PapersFlow Research Brief
Research Data Management Practices
Research Guide
What is Research Data Management Practices?
Research Data Management Practices are the systematic processes for organizing, storing, sharing, and stewarding scientific data to enable reuse, compliance with FAIR principles, and support for open science.
Research Data Management Practices encompass data sharing, metadata standards, digital repositories, and data citation, with 329,639 works in the field. The FAIR Guiding Principles for scientific data management and stewardship by Wilkinson et al. (2016) provide a foundational framework cited 16,387 times for making data findable, accessible, interoperable, and reusable. Tools like REDCap by Harris et al. (2019) and Galaxy by Goecks et al. (2010) facilitate practical implementation in biomedical and life sciences research.
Topic Hierarchy
Research Sub-Topics
FAIR Data Principles Implementation
This sub-topic addresses strategies for making research data Findable, Accessible, Interoperable, and Reusable across disciplines. Researchers develop guidelines, tools, and case studies for compliance in repositories.
Research Data Sharing Barriers
Investigating institutional, legal, cultural, and technical obstacles to open data sharing in academia. Surveys and interviews identify solutions like policy incentives and trust-building measures.
Metadata Standards for Scientific Data
Focusing on schema development, ontologies, and best practices for descriptive metadata in digital repositories. Studies evaluate interoperability across domains like ecology and biomedicine.
Data Reuse in Computational Workflows
Examining practices for integrating shared datasets into reproducible pipelines using tools like Nextflow and Galaxy. Research assesses reuse metrics and workflow portability.
Data Citation Practices and Metrics
This area explores standards for citing datasets, tracking citations, and incentivizing data publication. Analyses use bibliometric methods to quantify impact and adoption.
Why It Matters
Research Data Management Practices enable reproducible research and data reuse across disciplines, as demonstrated by Nextflow for reproducible computational workflows (Di Tommaso et al., 2017, 3855 citations) and Galaxy's platform for transparent genomic research (Goecks et al., 2010, 3491 citations). In policy contexts, Canada's Tri-Agency Research Data Management Policy requires data management plans for funding, with institutions like SFU mandating them for strategic awards. Tools such as RDMO support project planning, while frameworks like NASA's modern-dgf ensure auditable governance aligned with community standards, maximizing public funding value and ethical research conduct.
Reading Guide
Where to Start
'The FAIR Guiding Principles for scientific data management and stewardship' by Wilkinson et al. (2016) first, as it establishes the core FAIR framework cited 16,387 times that underpins all modern practices.
Key Papers Explained
Wilkinson et al. (2016) 'The FAIR Guiding Principles for scientific data management and stewardship' sets metadata and accessibility standards, which Harris et al. (2019) 'The REDCap consortium: Building an international community of software platform partners' implements in secure platforms (21,723 citations), while Goecks et al. (2010) 'Galaxy: a comprehensive approach...' builds reproducible workflows atop these principles (3491 citations). Di Tommaso et al. (2017) 'Nextflow enables reproducible computational workflows' (3855 citations) and Ewels et al. (2020) 'The nf-core framework...' extend them to community-curated pipelines. Wessel et al. (2013) 'Generic Mapping Tools: Improved Version Released' applies them in geoscience data handling.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Recent preprints like 'Ten simple rules for effective research data management' address institutional storage for reuse, while 'Research Data Management (Health Sciences): Overview' covers planning to closing phases. News on Canada's Tri-Agency Policy and SFU's DMP requirements signal policy-driven mandates, with tools like RDMO and ELIXIR RDMkit advancing maturity assessments.
Papers at a Glance
| # | Paper | Year | Venue | Citations | Open Access |
|---|---|---|---|---|---|
| 1 | The REDCap consortium: Building an international community of ... | 2019 | Journal of Biomedical ... | 21.7K | ✓ |
| 2 | Welcome to the Tidyverse | 2019 | The Journal of Open So... | 19.2K | ✓ |
| 3 | The FAIR Guiding Principles for scientific data management and... | 2016 | Scientific Data | 16.4K | ✓ |
| 4 | Generic Mapping Tools: Improved Version Released | 2013 | Eos | 3.9K | ✕ |
| 5 | Nextflow enables reproducible computational workflows | 2017 | Nature Biotechnology | 3.9K | ✕ |
| 6 | Galaxy: a comprehensive approach for supporting accessible, re... | 2010 | Genome biology | 3.5K | ✓ |
| 7 | The nf-core framework for community-curated bioinformatics pip... | 2020 | Nature Biotechnology | 3.4K | ✕ |
| 8 | XSEDE: Accelerating Scientific Discovery | 2014 | Computing in Science &... | 3.3K | ✓ |
| 9 | Networks of Scientific Papers | 1965 | Science | 3.0K | ✕ |
| 10 | The bibliometric analysis of scholarly production: How great i... | 2015 | Scientometrics | 2.8K | ✓ |
In the News
Is Canada poised to implement a data deposit requirement?
In 2021, Canada’s Tri-Agency introduced their Research Data Management Policy ( Government of Canada 2021*a* ), building upon an earlier 2016 Statement on Principles of Digital Data Management ([Go...
