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”
Test data for the shared task Ideology and Power Identification in Parliamentary Debates 2024
<p>This dataset contains a selection of speeches from <a href="https://www.clarin.eu/parlamint">ParlaMint</a> corpora (version 4.0) as the test set for the shared task on "<a href="https://touche.webis.de/clef24/touche24-web/ideology-and-power-identification-in-parliamentary-debates.html">Ideology and Power Identification in Parliamentary Debates</a>" in <a href="https://clef2024.imag.fr/">CLEF 2024</a>. The format of the files are similar to the <a title="training set" href="10450640">training set</a>, with the exception that the labels are not provided in this data set.</p>
Data and codes sharing for GRIST forecasts during Meiyu period in 2021
<p>This file provide data & scripts for the research "Assessment of rainfall forecasts over eastern China with the Global-to-Regional Integrated forecast SysTem during the Meiyu period". </p> <p>Please check ‘README.TXT’ for the information and usage of the dataset.</p> <p>You can run 'decompression_tar_file.sh' to get *tar.gz into data.</p>
Dataset of the study "Open Access and Data Sharing in Cancer Stem Cells research"
<p>Dataset of the study "Open Access and Data Sharing in Cancer Stem Cells research"</p>
Data of a meta analytical review on instructional strategies used in shared text reading for students with intellectual disability.
<p><span>The data provided was collected in the context of a meta analytical review entitled: “<strong>Effects of Shared Text Reading for Students With Intellectual Disability: A Meta-analytical Review of Instructional Strategies”</strong>conducted by R. Sermier Dessemontet, M. Geyer, A-L. Linder, M. Atzemian, C. Martinet, N. Meuli, C. Audrin, and A-F. de Chambrier, and published in the journal “Educational Research Review”.. The objective of the meta-analytical review was to measure the effect of shared text reading on the listening comprehension skills of students with ID and to identify efficient instructional strategies to teach these students listening comprehension. 22 single-case experimental studies were included in this meta-analysis. Data from each participant in each study was extracted and transformed into percentages of independent correct responses. Effect size estimates were performed based on these percentages.</span></p> <p><span>The shared data contains the following: raw data from each participant used for effect size estimate calculations, a table presenting effect size estimates and moderator-coding for each participant in each study, and the statistical analysis script. A pdf describing meta-data (meta-data_meta-analysis.pdf) is also provided for more information on each available document and describes data. </span></p>
Appendix to "Process Mining Pipelines with Controlled Sharing of Data and Algorithms"
<p><strong>Abstract: </strong>Process mining leverages execution traces within an organisation's IT systems to gain insights into its processes. Despite being a mature discipline in academia and industry, setting up process mining pipelines is still a complex task and involves programming, manual steps, and considerations of privacy and intellectual property.</p> <p>This paper introduces a platform based on a distributed architecture that helps define, deploy, and execute process mining pipelines across organisations. The requirements for this distributed architecture and platform are derived from a set of process mining scenarios, whose relevance is validated through a survey.</p> <p>Furthermore, this paper introduces a prototype for an initial version of the platform, demonstrating feasibility and supporting the specified requirements. This development is a major step in advancing process mining, offering simpler and more efficient ways of implementing and managing complex process mining pipelines on a larger scale.</p> <p><strong>Description: </strong>This dataset presents the support for non-functional requirements identified in the paper "Process Mining Pipelines with Controlled Sharing of Data and Algorithms" by existing process mining platforms.</p> <p><strong>Legend:</strong> Green cells indicate complete fulfilment. Yellow indicates partial fulfilment. Blue cells indicate uncertain fulfilment. Red indicates no fulfilmnet.</p>
Dataset for Spence et al., "Patient consent to publication and data sharing in industry and NIH-funded clinical trials" (Trials 2018 19:269).
