https://github.com/scilifelabdatacentre/annual-report-2021
This repository will contain the scripts required to produce the bibliometric analyses from the 2021 annual report
https://github.com/scilifelabdatacentre/annual-report-2021
Last synced: 3 months ago
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This repository will contain the scripts required to produce the bibliometric analyses from the 2021 annual report
- Host: GitHub
- URL: https://github.com/scilifelabdatacentre/annual-report-2021
- Owner: ScilifelabDataCentre
- License: mit
- Created: 2021-11-25T12:45:58.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-10-18T13:11:12.000Z (over 2 years ago)
- Last Synced: 2023-02-26T08:47:25.929Z (about 2 years ago)
- Language: Python
- Size: 70.3 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: citation_number.py
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README
# Annual Report 2021
This repository contains the code required to perform the bibliometric analysis presented in the 2021 SciLifeLab annual report (and a subfolder for scripts related to the infrastructure report in early 2022 for management). The scripts will be categorised according to the plot that they relate to.
## Infra_2022_update
This subfolder contains scripts related to the infrastructure report in early 2022 for management. See the readme file in this folder for details.
## Plot colour (multiple plots)
Used to assign colours in multiple graphs. There is no need to run this script or edit it before use, it is simply imported.
## Calculation of collaboration between infrastructure units
Used to calculate the level of collaboration between infrastructure units. Rules change periodically, this script uses rule applicable in 2021.
**infra_collab_calc.py** - This uses the rules set out by OO (infra) in 2021, some units are considered together, so collaborations between them are not counted.
## JIF stacked plot
This plot shows the number of publications produced in each year for a 5 year timespan (for 2021, use 2016-2021). It also shows the number of papers that fall into each category of JIF score. Next year, may consider using an updated measure of impact score here - the article influence score (AIS).
**JIF_aff_data_prep.py** - this script prepares the data for the affiliates dataset. You must ensure that the link to the data is set up the same way for the script to work unedited.
**JIF_fell_data_prep.py** - this script prepares the data for the fellows dataset. You must ensure that the link to the data is set up the same way for the script to work unedited.
**JIF_inf_data_prep.py** - this script prepares the data for the infrastructure dataset. You must ensure that the link to the data is set up the same way for the script to work unedited.
**JIF_plot.py** - this script uses the data from the data_prep scripts above to create the JIF plots for each group. If the prep scripts are set up, then this script can be used directly and unedited.
## Citation number plot
This plot represents the number of citations acquired by all scilifelab affiliates/infrastructure users in each year for the last 5 years 2016-21.
**citation_number.py** - this script completes the calculations required to sum the number of citations and produce the plots.
## Venn plot
**venn_diagram.py** - This plot shows the numbers of papers produced by scilifelab infrastructure users and affiliated researchers, and how many they collaborated on (2016-2021). All instructions for use are available in the script itself.
## Benchmarking plot
**benchmarking.py** - This plot shows the pptop(10) scores of papers produced by scilifelab infrastructure users and affiliated researchers, compared to overall scores for papers produced by Swedish researchers in general. We focus on the 6 subjects in which SciLifeLab completed the most work and on the timeframe 2016-2021. All instructions for use are available in the script itself.