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483 results for “semantics”
MESINESP: Medical Semantic Indexing in Spanish - Train dataset
<p><em><strong>Please use the <a href="https://doi.org/10.5281/zenodo.4612274">MESINESP2 corpus (the second edition of the shared-task)</a> since it has a higher level of curation, quality and is organized by document type (scientific articles, patents and clinical trials).</strong></em></p> <p> </p> <p> </p> <p><strong>INTRODUCTION</strong>:</p> <p>The Mesinesp (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) training set has a total of 369,368 records. </p> <p>The training dataset contains all records from LILACS and IBECS databases at the Virtual Health Library (VHL) with a non-empty abstract written in Spanish. The URL used to retrieve records is as follows:<br> http://pesquisa.bvsalud.org/portal/?output=xml&lang=es&sort=YEAR_DESC&format=abstract&filter[db][]=LILACS&filter[db][]=IBECS&q=&index=tw&</p> <p>We have filtered out empty abstracts and non-Spanish abstracts. </p> <p>The training dataset was crawled on 10/22/2019. This means that the data is a snapshot of that moment and that may change over time. In fact, it is very likely that the data will undergo minor changes as the different databases that make up LILACS and IBECS may add or modify the indexes.</p> <p> </p> <p><strong>ZIP STRUCTURE:</strong></p> <p>The training data sets contain 369,368 records from 26,609 different journals. Two different data sets are distributed as described below:</p> <p> - <em>Original Train set</em> with 369,368 records that also include the qualifiers, as retrieved from VHL. <br> - <em>Pre-processed Train set</em><strong> </strong>with the 318,658 records with at least one DeCS code and with no qualifiers. </p> <p> </p> <p> </p> <p><strong>STATISTICS</strong>:</p> <p>Abstracts’ length (measured in characters)<br> Min: 12<br> Avg: 1140.41<br> Median: 1094<br> Max: 9428</p> <p>Number of DeCS codes per file<br> Min: 1<br> Avg: 8.12<br> Median: 7<br> Max: 53</p> <p> </p> <p> </p> <p><strong>CORPUS FORMAT</strong>:</p> <p>The training data sets are distributed as a JSON file with the following format:</p> <pre><code>{ "articles": [ { "id": "Id of the article", "title": "Title of the article", "abstractText": "Content of the abstract", "journal": "Name of the journal", "year": 2018, "db": "Name of the database", "decsCodes": [ "code1", "code2", "code3" ] } ] } </code></pre> <p>Note that the decsCodes field lists the DeCs Ids assigned to a record in the source data. Since the original XML data contain descriptors (no codes), we provide a DeCs conversion table (https://temu.bsc.es/mesinesp/wp-content/uploads/2019/12/DeCS.2019.v5.tsv.zip) with:</p> <p> - DeCs codes<br> - Preferred descriptor (the label used in the European DeCs 2019 set)<br> - List of synonyms (the descriptors and synonyms from both European and Latin Spanish DeCs 2019 data sets, separated by pipes)</p> <p> </p> <p>For more details on the Latin and European Spanish DeCs codes see: http://decs.bvs.br and http://decses.bvsalud.org/ respectively.</p> <p>Please, cite: Krallinger M, Krithara A, Nentidis A, Paliouras G, Villegas M. BioASQ at CLEF2020: Large-Scale Biomedical Semantic Indexing and Question Answering. InEuropean Conference on Information Retrieval 2020 Apr 14 (pp. 550-556). Springer, Cham.</p> <p> </p> <p>Copyright (c) 2020 Secretaría de Estado de Digitalización e Inteligencia Artificial</p>
TweetsCOV19 - A Semantically Annotated Corpus of Tweets About the COVID-19 Pandemic (Part 1, October 2019 - April 2020)
<p><strong><a href="https://data.gesis.org/tweetscov19/">TweetsCOV19</a></strong><strong> </strong>is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of <a href="https://data.gesis.org/tweetskb">TweetsKB</a> and aims at capturing online discourse about various aspects of the pandemic and its societal impact. <strong>Metadata</strong> information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong>, and <strong>resolved URLs </strong>are exposed in RDF using established RDF/S vocabularies*.</p> <p>We also provide a <em><strong>tab-separated values (tsv)</strong></em> version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character ("\t"). The following list indicate the feature indices:</p> <ol> <li>Tweet Id: Long.