@@ -11,7 +11,7 @@ The **MetaDatabaseAnalyzer** checks metadata in the database against CRC standar
## Features
-**Compliance Check:** Validates metadata fields against CRC 1280 requirements.
-**Descriptive Statistic**s: Generates core statistics on metadata completeness and quality.
-**Descriptive Statistics**: Generates core statistics on metadata completeness and quality.
-**Visualizations**: Provides graphical insights into metadata trends and issues.
## Python Libraries Used
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# Project structure
This is organized into the following structure:
- Jupyter Notebook Script: The main analysis is performed within a Jupyter Notebook, which includes the core logic for evaluating the metadata.
- Jupyter Notebook Script: The main analysis is performed within a Jupyter Notebook`MetaDatabaseAnalyzer.ipynb`, which includes the core logic for evaluating the metadata.
-`databases` (Folder): Contains example SQLite databases (20230101_SFB1280Database.db, 20240101_SFB1280Database.db) with purely fictional data. These databases demonstrate the functionality of the MetaDatabaseAnalyzer, allowing users to explore the tool without concerns about confidentiality or data sensitivity.
-`metadata_info` (Folder): Includes text files that define the SFB metadata terms, vocabulary, and mappingsin the analysis. This folder is crucial for the perfomed checks that metadata adheres to the required standards and mappings.
-`data_curation` (Folder): This folder is required for the successful execution of the script in order to save certain results and thus data curation tasks as txt files. In this repo it coontains the output for the example databases.
-`data_curation` (Folder): This folder is required for the successful execution of the script in order to save certain results and thus data curation tasks as txt files. In this repo it contains the output for the example databases.
# Example datasets
Two example datasets (20230101_SFB1280Database.db, 20240101_SFB1280Database.db) are provided with this tool in the folder `databases`, containing purely fictional data. These datasets are intended to demonstrate the functionality of the MetaDatabaseAnalyzer. The fictitious data ensures that users can explore and understand the features of the tool without any concerns related to confidentiality or data sensitivity. The datasets mimic typical metadata structures and potential issues that might arise in real-world scenarios, allowing users to practice and experiment with the analysis process.
Two example datasets (20230101_SFB1280Database.db, 20240101_SFB1280Database.db) are provided in this repo in the folder `databases`, containing purely fictional data. These datasets are intended to demonstrate the functionality of the MetaDatabaseAnalyzer script. The fictitious data ensures that users can explore and understand the features of the tool without any concerns related to confidentiality or data sensitivity. The datasets mimic typical metadata structures and potential issues that might arise in real-world scenarios, allowing users to practice and experiment with the analysis process.
The metdata in the datasets was created using the [MetaDataApp](https://gitlab.ruhr-uni-bochum.de/sfb1280/metaapp_2), [ParticipantsTSVConverter](https://gitlab.ruhr-uni-bochum.de/sfb1280/participantstsvconverter) and [ChangeMetaApp](https://gitlab.ruhr-uni-bochum.de/sfb1280/changemetaapp). The databases themselves were compiled using the [DatabaseCreator ](https://gitlab.ruhr-uni-bochum.de/sfb1280/DatabaseCreate).
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## Acknowledgements
This project was developed as part of the final thesis work in the [Data Librarian course at TH Köln](https://www.th-koeln.de/weiterbildung/zertifikatskurs-data-librarian_63393.php), funded by SFB 1280. It builds on the substantial work undertaken within the SFB 1280 INF project, supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, project number 316803389).
I would like to thank the principal investigators of the SFB's INF project for their guidance and encouragement throughout this work. Special thanks are due to Prof. Dr. Dr. h.c. Onur Güntürkün, the speaker of CRC 1280, for his leadership and approval of this work. ChatGPT assisted with debugging and scripting in the Jupyter notebook.
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I would like to thank the principal investigators of the SFB's [INF project](https://sfb1280.ruhr-uni-bochum.de/en/research/projects/inf/) Tobias Otto and Nina Winter for their guidance and encouragement throughout this work. Special thanks are due to Prof. Dr. Dr. h.c. Onur Güntürkün, the speaker of CRC 1280, for his leadership and approval of this work. ChatGPT assisted with debugging and scripting in the Jupyter notebook.
