Decrease in Free Computer Science Papers Found Through Google Scholar Lee A
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Google Apps: an Introduction to Docs, Scholar, and Maps
[Not for Circulation] Google Apps: An Introduction to Docs, Scholar, and Maps This document provides an introduction to using three Google Applications: Google Docs, Google Scholar, and Google Maps. Each application is free to use, some just simply require the creation of a Google Account, also at no charge. Creating a Google Account To create a Google Account, 1. Go to http://www.google.com/. 2. At the top of the screen, select “Gmail”. 3. On the Gmail homepage, click on the right of the screen on the button that is labeled “Create an account”. 4. In order to create an account, you will be asked to fill out information, including choosing a Login name which will serve as your [email protected], as well as a password. After completing all the information, click “I accept. Create my account.” at the bottom of the page. 5. After you successfully fill out all required information, your account will be created. Click on the “Show me my account” button which will direct you to your Gmail homepage. Google Docs Now that you have an account created with Google, you are able to begin working with Google Docs. Google Docs is an application that allows the creation and sharing of documents, spreadsheets, presentations, forms, and drawings online. Users can collaborate with others to make changes to the same document. 1. Type in the address www.docs.google.com, or go to Google’s homepage, http://www. google.com, click the more drop down tab at the top, and then click Documents. Information Technology Services, UIS 1 [Not for Circulation] 2. -
A Comparison of On-Line Computer Science Citation Databases
A Comparison of On-Line Computer Science Citation Databases Vaclav Petricek1,IngemarJ.Cox1,HuiHan2, Isaac G. Councill3, and C. Lee Giles3 1 University College London, WC1E 6BT, Gower Street, London, United Kingdom [email protected], [email protected] 2 Yahoo! Inc., 701 First Avenue, Sunnyvale, CA, 94089 [email protected] 3 The School of Information Sciences and Technology, The Pennsylvania State University, University Park, PA 16802, USA [email protected] [email protected] Abstract. This paper examines the difference and similarities between the two on-line computer science citation databases DBLP and CiteSeer. The database entries in DBLP are inserted manually while the CiteSeer entries are obtained autonomously via a crawl of the Web and automatic processing of user submissions. CiteSeer’s autonomous citation database can be considered a form of self-selected on-line survey. It is important to understand the limitations of such databases, particularly when citation information is used to assess the performance of authors, institutions and funding bodies. We show that the CiteSeer database contains considerably fewer single author papers. This bias can be modeled by an exponential process with intuitive explanation. The model permits us to predict that the DBLP database covers approximately 24% of the entire literature of Computer Science. CiteSeer is also biased against low-cited papers. Despite their difference, both databases exhibit similar and signif- icantly different citation distributions compared with previous analysis of the Physics community. In both databases, we also observe that the number of authors per paper has been increasing over time. 1 Introduction Several public1 databases of research papers became available due to the advent of the Web [1,22,5,3,2,4,8] These databases collect papers in different scientific disciplines, index them and annotate them with additional metadata. -
Linking Educational Resources on Data Science
The Thirty-First AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI-19) Linking Educational Resources on Data Science Jose´ Luis Ambite,1 Jonathan Gordon,2∗ Lily Fierro,1 Gully Burns,1 Joel Mathew1 1USC Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, California 90292, USA 2Department of Computer Science, Vassar College, 124 Raymond Ave, Poughkeepsie, New York 12604, USA [email protected], [email protected], flfierro, burns, [email protected] Abstract linked data (Heath and Bizer 2011) with references to well- known entities in DBpedia (Auer et al. 2007), DBLP (Ley The availability of massive datasets in genetics, neuroimag- 2002), and ORCID (orcid.org). Here, we detail the meth- ing, mobile health, and other subfields of biology and ods applied to specific problems in building ERuDIte. First, medicine promises new insights but also poses significant we provide an overview of our project. In later sections, we challenges. To realize the potential of big data in biomedicine, the National Institutes of Health launched the Big Data to describe our resource collection effort, the resource schema, Knowledge (BD2K) initiative, funding several centers of ex- the topic ontology, our linkage approach, and the linked data cellence in biomedical data analysis and a Training Coordi- resource provided. nating Center (TCC) tasked with facilitating online and in- The National Institutes of Health (NIH) launched the Big person training of biomedical researchers in data science. A Data to Knowledge (BD2K) initiative to fulfill the promise major initiative of the BD2K TCC is to automatically identify, of biomedical “big data” (Ohno-Machado 2014). The NIH describe, and organize data science training resources avail- BD2K program funded 15 major centers to investigate how able on the Web and provide personalized training paths for data science can benefit diverse fields of biomedical research users. -
