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We present some suggestions for improving the state of date sharing in archaeology; among these is a standard for citing datasets to ensure that researchers making their data publicly available receive appropriate credit."> <meta name="citation_abstract" lang="es" content="¿Cómo comparten los arqueólogos sus datos de investigación, si lo hacen? Revisamos qué datos son, de acuerdo con las actuales definiciones influyentes, y el trabajo previo sobre los beneficios, costos y estándares de intercambio de datos en las ciencias en general. Para comprender el intercambio de datos en arqueología, presentamos los resultados de tres estudios piloto: solicitudes de datos por correo electrónico; revisión de la disponibilidad de datos en artículos publicados y análisis de conjuntos de datos arqueológicos depositados en repositorios. Encontramos que los arqueólogos están a menudo dispuestos a compartir, pero el intercambio de toda la disciplina es desigual y ad hoc. La legislación y los mandatos son eficaces para aumentar el intercambio de datos, pero las políticas editoriales de las revistas carecen de aplicación adecuada. Aunque la mayoría de los datos disponibles en repositorios están autorizados para permitir la reutilización flexible, sólo una pequeña proporción de los datos se almacena en formatos estructurados para su fácil reutilización. Presentamos algunas sugerencias para mejorar el estado de la fecha de intercambio en arqueología, incluyendo un estándar para citar conjuntos de datos para asegurar que los investigadores que hacen sus datos disponibles públicamente reciben crédito apropiado."> <meta name="citation_doi" content="10.1017/aap.2018.3"> <link rel="alternate" href="/core/journals/advances-in-archaeological-practice/article/standard-for-the-scholarly-citation-of-archaeological-data-as-an-incentive-to-data-sharing/E7154AE4990ED752EC76F9AFF0977807" hreflang="en" /> <link rel="icon" href="/core/cambridge-core/public/images/favicon.ico" type="image/x-icon"/> <link rel="shortcut icon" href="/core/cambridge-core/public/images/favicon.ico" type="image/x-icon"/> <link href='//fonts.googleapis.com/css?family=Noto+Sans:400,700,400italic,700italic' rel='stylesheet' type='text/css'>  <link rel="stylesheet" href="/core/cambridge-core/public/css/app.css?version=v7.337.1-hotfix"/> <link rel="dns-prefetch" 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data-v-63dfaf6e>Advances in Archaeological Practice</span> </a></li><li class="page-breadcrumbs__item" data-v-3692cf84><span aria-hidden="true" class="breadcrumbs-wrapper__arrow" data-v-3692cf84>></span><a href="/core/journals/advances-in-archaeological-practice/issue/0E42182312FF97D724012F0CCA9E0993" class="app-link app-link__text app-link--accent" data-v-63dfaf6e data-v-3692cf84><span data-v-63dfaf6e>Volume 6 Issue 2</span> </a></li><li class="page-breadcrumbs__item" data-v-3692cf84><span aria-hidden="true" class="breadcrumbs-wrapper__arrow" data-v-3692cf84>></span><span data-v-3692cf84>A Standard for the Scholarly Citation of Archaeological...</span></li></ul></div></div> <div class="language" data-v-3692cf84><ul class="language-switch" data-v-6b1118dd data-v-3692cf84><li aria-label="English" data-v-6b1118dd><span class="language-option current divider" data-v-6b1118dd>English</span></li><li aria-label="Français" data-v-6b1118dd><span role="button" tabindex="0" href="#" lang="fr" class="language-option" data-v-6b1118dd> Français </span></li></ul></div></div></div></div> <div class="container container__modified" data-v-01274b1d>  <div class="row" data-v-01274b1d><div role="complementary" aria-label="table of content" class="column__left" data-v-01274b1d><div class="col journal-container row" data-v-146270e8 data-v-01274b1d><img src="https://static.cambridge.org/covers/AAP_0_0_0/advances-in-archaeological-practice.jpg" alt="" class="journal__image" data-v-146270e8> <a href="/core/journals/advances-in-archaeological-practice" class="app-link journal__title app-link__text app-link--underlined" data-v-63dfaf6e data-v-146270e8><span class="text" data-v-63dfaf6e>Advances in Archaeological Practice </span> </a> <hr aria-hidden="true" class="separator default" data-v-7036083a data-v-146270e8></div>  <div id="toc" class="table-of-content" data-v-01274b1d><h2>Article contents</h2> <div id="toc-list-wrapper" class="table-of-content__wrapper"><ul id="toc-list" class="list"><li class="list__item"><a href="#sec0" class="list__item__link"><span class="toc-title">Abstract</span></a></li> <li class="list__item"><a href="#sec1" class="list__item__link"> <span><div class="toc-title">WHAT ARE DATA?</div></span></a></li><li class="list__item"><a href="#sec2" class="list__item__link"> <span><div class="toc-title">WHAT ARE THE ETHICS OF DATA SHARING?</div></span></a></li><li class="list__item"><a href="#sec3" class="list__item__link"> <span><div class="toc-title">WHAT ARE THE BENEFITS OF DATA SHARING TO THE INDIVIDUAL RESEARCHER?</div></span></a></li><li class="list__item"><a href="#sec4" class="list__item__link"> <span><div class="toc-title">WHAT DO RESEARCHERS THINK ABOUT SHARING DATA?</div></span></a></li><li class="list__item"><a href="#sec5" class="list__item__link"> <span><div class="toc-title">WHAT ARE THE CURRENT NORMS OF DATA SHARING?</div></span></a></li><li class="list__item"><a href="#sec6" class="list__item__link"> <span><div class="toc-title">DO ARCHAEOLOGISTS SHARE DATA?</div></span></a></li><li class="list__item"><a href="#sec7" class="list__item__link"> <span><div class="toc-title">HOW TO SHARE ARCHAEOLOGICAL DATA?</div></span></a></li><li class="list__item"><a href="#sec8" class="list__item__link"> <span><div class="toc-title">HOW TO CITE ARCHAEOLOGICAL DATA?</div></span></a></li><li class="list__item"><a href="#sec9" class="list__item__link"> <span><div class="toc-title">SUMMARY AND CONCLUSION</div></span></a></li><li class="list__item"><a href="#sec11" class="list__item__link"> <span><div class="toc-title">Data Availability Statement</div></span></a></li>  <li class="list__item"><a href="#references-list" class="list__item__link"><span class="toc-title">References</span></a></li></ul></div></div></div> <div class="column__main" data-v-01274b1d><div class="row" data-v-01274b1d><div class="column__main__left" data-v-01274b1d><div id="maincontent" class="col" data-v-862424e6 data-v-01274b1d> <hgroup data-v-862424e6><h1 data-v-862424e6>A Standard for the Scholarly Citation of Archaeological Data as an Incentive to Data Sharing</h1> </hgroup>     <div class="row published-date" data-v-862424e6><p data-v-862424e6> Published online by Cambridge University Press: <strong data-v-862424e6>05 April 2018</strong></p></div>   <div class="contributors-details" data-v-99f6eb26 data-v-862424e6><div class="row contributors" data-v-99f6eb26><div class="col" data-v-99f6eb26><div class="row contributor-type" data-v-792406ce data-v-99f6eb26> <div class="contributor-type__contributor" data-v-792406ce><a href="/core/search?filters%5BauthorTerms%5D=Ben%20Marwick&eventCode=SE-AU" class="app-link app-link__text app-link--accent" data-v-63dfaf6e data-v-792406ce><span data-v-63dfaf6e>Ben Marwick</span> </a>  <span data-v-792406ce> and</span></div><div class="contributor-type__contributor" data-v-792406ce><a href="/core/search?filters%5BauthorTerms%5D=Suzanne%20E.%20Pilaar%20Birch&eventCode=SE-AU" class="app-link app-link__text app-link--accent" data-v-63dfaf6e data-v-792406ce><span data-v-63dfaf6e>Suzanne E. Pilaar Birch</span> </a>  <span data-v-792406ce></span></div> </div> </div> <div class="col-2 collapse-link" data-v-99f6eb26><a href="#authors-details" data-toggle="collapse" aria-expanded="false" aria-controls="authors-details" class="app-link collapsed app-link__text-icon app-link--secondary reverse" data-v-63dfaf6e data-v-99f6eb26><img src="data:image/svg+xml;base64,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" alt="" class="app-icon icon arrow-down" data-v-d2c09870 data-v-63dfaf6e><span class="text" data-v-63dfaf6e>Show author details </span> </a></div></div> <hr aria-hidden="true" class="separator default" data-v-7036083a data-v-99f6eb26> <dl id="authors-details" class="authors-details collapse" data-v-2edb8da6 data-v-99f6eb26><div data-test-author="Ben Marwick" class="row author" data-v-2edb8da6><dt class="col-12 col-sm-2 title" data-v-2edb8da6>Ben Marwick*</dt> <dd class="col content d-inline d-sm-flex" data-v-2edb8da6><span class="content__title" data-v-2edb8da6>Affiliation:</span> <div class="d-sm-flex flex-column flex-sm-1 d-inline" data-v-2edb8da6><span data-v-2edb8da6><span data-v-2edb8da6>University of Washington, Seattle, WA 98115, USA</span> </span></div></dd></div><div data-test-author="Suzanne E. Pilaar Birch" class="row author" data-v-2edb8da6><dt class="col-12 col-sm-2 title" data-v-2edb8da6>Suzanne E. Pilaar Birch</dt> <dd class="col content d-inline d-sm-flex" data-v-2edb8da6><span class="content__title" data-v-2edb8da6>Affiliation:</span> <div class="d-sm-flex flex-column flex-sm-1 d-inline" data-v-2edb8da6><span data-v-2edb8da6><span data-v-2edb8da6>University of Georgia, Athens, GA 30602, USA</span> </span></div></dd></div> <div class="row" data-v-2edb8da6><dt class="col-sm-2 col-12 title" data-v-2edb8da6> * </dt> <dd class="col content" data-v-2edb8da6><div class="row" data-v-2edb8da6><div class="d-sm-flex d-inline flex-sm-1 flex-sm-wrap" data-v-2edb8da6><div class="d-inline" data-v-2edb8da6><span data-v-2edb8da6><div class="corresp">(<a href="mailto:bmarwick@uw.edu">bmarwick@uw.edu</a>, corresponding author)</div></span></div></div></div></dd></div> <hr aria-hidden="true" class="separator default" data-v-7036083a data-v-2edb8da6></dl></div></div> <div id="app-tabs" class="tabs" data-v-1d90c6ce data-v-01274b1d><div id="app-tabs-wrapper" class="tabs__wrapper" 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data-v-1d90c6ce>Addendum </a></li><li role="none" class="tabs__tab" data-v-1d90c6ce><a href="#metrics-tab" role="tab" aria-controls="metrics-tab" tabindex="-1" data-v-1d90c6ce>Metrics </a></li></ul></div></div> <div tabindex="0" role="region" aria-label="Tab content" class="tab-content" data-v-1d90c6ce><div id="article-tab" class="tab-pane active" data-v-43a4d572><div id="toc-mobile" class="table-of-content-mobile d-sm-none" data-v-43a4d572><a data-toggle="collapse" href="#toc-list-mobile" role="button" aria-expanded="false" class="toc-collapse-switch collapsed"><span class="default-heading">Article contents</span>  <span class="toc-arrow-up"></span> <span class="toc-arrow-down"></span></a> <ul id="toc-list-mobile" class="list collapse"><li class="list__item"><a href="#sec0" class="list__item__link"><span class="toc-title">Abstract</span></a></li> <li class="list__item"><a href="#sec1" class="list__item__link"> <span>WHAT ARE DATA?</span></a></li><li class="list__item"><a href="#sec2" class="list__item__link"> <span>WHAT ARE THE ETHICS OF DATA SHARING?</span></a></li><li class="list__item"><a href="#sec3" class="list__item__link"> <span>WHAT ARE THE BENEFITS OF DATA SHARING TO THE INDIVIDUAL RESEARCHER?</span></a></li><li class="list__item"><a href="#sec4" class="list__item__link"> <span>WHAT DO RESEARCHERS THINK ABOUT SHARING DATA?</span></a></li><li class="list__item"><a href="#sec5" class="list__item__link"> <span>WHAT ARE THE CURRENT NORMS OF DATA SHARING?</span></a></li><li class="list__item"><a href="#sec6" class="list__item__link"> <span>DO ARCHAEOLOGISTS SHARE DATA?</span></a></li><li class="list__item"><a href="#sec7" class="list__item__link"> <span>HOW TO SHARE ARCHAEOLOGICAL DATA?</span></a></li><li class="list__item"><a href="#sec8" class="list__item__link"> <span>HOW TO CITE ARCHAEOLOGICAL DATA?</span></a></li><li class="list__item"><a href="#sec9" class="list__item__link"> <span>SUMMARY AND CONCLUSION</span></a></li><li class="list__item"><a href="#sec11" class="list__item__link"> <span>Data Availability Statement</span></a></li>  <li class="list__item"><a href="#references-list" class="list__item__link"><span class="toc-title">References</span></a></li></ul></div> <div class="action-bar" data-v-43a4d572><div class="row items"> <div class="app-dropdown d-print-none" data-v-fab090b8 data-v-113567da data-v-43a4d572><button aria-expanded="false" data-test-id="buttonSavePDFOptions" id="save-pdf-dropdown" class="app-button dropdown-menu-button app-button__text-icon app-button--secondary" data-v-2a038744 data-v-fab090b8><img src="/core/page-component/img/save-pdf-icon.080470e.svg" alt="" class="app-icon icon save-pdf" data-v-d2c09870 data-v-2a038744> <span class="text" data-v-2a038744>Save PDF</span></button> <div aria-labelledby="save-pdf-dropdown" class="app-dropdown__menu" style="display:none;" data-v-fab090b8><div class="pdf-buttons" data-v-fab090b8 data-v-113567da><a 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data-v-7036083a></div> <div class="share-modal-overlay" style="display:none;" data-v-43a4d572></div> <div data-spy="scroll" data-target="#toc" class="scrollspy-content" data-v-43a4d572> <div id="sec0" class="col article-abstract sec" data-v-2fa8b348 data-v-43a4d572><div class="abstract-text-container" data-v-2fa8b348><div lang="en"><h2>Abstract</h2>  <div class="abstract-content"><div class="abstract" data-abstract-type="normal"><p>How do archaeologists share their research data, if at all? We review what data are, according to current influential definitions, and previous work on the benefits, costs, and norms of data sharing in the sciences broadly. To understand data sharing in archaeology, we present the results of three pilot studies: requests for data by e-mail, review of data availability in published articles, and analysis of archaeological datasets deposited in repositories. We find that archaeologists are often willing to share but that discipline-wide sharing is patchy and ad hoc. Legislation and mandates are effective at increasing data sharing, but editorial policies at journals lack adequate enforcement. Although most of the data available at repositories are licensed to enable flexible reuse, only a small proportion of the data are stored in structured formats for easy reuse. We present some suggestions for improving the state of date sharing in archaeology; among these is a standard for citing datasets to ensure that researchers making their data publicly available receive appropriate credit.</p></div></div> <hr aria-hidden="true" class="abstract-divider separator default" data-v-7036083a></div><div lang="es">  <div class="abstract-content"><div class="abstract" data-abstract-type="translated"><p>¿Cómo comparten los arqueólogos sus datos de investigación, si lo hacen? Revisamos qué datos son, de acuerdo con las actuales definiciones influyentes, y el trabajo previo sobre los beneficios, costos y estándares de intercambio de datos en las ciencias en general. Para comprender el intercambio de datos en arqueología, presentamos los resultados de tres estudios piloto: solicitudes de datos por correo electrónico; revisión de la disponibilidad de datos en artículos publicados y análisis de conjuntos de datos arqueológicos depositados en repositorios. Encontramos que los arqueólogos están a menudo dispuestos a compartir, pero el intercambio de toda la disciplina es desigual y ad hoc. La legislación y los mandatos son eficaces para aumentar el intercambio de datos, pero las políticas editoriales de las revistas carecen de aplicación adecuada. Aunque la mayoría de los datos disponibles en repositorios están autorizados para permitir la reutilización flexible, sólo una pequeña proporción de los datos se almacena en formatos estructurados para su fácil reutilización. Presentamos algunas sugerencias para mejorar el estado de la fecha de intercambio en arqueología, incluyendo un estándar para citar conjuntos de datos para asegurar que los investigadores que hacen sus datos disponibles públicamente reciben crédito apropiado.</p></div></div> <hr aria-hidden="true" class="abstract-divider separator default" data-v-7036083a></div></div>   </div>    <dl class="article-details" data-v-6e32a161 data-v-43a4d572><div class="row" data-v-6e32a161><dt class="col-12 col-sm-3 col-md-2_5 title" data-v-6e32a161> Type </dt> <dd class="col content" data-v-6e32a161>SPECIAL SECTION: DIGITAL DATA REUSE IN ARCHAEOLOGY</dd></div> <div class="row" data-v-6e32a161><dt class="col-12 col-sm-3 col-md-2_5 title" data-v-6e32a161> Information </dt> <dd class="col content" data-v-6e32a161><div class="content__journal" data-v-6e32a161><a href="/core/journals/advances-in-archaeological-practice" class="app-link app-link__text app-link--underlined" data-v-63dfaf6e data-v-6e32a161><span class="text" data-v-63dfaf6e>Advances in Archaeological Practice </span> </a> <span data-v-6e32a161> , <a href="/core/journals/advances-in-archaeological-practice/volume/58F5EC148CF77D8DA11AA56EB188B695" class="app-link app-link__text app-link--underlined" data-v-63dfaf6e data-v-6e32a161><span class="text" data-v-63dfaf6e>Volume 6 </span> </a></span> <span data-v-6e32a161> , <a href="/core/journals/advances-in-archaeological-practice/issue/0E42182312FF97D724012F0CCA9E0993" class="app-link app-link__text app-link--underlined" data-v-63dfaf6e data-v-6e32a161><span class="text" data-v-63dfaf6e>Issue 2 </span> </a></span> <span data-v-6e32a161>, May 2018</span>  <span data-v-6e32a161>, pp. 125 - 143</span> </div> <div class="doi-data" data-v-6e32a161><div data-v-6e32a161>DOI: <a target="_blank" href="https://doi.org/10.1017/aap.2018.3" class="app-link app-link__text app-link--accent" data-v-63dfaf6e data-v-6e32a161><span class="text" data-v-63dfaf6e>https://doi.org/10.1017/aap.2018.3 </span> <span class="sr-only" data-v-63dfaf6e>[Opens in a new window]</span></a></div> </div>  </dd></div>   <div class="row" data-v-6e32a161><dt class="col-12 col-sm-3 col-md-2_5 title" data-v-6e32a161> Copyright </dt> <dd class="col content" data-v-6e32a161><div data-v-6e32a161> Copyright 2018 © Society for American Archaeology </div></dd></div></dl>  <div id="content-container" class="content-container" data-v-43a4d572><div class="content-box"><div class="article research-article JATS"><div class="body"><h2 class="A clr-fn-label"> </h2><div class="disp-quote"><p class="p"> Data are the building blocks of research, and access to data is necessary to fully understand and extend the work of others (Fienberg, Martin, and Straf <a class="xref bibr" href="#ref018"><span class="show-for-sr">Reference Fienberg, Martin and Straf</span>1985</a>). Yet archaeological data are often not available for a variety of reasons, including a perceived lack of credit for making data available. The goal of this article is to present a standard for the scholarly citation of archaeological data, akin to the way we cite publications. The need for this has been motivated by Huggett's (<a class="xref bibr" href="#ref027"><span class="show-for-sr">Reference Huggett</span>2017</a>) findings of low levels of data citation and reuse in archaeology and Clarke's (<a class="xref bibr" href="#ref009"><span class="show-for-sr">Reference Clarke</span>2015</a>) analysis that digital data preservation and management systems are underdeveloped in archaeology, despite our obligations as stewards of cultural heritage. A standard for data citation is important because it gives credit to the authors or compilers of the data and so accords them visibility and recognition for their scholarly contribution in a way that is valued broadly in the discipline. A citation standard is also important for tracking the use of data and generating metrics about the impact of a dataset on the discipline. Understanding the use of resources such as datasets is important for allocating resources within archaeology and identifying novel research directions.</p></div><p class="p"> Archaeologists work with data in research, commercial, government, and other contexts. A full investigation of data in each of these contexts is beyond the scope of this article, so we limit this study to what we are most familiar with, the research context. However, many of the practical details we discuss here will also be relevant to archaeologists working in other contexts. We begin by defining what we mean by “data” and reviewing previous work on researchers’ attitudes to sharing data. We motivate data sharing and citing behaviors by explaining why data sharing is beneficial to archaeology as a discipline and to individual researchers. Then we survey some of the data sharing and citing norms that have emerged in other research areas. From these norms we derive a standard suitable for the citation of archaeological data. Finally, we present some basic instructions on how to share archaeological data.</p><div class="sec" data-magellan-destination="sec1" id="sec1"><h2 class="A"> WHAT ARE DATA?</h2><p class="p"> Defining what data are is an ongoing research problem for information scientists (Borgman <a class="xref bibr" href="#ref006"><span class="show-for-sr">Reference Borgman</span>2012</a>; Leonelli <a class="xref bibr" href="#ref041"><span class="show-for-sr">Reference Leonelli</span>2016</a>). <a class="xref table" href="#tbl001">Table 1</a> summarizes some of the definitions debated in the philosophy of science literature. A more concrete starting point is this definition from the National Academies of Sciences: </p><div class="disp-quote"><p class="p"> The term “data” as used in this document is meant to be broadly inclusive. In addition to digital manifestations of literature (including text, sound, still images, moving images, models, games, or simulations), it refers as well to forms of data and databases that generally require the assistance of computational machinery and software in order to be useful, such as various types of laboratory data including spectrographic, genomic sequencing, and electron microscopy data; observational data, such as remote sensing, geospatial, and socioeconomic data; and other forms of data either generated or compiled, by humans or machines [Uhlir and Cohen <a class="xref bibr" href="#ref083"><span class="show-for-sr">Reference Uhlir and Cohen</span>2011</a>, cited in Borgman <a class="xref bibr" href="#ref007"><span class="show-for-sr">Reference Borgman</span>2015</a> p. 1061].</p></div> <div class="table-wrap" data-magellan-destination="tbl001" id="tbl001"><div class="caption"><p class="p"><span class="label">TABLE 1.</span> Selection of Definitions of Research Data.</p></div><span><div class="figure-thumb"><img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab1.gif?pub-status=live" class="aop-lazy-load-image" width="466" height="309" data-original-image="/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab1.gif" data-zoomable="false"></div></span></div><p class="p"> This is a useful definition because it emphasizes data as digital manifestations and so suitable for transmission, storage, and retrieval using the internet. Physical objects (such as samples, collections, hard copies of notes, etc.) are data also, but they are out of the scope of this discussion because of the different challenges and requirements for access, archiving, and so on. The categories shown in <a class="xref table" href="#tbl002">Table 2</a> are useful as a rough guide to what data can be, although this table is not comprehensive and does not represent the full diversity of data used by archaeologists. Ultimately, data are whatever the community decides, as the National Science Foundation notes in its commentary on data management plans: “‘Data’ are defined as the recorded factual material commonly accepted in the scientific community as necessary to validate research findings. This includes original data, but also ‘metadata’ (e.g. experimental protocols, code written for statistical analyses, etc.)” (2010). </p><div class="table-wrap" data-magellan-destination="tbl002" id="tbl002"><div class="caption"><p class="p"><span class="label">TABLE 2.</span> Selection of Types of Data Encountered by Archaeologists.</p></div><span><div class="figure-thumb"><img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab2.gif?pub-status=live" class="aop-lazy-load-image" width="466" height="378" data-original-image="/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab2.gif" data-zoomable="false"></div></span></div><p class="p"> These definitions in policy are consistent with the relational account of data in philosophy of science scholarship, which holds that what data are is dependent on who uses it, how, and for what purpose (Daston and Galison <a class="xref bibr" href="#ref012"><span class="show-for-sr">Reference Daston and Galison</span>1992</a>; Leonelli <a class="xref bibr" href="#ref040"><span class="show-for-sr">Reference Leonelli</span>2015</a>). How can these definitions and philosophical discussions inform our practice? They tell us that what count as data are emergent from our professional interactions. Data are the materials we request when we do peer review of manuscripts and grant proposals or when we make an informal request for more information by e-mail. They are not exclusively objective archaeometric records but, rather, include interpretive and derivative results. In short, data are anything another person needs to understand our work better.</p><p class="p"> To translate this into a more applied context, our practice of data sharing is to make available the computer files (e.g., containing text, raw structured data, images, etc.) that we analyze or otherwise use to create the figures, tables, and quantitative results that we include in our reports and publications. These include our derived results and secondary data, such as taxonomic and typological classifications. This selection omits materials that could also be considered data according to the definitions noted above (e.g., field notes), but we feel that our selection represents the most information-dense and meaningful items that are useful for our community of interest. These are also the items that have been most frequently requested from us by others in our professional interactions. Our citation standard is inclusive and imposes no constraints on what data can be, but we propose a few minimal qualities for how data should be organized that will help ground our discussion.</p></div><div class="sec" data-magellan-destination="sec2" id="sec2"><h2 class="A"> WHAT ARE THE ETHICS OF DATA SHARING?</h2><p class="p"> A full treatment of the ethical issues of data sharing is beyond the scope of this article, but we can highlight some points specifically relevant to the question of when <em class="italic">not</em> to share archaeological data. Much of the discussion on ethics and data sharing generally relates to appropriate attribution (assigning credit for the data) in data reuse (Duke and Porter <a class="xref bibr" href="#ref015"><span class="show-for-sr">Reference Duke and Porter</span>2013</a>). The default position for archaeological data, like data from many other sciences, is that they are part of the global human patrimony and should be accessible to other scholars and the public (Vitelli and Colwell-Chanthaphonh <a class="xref bibr" href="#ref086"><span class="show-for-sr">Reference Vitelli and Colwell-Chanthaphonh</span>2006</a>; Zimmerman et al. <a class="xref bibr" href="#ref094"><span class="show-for-sr">Reference Zimmerman, Vitelli and Hollowell-Zimmer</span>2003</a>). However, archaeologists, like other researchers working with local and indigenous communities, governments, and international collaborators, generate research data that are intellectual property with many stakeholders. With diverse stakeholders, archaeological data can become entangled in economics, nationalism, cultural politics, and identity (Nicholas and Wylie <a class="xref bibr" href="#ref055"><span class="show-for-sr">Reference Nicholas, Wylie, Young and Brunk</span>2009</a>). Often, one of these stakeholders is an indigenous community, and archaeologists may find differences between Western and indigenous conceptions of what data are and how they may be generated, used, shared, and “owned” (Harding et al. <a class="xref bibr" href="#ref024"><span class="show-for-sr">Reference Harding, Harper, Stone, O'Neill, Berger, Harris and Donatuto</span>2012</a>).