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  • Visual Exploration and Knowledge Discovery from Biomedical Dark Data
    arXiv.cs.DL Pub Date : 2020-09-28
    Shashwat Aggarwal; Ramesh Singh

    Data visualization techniques proffer efficient means to organize and present data in graphically appealing formats, which not only speeds up the process of decision making and pattern recognition but also enables decision-makers to fully understand data insights and make informed decisions. Over time, with the rise in technological and computational resources, there has been an exponential increase

  • Can pandemics transform scientific novelty? Evidence from COVID-19
    arXiv.cs.DL Pub Date : 2020-09-26
    Meijun Liu; Yi Bu; Chongyan Chen; Jian Xu; Daifeng Li; Yan Leng; Richard Barry Freeman; Eric Meyer; Wonjin Yoon; Mujeen Sung; Minbyul Jeong; Jinhyuk Lee; Jaewoo Kang; Min Song; Yujia Zhai; Ying Ding

    Scientific novelty is important during the pandemic due to its critical role in generating new vaccines. Parachuting collaboration and international collaboration are two crucial channels to expand teams' search activities for a broader scope of resources required to address the global challenge. Our analysis of 58,728 coronavirus papers suggests that scientific novelty measured by the BioBERT model

  • Virtual Proximity Citation (VCP): A Supervised Deep Learning Method to Relate Uncited Papers On Grounds of Citation Proximity
    arXiv.cs.DL Pub Date : 2020-09-25
    Rohit Rawat

    Citation based approaches have seen good progress for recommending research papers using citations in the paper. Citation proximity analysis which uses the in-text citation proximity to find relatedness between two research papers is better than co-citation analysis and bibliographic analysis. However, one common problem which exists in each approach is that paper should be well cited. If documents

  • CAT STREET: Chronicle Archive of Tokyo Street-fashion
    arXiv.cs.DL Pub Date : 2020-09-28
    Satoshi Takahashi; Keiko Yamaguchi; Asuka Watanabe

    The analysis of daily life fashion trends can help us understand our societies and human cultures profoundly. However, no appropriate database exists that includes images illustrating what people wore in their daily lives over an extended period. In this study, we propose a new fashion image archive, Chronicle Archive of Tokyo Street-fashion (CAT STREET), to shed light on daily life fashion trends

  • An Analysis of the Impact of SEO on University Website Ranking
    arXiv.cs.DL Pub Date : 2020-09-25
    Mohammad Javad Shayegan; Maasoumeh Kouhzadi

    Today, ranking systems in universities have been considered by the academic community, and there is a tight competition between world universities to achieve higher ranks. In the meantime, the ranking of university websites is also in the spotlight, and the Webometric research center announces the ranks of university websites twice a year. Examining university rankings indicators and the Webometric

  • Funding CRISPR: Understanding the role of government and private sector actors in transformative innovation systems
    arXiv.cs.DL Pub Date : 2020-09-24
    David Fajardo-Ortiz; Stephan Hornbostel; Maywa Montenegro-de-Wit; Annie Shattuck

    CRISPR/Cas has the potential to revolutionize medicine, agriculture, and the way we understand life itself. Understanding the trajectory of innovation, how it is influenced and who pays for it, is essential for such a transformative technology. The University of California and the Broad/Harvard/MIT systems are the two most prominent academic institutions involved in the research and development of

  • Crosslingual Topic Modeling with WikiPDA
    arXiv.cs.DL Pub Date : 2020-09-23
    Tiziano Piccardi; Robert West

    We present Wikipedia-based Polyglot Dirichlet Allocation (WikiPDA), a crosslingual topic model that learns to represent Wikipedia articles written in any language as distributions over a common set of language-independent topics. It leverages the fact that Wikipedia articles link to each other and are mapped to concepts in the Wikidata knowledge base, such that, when represented as bags of links, articles

  • Open Access Books in the Humanities and Social Sciences: an Open Access Altmetric Advantage
    arXiv.cs.DL Pub Date : 2020-09-22
    Michael Taylor

