| El manejo de datos. Aproximación desde los estudios de la información |
| Georgina Araceli Torres Vargas |
| DR ©Universidad Nacional Autónoma de México, Instituto de Investigaciones Bibliotecológicas y de la Información |
| ISBN: 978-607-30-2710-6 |
| Primera edición 2020 |
| Colección: Tecnologías de la Información |
| Publicación dictaminada |
| La presente obra está bajo una licencia de Creative Commons by nc sa 4.0 |
Contenido
Minería de Texto y Minería de Datos
Sistematización de datos y servicios de información
Research Data Management and Libraries: Opportunities and Challenges
Integración de los principios de linked data en el registro bibliográfico
Research Data Management and Libraries: Opportunities and Challenges
KRYSTYNA K. MATUSIAK
University of Denver
INTRODUCTION
Research Data Management (rdm) is a new area of service and infrastructure development at universities and research centers worldwide. The increasing volume and complexity of digital data, as well as the challenges associated with organization, preservation, and reuse of data, have contributed to the emergence of RDM as a priority in recent years. Modern science has increasingly become data-intensive with researchers using new methodology and instruments and producing an unprecedented amount of data (Borgman 2012). Digital technology has accelerated this process by providing new tools for collecting scientific evidence but also enabled building technical infrastructure for storing and sharing data. The researchers studying the growth of science found that global scientific output doubles every 9 years. Most of the scientific expansion has taken place in the modern era with the growth rate of 8 to 9% (Bornmann & Mutz 2015).
The motivations for deployment of RDM services are diverse, often emerging from a pragmatic need to comply with requests from funding agencies for data management planning, but also responding to the policy environment and calls for openness in science (Ayris et al. 2016; Fearon et al 2013; Pryor et al. 2013). National funding agencies in several countries now require researchers to prepare data management plans and to provide open access to data (NSF; UK Research and Innovation). The European Research Council (ERC) supports the principle of open access to research data and scholarly publications. It conducted a Pilot on Open Research Data for research projects funded through the Horizon 2020 program. As of 2017, the Pilot on Open Research Data has been extended and open access became the default for the research data generated as a result of the Horizon 2020 funding, although researchers can still opt out in some circumstances (ERC 2018). In addition to funder requirements, journal editors and publishers are increasingly requesting authors to provide open access to source data underpinning publications.
This paper provides an overview of RDM services and their importance in the context of Open Science. It summarizes the findings from the Data Curation project sponsored by the International Federation of Library Associations (IFLA) Library Theory and Research (LTR) Section. The IFLA study focused on the roles and responsibilities of RDM professionals in international and interdisciplinary contexts. This paper discusses the opportunities and challenges in providing RDM services in light of the findings from the IFLA Data Curation project.
OPEN DATA AND THE OPEN SCIENCE MOVEMENT
In the traditional scholarly communication model, scholars disseminated the results of their research through conference presentations, books, and articles published in peer-review, subscription-based journals. The Open Access (OA) movement has changed the model of scholarly publishing encouraging scholars to share their papers through open access publishing or depositing published articles in institutional or disciplinary repositories (Swan 2012). The emphasis of OA, however, has been almost exclusively on opening access to journal articles, not so much on research data. As Borgman (2015) notes open data is “substantially distinct from open access to scholarly literature” (p. 44). Researchers would sometimes share data sets with colleagues in the scholarly community but rarely provide open access as part of the traditional scholarly communication practice.
Data is a valuable output of scholarly work and the calls for providing open access to research data come not only from the funding agencies but also from the members of the scholarly community. Opening access to data is believed to contribute to transparency and reproducibility of research and to the more efficient scientific process (Kraker et al. 2011; Molloy 2011; Nosek et al. 2015). Open research data can be freely accessed, reused, and redistributed for scholarly purposes. The principles of FAIR data (findable, accessible, interoperable and reusable) provide a foundation for access and reuse of research data across disciplines and borders (Wilkinson et al. 2016). Open Data is a key component of the Open Science movement.
The Open Science movement advocates for opening all phases of the research cycle and sharing all outcomes of the scientific work (Foster 2018). It emphasizes a more open, inclusive, and collaborative research process and encourages new ways of diffusing knowledge by using digital technology. The term “Open Science” often serves as an umbrella term encompassing scholarly outputs, practices, and collaborative digital tools. In its broad understanding, it includes open data, open publications, open educational resources (OER), open source software, open peer review, and citizen science (Bezjak et al. 2018). Fecher and Friesike (2014) note the diversity and even ambiguity of the discourse on Open Science and identify several perspectives or “schools of thoughts,” ranging from making knowledge freely available for everyone to developing an alternative system for evaluating quality and measuring impact.
Vicente-Sáez and Martínez-Fuentes (2018) acknowledge the diversity of perspectives and concepts of Open Science in their systematic review of the scholarly literature. The authors