</p><p class="p"> One of these differences is that Western researchers often have individual autonomy in sharing data, and once data are shared, they are free for all to use. In contrast, within many indigenous communities, data are part of the group's identity and property, akin to tangible heritage (Nicholas <a class="xref bibr" href="#ref054"><span class="show-for-sr">Reference Nicholas, Coombe, Wershler and Zeilinger</span>2014</a>), with certain members entrusted to keep that data on behalf of the group, and sometimes it may be inappropriate for these people to share data with other members of the group or beyond the group (Tsosie <a class="xref bibr" href="#ref082"><span class="show-for-sr">Reference Tsosie</span>2007</a>). Differences between Western and indigenous concepts of data are not only cultural—substantial inequalities exist in access to resources for converting archaeological data into economic and political benefit (e.g., graduate degrees, employment, publications, grants, promotions). On one hand, making archaeological data openly available benefits indigenous communities because it removes some of the financial and technical barriers to accessing that data (e.g., subscription fees to journals). On the other hand, this may be little more than a useless courtesy (Hymes <a class="xref bibr" href="#ref028"><span class="show-for-sr">Reference Hymes</span>1972</a>), because simply making data openly available does not give indigenous people access to the contexts where data can be used to generate economic and political benefits. Thus, open data have different values for Western and indigenous communities, and if archaeologists want to make a compelling case to their indigenous partners for data sharing, they should also make a commitment to reducing some of the inequalities in making use of the data, for example, by providing some training in data analysis or adapting the data to a context that is valued by the indigenous community.</p><p class="p"> In short, there is more to the ethics of data sharing than avoiding risk of damage. Redacting the locations of archaeological sites to prevent looting is an obvious case of preparing data for sharing to reduce the risk of damage to the sites (and in some jurisdictions, mandated by law). However, because of cultural, economic, and political differences between Western archaeologists and the communities they work with, archaeologists cannot consider their own risk assessment to be comprehensive and exhaustive (Nicholas and Wylie <a class="xref bibr" href="#ref056"><span class="show-for-sr">Reference Nicholas, Wylie, Coningham and Scarre</span>2012</a>). It is crucial for archaeologists to discuss data sharing plans during negotiations with representatives authorized by the indigenous peoples whose cultural heritage is the subject of investigation. This negotiation process is important because only indigenous peoples themselves can identify potential adverse outcomes to data sharing, and they can do this only if they understand the proposed research and anticipated results. The concrete outcome of this process is often a memorandum of understanding between the indigenous community and the archaeologist's employer that documents the outcome of negotiations about data sharing. If an archaeologist's indigenous partners have reasons not to share data in public, then the archaeologist should respect that position and note in publication that the data are restricted at the request of the indigenous collaborators.</p></div><div class="sec" data-magellan-destination="sec3" id="sec3"><h2 class="A"> WHAT ARE THE BENEFITS OF DATA SHARING TO THE INDIVIDUAL RESEARCHER?</h2><p class="p"> While public and community benefits and costs are well rehearsed and widely agreed on (<a class="xref table" href="#tbl003">Tables 3</a> and <a class="xref table" href="#tbl004">4</a>), they offer no, or only weak, tangible incentives to the individual researcher (Rowhani-Farid, Allen, and Barnett <a class="xref bibr" href="#ref070"><span class="show-for-sr">Reference Rowhani-Farid, Allen and Barnett</span>2017</a>). Previous work has identified citation advantages as a tangible benefit to individual researchers who share data with publications, although the magnitude of the advantage varies greatly (see <a class="xref table" href="#tbl005">Table 5</a> for a summary). Increased publication productivity also appeared to be associated with data sharing behaviors. Across more than 7,000 National Science Foundation and National Institute of the Humanities awards, Pienta, Alter, and Lyle (<a class="xref bibr" href="#ref064"><span class="show-for-sr">Reference Pienta, Alter and Lyle</span>2010</a>) report that research projects with archived data produced a median of 10 publications, vs. only five for projects without archived data. </p><div class="table-wrap" data-magellan-destination="tbl003" id="tbl003"><div class="caption"><p class="p"><span class="label">TABLE 3.</span> Summary of Public and Community Costs of Data Sharing.</p></div><span><div class="figure-thumb"><img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab3.gif?pub-status=live" class="aop-lazy-load-image" width="466" height="456" data-original-image="/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab3.gif" data-zoomable="false"></div></span><div class="table-wrap-foot"><div class="fn-group footnotes"><div class="fn" data-magellan-destination="manual_ref-1" id="manual_ref-1"><p class="p"> <em class="italic">Source:</em> From Borgman <a class="xref bibr" href="#ref006"><span class="show-for-sr">Reference Borgman</span>2012</a>; Stodden <a class="xref bibr" href="#ref077"><span class="show-for-sr">Reference Stodden</span>2010</a>.</p></div></div></div></div> <div class="table-wrap" data-magellan-destination="tbl004" id="tbl004"><div class="caption"><p class="p"><span class="label">TABLE 4.</span> Summary of Public and Community Benefits of Data Sharing.</p></div><span><div class="figure-thumb"><img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab4.gif?pub-status=live" class="aop-lazy-load-image" width="466" height="378" data-original-image="/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab4.gif" data-zoomable="false"></div></span><div class="table-wrap-foot"><div class="fn-group footnotes"><div class="fn" data-magellan-destination="manual_ref-2" id="manual_ref-2"><p class="p"> <em class="italic">Source:</em> From Borgman <a class="xref bibr" href="#ref006"><span class="show-for-sr">Reference Borgman</span>2012</a>; Stodden <a class="xref bibr" href="#ref077"><span class="show-for-sr">Reference Stodden</span>2010</a>.</p></div></div></div></div> <div class="table-wrap" data-magellan-destination="tbl005" id="tbl005"><div class="caption"><p class="p"><span class="label">TABLE 5.</span> Summary of Individual Benefits to Data Sharing.</p></div><span><div class="figure-thumb"><img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab5.gif?pub-status=live" class="aop-lazy-load-image" width="466" height="208" data-original-image="/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab5.gif" data-zoomable="false"></div></span></div></div><div class="sec" data-magellan-destination="sec4" id="sec4"><h2 class="A"> WHAT DO RESEARCHERS THINK ABOUT SHARING DATA?</h2><p class="p"> Sharing of data is a quality that is fundamental to the definition of science (cf. Merton's [<a class="xref bibr" href="#ref048"><span class="show-for-sr">Reference Merton and Merton</span>1942</a>] communalism norm, where intellectual property rights are normally given up in exchange for recognition and esteem) and to data both as an abstract quality (cf. the definitions in <a class="xref table" href="#tbl001">Table 1</a> that mention data as “portable,” “stable across different domains”) and as a concrete detail to be managed with policy. For example, for over a decade the National Science Foundation has had a data sharing policy that states: </p><div class="disp-quote"><p class="p"> Investigators are expected to share with other researchers, at no more than incremental cost and within a reasonable time, the primary data, samples, physical collections and other supporting materials created or gathered in the course of work under NSF grants. Grantees are expected to encourage and facilitate such sharing [2016].</p></div><p class="p"> Surveys of researchers across different domains repeatedly show that people value the ideal of data sharing (Kratz and Strasser <a class="xref bibr" href="#ref036"><span class="show-for-sr">Reference Kratz and Strasser</span>2014</a>, <a class="xref bibr" href="#ref036"><span class="show-for-sr">Reference Kratz and Strasser</span>2015</a>). However, data sharing is not widespread in many disciplines, raising questions about why researchers do or do not share their data. Three recent studies have attempted to answer these questions, in the search for strategies to encourage sharing.</p><p class="p"> Stodden (<a class="xref bibr" href="#ref077"><span class="show-for-sr">Reference Stodden</span>2010</a>) surveyed registrants at a top machine learning conference (the Neural Information Processing Systems conference) to identify the factors that affect researchers’ decisions to reveal data (<a class="xref table" href="#tbl004">Table 4</a>). Stodden found that communitarian factors influence scientists to share their data but private incentives determine when scientists choose not to share their code and data. In the top three reasons not to share, we see a lack of incentives to invest time in data sharing (however, there are many other time-consuming tasks in research, and they get done because clear reward structures are in place), a lack of faith in data citation (which we hope this article will help with), and a lack of an efficient copyright process. Stodden's (<a class="xref bibr" href="#ref075"><span class="show-for-sr">Reference Stodden</span>2009</a>a, <a class="xref bibr" href="#ref076"><span class="show-for-sr">Reference Stodden</span>2009</a>b) “Reproducible Research Standard,” summarized in <a class="xref table" href="#tbl006">Table 6</a>, is a convenient and robust copyright schema that is in wide use for many typical research projects. </p><div class="table-wrap" data-magellan-destination="tbl006" id="tbl006"><div class="caption"><p class="p"><span class="label">TABLE 6.</span> Summary of Stodden's “Reproducible Research Standard.”</p></div><span><div class="figure-thumb"><img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab6.gif?pub-status=live" class="aop-lazy-load-image" width="977" height="336" data-original-image="/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab6.gif" data-zoomable="true"></div></span><div class="table-wrap-foot"><div class="fn-group footnotes"><div class="fn" data-magellan-destination="manual_ref-3" id="manual_ref-3"><p class="p"> <em class="italic">Note:</em> This standard realigns legal rights with scientific norms by freeing scientific work from copying and reuse restrictions, with attribution. It encourages researchers to release their products with standardized instructions for reuse and attribution.</p></div><div class="fn" data-magellan-destination="manual_ref-4" id="manual_ref-4"><p class="p"> <em class="italic">Source:</em> Stodden <a class="xref bibr" href="#ref075"><span class="show-for-sr">Reference Stodden</span>2009a</a>.</p></div></div></div></div><p class="p"> Other surveys have found similar results. Tenopir and colleagues (<a class="xref bibr" href="#ref079"><span class="show-for-sr">Reference Tenopir, Allard, Douglass, Aydinoglu, Wu and Frame</span>2011</a>) received 1,329 responses to a survey conducted by the research team of the National Science Foundation–funded DataONE project. They found that when researchers did not share data it was due to reasons that included insufficient time (54%), a lack of funding (40%), having no place to put the data (24%), a lack of standards (20%), and the fact that their sponsor did not require it (17%). A remarkable observation by Tenopir and colleagues is that while 75% of respondents said that they shared data, only 36% of those respondents said that their data are easy to access, while only 6% said that they make all their data available. This suggests that most data sharing occurs in private directly between individual researchers, rather than in public via open repositories. A similar focus on private, peer-to-peer sharing is evident in interviews conducted by Wallis and colleagues (<a class="xref bibr" href="#ref087"><span class="show-for-sr">Reference Wallis, Rolando and Borgman</span>2013</a>) with 43 researchers at the Center for Embedded Networked Sensing. They found that the most commonly cited conditions for sharing data were (1) first rights to publish results, (2) proper attribution to the data source, (3) familiarity between sharer and recipient, (4) funding agency expectations, and (5) the amount of effort required to share.</p></div><div class="sec" data-magellan-destination="sec5" id="sec5"><h2 class="A"> WHAT ARE THE CURRENT NORMS OF DATA SHARING?</h2><p class="p"> The surveys above show that data sharing practices tend to be a private activity, but where public sharing does occur, what forms does it take, and how do these vary across different fields of research? Wouters and Haak (<a class="xref bibr" href="#ref093"><span class="show-for-sr">Reference Wouters and Haak</span>2017</a>) surveyed 1,200 researchers about data sharing behaviors and found a spectrum from “intensive data-sharing fields” to “restricted data-sharing fields.” Intensive data-sharing fields often depend on access to global databases, for example, human genetics, while restricted data-sharing fields depend mostly on data collected by the individual researcher or his or her immediate collaborators, such as digital humanities. These differences in disciplinary norms have implications for data ownership (intensive: collectively owned, willing to share; restrictive: owned by the individual, reluctant to share) and the effort required to share (intensive: low effort because data are prepared for sharing as an integral part of the research process; restrictive: high effort because data are prepared for sharing after the research, or apart from it, in response to a request from another researcher).</p><p class="p"> This intensive-restricted spectrum also maps onto Sawyer's (<a class="xref bibr" href="#ref072"><span class="show-for-sr">Reference Sawyer</span>2008</a>) analysis of data sharing in data-rich fields compared with data-poor fields. Data-rich fields, such as astronomy, oceanography, and ecology, have three common characteristics: Pooling and sharing data resources is expected; the cumulative nature of work that builds on common data and common methods reduces the likelihood of alternative data sources and other methods being widespread; and the number of theoretical choices is limited because there is enough data to adequately test competing theories. Data-poor fields have three common characteristics: Data are rare, or hard to get, or both; the types of data available often dictate the methods used; and theories are relatively easy to generate and more difficult to validate. In data-poor fields, researchers often invest substantial time and resources on gathering their own data. The accumulation of data, and the quality of the data collected, is often a key determinant of the status of a researcher, leading to hoarding of data as an asset to be traded for professional benefit.</p><p class="p"> Archaeologists can be found all over this spectrum because of the diversity of archaeological research. However, our observations of data sharing in archaeology, and the nature of archaeological research generally, indicate that it is, for the most part, currently a restricted data-sharing and data-poor field. There are few standardized and widely used methods for sharing and citing data, a challenge for archaeological data sharing that we hope this article will help to address.</p><p class="p"> Even though there is a wide spectrum of data sharing behaviors, overall rates of data sharing by individual researchers are very low in many fields. Wicherts and colleagues (<a class="xref bibr" href="#ref090"><span class="show-for-sr">Reference Wicherts, Borsboom, Kats and Molenaar</span>2006</a>) e-mailed authors of 141 empirical articles recently published by American Psychological Association (APA) journals to request a copy of the original data reported in the article. They received 64 of the 249 datasets they requested, resulting in a data sharing rate of about 26%, despite all the authors of the articles in their study being bound by the APA's Ethical Principles, which stated that authors must “not withhold the data on which their conclusions are based from other competent professionals who seek to verify the substantive claims” (APA <a class="xref bibr" href="#ref002">2001</a>:396). Vanpaemel and colleagues (<a class="xref bibr" href="#ref084"><span class="show-for-sr">Reference Vanpaemel, Vermorgen, Deriemaecker and Storms</span>2015</a>) similarly requested data for 394 articles, published in all issues of four APA journals in 2012, and found that 38% of authors responded with their data. They conclude that although the data sharing rate increased as compared with the study by Wicherts and colleagues, their findings are “worrisome” and indicate a “poor availability of data” (Vanpaemel et al. <a class="xref bibr" href="#ref084"><span class="show-for-sr">Reference Vanpaemel, Vermorgen, Deriemaecker and Storms</span>2015</a>: 3).</p><p class="p"> Savage and Vickers (<a class="xref bibr" href="#ref071"><span class="show-for-sr">Reference Savage and Vickers</span>2009</a>) requested data from the authors of 10 papers published in PLOS journals and received only one reply. Although this is a very small sample, it is notable because PLOS journals require authors to share data as a condition of publication: “PLOS journals require authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception” (<em class="italic">PLOS</em> Editorial and Publishing Policies <a class="xref bibr" href="#ref067">2017</a>); and yet only one out of the 10 authors actually complied with this requirement. Alsheikh-Ali and colleagues (<a class="xref bibr" href="#ref001"><span class="show-for-sr">Reference Alsheikh-Ali, Qureshi, Al-Mallah and Ioannidis</span>2011</a>) inspected 500 articles published in high-impact science journals in 2009 and found that only 47 essays (9%) deposited full primary raw data online. Similarly, Womack (<a class="xref bibr" href="#ref092"><span class="show-for-sr">Reference Womack</span>2015</a>) inspected 4,370 articles published in high-impact science journals in 2014 and found that 13% of articles with original data published had made the data available to others.</p><p class="p"> The absence of a standardized and sustainable approach to data sharing is starkly demonstrated by Vines and colleagues (<a class="xref bibr" href="#ref085"><span class="show-for-sr">Reference Vines, Albert, Andrew, Débarre, Bock, Franklin and Rennison</span>2014</a>) study of data availability over time in biology publications. They examined the availability of data from 516 studies of plants or animal morphology between two and 22 years old. Vines et al. found that the odds of a dataset referred to in a publication being available fell by 17% per year. They found that the key factors responsible for the unavailability of data were that the data were lost or on inaccessible storage media.</p></div><div class="sec" data-magellan-destination="sec6" id="sec6"><h2 class="A"> DO ARCHAEOLOGISTS SHARE DATA?</h2><p class="p"> Here we present the results of three small pilot studies aimed at understanding how archaeologists share their data. First, we e-mailed a sample of authors of research publications to request access to the data behind their publication. Second, we surveyed a sample of journal articles to see what data were available. Third, we analyzed information about archaeological data files in online repositories tracked by DataCite (an organization that provides persistent identifiers, or digital object identifiers [DOIs], for research data). The small sample sizes of the first two of these pilot studies limit how generalizable the results are, but as the first of their kind for archaeology, they are valuable for proposing hypotheses for larger-scale investigations.</p><div class="sec" data-magellan-destination="sec6-1" id="sec6-1"><h3 class="B"> Reproducibility and Open-Source Materials for This Study</h3><p class="p"> To enable reuse of our materials and improve reproducibility and transparency according to the principles outlined in Marwick <a class="xref bibr" href="#ref046"><span class="show-for-sr">Reference Marwick</span>2017</a>, we include the entire R code used for all the analysis and visualizations contained in this article in our compendium at <a class="uri simple" href="https://doi.org/10.17605/OSF.IO/KSRUZ">http://doi.org/10.17605/OSF.IO/KSRUZ</a>. Also in this version-controlled compendium are the raw data for all the tests reported here. All of the figures and quantitative results presented here can be independently reproduced with the code and data in this repository. In our compendium our code is released under the MIT license, our data as CC-0, and our figures as CC-BY, to enable maximum reuse (for more details, see Marwick <a class="xref bibr" href="#ref046"><span class="show-for-sr">Reference Marwick</span>2017</a>).</p><p class="continuation codeocean" style="margin: 2em 0 1em 0.2em;"> <iframe src="https://codeocean.com/algo.html?algorithmId=ca12b3f0-55a2-4eba-9687-168c8281e535" style="border: none; margin: 0; height: 650px; width: 100%"></iframe></p><div class="caption"><em class="italic">Reader note: The Code Ocean capsule above contains the code to replicate the results of this article. Users can run the code and view the outputs, but in order to do so they will need to</em> <a class="uri simple" href="https://codeocean.com/signup">register</a> <em class="italic">on the Code Ocean site (or</em> <a class="uri simple" href="https://codeocean.com/login">login</a> <em class="italic">if they have an existing Code Ocean account)</em>.</div></div><div class="sec" data-magellan-destination="sec6-2" id="sec6-2"><h3 class="B"> E-mail Requests for Data in a Published Article</h3><p class="p"> Our sample focused on <em class="italic">Journal of Archaeological Science</em> articles on experiments with stone artifacts that were published during 2009 and 2015. We focused on stone artifact experiments because all research materials in these projects are generated by the researcher, so there are no other stakeholders to consider when determining how to share the data: the researcher has complete control over the data. There are also no risks of damage to people or property in sharing these data because they do not include archaeological sites or human subjects. Because we contacted authors of essays at the intersection of (1) containing eminently sharable data and (2) our bona fide research interests (i.e., stone artifacts for BM), this is a targeted, nonrandom sample that may not be representative of the entire archaeological community. In contacting the authors, we requested the datasets for use in a graduate seminar with the aim of reproducing the published results and further exploring the data (this stage is still in progress). We promised to contact the authors if we discovered anything new and not to share the data further without their permission. The data included with this article are anonymized so the specific authors we contacted are not identifiable.</p><p class="p"> We sent out e-mails to the first authors of 23 stone artifact experiment articles and received 15 replies, resulting in a 70% response rate. We received five responses that included data files, giving an overall sharing rate of 20%. Our small sample size limits the robustness of our analysis but points to some observations that may be worth following up in a larger study. We found no clear relationship of data sharing and date of publication. We do not have any insights into why authors of older essays declined to share, but three of the six responses for articles published in 2014–2015 said that they declined to share data because they intended to use these data in a future publication. Other noteworthy reasons provided by respondents for declining to share include the fact that the authors were about to defend their thesis or about to go on vacation. We found no effect of the author's student status at time of publication on data sharing behaviors, with roughly equal proportions of students and nonstudents sharing and not sharing (<a class="xref fig" href="#fig001">Figure 1</a>).</p><p class="p"> Similarly there are no clear patterns of data sharing among different journals. It is remarkable that the authors of the <em class="italic">PLOS ONE</em> article we contacted declined to share because that refusal violates the policy of that journal at the time of publication of the essay (early 2014). Our finding is not unique—Chambers (<a class="xref bibr" href="#ref008"><span class="show-for-sr">Reference Chambers</span>2017</a>) examined 50 <em class="italic">PLOS ONE</em> articles on brain images published in November 2014–May 2015 and found that only 38% of the essays’ authors had archived their data in an open repository. Similarly, Nuijten and colleagues (<a class="xref bibr" href="#ref059"><span class="show-for-sr">Reference Nuijten, Borghuis, Veldkamp, Alvarez, van Assen and Wicherts</span>2017</a>) found that of 462 <em class="italic">PLOS ONE</em> articles that promised data availability, 29% of these did not have any available data. Unfulfilled promises to share data are not unique to <em class="italic">PLOS ONE</em> (Kidwell et al. <a class="xref bibr" href="#ref034"><span class="show-for-sr">Reference Kidwell, Lazarević, Baranski, Hardwicke, Piechowski, Falkenberg and Nosek</span>2016</a>), but these studies highlight the need to resolve ambiguity among authors about what count as data and for editors and peer reviewers to more critically assess whether authors have made their data openly available.</p><p class="p"> We see similar rates of sharing in response to e-mail requests among archaeologists to what have been described in other disciplines (<a class="xref table" href="#tbl007">Table 7</a>; Tenopir et al. <a class="xref bibr" href="#ref080"><span class="show-for-sr">Reference Tenopir, Dalton, Allard, Frame, Pjesivac, Birch and Dorsett</span>2015</a>; Vanpaemel et al. <a class="xref bibr" href="#ref084"><span class="show-for-sr">Reference Vanpaemel, Vermorgen, Deriemaecker and Storms</span>2015</a>). However, our results may overestimate the true rate because our sample did not include datasets that authors might not be able or willing to share because they contain sensitive location or cultural information. Analysis of the qualitative responses we received suggests two key reasons why authors are reluctant to share: they are fearful that they will lose the opportunity to produce further publications from those data (a typical concern of researchers in data-poor fields) and that their data are not in a form suitable for sharing and the task of organizing those data is not a high priority. These reasons not to share have also been documented in other research areas, noted above. The absence of patterns in the date of publication, student status of the author, and other variables (<a class="xref fig" href="#fig001">Figure 1</a>) may relate more to our small sample size than to a real absence of relationships in data sharing behavior. </p><div class="table-wrap" data-magellan-destination="tbl007" id="tbl007"><div class="caption"><p class="p"><span class="label">TABLE 7.</span> Summary of a Selection of Previous Studies of Data Sharing in Various Fields.</p></div><span><div class="figure-thumb"><img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab7.gif?pub-status=live" class="aop-lazy-load-image" width="977" height="374" data-original-image="/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab7.gif" data-zoomable="true"></div></span><div class="table-wrap-foot"><div class="fn-group footnotes"><div class="fn" data-magellan-destination="manual_ref-5" id="manual_ref-5"><p class="p"> <em class="italic">Note:</em> In the study by Peng, Dominici, and Zege (<a class="xref bibr" href="#ref063"><span class="show-for-sr">Reference Peng, Dominici and Zege</span>2006</a>), the authors did not attempt to contact other researchers but note the availability of data as described in the published article.</p></div><div class="fn" data-magellan-destination="manual_ref-6" id="manual_ref-6"><p class="p"> <em class="italic">Source:</em> From Spencer <a class="xref bibr" href="#ref074"><span class="show-for-sr">Reference Spencer</span>2010</a>.</p></div></div></div></div> <section><div class="fig" data-magellan-destination="fig001" id="fig001"><div class="figure-thumb"><img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig1g.jpeg?pub-status=live" class="aop-lazy-load-image" width="1394" height="930" data-original-image="/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig1g.jpeg" data-zoomable="true"></div><div class="caption"><p class="p"> </p><p class="p"><span class="label">FIGURE 1.