Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,684
datasets available to search
ShareScore release 0.9.0
Dataset results
3,684 results for “extract”
Data and Code for "Extracting reproductive parameters from GPS tracking data for a nesting raptor in Europe"
<p>Understanding population dynamics requires estimation of demographic parameters. We build on existing approaches to develop a new tool that uses GPS tracking data to estimate breeding propensity and breeding success, and show that this tool yielded accurate predictions for two red kite populations in Central Europe. The tool is available as an R package at <a href="https://github.com/Vogelwarte/NestTool">https://github.com/Vogelwarte/NestTool</a> and will facilitate the estimation of demographic parameters from tracking data to inform population assessments. The files in this repository contain the data and analytical code to replicate the results of the publication in the Journal of Avian Biology (DOI: 10.1111/jav.03246). The version contained in this repository does not include updates and improvements that occurred after the 29 August 2024.</p>
Comparison of conventional IgE assay and measurement of specific IgE to hemocyanin for the diagnosis of adult crab allergy (Running tile: Utility of crab extracts and hemocyanin in diagnosing crab allergy)
<p><span>Summary: </span></p> <p><span><span> </span>Specific IgE to hemocyanin was elevated in crab-allergic as compared to crab-tolerant patients.</span></p> <p><span><span> </span>The combination of specific IgE to hemocyanin and conventional IgE assays improved specificity.</span></p>
Comparison of conventional IgE assay and measurement of specific IgE to hemocyanin for the diagnosis of adult crab allergy (Running tile: Utility of crab extracts and hemocyanin in diagnosing crab allergy)
<p>Summary:</p> <p> Specific IgE to hemocyanin was elevated in crab-allergic as compared to crab-tolerant patients.</p> <p> The combination of specific IgE to hemocyanin and conventional IgE assays improved specificity.</p>
Machine Learning Articles Extracted from Google Scholar
<p>This dataset was created as part of a web scraping practice aimed at capturing academic information from Google Scholar. It contains data on <em>Machine Learning</em> research articles, including the article's title, authors, summary, direct link, citation count, and APA reference. This dataset was collected using Python and Selenium to develop skills in web scraping tools for extracting data from websites with dynamic content.</p> <p>The dataset was generated specifically as part of an academic exercise to learn and apply web scraping techniques, without a deep analysis intent for the data obtained. This dataset is intended as a resource for learning and evaluating the methods used in web data collection.</p> <p><strong>Included Fields</strong>:</p> <ul> <li><strong>title</strong>: Title of the research article.</li> <li><strong>link</strong>: Direct link to the article.</li> <li><strong>authors</strong>: Names of the article’s authors.</li> <li><strong>description</strong>: Summary or brief description of the article.</li> <li><strong>citations</strong>: Number of times the article has been cited on Google Scholar.</li> <li><strong>APA_citation</strong>: APA-formatted citation of the article.</li> </ul> <p>This dataset was created solely for educational purposes and to demonstrate the application of web scraping techniques in a controlled environment.</p>
Formal Methods in Railways: a Systematic Mapping Study - List of Primary Studies and Data Extraction
<p>This Excel file includes the list of papers analyzed in the systematic mapping study titled "Formal Methods in Railways: a Systematic Mapping Study". The study has been submitted for publication, and its preprint is also included in this repository. </p>
Building Large-Scale Gene-Disease Association Datasets for Biomedical Relation Extraction
<p>This repository contains the GDAb and GDAt datasets. GDAb and GDAt are large-scale, distantly supervised, and manually enhanced datasets for Gene-Disease Association (GDA) extraction. Each dataset consists of three text files, corresponding to train, validation, and test sets, plus an additional JSON file containing the mapping between relation names and IDs. Each record in train, validation, or test files corresponds to a single GDA extracted from a sentence. Records are represented as JSON objects with the following structure:</p> <ul> <li><strong>text:</strong> sentence from which the GDA was extracted.</li> <li><strong>relation:</strong> relation name associated to the given GDA.</li> <li><strong>h: </strong>JSON object representing the gene entity, composed of: <ul> <li><strong>id: </strong>UMLS CUI associated to the gene entity.</li> <li><strong>name:</strong> UMLS preferred name associated to the gene entity.</li> <li><strong>pos: </strong>list consisting of starting position and length of the gene mention within text.</li> </ul> </li> <li><strong>t:</strong> JSON object representing the disease entity, composed of: <ul> <li><strong>id: </strong>UMLS CUI associated to the disease entity.</li> <li><strong>name:</strong> UMLS preferred name associated to the disease entity.</li> <li><strong>pos:</strong> list consisting of starting position and length of the disease mention within text.</li> </ul> </li> </ul> <p>Both datasets contain over 2,500,000 sentences and 500,000 bags.<br> The zip file consists of two folders, GDAb and GDAt, containing the files corresponding to the two datasets, respectively.</p> <p> </p>