<p>Dataset for our journal publication (Spence et al. 2018 <a href="https://doi.org/10.1186/s13063-018-2651-2">https://doi.org/10.1186/s13063-018-2651-2</a>). This dataset contains</p> <ul> <li>Informed consent forms (ICFs) for 98 industry-funded clinical trials</li> <li>Informed consent forms (ICFs) for 46 NIH-funded clinical trials</li> <li>Our extraction datasheet</li> </ul>
Phenotypic landscape of schizophrenia-associated genes defines candidates and their shared functions - behavior data
<p>Processed behavior data for all zebrafish behavior runs included in manuscript. This includes data such as bouts / minute. The raw data used to generate this processed output is available upon request (too large for any public repository). These files can be used to generate all statistics and graphs using the script statsandgraphs.py (https://github.com/sthyme/ZFSchizophrenia/tree/master/BehaviorAnalysis). Each directory also contains scripts used, genotyping information, and output files for the statistical analyses presented in the manuscript.</p>
Supplementary material 3 from: Petersen M, Hoffmann J, Glöckler F (2019) Access to Geosciences – Ways and Means to share and publish collection data. Research Ideas and Outcomes 5: e32987. https://doi.org/10.3897/rio.5.e32987
Programm of the two-day workshop Access to Geosciences: sharing and publishing data related to paleontological, mineralogical, and petrological objects using a common data standard. Museum für Naturkunde, 29-30 May 2017
Supplementary material 2 from: Petersen M, Hoffmann J, Glöckler F (2019) Access to Geosciences – Ways and Means to share and publish collection data. Research Ideas and Outcomes 5: e32987. https://doi.org/10.3897/rio.5.e32987
Results of the survey on geoscientific collection data. Names, institutions and contact details are anonymized
Data from: Lupus and inflammatory bowel disease share a common set of microbiome features distinct from other autoimmune disorders
<p>Supplemental data for "Lupus and inflammatory bowel disease share a common set of microbiome features distinct from other autoimmune disorders"</p>
Data for National carbon footprint estimations of an electrified internet of energy with circular economy under future EV market share prediction in China
<p>This dataset is created for National carbon footprint estimations of an electrified internet of energy with circular economy under future EV market share prediction in China.</p> <p>The dataset includes the energy demand for buildings of each province in China, the Centralized and Distributed PV-battery system design of each province in China, The EV and ICEV carbon emission comparison of each province in China, the electricity price in China, The Carbon footprint and NPV calculation of current and future building-transportation system in China.</p>
Ethical Impact Assessment of sharing nanosafety data: raw data of the online survey among participants in the XX Seminar on Nanotechnology, Society and Environment in Brazil and online, on 18 October 2023
<p>One excel sheet includes the responses to the online survey on Ethical Impact Assessment of sharing nanosafety data of participants in the XX Seminar on Nanotechnology, Society and Environment in Brazil and online, on 18 October 2023, in Portuguese, and the other the translated responses in English.</p>
Clinical Trial Transparency and Data-Sharing Among Bio-Pharmaceutical Companies and the Role of Company Size, Location, and Product Type: A Cross-Sectional Descriptive Analysis
<p><b>Objective</b>: To examine company characteristics associated with better transparency and to apply a tool used to measure and improve clinical trial transparency among large companies and drugs, to smaller companies and biologics.</p> <p><b>Design</b>: Cross-sectional descriptive analysis.</p> <p><b>Setting and participants. </b>Novel drugs and biologics FDA approved in 2016 and 2017, and their company sponsors.</p> <p>Using established Good Pharma Scorecard (GPS) measures, companies and products were evaluated on their clinical trial registration, results dissemination, and FDA Amendments Act (FDAAA) implementation; Companies were ranked using these measures and a multi-component data sharing measure. Associations between company transparency scores with company size (large vs non-large), location (US vs non-US), and sponsored product type (drug vs biologic) were also examined. 26% of products (16/62) had publicly available results for all clinical trials supporting their FDA approval and 67% (39/58) had public results for trials in patients by 6 months after their FDA approval; 58% (32/55) were FDAAA compliant. Large companies were significantly more transparent than non-large companies (overall median transparency score of 95% [IQR 91-100] vs 59% [IQR 41-70], p<0.001), attributable to higher FDAAA compliance (median of 100% [IQR 88-100] vs 57% [0-100], p=0.01) and better data sharing (median of 100% [IQR 80-100] vs 20% [IQR 20-40], p<0.01). No significant differences were observed by company location or product type. It was feasible to apply the GPS transparency measures and ranking tool to non-large companies and biologics. Large companies are significantly more transparent than non-large companies, driven by better data sharing procedures and implementation of FDAAA trial reporting requirements. Greater research transparency is needed, particularly among non-large companies, to maximize the benefits of research for patient care and scientific innovation. </p>
Data from: Within-group relatedness is correlated with colony-level social structure and reproductive sharing in a social fish.