</li> <li>Username: String. Encrypted for privacy issues*.</li> <li>Timestamp: Format ( "EEE MMM dd HH:mm:ss Z yyyy" ).</li> <li>#Followers: Integer.</li> <li>#Friends: Integer.</li> <li>#Retweets: Integer.</li> <li>#Favorites: Integer.</li> <li>Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from <a href="https://github.com/yahoo/FEL">FEL</a> library. Each entity is separated from another entity by char ";". Also, each entity is separated by char ":" in order to store "original_text:annotated_entity:score;". If FEL did not find any entities, we have stored "null;".</li> <li>Sentiment: String. <a href="http://sentistrength.wlv.ac.uk/">SentiStrength</a> produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char " ". Positive sentiment was stored first and then negative sentiment (i.e. "2 -1").</li> <li>Mentions: String. If the tweet contains mentions, we remove the char "@" and concatenate the mentions with whitespace char " ". If no mentions appear, we have stored "null;".</li> <li>Hashtags: String. If the tweet contains hashtags, we remove the char "#" and concatenate the hashtags with whitespace char " ". If no hashtags appear, we have stored "null;".</li> <li>URLs: String: If the tweet contains URLs, we concatenate the URLs using ":-: ". If no URLs appear, we have stored "null;"</li> </ol> <p>This dataset consists of <strong>8,151,524 tweets</strong> in total, posted by <strong>3,664,518 users</strong> and reflects the societal discourse about COVID-19 on Twitter in the period of October 2019 until April 2020.</p> <p>To extract the dataset from <a href="https://data.gesis.org/tweetskb">TweetsKB</a>, we compiled a seed list of 268 COVID-19-related <a href="https://data.gesis.org/tweetscov19/keywords.txt">keywords</a>.</p> <p><em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em></p> <p> </p>
Dataset for semantic segmentation of the laboratory model of manufacturing environment
<p>This dataset includes images and labels used for semantic segmentation of the laboratory model of the manufacturing environment, at the University of Belgrade - Faculty of Mechanical Engineering. The dataset is gathered by using mobile robot RAICO (Robot with Artificial Intelligence based COgnition) and its stereo visual system made from two Basler acA1920-25uc cameras with Fujinon lens DF6HA-1B. The dataset includes close to 430 images with a resolution of 640x360. Images are acquired by both cameras at different mobile robot poses in the laboratory model of a manufacturing environment. Five classes are introduced in the dataset, machines 1 to 4, and a background class. The exact names of the classes are:</p> <p>classNames = ["Machine_1", "Machine_2", "Machine_3", "Machine_4", "Background"];</p> <p>while the labels of the classes (RGB values of label images) are:</p> <p>labelIDs = [ ...<br> 000 000 255; ... % "Machine 1"<br> 000 255 255; ... % "Machine 2"<br> 255 255 000; ... % "Machine 3"<br> 255 000 000; ... % "Machine 4"<br> 255 255 255; ... % "Background"<br> ];</p> <p>Image and label pairs are entitled 1 to 430, and e.g. label 5 corresponds to images 5.</p> <p>This dataset was developed with the support of the Science Fund of the Republic of Serbia, Grant No. 6523109, AI - MISSION4.0, 2020-2022.</p>
Semantic Segmentation of Time Series Imagery Using Deep Convolutional Neural Networks: A Case Study of Sandbars in Grand Canyon
<p>This dataset contains imagery used to train and test Deep Convolutional Neural Networks for the purpose of binary semantic segmentation of a time series of oblique imagery capturing sandbar monitoring sites in The Grand Canyon. In addition the scripts needed for removing image distortion, registering, rectifying, and labeling imagery is present. </p>
Human and Mouse Eyes for Pupil Semantic Segmentation
