At the ‘Reflection’ table participants were asked about the general situation regarding RDM at their home institutions. Although for most institutions the topic of RDM is still relatively new, progress in RDM has been achieved by all represented institutions over the last few years. In many organisations, institutional research data policies have been implemented and research data management positions have been created. In some institutions researchers already receive support in managing research data, for example through the provision of training courses and help desks. However, the topic of ‘Standards and Certification’ reveals a more heterogeneous picture. While some institutes trust the use of internationally accepted standards according to the research practices prevailing in their discipline, other institutes need to develop their own, more specific routines. In summary, all institutes demonstrated an awareness of the necessity of professional research data management, but it still needs to be put more into practice.

The ‘Technology’ table focused on the current state of RDM at the participants’ institutions, the tools and technologies being used and the technological requirements for further RDM development as perceived by the institutions’ academic staff.

It became evident that in terms of developing, evaluating, and implementing institution-wide RDM policies the situation at the different institutions is currently quite heterogeneous. Most institutions are in the process of developing standards for managing scientific data, bundling it within an institutional policy and testing it in pilot studies. In a few institutions this process is quite advanced, with their policies being published and routinely applied to research projects while others have only just begun to scrutinise how the availability and interoperability of their data can be improved by enforcing RDM best practices. Notably, the development and implementation of policies is more advanced in institutions where the management takes an active interest in RDM issues and allocates resources to the development of data management and plans. The participants stressed the importance of developing policies in close collaboration with researchers to ensure their relevance and adoption.

RDM is based on a variety of technologies at the participants’ institutions. While the institutional technological toolboxes have diverse components, practically all management efforts rely in one way or another on standardised metadata. This makes the development, dissemination and application of such standards essential.

Information system technologies range from relational databases, document-based data stores, such as wikis, to sharepoint services. The choice of technology is generally highly diverse and, as well as offering a choice between in-house development or customised solutions for software products, is motivated by the existing specific workflows and data flows designed by the researchers. Where experimental research is conducted, the use of electronic lab notebooks is generally perceived as desirable and a number of institutions already use them or are in the process of testing them.

The technical requirements for improving RDM practices and for fostering its adoption into the scientific workflow can be summarised in two major points:

Firstly, technology and tools are needed, which enable the integration of RDM practices into scientific workflows in a user-friendly, non-technical manner and with as little financial investment as possible. Important requirements in this respect are:

1. support for the integration of heterogeneous data from diverse and distributed sources,
2. compatibility with legacy systems,
3. automatic annotation of device-generated data,
4. support for enrichment with metadata, and
5. solutions for high-throughput and large volume data (petabytes).

Secondly, any technological solutions must be augmented by measures to increase the acceptance of RDM practices among users. This includes:

1. enabling users to make informed choices regarding usage and attribution of their data and their results,
2. addressing data privacy, licensing, and intellectual property rights concerns,
3. providing ways to measure data accuracy and trustworthiness of data sources and repositories, and
4. offering easy to understand documentation of the institution’s RDM goals and practices.

The ‘Society & values’ table posed the question: ‘In your opinion, what are the most important (social) problems and challenges that need to be addressed regarding the management of research data (research, awareness, implementation, politics)?’

The topics raised and discussed at this table can be clustered into four different areas:

One major aspect was the transparency and openness of data. The transfer, translation and dissemination of methodological knowledge was mentioned by the participants as a requirement for the ‘demystification’ of methods, increasing reproducibility and the avoidance of manipulated results. Although openness of science is favoured by all disciplines, patent-relevant research results are an exception. Also, this openness includes the risk of generating (incorrect) causal relationships with critical outcomes for society as a result of the partial evaluation of Big Data by third parties compare (O’Neil 2016).

In addition, it is important to protect open data from subsequent private appropriation. It may also be necessary to evaluate the risk of ‘Big Data’ methods producing causal relationships between formerly independent data segments.

The efficient use of data was another subject raised at this table. To reduce (personnel and financial) resources data must be available for use and reuse. This, however, requires a guaranteed level of reliability and quality of data as well as the provision of software for data evaluation and re-use. The participants stressed that excessive bureaucratisation caused by the need for performance criteria might prevent efficient data (re)use.

The participants also focused on the question of accountability. They were asked for input on how to find a balance between public funding and scientific freedom. What needs to be reported on if there is public funding and how can this be done without constraining scientific freedom? The issue of scientific reputation of published data was also a topic at this table. The acknowledgement of the value of data publication could promote its re-use, however the extension of the current rating system for scientific output might entail a yet unknown risk. The unlimited collection of digital data by various competing actors was also identified as problematic.

Finally, citizen science and the appreciation of citizen scientists was another topic at the ‘Society & values’ table. Participants posed the question, “To what extent can individual citizens benefit from contributing to science without being exploited and how can contributions from citizens be acknowledged?” Furthermore, there were discussions regarding whether citizen science is simply hype or whether it really takes the responsibility of society as a whole into account. Also, the data collected by citizen scientists was discussed, including its quality and (re-) usability.