Softcite software mention extraction from the CORD-19 publications
<p><strong>Softcite software mention extraction from the CORD-19 publications </strong></p> <p>This dataset is the result of the extraction of software mentions from the set of publications of the CORD-19 corpus (<a href="https://allenai.org/data/cord-19">https://allenai.org/data/cord-19</a>) by the Softcite software recognizer (SciBERT-CRF fine-tuned model), see <a href="https://github.com/ourresearch/software-mentions">https://github.com/ourresearch/software-mentions</a>.</p> <p>The CORD-19 version used for this dataset is the one dated <strong>2021-07-26,</strong> using the <em>metadata.csv</em> file only. We re-harvested the PDF with <a href="https://github.com/kermitt2/article-dataset-builder">https://github.com/kermitt2/article-dataset-builder</a> in order to also extract coordinates of software mentions in the PDF and to take advantage of the latest version of GROBID to produce better full text extraction from PDF. We also harvested 61,230 full-texts more than the standard CORD-19 distribution.</p> <p>Note that this is the third version of this dataset (version 0.3.0). The previous Softcite software mention extraction from the CORD-19 was based on 2020-09-11 and 2021-03-22 versions. The new version cover a larger set of documents and is using an improved version of the extraction tools.</p> <p><strong>Data format </strong></p> <p>The extraction consists of 3 JSON files:</p> <p><strong>annotations.jsonl</strong> contains the individual software annotations including <em>software name</em> and possible attached attributes (<em>publisher</em>, <em>URL</em> and <em>version</em>). Each annotation is associated with coordinates expressed as bounding boxes in the original PDF. See <a href="https://grobid.readthedocs.io/en/latest/Coordinates-in-PDF/">Coordinates of structures in the original PDF</a> for more details on the coordinate format.</p> <p>The context of citation is the sentence where the software name and its attributes are extracted. It is added to the JSON structure (field <em>context</em>), as well as the identifier of the document where the annotation belongs (field <em>document</em>, pointing to entries available in <em>documents.json</em>) and a list of bibliographical references attached to the software name (field <em>references</em>, pointing to entries available in <em>references.json</em>, with the used reference marker string). See <a href="https://github.com/ourresearch/software-mentions">https://github.com/ourresearch/software-mentions</a> for more details on the extracted attributes.</p> <p>If the software name was sucessfully disambiguated against WikiData ("entity linking"), it appears in the field <em>wikidataId</em> as Wikidata entity identifier and in the field <em>wikipediaExternalRef</em> as a Wikipedia PageID from the English Wikipedia. Entity linking is realized with <a href="https://github.com/kermitt2/entity-fishing">entity-fishing</a>.</p> <p><strong>documents.jsonl</strong> contains the metadata of the all the CORD-19 documents containing at least one software annotation. The metadata are given as a CrossRef JSON structure. The abstract should be included in the metadata most of the time, as well as some complements extracted by GROBID directly from the PDF. In addition, the size of the pages and the unique file path to the PDF can be found to allow annotations directly on the PDF (see <a href="https://grobid.readthedocs.io/en/latest/Coordinates-in-PDF/">Coordinates of structures in the original PDF</a> for more details on the PDF annotation display mechanism).</p> <p><strong>references.jsonl </strong>contains the parsed reference entries associated to software mentions. These references are given in the field <em>tei</em> encoded in the XML TEI format of GROBID extraction. The extracted raw references have been matched against CrossRef to get a DOI and more complete metadata with <a href="https://github.com/kermitt2/biblio-glutton">biblio-glutton</a>.</p> <p><strong>Statistics</strong></p> <p>CORD-19 version: 2021-07-26</p> <p>- total Open Access full texts: 296,686<br> - with at least one software mention: 115,073</p> <p>- total software name annotations: 652,518<br> - with linked Wikidata ID: 231,599</p> <p>- associated field <br> - publisher: 107,421<br> - version: 188,724<br> - URL: 59,366<br> - references: 230,145</p> <p>- associated bibliographical references: 92,573<br> - references with matched DOI: 49,350<br> - references with matched PMID: 32,895<br> - references with matched PMC ID: 18,741</p> <p><strong>License and acknowledgements</strong></p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International License.</p> <p>We thank the Alfred P. Sloan Foundation and of the Gordon and Betty Moore Foundation for supporting this work.</p>
Morphological profiles extracted from Human MCF7 cells – compound-profiling experiment BBBC021.
