Whose articles cite a body of work? Is this a high-impact journal? How might others assess my scholarly impact? Citation analysis is one of the primary methods used to answer these questions.
This document provides an introduction to bibliometrics for researchers. It aims to look at methods of identifying and interpreting research performance data as a measure of research impact. The outcomes are to use citation analysis tools to evaluate research impact, understand the limitations of bibliometrics, and utilize publishing strategies to improve citation performance. The format includes an introduction to research evaluation, citation impact, journal impact, caveats to bibliometrics, and publishing strategies including open access. It then discusses citation impact, journal impact factors, limitations of bibliometrics, and exercises to find citation counts and impact factors.
The document discusses various research methods and survey design. It provides information on survey research classifications including exploratory, descriptive, and explanatory surveys. The survey research process is outlined including design, data collection, analysis, and presentation of results. Issues to consider for survey research such as the research question, population, sampling, questions, and biases are presented. Probability and non-probability sampling methods are covered along with examples like simple random sampling, stratified sampling, and cluster sampling. Key steps in the sampling process including defining the population, determining the sample frame and size, and choosing a sampling method or procedure are also summarized.
Bibliometric Analysis Tutorial by Dayu JinDayu Tony Jin
This slide presents practical ways to conduct bibliometric analysis. Based on my own experience on bibliometric analysis research, I wrote this step-by-step guide to help students and professionals who will use citation, co-citation, biliographic coupling techniques in their research. Visit my service research blog for more updates: http://servicesresearch.blogspot.com/
The document provides information on various aspects of research methodology. It defines key terms such as research, theory, data types, data collection methods, research design and sampling. It discusses primary and secondary data sources and the advantages and limitations of each. Various data collection techniques for qualitative and quantitative data are also outlined.
Practice with PoP: How to use Publish or Perish EffectivelyAnne-Wil Harzing
Covers four key ways in which Publish or Perish can be used:
1. Search for an individual's citation metrics
2. Do a literature review
3. Prepare your case for tenure or promotion
4. Prepare for a meeting with your "academic hero"
Also covers the why's of citation analysis, different metrics and diffferent databases and shows how to use PoP's multi-query center.
This document provides an introduction to bibliometrics for researchers. It aims to look at methods of identifying and interpreting research performance data as a measure of research impact. The outcomes are to use citation analysis tools to evaluate research impact, understand the limitations of bibliometrics, and utilize publishing strategies to improve citation performance. The format includes an introduction to research evaluation, citation impact, journal impact, caveats to bibliometrics, and publishing strategies including open access. It then discusses citation impact, journal impact factors, limitations of bibliometrics, and exercises to find citation counts and impact factors.
The document discusses various research methods and survey design. It provides information on survey research classifications including exploratory, descriptive, and explanatory surveys. The survey research process is outlined including design, data collection, analysis, and presentation of results. Issues to consider for survey research such as the research question, population, sampling, questions, and biases are presented. Probability and non-probability sampling methods are covered along with examples like simple random sampling, stratified sampling, and cluster sampling. Key steps in the sampling process including defining the population, determining the sample frame and size, and choosing a sampling method or procedure are also summarized.
Bibliometric Analysis Tutorial by Dayu JinDayu Tony Jin
This slide presents practical ways to conduct bibliometric analysis. Based on my own experience on bibliometric analysis research, I wrote this step-by-step guide to help students and professionals who will use citation, co-citation, biliographic coupling techniques in their research. Visit my service research blog for more updates: http://servicesresearch.blogspot.com/
The document provides information on various aspects of research methodology. It defines key terms such as research, theory, data types, data collection methods, research design and sampling. It discusses primary and secondary data sources and the advantages and limitations of each. Various data collection techniques for qualitative and quantitative data are also outlined.
Practice with PoP: How to use Publish or Perish EffectivelyAnne-Wil Harzing
Covers four key ways in which Publish or Perish can be used:
1. Search for an individual's citation metrics
2. Do a literature review
3. Prepare your case for tenure or promotion
4. Prepare for a meeting with your "academic hero"
Also covers the why's of citation analysis, different metrics and diffferent databases and shows how to use PoP's multi-query center.
This document discusses referencing styles including Harvard and Oxford systems. [1] It explains that direct quotes require quotation marks while paraphrases do not but both need an in-text citation. [2] The Harvard system uses an in-text citation with author surname and date, and a reference list provides full details. [3] The Oxford system uses footnotes instead of in-text citations that correspond to a bibliography.
The presentation discusses about a Thesis, Research paper, Review Article & Technical Reports: Organization of thesis and reports, formatting issues, citation methods, references, effective oral presentation of research. Quality indices of research publication: impact factor, immediacy factor, H- index and other citation indices. A verbal consent of Prof. Dr. C. B. Bhatt was obtained (at 4.15pm on Dt. 26-11-2016 at Hall A-2, GTU, Chandkheda) to float the presentation online in benefits of the research scholar society.
The document provides an overview of how to conduct a literature review. It begins by defining a literature review as an interpretation and synthesis of published work on a topic. It then outlines the main reasons for conducting a literature review, including finding a research problem worth studying and contextualizing one's own research. The document discusses when a literature review should be conducted, primarily early on to establish context and confirm the research focus. It provides details on how to conduct a literature review through identifying topics, locating sources, reading, analyzing, and organizing the literature. The document also offers tips on how to present a literature review and concludes by listing additional resources for conducting literature reviews.
The document provides an overview of key aspects of research methodology. It discusses that research is a systematic, careful investigation aimed at establishing facts or principles. Some key characteristics of research outlined are that it must be controlled, rigorous, systematic, valid and verifiable. The research process involves formulating a research problem, designing the study, developing instruments, selecting samples, collecting and analyzing data, and reporting findings. Important steps include reviewing literature, identifying variables, developing hypotheses, writing a proposal, and considering ethical issues.
Topics for the class include multiple regression, dummy variables, interaction effects, hypothesis tests, and model diagnostics. Prerequisites include a general familiarity with Stata, including importing and managing datasets and data exploration, the linear regression model, and the ordinary least squares estimation.
Workshop materials including do files and example data sets are available from http://projects.iq.harvard.edu/rtc/event/regression-stata
This document provides an overview of key concepts in biostatistics and how to use SPSS software for data analysis. It discusses learning objectives for understanding biostatistics, different types of data (nominal, ordinal, interval, ratio) and variables (independent, dependent
Data Analysis, Presentation and Interpretation of DataRoqui Malijan
The document defines and describes various types of data analysis techniques:
- Descriptive statistics summarize and describe data through methods like frequency distributions and descriptive graphs.
- Bivariate analysis examines the relationship between two variables.
- Multivariate analysis studies more than two variables simultaneously.
- Comparative analysis examines similarities and differences between alternatives.
- Evaluation assesses subjects using defined criteria to aid decision making.
1) Citation metrics have evolved over time from bibliometrics in the 1960s to more recent metrics like altmetrics and webometrics. They are used to assess the influence of published research.
2) Key citation metrics include the journal impact factor, h-index, and article-level metrics like citation counts and altmetrics. Data sources include Web of Science, Scopus, and Google Scholar.
3) Citation indexing links cited and citing articles, allowing researchers to trace the development of ideas over time. Citation analysis helps understand why authors cite other works.
Peer review is the most widely accepted model for setting a threshold of published scholarly material. With the move to digital publishing, it has come under attack with suggestions that it is 'broken', overloading reviewers and possibly no longer fit for purpose. This presentation discusses the challenges for peer review and some emerging new models. Ultimately, we may need to take a step back to ask what peer review is for and how these aims can best be achieved. Video available at https://www.youtube.com/watch?v=7YMla0Uc5ZE&x-yt-ts=1422327029&x-yt-cl=84838260
This document discusses data management and analysis for monitoring and evaluation. It covers topics such as data capture, data cleaning, data security, and data analysis. The objectives are to understand data management rules and roles, implement a data management system, and strengthen skills in data analysis and interpretation. Data capture methods include paper forms, databases, and personal digital assistants. Data cleaning involves checking for completeness, consistency, plausibility, duplicates, and outliers. Data security requires restricting access, backups, and anonymous storage. Data analysis turns raw data into useful information by answering questions through comparison, statistics, and interpretation.
This document provides an overview of key concepts related to literature reviews including definitions, processes, elements, and citation styles. It defines a literature review as a comprehensive analysis and synthesis of previous scholarly works on a topic used to provide context and identify gaps. The document outlines the main components of conducting a literature review such as searching literature databases, analyzing sources, and identifying themes. It also summarizes several common citation styles used in academic writing including APA, MLA, IEEE, and Chicago/Turabian styles.
This document provides an overview of bibliometrics and discusses various bibliometric indicators and tools. It describes what bibliometrics is, why it is used, and different bibliometric indicators like the impact factor, h-index, SNIP, SJR, and altmetrics. It discusses bibliometric data sources like Web of Science, Scopus, Google Scholar, and provides pros and cons of each. The document concludes that no single metric can provide a complete picture and that metrics should be used to improve research assessment rather than rely on a single number or tool.
This document discusses referencing styles and provides guidance on citing sources. It defines referencing and citing, and distinguishes between references and bibliographies. Reasons for referencing include acknowledging others' work, allowing readers to find sources, avoiding plagiarism, and adding credibility. The document reviews several referencing styles including APA, Chicago, and MLA styles. It provides examples of how to reference different source types such as books, journal articles, and websites. Referencing tools that can help manage citations are also introduced.
1. The document discusses selecting and formulating a research problem. It defines research as a process of observing phenomena repeatedly to collect data and draw conclusions.
