Citeseerx: 20 Years of Service to Scholarly Big Data
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Don's Conference Notes
Don’s Conference Notes by Donald T. Hawkins (Freelance Conference Blogger and Editor) <[email protected]> Information Transformation: Open. Global. Plenary Sessions Collaborative: NFAIS’s 60th Anniversary Meeting Regina Joseph, founder of Sibylink (http://www.sibylink.com/) and co-founder of pytho (http://www.pytho.io/), consultancies that Column Editor’s Note: Because of space limitations, this is an specialize in decision science and information design, said that we are abridged version of my report on this conference. You can read the gatekeepers of knowledge and information. Information has never full article which includes descriptions of additional sessions at been more accessible, in demand, but simultaneously under attack. https://against-the-grain.com/2018/04/nfaiss-60th-anniversary- There is both a challenge and an opportunity in information system meeting/. — DTH availability and diversity. News outlets have become organs of influ- ence, and social networks are changing our consumption of information (for example, 26% of news retrieval is through social media). We are In 1958, G. Miles Conrad, director of Biological Abstracts, con- willingly allowing ourselves to be controlled. How will we be able to vened a meeting of representatives from 14 information services to harness the advantages of open access to information when the ability collaborate and cooperate in sharing technology and discussing issues of to access it might be compromised? We need people with multiple mutual interest. The National Federation of Abstracting and Indexing areas of specialist knowledge but who are also connected with broad (now Advanced Information) Services (NFAIS) was formed as a result and general knowledge. -
Nonconvex Online Support Vector Machines
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 33, NO. XX, XXXXXXX 2011 1 Nonconvex Online Support Vector Machines S¸eydaErtekin,Le´on Bottou, and C. Lee Giles, Fellow, IEEE Abstract—In this paper, we propose a nonconvex online Support Vector Machine (SVM) algorithm (LASVM-NC) based on the Ramp Loss, which has the strong ability of suppressing the influence of outliers. Then, again in the online learning setting, we propose an outlier filtering mechanism (LASVM-I) based on approximating nonconvex behavior in convex optimization. These two algorithms are built upon another novel SVM algorithm (LASVM-G) that is capable of generating accurate intermediate models in its iterative steps by leveraging the duality gap. We present experimental results that demonstrate the merit of our frameworks in achieving significant robustness to outliers in noisy data classification where mislabeled training instances are in abundance. Experimental evaluation shows that the proposed approaches yield a more scalable online SVM algorithm with sparser models and less computational running time, both in the training and recognition phases, without sacrificing generalization performance. We also point out the relation between nonconvex optimization and min-margin active learning. Index Terms—Online learning, nonconvex optimization, support vector machines, active learning. Ç 1INTRODUCTION N supervised learning systems, the generalization perfor- solutions are guaranteed to reach global optima and are not Imance of classification algorithms is known to be greatly sensitive to initial conditions. The popularity of convexity improved with large margin training. Large margin classi- further increased after the success of convex algorithms, fiers find the maximal margin hyperplane that separates the particularly with SVMs, which yield good generalization training data in the appropriately chosen kernel-induced performance and have strong theoretical foundations. -
Tipping Points: Cancelling Journals When Arxiv Access Is Good Enough
Tipping points: cancelling journals when arXiv access is good enough Tony Aponte Sciences Collection Coordinator UCLA Library ASEE ELD Lightning Talk June 17, 2019 Preprint explosion! Brian Resnick and Julia Belluz. (2019). The war to free science. Vox https://www.vox.com/the-highlight/2019/6/3/18271538/open- access-elsevier-california-sci-hub-academic-paywalls Preprint explosion! arXiv. (2019). arXiv submission rate statistics https://arxiv.org/help/stats/2018_by_area/index 2018 Case Study: two physics journals and arXiv ● UCLA: heavy users of arXiv. Not so heavy users of version of record ● Decent UC authorship ● No UC editorial board members 2017 Usage Annual cost Cost per use 2017 Impact Factor Journal A 103 $8,315 ~$80 1.291 Journal B 72 $6,344 ~$88 0.769 Just how many of these articles are OA? OAISSN.py - Enter a Journal ISSN and a year and this python program will tell you how many DOIs from that year have an open access version2 Ryan Regier. (2018). OAISSN.py https://github.com/ryregier/OAcounts. Just how many of these articles are OA? Ryan Regier. (2018). OAISSN.py https://github.com/ryregier/OAcounts. Just how many of these articles are OA? % OA articles from 2017 % OA articles from 2018 Journal A 68% 64% Journal B 11% 8% Ryan Regier. (2018). OAISSN.py https://github.com/ryregier/OAcounts. arXiv e-prints becoming closer to publisher versions of record according to UCLA similarity study of arXiv articles vs versions of record Martin Klein, Peter Broadwell, Sharon E. Farb, Todd Grappone. 2018. Comparing Published Scientific Journal Articles to Their Pre-Print Versions -- Extended Version. -
Microsoft Academic Search and Google Scholar Citations
