Research Data Management Jobs: Roles, Skills, and Career Opportunities

Research data management (RDM) careers sit at the intersection of scientific inquiry, information science, and information technology. Professionals in this field organize, document, store, share, and preserve research data so that it remains usable and trustworthy over time. This article explains the main RDM job families, the skills employers seek, the tools and certifications that support the work, and practical strategies for entering and advancing in the field. The content draws on official occupational sources and peer-reviewed studies of data management practice.

What Research Data Management Work Involves

Effective data management is crucial for scientific integrity and reproducibility, which are cornerstones of scientific progress. Well-organized and well-documented data enable validation and building on results. Data management encompasses activities including organization, documentation, storage, sharing, and preservation. Robust data management establishes credibility, fostering trust within the scientific community and benefiting researchers' careers. In experimental biomedicine, comprehensive data management is vital due to the typically intricate protocols, extensive metadata, and large datasets. Low-throughput experiments, in particular, require careful management to address variations and errors in protocols and raw data quality. Transparent and accountable research practices rely on accurate documentation of procedures, data collection, and analysis methods. Proper data management ensures long-term preservation and accessibility of valuable datasets. Well-managed data can be revisited, contributing to cumulative knowledge and potential new discoveries. Publicly funded research has an added responsibility for transparency, resource allocation, and avoiding redundancy. Meeting funding agency expectations increasingly requires rigorous methodologies, adherence to standards, comprehensive documentation, and widespread sharing of data, code, and other auxiliary resources. These findings come from a 2024 review in Physiological Reviews on best practices for data management and sharing in experimental biomedical research (Best practices for data management and sharing in experimental biomedical research).

RDM professionals translate these principles into daily operations. They build data management plans, design metadata schemas, select repositories, clean datasets, ensure compliance with funder and journal policies, and train researchers in good practices. The work spans many sectors, including universities, hospitals, government agencies, nonprofit research institutes, and private companies in pharmaceuticals, biotechnology, and agriculture.

At a Glance: Research Data Management Job Families

Core Principles Guiding Research Data Management Careers

FAIR Data Principles as Professional Standards

The FAIR principles, which stand for findable, accessible, interoperable, and reusable, provide a common framework for RDM work. A 2022 survey of the bioimaging community conducted through Germany's National Research Data Infrastructure found that knowing the needs of the bioimaging community with respect to RDM is essential for identifying measures that enable adoption of the FAIR principles for microscopy and bioimage analysis data across disciplines. The survey included 203 entries covering perspectives from various life and biomedical science disciplines and from participants at different career levels. Results highlighted the importance and value of bioimaging RDM and data sharing. However, the practical implementation of FAIR practices is impeded by technical hurdles, lack of knowledge, and insecurity about the legal aspects of data sharing. Survey participants requested metadata guidelines and annotation tools and endorsed the usage of image data management platforms. OMERO, which stands for Open Microscopy Environment Remote Objects, was the best known and most widely used platform at the time of the survey. Most respondents relied on image processing and analysis, which they regarded as the most time-consuming step of the bioimage data workflow. While knowledge about and implementation of electronic lab notebooks and data management plans was limited, respondents acknowledged their potential value for data handling and publication (Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey).

For RDM professionals, this survey illustrates a recurring pattern. Researchers value data sharing in principle but struggle with implementation. The RDM professional's job is to lower those practical barriers by providing metadata guidelines, annotation tools, and training.

Documentation as the Foundation of Trustworthy Data

Accurate documentation of procedures, data collection, and analysis methods underpins transparent and accountable research practices. A 2024 position paper from the German Cardiac Society and German Centre for Cardiovascular Research noted that the sharing and documentation of cardiovascular research data are essential for efficient use and reuse of data, thereby aiding scientific transparency, accelerating the progress of cardiovascular research and healthcare, and contributing to the reproducibility of research results. The paper identified challenges including lack of time, awareness, incentives, and funding for implementing effective RDM, lack of standardization in RDM processes, a need to better identify meaningful and actionable data among the increasing volume and complexity of data being acquired, and a lack of understanding of the legal aspects of data sharing. The authors called for a culture of open science with FAIR research data fostered through education and training of early-career and established research professionals (The challenges of research data management in cardiovascular science: a DGK and DZHK position paper-executive summary).

