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267 results for “entities”
Line-level Named Entity Recognition annotation for the George Washington and IAM datasets
<p>Line-level Named Entity annotation for the George Washington and IAM datasets. The word-level annotations from Oliver Tüselmann [3] were extended to line-level to enable experimentation with line-level coupled HTR+NER models. We also publish the line-level partition files that result from the partition proposed by [3].</p>
The relationship between the leadership of business organizations to manage conflicts within their organizations and with other entities
<p>Descripition of the dataset:</p> <p>The questionnaire was made to the leaders of the companies was made in July 2019, consisting of 10 questions where the subject under study is addressed. <br> The questionnaire was personally conducted to 390 leaders of Madrid, consisting of 10 questions ranging from demographic variables, leadership style in conflict management and methods used for conflict resolution.<br> The database has been subsequently anonymised, so that each respondent cannot be identified.</p> <p>Dataset relations:</p> <p>Paper: What are the determinants in conflict management and leadership employed? An analysis of the determinants for the Spanish case</p> <p>Abstract:The purpose of this study is to examine the relationship between the leadership of business organizations to manage conflicts within their organizations and with other entities. <br> For this reason, an exhaustive review of the literature on leadership decision-making and conflict management has been carried out, focusing on sociodemographic variables that have the greatest influence. <br> In addition, a study of conflict management has been carried out in the community of Madrid. The purpose of the study was to identify the most decisive sociodemographic variables that explain how leaders manage conflicts in the most important business nucleus in Spain. <br> The statistical study was based on information collected on demographic variables and leadership styles in conflict management through a survey carried out in July 2019 of 394 leaders of Madrid organizations.</p>
morethanbooks/XML-TEI-Bible: XML-TEI Bible: Entities and communication (66 Books)
<p>This release contains the biblical text in XML-TEI (66 books). The encoded text is in Spanish, but the codification (elements, attributes, values, ids) is in English. It makes explicit following information:</p> <ul> <li>Books, chapters, pericopes and verses.</li> <li>References to peoples, places, times, groups and books, using ids.</li> <li>Direct speech, including who is communicating, to whom and how (written, oral, prayer...).</li> </ul>
Benchmark for the evaluation of named entity recognition over ancient documents
<p>The dataset consists of a multilingual noisy corpora for named entity recognition (NER).<br> The noisy versions are simulated from the CoNLL-02 (Spanish and Dutch) and CoNLL-03 (English) NER corpora.<br> The original collections are re-OCRed and four types of noises at two different levels are added in order to simulate various OCR output.</p> <p>More precisely, we first extracted raw texts and converted them into images. These images have been contaminated by adding some common noises when using a scanner. We further extract OCRed data using tesseract open source<br> OCR engine v-3.04.01. Consequently to the image noise insertions, OCRed data contains degradations. Original and noisy texts are finally aligned.</p> <p>This archive contains three folders (one per language). The folders contain the degraded images, the noisy texts extracted by the OCR and their aligned version with clean data.</p> <p>These are the supplementary materials for the TPDL 2020 paper <a href="https://zenodo.org/record/4734376#.YJKAcKE6-Uk">Assessing and minimizing the impact of OCR quality on named entity recognition</a>. If you end up using whole or parts of this resource,<br> please cite this paper:</p> <pre><code>@InProceedings{10.1007/978-3-030-54956-5_7, author="Hamdi, Ahmed and Jean-Caurant, Axel and Sid{\`e}re, Nicolas and Coustaty, Micka{\"e}l and Doucet, Antoine", editor="Hall, Mark and Mer{\v{c}}un, Tanja and Risse, Thomas and Duchateau, Fabien", title="Assessing and Minimizing the Impact of OCR Quality on Named Entity Recognition", booktitle="Digital Libraries for Open Knowledge", year="2020", publisher="Springer International Publishing", address="Cham", pages="87--101", isbn="978-3-030-54956-5" }</code></pre> <p><strong>Acknowledgments</strong><br> This work has been supported by the European Union's Horizon 2020 research and innovation programme under grant 770299 [NewsEye](https://www.newseye.eu/).</p>
Point, polygon, or marker? In search of the best geographic entity for mapping Cultural Ecosystem Services using the online PPGIS tool, "My Green Place."
