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/><path d="M0,37.16c.03,.09,.08,.19,.08,.28v1.72c0,.09-.05,.19-.08,.28v-2.28Z" /><path d="M24.07,8.62s-.01-.06-.02-.09c.03,0,.05,.02,.08,.03-.02,.02-.04,.04-.06,.06Z" /></g ></svg></a></div> <nav class="navbar-center hidden lg:flex"> <ul class="menu menu-horizontal gap-2 text-[18px]" data-svelte-h="svelte-1ueipvt"><li><a href="/datasets">Datasets</a></li> <li class="dropdown dropdown-hover"><a href="/contribute/donation">Contribute Dataset</a> <ul class="menu dropdown-content rounded-box z-10 ml-0 w-40 gap-1 bg-base-100 text-[18px] shadow"><li><a href="/contribute/donation">Donate New</a></li> <li><a href="/contribute/linking">Link External</a></li></ul></li> <li class="dropdown dropdown-hover"><a href="/about">About Us</a> <ul class="menu dropdown-content rounded-box z-10 ml-0 gap-1 bg-base-100 text-[18px] shadow"><li><a href="/about">Who We Are</a></li> <li><a href="/citation">Citation Metadata</a></li> <li><a href="/contact">Contact Information</a></li></ul></li></ul></nav> <div 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width="1.2em" height="1.2em" class="icon text-lg"><path fill="currentColor" d="m19.6 21l-6.3-6.3q-.75.6-1.725.95T9.5 16q-2.725 0-4.612-1.888T3 9.5t1.888-4.612T9.5 3t4.613 1.888T16 9.5q0 1.1-.35 2.075T14.7 13.3l6.3 6.3zM9.5 14q1.875 0 3.188-1.312T14 9.5t-1.312-3.187T9.5 5T6.313 6.313T5 9.5t1.313 3.188T9.5 14"/></svg></button></div> </div></div></div></form></div> <a href="/auth/login" class="link-hover link-primary hidden items-center md:flex"><svg viewBox="0 0 24 24" width="24" height="24" class="text-primary"><path fill="currentColor" d="M12 21v-2h7V5h-7V3h7q.825 0 1.413.588T21 5v14q0 .825-.587 1.413T19 21zm-2-4l-1.375-1.45l2.55-2.55H3v-2h8.175l-2.55-2.55L10 7l5 5z"/></svg> <span class="font-semibold" data-svelte-h="svelte-1fjyivv">Login</span></a></div></header> <main class="min-h-screen"> <div class="mx-auto max-w-screen-xl p-4 2xl:max-w-screen-2xl"><div class="hero"><div class="hero-content"><div class="flex max-w-7xl flex-col gap-6"><h1 class="text-center text-3xl font-bold text-primary" data-svelte-h="svelte-s178ni">Welcome to the UC Irvine Machine Learning Repository</h1> <p>We currently maintain 677 datasets as a service to the machine learning community. Here, you can donate and find datasets used by millions of people all around the world!</p> <div class="flex flex-wrap justify-center gap-5" data-svelte-h="svelte-1s6m5ni"><a href="/datasets" class="btn btn-primary">View Datasets</a> <a href="/contribute/donation/" class="btn btn-secondary">Contribute a Dataset</a></div></div></div></div> <div class="grid grid-cols-1 gap-12 lg:grid-cols-2 lg:gap-4"><section class="col-span-1 rounded-lg border shadow"><h1 class="p-4 text-center text-2xl font-semibold text-primary" data-svelte-h="svelte-1g46wz4">Popular Datasets</h1> <div class="divider my-0 h-2"></div> <div class="flex flex-col gap-1"> <div role="row" tabindex="0" class="rounded-box bg-base-100"><div class="grid grid-cols-8 gap-2 p-2 hover:bg-base-200 cursor-pointer"><div class="avatar col-span-8 flex items-center justify-center sm:col-span-1"><div class="h-12 w-12 rounded"><a href="/dataset/53/iris"> <img src="/static/public/53/Thumbnails/Small.jpg" alt="Iris" width="48px" height="48px"></a></div></div> <div class="relative col-span-8 sm:col-span-7"><h2 class="truncate text-primary" data-sveltekit-preload-data="hover"><a href="/dataset/53/iris" class="link-hover link text-xl font-semibold">Iris</a></h2> <p class="truncate">A small classic dataset from Fisher, 1936. One of the earliest known datasets used for evaluating classification methods. </p> <div class="my-2 hidden gap-4 md:grid grid-cols-9"><div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="m19.6 21l-6.3-6.3q-.75.6-1.725.95T9.5 16q-2.725 0-4.612-1.888T3 9.5t1.888-4.612T9.5 3t4.613 1.888T16 9.5q0 1.1-.35 2.075T14.7 13.3l6.3 6.3zM9.5 14q1.875 0 3.188-1.312T14 9.5t-1.312-3.187T9.5 5T6.313 6.313T5 9.5t1.313 3.188T9.5 14"/></svg></div> <span class="truncate">Classification</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M3.025 17V7q0-.825.588-1.412T5.025 5H19q.825 0 1.413.588T21 7v10q0 .825-.587 1.413T19 19H5.025q-.825 0-1.412-.587T3.025 17M5 17h3.325V7H5zm5.325 0h3.325V7h-3.325zm5.325 0h3.325V7H15.65z"/></svg></div> <span class="truncate">150 Instances</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M19 19v-3H5v3zm0-5v-4H5v4zm0-6V5H5v3zM5 21q-.825 0-1.412-.587T3 19V5q0-.825.588-1.412T5 3h14q.825 0 1.413.588T21 5v14q0 .825-.587 1.413T19 21z"/></svg></div> <span