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ShareScore release 0.7.1
Dataset results
379 results for “data sharing”
Supplementary material 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
List of selected tools.
Figure 2 from: Neylon C (2017) Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers. Research Ideas and Outcomes 3: e14673. https://doi.org/10.3897/rio.3.e14673
Figure 2 - The core issues and principles that a Research Data Management policy should address, adapted from Hodson and Molloy 2015
Figure 1 from: Neylon C (2017) Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers. Research Ideas and Outcomes 3: e14673. https://doi.org/10.3897/rio.3.e14673
Figure 1 - Illustration of the categories through which many research data management and sharing policies develop, with examples of the language used.
Figure 2 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 2 - The Cultural Science model of Hartley and Potts (2014).The co-creation of culture and group in the context of an external environment.
Figure 2 from: Neylon C (2017) Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers. Research Ideas and Outcomes 3: e21705. https://doi.org/10.3897/rio.3.e21705
Figure 2 - The core issues and principles that a Research Data Management policy should address, adapted from Hodson and Molloy (2015)
Figure 1 from: Neylon C (2017) Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers. Research Ideas and Outcomes 3: e21705. https://doi.org/10.3897/rio.3.e21705
Figure 1 - Illustration of the categories through which many research data management and sharing policies develop, with examples of the language used.
Dallilar 2017 Science Data Share
<p>Data share for: http://science.sciencemag.org/content/358/6368/1299</p>
Figure 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
Figure 1 Types of collection objects. Given are the frequencies of particular objects the survey participants' institution or department holds. Other objects include soil, crystal models, historical instruments, boreholes, etc.
Figure 5 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 5 Elements identified by workshop and survey participants as being important for the publication of geoscientific collection objects. The four different object classes (fossils, rocks, meteorites, and minerals) are highlighted in different shades of green, terms important for the respective class are assigned around the object name and likewise colored, terms in the center (grey) are relevant for all four object classes. Note: The figure only summarizes the mentioned terms, there might be more properties that are important for the publication of collection object associated data for the different object classes and / or more terms mentioned for one class only but important for several.
Figure 4 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 4 Awareness and usage of Geoscientific Collection Access Service (GeoCASe). Shown is the frequency of answers regarding the awareness of GeoCASe (a), the data provision through the GeoCASe portal (b), the readiness to support the progress of improvements (c), and the interest of being part of the GeoCASe community (d).
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).
Data archiving and sharing survey
<p><span>Open data sharing</span> improves the quality and reproducibility of scholarly research<span>. </span>Open data are defined as data that can be freely used, reused, and redistributed by anyone. Open data sharing democratizes science by making data more equitably available throughout the world regardless of funding or access to other resources necessary for generating cutting-edge data.<span> For an interdisciplinary field like biological anthropology, data </span>sharing is critical since one person cannot easily collect data across multiple domains.<span> The goal of this paper is to encourage broader data sharing by exploring the state of data sharing in the field of biological anthropology. Our paper is divided into four parts: the first section describes the benefits, challenges, and emerging solutions to open data sharing; the second section presents the results of our data archiving and sharing survey that was completed by over 700 researchers; the third section presents personal experiences of data sharing by the authors; and the fourth section discusses the strengths of different types of data repositories and provides a list of recommended data repositories.</span></p> <p><span>The data archiving and sharing survey and the raw data collected from the survey are deposited here.</span></p>
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
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.