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.
12,346
datasets available to search
ShareScore release 0.9.0
Dataset results
12,346 results for “derivatives”
CLDF dataset derived from Feleke's "Ethiosemitic Languages" from 2021
<p>Cite the source of the dataset as:</p> <blockquote> <p>Feleke, Tekabe Legesse (2021): Ethiosemitic languages: classifications and classification determinants. Ampersand. 2021. DOI: 10.1016/j.amper.2021.100074.</p> </blockquote>
CLDF dataset derived from Dellert et al.'s "NorthEuraLex (Version 0.9)" from 2020
<p>Cite the source of the dataset as:</p> <blockquote> <p>Dellert, J., Daneyko, T., Münch, A. et al (2020). NorthEuraLex (Version 0.9). Lang Resources and Evaluation. https://doi.org/10.1007/s10579-019-09480-6</p> </blockquote>
CLDF dataset derived from Nagaraja et al.'s "A Lexicostatistical Study of the Khasian Languages" from 2013
<p>Cite the source of the dataset as:</p> <blockquote> <p>Nagaraja KS, Sidwell P & Greenhill SJ. 2013. A Lexicostatistical Study of the Khasian Languages: Khasi, Pnar, Lyngngam, and War. Mon-Khmer Studies Journal, 42, 1-11.</p> </blockquote>
CLDF dataset derived from McElhanon's "Preliminary Observations on Huon Peninsula Languages" from 1967
<p>Cite the source of the dataset as:</p> <blockquote> <p> McElhanon, K.A. 1967. Preliminary Observations on Huon Peninsula Languages. Oceanic Linguistics. 6, 1-45.</p> </blockquote>
CLDF dataset derived from Hsiu's "Classification of Na-Meo" from 2015
<p>Cite the source of the dataset as:</p> <blockquote> <p>Hsiu, Andrew (2015): The classification of Na Meo, a Hmong-Mien language of Vietnam. Handout prepared for SEALS 25 (Chiang Mai, 2015/05/27-29).</p> </blockquote>
CLDF dataset derived from Sūn's "Tibeto-Burman Phonology and Lexicon" from 1991
<p>Cite the source of the dataset as:</p> <blockquote> <p>Sūn, Hóngkāi 孙宏开 (1991): Zangmianyu yuyin he cihui 藏缅语音和词汇 [Tibeto-Burman phonology and lexicon]. Beijing: Chinese Social Sciences Press.</p> </blockquote>
CLDF dataset derived from Heath et al's "Dogon Comparative Wordlist" from 2016
<p>Cite the source of the dataset as:</p> <blockquote> <p>Moran, Steven & Forkel, Robert & Heath, Jeffrey (eds.) 2016. Dogon and Bangime Linguistics. Jena: Max Planck Institute for the Science of Human History. (Available online at http://dogonlanguages.info, Accessed on 2024-08-12.)</p> </blockquote>
CLDF dataset derived from Greenhill et al.'s "Austronesian Basic Vocabulary Database" from 2020 focusing on Philippine languages
<p>Cite the source of the dataset as:</p> <blockquote> <p>Greenhill, S.J., Blust. R, & Gray, R.D. (2008). The Austronesian Basic Vocabulary Database: From Bioinformatics to Lexomics. Evolutionary Bioinformatics, 4:271-283.</p> </blockquote>
CLDF dataset derived from Pharao Hansen's "Investigation of the Relation between Proto-Náhuatl and Proto-Corachol" from 2020
<p>Cite the source of the dataset as:</p> <blockquote> <p>Pharao Hansen, Magnus (2020): ¿Familia o vecinos? Investigando la relación entre el proto-náhuatl y el proto-corachol [Family or neighbors? Investigating the relation between Proto-Náhuatl and Proto-Corachol]. IN: Lenguas yutoaztecas: historia, estructuras y contacto lingüístico. Homenaje a Karen Dakin. Rosa Yañez (ed.) Guadalajara: Universidad de Guadalajara.</p> </blockquote>
CLDF dataset derived from Heggarty, Paul & Anderson, Cormac & Scarborough, Matthew's "Indo-European Cognate Relationships database" ([IE-CoR version 1.0](https://github.com/lexibank/iecor/releases/tag/v1.0)) from 2019
<p>Cite the source of the dataset as:</p> <blockquote> <p>Heggarty, Paul & Anderson, Cormac & Scarborough, Matthew 2024. Indo-European Cognate Relationships database (IE-CoR version 1.1). Leipzig: Max Planck Institute for Evolutionary Anthropology</p> </blockquote>
CLDF dataset derived from Yang's "Lalo Regional Varieties" from 2011
<p>Cite the source of the dataset as:</p> <blockquote> <p>Yang, Cathryn (2011): Lalo regional varieties: Phylogeny, dialectometry and sociolinguistics. Bundoora: La Trobe University.</p> </blockquote>
D-PLACE dataset derived from Bertolo et al. 2023 'Cross-cultural music corpus: The Expanded Natural History of Song Discography'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Mila Bertolo, Martynas Snarskis, Manvir Singh, & Samuel Mehr. (2023, August 8). Cross-cultural music corpus: The Expanded Natural History of Song Discography. Zenodo. https://doi.org/10.5281/zenodo.8378337</p> </blockquote>
CLDF dataset derived from Lundgren's "Phonological Reconstruction of Proto-Omagua-Kokama-Tupinambá" from 2020
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lundgren, Olof (2020): A phonological reconstruction of Proto-Omagua-Kokama-Tupinambá. Master's thesis. Lund: Lund University.</p> </blockquote>
D-PLACE dataset derived from Wessel and Smith 2015 'Global Self-consistent, Hierarchical, High-resolution Geography Database'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Wessel, P., and W. H. F. Smith (1996), A global, self-consistent, hierarchical, high-resolution shoreline database, J. Geophys. Res., 101(B4), 8741–8743, doi:10.1029/96JB00104. Wessel P, Smith, W. H. F. Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHS) v2.3.4 [Internet]. 2015. Available: https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html</p> </blockquote>
D-PLACE dataset derived from Jenkins et al. 2013 'Global patterns of terrestrial vertebrate diversity and conservation'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Jenkins CN, Pimm SL, Joppa LN. Global patterns of terrestrial vertebrate diversity and conservation. Proc Natl Acad Sci. 2013;110: E2602–E2610.</p> </blockquote>
