Data sharing is the process of sharing with other researchers the deidentified individual patient data underlying the results presented in scientific articles. Recently, the US Institute of Medicine urged biomedical journals, as evaluators and publishers of research results, and implementers of academic standards, to enforce policies that require sharing of clinical trial data (Institute of Medicine, 2015). The International Committee of Medical Journal Editors (ICMJE) pointed out that there is an ethical obligation to responsibly share data generated by interventional clinical trials because participants have put themselves at risk (Longo & Drazen, Reference Longo and Drazen2016; Taichman et al. Reference Taichman, Backus, Baethge, Bauchner, Leeuw, Drazen, Fletcher and Frizelle2016). In addition to clinical trial data, it has been argued that results from observational studies should similarly be shared for the same reasons (Barbui, Reference Barbui2016).

Accountability is a first reason for sharing data. By accessing the raw information underlying the results presented in an article, other researchers may re-run the same analyses that are presented by the study authors, or may plan different analyses to answer the same research question, thus confirming (replicating) the main findings or raising concerns about their robustness and validity under different analytical or statistical assumptions. A second reason for sharing the data is that other researchers may use the shared dataset to answer a different research question. A third reason is that shared datasets of similarly collected data may be used within systematic reviews, to run meta-analyses and individual-patient data meta-analyses. Some also argue that shared datasets may be effectively used for educational purposes (Feldman et al. Reference Feldman, Patel, Ortmann, Robinson and Popovic2012).

There are also challenges in sharing data (Gewin, Reference Gewin2016), such as preserving patient privacy and confidentiality (Sarpatwari et al. Reference Sarpatwari, Kesselheim, Malin, Gagne and Schneeweiss2014), giving credit to those who conducted the study and collected the data (Longo & Drazen, Reference Longo and Drazen2016), the cost for developing reliable repositories of data, and the additional work and cost for researchers, who may be asked to develop datasets suitable for use by others and perhaps to pay for hosting the data in a repository. Running new analyses on shared datasets may also pose scientific issues, as these new analyses, by definition, cannot be pre-planned and may therefore suffer from being guided by the data.

However, solutions to these challenges are coming. Researchers should consider the issue of data sharing from the very beginning of their research projects, by including for example a data sharing plan in the study protocol. It may report a procedure for including provision for data sharing when gaining informed consent, preserving patient confidentiality and privacy when data are shared (Sarpatwari et al. Reference Sarpatwari, Kesselheim, Malin, Gagne and Schneeweiss2014; El Emam et al. Reference El Emam, Rodgers and Malin2015), and details of what is planned to be shared and how, with a timeline. This is relevant as developing a high-quality database in a way suitable for secondary uses may require considerable work, and how the raw data are shared may depend on the type of data that are collected (sometimes it is possible to present the raw data in the main manuscript or in additional supporting files, sometimes a web-based repository is needed). Researchers should also draft a detailed publication plan, with a timeline, in order to give those who gathered the data a chance to make the best use of the database (Drazen, Reference Drazen2014).

Current policies of biomedical journals are very heterogeneous (Barbui, Reference Barbui2016). Some journals do not mention data sharing, and do not require any statement to be published along with the study report on the possibility to access the raw data. Other journals encourage data sharing, and require a formal statement describing under which conditions raw data are accessible. A third policy is implemented by PLOS journals, which require fully availability of all data underlying the findings described in published study reports.

Epidemiology and Psychiatric Sciences announces the implementation of the following requirement on data sharing:

We hope to contribute to the implementation of a new data sharing culture. Currently, a scientific output only corresponds to a study report published in a medical journal, while in the near future it might consist of all materials described in the manuscript, including all relevant raw data. We need to change how we think about data (Drazen, Reference Drazen2015). Data sharing and open science are the future of science.