In group-living species, the degree of relatedness among group members often governs the extent of reproductive sharing, cooperation and conflict within a group. Kinship among group members can be shaped by the presence and location of neighbouring groups, as these provide dispersal or mating opportunities that can dilute kinship among current group members. Here, we assessed how within-group relatedness varies with the density and position of neighbouring social groups in Neolamprologus pulcher, a colonial and group-living cichlid fish. We used restriction site-associated DNA sequencing (RADseq) methods to generate thousands of polymorphic SNPs. Relative to microsatellite data, RADseq data provided much tighter confidence intervals around our relatedness estimates. These data allowed us to document novel patterns of relatedness in relation to colony-level social structure. First, the density of neighbouring groups was negatively correlated with relatedness between subordinates and dominant females within a group, but no such patterns were observed between subordinates and dominant males. Second, subordinates at the colony edge were less related to dominant males in their group than subordinates in the colony centre, suggesting a shorter breeding tenure for dominant males at the colony edge. Finally, subordinates who were closely related to their same-sex dominant were more likely to reproduce, supporting some restraint models of reproductive skew. Collectively, these results demonstrate that within-group relatedness is influenced by the broader social context, and variation between groups in the degree of relatedness between dominants and subordinates can be explained by both patterns of reproductive sharing and the nature of the social landscape.
Identifying and classifying shared selective sweeps from multilocus data
Positive selection causes beneficial alleles to rise to high frequency, resulting in a selective sweep of the diversity surrounding the selected sites. Accordingly, the signature of a selective sweep in an ancestral population may still remain in its descendants. Identifying signatures of selection in the ancestor that are shared among its descendants is important to contextualize the timing of a sweep, but few methods exist for this purpose. We introduce the statistic SS-H12, which can identify genomic regions under shared positive selection across populations and is based on the theory of the expected haplotype homozygosity statistic H12, which detects recent hard and soft sweeps from the presence of high-frequency haplotypes. SS-H12 is distinct from comparable statistics because it requires a minimum of only two populations, and properly identifies and differentiates between independent convergent sweeps and true ancestral sweeps, with high power and robustness to a variety of demographic models. Furthermore, we can apply SS-H12 in conjunction with the ratio of statistics we term H2Tot and H1Tot to further classify identified shared sweeps as hard or soft. Finally, we identified both previously-reported and novel shared sweep candidates from human whole-genome sequences. Previously-reported candidates include the well-characterized ancestral sweeps at LCT and SLC24A5 in Indo-Europeans, as well as GPHN worldwide. Novel candidates include an ancestral sweep at RGS18 in sub-Saharan Africans involved in regulating the platelet response and implicated in sudden cardiac death, and a convergent sweep at C2CD5 between European and East Asian populations that may explain their different insulin responses.
Data Sharing - Article GIScience e Remote Sensing
<p>Confidence Interval: Comparative of the Delta Models.</p>
Dataset for How open is innovation research?–An empirical analysis of data sharing among innovation scholars
<p>This dataset comprises responses from 241 innovation researchers on their personal data sharing behavior as well as their perceptions of and attitudes towards open research data. This dataset is the supplementary material to Barczak et al. (2021) (<a href="https://doi.org/10.1080/13662716.2021.1967727" target="_blank" rel="noopener">https://doi.org/10.1080/13662716.2021.1967727</a>).</p>
UnFAIR dataset for hands-on FAIR data sharing course
<p>UnFAIR dataset for hands-on FAIR data sharing course</p>
National Institute of Diabetes and Digestive Kidney Diseases* Share Plus: Continuous Glucose Monitoring with Data Sharing in Older Adults with Type 1 Diabetes* and Their Care Partners to Improve Time
ClinicalTrials.gov study NCT05937321. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Historical changes in northeastern US bee pollinators related to shared ecological traits
Open the record for dataset details and reuse information.
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.