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62 results for “open research data”
Results from CODATA-RDA Schools for Research Data Science "being open and responsible at home" exercise 2022
<p>This is an exercise in identifying potential influence of environmental factors on open and responsible research practices. Students were asked to write down concerns about conducting open and responsible research in their home environments (column 2), and to group these concerns around institutional/cultural issues, infrastructural issues and personal concerns (column 1). Through a facilitated discussion, the class identified tools, communities and resources to use to help overcome these concerns (column 3).</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>
pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage - Validation data
<p>Image data used in the paper introducing pyKNEEr. Explanations about these data are in the <a href="https://github.com/sbonaretti/pyKNEEr/tree/master/publication">GitHub</a> repository</p> <p>Changes in version 0.2.0: </p> <p>- Added inHouse images </p> <p>- Segmented images casted to int16 for smaller file size</p>
Demystifying Open Science and Research Data Management: A practical workshop for researchers
<p>Have you ever wondered why is everyone discussing about Open Science and Research Data Management (RDM) these days? Good Research Data Management (RDM) is crucial for reproducible and robust scientific research. Consequently, more and more funding bodies, governments, research institutions and other agencies have emphasised the value and importance of good data management and introduced policies on data management and sharing. However, it is easier said than done.<br> Like most researchers you might have more questions than answers about the topic. You are not alone! Come join us on the 24th at an interactive workshop where you can understand the why and how of open science and research data management in practical terms.</p> <p> </p> <p>This zenodo entry is a recording of the workshop described above. </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>
Data from: tableone: An open source Python package for producing summary statistics for research papers
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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: In an age of open access to research policies: physician and public health NGO staff research use and policy awareness
Introduction: Through funding agency and publisher policies, an increasing proportion of the health sciences literature is being made open access. Such an increase in access raises questions about the awareness and potential utilization of this literature by those working in health fields. Methods: A sample of physicians (N=336) and public health non-governmental organization (NGO) staff (N=92) were provided with relatively complete access to the research literature indexed in PubMed, as well as access to the point-of-care service UpToDate, for up to one year, with their usage monitored through the tracking of web-log data. The physicians also participated in a one-month trial of relatively complete or limited access. Results: The study found that participants' research interests were not satisfied by article abstracts alone nor, in the case of the physicians, by a clinical summary service such as UpToDate. On average, a third of the physicians viewed research a little more frequently than once a week, while two-thirds of the public health NGO staff viewed more than three articles a week. Those articles were published since the 2008 adoption of the NIH Public Access Policy, as well as prior to 2008 and during the maximum 12-month embargo period. A portion of the articles in each period was already open access, but complete access encouraged a viewing of more research articles. Conclusion: Those working in health fields will utilize more research in the course of their work as a result of (a) increasing open access to research, (b) improving awareness of and preparation for this access, and (c) adjusting public and open access policies to maximize the extent of potential access, through reduction in embargo periods and access to pre-policy literature.
Supplementary material 1 from: Cardoso A, Tsiamis K, Gervasini E, Schade S, Taucer F, Adriaens T, Copas K, Flevaris S, Galiay P, Jennings E, Josefsson M, López B, Magan J, Marchante E, Montani E, Roy H, von Schomberg R, See L, Quintas M (2017) Citizen Science and Open Data: a model for Invasive Alien Species in Europe. Research Ideas and Outcomes 3: e14811. https://doi.org/10.3897/rio.3.e14811
Appendix 2.
Supplementary material 1 from: Woolfrey L (2017) Data Management Plan: Opening access to economic data to prevent tobacco related diseases in Africa. Research Ideas and Outcomes 3: e14837. https://doi.org/10.3897/rio.3.e14837
Data quality issues: Accuracy - issues around data collection.
Figure 1 from: Cardoso A, Tsiamis K, Gervasini E, Schade S, Taucer F, Adriaens T, Copas K, Flevaris S, Galiay P, Jennings E, Josefsson M, López B, Magan J, Marchante E, Montani E, Roy H, von Schomberg R, See L, Quintas M (2017) Citizen Science and Open Data: a model for Invasive Alien Species in Europe. Research Ideas and Outcomes 3: e14811. https://doi.org/10.3897/rio.3.e14811
Figure 1 - Participants of the workshop on "Citizen Science and Open Data: a model for Invasive Alien Species in Europe". Image: COST Association.
Figure 4 from: Cardoso A, Tsiamis K, Gervasini E, Schade S, Taucer F, Adriaens T, Copas K, Flevaris S, Galiay P, Jennings E, Josefsson M, López B, Magan J, Marchante E, Montani E, Roy H, von Schomberg R, See L, Quintas M (2017) Citizen Science and Open Data: a model for Invasive Alien Species in Europe. Research Ideas and Outcomes 3: e14811. https://doi.org/10.3897/rio.3.e14811
Figure 4 - Flipchart with participants notes addressing the topic "Main characteristics of a model for a citizen participation replicable across different policies" during Session 1. Image: COST Association.