2. A research problem is a question a researcher wants to answer or a problem they want to solve. It is the first step in the research process. Without a problem, research cannot proceed.
3. Formulating a research problem involves selecting a broad research topic, reviewing literature and theories, delimiting the topic to something more specific, evaluating the problem's significance and feasibility, and finally stating the problem in declarative or interrogative format.
Using Reference Management Tools: EndNote and ZoteroUCD Library
Presentation by Diarmuid Stokes, College Liaison Librarian, University College Dublin Library, to the Health Sciences Libraries Group (HSLG) 2014 Annual Conference on May 23, 2014 in Dublin, Ireland.
This document provides general guidelines for writing a literature review, including introducing the major research topic, selecting the most relevant research to the topic being studied, covering research related to all variables, and planning the structure of the literature review before writing. Key points are to introduce the topic, focus on the most pertinent studies, and ensure all aspects of the research question are addressed through the included studies. The review is meant to use prior work to establish the importance of the research question.
Impact factor (using impact factor to assess the impact of a journal)shri mangalambikai
The impact factor (IF) is a measure of the frequency with which the average article in a journal has been cited in a particular year. It is used to measure the importance or rank of a journal by calculating the times it's articles are cited.
Impact Factors are useful, but they should not be the only consideration when judging quality. Not all journals are tracked in the JCR database and, as a result, do not have impact factors. New journals must wait until they have a record of citations before even being considered for inclusion. The scientific worth of an individual article has nothing to do with the impact factor of a journal.
This document discusses various metrics used for assessing research outputs and impact. It describes journal impact factor, which measures the average number of citations to recent articles published in that journal. It also discusses author-level metrics like the h-index, g-index, and i10-index, which measure an individual researcher's productivity and citation impact. These metrics are useful for tasks like grant allocation, benchmarking, hiring, promotions, and reviewing faculty/departments. However, no single metric should be considered in isolation as results can vary depending on the database or time period used.
This document provides an overview of various bibliometric tools and metrics for measuring scientific output and impact. It discusses journal ranking metrics like impact factor, eigenfactor, SNIP, and SJR. It also covers article-level metrics including F1000 factors and citation analysis tools from Google Scholar, Web of Science, and Scopus. Additionally, it introduces author-level metrics such as the h-index and its variants that can be calculated using various databases and tools. Finally, the document briefly discusses altmetrics and ways to track scholarly impact on social media and the open web.
The document discusses using Web of Science and related databases to strengthen research discovery, assessment, and identification of producers of research. It outlines how the databases can be used to discover more relevant papers, assess the impact and performance of articles, authors, journals and institutions, and improve author identification. The document provides examples and screenshots related to searching topics, analyzing citation metrics, and identifying highly cited research.
This document discusses referencing styles including Harvard and Oxford systems. [1] It explains that direct quotes require quotation marks while paraphrases do not but both need an in-text citation. [2] The Harvard system uses an in-text citation with author surname and date, and a reference list provides full details. [3] The Oxford system uses footnotes instead of in-text citations that correspond to a bibliography.
The presentation discusses about a Thesis, Research paper, Review Article & Technical Reports: Organization of thesis and reports, formatting issues, citation methods, references, effective oral presentation of research. Quality indices of research publication: impact factor, immediacy factor, H- index and other citation indices. A verbal consent of Prof. Dr. C. B. Bhatt was obtained (at 4.15pm on Dt. 26-11-2016 at Hall A-2, GTU, Chandkheda) to float the presentation online in benefits of the research scholar society.
The document provides an overview of how to conduct a literature review. It begins by defining a literature review as an interpretation and synthesis of published work on a topic. It then outlines the main reasons for conducting a literature review, including finding a research problem worth studying and contextualizing one's own research. The document discusses when a literature review should be conducted, primarily early on to establish context and confirm the research focus. It provides details on how to conduct a literature review through identifying topics, locating sources, reading, analyzing, and organizing the literature. The document also offers tips on how to present a literature review and concludes by listing additional resources for conducting literature reviews.
The document provides an overview of key aspects of research methodology. It discusses that research is a systematic, careful investigation aimed at establishing facts or principles. Some key characteristics of research outlined are that it must be controlled, rigorous, systematic, valid and verifiable. The research process involves formulating a research problem, designing the study, developing instruments, selecting samples, collecting and analyzing data, and reporting findings. Important steps include reviewing literature, identifying variables, developing hypotheses, writing a proposal, and considering ethical issues.
Topics for the class include multiple regression, dummy variables, interaction effects, hypothesis tests, and model diagnostics. Prerequisites include a general familiarity with Stata, including importing and managing datasets and data exploration, the linear regression model, and the ordinary least squares estimation.
Workshop materials including do files and example data sets are available from http://projects.iq.harvard.edu/rtc/event/regression-stata
This document provides an overview of key concepts in biostatistics and how to use SPSS software for data analysis. It discusses learning objectives for understanding biostatistics, different types of data (nominal, ordinal, interval, ratio) and variables (independent, dependent
Data Analysis, Presentation and Interpretation of DataRoqui Malijan
The document defines and describes various types of data analysis techniques:
- Descriptive statistics summarize and describe data through methods like frequency distributions and descriptive graphs.
- Bivariate analysis examines the relationship between two variables.
- Multivariate analysis studies more than two variables simultaneously.
- Comparative analysis examines similarities and differences between alternatives.
- Evaluation assesses subjects using defined criteria to aid decision making.
1) Citation metrics have evolved over time from bibliometrics in the 1960s to more recent metrics like altmetrics and webometrics. They are used to assess the influence of published research.
2) Key citation metrics include the journal impact factor, h-index, and article-level metrics like citation counts and altmetrics. Data sources include Web of Science, Scopus, and Google Scholar.
3) Citation indexing links cited and citing articles, allowing researchers to trace the development of ideas over time. Citation analysis helps understand why authors cite other works.
Peer review is the most widely accepted model for setting a threshold of published scholarly material. With the move to digital publishing, it has come under attack with suggestions that it is 'broken', overloading reviewers and possibly no longer fit for purpose. This presentation discusses the challenges for peer review and some emerging new models. Ultimately, we may need to take a step back to ask what peer review is for and how these aims can best be achieved. Video available at https://www.youtube.com/watch?v=7YMla0Uc5ZE&x-yt-ts=1422327029&x-yt-cl=84838260
This document discusses data management and analysis for monitoring and evaluation. It covers topics such as data capture, data cleaning, data security, and data analysis. The objectives are to understand data management rules and roles, implement a data management system, and strengthen skills in data analysis and interpretation. Data capture methods include paper forms, databases, and personal digital assistants. Data cleaning involves checking for completeness, consistency, plausibility, duplicates, and outliers. Data security requires restricting access, backups, and anonymous storage. Data analysis turns raw data into useful information by answering questions through comparison, statistics, and interpretation.
This document provides an overview of key concepts related to literature reviews including definitions, processes, elements, and citation styles. It defines a literature review as a comprehensive analysis and synthesis of previous scholarly works on a topic used to provide context and identify gaps. The document outlines the main components of conducting a literature review such as searching literature databases, analyzing sources, and identifying themes. It also summarizes several common citation styles used in academic writing including APA, MLA, IEEE, and Chicago/Turabian styles.
This document provides an overview of bibliometrics and discusses various bibliometric indicators and tools. It describes what bibliometrics is, why it is used, and different bibliometric indicators like the impact factor, h-index, SNIP, SJR, and altmetrics. It discusses bibliometric data sources like Web of Science, Scopus, Google Scholar, and provides pros and cons of each. The document concludes that no single metric can provide a complete picture and that metrics should be used to improve research assessment rather than rely on a single number or tool.
This document discusses referencing styles and provides guidance on citing sources. It defines referencing and citing, and distinguishes between references and bibliographies. Reasons for referencing include acknowledging others' work, allowing readers to find sources, avoiding plagiarism, and adding credibility. The document reviews several referencing styles including APA, Chicago, and MLA styles. It provides examples of how to reference different source types such as books, journal articles, and websites. Referencing tools that can help manage citations are also introduced.
1. The document discusses selecting and formulating a research problem. It defines research as a process of observing phenomena repeatedly to collect data and draw conclusions.
2. A research problem is a question a researcher wants to answer or a problem they want to solve. It is the first step in the research process. Without a problem, research cannot proceed.
3. Formulating a research problem involves selecting a broad research topic, reviewing literature and theories, delimiting the topic to something more specific, evaluating the problem's significance and feasibility, and finally stating the problem in declarative or interrogative format.
Using Reference Management Tools: EndNote and ZoteroUCD Library
Presentation by Diarmuid Stokes, College Liaison Librarian, University College Dublin Library, to the Health Sciences Libraries Group (HSLG) 2014 Annual Conference on May 23, 2014 in Dublin, Ireland.
This document provides general guidelines for writing a literature review, including introducing the major research topic, selecting the most relevant research to the topic being studied, covering research related to all variables, and planning the structure of the literature review before writing. Key points are to introduce the topic, focus on the most pertinent studies, and ensure all aspects of the research question are addressed through the included studies. The review is meant to use prior work to establish the importance of the research question.
Impact factor (using impact factor to assess the impact of a journal)shri mangalambikai
The impact factor (IF) is a measure of the frequency with which the average article in a journal has been cited in a particular year. It is used to measure the importance or rank of a journal by calculating the times it's articles are cited.
Impact Factors are useful, but they should not be the only consideration when judging quality. Not all journals are tracked in the JCR database and, as a result, do not have impact factors. New journals must wait until they have a record of citations before even being considered for inclusion. The scientific worth of an individual article has nothing to do with the impact factor of a journal.
