Étiquette : data sharing

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How to responsibly acknowledge research work in the era of big data and biobanks: ethical aspects of the Bioresource Research Impact Factor (BRIF)

Authors : Heidi Carmen Howard, Deborah Mascalzoni, Laurence Mabile, Gry Houeland, Emmanuelle Rial-Sebbag, Anne Cambon-Thomsen

Currently, a great deal of biomedical research in fields such as epidemiology, clinical trials and genetics is reliant on vast amounts of biological and phenotypic information collected and assembled in biobanks.

While many resources are being invested to ensure that comprehensive and well-organised biobanks are able to provide increased access to, and sharing of biomedical samples and information, many barriers and challenges remain to such responsible and extensive sharing.

Germane to the discussion herein is the barrier to collecting and sharing bioresources related to the lack of proper recognition of researchers and clinicians who developed the bioresource. Indeed, the efforts and resources invested to set up and sustain a bioresource can be enormous and such work should be easily traced and properly recognised.

However, there is currently no such system that systematically and accurately traces and attributes recognition to those doing this work or the bioresource institution itself. As a beginning of a solution to the “recognition problem”, the Bioresource Research Impact Factor/Framework (BRIF) initiative was proposed almost a decade and a half ago and is currently under further development.

With the ultimate aim of increasing awareness and understanding of the BRIF, in this article, we contribute the following: (1) a review of the objectives and functions of the BRIF including the description of two tools that will help in the deployment of the BRIF, the CoBRA (Citation of BioResources in journal Articles) guideline, and the Open Journal of Bioresources (OJB); (2) the results of a small empirical study on stakeholder awareness of the BRIF and (3) a brief analysis of the ethical dimensions of the BRIF which allow it to be a positive contribution to responsible biobanking.

URL : How to responsibly acknowledge research work in the era of big data and biobanks: ethical aspects of the Bioresource Research Impact Factor (BRIF)

Alternative locaton : https://link.springer.com/article/10.1007/s12687-017-0332-6

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Rethinking Data Sharing and Human Participant Protection in Social Science Research: Applications from the Qualitative Realm

Authors : Dessi Kirilova, Sebastian Karcher

While data sharing is becoming increasingly common in quantitative social inquiry, qualitative data are rarely shared. One factor inhibiting data sharing is a concern about human participant protections and privacy.

Protecting the confidentiality and safety of research participants is a concern for both quantitative and qualitative researchers, but it raises specific concerns within the epistemic context of qualitative research.

Thus, the applicability of emerging protection models from the quantitative realm must be carefully evaluated for application to the qualitative realm. At the same time, qualitative scholars already employ a variety of strategies for human-participant protection implicitly or informally during the research process.

In this practice paper, we assess available strategies for protecting human participants and how they can be deployed. We describe a spectrum of possible data management options, such as de-identification and applying access controls, including some already employed by the Qualitative Data Repository (QDR) in tandem with its pilot depositors.

Throughout the discussion, we consider the tension between modifying data or restricting access to them, and retaining their analytic value.

We argue that developing explicit guidelines for sharing qualitative data generated through interaction with humans will allow scholars to address privacy concerns and increase the secondary use of their data.

URL : Rethinking Data Sharing and Human Participant Protection in Social Science Research: Applications from the Qualitative Realm

DOI : http://doi.org/10.5334/dsj-2017-043

 

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The new alchemy: Online networking, data sharing and research activity distribution tools for scientists

Authors : Antony J. Williams, Lou Peck, Sean Ekins

There is an abundance of free online tools accessible to scientists and others that can be used for online networking, data sharing and measuring research impact. Despite this, few scientists know how these tools can be used or fail to take advantage of using them as an integrated pipeline to raise awareness of their research outputs.

In this article, the authors describe their experiences with these tools and how they can make best use of them to make their scientific research generally more accessible, extending its reach beyond their own direct networks, and communicating their ideas to new audiences.

These efforts have the potential to drive science by sparking new collaborations and interdisciplinary research projects that may lead to future publications, funding and commercial opportunities.

The intent of this article is to: describe some of these freely accessible networking tools and affiliated products; demonstrate from our own experiences how they can be utilized effectively; and, inspire their adoption by new users for the benefit of science.

URL : The new alchemy: Online networking, data sharing and research activity distribution tools for scientists

DOI : http://dx.doi.org/10.12688/f1000research.12185.1

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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

 

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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

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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

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Journal Data Sharing Policies and Statistical Reporting Inconsistencies in Psychology

Authors : Michele Nuijten, Jeroen Borghuis, Coosje Veldkamp, Linda Alvarez, Marcel van Assen, Jelte Wicherts

In this paper, we present three studies that investigate the relation between data sharing and statistical reporting inconsistencies. Previous research found that reluctance to share data was related to a higher prevalence of statistical errors, often in the direction of statistical significance (Wicherts, Bakker, & Molenaar, 2011).

We therefore hypothesized that journal policies about data sharing and data sharing itself would reduce these inconsistencies. In Study 1, we compared the prevalence of reporting inconsistencies in two similar journals on decision making with different data sharing policies.

In Study 2, we compared reporting inconsistencies in articles published in PLOS (with a data sharing policy) and Frontiers in Psychology (without a data sharing policy). In Study 3, we looked at papers published in the journal Psychological Science to check whether papers with or without an Open Practice Badge differed in the prevalence of reporting errors.

Overall, we found no relationship between data sharing and reporting inconsistencies. We did find that journal policies on data sharing are extremely effective in promoting data sharing.

We argue that open data is essential in improving the quality of psychological science, and we discuss ways to detect and reduce reporting inconsistencies in the literature.

DOI : https://dx.doi.org/10.17605/OSF.IO/SGBTA