</span> Summary of results of e-mail requests for data: (a) responses by year of journal article publication; (b) student status of first author; (c) country location of first author; (d) journal. AAS = <em class="italic">Archaeological and Anthropological Sciences</em>; AQ = <em class="italic">American Antiquity</em>; ARCHMTRY = <em class="italic">Archaeometry</em>; JAS = <em class="italic">Journal of Archaeological Science</em>; JFA = <em class="italic">Journal of Field Archaeology</em>; JHE = <em class="italic">Journal of Human Evolution</em>; LT = <em class="italic">Lithic Technology</em>; PLOSONE = <em class="italic">PLOS ONE</em>; SCI = <em class="italic">Science</em>.</p></div></div></section></div><div class="sec" data-magellan-destination="sec6-3" id="sec6-3"><h3 class="B"> Survey of Articles Published in the <em class="italic">Journal of Archaeological Science</em></h3><p class="p"> In addition to our e-mail survey, we also conducted a pilot study of a random sample of 48 articles published during February–May 2017 in the <em class="italic">Journal of Archaeological Science</em> to investigate data sharing behaviors. In this sample we found openly available raw data for 18 essays (53%), even though only seven articles (21%) include a data availability statement. For essays where data are available, nine (50%) have data contained in supplementary files published with the article, six (33%) have all the raw data in tables in the text of the essays, and three (17%) have data in an online repository. The most frequently shared type of data is compositional (e.g., element concentrations; <em class="italic">n</em> = 9, 28%), followed by radiocarbon and luminescence age data (where the dates are the primary object of analysis; <em class="italic">n</em> = 5, 16%) and DNA sequences (<em class="italic">n</em> = 4, 12%). Generally, compositional data are presented in tables in the text, dating data are presented in supplementary files, and DNA data are in repositories (as is typical for DNA data; <a class="xref fig" href="#fig002">Figure 2</a>). Data shared in supplementary files and online repositories were most often in Microsoft Excel format (<em class="italic">n</em> = 38, 38% of the files in supplementary files and repositories), followed by CSV (aka comma-separated values, an open-source plain text spreadsheet format; <em class="italic">n</em> = 4, 31%), and tables embedded in Microsoft Word or PDF documents (<em class="italic">n</em> = 4, 31%). When a scripting language such as R, Python, MATLAB, or OxCal was used as the analysis software for an article (<em class="italic">n</em> = 7, 54% of the essays would identify the software), only two also provided the script with the article. </p><section><div class="fig" data-magellan-destination="fig002" id="fig002"><div class="figure-thumb"><img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig2g.jpeg?pub-status=live" class="aop-lazy-load-image" width="933" height="714" data-original-image="/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig2g.jpeg" data-zoomable="true"></div><div class="caption"><p class="p"> </p><p class="p"><span class="label">FIGURE 2.</span> Summary of relationships among file types, data types, and data locations: (a) file types and data locations; (b) data types and data locations.</p></div></div></section><p class="p"> The <em class="italic">Journal of Archaeological Science</em> has had a “data disclosure” policy since at least 2013 that states that “all data relating to the article must be made available in Supplementary files or deposited in external repositories and linked to within the article” (<a class="xref bibr" href="#ref030">2013</a>). The data availability rate of 53% reported in our pilot study reflects either weak enforcement of this policy or an incomplete understanding among editors, reviewers, and authors of how to interpret and implement this policy. The prominence of compositional and dating data among the shared data types suggests a least effort strategy, with authors sharing data that do not require extensive cleaning and tidying (because the data are generated by instruments rather than entered by hand, such as artifact attribute data). The size of compositional and dating datasets means that they mostly fit easily into tables in the text or supplementary files and do not require much effort to organize (e.g., very few metadata are required for others to use these data). The small proportion of articles in our sample using or sharing a scripting language suggests that, unlike authors in other fields (Eglen et al. <a class="xref bibr" href="#ref016"><span class="show-for-sr">Reference Ellison</span>2017</a>), archaeologists do not appear to recognize code for data analysis as a primary research object to share with the research community. Code written by the researcher for even the simplest operations should be shared along with data because code communicates decisions made about the data analysis that can help others understand, analyze, and reuse the data.</p></div><div class="sec" data-magellan-destination="sec6-4" id="sec6-4"><h3 class="B"> Datasets in Repositories Tracked by DataCite</h3><p class="p"> Our third investigation of archaeologists’ data sharing behaviors is an analysis of all records (<em class="italic">n</em> = 118,311, as of August 2017) of archaeological data files in online repositories tracked by DataCite. DataCite is the primary (but not the only) organization that provides persistent identifiers, or DOIs, for research data. Although DataCite is an international organization, our results are highly concentrated in just a few locations in Europe. The majority of records come from the Archaeology Data Service (ADS) in the United Kingdom, followed by the Dutch Data Archiving and Networked Services (DANS-EASY; <a class="xref fig" href="#fig003">Figure 3)</a>. Although the earliest data date to 1640, file deposition started and steadily increased during 1990–2008, as personal computers became more affordable and data became easier to digitize. There was a substantial increase in 2009, when the DANS-EASY repository saw an influx of records from Dutch cultural resource management firms (<a class="xref fig" href="#fig004">Figure 4a</a>). This can be explained as a result of Dutch archaeology legislation, the Wet op de archeologische monumentenzorg, in 2007 that formally obligated archaeologists in the Netherlands to archive their data via DANS within two years of completion of their projects (Keers et al. <a class="xref bibr" href="#ref033"><span class="show-for-sr">Reference Keers, van derReijden and van Rossum</span>2011</a>). Another spike occurred in 2012, when tDAR: The Digital Archaeological Record had a large increase in deposits, likely due to it waiving its deposit fees for that year (Ellison <a class="xref bibr" href="#ref016"><span class="show-for-sr">Reference Ellison</span>2012</a>). After 2009 the volume of deposits declined in many repositories, with the exception of generic repositories such as Figshare, Zenodo, and ResearchGate, which continued to grow (<a class="xref fig" href="#fig004">Figure 4b</a>). </p><section><div class="fig" data-magellan-destination="fig003" id="fig003"><div class="figure-thumb"><img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig3g.jpeg?pub-status=live" class="aop-lazy-load-image" width="1394" height="1227" data-original-image="/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig3g.jpeg" data-zoomable="true"></div><div class="caption"><p class="p"> </p><p class="p"><span class="label">FIGURE 3.</span> Distributions of data centers, languages, and licenses (see text for details) among archaeology datasets known to DataCite. Horizontal axis is the number of digital object identifiers. DANS = Data Archiving and Networked Services; en = English; nl = Dutch; swe = Swedish; de = German; it = Italian.</p></div></div></section> <section><div class="fig" data-magellan-destination="fig004" id="fig004"><div class="figure-thumb"><img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig4g.jpeg?pub-status=live" class="aop-lazy-load-image" width="1394" height="1272" data-original-image="/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig4g.jpeg" data-zoomable="true"></div><div class="caption"><p class="p"> </p><p class="p"><span class="label">FIGURE 4.</span> Change over time in archaeology datasets known to DataCite. (a) Counts of datasets per year by the major data centers; red lines indicate major increases in the number of datasets in repositories. Note that the date on the horizontal axis indicates when the data were collected, not when they were deposited in the data center. (b) Temporal trends in the number of digital object identifiers (DOIs) at individual data centers. DANS = Data Archiving and Networked Services.</p></div></div></section><p class="p"> Of the 75,632 records with copyright information, the majority are released under the ADS conditions of use (<em class="italic">n</em> = 43,661, 58%), which is broadly similar to the CC-BY-NC (Moore and Richards <a class="xref bibr" href="#ref049"><span class="show-for-sr">Reference Moore, Richards, Wilson and Edwards</span>2015</a>; <a class="xref fig" href="#fig005">Figure 5</a>). The next most frequently used condition is the EU Open Access license (<em class="italic">n</em> = 22,198, 29%), similar to the CC-BY license. Creative Commons licenses such as CC-BY, CC-0, and others are used by less than 3% of records. </p><section><div class="fig" data-magellan-destination="fig005" id="fig005"><div class="figure-thumb"><img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig5g.jpeg?pub-status=live" class="aop-lazy-load-image" width="833" height="1189" data-original-image="/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig5g.jpeg" data-zoomable="true"></div><div class="caption"><p class="p"> </p><p class="p"><span class="label">FIGURE 5.</span> Summary of licenses used by data centers with archaeological data. ADS = Archaeology Data Service; DANS = Data Archiving and Networked Services.</p></div></div></section><p class="p"> Many of the records are gray literature such as unpublished reports (<a class="xref fig" href="#fig006">Figure 6</a>), produced by commercial consultants or research projects. Of the 66,121 (56%) records that include information about the file types in the deposit, 86% (<em class="italic">n</em> = 56,935) contain only PDFs. Structured data files such as spreadsheets, databases, and shapefiles are found either alone or together with other file types (typically PDFs) in 7% (<em class="italic">n</em> = 4,481) of records. The highest proportion of records with structured data files is found in the DANS-EASY repository (<em class="italic">n</em> = 4,304, 14%), followed by the ADS with 0.4% (<em class="italic">n</em> = 174). Note that file format information is not present for all records in this sample, and some of the smaller repositories do not supply this information to DataCite at all, so it is possible that the true proportion of structured files in these records is higher than observed here. </p><section><div class="fig" data-magellan-destination="fig006" id="fig006"><div class="figure-thumb"><img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig6g.jpeg?pub-status=live" class="aop-lazy-load-image" width="1394" height="553" data-original-image="/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig6g.jpeg" data-zoomable="true"></div><div class="caption"><p class="p"> </p><p class="p"><span class="label">FIGURE 6.</span> Relationships between data centers and file formats. The 10 most frequently found file formats are shown, in the data centers with these files from the top 10 data centers by number of digital object identifiers. Open file formats for structured data, such as CSV, are not common. DANS = Data Archiving and Networked Services.</p></div></div></section><p class="p"> From this pilot study of repository data we see that policy mandates in the United Kingdom and the Netherlands have been effective in populating repositories with archaeological data. With the 2012 spike in records deposited in tDAR we also see that archaeologists are sensitive to the price of sharing data. Nearly all records are made available under conditions that permit others to use the data while requiring that the original author of the data receive credit in reuse. In most cases this is because the repository hosting the data requires the author to choose one of these permissive licenses as a condition of making the deposit. However, although many items are <em class="italic">able</em> to be reused because of the licenses, the ease of reuse of these items is low. Only a small fraction of the items deposited are machine-readable structured data files. The majority of items are PDFs, which require extensive manual handling to extract tabular data for convenient reuse and combination with other datasets.</p><p class="p"> We can get some insights into the impact of repository data by querying the Crossref (<a class="xref bibr" href="#ref011">2017</a>) Event Data service, a freely accessible DOI citation dataset produced by periodic scanning of a variety of online sources. Crossref is an official DOI registration agency, focused on issuing DOIs for scholarly materials such as journal articles, books, e-books, and so on. Another source for data citation data is the Thomson Reuters Data Citation Index (Pavlech <a class="xref bibr" href="#ref061"><span class="show-for-sr">Reference Pavlech</span>2016</a>), but this resource is not free, and we did not have access to a subscription. Of the 118,311 DOIs in our sample, we found a total of 366 citations of 80 unique DOIs in the Crossref events data. The majority of these citations were found on Wikipedia (<a class="xref fig" href="#fig007">Figure 7</a>), followed by other data repositories (i.e., cross-citations from one dataset to another in the same repository). The largest source of citations is the Zenodo repository, followed by articles in the journal <em class="italic">Medieval Archaeology</em>, which appear here because Volumes 1–50 are hosted by the ADS. According to the Crossref data, citation of archaeological data in repositories is a recent phenomenon, with the majority in our sample occurring over the last 12 months. We found only one example of a dataset DOI cited in a journal article (McCoy and Codlin <a class="xref bibr" href="#ref042"><span class="show-for-sr">Reference McCoy and Codlin</span>2016</a>). This may be an underestimate, given Huggett's (<a class="xref bibr" href="#ref027"><span class="show-for-sr">Reference Huggett</span>2017</a>) finding that 56 of 476 archaeology data sources in the Thomson Reuters Data Citation Index have citations in Web of Science databases. However, Huggett notes that the Thomson Reuters citation data are unreliable, for example, with incomplete citations and citations that predate the cited dataset. Huggett also shows an example of archaeologists citing datasets but omitting the DOI (e.g., Nugent and Williams <a class="xref bibr" href="#ref058"><span class="show-for-sr">Reference Nugent, Williams, Danielsson, Fahlander and Sjöstrand</span>2012</a>), so the citation is not captured by services such as Crossref or Thomson Reuters. These malformed citations add to the difficulty of accurately measuring data citation in archaeology (cf. Belter <a class="xref bibr" href="#ref005a"><span class="show-for-sr">Reference Belter</span>2014</a>). </p><section><div class="fig" data-magellan-destination="fig007" id="fig007"><div class="figure-thumb"><img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig7g.jpeg?pub-status=live" class="aop-lazy-load-image" width="1394" height="972" data-original-image="/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig7g.jpeg" data-zoomable="true"></div><div class="caption"><p class="p"> </p><p class="p"><span class="label">FIGURE 7.</span> Citation of archaeological data digital object identifiers tracked by the Crossref Event Data service. Inset shows the distribution of these citations over time. DANS = Data Archiving and Networked Services, GBIF = Global Biodiversity Information Facility.</p></div></div></section></div></div><div class="sec" data-magellan-destination="sec7" id="sec7"><h2 class="A"> HOW TO SHARE ARCHAEOLOGICAL DATA?</h2><p class="p"> In the pilot studies reported above we see three common approaches to sharing data: sending files privately by e-mail, following a personal request; including data as supplementary files submitted for publication with a journal article; and sharing data by depositing files in a trustworthy, public, DOI-issuing repository. The third method, via a public repository, is by far the most widely recommended method both specifically by archaeologists (Kansa and Kansa <a class="xref bibr" href="#ref031"><span class="show-for-sr">Reference Kansa and Kansa</span>2013</a>; Kansa et al. <a class="xref bibr" href="#ref032"><span class="show-for-sr">Reference Kansa, Kansa and Watrall</span>2011</a>; Kintigh <a class="xref bibr" href="#ref035"><span class="show-for-sr">Reference Kintigh</span>2006</a>) and in other sciences (see Cranston et al. <a class="xref bibr" href="#ref010"><span class="show-for-sr">Reference Cranston, Harmon, O'Leary and Lisle</span>2014</a>; Mounce <a class="xref bibr" href="#ref050"><span class="show-for-sr">Reference Mounce and Moore</span>2014</a>; Penev et al. <a class="xref bibr" href="#ref062"><span class="show-for-sr">Reference Penev, Mietchen, Chavan, Hagedorn, Smith, Shotton, Tuama, Senderov, Georgiev, Stoev, Groom, Remsen and Edmunds</span>2017</a>; Thessen and Patterson <a class="xref bibr" href="#ref081"><span class="show-for-sr">Reference Thessen and Patterson</span>2011</a>; Whitlock <a class="xref bibr" href="#ref089"><span class="show-for-sr">Reference Whitlock</span>2011</a>). Lists of trustworthy repositories are maintained by the editors of the journal <em class="italic">Scientific Data</em> (Nature: <em class="italic">Scientific Data</em> Editors <a class="xref bibr" href="#ref053">2017</a>) and <a class="uri simple" href="http://www.re3data.org">http://www.re3data.org</a>, a global registry of research data repositories (Pampel et al. <a class="xref bibr" href="#ref060"><span class="show-for-sr">Reference Pampel, Vierkant, Scholze, Bertelmann, Kindling, Klump and Dierolf</span>2013</a>). University librarians are another resource for advice on choosing repositories and preparing data for sharing. There are many guidelines and conventions from other disciplines that are relevant to sharing archaeological data (Inter-university Consortium for Political and Social Research <a class="xref bibr" href="#ref029">2012</a>; Strasser et al. <a class="xref bibr" href="#ref078"><span class="show-for-sr">Reference Strasser, Cook, Michener and Budden</span>2012</a>; Wilkinson et al. <a class="xref bibr" href="#ref091"><span class="show-for-sr">Reference Wilkinson, Dumontier, Aalbersberg, Appleton, Axton, Baak and Mons</span>2016</a>). <a class="xref table" href="#tbl008">Table 8</a> summarizes some of these, and recommendations specifically written for archaeologists can be found in Archaeology Data Service/Digital Antiquity (<a class="xref bibr" href="#ref004">2011</a>) and Archaeological Resources in Cultural Heritage: A European Standard (<a class="xref bibr" href="#ref003">2014</a>). Many of these guidelines may create new technical and practical challenges for archaeologists who are not accustomed to sharing data, and many archaeologists do not want to dedicate resources to sharing data. However, we should not let the perfect be the enemy of the good. In our own work we try to follow these guidelines with no dedicated resources for data sharing and with an incomplete knowledge of some of the technical details of repository operations. While we recognize that our data sharing practices fall short of all the guidelines, we still see value in sharing data imperfectly, rather than not at all. </p><div class="table-wrap" data-magellan-destination="tbl008" id="tbl008"><div class="caption"><p class="p"><span class="label">TABLE 8.</span> Nine Key Things to Do before Sharing Your Data.</p></div><span><div class="figure-thumb"><img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab8.gif?pub-status=live" class="aop-lazy-load-image" width="977" height="863" data-original-image="/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab8.gif" data-zoomable="true"></div></span><div class="table-wrap-foot"><div class="fn-group footnotes"><div class="fn" data-magellan-destination="manual_ref-7" id="manual_ref-7"><p class="p"> <em class="italic">Source:</em> Hart et al. <a class="xref bibr" href="#ref025"><span class="show-for-sr">Reference Hart, Barmby, LeBauer, Michonneau, Mount, Mulrooney and Hollister</span>2016</a>; White et al. <a class="xref bibr" href="#ref088"><span class="show-for-sr">Reference White, Baldridge, Brym, Locey, McGlinn and Supp</span>2013</a>.</p></div></div></div></div><p class="p"> Data sharing is a relatively new behavior for most archaeologists, so it is important to clarify what does not count as data sharing. Making data available on a website that is not a trustworthy, persistent-identifier-issuing repository is not data sharing. For example, data files hosted on a personal faculty web page or the Web server of a research project is problematic because these files could disappear at any time, the data can change in untracked ways, and published links to the files are broken when these websites are reorganized, making the files inaccessible. Sharing data via supplementary files attached to a journal article is problematic because they are inaccessible to nonsubscribers for paywalled journals, because publishers often alter the data file formats and file names during the production process, and because publishers own those data and the original authors may need to request permission to reuse their own data. For example, machine-readable formats such as CSV are sometimes presented as PDF or Microsoft Word files in supplementary files. The modification of supplementary file names during the article production process can break scripts that attempt to read files by referring to file names as they were prior to the publisher handling the files.</p><p class="p"> Finally, authors who write in their publications that “the data are available by request” are not sharing data. Not only are the data not actually available, but there is no way to enforce that the authors will actually honor any requests for their data. A simple strategy for avoiding these pseudo-sharing behaviors is to deposit data files in a trustworthy data repository and use persistent links to the files in reports and manuscripts. In our own work we have used figshare.com, zenodo.org, osf.io, neotomadb.org, opencontext.org, and our university repositories. However, there are many other options, and we recommend seeking advice from librarians and funding agencies to help with finding the most suitable service.</p></div><div class="sec" data-magellan-destination="sec8" id="sec8"><h2 class="A"> HOW TO CITE ARCHAEOLOGICAL DATA?</h2><p class="p"> The Joint Declaration of Data Citation Principles is the current standard in many disciplines for data citation within any research object (Data Citation Synthesis Group <a class="xref bibr" href="#ref013"><span class="show-for-sr">Reference Martone</span>2014</a>). The result of these principles is a citation formation as follows (the exact order of the items may vary slightly across different referencing styles): </p><div class="disp-quote"><p class="p"> Creator (PublicationYear). Title. Version. Publisher. Identifier</p></div><p class="p"> The “PublicationYear,” “Title,” and “Publisher” are familiar from traditional citation formats, except that the publisher may not be a traditional book or journal publisher but, rather, a dedicated data repository organization. “Creator” is equivalent to the author or primary collector and arranger of the data (e.g., Open Context or tDAR). The “Version” and “Identifier” fields are unique to data citation and can take a variety of forms. The identifier is a special type of persistent internet address. The identifier must have two important qualities: the address contains some metadata (such as the identity of the publisher, but not necessarily in a human-readable format), and it remains fixed to a specific file or bundle of files over the lifetime of those objects, even if their location on the internet changes. There are several varieties of these in use, with DOIs currently the most common form. When a data publisher changes the internet address of a dataset, it is responsible for informing the DOI-issuing authority so that the DOI resolves to the new location of the files.</p><p class="p"> A DOI consists of three parts: </p><ul class="list nomark"><li class="list-item"><p class="p"><span class="label">•</span> the proxy, usually <a class="uri simple" href="https://doi.org">https://doi.org</a> but sometimes also <a class="uri simple" href="https://hdl.handle.net/">https://hdl.handle.net/</a> or just DOI</p></li><li class="list-item"><p class="p"><span class="label">•</span> the prefix, usually a string of numbers that is unique to the organization publishing the DOI. For example, the tDAR prefix is 10.6067</p></li><li class="list-item"><p class="p"><span class="label">•</span> the suffix, a random-looking string of any combination of characters and numbers that uniquely identify the file or bundle of files; this is also known as a local identifier (local ID) or an “accession,” an identifier that, by itself, is only guaranteed to be locally unique within the database or source (McMurry et al. <a class="xref bibr" href="#ref045"><span class="show-for-sr">Reference McMurry, Juty, Blomberg, Burdett, Conlin, Conte and Parkinson</span>2017</a>)</p></li></ul><p class="p"> A DOI alone is not sufficient for citing a dataset, because, as with many things on the internet, the files can change over time. For example, we might upload data files to the repository when we submit a paper for peer review and then get a DOI and cite that in our manuscript so the peer reviewers can see them (some services provide a private URL that can be shared with reviewers but is not publicly accessible or discoverable by a search engine). Then when we resubmit our manuscript after peer review, we might update the repository to include additional data files relating to additional analyses requested by the reviewers. We still have the same DOI in our manuscript for this dataset, but the files have changed because of the revisions we did in response to the peer reviewers’ comments. Most data repositories will keep each version of the files that you upload, so in this scenario there will be at least two versions of the files in our data repository. Even if we do not follow this exact sequence of updating the files, we may still change them in the future, after our article has been published. Because of this potential to change the files stored at the DOI, it is necessary to identify specific file versions in our citation of the data. A simple method to record the version is to include the date that the data files were accessed, as when citing any other resource found on the internet. Some repositories also provide DOIs that have a version number suffix such as “.v2,” “.2,” “/2.” However, this is not formally a functionality of the DOI system, so including the date accessed is a more robust and general method of indicating the version. Technological solutions to issues of transience, permanence, and granularity in data citation are still in development.</p><p class="p"> When citing data in the text of a manuscript or report, the ideal practice is to cite the dataset and the traditional publication that first described it, like so (Penev et al. <a class="xref bibr" href="#ref062"><span class="show-for-sr">Reference Penev, Mietchen, Chavan, Hagedorn, Smith, Shotton, Tuama, Senderov, Georgiev, Stoev, Groom, Remsen and Edmunds</span>2017</a>): “This paper uses data from the [name] data repository at <a class="uri simple" href="https://doi.org/xxx/xxx">http://doi.org/xxx/xxx</a> (Author YYYY), first described in Author YYYY.” For example: “This paper uses data from the Open Science Framework data repository at <a class="uri simple" href="https://doi.org/10.17605/OSF.IO/32A87">http://doi.org/10.17605/OSF.IO/32A87</a> (Marwick <a class="xref bibr" href="#ref046"><span class="show-for-sr">Reference Marwick</span>2017</a>), first described in Marwick et al. <a class="xref bibr" href="#ref047"><span class="show-for-sr">Reference Marwick, Hayes, Clarkson and Fullagar</span>2017</a>.” Citing both the dataset and the publication helps to resolve ambiguity about the source of the data and gives context to the data. Furthermore, the authorship of the data files may differ from that of the paper describing the data, and it is important that both authors (or sets of authors) receive credit for their work through the normal mechanisms of citation recognition. In cases where authors have published their data and then make an intellectual contribution to a separate study led by others that uses the published data, then the data author should also be a coauthor of the separate study. If other researchers use published data in their publication without any input from the data author, then citation of the data is all that is required, and adding the data author as a coauthor is not necessary.