class="truncate">4 Features</span></div></div> </div></div> </div> <div class="divider my-0 h-0"></div> <div role="row" tabindex="0" class="rounded-box bg-base-100"><div class="grid grid-cols-8 gap-2 p-2 hover:bg-base-200 cursor-pointer"><div class="avatar col-span-8 flex items-center justify-center sm:col-span-1"><div class="h-12 w-12 rounded"><a href="/dataset/45/heart+disease"> <img src="/static/public/default/Small.jpg" alt="Heart Disease" width="48px" height="48px"></a></div></div> <div class="relative col-span-8 sm:col-span-7"><h2 class="truncate text-primary" data-sveltekit-preload-data="hover"><a href="/dataset/45/heart+disease" class="link-hover link text-xl font-semibold">Heart Disease</a></h2> <p class="truncate">4 databases: Cleveland, Hungary, Switzerland, and the VA Long Beach</p> <div class="my-2 hidden gap-4 md:grid grid-cols-9"><div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="m19.6 21l-6.3-6.3q-.75.6-1.725.95T9.5 16q-2.725 0-4.612-1.888T3 9.5t1.888-4.612T9.5 3t4.613 1.888T16 9.5q0 1.1-.35 2.075T14.7 13.3l6.3 6.3zM9.5 14q1.875 0 3.188-1.312T14 9.5t-1.312-3.187T9.5 5T6.313 6.313T5 9.5t1.313 3.188T9.5 14"/></svg></div> <span class="truncate">Classification</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M3.025 17V7q0-.825.588-1.412T5.025 5H19q.825 0 1.413.588T21 7v10q0 .825-.587 1.413T19 19H5.025q-.825 0-1.412-.587T3.025 17M5 17h3.325V7H5zm5.325 0h3.325V7h-3.325zm5.325 0h3.325V7H15.65z"/></svg></div> <span class="truncate">303 Instances</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M19 19v-3H5v3zm0-5v-4H5v4zm0-6V5H5v3zM5 21q-.825 0-1.412-.587T3 19V5q0-.825.588-1.412T5 3h14q.825 0 1.413.588T21 5v14q0 .825-.587 1.413T19 21z"/></svg></div> <span class="truncate">13 Features</span></div></div> </div></div> </div> <div class="divider my-0 h-0"></div> <div role="row" tabindex="0" class="rounded-box bg-base-100"><div class="grid grid-cols-8 gap-2 p-2 hover:bg-base-200 cursor-pointer"><div class="avatar col-span-8 flex items-center justify-center sm:col-span-1"><div class="h-12 w-12 rounded"><a href="/dataset/186/wine+quality"> <img src="/static/public/186/Thumbnails/Small.jpg" alt="Wine Quality" width="48px" height="48px"></a></div></div> <div class="relative col-span-8 sm:col-span-7"><h2 class="truncate text-primary" data-sveltekit-preload-data="hover"><a href="/dataset/186/wine+quality" class="link-hover link text-xl font-semibold">Wine Quality</a></h2> <p class="truncate">Two datasets are included, related to red and white vinho verde wine samples, from the north of Portugal. The goal is to model wine quality based on physicochemical tests (see [Cortez et al., 2009], http://www3.dsi.uminho.pt/pcortez/wine/).</p> <div class="my-2 hidden gap-4 md:grid grid-cols-9"><div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="m19.6 21l-6.3-6.3q-.75.6-1.725.95T9.5 16q-2.725 0-4.612-1.888T3 9.5t1.888-4.612T9.5 3t4.613 1.888T16 9.5q0 1.1-.35 2.075T14.7 13.3l6.3 6.3zM9.5 14q1.875 0 3.188-1.312T14 9.5t-1.312-3.187T9.5 5T6.313 6.313T5 9.5t1.313 3.188T9.5 14"/></svg></div> <span class="truncate">Classification, Regression</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M3.025 17V7q0-.825.588-1.412T5.025 5H19q.825 0 1.413.588T21 7v10q0 .825-.587 1.413T19 19H5.025q-.825 0-1.412-.587T3.025 17M5 17h3.325V7H5zm5.325 0h3.325V7h-3.325zm5.325 0h3.325V7H15.65z"/></svg></div> <span class="truncate">4.9K Instances</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M19 19v-3H5v3zm0-5v-4H5v4zm0-6V5H5v3zM5 21q-.825 0-1.412-.587T3 19V5q0-.825.588-1.412T5 3h14q.825 0 1.413.588T21 5v14q0 .825-.587 1.413T19 21z"/></svg></div> <span class="truncate">12 Features</span></div></div> </div></div> </div> <div class="divider my-0 h-0"></div> <div role="row" tabindex="0" class="rounded-box bg-base-100"><div class="grid grid-cols-8 gap-2 p-2 hover:bg-base-200 cursor-pointer"><div class="avatar col-span-8 flex items-center justify-center sm:col-span-1"><div class="h-12 w-12 rounded"><a href="/dataset/17/breast+cancer+wisconsin+diagnostic"> <img src="/static/public/default/Small.jpg" alt="Breast Cancer Wisconsin (Diagnostic)" width="48px" height="48px"></a></div></div> <div class="relative col-span-8 sm:col-span-7"><h2 class="truncate text-primary" data-sveltekit-preload-data="hover"><a href="/dataset/17/breast+cancer+wisconsin+diagnostic" class="link-hover link text-xl font-semibold">Breast Cancer Wisconsin (Diagnostic)</a></h2> <p class="truncate">Diagnostic Wisconsin Breast Cancer Database.