Microsoft Academic Search and Google Scholar Citations Microsoft Academic Search and Google Scholar Citations: A comparative analysis of author profiles José Luis Ortega1 VICYT-CSIC, Serrano, 113 28006 Madrid, Spain, [email protected] Isidro F. Aguillo Cybermetrics Lab, CCHS-CSIC, Albasanz, 26-28 28037 Madrid, Spain, [email protected] Abstract This paper aims to make a comparative analysis between the personal profiling capabilities of the two most important free citation-based academic search engines, namely Microsoft Academic Search (MAS) and Google Scholar Citations (GSC). Author profiles can be very useful for evaluation purposes once the advantages and the shortcomings of these services are described and taken into consideration. A total of 771 personal profiles appearing in both the MAS and the GSC databases are analysed. Results show that the GSC profiles include more documents and citations than those in MAS, but with a strong bias towards the Information and Computing sciences, while the MAS profiles are disciplinarily better balanced. MAS shows technical problems such as a higher number of duplicated profiles and a lower updating rate than GSC. It is concluded that both services could be used for evaluation proposes only if they are applied along with other citation indexes as a way to supplement that information. Keywords: Microsoft Academic Search; Google Scholar Citations; Web bibliometrics; Academic profiles; Research evaluation; Search engines Introduction In November 2009, Microsoft Research Asia started a new web search service specialized in scientific information. Even though Google (Google Scholar) already introduced an academic search engine in 2004, the proposal of Microsoft Academic Search (MAS) went beyond a mere document retrieval service that counts citations. -
Bibliometric Study of Indian Open Access Social Science Literature Deep Kumar Kirtania [email protected]
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Library Philosophy and Practice (e-journal) Libraries at University of Nebraska-Lincoln 2018 Bibliometric Study of Indian Open Access Social Science Literature Deep Kumar Kirtania [email protected] Follow this and additional works at: http://digitalcommons.unl.edu/libphilprac Part of the Library and Information Science Commons Kirtania, Deep Kumar, "Bibliometric Study of Indian Open Access Social Science Literature" (2018). Library Philosophy and Practice (e-journal). 1867. http://digitalcommons.unl.edu/libphilprac/1867 Bibliometric Study of Indian Open Access Social Science Literature Deep Kumar Kirtania Library Trainee, West Bengal Secretariat Library & M.Phil Scholar, Department of Library & Information Science University of Calcutta [email protected] Abstract: the purpose of this study is to trace out the growth and development social science literature in open access environment published from India. Total 1195 open access papers published and indexed in Scopus database in ten years have considered for the present study. Research publication from 2008 to 2017 have been analyzed based on literature growth, authorship pattern, activity index, prolific authors and institutions, publication type, channel and citation count have examined to provide a clear picture of Indian social science research. The study shows the dominance of shared authorship and sixty percentages of total articles have been cited. This original research paper described the research productivity of social science in open access context and will be helpful to the social scientist and library professional as a whole. Key Words: Bibliometric study, Research Growth, Social Sciences, Open Access, Scopus, India Introduction: Scholarly communications have been the primary source of creating and sharing knowledge by academics and researchers from the mid 1600s (Chan, Gray & Kahn, 2012). -
Applied Computing 2004
AI and Computational Logic and Image Analysis (AI) Track Chair: C.C. Hung, Southern Polytechnic State University, USA Track Co-Chair: Agostinho Rosa, LaSEEB –ISR – IST, Portugal Track Editorial...........................................................................................................................................3 Experimenting with a Real-Size Man-Hill to Optimize Pedagogical Paths..........................................3 Gregory Valigiani, University of Calais Yannick Jamont, Paraschool Company Claire Bourgeois Republique, University of Bourgogne Raphael Biojout, Paraschool Company Evelyne Lutton, INRIA Rocquencourt Pierre Collet, University of Calais An Hybridization of an Ant-based Clustering Algorithm with Growing Neural Gas Networks for Classification Tasks ...................................................................................................................................8 Marco A. Montes de Oca, Monterrey Institute of Technology, Mexico Leonardo Garrido, Monterrey Institute of Technology, Mexico José L. Aguirre, Monterrey Institute of Technology, Mexico Reinforcement Learning Agents with Primary Knowledge Designed by Analytic Hierarchy Process.......................................................................................................................................................18 Kengo Katayama, Okayama University of Science, Japan Takahiro Koshiishi, Okayama University of Science, Japan Hiroyuki Narihisa, Okayama University of Science, Japan Estimating Manifold Dimension by Inverion -
JCDL 2004) Global Reach and Diverse Impact June 7-11, 2004 Tucson, Arizona, USA
