Opportunities and Challenges of “Opening” Naturally Occurring Data
Researchers working with naturally occurring data often have to weigh the “costs” and “benefits” of designing studies in which they will collect data that can be shared with others and or reused in future studies. Often, some of the opportunities afforded by “opening” naturally occurring data may not be apparent in the early stages of a project. Similarly, some of the challenges posed by, say, publishing the data in a data archive, may remain hidden until the end stages of a project. Awareness of these potential advantages and disadvantages can be very helpful. That’s why, below, we aim to provide a comprehensive list of opportunities and challenges associated with “opening” naturally occurring data.
Opportunities afforded by “opening” naturally occurring data
Collaborative Potential
Facilitates collaboration across research groups, methods, and disciplines, and encourages multidisciplinary approaches.
Accelerated Knowledge Building
Other researchers can build on, verify, or reinterpret your findings, generating new insights.
Efficient Resource Use
Access to shared data reduces the need for new data collection, saving time and resources.
Transparency and Integrity
Improves research transparency, fostering trust and integrity within the scientific community.
Advancement of FAIR Principles
Promotes findability, accessibility, interoperability, and reusability of data.
Enhanced Training and Teaching
Existing open datasets can be utilised for education and training purposes, fostering skill development.
Recognition
Open sharing can lead to increased citations, acknowledgement as a data contributor, or eligibility for awards.
Long-Term Comparisons
Enables longitudinal studies and retrospective analyses using existing datasets.
Increased Visibility
Sharing data and research allows others to learn about your work and enhances your reputation.
Participant Benefits
Reusing data reduces participant burden; and may even encourage future participation due to individuals being aware their contributions might inspire and encourage future research.
Diverse Perspectives
Allows researchers from different contexts or regions to access and contribute to research.
Unique Data Creation
Encourages the development of unique datasets, provides access to underrepresented or usually less accessible data, and makes innovative research questions possible.
Reproducibility and Reliability
Strengthens the rigor of research by enabling independent verification of findings and imporving overall reproducibility and reliability.
Knowledge Sharing
Makes it easier to inspire the wider community and other researchers by showcasing impactful work of good open science practices.
Minimised Bias
Reduces potential influence or bias of the researcher on the research processes and outcomes.
Global Accessibility
Levels the playing field for researchers worldwide by providing equitable access to data.
Meta-research
Allows for researchers to study how researchers do research
Easier Data Handover
Allows for a more effective data handover between (changing) staff, universities or institutions.
Challenges posed by “opening” naturally occurring data
Metadata Complexity
Creating detailed, accurate, and standardized metadata can be challenging.
Privacy Risks
(sensitive) Data may raise privacy concerns, including the risk of participant identification and the challenge of ensuring compliance with regulations such as GDPR.
Consent Forms
Writing informed consent forms that are comprehensive, participant-friendly, and compliant with regulations can be highly challenging.
Consent Limitations
Participants’ consent may not extend to unforeseen future research uses or different research questions, complicating ethical compliance.
Ethical Considerations
Using open data raises questions about participant impact, informed consent, and potential misuse.
Technical Barriers
Challenges include managing large-scale data storage, ensuring effective anonymization and security, processing data efficiently, and selecting widely accepted file formats.
Resource Gaps
Lack of institutional resources or platforms to support open data sharing.
Cultural and Institutional Barriers
Lack of (institutional) support and differing regulations complicate the implementation of Open Science.
Recognition Deficit
Researchers may not receive proper credit or acknowledgement for shared data.
Legal Uncertainty
Ambiguities about data ownership and usage rights create a lot of legal uncertainties.
Time and Effort
Preparing data for open sharing, including anonymization and formatting, requires significant time and planning.
Competition Risks
Early sharing might lead to others using or publishing findings prematurely.
Data Quality Issues
Open data might lack context, structure, or consistency, impacting its reliability or even ‘readability’.
Bias and Representation
Open data may introduce new biases or fail to capture the context in which it was created.
Ethical Approvals
Securing approval for open data use can be complex and time-consuming.
Loss of Data Opportunities
Open data might discourage some individuals from participating in research
Funding Restrictions
Some funding bodies may wish to not have the data available for re-use or open sharing.
Communication Challenges
Maintaining clear communication between researchers and participants can be difficult, especially explaining complex concepts such as re-use and open data sharing.
Advanced Skills Requirement
Processing, structuring, and interpreting open data might require specialized expertise and/or specific contexts.
Unexpected costs
Anonymization, data storing or the use of special equipment might bring unexpected expenses.
Lack of Standardization
Variations in data-sharing practices, including file formats and platform usage, can create significant challenges in ensuring consistency and interoperability.