Measures for recognition and reward

Foster, and require where appropriate, the adoption of measures to recognise and reward the provision of access to, and maintenance of, research data and other research-relevant digital objects from public funding as a recognised research output, including by:

a.developing criteria for researcher recruitment, advancement, and grant review that take into account the accessibility, quality, and impact of research data and other research-relevant digital objects from public funding, while taking into account the specific constraints resulting from potential sensitivity of certain datasets;

b.supporting the development of robust and open indicators on the impact of access to research data and other research-relevant digital objects from public funding, including through the tracking of data and software citations;

c.developing and adopting approaches such as contributor taxonomies that would be useful to facilitate giving credit for all contributions to the research endeavour, starting with the conceptualisation and research design, through data acquisition, curation, analysis, validation, documentation, packaging, and final write-up;

d.fostering the creation of an environment favourable to new types of actions that facilitate the dissemination of research outputs beyond publications and their recognition within the research assessment process;

e.promoting data and software citation in academic practice, including the development of data and software citation standards and acknowledgement of data and code creators and maintainers as key contributors with a view to fostering the adoption of such practices as a standard publishing requirement formulated by funding agencies.

Machine actionable Data Management Plans - maDMPs  could become a strong vehicle raising positive perceptions and enabling incentives, among other things, for data sharing to be created through automations employed by

Data Management Platforms (DMP) systems integrating and exchanging information with diverse data services. Use of the incentives could serve as an indirect funding mechanism. 

Policymakers may promote to: 

• Explicitly include data and software publication in assessing a researcher’s impact (e.g. for job or grant applications)

• Have citation services reliantly collect information about data citations

• Develop bibliometric measures to include data publications in impact

• Agree on (discipline-specific) standards how to measure impact of data publications and citations

• Enourage the use of Persisdent Identifiers (PIDs).

• Foster academic promotion conditional on OA publications. Public research institutions (PRIs) and funding agencies should incoporate open science criteria and indicators to its evaluation and grant review procedures. 

• Support international initiatives for a uniform push towards recognition of open science contributions, covering several research output types. European Commission initiative is an example of this.

• Encourage that data archived at disciplinary level in digital platforms are federated in European Open Science Cloud (EOSC) and create and maintain a National metadata catalogue.

Implementing this provision faces several hurdles.

• Institutions often lack cohesive strategies to promote robust Research Data Management (RDM) practices, creating barriers for researchers in meeting data sharing and maintenance requirements.

• Academic promotion and grant evaluation criteria primarily favour traditional outputs, disincentivising researchers from prioritising open access to data and software.

• Achieving international recognition for open science contributions is further complicated by varied policies and practices across countries, impeding cohesive standards. As long as academic careers are solely dependent on publishing in a select group of international journals, open access to research data development will be hampered.

• Data citation and metrics face inconsistent standards, making it difficult to measure the impact of data publications reliably. Publishers often do not uniformly endorse data citation practices, limiting their integration into scholarly communication.

• Moreover, policy changes are needed to explicitly incorporate open science contributions, such as data and software, into researcher evaluations and grant review processes.

Addressing these challenges requires policy alignment, international collaboration, and a cultural shift to value and incentivise data sharing, citation, and open science practices.