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ShareScore release 0.9.0
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81 results for “research data sharing”
Supplementary material 1 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
Sheet on the survey on geoscientific collection data
Figure 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
Figure 3 Reasons for publication of (meta)data on geoscientific collection objects (a) and not publishing particular datasets through a data portal (b). Shown is the frequency of answers per reason. Note that the y-axes are scaled differently.
Figure 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
Figure 2 Estimated value of cross-institutional search possibilities through data portals on geoscientific object (meta)data for different target groups. Shown is the frequency of answers for different scopes and their respective importance (see legend).
Figure 8 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 8 - The patchiness of survey coverage in Europe illustrated by the distribution map of Plantago lanceolata taken from GBIF in 2016. This species is one of the commonest and most widespread in Europe, it should occur in almost all areas of this map, but in fact the data traces out the borders of countries and area who have published data on GBIF.
Figure 9 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 9 - Information flows between EU BON and LTER Europe, as envisaged on the 3rd EU BON Stakeholder Roundtable in Granada on 9-11 December 2015.
Figure 6 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 6 - Individual Metacat instances can be connected to DataOne which replicates public files. Thus the data is still available if a single instance goes offline. https://search.dataone.org/#data/page/0
Figure 5 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 5 - PPBio has installed a Metacat instance for their researchers to upload and make publicly available the results of work related to biodiversity in the Western Amazon. https://ppbiodata.inpa.gov.br/metacatui/
Figure 3 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 3 - The implementation of Darwin Core Archive in Plazi to transfer treatment data. Observation data described with Darwin Core terms.
Figure 4 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 4 - The public data repository provided by the Knowledge Network for Biocomplexity (KNB). https://knb.ecoinformatics.org/#data/page/0
Figure 1 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 1 - ARPHA consists of two integrated workflows: in ARPHA-XML, the manuscript is written and processed via the ARPHA Writing Tool, and in ARPHA-DOC, the manuscript is submitted and processed as document file(s).
Data from: Public sharing of research datasets: a pilot study of associations
Open the record for dataset details and reuse information.
Data from: Sharing detailed research data is associated with increased citation rate
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Data from: Who shares? Who doesn’t? Factors associated with openly archiving raw research data
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Data from: Data management, archiving and sharing for biologists and the role of research institutions in the technology-oriented age
Data are one of the primary outputs of science. Although certain sub-disciplines of biology have pioneered efforts to ensure their long-term preservation and facilitate collaborations, data continue to disappear, owing mostly to technological, regulatory and ideological hurdles. In this review, we describe the important steps towards proper data management and archiving, and provide a critical discussion on the importance of long term data conservation. We then illustrate the rise in data archiving through the Joint Data Archiving Policy and the Dryad Digital Repository. In particular, we discuss data integration and how the limited availability of large scale datasets can hinder new discoveries. Finally, we propose solutions to increase the rate of data preservation. For example, by generating mechanisms insuring proper data management and archiving, by providing training in data management, and by transforming the traditional role of research institutions and libraries as data generators towards managers and archivers.
Figure 1 from: Neylon C (2017) Building a Culture of Data Sharing: Policy Design and Implementation for Research Data Management in Development Research. Research Ideas and Outcomes 3: e21773. https://doi.org/10.3897/rio.3.e21773
Figure 1 - The Institutional Analysis and Design framework adapted from (Ostrom 2005).
Figure 1 from: Irawan D, Rachmi C (2018) Promoting data sharing among Indonesian scientists: A proposal of generic university-level Research Data Management Plan (RDMP). Research Ideas and Outcomes 4: e28163. https://doi.org/10.3897/rio.4.e28163
Figure 1 Current situation of data lifecycle (Irawan 2018).
Figure 7 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 7 - Mobile app for sporadic observations reporting.
Figure 2 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 2 - The Plazi workflow (green) within EU BON.
Data from: Data management, archiving and sharing for biologists and the role of research institutions in the technology-oriented age
Open the record for dataset details and reuse information.
Anonymized interview transcripts from the study "Information Scientists' Motivations for Research Data Sharing and Reuse"
<p>These are eleven transcripts of the interviews conducted in the context of the study “Information Scientists’ Motivations for Research Data Sharing and Reuse”.</p> <p>Interview partners are information science researchers who work with data and shared (or not) their research data and/or reused (or not) the data from third parties. Study sample included six women and five men working either at a German university and/or a research performing organization with working experience as researchers from six to over 40 years. Nine persons have a PhD degree, two persons are the PhD candidates. One researcher belongs to the Net Generation (i.e. born approx. between 1946 and 1960), three researchers – to Generation X (i.e. born approx. 1960 and 1980), seven researchers – to Generation Y (i.e. born approx. between 1980 and 1996).</p> <p>Data collection took place between October, 13 and November, 24 2022. The interviews were conducted in German, online and video-recorded. The shortest conversation took 43 minutes and the longest – 63 minutes. With participants’ consent, all interviews were transcribed using f4x Automatic Speech Recognition. The automatically created transcripts were then manually checked and corrected if needed.</p> <p>The interview transcripts are anonymized by replacing original words or phrases with more generic words or phrases. The replaced texts are marked in blue and taken in brackets. The access to the anonymized transcripts will be granted on request with concrete agreement on use.</p> <p>The <a href="https://doi.org/10.5281/zenodo.8230979">interview guide template</a> used for conducting the interviews is available. For more information about study design and results, please visit the <a href="https://doi.org/10.5281/zenodo.8186518">project page</a> and/or read the article "Information Scientists' Motivations for Research Data Sharing and Reuse" by Shutsko and Stock (2023).</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.