2020, Oelen, Allard, Jaradeh, Mohamad Yaser, Stocker, Markus, Auer, Sören

Reviewing scientific literature is a cumbersome, time consuming but crucial activity in research. Leveraging a scholarly knowledge graph, we present a methodology and a system for comparing scholarly literature, in particular research contributions describing the addressed problem, utilized materials, employed methods and yielded results. The system can be used by researchers to quickly get familiar with existing work in a specific research domain (e.g., a concrete research question or hypothesis). Additionally, it can be used to publish literature surveys following the FAIR Data Principles. The methodology to create a research contribution comparison consists of multiple tasks, specifically: (a) finding similar contributions, (b) aligning contribution descriptions, (c) visualizing and finally (d) publishing the comparison. The methodology is implemented within the Open Research Knowledge Graph (ORKG), a scholarly infrastructure that enables researchers to collaboratively describe, find and compare research contributions. We evaluate the implementation using data extracted from published review articles. The evaluation also addresses the FAIRness of comparisons published with the ORKG.

2020, Tennant, Jonathan P., Agrawal, Ritwik, Baždarić, Ksenija, Brassard, David, Crick, Tom, Dunleavy, Daniel J., Evans, Thomas Rhys, Gardner, Nicholas, Gonzalez-Marquez, Monica, Graziotin, Daniel, Greshake Tzovaras, Bastian, Gunnarson, Daniel, Havemann, Johanna, Hosseini, Mohammad, Katz, Daniel S., Knöchelmann, Marcel, Lahti, Leo, Madan, Christopher R., Manghi, Paolo, Marocchino, Alberto, Masuzzo, Paola, Murray-Rust, Peter, Narayanaswamy, Sanjay, Nilsonne, Gustav, Pacheco-Mendoza, Josmel, Penders, Bart, Pourret, Olivier, Rera, Michael, Samuel, John, Steiner, Tobias, Stojanovski, Jadranka, Uribe Tirado, Alejandro, Vos, Rutger, Worthington, Simon, Yarkoni, Tal

There is no clear-cut boundary between Free and Open Source Software and Open Scholarship, and the histories, practices, and fundamental principles between the two remain complex. In this study, we critically appraise the intersections and differences between the two movements. Based on our thematic comparison here, we conclude several key things. First, there is substantial scope for new communities of practice to form within scholarly communities that place sharing and collaboration/open participation at their focus. Second, Both the principles and practices of FOSS can be more deeply ingrained within scholarship, asserting a balance between pragmatism and social ideology. Third, at the present, Open Scholarship risks being subverted and compromised by commercial players. Fourth, the shift and acceleration towards a system of Open Scholarship will be greatly enhanced by a concurrent shift in recognising a broader range of practices and outputs beyond traditional peer review and research articles. In order to achieve this, we propose the formulation of a new type of institutional mandate. We believe that there is substantial need for research funders to invest in sustainable open scholarly infrastructure, and the communities that support them, to avoid the capture and enclosure of key research services that would prevent optimal researcher behaviours. Such a shift could ultimately lead to a healthier scientific culture, and a system where competition is replaced by collaboration, resources (including time and people) are shared and acknowledged more efficiently, and the research becomes inherently more rigorous, verified, and reproducible.

2020, Fathalla, Said, Auer, Sören, Lange, Christoph

The past decades have witnessed a huge growth in scholarly information published on the Web, mostly in unstructured or semi-structured formats, which hampers scientific literature exploration and scientometric studies. Past studies on ontologies for structuring scholarly information focused on describing scholarly articles' components, such as document structure, metadata and bibliographies, rather than the scientific work itself. Over the past four years, we have been developing the Science Knowledge Graph Ontologies (SKGO), a set of ontologies for modeling the research findings in various fields of modern science resulting in a knowledge graph. Here, we introduce this ontology suite and discuss the design considerations taken into account during its development. We deem that within the next years, a science knowledge graph is likely to become a crucial component for organizing and exploring scientific work.

2020, Say, Aysegul, Fathalla, Said, Vahdati, Sahar, Lehmann, Jens, Auer, Sören, Aveiro, David, Dietz, Jan, Filipe, Joaquim

Improvements in web technologies and artificial intelligence enable novel, more data-driven research practices for scientists. However, scientific knowledge generated from data-intensive research practices is disseminated with unstructured formats, thus hindering the scholarly communication in various respects. The traditional document-based representation of scholarly information hampers the reusability of research contributions. To address this concern, we developed the Physics Ontology (PhySci) to represent physics-related scholarly data in a machine-interpretable format. PhySci facilitates knowledge exploration, comparison, and organization of such data by representing it as knowledge graphs. It establishes a unique conceptualization to increase the visibility and accessibility to the digital content of physics publications. We present the iterative design principles by outlining a methodology for its development and applying three different evaluation approaches: data-driven and criteria-based evaluation, as well as ontology testing.