Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

ABSTRACT The Big Data phenomenon and the fourth science paradigm - e-Science - demand from science and technology institutions proper management and preservation of research data, for access, use and sharing of original data and thus achieve sustainability. and competitiveness in the modern scientific and technological system. This paper comments and analyzes Embrapa’s Data Governance, Information and Knowledge Policy, focusing on issues related to scientific data management. It is hoped that this Policy can be instrumental to other organizations in the national S&T system in developing their own standards.Keywords: Scientific Data; Data Intensive Science; Access; Sharing; Preservation; Management.

ABSTRACT This article proposes to discuss the role of data management plans as a tool to facilitate data management during researches life cycle. Today, research data opening is a primary agenda at scientific agencies as it may boost investigations’ visibility and transparency as well as the ability to reproduce and reuse its data on new researches. Within this context, FAIR principles, an acronym for Findable, Accessible, Interoperable and Reusable, is paramount, as it establishes basic and guiding orientations for research data management, curatorship and preservation with an intent on its sharing and reuse. The current work intends to present to the Fundação Oswaldo Cruz a new Data Management Plan template proposal, aligned with FAIR principles. The methodology used is bibliographical research and documental analysis of several European data management plans. We conclude that the adoption of a management plan on universities and research institutions scientific activities is paramount. However, to be fully benefited from this activity, all actors involved in the process must participate, and, on top of that, this plan must be machine-actionable.Keywords: Data Management Plan; Research Data; FAIR Principles; DMP Machine-Actionable; Open Science.

ABSTRACT The availability of scientific assets through data repositories has been greatly increased as a result of government and institutional data sharing policies and mandates for publicly funded research, allowing data to be reused for purposes not always anticipated by primary researchers. Despite the fact that the argument favoring data sharing is strongly grounded in the possibilities of data reuse and its contributions to scientific advancement, this subject remains unobserved in discussions about data science and open science. This paper follows a narrative review method to take a closer look at data reuse in order to better conceptualize this term, while proposing an early classification of five distinct data reuse approaches (repurposing, aggregation, integration, meta-analysis and reanalysis) based on hypothetical cases and literature examples. It also explores the determinants of what constitutes reusable data, and the relationship between data reusability and documentation quality. It presents some challenges associated with data documentation and points out some initiatives and recommendations to overcome such problems. It expects to contribute not only for the conceptual advancement around the reusability and effective reuse of the data, but also to result in initiatives related to data documentation in order to increase the reuse potential of these scientific assets.Keywords:Data Reuse; Scientific Reproducibility; Reusability; Open Science; Research Data.

ABSTRACT Open access to research outcomes and data obtained in Science and Technology production has risen debates about the adoption and use of sharing tools for those outcomes. This research proposes a maturity model that unifies trusted document repositories and data repositories to form a notion of an integrated digital repository, setting this institution on a maturity level regarding these scientific production sharing tools. It also presents studies on these maturity models as ways to evaluate the quality of those data repositories. It adopts the CMMI model as a reference, relating to its respective levels. It presents a study on trustworthy repositories and evaluation criteria and concludes with the incorporation of FAIR principles into the evaluating process. Using a combination of methods to develop its investigation, the research proposes the use of this maturity model to evaluate Rio de Janeiro state repositories. Finally, it proposes the consummation of its results using an evaluation scheme structured in maturity levels, which includes an evaluation method for integrated repositories.Keywords: Maturity Model; Digital Repository; Quality Management; Open Science; Trustworthy Repository; FAIR Principles.

ABSTRACT The tasks of managing research data throughout the research process have become very important due to the high production of data and the requirement for its preservation. Both libraries and research support sections of various research institutions have started to implement services for data management and the professionalization of this management. With a qualitative character, and after a bibliographic search in open databases, research data management is contextualized, professional profiles are analyzed, and three phases are determined for the institutional implementation of these services: the elaboration of a policy, the establishment of an information unit and the integration of data management professionals.Keywords: Data Stewardship; Research Data; Research Data Management; Data Policy; Institutional Services.

ABSTRACT The fundamental principles of data science and the generalities of one of its areas of study are exposed: Data Visualization. The article discusses how multivariate data has been represented through illustrated images and graphs that relate the elements of syntax and semantics that can include analytical thinking in visual margins. It analyzes how Data Visualization has been developed over time, using examples recognized as cutting edge in this field, validating research with basic cognitive analysis on principles of evidence presentation in information displays.Keywords: Data Visualization; Infographics; Scientific Data; Storytelling, Big Data.

Barend Mons is a molecular biologist and, since 2012, he has been a professor of BioSemantics in the Department of Human Genetics at the Leiden University Medical Center (LUMC) in the Netherlands. In 2015, he was chair of the High-Level Expert Group on the European Open Science Cloud (EOSC). Since 2017, he has been heading the GO FAIR initiative’s Dutch International Support and Coordination office. In 2018, he was elected president of the Committee on Data for Science and Technology (CODATA) of the International Science Council for a four-year term through 2022. He is also a member of the Netherlands Academy of Technology and Innovation (AcTI), and he is a representative of the Board on Research Data and Information (BRDI) of the National Academies of Science, Engineering, and Medicine in the USA.

Agradecimentos | Acknowledgments

ABSTRACT Since the Berlin Declaration on Open Access to Knowledge in Science and Humanities published in 2003, the demand for an "open science" whose primary concern is to make research activity more transparent, more collaborative and more efficient, has grown at the academy. Added to this, the perception that the access and sharing of research data contribute significantly to science advance and maximize the investments applied in research programs has been consolidated. In this sense, the present work presents a proposal composed of digital repositories and computational tools aimed at publishing and sharing of information resources in research institutes. The proposed architecture, based on free and open-source tools, proved adequate for the management and publication of information resources in research institutions. However, this approach pointed to the need for a search tool that integrates the different tools, as well as the existence of a controlled vocabulary, capable of indexing resources in their different contexts.Keywords: Open Data; Open Science; Scientific Data Publishing.

Letter from the Editors