</p> <div class="my-2 hidden gap-4 md:grid grid-cols-9"><div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="m19.6 21l-6.3-6.3q-.75.6-1.725.95T9.5 16q-2.725 0-4.612-1.888T3 9.5t1.888-4.612T9.5 3t4.613 1.888T16 9.5q0 1.1-.35 2.075T14.7 13.3l6.3 6.3zM9.5 14q1.875 0 3.188-1.312T14 9.5t-1.312-3.187T9.5 5T6.313 6.313T5 9.5t1.313 3.188T9.5 14"/></svg></div> <span class="truncate">Classification</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M3.025 17V7q0-.825.588-1.412T5.025 5H19q.825 0 1.413.588T21 7v10q0 .825-.587 1.413T19 19H5.025q-.825 0-1.412-.587T3.025 17M5 17h3.325V7H5zm5.325 0h3.325V7h-3.325zm5.325 0h3.325V7H15.65z"/></svg></div> <span class="truncate">569 Instances</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M19 19v-3H5v3zm0-5v-4H5v4zm0-6V5H5v3zM5 21q-.825 0-1.412-.587T3 19V5q0-.825.588-1.412T5 3h14q.825 0 1.413.588T21 5v14q0 .825-.587 1.413T19 21z"/></svg></div> <span class="truncate">30 Features</span></div></div> </div></div> </div> <div class="divider my-0 h-0"></div> <div role="row" tabindex="0" class="rounded-box bg-base-100"><div class="grid grid-cols-8 gap-2 p-2 hover:bg-base-200 cursor-pointer"><div class="avatar col-span-8 flex items-center justify-center sm:col-span-1"><div class="h-12 w-12 rounded"><a href="/dataset/2/adult"> <img src="/static/public/default/Small.jpg" alt="Adult" width="48px" height="48px"></a></div></div> <div class="relative col-span-8 sm:col-span-7"><h2 class="truncate text-primary" data-sveltekit-preload-data="hover"><a href="/dataset/2/adult" class="link-hover link text-xl font-semibold">Adult</a></h2> <p class="truncate">Predict whether annual income of an individual exceeds $50K/yr based on census data. Also known as "Census Income" dataset. </p> <div class="my-2 hidden gap-4 md:grid grid-cols-9"><div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="m19.6 21l-6.3-6.3q-.75.6-1.725.95T9.5 16q-2.725 0-4.612-1.888T3 9.5t1.888-4.612T9.5 3t4.613 1.888T16 9.5q0 1.1-.35 2.075T14.7 13.3l6.3 6.3zM9.5 14q1.875 0 3.188-1.312T14 9.5t-1.312-3.187T9.5 5T6.313 6.313T5 9.5t1.313 3.188T9.5 14"/></svg></div> <span class="truncate">Classification</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M3.025 17V7q0-.825.588-1.412T5.025 5H19q.825 0 1.413.588T21 7v10q0 .825-.587 1.413T19 19H5.025q-.825 0-1.412-.587T3.025 17M5 17h3.325V7H5zm5.325 0h3.325V7h-3.325zm5.325 0h3.325V7H15.65z"/></svg></div> <span class="truncate">48.84K Instances</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M19 19v-3H5v3zm0-5v-4H5v4zm0-6V5H5v3zM5 21q-.825 0-1.412-.587T3 19V5q0-.825.588-1.412T5 3h14q.825 0 1.413.588T21 5v14q0 .825-.587 1.413T19 21z"/></svg></div> <span class="truncate">14 Features</span></div></div> </div></div> </div> <div class="divider my-0 h-0"></div> <div role="row" tabindex="0" class="rounded-box bg-base-100"><div class="grid grid-cols-8 gap-2 p-2 hover:bg-base-200 cursor-pointer"><div class="avatar col-span-8 flex items-center justify-center sm:col-span-1"><div class="h-12 w-12 rounded"><a href="/dataset/222/bank+marketing"> <img src="/static/public/default/Small.jpg" alt="Bank Marketing" width="48px" height="48px"></a></div></div> <div class="relative col-span-8 sm:col-span-7"><h2 class="truncate text-primary" data-sveltekit-preload-data="hover"><a href="/dataset/222/bank+marketing" class="link-hover link text-xl font-semibold">Bank Marketing</a></h2> <p class="truncate">The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y).</p> <div class="my-2 hidden gap-4 md:grid grid-cols-9"><div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="m19.6 21l-6.3-6.3q-.75.6-1.725.95T9.5 16q-2.725 0-4.612-1.888T3 9.5t1.888-4.612T9.5 3t4.613 1.888T16 9.5q0 1.1-.35 2.075T14.7 13.3l6.3 6.3zM9.5 14q1.875 0 3.188-1.312T14 9.5t-1.312-3.187T9.5 5T6.313 6.313T5 9.5t1.313 3.188T9.5 14"/></svg></div> <span class="truncate">Classification</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M3.025 17V7q0-.825.588-1.412T5.025 5H19q.825 0 1.413.588T21 7v10q0 .825-.587 1.413T19 19H5.025q-.825 0-1.412-.587T3.025 17M5 17h3.325V7H5zm5.325 0h3.325V7h-3.325zm5.325 0h3.325V7H15.65z"/></svg></div> <span class="truncate">45.21K Instances</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M19 19v-3H5v3zm0-5v-4H5v4zm0-6V5H5v3zM5 21q-.825 0-1.412-.587T3 19V5q0-.825.588-1.412T5 3h14q.825 0 1.413.588T21 5v14q0 .825-.587 1.413T19 21z"/></svg></div> <span class="truncate">17 Features</span></div></div> </div></div> </div> <div class="divider my-0 h-0"></div> <a href="/datasets" class="btn btn-ghost rounded-t-none text-center text-primary hover:underline" data-svelte-h="svelte-186a50l">See More Popular