This document discusses various metrics used for assessing research outputs and impact. It describes journal impact factor, which measures the average number of citations to recent articles published in that journal. It also discusses author-level metrics like the h-index, g-index, and i10-index, which measure an individual researcher's productivity and citation impact. These metrics are useful for tasks like grant allocation, benchmarking, hiring, promotions, and reviewing faculty/departments. However, no single metric should be considered in isolation as results can vary depending on the database or time period used.
This document provides an overview of various bibliometric tools and metrics for measuring scientific output and impact. It discusses journal ranking metrics like impact factor, eigenfactor, SNIP, and SJR. It also covers article-level metrics including F1000 factors and citation analysis tools from Google Scholar, Web of Science, and Scopus. Additionally, it introduces author-level metrics such as the h-index and its variants that can be calculated using various databases and tools. Finally, the document briefly discusses altmetrics and ways to track scholarly impact on social media and the open web.
The document discusses using Web of Science and related databases to strengthen research discovery, assessment, and identification of producers of research. It outlines how the databases can be used to discover more relevant papers, assess the impact and performance of articles, authors, journals and institutions, and improve author identification. The document provides examples and screenshots related to searching topics, analyzing citation metrics, and identifying highly cited research.
The document discusses various topics related to scientometrics including bibliometrics, informetrics, cybermetrics, scientometrics, altmetrics, and scientometric tools. It provides definitions and examples of each topic. For scientometric tools, it mentions citation mapping, visualization, bibliographic coupling, co-authorship networks, and co-word mapping. It also discusses the h-index and impact factor as important metrics for measuring research.
Bibliometrics, Journal Impact Factors and Maximising the Cite-ability of Jour...Jamie Bisset
Most recent version of slides from Durham "Bibliometrics, Journal Impact Factors and Maximising the Cite-ability of Journal Articles" session.. Delivered as part of the Durham University Researcher Development Programme.
[Last Devlivered November 2014]
Further Training available at https://www.dur.ac.uk/library/research/training/
This document provides an overview of various bibliometric products and metrics that can be used to measure research impact, including journal impact factor, h-index, citation counts, and journal/article ranking tools from Journal Citation Reports, Scopus, and Google Scholar. It discusses the purpose and calculations of metrics like impact factor, eigenfactor, and source normalized impact per paper (SNIP). It also covers limitations of bibliometrics and recommends using multiple metrics and tools to evaluate research. Exercises are provided to help understand how to analyze journals, articles, and individual researchers using different bibliometric resources.
The document discusses various citation databases and research metrics used to evaluate scholarly publications and researchers. It describes major citation databases like Web of Science, Scopus, and Google Scholar that compile citations from bibliographies. It also explains common research metrics like the Impact Factor, h-index, g-index, i10 Index, Cite Score, SJR, and SNIP used to measure the influence and impact of publications and researchers. These metrics are calculated based on factors like the number of citations a publication or researcher receives.
In the competitive landscape of academia, the visibility of your research is crucial. It not only reflects the impact of your work but also contributes to the advancement of your career
How to prepare a research paper and its evaluation toolsMohanapriya Suresh
This document provides guidance on preparing and structuring a research paper, including:
1. The general structure of a full research paper includes sections like the title, abstract, introduction, methods, results, discussion, and references.
2. Key elements that should be addressed in each section are described, such as keeping the title concise but informative, summarizing key findings in the abstract, and clearly explaining methodology.
3. Various tools for evaluating research impact are discussed, including journal indexing in databases like Web of Science, Scopus, and Google Scholar as well as metrics like the h-index, g-index, and i10-index that measure citations and author productivity. Proper formatting of references
Identifying and understanding research impact:
A comprehensive suite of metrics embedded throughout Scopus is designed to help facilitate evaluation and provide a better view of your research interests. Whether you are looking for metrics at the journal, article or author level, Scopus combines its sophisticated analytical capabilities with its unbiased and broad content coverage to help you build valuable insights.
Here we look at:
Author level metrics
Journal metrics
Article level metrics
This slide aims to help and guide students on how to start finding literature review through WOS and SCOPUS. The content is excerpted from various sources available from the internet. This is solely meant for education purpose.
Durham Researcher Development Programme 2015-16: Bibliometric Research Indica...Jamie Bisset
There is an ever-increasing need to make your research more visible as you establish your career, and metrics to measure your research performance when it comes to thinking about promotion and probation.
This session will focus on bibliometric research indicators (such as the Journal Impact Factor and SCImago, author metrics such as the h-index and g-index) and sources for accessing citation data (Web of Science, Journal Citation Reports and Google Scholar). These may be one of several factors to consider when thinking about where to submit an article manuscript for publication to maximise the potential academic impact of the research, and tools useful to be familiar with if they form part of any research evaluation you and your authored journal papers may be subject to.
An additional section will also look at tips to consider when writing an article abstract to maximise its discoverability and cite-ability.
Learning Outcomes:
• Understanding of meaning and intended uses of bibliometric research indicators
• Understanding of how some key indicators (JIF, H-index) are calculated
• Ability to make a judgement as to the appropriateness and limitations of such indicators
• Ability to use online datasets to view and calculate key bibliometric measures
• Awareness of some factors which can increase the visibility and discoverability of your own research in bibliographic databases.
Previous participants have said:
"The session has helped provide me with the basic information on Journal Impact and where to find information such as an author's h-index. It will be useful for future journal submission consideration."
"This session was very useful for me to become familiar with the topic."
This review demonstrates that using these websites can provide researchers with valuable sources of data and research, facilitating access to current literature and specialized scientific content. For optimal results, diversifying sources of research and using multiple search engines based on need and specialization is recommended
Citation Management Using Mendeley SoftwareDave Marcial
This document discusses citation indexes and Mendeley software for citation management. It defines citation indexes as indexes of citations between publications that allow users to establish which later documents cite earlier works. It provides details on major citation indexes like the Science Citation Index and Scopus, as well as free sources like Google Scholar and CiteSeer. The document also discusses bibliometric indicators calculated from citation data, like the h-index, i10-index, and g-index. Finally, it summarizes Mendeley software, describing it as a citation management tool that allows users to organize references and papers into a personal or shared library, and includes social networking features to collaborate with other researchers.
This document provides an overview of digitization practices for scholarly publications and international databases. It discusses registering with the Egyptian Knowledge Bank and obtaining identifiers like ORCID and Researcher IDs. Metrics for evaluating impact like the h-index and journal impact factors are explained. The importance of optimizing discoverability of research outputs through inclusion in databases like Scopus, Web of Science, and Google Scholar is emphasized. The take home message is the importance for researchers to digitize their scholarly works and navigate the world of academic publishing.
The document discusses the establishment of a national knowledge bank in Egypt with the following key points:
1. The knowledge bank will have multiple portals for researchers, students, children, and the general public.
2. Registration will require identifying the user's access level and providing basic personal and institutional details.
3. Registered users can access databases of scientific resources, publications, and datasets through their designated portal.
Identifying journals for publication youtubeDr. Chinchu C
The presentation is about how to be careful while selecting academic journals for publication.
Malayalam YouTube video based on this presentation is available at https://youtu.be/z5_LD7qqzbw
Content:
When to start searching for journals
General and Specialized Journals
Acceptance Rates
Journal Selection Tools
Journal Indexing
Web of Science
Scopus
Medline, PubMed, and PubMed Central
UGC CARE
Journal Metrics
Impact Factor
CiteScore
Checklist for Journal Selection
Predatory Journals
Cloned/Hijacked Journals
Some Useful Places
Similar to Overview of Bibliometrics - IAP Course version 1.1 (20)
Selecting efficient and reliable preservation strategiesMicah Altman
This article addresses the problem of formulating efficient and reliable operational preservation policies that ensure bit-level information integrity over long periods, and in the presence of a diverse range of real-world technical, legal, organizational, and economic threats. We develop a systematic, quantitative prediction framework that combines formal modeling, discrete-event-based simulation, hierarchical modeling, and then use empirically calibrated sensitivity analysis to identify effective strategies.
This discussion, covened by the Dubai Future Foundation, focusses on identifying the significance of the concept of well-being for social-science and policy; and the opportunities to measure it at scale.
Matching Uses and Protections for Government Data Releases: Presentation at t...Micah Altman
In the work included below, and presented at the Simons Institute, we describe work-in progress that aims to align emerging methods of data protections with research uses.
Privacy Gaps in Mediated Library Services: Presentation at NERCOMP2019Micah Altman
Libraries enable patrons to access a wide range of information, but much of the access to this information is now directly managedy publishers. This has lead to a significant gap across library values, patrons perception of privacy, and effective privacy protection for access to digital resources.
In the work included below, and presented at NERCOMP 2019, we review privacy principles based on ALA, IFLA, and NISO policies. We then organizing and comparing high level privacy protections required by ALA checklist, NISO, and GDPR. This framework of principles and controls is then used to score the privacy policies and practices of major vendors of research library content. We evaluate each element of the vendors privacy policy, and use instrumented browsers to identify the types of tracking mechanisms used by different vendors. We use this set of privacy scores to support analyses of change over time, and of potential gaps between patron expectations and privacy policies and practices.
Presentation by Philip Cohen on collaborative work with Micah Altman as part of the MIT CREOS research talk series. Presented in fall 2018, in Cambridge, MA.
