Science Score: 36.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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✓Academic publication links
Links to: zenodo.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (8.2%) to scientific vocabulary
Repository
Repository for creating and working with climate networks.
Basic Info
- Host: GitHub
- Owner: mlcs
- License: cc0-1.0
- Language: Jupyter Notebook
- Default Branch: main
- Size: 1.93 MB
Statistics
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 1
- Releases: 4
Metadata Files
README.md
climnet
Repository for creating and working with climate networks.
Clone the repo and install all required packages
1. Clone repository with submodules:
git clone --recurse-submodules git@github.com:mlcs/climnet.git
2. Installing packages
Due to dependencies we recommend using conda. We provided a list of packages in the 'condaEnv.yml' file. The following steps set up a new environment with all required packages: 1. Install packages:
conda env create -f condaEnv.yml 2. Activate environment: conda activate climnetenv 3. Install packages which are only available on pip pip install graphriccicurvature 3. Make your local version of climnet a package by running pip install -e .
3. Tutorial
A tutorial for reading data, processing and creating a simple correlation based climate network can be found at this .
Owner
- Name: Machine Learning in Climate Science
- Login: mlcs
- Kind: organization
- Email: machinelearning.climatescience@protonmail.com
- Location: Tübingen, Germany
- Website: https://machineclimate.de
- Twitter: MachineClimate
- Repositories: 2
- Profile: https://github.com/mlcs
Independent Research Group within the Cluster of Excellence "Machine Learning" at the University of Tübingen