Glottocodes: Identifiers linking families, languages and dialects to comprehensive reference information

Glottocodes constitute the backbone identification system for the language, dialect and family inventory Glottolog (https://glottolog.org). In this paper, we summarize the motivation and history behind the system of glottocodes and describe the principles and practices of data curation, technical infrastructure and update/version-tracking systematics. Since our understanding of the target domain – the dialects, languages and language families of the entire world – is continually evolving, changes and updates are relatively common. The resulting data is assessed in terms of the FAIR (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship. As such the glottocode-system responds to an important challenge in the realm of Linguistic Linked Data with numerous NLP applications.

Research libraries as digital publishers: The foreign experience

The author discusses the current experience of building the service of digital science publishing obtained by the libraries in the Western countries. She emphasizes that this process is incorporated into further informatization of libraries and their increasing role in managing science data. The digital publications integrate a number of interrelated programs comprising the whole cycle of scientific data management accomplished within the wider context of innovations. Digital publishing is a new type of library activities; it requires integrating competences of modern librarians (i.e. content selection, data supervising, metadata management, building digital collections, their preservation and archiving) and publishers (monitoring new trends in science and technology, selecting materials for publication, abstracting, scientific editing, developing marketing strategies).For the first time in the domestic library studies, the dynamics of this service in foreign countries is examined based on Library Publishing Directory for 2013– 2018. The author compares digital publishing services in four university libraries in different world regions and offers the findings of preliminary analysis of online publication services in foreign research libraries.

Making FAIR trustworthy

The life sciences community is now increasingly leaning on the processing powers of machines to carry out advanced scientific research. So in order to adequately exploit the capabilities of machines in science, the FAIR (Findable, Accessible, Interoperable, Reusable) principles for scientific data management and stewardship have been proposed. These principles are to assist scientists in tweaking their established research routines so as to unlock the true potential of machines and contribute to better science. However, through interviews with key scientist groups implicated by FAIR we have learned that doing what FAIR demands also presents certain epistemic concerns that discourage scientists to trust FAIR. One such concern is the loss of valuable knowledge in the translation of versatile human readable research output to a restricted, machine friendly language to enable machine action (semantic freedom). A second concern is evident in the displacement of human expertise by this increasing reliance on machines and the ensuing loss of knowledge contributed by these displaced experts (expert intuition). Thus, through this article, we highlight how incorporating FAIR also presents an epistemic loss to the scientific community. But the lack of attention given to these concerns by the proponents of FAIR offers scientists who have to abide by FAIR sufficient reason to resist it. We thus propose that while the implementation of FAIR has so far been paternalistic and top-down, such concerns have also made the scientist sceptical. So by initiating a more balanced, open and honest discussion of not just the benefits and promises of FAIR but also such epistemic costs, FAIR could lay to rest reasons for such scepticism and foster trust within the stakeholders of FAIR.

Dynamo: Handling Scientific Data Across Sites and Storage Media

Dynamo is a full-stack software solution for scientific data management. Dynamo’s architecture is modular, extensible, and customizable, making the software suitable for managing data in a wide range of installation scales, from a few terabytes stored at a single location to hundreds of petabytes distributed across a worldwide computing grid. This article documents the core system design of Dynamo and describes the applications that implement various data management tasks. A brief report is also given on the operational experiences of the system at the CMS experiment at the CERN Large Hadron Collider and at a small-scale analysis facility.

Experience with Rucio in the wider HEP community

Managing the data of scientific projects is an increasingly complicated challenge, which was historically met by developing experiment-specific solutions. However, the ever-growing data rates and requirements of even small experiments make this approach very difficult, if not prohibitive. In recent years, the scientific data management system Rucio has evolved into a successful open-source project that is now being used by many scientific communities and organisations. Rucio is incorporating the contributions and expertise of many scientific projects and is offering common features useful to a diverse research community. This article describes the recent experiences in operating Rucio, as well as contributions to the project, by ATLAS, Belle II, CMS, ESCAPE, IGWN, LDMX, Folding@Home, and the UK’s Science and Technology Facilities Council (STFC).