Scientometric Analysis of Interdisciplinary Collaboration and Gender Trends in 30 Years of IEEE VIS Publications.

We present the results of a scientometric analysis of 30 years of IEEE VIS publications between 1990-2020, in which we conducted a multifaceted analysis of interdisciplinary collaboration and gender composition among authors. To this end, we curated BiblioVIS, a bibliometric dataset that contains rich metadata about IEEE VIS publications, including 3032 articles and 6113 authors. One of the main factors differentiating BiblioVIS from similar datasets is the authors' gender and discipline data, which we inferred through iterative rounds of computational and manual processes. Our analysis shows that, by and large, inter-institutional and interdisciplinary collaboration has been steadily growing over the past 30 years. However, interdisciplinary research was mainly between a few fields, including Computer Science, Engineering and Technology, and Medicine and Health disciplines. Our analysis of gender shows steady growth in women's authorship. Despite this growth, the gender distribution is still highly skewed, with men dominating ( ≈ 75%) of this space. Our predictive analysis of gender balance shows that if the current trends continue, gender parity in the visualization field will not be reached before the third quarter of the century ( ≈ 2070). Our primary goal in this work is to call the visualization community's attention to the critical topics of collaboration, diversity, and gender. Our research offers critical insights through the lens of diversity and gender to help accelerate progress towards a more diverse and representative research community.

Building visualization skills through investigating the Journal of the Medical Library Association coauthorship network from 2006-2017.

OBJECTIVE: The primary objective of this study was to explore different dimensions of Journal of the Medical Library Association (JMLA) authorship from 2006-2017. Dimensions that were evaluated using coauthorship networks and affiliation data included collaboration, geographical reach, and relationship between Medical Library Association (MLA) member and nonmember authors. A secondary objective was to analyze the practice and practical application of data science skills. METHODS: A team of librarians who attended the 2017 Data Science and Visualization Institute used JMLA bibliographic metadata extracted from Scopus, together with select MLA membership data from 2006-2017. Data cleaning, anonymization, analysis, and visualization were done collaboratively by the team members to meet their learning objectives and to produce insights about the nature of collaborative authorship at JMLA. RESULTS: Sixty-nine percent of the 1,351 JMLA authors from 2006-2017 were not MLA members. MLA members were more productive and collaborative, and tended to author articles together. The majority of the authoring institutions in JMLA are based in the United States. Global reach outside of the United States and Canada shows higher authorship in English-speaking countries (e.g., Australia, United Kingdom), as well as in Western Europe and Japan. CONCLUSIONS: MLA support of JMLA may benefit a wider network of health information specialists and medical professionals than is reflected in MLA membership. Conducting coauthorship network analyses can create opportunities for health sciences librarians to practice applying emerging data science and data visualization skills.

Scholarly Productivity and Impact: Developing a Quantifiable, Norm-based Benchmarking Methodology for Academic Emergency Medicine.

BACKGROUND: Quantifying and benchmarking scholarly productivity of emergency medicine faculty is challenging. While performance indicators including publication and citation counts are available, use of indicators to create normative references has lagged. The authors developed methodology to benchmark emergency medicine academician scholarly productivity (e.g., publications over time) and impact (e.g., citations per publication over time) against an appropriate reference group. METHODS: The methodology includes: 1) define time frame and scholarly metrics; 2) identify representative population; 3) reconcile alternative author names; 4) use analytic tool to identify scholarly output; 5) build database containing metrics; and 6) create benchmarking statistics, including subsamples. This study included emergency medicine faculty from 2011 to 2015, with total peer-reviewed publications and citations per publication as scholarly metrics. RESULTS: In the United States at the time of the search (2016) there were 200 academic emergency departments, 186 with public faculty listings, which yielded 6,727 academicians. For each academician, the authors calculated statistics about peer-reviewed publications and average citations per publication from 2011 to 2015 and created benchmarking rulers using percentile ranks. Productivity by year of graduation with terminal degree was compared within each subsample, finding that newly graduated faculty demonstrated higher productivity than their within-rank peers who graduated earlier. Finally, benchmarking tables were created that allow comparison of peer-reviewed publication counts and citations per publication for individual academicians against the norm. CONCLUSIONS: This benchmarking method can serve as a model for norm-based scaling of scholarly productivity for emergency medicine. This has important implications for performance review, promotion and hiring, and evaluating group productivity.

Research evaluation support services in biomedical libraries.

Objective: The paper provides a review of current practices related to evaluation support services reported by seven biomedical and research libraries. Methods: A group of seven libraries from the United States and Canada described their experiences with establishing evaluation support services at their libraries. A questionnaire was distributed among the libraries to elicit information as to program development, service and staffing models, campus partnerships, training, products such as tools and reports, and resources used for evaluation support services. The libraries also reported interesting projects, lessons learned, and future plans. Results: The seven libraries profiled in this paper report a variety of service models in providing evaluation support services to meet the needs of campus stakeholders. The service models range from research center cores, partnerships with research groups, and library programs with staff dedicated to evaluation support services. A variety of products and services were described such as an automated tool to develop rank-based metrics, consultation on appropriate metrics to use for evaluation, customized publication and citation reports, resource guides, classes and training, and others. Implementing these services has allowed the libraries to expand their roles on campus and to contribute more directly to the research missions of their institutions. Conclusions: Libraries can leverage a variety of evaluation support services as an opportunity to successfully meet an array of challenges confronting the biomedical research community, including robust efforts to report and demonstrate tangible and meaningful outcomes of biomedical research and clinical care. These services represent a transformative direction that can be emulated by other biomedical and research libraries.

Informatics and standards for nanomedicine technology.

There are several issues to be addressed concerning the management and effective use of information (or data), generated from nanotechnology studies in biomedical research and medicine. These data are large in volume, diverse in content, and are beset with gaps and ambiguities in the description and characterization of nanomaterials. In this work, we have reviewed three areas of nanomedicine informatics: information resources; taxonomies, controlled vocabularies, and ontologies; and information standards. Informatics methods and standards in each of these areas are critical for enabling collaboration; data sharing; unambiguous representation and interpretation of data; semantic (meaningful) search and integration of data; and for ensuring data quality, reliability, and reproducibility. In particular, we have considered four types of information standards in this article, which are standard characterization protocols, common terminology standards, minimum information standards, and standard data communication (exchange) formats. Currently, because of gaps and ambiguities in the data, it is also difficult to apply computational methods and machine learning techniques to analyze, interpret, and recognize patterns in data that are high dimensional in nature, and also to relate variations in nanomaterial properties to variations in their chemical composition, synthesis, characterization protocols, and so on. Progress toward resolving the issues of information management in nanomedicine using informatics methods and standards discussed in this article will be essential to the rapidly growing field of nanomedicine informatics.