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: Strategies of resource sharing in clonal plants: A conceptual model and an example of contrasting strategies in two closely related species
<p>These experimental data were collected to quantify amount of C and N translocated between mother and daughter ramets of two stoloniferous species. Data includes concentrations of 13-C and 15-N in plants samples originating from pulse-chase labelling, absolute amounts of the labels present, as well as dry mass of the samples. Details are described in the relevant paper.</p> <p> </p>
Data Sharing Practices in the MRC Circadian Mental Health Network.
<p>This dataset supports the research conducted within the MRC Circadian Mental Health Network which assesses data sharing practices among Principal Investigators' publications in 2023. This work aims to identify trends, challenges, and inform future recommendations and policies based on the findings. The dataset includes various files that detail the methodology, data collected, and analyses performed.</p> <p> </p> <p><strong>Repository Contents:</strong></p> <ol> <li> <p><strong>Methods and Analysis Report - Data Sharing Practices in the MRC CMHN.pdf</strong></p> <ul> <li>This report provides the methodologies used for selecting and assessing research papers within the network, along with detailed results, tables, and discussions from the evaluation.</li> </ul> </li> <li> <p><strong>CMHN_All_Data.xlsx</strong></p> <ul> <li>An Excel workbook containing: <ul> <li><strong>Sheet 1</strong>: All data and variables collected and analysed for this project.</li> <li><strong>Sheet 2</strong>: A README file that explains each variable and its values.<br><br></li> </ul> </li> </ul> </li> <li> <p><strong>CMHN DataType Scoring.xlsx</strong></p> <ul> <li>An Excel workbook detailing: <ul> <li><strong>Sheet 1</strong>: All datatypes, both code and datasets, evaluated in this study.</li> <li><strong>Sheet 2</strong>: A README explaining the variables evaluated and their specific values.<br><br></li> </ul> </li> </ul> </li> <li> <p><strong>CMHN Data Extraction Survey.pdf</strong></p> <ul> <li>A copy of the Microsoft Form used to systematically evaluate data-sharing practices from selected publications, describing the structured data extraction process used.<br><br></li> </ul> </li> <li> <p><strong>CMHN DataType Scoring Survey.pdf</strong></p> <ul> <li>A Microsoft Form used to assess the types of data (code and datasets) shared.<br><br></li> </ul> </li> <li> <p><strong>Data_CSV_Code.csv</strong></p> <ul> <li>This file is the original, uncleaned dataset directly extracted from the initial response data of the Microsoft Form used in the project. It served as the primary dataset for all subsequent data analysis and code execution within the study.<br><br></li> </ul> </li> <li> <p><strong>CMHN Code.Rmd</strong></p> <ul> <li>An R Markdown file containing the code used for data analysis; predominantly descriptive statistics due to the limited number of papers with shared data.</li> </ul> </li> </ol> <p><strong><br>Recommended Use:</strong> For comparative purposes or further analysis, researchers are encouraged to utilise the cleaned datasets available in "CMHN_All_Data.xlsx" and "CMHN DataType Scoring.xlsx."<br><br><strong>Contact:</strong> For further inquiries, please email us at <a href="mailto:bio_rdm@ed.ac.uk" target="_blank" rel="noopener">bio_rdm@ed.ac.uk</a>.</p>
MoveD - Example of shared data with metadata
<p>The example of Normdata contains a dataset of one person for spine movement while walking at four different walking speeds. Movement data are saved in json-files (fw: fast walking, nw: normal walking, sw: slow walking, xw: walking at self-selected speed). To convert the json files to mat files (for Matlab), a script (Normdata_Code_mat_to_json.m) is added. </p> <p>The metadata (csv-files) are reported for the steps of the data life cycle: </p> <ul> <li>data collection, participant-specific information</li> <li>data collection, general information (valid for all subjects)</li> <li>data processing</li> <li>data analysis</li> <li>data sharing</li> </ul> <p>A figure of the used marker set can be found in the publication by Rast et al, 2016, Between-day reliability of three-dimensional motion analysis of the trunk: A comparison of marker based protocols, <a href="http://dx.doi.org/10.1016/j.jbiomech.2016.02.030">http://dx.doi.org/10.1016/j.jbiomech.2016.02.030</a>. </p> <p>The example of shared metadata of a project called ExerUP contains txt-files for metadata of each step of the data life cycle:</p> <ul> <li>data collection (ExerUP_Metadata_Collect) and marker model (ExerUP_MarkerModel_Collect and ExerUP_Marker_Placement_anonymised.jpg)</li> <li>data processing (ExerUP_Metadata_Process)</li> <li>data analysis (ExerUP_Metadata_Analyze)</li> <li>data sharing (ExerUP_Metadata_Share)</li> </ul> <p>The corresponding data is shared elsewhere. <a href="https://doi.org/10.7910/DVN/XBJXC4">https://doi.org/10.7910/DVN/XBJXC4</a></p> <p>For more information on the project MoveD, see the <a href="https://zenodo.org/communities/moved/records?q=&l=list&p=1&s=10&sort=newest">Open Research Data Guidelines for Movement Laboratories</a>. </p>
