Étiquette : data sharing

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Open Science: What, Why, and How

Authors : Barbara A. Spellman, Elizabeth A. Gilbert, Katherine S. Corker

Open Science is a collection of actions designed to make scientific processes more transparent and results more accessible. Its goal is to build a more replicable and robust science; it does so using new technologies, altering incentives, and changing attitudes.

The current movement towards open science was spurred, in part, by a recent “series of unfortunate events” within psychology and other sciences.

These events include the large number of studies that have failed to replicate and the prevalence of common research and publication procedures that could explain why.

Many journals and funding agencies now encourage, require, or reward some open science practices, including pre-registration, providing full materials, posting data, distinguishing between exploratory and confirmatory analyses, and running replication studies.

Individuals can practice and encourage open science in their many roles as researchers, authors, reviewers, editors, teachers, and members of hiring, tenure, promotion, and awards committees.

A plethora of resources are available to help scientists, and science, achieve these goals.

URL : https://osf.io/preprints/psyarxiv/ak6jr

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Perseids: Experimenting with Infrastructure for Creating and Sharing Research Data in the Digital Humanities

Author : Bridget Almas

The Perseids project provides a platform for creating, publishing, and sharing research data, in the form of textual transcriptions, annotations and analyses. An offshoot and collaborator of the Perseus Digital Library (PDL),

Perseids is also an experiment in reusing and extending existing infrastructure, tools, and services.

This paper discusses infrastructure in the domain of digital humanities (DH). It outlines some general approaches to facilitating data sharing in this domain, and the specific choices we made in developing Perseids to serve that goal.

It concludes by identifying lessons we have learned about sustainability in the process of building Perseids, noting some critical gaps in infrastructure for the digital humanities, and suggesting some implications for the wider community.

URL : Perseids: Experimenting with Infrastructure for Creating and Sharing Research Data in the Digital Humanities

DOI : http://doi.org/10.5334/dsj-2017-019

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Strengthening institutional data management and promoting data sharing in the social and economic sciences

Authors : Monika Linne, Wolfgang Zenk-Möltgen

In the German social and economic sciences there is a growing awareness of flexible data distribution and research data reuse, especially as increasing numbers of research funders recommend publishing research data as the basis for scientific insight.

However, a data-sharing mentality has not yet been established in Germany attributable to researchers’ strong reservations about publishing their data.

This attitude is exacerbated by the fact that, at present, there is no trusted national data sharing repository that covers the particular requirements of institutions regarding research data.

This article discusses how this objective can be achieved with the project initiative SowiDataNet.

The development of a community-driven data repository is a logically consistent and important step towards an attitude shift concerning data sharing in the social and economic sciences.

DOI : http://doi.org/10.18352/lq.10195

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Evaluating and Promoting Open Data Practices in Open Access Journals

Authors : Eleni Castro, Mercè Crosas, Alex Garnett, Kasey Sheridan, Micah Altman

In the last decade there has been a dramatic increase in attention from the scholarly communications and research community to open access (OA) and open data practices.

These are potentially related, because journal publication policies and practices both signal disciplinary norms, and provide direct incentives for data sharing and citation. However, there is little research evaluating the data policies of OA journals.

In this study, we analyze the state of data policies in open access journals, by employing random sampling of the Directory of Open Access Journals (DOAJ) and Open Journal Systems (OJS) journal directories, and applying a coding framework that integrates both previous studies and emerging taxonomies of data sharing and citation.

This study, for the first time, reveals both the low prevalence of data sharing policies and practices in OA journals, which differs from the previous studies of commercial journals’ in specific disciplines.

URL : Evaluating and Promoting Open Data Practices in Open Access Journals

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On the Reuse of Scientific Data

Authors : Irene V. Pasquetto, Bernadette M. Randles, Christine L. Borgman

While science policy promotes data sharing and open data, these are not ends in themselves. Arguments for data sharing are to reproduce research, to make public assets available to the public, to leverage investments in research, and to advance research and innovation.

To achieve these expected benefits of data sharing, data must actually be reused by others. Data sharing practices, especially motivations and incentives, have received far more study than has data reuse, perhaps because of the array of contested concepts on which reuse rests and the disparate contexts in which it occurs.

Here we explicate concepts of data, sharing, and open data as a means to examine data reuse. We explore distinctions between use and reuse of data.

Lastly we propose six research questions on data reuse worthy of pursuit by the community: How can uses of data be distinguished from reuses? When is reproducibility an essential goal? When is data integration an essential goal? What are the tradeoffs between collecting new data and reusing existing data? How do motivations for data collection influence the ability to reuse data? How do standards and formats for data release influence reuse opportunities?

We conclude by summarizing the implications of these questions for science policy and for investments in data reuse.

URL : On the Reuse of Scientific Data

DOI : http://doi.org/10.5334/dsj-2017-008

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Research Data Reusability: Conceptual Foundations, Barriers and Enabling Technologies

Author : Costantino Thanos

High-throughput scientific instruments are generating massive amounts of data. Today, one of the main challenges faced by researchers is to make the best use of the world’s growing wealth of data. Data (re)usability is becoming a distinct characteristic of modern scientific practice.

By data (re)usability, we mean the ease of using data for legitimate scientific research by one or more communities of research (consumer communities) that is produced by other communities of research (producer communities).

Data (re)usability allows the reanalysis of evidence, reproduction and verification of results, minimizing duplication of effort, and building on the work of others. It has four main dimensions: policy, legal, economic and technological. The paper addresses the technological dimension of data reusability.

The conceptual foundations of data reuse as well as the barriers that hamper data reuse are presented and discussed. The data publication process is proposed as a bridge between the data author and user and the relevant technologies enabling this process are presented.

URL : Research Data Reusability: Conceptual Foundations, Barriers and Enabling Technologies

DOI : http://dx.doi.org/10.3390/publications5010002

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Knowledge Sharing as a Social Dilemma in Pharmaceutical Innovation

Author : Daria Kim

The article addresses the problem of restricted access to industry-sponsored clinical trial data. In particular, it analyses the intersection of the competing claims that mandatory disclosure of pharmaceutical test data impedes innovation incentives, and that access facilitates new drug development.

These claims are characterised in terms of public-good and common-resource dilemmas. The analysis finds that confidentiality protection of primary research data plays an ambiguous role.

While secrecy, as such, does not solve the public-good problem in pharmaceutical innovation (in the presence of regulatory instruments that protect the originator drug against generic competition), it is likely to exacerbate the common-resource problem, in view of data as a source of verified and new knowledge.

It is argued that the claim of the research-based industry that disclosure of clinical data impedes innovation incentives is misplaced and should not be leveraged against the pro-access policies. The analysis proposes that regulation should adhere to the principle that protection should be confined to competition by imitation.

This implies that the rules of access should be designed in such a way that third-party use of data does not interfere with protection against generic competition. At the same time, the long-term collective benefit can be maximised when the ‘cooperative choice’ – i.e. when everyone shares data – becomes the ‘dominant strategy’.

This can be achieved only when access is not subject to the authorisation of the initial trial sponsors, and when primary data is aggregated, refined and managed on the collective basis.

URL : https://ssrn.com/abstract=2834493