The pros and cons of the use of altmetrics in research assessment

© 2020 The Authors. Published by Levi Library Press. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: http://doi.org/10.29024/sar.10Many indicators derived from the web have been proposed to supplement citation-based indicators in support of research assessments. These indicators, often called altmetrics, are available commercially from Altmetric.com and Elsevier’s Plum Analytics or can be collected directly. These organisations can also deliver altmetrics to support institutional selfevaluations. The potential advantages of altmetrics for research evaluation are that they may reflect important non-academic impacts and may appear before citations when an article is published, thus providing earlier impact evidence. Their disadvantages often include susceptibility to gaming, data sparsity, and difficulties translating the evidence into specific types of impact. Despite these limitations, altmetrics have been widely adopted by publishers, apparently to give authors, editors and readers insights into the level of interest in recently published articles. This article summarises evidence for and against extending the adoption of altmetrics to research evaluations. It argues that whilst systematicallygathered altmetrics are inappropriate for important formal research evaluations, they can play a role in some other contexts. They can be informative when evaluating research units that rarely produce journal articles, when seeking to identify evidence of novel types of impact during institutional or other self-evaluations, and when selected by individuals or groups to support narrative-based non-academic claims. In addition, Mendeley reader counts are uniquely valuable as early (mainly) scholarly impact indicators to replace citations when gaming is not possible and early impact evidence is needed. Organisations using alternative indicators need recruit or develop in-house expertise to ensure that they are not misused, however

Metrics of the Biometrics: The Steady Growth of an Interdisciplinary Field

The word biometry or its derivative, biometrics, depicts the need for close interdisciplinary research among big data systems, mathematics, and statistical sciences. Reports headed with the word biometrics are constantly increasing. Below, the information provided by databases evidences the expansion and evolution from the original main biological topics to new research areas.Centro de Investigación de Proteínas Vegetale

Stalking the Wild X Patent

For most of the history of the patent office, recorded patents were used primarily to enforce the patent holder’s rights during the life of the patent and to evaluate prior art, in determining patentability. The limits of manual indexes and hand counts of entries made more sophisticated analyses impractical. Recently, a number of researchers have begun to apply scientometric methods to assess trends and causation in patterns of innovation in the United States by organizing data elements from patent documents. Although most patents are now searchable, fully digital records, the records of the earliest patents (1790–1836) were incinerated in a fire at the Patent Office in Washington, D.C. Of approximately 10,000 patents destroyed, original duplicate copies have been located and re-recorded for about one–quarter of the total. These patents are now available in the USPTO PatFT database. Occasionally, additional duplicate originals are still being found. A more complete record of these Early American patents would allow better and more complete analysis. This article suggests methods that librarians and archivists can use to contribute to additional recoveries of the missing patents

Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network

The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (i) identifies actual clusters of patents: i.e. technological branches, and (ii) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the {citation vector}, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action

Supply Chain in Library: A Bibliometric Analysis

The research objective of this paper is to identify and map supply chain research in a library. This study conducted a bibliometric analysis using the Scopus electronic database. The article search is done by filtering search results in the system and manual exploration to get relevant articles. The collected articles are processed with the VOSviewer application to obtain research mapping results. A total of 81 articles were obtained from 1999 to 2021, with an increasing trend in the number of articles each year. The United States and China are the most productive countries in this field. Three cluster topics were obtained: supply chain integration in digital libraries, the benefits of open source applications in libraries, and digital data issues in libraries. The results of this study provide an additional overview of supply chain mapping in libraries