DataCite – services and support for opening up research data

Scientific Information is more than a journal article or a book, are data. Libraries should open their catalogues to any kind of information: data. URLs are not persistent to identify datasets. DOI (Digital Object Identifier) offer a solution for the Data citation: persistent identifier enabling citation and providing a stable link to digital resources, like research data sets. DOI names for access and citations. DataCite is a global consortium focused on working with data centres and organisations that hold data, providing standards, workflows and best-practice

Theory and Practice of Data Citation

Citations are the cornerstone of knowledge propagation and the primary means of assessing the quality of research, as well as directing investments in science. Science is increasingly becoming "data-intensive", where large volumes of data are collected and analyzed to discover complex patterns through simulations and experiments, and most scientific reference works have been replaced by online curated datasets. Yet, given a dataset, there is no quantitative, consistent and established way of knowing how it has been used over time, who contributed to its curation, what results have been yielded or what value it has. The development of a theory and practice of data citation is fundamental for considering data as first-class research objects with the same relevance and centrality of traditional scientific products. Many works in recent years have discussed data citation from different viewpoints: illustrating why data citation is needed, defining the principles and outlining recommendations for data citation systems, and providing computational methods for addressing specific issues of data citation. The current panorama is many-faceted and an overall view that brings together diverse aspects of this topic is still missing. Therefore, this paper aims to describe the lay of the land for data citation, both from the theoretical (the why and what) and the practical (the how) angle.Comment: 24 pages, 2 tables, pre-print accepted in Journal of the Association for Information Science and Technology (JASIST), 201

A Data Citation Roadmap for Scholarly Data Repositories [preprint]

This article presents a practical roadmap for scholarly data repositories to implement data citation in accordance with the Joint Declaration of Data Citation Principles, a synopsis and harmonization of the recommendations of major science policy bodies. The roadmap was developed by the Repositories Expert Group, as part of the Data Citation Implementation Pilot (DCIP) project, an initiative of FORCE11.org and the NIH BioCADDIE (https://biocaddie.org) program. The roadmap makes 11 specific recommendations, grouped into three phases of implementation: a) required steps needed to support the Joint Declaration of Data Citation Principles, b) recommended steps that facilitate article/data publication workflows, and c) optional steps that further improve data citation support provided by data repositories

Out of cite, out of mind: the current state of practice, policy, and technology for the citation of data

PREFACE The growth in the capacity of the research community to collect and distribute data presents huge opportunities. It is already transforming old methods of scientific research and permitting the creation of new ones. However, the exploitation of these opportunities depends upon more than computing power, storage, and network connectivity. Among the promises of our growing universe of online digital data are the ability to integrate data into new forms of scholarly publishing to allow peer-examination and review of conclusions or analysis of experimental and observational data and the ability for subsequent researchers to make new analyses of the same data, including their combination with other data sets and uses that may have been unanticipated by the original producer or collector. The use of published digital data, like the use of digitally published literature, depends upon the ability to identify, authenticate, locate, access, and interpret them. Data citations provide necessary support for these functions, as well as other functions such as attribution of credit and establishment of provenance. References to data, however, present challenges not encountered in references to literature. For example, how can one specify a particular subset of data in the absence of familiar conventions such as page numbers or chapters? The traditions and good practices for maintaining the scholarly record by proper references to a work are well established and understood in regard to journal articles and other literature, but attributing credit by bibliographic references to data are not yet so broadly implemented

Sharing and citing research data: A repository's perspective

Formal data citation is a key element of the growing data-sharing infrastructure, not only facilitating sharing, discovery, and proper use, but also enabling data impact tracking that allows researchers to receive credit for their contributions. Specialized data repositories, such as ICPSR, integrate data citations within study metadata to enhance access and encourage data sharing. National and international efforts are underway to encourage adoption of these types of practices. The eventual result should be that more data creators will benefit from citations by receiving credit for their work. More researchers will benefit by readily finding reproducible research. And more funding agencies will benefit by tracking supported projects’ usage and gauging impact beyond the initial funding. ICPSR and its topical archives, like NACJD, provide an example of how data citation can encourage data archiving and secondary use. They support the growth of the ICPSR Bibliography of Data-related Literature and see the collection as evidence of new scientific findings for consideration in shaping public policy. The Bibliography’s two-way linkages between data and data-associated publications have improved the discovery and the chances of good secondary use of ICPSR data. Due to inconsistent and inadequate data-citing practices in the scholarly literature, tracking data reuse is costly and labor-intensive. Despite this, ICPSR continues to value and invest in the collection of data-related publications, while promoting the creation and use of standards for citing and sharing research data according to best practices.http://deepblue.lib.umich.edu/bitstream/2027.42/115490/1/012_Ch04_Sharing_and_Citing_Resear_final.pdfDescription of 012_Ch04_Sharing_and_Citing_Resear_final.pdf : Book chapte

Enabling sharing and reuse of scientific data

The purpose of this study was to develop an understanding of the current state of scientific data sharing that stakeholders could use to develop and implement effective data sharing strategies and policies. The study developed a conceptual model to describe the process of data sharing, and the drivers, barriers, and enablers that determine stakeholder engagement. The conceptual model was used as a framework to structure discussions and interviews with key members of all stakeholder groups. Analysis of data obtained from interviewees identified a number of themes that highlight key requirements for the development of a mature data sharing culture

Notes from Research Data Alliance Plenary Meeting in Dublin, Ireland

Notes from the Third Plenary for the Research Data Alliance in Dublin, Ireland on March 26 to 28, 2014 with focus on starting an institutional research data repository

DataCite – services and support for opening up research data

Scientific Information is more than a journal article or a book, are data. Libraries should open their catalogues to any kind of information: data. URLs are not persistent to identify datasets. DOI (Digital Object Identifier) offer a solution for the Data citation: persistent identifier enabling citation and providing a stable link to digital resources, like research data sets. DOI names for access and citations. DataCite is a global consortium focused on working with data centres and organisations that hold data, providing standards, workflows and best-practice