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103 results for “research data management”
Quantitative Assessment of Research Data Management Practices - 2023
<p>This survey investigates <strong>Research Data Management (RDM) practices across five Swiss higher education institutions</strong>, including EPFL, ETH Zürich, Eawag, FHNW, and DaSCH, with the goal of gathering insights into how researchers manage data and code throughout the lifecycle of their projects, as well as using such findings to inform academic services related to RDM for researchers. Previous surveys, conducted at EPFL in 2017, 2019, and 2021, primarily focused on the planning and publishing stages of the research data lifecycle, such as data management planning and open data dissemination. The 2023 edition expanded to other institutes and places a stronger emphasis on <strong>Active Data Management</strong>, particularly during research projects, including a range of topics such as:</p> <ul> <li>Storage and backup solutions</li> <li>Data and code sharing platforms</li> <li>Documentation and metadata usage</li> <li>Compliance with legal and ethical standards</li> <li>Long-term data preservation strategies</li> <li>Use of open formats and open-source software</li> <li>Adoption of Data Management Plans (DMPs)</li> </ul> <p>This dataset was collected using the SurveyHero platform in compliance with GDPR and Swiss FADP regulations. enuvo GmbH acted as the data processor under a signed Data Processing Agreement. No personal identifiable information was purposefully collected, and data has been aggregated to further ensure respondents’ privacy.</p> <p>Included in this dataset:</p> <ul> <li>A CSV and XLSX file with the aggregated, anonymized data from the survey.</li> <li>Two PDF files containing graphical representations of the survey results, automatically generated by the SurveyHero platform in portrait and landscape mode.</li> <li>A README file providing context.</li> </ul> <p>This dataset is made openly available under the CC-BY 4.0 license. Users are encouraged to reuse it with appropriate attribution.</p>
Multi-stakeholder research data management training as a tool to improve the quality, integrity, reliability and reproducibility of research: Quantitative data of the post-course surveys
<p>Data contains doctoral students' and postdoc researchers' (n=168) self-ratings of their RDM competencies before and after the 3 ECTS credits "Basics of Research Data Management" (BRDM) trainings held 2019-2021 in the University of Turku and Åbo Akademi University, Finland. Moreover, data contains respondents' self-reported further learning needs.</p>
Quantitative assessment of research data management practice - University of Bordeaux
<p>This survey was run at the University of Bordeaux in January 2019 using the questionnaire "Quantitative assessment of research data management practice" :</p> <p>Teperek, M., Krause, J., Lambeng, N., Blumer, E., van Dijck, J., Eggermont, R., … der Velden, Y. T. (2019). Quantitative assessment of research data management practice. Retrieved from : <a href="https://osf.io/mz3fx/">https://osf.io/mz3fx/</a></p> <p>The questionnaire included all the primary and secondary common questions, institution-specific questions regarding services and file sharing (EPFL questions), institution-specific questions for profile information.</p> <p>Data from the 425 responses collected are published here.</p> <p>Details regarding data collection and curation are included in the README file.</p> <p> </p>
Research Data Management and Sharing for images: beautiful fountains require ugly piping!