<p>This contains morphological profiles extracted from Human MCF-7 cells from the compound-profiling experiment BBBC021 hosted at the Broad Bioimage Benchmark Collection.</p> <p>The profiles were extracted using the software SCIP.</p>
Supporting Data -- Evaluating Mask R-CNN Models to Extract Terracing across Oceanic High Islands: an example from Sāmoa.
<p>This dataset provides supplemental information for the manuscript, "Diverse terracing practices revealed by automated lidar analysis across the Sāmoan islands", submitted to Archaeological Prospection. The dataset contains a trained Mask R-CNN deep learning model designed for detecting archaeological terracing features on the islands of American Samoa, associated training data, and the raw and cleaned output of detected terraces.</p>
Extracting interpretable rules with Bayesian Networks. A case study of intrinsic human hazardous properties of silver nanoforms for the Safety Dimension of Safe and Sustainable by design paradigm.
<p>Three different datasets: toxicological attributes in i) lung and ii) intestinal cell line along with system dependent features and iii) system independent pchem properties) were merged. Each row represents one set of experimental testing conditions and related system dependent nanodescriptors based on the exposure dose and NFs pre-treatment (for intestinal assessments). The system independent inputs are NF specific and independent of experimental conditions. Data is captured via FAIR principles where the reader can find the origin (institution) of each data, the responsible data creators (experimentalists), the raw measurements, the protocols followed and the instrumentations used for each experiment. .</p>
Artifacts for Keyword Extraction From Specification Documents for Planning Security Mechanisms
<p>This dataset contains the data used for evaluating VDocScan - a keyword extraction based security vulnerability prediction method. The repository includes an extensive list of Products and Vulnerability reports from CVE, a custom created dataset mapping vulnerability reports to product documentations, as well as, intermediate results from the study such as decision trees rendered for each vulnerability, correlation matrix of vulnerabilities etc. </p>
Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Open Pit Extraction, Valea Sesei and Roșia Poieni (Romania)).
<p>Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Open Pit Extraction (mine located at Valea Sesei and Roșia Poieni (Romania)) (3D view mode).</p> <p>Accessing the GOLDENAI GUI, please refer to the following link (<strong>login required</strong>): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a></p>
Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Underground Extraction, Pyhäsalmi (Finland)).