<p>A dataset composed of 11897 grayscale images of humans (4285) and mouse (7612) eyes. In different experimental conditions: head-fixation sessions (HF: 5061), 2-photon Ca2+ imaging ( 2P: 2551), and human eyes (H: 4285). The dataset contains 1596 eye blinks, 841 images in the mouse, and 755 photos in the human datasets. Five human raters segmented the pupil in all pictures (one per image) by manual placement of an ellipse or polygon over the pupil area. Raters flagged blinks using the same code. All the photos are illuminated using infrared (IR, 850 nm) light sources.</p> <p>The dataset contains 2 folders:</p> <p>'fullFrames': contains all the grayscale images in png format.</p> <p>'annotation': contains a folder called 'png' with pupil mask in the red channel. There is also a file called 'annotations.csv' containing a list with a description of each file in the dataset in this folder.</p> <p>Description of the fields in annotations.csv:</p> <p>filename: [string] with the file name </p> <p>eye: [0,1] if true an eye is present in the picture</p> <p>blink: [0,1] if true the subject is blinking</p> <p>exp: [string] what kind of experiments </p> <p>w: [int] resolution width</p> <p>h: [int] resolution height</p> <p>roi_x: [int] roi x coordinate</p> <p>roi_y: [int] roi y coordinate</p> <p>roi_w: [int] roi width-height (128x128)</p> <p>sub: [int] subject's label</p> <p> </p> <p> </p>
A Static-Based Approach to Detect SQL Semantic Bugs Dataset
<p>The dataset used for our study: A Static-Based Approach to Detect SQL Semantic Bugs.</p> <p>This dataset contains more than 172,000 queries extracted from StackOverflow posts. It was built for analysing the prevalence of semantic bugs in SQL queries.</p> <p>For more information about our study and tools see our GitHub repository: <a href="https://github.com/SERG-Delft/sql-bug-finder">https://github.com/SERG-Delft/sql-bug-finder</a></p> <p>Description of included files:</p> <ul> <li>sql_db.png: database ER diagram</li> <li>homedb_queries.sql: contains queries extracted from StackOverflow posts</li> <li>homedb_questions.sql: contains SQL related question posts extracted from StackOverflow</li> <li>homedb_answers.sql: contains the answers to SQL related question posts extracted from StackOverflow</li> <li>homedb_bugs.sql: contains queries with semantic bugs extracted from StackOverflow posts</li> <li>homedb_owners.sql: contains data related to the owners (users) of SQL StackOverflow posts</li> <li>homedb_pages.sql: artifact from book-keeping script, tracking the StackOverflow pages from which SQL queries were extracted (SQL tagged pages, ordered by votes in descending order)</li> </ul>
A dataset used to determine a semantic similarity metric based on UMLS for PMC-OA
<p>We have performed a series of in-silico experiments in order to determine a semantic similarity metric based on UMLS annotations for PubMed Central Open Access. Here we have stored the data used for and obtained from such experiments. We have worked with relevant and partially relevant articles from the TREC-2005 Genomics Track Collection, from now referred as the initial collection, including a total of 4240 unique PubMed articles. From those 4240 articles, only 62 had publicly available; those 62 articles correspond to the full-text collection.</p> <p>Our data comprises flat files using tabs as separators and one Excel sheet. Tab separated values always include a first row with headings:</p> <ul> <li>Stems extracted from title and abstract for articles in the initial collection. Each row contains a stem with its inverse-document-frequency (IDF) within the initial collection. Stems were calculated following the Porter algorithm (available at http://tartarus.org/martin/PorterStemmer/java.txt) <ul> <li>stems.TA.tsv</li> </ul> </li> <li>Article profiles, i.e., terms (either word stems or UMLS concepts) found in the articles with term frequency (TF) and IDF. The first two columns correspond to PubMed Identifier (PMID) and PubMed Central identifier (PMC). PMC identifier was set to 0 whenever full-text was not available. <ul> <li>profiles.TA.tsv: Profiles according word stems in title and abstract for the initial collection</li> <li>profiles.PMID.tsv: Profiles according to UMLS concpets in title and abstract for the initial collection</li> <li>profiles.PMC_TA.tsv: Profiles according to UMLS concepts in title and abstract for the full-text collection</li> <li>profiles.PMC.tsv: Profiles according to UMLS concepts in the full-text for the full-text collection</li> </ul> </li> <li>Similarity matrixes calculated on the article profiles with PubMed Related Article metric (PMRA), BM25, and Cosine. There are matrixes for terms found in title-and-abstract as well as full-text. In a similarity matrix, a reference