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.
3,101
datasets available to search
ShareScore release 0.9.0
Dataset results
3,101 results for “tables”
Fig. 1. Map showing the localities where Acropora corals were sampled during the first Snellius expedition, numbers correspond with Table 1 in The Acropora Humilis Group (Scleractinia) Of The Snellius Expedition (1929-30)
Fig. 1. Map showing the localities where Acropora corals were sampled during the first Snellius expedition, numbers correspond with Table 1.
Fig. 5. Combined tree from Fig. 4 with mapped morphological characters. Numbers above branches represent characters from Table 3. Mapped using WinClada ver. 1.61 in A revision of Scipopus Enderlein including the subgenera Scipopus s. str., Phaeopterina Frey and Parascipopus subgen. nov. (Diptera, Micropezidae, Taeniapterinae)
Fig. 5. Combined tree from Fig. 4 with mapped morphological characters. Numbers above branches represent characters from Table 3. Mapped using WinClada ver. 1.61 (Nixon 1999–2002) with unambiguous characters only.
Metabolomics Peak Tables of the publication "Screening of leaf extraction and storage conditions for eco-metabolomics studies"
<p>This dataset is a metabolomics study of maize extracts. The dataset consists of peak tables (.csv files) and MS/MS fragment patterns (.mgf files) both exported from MetaboScape. Additionally the results of a Principal Component Analysis are provided for the LLE optimisation part of the dataset.</p> <p>All further details are available in the publication in Plant Direct: https://doi.org/10.1002/pld3.578</p>
Groundwater level data, aquifer system boundaries, and supplementary tables associated with Jasechko, S. et al. Rapid groundwater decline and some cases of recovery in aquifers globally. Nature, doi.org/10.1038/s41586-023-06879-8 (2024).
<p>Groundwater level data, aquifer system boundaries, and Supplementary Tables associated with Jasechko, S., Seybold, H., Perrone, D., Fan, Y., Shamsudduha, M., Taylor, R.G., Fallatah, O., Kirchner, J.W. Rapid groundwater decline and some cases of recovery in aquifers globally. Nature, https://doi.org/10.1038/s41586-023-06879-8 (2024).</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>
Tables of inchoative verbs in the Saami languages
<p>The tables in the .xlsx file present the occurrence of various types of derived inchoative verbs by tabulating the attested derivatives of each base verb in each Saami language. Base verbs are given in their Proto-Saami form and approximate meanings in Finnish. For more information about sources etc., see the paper which this is an appendix to:</p><p>Kuokkala, Juha. 2023. Inchoative verbs in Saami: Derivational types and their variation. In: <i>Saami Linguistics in Uppsala: The 2019 SAALS 4 Symposium</i>. Studia Uralica Upsaliensia 42.</p><p>Changelog:</p><ul><li>Version 1.1 - Added missing North Saami <i>balˈlát</i></li></ul>
Tables, figures, and country data complementing the European Union One Health Zoonoses 2022 Report
<p>European Food Safety Authority; European Centre for Disease Prevention and Control</p><p>All summary tables and figures produced for the European Union One Health 2022 Zoonoses Report are provided as archives containing Excel files for tables, and as PDF or PNG files for figures.</p><p><strong>All country data connected to this Report are published SEPARATELY on Knowledge Junction - see related identifiers. This is because DATA OWNERSHIP for country data stays with the organisation(s) of the country submitting the data - for further reference see doi:10.2903/sp.efsa.2019.EN-1544.