[GSoC Project Proposal]: Develop a Data Validation and Visualization Tool for Climate and Ocean Data

[dyakokarwan]

Project Description

The aim of this project is to carry out the implementation of a set of validation and visualization tools for large ocean and climate data in the IOOS Cloud Sandbox environment. The applicant will, throughout the GSoC coding duration, develop Python scripts that can validate oceanographic and climate data for consistency against observational data and also detect anomalies. The project will also entail creating interactive visualization tools that allow users to make graphs and plots (e.g., time-series and spatial maps) for examining trends in the data. The tools will be intuitive, allowing non-expert users to combine datasets and visualize findings easily. Background information such as existing data repositories, examples of validation methods, and documentation will be provided to assist in developing the project. The completion of this project will contribute to the ability to validate and analyze ocean and climate data in the IOOS Cloud Sandbox.

Expected Outcomes

The expected result is a comprehensive collection of data validation and visualization tools that will significantly improve the usability and quality of climate and ocean data in the IOOS Cloud Sandbox. The project will enable users to easily validate large datasets, compare model outputs with observational data, and detect anomalies or inconsistencies. In addition, the visualization tools will provide intuitive graphs and maps, allowing users to identify trends and anomalies in the data, ultimately informing decision-making and facilitating better climate and oceanographic research.

Skills Required

• Python: Advanced Python skills for scientific computing, data validation, and visualization (pandas, numpy, matplotlib, seaborn). • Data Analysis: Understanding of statistical methods used in oceanography and climate science for data validation and anomaly detection. • Visualization Tools: Experience with visualization tools such as Plotly, Matplotlib, or Dash for creating interactive maps and charts. • Climate and Ocean Data: Familiarity with oceanographic and climate data sets, including time-series and spatial data formats (e.g., NetCDF, HDF5). • Jupyter Notebooks: Familiarity with Jupyter Notebooks for interactive data analysis and visualization. • Linux/Bash: General knowledge of Linux shell scripting for task automation and data processing. • Collaboration Tools: Familiarity with version control (Git/GitHub) for collaborative development.

Additional Background/Issues

Additional Background/Issues:
• IOOS Cloud Sandbox GitHub Repository: Main repository where the project will take place. The candidates should visit the repository and view the organization and tools that are already there.
• Existing Validation Code Examples: Earlier code used for validation forecasting. This will allow applicants to understand the validation techniques already in use.
• Example Dataset 1:Sample oceanographic data that can potentially be used to validate. The applicants should become familiar with the dataset and consider how it might be validated.
• Issue: Data Validation Scripts: An existing problem that describes the need for more efficient data validation scripts. Prospects can have a look and provide recommendations on how to develop good validation tools.

Prospects are encouraged to read these materials, provide recommendations, and push changes to refine the validation tools and enhance the performance of the IOOS Cloud Sandbox overall.

Mentor(s)

@patrick-tripp

Mentor Contact Email(s)

mailto:[email protected]

Expected Project Size

175 hours

Project Difficulty

Intermediate

[mwengren]

Closing this per guidance already provided in: #94 (comment).

Please follow the established process for applying to GSoc with IOOS.