<p>The consensus is clear: research data funded by public resources should be shared. Globally, the advantages of sharing research data are widely recognized. It promotes transparency and validation, reduces redundant efforts, accelerates discovery, enhances equity, and increases the impact of research through collaboration and efficient use of resources.</p> <p>Image data, however, presents unique challenges. Advanced technologies produce large, multimodal, and multiplexed datasets that span multiple targets across various spatiotemporal scales.</p> <p>This image data comes from a range of sources—such as optical, electron microscopy, and medical imaging—each with specific technical requirements. Managing this complexity is a daunting task without global metadata standardization as well as robust Research Data Management and Sharing (RDMS) cyberinfrastructure to bring it all together.</p> <p>The figure illustrates a common issue: while the importance of the <strong>“beautiful fountains”</strong> of scientific discoveries and medical treatments is widely understood, fewer people recognize the <strong>need to invest in building the often ignored “ugly plumbing” </strong>required to build a strong RDMS cyberinfrastructure.</p> <p> </p>
Pirate Illustrations for Research Data Management Training
<p>This collection of icons and comics was created to illustrate a workshop on Data Management Plans (<a href="https://doi.org/10.5281/zenodo.5575920">https://doi.org/10.5281/zenodo.5575920</a>). It is provided here to allow further reuse, for example to illustrate presentations.</p> <p>The theme of this collection is revolving around pirates, their accessories, and maritime items in general.</p> <p>Created by Jeanne Wilbrandt.</p>
Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey - Extended Data 3 - Raw Data survey entries
<p>This document provides extended, supplementary data and information to the manuscript "Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey" by Schmidt C., Hanne J, Moore J, Meesters C, Ferrando-May E, Weidtkamp-Peters S, and members of the NFDI4BIOIMAGE initiative. [version 1; peer review: awaiting peer review] F1000Research 2022, 11:638, https://doi.org/10.12688/f1000research.121714.1</p> <p>This extended data includes:</p> <p>- The raw dataset of survey entries, anonymized (IP addresses and personal comments deleted)</p>
Including Data Management in Research Culture Increases the Reproducibility of Scientific Results
<p><strong>General Information:</strong></p> <p>This dataset contains artifacts related to Riedel et al. (2022) (https://dx.doi.org/10.18420/inf2022_114). Here, we investigate the reproducibility of 108 research papers published between 2017 and 2021 by members of the Collaborative Research Center 1294 – Data Assimilation. To that end, we relate to a previous study by Stagge et al. (2019) that relies on a questionnaire that we extended. </p> <p>The publication by Stagge et al. (2019) is available here: https://doi.org/10.5281/zenodo.2562268<br> The dataset by Stagge et al. (2019) is available here: https://doi.org/10.1038/sdata.2019.30</p> <p>This dataset contains the questionnaire that we used to evaluate the reproducibility of scientific publications, a csv file containing the questionnaire’s answers, and a Jupyter notebook script to evaluate the given data.</p> <p><strong>Run the code:</strong></p> <p>To run the code, you must install Anaconda [1] and then open the jupyter notebook. All necessary libraries are listed in "requirement.txt". </p> <p>Alternatively, you can import the .ipyab file in the colab [2] and run it. </p> <p><br> [1]. https://www.anaconda.com/<br> [2]. https://research.google.com/colaboratory/<br> </p>
Research Data Management Lifecycle
<p>The Research Data Management (RDM) lifecycle describes the various phases of a research project from a data management perspective. The Cycle diagram illustrates all the steps with multiple levels of granularity and details. The text free images can be used for other purposes where a cycle diagram is needed.</p>
Research data management in the German-speaking Sports Sciences - Survey on the Status Quo
<p>The data set contains survey data on the status quo of research data management within the German-speaking sports science community. The survey was conducted as an online survey in the period from August 16<sup>th</sup> to September 30<sup>th</sup>, 2023.</p>
Assessing Quality Variations in Early Career Researchers' Data Management Plans: Quantitative Data of the Content Analysis
<p>The data includes the numerical results of the ranking of the data management plans created during the Basics of Research Data Management (BRDM) courses worth 3 ECTS credits in the years 2020 - 2022. The ranking was made using the Finnish DMP Evaluation Guidance (https://doi.org/10.5281/zenodo.4729831). Additionally, the data contains the results of the analysis of the best RDM practices included in the DMPs.</p> <p>Note 1: The comma-separated coded CSV version 1 (5.2.2024) may not open correctly on MacOS. You can use the comma-delimited CSV file version 2 or 3 (31.5.2024).</p> <p>Note 2: Versions 1 (Quality_variations_in_ECRs_DMPs_data) and 3 (Quality_variations_in_ECRs_DMPs_data_ver_3) contain evaluations of DMPs, best practices for data management, as well as methods for data sharing, storage, and preservation. In version 2 (Quality_variations_in_ECRs_DMPs_data_ver_2), the methods for data sharing, storage, and preservation are missing.</p> <p>Data is related to the research article https://doi.org/10.2218/ijdc.v18i1.873.</p>