</strong></p><p>Supplementary datasets submitted are given in the related identifier section, however for clarity we give here the information on what they refer to:</p><p><strong>10.5281/zenodo.10255165 </strong>Foodborne outbreaks</p><p><strong>10.5281/zenodo.10256864 </strong>Disease status</p><p><strong>10.5281/zenodo.10255061 </strong>Animal Population</p><p><strong>10.5281/zenodo.10246432 </strong>Prevalence</p><p><i><strong>Sample-based data submitted by specific countries</strong></i></p><p><strong>10.5281/zenodo.10257184 </strong>Finland</p><p><strong>10.5281/zenodo.10257162 </strong>Croatia</p><p><strong>10.5281/zenodo.10257105 </strong>Norway</p><p><strong>10.5281/zenodo.10257034 </strong>Luxembourg</p><p><strong>10.5281/zenodo.10257142 </strong>United Kingdom (Northern Ireland)</p><p><strong>10.5281/zenodo.10257210 </strong>Ireland</p><p><strong>10.5281/zenodo.10257388 </strong>Sweden</p><p>Journal article: 10.2903/j.efsa. 2023.8442 </p><p>Citation</p><p>EFSA (European Food Safety Authority) & ECDC (European Centre for Disease Prevention and Control). (2023). Tables, figures, and country data complementing the European Union One Health Zoonoses 2022 Report [Dataset]. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.10057302&data=05%7C01%7C%7Cd53ce1fd0eeb4b027cb608dbf6528f55%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C638374606688640910%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=46n1ois6rCgqRamDnk4%2B42K2XGwwg1T1nvG5ezYqqyg%3D&reserved=0">https://doi.org/10.5281/zenodo.10057302</a></p>
Towards metadata for machine learning - Crosswalk tables
<p><strong>Crosswalks for Machine Learning models and datasets used for training</strong></p> <p>Here we present a collection of crosswalks for ML models (in TSV and XLSX formats) and datasets used for training (in TSV and XLSX formats). These crosswalks were created during an <a href="https://www.nfdi4datascience.de/">NFDI4DataScience</a> hackathon organized by the Semantic Technologies team (SemTec) at<a href="https://www.zbmed.de/en/"> ZB MED Information Centre for Life Sciences (ZB MED)</a> with the aim of provindg a starting point for a common proposal towards a metadata schema for ML models based on <a href="http://schema.org">schema.org</a>.</p> <p><strong>Files</strong></p> <ul> <li>2023.11.23 Metadata for ML - ML dataset Union.tsv: Crosswalks for datasets in TSV format</li> <li>2023.11.23 Metadata for ML - ML dataset Union.xlsx: Crosswalks for datasets in XLSX format</li> <li>2023.11.23 Metadata for ML - ML model union.tsv: Crosswalks for ML models in TSV format</li> <li>2023.11.23 Metadata for ML - ML model union.xlsx: Crosswalks for ML models in XLSX format</li> </ul>
Manual active layer and and water table depth measurements from the autochamber sites at Stordalen Mire, northern Sweden (2003-2017)
<p>Files:</p> <ul> <li><strong>Active_Layer_Water_Table_03-17.xlsx</strong> - Data file, with main data in the "DATA" tab.</li> <li><strong>IsoGenieSite_AL_WTD_MapsVisualNotes_200310.pdf</strong> - Visual notes on the measurement locations.</li> </ul> <p>The following site labels (with chamber numbers in parentheses) correspond to the main autochamber sites:</p> <ul> <li>Dry (1,3,5) = Palsa Autochamber Site</li> <li>Mesic (2,4,6) = Sphagnum Autochamber Site</li> <li>Wet (7,8) = Eriophorum Autochamber Site</li> </ul> <p>Water table depth (W D) was measured in wells.</p> <p>Active layer depth (A L) was measured by inserting a metal rod into the surface. The original instruction page is included in page 3 of the pdf.</p> <p>All depths are in centimeters (cm) below peat surface (i.e. peat or <em>Sphagnum</em> spp. vegetation surface = 0), with negative values indicating depth below the surface and positive values (for water table) indicating height of standing water above the surface. Blank data in the Palsa or water table column means no water table observed.</p> <p>Staff gauge was added July 2006 at the edge of a small pond in the fen visible from the shack, with measurements reported in meters. All other measures are in cm.</p> <p> </p> <p>FUNDING:</p> <p>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.</p> <p>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070. The IsoGenie Project (which funded much of the work at these sites during the measurement period) was funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440.</p>
BIObec dissemination table
<p>This sheet specifies all the partners' contributions to communicate and disseminate the BIObec project along its entire duration. Links to social media posts, website articles or scientific publications can be found inside it.</p>
Effect of shade and nitrogen content on Arabidopsis Col-0 and cytokinin mutants abcg14 and cypDM mRNA-seq gene expression processed tables.