article (an interest has been already expressed for it) correspond to a row, while the columns correspond to all the other articles for which the similarity was calculated. <ul> <li>Matrixes for our initial collection <ul> <li>similarity.PMRA.TA.profiles.TA.tsv: Similarity matrix for profiles.TA.tsv following the algorithm PMRA. This matrix is considered the baseline for further analyses</li> <li>similarity.PMRA.profiles.PMID.tsv: Similarity matrix for profiles.PMID.tsv following the algorithm PMRA</li> <li>similarity.BM25_1.2_0.75.profiles.PMID.tsv: Similarity matrix for profiles.PMID.tsv following the algorithm BM25 with k=1.2 and b=0.75</li> <li>similarity.COSINE.profiles.PMID.tsv: Similarity matrix for profiles.PMID.tsv following the algorithm Cosine</li> </ul> </li> <li>Matrixes for our full-text collection <ul> <li>similarity.PMRA.profiles.PMC_TA.tsv: Similarity matrix for profiles.PMC_TA.tsv following the algorithm PMRA</li> <li>similarity.PMRA.profiles.PMC.tsv: Similarity matrix for profiles.PMC.tsv following the algorithm PMRA</li> <li>similarity.BM25.profiles.PMC_TA.tsv: Similarity matrix for profiles.PMC_TA.tsv following the algorithm BM25 with k=1.2 and b=0.75</li> <li>similarity.BM25.profiles.PMC.tsv: Similarity matrix for profiles.PMC.tsv following the algorithm BM25 with k= 1.2 and b= 0.75</li> <li>similarity.COSINE.profiles.PMC_TA.tsv: Similarity matrix for profiles.PMC_TA.tsv following the algorithm Cosine</li> <li>similarity.COSINE.profiles.PMC.tsv: Similarity matrix for profiles.PMC.tsv following the algorithm Cosine</li> </ul> </li> </ul> </li> <li>Correlation matrixes for similarities calculated for title-and-abstract taking as reference the similarity values obtained with PMRA for word stems on title-and-abstract. <ul> <li>pearsonCorrelation.PMRA.tsv: Correlation for similarity.PMRA.profiles.PMID.tsv</li> <li>pearsonCorrelationTopic.PMRA.tsv: Correlation for similarity.PMRA.profiles.PMID.tsv discriminated by TREC topics</li> <li>pearsonCorrelation.BM25_1.2_0.75.tsv: Correlation for similarity.BM25_1.2_0.75.profiles.PMID.tsv</li> <li>pearsonCorrelationTopic.BM25_1.2_0.75.tsv: Correlation for similarity.BM25_1.2_0.75.profiles.PMID.tsv discriminated by TREC topics</li> <li>pearsonCorrelation.COSINE.tsv: Correlation for similarity.COSINE.profiles.PMID.tsv</li> <li>pearsonCorrelationTopic.COSINE.tsv: Correlation for similarity.COSINE.profiles.PMID.tsv discriminated by TREC topics</li> </ul> </li> <li>Precision and recall summaries for the similarities calculated based on title-and-abstract. <ul> <li>StatsAllSummary.xlsx: Precision and recall at a global level, i.e., without considering TREC topics. This file includes information for BM25 with multiples values for constants k and b</li> </ul> </li> </ul> <p>Visualization for correlation matrixes as well as scattered plots for full-text based similarity is available at http://ljgarcia.github.io/semsim.benchmark</p>
Human and Machine Judgements for Russian Semantic Relatedness
<p>Semantic relatedness of terms represents similarity of meaning by a numerical score. On the one hand, humans easily make judgements about semantic relatedness. On the other hand, this kind of information is useful in language processing systems. While semantic relatedness has been extensively studied for English using numerous language resources, such as associative norms, human judgements and datasets generated from lexical databases, no evaluation resources of this kind have been available for Russian to date. Our contribution addresses this problem. We present five language resources of different scale and purpose for Russian semantic relatedness, each being a list of triples (wordi, wordj , similarityij ). Four of them are designed for evaluation of systems for computing semantic relatedness, complementing each other in terms of the semantic relation type they represent. These benchmarks were used to organise a shared task on Russian semantic relatedness, which attracted 19 teams. We use one of the best approaches identified in this competition to generate the fifth high-coverage resource, the first open distributional thesaurus of Russian. Multiple evaluations of this thesaurus, including a large-scale crowdsourcing study involving native speakers, indicate its high accuracy.