Datasets</a></div></section> <section class="col-span-1 rounded-lg border shadow"><h1 class="p-4 text-center text-2xl font-semibold text-primary" data-svelte-h="svelte-18uiyod">New Datasets</h1> <div class="divider my-0 h-2"></div> <div class="flex flex-col gap-1"> <div role="row" tabindex="0" class="rounded-box bg-base-100"><div class="grid grid-cols-8 gap-2 p-2 hover:bg-base-200 cursor-pointer"><div class="avatar col-span-8 flex items-center justify-center sm:col-span-1"><div class="h-12 w-12 rounded"><a href="/dataset/1104/drug_induced_autoimmunity_prediction"> <img src="/static/public/1104/Thumbnails/Small.jpg" alt="Drug_induced_Autoimmunity_Prediction " width="48px" height="48px"></a></div></div> <div class="relative col-span-8 sm:col-span-7"><h2 class="truncate text-primary" data-sveltekit-preload-data="hover"><a href="/dataset/1104/drug_induced_autoimmunity_prediction" class="link-hover link text-xl font-semibold">Drug_induced_Autoimmunity_Prediction </a></h2> <p class="truncate">This dataset comprises molecular descriptors generated using RDKit, specifically curated for the study of drug-induced autoimmunity through ensemble machine learning approaches. It is divided into a training set and a testing set, containing numerical features that represent molecular properties and structural characteristics of drugs. The dataset supports predictive modeling tasks aimed at identifying potential autoimmune risks associated with drug candidates. These molecular descriptors include physicochemical properties, providing a comprehensive foundation for machine learning analysis. The dataset facilitates the development of interpretable models for drug toxicity prediction, contributing to advancements in computational toxicology and drug safety assessment.</p> <div class="my-2 hidden gap-4 md:grid grid-cols-9"><div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="m19.6 21l-6.3-6.3q-.75.6-1.725.95T9.5 16q-2.725 0-4.612-1.888T3 9.5t1.888-4.612T9.5 3t4.613 1.888T16 9.5q0 1.1-.35 2.075T14.7 13.3l6.3 6.3zM9.5 14q1.875 0 3.188-1.312T14 9.5t-1.312-3.187T9.5 5T6.313 6.313T5 9.5t1.313 3.188T9.5 14"/></svg></div> <span class="truncate">Classification</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M3.025 17V7q0-.825.588-1.412T5.025 5H19q.825 0 1.413.588T21 7v10q0 .825-.587 1.413T19 19H5.025q-.825 0-1.412-.587T3.025 17M5 17h3.325V7H5zm5.325 0h3.325V7h-3.325zm5.325 0h3.325V7H15.65z"/></svg></div> <span class="truncate">477 Instances</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M19 19v-3H5v3zm0-5v-4H5v4zm0-6V5H5v3zM5 21q-.825 0-1.412-.587T3 19V5q0-.825.588-1.412T5 3h14q.825 0 1.413.588T21 5v14q0 .825-.587 1.413T19 21z"/></svg></div> <span class="truncate">196 Features</span></div></div> </div></div> </div> <div class="divider my-0 h-0"></div> <div role="row" tabindex="0" class="rounded-box bg-base-100"><div class="grid grid-cols-8 gap-2 p-2 hover:bg-base-200 cursor-pointer"><div class="avatar col-span-8 flex items-center justify-center sm:col-span-1"><div class="h-12 w-12 rounded"><a href="/dataset/1101/pirvision_fog_presence_detection"> <img src="/static/public/default/Small.jpg" alt="PIRvision_FoG_presence_detection" width="48px" height="48px"></a></div></div> <div class="relative col-span-8 sm:col-span-7"><h2 class="truncate text-primary" data-sveltekit-preload-data="hover"><a href="/dataset/1101/pirvision_fog_presence_detection" class="link-hover link text-xl font-semibold">PIRvision_FoG_presence_detection</a></h2> <p class="truncate">The PIRvision dataset contains occupancy detection data collected from a Synchronized Low-Energy Electronically-chopped Passive Infra-Red sensing node in residential and office environments. Each observation represents 4 seconds of recorded human activity within the sensor Field-of-View (FoV).</p> <div class="my-2 hidden gap-4 md:grid grid-cols-9"><div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="m19.6 21l-6.3-6.3q-.75.6-1.725.95T9.5 16q-2.725 0-4.612-1.888T3 9.5t1.888-4.612T9.5 3t4.613 1.888T16 9.5q0 1.1-.35 2.075T14.7 13.3l6.3 6.3zM9.5 14q1.875 0 3.188-1.312T14 9.5t-1.312-3.187T9.5 5T6.313 6.313T5 9.5t1.313 3.188T9.5 14"/></svg></div> <span class="truncate">Classification</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M3.025 17V7q0-.825.588-1.412T5.025 5H19q.825 0 1.413.588T21 7v10q0 .825-.587 1.413T19 19H5.025q-.825 0-1.412-.587T3.025 17M5 17h3.325V7H5zm5.325 0h3.325V7h-3.325zm5.325 0h3.325V7H15.65z"/></svg></div> <span class="truncate">15.3K Instances</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M19 