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103 results for “Research data management·”
FAIRmat Tutorial 11: Research data management, from fundamentals to implementation
<p>Scientific data are the key outcome of research activities at universities, research institutes, and industrial R&D centers. With the recent surge in research data production driven by high-throughput techniques, and the digitalization of scientific methods, data management has become more critical than ever. Moreover, multidisciplinary collaborative research requires the seamless exchange of research data between team members and different laboratories.</p> <p>To ensure that these vast amounts of diverse research data are handled effectively and result in valuable human knowledge and discoveries, it is imperative that proper data management practices are implemented. This ensures that data are produced in a high-quality, reproducible, and well-documented manner so that they can be machine-actionable and reusable in the future. </p> <p>Research Data Management (RDM) refers to the practices used in handling scientific data throughout their lifecycle, from collection to reuse.</p> <p>In this interactive tutorial, we will explain the different stages of the research data lifecycle and identify the best practices for each stage. We will explore the FAIR data principles —Findable, Accessible, Interoperable, and Reusable— and provide insights into their implementation during the research process. In addition, we will introduce the concept of data management plans, which define the data management process during research projects, along with practical tips on the various components and how to comply with funder requirements. </p> <p>The tutorial is divided into two sessions:</p> <ol> <li>Basic concepts and definitions of FAIR data and RDM</li> <li>Practical tips and examples for applying best practices in RDM</li> </ol> <p>After completing this tutorial, you will be able to: </p> <ul> <li>Describe the FAIR principles and understand the different aspects and implementations of FAIR data.</li> <li>Describe and justify measures for good RDM at different stages of the data lifecycle.</li> <li>Understand and explain the concepts of persistent identifiers (PID), metadata, data storage systems, etc. </li> <li>List and understand the contents of the components of a DMP according to the requirements of different funders in Europe. </li> <li>Identify the tools available to create a DMP.</li> </ul>
FAIRmat Tutorial 12: Getting started with NOMAD and NOMAD Oasis for research data management (RDM)
<p>In this online tutorial we will cover the first steps with NOMAD and NOMAD Oasis. We will briefly cover the core NOMAD functionality on exploring, uploading, sharing and publishing data with NOMAD. We will then explore options for creating your own schemas and plugins to support new file formats and create custom electronic lab notebooks (ELNs), we show ways to customize an NOMAD Oasis, and how to contribute to the development of NOMAD and its ecosystem.</p> <p>The tutorial includes an introduction talk about NOMAD and FAIRmat, including the latest changes and features in NOMAD. This is followed by a practical follow along session, where we go through a Jupyter notebook that demonstrates how to use NOMAD for managing custom data and file types. Based on a simple given dataset, we show how to model the data in a schema, do parsing and normalization, process data, access existing data with NOMAD's API for analysis, and how to add visualization to your data.</p> <p> </p> <p><strong>Disclaimer: </strong>NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
Research Data Management - Persistent identifier (Video)
<p>Video of our latest presentation for the training seminars in Research Data Management of the FoDaKo-project: <a href="https://fodako.de/">https://fodako.de</a></p>
Research Data Management – Basics (Video)
<p>Video of our latest presentation for the training seminars in Research Data Management of the FoDaKo-project: <a href="https://fodako.de/">https://fodako.de</a></p>
Research Data Management – Reuse of Research Data (Video)
<p>Video of our latest presentation for the training seminars in Research Data Management of the FoDaKo-project: <a href="https://fodako.de/">https://fodako.de</a></p> <p> </p> <p>Slides can be found here: <a href="https://doi.org/10.5281/zenodo.3269237">10.5281/zenodo.3269237</a></p>
Prioriteringsscore op basis van weging aantal stemmen - bijlage bij: Landelijk Coördinatiepunt Research Data Management (LCRDM) Positioning paper voor 2019 en verder
<p>Bijlage bij Positinioningpaper (juni 2019) https://doi.org/10.5281/zenodo.3266833</p>
Introduction to Research Data Management for Ecologists Distributed Graduate Seminar
<p>This video was made to introduce students who had registered for the Spring 2021 <em>Research Data Management for Ecologists</em> distributed graduate seminar that was offered at Florida International University, University of New Mexico, and University of Wisconsin, Madison.</p>
Data for: A new GTSeq resource to facilitate multijurisdictional research and management of walleye Sander vitreus