Call for Papers Joint Conference on Digital Libraries (JCDL 2004) Global Reach and Diverse Impact June 7-11, 2004 Tucson, Arizona, USA http://www.jcdl2004.org/ Jointly sponsored by Association for Computing Machinery (ACM) Special Interest Group on Information Retrieval (SIGIR) Special Interest Group on Hypertext, Hypermedia, and the Web (ACM SIGWEB) and Institute of Electrical and Electronics Engineers Computer Society (IEEE Computer Society) Technical Committee on Digital Libraries (TCDL) In cooperation with The American Society for Information Science and Technology (ASIS&T) Coalition for Networked Information DELOS Network of Excellence on Digital Libraries The Joint Conference on Digital Libraries is a major international forum focusing on digital libraries and associated technical, practical, and social issues. JCDL encompasses the many meanings of the term “digital libraries,” including (but not limited to) new forms of information institutions; operational information systems with all manner of digital content; new means of selecting, collecting, organizing, and distributing digital content; digital preservation and archiving; and theoretical models of information media, including document genres and electronic publishing. The intended community for this conference includes those interested in aspects of digital libraries such as infrastructure; institutions; metadata; content; services; digital preservation; system design; implementation; interface design; human-computer interaction; performance evaluation; usability evaluation; -
Scholarly Metrics Recommendations for Research Libraries: Deciphering the Trees in the Forest Table of Contents
EUROPE’S RESEARCH LIBRARY NETWORK Scholarly Metrics Recommendations for Research Libraries: Deciphering the Trees in the Forest Table of Contents 01 3D. Learn About Platforms Before Implementing Them In Services & 15 INTRODUCTION 03 Homogenize Different Sources ABOUT LIBER 3E. Work With Researchers To Build Awareness Of Benefits But Educate 16 1. DISCOVERY & DISCOVERABILITY 05 About Weaknesses of Scholarly Metrics 1A. Provide Contextual Information To Allow the Discovery of Related 05 3F. Expand Your Perspective When Developing Services; Avoid Single- 17 Work & Users Purpose Approaches 1B. Exploit Rich Network Structures & Implement Bibliometric Methods To 07 3G. Teach Colleagues New Skills & Library Goals & Services 17 Enable Discovery 1C. Encourage Sharing Of Library Collections Under Open Licenses 08 4. RESEARCH ASSESSMENT 19 4A. Establish Appropriate Goals for Assessment Exercises Before Selecting 19 2. SHOWCASING ACHIEVEMENTS 09 Databases & Metrics; Be Transparent About Use & Interpretation 2A. Incentivize Researchers To Share Scholarly Works, Promote Achievements 09 4B. Use Different Data Sources to Include Various Scholarly Works & 20 Online & Engage With Audiences Disciplinary Communication & Publication Cultures 2B. Encourage Researchers To Showcase Scientific Contributions & Monitor 11 4C. Rely on Objective, Independent & Commonly-Accepted Data Sources 21 Impact To Provide Sound & Transparent Scholarly Metrics 4D. Avoid Using Composite Indicators & Conflating Different Aspects 21 3. SERVICE DEVELOPMENT 13 Of Scholarly Works & Impact; Don’t Lose The Multifaceted Nature of 3A. Join Forces With Stakeholders To Provide Services Reusing Existing 13 Metrics & Distort Interpretation Resources, Tools, Methods & Data 3B. Value Various Levels of Engagement; Favour Standardized, Well-Established 15 23 CONCLUSION & CALL FOR ACTION Practices & Easy-To-Use Tools 25 REFERENCES 3C. -
1 Scientometric Indicators and Their Exploitation by Journal Publishers
Scientometric indicators and their exploitation by journal publishers Name: Pranay Parsuram Student number: 2240564 Course: Master’s Thesis (Book and Digital Media Studies) Supervisor: Prof. Fleur Praal Second reader: Dr. Adriaan van der Weel Date of completion: 12 July 2019 Word count: 18177 words 1 Contents 1. Introduction ............................................................................................................................ 3 2. Scientometric Indicators ........................................................................................................ 8 2.1. Journal Impact Factor ...................................................................................................... 8 2.2. h-Index .......................................................................................................................... 10 2.3. Eigenfactor™ ................................................................................................................ 11 2.4. SCImago Journal Rank.................................................................................................. 13 2.5. Source Normalized Impact Per Paper ........................................................................... 14 2.6. CiteScore ....................................................................................................................... 15 2.6. General Limitations of Citation Count .......................................................................... 16 3. Conceptual Framework ....................................................................................................... -
Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and Opencitations’ COCI: a Multidisciplinary Comparison of Coverage Via Citations