19v-3H5v3zm0-5v-4H5v4zm0-6V5H5v3zM5 21q-.825 0-1.412-.587T3 19V5q0-.825.588-1.412T5 3h14q.825 0 1.413.588T21 5v14q0 .825-.587 1.413T19 21z"/></svg></div> <span class="truncate">54 Features</span></div></div> </div></div> </div> <div class="divider my-0 h-0"></div> <div role="row" tabindex="0" class="rounded-box bg-base-100"><div class="grid grid-cols-8 gap-2 p-2 hover:bg-base-200 cursor-pointer"><div class="avatar col-span-8 flex items-center justify-center sm:col-span-1"><div class="h-12 w-12 rounded"><a href="/dataset/1091/lattice-physics+(pwr+fuel+assembly+neutronics+simulation+results)"> <img src="/static/public/1091/Thumbnails/Small.jpg" alt="Lattice-physics (PWR fuel assembly neutronics simulation results)" width="48px" height="48px"></a></div></div> <div class="relative col-span-8 sm:col-span-7"><h2 class="truncate text-primary" data-sveltekit-preload-data="hover"><a href="/dataset/1091/lattice-physics+(pwr+fuel+assembly+neutronics+simulation+results)" class="link-hover link text-xl font-semibold">Lattice-physics (PWR fuel assembly neutronics simulation results)</a></h2> <p class="truncate">This dataset encompasses lattice-physics parameters—the infinite multiplication factor (k-inf) and the pin power peaking factor (PPPF)—modeled as functions of variations in fuel pin enrichments for the NuScale US600 fuel assembly type C-01 (NFAC-01) [NuScale FSAR]. These critical parameters were computed using the MCNP6 code, a Monte Carlo-based tool for nuclear reactor criticality simulations. Fuel pin enrichments were uniformly sampled within the range of 0.7–5.0 weight percent (w/o) U-235 to generate the dataset. The dataset contains 39 features, each representing the enrichment of a specific fuel rod in a one-eighth symmetry of the NFAC assembly. The outputs of interest are the k-inf and PPPF values associated with these enrichments.</p> <div class="my-2 hidden gap-4 md:grid grid-cols-9"><div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="m19.6 21l-6.3-6.3q-.75.6-1.725.95T9.5 16q-2.725 0-4.612-1.888T3 9.5t1.888-4.612T9.5 3t4.613 1.888T16 9.5q0 1.1-.35 2.075T14.7 13.3l6.3 6.3zM9.5 14q1.875 0 3.188-1.312T14 9.5t-1.312-3.187T9.5 5T6.313 6.313T5 9.5t1.313 3.188T9.5 14"/></svg></div> <span class="truncate">Regression</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M3.025 17V7q0-.825.588-1.412T5.025 5H19q.825 0 1.413.588T21 7v10q0 .825-.587 1.413T19 19H5.025q-.825 0-1.412-.587T3.025 17M5 17h3.325V7H5zm5.325 0h3.325V7h-3.325zm5.325 0h3.325V7H15.65z"/></svg></div> <span class="truncate">24K Instances</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M19 19v-3H5v3zm0-5v-4H5v4zm0-6V5H5v3zM5 21q-.825 0-1.412-.587T3 19V5q0-.825.588-1.412T5 3h14q.825 0 1.413.588T21 5v14q0 .825-.587 1.413T19 21z"/></svg></div> <span class="truncate">39 Features</span></div></div> </div></div> </div> <div class="divider my-0 h-0"></div> <div role="row" tabindex="0" class="rounded-box bg-base-100"><div class="grid grid-cols-8 gap-2 p-2 hover:bg-base-200 cursor-pointer"><div class="avatar col-span-8 flex items-center justify-center sm:col-span-1"><div class="h-12 w-12 rounded"><a href="/dataset/1081/gas+sensor+array+low-concentration"> <img src="/static/public/default/Small.jpg" alt="Gas sensor array low-concentration " width="48px" height="48px"></a></div></div> <div class="relative col-span-8 sm:col-span-7"><h2 class="truncate text-primary" data-sveltekit-preload-data="hover"><a href="/dataset/1081/gas+sensor+array+low-concentration" class="link-hover link text-xl font-semibold">Gas sensor array low-concentration </a></h2> <p class="truncate">This dataset contains 6 gas responses collected by a sensor array consisting of 10 metal oxide semiconductor sensors, with gas concentrations at the ppb level (below the minimum detection limit of the sensors)</p> <div class="my-2 hidden gap-4 md:grid grid-cols-9"><div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="m19.6 21l-6.3-6.3q-.75.6-1.725.95T9.5 16q-2.725 0-4.612-1.888T3 9.5t1.888-4.612T9.5 3t4.613 1.888T16 9.5q0 1.1-.35 2.075T14.7 13.3l6.3 6.3zM9.5 14q1.875 0 3.188-1.312T14 9.5t-1.312-3.187T9.5 5T6.313 6.313T5 9.5t1.313 3.188T9.5 14"/></svg></div> <span class="truncate">Classification, Regression, Clustering</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M3.025 17V7q0-.825.588-1.412T5.025 5H19q.825 0 1.413.588T21 7v10q0 .825-.587 1.413T19 19H5.025q-.825 0-1.412-.587T3.025 17M5 17h3.325V7H5zm5.325 0h3.325V7h-3.325zm5.325 0h3.325V7H15.65z"/></svg></div> <span class="truncate">90 Instances</span></div> <div class="col-span-3 