    The last decade has seen two significant phenomena emerge in research communication: the rise of open access (OA) publishing, and evidence of online sharing in the form of altmetrics. There has been limited examination of the effect of OA on online sharing for journal articles, and little for books. This paper examines the altmetrics of a set of 32,222 books (of which 5% are OA) and a set of 220,527

  • COVID-19 Literature Topic-Based Search via Hierarchical NMF
    arXiv.cs.DL Pub Date : 2020-09-07
    Rachel Grotheer; Yihuan Huang; Pengyu Li; Elizaveta Rebrova; Deanna Needell; Longxiu Huang; Alona Kryshchenko; Xia Li; Kyung Ha; Oleksandr Kryshchenko

    A dataset of COVID-19-related scientific literature is compiled, combining the articles from several online libraries and selecting those with open access and full text available. Then, hierarchical nonnegative matrix factorization is used to organize literature related to the novel coronavirus into a tree structure that allows researchers to search for relevant literature based on detected topics

  • Examining the Impact of Algorithm Awareness on Wikidata's Recommender System Recoin
    arXiv.cs.DL Pub Date : 2020-09-18
    Jesse Josua Benjamin; Claudia Müller-Birn; Simon Razniewski

    The global infrastructure of the Web, designed as an open and transparent system, has a significant impact on our society. However, algorithmic systems of corporate entities that neglect those principles increasingly populated the Web. Typical representatives of these algorithmic systems are recommender systems that influence our society both on a scale of global politics and during mundane shopping

  • A Glimpse of the First Eight Months of the COVID-19 Literature on Microsoft Academic Graph: Themes, Citation Contexts, and Uncertainties
    arXiv.cs.DL Pub Date : 2020-09-17
    Chaomei Chen

    As scientists worldwide search for answers to the overwhelmingly unknown behind the deadly pandemic, the literature concerning COVID-19 has been growing exponentially. Keeping abreast of the body of literature at such a rapidly advancing pace poses significant challenges not only to active researchers but also to the society as a whole. Although numerous data resources have been made openly available

  • A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching
    arXiv.cs.DL Pub Date : 2020-09-17
    Mariona Coll Ardanuy; Kasra Hosseini; Katherine McDonough; Amrey Krause; Daniel van Strien; Federico Nanni

    Recognizing toponyms and resolving them to their real-world referents is required for providing advanced semantic access to textual data. This process is often hindered by the high degree of variation in toponyms. Candidate selection is the task of identifying the potential entities that can be referred to by a toponym previously recognized. While it has traditionally received little attention in the

  • Beyond the Western Core-Periphery Model: Analysing Scientific Mobility and Collaboration in the Middle East and North Africa
    arXiv.cs.DL Pub Date : 2020-09-16
    Jamal El Ouahi; Nicolas Robinson-Garcia; Rodrigo Costas

    This study investigates the scientific mobility and international collaboration networks in the Middle East and North Africa (MENA) region between 2008 and 2017. The main goal is to establish mobility and collaboration profiles at the region and country levels. By using affiliation metadata available in scientific publications, we track international scientific mobility and collaboration networks in

  • Representing Semantified Biological Assays in the Open Research Knowledge Graph
    arXiv.cs.DL Pub Date : 2020-09-16
    Marco Anteghini; Jennifer D'Souza; Vitor A. P. Martins dos Santos; Sören Auer

    In the biotechnology and biomedical domains, recent text mining efforts advocate for machine-interpretable, and preferably, semantified, documentation formats of laboratory processes. This includes wet-lab protocols, (in)organic materials synthesis reactions, genetic manipulations and procedures for faster computer-mediated analysis and predictions. Herein, we present our work on the representation

  • How to Measure the Performance of a Collaborative Research Center
    arXiv.cs.DL Pub Date : 2020-09-16
    Alona Zharova; Janine Tellinger-Rice; Wolfgang Karl Härdle

