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81 results for “research data sharing”
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
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>
Data from: Research data sharing: practices and attitudes of geophysicists
Open the record for dataset details and reuse information.
Data of the article "Journal research data sharing policies: a study of highly-cited journals in neuroscience, physics, and operations research"
<p>The journals’ author guidelines and/or editorial policies were examined on whether they take a stance with regard to the availability of the underlying data of the submitted article. The mere explicated possibility of providing supplementary material along with the submitted article was not considered as a research data policy in the present study. Furthermore, the present article excluded source codes or algorithms from the scope of the paper and thus policies related to them are not included in the analysis of the present article.</p> <p>For selection of journals within the field of neurosciences, Clarivate Analytics’ InCites Journal Citation Reports database was searched using categories of neurosciences and neuroimaging. From the results, journals with the 40 highest Impact Factor (for the year 2017) indicators were extracted for scrutiny of research data policies. Respectively, the selection journals within the field of physics was created by performing a similar search with the categories of physics, applied; physics, atomic, molecular & chemical; physics, condensed matter; physics, fluids & plasmas; physics, mathematical; physics, multidisciplinary; physics, nuclear and physics, particles & fields. From the results, journals with the 40 highest Impact Factor indicators were again extracted for scrutiny. Similarly, the 40 journals representing the field of operations research were extracted by using the search category of operations research and management. </p> <p>Journal-specific data policies were sought from journal specific websites providing journal specific author guidelines or editorial policies. Within the present study, the examination of journal data policies was done in May 2019. The primary data source was journal-specific author guidelines. If journal guidelines explicitly linked to the publisher’s general policy with regard to research data, these were used in the analyses of the present article. If journal-specific research data policy, or lack of, was inconsistent with the publisher’s general policies, the journal-specific policies and guidelines were prioritized and used in the present article’s data. If journals’ author guidelines were not openly available online due to, e.g., accepting submissions on an invite-only basis, the journal was not included in the data of the present article. Also journals that exclusively publish review articles were excluded and replaced with the journal having the next highest Impact Factor indicator so that each set representing the three field of sciences consisted of 40 journals. The final data thus consisted of 120 journals in total. </p> <p>‘Public deposition’ refers to a scenario where researcher deposits data to a public repository and thus gives the administrative role of the data to the receiving repository. ‘Scientific sharing’ refers to a scenario where researcher administers his or her data locally and by request provides it to interested reader. Note that none of the journals examined in the present article required that all data types underlying a submitted work should be deposited into a public data repositories. However, some journals required public deposition of data of specific types. Within the journal research data policies examined in the present article, these data types are well presented by the Springer Nature policy on “Availability of data, materials, code and protocols” (Springer Nature, 2018), that is, DNA and RNA data; protein sequences and DNA and RNA sequencing data; genetic polymorphisms data; linked phenotype and genotype data; gene expression microarray data; proteomics data; macromolecular structures and crystallographic data for small molecules. Furthermore, the registration of clinical trials in a public repository was also considered as a data type in this study. The term <em>specific data types</em> used in the custom coding framework of the present study thus refers to both life sciences data and public registration of clinical trials. These data types have community-endorsed public repositories where deposition was most often mandated within the journals’ research data policies.</p> <p>The term ‘location’ refers to whether the journal’s data policy provides suggestions or requirements for the repositories or services used to share the underlying data of the submitted works. A mere general reference to ‘public repositories’ was not considered a location suggestion, but only references to individual repositories and services. The category of ‘immediate release of data’ examines whether the journals’ research data policy addresses the timing of publication of the underlying data of submitted works. Note that even though the journals may only encourage public deposition of the data, the editorial processes could be set up so that it leads to either publication of the research data or the research data metadata in conjunction to publishing of the submitted work.<em> </em></p> <p> </p>
Raw data belonged to the study: The sharing of research data facing the COVID-19 pandemic
<p>Raw data belonged to the study: "The sharing of research data facing the COVID-19 pandemic"</p>
The sharing of research raw data in journals indexed in the Reproductive Biology JCR category
<p><a href="https://zenodo.org/record/2384871">Raw data belonged to the study of use and sharing of raw research data in the Journal Citation Reports' Reproductive Biology Category</a>.</p>
Data from: Who shares? Who doesn't? Factors associated with openly archiving raw research data
Many initiatives encourage investigators to share their raw datasets in hopes of increasing research efficiency and quality. Despite these investments of time and money, we do not have a firm grasp of who openly shares raw research data, who doesn't, and which initiatives are correlated with high rates of data sharing. In this analysis I use bibliometric methods to identify patterns in the frequency with which investigators openly archive their raw gene expression microarray datasets after study publication. Automated methods identified 11,603 articles published between 2000 and 2009 that describe the creation of gene expression microarray data. Associated datasets in best-practice repositories were found for 25% of these articles, increasing from less than 5% in 2001 to 30%-35% in 2007-2009. Accounting for sensitivity of the automated methods, approximately 45% of recent gene expression studies made their data publicly available. First-order factor analysis on 124 diverse bibliometric attributes of the data creation articles revealed 15 factors describing authorship, funding, institution, publication, and domain environments. In multivariate regression, authors were most likely to share data if they had prior experience sharing or reusing data, if their study was published in an open access journal or a journal with a relatively strong data sharing policy, or if the study was funded by a large number of NIH grants. Authors of studies on cancer and human subjects were least likely to make their datasets available. These results suggest research data sharing levels are still low and increasing only slowly, and data is least available in areas where it could make the biggest impact. Let's learn from those with high rates of sharing to embrace the full potential of our research output.
Data from: Public sharing of research datasets: a pilot study of associations
The public sharing of primary research datasets potentially benefits the research community but is not yet common practice. In this pilot study, we analyzed whether data sharing frequency was associated with funder and publisher requirements, journal impact factor, or investigator experience and impact. Across 397 recent biomedical microarray studies, we found investigators were more likely to publicly share their raw dataset when their study was published in a high-impact journal and when the first or last authors had high levels of career experience and impact. We estimate the USA's National Institutes of Health (NIH) data sharing policy applied to 19% of the studies in our cohort; being subject to the NIH data sharing plan requirement was not found to correlate with increased data sharing behavior in multivariate logistic regression analysis. Studies published in journals that required a database submission accession number as a condition of publication were more likely to share their data, but this trend was not statistically significant. These early results will inform our ongoing larger analysis, and hopefully contribute to the development of more effective data sharing initiatives. **Earlier version presented at ASIS&T and ISSI Pre-Conference: Symposium on Informetrics and Scientometrics 2009**
Supplementary material 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
List of tested and analyzed data sharing tools (non-exhaustive)
Supplementary material 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
Definitions and concepts in the context of the main paper.
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.
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).
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.