<p>Excel files include the raw database and the processed data that led to the quadrat analyses. The "Matrix_raw data" file includes the raw data as downloaded from the server. This data was cleaned and organized for its posterior use. "Quadrat analyzes "file includes all the quadrat analyses resulting in each research question in the paper except question four. Question 4 can be seen in the file "Water analysis_Blaarmeersen." All excel files come with a "CODE" tab that describes each of the codes used, their meaning, and ways that were calculated where necessary. Two zip files include all the GIS files. The first one includes the GIS files from which "Matrix_raw data" was built from. The second folder includes the resulting maps from the quadrat analyses. In order to visualize them as in the paper, configure the symbology tab at the GIS software in quantile and the categories number, as shown in the paper.</p> <p>The production of the files in the "GIS_Processed data" folder was done via a repetitive line of commands in ArcGIS pro. The same process was followed for each one of the quadrat analysis described in the paper. Refer to "<a href="https://zenodo.org/api/files/72403e0b-78a9-4bbf-8dca-ed9b8702e3ae/Reproduction%20commands%20and%20parameters.pdf">Reproduction commands and parameters.pdf</a>" for further information.</p>
Contextualizing Trending Entities in News Stories
<p>This repository contains the enrichments for the dataset <a href="https://catalog.ldc.upenn.edu/LDC2008T19">The New York Times Annotated Corpus</a> developed for the paper:</p> <p>“Marco Ponza, Diego Ceccarelli, Paolo Ferragina, Edgar Meij, Sambhav Kothari. Contextualizing Trending Entities in News Stories. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM 2021).”</p> <p>It includes a total of 149 trends constituted by 120K entities. The goal is to retrieve a set of entities ranked with respect to their usefulness in explaining why a given trending entity is actually trending.</p> <p><strong>Format</strong></p> <p>The repository contains the enrichments in JSON format.</p> <p>The news stories of the New York Times from which these enrichments have been developed are available from <a href="https://catalog.ldc.upenn.edu/LDC2008T19">LDC</a>.</p> <p><strong>Data Splits</strong></p> <p>We perform two kinds of evaluation.</p> <ol> <li>Unsupervised evaluation, where we use the complete dataset of 149 trends as a benchmark.</li> <li>Supervised evaluation, where we train/tune our models on a training/development set and we test them on a test set.</li> </ol> <ul> <li>The training set contains 50 trends constituted by 36.3K entities from 1996 to 2000.</li> <li>The development set contains 34 trends constituted by 26.7K entities from 2000 to 2002.</li> <li>The test set contains 65 trends constituted by 57K entities from 2002 to 2007.</li> </ul> <p>Use</p> <p>Please cite the data set and the accompanying paper if you found the resources in this repository useful:</p> <p>@inproceedings{ponza2021,<br> Title = {Contextualizing Trending Entities in News Stories},<br> author = {Ponza, Marco and Ceccarelli, Diego and Ferragina, Paolo and Meij, Edgar and Kothari, Sambhav},<br> Booktitle = {Proceedings of the 14th ACM International Conference on Web Search and Data Mining},<br> Year = {2021},<br> }</p>
Ground Truth Dataset with mappings of companies to OpenCorporates legal entities
<p>Ground Truth Dataset with mappings of companies to OpenCorporates legal entities. Available data per company: company name, country of headquarters, state of headquarters (in case of US companies) and address of headquarters.</p>
GRN-AV-traffic-entity
<p>The GRN-AV-traffic-entity dataset is a collection of labelled multi-modal synched audio-video (AV) data, which is labelled with traffic sounds (e.g., engine noise, wheel rolling and vehicle horn). The video frames are not labelled, but are included.</p>
GeoEDdA: A Gold Standard Dataset for Named Entity Recognition and Span Categorization Annotations of Diderot & d'Alembert's Encyclopédie
