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.
379
datasets available to search
ShareScore release 0.7.1
Dataset results
379 results for “data sharing”
Data from: Shifts to earlier selfing in sympatry may reduce costs of pollinator sharing
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Data from: Avian phylogenetic and functional diversity are better conserved by land-sparing than land-sharing farming in lowland tropical forests
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Data for: Paternity sharing in insects with female competition for nuptial gifts
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Data from: Implications of shared predation for space use in two sympatric leporids
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Data from: A framework for sharing power in research teams and promoting justice in scientific publication
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Data from: Inter-individual spacing affects the finder’s share in ring-tailed coatis (Nasua nasua)
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Data from: The Chord-Normalized Expected Species Shared (CNESS)-distance represents a superior measure of species turnover patterns
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Data and code from: The shared selection landscape of dog and human cancers
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Data from: Patterns of constitutive and induced herbivore defense are complex, but share a common genetic basis in annual and perennial monkeyflower
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Data from: Repeated successful nest sharing and cooperation between Western Kingbirds (Tyrannus verticalis) and a female western kingbird X scissor-tailed flycatcher (T. forficatus) hybrid
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Data and codes from: Species divergence under competition and shared predation
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Data from: Bacterial and fungal growth on fungal necromass and its diverse components: shared profiles and divergent constraints revealed by high-throughput phenotyping
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Data from: Shared and modality-specific brain regions that mediate auditory and visual word comprehension
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Data from: We get by with a little help from our friends: shared adaptive variation provides a bridge to novel ecological specialists during adaptive radiation
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Data: Researcher Perspectives on the Use and Sharing of Software
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Building infrastructure leading to diversity (BUILD) initiative production data share
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Good scientists share data?
<p><strong>Episode Summary:</strong></p> <p>In this episode we are discussing data sharing and who owns research data. Our interview guest will be Dr Daniel Barron from Yale University, who wrote an article on these issues in Scientific American. We will cover the issue of what the priorities for research data are, the Jack Gallant controversy, and should Open Science principles be enforced.</p> <p><strong>Resources and Links:</strong></p> <ul> <li><a href="https://medicine.yale.edu/psychiatry/nrtp/residents/daniel_s_barron.profile">Daniel Barron</a> <ul> <li><a href="http://danielsbarron.com/">Daniel Barron Articles</a></li> <li><a href="https://twitter.com/daniel__barron">Daniel Barron Twitter</a></li> </ul> </li> <li><a href="https://blogs.scientificamerican.com/observations/how-freely-should-scientists-share-their-data/">Barron’s Scientific American article</a></li> <li><a href="https://www.nature.com/articles/s41467-018-05227-z">Nature article: Data sharing and the future of science</a></li> </ul> <p><strong>Episode Quotes:</strong></p> <p>“Who owns research data?”</p>
A Metric for Optimism: John Ioannidis on Reproducibility, Preregistration, and Data Sharing
<p><strong>Episode Summary:</strong></p> <p>In this episode we are discussing data sharing and Open Science. Our interview guest will be Stanford University Professor of Medicine: John Ioannidis who has now come to the Berlin Institute of Health as an Einstein BIH Visiting Fellow at the BIH QUEST Center to establish the Meta-Research Innovation Center Berlin (METRIC-Berlin), the European “sister” of the Meta-Research Innovation Center at Stanford (METRICS). We will cover his research and opinions on data sharing, reproducibility, and how to improve research.</p> <p><strong>Links: </strong></p> <p><a href="https://profiles.stanford.edu/john-ioannidis">John Ioannidis</a></p> <p><a href="https://www.bihealth.org/en/research/quest-center/mission-approaches/">BIH Quest Centre</a></p> <p><a href="https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0020124">Why Most Published Research Findings Are False</a></p> <p><strong>Quotes:</strong></p> <p>'I think that scientists, by themselves, are recognizing that it is important to share [data] and in many fields, like in Genetics, they realize that unless they share they cannot really go very far'</p> <p>'Clearly over the years we have seen more scientific sharing of data'</p>
Edinburgh Bike Sharing Data
<p>Accompanying datasets for the simulation study found here: <a href="https://doi.org/10.5281/zenodo.3702267">doi</a> / <a href="https://github.com/justinnk/bss-simulation-study">GitHub</a></p> <p>Please note the restricted usecases defined in the LICENSE.</p>
Data Supplement: U.S. state-level projections of the spatial distribution of population consistent with Shared Socioeconomic Pathways.