</p><p class="p"> In the paper in which these data are first described, the citation statement should be included in the body of the paper, in the “Material and Methods” section. In addition, the formal data reference should be included in the paper's reference list, using the recommended journal's reference format. For example, here is a statement that would appear in the paper in which the data are first presented: “The data underpinning the analysis reported in this paper were deposited in the Open Science Framework Repository at <a class="uri simple" href="https://doi.org/10.17605/OSF.IO/32A87">http://doi.org/10.17605/OSF.IO/32A87</a> (Marwick <a class="xref bibr" href="#ref046"><span class="show-for-sr">Reference Marwick</span>2017</a>).” The reference list of that paper would include Marwick <a class="xref bibr" href="#ref046"><span class="show-for-sr">Reference Marwick</span>2017</a> as </p><div class="disp-quote"><p class="p"> Marwick, B. (<a class="xref bibr" href="#ref047"><span class="show-for-sr">Reference Marwick, Hayes, Clarkson and Fullagar</span>2017</a>). Dataset and R code for “Movement of lithics by trampling: An experiment in the Madjedbebe sediments,” <a class="uri simple" href="https://doi.org/10.17605/OSF.IO/32A87">http://doi.org/10.17605/OSF.IO/32A87</a>, Open Science Framework, Accessed 7 Sept 2017.</p></div></div><div class="sec conclusions" data-magellan-destination="sec9" id="sec9"><h2 class="A"> SUMMARY AND CONCLUSION</h2><p class="p"> The status of research data is slowly changing in many fields from a gift culture where data are privately held commodities traded for coauthorship, invitations, and other status tokens (Hagstrom <a class="xref bibr" href="#ref023"><span class="show-for-sr">Reference Hagstrom, Barnes and Edge</span>1982</a>; Wallis et al. <a class="xref bibr" href="#ref087"><span class="show-for-sr">Reference Wallis, Rolando and Borgman</span>2013</a>) to part of the public commons, accessible to anyone with an internet connection. This change has been fastest in data-rich “big science” fields, such as astronomy, physics, and oceanography, that use expensive instruments that generate data shared with large teams of collaborators to produce new discoveries (Reichman and Uhlir <a class="xref bibr" href="#ref068"><span class="show-for-sr">Reference Reichman and Uhlir</span>2003</a>). However, in “smaller” data-poor sciences such as archaeology, where collaborative groups are modest, data sharing is still mostly ad hoc and disaggregated. Our pilot studies, while small and limited in many ways, support this statement, with an overall sharing rate of 20% in responses to private requests for data and 53% of sampled journal articles with openly available data, in a wide variety of formats and locations. We found that legislation requiring data sharing resulted in a substantial increase in the rate of data deposition and that archaeologists are sensitive to repository fees. In examining archaeological data deposited in repositories, we found that most of the contents are PDF files of reports and only a very small fraction of the data files are structured data such as spreadsheets. Archaeologists still have a long way to go in making their data easier to reuse by sharing data in plain text structured data formats such as CSV. We found that citation of datasets, although problematic to measure, appears to be almost nonexistent in archaeological literature. <a class="xref table" href="#tbl008">Table 8</a> summarizes nine key tasks archaeologists should do with their data to optimize their discovery and reuse.</p><p class="p"> Among the many challenges of increasing the rate of data sharing in archaeology and other “small” sciences is that data sharing is unfunded, unrewarded, and only rarely required. Who is responsible for changing this? There is no simple answer, but we believe that the task must be shared between individuals and institutions, with individuals pushing for institutional change. The challenge of funding can be tackled by practical action from members of professional societies. For example, the Society for American Archaeology should communicate to funding bodies, such as the National Science Foundation, about the importance of allocating funds specifically for data sharing and providing reliable information on how to choose and use data repositories. The challenge of requiring data sharing can also be addressed by funding bodies and federal agencies, through more rigorous evaluation and enforcement of data management plans during the review process. However, changes in the policies of professional societies and other agencies are primarily responses to changes in norms in the community of researchers. In practice, individual researchers are responsible for communicating the value of data sharing to professional societies and funding agencies (e.g., by holding peers to high standards of data sharing during grant reviews). Changes in individual practice will demonstrate a change in norms and motivate our institutions to formalize these new data sharing values through policies and regulations.</p><p class="p"> One area where changes in individual practice may be especially effective is among journal editors and editorial boards. For example, editors of archaeology journals should adopt the Transparency and Openness Protocols of the Center for Open Science (Nosek et al. <a class="xref bibr" href="#ref057"><span class="show-for-sr">Reference Nosek, Alter, Banks, Borsboom, Bowman, Breckler and Yarkoni</span>2015</a>). Editors should then educate their editorial board members, peer reviewers, and contributors about their obligations regarding these protocols. Editors should only accept papers that, minimally, provide the raw data used to produce each table and visualization in the manuscript. Changes in journal-specific submission guidelines can result in substantial changes in authors’ research practices (Giofrè et al. <a class="xref bibr" href="#ref020"><span class="show-for-sr">Reference Giofrè, Cumming, Fresc, Boedker and Tressoldi</span>2017</a>; Macleod et al. <a class="xref bibr" href="#ref042a"><span class="show-for-sr">Reference Macleod</span>2017</a>). For example, if archaeology journals adopt incentives such as badging (Kidwell et al. <a class="xref bibr" href="#ref034"><span class="show-for-sr">Reference Kidwell, Lazarević, Baranski, Hardwicke, Piechowski, Falkenberg and Nosek</span>2016</a>), and require as a condition of publication that submitted articles include in the text a properly formatted citation to the data analyzed in the article (or if the data cannot be shared, an explanation why the data are not available), authors would quickly adapt to this new requirement to ensure that their articles will be published.</p><p class="p"> Individual researchers can also contribute to changing the norms on data sharing via several practical actions suitable for typical day-to-day work: </p><ul class="list nomark"><li class="list-item"><p class="p"><span class="label">•</span> When preparing research for publication, we can upload data to a trustworthy repository and cite the data in the manuscript.</p></li><li class="list-item"><p class="p"><span class="label">•</span> When writing peer reviews of journal articles, we can tell editors that we require data to be available for papers that we read and that the manuscripts we review must include citations of datasets analyzed in them. We can write in our peer reviews that data need to be available for us to make a proper assessment of the manuscript.</p></li><li class="list-item"><p class="p"><span class="label">•</span> When reviewing grant applications, we can critically evaluate data management plans and report on the applicants’ past record on data sharing. We can offer helpful suggestions about how to improve data availability (e.g., <a class="xref table" href="#tbl008">Table 8</a>).</p></li><li class="list-item"><p class="p"><span class="label">•</span> When teaching students, we can instruct them how to prepare data for sharing, and how to find and use publicly available datasets to give students experience analyzing and writing about shared data, and cultivate the expectation among students that data sharing is a normal part of archaeological research.</p></li><li class="list-item"><p class="p"><span class="label">•</span> When evaluating candidates for hiring and promotion, we can ensure that commitments to data sharing are valued.</p></li></ul><p class="p"> The challenge of data sharing as an unrewarded behavior was the primary motivation for this article. By specifying a standard for the scholarly citation of archaeological data we provide a method for giving credit to the authors or compilers of data. This rewards those authors with visibility and recognition for their effort with citations, a currency that is valued broadly in the discipline. The data citation standard outlined above is easy for humans and search engines to understand and is highly functional, with a full internet address for the data contained in the citation. The standard can accommodate the addition of further information required by different citation styles and publishers’ requirements. The data citation standard is not dependent on any specific choices of software, archive, data producer, publisher, or author and is easy to adapt into existing writing and research workflows. There are still many unresolved challenges in data sharing, such as how to manage high volumes of data generated by photogrammetry. However, we are confident that solutions to many of these challenges will emerge as data sharing is increasingly valued by the research community.</p></div></div><div class="back"><div class="ack" data-magellan-destination="ack1" id="ack1"><h2 class="A"> Acknowledgments</h2><p class="p"> This article emerged out of discussions at the 2017 Society for American Archaeology meeting, specifically the forum “Beyond Data Management: A Conversation about ‘Digital Data Realities,’” organized by Sarah Kansa and Eric Kansa, and the Society for American Archaeology Publications Committee. Thanks go to Canan Çakirlar for insights into the history of archaeological data sharing in the Netherlands and Stefano Costa for many helpful suggestions. BM was supported by an Australian Research Council Future Fellowship (FT140100101). Thanks go to Stefanie Crabtree and the other four peer reviewers for their constructive suggestions.</p></div><div class="sec" data-magellan-destination="sec11" id="sec11"><h2 class="A"> Data Availability Statement</h2><p class="p"> This article uses data from the Open Science Framework repository at <a class="uri simple" href="https://doi.org/10.17605/OSF.IO/KSRUZ">http://doi.org/10.17605/OSF.IO/KSRUZ</a>. (Marwick and Pilaar Birch <a class="xref bibr" href="#ref048a"><span class="show-for-sr">Reference Marwick and Birch</span>2018</a>), first described in this article. See the methods section of the text for more details. A capsule of text, data and code to reproduce this article is also available in Code Ocean, a computational reproducibility platform at <a class="uri simple" href="https://doi.org/10.24433/CO.ca12b3f0-55a2-4eba-9687-168c8281e535">https://doi.org/10.24433/CO.ca12b3f0-55a2-4eba-9687-168c8281e535</a>.</p></div></div></div></div>   <hr aria-hidden="true" class="list-divider separator default" data-v-7036083a> <div id="references-list" class="circle-list"><h2>References</h2> <div id="reference-header-mjet4i" aria-flowto="reference-1-content reference-1-button"><h4> REFERENCES CITED </h4> <div class="circle-list__item__indicator"></div> <div aria-hidden="true" data-test-hidden="true" class="circle-list__item__number"> </div> <div class="circle-list__item__grouped"><div id="reference-1-content" class="circle-list__item__grouped__content"></div></div></div><div id="manual_ref-8" aria-flowto="reference-2-content reference-2-button" class="circle-list__item"> <div class="circle-list__item__indicator"></div> <div aria-hidden="true" data-test-hidden="true" class="circle-list__item__number"> </div> <div class="circle-list__item__grouped"><div id="reference-2-content" class="circle-list__item__grouped__content"><span class="name"><span class="surname">Alsheikh-Ali</span>, <span class="given-names">Alawi A.</span></span>, <span class="string-name"><span class="surname">Qureshi</span>, <span class="given-names">Waqas</span></span>, <span class="string-name"><span class="surname">Al-Mallah</span>, <span class="given-names">Mouaz H.</span></span>, and <span class="string-name"><span class="surname">Ioannidis</span>, <span class="given-names">John P. 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data-v-241a4b23><button class="figures__ref" data-v-241a4b23> View in content </button> <img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" alt="Figure 0" data-zoomable="true" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab1.gif" data-enlarged-image="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab1.gif" class="graphic" data-v-241a4b23></div> <div data-v-241a4b23><div class="caption" data-v-241a4b23><p data-v-241a4b23><span class="label" data-v-241a4b23>TABLE 1.</span> <span data-v-241a4b23><span class="p">Selection of Definitions of Research Data.</span></span></p></div> </div></div> <hr aria-hidden="true" class="separator dashed" data-v-7036083a data-v-241a4b23></div><div data-v-241a4b23><div class="figures__item" data-v-241a4b23><div class="figures__item__image-box" data-v-241a4b23><button class="figures__ref" data-v-241a4b23> View in content </button> <img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" alt="Figure 1" data-zoomable="true" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab2.gif" data-enlarged-image="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab2.gif" class="graphic" data-v-241a4b23></div> <div data-v-241a4b23><div class="caption" data-v-241a4b23><p data-v-241a4b23><span class="label" data-v-241a4b23>TABLE 2.</span> <span data-v-241a4b23><span class="p">Selection of Types of Data Encountered by Archaeologists.</span></span></p></div> </div></div> <hr aria-hidden="true" class="separator dashed" data-v-7036083a data-v-241a4b23></div><div data-v-241a4b23><div class="figures__item" data-v-241a4b23><div class="figures__item__image-box" data-v-241a4b23><button class="figures__ref" data-v-241a4b23> View in content </button> <img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" alt="Figure 2" data-zoomable="true" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab3.gif" data-enlarged-image="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab3.gif" class="graphic" data-v-241a4b23></div> <div data-v-241a4b23><div class="caption" data-v-241a4b23><p data-v-241a4b23><span class="label" data-v-241a4b23>TABLE 3.</span> <span data-v-241a4b23><span class="p">Summary of Public and Community Costs of Data Sharing.</span></span></p></div> </div></div> <hr aria-hidden="true" class="separator dashed" data-v-7036083a data-v-241a4b23></div><div data-v-241a4b23><div class="figures__item" data-v-241a4b23><div class="figures__item__image-box" data-v-241a4b23><button class="figures__ref" data-v-241a4b23> View in content </button> <img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" alt="Figure 3" data-zoomable="true" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab4.gif" data-enlarged-image="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab4.gif" class="graphic" data-v-241a4b23></div> <div data-v-241a4b23><div class="caption" data-v-241a4b23><p data-v-241a4b23><span class="label" data-v-241a4b23>TABLE 4.</span> <span data-v-241a4b23><span class="p">Summary of Public and Community Benefits of Data Sharing.</span></span></p></div> </div></div> <hr aria-hidden="true" class="separator dashed" data-v-7036083a data-v-241a4b23></div><div data-v-241a4b23><div class="figures__item" data-v-241a4b23><div class="figures__item__image-box" data-v-241a4b23><button class="figures__ref" data-v-241a4b23> View in content </button> <img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" alt="Figure 4" data-zoomable="true" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab5.gif" data-enlarged-image="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab5.gif" class="graphic" data-v-241a4b23></div> <div data-v-241a4b23><div class="caption" data-v-241a4b23><p data-v-241a4b23><span class="label" data-v-241a4b23>TABLE 5.</span> <span data-v-241a4b23><span class="p">Summary of Individual Benefits to Data Sharing.</span></span></p></div> </div></div> <hr aria-hidden="true" class="separator dashed" data-v-7036083a data-v-241a4b23></div><div data-v-241a4b23><div class="figures__item" data-v-241a4b23><div class="figures__item__image-box" data-v-241a4b23><button class="figures__ref" data-v-241a4b23> View in content </button> <img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" alt="Figure 5" data-zoomable="true" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab6.gif" data-enlarged-image="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab6.gif" class="graphic" data-v-241a4b23></div> <div data-v-241a4b23><div class="caption" data-v-241a4b23><p data-v-241a4b23><span class="label" data-v-241a4b23>TABLE 6.</span> <span data-v-241a4b23><span class="p">Summary of Stodden's “Reproducible Research Standard.”</span></span></p></div> </div></div> <hr aria-hidden="true" class="separator dashed" data-v-7036083a data-v-241a4b23></div><div data-v-241a4b23><div class="figures__item" data-v-241a4b23><div class="figures__item__image-box" data-v-241a4b23><button class="figures__ref" data-v-241a4b23> View in content </button> <img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" alt="Figure 6" data-zoomable="true" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab7.gif" data-enlarged-image="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab7.gif" class="graphic" data-v-241a4b23></div> <div data-v-241a4b23><div class="caption" data-v-241a4b23><p data-v-241a4b23><span class="label" data-v-241a4b23>TABLE 7.</span> <span data-v-241a4b23><span class="p">Summary of a Selection of Previous Studies of Data Sharing in Various Fields.</span></span></p></div> </div></div> <hr aria-hidden="true" class="separator dashed" data-v-7036083a data-v-241a4b23></div><div data-v-241a4b23><div class="figures__item" data-v-241a4b23><div class="figures__item__image-box" data-v-241a4b23><button class="figures__ref" data-v-241a4b23> View in content </button> <img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" alt="Figure 7" data-zoomable="true" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary-alt:20231030111511-90556-mediumThumb-S2326376818000037_fig1g.jpg" data-enlarged-image="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary-alt:20231030111511-39828-optimisedImage-S2326376818000037_fig1g.jpg" class="graphic" data-v-241a4b23></div> <div data-v-241a4b23><div class="caption" data-v-241a4b23><p data-v-241a4b23><span class="label" data-v-241a4b23>FIGURE 1.</span> <span data-v-241a4b23><span class="p">Summary of results of e-mail requests for data: (a) responses by year of journal article publication; (b) student status of first author; (c) country location of first author; (d) journal. AAS = <span class="italic">Archaeological and Anthropological Sciences</span>; AQ = <span class="italic">American Antiquity</span>; ARCHMTRY = <span class="italic">Archaeometry</span>; JAS = <span class="italic">Journal of Archaeological Science</span>; JFA = <span class="italic">Journal of Field Archaeology</span>; JHE = <span class="italic">Journal of Human Evolution</span>; LT = <span class="italic">Lithic Technology</span>; PLOSONE = <span class="italic">PLOS ONE</span>; SCI = <span class="italic">Science</span>.</span></span></p></div> </div></div> <hr aria-hidden="true" class="separator dashed" data-v-7036083a data-v-241a4b23></div><div data-v-241a4b23><div class="figures__item" data-v-241a4b23><div class="figures__item__image-box" data-v-241a4b23><button class="figures__ref" data-v-241a4b23> View in content </button> <img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" alt="Figure 8" data-zoomable="true" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig2g.jpeg" data-enlarged-image="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig2g.jpeg" class="graphic" data-v-241a4b23></div> <div data-v-241a4b23><div class="caption" data-v-241a4b23><p data-v-241a4b23><span class="label" data-v-241a4b23>FIGURE 2.</span> <span data-v-241a4b23><span class="p">Summary of relationships among file types, data types, and data locations: (a) file types and data locations; (b) data types and data locations.</span></span></p></div> </div></div> <hr aria-hidden="true" class="separator dashed" data-v-7036083a data-v-241a4b23></div><div data-v-241a4b23><div class="figures__item" data-v-241a4b23><div class="figures__item__image-box" data-v-241a4b23><button class="figures__ref" data-v-241a4b23> View in content </button> <img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" alt="Figure 9" data-zoomable="true" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary-alt:20231030111511-83403-mediumThumb-S2326376818000037_fig3g.jpg" data-enlarged-image="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary-alt:20231030111511-00856-optimisedImage-S2326376818000037_fig3g.jpg" class="graphic" data-v-241a4b23></div> <div data-v-241a4b23><div class="caption" data-v-241a4b23><p data-v-241a4b23><span class="label" data-v-241a4b23>FIGURE 3.</span> <span data-v-241a4b23><span class="p">Distributions of data centers, languages, and licenses (see text for details) among archaeology datasets known to DataCite. Horizontal axis is the number of digital object identifiers. DANS = Data Archiving and Networked Services; en = English; nl = Dutch; swe = Swedish; de = German; it = Italian.</span></span></p></div> </div></div> <hr aria-hidden="true" class="separator dashed" data-v-7036083a data-v-241a4b23></div><div data-v-241a4b23><div class="figures__item" data-v-241a4b23><div class="figures__item__image-box" data-v-241a4b23><button class="figures__ref" data-v-241a4b23> View in content </button> <img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" alt="Figure 10" data-zoomable="true" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary-alt:20231030111511-09203-mediumThumb-S2326376818000037_fig4g.jpg" data-enlarged-image="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary-alt:20231030111511-74280-optimisedImage-S2326376818000037_fig4g.jpg" class="graphic" data-v-241a4b23></div> <div data-v-241a4b23><div class="caption" data-v-241a4b23><p data-v-241a4b23><span class="label" data-v-241a4b23>FIGURE 4.</span> <span data-v-241a4b23><span class="p">Change over time in archaeology datasets known to DataCite. (a) Counts of datasets per year by the major data centers; red lines indicate major increases in the number of datasets in repositories. Note that the date on the horizontal axis indicates when the data were collected, not when they were deposited in the data center. (b) Temporal trends in the number of digital object identifiers (DOIs) at individual data centers. DANS = Data Archiving and Networked Services.</span></span></p></div> </div></div> <hr aria-hidden="true" class="separator dashed" data-v-7036083a data-v-241a4b23></div><div data-v-241a4b23><div class="figures__item" data-v-241a4b23><div class="figures__item__image-box" data-v-241a4b23><button class="figures__ref" data-v-241a4b23> View in content </button> <img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" alt="Figure 11" data-zoomable="true" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary-alt:20231030111511-91667-mediumThumb-S2326376818000037_fig5g.jpg" data-enlarged-image="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary-alt:20231030111511-29896-optimisedImage-S2326376818000037_fig5g.jpg" class="graphic" data-v-241a4b23></div> <div data-v-241a4b23><div class="caption" data-v-241a4b23><p data-v-241a4b23><span class="label" data-v-241a4b23>FIGURE 5.</span> <span data-v-241a4b23><span class="p">Summary of licenses used by data centers with archaeological data. ADS = Archaeology Data Service; DANS = Data Archiving and Networked Services.</span></span></p></div> </div></div> <hr aria-hidden="true" class="separator dashed" data-v-7036083a data-v-241a4b23></div><div data-v-241a4b23><div class="figures__item" data-v-241a4b23><div class="figures__item__image-box" data-v-241a4b23><button class="figures__ref" data-v-241a4b23> View in content </button> <img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" alt="Figure 12" data-zoomable="true" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig6g.jpeg" data-enlarged-image="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig6g.jpeg" class="graphic" data-v-241a4b23></div> <div data-v-241a4b23><div class="caption" data-v-241a4b23><p data-v-241a4b23><span class="label" data-v-241a4b23>FIGURE 6.</span> <span data-v-241a4b23><span class="p">Relationships between data centers and file formats. The 10 most frequently found file formats are shown, in the data centers with these files from the top 10 data centers by number of digital object identifiers. Open file formats for structured data, such as CSV, are not common. DANS = Data Archiving and Networked Services.</span></span></p></div> </div></div> <hr aria-hidden="true" class="separator dashed" data-v-7036083a data-v-241a4b23></div><div data-v-241a4b23><div class="figures__item" data-v-241a4b23><div class="figures__item__image-box" data-v-241a4b23><button class="figures__ref" data-v-241a4b23> View in content </button> <img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" alt="Figure 13" data-zoomable="true" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary-alt:20231030111511-14858-mediumThumb-S2326376818000037_fig7g.jpg" data-enlarged-image="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary-alt:20231030111511-83793-optimisedImage-S2326376818000037_fig7g.jpg" class="graphic" data-v-241a4b23></div> <div data-v-241a4b23><div class="caption" data-v-241a4b23><p data-v-241a4b23><span class="label" data-v-241a4b23>FIGURE 7.</span> <span data-v-241a4b23><span class="p">Citation of archaeological data digital object identifiers tracked by the Crossref Event Data service. Inset shows the distribution of these citations over time. DANS = Data Archiving and Networked Services, GBIF = Global Biodiversity Information Facility.</span></span></p></div> </div></div> <hr aria-hidden="true" class="separator dashed" data-v-7036083a data-v-241a4b23></div><div data-v-241a4b23><div class="figures__item" data-v-241a4b23><div class="figures__item__image-box" data-v-241a4b23><button class="figures__ref" data-v-241a4b23> View in content </button> <img src="data:image/gif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==" alt="Figure 14" data-zoomable="true" data-src="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab8.gif" data-enlarged-image="https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab8.gif" class="graphic" data-v-241a4b23></div> <div data-v-241a4b23><div class="caption" data-v-241a4b23><p data-v-241a4b23><span class="label" data-v-241a4b23>TABLE 8.</span> <span data-v-241a4b23><span class="p">Nine Key Things to Do before Sharing Your Data.</span></span></p></div> </div></div> </div></div>    <div id="addendum-tab" class="addendums tab-pane" data-v-0947a2a3><h2 class="addendums__header" data-v-0947a2a3> An addendum has been issued for this article: </h2> <div class="container-fluid" data-v-0947a2a3><div class="row addendums__articles-box" data-v-0947a2a3><div class="article-card col-sm-6" data-v-a97137fe data-v-0947a2a3><p class="article-title" data-v-a97137fe><a href="/core/product/0FC4AFB0A9F932E96E0C2D6C07AFF171" class="app-link app-link__text app-link--accent" data-v-63dfaf6e data-v-a97137fe><span class="text" data-v-63dfaf6e>A Standard for the Scholarly Citation of Archaeological Data as an Incentive to Data Sharing – ADDENDUM </span> </a></p> <div class="authors" data-v-a97137fe><span data-v-a97137fe><a href="/core/search?filters%5BauthorTerms%5D=Ben%20Marwick&eventCode=SE-AU" class="app-link app-link__text app-link--accent" data-v-63dfaf6e data-v-a97137fe><span class="text" data-v-63dfaf6e>Ben Marwick </span> </a><span data-v-a97137fe> and</span> </span><span data-v-a97137fe><a href="/core/search?filters%5BauthorTerms%5D=Suzanne%20E.