<p>This repository contains a gold standard dataset for named entity recognition and span categorization annotations from Diderot & d’Alembert’s Encyclopédie entries.</p> <p>The dataset is available in the following formats:</p> <ul> <li>JSONL format provided by <a href="https://prodi.gy/" rel="nofollow">Prodigy</a></li> <li>binary spaCy format (ready to use with the spaCy train pipeline)</li> </ul> <p>The Gold Standard dataset is composed of 2,200 paragraphs out of 2,001 Encyclopédie's entries randomly selected. All paragraphs were written in 19th-century French.</p> <p>The spans/entities were labeled by the project team along with using pre-labelling with early machine learning models to speed up the labelling process. A train/val/test split was used. Validation and test sets are composed of 200 paragraphs each: 100 classified under 'Géographie' and 100 from another knowledge domain. The datasets have the following breakdown of tokens and spans/entities.</p> <h2>Tagset</h2> <ul> <li><strong>NC-Spatial</strong>: a common noun that identifies a spatial entity (nominal spatial entity) including natural features, e.g. <code>ville</code>, <code>la rivière</code>, <code>royaume</code>.</li> <li><strong>NP-Spatial</strong>: a proper noun identifying the name of a place (spatial named entities), e.g. <code>France</code>, <code>Paris</code>, <code>la Chine</code>.</li> <li><strong>ENE-Spatial</strong>: nested spatial entity , e.g. <code>ville de France</code> , <code>royaume de Naples</code>, <code>la mer Baltique</code>.</li> <li><strong>Relation</strong>: spatial relation, e.g. <code>dans</code>, <code>sur</code>, <code>à 10 lieues de</code>.</li> <li><strong>Latlong</strong>: geographic coordinates, e.g. <code>Long. 19. 49. lat. 43. 55. 44.</code></li> <li><strong>NC-Person</strong>: a common noun that identifies a person (nominal spatial entity), e.g. <code>roi</code>, <code>l'empereur</code>, <code>les auteurs</code>.</li> <li><strong>NP-Person</strong>: a proper noun identifying the name of a person (person named entities), e.g. <code>Louis XIV</code>, <code>Pline</code>.</li> <li><strong>ENE-Person</strong>: nested people entity, e.g. <code>le czar Pierre</code>, <code>roi de Macédoine</code>.</li> <li><strong>NP-Misc</strong>: a proper noun identifying entities not classified as spatial or person, e.g. <code>l'Eglise</code>, <code>1702</code>, <code>Pélasgique</code></li> <li><strong>ENE-Misc</strong>: nested named entity not classified as spatial or person, e.g. <code>l'ordre de S. Jacques</code>, <code>la déclaration du 21 Mars 1671</code>.</li> <li><strong>Head</strong>: entry name</li> <li><strong>Domain-Mark</strong>: words indicating the knowledge domain (usually after the head and between parenthesis), e.g. <code>Géographie</code>, <code>Geog.</code>, <code>en Anatomie</code>.</li> </ul> <h2>HuggingFace</h2> <p>The GeoEDdA dataset is available on the HuggingFace Hub: <a href="https://huggingface.co/datasets/GEODE/GeoEDdA">https://huggingface.co/datasets/GEODE/GeoEDdA</a></p> <h2>spaCy Custom Spancat trained on Diderot & d’Alembert’s Encyclopédie entries</h2> <p>This dataset was used to train and evaluate a custom spancat model for French using <a href="https://spacy.io/" rel="nofollow">spaCy</a>. The model is available on HuggingFace's model hub: <a href="https://huggingface.co/GEODE/fr_spacy_custom_spancat_edda" rel="nofollow">https://huggingface.co/GEODE/fr_spacy_custom_spancat_edda</a>.</p> <h2>Acknowledgement</h2> <p>The authors are grateful to the <a href="https://aslan.universite-lyon.fr/" rel="nofollow">ASLAN project</a> (ANR-10-LABX-0081) of the Université de Lyon, for its financial support within the French program "Investments for the Future" operated by the National Research Agency (ANR). Data courtesy the <a href="https://artfl-project.uchicago.edu/" rel="nofollow">ARTFL Encyclopédie Project</a>, University of Chicago.</p>
Extracting Biomedical Entities from Noisy Audio Transcripts--Dataset