The data for Non-Dikarya fungi share the TORC1 pathway with animals, not with yeasts
<p>This dataset provides all the raw data used in the<strong> Non-Dikarya fungi share the TORC1 pathway with animals, not with yeasts </strong>manuscript including:</p> <p><strong>Supplementary file S1<br></strong></p> <p>All protein identifiers, genomic assemblies, transcriptomic datasets </p> <p><strong>Supplementary file S2<br></strong></p> <p>The figures of phylogenetic trees for TORC1 proteins.</p> <p><strong>Supplementary Dataset SD1.</strong></p> <p>The phylogenetic trees for TORC1 proteins</p> <p><strong>HMM profiles</strong> for EGO1 EGO2 EGO3 and Tco89 proteins</p> <p>This work was supported by National Science Centre grants (#2021/41/B/NZ2/02426 to A.M.)</p>
Use and sharing of raw data in the Journal Citation Reports' Emergency Medicine Category: Metrics and Journals including supplementary material classification sorted by quartile of the JCR emergency medicine category.
<p>Raw data belonged to the study of use and sharing of raw research data in the Journal Citation Reports' Emergency Medicine Category.</p>
Is data sharing in LCA FAIR
<p>Data for Setac Abstract - </p> <p>In the light of rising awareness, and growing need of better data management across disciplines, it is pertinent to investigate how the FAIR principles are utilized in the LCA domain. The purpose of this research is to investigate the status quo of data sharing by LCA practitioners. This study will look into data shared in relation to research outputs (e.g. peer reviewed articles). The life cycle inventory (LCI) is the most common data that is re-used, hence this study investigates how the LCI is shared in peer reviewed articles. A review of the findability, accessibility, interoperability and re-usability of the LCI data of 25 peer reviewed articles was performed. This study highlights although there is growing awareness on the importance of data sharing , the lack of a clear guidelines within the LCA domain as well as limited infrastructure is a major barrier in implementing FAIR principles. Research data sharing is beyond the sole responsibility of individual researchers. The overall research and funding infrastructure must consider the diversity of practices, differences in fields, facilitate the sharing of a broad range of research outputs. Current gaps in academic data sharing can be seen as opportunities to build a common framework</p>
Platforms & registries for sharing participant-level COVID-19 data
<p>This dataset presents an overview of platforms and registries that store, harmonize, and, in some cases, share COVID-19-related participant-level data, including clinical-epidemiological, human and pathogen OMICs, and high dimensional imaging data. The dataset provides an in-depth review of adherence to the <a href="https://www.go-fair.org/fair-principles/">FAIR principles</a>, governance, benefit sharing and other ethical concerns related to resources that share harmonized, participant-level data for the research response to COVID-19. Systematic searches were conducted between April 2020 and June 2021 to identify relevant platforms and registries. We applied natural language processing to the CORD-19 dataset in March of 2021 and consulted with COVID-19 focused researchers in Asia, Africa, and Latin America to identify non-English language COVID-19-related data sharing resources.</p>
Data sharing: an integral part of research practice? Codebook
<p>List of thematic codes for qualitative review of studies focusing on data sharing motives and barriers.</p>
Data from the manuscript 'Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context'
<p>Data sets from the manuscript 'Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context'. These include the test statistics from analyses of real and simulated data, and the data used to generate the figures relating to the goodness-of-fit of the generalised extreme value distribution to the GPS test statistics under the null. Please see the enclosed README for more details.</p>
Model simulation data used in "The global impact of the transport sectors on the atmospheric aerosol and the resulting climate effects under the Shared Socioeconomic Pathways (SSPs)" (Righi et al., Earth Syst. Dynam., 2023)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Earth Syst. Dynam.</i>, 2023). For details see the README.md file.</p>
Results of the poll in the study "Information Scientists' Motivations for Research Data Sharing and Reuse"