</p> <p>For more details see: </p> <ul> <li>The web page of the RUSSE evaluation campaign: http://russe.nlpub.ru/downloads</li> <li>The original publication "Panchenko A., Ustalov D., Arefyev N., Paperno D. Konstantinova N., Loukachevitch N. and Biemann C. undefinedHuman and Machine Judgements about Russian Semantic Relatedness. In Proceedings of the 5th Conference on Analysis of Images, Social Networks and Texts (AIST'2016). Communications in Computer and Information Science (CCIS). Springler-Verlag Berlin Heidelberg": https://www.lt.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_LangTech/publications/aist_2016_hmj.pdf</li> </ul>
On the Understandability of Semantic Constraints for Behavioral Software Architecture Compliance: A Controlled Experiment
<p>Software architecture compliance is concerned with the alignment of implementation with its desired architecture and detecting potential inconsistencies. The study is specifically concerned with behavioral architecture compliance. That is, the focus is on semantic alignment of implementation and architecture. In particular, the study evaluates three representative approaches for describing semantic constraints in terms of their understandability, namely natural language descriptions as used in many architecture documentations today, a structured language based on specification patterns that abstract underlying temporal logic formulas, and a structured cause-effect language that is based on Complex Event Processing. We conducted a controlled experiment with 190 participants using a simple randomized design with one alternative per experimental unit.</p>
geoscience-semantics: First release--stripped down version
<p>Stripped down version of geoscience semantic models "review"</p>
SeMRA Raw Semantic Mappings Database
<p>An automatically assembled dataset of raw semantic mappings produced by <code>python -m semra.database</code>. This incorporates mappings from the following places:</p> <ol> <li>Ontologies indexed in the Bioregistry (primary)</li> <li>Databases integrated in PyOBO (primary)</li> <li>Biomappings (secondary)</li> <li>Wikidata (primary/secondary)</li> <li>Custom resources integrated in SeMRA (primary)</li> </ol> <p>This is a database of raw mapping without further processing. For processed mapping datasets, we suggest smaller domain-specific processing rules (see <a href="https://github.com/biopragmatics/semra/tree/main/notebooks/landscape">https://github.com/biopragmatics/semra/tree/main/notebooks/landscape</a> for examples). It can be accessed directly via:</p> <ul> <li><code>mappings.sssom.tsv.gz</code> - loadable through any tools supporting SSSOM</li> <li><code>mappings.jsonl.gz</code> - loadable through SeMRA using <a href="https://semra.readthedocs.io/en/latest/api/semra.io.from_jsonl.html" target="_blank" rel="noopener"><code>semra.from_jsonl</code></a></li> </ul> <h2>How to Run the Web App</h2> <ol> <li>Download all artifacts from this Record</li> <li>Make sure that you have Docker running locally</li> <li>Run <code>sh run_on_docker.sh</code> from the command line</li> <li>Navigate to http://localhost:8773 to see the SeMRA dashboard or to http://localhost:7474 for direct access to the Neo4j graph database</li> </ol> <h2>Licensing</h2> <p>Mappings are licensed according to their primary resources. These are explicitly annotated in the SSSOM file on each row (when available) and on the mapping set level in the Neo4j graph database artifacts.</p>
Semantic Enrichment of the Laboratory Data Dictionary of the Study of Health in Pomerania (SHIP-START-4) with LOINC; Detailed Mapping Results
<p>Unlike West Germany, high morbidity and mortality have been observed in East Germany over the last century. The regional population-based Study of Health in Pomerania (SHIP) therefore investigates the long-term progression of sub-clinical findings, their determinants and prognostic values, to acquire knowledge that facilitates early diagnosis and thus helps prevent the progression of disease. The SHIP covers various areas of patient health. Each SHIP data set is accompanied by a data dictionary (DD) which provides descriptions of variables and definitions.</p> <p>This work shows the detailed mapping results of the semantic enrichment of the SHIP-START-4 medical laboratory data dictionary with LOINC codes. This work also provides detailed descriptions of the concepts applied in the semnatic enrichment. The results of this work serve as a critical step towards improving its interoperability and hence FAIRness for the SHIP laboratory-related measurements. </p>