The Informal Sector and Economic Growth of South Africa and Nigeria: a Comparative Systematic Review
Journal of Open Innovation: Technology, Market, and Complexity Review The Informal Sector and Economic Growth of South Africa and Nigeria: A Comparative Systematic Review Ernest Etim and Olawande Daramola * Department of Information Technology, Cape Peninsula University of Technology, P.O. Box 652, South Africa; [email protected] * Correspondence: [email protected] Received: 17 August 2020; Accepted: 10 October 2020; Published: 6 November 2020 Abstract: The informal sector is an integral part of several sub-Saharan African (SSA) countries and plays a key role in the economic growth of these countries. This article used a comparative systematic review to explore the factors that act as drivers to informality in South Africa (SA) and Nigeria, the challenges that impede the growth dynamics of the informal sector, the dominant subsectors, and policy initiatives targeting informal sector providers. A systematic search of Google Scholar, Scopus, ResearchGate was performed together with secondary data collated from grey literature. Using Boolean string search protocols facilitated the elucidation of research questions (RQs) raised in this study. An inclusion and exclusion criteria became necessary for rigour, comprehensiveness and limitation of publication bias. The data collated from thirty-one (31) primary studies (17 for SA and 14 for Nigeria) revealed that unemployment, income disparity among citizens, excessive tax burdens, excessive bureaucratic hurdles from government, inflationary tendencies, poor corruption control, GDP per capita, and lack of social protection survival tendencies all act as drivers to the informal sector in SA and Nigeria. Several challenges are given for both economies and policy incentives that might help sustain and improve the informal sector in these two countries. -
Analysis of Computer Science Communities Based on DBLP
Analysis of Computer Science Communities Based on DBLP Maria Biryukov1 and Cailing Dong1,2 1 Faculty of Science, Technology and Communications, MINE group, University of Luxembourg, Luxembourg [email protected] 2 School of Computer Science and Technology, Shandong University, Jinan, 250101, China cailing [email protected] Abstract. It is popular nowadays to bring techniques from bibliometrics and scientometrics into the world of digital libraries to explore mecha- nisms which underlie community development. In this paper we use the DBLP data to investigate the author’s scientific career, and analyze some of the computer science communities. We compare them in terms of pro- ductivity and population stability, and use these features to compare the sets of top-ranked conferences with their lower ranked counterparts.1 Keywords: bibliographic databases, author profiling, scientific commu- nities, bibliometrics. 1 Introduction Computer science is a broad and constantly growing field. It comprises various subareas each of which has its own specialization and characteristic features. While different in size and granularity, research areas and conferences can be thought of as scientific communities that bring together specialists sharing simi- lar interests. What is specific about conferences is that in addition to scope, par- ticipating scientists and regularity, they are also characterized by level. In each area there is a certain number of commonly agreed upon top ranked venues, and many others – with the lower rank or unranked. In this work we aim at finding out how the communities represented by different research fields and conferences are evolving and communicating to each other. To answer this question we sur- vey the development of the author career, compare various research areas to each other, and finally, try to identify features that would allow to distinguish between venues of different rank. -
How Do Computer Scientists Use Google Scholar?: a Survey of User Interest in Elements on Serps and Author Profile Pages
BIR 2019 Workshop on Bibliometric-enhanced Information Retrieval How do Computer Scientists Use Google Scholar?: A Survey of User Interest in Elements on SERPs and Author Profile Pages Jaewon Kim, Johanne R. Trippas, Mark Sanderson, Zhifeng Bao, and W. Bruce Croft RMIT University, Melbourne, Australia fjaewon.kim, johanne.trippas, mark.sanderson, zhifeng.bao, [email protected] Abstract. In this paper, we explore user interest in elements on Google Scholar's search engine result pages (SERPs) and author profile pages (APPs) through a survey in order to predict search behavior. We inves- tigate the effects of different query intents (keyword and title) on SERPs and research area familiarities (familiar and less-familiar) on SERPs and APPs. Our findings show that user interest is affected by the re- spondents' research area familiarity, whereas there is no effect due to the different query intents, and that users tend to distribute different levels of interest to elements on SERPs and APPs. We believe that this study provides the basis for understanding search behavior in using Google Scholar. Keywords: Academic search · Google Scholar · Survey study 1 Introduction Academic search engines (ASEs) such as Google Scholar1 and Microsoft Aca- demic2, which are specialized for searching scholarly literature, are now com- monly used to find relevant papers. Among the ASEs, Google Scholar covers about 80-90% of the publications in the world [7] and also provides individual profiles with three types of citation namely, total citation number, h-index, and h10-index. Although the Google Scholar website3 states that the ranking is de- cided by authors, publishers, numbers and times of citation, and even considers the full text of each document, there is a phenomenon called the Google Scholar effect [11]. -
