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103 results for “Research data management·”
Supplementary material 6 from: Borghi J, Abrams S, Lowenberg D, Simms S, Chodacki J (2018) Support Your Data: A Research Data Management Guide for Researchers. Research Ideas and Outcomes 4: e26439. https://doi.org/10.3897/rio.4.e26439
A draft guide that corresponds with the "Analyzing your data and handling the outputs" row of the RDM rubric. Suggested points of customization are highlighted in yellow (discipline-specific) and red (institution-specific).
Supplementary material 4 from: Borghi J, Abrams S, Lowenberg D, Simms S, Chodacki J (2018) Support Your Data: A Research Data Management Guide for Researchers. Research Ideas and Outcomes 4: e26439. https://doi.org/10.3897/rio.4.e26439
A draft guide that corresponds with the "Saving and backing up your data" row of the RDM rubric. Suggested points of customization are highlighted in yellow (discipline-specific) and red (institution-specific).
Supplementary material 3 from: Borghi J, Abrams S, Lowenberg D, Simms S, Chodacki J (2018) Support Your Data: A Research Data Management Guide for Researchers. Research Ideas and Outcomes 4: e26439. https://doi.org/10.3897/rio.4.e26439
A draft guide that corresponds with the "Organizing your data" row of the RDM rubric. Suggested points of customization are highlighted in yellow (discipline-specific) and red (institution-specific).
Supplementary material 5 from: Borghi J, Abrams S, Lowenberg D, Simms S, Chodacki J (2018) Support Your Data: A Research Data Management Guide for Researchers. Research Ideas and Outcomes 4: e26439. https://doi.org/10.3897/rio.4.e26439
A draft guide that corresponds with the "Getting your data ready for analysis" row of the RDM rubric. Suggested points of customization are highlighted in yellow (discipline-specific) and red (institution-specific).
Supplementary material 2 from: Borghi J, Abrams S, Lowenberg D, Simms S, Chodacki J (2018) Support Your Data: A Research Data Management Guide for Researchers. Research Ideas and Outcomes 4: e26439. https://doi.org/10.3897/rio.4.e26439
A draft guide that corresponds with the "Planning your project" row of the RDM rubric. Suggested points of customization are highlighted in yellow (discipline-specific) and red (institution-specific).
Supplementary material 1 from: Van Tuyl S, Whitmire A (2018) Investigation of Non-Academic Data Management Practices to Inform Academic Research Data Management. Research Ideas and Outcomes 4: e30829. https://doi.org/10.3897/rio.4.e30829
Interview protocol used for the project, "Investigation of Non-Academic Data Management Practices to Inform Academic Research Data Management"
Why research data management: four videos
<p>See https://rdmpromotion.rbind.io, and doi: <a href="https://doi.org/10.5281/zenodo.1293375">10.5281/zenodo.1293375</a> </p> <p>The four videos are presented in their own folder, we have one version without sound, and the music and the voice over sound in three different files. Finally, the version with all 4 elements is also there. The videos were also pushed to figshare and got their own doi there (they are also on youtube at the moment).</p> <p>01_opendata, doi: https://doi.org/10.6084/m9.figshare.7379942<br> 02_retrievedata, doi: https://doi.org/10.6084/m9.figshare.7163396<br> 03_analysedata, doi: https://doi.org/10.6084/m9.figshare.7673543<br> 04_data_champions, doi: https://doi.org/10.6084/m9.figshare.7053797</p> <p>subtitles (including translations) can be found here and at https://github.com/open-science-promoters/RDM-promotion/tree/master/subtitlesvideo. You may want to check youtube for later translations. </p> <p>Note that timing of the voice over of the 3d movie had to be modified, and a second file with the modification is included.</p> <p><br> Source of videos:</p> <p>01_opendata:</p> <p>Superman, Secret Agent<br> Superman, Billion dollar limited<br> Superman, Superman (the mad scientist)<br> Superman, Japoteurs<br> Superman, the magnetic telescope (music only)</p> <p>source: toonamiarsenal.com (deprecated, see archive.org to get it)</p> <p>02_retrievedata:<br> Popeye, Parlez Vous Woo<br> Popeye, A date to skate<br> Popeye, A Haul in One</p> <p>source: archive.org</p> <p>03_analysedata:<br> The haunted house<br> One week</p> <p>source: archive.org</p> <p>04_data_champions:<br> classic TV commercial from the 50s,<br> ClassicT1948_7<br> ClassicT1948_8<br> Televisi1960</p> <p>source: prellinger collection, archive.org</p>