<p>These data are to supplement the following in-press publication: </p> <p>Zoraghein, H., and O'Neill B. (2020). U.S. state-level projections of the spatial distribution of population consistent with Shared Socioeconomic Pathways. Sustainability.</p> <p>The data herein were generated using the `population_gravity` model which can be found here: <a href="https://github.com/IMMM-SFA/population_gravity">https://github.com/IMMM-SFA/population_gravity</a></p> <p>CONTENTS:</p> <p><strong>zoraghein-oneill_population_gravity_inputs_outputs.zip</strong></p> <ul> <li>contains a directory for each U.S. state for inputs and outputs</li> <li><strong>inputs</strong> contain the following: <ul> <li><strong><state-name>_<urban or rural>_<yr>_1km.tif: </strong> Urban and Rural population GeoTIF rasters at a 1km resolution <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> <li><strong><state-name>_mask_short_term.tif: </strong> Mask GeoTIF rasters at a 1km resolution that contain values from 0.0 to 1.0 for each 1 km grid cell to help calculate suitability depending on topographic and land use and land cover characteristics <ul> <li>value per grid cell: values from 0.0 to 1.0 (float) that are generated from topographic and land use and land cover characteristics to inform suitability as outlined in the companion publication</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> <li><strong><state-name>_<ssp>_popproj.csv: </strong> Population projection CSV files for urban, rural, and total population (number of humans; float) for SSPs 2, 3, and 5 for years 2010-2100</li> <li><strong><state-name>_coordinates.csv: </strong>CSV file containing the coordinates for each 1 km grid cell within the target state. File includes a header with the fields XCoord, YCoord, FID.,Where data types and field descriptions are as follows: (XCoord, float, X coordinate in meters),(YCoord, float, Y coordinate in meters),(FID, int, Unique feature id)</li> <li><strong><state-name>_within_indices.txt: </strong>text file containing a file structured as a Python list (e.g. [0, 1]) that contains the index of each grid cell when flattened from a 2D array to a 1D array for the target state.</li> <li><strong><state-name>_<ssp>_params.csv: </strong>CSV file containing the calibration parameters (alpha_rural, beta_rural, alpha_urban, beta_urban; float) for the `population_gravity` model for each year from 2010-2100 in 10-year time-steps as described in the companion publication</li> </ul> </li> <li><strong>outputs</strong> contain the following: <ul> <li><strong>jones_oneill</strong> directory; these are the comparison datasets used to build Figures 7 and 8 in the companion publication <ul> <li>contains three directories: SSP2, SSP3, and SSP5 that each contain a GeoTIF representing total population (number of humans; float) at 1km resolution for years 2050 and 2100. <ul> <li><strong><state-name>_1km_<ssp>_total_<year>_jones_oneill.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>model</strong> directory; these are the model outputs from `population_gravity` for SSP2, SSP3, and SSP5 that each contain a GeoTIF representing urban, rural, and total population (number of humans; float) at 1km resolution for years 2020-2100 in 10-year time-steps. <ul> <li><strong><state-name>_1km_<ssp>_<urban, rural, or total>_<year>_jones_oneill.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p> </p> <p><strong>zoraghein-oneill_population_gravity_national-ssp-maps.zip</strong></p> <ul> <li>Results of the `population_gravity` model mosaicked to the National scale at a 1km resolution and the comparison Jones and O'Neill research. These are used to generate Figure 6 of the companion paper <ul> <li><strong>National_1km_<ssp>_<urban, rural, or total>_<year>_jones_oneill.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> <li><strong>National_1km_<ssp>_<urban, rural, or total>_<year>.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> </ul> </li> </ul> <p> </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.