Figure 3 from: Cardoso A, Tsiamis K, Gervasini E, Schade S, Taucer F, Adriaens T, Copas K, Flevaris S, Galiay P, Jennings E, Josefsson M, López B, Magan J, Marchante E, Montani E, Roy H, von Schomberg R, See L, Quintas M (2017) Citizen Science and Open Data: a model for Invasive Alien Species in Europe. Research Ideas and Outcomes 3: e14811. https://doi.org/10.3897/rio.3.e14811
Figure 3 - Discussion of a round table through the "world café" method, addressing the topic "List of methods for mainstreaming inputs from CS in policy making including quality assurance and validations and other parameters" during Session 1. Image: COST Association.
Figure 2 from: Cardoso A, Tsiamis K, Gervasini E, Schade S, Taucer F, Adriaens T, Copas K, Flevaris S, Galiay P, Jennings E, Josefsson M, López B, Magan J, Marchante E, Montani E, Roy H, von Schomberg R, See L, Quintas M (2017) Citizen Science and Open Data: a model for Invasive Alien Species in Europe. Research Ideas and Outcomes 3: e14811. https://doi.org/10.3897/rio.3.e14811
Figure 2 - Discussion of a round table through the "world café" method, addressing the topic "List of successful case-studies and examples of good practices in environment and IAS" during Session 1. Image: COST Association.
Figure 1 from: Woolfrey L (2017) Data Management Plan: Opening access to economic data to prevent tobacco related diseases in Africa. Research Ideas and Outcomes 3: e14837. https://doi.org/10.3897/rio.3.e14837
Figure 1 - Tobacco Data in Africa Project Data Inventory 2016. Original data available as Suppl. material 1.
Data underlying research paper "Exploring potential contributions of open data intermediaries"
<p>This folder contains data underlying the research paper “Exploring the Contributions of Open Data Intermediaries for a Sustainable Open Data Ecosystem”. The research is about open data ecosystem and the role of open data intermediaries. <strong>The folder consists of 4 items</strong>:</p> <p>1. Tentative interview questions (.pdf and .odt formats)</p> <p>2. Informed consent form template (for verbal interview & written interview) (.pdf and .odt formats)</p> <p>3. De-identified interview transcripts (.pdf and .odt formats)</p> <p>4. Coding results (.pdf and .ods formats)</p> <p> </p> <p><strong>Note about the tentative interview questions:</strong></p> <p>The interviews were conducted between May and July 2023 based on the semi-structured approach. We customise the tentative interview questions accordingly for each interview and share them with the interviewees in advance (for the majority, at least three working days in advance). As semi-structured interviews, the ultimate interview questions may differ from the tentative questions based on the information provided by the interviewees and time constraints (refer to item #3).</p> <p> </p> <p><strong>Note about the informed consent form:</strong></p> <p>We sent the informed consent form to every interviewee in advance and requested them to return it to us before the interview. The consent form has been reviewed by TU Delft's Human Research Ethics Committee (HREC).</p> <p> </p> <p><strong>Note about the de-identified interview transcripts (and coding results):</strong></p> <p><u>The de-identified interview transcripts should be read in the context of the research on open data ecosystem and the role of open data intermediaries.</u> We removed personally identifiable information from the transcripts. A few interviewees may risk being identifiable if their organisation is known. Hence, we removed the identification of the organisation and country in all transcripts. Partially disclosing the organisation or country for some transcripts increases the risks of identifying the non-disclosed transcripts. With verbal communication, some sentences may be less incomprehensible in writing. Thus, we did minimal edits when transcribing to improve the comprehensibility where necessary, but the main objective was to keep the transcript as close to verbatim as possible. All interviewees whose interview transcripts are recorded in this document give permission for the anonymised transcript of their interview, with personally identifiable information redacted, to be shared in 4TU.ResearchData repository so it can be used for future research and learning.</p> <p> </p> <p><strong>Acknowledgement:</strong></p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955569. The opinions expressed in this document reflect only the author’s view and in no way reflect the European Commission’s opinions. The European Commission is not responsible for any use that may be made of the information it contains.</p>
Poster_YouTube_as_Source_of_Open_Data_for_Traffic_Safety_Research
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(re)Use Indications of High Energy Physics related Research Data and Software in CERN Open Data Portal
<p>This dataset contains High Energy Physics related research data and software (re)use indications (formal citations, informal mentions) in scholarly works. All research data and software resources were identified and extracted from CERN Open Data Portal. The (re)use indications were identified by a mix of approaches: use of citation discovery services and multiple search approaches in Google Scholar. All identified research data and software (re)use indications were classified according to their purpose, location, and elements.</p> <p>The data was collected in 2018 for a PhD thesis on research data and software (re)use indications in scholarly works.</p>
Open Research Data for "A New Framework for Evaluating Model Simulated Inland Tropical Cyclone Wind Fields"
<p>The (1) NOAA GFDL T-SHiELD outputs, (2) processed ASOS data, and (3) observation-based, theory-driven wind profiles data used in the manuscript "A New Framework for Evaluating Model Simulated Inland Tropical Cyclone Wind Fields". </p>
Data from: In an age of open access to research policies: physician and public health NGO staff research use and policy awareness
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ScienceDex guides
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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.