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1. Mapping the co-evolution of artificial intelligence, robotics, and the internet of things over 20 years (1998-2017)

   https://doi.org/10.1371/journal.pone.0242984  

   Börner, Katy; Scrivner, Olga; Cross, Leonard E.; Gallant, Michael; Ma, Shutian; Martin, Adam S.; Record, Lisel; Yang, Haici; Dilger, Jonathan M. (December 2020, PLOS ONE)

   Bouffanais, Roland (Ed.)

   Understanding the emergence, co-evolution, and convergence of science and technology (S&T) areas offers competitive intelligence for researchers, managers, policy makers, and others. This paper presents new funding, publication, and scholarly network metrics and visualizations that were validated via expert surveys. The metrics and visualizations exemplify the emergence and convergence of three areas of strategic interest: artificial intelligence (AI), robotics, and internet of things (IoT) over the last 20 years (1998-2017). For 32,716 publications and 4,497 NSF awards, we identify their topical coverage (using the UCSD map of science), evolving co-author networks, and increasing convergence. The results support data-driven decision making when setting proper research and development (R&D) priorities; developing future S&T investment strategies; or performing effective research program assessment. 

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2. Reproducibility Signals in Science: A preliminary analysis

   Akella, Akhil Pandey; Alhoori, Hamed; Koop, David (November 2022, The first Workshop on Information Extraction from Scientific Publications)

   Tirthankar Ghosal, Sergi Blanco-Cuaresma (Ed.)

   Reproducibility is an important feature of science; experiments are retested, and analyses are repeated. Trust in the findings increases when consistent results are achieved. Despite the importance of reproducibility, significant work is often involved in these efforts, and some published findings may not be reproducible due to oversights or errors. In this paper, we examine a myriad of features in scholarly articles published in computer science conferences and journals and test how they correlate with reproducibility. We collected data from three different sources that labeled publications as either reproducible or irreproducible and employed statistical significance tests to identify features of those publications that hold clues about reproducibility. We found the readability of the scholarly article and accessibility of the software artifacts through hyperlinks to be strong signals noticeable amongst reproducible scholarly articles. 

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3. Cascading Citation Expansion

   https://doi.org/10.1633/JISTaP.2018.6.2.1  

   Chen, Chaomei (June 2018, Journal of information science theory and practice)

   Digital Science's Dimensions is envisaged as a next-generation research and discovery platform for a better and more efficient access to cross-referenced scholarly publications, grants, patents, and clinical trials. As a new addition to the growing open citation resources, it offers opportunities that may benefit a wide variety of stakeholders of scientific publications from researchers, policy makers, and the general public. In this article, we explore and demonstrate some of the practical potentials in terms of cascading citation expansions. Given a set of publications, the cascading citation expansion process can be successively applied to a set of articles so as to extend the coverage to more and more relevant articles through citation links. Although the conceptual origin can be traced back to Garfield's citation indexing, it has been largely limited, until recently, to the few who have unrestricted access to a citation database that is large enough to sustain such iterative expansions. Building on the open API of Dimensions, we integrate cascading citation expansion functions in CiteSpace and demonstrate how one may benefit from these new capabilities. In conclusion, cascading citation expansion has the potential to improve our understanding of the structure and dynamics of scientific knowledge. 

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4. Keyphrase Extraction in Scholarly Digital Library Search Engines

   https://doi.org/10.1007/978-3-030-59618-7_12  

   Patel, K.; Caragea, C.; Wu, J; Giles, C.L. (September 2020, International Conference on Web Services)

   Scholarly digital libraries provide access to scientific publications and comprise useful resources for researchers who search for literature on specific subject areas. CiteSeerX is an example of such a digital library search engine that provides access to more than 10 million academic documents and has nearly one million users and three million hits per day. Artificial Intelligence (AI) technologies are used in many components of CiteSeerX including Web crawling, document ingestion, and metadata extraction. CiteSeerX also uses an unsupervised algorithm called noun phrase chunking (NP-Chunking) to extract keyphrases out of documents. However, often NP-Chunking extracts many unimportant noun phrases. In this paper, we investigate and contrast three supervised keyphrase extraction models to explore their deployment in CiteSeerX for extracting high quality keyphrases. To perform user evaluations on the keyphrases predicted by different models, we integrate a voting interface into CiteSeerX. We show the development and deployment of the keyphrase extraction models and the maintenance requirements. 

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5. Towards Concept-Driven Visual Analytics

   Choi, In Kwon; Mishra, Swati; Harris, Kyle; Raveendranath, Nirmal Kumar; Childers, Taylor; Reda, Khairi (October 2018, IEEE Conference on Visual Analytics Science and Technology (extended abstract))

   Visualizations of data provide a proven method for analysts to explore and make data-driven discoveries. However, current visualization tools provide only limited support for hypothesis-driven analyses, and often lack capabilities that would allow users to visually test the fit of their conceptual models against the data. This imbalance could bias users to overly rely on exploratory visual analysis as the principal mode of inquiry, which can be detrimental to discovery. To address this gap, we propose a new paradigm for ‘concept-driven’ visual analysis. In this style of analysis, analysts share their conceptual models and hypotheses with the system. The system then uses those inputs to drive the generation of visualizations, while providing plots and interactions to explore places where models and data disagree. We discuss key characteristics and design considerations for concept-driven visualizations, and report preliminary findings from a formative study. 

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