flex items-center gap-2 invisible"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M19 19v-3H5v3zm0-5v-4H5v4zm0-6V5H5v3zM5 21q-.825 0-1.412-.587T3 19V5q0-.825.588-1.412T5 3h14q.825 0 1.413.588T21 5v14q0 .825-.587 1.413T19 21z"/></svg></div> <span class="truncate">0 Features</span></div></div> </div></div> </div> <div class="divider my-0 h-0"></div> <div role="row" tabindex="0" class="rounded-box bg-base-100"><div class="grid grid-cols-8 gap-2 p-2 hover:bg-base-200 cursor-pointer"><div class="avatar col-span-8 flex items-center justify-center sm:col-span-1"><div class="h-12 w-12 rounded"><a href="/dataset/1050/twitter+geospatial+data"> <img src="/static/public/1050/Thumbnails/Small.jpg" alt="Twitter Geospatial Data" width="48px" height="48px"></a></div></div> <div class="relative col-span-8 sm:col-span-7"><h2 class="truncate text-primary" data-sveltekit-preload-data="hover"><a href="/dataset/1050/twitter+geospatial+data" class="link-hover link text-xl font-semibold">Twitter Geospatial Data</a></h2> <p class="truncate">Seven days of geo-tagged Tweet data from the United States with exact GPS location and timestamp.</p> <div class="my-2 hidden gap-4 md:grid grid-cols-9"><div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="m19.6 21l-6.3-6.3q-.75.6-1.725.95T9.5 16q-2.725 0-4.612-1.888T3 9.5t1.888-4.612T9.5 3t4.613 1.888T16 9.5q0 1.1-.35 2.075T14.7 13.3l6.3 6.3zM9.5 14q1.875 0 3.188-1.312T14 9.5t-1.312-3.187T9.5 5T6.313 6.313T5 9.5t1.313 3.188T9.5 14"/></svg></div> <span class="truncate">Classification, Regression, Clustering</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M3.025 17V7q0-.825.588-1.412T5.025 5H19q.825 0 1.413.588T21 7v10q0 .825-.587 1.413T19 19H5.025q-.825 0-1.412-.587T3.025 17M5 17h3.325V7H5zm5.325 0h3.325V7h-3.325zm5.325 0h3.325V7H15.65z"/></svg></div> <span class="truncate">14.26M Instances</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M19 19v-3H5v3zm0-5v-4H5v4zm0-6V5H5v3zM5 21q-.825 0-1.412-.587T3 19V5q0-.825.588-1.412T5 3h14q.825 0 1.413.588T21 5v14q0 .825-.587 1.413T19 21z"/></svg></div> <span class="truncate">4 Features</span></div></div> </div></div> </div> <div class="divider my-0 h-0"></div> <div role="row" tabindex="0" class="rounded-box bg-base-100"><div class="grid grid-cols-8 gap-2 p-2 hover:bg-base-200 cursor-pointer"><div class="avatar col-span-8 flex items-center justify-center sm:col-span-1"><div class="h-12 w-12 rounded"><a href="/dataset/1035/can-mirgu"> <img src="/static/public/default/Small.jpg" alt="CAN-MIRGU" width="48px" height="48px"></a></div></div> <div class="relative col-span-8 sm:col-span-7"><h2 class="truncate text-primary" data-sveltekit-preload-data="hover"><a href="/dataset/1035/can-mirgu" class="link-hover link text-xl font-semibold">CAN-MIRGU</a></h2> <p class="truncate">A Comprehensive CAN Bus Attack Dataset from Moving Vehicles for Intrusion Detection System Evaluation This dataset includes CAN bus attacks collected from a modern automobile equipped with autonomous driving capabilities, operating in real-world driving scenarios. The dataset encompasses physically verified attacks to enhance the comparison and validation of in-vehicle network Intrusion Detection Systems.</p> <div class="my-2 hidden gap-4 md:grid grid-cols-9"><div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="m19.6 21l-6.3-6.3q-.75.6-1.725.95T9.5 16q-2.725 0-4.612-1.888T3 9.5t1.888-4.612T9.5 3t4.613 1.888T16 9.5q0 1.1-.35 2.075T14.7 13.3l6.3 6.3zM9.5 14q1.875 0 3.188-1.312T14 9.5t-1.312-3.187T9.5 5T6.313 6.313T5 9.5t1.313 3.188T9.5 14"/></svg></div> <span class="truncate">Classification</span></div> <div class="col-span-3 flex items-center gap-2"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M3.025 17V7q0-.825.588-1.412T5.025 5H19q.825 0 1.413.588T21 7v10q0 .825-.587 1.413T19 19H5.025q-.825 0-1.412-.587T3.025 17M5 17h3.325V7H5zm5.325 0h3.325V7h-3.325zm5.325 0h3.325V7H15.65z"/></svg></div> <span class="truncate">48 Instances</span></div> <div class="col-span-3 flex items-center gap-2 invisible"><div><svg viewBox="0 0 24 24" width="20" height="20" class="text-primary"><path fill="currentColor" d="M19 19v-3H5v3zm0-5v-4H5v4zm0-6V5H5v3zM5 21q-.825 0-1.412-.587T3 19V5q0-.825.588-1.412T5 3h14q.825 0 1.413.588T21 5v14q0 .825-.587 1.413T19 21z"/></svg></div> <span class="truncate">0 Features</span></div></div> </div></div> </div> <div class="divider my-0 h-0"></div> <a href="/datasets?orderBy=DateDonated&sort=desc" class="btn btn-ghost rounded-t-none text-center text-primary hover:underline" data-svelte-h="svelte-1xjbd5g">See