FAIRness of Repositories & Their Data: A Report from LIBER's Research Data Management Working Group
<p>Data repositories play a crucial role in the evolution of Open Science. The FAIR Data Principles establish how to make data Findable, Accessible, Interoperable and Reusable (Wilkinson et al., 2016). The FAIR principles are as follows: </p> <p><strong>To Be Findable</strong></p> <ul> <li>F1. (meta)data are assigned a globally unique and eternally persistent identifier.</li> <li>F2. data are described with rich metadata.</li> <li>F3. (meta)data are registered or indexed in a searchable resource.</li> <li>F4. metadata specify the data identifier.</li> </ul> <p><strong>To Be Accessible:</strong></p> <ul> <li>A1 (meta)data are retrievable by their identifier using a standardized communications protocol.</li> <li>A1.1 the protocol is open, free, and universally implementable.</li> <li>A1.2 the protocol allows for an authentication and authorization procedure, where necessary.</li> <li>A2 metadata are accessible, even when the data are no longer available.</li> </ul> <p><strong>To Be Interoperable</strong></p> <ul> <li>I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.</li> <li>I2. (meta)data use vocabularies that follow FAIR principles.</li> <li>I3. (meta)data include qualified references to other (meta)data.</li> </ul> <p><strong>To Be Reusable</strong></p> <ul> <li>R1. meta(data) have a plurality of accurate and relevant attributes.</li> <li>R1.1. (meta)data are released with a clear and accessible data usage license.</li> <li>R1.2. (meta)data are associated with their provenance.</li> <li>R1.3. (meta)data meet domain-relevant community standards. </li> </ul> <p><strong>Methodology</strong></p> <p>Based on the FAIR Data Principles, two questionnaires were created. The first (hereafter #Q1 - see Appendix #1) targeted repository managers and/or librarians and consisted of 40 questions. The second (hereafter #Q2 - see Appendix #2) targeted technical staff responsible for repository development and maintenance and consisted of 25 questions. </p> <p>Members of LIBER’s <a href="https://libereurope.eu/strategy/research-infrastructures/rdm/">Research Data Management (RDM) Working Group</a> circulated the questionnaires between December 2018 and February 2019. Responses were collected from managers and/or librarians of 29 repositories for the first (#Q1) questionnaire. </p> <p>In addition, technical staff responsible for the development and maintenance of 14 repositories (Table 1) responded to the second (#Q2) questionnaire. In 11 cases, repositories filled out both #Q1 and #Q2. </p> <p>In this report, the responses for both questionnaires have been merged and analyzed to gain a comprehensive picture about FAIRness at the level of repositories and their data.<br> </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>
Data from: Integrating local knowledge and research to refine the management of an invasive non-native grass in critically endangered grassy woodlands
Open the record for dataset details and reuse information.
Figure 1 from: Petersen M, Pramann B, Toepfer R, Neumann J, Enke H, Hoffmann J, Mauer R (2020) Research Data Management - Current status and future challenges for German non-university research institutions. Research Ideas and Outcomes 6: e55141. https://doi.org/10.3897/rio.6.e55141
Figure 1 Main results of the World Café on experts' opinion to different aspects and challenges in research data management. Shown are the five stations ('Community Perspective', 'Reflection', 'Technology', 'Society & Values', and 'Personal Objectives') and the main points raised and discussed by the participants during the sessions.
Supplementary material 1 from: Petersen M, Pramann B, Toepfer R, Neumann J, Enke H, Hoffmann J, Mauer R (2020) Research Data Management - Current status and future challenges for German non-university research institutions. Research Ideas and Outcomes 6: e55141. https://doi.org/10.3897/rio.6.e55141
Programm FDM-Expert*innentreffen 18.06.2019 (german)
Supplementary material 2 from: Petersen M, Pramann B, Toepfer R, Neumann J, Enke H, Hoffmann J, Mauer R (2020) Research Data Management - Current status and future challenges for German non-university research institutions. Research Ideas and Outcomes 6: e55141. https://doi.org/10.3897/rio.6.e55141
Forschungsdatenmanagement mit NFDI: Wie sind wir vorbereitet? Wie partizipieren wir?