    New Public Management helps universities and research institutions to perform in a highly competitive research environment. Evaluating publicly financed research improves transparency, helps in reflection and self-assessment, and provides information for strategic decision making. In this paper we provide empirical evidence using data from a Collaborative Research Center (CRC) on financial inputs and

  • Comment on "Open is not forever: a study of vanished open access journals"
    arXiv.cs.DL Pub Date : 2020-09-16
    Matan Shelomi

    We comment on a recent article by Laakso et al. (arXiv:2008.11933 [cs.DL]), in which the disappearance of 176 open access journals from the Internet is noted. We argue that one reason these journals may have vanished is that they were predatory journals. The de-listing of predators from the Directory of Open Access Journals in 2014 and the abundance of predatory journals and awareness thereof in North

  • Same data may bring conflict results: a caution to use the disruptive index
    arXiv.cs.DL Pub Date : 2020-09-15
    Guoqiang Liang; Yi Jiang; Haiyan Hou

    In the last two decades, scholars have designed various types of bibliographic related indicators to identify breakthrough-class academic achievements. In this study, we take a further step to look at properties of the promising disruptive index, thus deepening our understanding of this index and further facilitating its wise use in bibliometrics. Using publication records for Nobel laureates between

  • Towards an RDF Knowledge Graph of Scholars from Early Modern History
    arXiv.cs.DL Pub Date : 2020-09-14
    Jennifer Blanke; Thomas Riechert

    The use of Semantic Web Technologies supports research in the field of digital humanities. In this paper we focus on the creation of semantic independent online databases such as those of historical prosopography. These databases contain biographical information of historical persons. We focus on this information with an interest in German professorial career patterns from the 16th to the 18th century

  • A matter of time: publication dates in Web of Science Core Collection
    arXiv.cs.DL Pub Date : 2020-09-14
    Weishu Liu

    Web of Science Core Collection, one of the most authoritative bibliographic databases, is widely used in academia to track high-quality research. This database has begun to index online-first articles since December 2017. This new practice has introduced two different publication dates (online and final publication dates) into the database for more and more early access publications. It may confuse

  • Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis
    arXiv.cs.DL Pub Date : 2020-09-13
    Jannis Born; Nina Wiedemann; Gabriel Brändle; Charlotte Buhre; Bastian Rieck; Karsten Borgwardt

    Controlling the COVID-19 pandemic largely hinges upon the existence of fast, safe, and highly-available diagnostic tools. Ultrasound, in contrast to CT or X-Ray, has many practical advantages and can serve as a globally-applicable first-line examination technique. We provide the largest publicly available lung ultrasound (US) dataset for COVID-19 consisting of 106 videos from three classes (COVID-19

  • Data mining and analysis of scientific research data records on Covid 19 mortality, immunity, and vaccine development in the first wave of the Covid 19 pandemic
    arXiv.cs.DL Pub Date : 2020-09-12
    Petar Radanliev; David De Roure; Rob Walton

    In this study, we investigate the scientific research response from the early stages of the pandemic, and we review key findings on how the early warning systems developed in previous epidemics responded to contain the virus. The data records are analysed with commutable statistical methods, including R Studio, Bibliometrix package, and the Web of Science data mining tool. We identified few different

  • Citing and referencing habits in Medicine and Social Sciences journals in 2019
    arXiv.cs.DL Pub Date : 2020-09-11
    Erika Alves dos Santos; Silvio Peroni; Marcos Luiz Mucheroni

    Purpose. This article explores citing and referencing systems in Social Sciences and Medicine articles from different theoretical and practical perspectives, considering bibliographic references as a facet of descriptive representation. Methodology. The analysis of citing and referencing elements (i.e. bibliographic references, mentions, quotations, and respective in-text reference pointers) identified

  • How reliable and useful is Cabell's Blacklist ? A data-driven analysis
    arXiv.cs.DL Pub Date : 2020-09-11
    Christophe Dony; Maurane Raskinet; François Renaville; Stéphanie Simon; Paul Thirion

