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379
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ShareScore release 0.7.1
Dataset results
379 results for “data sharing”
Data and codes from: Species divergence under competition and shared predation
<p><span>Species competing for resources also commonly share predators. While competition often drives divergence between species, effects of shared predation are less understood. Theoretically, competing prey species could either diverge or evolve in the same direction under shared predation depending on the strength and symmetry of their interactions. We took an empirical approach to this question, comparing antipredator and trophic phenotypes between sympatric and allopatric populations of threespine stickleback and prickly sculpin fish that all live in the presence of a trout predator. We found divergence in antipredator traits between the species: in sympatry, antipredator adaptations were relatively increased in stickleback but decreased in sculpin. Shifts in feeding morphology, diet and habitat use were also divergent but driven primarily by stickleback evolution. Our results suggest that asymmetric ecological character displacement indirectly made stickleback more and sculpin less vulnerable to shared predation, driving divergence of antipredator traits between sympatric species.</span></p>
Data for: Paternity sharing in insects with female competition for nuptial gifts
<p>Male parental investment is expected to be associated with high confidence of paternity. Studies of species with exclusive male parental care have provided support for this hypothesis because mating typically co-occurs with each oviposition, allowing control over paternity and the allocation of care. However, in systems where males invest by feeding mates (typically arthropods) mating (and thus the investment) is separated from egg-laying, resulting in less control over insemination (as male ejaculates compete with rival sperm stored by females) and a greater risk of investing in unrelated offspring (cuckoldry). As strong selection on males to increase paternity would compromise the fitness of all a female's other mates that make costly nutrient contributions, paternity sharing (males not excluded from siring offspring) is an expected outcome of sperm competition. Using wild-caught females in an orthopteran and a dipteran species, in which sexually selected, ornamented females compete for male nuptial food gifts needed for successful reproduction, we examined paternity patterns and compared them to findings in other insects. We used microsatellite analysis of offspring (lifetime reproduction in the orthopteran) and stored sperm from wild-caught females in both study species, and as predicted there was evidence of shared paternity as few males failed to sire offspring. Further support for paternity-sharing is the lack of last-male sperm precedence in our study species. Although paternity was not equal among sires, our estimates of paternity bias were similar to other insects with valuable nuptial gifts and contrasted with the finding that males are frequently excluded from siring offspring in species where males supply little more than sperm. This suggests paternity bias may be reduced in nuptial-gift systems and may help facilitate the evolution of these paternal investments.</p>
Dataset for "Initial insight of three modes of data sharing: Prevalence of primary reuse, data integration and dataset release in research articles"
<p>The dataset for "Initial insight of three modes of data sharing: Prevalence of primary reuse, data integration and dataset release in research articles" is coded as follows:</p> <p>01 DOI: DOI<br> 02 article number: the accession number in Web of Science<br> 03 article title: title of the articles<br> 04 exclude: if the article was excluded from the sample, assign 1.<br> 05 research_field: the categories of research fields are described in the Appendix (Table S1)<br> 06 target_of_study: the categories of the target of studies are described in the Appendix (Table S1)<br> 29 release_location_nameofpublicarchive: the names of the deposited public archives (comma separated)</p> <p>The following items, if they occur, are assigned a value of 1:<br> 07 No_datause: The article did not use data<br> 08 primary_reuse: primary reuse<br> 09 primary_data_specificresarchdata: primary reuse of specific research data<br> 10 primary_data_resource: primary reuse of resource<br> 11 primary_source_self: primary reuse from self-constructed data<br> 12 primary_source_citation: primary reuse from citation<br> 13 primary_source_archive: primary reuse from an archive<br> 14 primary_source_others: primary reuse from the other source<br> 15 primary_souce_na: primary reuse source is not available<br> 16 data_integration: data integration<br> 17 integration_type_empirical: data integration as empirical type<br> 18 integration_type_Introductionmaterialresearchmethod: data integration as introduction/material/research methods type<br> 19 integration_type_combinedanalysis: data integration as introduction/material/research methods type<br> 20 integration_source_self: data integration from self-constructed data<br> 21 integration_source_citation: data integration from citation<br> 22 integration_source_archive: data integration from an archive<br> 23 integration_source_others: data integration from the other source<br> 24 integration_source_na: data integration source is not available<br> 25 dataset_release: dataset release<br> 26 release_location_publicarchive: dataset deposit to a public archive <br> 27 release_location_supporting: dataset release in Supporting Information<br> 28 release_location_onrequest: dataset release through personal contacts </p> <p> </p> <p>The appendix includes following tables:<br> Table S1. Coding schema for analysis<br> Table S2. Primary reuse by research field and reused data<br> Table S3. Primary reuse by target of study and reused data<br> Table S4. Data integration by research field and reuse type<br> Table S5. Data integration by target of study and reuse type<br> Table S6. Dataset release by research field<br> Table S7. Dataset release by target of study and methods<br> Table S8. List of names of public data archives for dataset release</p>
