Research Lines
The OpenScienceLab research group encompasses all aspects of open science through a holistic and interdisciplinary approach, supported by our research in Metascience, aimed at generating evidence-based Open Science. This perspective allows for a critical and rigorous analysis of research processes and practices, evaluating their real impact and contributing to the continuous improvement of science, its infrastructures, and its relationship with society.
Axis A. Infrastructures, and Technologies for Open Science and FAIR
This axis addresses the development of infrastructures, standards, and technical capabilities that enable the consolidation of open, interoperable, and sustainable research ecosystems. Meta-research plays a central role in analyzing the effectiveness of FAIR environments, data management models, and interoperability mechanisms, as well as identifying barriers and opportunities for their large-scale adoption. The knowledge generated provides a solid empirical foundation for designing technological and organizational solutions that make real and operational open science possible.
1. FAIR Research Data Infrastructures and Management
Design, development, and implementation of research data infrastructures; promotion of effective data management and sharing practices, including the adoption of open data and FAIR principles; development and use of repositories, open-source platforms, and data management plans (DMPs).
2. Vocabularies, Metadata, and Standards for Interoperability
This line addresses the creation, standardization, and application of controlled vocabularies, metadata schemes, and persistent identifiers (PIDs) that allow research data to be integrated in a coherent and reusable way across disciplines. Its purpose is to ensure the technical and semantic interoperability of scientific resources, facilitating the discovery, connection, and utilization of information. Meta-research in this area allows for evaluating the actual degree of adoption of standards and their impact on the quality and openness of science.
3. New Roles and Competencies in the Management of Responsible Research and Open Science
This line explores the emerging profiles that are transforming research ecosystems, such as data managers, open science officers, and other specialized professionals. Its objectives include analyzing the competencies necessary to support open, ethical, and sustainable research practices, and promoting training, capacity development, and skill diversification. The study of these new roles provides evidence of how organizational structures and frameworks supporting open science are evolving.
Axis B. Quality, Integrity, and Responsible Evaluation of Science
This axis focuses on understanding and improving the processes that ensure quality, integrity, and accountability in research, as well as developing evaluation approaches aligned with the principles of open science. Meta-research enables a critical examination of metrics, indicators, and evaluation practices, including the emerging challenges associated with the use of artificial intelligence and the need for forensic scientometrics approaches. This evidence is crucial for designing governance frameworks, policies, and incentive systems that promote more responsible, transparent, and socially relevant research.
4. Research Evaluation, Metrics, and Monitoring in Open Science
This line investigates and develops alternative and next-generation metrics, such as altmetrics and methodologies addressed by forensic scientometrics, that allow the identification of irregular patterns and a more accurate assessment of the impact and quality of research. It also includes the study of monitoring tools for open access and open data practices. Its approach integrates both quantitative and qualitative methods and seeks to align evaluation systems with the principles of open science and reform frameworks promoted by initiatives like CoARA and DORA. This knowledge contributes to building responsible, transparent evaluation models focused on social impact and sustainable development.
5. Reproducibility, Replicability, and Good Scientific Practices
This line promotes the advancement of reproducibility and replicability as fundamental pillars of trust in science. It focuses on the development and evaluation of open protocols, study pre-registration, and transparent analysis workflows, as well as identifying and mitigating questionable research and publication practices (QRPs and QPPs). Through meta-research, evidence is generated to help improve methodological quality, transparency, and integrity in scientific production.
6. Ethical, Legal, Social (ELSI), and Governance Aspects
This line critically analyzes the legal, social, and ethical implications of open science, with particular attention to issues such as data protection, intellectual property rights, and research integrity. It also examines the impact of artificial intelligence on the generation and evaluation of scientific knowledge, assessing its alignment with ethical and responsibility principles. The goal is to provide knowledge and recommendations that help design solid, reliable governance models aligned with the values of open and responsible research.
Axis C. Participation, Visibility, and Social Impact of Open Science
This axis promotes the active involvement of different actors in the production and dissemination of knowledge, strengthening the relationship between science and society. Meta-research allows for a rigorous evaluation of the effectiveness of open access strategies, scientific communication, citizen science, and knowledge transfer. Through systematic analysis, data and recommendations are generated that facilitate the design of policies and practices aimed at increasing the visibility, legitimacy, and social impact of open research.
7. Open Access and Visibility of Scientific Results
This line aims to analyze and promote open access to academic publications, data, and other research outcomes as a fundamental means to democratize knowledge. It includes the development of strategies and tools that improve the visibility, discoverability, and reuse of scientific results in various contexts. It also analyzes the design and implementation of open access policies, evaluating their effectiveness and alignment with the goals of transparency, equity, and social impact that define open science.
8. Citizen Science and Public Participation
This line encourages the active involvement of citizens in all phases of the research cycle, fostering co-creation of knowledge and the democratization of science. Its approach combines the analysis of participation experiences from three complementary perspectives: (a) the evaluation of the level of involvement and recognition of citizen contributions; (b) the study of how these methodologies are perceived by both researchers and project coordinators, as well as by the citizens themselves; and (c) the assessment of their coherence with the principles of open science and scientific evaluation reform frameworks. The goal is to provide evidence that helps design inclusive, effective, and sustainable practices.
9. Scientific Communication and Data Journalism
This line develops innovative strategies to bring science closer to diverse audiences through accessible formats, comprehensible narratives, and rigorous use of data. It focuses on integrating open science principles into mainstream media and strengthening data journalism as a tool to improve transparency and public trust. Its purpose is to evaluate and disseminate best practices in scientific communication that contribute to building a shared and socially relevant scientific culture.
10. Open Innovation and Knowledge Transfer
This line critically examines the processes that allow the transfer and valorization of research outcomes beyond the academic sphere, with a focus on open innovation and cross-sector collaboration. Research in this area involves analyzing the factors that favor the adoption of scientific knowledge in society, industry, and public policies, as well as the challenges that affect its real impact. The goal is to generate evidence that allows for the definition of both social impact indicators and responsible, sustainable techniques for transfer and innovation based on the principles of open science.