We describe the struggle to get proper data on research information systems from the universities of the Netherlands. A working group involving various stakeholders to develop a common research data governance framework in the Netherlands is proposed. The working group would propose standards in higher education, research and impact specific to data collection, metadata and their interoperability across various stakeholders.

This article is a simplied version of the following communication article:

R. Thorat and R. Van Brakel. “Brief communication: The need for a national-level working group for higher education research data in the Netherlands”. In: Knowledge Organization 46.5 (2019). pp. 380–386. doi: 10.5771/0943–7444–2019–5–380

Introduction

The objective of data collection and visualization from the universities of the Netherlands is to offer transparency and accountability of the universities to the government and to the public. However, the data collection, update and visualization from multiple sources is a tedious task. A collective research information (CRIS) system for research data as a joint venture with other stakeholders (universities, semi-government, government institutes) would ease the task. The stakeholders, involved in governing the information on research output, are individual universities, national-international organizations, expert working groups, ranking agencies, government institutions and research companies. There is no consensus on the classification among the aforementioned stakeholders to ensure that the information is relevant for policymakers and used in decision making on the national level politics. Further disadvantages of fragmented research data governance lead to labor-intensiveness, and to unclear legal and operational status of research data.

Alternatives to current data flow

Two options to solving the problems of fragmented governance of the data, with pro and counterarguments, are described below.

Option 1: Retrieve the data directly from the research information systems (CRISs) of universities.

Pro arguments:
– Easier for the universities to deliver the data;
– The work has already been partially done within research information systems;
– Possible Machine2machine automation.

Counter arguments:
– The universities could lose control over the delivering the data;
– Not all universities use the same research information systems. A link among various research information systems must be developed for each system.

Option 2: Request aggregated research output via national platform .

Pro arguments:
– Possible Machine2machine automation;
– Information on key research outputs can be retrieved at various levels (university, professor) continuously instead of periodically;
– A national standard.
Counter arguments:
–API linking problem among research information systems from the universities with national platform;
– Difference in definitions for research output among research information systems
– The national platform.

Need for a national level governance framework for data

A common data governance across the Netherlands is necessary to enable Dutch universities, research organizations and industry to benchmark education, research and innovation. It can be used to investigate the links between education strategies, research practices and business outcomes.
Furthermore, such framework could help researchers, companies and also the government with decision making in higher education. The definitive product of national level research data governance framework could be a national depo for the data collection, automation and as the “open data portal” for research output. Or as an alternative, it can be a federative model of data collection, where data are stored in various places, but there are common definitions and agreements on exchange of aggregated data. The current fragmented research output monitoring system can be
replaced by such a common system.

A number of criteria have been considered for a common framework: it must be usable at the national level, it must be public, and it must encapsulate strategic goals such as the climate agreement and sustainable developments. In such a framework, all data can be mapped at a glance, ar-
ranged per theme. Considerable attention will be given to social impact in relation to research. It can be used as a good tool for participating universities to learn from each other’s research data. The framework should have the possibility to incorporate the updated synchronization of data, new functionalities and topics. The information is free of charge and up-to-date and can, therefore, be used by anyone interested in the higher education sector: from journalist to student, from civil servant to professor.

Call for a working group to develop a national governance framework

A working group is needed to develop a national governance framework for the aforementioned data flow.

The tasks of the working group could be to:

– develop consensus to collect, edit and use research data in the wake of GDPR;
– cluster publication to support research domains;
– determine the role of private parties in the research data exchange;
– develop key figures for policy information about research; and to
– focus on research community proposed public needs.

In order to achieve these goals, the working group might consider following governance framework.

Classification

Currently available commercial data classification and aggregation systems are mentioned Legendre (2019) and Vancauwenbergh and Poelmans (2019). These classification systems give a first indication of number of publications, information about researchers, etc., subdivided into themes. The following considerations must be taken into account while developing a classification strategy for common data governance:

– Complete list of data providers, collectors and analyzers;
– Finding stakeholder institutes, research centers ;
– Finding people involved in the similar efforts;
– International initiatives, like the Common European Research Information Format (CERIF) and EuroCris;
– Comparison of definitions among various systems;
– Classifying available systems: type of data, search hits, corresponding licenses;
– Designing a terminology and sections;
– Research and Valorisation: Publications, PhDs, events, rankings, finance;
– Impact: Economic benefits, patents, collaboration with industry;
– Key Performance Indicators per section of information system of data (education, research and impact) for bench marking and analysis for the sector-wide performance of universities. The indicators could give an
overview of how domains within sectors are performing across a broader range of universities. The indicators should give information about domains within sectors compare against their peers abroad and the higher education sector on average.

Technology

Currently, every university has its own research information system. The universities will have to accommodate such interoperability among different metadata standards and schemas, extra metadata. A standalone website is suitable for using such a content management system supported by worldpress.org/.com. The following considerations must be taken into account while developing a technical framework for common data governance:

– Checking the publications for digital object identifier (DOI);
– Initiative by university libraries for the indicators for research and knowledge transfer domain;
– Include more information on metadata;
– Extracting the data from the various sources (scraping information from webpages).

Legal status

A common governance framework based on the open source is easier to deal with the legal issues for digital rights as compared to systems with commercial interests. With its creative commons license it will provide public access to data and to official publications of universities regarding
education, research and valorisation.

Concluding remarks

There is a need for more complete, up-to-date, even timely information, both for science policy on a national level and for its application at university level.

References

Legendre, Ariadne. 2019. “The Development of the Canadian Research and Development Classification.’” Knowledge Organization 46(5): 371–379. DOI:10.5771/0943-7444–2019–5–371
Vancauwenbergh, Sadia and Hanne Poelmans. 2019. “The Flemish Research Discipline Classification Standard: A Practical Approach.” Knowledge Organization 46(5): 354-363. DOI:10.5771/0943–7444–2019–5–354.

Various data sources

– National level Data sets: 1cijfer HO, WOPI, KUOZ, CROHO
– Survey data: National Student Survey, National Alumni Survey (NAE)
– Data collectors / users : OCW, VSNU, DUO, Nuffic,CBS, Eurostat, NVAO
– Ranking agencies: THE, ARWU, CWTS,
– Awarding/Funding agencies: NWO, Horizon 2020