Gummern - Mining Waste Deposits 5.73cm DEM UAV-derived
<h2>Abstract</h2> <p>Mining Waste Deposits High Detailed Digital Elevation Model derived from Multispectral UAV DJI Mavic 3M.</p> <p>This depository contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Mining Waste Deposits 5.73cm DEM UAV-derived</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Mining Waste Deposits High Detailed Digital Elevation Model derived from Multispectral UAV DJI Mavic 3M</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Mining Waste Deposits, DEM, DTM, DSM, UAV, Drone</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Gummern</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>See "Mining Waste Deposits Multispectral Drone Imagery" <a href="https://doi.org/10.5281/zenodo.13622458">https://doi.org/10.5281/zenodo.13622458</a></p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Hydrography</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>3.10.2023</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>3.10.2023</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Raster</p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p>GeoTIFF</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>See "Mining Waste Deposits Multispectral Drone Imagery"</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.0573m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0.05m</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 4258</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Enoc Sanz Ablanedo (esana@unileon.es)</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>UNILEON</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Enoc Sanz Ablanedo (esana@unileon.es)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
DATA: Diversity of extracellular vesicles derived from calli, cell culture and apoplastic fluid of tobacco
<p><span>The data includes .jpg files with fluorescence readings and .csv files with results of concentration and size measurements.</span></p>
Country-wide data products for the ecosystem structure metrics derived from ALS data across the Netherlands (AHN3)
<p>This data repository contains country-wide data products for the ecosystem structure metrics generated from Airborne Laser Scanning (ALS) data across the Netherlands (AHN3). Twenty-five ecosystem structure metrics (at 10-meter resolution, GeoTIFF format) were derived from AHN3 dataset (<a href="https://downloads.pdok.nl/ahn3-downloadpage/">https://downloads.pdok.nl/ahn3-downloadpage/</a>) using <a href="https://laserfarm.readthedocs.io/en/latest/">Laserfarm</a> workflow (<a href="../record/5636773">https://zenodo.org/record/5636773</a>). Laserfarm is a free and open-source workflow that enables efficient, scalable, and distributed processing of multi-terabyte LiDAR point clouds from national and regional ALS surveys into LiDAR metrics of ecosystem structure. All code of Laserfarm is hosted and freely available on GitHub (<a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a>). The Jupyter Notebooks for the processing of the AHN3 dataset are available on GitHub (<a href="https://github.com/eEcoLiDAR/AHN/tree/main/AHN3">https://github.com/eEcoLiDAR/AHN/tree/main/AHN3</a>).</p> <p>The twenty-five LiDAR metrics are related to three key dimensions of ecosystem structure (ecosystem height, ecosystem cover, and ecosystem structural complexity), and a layer of point density and a layer of building/road/water mask are also provided. Each GeoTIFF layer represents one LiDAR metric at 10 m resolution covering the whole Netherlands (file name as "ahn3_10m_feature_name.tiff").</p> <p>An overview of all the listed metrics (maps) is also provided in the PDF version (AHN3.pdf).</p> <p>A detailed description of the dataset is available from the following data publication:<br>Kissling, W. D., Y. Shi, Z. Koma, C. Meijer, O. Ku, F. Nattino, A. C. Seijmonsbergen, and M. W. Grootes. 2022. Country-wide data of ecosystem structure from the third Dutch airborne laser scanning survey. Data in Brief: 108798.<br><a href="https://eur04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1016%2Fj.dib.2022.108798&data=05%7C01%7Cy.shi%40uva.nl%7C177a19a4359a422b0ef808dad9d30ef8%7Ca0f1cacd618c4403b94576fb3d6874e5%7C0%7C0%7C638061797757145956%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=2R7NSGli4Mw6Pp5FAIyOzBu4USPZXigng46EFVT4X68%3D&reserved=0">https://doi.org/10.1016/j.dib.2022.108798</a></p> <p>A detailed description of all the metrics can be found in the README file (README.docx). </p> <p>A .zip file is also provided containing all the data for the validation of the AHN3 data products (AHN3_validation.zip). </p>
Dataset for the publication "Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site"
<p>This datasset contains data to reproduce the following figures of the paper <em>Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site</em>:</p> <ul> <li> <p>Time series data of Figures 1c and 2</p> </li> <li> <p>Data (*.asc) used for plotting Figures 1d and 1e (as well as Figure S3 and S4)</p> </li> <li>Pléiades snow depth map (Figure S1)</li> <li> <p>Data used for plotting Figure S2</p> </li> </ul> <p> </p>
CLDF dataset derived from Bremer's "Sociolinguistic Survey of Six Berta Speech Varieties in Ethiopia" from 2016
<p>Cite the source of the dataset as:</p> <blockquote> <p>Bremer, Nate D. (2016): A Sociolinguistic Survey of Six Berta Speech Varieties in Ethiopia. SIL Electronic Survey Reports 2016-007. Dallas: SIL International.</p> </blockquote>
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.