Supplementary material 4 from: Petersen M, Pramann B, Toepfer R, Neumann J, Enke H, Hoffmann J, Mauer R (2020) Research Data Management - Current status and future challenges for German non-university research institutions. Research Ideas and Outcomes 6: e55141. https://doi.org/10.3897/rio.6.e55141
Photographs of notes from the discussions in the Leibniz Sessions
Survey on Research Data Management at a Technical University
<p>This dataset is based on a survey with researchers and engineers about data management practices. The survey was hosted via LimeSurvey. The data displayed here were retrieved directly from LimeSurvey. For analysis, we recoded the survey items as most of them were in a textual format (e.g. answer options such as "To a large extent" etc.). Here, only the original data are provided. Here, the answers and questions should be fairly self-explanatory, eliminating the need for extensive metadata.</p> <p>In total, 498 people started to fill in the survey, but only 259 completed the process.</p>
Research Data Management in Health and Biomedical Citizen Science: Practices and Prospects
<p><b>Background:</b> Public engagement in health and biomedical research is being influenced by the paradigm of citizen science. However, conventional health and biomedical research relies on sophisticated research data management tools and methods. Considering these, what contribution can citizen science make in this field of research? How can it follow research protocols and produce reliable results?</p> <p><b>Objective:</b> The aim of this paper is to analyse research data management practices in existing biomedical citizen science studies, so as to provide insights for members of the public and of the research community considering this approach to research.</p> <p><b>Methods:</b> A scoping review was conducted on this topic to determine data management characteristics of health and bio medical citizen science research. From this review and related web searching, we chose five online platforms and a specific research project associated with each, to understand their research data management approaches and enablers.</p> <p><b>Results:</b> Health and biomedical citizen science platforms and projects are diverse in terms of types of work with data and data management activities that in themselves may have scientific merit. However, consistent approaches in the use of research data management models or practices seem lacking, or at least are not evident.</p> <p><b>Conclusions:</b> There is potential for important data collection and analysis activities to be opaque or irreproducible in health and biomedical citizen science initiatives without the implementation of a research data management model that is transparent and accessible to team members and to external audiences. This situation might be improved with participatory development of standards that can be applied to diverse projects and platforms, across the research data life cycle.<b> </b></p>
Data from: An ecosystem services perspective on brush management: research priorities for competing land use objectives
1. The vegetation of semi-arid and arid landscapes is often comprised of mixtures of herbaceous and woody vegetation. Since the early 1900s, shifts from herbaceous to woody plant dominance, termed woody plant encroachment and widely regarded as a state change, have occurred world-wide. This shift presents challenges to the conservation of grassland and savanna ecosystems and to animal production in commercial ranching systems and pastoral societies. 2. Dryland management focused on cattle and sheep grazing has historically attempted to reduce the abundance of encroaching woody vegetation (hereafter, 'brush management') with the intent of reversing declines in forage production, stream flow or groundwater recharge. Here, we assess the known and potential consequences of brush management actions, both positive and negative, on a broader suite of ecosystem services, the scientific challenges to quantifying these services and the trade-offs among them. 3. Our synthesis suggests that despite considerable investments accompanying the application of brush management practices, the recovery of key ecosystem services may be short-lived or absent. However, in the absence of such interventions, those and other ecosystem services may be compromised, and the persistence of grassland and savanna ecosystem types and their endemic plants and animals threatened. 4. Addressing the challenges posed by woody plant encroachment will require integrated management systems using diverse theoretical principles to design the type, timing and spatial arrangement of initial management actions and follow-up treatments. These management activities will need to balance cultural traditions and preferences, socio-economic constraints and potentially competing land-use objectives. 5. Synthesis. Our ability to predict ecosystem responses to management aimed at recovering ecosystem services where grasslands and savannas have been invaded by native or exotic woody plants is limited for many attributes (e.g. primary production, land surface–atmosphere interactions, biodiversity conservation) and inconsistent for others (e.g. forage production, herbaceous diversity, water quality/quantity, soil erosion, carbon sequestration). The ecological community is challenged with generating robust information about the response of ecosystem services and their interactions if we are to position land managers and policymakers to make objective, science-based decisions regarding the many trade-offs and competing objectives for the conservation and dynamic management of grasslands and savannas.
Supplementary material 1 from: Borisenko A, Young R, Hanner R (2024) A lab-centric, workflow-based data management system for environmental DNA research. Research Ideas and Outcomes 10: e120483. https://doi.org/10.3897/rio.10.e120483
eDNA Laboratory Database Schema Outline
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: 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.
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.