The NECCTON-algo organization hosts the algorithms developed in the framework of the NECCTON project.
Requirements to add a repository to the organization https://github.com/neccton-algo:
- Be a member of the NECCTON project
- Put the mandatory files in the respository as described here.
- Follow the recommendations as much as possible
- Complete the table here :
| Repository name | Branch (default: main) | Owner1 | NECCTON task | short description |
|---|---|---|---|---|
| .github | @brajard | 4.1 | description of the github organization | |
| Neccton_Super_Resolution | @AntoineBernigaud | 4.4.3 | Super Resolution Data Assimilation | |
| DINCAE-benthic-traits | @Alexander-Barth,@AbelDechN | 4.2.2 Interpolation | data products of benthic traits | |
| NECCTON_PNMI | @dlaetitia | 4.3.1 | Spatial distribution of zooplankton diversity in the Parc Naturel Marin Iroise (PNMI) | |
| BFMFORFABM | @plazzari | 5.2.3 and 5.2.4 | POC and bio-optic module used within BFM | |
| plasticparcels | @michaeldenes | 8.3 | Microplastic transport and dispersion simulation tool based on the parcels Lagrangian framework |
|
| ERSEM-NECCTON | spm | @jimc101 | 5.2.2 | SPM Model in ERSEM |
| ERSEM-NECCTON | dvm | @r-millington | 5.2.1 | DVM Model in ERSEM |
| ECOSMO | @caglartac | 4.3.1 and 5.2.1 | main ECOSMO and diel vertical migration codes | |
| SPMmodule | @giubonino | 5.2.2 | SPM module | |
| ERSEM-NECCTON | DOC | @hpowley | 5.2.3 | NECCTON DOC changes in ERSEM |
| ERSEM-NECCTON | CDOM | @hpowley | 5.2.4 | CDOM additions for bio-optical model in ERSEM |
| fabm-spectral | rrs | @hpowley | 5.2.4 | Bio-optical model used with ERSEM |
A GitHub repository of the NECCTON GitHub organization contains the following file:
- A
LICENCEfile: NECCTON encourages the use of open-source licences. - A
CODEOWNERSfile: indicate the main contacts for the repository. See here for more details. - A
READMEfile: see the minimum requirement for the README file here - One or several Jupyter notebooks to demonstrate the algorithm and the baseline. The baseline corresponds to an existing algorithm or a minimal solution (e.g. linear regression) that the algorithm is expected to outperform.
The README file must contain a description for:
- the data source
- the baseline (or a link to the jupyter notebook of the baseline)
- the metrics used to validate the output(s) of the algorithm
- the list of dependencies (name of the dependency and full version number used) needed to use the code, and use language-specific tools to install the dependencies (recommended)
- the documentation (e.g., via a link). It should allow a potential user to understand the code and reuse it. The documentation will be available at the M36 of the NECCTON project.
- Citations and links for NECCTON publications using or introducing the code, when applicable.
In addition to the points mentionned above, it is strongly suggested to:
- Use a data API for easy access to the data when testing the code
- Make use of GitHub actions to run unit tests when pushing the code on the repository (or when merging with the
mainbranch). See here for a documentation of GitHub actions. - Use language specific tools (e.g. conda, pipenv) to define the running environment.
- Use the latest best coding practices. For more details, see here
- Upload code to the organization code that is specific to the NECCTON project. Other generic tools can be hosted elsewhere.
Footnotes
-
indicate here the github login of the main contact for the code. ↩