%20Pilaar%20Birch&eventCode=SE-AU" class="app-link app-link__text app-link--accent" data-v-63dfaf6e data-v-a97137fe><span class="text" data-v-63dfaf6e>Suzanne E. Pilaar Birch </span> </a><span data-v-a97137fe></span> </span></div> <div data-v-a97137fe><a href="/core/product/738B2AB7F925D064AB96A2E664ED2521" class="app-link app-link__text app-link--underlined" data-v-63dfaf6e data-v-a97137fe><span class="text" data-v-63dfaf6e>Advances in Archaeological Practice </span> </a><span data-v-a97137fe>,</span> <a href="/core/product/58F5EC148CF77D8DA11AA56EB188B695" class="app-link app-link__text app-link--underlined" data-v-63dfaf6e data-v-a97137fe><span class="text" data-v-63dfaf6e>Volume 6 </span> </a><span data-v-a97137fe>,</span> <a href="/core/product/56A227A968928065DAEF288D0B806C8B" class="app-link app-link__text app-link--underlined" data-v-63dfaf6e data-v-a97137fe><span class="text" data-v-63dfaf6e>Special Issue 3: Interpreting and Presenting Archaeology </span> </a></div></div></div></div></div>     <div id="metrics-tab" publication-date="05 April 2018" class="metrics tab-pane" data-v-c41a0c86><div class="app-loader" 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{layout:"default",data:[{article:{id:"E7154AE4990ED752EC76F9AFF0977807",metadata:{title:k,htmlTitle:k,subtitle:a,authorsGroup:{authors:{contributors:[{givenNames:l,surname:m,nameStyle:i,affiliations:[{text:"University of Washington, Seattle, WA 98115, USA"}],isCorresponding:c,notes:"\u003Cdiv class=\"corresp\"\u003E(\u003Ca href=\"mailto:bmarwick@uw.edu\"\u003Ebmarwick@uw.edu\u003C\u002Fa\u003E, corresponding author)\u003C\u002Fdiv\u003E",isAnonymous:b,fullName:n,searchUrl:o,orcidUrl:e},{givenNames:p,surname:q,nameStyle:i,affiliations:[{text:"University of Georgia, Athens, GA 30602, USA"}],isCorresponding:b,notes:a,isAnonymous:b,fullName:r,searchUrl:s,orcidUrl:e}]},translators:{contributors:[],label:"Translated by"}},collections:[],publishedDate:"05 April 2018",keywords:[],openPracticeBadges:[],doi:{url:"https:\u002F\u002Fdoi.org\u002F10.1017\u002Faap.2018.3",value:"10.1017\u002Faap.2018.3"},copyright:{statement:["Copyright 2018 © Society for American Archaeology "],holder:["Society for American Archaeology"],year:[2018]},creativeCommons:e,acceptedManuscript:b,type:"research-article",pageRange:{range:"125 - 143",firstPage:"125",lastPage:"143"},typeDescription:"SPECIAL SECTION: DIGITAL DATA REUSE IN ARCHAEOLOGY",resultTypes:[],commentsCount:t,topicsAndSubtopics:e},journal:{id:u,title:g,titleSlug:v,mnemonic:"AAP",titleHistory:[],isFirstView:b,journalSlug:v,isCompanion:b,parentCompanionJournalName:g,associatedParentCollection:e,paymentInfo:{prices:{"£":{price:21,sku:j,skuNew:h,currency:"£"},"€":{price:24,sku:j,skuNew:h,currency:"€"},US$:{price:26,sku:j,skuNew:h,currency:"US$"},AU$:{price:41,sku:j,skuNew:h,currency:"AU$"}}},url:w,firstViewUrl:"\u002Fcore\u002Fjournals\u002Fadvances-in-archaeological-practice\u002Ffirstview",coverUrl:"https:\u002F\u002Fstatic.cambridge.org\u002Fcovers\u002FAAP_0_0_0\u002Fadvances-in-archaeological-practice.jpg",submitMaterialsUrl:"\u002Fcore\u002Fjournals\u002Fadvances-in-archaeological-practice\u002Finformation\u002Fauthor-instructions\u002Fsubmitting-your-materials",hasHistory:b,latestTitle:g,latestId:u,hasPastTitle:b,issue:{id:"0E42182312FF97D724012F0CCA9E0993",number:"2",title:"Issue 2",publishedDate:x,printPublishTimestamp:y,url:z},volume:{id:"58F5EC148CF77D8DA11AA56EB188B695",number:"6",title:A,publishedDate:x,printPublishTimestamp:y,url:"\u002Fcore\u002Fjournals\u002Fadvances-in-archaeological-practice\u002Fvolume\u002F58F5EC148CF77D8DA11AA56EB188B695"}},abstract:{textAbstracts:[{title:"Abstract",content:"\u003Cdiv class=\"abstract\" data-abstract-type=\"normal\"\u003E\u003Cp\u003EHow do archaeologists share their research data, if at all? We review what data are, according to current influential definitions, and previous work on the benefits, costs, and norms of data sharing in the sciences broadly. To understand data sharing in archaeology, we present the results of three pilot studies: requests for data by e-mail, review of data availability in published articles, and analysis of archaeological datasets deposited in repositories. We find that archaeologists are often willing to share but that discipline-wide sharing is patchy and ad hoc. Legislation and mandates are effective at increasing data sharing, but editorial policies at journals lack adequate enforcement. Although most of the data available at repositories are licensed to enable flexible reuse, only a small proportion of the data are stored in structured formats for easy reuse. We present some suggestions for improving the state of date sharing in archaeology; among these is a standard for citing datasets to ensure that researchers making their data publicly available receive appropriate credit.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E",lang:B},{title:a,content:"\u003Cdiv class=\"abstract\" data-abstract-type=\"translated\"\u003E\u003Cp\u003E¿Cómo comparten los arqueólogos sus datos de investigación, si lo hacen? Revisamos qué datos son, de acuerdo con las actuales definiciones influyentes, y el trabajo previo sobre los beneficios, costos y estándares de intercambio de datos en las ciencias en general. Para comprender el intercambio de datos en arqueología, presentamos los resultados de tres estudios piloto: solicitudes de datos por correo electrónico; revisión de la disponibilidad de datos en artículos publicados y análisis de conjuntos de datos arqueológicos depositados en repositorios. Encontramos que los arqueólogos están a menudo dispuestos a compartir, pero el intercambio de toda la disciplina es desigual y ad hoc. La legislación y los mandatos son eficaces para aumentar el intercambio de datos, pero las políticas editoriales de las revistas carecen de aplicación adecuada. Aunque la mayoría de los datos disponibles en repositorios están autorizados para permitir la reutilización flexible, sólo una pequeña proporción de los datos se almacena en formatos estructurados para su fácil reutilización. Presentamos algunas sugerencias para mejorar el estado de la fecha de intercambio en arqueología, incluyendo un estándar para citar conjuntos de datos para asegurar que los investigadores que hacen sus datos disponibles públicamente reciben crédito apropiado.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E",lang:"es"}]},content:{html:"\u003Cdiv class=\"article research-article JATS\"\u003E\u003Cdiv class=\"body\"\u003E\u003Ch2 class=\"A clr-fn-label\"\u003E \u003C\u002Fh2\u003E\u003Cdiv class=\"disp-quote\"\u003E\u003Cp class=\"p\"\u003E Data are the building blocks of research, and access to data is necessary to fully understand and extend the work of others (Fienberg, Martin, and Straf \u003Ca class=\"xref bibr\" href=\"#ref018\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Fienberg, Martin and Straf\u003C\u002Fspan\u003E1985\u003C\u002Fa\u003E). Yet archaeological data are often not available for a variety of reasons, including a perceived lack of credit for making data available. The goal of this article is to present a standard for the scholarly citation of archaeological data, akin to the way we cite publications. The need for this has been motivated by Huggett's (\u003Ca class=\"xref bibr\" href=\"#ref027\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Huggett\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E) findings of low levels of data citation and reuse in archaeology and Clarke's (\u003Ca class=\"xref bibr\" href=\"#ref009\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Clarke\u003C\u002Fspan\u003E2015\u003C\u002Fa\u003E) analysis that digital data preservation and management systems are underdeveloped in archaeology, despite our obligations as stewards of cultural heritage. A standard for data citation is important because it gives credit to the authors or compilers of the data and so accords them visibility and recognition for their scholarly contribution in a way that is valued broadly in the discipline. A citation standard is also important for tracking the use of data and generating metrics about the impact of a dataset on the discipline. Understanding the use of resources such as datasets is important for allocating resources within archaeology and identifying novel research directions.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cp class=\"p\"\u003E Archaeologists work with data in research, commercial, government, and other contexts. A full investigation of data in each of these contexts is beyond the scope of this article, so we limit this study to what we are most familiar with, the research context. However, many of the practical details we discuss here will also be relevant to archaeologists working in other contexts. We begin by defining what we mean by “data” and reviewing previous work on researchers’ attitudes to sharing data. We motivate data sharing and citing behaviors by explaining why data sharing is beneficial to archaeology as a discipline and to individual researchers. Then we survey some of the data sharing and citing norms that have emerged in other research areas. From these norms we derive a standard suitable for the citation of archaeological data. Finally, we present some basic instructions on how to share archaeological data.\u003C\u002Fp\u003E\u003Cdiv class=\"sec\" data-magellan-destination=\"sec1\" id=\"sec1\"\u003E\u003Ch2 class=\"A\"\u003E WHAT ARE DATA?\u003C\u002Fh2\u003E\u003Cp class=\"p\"\u003E Defining what data are is an ongoing research problem for information scientists (Borgman \u003Ca class=\"xref bibr\" href=\"#ref006\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Borgman\u003C\u002Fspan\u003E2012\u003C\u002Fa\u003E; Leonelli \u003Ca class=\"xref bibr\" href=\"#ref041\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Leonelli\u003C\u002Fspan\u003E2016\u003C\u002Fa\u003E). \u003Ca class=\"xref table\" href=\"#tbl001\"\u003ETable 1\u003C\u002Fa\u003E summarizes some of the definitions debated in the philosophy of science literature. A more concrete starting point is this definition from the National Academies of Sciences:\n\u003C\u002Fp\u003E\u003Cdiv class=\"disp-quote\"\u003E\u003Cp class=\"p\"\u003E The term “data” as used in this document is meant to be broadly inclusive. In addition to digital manifestations of literature (including text, sound, still images, moving images, models, games, or simulations), it refers as well to forms of data and databases that generally require the assistance of computational machinery and software in order to be useful, such as various types of laboratory data including spectrographic, genomic sequencing, and electron microscopy data; observational data, such as remote sensing, geospatial, and socioeconomic data; and other forms of data either generated or compiled, by humans or machines [Uhlir and Cohen \u003Ca class=\"xref bibr\" href=\"#ref083\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Uhlir and Cohen\u003C\u002Fspan\u003E2011\u003C\u002Fa\u003E, cited in Borgman \u003Ca class=\"xref bibr\" href=\"#ref007\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Borgman\u003C\u002Fspan\u003E2015\u003C\u002Fa\u003E p. 1061].\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\n\u003Cdiv class=\"table-wrap\" data-magellan-destination=\"tbl001\" id=\"tbl001\"\u003E\u003Cdiv class=\"caption\"\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003ETABLE 1.\u003C\u002Fspan\u003E Selection of Definitions of Research Data.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cspan\u003E\u003Cdiv class=\"figure-thumb\"\u003E\u003Cimg src=\"data:image\u002Fgif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==\" data-src=\"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab1.gif?pub-status=live\" class=\"aop-lazy-load-image\" width=\"466\" height=\"309\" data-original-image=\"\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab1.gif\" data-zoomable=\"false\"\u003E\u003C\u002Fdiv\u003E\u003C\u002Fspan\u003E\u003C\u002Fdiv\u003E\u003Cp class=\"p\"\u003E This is a useful definition because it emphasizes data as digital manifestations and so suitable for transmission, storage, and retrieval using the internet. Physical objects (such as samples, collections, hard copies of notes, etc.) are data also, but they are out of the scope of this discussion because of the different challenges and requirements for access, archiving, and so on. The categories shown in \u003Ca class=\"xref table\" href=\"#tbl002\"\u003ETable 2\u003C\u002Fa\u003E are useful as a rough guide to what data can be, although this table is not comprehensive and does not represent the full diversity of data used by archaeologists. Ultimately, data are whatever the community decides, as the National Science Foundation notes in its commentary on data management plans: “‘Data’ are defined as the recorded factual material commonly accepted in the scientific community as necessary to validate research findings. This includes original data, but also ‘metadata’ (e.g. experimental protocols, code written for statistical analyses, etc.)” (2010).\n\u003C\u002Fp\u003E\u003Cdiv class=\"table-wrap\" data-magellan-destination=\"tbl002\" id=\"tbl002\"\u003E\u003Cdiv class=\"caption\"\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003ETABLE 2.\u003C\u002Fspan\u003E Selection of Types of Data Encountered by Archaeologists.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cspan\u003E\u003Cdiv class=\"figure-thumb\"\u003E\u003Cimg src=\"data:image\u002Fgif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==\" data-src=\"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab2.gif?pub-status=live\" class=\"aop-lazy-load-image\" width=\"466\" height=\"378\" data-original-image=\"\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab2.gif\" data-zoomable=\"false\"\u003E\u003C\u002Fdiv\u003E\u003C\u002Fspan\u003E\u003C\u002Fdiv\u003E\u003Cp class=\"p\"\u003E These definitions in policy are consistent with the relational account of data in philosophy of science scholarship, which holds that what data are is dependent on who uses it, how, and for what purpose (Daston and Galison \u003Ca class=\"xref bibr\" href=\"#ref012\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Daston and Galison\u003C\u002Fspan\u003E1992\u003C\u002Fa\u003E; Leonelli \u003Ca class=\"xref bibr\" href=\"#ref040\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Leonelli\u003C\u002Fspan\u003E2015\u003C\u002Fa\u003E). How can these definitions and philosophical discussions inform our practice? They tell us that what count as data are emergent from our professional interactions. Data are the materials we request when we do peer review of manuscripts and grant proposals or when we make an informal request for more information by e-mail. They are not exclusively objective archaeometric records but, rather, include interpretive and derivative results. In short, data are anything another person needs to understand our work better.\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E To translate this into a more applied context, our practice of data sharing is to make available the computer files (e.g., containing text, raw structured data, images, etc.) that we analyze or otherwise use to create the figures, tables, and quantitative results that we include in our reports and publications. These include our derived results and secondary data, such as taxonomic and typological classifications. This selection omits materials that could also be considered data according to the definitions noted above (e.g., field notes), but we feel that our selection represents the most information-dense and meaningful items that are useful for our community of interest. These are also the items that have been most frequently requested from us by others in our professional interactions. Our citation standard is inclusive and imposes no constraints on what data can be, but we propose a few minimal qualities for how data should be organized that will help ground our discussion.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"sec\" data-magellan-destination=\"sec2\" id=\"sec2\"\u003E\u003Ch2 class=\"A\"\u003E WHAT ARE THE ETHICS OF DATA SHARING?\u003C\u002Fh2\u003E\u003Cp class=\"p\"\u003E A full treatment of the ethical issues of data sharing is beyond the scope of this article, but we can highlight some points specifically relevant to the question of when \u003Cem class=\"italic\"\u003Enot\u003C\u002Fem\u003E to share archaeological data. Much of the discussion on ethics and data sharing generally relates to appropriate attribution (assigning credit for the data) in data reuse (Duke and Porter \u003Ca class=\"xref bibr\" href=\"#ref015\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Duke and Porter\u003C\u002Fspan\u003E2013\u003C\u002Fa\u003E). The default position for archaeological data, like data from many other sciences, is that they are part of the global human patrimony and should be accessible to other scholars and the public (Vitelli and Colwell-Chanthaphonh \u003Ca class=\"xref bibr\" href=\"#ref086\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Vitelli and Colwell-Chanthaphonh\u003C\u002Fspan\u003E2006\u003C\u002Fa\u003E; Zimmerman et al. \u003Ca class=\"xref bibr\" href=\"#ref094\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Zimmerman, Vitelli and Hollowell-Zimmer\u003C\u002Fspan\u003E2003\u003C\u002Fa\u003E). However, archaeologists, like other researchers working with local and indigenous communities, governments, and international collaborators, generate research data that are intellectual property with many stakeholders. With diverse stakeholders, archaeological data can become entangled in economics, nationalism, cultural politics, and identity (Nicholas and Wylie \u003Ca class=\"xref bibr\" href=\"#ref055\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Nicholas, Wylie, Young and Brunk\u003C\u002Fspan\u003E2009\u003C\u002Fa\u003E). Often, one of these stakeholders is an indigenous community, and archaeologists may find differences between Western and indigenous conceptions of what data are and how they may be generated, used, shared, and “owned” (Harding et al. \u003Ca class=\"xref bibr\" href=\"#ref024\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Harding, Harper, Stone, O'Neill, Berger, Harris and Donatuto\u003C\u002Fspan\u003E2012\u003C\u002Fa\u003E).\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E One of these differences is that Western researchers often have individual autonomy in sharing data, and once data are shared, they are free for all to use. In contrast, within many indigenous communities, data are part of the group's identity and property, akin to tangible heritage (Nicholas \u003Ca class=\"xref bibr\" href=\"#ref054\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Nicholas, Coombe, Wershler and Zeilinger\u003C\u002Fspan\u003E2014\u003C\u002Fa\u003E), with certain members entrusted to keep that data on behalf of the group, and sometimes it may be inappropriate for these people to share data with other members of the group or beyond the group (Tsosie \u003Ca class=\"xref bibr\" href=\"#ref082\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Tsosie\u003C\u002Fspan\u003E2007\u003C\u002Fa\u003E). Differences between Western and indigenous concepts of data are not only cultural—substantial inequalities exist in access to resources for converting archaeological data into economic and political benefit (e.g., graduate degrees, employment, publications, grants, promotions). On one hand, making archaeological data openly available benefits indigenous communities because it removes some of the financial and technical barriers to accessing that data (e.g., subscription fees to journals). On the other hand, this may be little more than a useless courtesy (Hymes \u003Ca class=\"xref bibr\" href=\"#ref028\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Hymes\u003C\u002Fspan\u003E1972\u003C\u002Fa\u003E), because simply making data openly available does not give indigenous people access to the contexts where data can be used to generate economic and political benefits. Thus, open data have different values for Western and indigenous communities, and if archaeologists want to make a compelling case to their indigenous partners for data sharing, they should also make a commitment to reducing some of the inequalities in making use of the data, for example, by providing some training in data analysis or adapting the data to a context that is valued by the indigenous community.\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E In short, there is more to the ethics of data sharing than avoiding risk of damage. Redacting the locations of archaeological sites to prevent looting is an obvious case of preparing data for sharing to reduce the risk of damage to the sites (and in some jurisdictions, mandated by law). However, because of cultural, economic, and political differences between Western archaeologists and the communities they work with, archaeologists cannot consider their own risk assessment to be comprehensive and exhaustive (Nicholas and Wylie \u003Ca class=\"xref bibr\" href=\"#ref056\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Nicholas, Wylie, Coningham and Scarre\u003C\u002Fspan\u003E2012\u003C\u002Fa\u003E). It is crucial for archaeologists to discuss data sharing plans during negotiations with representatives authorized by the indigenous peoples whose cultural heritage is the subject of investigation. This negotiation process is important because only indigenous peoples themselves can identify potential adverse outcomes to data sharing, and they can do this only if they understand the proposed research and anticipated results. The concrete outcome of this process is often a memorandum of understanding between the indigenous community and the archaeologist's employer that documents the outcome of negotiations about data sharing. If an archaeologist's indigenous partners have reasons not to share data in public, then the archaeologist should respect that position and note in publication that the data are restricted at the request of the indigenous collaborators.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"sec\" data-magellan-destination=\"sec3\" id=\"sec3\"\u003E\u003Ch2 class=\"A\"\u003E WHAT ARE THE BENEFITS OF DATA SHARING TO THE INDIVIDUAL RESEARCHER?\u003C\u002Fh2\u003E\u003Cp class=\"p\"\u003E While public and community benefits and costs are well rehearsed and widely agreed on (\u003Ca class=\"xref table\" href=\"#tbl003\"\u003ETables 3\u003C\u002Fa\u003E and \u003Ca class=\"xref table\" href=\"#tbl004\"\u003E4\u003C\u002Fa\u003E), they offer no, or only weak, tangible incentives to the individual researcher (Rowhani-Farid, Allen, and Barnett \u003Ca class=\"xref bibr\" href=\"#ref070\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Rowhani-Farid, Allen and Barnett\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E). Previous work has identified citation advantages as a tangible benefit to individual researchers who share data with publications, although the magnitude of the advantage varies greatly (see \u003Ca class=\"xref table\" href=\"#tbl005\"\u003ETable 5\u003C\u002Fa\u003E for a summary). Increased publication productivity also appeared to be associated with data sharing behaviors. Across more than 7,000 National Science Foundation and National Institute of the Humanities awards, Pienta, Alter, and Lyle (\u003Ca class=\"xref bibr\" href=\"#ref064\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Pienta, Alter and Lyle\u003C\u002Fspan\u003E2010\u003C\u002Fa\u003E) report that research projects with archived data produced a median of 10 publications, vs. only five for projects without archived data.\n\u003C\u002Fp\u003E\u003Cdiv class=\"table-wrap\" data-magellan-destination=\"tbl003\" id=\"tbl003\"\u003E\u003Cdiv class=\"caption\"\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003ETABLE 3.\u003C\u002Fspan\u003E Summary of Public and Community Costs of Data Sharing.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cspan\u003E\u003Cdiv class=\"figure-thumb\"\u003E\u003Cimg src=\"data:image\u002Fgif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==\" data-src=\"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab3.gif?pub-status=live\" class=\"aop-lazy-load-image\" width=\"466\" height=\"456\" data-original-image=\"\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab3.gif\" data-zoomable=\"false\"\u003E\u003C\u002Fdiv\u003E\u003C\u002Fspan\u003E\u003Cdiv class=\"table-wrap-foot\"\u003E\u003Cdiv class=\"fn-group footnotes\"\u003E\u003Cdiv class=\"fn\" data-magellan-destination=\"manual_ref-1\" id=\"manual_ref-1\"\u003E\u003Cp class=\"p\"\u003E \u003Cem class=\"italic\"\u003ESource:\u003C\u002Fem\u003E From Borgman \u003Ca class=\"xref bibr\" href=\"#ref006\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Borgman\u003C\u002Fspan\u003E2012\u003C\u002Fa\u003E; Stodden \u003Ca class=\"xref bibr\" href=\"#ref077\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Stodden\u003C\u002Fspan\u003E2010\u003C\u002Fa\u003E.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\n\u003Cdiv class=\"table-wrap\" data-magellan-destination=\"tbl004\" id=\"tbl004\"\u003E\u003Cdiv class=\"caption\"\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003ETABLE 4.\u003C\u002Fspan\u003E Summary of Public and Community Benefits of Data Sharing.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cspan\u003E\u003Cdiv class=\"figure-thumb\"\u003E\u003Cimg src=\"data:image\u002Fgif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==\" data-src=\"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab4.gif?pub-status=live\" class=\"aop-lazy-load-image\" width=\"466\" height=\"378\" data-original-image=\"\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab4.gif\" data-zoomable=\"false\"\u003E\u003C\u002Fdiv\u003E\u003C\u002Fspan\u003E\u003Cdiv class=\"table-wrap-foot\"\u003E\u003Cdiv class=\"fn-group footnotes\"\u003E\u003Cdiv class=\"fn\" data-magellan-destination=\"manual_ref-2\" id=\"manual_ref-2\"\u003E\u003Cp class=\"p\"\u003E \u003Cem class=\"italic\"\u003ESource:\u003C\u002Fem\u003E From Borgman \u003Ca class=\"xref bibr\" href=\"#ref006\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Borgman\u003C\u002Fspan\u003E2012\u003C\u002Fa\u003E; Stodden \u003Ca class=\"xref bibr\" href=\"#ref077\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Stodden\u003C\u002Fspan\u003E2010\u003C\u002Fa\u003E.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\n\u003Cdiv class=\"table-wrap\" data-magellan-destination=\"tbl005\" id=\"tbl005\"\u003E\u003Cdiv class=\"caption\"\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003ETABLE 5.\u003C\u002Fspan\u003E Summary of Individual Benefits to Data Sharing.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cspan\u003E\u003Cdiv class=\"figure-thumb\"\u003E\u003Cimg src=\"data:image\u002Fgif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==\" data-src=\"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab5.gif?pub-status=live\" class=\"aop-lazy-load-image\" width=\"466\" height=\"208\" data-original-image=\"\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab5.gif\" data-zoomable=\"false\"\u003E\u003C\u002Fdiv\u003E\u003C\u002Fspan\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"sec\" data-magellan-destination=\"sec4\" id=\"sec4\"\u003E\u003Ch2 class=\"A\"\u003E WHAT DO RESEARCHERS THINK ABOUT SHARING DATA?\u003C\u002Fh2\u003E\u003Cp class=\"p\"\u003E Sharing of data is a quality that is fundamental to the definition of science (cf. Merton's [\u003Ca class=\"xref bibr\" href=\"#ref048\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Merton and Merton\u003C\u002Fspan\u003E1942\u003C\u002Fa\u003E] communalism norm, where intellectual property rights are normally given up in exchange for recognition and esteem) and to data both as an abstract quality (cf. the definitions in \u003Ca class=\"xref table\" href=\"#tbl001\"\u003ETable 1\u003C\u002Fa\u003E that mention data as “portable,” “stable across different domains”) and as a concrete detail to be managed with policy. For example, for over a decade the National Science Foundation has had a data sharing policy that states:\n\u003C\u002Fp\u003E\u003Cdiv class=\"disp-quote\"\u003E\u003Cp class=\"p\"\u003E Investigators are expected to share with other researchers, at no more than incremental cost and within a reasonable time, the primary data, samples, physical collections and other supporting materials created or gathered in the course of work under NSF grants. Grantees are expected to encourage and facilitate such sharing [2016].