Contemporary journal peer review is beset by a range of problems. These include (a) long delay times to publication, during which time research is inaccessible; (b) weak incentives to conduct reviews, resulting in high refusal rates as the pace of journal publication increases; (c) quality control problems that produce both errors of commission (accepting erroneous work) and omission (passing over important work, especially null findings); (d) unknown levels of bias, affecting both who is asked to perform peer review and how reviewers treat authors, and; (e) opacity in the process that impedes error correction and more systematic learning, and enables conflicts of interest to pass undetected. Proposed alternative practices attempt to address these concerns -- especially open peer review, and post-publication peer review. However, systemic solutions will require revisiting the functions of peer review in its institutional context.
The document discusses peer review reform and proposes a new system called IOTA (I Owe the Academy Review). IOTA would allow academics to donate review tokens that represent a pledge to conduct peer reviews. These tokens could then be granted to various peer review initiatives in exchange for conducting experiments and sharing results openly. The goal is to better match reviewers with projects promoting high-quality scholarship, while generating more empirical evidence on effective peer review methods. Several example scenarios for how IOTA could work with different types of journals or research pools are provided.
Redistricting in the US -- An OverviewMicah Altman
This presentation was prepared for the International Seminar on Electoral Districting, National Electoral Institute El Colegio de México. http://www.ine.mx/seminario-internacional-distritacion-electoral/
This document summarizes a presentation about the future of electoral redistricting. It discusses how open data, transparent criteria, public participation, and open-source software can improve redistricting. Key points include:
- Open data is needed for evaluating proposals, modifying plans, and auditing the process. Keeping data open requires coordination.
- Criteria should be based on social science, validated, and allow for reproducibility and robustness against small changes.
- Public participation exists on a continuum from information seeking to proposing alternatives. It can increase engagement, legitimacy and accountability.
- Independent institutions are important to support criteria, funding, transparency and public participation in the process.
- Open-source software can enable
A History of the Internet :Scott Bradner’s Program on Information Science Talk Micah Altman
Scott Bradner is a Berkman Center affiliate who worked for 50 at Harvard in the areas of computer programming, system management, networking, IT security, and identity management. Scott Bradner was involved in the design, operation and use of data networks at Harvard University since the early days of the ARPANET and served in many leadership roles in the IETF. He presented the talk recorded below, entitled, A History of the Internet -- as part of Program on Information Science Brown Bag Series:
Bradner abstracted his talk as follows:
In a way the Russians caused the Internet. This talk will describe how that happened (hint it was not actually the Bomb) and follow the path that has led to the current Internet of (unpatchable) Things (the IoT) and the Surveillance Economy.
SAFETY NETS: RESCUE AND REVIVAL FOR ENDANGERED BORN-DIGITAL RECORDS- Program ...Micah Altman
The web is now firmly established as the primary communication and publication platform for sharing and accessing social and cultural materials. This networked world has created both opportunities and pitfalls for libraries and archives in their mission to preserve and provide ongoing access to knowledge. How can the affordances of the web be leveraged to drastically extend the plurality of representation in the archive? What challenges are imposed by the intrinsic ephemerality and mutability of online information? What methodological reorientations are demanded by the scale and dynamism of machine-generated cultural artifacts? This talk will explore the interplay of the web, contemporary historical records, and the programs, technologies, and approaches by which libraries and archives are working to extend their mission to preserve and provide access to the evidence of human activity in a world distinguished by the ubiquity of born-digital materials.
Information Science Brown Bag talks, hosted by the Program on Information Science, consists of regular discussions and brainstorming sessions on all aspects of information science and uses of information science and technology to assess and solve institutional, social and research problems. These are informal talks. Discussions are often inspired by real-world problems being faced by the lead discussant.
Labor And Reward In Science: Commentary on Cassidy Sugimoto’s Program on Info...Micah Altman
Cassidy Sugimoto is Associate Professor in the School of Informatics and Computing, Indiana University Bloomington, who researches within the domain of scholarly communication and scientometrics, examining the formal and informal ways in which knowledge producers consume and disseminate scholarship. She presented this talk, entitled Labor And Reward In Science: Do Women Have An Equal Voice In Scholarly Communication? A Brown Bag With Cassidy Sugimoto, as part of the Program on Information Science Brown Bag Series.
Despite progress, gender disparities in science persist. Women remain underrepresented in the scientific workforce and under rewarded for their contributions. This talk will examine multiple layers of gender disparities in science, triangulating data from scientometrics, surveys, and social media to provide a broader perspective on the gendered nature of scientific communication. The extent of gender disparities and the ways in which new media are changing these patterns will be discussed. The talk will end with a discussion of interventions, with a particular focus on the roles of libraries, publishers, and other actors in the scholarly ecosystem..
Utilizing VR and AR in the Library Space:Micah Altman
Matt Bernhardt is a web developer in the MIT libraries and a collaborator in our program. He presented this talk, entitled Reality Bytes - Utilizing VR and AR in The Library Space, as part of Program on Information Science Brown Bag Series.
Terms like "virtual reality" and "augmented reality" have existed for a long time. In recent years, thanks to products like Google Cardboard and games like Pokemon Go, an increasing number of people have gained first-hand experience with these once-exotic technologies. The MIT Libraries are no exception to this trend. The Program on Information Science has conducted enough experimentation that we would like to share what we have learned, and solicit ideas for further investigation.
For slides and comments see: http://informatics.mit.edu/blog
Creative Data Literacy: Bridging the Gap Between Data-Haves and Have-NotsMicah Altman
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
SOLARSPELL: THE SOLAR POWERED EDUCATIONAL LEARNING LIBRARY - EXPERIENTIAL LEA...Micah Altman
Access to high-quality, relevant information is absolutely foundational for a quality education. Yet, so many schools across the developing world lack fundamental resources, like textbooks, libraries, electricity and Internet connectivity. The SolarSPELL (Solar Powered Educational Learning Library) is designed specifically to address these infrastructural challenges, by bringing relevant, digital educational content to offline, off-grid locations. SolarSPELL is a portable, ruggedized, solar-powered digital library that broadcasts a webpage with open-access educational content over an offline WiFi hotspot, content that is curated for a particular audience in a specified locality—in this case, for schoolchildren and teachers in remote locations. It is a hands-on, iteratively developed project that has involved undergraduate students in all facets and at every stage of development. This talk will examine the design, development, and deployment of a for-the-field technology that looks simple but has a quite complex background.
Laura Hosman is Assistant Professor at Arizona State University, holding a joint appointment in the School for the Future of Innovation in Society and in The Polytechnic School. Her work is action-oriented and focuses on the role for information and communications technology (ICT) in developing countries. Presently, she focuses on ICT-in-education projects, and brings her passion for experiential learning to the classroom by leading real-world-focused, project-based courses that have seen student-built technology deployed in schools in Haiti, Vanuatu, Micronesia, Samoa, and Tonga.
Information Science Brown Bag talks, hosted by the Program on Information Science, consists of regular discussions and brainstorming sessions on all aspects of information science and uses of information science and technology to assess and solve institutional, social and research problems. These are informal talks. Discussions are often inspired by real-world problems being faced by the lead discussant.
Making Decisions in a World Awash in Data: We’re going to need a different bo...Micah Altman
In his abstract, Scriffignano summarizes as follows:
l explore some of the ways in which the massive availability of data is changing and the types of questions we must ask in the context of making business decisions. Truth be told, nearly all organizations struggle to make sense out of the mounting data already within the enterprise. At the same time, businesses, individuals, and governments continue to try to outpace one another, often in ways that are informed by newly-available data and technology, but just as often using that data and technology in alarmingly inappropriate or incomplete ways. Multiple “solutions” exist to take data that is poorly understood, promising to derive meaning that is often transient at best. A tremendous amount of “dark” innovation continues in the space of fraud and other bad behavior (e.g. cyber crime, cyber terrorism), highlighting that there are very real risks to taking a fast-follower strategy in making sense out of the ever-increasing amount of data available. Tools and technologies can be very helpful or, as Scriffignano puts it, “they can accelerate the speed with which we hit the wall.” Drawing on unstructured, highly dynamic sources of data, fascinating inference can be derived if we ask the right questions (and maybe use a bit of different math!). This session will cover three main themes: The new normal (how the data around us continues to change), how are we reacting (bringing data science into the room), and the path ahead (creating a mindset in the organization that evolves). Ultimately, what we learn is governed as much by the data available as by the questions we ask. This talk, both relevant and occasionally irreverent, will explore some of the new ways data is being used to expose risk and opportunity and the skills we need to take advantage of a world awash in data.
Software Repositories for Research-- An Environmental ScanMicah Altman
This document provides a summary of the state of software curation based on an environmental scan of research software repositories and related practices. The summary finds:
1) There are no comprehensive indices of software archives and orders of magnitude fewer software archives than data archives. Institutional repositories offer little functionality for software archiving.
2) Very few funders have policies addressing software curation. There is little available advice for researchers who wish to curate, cite, and preserve software.
3) Substantial reproducibility failures continue to be reported due to a lack of software preservation. In summary, software curation looks a lot like data curation did a decade ago, with no universal standards for citing and archiving software.
The Open Access Network: Rebecca Kennison’s Talk for the MIT Prorgam on Infor...Micah Altman
Rebecca Kennison, who is the Principal of K|N Consultants, the co-founder of the Open Access Network; and was was the founding director of the Center for Digital Research and Scholarship, gave this talk on Come Together Right Now: An Introduction To The Open Access Network as part of the Program on Information Science Brown Bag Series.
