How To Combine And Clean Bibliometric Data And Use Bibliometric Tools

Leo Migdal

-Dec 5, 2025, 7:01 PM

how to combine and clean bibliometric data and use bibliometric tools

Correspondence: rahmat.ullah@southwales.ac.uk Received 2022 Nov 22; Revised 2022 Dec 14; Accepted 2022 Dec 18; Collection date 2023 Jan. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). This paper presents an integrated and easy methodology for bibliometric analysis. The proposed methodology is evaluated on recent research activities to highlight the role of the Internet of Things in healthcare applications.

Different tools are used for bibliometric studies to explore the breadth and depth of different research areas. However, these Methods consider only the Web of Science or Scopus data for bibliometric analysis. Furthermore, bibliometric analysis has not been fully utilised to examine the capabilities of the Internet of Things for medical devices and their applications. There is a need for an easy methodology to use for a single integrated analysis of data from many sources rather than just the Web of Science or Scopus. A few bibliometric studies merge the Web of Science and Scopus to conduct a single integrated piece of research. This paper presents a methodology that could be used for a single bibliometric analysis across multiple databases.

Three freely available tools, Excel, Perish or Publish and the R package Bibliometrix, are used for the purpose. The proposed bibliometric methodology is evaluated for studies related to the Internet of Medical Things (IoMT) and its applications in healthcare settings. An inclusion/exclusion criterion is developed to explore relevant studies from the seven largest databases, including Scopus, Web of Science, IEEE, ACM digital library, PubMed, Science Direct and Google Scholar. The study focuses on factors such as the number of publications, citations per paper, collaborative research output, h-Index, primary research and healthcare application areas. Data for this study are collected from the seven largest academic databases for 2012 to 2022 related to IoMT and their applications in healthcare. The bibliometric data analysis generated different research themes within IoMT technologies and their applications in healthcare research.

The study has also identified significant research areas in this field. The leading research countries and their contributions are another output from the data analysis. Finally, future research directions are proposed for researchers to explore this area in further detail. Keywords: bibliometrics, internet of medical things, internet of things for medical devices, healthcare applications, bibliometric study The last post dealt with extracting bibliometric data from Scopus and presented some steps to clean these data, notably references data, with R. We will do something similar here, but for another database: Dimensions.

Dimensions is a relatively newcomer in the world of bibliometric database, in comparison to Scopus or Web of Science. The initial advantage of Dimensions is that the search application is open to any one (if I remember well, you just have to create an account).1 So let’s begin with the same search than in the last post: publications using Dynamic Stochastic General Equilibrium model.2 Like with Scopus, we search for “DSGE” or “Dynamic Stochastic General Equilibrium”. A difference here is that we can choose between search in “full data” (meaning searching also in the full-text) or in “Title and abstract” (see Figure 1). Doing the first search, we get 27 693 results on February 4, 2022. The fact that we get close to 100 results before the 1990s (when the label DSGE began to be used) means that we could have a lot of false positive.

It should be explored more in-depth. But for now and for this tutorial, we will opt for a more secure search, by focusing on titles and abstracts. We obtain 4106 results on February 4, 2022. That is quite much than the 2633 results of our Scopus search in the last post. But I think that it can be partly explain by the fact that Dimensions integrate “preprints” and we have 1532 preprints in our results. To download the result, click on “Save / Export” on the right of the search bar, and then “Export results”.

Two types of export are of interest for us: “Export full record” and “Export for bibliometric mapping”. In the first case, we can export only 500 results against 2500 for the second one (see Figure 2). This is not obvious, but the second option allows you to export most metadata, including affiliations and the references cited. What do we lose in comparison to the first export? The publication type (article, preprints, etc…), acknowledgements (that can be useful; see for instance Paul-Hus et al. 2017) and categorization of publications.

That is not so much. Besides, it means that, by using the second option, you can extract more results than in Scopus (limited to 2000). Another advantage of Dimensions over Scopus is that the data exported are quite easier to clean. So let’s load the needed packages and start the cleaning. As the access to this document is restricted, you may want to All material on this site has been provided by the respective publishers and authors.

You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jbrese:v:182:y:2024:i:c:s0148296324002649. See general information about how to correct material in RePEc. If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form . If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation. TOBI portfolio cover page (ETH Zurich / Elisabeth Giryes) In today’s research environment, bibliometric analysis has become an essential tool for understanding the reach and impact of scientific work. Yet, for many researchers, especially those without a technical background, getting started with bibliometric data can be overwhelming.

Tools are often complex, and data access can feel out of reach. That’s where the TOBI Portfolio comes in. With a focus on accessibility, this collection of Jupyter Notebooks offers a beginner-friendly approach to bibliometric analysis, for all levels of technical expertise. TOBI provides a flexible and intuitive way to explore key bibliometric questions. The TOBI Portfolio simplifies bibliometric analysis by offering notebooks that guide you step-by-step through various research scenarios. Each notebook comes with clear instructions, example API retrievals, and plotted visualizations — so you can jump straight into the analysis without having to start from scratch.

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How To Combine And Clean Bibliometric Data And Use Bibliometric Tools

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