At the ‘Personal Objectives’ table the experts were asked about their personal experiences, expectations and wishes related to the main topics arising from the incentive talks on RDM and NFDI.

For RDM, one thematic cluster dealt with the structure for RDM, including structured processes, structured documentation, folder and file management and consistent data formats. These are as important as the structured implementation of working groups, preferably using a top-down approach. RDM should be an integral element in the processing of projects. Most participants wished that RDM was seen by researchers as an integral part of research and not as an additional task.

RDM implementation was another major topic. In many institutions, the acceptance of RDM is higher among younger researchers, and acceptance by research group leaders and professors, for example, is urgently needed. RDM needs to be seen as an institutional task, even as a key task, within an organisation. Management must acknowledge that the implementation of RDM and RDM policies is required. As far as funding bodies are concerned, the participants would support an intense review of RDM in grant applications. All in all, the experts present stated that the status of RDM and managers should be improved.

The experts present expressed a need to motivate researchers and institutions to pay more attention to RDM by making clear:

1. that documenting the research process, as part of RDM, is an important part of good research practice and of science in general;
2. that RDM should be presented as a method for smooth work processes, as working with more structured data is much more efficient; and
3. that RDM makes their work easier in the end.

In this context, participants claimed that a cost-benefit analysis between RDM and data re-use could be helpful incentives. Researchers could be motivated to practice RDM by:

1. a reward system,
2. more pressure through (financial) incentives and
3. an improved awareness of, and reputation for RDM. All participants agreed that the publication of research data needs to be acknowledged as a valid (scientific) publication.

A general lack of willingness to share data again highlights the need for better training and awareness of the importance of RDM. As universities do not emphasize the benefits of collaboration, young researchers might need additional conversations and training workshops to adopt cooperation and share their data. Junior scientists need to be trained as RDM-promoter so that they can pass their knowledge on to other staff members.

In the participants’ assessments of NFDI, collaborative working was an important point. The experts present appreciated the initiative because they hope that parallel structures will be reduced, that duplications will be avoided and that institutes can benefit from the expertise of others. Common workflows, processes and data access should be integrated into the process. The overall goal would be to achieve a community culture change and to strengthen the acceptance of RDM.

Yet, in quite a number of institutes the prevailing situation seems to be one of uncertainty and missing information. It was stated that outreach and communication as well as support and orientation are important. At the moment it seems rather unclear how to contribute best since the process seems to be difficult to understand and is seen to be too abstract and not related to daily practices. This especially applies to smaller academic communities where institutions must first address RDM before committing to NFDI.

All in all, the experts argued that NDFI should be a European initiative, and not be restricted to the national level.

With regard to how research data is handled, it is regularly pointed out that it is necessary to take into account discipline-specific approaches and working methods in RDM as well as the particularities resulting from the respective research data itself. The ‘Community Perspective’ table dealt primarily with the question which themes or fields of action are topical within the respective communities or require special attention, and whether solutions or approaches already exist in the respective fields of action. In addition, participants were asked whether there are particularities in the respective fields of work for which specific solutions are needed; or conversely, whether there are solutions that could also be relevant for other disciplines or communities.

The lack of (subject-specific and interdisciplinary) standards was regularly referred to as a central challenge. It is a deficit that affects all areas, from the collection or generation of data, its processing and analysis, through to publication and archiving. The main desires of the participants mentioned in this context were standards related to the quality of research data and standards for the description, documentation and indexing of research data (metadata, ontologies). A further important aspect in this context is that it is necessary to apply discipline-specific standards while also ensuring that they can be linked to comprehensive and global standards.

In addition to the lack of common standards there is also the issue of complexity, caused not least by the wide variety of discipline- and community-specific approaches and solutions. Participants cited the large and growing number of repositories as an example. While this fundamentally positive development leads to more research data being accessible, it also greatly reduces findability. Existing meta-searches mitigate the problem, but do not completely solve it.

Another challenge that was repeatedly mentioned by the participants is the lack of willingness on the part of researchers to share or publish their research data. However, it was not possible to determine from the discussions whether there are community-specific reasons or conditions for this. It seems that similar structural conditions and individual motives are the obstacles to data sharing in the various communities.

An additional central topic area discussed by the experts goes hand in hand with the increased expectations regarding scientific work, namely the added emphasis on open scientific practices, which produce comprehensible results. With regard to how research data is handled, all communities must be able to answer the question of how the reproducibility of research results can be ensured in concrete terms.

Finally, it is important to mention a fundamental aspect that arose during the discussion of subject-specific approaches. The experts present agreed that many areas of RDM require subject-specific approaches. At the same time, however, this poses special challenges. One is that many Leibniz Institutions comprise various communities, and sometimes also disciplines, under one roof. Due to the different subject-specific approaches used in RDM, this creates the problem of applying uniform policies and standards within the individual institutes and of developing and offering uniform solutions for dealing with data generated or processed in-house. This often creates a conflict between the need to develop a uniform institutional view of RDM and the (heterogeneous) needs of the different in-house communities.