Gary Price, MIT Program on Information ScienceMicah Altman
This document discusses maximizing the use of open web resources in libraries. It argues that libraries should better utilize free and openly available web content for research and users. However, curating and selecting quality resources from the vast amount on the open web presents challenges including the volume of content, lack of metadata, scalability, and ephemeral nature of some resources. The document outlines potential workflows for discovering, ingesting, reviewing, archiving, and sharing open web resources and suggests tools that can help with curation tasks. It also discusses the types of materials that could be curated from the open web like reports, datasets, digital collections, and videos.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
2. Overview of Citation Analysis
Micah Altman
Director of Research
MIT Libraries
Prepared for
IAPril
MIT
April 2014
3. DISCLAIMER
These opinions are my own, they are not the opinions
of MIT, Brookings, any of the project funders, nor (with
the exception of co-authored previously published
work) my collaborators
Secondary disclaimer:
“It’s tough to make predictions, especially about the
future!”
-- Attributed to Woody Allen, Yogi Berra, Niels Bohr, Vint Cerf, Winston Churchill,
Confucius, Disreali [sic], Freeman Dyson, Cecil B. Demille, Albert Einstein, Enrico Fermi,
Edgar R. Fiedler, Bob Fourer, Sam Goldwyn, Allan Lamport, Groucho Marx, Dan Quayle,
George Bernard Shaw, Casey Stengel, Will Rogers, M. Taub, Mark Twain, Kerr L. White,
etc.
Overview of Citation AnalysisVersion: 4/30/15
4. Collaborators & Co-Conspirators
• Thanks To…
– Sean Thomas
Program Manager for Scholarly Repository
Services and the Product Manager of
DSpace@MIT
– Michael Noga
– Peter Cohn
– Courtney Crummett
Overview of Citation AnalysisVersion: 4/30/15
5. Related Work
• K. Smith-Yoshimura, et al., 2014, Registering Researchers in
Authority Files, OCLC Research.
• Liz Allen, Jo Scott, Amy Brand, Marjorie M.K. Hlava, Micah
Altman 2014, Beyond authorship: recognising the
contributions to research; Nature.
• Data Synthesis Task Group. 2014. Joint Principles for Data
Citation.
• CODATA Data Citation Task Group, 2013. Out of Cite, Out of
Mind: The Current State of Practice, Policy and Technology for
Data Citation. Data Science Journal. 2013;12:1–75.
Slides and reprints available from:
informatics.mit.edu
Overview of Citation AnalysisVersion: 4/30/15
6. The MIT libraries provide support for all researchers at MIT:
• Research consulting, including:
bibliographic information management; literature searches; subject-specific
consultation
• Data management, including:
data management plan consulting; data archiving; metadata creation
• Data acquisition and analysis, including:
database licensing; statistical software training; GIS consulting, analysis & data
collection
• Scholarly publishing:
open access publication & licensing
libraries.mit.edu
Overview of Citation AnalysisVersion: 4/30/15
7. Roadmap
* Background *
* Metrics *
* Data *
* Tools *
* Data Processing *
* Hacking *
* Resources *
Overview of Citation AnalysisVersion: 4/30/15
9. What are bibliometrics?
(simple definition)
Bibliometrics are measures
of scholarly outputs.
Overview of Citation AnalysisVersion: 4/30/15
10. Scholarly output effects reputation, ranking, and funding
of the discipline, institution, and individual scholar
We initially use bibliometric analysis to look at
the top institutions, by publications and
citation count for the past ten years…
Universities are ranked by several
indicators of academic or
research performance, including…
highly cited researchers…
Citations… are the best understood and most
widely accepted measure of research strength.
Version: 4/30/15 Overview of Citation Analysis
11. Then
Overview of Citation Analysis
Clarke, Beverly L. "Multiple authorship
trends in scientific papers." Science
143.3608 (1964): 822-824.
Version: 4/30/15
14. What are bibliometrics?
(Extended Definition)
• Analysis of
characteristics of/relationships among
research/scholarly
outputs/publications
– Analysis includes:
lists, descriptive statistics, visualization, inference
– Outputs include:
grants, articles, books, databases, software,
patents
Overview of Citation AnalysisVersion: 4/30/15
15. Which questions are bibliometrics being
used to answer?
Some examples:
• What are the most influential journals in a
particular field?
• How influential is this scholar?
• Where is interdisciplinary research occurring?
• Which groups of people effectively collaborate?
• Which institutions are using funding most
productively?
Overview of Citation AnalysisVersion: 4/30/15
16. Overview of Citation Analysis
Data
(Leading Databases)
(Subject-Specific)
(MIT Internal)
(Selection)Version: 4/30/15
17. Google Scholar
Data Sources
• Unspecified coverage, but…
• Wide coverage of books,
preprint, conference
proceedings, non-english
work, working papers,
patents, institutional
repositories
Built-in Metrics
• Journal H-Index
• Author Profiles
– Total & Five-Year Counts
– I-10 index and H-index
– Yearly citations
• Limited filtering
Overview of Citation Analysis
scholar.google.com
Version: 4/30/15
18. Data
• Frequently updated/current
• Covers journal articles
published after 1995
• Wide disciplinary coverage
• Includes theses and patents,
and citations from these
• Includes some institutional
repositories
• Commercial
Metrics
• Citation lists & counts
• Author impact & articles
– Statistics
– Metrics
– Graphs
• Journal impact
– Statistics
– Metrics
– graphs
Overview of Citation Analysis
scopus.com
Scopus
Version: 4/30/15
19. Data
• Journal coverage after 1899
• Many conference proceedings
since 1990
• Many books since 2005
• Limited coverage of non-
english works
• Doesn’t index institutional
repositories and e-print
servers
• Commercial
Metrics
• Citation lists & counts
• Author impact & articles
– Statistics
– Metrics
– Graphs
• Journal impact
– Statistics
– Metrics
– graphs
Overview of Citation Analysis
apps.webofknowledge.com
Web of Science
Version: 4/30/15
20. Major Subject Specific Catalogs With Citation Metrics
• SciFinder:
chemical abstracts
scifinder.cas.org
• PsycInfo:
psychological literature
www.apa.org/pubs/databases/psycinfo/
• Business Source Complete:
business articles
www.ebscohost.com/academic/business-
source-complete
• arXiv:
physics, mathematics, nonlinear sciences,
computer science, quantitative biology,
quantitative finance, statistics (Integrates
w/NASA-ADS and INSPIRE)
arxiv.org
• mathSciNet
Mathematical Reviews. Computes
collaboration distances.
www.ams.org/mathscinet/
• IEEE Digital Library
content published by the IEEE including
citing references
• USPTO:
find patents that are cited by/cite others
uspto.gov/patft/
• ACM Digital Libraries
Full text and citation of ACM articles and
proceedings
dl.acm.org
Overview of Citation Analysis
VERA: owens.mit.edu/sfx_local/az/mit_db
Version: 4/30/15
21. APIs for Scholarly Resources
What are API’s?
• Application programming interface
(APIs), are tools used to expose raw
data, query interfaces, or other
functions to other software
applications
• Typically more flexible than
interactive interfaces
Challenges
• Requires programming
• Requires data manipulation and
reorganization
• Variety of interfaces, coverage,
results and terms of service
Overview of Citation Analysis
libguides.mit.edu/apis
API What it does
arXiv API
Gives programmatic access to all of the arXiv data, search and linking
facilities
BioMed Central API
Retrieves: 1) BMC Latest Articles; 2) BMC Editors picks; 3) Data on article
subscription and access; 4) Bibliographic search data
DVN (Dataverse
Network) API for
Data Sharing
Allows programmatic access to data and metadata in the Dataverse
Network, which includes theHarvard Dataverse Network, MIT Libraries-
purchased data, and data deposited inother Dataverse Network repositories.
Two modules exist: Metadata/Search and Data Access.
Digital Public
Library of America
(DPLA) API
Allows programmatic access to metadata in DPLA collections, including
partner data from Harvard, New York Public Library, ARTstor, and others.
IEEE Xplore XML
Search API
Allows IEEE customers and 3rd parties such as federated search vendors to
query the IEEE Xplore content repository and retrieve results for
manipulation and presentation on local web interfaces
JSTOR Data for
Research
Not a true API, but allows computational analysis and selection of JSTOR's
scholarly journal and primary resource collections Includes tools for faceted
searching and filtering, text analysis, topic modeling, data extraction, and
visualization.
Nature Blogs API
Blog tracking and indexing service; tracks Nature blogs and other third-party
science blogs
Nature OpenSearch
API
Bibliographic search service for Nature content
NLM APIs NLM offers 21 different APIs for accessing various NLM databases.
ORCID API
Queries and searches the ORCID researcher identifier system and obtain
researcher profile data
PLoS Article-Level
Metrics API
Retrieves article-level metrics (including usage statistics, citation counts,
and social networking activity) for articles published in PLOS journals and
articles added to PLOS Hubs: Biodiversity
PLoS Search API
Allows PLoS content to be queried using the 23 terms in the PLoS search,
for integration into web, desktop, or mobile applications
PubMed E-Utilities
API
Set of 8 server-side programs for searching 38 NCBI Entrez databases of
biomedical literature and data
Scopus Integration
Scopus Document Search API displays search results on a website. Scopus
Cited-By Count API, displays cited-by count for a publication as an image on
a web site.
Springer Images
API
Provides images and related text for over 300,000 free images available on
Springer Images.
Springer Metadata
API
Provides metadata for over 5 million online documents (e.g. journal articles,
book chapters, protocols).
Springer Open
Access API
Provides metadata, full-text content, and images for over 80,000 open
access articles from BioMed Central and SpringerOpen journals.
STAT!Ref
OpenSearch API
Bibliographic search service for displaying syndicated results on a website.