<p>Conservation and management professionals often work across jurisdictional boundaries to identify broad ecological patterns. These collaborations help to protect populations whose distributions span political borders. One common limitation to multijurisdictional collaboration is consistency in data recording and reporting. This limitation can impact genetic research which relies on data about specific markers in an organism's genome. Incomplete overlap of markers between separate studies can prevent direct comparisons of results. Standardized marker panels can reduce the impact of this issue and provide a common starting place for new research. Genotyping-in-thousands (GTSeq) is one approach used to create standardized marker panels for non-model organisms. Here we describe the development, optimization, and early assessments of a new GTSeq panel for use with walleye (<em>Sander vitreus</em>) from the Great Lakes region of North America. High genome-coverage sequencing conducted using RAD-capture provided genotypes for thousands of single nucleotide polymorphisms (SNPs). From these markers, SNP and microhaplotype markers were chosen that were informative for genetic stock identification (GSI) and kinship analysis. The final GTSeq panel contained 500 markers, including 197 microhaplotypes and 303 SNPs. Leave-one-out GSI simulations indicated that GSI accuracy should be greater than 80% in most jurisdictions. The false positive rates of parent-offspring and full-sibling kinship identification was found to be low. Finally, genotypes could be consistently scored among separate sequencing runs >94% of the time. Results indicate that the GTSeq panel that we developed should perform well for multijurisdictional walleye research throughout the Great Lakes region.</p>
Data Management and Sharing: Practices and Perceptions of Psychology Researchers
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Data from: Phosphorus budgets of intensively managed row crops at a long-term agroecosystem research site in the upper US Midwest
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Data and code from: Public participation in tropical conservation and environmental management research: Towards a locally grounded and reflexive practice
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Case study data of the paper: A methodological guide to observe local-scale geodiversity for biodiversity research and management
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Data for: A new GTSeq resource to facilitate multijurisdictional research and management of walleye Sander vitreus
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Supplementary material 3 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
Praxisbericht: Entwicklung eines Maßnahmenkatalogs zur Verbesserung des Forschungsdatenmanagements am Herder-Institut für historische Ostmitteleuropaforschung
Humboldt-Universität zu Berlin Research Data Management Survey Results. Comparing respondent groups "Professor" and "Research associate"
<p>This spreadsheet represents results of the research data management survey at Humboldt-Universität zu Berlin, comparing respondents groups "Professor" and "Research associate".</p>
Humboldt-Universität zu Berlin Research Data Management Survey Results
<p>This spreadsheet represents anonymized summary results of the research data management survey at Humboldt-Universität zu Berlin.</p>
The Role of Research University Libraries in Research Data Management: The Case of Türkiye
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Quantitative assessment of research data management practice - 2021
<p>This survey aims to investigate research data management practices at EPFL and integrate their results into specific academic services. The previous two editions, in collaboration with TU Delft, Cambridge University and Illinois University, were carried out in 2017 and 2019.</p> <p>The objective of these surveys is to collect information on researchers' habits in terms of management of their research data, as well as to identify their needs for data curation services/support. For this edition of the survey, a particular focus has been given to the ways in which they disseminate data and code.</p> <p>You can find here a file corresponding to the report, in PDF, highlighting the findings of the survey, plus the file of the underlying data, in CSV, and a file with the graphical representation of such data, in PDF.<br> <br> For more information about this survey, a description on how the survey might be re-used by other institutions, and RDM services offered by the EPFL Library, please contact <a href="mailto:researchdata@epfl.ch?subject=Concerning%20the%20EPFL%20survey%20on%20RDM%20practices">researchdata@epfl.ch</a>.</p>
Data literacy and research data management survey
<p>Data from Czech version of Data literacy and research data management multinational study.</p>
Supplementary material 7 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 "Sharing and publishing your data" row of the RDM rubric. Suggested points of customization are highlighted in yellow (discipline-specific) and red (institution-specific).
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.