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103 results for “Research data management·”
Research Data Management Aspects - A Mindmap
<p>Just my personal mind-map of research data management aspects. No guarantee to be complete, feel free to use it and give me feedback.</p>
Research Data Management Framework - POC-Study: Guideline and protocol
<p>Dataset for the following paper:<br><br>Proof-Of-Concept-Studie für das FDM in den Ingenieur:innenwissenschaften</p> <p><strong>Ein Forschungsdatenmanagement-Rahmenwerk</strong><br>T. Hamann, C. Florides, A. Abdelrazeq, R. H. Schmitt</p> <p>Forschungsdatenmanagement (FDM) gewinnt seit Jahren an Bedeutung. Das Ziel, Daten wiederverwendbar aufzubereiten und nachzunutzen anstatt sie aufwändig neu zu erheben, wird von Forschenden der deutschen Ingenieur:innenwissenschaften jedoch nur selten verfolgt. Um dem entgegenzuwirken, wurde ein Rahmenwerk für das FDM in den Ingenieur:innenwissenschaften entwickelt. In einer Proof-Of-Concept-Studie soll dieses nun erstmals anhand des Forschungsprojekts KIOptiPack validiert werden.</p> <p><strong>A Research Data Management Framework</strong></p> <p>Research data management (RDM) has been gaining in importance for years. However, the goal of preparing data sustainably and reusing existing data instead of laboriously collecting it from scratch is rarely pursued by researchers in the German engineering sciences. To counteract this, a framework for RDM in the engineering sciences was developed. In a proof-of-concept study, this framework will be validated for the first time using the research project KIOptiPack.<br>Stichwörter: Forschung, Informationsmanagement, Digitalisierung<br><br><br>The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) - project number 442146713.</p>
Data from Finnish Research Data Management Training Survey 2020-2021
<p>This is the survey data used in The Finnish Research Data Management Training Survey 2020-2021. The survey was sent to 74 Finnish research organizations of which 36 responded. The aim of the report was to gain a deeper understanding of what kind of research data management (RDM) training activities are provided by different Finnish organizations.</p> <p> </p>
Survey used and data gathered for research into adoption of carbon management strategies amongst universities E Lewis-Brown et al 2022
<p>Survey used and data gathered for research into adoption of carbon management strategies amongst universities 2022, which forms part of a PhD thesis and will be submitted for publication in a journal. </p>
Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey - Extended Data 4 - Analysis Data Sheet
<p>This dataset is extended data 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>- Data Analysis Sheet and results table</p> <p>Note: The data is anonymized (i.e., all IP addresses as well as personal comments were deleted)</p> <p>The revised version was published after the peer-review process of the original article on zenodo.org</p>
Research Data Management: Data Lifecycle
<p>This diagram has been created by the team of data steward at the University of Bologna (Alma Mater Studiorum - Università di Bologna, UniBo) in October 2022. It proposes a data lifecycle model inspired by the University of Virginia Library’s model (<a href="https://guides.lib.virginia.edu/c.php?g=515290&p=3522215">https://guides.lib.virginia.edu/c.php?g=515290&p=3522215</a>). It has been developed in parallel to the Research Data Management Decision Tree, available here: <a href="https://doi.org/10.5281/zenodo.7190004">https://doi.org/10.5281/zenodo.7190004</a></p> <p>Emphasis is put on a careful planning of data management, which should always precede data collection (and re-use of existing data). A reference to the opportunity of creating and maintaining a Data Management Plan (DMP) has been added. This is not always compulsory, but is increasingly required by funders.</p> <p>Collecting, analysing and storing data (and possibly sharing them with a group) remain at the heart of the lifecycle, constituting what we called “data handling”. Here, the process is not linear, and researchers tend to move from one stage to another in a recursive fashion. </p> <p>At any point during data handling, it is possible to deposit data: the responsibility for storing and safekeeping is passed on to the repository, the time-scale shifts from short-term to long-term, and data become citable (and possibly discoverable) by the wider scholarly community and beyond. Importantly, deposited data can always be re-used as the basis for a new round of collection/analysis/storage that will lead to a new deposit, and so on.</p>
Dataset for: Developing research data management services and support for researchers: a mixed methods study
<p><strong>Overview</strong></p> <p>This dataset contains the raw data for the manuscript: <br> Perrier L, Barnes L. Developing research data management services and support for researchers: a mixed methods study. Partnership. 2018;13(1). doi: doi.org/10.21083/partnership.v13i1.4115.</p> <p>Full-text available at: <a href="https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202">https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202</a></p> <p><strong>Data and Documentation Files</strong></p> <p>Five files make up the dataset: </p> <ol> <li>Coding Scheme: RDMServicesSupport_Codes.txt</li> <li>Transcript, Focus Group 01 (anonymized): RDMServicesSupport_FocusGroup01.pdf</li> <li>Transcript, Focus Group 02 (anonymized): RDMServicesSupport_FocusGroup02.pdf</li> <li>Transcript, Focus Group 03 (anonymized): RDMServicesSupport_FocusGroup03.pdf</li> <li>Transcript, Focus Group 04 (anonymized): RDMServicesSupport_FocusGroup04.pdf</li> </ol> <p>Contact: Laure Perrier: <a href="https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202">orcid.org/0000-0001-9941-7129</a></p>