    In scholarly publishing, blacklists aim to register fraudulent or deceptive journals and publishers, also known as "predatory", to minimise the spread of unreliable research and the growing of fake publishing outlets. However, blacklisting remains a very controversial activity for several reasons: there is no consensus regarding the criteria used to determine fraudulent journals, the criteria used

  • RapidLearn: A General Purpose Toolkit for Autonomic Networking
    arXiv.cs.DL Pub Date : 2020-09-09
    Jatin Sharma; Nikhilesh Behera; Priya Venkatraman; Boon Thau Loo

    Software Defined Networking has unfolded a new area of opportunity in distributed networking and intelligent networks. There has been a great interest in performing machine learning in distributed setting, exploiting the abstraction of SDN which makes it easier to write complex ML queries on standard control plane. However, most of the research has been made towards specialized problems (security,

  • The aftermath of Big Deal cancellations and their impact on interlibrary loans
    arXiv.cs.DL Pub Date : 2020-09-09
    Marc-Andre Simard; Jason Priem; Heather Piwowar

    A "Big Deal" is a bundle of journals that is offered to libraries by publishers as a "one-price, one size fits all package" (Frazier, 2001). There have been several accounts of Big Deals cancellations by academic libraries in the scientific literature. This paper presents the finding of a literature review aimed at documenting the aftermath of Big Deal cancellation in University Libraries, particularly

  • How much does an interlibrary loan request cost? A review of the literature
    arXiv.cs.DL Pub Date : 2020-09-09
    Marc-Andre Simard; Jason Priem; Heather Piwowar

    Interlibrary loan (ILL) services are used to fill the gap between academic libraries' collections and the information needs of their users. Today's trend toward the cancellation of serials "Big Deals" has increased the importance of clear information around ILL to support decision-making. In order to plan the cancellation of a journal package, academic libraries need to be able to forecast their total

  • A Review of Geospatial Content in IEEE Visualization Publications
    arXiv.cs.DL Pub Date : 2020-09-07
    Alexander Yoshizumi; Megan M. Coffer; Elyssa L. Collins; Mollie D. Gaines; Xiaojie Gao; Kate Jones; Ian R. McGregor; Katie A. McQuillan; Vinicius Perin; Laura M. Tomkins; Thom Worm; Laura Tateosian

    Geospatial analysis is crucial for addressing many of the world's most pressing challenges. Given this, there is immense value in improving and expanding the visualization techniques used to communicate geospatial data. In this work, we explore this important intersection -- between geospatial analytics and visualization -- by examining a set of recent IEEE VIS Conference papers (a selection from 2017-2019)

  • Universal Layout Emulation for Long-Term Database Archival
    arXiv.cs.DL Pub Date : 2020-09-06
    Raja Appuswamy; Vincent Joguin

    Research on alternate media technologies, like film, synthetic DNA, and glass, for long-term data archival has received a lot of attention recently due to the media obsolescence issues faced by contemporary storage media like tape, Hard Disk Drives (HDD), and Solid State Disks (SSD). While researchers have developed novel layout and encoding techniques for archiving databases on these new media types

  • The Pace of Artificial Intelligence Innovations: Speed, Talent, and Trial-and-Error
    arXiv.cs.DL Pub Date : 2020-09-03
    Xuli Tang; Xin Li; Ying Ding; Min Song; Yi Bu

    Innovations in artificial intelligence (AI) are occurring at speeds faster than ever witnessed before. However, few studies have managed to measure or depict this increasing velocity of innovations in the field of AI. In this paper, we combine data on AI from arXiv and Semantic Scholar to explore the pace of AI innovations from three perspectives: AI publications, AI players, and AI updates (trial

  • Identifying Documents In-Scope of a Collection from Web Archives
    arXiv.cs.DL Pub Date : 2020-09-02
    Krutarth Patel; Cornelia Caragea; Mark Phillips; Nathaniel Fox

    Web archive data usually contains high-quality documents that are very useful for creating specialized collections of documents, e.g., scientific digital libraries and repositories of technical reports. In doing so, there is a substantial need for automatic approaches that can distinguish the documents of interest for a collection out of the huge number of documents collected by web archiving institutions