<p>Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Underground Extraction (mine located at Pyhäsalmi (Finland)) (2D view mode).</p> <p>Accessing the GOLDENAI GUI, please refer to the following link (<strong>login required</strong>): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a></p>
Medical Temporal Constraint Extraction Dataset
<p>This dataset contains 836 drug usage guidelines labeled with medical temporal constraints. These constraints conform to a context-free grammar, allowing their semantics to be computationally represented.</p>
Extract from the glazed tile heritage database of Covilhã-Portugal (12 elements)
<p>Extract from the tile heritage database of Covilhã - Portugal: geographic and alphanumeric data of 12 buildings with facades fully or partially covered with tiles, of heritage interest (registered in the SIPA), located in the municipality of Covilhã.</p> <p>The information is related to the buildings and tile compositions on its facades.</p> <p>Building data</p> <p>Building location: country, region, district, municipality, parish, street and house number, Lon WGS84, Lat WGS84, X ETRS89, Y ETRS89, Z elevation. SIPA: designation, identification number, link to SIPA data. Building identification: if it does not exist, an ID must be assigned. Protection and conditioning: world heritage, built heritage, benefits from a protection zone, in the process of classification, in study, non-existent. Public interest, national monument, municipal interest. Property type: public, private. Ownership: ownership information. Case number at municipal services: number ID. Typology: residence, industrial, commercial, religious, panel or other. Occupation: property occupation. Architectural style: dominant, secondary. Other decorative elements than tiles: if existing. General building condition (exterior): good, reasonable, bad, doom. Interventions chronology: if existing. Main moments of design, application, conservation, remodeling, restoration, and management. Bibliography, drawings and documents: source, link. Pictures: images.</p> <p>Glazed tile data</p> <p>Tile location: facade, inside building. Tile identification: ID number. Construction period: main construction periods. Local context: brief description of the glazed tile object surroundings, in its geophysical, historical, and socio-cultural aspects. Classification 1: coating, ceramic panel, toponymical plate or other. Classification 2: facade, patio, external staircase, balcony, wall, wall panel, small religious panel, caption. Classification 3: pattern, repeat composition, figurative, ornamental, or loose figure. Iconography: religion, biblical, magic, mysticism, nature, human being – humanity, society - civilization – culture, history, ancient history, literature, classical mythology, abstract ideas and concepts, abstract - non-figurative art. Monochrome or polychromatic: monochrome, polychromatic. Objective and succinct tile description: From general to particular: building, space, glazed tile, and section. Material (ceramic product): faience, stone dust, stoneware, porcelain, terracotta. Tile decoration technique: information on tile manufacture, divided into conformation, decoration and application. Composition dimensions: height x width (meters). Tile dimensions: height x width (centimetres). Tile condition: good, reasonable, bad. Authorship: company, artist, painter or other. Authorship nationality: country. Interventions chronology: if existing. Main moments of design, application, conservation, remodelling, restoration, and management. Bibliography, drawings and documents: source, link. Pictures: images.</p>
Numerical convergence of model Cauchy-Characteristic Extraction and Matching (data)
<p>This dataset was used to produce the convergence plots in the paper "Numerical convergence of model Cauchy-Characteristic Extraction and Matching", as well as additional convergence tests that can be found in the repository https://github.com/ThanasisGiannakopoulos/model_CCE_CCM_public. The data can be used to reproduce the aforementioned convergence plots or for comparison against data obtained if one performs the same simulations independently.</p>
Single-cell information extracted from IMC example data
<p>If you are working with these files, please cite them as follows:<br><br>Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). <a href="https://doi.org/10.1038/s41596-023-00881-0">https://doi.org/10.1038/s41596-023-00881-0</a></p><p>This repository contains additional information related to IMC example data available at <a href="https://zenodo.org/record/5949116">zenodo.org/record/5949116</a>. The following files are available and are part of the <a href="https://bodenmillergroup.github.io/IMCDataAnalysis/">IMC Data Analysis workflow</a></p><ul><li><strong>gated_cells.zip:</strong> contains SpatialExperiment objects storing cells that were manually gated based on their expression values to derive ground truth cell phenotype labels.</li><li><strong>spe.rds:</strong> SpatialExperiment object containing the single-cell information (mean intensity per cell and per channel; cellular metadata; channel metadata) of the processed data.</li><li><strong>images.rds:</strong> CytoImageList object containing the spillover-corrected images.</li><li><strong>masks.rds:</strong> CytoImageList object containing the segmentation masks.</li></ul>
SI2: How circular is an extractive economy? South Africa's export orientation results in low circularity and insufficient societal stocks for service-provisioning
<p>Supporting information SI2 for the manuscript under review:</p> <p>How circular is an extractive economy? South Africa’s export orientation results in low circularity and insufficient societal stocks for service-provisioning </p> <p> </p> <p>It provides the data used and the basic mass balanced calculation for a circularity assessment.</p>
Evaluation of an adapted semi-automated DNA extraction for human salivary shotgun metagenomics
<p>This deposit contains :</p> <p>- a RMarkdown filte containing the codes for the mcirobial analysis of saliva samples</p> <p>- the html report with codes, results and figures</p> <p>- a RData containing microbial datasets (MSp species abundance table, genus, family and phylum abundance tables, matrix of genes correlations, taxonomy)</p> <p>- a RData containing associated metadata </p>
Data Extraction table summarizing studies in the scoping review on co-creation of patient education materials
<p>Data extraction table for scoping review on best practices for co-creating patient-facing educational materials</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.