RDM professionals address these challenges by creating documentation templates, standard operating procedures, and training programs that make good practice achievable within real research workflows.

Training as a Core Professional Activity

Research data management has become an indispensable skill in modern neuroscience, and researchers benefit from following good practices as well as from having proficiency in using particular software solutions. Because these domain-agnostic skills are commonly not included in domain-specific graduate education, community efforts increasingly provide early career scientists with opportunities for organized training and materials for self-study. A 2024 report from the Neuroinformatics journal described a multi-modal teaching approach to RDM in the DataLad ecosystem, spanning an online and printed handbook, a modular course suitable for in-person and virtual teaching, and a flexible collection of RDM tips in a knowledge base. This free and open source collection of training material made RDM and software training available to various stakeholders over a five-year period (Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort).

This finding matters for job seekers. RDM roles frequently include a teaching component, whether formal classroom instruction, one-on-one consultations, or the creation of online learning materials. Candidates who can demonstrate teaching or mentoring experience have a measurable advantage.

Research Data Librarian Roles

Scope of Work in Academic and Health Sciences Libraries

Data-related librarians hold conventional titles related to fields of data librarianship, including research data management, data services, geographic information systems, data visualization, and data science. They may also be librarians whose duties include providing data-related services within an academic institution. A 2023 qualitative study in Evidence Based Library and Information Practice interviewed 12 data-related librarians in Canadian academic libraries and identified four major themes related to the skills required for library-related data services positions. These themes included the perceived importance of experience conducting original research, proficiency in computational coding and quantitative methods, MLIS-related skills such as understanding metadata, and the ability to learn new skills quickly on the job. The study concluded that although expertise in metadata, documentation, and information management are vital skills for data-related librarians, the MLIS is increasingly less competitive compared with degree programs that offer more intensive training in computational methods (It Takes a Researcher to Know a Researcher: Academic Librarian Perspectives Regarding Skills and Training for Research Data Support in Canada).

This finding has direct implications for aspiring data librarians. A library science degree provides essential grounding in metadata and information organization, but employers increasingly expect hands-on research experience and coding proficiency. Candidates should seek opportunities to conduct original research, learn programming languages such as Python or R, and build a portfolio of data-related projects.

How Librarians Build RDM Services

A 2021 review in Evidence Based Library and Information Practice examined a pilot program to initiate RDM services within academic libraries serving health sciences populations. Six institutions participated, consisting of three academic health sciences and three university libraries within the National Network of Libraries of Medicine Middle Atlantic Region in the United States. Between two and eight librarians participated from each institution, for a total of twenty-six librarian participants. Participants completed eight educational modules that included text, videos, and quizzes. The participating institutions received data interview questions to connect with their research community to be better informed about their attitudes, language, and practices. Participants also received a Teaching Toolkit with slides, a script, and an attendee evaluation form, plus a data series of branded classes for teaching over a designated period with instructors from within and outside of the library (A Pilot to Initiate Research Data Management Services Within Academic Libraries Helps Librarians to Learn About, Engage With, and Enhance Skills Within Their Research Communities).

This pilot model reveals the practical skills that data librarian jobs demand. Successful candidates must be able to conduct informational interviews with researchers, develop and deliver training, and collaborate with colleagues across departments. The ability to learn new domains quickly is essential because RDM librarians serve researchers across many disciplines.

Career Entry Points for Data Librarians

Occupations related to research data include the data librarian role, which has been recognized in the professional literature since at least 2013 (Occupations Related to Research Data: Data Librarian). More recent work has examined what makes a data librarian effective in practice (What Makes a Data Librarian). Entry-level positions typically require an MLIS degree, though some employers accept equivalent experience in research support, data management, or information technology. Postgraduate certificates in data curation or digital stewardship can strengthen an application. The U.S. Bureau of Labor Statistics groups many librarian roles under Life, Physical, and Social Science Occupations, which provides a useful reference for understanding the broader occupational landscape (Life, Physical, and Social Science Occupations).