More New Datasets</a></div></section></div></div></main> <div class="alert fixed bottom-0"><div class="flex"><svg viewBox="0 0 24 24" width="1.2em" height="1.2em" class="text-primary"><path fill="currentColor" d="M11 9h2V7h-2m1 13c-4.41 0-8-3.59-8-8s3.59-8 8-8s8 3.59 8 8s-3.59 8-8 8m0-18A10 10 0 0 0 2 12a10 10 0 0 0 10 10a10 10 0 0 0 10-10A10 10 0 0 0 12 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/><path d="M116.05,3.62c.97-1,2.21-1.49,3.72-1.49,2.01,0,3.48,.67,4.42,2,.51,.75,.79,1.5,.83,2.26h-2.53c-.16-.58-.37-1.02-.62-1.31-.45-.53-1.12-.79-2-.79s-1.61,.37-2.13,1.11c-.52,.74-.78,1.79-.78,3.15s.27,2.37,.82,3.05c.55,.68,1.25,1.01,2.09,1.01s1.53-.29,1.98-.87c.25-.31,.46-.78,.63-1.4h2.51c-.22,1.32-.77,2.39-1.66,3.22s-2.03,1.24-3.42,1.24c-1.72,0-3.07-.56-4.06-1.67-.98-1.12-1.48-2.66-1.48-4.61,0-2.11,.56-3.74,1.68-4.88Z" /><path d="M134,14.45h-2.51V2.35h2.51V14.45Z" /><path d="M141.14,5.3s.1,0,.2,.01v2.4c-.15-.02-.28-.03-.39-.03s-.21,0-.28,0c-.94,0-1.57,.31-1.89,.92-.18,.34-.27,.88-.27,1.59v4.27h-2.35V5.5h2.23v1.56c.36-.6,.67-1,.94-1.22,.44-.37,1.01-.55,1.71-.55,.04,0,.08,0,.11,0Z" /><path d="M148.22,5.5h2.51l-3.23,8.94h-2.46l-3.21-8.94h2.62l1.86,6.6,1.9-6.6Z" /><path d="M154.46,4.44h-2.37V2.28h2.37v2.16Zm-2.37,1.07h2.37V14.45h-2.37V5.5Z" /><path d="M163.93,6.02c.59,.48,.88,1.29,.88,2.41v6.02h-2.4v-5.44c0-.47-.06-.83-.19-1.08-.23-.46-.66-.69-1.3-.69-.79,0-1.33,.34-1.62,1.01-.15,.36-.23,.81-.23,1.36v4.84h-2.34V5.52h2.26v1.3c.3-.46,.58-.79,.85-.99,.48-.36,1.09-.54,1.82-.54,.92,0,1.67,.24,2.26,.73Z" /><path d="M172.81,5.69c.62,.28,1.14,.72,1.54,1.32,.37,.53,.6,1.15,.71,1.85,.06,.41,.09,1,.08,1.77h-6.51c.04,.9,.35,1.53,.93,1.89,.36,.22,.79,.34,1.29,.34,.53,0,.96-.14,1.3-.41,.18-.15,.34-.35,.48-.62h2.39c-.06,.53-.35,1.07-.87,1.62-.8,.87-1.92,1.3-3.36,1.3-1.19,0-2.24-.37-3.15-1.1-.91-.73-1.36-1.93-1.36-3.58,0-1.55,.41-2.74,1.23-3.56s1.89-1.24,3.2-1.24c.78,0,1.48,.14,2.1,.42Zm-3.5,2.02c-.33,.34-.54,.8-.62,1.38h4.03c-.04-.62-.25-1.09-.62-1.41s-.83-.48-1.39-.48c-.6,0-1.07,.17-1.4,.51Z" /><path d="M102.72,22.56h2.34l3.46,10.19,3.44-10.19h2.31v12.05h-1.55v-7.11c0-.25,0-.65,.02-1.22,.01-.57,.02-1.18,.02-1.83l-3.44,10.16h-1.62l-3.46-10.16v.37c0,.3,0,.75,.02,1.35,.01,.6,.02,1.05,.02,1.33v7.11h-1.56v-12.05Z" /><path 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class="text-primary"><path fill="currentColor" d="M12 21v-2h7V5h-7V3h7q.825 0 1.413.588T21 5v14q0 .825-.587 1.413T19 21zm-2-4l-1.375-1.45l2.55-2.55H3v-2h8.175l-2.55-2.55L10 7l5 5z"/></svg> <span data-svelte-h="svelte-sdljcx">Login</span></a></li> </ul></div></div> <script type="application/json" data-sveltekit-fetched data-url="/api/trpc/donated_datasets.find,donated_datasets.find,donated_datasets.count?batch=1&input=%7B%220%22%3A%7B%22json%22%3A%7B%22orderBy%22%3A%22NumHits%22%2C%22take%22%3A6%7D%7D%2C%221%22%3A%7B%22json%22%3A%7B%22orderBy%22%3A%22DateDonated%22%2C%22take%22%3A6%7D%7D%2C%222%22%3A%7B%22json%22%3Anull%2C%22meta%22%3A%7B%22values%22%3A%5B%22undefined%22%5D%7D%7D%7D" data-hash="u87jg8">{"status":200,"statusText":"","headers":{},"body":"[{\"result\":{\"data\":{\"json\":{\"datasets\":[{\"ID\":53,\"userID\":1,\"introPaperID\":2381,\"Name\":\"Iris\",\"Abstract\":\"A small classic dataset from Fisher, 1936. 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The goal is to model wine quality based on physicochemical tests (see [Cortez et al., 2009], http://www3.dsi.uminho.pt/pcortez/wine/).\",\"Area\":\"Business\",\"Task\":\"Classification, Regression\",\"Types\":\"Multivariate\",\"DOI\":\"10.24432/C56S3T\",\"DateDonated\":\"2009-10-07T00:00:00.000Z\",\"YearCreated\":2009,\"isTabular\":true,\"URLFolder\":\"../machine-learning-databases/wine-quality/\",\"URLLink\":null,\"Graphics\":\"/static/public/186/Thumbnails\",\"isAvailablePython\":true,\"Status\":\"APPROVED\",\"NumHits\":503784,\"NumDownloads\":168675,\"NumInstances\":4898,\"NumFeatures\":12,\"FeatureTypes\":\"Real\",\"slug\":\"wine+quality\"},{\"ID\":17,\"userID\":1,\"introPaperID\":2420,\"Name\":\"Breast Cancer Wisconsin (Diagnostic)\",\"Abstract\":\"Diagnostic Wisconsin Breast Cancer Database.