Dataset for: Exploring the experiences of academic libraries with research data management: a meta-ethnographic analysis of qualitative studies
<p><strong>Overview</strong></p> <p>This dataset contains the raw data for the manusript:<br> Perrier L, Blondal E, MacDonald H. Exploring the experiences of academic libraries with research data management: a meta-ethnographic analysis of qualitative studies. 2018; 40(3-4): 173-183. doi: 10.1016/j.lisr.2018.08.002</p> <p>Full-text available at: <a href="https://doi.org/10.1016/j.lisr.2018.08.002">https://doi.org/10.1016/j.lisr.2018.08.002</a> </p> <p><strong>Data and Documentation Files</strong></p> <p>Five files make up the dataset:</p> <ol> <li>Data Dictionary: RDMMetaEthnography_DataDictionary_v1.pdf</li> <li>Data Abstraction Sheet: RDMMetaEthnography_StudyCharacteristics.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_ParticipantCharacteristics.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_Outcomes.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_COREQ,csv</li> </ol> <p>Contact: Laure Perrier: <a href="https://orcid.org/0000-0001-9941-7129">orcid.org/0000-0001-9941-7129</a></p>
Quantitative assessment of research data management practice
<p>This survey aims to investigate research data management practices in academic institutions. The survey comprises questions common to all institutions as well as institution-specific ones. Common questions were drafted in the frame of a collaboration between several RDM services: Tu Delft (team effort), EPFL (team effort), University of Cambridge (notably Marta Busse) and University of Illinois (notably Heidi Imker). The first survey was run by TU Delft and EPFL only end of 2017. In total, 1263 responses where collected (680 from TU Delft, 235 from EPFL and 348 from the University of Cambridge) and are published here. The results of each institution are provided in Microsoft Excel 2007 (XLSX) format. Consolidated results are provided in CSV format</p> <p>The first lines of the CSV file contains the question asked to researchers. Each further line contains the response of a researcher; answers to institution-specific questions are set to N/A for researchers of the other institutions. Column delimiters are commas(,), quote chars are double-quotes (") and subfield separators are semi-columns (;). The text encoding is UTF-8.</p> <p>More information about this survey as well as the exact survey questions and a detailed description how the survey might be re-used by other institutions is available on the project page on the Open Science Framework: htts://osf.io/mz3fx/ For any questions contact datastewards@tudelft.nl or researchdata@epfl.ch</p>
Research data management consulting requests at Charité - Universitätsmedizin Berlin
<p><strong>The dataset documents</strong> <strong>consulting requests and corresponding consultations on topics related to research data management </strong>at the Charité - Universitätsmedizin Berlin, a university hospital and large biomedical research institution. Version v2.0 includes requests between late 2018 and October 2024.</p> <p>Please note that the documentation is not complete. Sometimes it is detailed, sometimes very brief, and rarely notes are completely absent. In many cases, I made detailed notes outside of this table, and these are not included in the dataset.</p> <p>The shared files all contain the same information or a subset of it. Different file formats have been shared to facilitate reuse and text search.</p> <p>See the readme file for detailed description of data fields and further disclaimers.</p>
Doctoral Students' Educational Needs in Research Data Management: Quantitative Data of Perceived Importance and Current Competencies
<p>These data sets include numerically coded answers to Likert-like scale questions concerning the importance and perceived current research data management competencies of doctoral students. Interviewees were 35 doctoral students and faculty members. Interview forms are attached. The data is connected with the research article: https://doi.org/10.2218/ijdc.v16i1.684</p>
Data From: Toward a Better Data Management Plan: The Impact of DMPs on Grant Funded Research Practices
<p>Researchers from Montana State University analyzed 186 National Science Foundation (NSF) data management plans, using the Data Management Plans As a Research Tool (DART) rubric.</p>
Research Beyond the Lab, Spring Term 2022, Global Health Engineering, ETH Zurich. Raw data and analysis-ready derived data on waste management in public spaces in Zurich, Switzerland.