Identifying and Implementing Relevant Research Data Management Services for the Library at the University of Dodoma, Tanzania
<p>This data set presents the results of research conducted at the University of Dodoma, Tanzania. The purpose of the research was to identify and report on relevant RDM services that need to be implemented so that researchers and university management could collaborate and make our research data accessible to the international community.</p> <p>The data set was used to support both the mini-dissertation as well as a paper published in the Data Science Journal. The journal paper presents findings on important issues for consideration when planning to develop and implement RDM services at a developing country, academic institution. The paper also mentions the requirements for the sustainability of these initiatives.</p>
Guideline for a FAIR Cultural Studies Research Data Management
<p>Dies ist die <strong>Datenpublikation</strong> (Ausgangsdateien, Abbildungen, Materialien zur Nachnutzung) für die NFDI4Culture Handreichung „Handreichung für ein FAIRes Management kulturwissenschaftlicher Forschungsdaten“.</p> <p>Die <strong>Online-Handreichung</strong> ist verfügbar unter <a href="https://nfdi4culture.de/go/E3625">https://nfdi4culture.de/go/E3625</a>.</p> <p>Als <strong>PDF</strong> ist diese Handreichung verfügbar unter <a href="https://doi.org/10.5281/zenodo.7716941">https://doi.org/10.5281/zenodo.7716941</a>.</p>
Questionnaire on the current status of Research Data Management in Mecklenburg-Vorpommern
<p>This dataset contains the questionnaires used in order to survey the current status of research data management in Mecklenburg-Vorpommern (MV). One questionnaire was used for the University of Rostock (<em>FDM_in_VREs_UR</em>) while the other was used for all other research institutes in MV. While the survey was conducted online, this dataset contains a PDF export as well as the Evasys questionnaire export which was used to conduct the online survey. Note that the PDF version does not cover the filter functionality which was used in the Evasys online survey.</p>
Persistence and interoperability in FAIR research data management - 1st FAIRsFAIR webinar
<p><strong>Webinar page: </strong>https://zenodo.org/record/3726149#.XnpRJXJ7k1k</p> <p>The main principles of FAIR data (findable, accessible, interoperable and reusable) have received wide acceptance in scientific data management circles. The work of further defining these principles and applying them in day-to-day knowledge sharing is ongoing.</p> <p>The FAIRsFAIR working group "<a href="https://www.fairsfair.eu/fair-practices-semantics-interoperability-and-services">FAIR practices: semantics, interoperability and services</a>" recently published the <a href="http://zenodo.org/record/3557381#.XiVxKSN7nIV">first iteration</a> of three annual reports on the state of FAIR in European scientific data. Based on studies of public information, especially EOSC infrastructure efforts, and on limited surveying and interviews, the report reviews and documents commonalities between infrastructures and obstacles to semantic interoperability - that is the use of metadata and persistent identifiers to enhance dissemination across infrastructures.</p>
Data Management and Sharing: Practices and Perceptions of Psychology Researchers
<p class="CxSpFirst">Research data is increasingly viewed as an important scholarly output. While a growing body of studies have investigated researcher practices and perceptions related to data sharing, information about data-related practices throughout the research process (including data collection and analysis) remains largely anecdotal. Building on our previous study of data practices in neuroimaging research, we conducted a survey of data management practices in the field of psychology. Our survey included questions about the type(s) of data collected, the tools used for data analysis, practices related to data organization, maintaining documentation, backup procedures, and long-term archiving of research materials. Our results demonstrate the complexity of managing and sharing data in psychology. Data is collected in multifarious forms from human participants, analyzed using a range of software tools, and archived in formats that may become obsolete. As individuals, our participants demonstrated relatively good data management practices, however they also indicated that there was little standardization within their research group. Participants generally indicated that they were willing to change their current practices in light of new technologies, opportunities, or requirements. </p>
IPBES Data Management Tutorials - Session 4.1: General introduction to the management of active research data
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>Data management of active research data </em>chapter will provide an introduction for IPBES experts on how to manage data while actively being used, analyzed, and produced to fulfill the criteria of the IPBES data management policy.</p> <p>This session,<em> General introduction to the management of active research data</em>, covers what active data management is, why it is important, and considerations for key decisions made in this process. </p>
