Purpose: – The purpose of this paper is to contribute to the debate on the development of academic libraries, by the introduction of the concepts of co-working and innovation to the learning centres.Design/methodology/approach: – The paper builds on published case studies and French initiatives.Findings: – The proposal of this paper is that the academic library can meet its social responsibility on the campus and in society by drawing on the model of the co-working spaces and communities, by the support of innovation and the transfer of knowledge to the world of work. Moreover, the proposal is to include these new functions into the concept of learning centre, i.e. to develop the work-related aspects of the learning centre.Research limitations/implications: – Future research on academic libraries should focus on social responsibility and their contribution not only to students’ academic success but also to students’ employability and to the transfer of technology.Practical implications: – The paper contributes to the development and marketing of new academic library services and to its strategic positioning on the campus.Originality/value: – Co-working and innovation are relatively new but promising concepts for academic libraries. Except for some recent case studies, conceptual papers are still missing that combine empirical experience with a theoretical approach.

Can the smart city provide a new perspective for public and academic libraries? How does the smart city impact the libraries as cultural and scientific assets? And how can libraries contribute to the development of the smart city? An overview of recent library models, like the learning center or the green library, reveals affinities with the concept of the smart city, especially regarding the central role of information and the integration of technology, people, and institutions. From this observation, the paper develops the outline of a new concept of the smart library, which can be described in four dimensions, i.e., smart services, smart people, smart place, and smart governance. However, the smart library concept does not constitute a unique model or project, but a process, a way of how to get things done, that is less linear, less structured, and more creative and innovative. Also, smartness may not be a solution for all library problems.

Research infrastructures (RIs) offer researchers a multitude of research opportunities and services and play a key role in the performance, innovative strength, and international competitiveness of science. As an important part of the generation and use of new knowledge and technologies, they are essential for research policies. Because of their strategic importance and their need for significant funding, there is a growing demand for the assessment of their scientific output and impact. Current research information systems (CRIS) have contributed for many years now to the evaluation of universities and research organizations. Based on studies on the application of CRIS to infrastructures and on a recent French report on the scientometric assessment of RI, this paper analyzes the potential of CRIS and their data models and standards (in particular the international CERIF format and the German RDC model) for the monitoring and evaluation of RIs. The interaction between functional specificities of RI and standards for their assessment is outlined, with reference to their own potential to stimulate and share innovation in the networks located inside and outside RIs. This societal challenge, more than an academic issue, is on the way to further harmonization and consolidation of shared and common RI metrics.

Collecting, integrating, storing and analyzing data in a database system is nothing new in itself. To introduce a current research information system (CRIS) means that scientific institutions must provide the required information on their research activities and research results at a high quality. A one-time cleanup is not sufficient; data must be continuously curated and maintained. Some data errors (such as missing values, spelling errors, inaccurate data, incorrect formatting, inconsistencies, etc.) can be traced across different data sources and are difficult to find. Small mistakes can make data unusable, and corrupted data can have serious consequences. The sooner quality issues are identified and remedied, the better. For this reason, new techniques and methods of data cleansing and data monitoring are required to ensure data quality and its measurability in the long term. This paper examines data quality issues in current research information systems and introduces new techniques and methods of data cleansing and data monitoring with which organizations can guarantee the quality of their data.

Purpose: This paper aims to present empirical evidence on the opinion and behaviour of French scientists (senior management level) regarding open access (OA) to scientific and technical information. Design/methodology/approach: The results are part of a nationwide survey on scientific information and documentation with 432 directors of French public research laboratories conducted by the French National Research Center (CNRS) in 2014. Findings: The CNRS senior research managers (laboratory directors) globally share the positive opinion towards OA revealed by other studies with researchers from the UK, Germany, the USA and other countries. However, they are more supportive of open repositories (green road) than of OA journal publishing (gold). The response patterns reveal a gap between generally positive opinions about OA and less supportive behaviours, principally publishing articles with article processing charges (APCs). A small group of senior research managers does not seem to be interested in green or gold OA and reluctant to self-archiving and OA publishing. Similar to other studies, the French survey confirms disciplinary differences, i.e. a stronger support for self-archiving of records and documents in HAL by scientists from Mathematics, Physics and Informatics than from Biology, Earth Sciences and Chemistry; and more experience and positive feelings with OA publishing and payment of APCs in Biology than in Mathematics or in Social Sciences and Humanities. Disciplinary differences and specific French factors are discussed, in particular in the context of the new European policy in favour of Open Science. Originality/value: For the first time, a nationwide survey was conducted with the senior research management level from all scientific disciplines. The response rate was high (>30 per cent), and the results provide good insight into the real awareness, support and uptake of OA by senior research managers who provide both models (examples for good practice) and opinion leadership.

Data papers have been defined as scholarly journal publications whose primary purpose is to describe research data. Our survey provides more insights about the environment of data papers, i.e., disciplines, publishers and business models, and about their structure, length, formats, metadata, and licensing. Data papers are a product of the emerging ecosystem of data-driven open science. They contribute to the FAIR principles for research data management. However, the boundaries with other categories of academic publishing are partly blurred. Data papers are (can be) generated automatically and are potentially machine-readable. Data papers are essentially information, i.e., description of data, but also partly contribute to the generation of knowledge and data on its own. Part of the new ecosystem of open and data-driven science, data papers and data journals are an interesting and relevant object for the assessment and understanding of the transition of the former system of academic publishing.