<p>This dataset is an add-on for Gautrat et al., containing processed files for the mRNAseq data in tab delimited txt format.</p> <p>Here, you can obtain the raw counts file, the normalized CPM values, and the normalized logCPM values</p> <p>The RNA-seq raw data supporting the conclusions of this article have been deposited in ArrayExpress (Kolesnikov et al., 2015) at EMBL-EBI (www.ebi.ac.uk/arrayexpress), under accession numbers E-MTAB-13638.</p> <p>All relevant custom r scripts are available at https://github.com/aromanowski/shade_N_ck</p>
Supporting Dataset (Tables S1-S7, Figure S1) for "Heavy-mineral grain counting: Counting techniques, error estimation, and the number of grains to be counted"
<p>This Dataset comprises Tables S1-S7 for the article <strong>"Heavy-mineral grain counting: Counting techniques, error estimation, and the number of grains to be counted"</strong> authored by Jan Schönig and submitted to J<em>ournal of Geophysical Research: Earth Surface</em>.</p> <p>Figure S1: Error of different counting methods in comparison with theory and without considering the finite population correction</p> <p>Table S1: Heavy-mineral dataset from semi-automated Raman analysis</p> <p>Table S2: Summary of heavy-mineral composition for individual samples in percent</p> <p>Table S3: Computed ribbon compositions for individual samples and ribbon sizes given in numbers of counts</p> <p>Table S4: Relative errors at 95 % quantile for consecutive ribbon counting simulations</p> <p>Table S5: Relative errors at 95 % quantile for maximum distance ribbon counting simulations</p> <p>Table S6: Relative errors at 95 % quantile for cluster counting simulations</p> <p>Table S7: Numerical solution for determining the number of counts required to discriminate the content of two mineral species in an aliquot</p>
Neolithic life tables de 2024.02
<p>Data from the literature containing information on the age at death of Neolithic burials from Germany. The data were taken from mortality tables (Dx), individual data or bar charts. The data set does not claim to be complete, but focuses on larger burial groups.</p>
Data and code for: Nonlinear life table response analysis: Decomposing nonlinear and nonadditive population growth responses to changes in environmental drivers
<p>Life table response experiments (LTREs) decompose differences in population growth rate between environments into separate contributions from each underlying demographic rate. However, most LTRE analyses make the unrealistic assumption that the relationships between demographic rates and environmental drivers are linear and independent, which may result in diminished accuracy when these assumptions are violated. In this study, we compare the relative efficacy of linear and second-order LTRE analyses in capturing changes in population growth rate caused by environmental driver changes. To explore this question, we analyze demographic data collected for three long-lived plant species: <em>Ardisia escallonioides</em> (Pascarella & Horvitz, 1998), <em>Silene acaulis</em>, and <em>Bistorta vivipara</em> (Doak & Morris, 2010). This repository includes data files containing vital rate (survival, growth, reproduction) observations or models for our three case studies, as well as an R script in which we use these demographic data to calculate linear and second-order LTRE approximations of changes in population growth rate for each system and generate the figures we present in our paper.</p>
Figure 2 in Development and life table parameters of the Phytoseius corniger Wainstein (Acari: Phytoseiidae) feeding on the two-spotted spider mite, Tetranychus urticae Koch (Acari: Tetranychidae) under laboratory conditions
Figure 2. The age-specific survival rate (lx), and fecundity (mx) of Phytoseius corniger fed on Tetranychus urticae under laboratory conditions (25 ± 2 °C, 55 ± 5% of RH, and 16L: 8D h photoperiod).
Figure 1 in Development and life table parameters of the Phytoseius corniger Wainstein (Acari: Phytoseiidae) feeding on the two-spotted spider mite, Tetranychus urticae Koch (Acari: Tetranychidae) under laboratory conditions
Figure 1. Age-stage specific survival rate (sjx) of the parent cohort of bisexual Phytoseius corniger fed on Tetranychus urticae under laboratory conditions (25 ± 2 °C, 55 ± 5% of RH, and 16L: 8D h photoperiod). Note: L stands for larva, N1 for protonymph, and N2 for deutonymph, respectively.
Figure 1 in Influence of temperature and prey type on life-table parameters and consumption rate of Stethorus gilvifrons (Mulsant) (Coleoptera: Coccinellidae) on three tetranychid mites
Figure 1. Age-stage-specific survival rate (lx) and age-specific fecundity (mx) curves of Stethorus gilvifrons on different prey types and different temperatures.
tBiomed: Semantic Table Annotations Benchmark for Biomedical Domain
<p><strong>tBiomed </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>tBiomed </strong>is generated by <a href="https://github.com/fusion-jena/KG2Tables">KG2Tables </a>using two levels of a recursive hierarchy of related concepts in Wikidata.</p> <p><strong>tBiomed </strong>contains <strong>26,778</strong> entity and horizontal tables, while this repository contains only a <strong>validation fold</strong> of the original data representing <strong>20%</strong> of the total of the entire benchmark with its ground truth data (gt). The Full size of this dataset is <strong>1</strong> <strong>GB</strong>.</p> <p>We included the full version of the dataset. We will update this repository ground truth data of the test set in the Future.</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.