Andreas Strotmann

Author name disambiguation: What difference does it make in author-based citation analysis?

In this article, we explore how strongly author name disambiguation (AND) affects the results of an author-based citation analysis study, and identify conditions under which the traditional simplified approach of using surnames and first initials may suffice in practice. We compare author citation ranking and cocitation mapping results in the stem cell research field from 2004 to 2009 using two AND approaches: the traditional simplified approach of using author surname and first initial and a sophisticated algorithmic approach. We find that the traditional approach leads to extremely distorted rankings and substantially distorted mappings of authors in this field when based on first- or all-author citation counting, whereas last-author-based citation ranking and cocitation mapping both appear relatively immune to the author name ambiguity problem. This is largely because Romanized names of Chinese and Korean authors, who are very active in this field, are extremely ambiguous, but few of these researchers consistently publish as last authors in bylines. We conclude that a more earnest effort is required to deal with the author name ambiguity problem in both citation analysis and information retrieval, especially given the current trend toward globalization. In the stem cell research field, in which laboratory heads are traditionally listed as last authors in bylines, last-author-based citation ranking and cocitation mapping using the traditional approach to author name disambiguation may serve as a simple workaround, but likely at the price of largely filtering out Chinese and Korean contributions to the field as well as important contributions by young researchers.

Comparing all-author and first-author co-citation analyses of information science

Although it is generally understood that different citation counting methods can produce quite different author rankings, and although “optimal” author co-citation counting methods have been identified theoretically, studies that compare author co-citation counting methods in author co-citation analysis (ACA) studies are still rare. The present study applies strict all-author-based ACA to the Information Science (IS) field, in that all authors of all cited references in a classic IS dataset are counted, and in that even the diagonal values of the co-citation matrix are computed in their theoretically optimal form. Using Scopus instead of SSCI as the data source, we find that results from a theoretically optimal all-author ACA appear to be excellent in practice, too, although in a field like IS where co-authorship levels are relatively low, its advantages over classic first-author ACA appear considerably smaller than in the more highly collaborative ones targeted before. Nevertheless, we do find some differences between the two approaches, in that first-author ACA appears to favor theorists who presumably tend to work alone, while all-author ACA appears to paint a somewhat more recent picture of the field, and to pick out some collaborative author clusters.

Counting first, last, or all authors in citation analysis: A comprehensive comparison in the highly collaborative stem cell research field

How can citation analysis take into account the highly collaborative nature and unique research and publication culture of biomedical research fields? This study explores this question by introducing last-author citation counting and comparing it with traditional first-author counting and theoretically optimal all-author counting in the stem cell research field for the years 2004–2009. For citation ranking, last-author counting, which is directly supported by Scopus but not by ISI databases, appears to approximate all-author counting quite well in a field where heads of research labs are traditionally listed as last authors; however, first author counting does not. For field mapping, we find that author co-citation analyses based on different counting methods all produce similar overall intellectual structures of a research field, but detailed structures and minor specialties revealed differ to various degrees and thus require great caution to interpret. This is true especially when authors are selected into the analysis based on citedness, because author selection is found to have a greater effect on mapping results than does choice of co-citation counting method. Findings are based on a comprehensive, high-quality dataset extracted in several steps from PubMed and Scopus and subjected to automatic reference and author name disambiguation.

The knowledge base and research front of information science 2006-2010: An author cocitation and bibliographic coupling analysis

This study continues a long history of author cocitation analysis (and more recently, author bibliographic coupling analysis) of the intellectual structure of information science (IS) into the time period 2006 to 2010 (IS 2006–2010). We find that web technologies continue to drive developments, especially at the research front, although perhaps more indirectly than before. A broadening of perspectives is visible in IS 2006–2010, where network science becomes influential and where full‐text analysis methods complement traditional computer science influences. Research in the areas of the h‐index and mapping of science appears to have been highlights of IS 2006–2011. This study tests and confirms a forecast made previously by comparing knowledge‐base and research‐front findings for IS 2001–2005, which expected both the information retrieval (IR) systems and webometrics specialties to shrink in 2006 to 2010. A corresponding comparison of the knowledge base and research front of IS 2006–2010 suggests a continuing decline of the IR systems specialty in the near future, but also a considerable (re)growth of the webometrics area after a period of decline from 2001 to 2005 and 2006 to 2010, with the latter due perhaps in part to its contribution to an emerging web science.