Vine Trunk Semantic Segmentation using Individual Vine Trunks
<p>Automatically labeled semantic segmentation dataset of vine trunk thumbnails extracted from VIneSet and other supplementary data.</p> <p>Source paper of this dataset is "Generating vine trunk semantic segmentation dataset via semi-supervised learning and object detection" published in MDPI Robotics https://www.mdpi.com/2218-6581/13/2/20.</p>
An urban traffic dataset composed of visible images and their semantic segmentation generated by the CARLA simulator
<p><strong>If you use this dataset please cite this paper: Rosende, S.B.; Gavilán, D.S.J.; Fernández-Andrés, J.; Sánchez-Soriano, J. An Urban Traffic Dataset Composed of Visible Images and Their Semantic Segmentation Generated by the CARLA Simulator. <em>Data</em> 2024, <em>9</em>, 4. <a href="https://doi.org/10.3390/data9010004">https://doi.org/10.3390/data9010004</a></strong></p> <p>A dataset of aerial urban traffic images and their semantic segmentation is presented to be used to train computer vision algorithms, among which those based on convolutional neural networks stand out. The images have been generated using the CARLA simulator (but would be like those that could be obtained with fixed aerial cameras or by using AUVs) in the field of intelligent transportation management. The presented dataset is available and accessible to improve the performance of vision and road traffic management systems, especially for the detection of incorrect or dangerous maneuvers.</p>
Semantic annotation of PLoS journal citation contexts
<p>Dataset </p>
Demonstration of semantic and inter-input constraints on software in OWL 2 and SPARQL for fulfilling the M1 Machine FAIR Use Case
<p>This video demonstrates using hypothetical examples how to (1) find a valid dataset for input into a software using OWL 2 classification inference, (2) validly combine two software using OWL 2 subsumption inference to infer that the output of software 1 is valid input to software 2, and (3) combine OWL 2 inference with a SPARQL query to find two datasets that satisfy a software's inter-input constraints.</p>
Large Spots DeepMIB project, synthetic dataset for testing 2D semantic segmentation
<p>A complete DeepMIB project with a synthetic dataset generated for quick tests of semantic segmentation approaches.<br>The dataset includes a trained DeepLabV3-Resnet18 network for detection of large spots on a black background. </p><p>The network can be opened by loading "2D_LargeSpots_2cl_DeepLabV3.mibCfg" file by</p><ul><li><i>MIB->Menu->Tools->Deep learning segmentation->Options tab->Config files->Load </i></li><li>Drag and drop of the config file into DeepMIB window</li></ul><p>Microscopy Image Browser: <a href="https://mib.helsinki.fi">https://mib.helsinki.fi</a></p>
tFoodL: Larger Semantic Table Annotations Benchmark for Food Domain
<p><strong>tFoodL</strong> is the successor work of <a href="https://zenodo.org/records/10048187">tFood</a> that is generated by <a href="https://github.com/fusion-jena/KG2Tables">KG2Tables </a>using 10 levels of a recursive hierarchy of related concepts in Wikidata.</p><p>Similar to tFood, it is a dataset for tabular data to knowledge graph matching. It is derived for the Food domain and has two types of tables. On the one hand, <strong>Horizontal Relational Tables</strong> are where each table represents a collection of entities. On the other hand, <strong>Entity Tables</strong> represent a single entity. We supported ground truth data from Wikidata as a target knowledge graph (KG).</p><p><strong>tFoodL</strong> contains 43,255 entity and horizontal tables, while this repository contains only the validation fold (10%) of the entire benchmark with its ground truth data (gt). </p><p>The supported tasks for semantic table annotations are: </p><ol><li>Topic Detection (<strong>TD</strong>) links the entire table to an entity or a class from the target KG.</li><li>Cell Entity Annotation (<strong>CEA</strong>) maps individual table cells to entities from the target KG.</li><li>Column Type Annotation (<strong>CTA</strong>) links individual table columns to classes from the target KG.</li><li>Column Property Annotation (<strong>CPA</strong>) detects the relations between column pairs from the target knowledge graph.</li><li>Row Annotation (<strong>RA) </strong>annotates the entire row to a KG entity or property.</li></ol>
