How to share data for collaboration

Authors : Shannon E Ellis, Jeffrey T Leek

Within the statistics community, a number of guiding principles for sharing data have emerged; however, these principles are not always made clear to collaborators generating the data. To bridge this divide, we have established a set of guidelines for sharing data.

In these, we highlight the need to provide raw data to the statistician, the importance of consistent formatting, and the necessity of including all essential experimental information and pre-processing steps carried out to the statistician. With these guidelines we hope to avoid errors and delays in data analysis.

URL : How to share data for collaboration

DOI : https://doi.org/10.7287/peerj.preprints.3139v1

 

Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data

Authors : Julie A. McMurry, Nick Juty, Niklas Blomberg, Tony Burdett, Tom Conlin, Nathalie Conte, Mélanie Courtot, John Deck, Michel Dumontier, Donal K. Fellows, Alejandra Gonzalez-Beltran, Philipp Gormanns, Jeffrey Grethe, Janna Hastings, Jean-Karim Hériché, Henning Hermjakob, Jon C. Ison, Rafael C. Jimenez, Simon Jupp, John Kunze, Camille Laibe, Nicolas Le Novère, James Malone, Maria Jesus Martin, Johanna R. McEntyre, Chris Morris, Juha Muilu, Wolfgang Müller, Philippe Rocca-Serra, Susanna-Assunta Sansone, Murat Sariyar, Jacky L. Snoep, Stian Soiland-Reyes, Natalie J. Stanford, Neil Swainston, Nicole Washington, Alan R. Williams, Sarala M. Wimalaratne, Lilly M. Winfree, Katherine Wolstencroft, Carole Goble, Christopher J. Mungall, Melissa A. Haendel, Helen Parkinson

In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure.

Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers.

We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability.

We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

URL : Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data

DOI : https://doi.org/10.1371/journal.pbio.2001414

The legal and policy framework for scientific data sharing, mining and reuse

Author : Mélanie Dulong de Rosnay

Text and Data Mining, the automatic processing of large amounts of scientific articles and datasets, is an essential practice for contemporary researchers. Some publishers are challenging it as a lawful activity and the topic is being discussed during European copyright law reform process.

In order to better understand the underlying debate and contribute to the policy discussion, this article first examines the legal status of data access and reuse and licensing policies. It then presents available options supporting the exercise of Text and Data Mining: publication under open licenses, open access legislations and a recognition of the legitimacy of the activity.

For that purpose, the paper analyses the scientific rational for sharing and its legal and technical challenges and opportunities. In particular, it surveys existing open access and open data legislations and discusses implementation in European and Latin America jurisdictions.

Framing Text and Data mining as an exception to copyright could be problematic as it de facto denies that this activity is part of a positive right to read and should not require additional permission nor licensing.

It is crucial in licenses and legislations to provide a correct definition of what is Open Access, and to address the question of pre-existing copyright agreements. Also, providing implementation means and technical support is key. Otherwise, legislations could remain declarations of good principles if repositories are acting as empty shells.

URL ; https://books.openedition.org/editionsmsh/9082

Scientific data from and for the citizen

Authors : Sven Schade, Chrisa Tsinaraki, Elena Roglia

Powered by advances of technology, today’s Citizen Science projects cover a wide range of thematic areas and are carried out from local to global levels. This wealth of activities creates an abundance of data, for example, in the forms of observations submitted by mobile phones; readings of low-cost sensors; or more general information about peoples’ activities.

The management and possible sharing of this data has become a research topic in its own right. We conducted a survey in the summer of 2015 in order to collectively analyze the state of play in Citizen Science.

This paper summarizes our main findings related to data access, standardization and data preservation. We provide examples of good practices in each of these areas and outline actions to address identified challenges.

URL : http://firstmonday.org/ojs/index.php/fm/article/view/7842

Towards a paradigm for open and free sharing of scientific data on global change science in China

Authors : Changhui Peng, Xinzhang Song, Hong Jiang, Qiuan Zhu, Huai Chen, Jing M. Chen, Peng Gong, Chang Jie, Wenhua Xiang, Guirui Yu, Xiaolu Zhou

Despite great progress in data sharing that has been made in China in recent decades, cultural, policy, and technological challenges have prevented Chinese researchers from maximizing the availability of their data to the global change science community.

To achieve full and open exchange and sharing of scientific data, Chinese research funding agencies need to recognize that preservation of, and access to, digital data are central to their mission, and must support these tasks accordingly.

The Chinese government also needs to develop better mechanisms, incentives, and rewards, while scientists need to change their behavior and culture to recognize the need to maximize the usefulness of their data to society as well as to other researchers.

The Chinese research community and individual researchers should think globally and act personally to promote a paradigm of open, free, and timely data sharing, and to increase the effectiveness of knowledge development.

URL : Towards a paradigm for open and free sharing of scientific data on global change science in China

DOI : http://dx.doi.org/10.1002/ehs2.1225

Issues in the development of open access to research data

This paper explores key issues in the development of open access to research data. The use of digital means for developing, storing and manipulating data is creating a focus on ‘data-driven science’. One aspect of this focus is the development of ‘open access’ to research data.

Open access to research data refers to the way in which various types of data are openly available to public and private stakeholders, user communities and citizens. Open access to research data, however, involves more than simply providing easier and wider access to data for potential user groups. The development of open access requires attention to the ways data are considered in different areas of research.

We identify how open access is being unevenly developed across the research environment and the consequences this has in terms of generating data gaps. Data gaps refer to the way data becomes detached from published conclusions. To address these issues, we examine four main areas in developing open access to research data: stakeholder roles and values; technological requirements for managing and sharing data; legal and ethical regulations and procedures; institutional roles and policy frameworks.

We conclude that problems of variability and consistency across the open access ecosystem need to be addressed within and between these areas to ensure that risks surrounding a data gap are managed in open access.

URL : http://dx.doi.org/10.1080/08109028.2014.956505

Publishing without Publishers: a Decentralized Approach to Dissemination, Retrieval, and Archiving of Data

« Making available and archiving scientific results is for the most part still considered the task of classical publishing companies, despite the fact that classical forms of publishing centered around printed narrative articles no longer seem well-suited in the digital age. In particular, there exist currently no efficient, reliable, and agreed-upon methods for publishing scientific datasets, which have become increasingly important for science. Here we propose to design scientific data publishing as a Web-based bottom-up process, without top-down control of central authorities such as publishing companies. We present a protocol and a server network to decentrally store and archive data in the form of nanopublications, an RDF-based format to represent scientific data with formal semantics. We show how this approach allows researchers to produce, publish, retrieve, address, verify, and recombine datasets and their individual nanopublications in a reliable and trustworthy manner, and we argue that this architecture could be used for the Semantic Web in general. Our evaluation of the current small network shows that this system is efficient and reliable, and we discuss how it could grow to handle the large amounts of structured data that modern science is producing and consuming. »

URL : http://arxiv-web3.library.cornell.edu/abs/1411.2749