Data science is facing the following major challenges: (1) developing scalable cross-disciplinary capabilities, (2) dealing with the increasing data volumes and their inherent complexity, (3) building tools that help to build trust, (4) creating mechanisms to efficiently operate in the domain of scientific assertions, (5) turning data into actionable knowledge units and (6) promoting data interoperability. As a way to overcome these challenges, we further develop the proposals by early Internet pioneers for Digital Objects as encapsulations of data and metadata made accessible by persistent identifiers. In the past decade, this concept was revisited by various groups within the Research Data Alliance and put in the context of the FAIR Guiding Principles for findable, accessible, interoperable and reusable data. The basic components of a FAIR Digital Object (FDO) as a self-contained, typed, machine-actionable data package are explained. A survey of use cases has indicated the growing interest of research communities in FDO solutions. We conclude that the FDO concept has the potential to act as the interoperable federative core of a hyperinfrastructure initiative such as the European Open Science Cloud (EOSC).

Data Science (DS) as defined by Jim Gray is an emerging paradigm in all research areas to help finding non-obvious patterns of relevance in large distributed data collections. “Open Science by Design” (OSD), i.e., making artefacts such as data, metadata, models, and algorithms available and re-usable to peers and beyond as early as possible, is a pre-requisite for a flourishing DS landscape. However, a few major aspects can be identified hampering a fast transition: (1) The classical “Open Science by Publication” (OSP) is not sufficient any longer since it serves different functions, leads to non-acceptable delays and is associated with high curation costs. Changing data lab practices towards OSD requires more fundamental changes than OSP. 2) The classical publication-oriented models for metrics, mainly informed by citations, will not work anymore since the roles of contributors are more difficult to assess and will often change, i.e., other ways for assigning incentives and recognition need to be found. (3) The huge investments in developing DS skills and capacities by some global companies and strong countries is leading to imbalances and fears by different stakeholders hampering the acceptance of Open Science (OS). (4) Finally, OSD will depend on the availability of a global infrastructure fostering an integrated and interoperable data domain—“one data-domain” as George Strawn calls it—which is still not visible due to differences about the technological key pillars. OS therefore is a need for DS, but it will take much more time to implement it than we may have expected.

Data-intensive science is reality in large scientific organizations such as the Max Planck Society, but due to the inefficiency of our data practices when it comes to integrating data from different sources, many projects cannot be carried out and many researchers are excluded. Since about 80% of the time in data-intensive projects is wasted according to surveys we need to conclude that we are not fit for the challenges that will come with the billions of smart devices producing continuous streams of data—our methods do not scale. Therefore experts worldwide are looking for strategies and methods that have a potential for the future. The first steps have been made since there is now a wide agreement from the Research Data Alliance to the FAIR principles that data should be associated with persistent identifiers (PID) and metadata (MD). In fact after 20 years of experience we can claim that there are trustworthy PID systems already in broad use. It is argued, however, that assigning PIDs is just the first step. If we agree to assign PIDs and also use the PID to store important relationships such as pointing to locations where the bit sequences or different metadata can be accessed, we are close to defining Digital Objects (DO) which could indeed indicate a solution to solve some of the basic problems in data management and processing. In addition to standardizing the way we assign PIDs, metadata and other state information we could also define a Digital Object Access Protocol as a universal exchange protocol for DOs stored in repositories using different data models and data organizations. We could also associate a type with each DO and a set of operations allowed working on its content which would facilitate the way to automatic processing which has been identified as the major step for scalability in data science and data industry. A globally connected group of experts is now working on establishing testbeds for a DO-based data infrastructure.

Since 2009 initiatives that were selected for the roadmap of the European Strategy Forum on Research Infrastructures started working to build research infrastructures for a wide range of research disciplines. An important result of the strategic discussions was that distributed infrastructure scenarios were now seen as “complex research facilities” in addition to, for example traditional centralised infrastructures such as CERN. In this paper we look at five typical examples of such distributed infrastructures where many researchers working in different centres are contributing data, tools/services and knowledge and where the major task of the research infrastructure initiative is to create a virtually integrated suite of resources allowing researchers to carry out state-of-the-art research. Careful analysis shows that most of these research infrastructures worked on the Findability, Accessibility, Interoperability and Reusability dimensions before the term “FAIR” was actually coined. The definition of the FAIR principles and their wide acceptance can be seen as a confirmation of what these initiatives were doing and it gives new impulse to close still existing gaps. These initiatives also seem to be ready to take up the next steps which will emerge from the definition of FAIR maturity indicators. Experts from these infrastructures should bring in their 10-years' experience in this definition process.

Institutions driving fundamental research at the cutting edge such as for example from the Max Planck Society (MPS) took steps to optimize data management and stewardship to be able to address new scientific questions. In this paper we selected three institutes from the MPS from the areas of humanities, environmental sciences and natural sciences as examples to indicate the efforts to integrate large amounts of data from collaborators worldwide to create a data space that is ready to be exploited to get new insights based on data intensive science methods. For this integration the typical challenges of fragmentation, bad quality and also social differences had to be overcome. In all three cases, well-managed repositories that are driven by the scientific needs and harmonization principles that have been agreed upon in the community were the core pillars. It is not surprising that these principles are very much aligned with what have now become the FAIR principles. The FAIR principles confirm the correctness of earlier decisions and their clear formulation identified the gaps which the projects need to address.