Fern provides a no‑code platform for cleaning, transforming, and preparing climate and earth observation datasets, enabling users to ask natural‑language questions and instantly receive visualizations such as maps and trend lines. The service automatically tracks dataset provenance and scores trustworthiness, facilitating reliable model training and collaborative sharing of insights within the climate data community.
Funding
Funding not disclosed
Founders
Product
Problem
Researchers and analysts working with climate and earth observation data often face time‑consuming, code‑heavy processes to clean, transform, and structure raw datasets for AI model training. Lack of standardized provenance and quality metrics makes it difficult to assess trustworthiness and to share reproducible insights.
Solution
Fern offers a no‑code platform that lets users describe data preparation tasks in plain language, automatically cleaning, transforming, and structuring climate and earth observation datasets for AI applications. The system provides instant, conversational visualizations such as maps, trend lines, and breakdowns, enabling rapid exploration of results. Each dataset is automatically tracked, with provenance details and trust scores that document source, processing steps, and quality. Users can share visualizations and data pipelines with a single click, ensuring that insights are reproducible and auditable. The platform also supports community collaboration, allowing analysts to build on shared datasets and best‑practice workflows.
Target Audience
Primary users are climate scientists, data analysts, and AI researchers who need to prepare and visualize earth observation data for modeling and policy analysis.
Features
- Natural‑language interface for specifying data cleaning, transformation, and structuring without writing code
- Automated generation of visualizations (maps, trend lines, breakdowns) via a conversational chat UI
- Built‑in provenance tracking that records data source, processing history, and assigns trust/quality scores
- One‑click sharing of datasets, visualizations, and analysis pipelines for reproducible research
- Community workspace for discovering, reusing, and contributing climate and earth observation datasets
- Compatibility with AI model training pipelines through export of cleaned, structured data in common formats