Le mouvement du libre-accès aux publications scientifiques s’élargit de plus en plus aux données de la recherche. Des initiatives pour garantir l’accessibilité et la complète réutilisation de ces données sont prises par une grande diversité d’acteurs – États, agences de financement de la recherche, éditeurs, communautés scientifiques.
L’ouverture des données de la recherche est rendue possible par la définition de politiques incitatives ou contraignantes, l’adoption de solutions juridiques et techniques, mais repose avant tout sur de bonnes pratiques de gestion des données. Tandis que la France s’insère progressivement dans la dynamique de l’Open research data, les universités sont appelées à définir leur politique de données.
Les bibliothécaires ont un rôle majeur à jouer dans l’élaboration de ces politiques, peuvent contribuer à identifier les besoins des chercheurs et les assister sur le volet « métadonnées ». Aussi, la question de l’ouverture des données de recherche offre une opportunité unique à ces professionnels de la documentation : celle de remodeler, à l’échelle des établissements de recherche, leur(s) lien(s) avec la communauté des chercheurs. »
Growing Institutional Support for Data Citation : Results of a Partnership Between Griffith University and the Australian National Data Service :
« Data is increasingly recognised as a valuable product of research and a number of international initiatives are underway to ensure it is better managed, connected, published, discovered, cited and reused. Within this context, data citation is an emergent practice rather than a norm of scholarly attribution. In 2012, a data citation project at Griffith University funded by the Australian National Data Service (ANDS) commenced that aimed to: enhance existing infrastructure for data citation at the University; test methodologies for tracking impact; and provide targeted outreach to researchers about the benefits of data citation. The project extended previous collaboration between Griffith and ANDS that built infrastructure at the University to assign DOI names (Digital Object Identifiers) to research data produced by Griffith’s researchers. This article reports on the findings of the project and provides a case study of what can be achieved at the institutional level to support data citation. »
Data sharing and its implications for academic libraries :
« Purpose : As an important aspect of the scientific process, research data sharing is the practice of making data used for scholarly research publicly available for use by other researchers. This paper seeks to provide a more comprehensive understanding of the data-sharing challenges and opportunities posed by the data deluge in academics. An attempt is made to discuss implications for the changing role and functioning of academic libraries.
Design/methodology/approach : An extensive review of literature on current trends and the impact of data sharing are performed.
Findings : The context in which the increasing demands for data sharing have arisen is presented. Some of the practices, trends, and issues central to data sharing among academics are presented. Emerging implications for academic libraries that are expected to provide a data service are discussed.
Originality/value : An insightful review and synthesis of context, issues, and trends in data sharing will help academic libraries to plan and develop programs and policies for their data services. »
This report examines policies and strategies towards open access (OA) of scientific data in the European Research Area (ERA), Brazil, Canada, Japan and the US from 2000 onwards. The analysis examines strategies that aim to foster OA scientific data—such as the types of incentives given at the researcher and institutional levels and the level of compliance by researchers and funded organisations —and also examines how, and whether, these policies are monitored and enforced. The infrastructures developed to store and share OA scientific data are also examined.
The analysis is supported by findings from the literature on the global progression of OA scientific data since 2000—including its growth as a segment of scholarly publishing—as well as some of the broader trends, themes and debates that have emerged from the movement.
Scientific research in the 21st century is more data intensive and collaborative than in the past. It is important to study the data practices of researchers – data accessibility, discovery, re-use, preservation and, particularly, data sharing. Data sharing is a valuable part of the scientific method allowing for verification of results and extending research from prior results.
A total of 1329 scientists participated in this survey exploring current data sharing practices and perceptions of the barriers and enablers of data sharing. Scientists do not make their data electronically available to others for various reasons, including insufficient time and lack of funding. Most respondents are satisfied with their current processes for the initial and short-term parts of the data or research lifecycle (collecting their research data; searching for, describing or cataloging, analyzing, and short-term storage of their data) but are not satisfied with long-term data preservation.
Many organizations do not provide support to their researchers for data management both in the short- and long-term. If certain conditions are met (such as formal citation and sharing reprints) respondents agree they are willing to share their data. There are also significant differences and approaches in data management practices based on primary funding agency, subject discipline, age, work focus, and world region.
Barriers to effective data sharing and preservation are deeply rooted in the practices and culture of the research process as well as the researchers themselves. New mandates for data management plans from NSF and other federal agencies and world-wide attention to the need to share and preserve data could lead to changes. Large scale programs, such as the NSF-sponsored DataNET (including projects like DataONE) will both bring attention and resources to the issue and make it easier for scientists to apply sound data management principles.
There is a growing chorus of voices in the scientific community calling for greater openness in the sharing of raw data that leads to a publication. In this commentary, we discuss the merits of sharing, common concerns that are raised, and practical issues that arise in developing a sharing policy. We suggest that the cognitive science community discuss the topic and establish a data sharing policy.