<p>This is a dataset with results of the poll conducted in the study “Information Scientists’ Motivations for Research Data Sharing and Reuse”.</p> <p>In terms of the Uses and Gratifications Theory (Questions 1 and 2), the most popular uses relate to the categories of research support and information. Researchers share, or would share, their research data in general for any reusability purposes and especially for combination of different datasets to produce new evidence. Also, the vast majority of study participants associate research data sharing with possibilities to accelerate scientific progress and to increase research efficiency. In case of research data reuse, all the researchers indicated that they use, or would use, others’ data first of all for inspiration. Interestingly, study participants put relatively high the category of recognition in case of sharing, but at the same time they do not associate increased recognition among colleagues and other researchers with research data reuse. The remaining categories belonging to the categories of self-esteem and social interaction, i.e. increased citation level and visibility of the research as well as enhanced scientific reputation, possible cooperations and co-authorship, were selected only by few respondents. Also remarkably, data reuse is more frequently linked to entertainment then data sharing. </p> <p>In terms of the Self-Determination Theory (Questions 3 and 4), all but one of the interviewees indicated that they have shared or would share their research data because it can accelerate scientific progress which they consider important and would like to contribute to it (i.e., identified regulation). The second most popular motivation turned out to be the obligation by employer, project funder and/or journals (i.e., external regulation). The third most popular option was social influence, i.e. because many other researchers participate in data sharing and they feel obligated to do the same (i.e., external regulation).This way, the participants demonstrate a mixture of identified motivation and external regulation, both material and social. In the case of data reuse, the participants demonstrate more homogeneous results with identification and intrinsic motivation having most of the votes. The role of external regulation seems to be much less important as in the case with data sharing. So, researchers reuse, or would reuse, research data because it can accelerate scientific progress which is important for them. Additionally, researchers enjoy exploring and using third party research data. Thus, interviewees participate or would participate in data sharing because they consider it important, but also feel or are obliged to do so. At the same time, study participants do not feel pressure from outside when deciding whether to reuse data or not.</p> <p>For more information about the study and its results, please read the article “Information Scientists’ Motivations for Research Data Sharing and Reuse” by Shutsko and Stock (2023).</p>
Data from: Honeybee visitation to shared flowers increases Vairimorpha ceranae prevalence in bumblebees
<p><em>Vairimorpha</em> (=<em>Nosema</em>) <em>ceranae</em> is a widespread pollinator parasite that commonly infects honeybees and wild pollinators, including bumblebees. Honeybees are highly competent <em>V. ceranae</em> hosts and previous work in experimental flight cages suggests <em>V. ceranae </em>can be transmitted during visitation to shared flowers. However, the relationship between floral visitation in the natural environment and the prevalence of <em>V. ceranae </em>among multiple bee species has not been explored. Here, we analyzed the number and duration of pollinator visits to particular components of squash flowers—including the petals, stamen, and nectary—at six farms in southeastern Michigan, USA. We also determined the prevalence of <em>V. ceranae </em>in honeybees and bumblebees at each site. Our results showed that more honeybee flower contacts and longer duration of contacts with pollen and nectar was linked with greater <em>V. ceranae</em> prevalence in bumblebees. Honeybee visitation patterns appear to have a disproportionately large impact on <em>V. ceranae</em> prevalence in bumblebees even though honeybees are not the most frequent flower visitors. Floral visitation by squash bees or other pollinators were not linked with <em>V. ceranae</em> prevalence in bumblebees. Further, <em>V. ceranae</em> prevalence in honeybees was unaffected by floral visitation behaviors by any pollinator species. These results suggest that honeybee visitation behaviors on shared floral resources may be an important contributor to increased <em>V. ceranae</em> spillover to bumblebees in the field. Understanding how <em>V. ceranae</em> prevalence is influenced by pollinator behavior in the shared floral landscape is critical for reducing parasite spillover into declining native bee populations.</p>
Data from: The MRi-Share database: Brain imaging in a cross-sectional cohort of 1,870 university students
Open the record for dataset details and reuse information.
Data from: Honeybee visitation to shared flowers increases Vairimorpha ceranae prevalence in bumblebees
Open the record for dataset details and reuse information.
