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Dataset results
16 results for “RDM”
Results from the RDM Survey - LEARN project (December 2016)
<p>Data obtained from the open survey developed by the LEARN project (http://www.learn-rdm.eu/) as a self-assessment tool to assist institutions discover how ready they are for managing research data. This dataset replaces the first one published at http://doi.org/10.5281/zenodo.61903. The survey is based on the issues posed to institutions by the LERU Roadmap for Research Data published at the end of 2013, and available at: http://www.learn-rdm.eu/material/leru_roadmap_for_research_data<br> The survey has thirteen questions addressing the main elements to be taken into account in developing an institutional strategy for research data management. Each question has three possible answers representing green, yellow or red light. The more ‘green light’ responses recorded, the readier an institution probably is for managing its research data.</p> <p>The survey is available in English at http://learn-rdm.eu/en/rdm-readiness-survey/ and in Spanish at http://learn-rdm.eu/encuesta-rdm/</p>
Final Results from the RDM Survey - LEARN project (June 2017)
<p> </p> <p>Data obtained from the open survey developed by the LEARN project (http://www.learn-rdm.eu/) as a self-assessment tool to assist institutions discover how ready they are for managing research data. This dataset replaces the previous ones published at http://doi.org/10.5281/zenodo.61903 and http://doi.org/10.5281/zenodo.290635. The survey is based on the issues posed to institutions by the LERU Roadmap for Research Data published at the end of 2013, and available at: http://www.learn-rdm.eu/material/leru_roadmap_for_research_data<br> The survey has thirteen questions addressing the main elements to be taken into account in developing an institutional strategy for research data management. Each question has three possible answers representing green, yellow or red light. The more ‘green light’ responses recorded, the readier an institution probably is for managing its research data.</p> <p>The survey is available in English at http://learn-rdm.eu/en/rdm-readiness-survey/ and in Spanish at http://learn-rdm.eu/encuesta-rdm/</p>
Roles and responsibilities of stakeholders in the RDM lifecycle
<p>In efforts to better articulate the roles and responsibilities of key stakeholders (not exhaustive) involved in data management and stewardship throughout the research data management (RDM) lifecycle, this infographic was developed as part of RDM training. The infographic is an integrative adaptation of the Data Asset Framework Implementation Guide (JISC et al., 2009, page 3) and Research Data Governance & Materials Handling Policy (UNSW, 2019, page 10).</p>
Results from the RDM Survey - LEARN project (June 2016)
<p>First data obtained from the open survey developed by the LEARN project (http://www.learn-rdm.eu/) as a self-assessment tool to assist institutions discover how ready they are for managing research data. The survey is based on the issues posed to institutions by the LERU Roadmap for Research Data published at the end of 2013, and available at: http://www.learn-rdm.eu/material/leru_roadmap_for_research_data<br /> The survey has thirteen questions addressing the main elements to be taken into account in developing an institutional strategy for research data management. Each question has three possible answers representing green, yellow or red light. The more ‘green light’ responses recorded, the readier an institution probably is for managing its research data.</p> <p>The survey is available in English at http://learn-rdm.eu/en/rdm-readiness-survey/ and in Spanish at http://learn-rdm.eu/encuesta-rdm/</p>
A collection of AI generated images visualising various RDM aspects
<p>This publication contains images visualising various RDM aspects. These images were generated by the <a href="https://www.forschungsdaten.uni-bonn.de/en" target="_blank" rel="noopener">Research Data Service Center</a> team at the University of Bonn and are used in the workshop "Research Data Management: A Crash Course" conducted since 2021 by the Research Data Service Center. The slide deck is available as a related publication (see the related works section below for details).</p> <p>The images were generated with the help of <a href="https://help.openai.com/en/articles/8932459-creating-images-in-chatgpt">ChatGPT</a>. </p> <p>In this version, due to legal reasons, we changed the images.</p>
DCC RDM 2014 Survey Responses - Deduplicated
<p>Responses de-duplicated and analysed on questions about current and planned storage, staffing and resourcing of Research Data Management services.</p>
DCC RDM 2014 Survey Responses by Respondent's Role
<p>Responses analysed according to the respondents self-identified affiliation to service unit, e.g. Library, Research office, IT</p>
Imperial College London Library RDM Workflow
<p>Workflow diagram for Imperial College London RDM Service.</p>
COAR RDM (Research Data Management) Working Group
<p>This presentation was made in the internal meeting of COAR RDM (Research Data Management) Working Group. It covered building up support structure for RDM and the requirements for infrastructures including a use case from Vienna University.</p>
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>
RDM dataset
<p>This dataset contains images of the CNR-STIIMA ReDeManufacturing Lab, focusing on three classes of objects: conveyor, pallet, PCB.</p> <p>The dataset comprises real data as pictures of real assets and synthetic data generated using the <a href="https://difactory.github.io/DF/scenes/VL/RdmPlant.html">VR digital twin</a> of the lab.</p>
Dataset: 'Survey: Advancing RDM at CESAER institutions'
<p>This dataset includes the anonymized raw and analyzed data of responses to the Survey ‘Advancing RDM at CESAER institutions' </p> <p>The data accompanies the CESAER white paper ‘Advancing Research Data Management in universities of Science and Technology’ - <a href="https://urldefense.proofpoint.com/v2/url?u=http-3A__doi.org_10.5281_zenodo.3665372&d=DwMFAw&c=XYzUhXBD2cD-CornpT4QE19xOJBbRy-TBPLK0X9U2o8&r=EeFoIs6zuxqCZzFjrEp29mxBoxjuaBBBEDRDP5cCvEw&m=qay0oyShurnXA4yYZOsQX4FTI5e7U33p0HtERgRDbO8&s=q_e7Ui2_ct6NS1XUGcgZ1SQRo7VsSkFFG3pXqpVKWos&e=">http://doi.org/10.5281/zenodo.3665372</a></p> <p>The survey aimed at gaining a better understanding of the challenges faced by the RDM support teams at the CESAER institutions when providing services to researchers working in technical/engineering disciplines (see also <a href="https://www.cesaer.org/">https://www.cesaer.org/</a>). </p> <p>The specific target group of this survey were persons responsible for RDM support and/or services at the CESAER institutions.</p>
High-resolution residual dry matter (RDM) map for a California oak savanna/annual grassland derived from drone multispectral remote sensing imagery and in-situ grass biomass data
Open the record for dataset details and reuse information.
Educational Resources from UC Berkeley RDM Librarian Training Program
Open the record for dataset details and reuse information.
Institute Selection for the Study of RDM Practices in HEIs
<p>This dataset provides the information of higher education institutions (HEIs) regarding the presence of their research data management (RDM) website and research data repository. </p>
RDM - Session 8: Exercise 2
<p>Exercise 2 in session 8 of the RDM course</p>
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.