Clinical Research Data Management Roles

Professional Competencies and Job Tasks

Clinical research data management is a distinct career path with its own competency framework. A job analysis conducted for the clinical research data management profession administered questionnaires in 2015 and 2018 to collect information from data managers on professional competencies, types of data managed, types of studies supported, and necessary foundational knowledge. The 2018 survey identified 67 professional competencies. Job tasks differed between early- to mid-career and mid- to late-career practitioners. A large variation in the types of studies conducted and variation in the data managed by the participants was observed. Clinical research data managers managed different types of data with variety of research settings, which indicated a need for training in methods and concepts that could be applied across therapeutic areas and types of data. The competency survey served as the foundation for the upcoming revision of the Certified Clinical Data Manager exam (Analysis of Professional Competencies for the Clinical Research Data Management Profession).

For job seekers, this study signals that clinical data management skills transfer across therapeutic areas. A candidate who learns the underlying methods and concepts can move between oncology, cardiology, infectious disease, and other specialties without starting over.

Where Clinical Data Managers Work

Clinical data management is a career option for pharmacy graduates and other life science degree holders (Clinical data management (CDM) - A career option for pharma graduates). Employers include pharmaceutical companies, biotechnology firms, contract research organizations, academic medical centers, and hospitals conducting clinical trials. The U.S. Bureau of Labor Statistics provides occupational information for healthcare occupations that may include related roles such as medical records specialists and health information technologists (Healthcare Occupations).

Regulatory and Quality Context

Clinical data managers work under regulatory frameworks that govern the conduct of clinical trials. These frameworks require accurate data capture, validation checks, audit trails, and documentation of data management procedures. The competency survey noted that clinical research data managers need foundational knowledge that supports work across different types of studies and data (Analysis of Professional Competencies for the Clinical Research Data Management Profession). Employers typically require familiarity with Good Clinical Practice guidelines, though specific certification requirements vary by jurisdiction and employer policy.

Research Data Steward and Data Curator Roles

Responsibilities in Research Institutions

Research data stewards work within universities, research institutes, and government laboratories to implement institutional data policies. Their responsibilities include developing data management plans, advising researchers on documentation standards, coordinating data deposit into repositories, and ensuring compliance with funder requirements. The 2024 Physiological Reviews article emphasized that meeting funding agency expectations increasingly requires rigorous methodologies, adherence to standards, comprehensive documentation, and widespread sharing of data, code, and other auxiliary resources (Best practices for data management and sharing in experimental biomedical research). Data stewards are the professionals who make those expectations achievable in practice.

Data Curation as a Specialized Function

Data curators focus on the preparation and quality control of datasets for deposit and reuse. Their work includes appraising datasets for long-term value, creating metadata, converting file formats, and reviewing data for completeness and consistency. The bioimaging survey found that respondents requested metadata guidelines and annotation tools and endorsed the usage of image data management platforms (Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey). Data curators are the professionals who develop and apply those guidelines and tools.

Skills That Transfer Across Settings

The competency survey for clinical data managers found that job tasks differed between early- to mid-career and mid- to late-career practitioners, and that a large variation existed in the types of studies conducted and data managed (Analysis of Professional Competencies for the Clinical Research Data Management Profession). The same pattern holds for data stewards and curators. Early-career professionals focus on hands-on data work, while senior professionals take on policy development, staff supervision, and institutional strategy.

Essential Tools and Technologies

Data Management Platforms

RDM professionals work with a range of platforms for storing, organizing, and sharing data. The bioimaging survey identified OMERO as the best known and most widely used image data management platform among respondents (Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey). Other platforms include institutional repository systems, electronic lab notebooks, and cloud storage services. The DataLad ecosystem provides a version-controlled approach to data management that supports training and collaborative research (Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort).

Metadata Standards and Documentation Tools

Metadata standards vary by discipline. The bioimaging community survey found that respondents requested metadata guidelines and annotation tools (Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey). RDM professionals must be able to identify the relevant standards for their domain, apply them correctly, and train researchers in their use. Common documentation tools include data dictionaries, codebooks, and README files.

