Not as easy as it sounds
Despite these requirements, sharing of health data in a meaningful manner is neither straightforward nor commonplace. We suggest that this is at least partially due to the lack of clear standards and established guidelines explaining where, when and how to share data. We carried out a gap analysis in order to assess the needs of researchers, as well as the resources and training available to them. Our approach was threefold. (1) We used specialist web browsing software to carry out a comprehensive audit of online training courses, learning materials and educational videos related to data sharing in health research by querying Bing, Exalead, Google, Yahoo and YouTube. (2) We conducted a workshop on data sharing and obtained feedback from the attendees regarding their training needs in this field. (The workshop was organised by The Global Health Network in collaboration with the Infectious Diseases Data Observatory and carried out during the EDCTP Ninth Forum (17–21 September 2018) in Lisbon, Portugal.) (3) We investigated repository availability and their characteristics in order to develop a tool that will guide researchers to repositories appropriate for their datasets.
Data sharing is complicated and costly in terms of time, effort, expertise and resources. There are of course other obstacles, including concerns about data sensitivity and patient privacy, as well as the technical aspects of data processing before the data can be shared.2–4 6 7
These challenges hold true across multiple contexts, for example, not just among researchers but also the public8, in high-income settings as well as low-income and middle-income settings. Overall inequality in health data can be linked to poverty,9 and similarly data sharing may be particularly challenging for researchers in low-income and middle-income countries (LMICs).10 For instance, inequities exist between high-income countries (HICs) and LMICs when it comes to data ownership and reuse.11 One of the main concerns of primary researchers is that while they spend time and effort collecting and sharing data, secondary researchers will focus on reusing these data and reaping the benefits, potentially without proper acknowledgment of the primary researchers, and without having contributed to the costs of data generation and processing.12 13 Furthermore, LMIC researchers may not even be able to access outputs of such secondary analyses produced using their own data, particularly if these are published behind a paywall in a HIC, and thus their communities will not be able to benefit from the advancements. Moreover, LMIC researchers will likely be responsible for the necessary community engagement and any ethical concerns of their study participants relating to informed consent and data sharing. On top all of these challenges, LMICs also face problems of limited resources and difficulties in accessing the training necessary to build research capacity for data management, processing, analysis and sharing.11 14–17
The nature of working with data is changing at an unprecedented rate due to advancements in technology and analytics techniques.18 19 Therefore, it is not sufficient to simply require data to be shared, without providing guidance and assistance with the process, especially if the objective is to share the data in a responsible and useful way. Yet, we struggled to find organisations that provide tools and resources necessary to fulfil their requirements of data sharing. Furthermore, the situation is not helped by the lack of follow-up from the organisations requiring that data are shared. Given that there are few incentives and multiple barriers to data sharing, regardless of whether these incentives and barriers are actual or perceived, as well as lack of support and, ultimately, of consequences, perhaps it is not surprising that data sharing has not been taken up more quickly.