39d9c177-11da-41b2-9b64-63f4c1c834b3

Variational data assimilation with deep prior

https://github.com/eds-book/39d9c177-11da-41b2-9b64-63f4c1c834b3

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
✓
DOI references
Found 2 DOI reference(s) in README
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Academic publication links
Links to: zenodo.org
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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 (10.0%) to scientific vocabulary

Keywords

environmental-data-science modelling oceanography reproducibility-challenge

Last synced: 6 months ago · JSON representation ·

Repository

Variational data assimilation with deep prior

Basic Info

Host: GitHub
Owner: eds-book
License: other
Language: Jupyter Notebook
Default Branch: main
Homepage:
Size: 4.33 MB

Statistics

Stars: 0
Watchers: 1
Forks: 0
Open Issues: 2
Releases: 5

Topics

environmental-data-science modelling oceanography reproducibility-challenge

Created almost 3 years ago · Last pushed 6 months ago

Metadata Files

Readme License Citation

README.md

Variational data assimilation with deep prior (CIRC23)

How to run

Running locally

You may also download the notebook from GitHub to run it locally: 1. Open your terminal

Check your conda install with conda --version. If you don't have conda, install it by following these instructions (see here)
Clone the repository bash git clone https://github.com/eds-book-gallery/39d9c177-11da-41b2-9b64-63f4c1c834b3.git
Move into the cloned repository bash cd 39d9c177-11da-41b2-9b64-63f4c1c834b3
Create and activate your environment from the .binder/environment.yml file bash conda env create -f .binder/environment.yml conda activate 39d9c177-11da-41b2-9b64-63f4c1c834b3
Launch the jupyter interface of your preference, notebook, jupyter notebook or lab jupyter lab

Owner

Name: Environmental Data Science Book
Login: eds-book
Kind: organization
Email: environmental.ds.book@gmail.com

Website: https://edsbook.org
Twitter: eds_book
Repositories: 1
Profile: https://github.com/eds-book

Organisation repo of EDS book for governance, outreach and other community-led activities

Citation (CITATION.cff)

cff-version: 1.2.0
message: Please cite the following works when using this project.
abstract: >-
  Notebook developed to demonstrate the computational reproduction of the paper
  Deep prior in variational assimilation to estimate an ocean circulation
  without explicit regularization, published in Environmental Data Science
  journal.
title: >-
  Variational data assimilation with deep prior (Jupyter Notebook) published in
  the Environmental Data Science book
authors:
  - family-names: Pahari
    given-names: Mukulika
    affiliation: University of Mumbai
    email: mukulikapahari@gmail.com
  - family-names: Bhoir
    given-names: Rutika
    affiliation: University of Mumbai
date-released: '2024-09-13'
contact:
  - family-names: Pahari
    given-names: Mukulika
    affiliation: University of Mumbai
    email: mukulikapahari@gmail.com
identifiers:
  - description: Open review report for this notebook
    type: url
    value: https://github.com/eds-book/notebooks-reviews/issues/8
keywords:
  - Oceans
  - Modelling
  - Special Issue
  - Python
license: MIT
license-url: https://opensource.org/license/MIT
repository: https://github.com/eds-book/39d9c177-11da-41b2-9b64-63f4c1c834b3
references:
  - authors:
      - family-names: Filoche
        given-names: Arthur
      - family-names: Béréziat
        given-names: Dominique
      - family-names: Charantonis
        given-names: Anastase
    doi: 10.1017/eds.2022.31
    type: article
    scope: >-
      Reproduced paper as part of the 2023 Climate Informatics Reproducibility
      Challenge.
    title: >-
      Deep prior in variational assimilation to estimate an ocean circulation
      without explicit regularization
    journal: Environmental Data Science journal
    year: 2022
type: software
version: v2025.6.0

GitHub Events

Total

Issue comment event: 1
Push event: 26
Pull request event: 2
Fork event: 1
Create event: 3

Last Year

Issue comment event: 1
Push event: 26
Pull request event: 2
Fork event: 1
Create event: 3

Dependencies

.github/workflows/binder.yaml actions

actions/github-script v3 composite

.github/workflows/build.yaml actions

actions/checkout main composite
jupyterhub/repo2docker-action master composite
notiz-dev/github-action-json-property release composite

.github/workflows/preview.yaml actions

actions/checkout v2 composite
ad-m/github-push-action master composite
addnab/docker-run-action v3 composite
notiz-dev/github-action-json-property release composite

.github/workflows/render.yaml actions

actions/checkout v2 composite
ad-m/github-push-action master composite
addnab/docker-run-action v3 composite
notiz-dev/github-action-json-property release composite

.github/workflows/test.yaml actions

actions/checkout main composite
jupyterhub/repo2docker-action master composite
notiz-dev/github-action-json-property release composite

.binder/environment.yml conda

jupyter
matplotlib
numpy
pooch
python 3.10.*
pytorch
scipy
tabulate

ecosyste.ms

Data

Tools

Indexes

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Experiments

Open Source Science