Web of Science
Web Services
Bibliographic search service. Allows automatic, real-time querying of
records. Primarly for populating an institutional repository.
World Bank
Indicators Provides access to nine World Bank statistical databases:
Version: 4/30/15
22. Full (text) Monte API’s
Why use Full Text?
• Name-entity extraction for
relationship analysis:
e.g. acknowledged people,
funders, grants, institutions,
software, datasets
• Name-entity extraction for
subject analysis: e.g.
locations, reagents, genes,
chemicals
• Topic extraction and
clustering
Primacy Sources for Full-Text
• CrossRef TDM API:
tdmsupport.crossref.org/research
ers/
• ArXiv bulk
data:arxiv.org/help/bulk_data_s3
• PLOS API
api.plos.org
• Pubmed API:
– www.ncbi.nlm.nih.gov/pmc/tools/
oai/
– www.ncbi.nlm.nih.gov/books/NBK
25501/
• OpenAire
api.openaire.eu
Version: 4/30/15 Overview of Citation Analysis
23. Using API’s
Choosing tools
• Recommend python or R
• Many resources such as
PUBMED, DataVerse, and
arXiv are accessible through
OAI-PMH protocol
• More in tools section and
resources section
Example: Harvesting ArXiv with pyoai
Version: 4/30/15 Overview of Citation Analysis
from oaipmh.client import Client
from oaipmh.metadata import MetadataRegistry
from lxml import etree
URL = 'http://export.arxiv.org/oai2’
registry = MetadataRegistry()
class Reader(object):
def __call__(self, element):
return etree.tostring(element, pretty_print=True,
encoding='UTF8')
registry.registerReader('oai_dc', Reader())
client = Client(URL, registry)
for count, record in
enumerate(client.listRecords(metadataPrefix='oai_dc')):
header = record[0]
metadata = record[1] or '’
print header.identifier()
print metadata
24. MIT Internal Data
Institute Data
(Restricted Use)
• IS&T DataWarehouse
Data from administrative systems. E.g.
MIT people, organizations, grants and
awards
ist.mit.edu/warehouse
• Office of the Provost – Institutional
Research
Provides analytical and research
support to the Provost, academic
departments, research laboratories and
centers.
web.mit.edu/ir/
Libraries Data
• DSpace@MIT
lists of publications in Dspace
by author/department
dspace.mit.edu
• Barton
lists of MIT these by
author/advisor
library.mit.edu
Overview of Citation AnalysisVersion: 4/30/15
25. Comparing Databases
Coverage
• Years
• Disciplines
• Publishers/sources
• Venue –
journals/conferences/worki
ng paper/IR/personal web
sites
• Documentation of coverage
• Completeness
Characteristics
• Internal vs. external
• Free vs. fee-based
• API vs. interactive
• Open data vs. restrictive
licensed
• Structured vs. unstructured
• Full text vs. metadata
Overview of Citation AnalysisVersion: 4/30/15
26. Selecting a Database
• Free, quick, and useful Google Scholar
• Extract data for further simple analysis
Scholarometer (google scholar extract),
Scopus, WOS
• More complete coverage use multiple
databases
• Specialized subject/single article
disciplinary database/API
• Extract data for network analysis, text
mining API
Overview of Citation Analysis
Free & Easy
$$ and/or
programmatic
Version: 4/30/15
28. Article Metrics: Overview
What are article-level metrics?
• Measures on specific
published articles
• Typically used in
construction of literature
reviews; or as building
blocks for other measures
Common measures
• Citations list
• Citation counts
• References
• Captures/bookmarks
• Downloads
• Mentions
• Likes
• Views
• Readers
Overview of Citation Analysis
sparc.arl.org/sites/default/files/sparc-alm-primer.pdf
Version: 4/30/15
29. Article Metrics: Using Google Scholar
Steps
1. Go to scholar.google.com
2. Search (Full Text + Metadata)
– Unstructured keyword search
OR
– “Advanced” fielded search
3. Sort
– by relevance
OR
– ny date
4. Filter
– By Date range AND/OR
– By Corpus (case law, patents)
Results
• Number of citations to
article indexed google
scholar
• List of citing articles
• Article text
(sometimes)
Overview of Citation AnalysisVersion: 4/30/15
32. Article Metrics: Database Comparison
Google Scholar,
Scopus,
WOS
PLOS
Plos Articles Only
PlumX
Coverage Wide variety PLOS Articles Wide Variety
Measures Citation count
Citation list
Citation count
Citation list
Views
Downloads
Mentions
Bookmarks
Comments
Citation count
Citation list
Views
Downloads
Mentions
Bookmarks
Comments
Overview of Citation AnalysisVersion: 4/30/15
33. ‘Impact’ Factors: Overview
What are impact factors?
• Descriptive statistics
• Usually based on citations
• Commonly treated as a
proxy for the level of
influence of an article,
person, or journal
Common measures
• ISI Journal Impact Factor:
The frequency with which the “average
article” has been cited in a particular year. It
is based on the most recent two years of
citations. It is only supplied for journals
indexed by ISI in the Web of Science.
• Article Citation Count:
Total number of citations received from other
articles to target article.
• H-Index:
The maximum number of articles h such that
each has received at least h citations
Overview of Citation Analysis
libraries.mit.edu/scholarly/publishing/impact-factors/
Version: 4/30/15
37. Author Impact: Database Comparison
Google Scholar Scholar+Scholaro
meter
Scopus Web of Science
Select Any
Author
Only w/profiles Yes Yes Yes
Export data No Yes Yes Yes
Exclude articles No Yes Yes Yes
Metrics H-
index,I10,num
cites
H-index,I10,num
cites
H-index,… H-index
Visualization Minimal Minimal Yes Yes
Longitudinal Minimal Minimal Yes Yes
Overview of Citation AnalysisVersion: 4/30/15
38. Journal Impact: Using Online Services
Scholar
1. Go to
scholar.google.
com
2. Click on
METRICS
3. Google rank
and journal h-5
factor
displayed
4. Filter by
country & field
Overview of Citation Analysis
Scopus
• Go to
scopus.com
• Click on
Journal
Analyzer
• Select journal
• Select statistics
Web of Science
1. Go to admin-
apps.webofkno
wledge.com/JC
R/
2. Select field and
year + SUBMIT
3. Select subject +
SUBMIT
Version: 4/30/15
42. Journal Impact: Database Comparison
Google Scholar Scopus Web of Science
Journals Covered Top 100 ranked in
each language
Mostly english-language Many (selected)
Journals
Metrics H5 Median Many Impact factor, Many
others
Visualization No Yes Yes
Longitudinal
analysis
No Yes Yes
Discipline Rankings No No Yes
Overview of Citation AnalysisVersion: 4/30/15
43. Network Analysis
What is network analysis?
• Study of objects and
interactions modeled as an
induced network (or graph)
• Units of observation form
nodes
• Relationships form edges
Common measures
• Community detection
– Modularity
– Clustering
– Clique
• Centrality
– Betweeness
– Degree
– Closeness
• Diameter
• Visualization
Overview of Citation AnalysisVersion: 4/30/15
45. Network Analysis: Example – CitNetExplorer
Overview of Citation Analysis
1. Use WOS to locate records
2. Add records to “marked list”
3. Click “marked list”
4. Check “cited references”
5. Save to other file formats
6. Select windows tab delimeted
7. Open in CitNetExplorerVersion: 4/30/15
47. % cut -d"," -f 1-11 citations.CSV >areastudies2003.csv
R> areastudies.df< read.table(file="citations.CSV",row.names=NULL
,sep=",",quote="",stringsAsFactors=F,header=T)
R> authorList <- strsplit(areastudies.df$author,perl=TRUE,split="t")
R> plot(table(sapply(authorList,length)))
CoAuthorship Analysis Example –
Using R and JSTOR – Part 2
Overview of Citation AnalysisVersion: 4/30/15
48. createCoauthorlist<-function(pl){
coauthors<-list()
updateCoauthor<-function(co,paperAuthors) {
tmp <- unlist( coauthors[co] )
tmp <- union(tmp,unlist(paperAuthors))
coauthors[[co]]<<-tmp
}
sapply(pl, function(x)sapply(x,function(y)updateCoauthor(y,x)))
return (coauthors)
}
CoAuthorship Analysis Example –
Using R and JSTOR – Part 3
Overview of Citation Analysis
R> R> coa
<-createCoauthorlist(authorList)
R> plot(table(sapply(coa,length)))
Note: Results are biased down, if a
sample of records is used!
