The Current State of Open Research Data in Spanish University Repositories: An Analysis

核心概念

Despite existing open access policies, Spanish university repositories show limited adoption and support for open research data, highlighting a crucial need for improved data management policies, infrastructure, and practices, particularly Data Curation, to realize the full potential of open science in Spain.

摘要

• Bibliographic Information: Monteagudo-Haro, P., & Prieto-Gutierrez, J. J. (2023). Open research data in Spanish university repositories. Revista Española de Documentación Científica.

• Research Objective: This research paper examines the current status of open research data within Spanish university repositories, aiming to analyze their adoption and identify areas for improvement.

• Methodology: The researchers employed a comparative methodology, analyzing 75 Spanish university repositories linked to REBIUN (Spanish Network of University Libraries). They developed and utilized twelve indicators to assess the presence, accessibility, and management of research data within these repositories.

• Key Findings: The study reveals a limited presence of open research data in Spanish university repositories. Only a small fraction of universities store a significant amount of research data. While a majority of repositories have open access policies, very few have specific policies dedicated to research data management. The analysis also highlights a heavy reliance on the Dublin Core metadata standard, potentially limiting data discoverability and interoperability.

• Main Conclusions: The authors conclude that despite the existence of open access policies, there is a significant gap in the implementation and support for open research data within Spanish universities. This gap necessitates a stronger emphasis on Data Curation, including the development of comprehensive data management policies, promotion of data sharing practices, and adoption of broader metadata standards.

• Significance: This research provides a crucial snapshot of the current state of open research data in Spain, highlighting the urgent need for improved infrastructure, policies, and practices to facilitate data sharing and enhance the impact of research conducted within Spanish universities.

• Limitations and Future Research: The study focuses solely on institutional repositories, warranting further investigation into the use of subject-specific or centralized repositories for research data management in Spain. Future research could also explore the impact of specific data management policies and Data Curation practices on the accessibility and reusability of research data.

统计

Only 39 out of 75 Spanish universities (52%) store research data in their repositories.
Only 7 universities (9%) have a specific policy for research data.
Only four universities have stored more than 200 research data sets.
36% of research data in the repositories comes from the Madroño Consortium, representing only 9% of the total REBIUN institutions.
76% of repositories with a section for research data only use Dublin Core as a metadata standard.

引用

"Research data repositories serve, among other purposes, to validate research results and, therefore, must be linked in some way to scientific publications where it is shown what those data were used for."
"The situation in 2022 of open research data in repositories of Spanish universities suggests that the situation is one of transition, as if awaiting applied and practical policies that will establish in our context where and how research data will be stored, and specifically, what will happen to the research data that are currently in some institutional repositories."

从中提取的关键见解

Open Research Data in Spanish University Repositories

by Pablo Montea... 在 arxiv.org 11-19-2024

https://arxiv.org/pdf/2411.10470.pdf

更深入的查询

How can Spanish universities learn from international best practices in open research data management and adapt them to their context?

Spanish universities can bridge the gap in open research data management by drawing upon international best practices and tailoring them to their unique context. Here's a breakdown of how:
1. Learning from Leaders:

Identifying Exemplary Institutions: Begin by identifying universities and research institutions globally recognized for their robust open research data practices. Institutions within the EU, UK, and Canada often serve as strong examples.
Analyzing Policies and Frameworks: Deeply examine the open data policies, data management plans (DMPs), and governance structures these institutions have implemented. Pay close attention to how they address issues like:

Data Ownership and Intellectual Property Rights
Data Security and Privacy
Long-Term Data Preservation
Metadata Standards and Interoperability (e.g., FAIR principles)

Case Studies and Success Stories: Explore case studies and success stories from these institutions to understand the practical implementation of their policies and the impact of open data on research.
2. Adaptation to the Spanish Context:

Regulatory Alignment: Ensure that any adopted practices align with Spanish and EU legislation regarding open data and data protection (e.g., GDPR).
Funding Landscape: Consider the specific requirements of Spanish research funding agencies that encourage or mandate data sharing.
Institutional Collaboration: Foster collaboration and knowledge exchange between Spanish universities. The Madroño Consortium's collaborative data management approach provides a good model.
Cultural Considerations: Recognize and address any cultural barriers to data sharing within the Spanish research community. This might involve:

Raising Awareness: Conducting workshops and training sessions to educate researchers about the benefits and importance of open data.
Language Accessibility: Providing resources and support in Spanish to facilitate wider adoption.
3. Key Strategies for Implementation:

Developing Institutional Data Policies: Establish clear, comprehensive open research data policies that outline expectations for data management, sharing, and preservation.
Providing Data Management Planning Support: Offer guidance and resources to researchers for creating DMPs, ensuring data is managed effectively throughout the research lifecycle.
Investing in Infrastructure: Invest in robust data repositories and data management platforms that meet international standards and support FAIR principles.
Recognizing and Rewarding Open Data Practices: Integrate open data sharing into researcher evaluation metrics and provide incentives for participation.
By embracing these strategies, Spanish universities can foster a thriving open research data ecosystem that enhances the visibility, impact, and reproducibility of Spanish research on a global scale.

Could the lack of incentives or recognition for researchers to share their data be a significant barrier to wider adoption of open research data practices in Spanish universities?

Yes, the lack of clear incentives and recognition for researchers to share their data is a major hurdle to the widespread adoption of open research data practices in Spanish universities.
Here's why:

Academic Culture and Evaluation: Traditional academic reward systems heavily emphasize publications and citations as primary indicators of research impact. Data sharing is often seen as an extra burden with little direct benefit to researchers' careers.
Time and Effort: Properly documenting, cleaning, and preparing data for sharing requires significant time and effort, which researchers may not have or prioritize without recognition.
Fear of Scooping or Misuse: Some researchers hesitate to share data due to concerns about being "scooped" by competitors or having their data misinterpreted or misused.
Lack of Awareness: There may be a lack of awareness among some researchers about the benefits of open data, available resources, and best practices for data sharing.
Addressing the Incentive Gap:
To overcome this barrier, Spanish universities need to implement a system that values and rewards open data practices:

Integrating Data Sharing into Evaluation Metrics:

Include data sharing as a criterion in hiring, promotion, and tenure decisions.
Recognize data citations and alternative metrics (altmetrics) that demonstrate the impact of shared data.

Funding and Grant Requirements:

Funding agencies should mandate data management plans and encourage or require data sharing as a condition of funding.

Institutional Recognition and Awards:

Establish awards or recognition programs specifically for researchers who demonstrate exemplary open data practices.

Building a Culture of Data Sharing:

Promote the value of open data through workshops, seminars, and training programs.

Highlighting Success Stories:

Showcase successful examples of data sharing within the university to demonstrate its benefits.
By creating a system that values and rewards open data, Spanish universities can incentivize researchers to embrace these practices, leading to a more open and collaborative research environment.

How can the concept of data as a valuable research output be further ingrained in the academic culture to encourage broader participation in open science initiatives?

Shifting academic culture to recognize data as a valuable research output is crucial for broader participation in open science. Here's how to achieve this:
1. Reframing the Narrative:

Data as a First-Class Research Object: Move away from the perception of data as a mere byproduct of research. Emphasize that data, when properly managed and shared, is a valuable research output in its own right.
Highlighting the Benefits: Clearly articulate the benefits of treating data as a valuable output:

Increased Research Impact and Visibility: Shared data can be discovered and reused by others, leading to new collaborations and citations.
Enhanced Reproducibility and Transparency: Open data practices promote scientific rigor and build trust in research findings.
Accelerated Discovery and Innovation: Sharing data allows researchers to build upon each other's work, potentially leading to faster breakthroughs.
Greater Research Efficiency: Reusing existing data can save time and resources compared to collecting new data for every study.
2. Embedding Data in Academic Practices:

Data Management Plans (DMPs): Make DMPs a standard requirement for all research projects. This encourages researchers to think about data management and sharing from the outset.
Data Citation and Recognition: Promote the use of persistent identifiers (PIDs) for datasets and encourage researchers to cite data in publications.
Data Journals and Repositories: Support the development and use of data journals and repositories that specifically publish and curate datasets.
Data-Related Skills Development: Integrate data management, analysis, and sharing skills into research training programs for students and early career researchers.
3. Fostering a Supportive Environment:

Leadership and Advocacy: University leaders and senior researchers must champion the importance of data as a research output and actively promote open science principles.
Community Building: Create communities of practice and provide platforms for researchers to share their experiences, best practices, and challenges related to data sharing.
Incentives and Rewards: Align evaluation and reward systems to recognize and value data sharing contributions alongside traditional publication metrics.
By implementing these strategies, Spanish universities can cultivate a cultural shift that views data as a valuable research output, fostering a more open and collaborative research environment that drives innovation and advances knowledge.