Supplementary Material: Patterns in research and data sharing for the study of form and function in caviomorph rodents
<p>Supplementary Material for the publication <em>Patterns in research and data sharing for the study of form and function in caviomorph rodents</em> by Luis D. Verde Arregoitia*, Pablo Teta, and Guillermo D'Elía. <em>Journal of Mammalogy</em></p>
Simulation Data for Project on Incentivizing Participation in Peer-to-Peer Ride-Sharing Platform
<p>Unlike commercial ridesharing, non-commercial peer-to-peer (P2P) ridesharing has been subject to limited research---although it can promote viable solutions in non-urban communities. This paper focuses on the core problem in P2P ridesharing: the matching of riders and drivers. We elevate users' preferences as a first-order concern and introduce novel notions of fairness and stability in P2P ridesharing. We propose algorithms for efficient matching while considering user-centric factors including users' preferred departure time, fairness, and stability.</p> <p>The dataset includes our simulation settings and results. Results suggest that individually rational, fair and stable solutions can be obtained in reasonable computational times, and can improve baseline outcomes based on system-wide efficiency exclusively. </p>
Data Management and Sharing: Practices and Perceptions of Psychology Researchers
<p class="CxSpFirst">Research data is increasingly viewed as an important scholarly output. While a growing body of studies have investigated researcher practices and perceptions related to data sharing, information about data-related practices throughout the research process (including data collection and analysis) remains largely anecdotal. Building on our previous study of data practices in neuroimaging research, we conducted a survey of data management practices in the field of psychology. Our survey included questions about the type(s) of data collected, the tools used for data analysis, practices related to data organization, maintaining documentation, backup procedures, and long-term archiving of research materials. Our results demonstrate the complexity of managing and sharing data in psychology. Data is collected in multifarious forms from human participants, analyzed using a range of software tools, and archived in formats that may become obsolete. As individuals, our participants demonstrated relatively good data management practices, however they also indicated that there was little standardization within their research group. Participants generally indicated that they were willing to change their current practices in light of new technologies, opportunities, or requirements. </p>
Data from: Shared and modality-specific brain regions that mediate auditory and visual word comprehension
<p>Visual speech carried by lip movements is an integral part of communication. Yet, it remains unclear in how far visual and acoustic speech comprehension are mediated by the same brain regions. Using multivariate classification of full-brain MEG data, we first probed where the brain represents acoustically and visually conveyed word identities. We then tested where these sensory-driven representations are predictive of participants' trial-wise comprehension. The comprehension-relevant representations of auditory and visual speech converged only in anterior angular and inferior frontal regions and were spatially dissociated from those representations that best reflected the sensory-driven word identity. These results provide a neural explanation for the behavioural dissociation of acoustic and visual speech comprehension and suggest that cerebral representations encoding word identities may be more modality-specific than often upheld.</p>
Data from: Evaluating the taxa that provide shared pollination services across multiple crops and regions
Many pollinator species visit multiple crops in multiple regions, yet we know little about their pollination service provisioning at local and regional scales. We investigated the floral visitors (n = 13,200), their effectiveness (n = 1718 single visits) and response to landscape composition across three crops avocado, mango and macadamia within a single growing region (1 year), a single crop (3 years) and across different growing regions in multiple years. In total, eight wild visitor groups were shared across all three crops. The network was dominated by three pollinators, two bees (Apis mellifera and Tetragonula spp.) and a fly, Stomorhina discolour. The visitation network for the three crops was relatively generalised but with the addition of pollen deposition data, specialisation increased. Sixteen managed and wild taxa were consistently present across three years in avocado, yet their contribution to annual network structure varied. Node specialisation (d') analyses indicated many individual orchard sites across each of the networks were significantly more specialised compared to that predicted by null models, suggesting the presence of site-specific factors driving these patterns. Identifying the taxa shared across multiple crops, regions and years will facilitate the development of specific pollinator management strategies to optimize crop pollination services in horticultural systems.
Data from: Landscape genetics reveals unique and shared effects of urbanization for two sympatric pool-breeding amphibians
Metapopulation-structured species can be negatively affected when landscape fragmentation impairs connectivity. We investigated the effects of urbanization on genetic diversity and gene flow for two sympatric amphibian species, spotted salamanders (Ambystoma maculatum) and wood frogs (Lithobates sylvaticus), across a large (>35,000 km2) landscape in Maine, USA, containing numerous natural and anthropogenic gradients. Isolation by distance (IBD) patterns differed between the species. Spotted salamanders showed a linear and relatively high variance relationship between genetic and geographic distance (r = 0.057; p < 0.001); whereas, wood frogs exhibited a strongly non-linear and lower variance relationship (r = 0.429; p < 0.001). Scale dependence analysis of IBD found gene flow has its most predictable influence (strongest IBD correlations) at distances up to 9 km for spotted salamanders and up to 6 km for wood frogs. Estimated effective migration surfaces revealed contrasting patterns of high and low genetic diversity and gene flow between the two species. Population isolation, quantified as the mean IBD residuals for each population, was associated with local urbanization and less genetic diversity in both species. The influence of geographic proximity and urbanization on population connectivity was further supported by distance-based redundancy analysis and multiple matrix regression with randomization. Resistance surface modelling found interpopulation connectivity to be influenced by developed land cover, light roads, interstates, and topography for both species, plus secondary roads and rivers for wood frogs. Our results highlight the influence of anthropogenic landscape features within the context of natural features and broad spatial genetic patterns, in turn supporting the premise that while urbanization significantly restricts interpopulation connectivity for wood frogs and spotted salamanders, specific landscape elements have unique effects on these two sympatric species.
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.