<p><strong>SUMMARY</strong>:</p> <p>This repo contains the CADEC and Synthetic BTACT datasets that were used for the paper titled "<em>Extracting Biomedical Entities from Noisy Audio Transcripts</em>."</p> <p>The dataset includes two sets: i) CADEC (Karimi et al., 2015) and ii) Synthetic BTACT. CADEC is a well-known NER dataset used to identify adverse drug reactions based on what patients have written about their experiences. Synthetic BTACT is the data that we have made up. It is created based on questions similar to those in the Brief Test of Adult Cognition by Telephone (BTACT)(Tun et al., 2006).</p> <p>CADEC includes two sets of audio files; one is read from the original CADEC, and the other one is with additional audio noise. It also includes the original CADEC scripts, annotations, and the transcripts of the noisy audio. The transcripts are generated using Whisper. The annotations encompass named entities, their types, and string indexes of their occurrence in the text. Annotations also include "AnnotatorNotes" which explains some of the annotations.</p> <p>The synthetic BTACT data include two types: i) animals and ii) fruits. Similar to CADEC, it includes two sets of audio files: one that is read from the original scripts and another one with additional audio noise. The text files include the original scripts, annotations, and the Whisper-transcribed of the noisy audio files. The annotations include indexes of named entities, their string indices and types.</p> <p><strong>REFERENCES</strong>:</p> <p>Karimi, S., Metke-Jimenez, A., Kemp, M., & Wang, C. (2015). Cadec: A corpus of adverse drug event annotations. Journal of biomedical informatics, 55, 73-81.</p> <p>Tun, P. A., & Lachman, M. E. (2006). Telephone assessment of cognitive function in adulthood: the Brief Test of Adult Cognition by Telephone. Age and Ageing, 35(6), 629-632.</p> <p><strong>DETAILS</strong>:</p> <p>Data_1: CADEC (1250 TextFiles, 1000 Audio, types=5):</p> <p> General Categories and Counts<br> ADR (Adverse Drug Reactions): 5316<br> DRUG: 1797<br> FINDING: 397<br> DISEASE: 280<br> SYMPTOM: 255<br> Specific Items (Drugs) and Counts<br> Arthrotec: 145<br> cambia: 4<br> cataflam: 10<br> diclofenac-potassium: 3<br> diclofenac-sodium: 7<br> flector: 1<br> Lipitor: 997<br> Pennsaid: 4<br> solarez: 3<br> voltaren: 46<br> voltaren-rx: 22<br> zipsor: 5</p> <p>Data_2: Synthetic BTACT (500 Fruits, 500 Animals, types=2)</p> <p>>> Audios can be matched with annotations, scripts and transcripts using their filenames. </p> <p>---<br>audio [original&noisy]:<br> 1. cadec<br> 1.1 cadec original<br> 1.2 cadec noisy<br> 2. synthetic btact<br> 2.1 btact original<br> 2.1.1 fruits<br> fruit-script[0:500].mp3<br> 2.1.2 animals<br> script-[0:500].mp3<br> 2.2 btact noisy<br> 2.2.1 fruits<br> fruit-script[0:500].mp3<br> 2.2.2 animals<br> script-[0:500].mp3<br>text[scripts, annotations, transcripts]:<br> 1. cadec<br> 1.1 scripts [1,250]<br> 1.2 annotations [1,250] (index/AnnotatorsNote, type, indices, named-entities)<br> 1.3 transcripts [1,000]<br> 2. synthetic btact<br> 2.1 animals<br> 2.1 scripts (original scripts)<br> script-[0:500].txt<br> 2.2 annotations<br> script-[0:500].ann (index, type, start/end indices, named entity)<br> 2.3 transcripts<br> script-[0:500].txt<br> 2.2. fruits<br> 2.1 scripts (original scripts)<br> script-[0:500].txt<br> 2.2 annotations<br> script-[0:500].ann (index, type, start/end indices, named entity)<br> 2.3 transcripts<br> fruit-script-[0:500].txt</p> <p> </p> <p><strong>CITATION</strong>:</p> <p>Ebadi, N., Morgan, K., Tan, A., Linares, B., Osborn, S., Majors, E., Davis, J., & Rios, A. (2024). Extracting biomedical entities from noisy audio transcripts. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024).</p>
Resource Description Framework (RDF) Modeling of Named Entity Co-occurrences in Biomedical Literature and Its Integration with PubChemRDF
<p>This Zenodo record contains the co-occurrence RDF data generated in the work described in the paper “<strong>A resource description framework (RDF) model of named entity co-occurrences in biomedical literature and its integration with PubChemRDF</strong>” by Li et al., published in the Journal of Cheminformatics (<a href="https://doi.org/10.1186/s13321-025-01017-0" target="_blank" rel="noopener">https://doi.org/10.1186/s13321-025-01017-0</a>). It also contains the SPARQL query examples, the RDF schema in SHACL and ShEx, and the validation scripts.</p> <p>All content in this Zenodo record is for archival purposes. The latest version of the co-occurrence RDF data and other PubChemRDF data can be accessed via the PubChem FTP site (<a href="https://ftp.ncbi.nlm.nih.gov/pubchem/RDF/" target="_blank" rel="noopener">https://ftp.ncbi.nlm.nih.gov/pubchem/RDF/</a>). The up-to-date RDF schema in various formats is available on the PubChemRDF Schema page (<a href="https://pubchem.ncbi.nlm.nih.gov/docs/rdf-schema" target="_blank" rel="noopener">https://pubchem.ncbi.nlm.nih.gov/docs/rdf-schema</a>). A set of SPARQL query examples can be found on the PubChemRDF use case pages (<a href="https://pubchem.ncbi.nlm.nih.gov/docs/rdf-use-cases" target="_blank" rel="noopener">https://pubchem.ncbi.nlm.nih.gov/docs/rdf-use-cases</a>).</p>