Programming and Data Processing Skills

Proficiency in computational coding and quantitative methods emerged as a key skill for data-related librarians in the Canadian study (It Takes a Researcher to Know a Researcher: Academic Librarian Perspectives Regarding Skills and Training for Research Data Support in Canada). Python and R are the most commonly requested languages in RDM job postings. SQL is valuable for working with relational databases, particularly in clinical data management settings.

Certifications and Professional Development

Certified Clinical Data Manager

The Certified Clinical Data Manager credential, abbreviated as CCDM, is administered by the Society for Clinical Data Management. The competency survey reported in Studies in Health Technology and Informatics served as the foundation for the upcoming revision of the CCDM exam (Analysis of Professional Competencies for the Clinical Research Data Management Profession). Candidates for this certification typically need a combination of education and hands-on clinical data management experience.

Other Relevant Credentials

Several other certifications support RDM careers, though specific requirements vary by provider and jurisdiction. These include credentials in health information management, data curation, and project management. The National Institutes of Health Office of Intramural Training and Education offers training resources that can support career development in research-related fields (Office of Intramural Training and Education). The U.S. Department of Labor's O*NET OnLine provides detailed information about the tasks, skills, and knowledge required for specific occupations, which can help job seekers identify relevant qualifications (O*NET OnLine).

Continuing Education and Self-Study

The DataLad teaching effort demonstrated that free and open source training materials can make RDM and software training available to various stakeholders over multiple years (Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort). Job seekers should look for similar open educational resources in their target domain. The ability to learn new skills quickly on the job was identified as a key competency for data-related librarians (It Takes a Researcher to Know a Researcher: Academic Librarian Perspectives Regarding Skills and Training for Research Data Support in Canada).

Job Search Strategies for Research Data Management Positions

Building Relevant Experience

Experience conducting original research was identified as a key skill for data-related librarians (It Takes a Researcher to Know a Researcher: Academic Librarian Perspectives Regarding Skills and Training for Research Data Support in Canada). For job seekers without formal RDM experience, opportunities to build relevant skills include volunteering to manage data for a research lab, assisting with data deposit into a repository, or creating documentation for an existing dataset. The library pilot program demonstrated that librarians can learn RDM skills through structured educational modules and hands-on engagement with their research communities (A Pilot to Initiate Research Data Management Services Within Academic Libraries Helps Librarians to Learn About, Engage With, and Enhance Skills Within Their Research Communities).

Tailoring Applications to Role Families

RDM job postings vary widely in their requirements. The Canadian study found that the MLIS is increasingly less competitive compared with degree programs that offer more intensive training in computational methods (It Takes a Researcher to Know a Researcher: Academic Librarian Perspectives Regarding Skills and Training for Research Data Support in Canada). Candidates should read each posting carefully and emphasize the specific skills that match the role. A data librarian position may emphasize metadata and instruction, while a clinical data manager position may emphasize regulatory compliance and database design.

Networking and Professional Communities

Professional associations, conferences, and online communities provide opportunities to learn about open positions and connect with practitioners. The radiation protection survey found that researchers in that community had limited awareness of existing data sharing platforms, highlighting the need for education and training in data management and for a significant program of improving awareness of open data issues (SURVEY ON DATA MANAGEMENT IN RADIATION PROTECTION RESEARCH). RDM professionals who participate in their professional communities can help close these awareness gaps while building their own networks.

Remote and Hybrid Work Opportunities

Many RDM tasks can be performed remotely, including metadata creation, data cleaning, documentation, and training. The DataLad teaching effort included a modular course suitable for in-person and virtual teaching (Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort). Job seekers should look for postings that specify remote or hybrid work arrangements and should be prepared to demonstrate their ability to collaborate effectively in distributed teams.

Common Failure Patterns in RDM Careers

Overemphasis on Tools Without Understanding Research Context

RDM professionals who focus exclusively on tools and platforms without understanding the research context struggle to serve their communities effectively. The bioimaging survey found that practical implementation of FAIR practices is impeded by technical hurdles, lack of knowledge, and insecurity about the legal aspects of data sharing (Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey). Successful RDM professionals combine technical skills with the ability to understand researchers' workflows and constraints.

