springtime
Spatiotemporal phenology research with interpretable models
Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 3 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Committers with academic emails
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (12.3%) to scientific vocabulary
Keywords
Repository
Spatiotemporal phenology research with interpretable models
Basic Info
- Host: GitHub
- Owner: phenology
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://springtime.readthedocs.io
- Size: 10.5 MB
Statistics
- Stars: 3
- Watchers: 6
- Forks: 2
- Open Issues: 20
- Releases: 4
Topics
Metadata Files
README.md
For detailed information and instruction, please refer to the documentation
Springtime
Springtime is both a project and a python packaged aimed at streamlining workflows for doing machine learning with phenological datasets.
Phenology is the scientific discipline in which we study the lifecycle of plants and animals. A common objective is to develop (Machine Learning) models that can explain or predict the occurrence of phenological events, such as the blooming of plants. Since there is a variety of data sources and existing tools to retrieve and analyse phenology data, it is easy to get lost and disorganized.
At the heart of springtime is a data representation following the scikit-learn standard structure. The springtime python package implements (down)loaders for various datasets that are able to convert the data to this same structure. Data loading specifications can be exported to yaml recipes for easy sharing.
The documentation has an extensive user guide that shows how each of the data loaders convert from the raw to the standardized data format. It also includes examples of using various (combinations of) models.
The data structure proposed here is still not ideal, and should rather be seen as a first step in standardizing workflows in phenological modelling. We hope it will serve as a basis for discussion and further developments.
Example task
Predict the day of first bloom of the common lilac given indirect observations (e.g. satellite data) and/or other indicators (e.g. sunshine and temperature).
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Owner
- Name: Netherlands eScience Center & University of Twente
- Login: phenology
- Kind: organization
- Email: r.zurita-milla@utwente.nl, j.maassen@esciencecenter.nl, r.goncalves@esciencecenter.nl
- Repositories: 7
- Profile: https://github.com/phenology
High spatial resolution phenological modelling at continental scales.
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: Springtime
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Peter
family-names: Kalverla
affiliation: Netherlands eScience Center
orcid: 'https://orcid.org/0000-0002-5025-7862'
- given-names: Fakhereh
family-names: Alidoost
affiliation: Netherlands eScience Center
orcid: 'https://orcid.org/0000-0001-8407-6472'
- given-names: Stefan
family-names: Verhoeven
orcid: 'https://orcid.org/0000-0002-5821-2060'
affiliation: Netherlands eScience Center
- given-names: Mahdi
family-names: Khodadadzadeh
orcid: 'https://orcid.org/0000-0001-7899-738X'
affiliation: University of Twente
repository-code: 'https://github.com/phenology/springtime'
url: 'https://springtime.readthedocs.io/'
abstract: >-
The Springtime Python package helps to streamline
workflows for doing machine learning with phenological
datasets.
keywords:
- phenology
- machine learning
- geospatial
identifiers:
- description: Latest version of software
type: doi
value: "10.5281/zenodo.7835299"
GitHub Events
Total
Last Year
Committers
Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Peter Kalverla | p****a@g****m | 48 |
| SarahAlidoost | 5****t | 29 |
| Stefan Verhoeven | s****n@e****l | 26 |
| Mahdi Khodadadzadeh | m****h@g****m | 1 |
| khzadeh | 9****h | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: over 1 year ago
All Time
- Total issues: 90
- Total pull requests: 57
- Average time to close issues: 4 months
- Average time to close pull requests: 6 days
- Total issue authors: 5
- Total pull request authors: 5
- Average comments per issue: 1.23
- Average comments per pull request: 0.72
- Merged pull requests: 47
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 36
- Pull requests: 13
- Average time to close issues: 2 months
- Average time to close pull requests: 10 days
- Issue authors: 3
- Pull request authors: 2
- Average comments per issue: 0.53
- Average comments per pull request: 0.46
- Merged pull requests: 9
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- Peter9192 (47)
- SarahAlidoost (12)
- sverhoeven (10)
- khzadeh (3)
- fnattino (3)
Pull Request Authors
- Peter9192 (26)
- sverhoeven (17)
- fnattino (12)
- SarahAlidoost (8)
- khzadeh (2)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 12 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 2
- Total maintainers: 2
pypi.org: springtime
Spatiotemporal phenology research with interpretable models
- Documentation: https://springtime.readthedocs.io/en/latest/
- License: Apache Software License
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Latest release: 0.2.2
published over 1 year ago