balancing alpha for tada-entity
<p>This contains a list of figures which represents the accuracy of alpha prediction using different datasets and settings.</p>
Dataset for "Evaluation of Publicly Available Information on Sex-related Differences in the Efficacy and Safety of New Molecular Entities and Therapeutic Biological Products"
<p>Contains our extraction sheets with additional documents/notes on methods used in our study.</p>
Maritime entities of geographic transportation network of Acclimate
<p>maritime_entities.csv: Centroids of maritime entities and coordination of ports used by geographic transportation network of Acclimate</p> <p>XYZ_coordinates.csv: Polygon coordinates of maritime entites XYZ of the East Asian maritime trading route. Used to compute time tropical cyclone passed by based on observed trajectories from IBTrACS [http://ibtracs.unca.edu/]</p>
"Genome binning of viral entities from bulk metagenomics data" - CAMISIM simulated datasets and genomes
<p><strong>Genome binning of viral entities from bulk metagenomics data</strong></p> <p> </p> <p><strong>Authors</strong></p> <p><strong>Joachim Johansen1,2, Damian R. Plichta2, Jakob Nybo Nissen1,3, Marie Louise Jespersen1,4, Shiraz A. Shah5, Ling Deng6, Jakob Stokholm5,6, Hans Bisgaard5, Dennis Sandris Nielsen6, Søren Sørensen7, Simon Rasmussen1</strong></p> <p> </p> <p><strong>Affiliations</strong></p> <p>1 Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark</p> <p>2 Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA</p> <p>3 Statens Serum Institut, Viral & Microbial Special diagnostics, Copenhagen, Denmark</p> <p>4 National Food Institute, Technical University of Denmark, Kongens Lyngby, Denmark</p> <p>5 Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark</p> <p>6 Section of Food Microbiology and Fermentation, Department of Food Science, Faculty of Science, University of Copenhagen, Copenhagen, Denmark</p> <p>7 Section of Microbiology, Department of Biology, University of Copenhagen, Copenhagen, Denmark</p> <p><strong>Methods description</strong></p> <p>We compared the viral binning performance of VAMB and MetaBAT2 using the official CAMI consortium method to create assemblies and metagenome profiles. To this end we generated 3 different metagenome compositions with up to 308 reference genomes; one mixed with bacteria, plasmids and viruses to test binning in complex samples i.e. high diversity (1), one with only crass-like viruses to test binning with highly similar viruses i.e. high relatedness (2) and a set of small-viruses (<6,000 bp) including members of the Microviridae family to address the bias of size (3). Bacterial genomes were gathered from NCBIs refseq genome repository 2021, plasmids from the PLSDB database (v. 2021_06_23) and viral genomes from the recent MGV database. </p> <p>Dataset A contained a mixture of bacteria (N=8), plasmids (N=20) and viruses (N=280) to test binning in complex samples, i.e. high diversity. Dataset B contained only crass-like viruses (N=80) to test binning with highly similar viruses i.e. high relatedness. Dataset C contained small-viruses (N=50, <6,000 bp) of the Microviridae family to address the bias of size. Bacterial genomes were sampled from the Refseq genome repository 2021, plasmids from the PLSDB database and viral genomes from the recent MGV database (Nayfach, et al. Nature Microbiology 2021).</p> <p> </p> <p> </p>
CLEF-HIPE-2020 Shared Task Named Entity Datasets