Version: 4/30/15
50. Limitations
Limitations of data
• Citation differs systematically from
sharing, reading, or ‘use’
• Relationships signaled by citation are
heterogenous: citations may indicate
evidentiary support, definitions,
disagreement, kudos,…
• Cited objects are heterogenous – e.g.
journals include letters, comments,
reviews and original research
• Databases may have limited or
inconsistent coverage of publishers,
fields, years, or types of publications
(e.g. conference proceedings), types of
objects (databases, software, books,
articles)
• Some types of objects are often used
without being cited
Limitations of measures
• Most measures are vulnerable
to self-citation and other sorts
of manipulation
• Most measures are descriptive
estimates – they are not
forecasting or causal
inferences
• Few studies of the external
validity of measures
• Few studies on error and bias
in estimators
Overview of Citation AnalysisVersion: 4/30/15
52. Built-in Tools
• Database portals have built-in tools:
Google Scholar; Scholarometer; Web of Science …
• Typical restrictions of built-in tools
– Single database
– Number of records
– Usually single-author/single journal metrics
– Lacks statistical forecasting/causal models
– Limited data-cleaning options
– Simple visualizations
Overview of Citation AnalysisVersion: 4/30/15
53. External Tools
Feature sets
• Data retrieval
• Data processing
(next section)
• Core statistics
• Visualization
• Exploratory network
analysis
• Network modeling
Choosing a tool
• Open vs. closed source
• Free vs. commercial
• GUI vs. CLI
• Scalability
• Single Platform/Multi-
Platform
• Feature Set
• Maintenance/support
Overview of Citation AnalysisVersion: 4/30/15
54. Publish or Perish
• Automatic data retrieval
– MS Academic Search
– Google Scholar
• Standard single-author
metrics
– Total number of papers and
total number of citations
– Average citations per paper,
citations per author, papers
per author, and citations per
year
– Hirsch's h-index and related
parameters and variations
• Data export to CSV
www.harzing.com/pop.htm
Overview of Citation AnalysisVersion: 4/30/15
55. Scholarometer
Data
• Google Scholar
• Crowd-source tags
(disciplines) – available
through API
• Data export to CSV
Metrics
• Single/combined author
citation count/h-index rank
• Discipline rank/
• Author network
visualization
• Discipline network
visualization
Overview of Citation Analysis
scholarometer.indiana.edu
Version: 4/30/15
56. Pajek
Analysis
• Network visualization
• Supports complex networks:
multi-relational,
longitudinal, 2-mode
• Layout control
• Clustering
• Community detection
Overview of Citation Analysis
pajek.imfm.si
Source: www.public.asu.edu/~majansse/pubs/SupplementIHDP.htm
Version: 4/30/15
57. CitNetExplorer
Features
• Citation/bibliometric specific
tool
• Web of Science import.
• Pajek export.
• Large networks.
(millions of publications)
• Simple network visualizations
• Network measures: connected
components, clusters, core
publications …
Overview of Citation Analysis
citnetexplorer.nl
Version: 4/30/15
58. CiteSpace
Features
• Citation/bibliometric tool
• Import from
WOS, ArXiV, NSF, ADS,Pubmed
• Export to CSV, GraphML, Pajek
• Time slicing
• Network measures: connected
components, clusters, core
publications …
• Topic clustering
Overview of Citation Analysis
cluster.cis.drexel.edu/~cchen/citespace
Version: 4/30/15
59. SciMat
Features
• Workflow support
• Network visualization
• Data processing and
cleanup
• Longitudinal analysis
• Metrics: h-index
Overview of Citation Analysis
sci2s.ugr.es/scimat/
Version: 4/30/15
61. Sci2Tool
Analysis and Visualization
• Temporal – burst detection
• Geospatial
• Topical
• Networks – trees and
graphs
Additional Benefits
• Parsers for citation data
• Bibliometric analysis tools
• Portable output files
• Direct connections to R and
Gephi
Overview of Citation Analysis
http://sci2.cns.iu.edu
Version: 4/30/15
62. Command-Line Tools
Using Python
• Scipy:
scientific data processing, statistics, visualization
scipy.org
• NLTK:
text processing and analysis
nltk.org
• NetworkX:
network measures (descriptive)
networkx.github.io
• Bibtools:
parse WOS data, and identify comunities of
cocitation
www.sebastian-grauwin.com/?page_id=492
• PythonOAI:
retrieve bibliographic metadata from OAI sources,
such as arXiv
pypi.python.org/pypi/pyoai/
Using R
• tm:
simple text processing and analysis
cran.r-project.org/web/packages/tm/
• StatNet:
network measures (descriptive); social network
analysis (forecasting, causal); visualization
statnet.org
• Citan:
citation analysis
cran.r-project.org/web/packages/CITAN
• Rplos:
retrieve citation data from PLOS
http://cran.r-project.org/web/packages/rplos/
• Rmendeley
retrieve citation data from Mendeley
http://ropensci.org/packages/rmendeley.html
• RISmed
retrieve data from NCBI
http://cran.r-
project.org/web/packages/RISmed/index.html
• OAIHarvester
retrieve data from OAI-PMH Sources
cran.r-project.org/web/packages/OAIHarvester/
Overview of Citation Analysis
Web integration for interactive visualization: d3js.org
Version: 4/30/15
63. Characteristics of Tools
• Built-in vs. external
• Free vs. fee-based
• Command line vs. interactive
• Open source vs. closed source
• Domain
– Data extraction, retrieval, integration
– Data cleaning and manipulation
– Network visualization
– Advanced measures
– Statistical analysis
Overview of Citation AnalysisVersion: 4/30/15
64. Choosing tools.
• Simple standard impact
built-in database tools; Publish or Perish;
Scholarometer
• Messy data OpenRefine + …
• Network analysis measures
– Network measures Sci2,SciMat, Pajek
– Visualizations Gephi, Pajek, CitNet, SciMat
• Need to estimate complex statistical
(predictive, statistical) models R
• Need maximum software flexibility,
integration with software Python
Overview of Citation Analysis
Quick Start
Power Tools
Version: 4/30/15
65. Overview of Citation Analysis
Data Processing
(reorganizing data)
(cleaning data)
(matching names)
Version: 4/30/15
66. Open Refine
• Spreadsheet/database combination
– Ease of use of spreadsheets
– Reporting and manipulative power of databases
• Filters, facets, and clustering
– Allow granular overview of what’s in your data
– Easily see occurrence distribution of values
– Easily make global corrections
• Supports both row-level and record-level
(multi-row) operations
Overview of Citation Analysis
openrefine.org
Version: 4/30/15
67. Open Refine – Reorganize Data
Reorganizing Data
• Splitting/joining multi-
valued cells
• Transposing rows/columns
• Supports logic-based
transformation
– Google Refine Expression
Language (GREL)
– Clojure
– Jython
Overview of Citation Analysis
openrefine.org
Version: 4/30/15
68. Open Refine – Cleaning Data
Cleaning Data
• Duplicate detection
• Common data
transformations
– Trimming whitespace
– Normalizing text case
• Cluster/edit for matching
and normalization
Additional Benefits
• Perform mass edits
efficiently
• Revision history allows for
roll-back to earlier state
• Transformations recorded
as JSON
– Portable for future data sets
• Browser-based
Overview of Citation Analysis
openrefine.org
Version: 4/30/15
69. Open Refine – Matching Names
Matching names
• Create filters to navigate
larger datasets
• Create facets to see all
unique values/occurrences
• Auto-detect variant entries
• Cluster/edit for matching
and normalization
• Reconciliation services
against external data for
normalization/aggregation
Overview of Citation Analysis
openrefine.org
Version: 4/30/15
71. Matching Names – Author Identifiers
What are Author Identifiers?
• Author identifiers give you a way to
reliably and unambiguously connect
your names(s) with your work
throughout your career, including your
papers, data, biographical information,
etc. This can be helpful in a number of
ways:
• Provides a means to distinguish
between you and other authors with
identical or similar names.
• Links together all of your works even if
you have used different names over the
course of your career.
• Makes it easy for others (grant funders,
other researchers etc.) to find your
research output.
• Ensures that your work is clearly
attributed to you.
Getting started with ORCID...
• ORCID (Open Researcher and Contributor ID)
is a non-prorietary, non-profit community-
based registry of research identifiers.
• Links authors to their datasets and other
works in addition to articles.
• Authors can control what information in their
ORCID profile they share. Only the ORCID ID
is automatically shared. (See their privacy
policy.)
• It is easy to import research output from
other sources (including ResearcherID,
Scopus Author ID, and Datacite Metadata
Store to your ORCID profile. (See ORCID's
import works page.)
• Many organizations and publishers have
created integrations with ORCID including
Nature Publishing Group, Elsevier, and the
American Physical Society.
• Free, private, 30-second registration:
orcid.org/register
Overview of Citation Analysis
libguides.mit.edu/content.php?pid=573578&sid=4729602
Version: 4/30/15
73. Sharing, Creativity, Collaboration,
Clarity Associated with Citation Impact
• Collaboration/team science increases impact
• Open access associated with substantially higher citations
• Self Citation in moderation is associated with reinforced
impact
• Sharing data is associated with higher citation rates
• Publishing regularly is associated with much higher impact
• Citation measures only one type of use
– you can collect evidence and measure others
• Use clear, titles, and meaningful keywords and abstracts
• Mainstream social media, especially twitter, can indicate
broader use
• Creativity matters
Overview of Citation AnalysisVersion: 4/30/15
74. Not-so-positive
findings
Overview of Citation Analysis
Daniel
Schectman’s
Lab Notebook
Providing
Initial
Evidence of
Quasi Crystals
• Null results are less likely to be submitted and published
submit all your results
• Publication bias leads to overestimates of effects/significance in
many fields
• Many data sharing and replication policies are not followed
share even when you are not forced to
• Good science may not pass peer review
be persistent
• Much research is not replicable
make yours replicable
• Many publications are not cited
• Multidisciplinary work less cited
• Edited volumes are not well cited
think carefully about publication venue, significance of
research
• Retraction rates in scientific journals have substantially
increased
• Author order is overemphasized in evaluation
discuss authorship early, use other ways of describing
contributions and distributing credit
• Delays in peer-review, and publishing are frequent, and
important
track your submissions, and politely, but actively manage
delays
• Not enough time spent on research
develop a research habit, and build research in your
schedule
Version: 4/30/15
75. From 10 Simple Rules …
Graduate Students
• Share your scientific success with the world
Postdoctoral Positions
• Negotiate first authorship before you start.
Getting Published
• If you do not write well in the English
language, take lessons early
• Become a reviewer early in your career.
• Decide early on where to try to publish
your paper.
