https://github.com/climate-service-center/dacstore
Acceptance analysis DACStorE
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 2 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 (10.0%) to scientific vocabulary
Repository
Acceptance analysis DACStorE
Basic Info
- Host: GitHub
- Owner: climate-service-center
- License: mit
- Language: Jupyter Notebook
- Default Branch: main
- Size: 23.3 MB
Statistics
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 11
- Releases: 2
Metadata Files
README.md
Acceptance analysis of Direct Air Capture and Storage
Acceptance analysis of Direct Air Capture and Storage in the DACStorE project.
This study investigates public perceptions and acceptance of Direct Air Capture (DAC) and CO2 storage technologies through a comprehensive survey. The survey, conducted among a diverse sample, assesses awareness, knowledge, and attitudes towards climate change, DAC, and CO2 storage. Key findings reveal that while a majority acknowledge the reality and seriousness of climate change, awareness and understanding of DAC technologies and CO2 storage remain limited. The analysis identifies significant factors influencing support for DAC and CO2 storage, including perceived risks, trust in institutions, and concerns about technological tampering with nature.
Installation
This project utilizes the SurveyHero API to update and automate the data analysis. Clone the repository with the following command in the terminal:
bash
git clone https://github.com/climate-service-center/dacstore.git
To run the analyis go to the dacstore directory and run main.py, e.g.
bash
cd dacstore
pip install -r requirements.txt
pip install -e .
python main.py
Acknowledgments
We acknowledge the use of GitHub Copilot, an AI-based code completion tool, which assisted in the development of the code used in this research.
References
Seabold, S., & Perktold, J. (2010). statsmodels: Econometric and statistical modeling with python. In 9th Python in Science Conference.
GitHub Copilot. GitHub, Inc. Available at: https://github.com/features/copilot
Owner
- Name: GERICS
- Login: climate-service-center
- Kind: organization
- Location: Germany
- Website: www.gerics.de
- Twitter: GERICS_Germany
- Repositories: 2
- Profile: https://github.com/climate-service-center
Climate Service Center Germany
GitHub Events
Total
- Create event: 3
- Release event: 1
- Issues event: 1
- Delete event: 5
- Push event: 6
- Public event: 1
- Pull request event: 4
Last Year
- Create event: 3
- Release event: 1
- Issues event: 1
- Delete event: 5
- Push event: 6
- Public event: 1
- Pull request event: 4
Dependencies
- actions/checkout v4 composite
- actions/setup-python v5 composite
- actions/checkout v4 composite
- actions/setup-python v5 composite
- actions/upload-artifact v4 composite
- actions/checkout v4 composite
- actions/deploy-pages v4 composite
- actions/setup-python v5 composite
- actions/upload-pages-artifact v3 composite
- jupyter-book *
- jinja2 *
- matplotlib *
- numpy *
- openpyxl *
- pandas *
- requests *
- seaborn *
- statsmodels *
- xlsxwriter *