Sharing data and code supporting the article entitled "Thermodynamically enhanced precipitation extremes due to counterbalancing influences of anthropogenic greenhouse gases and aerosols"
<p>The public data repository contains the data and plotting code supporting the article entitled "Thermodynamically enhanced precipitation extremes due to counterbalancing influences of anthropogenic greenhouse gases and aerosols". The dataset includes annual maximum one-day precipitation (Rx1day), its proxy computed by a physical scaling diagnostic (scaling), and the decomposed components of the scaling (i.e., thermodynamic response, dynamic response, and their interaction). Several reanalyses (including ERA5 and JRA55) and CMIP6 simulations under several scenarios (including ALL, GHG, AER, and piControl) are applied to compute the historical Rx1day, scaling, and the associated components following a GitHub Python repository (<a href="https://github.com/oliverangelil/precip_extremes_scaling">https://github.com/oliverangelil/precip_extremes_scaling</a>). Note that the decomposed components are calculated as anomalies. </p> <p>The data results of the extreme precipitation decomposition in the NetCDF format are available in zip files “<em><strong>ERA5</strong></em>”, "<em><strong>JRA55</strong></em>", "<em><strong>ALL</strong></em>", "<em><strong>GHG</strong></em>", "<em><strong>AER</strong></em>", "<em><strong>NAT</strong></em>", and "<em><strong>piControl</strong></em>". Code for visualizations is available in the "<em><strong>Jupyter Notebooks</strong></em>" zip file.</p>
Towards a practical framework to "ethics by design" data sharing and machine learning applications
<p>Responsible AI and data-driven applications can only be developed when teams integrate the ethical principles directly into the development process. An important prerequisite is the involvement of a diverse group of stakeholders who build and are affected by AI and data systems. We present a practical framework that helps teams build trustworthy AI systems and data strategies by combining expertise and training from philosophy, law, machine learning and design.</p>
Data from: A shared pattern of midfacial bone modelling in hominids suggests deep evolutionary roots for human facial morphogenesis
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Data from: The holocephalan ratfish endoskeleton shares trabecular and areolar mineralization patterns, but not tesserae, with elasmobranchs little skate and catshark
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Data for: Social-ecological predictors of spotted hyena navigation through a shared landscape
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Data and code from: A colorful legacy of hybridization in wood-warblers includes frequent sharing of carotenoid genes among species and genera
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Data: Genomic signatures of admixture and selection are shared among populations of Zaprionus indianus across the western hemisphere
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Data from: RNA interference reveals that male nuptial gift proteins affect female behavior to increase male paternity share in decorated crickets
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Data from: Sharing detection heterogeneity information among species in community models of occupancy and abundance can strengthen inference
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Data from: Participatory mapping reveals biocultural and nature values in the shared landscape of a Nordic UNESCO Biosphere Reserve
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Data from: A shared numerical magnitude representation evidenced by the distance effect in frequency-tagging EEG
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Data Management and Sharing: Practices and Perceptions of Psychology Researchers
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Data from: Evidence of extensive home range sharing among mother-daughter bobcat pairs in the wildland-urban interface of the Tucson Mountains
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Post-trial survey data for shared micromobility pricing revealed preference experiment
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Data from: Landscape genetics reveals unique and shared effects of urbanization for two sympatric pool-breeding amphibians
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Data from: Evaluating the taxa that provide shared pollination services across multiple crops and regions
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Data from: Data sharing, management, use, and reuse: practices and perceptions of scientists worldwide
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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.