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https://github.com/akbaritabar/parallelised-analysis-of-large-scale-bibliometric-and-text-data
Materials for the lecture on "Parallelised analysis of large-scale bibliometric and text data (with Dask in Python, DuckDB and DBeaver in SQL)"
https://github.com/akbaritabar/parallelised-analysis-of-large-scale-bibliometric-and-text-data
Last synced: about 1 month ago
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Materials for the lecture on "Parallelised analysis of large-scale bibliometric and text data (with Dask in Python, DuckDB and DBeaver in SQL)"
- Host: GitHub
- URL: https://github.com/akbaritabar/parallelised-analysis-of-large-scale-bibliometric-and-text-data
- Owner: akbaritabar
- License: gpl-3.0
- Created: 2022-10-25T13:46:37.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-13T14:01:03.000Z (5 months ago)
- Last Synced: 2024-07-13T15:22:32.960Z (5 months ago)
- Language: Python
- Size: 9.44 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: Readme.md
- License: LICENSE
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README
# Materials for the lecture on "Parallelised analysis of large-scale bibliometric and text data (with Dask in Python, DuckDB and DBeaver in SQL)"
Speaker:
- **Aliakbar Akbaritabar**, research scientist at the Max Planck Institute for Demographic Research (MPIDR), GitHub: [https://github.com/akbaritabar](https://github.com/akbaritabar), ([email protected])
## Instructions for participants
0. Please clone or download this repository ([https://github.com/akbaritabar/Parallelised-analysis-of-large-scale-bibliometric-and-text-data](https://github.com/akbaritabar/Parallelised-analysis-of-large-scale-bibliometric-and-text-data.git)) by clicking on the green button on top right (see photo)
![Clone or download](99_images/download_from_github.png)
1. Please go over the presentation files in the `2_presentations` directory, some are *self-study* materials that in combination with Python and R scripts in `0_code` directory should help you set-up and get started. You can ask your clarification questions by email or during the Q&A sessions.
2. For `Python` users to replicate the results, you should follow steps outlined in `0_code\01_Required_installation_setup_python.md` to have all necessary libraries installed.
3. For `R` users to replicate the results, you should follow steps outlined in `0_code\02_Required_installation_setup_R.md` to have all necessary libraries installed.