Big data have become a global strategic issue, as increasingly large amounts of unstructured data challenge the IT infrastructure of global organizations and threaten their capacity for strategic forecasting. As experienced in former massive information issues, big data technologies, such as Hadoop, should efficiently tackle the incoming large amounts of data and provide organizations with relevant processed information that was formerly neither visible nor manageable. After having briefly recalled the strategic advantages of big data solutions in the introductory remarks, in the first part of this paper, we focus on the advantages of big data solutions in the currently difficult time of the COVID-19 pandemic. We characterize it as an endemic heterogeneous data context; we then outline the advantages of technologies such as Hadoop and its IT suitability in this context. In the second part, we identify two specific advantages of Hadoop solutions, globality combined with flexibility, and we notice that they are at work with a “Hadoop Fusion Approach” that we describe as an optimal response to the context. In the third part, we justify selected qualifications of globality and flexibility by the fact that Hadoop solutions enable comparable returns in opposite contexts of models of partial submodels and of models of final exact systems. In part four, we remark that in both these opposite contexts, Hadoop’s solutions allow a large range of needs to be fulfilled, which fits with requirements previously identified as the current heterogeneous data structure of COVID-19 information. In the final part, we propose a framework of strategic data processing conditions. To the best of our knowledge, they appear to be the most suitable to overcome COVID-19 massive information challenges.

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.

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.

Purpose: The purpose of this paper is to present empirical evidence on the opinion and behaviour of French scientists (senior management level) regarding research data management (RDM). 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 Research Center CNRS in 2014. Findings: The paper presents empirical results about data production (types), management (human resources, IT, funding, and standards), data sharing and related needs, and highlights significant disciplinary differences. Also, it appears that RDM and data sharing is not directly correlated with the commitment to open access. Regarding the FAIR data principles, the paper reveals that 68 per cent of all laboratory directors affirm that their data production and management is compliant with at least one of the FAIR principles. But only 26 per cent are compliant with at least three principles, and less than 7 per cent are compliant with all four FAIR criteria, with laboratories in nuclear physics, SSH and earth sciences and astronomy being in advance of other disciplines, especially concerning the findability and the availability of their data output. The paper concludes with comments about research data service development and recommendations for an institutional RDM policy. Originality/value: For the first time, a nationwide survey was conducted with the senior research management level from all scientific disciplines. Surveys on RDM usually assess individual data behaviours, skills and needs. This survey is different insofar as it addresses institutional and collective data practice. The respondents did not report on their own data behaviours and attitudes but were asked to provide information about their laboratory. The response rate was high (>30 per cent), and the results provide good insight into the real support and uptake of RDM by senior research managers who provide both models (examples for good practice) and opinion leadership.

The use of Elastic Stack (ELK) solutions and Knowledge Graphs (KGs) has attracted a lot of attention lately, with promises of vastly improving business performance based on new business insights and better decisions. This allows organizations not only to reap the ultimate benefits of data governance but also to consider the widest possible range of relevant information when deciding their next steps. In this paper, we examine how data management and data visualization are used in organizations that use ELK solutions to collect integrated data from different sources in one place and visualize and analyze them in near-real time. We also present some interpretable Knowledge Graphs, GRAPHYP, which are innovative by processing an analytical information geometry and can be used together with an ELK to improve data quality and visualize the data to make informed decisions in organizations. Good decisions are the backbone of successful organizations. Ultimately, this research is about integrating a combined solution between ELK and SKG GRAPHYP and showing users the advantages in this area.

The digital support for scientific reasoning presents contrasting results. Bibliometric services are improving, but not academic assessment; no service for scholars relies on logs of web usage to base query strategies for relevance judgments (or assessor shifts). Our Scientific Knowledge Graph GRAPHYP innovates with interpretable patterns of web usage, providing scientific reasoning with conceptual fingerprints and helping identify eligible hypotheses. In a previous article, we showed how usage log data, in the form of ‘documentary tracks’, help determine distinct cognitive communities (called adversarial cliques) within sub-graphs. A typology of these documentary tracks through a triplet of measurements from logs (intensity, variety and attention) describes the potential approaches to a (research) question. GRAPHYP assists interpretation as a classifier, with possibilistic graphical modeling. This paper shows what this approach can bring to scientific reasoning; it involves visualizing complete interpretable pathways, in a multi-hop assessor shift, which users can then explore toward the ‘best possible solution’—the one that is most consistent with their hypotheses. Applying the Leibnizian paradigm of scientific reasoning, GRAPHYP highlights infinitesimal learning pathways, as a ‘multiverse’ geometric graph in modeling possible search strategies answering research questions.