By Frank W. Pfrieger (fw-pfrieger@gmx.de or frank.pfrieger@unistra.fr) https://orcid.org/0000-0001-7085-1431
CNRS / University of Strasbourg, Institute of Cellular and Integrative Neurosciences, Strasbourg, France.
This repository provides free access to R code and example files enabling the TeamTree analysis. It also serves as launch pad for an open source project.
Here's the "scientific summary", for in-depth information see the article in PLOS ONE (Pfrieger, 2021, https://doi.org/10.1371/journal.pone.0253847): "Advances in science and technology depend on the work of research teams and the publication of results through peer-reviewed articles representing a growing socio-economic resource. Current methods to mine the scientific literature regarding a field of interest focus on content, but the workforce credited by authorship remains largely unexplored. Notably, appropriate measures of scientific production are debated. Here, a new bibliometric approach named TeamTree analysis is introduced that visualizes the development and composition of the workforce driving a field. A new citation-independent measure that scales with the H index estimates impact based on publication record, genealogical ties and collaborative connections. This author-centered approach complements existing tools to mine the scientific literature and to evaluate research across disciplines."
Imagine that you are interested in a specific topic of Science and Technology, for example a rare human disease, life on Mars or quantum computing. You will query specific bibliometric databases (PubMed, Scopus, Web of Science etc.) and read scientific articles about the topic. However, this will not give you the full picture. You will have hard times to answer the following questions:
- How has field developed over time?
- How many people are working on the topic?
- Who are the key players, experts and potential collaborators - or competitors?
To overcome this hurdle, I have come up with the TeamTree analysis, further referred to as TTA. This approach provides new visuals and measures to explore a field of Science & Technology based on relevant publications. The current version of TTA is based on the open source programming langugage R.
This repository contains the R code to run TTA (Teamtree_11.R) plus a list of PubMed publications (pub_clock.csv) and TTA-derived data for an exemplary field (Circadian Clock) as shown in the preprint (pub_clock_TTA_data.csv). The manuscript is currently under review.
To run the TeamTree analysis on the exemplary field of research, you need to
- Download the file with the R code (Teamtree_11.R).
- Download the file "pub_clock.csv" with the PubMed articles related to the keyword "Circadian clock" and the data file (pub_clock_TTA_data.csv).
- Make sure you have the required libraries/packages.
- In the R script, indicate the directories, where the csv file with the PubMed articles is located and where data should be saved to.
- If you have downloaded the data file, TTA will use the saved colors. If not, you need to set "new colors=T".
- Run the script by copy/paste in the RGui - or use your preferred method to run R code.
The need to find information about a field of S&T and to learn about the teams working in the field occurs frequently. Thus, TTA is probably of interest for a large and diverse range of users such as - for example - scientists, engineers, programmers, but also editors, consultants, investors, journalists, medical doctors, philanthropists etc. Even the general public, I imagine someone trying to find an expert on a rare disease. Therefore, the open source project aims to make TTA available to anybody interested through a website or an app. Since I am not a professional programmer, I decided to reach out for help through Github.
- Revise R code to optimize for speed/memory.
- Alternatively: Implement the TeamTree procedure in a different language to improve performance/visual display and allow for interactivity.
- Create html page/app where a user can upload articles on a specific topic or query directly a relevant (and accessible) database (e.g. PubMed, Google Scholar, Microsoft Academic, search engines etc.).
- Extract authors and years of publication from the articles
- Perform TTA, store data and display the new visuals as shown in the preprint figures
- Develop new features and visuals such as
- author disambiguation
- 3D display of TeamTrees (spiral-like timeline)
- interactive graphs allowing a user for example to obtain information on specific teams, to zoom into a specific time window.
- Application of TTA to other types of scientific output such as patents and preprints.