  • What Library Digitization Leaves Out: Predicting the Availability of Digital Surrogates of English Novels
    arXiv.cs.DL Pub Date : 2020-09-01
    Allen Riddell; Troy J. Bassett

    Library digitization has made more than a hundred thousand 19th-century English-language books available to the public. Do the books which have been digitized reflect the population of published books? An affirmative answer would allow book and literary historians to use holdings of major digital libraries as proxies for the population of published works, sparing them the labor of collecting a representative

  • Mapping Researchers with PeopleMap
    arXiv.cs.DL Pub Date : 2020-08-31
    Jon Saad-Falcon; Omar Shaikh; Zijie J. Wang; Austin P. Wright; Sasha Richardson; Duen Horng Chau

    Discovering research expertise at universities can be a difficult task. Directories routinely become outdated, and few help in visually summarizing researchers' work or supporting the exploration of shared interests among researchers. This results in lost opportunities for both internal and external entities to discover new connections, nurture research collaboration, and explore the diversity of research

  • The prestige and status of research fields within mathematics
    arXiv.cs.DL Pub Date : 2020-08-30
    Jean-Marc Schlenker

    While the ``hierarchy of science'' has been widely analysed, there is no corresponding study of the status of subfields within a given scientific field. We use bibliometric data to show that subfields of mathematics have a different ``standing'' within the mathematics community. Highly ranked departments tend to specialize in some subfields more than in others, and the same subfields are also over-represented

  • Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks
    arXiv.cs.DL Pub Date : 2020-08-30
    Qingyun Sun; Hao Peng; Jianxin Li; Senzhang Wang; Xiangyu Dong; Liangxuan Zhao; Philip S. Yu; Lifang He

    Name disambiguation aims to identify unique authors with the same name. Existing name disambiguation methods always exploit author attributes to enhance disambiguation results. However, some discriminative author attributes (e.g., email and affiliation) may change because of graduation or job-hopping, which will result in the separation of the same author's papers in digital libraries. Although these

  • A Decade of In-text Citation Analysis based on Natural Language Processing and Machine Learning Techniques: An overview of empirical studies
    arXiv.cs.DL Pub Date : 2020-08-29
    Sehrish Iqbal; Saeed-Ul Hassan; Naif Radi Aljohani; Salem Alelyani; Raheel Nawaz; Lutz Bornmann

    Citation analysis is one of the most frequently used methods in research evaluation. We are seeing significant growth in citation analysis through bibliometric metadata, primarily due to the availability of citation databases such as the Web of Science, Scopus, Google Scholar, Microsoft Academic, and Dimensions. Due to better access to full-text publication corpora in recent years, information scientists

  • CORAL: COde RepresentAtion Learning with Weakly-Supervised Transformers for Analyzing Data Analysis
    arXiv.cs.DL Pub Date : 2020-08-28
    Ge Zhang; Mike A. Merrill; Yang Liu; Jeffrey Heer; Tim Althoff

    Large scale analysis of source code, and in particular scientific source code, holds the promise of better understanding the data science process, identifying analytical best practices, and providing insights to the builders of scientific toolkits. However, large corpora have remained unanalyzed in depth, as descriptive labels are absent and require expert domain knowledge to generate. We propose a

  • COVID-19 Kaggle Literature Organization
    arXiv.cs.DL Pub Date : 2020-08-04
    Maksim Ekin Eren; Nick Solovyev; Edward Raff; Charles Nicholas; Ben Johnson

    The world has faced the devastating outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), or COVID-19, in 2020. Research in the subject matter was fast-tracked to such a point that scientists were struggling to keep up with new findings. With this increase in the scientific literature came a need to present that literature so that researchers and health professionals could find

  • Linked Credibility Reviews for Explainable Misinformation Detection
    arXiv.cs.DL Pub Date : 2020-08-28
    Ronald Denaux; Jose Manuel Gomez-Perez