Research Data Management: The Numbers
<p>Infographic illustrating the numbers associated with research data management. In particular the associated costs, the value and the volume of data</p>
Research Data Management Workflow
<p>This diagram illustrates essential ideas and useful tools during the RDM cycle.</p>
Sahana et al. Supplementary Data for Global Transboundary River Research: Databases, Case Study Analysis, and Regional Statistics for Sustainable Management
<p><span>This dataset supports our comprehensive review article on transboundary river research, exploring its implications for sustainable management worldwide. Utilizing machine learning, we analyzed 4,237 publications and conducted an in-depth desk review of 325 selected papers, examining a total of 4,713 case studies spanning 286 river basins globally. The study provides critical insights into upstream, midstream, and downstream regions, offering a complete view of challenges and opportunities in transboundary river management. Supplementary Data 1 contains the main database used in this study, sourced from Scopus, Web of Science, and Google Scholar. Additionally, Supplementary Data 2 and 3, included in the spreadsheet, offer statistics and further resources essential for understanding regional and cross-regional dynamics in river basin governance. These supplementary resources include key statistics, case study metadata, and tools, helping to facilitate a deeper exploration of basin-specific and global trends in transboundary water management. This collection of data and resources provides a valuable foundation for researchers and policymakers in advancing sustainable transboundary river management practices.</span></p>
Research Data Management Life Cycle
<p>An overview of the research data management life cycle with proper licensing.</p> <p> </p>
Case study data of the paper: A methodological guide to observe local-scale geodiversity for biodiversity research and management
<p><span>Current global environmental change calls for comprehensive and complementing approaches for biodiversity conservation. According to recent research, consideration of the diversity of Earth's abiotic features (i.e., geodiversity) could provide new insights and applications into the investigation and management of biodiversity. However, methods to map and quantify geodiversity at local scale have not been developed although this scale is important for conservation planning. </span><span>Here, we introduce a field methodology for observing plot-scale geodiversity, pilot the method in an Arctic-alpine tundra environment, provide empirical evidence on the plot-scale biodiversity-geodiversity relationship and give guidance for practitioners on the implementation of the method</span><span>.</span></p> <p><span>The field method is based on observation of geofeatures, i.e., elements of geology, geomorphology, and hydrology, from a given area surrounding a location of species observations. As a result, the method provides novel information on the variation of abiotic nature for biodiversity research and management. The method was piloted in northern Norway and Finland by observing geofeatures from 76 sites at three scales (5, 10 and 25 m radii). To explore the relationship between measures of biodiversity and geodiversity, the occurrence of vascular plant species was recorded from 2 m x 2 m plots at the same sites.</span></p> <p><span>According to the results, vascular plant species richness was positively correlated with the richness of geofeatures (R<sub>s</sub> = 0.18–0.59). The connection was strongest in habitats characterized by deciduous shrubs. The method has a high potential for observing geofeatures without extensive geological or geomorphological training or field survey experience and </span><span>could be applied by conservation practitioners</span><span>.</span></p> <p><span>Consideration of geodiversity in understanding, analysing and conserving biodiversity could facilitate environmental management and ensure the long-term sustainability of ecosystem functions. With the developed method, it is possible to cost-efficiently observe the elements of geodiversity that are useful in ecology and biodiversity conservation. Our approach can be adapted in different ecosystems and biodiversity investigations. The method can be adjusted depending on the abiotic conditions, expertise of the observer(s), and the equipment available.</span></p>
Research Data Management Decision Tree
<p>Researchers often face the same stumbling blocks. To support them, we have developed a <strong>RDM Decisional Tree</strong> starting from the fundamental bricks of the data lifecycle and posing a series of questions to help researchers navigate:</p> <p>1) the domain specific nature and origin of the data they are handling;</p> <p>2) Privacy/Ethics requirements (e.g. GDPR);</p> <p>3) Intellectual Property Rights;</p> <p>4) active data storage;</p> <p>5) long-term deposit and preservation.</p> <p><span>This diagram has been created by the team of data steward at the University of Bologna (UNIBO, Alma Mater Studiorum - Università di Bologna) in October 2022. </span>It has been developed in parallel to the Research Data Management: Data Lifecycle, available here: <a href="https://doi.org/10.5281/zenodo.7249050">10.5281/zenodo.7249050</a> </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>
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.