PP-DBLP: Modeling and Generating Public-Private Attributed Networks with DBLP
PP-DBLP: Modeling and Generating Public-Private Attributed Networks with DBLP Xin Huang∗, Jiaxin Jiang∗, Byron Choi∗, Jianliang Xu∗, Zhiwei Zhang∗, Yunya Song# ∗Department of Computer Science, Hong Kong Baptist University #Department of Journalism, Hong Kong Baptist University ∗fxinhuang, jxjian, bchoi, xujl, [email protected], #[email protected] Abstract—In many online social networks (e.g., Facebook, graph is visible only to the corresponding user. From each Google+, Twitter, and Instagram), users prefer to hide her/his users viewpoint, the social network is exactly the union of the partial or all relationships, which makes such private relation- public graph and her/his own private graph. Several sketching ships not visible to public users or even friends. This leads to a new graph model called public-private networks, where each and sampling approaches [2] have been proposed to address user has her/his own perspective of the network including the essential problems of graph processing, such as the size of private connections. Recently, public-private network analysis has reachability tree [5], all-pair shortest paths [6], pairwise node attracted significant research interest in the literature. A great similarities [7], correlation clustering [8] and so on. deal of important graph computing problems (e.g., shortest paths, centrality, PageRank, and reachability tree) has been studied. In social networks, vertices usually contain attributes, e.g., However, due to the limited data sources and privacy concerns, a person has information including name, interests, skills, proposed approaches are not tested on real-world datasets, but and so on. Recent study [2] focuses on one essential aspect on synthetic datasets by randomly selecting vertices as private of topological structure of public-private graphs. -
4 Google Scholar-Books
Scholarship Tools: Google Scholar Eccles Health Sciences Library NANOS 2010 What is Google Scholar? Google Scholar provides a simple way to broadly search for scholarly literature. From one place, you can search across many disciplines and sources: articles, theses, books, abstracts and court opinions, from academic publishers, professional societies, online repositories, universities and other web sites. Google Scholar helps you find relevant work across the world of scholarly research. Features of Google Scholar • Search diverse sources from one convenient place • Find articles, theses, books, abstracts or court opinions • Locate the complete document through your library or on the web • Cited by links you to abstracts of papers where the article has been cited • Related Articles links you to articles on similar topics Searching Google Scholar You can do a simple keyword search as is done in Google, or you can refine your search using the Advanced Search option. Advanced Search allows you t o limit by author, journal, date and collections. Results are ranked by weighing the full text of the article, where it is published, who it is written by and how often it has been n cited in other scholarly journals. Google Scholar Library Links Find an interesting abstract or citation that you wish you could read? In many cases you may have access to the complet e document through your library. You can set your preferences so that Google Scholar knows your primary library. Once this is set, it will determine whether your library can supply the article in Google Scholar preferences. You may need to be on campus or logged into a VPN or a Proxy server to get the full text of the article, even if it is in your library. -
The Best Nurturers in Computer Science Research
The Best Nurturers in Computer Science Research Bharath Kumar M. Y. N. Srikant IISc-CSA-TR-2004-10 http://archive.csa.iisc.ernet.in/TR/2004/10/ Computer Science and Automation Indian Institute of Science, India October 2004 The Best Nurturers in Computer Science Research Bharath Kumar M.∗ Y. N. Srikant† Abstract The paper presents a heuristic for mining nurturers in temporally organized collaboration networks: people who facilitate the growth and success of the young ones. Specifically, this heuristic is applied to the computer science bibliographic data to find the best nurturers in computer science research. The measure of success is parameterized, and the paper demonstrates experiments and results with publication count and citations as success metrics. Rather than just the nurturer’s success, the heuristic captures the influence he has had in the indepen- dent success of the relatively young in the network. These results can hence be a useful resource to graduate students and post-doctoral can- didates. The heuristic is extended to accurately yield ranked nurturers inside a particular time period. Interestingly, there is a recognizable deviation between the rankings of the most successful researchers and the best nurturers, which although is obvious from a social perspective has not been statistically demonstrated. Keywords: Social Network Analysis, Bibliometrics, Temporal Data Mining. 1 Introduction Consider a student Arjun, who has finished his under-graduate degree in Computer Science, and is seeking a PhD degree followed by a successful career in Computer Science research. How does he choose his research advisor? He has the following options with him: 1. Look up the rankings of various universities [1], and apply to any “rea- sonably good” professor in any of the top universities. -