tBiodivL: Larger Semantic Table Annotations Benchmark for Biodiversity Domain
<p><strong>tBiodivL</strong> is a dataset for tabular data to knowledge graph matching. It is derived from the Biodiversity domain and has two types of tables. On the one hand, <strong>Horizontal Relational Tables</strong> are where each table represents a collection of entities. On the other hand, <strong>Entity Tables</strong> represent a single entity. We supported ground truth data from Wikidata as a target knowledge graph (KG).</p><p><strong>tBiodivL</strong> is generated by <a href="https://github.com/fusion-jena/KG2Tables">KG2Tables </a>using 10 levels of a recursive hierarchy of related concepts in Wikidata. It is the successor work of <a href="https://doi.org/10.5281/zenodo.10283015">tBiodiv</a></p><p><strong>tBiodivL </strong>contains <strong>222,353</strong> entity and horizontal tables, while this repository contains only a sample of <strong>1% of the total generated tables</strong> of the entire benchmark with its ground truth data (gt). The Full size of this dataset is <strong>312 GB</strong>. We will update this repository with the full dataset in the Future.</p><p>Please get in touch if you are interested in the full dataset, </p><p>The supported tasks for semantic table annotations are: </p><ol><li>Topic Detection (<strong>TD</strong>) links the entire table to an entity or a class from the target KG.</li><li>Cell Entity Annotation (<strong>CEA</strong>) maps individual table cells to entities from the target KG.</li><li>Column Type Annotation (<strong>CTA</strong>) links individual table columns to classes from the target KG.</li><li>Column Property Annotation (<strong>CPA</strong>) detects the relations between column pairs from the target knowledge graph.</li><li>Row Annotation (<strong>RA) </strong>annotates the entire row to a KG entity or property.</li></ol>
tBiomedL: Larger Semantic Table Annotations Benchmark for Biomedical Domain
<p><strong>tBiomedL </strong>is a dataset for tabular data to knowledge graph matching. It is derived for the Biodiversity domain and has two types of tables. On the one hand, <strong>Horizontal Relational Tables</strong> are where each table represents a collection of entities. On the other hand, <strong>Entity Tables</strong> represent a single entity. We supported ground truth data from Wikidata as a target knowledge graph (KG).</p><p><strong>tBiomedL </strong>is generated by <a href="https://github.com/fusion-jena/KG2Tables">KG2Tables </a>using five levels of a recursive hierarchy of related concepts in Wikidata. It is the successor work of <a href="https://doi.org/10.5281/zenodo.10283103">tBiomed</a></p><p><strong>tBiomedL </strong>contains <strong>860,479</strong> entity and horizontal tables, while this repository contains only <strong>a sample of 1%</strong> of the total of the entire benchmark with its ground truth data (gt). The Full size of this dataset is <strong>27</strong> <strong>GB</strong>. We will update this repository with the full dataset, including the test fold with its ground truth data in the Future.</p><p>Please get in touch if you are interested in the full dataset, </p><p>The supported tasks for semantic table annotations are: </p><ol><li>Topic Detection (<strong>TD</strong>) links the entire table to an entity or a class from the target KG.</li><li>Cell Entity Annotation (<strong>CEA</strong>) maps individual table cells to entities from the target KG.</li><li>Column Type Annotation (<strong>CTA</strong>) links individual table columns to classes from the target KG.</li><li>Column Property Annotation (<strong>CPA</strong>) detects the relations between column pairs from the target knowledge graph.</li><li>Row Annotation (<strong>RA) </strong>annotates the entire row to a KG entity or property.</li></ol>
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.