Literature recommendation systems (LRS) assist readers in the discovery of relevant content from the overwhelming amount of literature available. Despite the widespread adoption of LRS, there is a lack of research on the user-perceived recommendation
characteristics for fundamentally different approaches to content-based literature recommendation. To complement existing quantitative studies on literature recommendation, we present qualitative study results that report on users perceptions for two contrasting recommendation classes: (1) link-based recommendation represented by the Co-Citation Proximity (CPA) approach, and (2) text-based recommendation represented by Lucenes MoreLikeThis (MLT) algorithm. The empirical data analyzed in our study with twenty users and a diverse set of 40 Wikipedia articles indicate a noticeable difference between text- and link-based recommendation generation approaches along several key dimensions. The text-based MLT method receives higher satisfaction ratings in terms of user-perceived similarity of recommended articles. In contrast, the CPA approach receives higher satisfaction scores in terms of diversity and serendipity of recommendations. We conclude that users of literature recommendation systems can benefit most from hybrid approaches that combine both link- and text-based approaches, where the users information needs and preferences should control the weighting for the approaches used. The optimal weighting of multiple approaches used in a hybrid recommendation system is highly dependent on a users shifting needs.
The vast amount of research produced at institutions world-wide is extremely diverse, and coarse-grained quantitative measures of impact often obscure the individual contributions of these institutions to specific research fields and topics. We show
that by applying an information retrieval model to index research articles which are faceted by institution and time, we can develop tools to rank institutions given a keyword query. We present an interactive atlas, Quoka, designed to enable a user to explore these rankings contextually by geography and over time. Through a set of use cases we demonstrate that the atlas can be used to perform sensemaking tasks to learn and collect information about the relationships between institutions and scholarly knowledge production.
Publish or perish is an expression describing the pressure on academics to consistently publish research to ensure a successful career in academia. With a global pandemic that has changed the world, how has it changed academic productivity? Here we s
how that academics are posting just as many publications on the arXiv pre-print server as if there were no pandemic: 168,630 were posted in 2020, a +12.6% change from 2019 and $+1.4sigma$ deviation above the predicted 162,577 $pm$ 4,393. However, some immediate impacts are visible in individual research fields. Conference cancellations have led to sharp drops in pre-prints, but laboratory closures have had mixed effects. Only some experimental fields show mild declines in outputs, with most being consistent on previous years or even increasing above model expectations. The most significant change is a 50% increase ($+8sigma$) in quantitative biology research, all related to the COVID-19 pandemic. Some of these publications are by biologists using arXiv for the first time, and some are written by researchers from other fields (e.g., physicists, mathematicians). While quantitative biology pre-prints have returned to pre-pandemic levels, 20% of the research in this field is now focussed on the COVID-19 pandemic, demonstrating a strong shift in research focus.
In contrast to other fields where conferences are typically for less polished or in-progress research, computing has long relied on referred conference papers as a venue for the final publication of completed research. While frequently a topic of inf
ormal discussion, debates about its efficacy, or library science research, the development of this phenomena has not been historically analyzed. This paper presents the first systematic investigation of the development of modern computing publications. It relies on semi-structured interviews with eight computing professors from diverse backgrounds to understand how researchers experienced changes in publication culture over time. Ultimately, the article concludes that the early presence of non-academic practitioners in research and a degree of path dependenceor a tendency to continue on the established path rather than the most economically optimal one allowed conferences to gain and hold prominence as the field exploded in popularity during the 1980s.
Computing such correlation coefficient would be straightforward had we had available the rankings given by the prize committee to all scientists in the pool. In reality we only have citation rankings for all scientists. This means, however, that we h
ave the ordinal rankings of the prize winners with regard to citation metrics. I use maximum likelihood method to infer the most probable correlation coefficient to produce the observed pattern of ordinal ranks of the prize winners. I get the correlation coefficients of 0.47 and 0.59 between the composite citation indicator and getting Abel Prize and Fields Medal, respectively. The correlation coefficient between getting a Nobel Prize and the Q-factor is 0.65. These coefficients are of the same magnitude as the correlation coefficient between Elo ratings of the chess players and their popularity measured as numbers of webpages mentioning the players.