    In recent years, misinformation on the Web has become increasingly rampant. The research community has responded by proposing systems and challenges, which are beginning to be useful for (various subtasks of) detecting misinformation. However, most proposed systems are based on deep learning techniques which are fine-tuned to specific domains, are difficult to interpret and produce results which are

  • An empirical review of the different variants of the Probabilistic Affinity Index as applied to scientific collaboration
    arXiv.cs.DL Pub Date : 2020-08-27
    Zaida Chinchilla-Rodríguez; Yi Bu; Nicolás Robinson-García; Cassidy R. Sugimoto

    Responsible indicators are crucial for research assessment and monitoring. Transparency and accuracy of indicators are required to make research assessment fair and ensure reproducibility. However, sometimes it is difficult to conduct or replicate studies based on indicators due to the lack of transparency in conceptualization and operationalization. In this paper, we review the different variants

  • Open is not forever: a study of vanished open access journals
    arXiv.cs.DL Pub Date : 2020-08-27
    Mikael Laakso; Lisa Matthias; Najko Jahn

    The preservation of the scholarly record has been a point of concern since the beginning of knowledge production. With print publications, the responsibility rested primarily with librarians, but the shift towards digital publishing and, in particular, the introduction of open access (OA) have caused ambiguity and complexity. Consequently, the long-term accessibility of journals is not always guaranteed

  • A 25 Year Retrospective on D-Lib Magazine
    arXiv.cs.DL Pub Date : 2020-08-26
    Michael L. Nelson; Herbert Van de Sompel

    In July, 1995 the first issue of D-Lib Magazine was published as an on-line, HTML-only, open access magazine, serving as the focal point for the then emerging digital library research community. In 2017 it ceased publication, in part due to the maturity of the community it served as well as the increasing availability of and competition from eprints, institutional repositories, conferences, social

  • PRINCIPIA: a Decentralized Peer-Review Ecosystem
    arXiv.cs.DL Pub Date : 2020-08-20
    Andrea Mambrini; Andrea Baronchelli; Michele Starnini; Daniele Marinazzo; Manlio De Domenico

    Peer review is a cornerstone of modern scientific endeavor. However, there is growing consensus that several limitations of the current peer review system, from lack of incentives to reviewers to lack of transparency, risks to undermine its benefits. Here, we introduce the PRINCIPIA (http://www.principia.network/) framework for peer-review of scientific outputs (e.g., papers, grant proposals or patents)

  • Understanding the Advisor-advisee Relationship via Scholarly Data Analysis
    arXiv.cs.DL Pub Date : 2020-08-20
    Jiaying Liu; Tao Tang; Xiangjie Kong; Amr Tolba; Zafer AL-Makhadmeh; Feng Xia

    Advisor-advisee relationship is important in academic networks due to its universality and necessity. Despite the increasing desire to analyze the career of newcomers, however, the outcomes of different collaboration patterns between advisors and advisees remain unknown. The purpose of this paper is to find out the correlation between advisors' academic characteristics and advisees' academic performance

  • Berlin: A Quantitative View of the Structure of Institutional Scientific Collaborations
    arXiv.cs.DL Pub Date : 2020-08-19
    Aliakbar Akbaritabar

    This paper examines the structure of scientific collaborations in a large European metropolitan area. It aims to identify strategic coalitions among organizations in Berlin as a specific case with high institutional and sectoral diversity. By adopting a global, regional and organization based approach we provide a quantitative, exploratory and macro view of this diversity. We use publications data

  • Prestige of scholarly book publishers: an investigation into criteria, processes, and practices across countries
    arXiv.cs.DL Pub Date : 2020-08-13
    Eleonora Dagiene

    Numerous national research assessment policies set the goal of promoting "excellence" and incentivise scholars to publish their research in the most prestigious journals or with the most prestigious book publishers. We investigate the practicalities of the assessment of book outputs based on the prestige of book publishers (Denmark, Finland, Flanders, Lithuania, Norway). Additionally, we test whether

