https://github.com/babayara/portal-teachingdb
A simplified version of the Portal Project database designed for teaching
Science Score: 13.0%
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
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○.zenodo.json file
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✓DOI references
Found 3 DOI reference(s) in README -
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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 (3.7%) to scientific vocabulary
Last synced: 10 months ago
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JSON representation
Repository
A simplified version of the Portal Project database designed for teaching
Basic Info
- Host: GitHub
- Owner: BabaYara
- Language: Python
- Default Branch: master
- Size: 4.78 MB
Statistics
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 0
Fork of weecology/portal-teachingdb
Created about 9 years ago
· Last pushed over 9 years ago
https://github.com/BabaYara/portal-teachingdb/blob/master/
The Portal Project Teaching Database is a simplified version of the Portal Project Database designed for teaching. It provides a real world example of life-history, population, and ecological data, with sufficient complexity to teach many aspects of data analysis and management, but with many complexities removed to allow students to focus on the core ideas and skills being taught. The database is currently available in csv, json, and sqlite. Use of this dataset should cite: http://dx.doi.org/10.6084/m9.figshare.1314459 This database is not designed for research as it intentionally removes some of the real-world complexities. The [original database is published at Ecological Archives](http://esapubs.org/archive/ecol/E090/118/) and this version of the database should be used for research purposes.
Owner
- Name: Baba-yara
- Login: BabaYara
- Kind: user
- Location: Portugal
- Company: Nova School of Business and Economics
- Website: www.babayara.com
- Twitter: baba_yara
- Repositories: 103
- Profile: https://github.com/BabaYara
I am a Ph.D. candidate at NOVA SBE who combines machine-learning with econometrics in the study of asset pricing.