Data sharing is key for replication and re-use in empirical research. Scientific journals can play a central role by establishing data policies and providing technologies. In this study factors of influence for data sharing are analyzed by investigating journal data policies and author behavior in sociology. The websites of 140 journals from sociology were consulted to check their data policy. The results are compared with similar studies from political science and economics. For five selected journals with a broad variety all articles from two years are examined to see if authors really cite and share their data, and which factors are related to this.
This issue of the Journal of Empirical Research on Human Research Ethics highlights the ethical issues that arise when researchers conducting projects in low- and middle-income countries seek to share the data they produce. Although sharing data is considered a best practice, the barriers to doing so are considerable and there is a need for guidance and examples. To that end, the authors of this article reviewed the articles in this special issue to identify challenges common to the five countries and to offer some practical advice to assist researchers in navigating this "uncharted territory," as some termed it. Concerns around informed consent, data management, data dissemination, and validation of research contributions were cited frequently as particularly challenging areas, so the authors focused on these four topics with the goal of providing specific resources to consult as well as examples of successful projects attempting to solve many of the problems raised.
Data is an important business resource. It forms the basis for various digital technologies such as artificial intelligence or smart services. However, access to data is unequally distributed in the market. Hence, some business ideas fail due to a lack of data sources. Although many governments have recognised the importance of open data and already make administrative data available to the public on a large scale, many companies are still reluctant to share their data among other firms and competitors. As a result, the economic potential of data is far from being fully exploited. Against this background, we analyse current developments in the area of open data. We compare the characteristics of open governmental and open company data in order to define the necessary framework conditions for data sharing. Subsequently, we examine the status quo of data sharing among firms. We use a qualitative analysis of survey data of European companies to derive the sufficient conditions to strengthen data sharing. Our analysis shows that government data is a public good, while company data can be seen as a club or private good. Latter frequently build the core for companies' business models and hence are less suitable for data sharing. Finally, we find that promoting legal certainty and the economic impact present important policy steps for fostering data sharing.
The current emphasis on broad sharing of human genomic data generated in research in order to maximize utility and public benefit is a significant legacy of the Human Genome Project. Concerns about privacy and discrimination have led to policy responses that restrict access to genomic data as the means for protecting research participants. Our research and experience show, however, that a considerable number of research participants agree to open access sharing of their genomic data when given the choice. General policies that limit access to all genomic data fail to respect the autonomy of these participants and, at the same time, unnecessarily limit the utility of the data. We advocate instead a more balanced approach that allows for individual choice and encourages informed decision making, while protecting against the misuse of genomic data through enhanced legislation.
Context and aims Researchers increasingly need to share their data. This requires both adherence to Australia's robust privacy legislation and preparation of comprehensive data management plans. This paper outlines the data-sharing issues managed by IMPACT, a 6-site Canadian-Australian collaborative research program designed to improve access to primary health care for vulnerable individuals. Each site used a common protocol to evaluate its own intervention, with the aim of pooling data across the sites. Ethics applications were submitted in each site.MethodsConsultations were conducted with key informants within one Australian university (UNSW Sydney) and external informants to develop a data sharing plan. The authors reflect upon the process and have identified lessons for others wanting to share data.Findings Data sharing for a multi-site multi-country study was complex. University policies and infrastructure have been changing, not all sharing tools were available and support personnel were still learning how to implement policies related to data sharing. Furthermore, site-specific ethics applications did not specify that the data was part of a larger study. Consequently, the other 5 sites were deemed as external.We needed multiple consultations with ethics, IT, and data governance units to understand data classification (patient data is inherently sensitive), who needed access, and how access could be enabled. Bringing these support units together assisted a common understanding – this had not been previous practice. Innovative contribution to policy, practice and/or researchEarly consultations with university ethics and data governance units is recommended for planning data sharing – particularly for patient data and complex projects.
This article addresses the role of pharmacoepidemiology in patient safety and the crucial role of data sharing in ensuring that such activities occur. Against the backdrop of proposed reforms of European data protection legislation, it considers whether the current legislative landscape adequately facilitates this essential data sharing. It is argued that rather than maximising and promoting the benefits of such activities by facilitating data sharing, current and proposed legislative landscapes hamper these vital activities. The article posits that current and proposed data protection approaches to pharmacoepidemiology — and more broadly, re-uses of data — should be reoriented towards enabling these important safety enhancing activities. Two potential solutions are offered: 1) a dedicated working party on data reuse for health research and 2) the introduction of new, dedicated legislation.
A model of cloud services is emerging whereby a few trusted providers manage the underlying hardware and communications whereas many companies build on this infrastructure to offer higher level, cloud-hosted PaaS services and/or SaaS applications. From the start, strong isolation between cloud tenants was seen to be of paramount importance, provided first by virtual machines (VM) and later by containers, which share the operating system (OS) kernel. Increasingly it is the case that applications also require facilities to effect isolation and protection of data managed by those applications. They also require flexible data sharing with other applications, often across the traditional cloud-isolation boundaries; for example, when government, consisting of different departments, provides services to its citizens through a common platform. These concerns relate to the management of data. Traditional access control is application and principal/role specific, applied at policy enforcement points, after which there is no subsequent control over where data flows;a crucial issue once data has left its owner's control by cloud-hosted applications andwithin cloud-services. Information Flow Control (IFC), in addition, offers system-wide, end-To-end, flow control based on the properties of the data. We discuss the potential of cloud-deployed IFC for enforcing owners' data flow policy with regard to protection and sharing, aswell as safeguarding against malicious or buggy software. In addition, the audit log associated with IFC provides transparency and offers system-wide visibility over data flows. This helps those responsible to meet their data management obligations, providing evidence of compliance, and aids in the identification ofpolicy errors and misconfigurations. We present our IFC model and describe and evaluate our IFC architecture and implementation (CamFlow). This comprises an OS level implementation of IFC with support for application management, together with an IFC-enabled middleware.
This article reports the results of a survey conducted between 18th November and 18th December 2017 about different aspects of data sharing: tools used in building metadata, problems encountered in order to share the data, the propensity to share the data, the satisfaction obtained over different working tasks. After a short description of the data gathering task, the report describes the sample, the univariate distribution of the most important variables related to the work of data archiving and the attitudes concerning the data sharing activity: problems encountered, propensity to share the data, satisfaction obtained. Part of the report illustrates models suitable for interpreting the results and finally gives some advice for promoting data services. Some international comparisons of the results are proposed in the annex.