<p>This repository contains all raw and derived data produced as part of the <a href="https://rbtl-fs22.github.io/website/">ETH Zurich course "Research Beyond the Lab: Open Science and Research Methods for a Global Engineer" (151-8102-00L)</a> offered in spring term 2022.</p> <p>Students were assigned teams of four to conduct a collaborative research project broadly addressing the theme of “Trash in the Public Spaces of Zurich” in collaboration with <a href="https://www.stadt-zuerich.ch/ted/de/index/entsorgung_recycling.html">Entsorgung & Recycling Zürich (ERZ)</a>, the waste management department at Stadt Zürich.</p> <p>Research methods and design are taught in the first half of the course. Surveys and a waste characterisation study are then designed based on the research questions students have developed in their respective teams. The collected raw data is used in the course to teach principles of research data management, tidy data structures, reproducible research with R & RStudio, and collaboration and version control with Git & GitHub.</p>
Research Data Management and data protection in the Social Sciences [Workshop recording]
<p>This online workshop organized by The Austrian Social Science Data Archive (AUSSDA) focused on the Research Data Management basics, Data Management Plans and common data protection issues in the Social Sciences.</p> <p>The first part of the workshop was dedicated to RDM basics and Data Management Plans (DMPs). In many projects, DMPs are mandatory deliverables that need to be submitted at the beginning of a project and are updated throughout the project life cycle. During the workshop, it was explained which aspect funders expect to be part of DMPs in Social Sciences and how researchers can benefit from (writing) these documents.</p> <p>In the second part of the workshop, data protection issues that are common in Social Sciences were addressed and how they can be handled. In particular, differences in the curation of quantitative and qualitative data need in order to comply with data protection regulations in general and AUSSDA deposit guidelines in particular. Presentation on how AUSSDA scans quantitative data for potential data protection violations using STATA and gives participants the opportunity to test the code on their own data and devices.</p> <p>The video is available on<a href="https://www.youtube.com/watch?v=DhiL9J-Iwqg"> the CESSDA Training YouTube channel</a>.</p> <p> </p>
Next Steps: How the FDNext Project is Tackling Research Data Management … and Farewell to Emma
<p>In this episode we talk to Kerstin Helbig about the research data management (RDM)project FDNext, which is also where our co-host Emma Harris' new role will be based. We discussed what the approach of FDNext is, the challenges of implementing effective RDM, and how it fits into the wider framework of Open and FAIR Data initiatives. </p> <p><strong>Episode Links</strong></p> <p><a href="https://www.forschungsdaten.org/index.php/FDNext">FDNext (German language)</a></p> <p><a href="https://zenodo.org/record/4071471#.X791NmhKhPY">FDMentor RDM Train-the-Trainer Concept</a></p> <p><a href="https://www.researchgate.net/profile/Kerstin_Helbig">Kerstin Helbig</a></p> <p><a href="https://www.linkedin.com/in/emma-a-harris-6bb865123/">Emma Harris</a></p>
Dataset for: Research data management in academic institutions: a scoping review
<p><strong>Overview</strong></p> <p>This dataset contains the raw data for the manuscript: <br> Perrier L, Blondal E, Ayala AP, Dearborn D, Kenny T, Lightfoot D, Reka R, Thuna M, Trimble L, MacDonald H. Research data management in academic institutions: A scoping review. PLOS One. 2017 May 23;12(5):e0178261. doi: 10.1371/journal.pone.0178261.</p> <p>Full-text available at: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0178261 ">http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0178261 </a></p> <p><strong>Data and Documentation Files</strong> </p> <p>Five files make up the dataset: </p> <ol> <li>Data Dictionary: RDMScopingReview_DataDictionary.pdf</li> <li>Data Abstraction Sheet: RDMScopingReview_StudyCharacteristics.csv</li> <li>Data Abstraction Sheet: RDMScopingReview_Setting.csv</li> <li>Data Abstraction Sheet: RDMScopingReview_DataCollectionTools.csv</li> <li>Data Abstraction Sheet: RDMScopingReview_Outcomes.csv</li> </ol> <p>Contact: Laure Perrier: <a href="https://orcid.org/0000-0001-9941-7129">orcid.org/0000-0001-9941-7129</a></p>
Research Data Management in Selected Health Research Institutions in Uganda
<p>This data set was collected from Researchers in three purposively selected health reseach Institutions in Uganda. The purpose of the study was to explore compliance to FAIR data princiles and Open science initiative given the increasing dependence on donor funding and need to fulfill the requirement for good research practices. </p>
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