  • Predicting the Citations of Scholarly Paper
    arXiv.cs.DL Pub Date : 2020-08-10
    Xiaomei Bai; Fuli Zhang; Ivan Lee

    Citation prediction of scholarly papers is of great significance in guiding funding allocations, recruitment decisions, and rewards. However, little is known about how citation patterns evolve over time. By exploring the inherent involution property in scholarly paper citation, we introduce the Paper Potential Index (PPI) model based on four factors: inherent quality of scholarly paper, scholarly paper

  • Nature, Science, and PNAS -- Disciplinary profiles and impact
    arXiv.cs.DL Pub Date : 2020-08-11
    Staša Milojević

    Nature, Science, and PNAS are the three most prestigious general-science journals, and Nature and Science are among the most influential journals overall, based on the journal Impact Factor (IF). In this paper we perform automatic classification of ~50,000 articles in these journals (published in the period 2005-2015) into 14 broad areas, to explore disciplinary profiles and to determine their field-specific

  • Towards a more realistic citation model: The key role of research team sizes
    arXiv.cs.DL Pub Date : 2020-08-11
    Staša Milojević

    We propose a new citation model which builds on the existing models that explicitly or implicitly include "direct" and "indirect" (learning about a cited paper's existence from references in another paper) citation mechanisms. Our model departs from the usual, unrealistic assumption of uniform probability of direct citation, in which initial differences in citation arise purely randomly. Instead, we

  • Quantifying Success in Science: An Overview
    arXiv.cs.DL Pub Date : 2020-08-10
    Xiaomei Bai; Hanxiao Pan; Jie Hou; Teng Guo; Ivan Lee; Feng Xia

    Quantifying success in science plays a key role in guiding funding allocations, recruitment decisions, and rewards. Recently, a significant amount of progresses have been made towards quantifying success in science. This lack of detailed analysis and summary continues a practical issue. The literature reports the factors influencing scholarly impact and evaluation methods and indices aimed at overcoming

  • Author Impact: Evaluations, Predictions, and Challenges
    arXiv.cs.DL Pub Date : 2020-08-10
    Fuli Zhang; Xiaomei Bai; Ivan Lee

    Author impact evaluation and prediction play a key role in determining rewards, funding, and promotion. In this paper, we first introduce the background of author impact evaluation and prediction. Then, we review recent developments of author impact evaluation, including data collection, data pre-processing, data analysis, feature selection, algorithm design, and algorithm evaluation. Thirdly, we provide

  • The decline of astronomical research in Venezuela
    arXiv.cs.DL Pub Date : 2020-08-11
    Nestor SanchezVIU, Spain

    During the last 15 years the number of astronomy-related papers published by scientists in Venezuela has been continuously decreasing, mainly due to emigration. If rapid corrective actions are not implemented, Venezuelan astronomy could disappear.

  • Measure the Impact of Institution and Paper via Institution-citation Network
    arXiv.cs.DL Pub Date : 2020-08-10
    Xiaomei Bai; Fuli Zhang; Jin Ni; Lei Shi; Ivan Lee

    This paper investigates the impact of institutes and papers over time based on the heterogeneous institution-citation network. A new model, IPRank, is introduced to measure the impact of institution and paper simultaneously. This model utilises the heterogeneous structural measure method to unveil the impact of institution and paper, reflecting the effects of citation, institution, and structural measure

  • Comprehensiveness of Archives: A Modern AI-enabled Approach to Build Comprehensive Shared Cultural Heritage
    arXiv.cs.DL Pub Date : 2020-08-11
    Abhishek GuptaMontreal AI Ethics InstituteMicrosoft, and; Nikitasha KapoorPure & Applied Group

    Archives play a crucial role in the construction and advancement of society. Humans place a great deal of trust in archives and depend on them to craft public policies and to preserve languages, cultures, self-identity, views and values. Yet, there are certain voices and viewpoints that remain elusive in the current processes deployed in the classification and discoverability of records and archives