\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cp class=\"p\"\u003E Surveys of researchers across different domains repeatedly show that people value the ideal of data sharing (Kratz and Strasser \u003Ca class=\"xref bibr\" href=\"#ref036\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Kratz and Strasser\u003C\u002Fspan\u003E2014\u003C\u002Fa\u003E, \u003Ca class=\"xref bibr\" href=\"#ref036\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Kratz and Strasser\u003C\u002Fspan\u003E2015\u003C\u002Fa\u003E). However, data sharing is not widespread in many disciplines, raising questions about why researchers do or do not share their data. Three recent studies have attempted to answer these questions, in the search for strategies to encourage sharing.\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E Stodden (\u003Ca class=\"xref bibr\" href=\"#ref077\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Stodden\u003C\u002Fspan\u003E2010\u003C\u002Fa\u003E) surveyed registrants at a top machine learning conference (the Neural Information Processing Systems conference) to identify the factors that affect researchers’ decisions to reveal data (\u003Ca class=\"xref table\" href=\"#tbl004\"\u003ETable 4\u003C\u002Fa\u003E). Stodden found that communitarian factors influence scientists to share their data but private incentives determine when scientists choose not to share their code and data. In the top three reasons not to share, we see a lack of incentives to invest time in data sharing (however, there are many other time-consuming tasks in research, and they get done because clear reward structures are in place), a lack of faith in data citation (which we hope this article will help with), and a lack of an efficient copyright process. Stodden's (\u003Ca class=\"xref bibr\" href=\"#ref075\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Stodden\u003C\u002Fspan\u003E2009\u003C\u002Fa\u003Ea, \u003Ca class=\"xref bibr\" href=\"#ref076\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Stodden\u003C\u002Fspan\u003E2009\u003C\u002Fa\u003Eb) “Reproducible Research Standard,” summarized in \u003Ca class=\"xref table\" href=\"#tbl006\"\u003ETable 6\u003C\u002Fa\u003E, is a convenient and robust copyright schema that is in wide use for many typical research projects.\n\u003C\u002Fp\u003E\u003Cdiv class=\"table-wrap\" data-magellan-destination=\"tbl006\" id=\"tbl006\"\u003E\u003Cdiv class=\"caption\"\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003ETABLE 6.\u003C\u002Fspan\u003E Summary of Stodden's “Reproducible Research Standard.”\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cspan\u003E\u003Cdiv class=\"figure-thumb\"\u003E\u003Cimg src=\"data:image\u002Fgif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==\" data-src=\"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab6.gif?pub-status=live\" class=\"aop-lazy-load-image\" width=\"977\" height=\"336\" data-original-image=\"\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab6.gif\" data-zoomable=\"true\"\u003E\u003C\u002Fdiv\u003E\u003C\u002Fspan\u003E\u003Cdiv class=\"table-wrap-foot\"\u003E\u003Cdiv class=\"fn-group footnotes\"\u003E\u003Cdiv class=\"fn\" data-magellan-destination=\"manual_ref-3\" id=\"manual_ref-3\"\u003E\u003Cp class=\"p\"\u003E \u003Cem class=\"italic\"\u003ENote:\u003C\u002Fem\u003E This standard realigns legal rights with scientific norms by freeing scientific work from copying and reuse restrictions, with attribution. It encourages researchers to release their products with standardized instructions for reuse and attribution.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"fn\" data-magellan-destination=\"manual_ref-4\" id=\"manual_ref-4\"\u003E\u003Cp class=\"p\"\u003E \u003Cem class=\"italic\"\u003ESource:\u003C\u002Fem\u003E Stodden \u003Ca class=\"xref bibr\" href=\"#ref075\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Stodden\u003C\u002Fspan\u003E2009a\u003C\u002Fa\u003E.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003Cp class=\"p\"\u003E Other surveys have found similar results. Tenopir and colleagues (\u003Ca class=\"xref bibr\" href=\"#ref079\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Tenopir, Allard, Douglass, Aydinoglu, Wu and Frame\u003C\u002Fspan\u003E2011\u003C\u002Fa\u003E) received 1,329 responses to a survey conducted by the research team of the National Science Foundation–funded DataONE project. They found that when researchers did not share data it was due to reasons that included insufficient time (54%), a lack of funding (40%), having no place to put the data (24%), a lack of standards (20%), and the fact that their sponsor did not require it (17%). A remarkable observation by Tenopir and colleagues is that while 75% of respondents said that they shared data, only 36% of those respondents said that their data are easy to access, while only 6% said that they make all their data available. This suggests that most data sharing occurs in private directly between individual researchers, rather than in public via open repositories. A similar focus on private, peer-to-peer sharing is evident in interviews conducted by Wallis and colleagues (\u003Ca class=\"xref bibr\" href=\"#ref087\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Wallis, Rolando and Borgman\u003C\u002Fspan\u003E2013\u003C\u002Fa\u003E) with 43 researchers at the Center for Embedded Networked Sensing. They found that the most commonly cited conditions for sharing data were (1) first rights to publish results, (2) proper attribution to the data source, (3) familiarity between sharer and recipient, (4) funding agency expectations, and (5) the amount of effort required to share.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"sec\" data-magellan-destination=\"sec5\" id=\"sec5\"\u003E\u003Ch2 class=\"A\"\u003E WHAT ARE THE CURRENT NORMS OF DATA SHARING?\u003C\u002Fh2\u003E\u003Cp class=\"p\"\u003E The surveys above show that data sharing practices tend to be a private activity, but where public sharing does occur, what forms does it take, and how do these vary across different fields of research? Wouters and Haak (\u003Ca class=\"xref bibr\" href=\"#ref093\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Wouters and Haak\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E) surveyed 1,200 researchers about data sharing behaviors and found a spectrum from “intensive data-sharing fields” to “restricted data-sharing fields.” Intensive data-sharing fields often depend on access to global databases, for example, human genetics, while restricted data-sharing fields depend mostly on data collected by the individual researcher or his or her immediate collaborators, such as digital humanities. These differences in disciplinary norms have implications for data ownership (intensive: collectively owned, willing to share; restrictive: owned by the individual, reluctant to share) and the effort required to share (intensive: low effort because data are prepared for sharing as an integral part of the research process; restrictive: high effort because data are prepared for sharing after the research, or apart from it, in response to a request from another researcher).\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E This intensive-restricted spectrum also maps onto Sawyer's (\u003Ca class=\"xref bibr\" href=\"#ref072\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Sawyer\u003C\u002Fspan\u003E2008\u003C\u002Fa\u003E) analysis of data sharing in data-rich fields compared with data-poor fields. Data-rich fields, such as astronomy, oceanography, and ecology, have three common characteristics: Pooling and sharing data resources is expected; the cumulative nature of work that builds on common data and common methods reduces the likelihood of alternative data sources and other methods being widespread; and the number of theoretical choices is limited because there is enough data to adequately test competing theories. Data-poor fields have three common characteristics: Data are rare, or hard to get, or both; the types of data available often dictate the methods used; and theories are relatively easy to generate and more difficult to validate. In data-poor fields, researchers often invest substantial time and resources on gathering their own data. The accumulation of data, and the quality of the data collected, is often a key determinant of the status of a researcher, leading to hoarding of data as an asset to be traded for professional benefit.\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E Archaeologists can be found all over this spectrum because of the diversity of archaeological research. However, our observations of data sharing in archaeology, and the nature of archaeological research generally, indicate that it is, for the most part, currently a restricted data-sharing and data-poor field. There are few standardized and widely used methods for sharing and citing data, a challenge for archaeological data sharing that we hope this article will help to address.\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E Even though there is a wide spectrum of data sharing behaviors, overall rates of data sharing by individual researchers are very low in many fields. Wicherts and colleagues (\u003Ca class=\"xref bibr\" href=\"#ref090\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Wicherts, Borsboom, Kats and Molenaar\u003C\u002Fspan\u003E2006\u003C\u002Fa\u003E) e-mailed authors of 141 empirical articles recently published by American Psychological Association (APA) journals to request a copy of the original data reported in the article. They received 64 of the 249 datasets they requested, resulting in a data sharing rate of about 26%, despite all the authors of the articles in their study being bound by the APA's Ethical Principles, which stated that authors must “not withhold the data on which their conclusions are based from other competent professionals who seek to verify the substantive claims” (APA \u003Ca class=\"xref bibr\" href=\"#ref002\"\u003E2001\u003C\u002Fa\u003E:396). Vanpaemel and colleagues (\u003Ca class=\"xref bibr\" href=\"#ref084\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Vanpaemel, Vermorgen, Deriemaecker and Storms\u003C\u002Fspan\u003E2015\u003C\u002Fa\u003E) similarly requested data for 394 articles, published in all issues of four APA journals in 2012, and found that 38% of authors responded with their data. They conclude that although the data sharing rate increased as compared with the study by Wicherts and colleagues, their findings are “worrisome” and indicate a “poor availability of data” (Vanpaemel et al. \u003Ca class=\"xref bibr\" href=\"#ref084\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Vanpaemel, Vermorgen, Deriemaecker and Storms\u003C\u002Fspan\u003E2015\u003C\u002Fa\u003E: 3).\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E Savage and Vickers (\u003Ca class=\"xref bibr\" href=\"#ref071\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Savage and Vickers\u003C\u002Fspan\u003E2009\u003C\u002Fa\u003E) requested data from the authors of 10 papers published in PLOS journals and received only one reply. Although this is a very small sample, it is notable because PLOS journals require authors to share data as a condition of publication: “PLOS journals require authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception” (\u003Cem class=\"italic\"\u003EPLOS\u003C\u002Fem\u003E Editorial and Publishing Policies \u003Ca class=\"xref bibr\" href=\"#ref067\"\u003E2017\u003C\u002Fa\u003E); and yet only one out of the 10 authors actually complied with this requirement. Alsheikh-Ali and colleagues (\u003Ca class=\"xref bibr\" href=\"#ref001\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Alsheikh-Ali, Qureshi, Al-Mallah and Ioannidis\u003C\u002Fspan\u003E2011\u003C\u002Fa\u003E) inspected 500 articles published in high-impact science journals in 2009 and found that only 47 essays (9%) deposited full primary raw data online. Similarly, Womack (\u003Ca class=\"xref bibr\" href=\"#ref092\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Womack\u003C\u002Fspan\u003E2015\u003C\u002Fa\u003E) inspected 4,370 articles published in high-impact science journals in 2014 and found that 13% of articles with original data published had made the data available to others.\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E The absence of a standardized and sustainable approach to data sharing is starkly demonstrated by Vines and colleagues (\u003Ca class=\"xref bibr\" href=\"#ref085\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Vines, Albert, Andrew, Débarre, Bock, Franklin and Rennison\u003C\u002Fspan\u003E2014\u003C\u002Fa\u003E) study of data availability over time in biology publications. They examined the availability of data from 516 studies of plants or animal morphology between two and 22 years old. Vines et al. found that the odds of a dataset referred to in a publication being available fell by 17% per year. They found that the key factors responsible for the unavailability of data were that the data were lost or on inaccessible storage media.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"sec\" data-magellan-destination=\"sec6\" id=\"sec6\"\u003E\u003Ch2 class=\"A\"\u003E DO ARCHAEOLOGISTS SHARE DATA?\u003C\u002Fh2\u003E\u003Cp class=\"p\"\u003E Here we present the results of three small pilot studies aimed at understanding how archaeologists share their data. First, we e-mailed a sample of authors of research publications to request access to the data behind their publication. Second, we surveyed a sample of journal articles to see what data were available. Third, we analyzed information about archaeological data files in online repositories tracked by DataCite (an organization that provides persistent identifiers, or digital object identifiers [DOIs], for research data). The small sample sizes of the first two of these pilot studies limit how generalizable the results are, but as the first of their kind for archaeology, they are valuable for proposing hypotheses for larger-scale investigations.\u003C\u002Fp\u003E\u003Cdiv class=\"sec\" data-magellan-destination=\"sec6-1\" id=\"sec6-1\"\u003E\u003Ch3 class=\"B\"\u003E Reproducibility and Open-Source Materials for This Study\u003C\u002Fh3\u003E\u003Cp class=\"p\"\u003E To enable reuse of our materials and improve reproducibility and transparency according to the principles outlined in Marwick \u003Ca class=\"xref bibr\" href=\"#ref046\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Marwick\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E, we include the entire R code used for all the analysis and visualizations contained in this article in our compendium at \u003Ca class=\"uri simple\" href=\"http:\u002F\u002Fdoi.org\u002F10.17605\u002FOSF.IO\u002FKSRUZ\"\u003Ehttp:\u002F\u002Fdoi.org\u002F10.17605\u002FOSF.IO\u002FKSRUZ\u003C\u002Fa\u003E. Also in this version-controlled compendium are the raw data for all the tests reported here. All of the figures and quantitative results presented here can be independently reproduced with the code and data in this repository. In our compendium our code is released under the MIT license, our data as CC-0, and our figures as CC-BY, to enable maximum reuse (for more details, see Marwick \u003Ca class=\"xref bibr\" href=\"#ref046\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Marwick\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E).\u003C\u002Fp\u003E\u003Cp class=\"continuation codeocean\" style=\"margin: 2em 0 1em 0.2em;\"\u003E\n \u003Ciframe src=\"https:\u002F\u002Fcodeocean.com\u002Falgo.html?algorithmId=ca12b3f0-55a2-4eba-9687-168c8281e535\" style=\"border: none; margin: 0; height: 650px; width: 100%\"\u003E\u003C\u002Fiframe\u003E\u003C\u002Fp\u003E\u003Cdiv class=\"caption\"\u003E\u003Cem class=\"italic\"\u003EReader note: The Code Ocean capsule above contains the code to replicate the results of this article. Users can run the code and view the outputs, but in order to do so they will need to\u003C\u002Fem\u003E \u003Ca class=\"uri simple\" href=\"https:\u002F\u002Fcodeocean.com\u002Fsignup\"\u003Eregister\u003C\u002Fa\u003E \u003Cem class=\"italic\"\u003Eon the Code Ocean site (or\u003C\u002Fem\u003E \u003Ca class=\"uri simple\" href=\"https:\u002F\u002Fcodeocean.com\u002Flogin\"\u003Elogin\u003C\u002Fa\u003E \u003Cem class=\"italic\"\u003Eif they have an existing Code Ocean account)\u003C\u002Fem\u003E.\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"sec\" data-magellan-destination=\"sec6-2\" id=\"sec6-2\"\u003E\u003Ch3 class=\"B\"\u003E E-mail Requests for Data in a Published Article\u003C\u002Fh3\u003E\u003Cp class=\"p\"\u003E Our sample focused on \u003Cem class=\"italic\"\u003EJournal of Archaeological Science\u003C\u002Fem\u003E articles on experiments with stone artifacts that were published during 2009 and 2015. We focused on stone artifact experiments because all research materials in these projects are generated by the researcher, so there are no other stakeholders to consider when determining how to share the data: the researcher has complete control over the data. There are also no risks of damage to people or property in sharing these data because they do not include archaeological sites or human subjects. Because we contacted authors of essays at the intersection of (1) containing eminently sharable data and (2) our bona fide research interests (i.e., stone artifacts for BM), this is a targeted, nonrandom sample that may not be representative of the entire archaeological community. In contacting the authors, we requested the datasets for use in a graduate seminar with the aim of reproducing the published results and further exploring the data (this stage is still in progress). We promised to contact the authors if we discovered anything new and not to share the data further without their permission. The data included with this article are anonymized so the specific authors we contacted are not identifiable.\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E We sent out e-mails to the first authors of 23 stone artifact experiment articles and received 15 replies, resulting in a 70% response rate. We received five responses that included data files, giving an overall sharing rate of 20%. Our small sample size limits the robustness of our analysis but points to some observations that may be worth following up in a larger study. We found no clear relationship of data sharing and date of publication. We do not have any insights into why authors of older essays declined to share, but three of the six responses for articles published in 2014–2015 said that they declined to share data because they intended to use these data in a future publication. Other noteworthy reasons provided by respondents for declining to share include the fact that the authors were about to defend their thesis or about to go on vacation. We found no effect of the author's student status at time of publication on data sharing behaviors, with roughly equal proportions of students and nonstudents sharing and not sharing (\u003Ca class=\"xref fig\" href=\"#fig001\"\u003EFigure 1\u003C\u002Fa\u003E).\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E Similarly there are no clear patterns of data sharing among different journals. It is remarkable that the authors of the \u003Cem class=\"italic\"\u003EPLOS ONE\u003C\u002Fem\u003E article we contacted declined to share because that refusal violates the policy of that journal at the time of publication of the essay (early 2014). Our finding is not unique—Chambers (\u003Ca class=\"xref bibr\" href=\"#ref008\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Chambers\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E) examined 50 \u003Cem class=\"italic\"\u003EPLOS ONE\u003C\u002Fem\u003E articles on brain images published in November 2014–May 2015 and found that only 38% of the essays’ authors had archived their data in an open repository. Similarly, Nuijten and colleagues (\u003Ca class=\"xref bibr\" href=\"#ref059\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Nuijten, Borghuis, Veldkamp, Alvarez, van Assen and Wicherts\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E) found that of 462 \u003Cem class=\"italic\"\u003EPLOS ONE\u003C\u002Fem\u003E articles that promised data availability, 29% of these did not have any available data. Unfulfilled promises to share data are not unique to \u003Cem class=\"italic\"\u003EPLOS ONE\u003C\u002Fem\u003E (Kidwell et al. \u003Ca class=\"xref bibr\" href=\"#ref034\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Kidwell, Lazarević, Baranski, Hardwicke, Piechowski, Falkenberg and Nosek\u003C\u002Fspan\u003E2016\u003C\u002Fa\u003E), but these studies highlight the need to resolve ambiguity among authors about what count as data and for editors and peer reviewers to more critically assess whether authors have made their data openly available.\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E We see similar rates of sharing in response to e-mail requests among archaeologists to what have been described in other disciplines (\u003Ca class=\"xref table\" href=\"#tbl007\"\u003ETable 7\u003C\u002Fa\u003E; Tenopir et al. \u003Ca class=\"xref bibr\" href=\"#ref080\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Tenopir, Dalton, Allard, Frame, Pjesivac, Birch and Dorsett\u003C\u002Fspan\u003E2015\u003C\u002Fa\u003E; Vanpaemel et al. \u003Ca class=\"xref bibr\" href=\"#ref084\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Vanpaemel, Vermorgen, Deriemaecker and Storms\u003C\u002Fspan\u003E2015\u003C\u002Fa\u003E). However, our results may overestimate the true rate because our sample did not include datasets that authors might not be able or willing to share because they contain sensitive location or cultural information. Analysis of the qualitative responses we received suggests two key reasons why authors are reluctant to share: they are fearful that they will lose the opportunity to produce further publications from those data (a typical concern of researchers in data-poor fields) and that their data are not in a form suitable for sharing and the task of organizing those data is not a high priority. These reasons not to share have also been documented in other research areas, noted above. The absence of patterns in the date of publication, student status of the author, and other variables (\u003Ca class=\"xref fig\" href=\"#fig001\"\u003EFigure 1\u003C\u002Fa\u003E) may relate more to our small sample size than to a real absence of relationships in data sharing behavior.\n\u003C\u002Fp\u003E\u003Cdiv class=\"table-wrap\" data-magellan-destination=\"tbl007\" id=\"tbl007\"\u003E\u003Cdiv class=\"caption\"\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003ETABLE 7.\u003C\u002Fspan\u003E Summary of a Selection of Previous Studies of Data Sharing in Various Fields.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cspan\u003E\u003Cdiv class=\"figure-thumb\"\u003E\u003Cimg src=\"data:image\u002Fgif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==\" data-src=\"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab7.gif?pub-status=live\" class=\"aop-lazy-load-image\" width=\"977\" height=\"374\" data-original-image=\"\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab7.gif\" data-zoomable=\"true\"\u003E\u003C\u002Fdiv\u003E\u003C\u002Fspan\u003E\u003Cdiv class=\"table-wrap-foot\"\u003E\u003Cdiv class=\"fn-group footnotes\"\u003E\u003Cdiv class=\"fn\" data-magellan-destination=\"manual_ref-5\" id=\"manual_ref-5\"\u003E\u003Cp class=\"p\"\u003E \u003Cem class=\"italic\"\u003ENote:\u003C\u002Fem\u003E In the study by Peng, Dominici, and Zege (\u003Ca class=\"xref bibr\" href=\"#ref063\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Peng, Dominici and Zege\u003C\u002Fspan\u003E2006\u003C\u002Fa\u003E), the authors did not attempt to contact other researchers but note the availability of data as described in the published article.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"fn\" data-magellan-destination=\"manual_ref-6\" id=\"manual_ref-6\"\u003E\u003Cp class=\"p\"\u003E \u003Cem class=\"italic\"\u003ESource:\u003C\u002Fem\u003E From Spencer \u003Ca class=\"xref bibr\" href=\"#ref074\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Spencer\u003C\u002Fspan\u003E2010\u003C\u002Fa\u003E.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\n\u003Csection\u003E\u003Cdiv class=\"fig\" data-magellan-destination=\"fig001\" id=\"fig001\"\u003E\u003Cdiv class=\"figure-thumb\"\u003E\u003Cimg src=\"data:image\u002Fgif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==\" data-src=\"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig1g.jpeg?pub-status=live\" class=\"aop-lazy-load-image\" width=\"1394\" height=\"930\" data-original-image=\"\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig1g.jpeg\" data-zoomable=\"true\"\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"caption\"\u003E\u003Cp class=\"p\"\u003E \u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003EFIGURE 1.\u003C\u002Fspan\u003E Summary of results of e-mail requests for data: (a) responses by year of journal article publication; (b) student status of first author; (c) country location of first author; (d) journal. AAS = \u003Cem class=\"italic\"\u003EArchaeological and Anthropological Sciences\u003C\u002Fem\u003E; AQ = \u003Cem class=\"italic\"\u003EAmerican Antiquity\u003C\u002Fem\u003E; ARCHMTRY = \u003Cem class=\"italic\"\u003EArchaeometry\u003C\u002Fem\u003E; JAS = \u003Cem class=\"italic\"\u003EJournal of Archaeological Science\u003C\u002Fem\u003E; JFA = \u003Cem class=\"italic\"\u003EJournal of Field Archaeology\u003C\u002Fem\u003E; JHE = \u003Cem class=\"italic\"\u003EJournal of Human Evolution\u003C\u002Fem\u003E; LT = \u003Cem class=\"italic\"\u003ELithic Technology\u003C\u002Fem\u003E; PLOSONE = \u003Cem class=\"italic\"\u003EPLOS ONE\u003C\u002Fem\u003E; SCI = \u003Cem class=\"italic\"\u003EScience\u003C\u002Fem\u003E.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fsection\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"sec\" data-magellan-destination=\"sec6-3\" id=\"sec6-3\"\u003E\u003Ch3 class=\"B\"\u003E Survey of Articles Published in the \u003Cem class=\"italic\"\u003EJournal of Archaeological Science\u003C\u002Fem\u003E\u003C\u002Fh3\u003E\u003Cp class=\"p\"\u003E In addition to our e-mail survey, we also conducted a pilot study of a random sample of 48 articles published during February–May 2017 in the \u003Cem class=\"italic\"\u003EJournal of Archaeological Science\u003C\u002Fem\u003E to investigate data sharing behaviors. In this sample we found openly available raw data for 18 essays (53%), even though only seven articles (21%) include a data availability statement. For essays where data are available, nine (50%) have data contained in supplementary files published with the article, six (33%) have all the raw data in tables in the text of the essays, and three (17%) have data in an online repository. The most frequently shared type of data is compositional (e.g., element concentrations; \u003Cem class=\"italic\"\u003En\u003C\u002Fem\u003E = 9, 28%), followed by radiocarbon and luminescence age data (where the dates are the primary object of analysis; \u003Cem class=\"italic\"\u003En\u003C\u002Fem\u003E = 5, 16%) and DNA sequences (\u003Cem class=\"italic\"\u003En\u003C\u002Fem\u003E = 4, 12%). Generally, compositional data are presented in tables in the text, dating data are presented in supplementary files, and DNA data are in repositories (as is typical for DNA data; \u003Ca class=\"xref fig\" href=\"#fig002\"\u003EFigure 2\u003C\u002Fa\u003E). Data shared in supplementary files and online repositories were most often in Microsoft Excel format (\u003Cem class=\"italic\"\u003En\u003C\u002Fem\u003E = 38, 38% of the files in supplementary files and repositories), followed by CSV (aka comma-separated values, an open-source plain text spreadsheet format; \u003Cem class=\"italic\"\u003En\u003C\u002Fem\u003E = 4, 31%), and tables embedded in Microsoft Word or PDF documents (\u003Cem class=\"italic\"\u003En\u003C\u002Fem\u003E = 4, 31%). When a scripting language such as R, Python, MATLAB, or OxCal was used as the analysis software for an article (\u003Cem class=\"italic\"\u003En\u003C\u002Fem\u003E = 7, 54% of the essays would identify the software), only two also provided the script with the article.\n\u003C\u002Fp\u003E\u003Csection\u003E\u003Cdiv class=\"fig\" data-magellan-destination=\"fig002\" id=\"fig002\"\u003E\u003Cdiv class=\"figure-thumb\"\u003E\u003Cimg src=\"data:image\u002Fgif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==\" data-src=\"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig2g.jpeg?pub-status=live\" class=\"aop-lazy-load-image\" width=\"933\" height=\"714\" data-original-image=\"\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig2g.jpeg\" data-zoomable=\"true\"\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"caption\"\u003E\u003Cp class=\"p\"\u003E \u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003EFIGURE 2.