<p>CLEF-HIPE-2020 (Identifying Historical People, Places and other Entities) is a <strong>evaluation campaign on named entity processing</strong> on <strong>historical newspapers</strong> in <strong>French</strong>, <strong>German</strong> and <strong>English</strong>, which was organized in the context of the <a href="http://impresso-project.ch"><em>impresso</em></a> project and run as a <a href="https://clef2020.clef-initiative.eu/">CLEF 2020</a> Evaluation Lab. Data consists of manually annotated historical newspapers in French, German and English.<br> <br> For more information, please refer to:</p> <ul> <li>the CLEF-HIPE-2020 <a href="https://impresso.github.io/CLEF-HIPE-2020/">website</a>;</li> <li>the <a href="https://github.com/impresso/CLEF-HIPE-2020-eval">CLEF-HIPE-2020-eval repository</a>, for the necessary material to replicate the results of the shared task;</li> <li>the <a href="https://zenodo.org/record/3539085">CLEF-HIPE-2020 poster</a> presented at CLEF 2019 in Lugano, Switzerland;</li> <li>the CLEF-HIPE-2020 <a href="https://zenodo.org/record/3677171">participation guidelines</a> (v1.1);</li> <li>the <em>impresso</em> <a href="https://doi.org/10.5281/zenodo.3585749">Named Entity Annotation Guidelines</a> (v2.2.0);</li> <li>the <a href="https://infoscience.epfl.ch/record/281054">CLEF-HIPE-2020 Extended Overview</a> paper (bibtex below);</li> <li>the participant team <a href="http://ceur-ws.org/Vol-2696/">CEUR working note papers</a>;</li> <li>the workshop presentation <a href="https://www.youtube.com/playlist?list=PLB45F159nVx-3bee7G_1jdTfUAtsLD0FU">video records</a>;</li> </ul> <p>A second edition of HIPE is organised in 2022: <a href="https://hipe-eval.github.io/HIPE-2022/">https://hipe-eval.github.io/HIPE-2022/ </a></p> <p>Please cite this paper if you are using the datasets or find the shared task results relevant to your research:</p> <pre><code>@inproceedings{ehrmann_extended_2020, title = {Extended {Overview} of {CLEF HIPE} 2020: {Named Entity Processing} on {Historical Newspapers}}, booktitle = {{CLEF 2020 Working Notes}. {Working Notes} of {CLEF} 2020 - {Conference} and {Labs} of the {Evaluation Forum}}, author = {Ehrmann, Maud and Romanello, Matteo and Fl{\"u}ckiger, Alex and Clematide, Simon}, editor = {Cappellato, Linda and Eickhoff, Carsten and Ferro, Nicola and N{\'e}v{\'e}ol, Aur{\'e}lie}, year = {2020}, volume = {2696}, pages = {38}, publisher = {{CEUR-WS}}, address = {{Thessaloniki, Greece}}, doi = {10.5281/zenodo.4117566}, url = {https://infoscience.epfl.ch/record/281054}, }</code></pre> <p> </p>
Dataset for WWW2022 accepted paper "SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs"
<p>Datasets for WWW2022 accepted paper "SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs"</p> <p>The code repository is <a href="https://github.com/THUDM/SelfKG">here</a>, and our paper is <a href="https://arxiv.org/abs/2203.01044">here</a>.</p>
EvaNIL: silver standard dataset for large-scale NIL entity linking evaluation
<p>The EvaNIL dataset can be used to train or evaluate approaches developed for NIL entity linking. It was built from several Biomedical and Life Sciences corpora:</p> <ul> <li>PubMed DS</li> <li>CRAFT corpus</li> <li>MedMentions</li> </ul> <p>These corpora contain entities associated with knowledge base concepts. To build the EvaNIL dataset, we assumed that those knowledge base concepts did not exist in the respective knowledge bases, so each entity is associated instead with the direct ancestors of those original concepts.</p> <p>The EvaNIL dataset is divided into 6 partitions including annotations from several knowledge bases:</p> <ul> <li>"medic" (CTD-MEDIC)</li> <li>"ctd_anatomy" (CTD-Anatomy)</li> <li>"ctd_chemicals" (CTD-Chemicals)</li> <li>"chebi" (ChEBI)</li> <li>"go_bp" (GO-Biological Process)</li> <li>"hp" (HPO)</li> </ul> <p> </p>
Dataset: Asset Entities Inc. (ASST) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Figure 5. Final Ranking-Data Conflict Resolution among Same Entities in Web of Data
<p>Finally, the last score with regard to the equation 4 has been calculated and is shown in<br> figure 5. As it shows Geonames gains the highest score. Considering the proposed idea, Geonames'<br> data is the most accurate. So when we are faced with data conflicts, Geonames' data must be chosen.<br> The WordFactbook is also illustrated in the above table. We can see if WordFactbook is not<br> removed from the synonyms list it would have more accurate data compared with DBpedia data.</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.