  • Scientific Article Recommendation: Exploiting Common Author Relations and Historical Preferences
    arXiv.cs.DL Pub Date : 2020-08-09
    Feng Xia; Haifeng Liu; Ivan Lee; Longbing Cao

    Scientific article recommender systems are playing an increasingly important role for researchers in retrieving scientific articles of interest in the coming era of big scholarly data. Most existing studies have designed unified methods for all target researchers and hence the same algorithms are run to generate recommendations for all researchers no matter which situations they are in. However, different

  • Waiving Article Processing Charges for Least Developed Countries. A Brick Stone of a Large-scale Open Access Transformation
    arXiv.cs.DL Pub Date : 2020-08-10
    Niels Taubert; Andre Bruns; Christopher Lenke; Graham Stone

    This article investigates the question, if it is economically feasible for a large publishing house to waive article processing charges for the group of 47 so called least developed countries (LDC). As an example Springer-Nature is selected. The analysis is based on the Web of Science, OpenAPC and the Jisc collections Springer compact journal list. As a result, it estimates an average yearly publication

  • An Overview on Evaluating and Predicting Scholarly Article Impact
    arXiv.cs.DL Pub Date : 2020-08-10
    Xiaomei Bai; Hui Liu; Fuli Zhang; Zhaolong Ning; Xiangjie Kong; Ivan Lee; Feng Xia

    Scholarly article impact reflects the significance of academic output recognised by academic peers, and it often plays a crucial role in assessing the scientific achievements of researchers, teams, institutions and countries. It is also used for addressing various needs in the academic and scientific arena, such as recruitment decisions, promotions, and funding allocations. This article provides a

  • Quantifying the Impact of Scholarly Papers Based on Higher-Order Weighted Citations
    arXiv.cs.DL Pub Date : 2020-08-10
    Xiaomei Bai; Fuli Zhang; Jie Hou; Ivan Lee; Xiangjie Kong; Amr Tolba; Feng Xia

    Quantifying the impact of a scholarly paper is of great significance, yet the effect of geographical distance of cited papers has not been explored. In this paper, we examine 30,596 papers published in Physical Review C, and identify the relationship between citations and geographical distances between author affiliations. Subsequently, a relative citation weight is applied to assess the impact of

  • The Role of Positive and Negative Citations in Scientific Evaluation
    arXiv.cs.DL Pub Date : 2020-08-10
    Xiaomei Bai; Ivan Lee; Zhaolong Ning; Amr Tolba; Feng Xia

    Quantifying the impact of scientific papers objectively is crucial for research output assessment, which subsequently affects institution and country rankings, research funding allocations, academic recruitment and national/international scientific priorities. While most of the assessment schemes based on publication citations may potentially be manipulated through negative citations, in this study

  • Navigating the landscape of COVID-19 research through literature analysis: A bird's eye view
    arXiv.cs.DL Pub Date : 2020-08-07
    Lana Yeganova; Rezarta Islamaj; Qingyu Chen; Robert Leaman; Alexis Allot; Chin-Hsuan Wei; Donald C. Comeau; Won Kim; Yifan Peng; W. John Wilbur; Zhiyong Lu

    Timely access to accurate scientific literature in the battle with the ongoing COVID-19 pandemic is critical. This unprecedented public health risk has motivated research towards understanding the disease in general, identifying drugs to treat the disease, developing potential vaccines, etc. This has given rise to a rapidly growing body of literature that doubles in number of publications every 20

  • Prediction Methods and Applications in the Science of Science: A Survey
    arXiv.cs.DL Pub Date : 2020-08-09
    Jie Hou; Hanxiao Pan; Teng Guo; Ivan Lee; Xiangjie Kong; Feng Xia

    Science of science has become a popular topic that attracts great attentions from the research community. The development of data analytics technologies and the readily available scholarly data enable the exploration of data-driven prediction, which plays a pivotal role in finding the trend of scientific impact. In this paper, we analyse methods and applications in data-driven prediction in the science

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