Neglecting Documentation Skills

Documentation is the foundation of trustworthy data, yet it is often the first thing to be cut when time is short. The cardiovascular position paper identified lack of time, awareness, incentives, and funding for implementing effective RDM as key challenges (The challenges of research data management in cardiovascular science: a DGK and DZHK position paper-executive summary). RDM professionals must be able to make documentation efficient and practical within real research workflows.

Underestimating the Legal and Policy Landscape

Lack of understanding of the legal aspects of data sharing was identified as a challenge in both the bioimaging survey and the cardiovascular position paper (Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey, The challenges of research data management in cardiovascular science: a DGK and DZHK position paper-executive summary). RDM professionals need working knowledge of data protection regulations, intellectual property considerations, and funder data policies. This knowledge must be applied within the specific legal framework of the relevant jurisdiction.

Failing to Build Training Skills

RDM roles increasingly include a teaching component. The DataLad effort demonstrated that organized training and self-study materials are valuable for early career scientists (Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort). RDM professionals who cannot communicate effectively with researchers will struggle to implement good practices across their institutions.

Observations and Measurements for Career Planning

Tracking Job Posting Requirements

Job seekers should track the qualifications listed in RDM postings over time. The clinical data management competency survey was conducted in 2015 and 2018, allowing the profession to compare and assess competencies over time (Analysis of Professional Competencies for the Clinical Research Data Management Profession). Individual job seekers can use a similar approach, noting which skills appear consistently across postings and which are specific to particular employers.

Documenting Your Own Competencies

The 67 professional competencies identified in the clinical data management survey provide a useful checklist for self-assessment (Analysis of Professional Competencies for the Clinical Research Data Management Profession). Job seekers can rate their proficiency in each area and identify gaps to address through training or experience.

Measuring Impact in RDM Roles

RDM professionals should track concrete outcomes of their work, such as the number of researchers trained, datasets deposited, or data management plans reviewed. The library pilot program included attendee evaluation forms and a focused marketing plan, demonstrating that RDM services can be evaluated systematically (A Pilot to Initiate Research Data Management Services Within Academic Libraries Helps Librarians to Learn About, Engage With, and Enhance Skills Within Their Research Communities).

Limitations and Professional Escalation Criteria

Recognizing the Boundaries of RDM Expertise

RDM professionals should recognize when a question falls outside their expertise. Legal questions about data sharing, intellectual property, and regulatory compliance should be referred to institutional legal counsel. Questions about specific research methods should be referred to the relevant domain experts. The cardiovascular position paper noted that lack of understanding of the legal aspects of data sharing is a common challenge (The challenges of research data management in cardiovascular science: a DGK and DZHK position paper-executive summary), which means RDM professionals must know when to escalate.

Escalation Criteria for Data Quality Issues

When RDM professionals encounter data quality issues that could affect research integrity, they should escalate to the principal investigator or research leadership. Examples include missing documentation, undocumented data transformations, or inconsistencies between raw and processed data. The Physiological Reviews article emphasized that accurate documentation of procedures, data collection, and analysis methods underpins transparent and accountable research practices (Best practices for data management and sharing in experimental biomedical research).

Escalation Criteria for Policy Compliance

When researchers are uncertain about funder or journal data policies, RDM professionals should help them locate the relevant requirements and connect them with institutional research offices when needed. The radiation protection survey found that researchers had limited awareness of data sharing infrastructures and policies (SURVEY ON DATA MANAGEMENT IN RADIATION PROTECTION RESEARCH). RDM professionals play a key role in closing these awareness gaps.

Safety and Regulatory Context

Data Protection and Privacy

RDM professionals who work with human subjects data must understand and apply data protection requirements. These requirements vary by jurisdiction and by the type of data involved. Clinical data managers work under regulatory frameworks that govern clinical trials, including requirements for accurate data capture and audit trails. The U.S. Bureau of Labor Statistics provides occupational information for healthcare occupations that may include related roles (Healthcare Occupations).

