Authors : Renata Gonçalves Curty, Kevin Crowston, Alison Specht, Bruce W. Grant, Elizabeth D. Dalton
The value of sharing scientific research data is widely appreciated, but factors that hinder or prompt the reuse of data remain poorly understood. Using the Theory of Reasoned Action, we test the relationship between the beliefs and attitudes of scientists towards data reuse, and their self-reported data reuse behaviour.
To do so, we used existing responses to selected questions from a worldwide survey of scientists developed and administered by the DataONE Usability and Assessment Working Group (thus practicing data reuse ourselves).
Results show that the perceived efficacy and efficiency of data reuse are strong predictors of reuse behaviour, and that the perceived importance of data reuse corresponds to greater reuse. Expressed lack of trust in existing data and perceived norms against data reuse were not found to be major impediments for reuse contrary to our expectations.
We found that reported use of models and remotely-sensed data was associated with greater reuse. The results suggest that data reuse would be encouraged and normalized by demonstration of its value.
We offer some theoretical and practical suggestions that could help to legitimize investment and policies in favor of data sharing.
Initiatives for sharing research data are opportunities to increase the pace of knowledge discovery and scientific progress. The reuse of research data has the potential to avoid the duplication of data sets and to bring new views from multiple analysis of the same data set.
For example, the study of genomic variations associated with cancer profits from the universal collection of such data and helps in selecting the most appropriate therapy for a specific patient. However, data sharing poses challenges to the scientific community.
These challenges are of ethical, cultural, legal, financial, or technical nature. This article reviews the impact that data sharing has in science and society and presents guidelines to improve the efficient sharing of research data.
Issues of balancing data accessibility with ethical considerations and governance of a genomics research biobank, Generation Scotland, are explored within the evolving policy landscape of the past ten years. During this time data sharing and open data access have become increasingly important topics in biomedical research.
Decisions around data access are influenced by local arrangements for governance and practices such as linkage to health records, and the global through policies for biobanking and the sharing of data with large-scale biomedical research data resources and consortia.
We use a literature review of policy relevant documents which apply to the conduct of biobanks in two areas: support for open access and the protection of data subjects and researchers managing a bioresource.
We present examples of decision making within a biobank based upon observations of the Generation Scotland Access Committee. We reflect upon how the drive towards open access raises ethical dilemmas for established biorepositories containing data and samples from human subjects.
Despite much discussion in science policy literature about standardisation, the contextual aspects of biobanking are often overlooked. Using our engagement with GS we demonstrate the importance of local arrangements in the creation of a responsive ethical approach to biorepository governance.
We argue that governance decisions regarding access to the biobank are intertwined with considerations about maintenance and viability at the local level. We show that in addition to the focus upon ever more universal and standardised practices, the local expertise gained in the management of such repositories must be supported.
A commitment to open access in genomics research has found almost universal backing in science and health policy circles, but repositories of data and samples from human subjects may have to operate under managed access, to protect privacy, align with participant consent and ensure that the resource can be managed in a sustainable way.
Data access committees need to be reflexive and flexible, to cope with changing technology and opportunities and threats from the wider data sharing environment. To understand these interactions also involves nurturing what is particular about the biobank in its local context.
This paper describes a preliminary study of research transparency, which draws on the findings from four focus group sessions with faculty in chemistry, law, urban and social studies, and civil and environmental engineering.
The multi-faceted nature of transparency is highlighted by the broad ways in which the faculty conceptualised the concept (data sharing, ethics, replicability) and the vocabulary they used with common core terms identified (data, methods, full disclosure).
The associated concepts of reproducibility and trust are noted. The research lifecycle stages are used as a foundation to identify the action verbs and software tools associated with transparency.
A range of transparency drivers and motivations are listed. The role of libraries and data scientists is discussed in the context of the provision of transparency services for researchers.
Authors : Pranammya Dey, Joseph S. Ross, Jessica D. Ritchie, Nihar R. Desai, Sanjeev P. Bhavnani, Harlan M. Krumholz
Sharing deidentified patient-level research data presents immense opportunities to all stakeholders involved in cardiology research and practice. Sharing data encourages the use of existing data for knowledge generation to improve practice, while also allowing for validation of disseminated research.
In this review, we discuss key initiatives and platforms that have helped to accelerate progress toward greater sharing of data. These efforts are being prompted by government, universities, philanthropic sponsors of research, major industry players, and collaborations among some of these entities.
As data sharing becomes a more common expectation, policy changes will be required to encourage and assist data generators with the process of sharing the data they create.
Patients also will need access to their own data and to be empowered to share those data with researchers. Although medicine still lags behind other fields in achieving data sharing’s full potential, cardiology research has the potential to lead the way.
Authors : Laura McDonald, Anna Schultze, Alex Simpson, Sophie Graham, Radek Wasiak, Sreeram V. Ramagopalan
In order to understand the current state of data sharing in observational research studies, we reviewed data sharing statements of observational studies published in a general medical journal, the British Medical Journal.
We found that the majority (63%) of observational studies published between 2015 and 2017 included a statement that implied that data used in the study could not be shared. If the findings of our exploratory study are confirmed, room for improvement in the sharing of real-world or observational research data exists.
Authors : Daniel S. Falster, Richard G. FitzJohn, Matthew W. Pennell, William K. Cornwell
The sharing and re-use of data has become a cornerstone of modern science. Multiple platforms now allow quick and easy data sharing. So far, however, data publishing models have not accommodated on-going scientific improvements in data: for many problems, datasets continue to grow with time — more records are added, errors fixed, and new data structures are created. In other words, datasets, like scientific knowledge, advance with time.
We therefore suggest that many datasets would be usefully published as a series of versions, with a simple naming system to allow users to perceive the type of change between versions. In this article, we argue for adopting the paradigm and processes for versioned data, analogous to software versioning.
We also introduce a system called Versioned Data Delivery and present tools for creating, archiving, and distributing versioned data easily, quickly, and cheaply. These new tools allow for individual research groups to shift from a static model of data curation to a dynamic and versioned model that more naturally matches the scientific process.