• Quality (of journals) is everything.
Building Reputation
• Think Before You Act
• Do not ignore criticism
• Do not ignore people
• Diligently check everything you publish
• Always declare conflicts of interest
• Do your share for the community
• Do not commit to tasks you cannot
complete
• Do not write poor reviews
• Do not write references for people who do
not deserve it
• Never plagiarize, or doctor your data
Overview of Citation Analysis
Bourne, Philip E. "Ten simple rules for getting published." PLoS computational biology 1, no. 5 (2005): e57.; Gu, Jenny, and
Philip E. Bourne. "Ten simple rules for graduate students." PLoS computational biology 3.11 (2007): e229.; Bourne, Philip E.,
and Virginia Barbour. "Ten simple rules for building and maintaining a scientific reputation." PLoS computational biology 7,
no. 6 (2011): e1002108. Bourne, Philip E., and Iddo Friedberg. "Ten simple rules for selecting a postdoctoral position." PLoS
computational biology 2, no. 11 (2006): e121.
Version: 4/30/15
76. Self Experimentation*: 10 Simple Steps
Identify yourself -- register for:
1. An identifier – ORCID
2. Information hubs: ORCID; LinkedIN; your own domain name forward to LinkedIN ; Slideshare
3. Communication channels: twitter, LinkedIN
Describe yourself
4. Write and share a 1-paragraph bio
5. Describe your research program in 2 paragraph
6. Create a CV
[Post these on your LinkedIn and ORCID profiles]
Share
7. Share (on Twitter & LinkedIN) news about something you did or published; an upcoming event in which
you will participate; interesting news and publications in your field
8. Make writing; data; publication; software available as Open Access (through your institutional
repository, SlideShare, FigShare, Dataverse)
Monitor
…check and record these things regularly, but not too frequently (once a month) -- and no need to react
or adjust immediately
9. Set up tracking– google scholar, google alert,
10. Find your klout schore, H-index,
Overview of Citation Analysis
*Question: How do you tell an
extroverted researcher?
Answer: When she talks, she looks
down at your shoes.
Version: 4/30/15
78. Recommended Reading
• Data Processing - General
– Getting Started:
programminghistorian.org/lessons/cleaning-data-with-openrefine
– References:
Verborgh, Ruben, and Max De Wilde. Using OpenRefine. Packt Publishing Ltd,
2013.
– Tutorials:
github.com/OpenRefine/OpenRefine/wiki/External-Resources
• Data Processing – Dealing with Names
– Getting Started -- author identifiers guide:
libguides.mit.edu/content.php?pid=573578&sid=4729602
– References:
Winkler 2012; Name Matching and Record Linkages, U.S.
Censushttp://www.census.gov/srd/papers/pdf/rr93-8.pdf
Overview of Citation AnalysisVersion: 4/30/15
79. Recommended Reading (Continued)
• Bibliometric Analysis
– Tutorials:
Anne-Wil Harzing ,2011 The Publish or Perish Book, part 3: Doing
bibliometric research with Google Scholar, Tarma software press
Wouter De Nooy , et al.,2011, Exploratory Social Network Analysis with Pajek,
2nd Edition, Cambridge University Press
author identifiers guide:
libguides.mit.edu/content.php?pid=573578&sid=4729602
article level metrics:
sparc.arl.org/sites/default/files/sparc-alm-primer.pdf
– References:
Eric D. Kolaczyk, 2009, Statistical Analysis of Network Data: Methods and
Models, Springer.
Overview of Citation AnalysisVersion: 4/30/15
80. Available Databases & API’s
• Scholarly APIs:
libguides.mit.edu/apis
• Google Scholar:
scholar.google.com
• Scopus:
scopus.com
• Web of science:
admin-apps.webofknowledge.com
• Author identifiers:
libguides.mit.edu/content.php?pid=573578&sid=4729602
• List of MIT-licensed Databases: owens.mit.edu/sfx_local/az/mit_db
• Altmetrics:
– PLOS article metrics article-level-metrics.plos.org
– Plum Analytics plumanalytics.com
– ImpactStory impactstory.org
Overview of Citation AnalysisVersion: 4/30/15
82. Glossary of Metrics
• Author H-Index:
The maximum number of articles h such that each has received at least h citations
• Centrality
A measure of the importance of some node in the network based on a selected abstract model of
influence/flow across network. Centrality measures include degree centrality (number of connections);
closeness centrality (distance of node to other nodes in network); betweenness centrality (proportion of
information that must pass through the node to go from one part of the network to another)
• (ISI Journal) Impact Factor:
The frequency with which the “average article” has been cited in a particular year. It is based on the most
recent two years of citations. It is only supplied for journals indexed by ISI in the Web of Science.
• Clustering:
Method that partition n observations into k clusters based on the characteristics of the object. Clusters are
defined either by a set of heuristics for forming the cluster, or according to a solution concept that the
clusters will satisfy.
One common algorithm, K-Means assigns each observation to a fixed-K number of clusters such that each
observation belongs to the cluster that has a mean value closest to that of the observation
• Network community structure measures:
The detection of highly-interconnected groups of nodes within a network. Methods include hierarchical-
clustering; information maximization; modularity; clique-detection
• Network Diameter:
The greatest distance between any two nodes in the network.
• Page Rank:
a family of iteratively-calculated recursive impact factors in which citations from other journals are weighted
by the impact of those journals
Overview of Citation Analysis
Maxi, j Min distance i, j( )( )( )
Version: 4/30/15
This work. \, by Micah Altman (http://redistricting.info) is licensed under the Creative Commons Attribution-Share Alike 3.0 United States License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/3.0/us/ or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA.
The structure and design of digital storage systems is a cornerstone of digital preservation. To better understand ongoing storage practices of organizations committed to digital preservation, the National Digital Stewardship Alliance conducted a survey of member organizations. This talk discusses findings from this survey, common gaps, and trends in this area.
(I also have a little fun highlighting the hidden assumptions underlying Amazon Glacier's reliability claims. For more on that see this earlier post: http://drmaltman.wordpress.com/2012/11/15/amazons-creeping-glacier-and-digital-preservation )
5 Minutes
First, some background and the problem statement. These are three university rankings issued annually, rankings that are of particular importance to research libraries. Some universities even incorporate raising their ranking in their strategic plans. All three use citations as a factor in determining the rankings, which skews the results towards universities focusing on the sciences rather than the humanities. It also puts added weight to authors of journal articles, which are usually not represented in national authority files.
5 Minutes
5 Minutes
- Who is that dashing fellow?
Hmm… same corpus, but different estimates of impact… note different methods of classification
Customizable – can exclude articles from estimate
Note export format will require cleanup on “Note” field to calculate other measures
- Who is that dashing fellow?
Google scholar is quite limited both in journal covered by metrics and in metrics available
Google scholar is quite limited both in journal covered by metrics and in metrics available
Google scholar is quite limited both in journal covered by metrics and in metrics available
Google scholar is quite limited both in journal covered by metrics and in metrics available
Google scholar is quite limited both in journal covered by metrics and in metrics available
5 Minutes
5 Minutes
5 Minutes
5 Minutes
Self Citation
Acts as impact enforcement, not illegitimate [van Raan 2008]
Collaboration – collaborations are responsible for disproportionate portion of impact [Wuchty, et al. 2007]
Regularity – strong association between part of very high impact group and publishing t least 3 times/year [Ionnadis, et al 2014]
Open access associated with higher citations:
Generally associated with higher citations [Eyesenback, 2006; Norris, et al. 2008]
For a possible interaction with higher impact, see journals [Koler-Povh, et al 2014]
Open access publishing of ETD’s does not obstruct publication as a print book (based on publisher surveys) [Seamsns 2013]
Sharing data
See CODATA 2013 for a review
Citation measures one type of use
Downloads are imperfectly correlated with citations, typically much larger, vary by discipline [Bollen et al 2005, Gorraiz et al., 2013]
Applied research impact correlates only weakly with journal impact [Sutherland, et al. 2011]
Clear Titles
Papers with compound titles more highly cited [Fatemah, et al 2014]
Pleasant titles and mild humor is ok, but avoid primarily humrous titles [Sagi & Yechiam 2008]
Clear Keywords and Abstract
Papers with keywords distinct from titles more highly cited [Fatemah, et al 2014]
Mainstream social meaia
See Bornman 2014a,b on value of twitter for measuring complementary impact
Creativity –Deweitt & Denisi 2004
Null results are less likely to be submitted & published [Franco, et al. 2014, Hopewell et al. 2009, and see CODATA 2012 for a review]
Publication bias leads to overestimates of effects/significance in many fields[Fanelli 2012]
Many data sharing and replication policies are not followed[See CODATA 2012 for a review]
Good science may not pass peer review[Peters & Ceci 1982; Gans & Shephard 1994; Ginther, et al. 2011]
Much research is not replicable[See CODATA 2012 for a review]
Many publications are not cited at all.[Hamilton 1991; Samuels 2011]
Edited volumes are not well cited - The evidence is preliminary but is reinforced by broad anecdotal evidence that edited volumes are typically down weighted in tenure review. [Bishop 2012]
Retraction rates in scientific journals have substantially increased[Brembs et. Al 2013 – check CODATA]
Author ordering weights too heavily in evaluation – be conscious of it[Liran & Yariv 2006]
Multidisciplinary work cited less [Levitt & Thelwall 2008]
Editorial delays are frequent, and important[Greenburg 2004; Ioannidis 1998]
Not enough time spent on research[> 75% of associate profs spend < 13 hours/week on research and writing, Hurtado 2012]