\u003C\u002Fspan\u003E Summary of relationships among file types, data types, and data locations: (a) file types and data locations; (b) data types and data locations.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fsection\u003E\u003Cp class=\"p\"\u003E The \u003Cem class=\"italic\"\u003EJournal of Archaeological Science\u003C\u002Fem\u003E has had a “data disclosure” policy since at least 2013 that states that “all data relating to the article must be made available in Supplementary files or deposited in external repositories and linked to within the article” (\u003Ca class=\"xref bibr\" href=\"#ref030\"\u003E2013\u003C\u002Fa\u003E). The data availability rate of 53% reported in our pilot study reflects either weak enforcement of this policy or an incomplete understanding among editors, reviewers, and authors of how to interpret and implement this policy. The prominence of compositional and dating data among the shared data types suggests a least effort strategy, with authors sharing data that do not require extensive cleaning and tidying (because the data are generated by instruments rather than entered by hand, such as artifact attribute data). The size of compositional and dating datasets means that they mostly fit easily into tables in the text or supplementary files and do not require much effort to organize (e.g., very few metadata are required for others to use these data). The small proportion of articles in our sample using or sharing a scripting language suggests that, unlike authors in other fields (Eglen et al. \u003Ca class=\"xref bibr\" href=\"#ref016\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Ellison\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E), archaeologists do not appear to recognize code for data analysis as a primary research object to share with the research community. Code written by the researcher for even the simplest operations should be shared along with data because code communicates decisions made about the data analysis that can help others understand, analyze, and reuse the data.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"sec\" data-magellan-destination=\"sec6-4\" id=\"sec6-4\"\u003E\u003Ch3 class=\"B\"\u003E Datasets in Repositories Tracked by DataCite\u003C\u002Fh3\u003E\u003Cp class=\"p\"\u003E Our third investigation of archaeologists’ data sharing behaviors is an analysis of all records (\u003Cem class=\"italic\"\u003En\u003C\u002Fem\u003E = 118,311, as of August 2017) of archaeological data files in online repositories tracked by DataCite. DataCite is the primary (but not the only) organization that provides persistent identifiers, or DOIs, for research data. Although DataCite is an international organization, our results are highly concentrated in just a few locations in Europe. The majority of records come from the Archaeology Data Service (ADS) in the United Kingdom, followed by the Dutch Data Archiving and Networked Services (DANS-EASY; \u003Ca class=\"xref fig\" href=\"#fig003\"\u003EFigure 3)\u003C\u002Fa\u003E. Although the earliest data date to 1640, file deposition started and steadily increased during 1990–2008, as personal computers became more affordable and data became easier to digitize. There was a substantial increase in 2009, when the DANS-EASY repository saw an influx of records from Dutch cultural resource management firms (\u003Ca class=\"xref fig\" href=\"#fig004\"\u003EFigure 4a\u003C\u002Fa\u003E). This can be explained as a result of Dutch archaeology legislation, the Wet op de archeologische monumentenzorg, in 2007 that formally obligated archaeologists in the Netherlands to archive their data via DANS within two years of completion of their projects (Keers et al. \u003Ca class=\"xref bibr\" href=\"#ref033\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Keers, van derReijden and van Rossum\u003C\u002Fspan\u003E2011\u003C\u002Fa\u003E). Another spike occurred in 2012, when tDAR: The Digital Archaeological Record had a large increase in deposits, likely due to it waiving its deposit fees for that year (Ellison \u003Ca class=\"xref bibr\" href=\"#ref016\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Ellison\u003C\u002Fspan\u003E2012\u003C\u002Fa\u003E). After 2009 the volume of deposits declined in many repositories, with the exception of generic repositories such as Figshare, Zenodo, and ResearchGate, which continued to grow (\u003Ca class=\"xref fig\" href=\"#fig004\"\u003EFigure 4b\u003C\u002Fa\u003E).\n\u003C\u002Fp\u003E\u003Csection\u003E\u003Cdiv class=\"fig\" data-magellan-destination=\"fig003\" id=\"fig003\"\u003E\u003Cdiv class=\"figure-thumb\"\u003E\u003Cimg src=\"data:image\u002Fgif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==\" data-src=\"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig3g.jpeg?pub-status=live\" class=\"aop-lazy-load-image\" width=\"1394\" height=\"1227\" data-original-image=\"\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig3g.jpeg\" data-zoomable=\"true\"\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"caption\"\u003E\u003Cp class=\"p\"\u003E \u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003EFIGURE 3.\u003C\u002Fspan\u003E Distributions of data centers, languages, and licenses (see text for details) among archaeology datasets known to DataCite. Horizontal axis is the number of digital object identifiers. DANS = Data Archiving and Networked Services; en = English; nl = Dutch; swe = Swedish; de = German; it = Italian.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fsection\u003E\n\u003Csection\u003E\u003Cdiv class=\"fig\" data-magellan-destination=\"fig004\" id=\"fig004\"\u003E\u003Cdiv class=\"figure-thumb\"\u003E\u003Cimg src=\"data:image\u002Fgif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==\" data-src=\"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig4g.jpeg?pub-status=live\" class=\"aop-lazy-load-image\" width=\"1394\" height=\"1272\" data-original-image=\"\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig4g.jpeg\" data-zoomable=\"true\"\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"caption\"\u003E\u003Cp class=\"p\"\u003E \u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003EFIGURE 4.\u003C\u002Fspan\u003E Change over time in archaeology datasets known to DataCite. (a) Counts of datasets per year by the major data centers; red lines indicate major increases in the number of datasets in repositories. Note that the date on the horizontal axis indicates when the data were collected, not when they were deposited in the data center. (b) Temporal trends in the number of digital object identifiers (DOIs) at individual data centers. DANS = Data Archiving and Networked Services.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fsection\u003E\u003Cp class=\"p\"\u003E Of the 75,632 records with copyright information, the majority are released under the ADS conditions of use (\u003Cem class=\"italic\"\u003En\u003C\u002Fem\u003E = 43,661, 58%), which is broadly similar to the CC-BY-NC (Moore and Richards \u003Ca class=\"xref bibr\" href=\"#ref049\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Moore, Richards, Wilson and Edwards\u003C\u002Fspan\u003E2015\u003C\u002Fa\u003E; \u003Ca class=\"xref fig\" href=\"#fig005\"\u003EFigure 5\u003C\u002Fa\u003E). The next most frequently used condition is the EU Open Access license (\u003Cem class=\"italic\"\u003En\u003C\u002Fem\u003E = 22,198, 29%), similar to the CC-BY license. Creative Commons licenses such as CC-BY, CC-0, and others are used by less than 3% of records.\n\u003C\u002Fp\u003E\u003Csection\u003E\u003Cdiv class=\"fig\" data-magellan-destination=\"fig005\" id=\"fig005\"\u003E\u003Cdiv class=\"figure-thumb\"\u003E\u003Cimg src=\"data:image\u002Fgif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==\" data-src=\"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig5g.jpeg?pub-status=live\" class=\"aop-lazy-load-image\" width=\"833\" height=\"1189\" data-original-image=\"\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig5g.jpeg\" data-zoomable=\"true\"\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"caption\"\u003E\u003Cp class=\"p\"\u003E \u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003EFIGURE 5.\u003C\u002Fspan\u003E Summary of licenses used by data centers with archaeological data. ADS = Archaeology Data Service; DANS = Data Archiving and Networked Services.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fsection\u003E\u003Cp class=\"p\"\u003E Many of the records are gray literature such as unpublished reports (\u003Ca class=\"xref fig\" href=\"#fig006\"\u003EFigure 6\u003C\u002Fa\u003E), produced by commercial consultants or research projects. Of the 66,121 (56%) records that include information about the file types in the deposit, 86% (\u003Cem class=\"italic\"\u003En\u003C\u002Fem\u003E = 56,935) contain only PDFs. Structured data files such as spreadsheets, databases, and shapefiles are found either alone or together with other file types (typically PDFs) in 7% (\u003Cem class=\"italic\"\u003En\u003C\u002Fem\u003E = 4,481) of records. The highest proportion of records with structured data files is found in the DANS-EASY repository (\u003Cem class=\"italic\"\u003En\u003C\u002Fem\u003E = 4,304, 14%), followed by the ADS with 0.4% (\u003Cem class=\"italic\"\u003En\u003C\u002Fem\u003E = 174). Note that file format information is not present for all records in this sample, and some of the smaller repositories do not supply this information to DataCite at all, so it is possible that the true proportion of structured files in these records is higher than observed here.\n\u003C\u002Fp\u003E\u003Csection\u003E\u003Cdiv class=\"fig\" data-magellan-destination=\"fig006\" id=\"fig006\"\u003E\u003Cdiv class=\"figure-thumb\"\u003E\u003Cimg src=\"data:image\u002Fgif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==\" data-src=\"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig6g.jpeg?pub-status=live\" class=\"aop-lazy-load-image\" width=\"1394\" height=\"553\" data-original-image=\"\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig6g.jpeg\" data-zoomable=\"true\"\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"caption\"\u003E\u003Cp class=\"p\"\u003E \u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003EFIGURE 6.\u003C\u002Fspan\u003E Relationships between data centers and file formats. The 10 most frequently found file formats are shown, in the data centers with these files from the top 10 data centers by number of digital object identifiers. Open file formats for structured data, such as CSV, are not common. DANS = Data Archiving and Networked Services.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fsection\u003E\u003Cp class=\"p\"\u003E From this pilot study of repository data we see that policy mandates in the United Kingdom and the Netherlands have been effective in populating repositories with archaeological data. With the 2012 spike in records deposited in tDAR we also see that archaeologists are sensitive to the price of sharing data. Nearly all records are made available under conditions that permit others to use the data while requiring that the original author of the data receive credit in reuse. In most cases this is because the repository hosting the data requires the author to choose one of these permissive licenses as a condition of making the deposit. However, although many items are \u003Cem class=\"italic\"\u003Eable\u003C\u002Fem\u003E to be reused because of the licenses, the ease of reuse of these items is low. Only a small fraction of the items deposited are machine-readable structured data files. The majority of items are PDFs, which require extensive manual handling to extract tabular data for convenient reuse and combination with other datasets.\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E We can get some insights into the impact of repository data by querying the Crossref (\u003Ca class=\"xref bibr\" href=\"#ref011\"\u003E2017\u003C\u002Fa\u003E) Event Data service, a freely accessible DOI citation dataset produced by periodic scanning of a variety of online sources. Crossref is an official DOI registration agency, focused on issuing DOIs for scholarly materials such as journal articles, books, e-books, and so on. Another source for data citation data is the Thomson Reuters Data Citation Index (Pavlech \u003Ca class=\"xref bibr\" href=\"#ref061\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Pavlech\u003C\u002Fspan\u003E2016\u003C\u002Fa\u003E), but this resource is not free, and we did not have access to a subscription. Of the 118,311 DOIs in our sample, we found a total of 366 citations of 80 unique DOIs in the Crossref events data. The majority of these citations were found on Wikipedia (\u003Ca class=\"xref fig\" href=\"#fig007\"\u003EFigure 7\u003C\u002Fa\u003E), followed by other data repositories (i.e., cross-citations from one dataset to another in the same repository). The largest source of citations is the Zenodo repository, followed by articles in the journal \u003Cem class=\"italic\"\u003EMedieval Archaeology\u003C\u002Fem\u003E, which appear here because Volumes 1–50 are hosted by the ADS. According to the Crossref data, citation of archaeological data in repositories is a recent phenomenon, with the majority in our sample occurring over the last 12 months. We found only one example of a dataset DOI cited in a journal article (McCoy and Codlin \u003Ca class=\"xref bibr\" href=\"#ref042\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference McCoy and Codlin\u003C\u002Fspan\u003E2016\u003C\u002Fa\u003E). This may be an underestimate, given Huggett's (\u003Ca class=\"xref bibr\" href=\"#ref027\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Huggett\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E) finding that 56 of 476 archaeology data sources in the Thomson Reuters Data Citation Index have citations in Web of Science databases. However, Huggett notes that the Thomson Reuters citation data are unreliable, for example, with incomplete citations and citations that predate the cited dataset. Huggett also shows an example of archaeologists citing datasets but omitting the DOI (e.g., Nugent and Williams \u003Ca class=\"xref bibr\" href=\"#ref058\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Nugent, Williams, Danielsson, Fahlander and Sjöstrand\u003C\u002Fspan\u003E2012\u003C\u002Fa\u003E), so the citation is not captured by services such as Crossref or Thomson Reuters. These malformed citations add to the difficulty of accurately measuring data citation in archaeology (cf. Belter \u003Ca class=\"xref bibr\" href=\"#ref005a\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Belter\u003C\u002Fspan\u003E2014\u003C\u002Fa\u003E).\n\u003C\u002Fp\u003E\u003Csection\u003E\u003Cdiv class=\"fig\" data-magellan-destination=\"fig007\" id=\"fig007\"\u003E\u003Cdiv class=\"figure-thumb\"\u003E\u003Cimg src=\"data:image\u002Fgif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==\" data-src=\"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig7g.jpeg?pub-status=live\" class=\"aop-lazy-load-image\" width=\"1394\" height=\"972\" data-original-image=\"\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_fig7g.jpeg\" data-zoomable=\"true\"\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"caption\"\u003E\u003Cp class=\"p\"\u003E \u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003EFIGURE 7.\u003C\u002Fspan\u003E Citation of archaeological data digital object identifiers tracked by the Crossref Event Data service. Inset shows the distribution of these citations over time. DANS = Data Archiving and Networked Services, GBIF = Global Biodiversity Information Facility.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fsection\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"sec\" data-magellan-destination=\"sec7\" id=\"sec7\"\u003E\u003Ch2 class=\"A\"\u003E HOW TO SHARE ARCHAEOLOGICAL DATA?\u003C\u002Fh2\u003E\u003Cp class=\"p\"\u003E In the pilot studies reported above we see three common approaches to sharing data: sending files privately by e-mail, following a personal request; including data as supplementary files submitted for publication with a journal article; and sharing data by depositing files in a trustworthy, public, DOI-issuing repository. The third method, via a public repository, is by far the most widely recommended method both specifically by archaeologists (Kansa and Kansa \u003Ca class=\"xref bibr\" href=\"#ref031\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Kansa and Kansa\u003C\u002Fspan\u003E2013\u003C\u002Fa\u003E; Kansa et al. \u003Ca class=\"xref bibr\" href=\"#ref032\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Kansa, Kansa and Watrall\u003C\u002Fspan\u003E2011\u003C\u002Fa\u003E; Kintigh \u003Ca class=\"xref bibr\" href=\"#ref035\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Kintigh\u003C\u002Fspan\u003E2006\u003C\u002Fa\u003E) and in other sciences (see Cranston et al. \u003Ca class=\"xref bibr\" href=\"#ref010\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Cranston, Harmon, O'Leary and Lisle\u003C\u002Fspan\u003E2014\u003C\u002Fa\u003E; Mounce \u003Ca class=\"xref bibr\" href=\"#ref050\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Mounce and Moore\u003C\u002Fspan\u003E2014\u003C\u002Fa\u003E; Penev et al. \u003Ca class=\"xref bibr\" href=\"#ref062\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Penev, Mietchen, Chavan, Hagedorn, Smith, Shotton, Tuama, Senderov, Georgiev, Stoev, Groom, Remsen and Edmunds\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E; Thessen and Patterson \u003Ca class=\"xref bibr\" href=\"#ref081\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Thessen and Patterson\u003C\u002Fspan\u003E2011\u003C\u002Fa\u003E; Whitlock \u003Ca class=\"xref bibr\" href=\"#ref089\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Whitlock\u003C\u002Fspan\u003E2011\u003C\u002Fa\u003E). Lists of trustworthy repositories are maintained by the editors of the journal \u003Cem class=\"italic\"\u003EScientific Data\u003C\u002Fem\u003E (Nature: \u003Cem class=\"italic\"\u003EScientific Data\u003C\u002Fem\u003E Editors \u003Ca class=\"xref bibr\" href=\"#ref053\"\u003E2017\u003C\u002Fa\u003E) and \u003Ca class=\"uri simple\" href=\"http:\u002F\u002Fwww.re3data.org\"\u003Ehttp:\u002F\u002Fwww.re3data.org\u003C\u002Fa\u003E, a global registry of research data repositories (Pampel et al. \u003Ca class=\"xref bibr\" href=\"#ref060\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Pampel, Vierkant, Scholze, Bertelmann, Kindling, Klump and Dierolf\u003C\u002Fspan\u003E2013\u003C\u002Fa\u003E). University librarians are another resource for advice on choosing repositories and preparing data for sharing. There are many guidelines and conventions from other disciplines that are relevant to sharing archaeological data (Inter-university Consortium for Political and Social Research \u003Ca class=\"xref bibr\" href=\"#ref029\"\u003E2012\u003C\u002Fa\u003E; Strasser et al. \u003Ca class=\"xref bibr\" href=\"#ref078\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Strasser, Cook, Michener and Budden\u003C\u002Fspan\u003E2012\u003C\u002Fa\u003E; Wilkinson et al. \u003Ca class=\"xref bibr\" href=\"#ref091\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Wilkinson, Dumontier, Aalbersberg, Appleton, Axton, Baak and Mons\u003C\u002Fspan\u003E2016\u003C\u002Fa\u003E). \u003Ca class=\"xref table\" href=\"#tbl008\"\u003ETable 8\u003C\u002Fa\u003E summarizes some of these, and recommendations specifically written for archaeologists can be found in Archaeology Data Service\u002FDigital Antiquity (\u003Ca class=\"xref bibr\" href=\"#ref004\"\u003E2011\u003C\u002Fa\u003E) and Archaeological Resources in Cultural Heritage: A European Standard (\u003Ca class=\"xref bibr\" href=\"#ref003\"\u003E2014\u003C\u002Fa\u003E). Many of these guidelines may create new technical and practical challenges for archaeologists who are not accustomed to sharing data, and many archaeologists do not want to dedicate resources to sharing data. However, we should not let the perfect be the enemy of the good. In our own work we try to follow these guidelines with no dedicated resources for data sharing and with an incomplete knowledge of some of the technical details of repository operations. While we recognize that our data sharing practices fall short of all the guidelines, we still see value in sharing data imperfectly, rather than not at all.\n\u003C\u002Fp\u003E\u003Cdiv class=\"table-wrap\" data-magellan-destination=\"tbl008\" id=\"tbl008\"\u003E\u003Cdiv class=\"caption\"\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003ETABLE 8.\u003C\u002Fspan\u003E Nine Key Things to Do before Sharing Your Data.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cspan\u003E\u003Cdiv class=\"figure-thumb\"\u003E\u003Cimg src=\"data:image\u002Fgif;base64,R0lGODlhAQABAIAAAMLCwgAAACH5BAAAAAAALAAAAAABAAEAAAICRAEAOw==\" data-src=\"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab8.gif?pub-status=live\" class=\"aop-lazy-load-image\" width=\"977\" height=\"863\" data-original-image=\"\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary:20231030111412058-0936:S2326376818000037:S2326376818000037_tab8.gif\" data-zoomable=\"true\"\u003E\u003C\u002Fdiv\u003E\u003C\u002Fspan\u003E\u003Cdiv class=\"table-wrap-foot\"\u003E\u003Cdiv class=\"fn-group footnotes\"\u003E\u003Cdiv class=\"fn\" data-magellan-destination=\"manual_ref-7\" id=\"manual_ref-7\"\u003E\u003Cp class=\"p\"\u003E \u003Cem class=\"italic\"\u003ESource:\u003C\u002Fem\u003E Hart et al. \u003Ca class=\"xref bibr\" href=\"#ref025\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Hart, Barmby, LeBauer, Michonneau, Mount, Mulrooney and Hollister\u003C\u002Fspan\u003E2016\u003C\u002Fa\u003E; White et al. \u003Ca class=\"xref bibr\" href=\"#ref088\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference White, Baldridge, Brym, Locey, McGlinn and Supp\u003C\u002Fspan\u003E2013\u003C\u002Fa\u003E.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003Cp class=\"p\"\u003E Data sharing is a relatively new behavior for most archaeologists, so it is important to clarify what does not count as data sharing. Making data available on a website that is not a trustworthy, persistent-identifier-issuing repository is not data sharing. For example, data files hosted on a personal faculty web page or the Web server of a research project is problematic because these files could disappear at any time, the data can change in untracked ways, and published links to the files are broken when these websites are reorganized, making the files inaccessible. Sharing data via supplementary files attached to a journal article is problematic because they are inaccessible to nonsubscribers for paywalled journals, because publishers often alter the data file formats and file names during the production process, and because publishers own those data and the original authors may need to request permission to reuse their own data. For example, machine-readable formats such as CSV are sometimes presented as PDF or Microsoft Word files in supplementary files. The modification of supplementary file names during the article production process can break scripts that attempt to read files by referring to file names as they were prior to the publisher handling the files.\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E Finally, authors who write in their publications that “the data are available by request” are not sharing data. Not only are the data not actually available, but there is no way to enforce that the authors will actually honor any requests for their data. A simple strategy for avoiding these pseudo-sharing behaviors is to deposit data files in a trustworthy data repository and use persistent links to the files in reports and manuscripts. In our own work we have used figshare.com, zenodo.org, osf.io, neotomadb.org, opencontext.org, and our university repositories. However, there are many other options, and we recommend seeking advice from librarians and funding agencies to help with finding the most suitable service.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"sec\" data-magellan-destination=\"sec8\" id=\"sec8\"\u003E\u003Ch2 class=\"A\"\u003E HOW TO CITE ARCHAEOLOGICAL DATA?\u003C\u002Fh2\u003E\u003Cp class=\"p\"\u003E The Joint Declaration of Data Citation Principles is the current standard in many disciplines for data citation within any research object (Data Citation Synthesis Group \u003Ca class=\"xref bibr\" href=\"#ref013\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Martone\u003C\u002Fspan\u003E2014\u003C\u002Fa\u003E). The result of these principles is a citation formation as follows (the exact order of the items may vary slightly across different referencing styles):\n\u003C\u002Fp\u003E\u003Cdiv class=\"disp-quote\"\u003E\u003Cp class=\"p\"\u003E Creator (PublicationYear). Title. Version. Publisher. Identifier\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cp class=\"p\"\u003E The “PublicationYear,” “Title,” and “Publisher” are familiar from traditional citation formats, except that the publisher may not be a traditional book or journal publisher but, rather, a dedicated data repository organization. “Creator” is equivalent to the author or primary collector and arranger of the data (e.g., Open Context or tDAR). The “Version” and “Identifier” fields are unique to data citation and can take a variety of forms. The identifier is a special type of persistent internet address. The identifier must have two important qualities: the address contains some metadata (such as the identity of the publisher, but not necessarily in a human-readable format), and it remains fixed to a specific file or bundle of files over the lifetime of those objects, even if their location on the internet changes. There are several varieties of these in use, with DOIs currently the most common form. When a data publisher changes the internet address of a dataset, it is responsible for informing the DOI-issuing authority so that the DOI resolves to the new location of the files.\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E A DOI consists of three parts:\n\u003C\u002Fp\u003E\u003Cul class=\"list nomark\"\u003E\u003Cli class=\"list-item\"\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003E•\u003C\u002Fspan\u003E the proxy, usually \u003Ca class=\"uri simple\" href=\"https:\u002F\u002Fdoi.org\"\u003Ehttps:\u002F\u002Fdoi.org\u003C\u002Fa\u003E but sometimes also \u003Ca class=\"uri simple\" href=\"https:\u002F\u002Fhdl.handle.net\u002F\"\u003Ehttps:\u002F\u002Fhdl.handle.net\u002F\u003C\u002Fa\u003E or just DOI\u003C\u002Fp\u003E\u003C\u002Fli\u003E\u003Cli class=\"list-item\"\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003E•\u003C\u002Fspan\u003E the prefix, usually a string of numbers that is unique to the organization publishing the DOI. For example, the tDAR prefix is 10.6067\u003C\u002Fp\u003E\u003C\u002Fli\u003E\u003Cli class=\"list-item\"\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003E•\u003C\u002Fspan\u003E the suffix, a random-looking string of any combination of characters and numbers that uniquely identify the file or bundle of files; this is also known as a local identifier (local ID) or an “accession,” an identifier that, by itself, is only guaranteed to be locally unique within the database or source (McMurry et al. \u003Ca class=\"xref bibr\" href=\"#ref045\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference McMurry, Juty, Blomberg, Burdett, Conlin, Conte and Parkinson\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E)\u003C\u002Fp\u003E\u003C\u002Fli\u003E\u003C\u002Ful\u003E\u003Cp class=\"p\"\u003E A DOI alone is not sufficient for citing a dataset, because, as with many things on the internet, the files can change over time. For example, we might upload data files to the repository when we submit a paper for peer review and then get a DOI and cite that in our manuscript so the peer reviewers can see them (some services provide a private URL that can be shared with reviewers but is not publicly accessible or discoverable by a search engine). Then when we resubmit our manuscript after peer review, we might update the repository to include additional data files relating to additional analyses requested by the reviewers. We still have the same DOI in our manuscript for this dataset, but the files have changed because of the revisions we did in response to the peer reviewers’ comments. Most data repositories will keep each version of the files that you upload, so in this scenario there will be at least two versions of the files in our data repository. Even if we do not follow this exact sequence of updating the files, we may still change them in the future, after our article has been published. Because of this potential to change the files stored at the DOI, it is necessary to identify specific file versions in our citation of the data. A simple method to record the version is to include the date that the data files were accessed, as when citing any other resource found on the internet. Some repositories also provide DOIs that have a version number suffix such as “.v2,” “.2,” “\u002F2.” However, this is not formally a functionality of the DOI system, so including the date accessed is a more robust and general method of indicating the version. Technological solutions to issues of transience, permanence, and granularity in data citation are still in development.