Research Integrity and Reproducibility

The Physiological Reviews article emphasized that effective data management is crucial for scientific integrity and reproducibility, and that well-managed data can be revisited, contributing to cumulative knowledge and potential new discoveries (Best practices for data management and sharing in experimental biomedical research). RDM professionals support research integrity by ensuring that data are properly documented, preserved, and made available for validation.

Funding Agency Requirements

Publicly funded research has an added responsibility for transparency, resource allocation, and avoiding redundancy. Meeting funding agency expectations increasingly requires rigorous methodologies, adherence to standards, comprehensive documentation, and widespread sharing of data, code, and other auxiliary resources (Best practices for data management and sharing in experimental biomedical research). RDM professionals help researchers navigate these requirements.

Career Progression and Long-Term Development

Early Career to Mid Career

The clinical data management competency survey found that job tasks differed between early- to mid-career and mid- to late-career practitioners (Analysis of Professional Competencies for the Clinical Research Data Management Profession). Early-career professionals typically focus on hands-on data work, while mid-career professionals take on more complex projects, supervise junior staff, and contribute to policy development.

Mid Career to Senior Roles

Senior RDM professionals lead institutional data programs, develop training curricula, and represent their organizations in national and international data initiatives. The DataLad teaching effort demonstrated that RDM professionals can contribute to open source training materials that serve broad communities (Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort). Senior professionals often contribute to such efforts as part of their institutional roles.

Maintaining Skills Over Time

The ability to learn new skills quickly on the job was identified as a key competency for data-related librarians (It Takes a Researcher to Know a Researcher: Academic Librarian Perspectives Regarding Skills and Training for Research Data Support in Canada). RDM professionals must commit to continuous learning as tools, standards, and policies evolve. The National Institutes of Health Office of Intramural Training and Education offers training resources that can support ongoing professional development (Office of Intramural Training and Education).

Frequently Asked Questions

What is the difference between a research data librarian and a research data steward?

A research data librarian typically works in a library setting and focuses on data discovery, metadata guidance, training, and repository services. A research data steward typically works within a research group or institute and focuses on data management planning, documentation standards, and data quality. The Canadian study found that data-related librarians need experience conducting original research, computational coding proficiency, MLIS-related skills such as understanding metadata, and the ability to learn new skills quickly (It Takes a Researcher to Know a Researcher: Academic Librarian Perspectives Regarding Skills and Training for Research Data Support in Canada). Data stewards often come from scientific backgrounds and apply domain knowledge to data management practice.

What degree do I need for a research data management job?

Degree requirements vary by role. Data librarian positions typically require a Master of Library and Information Science, though the Canadian study found that the MLIS is increasingly less competitive compared with degree programs that offer more intensive training in computational methods (It Takes a Researcher to Know a Researcher: Academic Librarian Perspectives Regarding Skills and Training for Research Data Support in Canada). Clinical data manager positions typically require a life science degree, and clinical data management is a career option for pharmacy graduates (Clinical data management (CDM) - A career option for pharma graduates). Research data steward positions typically require a graduate degree in a scientific field.

What programming languages should I learn for research data management?

Python and R are the most commonly requested programming languages in RDM job postings. The Canadian study identified proficiency in computational coding and quantitative methods as a key skill for data-related librarians (It Takes a Researcher to Know a Researcher: Academic Librarian Perspectives Regarding Skills and Training for Research Data Support in Canada). SQL is valuable for working with relational databases, particularly in clinical data management settings.

What certifications are available for clinical data managers?

The Certified Clinical Data Manager credential, abbreviated as CCDM, is the primary certification for clinical data managers. The competency survey reported in Studies in Health Technology and Informatics served as the foundation for the upcoming revision of the CCDM exam (Analysis of Professional Competencies for the Clinical Research Data Management Profession). Candidates typically need a combination of education and hands-on clinical data management experience.

Can I work remotely in research data management?

Many RDM tasks can be performed remotely, including metadata creation, data cleaning, documentation, and training. The DataLad teaching effort included a modular course suitable for in-person and virtual teaching (Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort). Remote work availability varies by employer and role, so job seekers should check individual postings.