Social Factors Associated with Chronic Non-Communicable
BMJ Open: first published as 10.1136/bmjopen-2019-035590 on 28 June 2020. Downloaded from PEER REVIEW HISTORY BMJ Open publishes all reviews undertaken for accepted manuscripts. Reviewers are asked to complete a checklist review form (http://bmjopen.bmj.com/site/about/resources/checklist.pdf) and are provided with free text boxes to elaborate on their assessment. These free text comments are reproduced below. ARTICLE DETAILS TITLE (PROVISIONAL) Social factors associated with chronic non-communicable disease and comorbidity with mental health problems in India: a scoping review AUTHORS M D, Saju; Benny, Anuja; Scaria, Lorane; Anjana, Nannatt; Fendt- Newlin, Meredith; Joubert, Jacques; Joubert, Lynette; Webber, Martin VERSION 1 – REVIEW REVIEWER Hoan Linh Banh University of Alberta, Canada REVIEW RETURNED 28-Nov-2019 GENERAL COMMENTS There is a major flaw with the search strategy. The authors did not include important databases such as PsycInfo and the Educational Resources Information Centre. Also, pubmed should have been used rather than medline. Finally, the authors did not even attempt to search google scholar for grey literature. The the study only included 10 papers and 6 were on multiple countries which was one of the exclusion critera. http://bmjopen.bmj.com/ REVIEWER Graham Thornicroft KCL, UK REVIEW RETURNED 06-Dec-2019 GENERAL COMMENTS MJ Open: Social factors associated with chronic non-communicable disease and comorbidity with mental health problems in India: a scoping review on October 2, 2021 by guest. Protected copyright. The aim of this paper is to examine the existing literature of the major social risk factors which are associated with diabetes, hypertension and the co- morbid conditions of depression and anxiety in India. -
Author Template for Journal Articles
Mining citation information from CiteSeer data Dalibor Fiala University of West Bohemia, Univerzitní 8, 30614 Plzeň, Czech Republic Phone: 00420 377 63 24 29, fax: 00420 377 63 24 01, email: [email protected] Abstract: The CiteSeer digital library is a useful source of bibliographic information. It allows for retrieving citations, co-authorships, addresses, and affiliations of authors and publications. In spite of this, it has been relatively rarely used for automated citation analyses. This article describes our findings after extensively mining from the CiteSeer data. We explored citations between authors and determined rankings of influential scientists using various evaluation methods including cita- tion and in-degree counts, HITS, PageRank, and its variations based on both the citation and colla- boration graphs. We compare the resulting rankings with lists of computer science award winners and find out that award recipients are almost always ranked high. We conclude that CiteSeer is a valuable, yet not fully appreciated, repository of citation data and is appropriate for testing novel bibliometric methods. Keywords: CiteSeer, citation analysis, rankings, evaluation. Introduction Data from CiteSeer have been surprisingly little explored in the scientometric lite- rature. One of the reasons for this may have been fears that the data gathered in an automated way from the Web are inaccurate – incomplete, erroneous, ambiguous, redundant, or simply wrong. Also, the uncontrolled and decentralized nature of the Web is said to simplify manipulating and biasing Web-based publication and citation metrics. However, there have been a few attempts at processing the Cite- Seer data which we will briefly mention. Zhou et al. -
The Claremont Report on Database Research
The Claremont Report on Database Research Rakesh Agrawal, Anastasia Ailamaki, Philip A. Bernstein, Eric A. Brewer, Michael J. Carey, Surajit Chaudhuri, AnHai Doan, Daniela Florescu, Michael J. Franklin, Hector Garcia‐ Molina, Johannes Gehrke, Le Gruenwald, Laura M. Haas, Alon Y. Halevy, Joseph M. Hellerstein, Yannis E. Ioannidis, Hank F. Korth, Donald Kossmann, Samuel Madden, Roger Magoulas, Beng Chin Ooi, Tim O’Reilly, Raghu Ramakrishnan, Sunita Sarawagi, Michael Stonebraker, Alexander S. Szalay, Gerhard Weikum Abstract In late May, 2008, a group of database researchers, architects, users and pundits met at the Claremont Resort in Berkeley, California to discuss the state of the research field and its impacts on practice. This was the seventh meeting of this sort in twenty years, and was distinguished by a broad consensus that we are at a turning point in the history of the field, due both to an explosion of data and usage scenarios, and to major shifts in computing hardware and platforms. Given these forces, we are at a time of opportunity for research impact, with an unusually large potential for influential results across computing, the sciences and society. This report details that discussion, and highlights the group’s consensus view of new focus areas, including new database engine architectures, declarative programming languages, the interplay of structured and unstructured data, cloud data services, and mobile and virtual worlds. We also report on discussions of the community’s growth, including suggestions for changes in community processes to move the research agenda forward, and to enhance impact on a broader audience. 1. A Turning Point in Database Research Over the last twenty years, small groups of database researchers have periodically gathered to assess the state of the field and propose directions for future research [BDD+89, SSU91, ASU95, AZ+96, BBC+98, AAB03].