\",\"Area\":\"Health and Medicine\",\"Task\":\"Classification\",\"Types\":\"Multivariate\",\"DOI\":\"10.24432/C5DW2B\",\"DateDonated\":\"1995-11-01T00:00:00.000Z\",\"YearCreated\":1993,\"isTabular\":true,\"URLFolder\":\"../machine-learning-databases/breast-cancer-wisconsin/\",\"URLLink\":null,\"Graphics\":\"\",\"isAvailablePython\":true,\"Status\":\"APPROVED\",\"NumHits\":430093,\"NumDownloads\":117055,\"NumInstances\":569,\"NumFeatures\":30,\"FeatureTypes\":\"Real\",\"slug\":\"breast+cancer+wisconsin+diagnostic\"},{\"ID\":2,\"userID\":1,\"introPaperID\":null,\"Name\":\"Adult\",\"Abstract\":\"Predict whether annual income of an individual exceeds $50K/yr based on census data. Also known as \\\"Census Income\\\" dataset. \",\"Area\":\"Social Science\",\"Task\":\"Classification\",\"Types\":\"Multivariate\",\"DOI\":\"10.24432/C5XW20\",\"DateDonated\":\"1996-05-01T00:00:00.000Z\",\"YearCreated\":1996,\"isTabular\":true,\"URLFolder\":\"../machine-learning-databases/adult/\",\"URLLink\":null,\"Graphics\":\"\",\"isAvailablePython\":true,\"Status\":\"APPROVED\",\"NumHits\":425312,\"NumDownloads\":115214,\"NumInstances\":48842,\"NumFeatures\":14,\"FeatureTypes\":\"Categorical, Integer\",\"slug\":\"adult\"},{\"ID\":222,\"userID\":1,\"introPaperID\":2467,\"Name\":\"Bank Marketing\",\"Abstract\":\"The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y).\",\"Area\":\"Business\",\"Task\":\"Classification\",\"Types\":\"Multivariate\",\"DOI\":\"10.24432/C5K306\",\"DateDonated\":\"2012-02-14T00:00:00.000Z\",\"YearCreated\":2014,\"isTabular\":true,\"URLFolder\":\"../machine-learning-databases/00222/\",\"URLLink\":null,\"Graphics\":\"\",\"isAvailablePython\":true,\"Status\":\"APPROVED\",\"NumHits\":396773,\"NumDownloads\":105417,\"NumInstances\":45211,\"NumFeatures\":17,\"FeatureTypes\":\"Categorical, Integer\",\"slug\":\"bank+marketing\"}],\"count\":677,\"nextCursor\":320},\"meta\":{\"values\":{\"datasets.0.DateDonated\":[\"Date\"],\"datasets.1.DateDonated\":[\"Date\"],\"datasets.2.DateDonated\":[\"Date\"],\"datasets.3.DateDonated\":[\"Date\"],\"datasets.4.DateDonated\":[\"Date\"],\"datasets.5.DateDonated\":[\"Date\"]}}}}},{\"result\":{\"data\":{\"json\":{\"datasets\":[{\"ID\":1104,\"userID\":35519,\"introPaperID\":2772,\"Name\":\"Drug_induced_Autoimmunity_Prediction \",\"Abstract\":\"This dataset comprises molecular descriptors generated using RDKit, specifically curated for the study of drug-induced autoimmunity through ensemble machine learning approaches. It is divided into a training set and a testing set, containing numerical features that represent molecular properties and structural characteristics of drugs. The dataset supports predictive modeling tasks aimed at identifying potential autoimmune risks associated with drug candidates. These molecular descriptors include physicochemical properties, providing a comprehensive foundation for machine learning analysis. The dataset facilitates the development of interpretable models for drug toxicity prediction, contributing to advancements in computational toxicology and drug safety assessment.\",\"Area\":\"Health and Medicine\",\"Task\":\"Classification\",\"Types\":\"Tabular\",\"DOI\":\"10.24432/C5332M\",\"DateDonated\":\"2025-01-06T00:00:00.000Z\",\"YearCreated\":2025,\"isTabular\":null,\"URLFolder\":\"/static/public/1104\",\"URLLink\":null,\"Graphics\":\"/static/public/1104/Thumbnails\",\"isAvailablePython\":null,\"Status\":\"APPROVED\",\"NumHits\":4371,\"NumDownloads\":1274,\"NumInstances\":477,\"NumFeatures\":196,\"FeatureTypes\":\"Categorical\",\"slug\":\"drug_induced_autoimmunity_prediction\"},{\"ID\":1101,\"userID\":35597,\"introPaperID\":2769,\"Name\":\"PIRvision_FoG_presence_detection\",\"Abstract\":\"The PIRvision dataset contains occupancy detection data collected from a Synchronized Low-Energy Electronically-chopped Passive Infra-Red sensing node in residential and office environments. Each observation represents 4 seconds of recorded human activity within the sensor Field-of-View (FoV).\",\"Area\":\"Climate and Environment\",\"Task\":\"Classification\",\"Types\":\"Tabular, Sequential, Multivariate, Time-Series, Text\",\"DOI\":\"10.24432/C56W5M\",\"DateDonated\":\"2025-01-05T00:00:00.000Z\",\"YearCreated\":2023,\"isTabular\":null,\"URLFolder\":\"/static/public/1101\",\"URLLink\":null,\"Graphics\":null,\"isAvailablePython\":null,\"Status\":\"APPROVED\",\"NumHits\":1722,\"NumDownloads\":412,\"NumInstances\":15302,\"NumFeatures\":54,\"FeatureTypes\":\"Real\",\"slug\":\"pirvision_fog_presence_detection\"},{\"ID\":1091,\"userID\":33165,\"introPaperID\":2759,\"Name\":\"Lattice-physics (PWR fuel assembly neutronics simulation results)\",\"Abstract\":\"This dataset encompasses lattice-physics parameters—the infinite multiplication factor (k-inf) and the pin power peaking factor (PPPF)—modeled as functions of variations in fuel pin enrichments for the NuScale US600 fuel assembly type C-01 (NFAC-01) [NuScale FSAR]. 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