What is the FAIR data framework and why does it matter for RDM careers?

The FAIR principles stand for findable, accessible, interoperable, and reusable. The bioimaging survey found that knowing the needs of the bioimaging community with respect to RDM is essential for identifying measures that enable adoption of the FAIR principles (Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey). RDM professionals apply FAIR principles in their daily work, and familiarity with FAIR is increasingly expected in job postings.

How do I get experience if I have no formal RDM background?

Experience conducting original research was identified as a key skill for data-related librarians (It Takes a Researcher to Know a Researcher: Academic Librarian Perspectives Regarding Skills and Training for Research Data Support in Canada). Opportunities to build relevant experience include volunteering to manage data for a research lab, assisting with data deposit into a repository, or creating documentation for an existing dataset. The library pilot program demonstrated that librarians can learn RDM skills through structured educational modules and hands-on engagement with their research communities (A Pilot to Initiate Research Data Management Services Within Academic Libraries Helps Librarians to Learn About, Engage With, and Enhance Skills Within Their Research Communities).

What are the main challenges facing RDM professionals?

The cardiovascular position paper identified challenges including lack of time, awareness, incentives, and funding for implementing effective RDM, lack of standardization in RDM processes, difficulty identifying meaningful and actionable data among increasing volume and complexity, and lack of understanding of the legal aspects of data sharing (The challenges of research data management in cardiovascular science: a DGK and DZHK position paper-executive summary). The bioimaging survey similarly found that practical implementation of FAIR practices is impeded by technical hurdles, lack of knowledge, and insecurity about the legal aspects of data sharing (Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey).

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References and Further Reading

• Life, Physical, and Social Science Occupations. U.S. Bureau of Labor Statistics.
• Healthcare Occupations. U.S. Bureau of Labor Statistics.
• O*NET OnLine. U.S. Department of Labor.
• Office of Intramural Training and Education. National Institutes of Health.
• NCBI Literature Resources. National Center for Biotechnology Information.
• PubMed. National Library of Medicine.
• Best practices for data management and sharing in experimental biomedical research.. Physiological reviews, 2024.
• Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey.. F1000Research, 2022.
• The challenges of research data management in cardiovascular science: a DGK and DZHK position paper-executive summary.. Clinical research in cardiology : official journal of the German Cardiac Society, 2024.
• Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort.. Neuroinformatics, 2024.
• SURVEY ON DATA MANAGEMENT IN RADIATION PROTECTION RESEARCH.. Radiation protection dosimetry, 2019.
• Analysis of Professional Competencies for the Clinical Research Data Management Profession.. Studies in health technology and informatics, 2020.
• Overhauling Morocco's healthcare system: Perception of healthcare professionals.. La Tunisie medicale, 2025.
• Proactive career management for female health professionals: a scoping review protocol.. BMJ open, 2023.
• An Analysis of Graduate Medical Education Program Coordinator Job Descriptions.. 2026.
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• Returning to work after treatment for a low-grade glioma: analysing return-to-work rates and exploring barriers experienced by patients.. 2026.
• It Takes a Researcher to Know a Researcher: Academic Librarian Perspectives Regarding Skills and Training for Research Data Support in Canada. Evidence Based Library and Information Practice, 2023.
• A Pilot to Initiate Research Data Management Services Within Academic Libraries Helps Librarians to Learn About, Engage With, and Enhance Skills Within Their Research Communities. Evidence Based Library and Information Practice, 2021.
• Occupations Related to Research Data: Data Librarian. 2013.
• What Makes a Data Librarian. 2019.
• Clinical data management (CDM) - A career option for pharma graduates. Pharma Times, 2016.
• Big data analysis on the relationship between the organizational career management and knowledge workers' work involved-take the high-tech enterprise as an example. Proceedings 15th IEEE International Conference on Trust Security and Privacy in Computing and Communications 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications IEEE Trustcom Bigdatase Ispa 2016, 2016.
• Bolstering a Learning Management System with Personalized Education Experience Aligned to Individual Career Aspirations Using the RIASEC Model and Data Mining. TEM Journal, 2026.
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This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.