Preservation pipelines demonstrate extended value when digitized content is also computation ready. Expanding this to historical controlled vocabularies published in analog format requires additional steps if they are to be fully leveraged for resear
ch. This paper reports on work addressing this challenge. We report on a pipeline and project progress addressing three key goals: 1) transforming the 1910 Library of Congress Subject Headings (LCSH) to the Simple Knowledge Organization System (SKOS) linked data standard, 2) implementing persistent identifiers (PIDs) and launching our prototype ARK resolver, and 3) importing the 1910 LCSH into the Helping Interdisciplinary Vocabulary Engineering (HIVE) System to support automatic metadata generation and scholarly analysis of the historical record. The discussion considers the implications of our work in the broader context of preservation, and the conclusion summarizes our work and identifies next steps.
This study aims to reveal what kind of topics emerged in the biomedical domain by retrospectively analyzing newly added MeSH (Medical Subject Headings) terms from 2001 to 2010 and how they have been used for indexing since their inclusion in the thes
aurus. The goal is to investigate if the future trend of a new topic depends on what kind of topic it is without relying on external indicators such as growth, citation patterns, or word co-occurrences. This topic perspective complements the traditional publication perspective in studying emerging topics. Results show that topic characteristics, including topic category, clinical significance, and if a topic has any narrower terms at the time of inclusion, influence future popularity of a new MeSH. Four emergence trend patterns are identified, including emerged and sustained, emerged not sustained, emerged and fluctuated, and not yet emerged. Predictive models using topic characteristics for emerging topic prediction show promise. This suggests that the characteristics of topics and domain should be considered when predicting future emergence of research topics. This study bridges a gap in emerging topic prediction by offering a topic perspective and advocates for considering topic and domain characteristics as well as economic, medical, and environmental impact when studying emerging topics in the biomedical domain.
The lack of scientific openness is identified as one of the key challenges of computational reproducibility. In addition to Open Data, Free and Open-source Software (FOSS) and Open Hardware (OH) can address this challenge by introducing open policies
, standards, and recommendations. However, while both FOSS and OH are free to use, study, modify, and redistribute, there are significant differences in sharing and reusing these artifacts. FOSS is increasingly supported with software repositories, but support for OH is lacking, potentially due to the complexity of its digital format and licensing. This paper proposes leveraging FAIR principles to make OH findable, accessible, interoperable, and reusable. We define what FAIR means for OH, how it differs from FOSS, and present examples of unique demands. Also, we evaluate dissemination platforms currently used for OH and provide recommendations.
A plethora of scholarly knowledge is being published on distributed scholarly infrastructures. Querying a single infrastructure is no longer sufficient for researchers to satisfy information needs. We present a GraphQL-based federated query service f
or executing distributed queries on numerous, heterogeneous scholarly infrastructures (currently, ORKG, DataCite and GeoNames), thus enabling the integrated retrieval of scholarly content from these infrastructures. Furthermore, we present the methods that enable cross-walks between artefact metadata and artefact content across scholarly infrastructures, specifically DOI-based persistent identification of ORKG artefacts (e.g., ORKG comparisons) and linking ORKG content to third-party semantic resources (e.g., taxonomies, thesauri, ontologies). This type of linking increases interoperability, facilitates the reuse of scholarly knowledge, and enables finding machine actionable scholarly knowledge published by ORKG in global scholarly infrastructures. In summary, we suggest applying the established linked data principles to scholarly knowledge to improve its findability, interoperability, and ultimately reusability, i.e., improve scholarly knowledge FAIR-ness.
This bibliometric review summarised the research trends and analysed research areas in multiple sclerosis (MS) over the last decade. The documents containing the term multiple sclerosis in the article title were retrieved from the Scopus database. We
found a total of 18003 articles published in journals in the English language between 2012 and 2021. The emerging keywords identified utilising the enhanced strategic diagram were covid-19, teriflunomide, clinical trial, microglia, b cells, myelin, brain, white matter, functional connectivity, pain, employment, health-related quality of life, meta-analysis and comorbidity. In conclusion, this study demonstrates the tremendous growth of MS literature worldwide, which is expected to grow more than double during the next decade especially in the identified emerging topics.
