Biobanks are well-organized resources comprising biological samples and associated information that are accessible to scientific investigation. Across Europe, millions of samples with related data are held in different types of collections. While individual collections can be well organized and accessible, the resources are subject to fragmentation, insecurity of funding and incompleteness. To address these issues, a Biobanking and BioMolecular Resources Infrastructure (BBMRI) is to be developed across Europe, thereby implementing a European 'roadmap' for research infrastructures that was developed by a forum of EU member states and that has been received by the European Commission. In this review, we describe the work involved in preparing for the construction of BBMRI in a European and global context.

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The successful and systematic collection of demographic and lifestyle data is central in the process of any epidemiological study. The traditionally used methods such as face-to-face and telephone interviews as well as paper-questionnaires are increasingly failing to produce good qualitative results within financially feasible limits. Tools that are better suited for the present dynamic populations are needed and the Internet presents a powerful alternative for the collection of data with several intrinsic features still unexplored.

The known challenge of underutilization of data and biological material from biorepositories as potential resources for medical research has been the focus of discussion for over a decade. Recently developed guidelines for improved data availability and reusability—entitled FAIR Principles (Findability, Accessibility, Interoperability, and Reusability)—are likely to address only parts of the problem. In this article, we argue that biological material and data should be viewed as a unified resource. This approach would facilitate access to complete provenance information, which is a prerequisite for reproducibility and meaningful integration of the data. A unified view also allows for optimization of long-term storage strategies, as demonstrated in the case of biobanks. We propose an extension of the FAIR Principles to include the following additional components: (1) quality aspects related to research reproducibility and meaningful reuse of the data, (2) incentives to stimulate effective enrichment of data sets and biological material collections and its reuse on all levels, and (3) privacy-respecting approaches for working with the human material and data. These FAIR-Health principles should then be applied to both the biological material and data. We also propose the development of common guidelines for cloud architectures, due to the unprecedented growth of volume and breadth of medical data generation, as well as the associated need to process the data efficiently.

Background Physical activity is associated with reduced risks of many chronic diseases. Data collected on physical activity in large epidemiological studies is often based on paper questionnaires. The validity of these questionnaires is debated, and more effective methods are needed.Objective This study evaluates repeated measures of physical activity level (PAL) and the feasibility of using a Java-based questionnaire downloaded onto cell phones for collection of such data. The data obtained were compared with reference estimates based on the doubly labeled water method and indirect calorimetry (PALref).Method Using a Java-based cell phone application, 22 women reported their physical activity based on two short questions answered daily over a 14-day period (PALcell). Results were compared with reference data obtained from the doubly labeled water method and indirect calorimetry (PALref). Results were also compared against physical activity levels assessed by two regular paper questionnaires completed by women at the end of the 14-day period (PALquest1 and PALquest2). PALcell, PALquest1, and PALquest2 were compared with PALref using the Bland and Altman procedure.Results The mean difference between PALcell and PALref was small (0.014) with narrow limits of agreement (2SD = 0.30). Compared with PALref, the mean difference was also small for PALquest1 and PALquest2 (0.004 and 0.07, respectively); however, the limits of agreement were wider (PALquest1, 2SD = 0.50 and PALquest2, 2SD = 0.90). The test for trend was statistically significant for PALquest1 (slope of regression line = 0.79, P = .04) as well as for PALquest2 (slope of regression line = 1.58, P < .001) when compared with PALref.Conclusion A Java-based physical activity questionnaire administered daily using cell phones produced PAL estimates that agreed well with PAL reference values. Furthermore, the limits of agreement between PAL obtained using cell phones, and reference values were narrower than for corresponding estimates obtained using paper questionnaires. Java-based questionnaires downloaded onto cell phones may be a feasible and cost-effective method of data collection for large-scale prospective studies of physical activity.