Combining commercial citation indexes and open-access bibliographic databases to delimit highly interdisciplinary research fields for citation analysis

Field delimitation for citation analysis, the process of collecting a set of bibliographic records with cited-reference information of research articles that represent a research field, is the first step in any citation analysis study of a research field. Due to a number of limitations, the commercial citation indexes have long made it difficult to obtain a comprehensive dataset in this step. This paper discusses some of the limitations imposed by these databases, and reports on a method to overcome some of these limitations that was used with great success to delimit an emerging and highly interdisciplinary biomedical research field, stem cell research. The resulting field delimitation and the citation network it induces are both excellent. This multi-database method relies on using PubMed for the actual field delimitation, and on mapping between Scopus and PubMed records for obtaining comprehensive information about cited-references contained in the resulting literature. This method provides high-quality field delimitations for citation studies that can be used as benchmarks for studies of the impact of data collection biases on citation metrics, and may help improve confidence in results of scientometric studies for an increased impact of scientometrics on research policy.

Analysis and Visualization of Citation Networks

Policy-based data management enables the creation of community-specific collections. Every collection is created for a purpose. e purpose defines the set of properties that will be associated with the collection.e properties are enforced bymanagement policies that control the execution of procedures that are applied whenever data are ingested or accessed. e procedures generate state information that defines the outcome of enforcing the management policy. e state information can be queried to validate assessment criteria and verify that the required collection properties have been conserved. e integrated Rule-Oriented Data System implements the data management framework required to support policy-based data management. Policies are turned into computer actionable Rules. Procedures are composed from a microservice-oriented architecture. e result is a highly extensible and tunable system that can enforce management policies, automate administrative tasks, and periodically validate assessment criteria. iRODS 4.0+ represents a major effort to analyze, harden, and package iRODS for sustainability, modularization, security, and testability. is has led to a fairly significant refactorization of much of the underlying codebase. iRODS has been modularized whereby existing iRODS 3.x functionality has been replaced and provided by small, interoperable plugins. e core is designed to be as immutable as possible and serve as a bus for handling the internal logic of the business of iRODS. Seven major interfaces have been exposed by the core and allow extensibility and separation of functionality into plugins.

Intellectual structure of stem cell research: a comprehensive author co-citation analysis of a highly collaborative and multidisciplinary field

This study is an attempt to approach the intellectual structure of the stem cell research field 2004–2009 through a comprehensive author co-citation analysis (ACA), and to contribute to a better understanding of a field that has been brought to the forefront of research, therapy and political and public debates, which, hopefully, will in turn better inform research and policy. Based on a nearly complete and clean dataset of stem cell literature compiled from PubMed and Scopus, and using automatic author disambiguation to further improve results, we perform an exclusive all-author ACA of the 200 top-ranked researchers of the field by fractional citation count. We find that, despite the theoretically highly interdisciplinary nature of the field, stem cell research has been dominated by a few central medical research areas—cancer and regenerative medicine of the brain, the blood, the skin, and the heart—and a core of cell biologists trying to understand the nature and the molecular biology of stem cells along with biotechnology researchers investigating the practical identification, isolation, creation, and culturing of stem cells. It is also remarkably self-contained, drawing only on a few related areas of cell biology. This study also serves as a baseline against which the effectiveness of a range of author-based bibliometric methods and indicators can be tested, especially when based on less comprehensive datasets using less optimal analysis methods.

Author bibliographic coupling: Another approach to citation‐based author knowledge network analysis

While bibliographic coupling (BC) as a measure of relatedness between documents was proposed a full decade before co-citation, interest in applying BC to mapping the intellectual structure of research areas has only recently resurged, perhaps because it allows researchers to circumvent problems of the so far dominant co-citation analysis. Especially for mapping the intellectual structure of a research field as represented by its authors, author co-citation analysis (ACA) has frequently been applied over the last two decades, but no author BC analysis has so far been attempted. In this paper, we define author BC and conduct an author BC analysis of the Information Science field using the same dataset as that used in our previous ACA study, which covers Information Science during 1996-2005. We find that these two citation-based author knowledge network analysis methods complement each other, with one providing a more realistic picture of the state of research within the IS field and the other revealing the structure of both internal and external influences on the IS research. In combination, the two methods provide a more comprehensive view of the intellectual structure of the IS field than either of them alone.

The Categorial Type of OpenMath Objects

We introduce a fundamental concept found in the formal semantics of language, categorial type logics, into the field of knowledge communication language design in general, and into mathematical content markup language design in particular.

Dimensions and uncertainties of author citation rankings: Lessons learned from frequency-weighted in-text citation counting

In‐text frequency‐weighted citation counting has been seen as a particularly promising solution to the well‐known problem of citation analysis that it treats all citations equally, be they crucial to the citing paper or perfunctory. But what is a good weighting scheme? We compare 12 different in‐text citation frequency‐weighting schemes in the field of library and information science (LIS) and explore author citation impact patterns based on their performance in these schemes. Our results show that the ranks of authors vary widely with different weighting schemes that favor or are biased against common citation impact patterns—substantiated, applied, or noted. These variations separate LIS authors quite clearly into groups with these impact patterns. With consensus rank limits, the hard upper and lower bounds for reasonable author ranks that they provide suggest that author citation ranks may be subject to something like an uncertainty principle.