\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E When citing data in the text of a manuscript or report, the ideal practice is to cite the dataset and the traditional publication that first described it, like so (Penev et al. \u003Ca class=\"xref bibr\" href=\"#ref062\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Penev, Mietchen, Chavan, Hagedorn, Smith, Shotton, Tuama, Senderov, Georgiev, Stoev, Groom, Remsen and Edmunds\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E): “This paper uses data from the [name] data repository at \u003Ca class=\"uri simple\" href=\"http:\u002F\u002Fdoi.org\u002Fxxx\u002Fxxx\"\u003Ehttp:\u002F\u002Fdoi.org\u002Fxxx\u002Fxxx\u003C\u002Fa\u003E (Author YYYY), first described in Author YYYY.” For example: “This paper uses data from the Open Science Framework data repository at \u003Ca class=\"uri simple\" href=\"http:\u002F\u002Fdoi.org\u002F10.17605\u002FOSF.IO\u002F32A87\"\u003Ehttp:\u002F\u002Fdoi.org\u002F10.17605\u002FOSF.IO\u002F32A87\u003C\u002Fa\u003E (Marwick \u003Ca class=\"xref bibr\" href=\"#ref046\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Marwick\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E), first described in Marwick et al. \u003Ca class=\"xref bibr\" href=\"#ref047\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Marwick, Hayes, Clarkson and Fullagar\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E.” Citing both the dataset and the publication helps to resolve ambiguity about the source of the data and gives context to the data. Furthermore, the authorship of the data files may differ from that of the paper describing the data, and it is important that both authors (or sets of authors) receive credit for their work through the normal mechanisms of citation recognition. In cases where authors have published their data and then make an intellectual contribution to a separate study led by others that uses the published data, then the data author should also be a coauthor of the separate study. If other researchers use published data in their publication without any input from the data author, then citation of the data is all that is required, and adding the data author as a coauthor is not necessary.\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E In the paper in which these data are first described, the citation statement should be included in the body of the paper, in the “Material and Methods” section. In addition, the formal data reference should be included in the paper's reference list, using the recommended journal's reference format. For example, here is a statement that would appear in the paper in which the data are first presented: “The data underpinning the analysis reported in this paper were deposited in the Open Science Framework Repository at \u003Ca class=\"uri simple\" href=\"http:\u002F\u002Fdoi.org\u002F10.17605\u002FOSF.IO\u002F32A87\"\u003Ehttp:\u002F\u002Fdoi.org\u002F10.17605\u002FOSF.IO\u002F32A87\u003C\u002Fa\u003E (Marwick \u003Ca class=\"xref bibr\" href=\"#ref046\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Marwick\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E).” The reference list of that paper would include Marwick \u003Ca class=\"xref bibr\" href=\"#ref046\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Marwick\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E as\n\u003C\u002Fp\u003E\u003Cdiv class=\"disp-quote\"\u003E\u003Cp class=\"p\"\u003E Marwick, B. (\u003Ca class=\"xref bibr\" href=\"#ref047\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Marwick, Hayes, Clarkson and Fullagar\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E). Dataset and R code for “Movement of lithics by trampling: An experiment in the Madjedbebe sediments,” \u003Ca class=\"uri simple\" href=\"http:\u002F\u002Fdoi.org\u002F10.17605\u002FOSF.IO\u002F32A87\"\u003Ehttp:\u002F\u002Fdoi.org\u002F10.17605\u002FOSF.IO\u002F32A87\u003C\u002Fa\u003E, Open Science Framework, Accessed 7 Sept 2017.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"sec conclusions\" data-magellan-destination=\"sec9\" id=\"sec9\"\u003E\u003Ch2 class=\"A\"\u003E SUMMARY AND CONCLUSION\u003C\u002Fh2\u003E\u003Cp class=\"p\"\u003E The status of research data is slowly changing in many fields from a gift culture where data are privately held commodities traded for coauthorship, invitations, and other status tokens (Hagstrom \u003Ca class=\"xref bibr\" href=\"#ref023\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Hagstrom, Barnes and Edge\u003C\u002Fspan\u003E1982\u003C\u002Fa\u003E; Wallis et al. \u003Ca class=\"xref bibr\" href=\"#ref087\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Wallis, Rolando and Borgman\u003C\u002Fspan\u003E2013\u003C\u002Fa\u003E) to part of the public commons, accessible to anyone with an internet connection. This change has been fastest in data-rich “big science” fields, such as astronomy, physics, and oceanography, that use expensive instruments that generate data shared with large teams of collaborators to produce new discoveries (Reichman and Uhlir \u003Ca class=\"xref bibr\" href=\"#ref068\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Reichman and Uhlir\u003C\u002Fspan\u003E2003\u003C\u002Fa\u003E). However, in “smaller” data-poor sciences such as archaeology, where collaborative groups are modest, data sharing is still mostly ad hoc and disaggregated. Our pilot studies, while small and limited in many ways, support this statement, with an overall sharing rate of 20% in responses to private requests for data and 53% of sampled journal articles with openly available data, in a wide variety of formats and locations. We found that legislation requiring data sharing resulted in a substantial increase in the rate of data deposition and that archaeologists are sensitive to repository fees. In examining archaeological data deposited in repositories, we found that most of the contents are PDF files of reports and only a very small fraction of the data files are structured data such as spreadsheets. Archaeologists still have a long way to go in making their data easier to reuse by sharing data in plain text structured data formats such as CSV. We found that citation of datasets, although problematic to measure, appears to be almost nonexistent in archaeological literature. \u003Ca class=\"xref table\" href=\"#tbl008\"\u003ETable 8\u003C\u002Fa\u003E summarizes nine key tasks archaeologists should do with their data to optimize their discovery and reuse.\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E Among the many challenges of increasing the rate of data sharing in archaeology and other “small” sciences is that data sharing is unfunded, unrewarded, and only rarely required. Who is responsible for changing this? There is no simple answer, but we believe that the task must be shared between individuals and institutions, with individuals pushing for institutional change. The challenge of funding can be tackled by practical action from members of professional societies. For example, the Society for American Archaeology should communicate to funding bodies, such as the National Science Foundation, about the importance of allocating funds specifically for data sharing and providing reliable information on how to choose and use data repositories. The challenge of requiring data sharing can also be addressed by funding bodies and federal agencies, through more rigorous evaluation and enforcement of data management plans during the review process. However, changes in the policies of professional societies and other agencies are primarily responses to changes in norms in the community of researchers. In practice, individual researchers are responsible for communicating the value of data sharing to professional societies and funding agencies (e.g., by holding peers to high standards of data sharing during grant reviews). Changes in individual practice will demonstrate a change in norms and motivate our institutions to formalize these new data sharing values through policies and regulations.\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E One area where changes in individual practice may be especially effective is among journal editors and editorial boards. For example, editors of archaeology journals should adopt the Transparency and Openness Protocols of the Center for Open Science (Nosek et al. \u003Ca class=\"xref bibr\" href=\"#ref057\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Nosek, Alter, Banks, Borsboom, Bowman, Breckler and Yarkoni\u003C\u002Fspan\u003E2015\u003C\u002Fa\u003E). Editors should then educate their editorial board members, peer reviewers, and contributors about their obligations regarding these protocols. Editors should only accept papers that, minimally, provide the raw data used to produce each table and visualization in the manuscript. Changes in journal-specific submission guidelines can result in substantial changes in authors’ research practices (Giofrè et al. \u003Ca class=\"xref bibr\" href=\"#ref020\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Giofrè, Cumming, Fresc, Boedker and Tressoldi\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E; Macleod et al. \u003Ca class=\"xref bibr\" href=\"#ref042a\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Macleod\u003C\u002Fspan\u003E2017\u003C\u002Fa\u003E). For example, if archaeology journals adopt incentives such as badging (Kidwell et al. \u003Ca class=\"xref bibr\" href=\"#ref034\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Kidwell, Lazarević, Baranski, Hardwicke, Piechowski, Falkenberg and Nosek\u003C\u002Fspan\u003E2016\u003C\u002Fa\u003E), and require as a condition of publication that submitted articles include in the text a properly formatted citation to the data analyzed in the article (or if the data cannot be shared, an explanation why the data are not available), authors would quickly adapt to this new requirement to ensure that their articles will be published.\u003C\u002Fp\u003E\u003Cp class=\"p\"\u003E Individual researchers can also contribute to changing the norms on data sharing via several practical actions suitable for typical day-to-day work:\n\u003C\u002Fp\u003E\u003Cul class=\"list nomark\"\u003E\u003Cli class=\"list-item\"\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003E•\u003C\u002Fspan\u003E When preparing research for publication, we can upload data to a trustworthy repository and cite the data in the manuscript.\u003C\u002Fp\u003E\u003C\u002Fli\u003E\u003Cli class=\"list-item\"\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003E•\u003C\u002Fspan\u003E When writing peer reviews of journal articles, we can tell editors that we require data to be available for papers that we read and that the manuscripts we review must include citations of datasets analyzed in them. We can write in our peer reviews that data need to be available for us to make a proper assessment of the manuscript.\u003C\u002Fp\u003E\u003C\u002Fli\u003E\u003Cli class=\"list-item\"\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003E•\u003C\u002Fspan\u003E When reviewing grant applications, we can critically evaluate data management plans and report on the applicants’ past record on data sharing. We can offer helpful suggestions about how to improve data availability (e.g., \u003Ca class=\"xref table\" href=\"#tbl008\"\u003ETable 8\u003C\u002Fa\u003E).\u003C\u002Fp\u003E\u003C\u002Fli\u003E\u003Cli class=\"list-item\"\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003E•\u003C\u002Fspan\u003E When teaching students, we can instruct them how to prepare data for sharing, and how to find and use publicly available datasets to give students experience analyzing and writing about shared data, and cultivate the expectation among students that data sharing is a normal part of archaeological research.\u003C\u002Fp\u003E\u003C\u002Fli\u003E\u003Cli class=\"list-item\"\u003E\u003Cp class=\"p\"\u003E\u003Cspan class=\"label\"\u003E•\u003C\u002Fspan\u003E When evaluating candidates for hiring and promotion, we can ensure that commitments to data sharing are valued.\u003C\u002Fp\u003E\u003C\u002Fli\u003E\u003C\u002Ful\u003E\u003Cp class=\"p\"\u003E The challenge of data sharing as an unrewarded behavior was the primary motivation for this article. By specifying a standard for the scholarly citation of archaeological data we provide a method for giving credit to the authors or compilers of data. This rewards those authors with visibility and recognition for their effort with citations, a currency that is valued broadly in the discipline. The data citation standard outlined above is easy for humans and search engines to understand and is highly functional, with a full internet address for the data contained in the citation. The standard can accommodate the addition of further information required by different citation styles and publishers’ requirements. The data citation standard is not dependent on any specific choices of software, archive, data producer, publisher, or author and is easy to adapt into existing writing and research workflows. There are still many unresolved challenges in data sharing, such as how to manage high volumes of data generated by photogrammetry. However, we are confident that solutions to many of these challenges will emerge as data sharing is increasingly valued by the research community.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"back\"\u003E\u003Cdiv class=\"ack\" data-magellan-destination=\"ack1\" id=\"ack1\"\u003E\u003Ch2 class=\"A\"\u003E Acknowledgments\u003C\u002Fh2\u003E\u003Cp class=\"p\"\u003E This article emerged out of discussions at the 2017 Society for American Archaeology meeting, specifically the forum “Beyond Data Management: A Conversation about ‘Digital Data Realities,’” organized by Sarah Kansa and Eric Kansa, and the Society for American Archaeology Publications Committee. Thanks go to Canan Çakirlar for insights into the history of archaeological data sharing in the Netherlands and Stefano Costa for many helpful suggestions. BM was supported by an Australian Research Council Future Fellowship (FT140100101). Thanks go to Stefanie Crabtree and the other four peer reviewers for their constructive suggestions.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003Cdiv class=\"sec\" data-magellan-destination=\"sec11\" id=\"sec11\"\u003E\u003Ch2 class=\"A\"\u003E Data Availability Statement\u003C\u002Fh2\u003E\u003Cp class=\"p\"\u003E This article uses data from the Open Science Framework repository at \u003Ca class=\"uri simple\" href=\"http:\u002F\u002Fdoi.org\u002F10.17605\u002FOSF.IO\u002FKSRUZ\"\u003Ehttp:\u002F\u002Fdoi.org\u002F10.17605\u002FOSF.IO\u002FKSRUZ\u003C\u002Fa\u003E. (Marwick and Pilaar Birch \u003Ca class=\"xref bibr\" href=\"#ref048a\"\u003E\u003Cspan class=\"show-for-sr\"\u003EReference Marwick and Birch\u003C\u002Fspan\u003E2018\u003C\u002Fa\u003E), first described in this article. See the methods section of the text for more details. A capsule of text, data and code to reproduce this article is also available in Code Ocean, a computational reproducibility platform at \u003Ca class=\"uri simple\" href=\"https:\u002F\u002Fdoi.org\u002F10.24433\u002FCO.ca12b3f0-55a2-4eba-9687-168c8281e535\"\u003Ehttps:\u002F\u002Fdoi.org\u002F10.24433\u002FCO.ca12b3f0-55a2-4eba-9687-168c8281e535\u003C\u002Fa\u003E.\u003C\u002Fp\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E\u003C\u002Fdiv\u003E",tableOfContent:[{level:f,current:b,title:"\u003Cdiv class=\"toc-title\"\u003EWHAT ARE DATA?\u003C\u002Fdiv\u003E",url:"sec1"},{level:f,current:b,title:"\u003Cdiv class=\"toc-title\"\u003EWHAT ARE THE ETHICS OF DATA SHARING?\u003C\u002Fdiv\u003E",url:"sec2"},{level:f,current:b,title:"\u003Cdiv class=\"toc-title\"\u003EWHAT ARE THE BENEFITS OF DATA SHARING TO THE INDIVIDUAL RESEARCHER?\u003C\u002Fdiv\u003E",url:"sec3"},{level:f,current:b,title:"\u003Cdiv class=\"toc-title\"\u003EWHAT DO RESEARCHERS THINK ABOUT SHARING DATA?\u003C\u002Fdiv\u003E",url:"sec4"},{level:f,current:b,title:"\u003Cdiv class=\"toc-title\"\u003EWHAT ARE THE CURRENT NORMS OF DATA SHARING?\u003C\u002Fdiv\u003E",url:"sec5"},{level:f,current:b,title:"\u003Cdiv class=\"toc-title\"\u003EDO ARCHAEOLOGISTS SHARE DATA?\u003C\u002Fdiv\u003E",url:"sec6"},{level:f,current:b,title:"\u003Cdiv class=\"toc-title\"\u003EHOW TO SHARE ARCHAEOLOGICAL DATA?\u003C\u002Fdiv\u003E",url:"sec7"},{level:f,current:b,title:"\u003Cdiv class=\"toc-title\"\u003EHOW TO CITE ARCHAEOLOGICAL DATA?\u003C\u002Fdiv\u003E",url:"sec8"},{level:f,current:b,title:"\u003Cdiv class=\"toc-title\"\u003ESUMMARY AND CONCLUSION\u003C\u002Fdiv\u003E",url:"sec9"},{level:f,current:b,title:"\u003Cdiv class=\"toc-title\"\u003EData Availability Statement\u003C\u002Fdiv\u003E",url:"sec11"}],footnotes:[],fulltextNotes:[],references:[{item:[],id:"reference-header-mjet4i",title:"REFERENCES CITED",description:a},{id:"manual_ref-8",displayNumber:a,existInContent:b,content:"\u003Cspan class=\"name\"\u003E\u003Cspan class=\"surname\"\u003EAlsheikh-Ali\u003C\u002Fspan\u003E, \u003Cspan class=\"given-names\"\u003EAlawi A.\u003C\u002Fspan\u003E\u003C\u002Fspan\u003E, \u003Cspan class=\"string-name\"\u003E\u003Cspan class=\"surname\"\u003EQureshi\u003C\u002Fspan\u003E, \u003Cspan class=\"given-names\"\u003EWaqas\u003C\u002Fspan\u003E\u003C\u002Fspan\u003E, \u003Cspan class=\"string-name\"\u003E\u003Cspan class=\"surname\"\u003EAl-Mallah\u003C\u002Fspan\u003E, \u003Cspan class=\"given-names\"\u003EMouaz H.\u003C\u002Fspan\u003E\u003C\u002Fspan\u003E, and \u003Cspan class=\"string-name\"\u003E\u003Cspan class=\"surname\"\u003EIoannidis\u003C\u002Fspan\u003E, \u003Cspan class=\"given-names\"\u003EJohn P. 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Electronic document, \u003Ca class=\"uri simple\" href=\"https:\u002F\u002Fwww.elsevier.com\u002Fabout\u002Fopen-science\u002Fresearch-data\u002Fopen-data-report\"\u003Ehttps:\u002F\u002Fwww.elsevier.com\u002Fabout\u002Fopen-science\u002Fresearch-data\u002Fopen-data-report\u003C\u002Fa\u003E, accessed October 11, 2017.\u003Ca class='ref-link' target='_blank' aria-label='Google Scholar link for ' href=https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Wouters,+Paul,+and+Haak,+Wouter+2017+Open+Data+Report:+The+Researcher+Perspective.+Electronic+document,+https:\u002F\u002Fwww.elsevier.com\u002Fabout\u002Fopen-science\u002Fresearch-data\u002Fopen-data-report,+accessed+October+11,+2017.\u003EGoogle Scholar\u003C\u002Fa\u003E",item:[{googleScholarLink:"https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Wouters,+Paul,+and+Haak,+Wouter+2017+Open+Data+Report:+The+Researcher+Perspective.+Electronic+document,+https:\u002F\u002Fwww.elsevier.com\u002Fabout\u002Fopen-science\u002Fresearch-data\u002Fopen-data-report,+accessed+October+11,+2017.",openUrlParams:{},innerRefId:"ref093",title:a,crossRefLink:a,pubMedLink:a}]},{id:"manual_ref-105",displayNumber:a,existInContent:b,content:"\u003Cspan class=\"name\"\u003E\u003Cspan class=\"surname\"\u003EZimmerman\u003C\u002Fspan\u003E, \u003Cspan class=\"given-names\"\u003ELarry J.\u003C\u002Fspan\u003E\u003C\u002Fspan\u003E, \u003Cspan class=\"string-name\"\u003E\u003Cspan class=\"surname\"\u003EVitelli\u003C\u002Fspan\u003E, \u003Cspan class=\"given-names\"\u003EKaren\u003C\u002Fspan\u003E\u003C\u002Fspan\u003E, and \u003Cspan class=\"string-name\"\u003E\u003Cspan class=\"surname\"\u003EHollowell-Zimmer\u003C\u002Fspan\u003E, \u003Cspan class=\"given-names\"\u003EJulie\u003C\u002Fspan\u003E\u003C\u002Fspan\u003E (editors) \u003Cspan class=\"year\"\u003E2003\u003C\u002Fspan\u003E \u003Cspan class=\"source\"\u003EEthical Issues in Archaeology\u003C\u002Fspan\u003E. \u003Cspan class=\"publisher-name\"\u003ERowman AltaMira\u003C\u002Fspan\u003E, \u003Cspan class=\"publisher-loc\"\u003ENew York\u003C\u002Fspan\u003E.\u003Ca class='ref-link' target='_blank' aria-label='Google Scholar link for ' href=https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Zimmerman,+Larry+J.,+Vitelli,+Karen,+and+Hollowell-Zimmer,+Julie+(editors)+2003+Ethical+Issues+in+Archaeology.+Rowman+AltaMira,+New+York.\u003EGoogle Scholar\u003C\u002Fa\u003E",item:[{googleScholarLink:"https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Zimmerman,+Larry+J.,+Vitelli,+Karen,+and+Hollowell-Zimmer,+Julie+(editors)+2003+Ethical+Issues+in+Archaeology.+Rowman+AltaMira,+New+York.",openUrlParams:{},innerRefId:"ref094",title:a,crossRefLink:a,pubMedLink:a}]}],figures:[{contentId:"tbl001",label:"TABLE 1.",description:"\u003Cspan class=\"p\"\u003ESelection of Definitions of Research Data.\u003C\u002Fspan\u003E",thumbnailSrc:C,enlargedSrc:C,attrib:[]},{contentId:"tbl002",label:"TABLE 2.",description:"\u003Cspan class=\"p\"\u003ESelection of Types of Data Encountered by Archaeologists.\u003C\u002Fspan\u003E",thumbnailSrc:D,enlargedSrc:D,attrib:[]},{contentId:"tbl003",label:"TABLE 3.",description:"\u003Cspan class=\"p\"\u003ESummary of Public and Community Costs of Data Sharing.\u003C\u002Fspan\u003E",thumbnailSrc:E,enlargedSrc:E,attrib:[]},{contentId:"tbl004",label:"TABLE 4.",description:"\u003Cspan class=\"p\"\u003ESummary of Public and Community Benefits of Data Sharing.\u003C\u002Fspan\u003E",thumbnailSrc:F,enlargedSrc:F,attrib:[]},{contentId:"tbl005",label:"TABLE 5.",description:"\u003Cspan class=\"p\"\u003ESummary of Individual Benefits to Data Sharing.\u003C\u002Fspan\u003E",thumbnailSrc:G,enlargedSrc:G,attrib:[]},{contentId:"tbl006",label:"TABLE 6.",description:"\u003Cspan class=\"p\"\u003ESummary of Stodden's “Reproducible Research Standard.”\u003C\u002Fspan\u003E",thumbnailSrc:H,enlargedSrc:H,attrib:[]},{contentId:"tbl007",label:"TABLE 7.",description:"\u003Cspan class=\"p\"\u003ESummary of a Selection of Previous Studies of Data Sharing in Various Fields.\u003C\u002Fspan\u003E",thumbnailSrc:I,enlargedSrc:I,attrib:[]},{contentId:"fig001",label:"FIGURE 1.",description:"\u003Cspan class=\"p\"\u003ESummary of results of e-mail requests for data: (a) responses by year of journal article publication; (b) student status of first author; (c) country location of first author; (d) journal. AAS = \u003Cspan class=\"italic\"\u003EArchaeological and Anthropological Sciences\u003C\u002Fspan\u003E; AQ = \u003Cspan class=\"italic\"\u003EAmerican Antiquity\u003C\u002Fspan\u003E; ARCHMTRY = \u003Cspan class=\"italic\"\u003EArchaeometry\u003C\u002Fspan\u003E; JAS = \u003Cspan class=\"italic\"\u003EJournal of Archaeological Science\u003C\u002Fspan\u003E; JFA = \u003Cspan class=\"italic\"\u003EJournal of Field Archaeology\u003C\u002Fspan\u003E; JHE = \u003Cspan class=\"italic\"\u003EJournal of Human Evolution\u003C\u002Fspan\u003E; LT = \u003Cspan class=\"italic\"\u003ELithic Technology\u003C\u002Fspan\u003E; PLOSONE = \u003Cspan class=\"italic\"\u003EPLOS ONE\u003C\u002Fspan\u003E; SCI = \u003Cspan class=\"italic\"\u003EScience\u003C\u002Fspan\u003E.\u003C\u002Fspan\u003E",thumbnailSrc:"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary-alt:20231030111511-90556-mediumThumb-S2326376818000037_fig1g.jpg",enlargedSrc:"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary-alt:20231030111511-39828-optimisedImage-S2326376818000037_fig1g.jpg",attrib:[]},{contentId:"fig002",label:"FIGURE 2.",description:"\u003Cspan class=\"p\"\u003ESummary of relationships among file types, data types, and data locations: (a) file types and data locations; (b) data types and data locations.\u003C\u002Fspan\u003E",thumbnailSrc:J,enlargedSrc:J,attrib:[]},{contentId:"fig003",label:"FIGURE 3.",description:"\u003Cspan class=\"p\"\u003EDistributions of data centers, languages, and licenses (see text for details) among archaeology datasets known to DataCite. Horizontal axis is the number of digital object identifiers. DANS = Data Archiving and Networked Services; en = English; nl = Dutch; swe = Swedish; de = German; it = Italian.\u003C\u002Fspan\u003E",thumbnailSrc:"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary-alt:20231030111511-83403-mediumThumb-S2326376818000037_fig3g.jpg",enlargedSrc:"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary-alt:20231030111511-00856-optimisedImage-S2326376818000037_fig3g.jpg",attrib:[]},{contentId:"fig004",label:"FIGURE 4.",description:"\u003Cspan class=\"p\"\u003EChange over time in archaeology datasets known to DataCite. (a) Counts of datasets per year by the major data centers; red lines indicate major increases in the number of datasets in repositories. Note that the date on the horizontal axis indicates when the data were collected, not when they were deposited in the data center. (b) Temporal trends in the number of digital object identifiers (DOIs) at individual data centers. DANS = Data Archiving and Networked Services.\u003C\u002Fspan\u003E",thumbnailSrc:"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary-alt:20231030111511-09203-mediumThumb-S2326376818000037_fig4g.jpg",enlargedSrc:"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary-alt:20231030111511-74280-optimisedImage-S2326376818000037_fig4g.jpg",attrib:[]},{contentId:"fig005",label:"FIGURE 5.",description:"\u003Cspan class=\"p\"\u003ESummary of licenses used by data centers with archaeological data. ADS = Archaeology Data Service; DANS = Data Archiving and Networked Services.\u003C\u002Fspan\u003E",thumbnailSrc:"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary-alt:20231030111511-91667-mediumThumb-S2326376818000037_fig5g.jpg",enlargedSrc:"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary-alt:20231030111511-29896-optimisedImage-S2326376818000037_fig5g.jpg",attrib:[]},{contentId:"fig006",label:"FIGURE 6.",description:"\u003Cspan class=\"p\"\u003ERelationships between data centers and file formats. The 10 most frequently found file formats are shown, in the data centers with these files from the top 10 data centers by number of digital object identifiers. Open file formats for structured data, such as CSV, are not common. DANS = Data Archiving and Networked Services.\u003C\u002Fspan\u003E",thumbnailSrc:K,enlargedSrc:K,attrib:[]},{contentId:"fig007",label:"FIGURE 7.",description:"\u003Cspan class=\"p\"\u003ECitation of archaeological data digital object identifiers tracked by the Crossref Event Data service. Inset shows the distribution of these citations over time. DANS = Data Archiving and Networked Services, GBIF = Global Biodiversity Information Facility.\u003C\u002Fspan\u003E",thumbnailSrc:"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary-alt:20231030111511-14858-mediumThumb-S2326376818000037_fig7g.jpg",enlargedSrc:"https:\u002F\u002Fstatic.cambridge.org\u002Fbinary\u002Fversion\u002Fid\u002Furn:cambridge.org:id:binary-alt:20231030111511-83793-optimisedImage-S2326376818000037_fig7g.jpg",attrib:[]},{contentId:"tbl008",label:"TABLE 8.",description:"\u003Cspan class=\"p\"\u003ENine Key Things to Do before Sharing Your 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id="b-comment-email-tip_--x--" x="0" y="0" width="19.972" height="19.832" fill="#fff"><use xlink:href="#a-comment-email-tip_--x--"/></mask> </defs> <g fill="none" fill-rule="evenodd"> <use stroke="#436FCC" mask="url(#b-comment-email-tip_--x--)" stroke-width="2" xlink:href="#a-comment-email-tip_--x--"/> <path d="M14.778 13.325a.983.983 0 0 1 0 1.385.982.982 0 0 1-.704.28c-.254 0-.507-.098-.704-.28l-3.352-3.329-3.353 3.329a.973.973 0 0 1-.69.28 1.01 1.01 0 0 1-.69-.28.983.983 0 0 1 0-1.385l3.352-3.328-3.352-3.33a.983.983 0 0 1 0-1.384 1 1 0 0 1 1.395 0l3.352 3.329 3.352-3.329a1 1 0 0 1 1.394 0 .983.983 0 0 1 0 1.385l-3.352 3.329 3.352 3.328z" fill="#436FCC"/> </g> </svg> </span> </a> </div> <p id="info-comment-email-tip_--x--" class="text-left">Your email address will be used in order to notify you when your comment has been reviewed by the moderator and in case the author(s) of the article or the moderator need to contact you directly.</p> </div> <input name="emailAddress_--x--" 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