✅ The content of this manuscript has been peer-reviewed and accepted for publication at Scientometrics (ISSN 1588-2861) Cite as: Martín-Martín, A., Thelwall, M., Orduna-Malea, E., & Delgado López-Cózar, E. (2021). Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: A multidisciplinary comparison of coverage via citations. Scientometrics, 126(1), 871-906. https://doi.org/10.1007/s11192-020-03690-4 Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations Alberto Martín-Martín 1 , Mike Thelwall 2 , Enrique Orduna- 3 1 Malea , Emilio Delgado López-Cózar Abstract (also in Spanish, Chinese) Introduction New sources of citation data have recently become available, such as Microsoft Academic, Dimensions, and the OpenCitations Index of CrossRef open DOI-to-DOI citations (COCI). Although these have been compared to the Web of Science (WoS), Scopus, or Google Scholar, there is no systematic evidence of their differences across subject categories. Methods In response, this paper investigates 3,073,351 citations found by these six data sources to 2,515 English-language highly-cited documents published in 2006 from 252 subject categories, expanding and updating the largest previous study. Results Google Scholar found 88% of all citations, many of which were not found by the other sources, and nearly all citations found by the remaining sources (89%-94%). A similar pattern held within most subject categories. Microsoft Academic is the second largest overall (60% of all citations), including 82% of Scopus citations and 86% of Web of Science citations. In most categories, Microsoft Academic found more citations than Scopus and WoS (182 and 223 subject categories, respectively), but had coverage gaps in some areas, such as Physics and some Humanities categories. -
Overview of the Altmetrics Landscape
Purdue University Purdue e-Pubs Charleston Library Conference Overview of the Altmetrics Landscape Richard Cave Public Library of Science, [email protected] Follow this and additional works at: https://docs.lib.purdue.edu/charleston Part of the Library and Information Science Commons An indexed, print copy of the Proceedings is also available for purchase at: http://www.thepress.purdue.edu/series/charleston. You may also be interested in the new series, Charleston Insights in Library, Archival, and Information Sciences. Find out more at: http://www.thepress.purdue.edu/series/charleston-insights-library-archival- and-information-sciences. Richard Cave, "Overview of the Altmetrics Landscape" (2012). Proceedings of the Charleston Library Conference. http://dx.doi.org/10.5703/1288284315124 This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact [email protected] for additional information. Overview of the Altmetrics Landscape Richard Cave, Director of IT and Computer Operations, Public Library of Science Abstract While the impact of article citations has been examined for decades, the “altmetrics” movement has exploded in the past year. Altmetrics tracks the activity on the Social Web and looks at research outputs besides research articles. Publishers of scientific research have enabled altmetrics on their articles, open source applications are available for platforms to display altmetrics on scientific research, and subscription models have been created that provide altmetrics. In the future, altmetrics will be used to help identify the broader impact of research and to quickly identify high-impact research. Altmetrics and Article-Level Metrics Washington as an academic research project to rank journals based on a vast network of citations The term “altmetrics” was coined by Jason Priem, (Eigenfactor.org, http://www.eigenfactor.org/ a PhD candidate at the School of Information and whyeigenfactor.php). -
The Visibility of Authority Records, Researcher Identifiers, Academic Social Networking
James Madison University JMU Scholarly Commons Libraries Libraries 4-23-2019 The iV sibility of Authority Records, Researcher Identifiers, Academic Social Networking Profiles, and Related Faculty Publications in Search Engine Results Rebecca B. French James Madison University, [email protected] Jody Condit Fagan James Madison University, [email protected] Follow this and additional works at: https://commons.lib.jmu.edu/letfspubs Part of the Library and Information Science Commons Recommended Citation French, Rebecca B. and Fagan, Jody Condit, "The iV sibility of Authority Records, Researcher Identifiers, Academic Social Networking Profiles, and Related Faculty Publications in Search Engine Results" (2019). Libraries. 148. https://commons.lib.jmu.edu/letfspubs/148 This Article is brought to you for free and open access by the Libraries at JMU Scholarly Commons. It has been accepted for inclusion in Libraries by an authorized administrator of JMU Scholarly Commons. For more information, please contact [email protected]. The visibility of authority records, researcher identifiers, academic social networking profiles, and related faculty publications in search engine results Rebecca B. French and Jody Condit Fagan James Madison University Author Note JMU Libraries, Carrier Library 1704, James Madison University, Harrisonburg, VA 22807 [email protected] [email protected] This is an Accepted Manuscript of an article published by Taylor & Francis in the Journal of Web Librarianship on April 23, 2019, available online: http://www.tandfonline.com/10.1080/19322909.2019.1591324. 1 Abstract Information about faculty and their publications can be found in library databases such as the Library of Congress Name Authority File, VIAF, WorldCat, and institutional repositories; in identifier registries like ORCID and ISNI; and on academic social networking sites like Academia, Google Scholar, and ResearchGate, but the way search engines use such identifiers and profiles is unclear.