Science Score: 49.0%
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
Found 3 DOI reference(s) in README -
✓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 (10.5%) to scientific vocabulary
Repository
Geoflow Tuna atlas workflow
Basic Info
- Host: GitHub
- Owner: firms-gta
- Language: HTML
- Default Branch: master
- Size: 9.35 MB
Statistics
- Stars: 3
- Watchers: 3
- Forks: 2
- Open Issues: 9
- Releases: 8
Metadata Files
README.md
output: html_document: default
pdf_document: default
Geoflow-tuna repository
This repository contains all material needed to update the Tuna atlas workflow by using geoflow package
1) Clone repository:
git clone -b sample https://github.com/firms-gta/geoflow-tunaatlas
2) Configure environment file:
To fully recreate the workflow and populate a database with the processed data, it is necessary to have a configuration file in the format of template.env. This file should include the connection details to a database where the data will be stored. Ensure that you have the proper database access and credentials before attempting to execute the workflow.
3) Execute GeoFlow
library(geoflow) # Load geoflow inside R
executeWorkflow("/path/to/geoflow-tunaatlas/launching_jsons_creating_GTA.R")
Workflow Overview
The creation of the datasets involves several stages:
- Pre-processing of tRFMOs data formats: Initial treatment to standardize the diverse data formats across tRFMOs.
- Data processing on tRFMOs data: Detailed processing to clean, validate, and prepare the data for aggregation.
- Aggregation of data: Combining and georeferencing the data by tRFMOs to create comprehensive datasets that include spatial information.
- Reporting: Generation of detailed reports at each stage to document the process and outcomes.
Datasets
Upon completion, the workflow produces five main datasets, one for each tRFMO. These datasets include:
- Raw dataset: The unprocessed collection of data as retrieved from the sources after pre-harmonisation.
- Level 0 dataset: A global dataset addressing overlaps in zones and standardizing spatial references.
- Level 1 dataset: Harmonized data with conversions from numbers to tonnes to unify measurement units.
- Level 2 dataset: Enhanced georeferenced data based on nominal data, tryng to increase the spatial detail and utility of the dataset.
All datasets created are accessible under DOI, ensuring easy access and citation for research and analysis purposes.
Owner
- Name: firms-gta
- Login: firms-gta
- Kind: organization
- Repositories: 6
- Profile: https://github.com/firms-gta
GitHub Events
Total
- Create event: 14
- Issues event: 10
- Release event: 5
- Watch event: 4
- Delete event: 5
- Issue comment event: 2
- Push event: 132
- Pull request event: 14
Last Year
- Create event: 14
- Issues event: 10
- Release event: 5
- Watch event: 4
- Delete event: 5
- Issue comment event: 2
- Push event: 132
- Pull request event: 14
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 5
- Total pull requests: 6
- Average time to close issues: 3 days
- Average time to close pull requests: 8 minutes
- Total issue authors: 1
- Total pull request authors: 1
- Average comments per issue: 0.0
- Average comments per pull request: 0.17
- Merged pull requests: 6
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 5
- Pull requests: 6
- Average time to close issues: 3 days
- Average time to close pull requests: 8 minutes
- Issue authors: 1
- Pull request authors: 1
- Average comments per issue: 0.0
- Average comments per pull request: 0.17
- Merged pull requests: 6
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- bastienird (5)
Pull Request Authors
- bastienird (7)