Interdisciplinary research figures high on today’s policy agendas. This short introduction and overview sketches the complexity of defining and mapping the nature of interdisciplinary research (IDR). The paper focuses on the different approaches to IDR and different methods applied in bibliometric studies that allow measuring it. These methods should not only be able to capture quantitative aspects of IDR but also to monitor evolutionary aspects and help answer the question of whether IDR stimulates collaboration and results in larger impact and visibility. Two specific indicators, variety and disparity, are developed, validated and applied to bibliometric data. They enable the visualization of the interdisciplinary nature of research activities at various levels of analysis (both institutional and individual). And, given the longitudinal character of bibliometric data and databases, both indicators allow for mapping time-dependent phenomena and evolutions. Relevant examples based on the literature and recent results from research conducted at the Leuven bibliometrics group of ECOOM (e.g., Glanzel et al., Proceedings of the 18th International Conference of the International Society of Scientometrics and Informetrics, 453–464, 2021; Huang et al., Proceedings of the 18th International Conference of the International Society of Scientometrics and Informetrics, 533–538, 2021) are given, and concrete proposals for future research are articulated.

Various aspects of interdisciplinarity in research and how to quantify and measure those

• the fast growing availability of open and linked data; • the rapid evolution of Big Data into a Big Data Science; • the wider perspective opened by the altmetrics movement with respect to traditional bibliometrics; • the proliferation of indicators (Wilsdon et al. 2015) for funding and evaluation purposes without clear interpretative frameworks; • the multidimensionality and growing complexity of research assessment (Moed and Halevi 2015); • the needs to overcome the logic of mono-dimensional and biased rankings together with the new trends in granularity and cross-referencing of science and technology (S&T) indicators (Daraio and Bonaccorsi 2016);

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Grand challenges in data integration--state of the art and future perspectives: an introduction

The paper summarizes the results of the recently completed project to derive a Research Core Dataset (RCD) for the German science system. It describes the basic principles and the architecture of the specification by introducing its main components and elements and by depicting the provisions with regard to aggregate and base data. In this context, the paper also explains the peculiarities of the German science system and the need for standardization given institutional heterogeneity and highly fragmented institutional reporting activities. The paper concludes with a short outlook on the potential chances and risks of the RCD to promote data integration and efficiency in reporting by research institutions in Germany.

The Research Core Dataset for the German science system: developing standards for an integrated management of research information

The research output of countries is among the indicators that help us understand the dynamics of science. Increasingly, these dynamics have been marked by changes in scientific communication. Researchers’ attitudes toward open science, alternative models of publication and toward originality are among the elements shaping the current scientific landscape. This changing panorama reflects on the attitude of authors, editors and publishers toward the correction of the literature, a practice that is encountered to different extents in different fields. This practice may suggest, among several issues, commitment of the scientific community to boosting the reliability of the research record. Would the research output of countries have any association with this panorama? We analyzed 1623 retractions issued in 2013–2015 and discussed in Retraction Watch (RW), www.retractionwatch.com
 . These retractions account for a considerable fraction of the total of retraction notices in PubMed in the same period. They were categorized by reason, field and country (that of the corresponding author). These retractions were distributed among 71 countries, with 15 countries accounting for a major share (85%)—most of those with the largest number of publications in the Scimago Journal & Country Rank (SJR). However, there is no consistent pattern for the relationship between ranking in SJR and ranking in number of retractions across countries in our RW dataset, which is skewed mostly by the fact that the RW website tends to post newsworthy retractions, with a bias toward biomedical and clinical sciences. This caveat notwithstanding, the prevalence of the most productive countries in our dataset of retractions is worth noting. Gradually, retractions have been permeating the dynamics of research productivity in many countries but, so far, there is limited knowledge of this interaction. We believe it should be further explored.

Retractions covered by Retraction Watch in the 2013–2015 period: prevalence for the most productive countries

The governance and standardisation of research information in different science systems: A comparative analysis of Germany and Italy

A work strand planned by the Science Europe Working Group on Research Policy and Programme Evaluation aims at mapping the state of affairs in data collection and their use at European funding and research organisations. In particular, the project identifies and proposes solutions for issues experienced by the Member Organisations (MO) regarding collection, standardisation and treatment of data related to the analysis and ex-post evaluation of activities funded or performed by MOs. This is implemented through a survey sent to the MOs. The survey was analysed with special attention to the particular needs of funding and performing organisations. On the basis of the results and the discussion among the work strand members and within the WG, we draw a preliminary set of conclusions to produce guidance on relevant topics, including researcher and funding identification, potential, properties and limitations of data and indicators that are used in the context of measurement of research output and its assessment, classification systems used in sciences systems including their various types and issues of availability, confidentiality and harmonisation of data and indicators. Feedback from such discussions will be used to identify areas for further action by Science Europe.

Data collection and use in research funding and performing organisations. General outlines and first results of a project launched by Science Europe

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Data collection and use in research funding and performing organisations. General outlines and first results of a project launched by Science Europe