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.

Associate professor in information sciences, ORCID 0000-0002-4000-807X Abstract 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 machinereadable.

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.

International audiencePurposeThis 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/approachThe 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.FindingsThe 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/valueFor 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

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.

The variety and diversity of published content are currently expanding in all fields of scholarly communication. Yet, scientific knowledge graphs (SKG) provide only poor images of the varied directions of alternative scientific choices, and in particular scientific controversies, which are not currently identified and interpreted. We propose to use the rich variety of knowledge present in search histories to represent cliques modeling the main interpretable practices of information retrieval issued from the same “cognitive community”, identified by their use of keywords and by the search experience of the users sharing the same research question. Modeling typical cliques belonging to the same cognitive community is achieved through a new conceptual framework, based on user profiles, namely a bipartite geometric scientific knowledge graph, SKG GRAPHYP. Further studies of interpretation will test differences of documentary profiles and their meaning in various possible contexts which studies on “disagreements in scientific literature” have outlined. This final adjusted version of GRAPHYP optimizes the modeling of “Manifold Subnetworks of Cliques in Cognitive Communities” (MSCCC), captured from previous user experience in the same search domain. Cliques are built from graph grids of three parameters outlining the manifold of search experiences: mass of users; intensity of uses of items; and attention, identified as a ratio of “feature augmentation” by literature on information retrieval, its mean value allows calculation of an observed “steady” value of the user/item ratio or, conversely, a documentary behavior “deviating” from this mean value. An illustration of our approach is supplied in a positive first test, which stimulates further work on modeling subnetworks of users in search experience, that could help identify the varied alternative documentary sources of information retrieval, and in particular the scientific controversies and scholarly disputes.

International audiencePurpose– Print theses and dissertations have regularly been submitted together with complementary material, such as maps, tables, speech samples, photos or videos, in various formats and on different supports. In the digital environment of open repositories and open data, these research results could become a rich source of research results and data sets, for reuse and other exploitation. The paper aims to discuss these issues.Design/methodology/approach– After introducing electronic theses and dissertations (ETD) into the context of eScience, the paper investigates some aspects that impact the availability and openness of data sets and other supplemental files related to ETD (system architecture, metadata and data retrieval, legal aspects).Findings– These items are part of the so-called “small data” of eScience, with a wide range of contents and formats. Their heterogeneity and their link to ETD need specific approaches to data curation and management, with specific metadata and identifiers and with specific services, workflows and systems. One size may not fit for all but it seems appropriate to separate text and data files. Regarding copyright and licensing, data sets must be evaluated carefully but should not be processed and disseminated under the same conditions as the related PhD theses. Some examples are presented.Research limitations/implications– The paper concludes with recommendations for further investigation and development to foster open access to research results produced along with PhD theses.Originality/value– ETDs are an important part of the content of open repositories. Yet, their potential as a gateway to underlying research results has not really been explored so far

In our present paper, the influence of data quality on the success of the user acceptance of research information systems (RIS) is investigated and determined. Until today, only a little research has been done on this topic and no studies have been carried out. So far, just the importance of data quality in RIS, the investigation of its dimensions and techniques for measuring, improving, and increasing data quality in RIS (such as data profiling, data cleansing, data wrangling, and text data mining) has been focused. With this work, we try to derive an answer to the question of the impact of data quality on the success of RIS user acceptance. An acceptance of RIS users is achieved when the research institutions decide to replace the RIS and replace it with a new one. The result is a statement about the extent to which data quality influences the success of users’ acceptance of RIS.