Research Data Capacity, Management and Sharing – CIHR
** Tri-Agency Research Data Management Policy **
Research data management: Grant and institutional ...
- **Data management plans:** An initial set of funding opportunities are subject to research data management requirements, including submitting a data management plan (DMP). Contact SFU Institution...
Bridging the Research Data Management Gap
With the rapid growth of digital research, managing data effectively has never been more important. Proper data management ensures research is conducted ethically, maximizes the value of public fun...
2026 International Joint Initiative for Research Harnessing ...
and best practices, data arising from their research (see also the tri-agency policies and guidelines on Research Data Management ).
Code & Tools
An open modern comprehensive, auditable science data management and governance framework aligned with policy guidelines as well as community best p...
# RDM Maturity Model The**Research Data Management (RDM) Maturity Model**is a framework designed to assess the capabilities of institutions in ma...
RDMkit is an online guide containing good data management practices applicable to research projects from the beginning to the end. Developed and ma...
RDMO is a tool to support the systematic planning, organisation and implementation of the data management throughout the course of a research proje...
reNEW-Data-Champions / ** Research-Data-Management ** Public - Notifications You must be signed in to change notification settings - Fork\ 0 - St...
Recent Preprints
Ten simple rules for effective research data management
topics. Certain institutional-level or holistic RDM matters remain unaddressed. Universities and research institutes increasingly seek to store data from individual projects systematically for reus...
Best Practices to Managing Research Data - Research Guides
This guide contains information about research data management and best practices for faculty, researchers and graduate students * Home * Data Management Plans * DMPTool * Best Practices to Mana...
Research Data Management (Health Sciences): Overview
This primer provides a high-level overview for researchers on research data management and sharing practices during the planning, implementation and closing phases of typical research projects. #...
Research Papers - Data Science Journal
Proper research data management facilitates collaboration and promotes research progress through the synergetic process of publishing and reusing research data. Despite these advantages, extant lit...
Research Data Management Overview: Introduction
Includes best practices, resources, and tools for managing and sharing research data. * Introduction * Data Management Plans * File Organization * Metadata and Documentation * Data Storage Togg...
Latest Developments
Recent developments in Research Data Management Practices research include the identification of emerging trends such as the integration of artificial intelligence and real-time analytics to streamline workflows, the emphasis on data quality, governance, and literacy, and the shift towards transforming long-term research data strategies to enhance innovation, sustainability, and data accessibility (montecarlodata.com, dataversity.net, campustechnology.com). Additionally, the release of NIST's version 2.0 of the Research Data Framework in February 2024 marks a significant step in standardizing research data practices (nist.gov).
Sources
Frequently Asked Questions
What are the FAIR principles in research data management?
The FAIR principles, outlined in 'The FAIR Guiding Principles for scientific data management and stewardship' by Wilkinson et al. (2016), require data to be Findable, Accessible, Interoperable, and Reusable. These guidelines promote data stewardship by standardizing metadata and identifiers. They have been cited 16,387 times and underpin practices in digital repositories.
How does REDCap support research data management?
REDCap, described in 'The REDCap consortium: Building an international community of software platform partners' by Harris et al. (2019), is a software platform for secure data capture and management in research. It builds an international community of partners with 21,723 citations. Researchers use it for clinical and collaborative studies.
What role do tools like Galaxy play in data management?
Galaxy provides a web-based platform for accessible, reproducible computational research, as in 'Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences' by Goecks et al. (2010, 3491 citations). It addresses reproducibility concerns in life sciences. The platform automates workflows for genomic data.
Why are data management plans required in funding?
Data management plans are mandated by policies like Canada's Tri-Agency Research Data Management Policy for certain funding opportunities. SFU requires them for institutional strategic awards. They ensure systematic data storage and reuse across project phases.
What are current tools for RDM planning?
RDMO is a tool for planning, implementing, and organizing research data management throughout projects, funded by the Deutsche Forschungsgemeinschaft. ELIXIR's RDMkit offers guidelines for life science data from project start to end. NASA's modern-dgf provides an auditable framework customizable for projects.
Open Research Questions
- ? How can institutional RDM maturity models like ELIXIR's be scaled to address unaddressed holistic matters in universities?
- ? What systematic relations exist between research data management practices and researcher adoption barriers revealed in extant literature?
- ? How do policies like Canada's Tri-Agency requirements influence long-term data deposit and reuse rates?
- ? In what ways can frameworks like NASA's modern-dgf integrate with diverse disciplinary data governance needs?
- ? How effective are ten simple rules for RDM in facilitating collaboration and progress through data publishing and reuse?
Recent Trends
Policies like Canada's Tri-Agency Research Data Management Policy now require data deposit plans, with SFU enforcing DMPs for awards as of 2025.
Preprints such as 'Ten simple rules for effective research data management' emphasize organizational reuse, and guides like 'Best Practices to Managing Research Data' highlight file organization and metadata.
2025Tools including RDMO and ELIXIR's RDM Maturity Model support systematic planning amid 329,639 works in the field.
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