Data Science

The increasing interest in analysing, describing, and improving the research process requires the development of new forms of scholarly data publication and analysis that integrates lessons and approaches from the field of Semantic Technologies, Science of Science, Digital Libraries, and Artificial Intelligence. This editorial summarises the content of the Special Issue on Scholarly Data Analysis (Semantics, Analytics, Visualisation), which aims to showcase some of the most interesting research efforts in the field. This issue includes an extended version of the best papers of the last two editions of the “Semantics, Analytics, Visualisation: Enhancing Scholarly Dissemination” (SAVE-SD 2017 and 2018) workshop at The Web Conference.

In the last decade, the research literature has reached an enormous volume with an unprecedented current annual increase of 1.5 million new publications. As research gets ever more global and new countries and institutions, either from academia or corporate environments, start to contribute, it is important to monitor this complex phenomenon and understand its dynamics and equilibria.

We present a study on a conference proceedings dataset extracted from Springer Nature SciGraph that illustrates insightful geographical trends and highlights the unbalanced growth of competitive research institutions worldwide in the 1996–2016 period. The main contribution of this work is fourfold. In the first instance, we found that the distributions of institutions and publications among countries follow a power law, consistently with previous literature, i.e., very few countries keep producing most of the papers accepted by high-tier conferences. Secondly, we show how the turnover rate of country rankings is extremely low and steadily declines over time, suggesting an alarmingly static landscape in which new entries struggle to emerge. We also performed an analysis of the venue locations and their effect on the distribution of countries involved in the publications, underlining the central role of Europe and China as knowledge hubs. Finally, we evidence the presence of an increasing gap between the number of institutions initiating and overseeing research endeavours (i.e. first and last authors’ affiliations) and the total number of institutions participating in research.

The paper also discusses our experience in working with authors’ affiliations: an utterly simple matter at first glance, that is instead revealed to be a complex research and technical challenge.

In this paper we introduce the latest version (Version 2.0) of OSCAR, the OpenCitations RDF Search Application, which has several improved features and extends the query workflow comparing with the previous version (Version 1.0) that we presented at the workshop entitled Semantics, Analytics, Visualisation: Enhancing Scholarly Dissemination (SAVE-SD 2018), held in conjunction with The Web Conference 2018. OSCAR is a user-friendly search platform that can be used to search any RDF triplestore providing a SPARQL endpoint, while hiding the complexities of SPARQL, thus making the search operations accessible to those who are not experts in Semantic Web technologies. We present here the basic features and the main extensions of this latest version of OSCAR. In addition, we demonstrate how it can be adapted to work with different SPARQL endpoints containing scholarly data, using as examples the OpenCitations Corpus (OCC) and the OpenCitations Index of Crossref open DOI-to-DOI citations (COCI), both provided by OpenCitations, and also the Wikidata dataset provided by the Wikimedia Foundation. We conclude by reporting the usage statistics of OSCAR, retrieved from the OpenCitations website logs, so as to demonstrate its uptake.

This is an extended, revised version of Philipson (2017). Findability and interoperability of some PIDs, Persistent Identifers, and their compliance with the FAIR data principles are explored, where ARKs, Archival Reource Keys, were added in this version. It is suggested that the wide distribution and findability (e.g. by simple ‘googling’) on the internet may be as important for the usefulness of PIDs as the resolvability of PID URIs – Uniform Resource Identifiers. This version also includes new reasoning about why sometimes PIDs such as DOIs, Digital Object Identifiers, are not used in citations. The prevalence of phenomena such as link rot implies that URIs cannot always be trusted to be persistently resolvable. By contrast, the well distributed, but seldom directly resolvable ISBN, International Standard Book Number, has proved remarkably resilient, with far-reaching persistence, inherent structural meaning and good validatability, through fixed string-length, pattern-recognition, restricted character set and check digit. Examples of regular expressions used for validation of PIDs are supplied or referenced. The suggestion to add context and meaning to PIDs, making them “identify themselves”, through namespace prefixes and object types is more elaborate in this version. Meaning can also be inherent through structural elements, such as well defined, restricted string patterns, that at the same time make PIDs more “validatable”. Concluding this version is a generic, refined model for a PID with these properties, in which namespaces are instrumental as custodians, meaning-givers and validation schema providers. A draft example of a Schematron schema for validation of “new” PIDs in accordance with the proposed model is provided.

The sharing of scientific and scholarly data has been increasingly promoted over the last decade, leading to open repositories in many different scientific domains. However, data sharing and open data are not final goals in themselves, the real benefit is in data reuse, which allows leveraging investments in research and enables large-scale data-driven research progress. Focusing on reuse, this paper discusses the design of an integrated framework to automatically take advantage of large amounts of scientific data extracted from the literature to support research, and in particular scientific model development. Scientific models reproduce and predict complex phenomena and their development is a rather challenging task, within which scientific experiments have a key role in their continuous validation. Starting from the combustion kinetics domain, this paper discusses a set of use cases and a first prototype for such a framework which leads to a set of new requirements and an architecture that can be generalized to other domains. The paper analyzes the needs, the challenges and the research directions for such a framework, in particular those related to data management, automatic scientific model validation, data aggregation and data analysis, to leverage large amounts of published scientific data for new knowledge extraction.

Ecological sustainability is the defining challenge of our time. Here we suggest a methodological approach that could help to investigate how environmental behavior (transport behavior, energy consumption, food consumption, goods consumption, wasting) dilemmas can be overcome on an individual level in real life by using smartphones to collect daily behavioral data in a field-experimental setup. Previous related studies are reviewed and we discuss how the boundaries of what can be done with smartphones for data collection and experimental purposes can be pushed further to allow for complex behavioral studies. Results from a pilot study are presented to discuss the feasibility and potential of this approach. The pilot shows that studying social dilemma behavior via smartphones is feasible and has potential value as a behavioral intervention tool.

Blockchains provide decentralised, tamper-free registries of transactions among partners that may not trust each other. For the scientific community, blockchain smart contracts have been proposed to decentralise and make more transparent multiple aspects of scholarly communications. We show how an Ethereum-based suite of smart contracts running on top of a Web-enabled governance framework can facilitate decentralised computation of citations that is trustworthy. We implement and evaluate Smart Papers, and extend it with a model for decentralised citation counts. We show how our approach complements current models for decentralised publishing and informetrics calculation, and analyse cost and performance implications.

Translational research applies findings from basic science to enhance human health and well-being. In translational research projects, academia and industry work together to improve healthcare, often through public-private partnerships. This “translation” is often not easy, because it means that the so-called “valley of death” will need to be crossed: many interesting findings from fundamental research do not result in new treatments, diagnostics and prevention. To cross the valley of death, fundamental researchers need to collaborate with clinical researchers and with industry so that promising results can be implemented in a product. The success of translational research projects often does not depend only on the fundamental science and the applied science, but also on the informatics needed to connect everything: the translational research informatics. This informatics, which includes data management, data stewardship and data governance, enables researchers to store and analyze their ‘big data’ in a meaningful way, and enable application in the clinic. The author has worked on the information technology infrastructure for several translational research projects in oncology for the past nine years, and presents his lessons learned in this paper in the form of ten commandments. These commandments are not only useful for the data managers, but for all involved in a translational research project. Some of the commandments deal with topics that are currently in the spotlight, such as machine readability, the FAIR Guiding Principles and the GDPR regulations. Others are mentioned less in the literature, but are just as crucial for the success of a translational research project.