The Research Data Alliance: globally co-ordinated action against barriers to data publishing and sharing


“This article discusses the drivers behind the formation of the Research Data Alliance (RDA), its current state, the lessons learned from its first full year of operation, and its anticipated impact on data publishing and sharing. One of the pressing challenges in data infrastructure (taken here to include issues relating to hardware, software and content format, as well as human actors) is how best to enable data interoperability across boundaries. This is particularly critical as the world deals with bigger and more complex problems that require data and insights from a range of disciplines. The RDA has been set up to enable more data to be shared across barriers to address these challenges. It does this through focused Working Groups and Interest Groups, formed of experts from around the world, and drawing from the academic, industry, and government sectors.”


Data Producers Courting Data Reusers: Two Cases from Modeling Communities


“Data sharing is a difficult process for both the data producer and the data reuser. Both parties are faced with more disincentives than incentives. Data producers need to sink time and resources into adding metadata for data to be findable and usable, and there is no promise of receiving credit for this effort. Making data available also leaves data producers vulnerable to being scooped or data misuse. Data reusers also need to sink time and resources into evaluating data and trying to understand them, making collecting their own data a more attractive option. In spite of these difficulties, some data producers are looking for new ways to make data sharing and reuse a more viable option. This paper presents two cases from the surface and climate modeling communities, where researchers who produce data are reaching out to other researchers who would be interested in reusing the data. These cases are evaluated as a strategy to identify ways to overcome the challenges typically experienced by both data producers and data reusers. By working together with reusers, data producers are able to mitigate the disincentives and create incentives for sharing data. By working with data producers, data reusers are able to circumvent the hurdles that make data reuse so challenging.”

URL : Data Producers Courting Data Reusers

Alternative URL :

Datasharing guía práctica para compartir datos de investigación…


Datasharing: guía práctica para compartir datos de investigación :

“Asociar los datos de investigación a la publicación favorece que la comunidad científica los reutilice, pero no tiene suficientes garantías de preservación. Almacenarlos en bases de datos solventa esta contingencia y aporta visibilidad, pero en España no existen demasiados servicios de estas características. Por esta razón, se describen, evalúan y exponen los pros y contras de depósitos de datos multidisciplinares extranjeros que pueden ser de utilidad para investigadores y gestores de información: Dryad, Figshare, Zenodo y Dataverse. Todavía es pronto para escoger de forma óptima y definitiva entre una u otra aplicación, por lo que se concluye con unas recomendaciones que orienten a la comunidad de usuarios e intermediarios.”

“To associate research data to the published results favors their reuse by the scientific community, but this does not afford sufficient guarantees of preservation. To store them in databases solves this contingency and provides visibility, but in Spain there are not many services of this kind. For this reason, we describe, evaluate and discuss the pros and cons of foreign multidisciplinary data repositories that can be useful for researchers and information managers: Dryad, Figshare, Zenodo and Dataverse. It is still early to choose optimally and definitively one or the other application, so we conclude with recommendations to guide the user community and intermediaries.”


The Open Access Divide This paper is…


The Open Access Divide :

“This paper is an attempt to review various aspects of the open access divide regarding the difference between those academics who support free sharing of data and scholarly output and those academics who do not. It provides a structured description by adopting the Ws doctrines emphasizing such questions as who, what, when, where and why for information-gathering. Using measurable variables to define a common expression of the open access divide, this study collects aggregated data from existing open access as well as non-open access publications including journal articles and extensive reports. The definition of the open access divide is integrated into the discussion of scholarship on a larger scale.”


Data sharing and its implications for academic libraries…


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.”


If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology


Research on practices to share and reuse data will inform the design of infrastructure to support data collection, management, and discovery in the long tail of science and technology. These are research domains in which data tend to be local in character, minimally structured, and minimally documented. We report on a ten-year study of the Center for Embedded Network Sensing (CENS), a National Science Foundation Science and Technology Center.

We found that CENS researchers are willing to share their data, but few are asked to do so, and in only a few domain areas do their funders or journals require them to deposit data. Few repositories exist to accept data in CENS research areas.. Data sharing tends to occur only through interpersonal exchanges. CENS researchers obtain data from repositories, and occasionally from registries and individuals, to provide context, calibration, or other forms of background for their studies. Neither CENS researchers nor those who request access to CENS data appear to use external data for primary research questions or for replication of studies.

CENS researchers are willing to share data if they receive credit and retain first rights to publish their results. Practices of releasing, sharing, and reusing of data in CENS reaffirm the gift culture of scholarship, in which goods are bartered between trusted colleagues rather than treated as commodities.

URL : If We Share Data, Will Anyone Use Them?

DOI : 10.1371/journal.pone.0067332

Data Sharing by Scientists: Practices and Perceptions


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.

Methodology/Principal Findings

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.