Update README.md

first version of redme

README.md

@@ -1,3 +1,96 @@
 ---
+language:
+- en
 license: apache-2.0
+size_categories:
+- 1K<n<10K
+task_categories:
+- text-generation
+- visual-question-answering
+- time-series-forecasting
+- text-classification
+tags:
+- climate-change
+- natural-disasters
+- causal-reasoning
+- multimodal
+- earth-observation
+- sentinel
+- open-meteo
+- em-dat
 ---
+
+# DisasterChain-MM: A Multimodal Dataset of Grounded Causal Impact Chains for Global Disasters
+
+## Dataset Description
+
+- **Paper:** [Link to your Scientific Data paper, e.g., Nature Scientific Data (Coming Soon)]
+- **Repository:** https://github.com/Franco7Scala/DisasterChain-MM
+- **Point of Contact:** [Your Name / Email]
+
+### Dataset Summary
+
+**DisasterChain-MM** is a high-fidelity, multimodal dataset designed to advance research in causal reasoning, environmental monitoring, and disaster impact assessment. Building upon the EM-DAT database, it provides a comprehensive reconstruction of over 1,400 global extreme environmental events from 2014 onwards. 
+
+Unlike traditional disaster catalogs that only provide static metadata, this dataset introduces **Grounded Causal Impact Chains**. Using advanced Large Language Models (Qwen 2.5 72B and Llama 3 70B), we extract step-by-step causal sequences of physical and socio-economic impacts (e.g., *Extreme Precipitation -> River Overflow -> Flood -> Infrastructure Damage -> Displacement*). Crucially, every step in the causal chain is strictly grounded to real-world news reports via verified supporting quotes.
+
+Furthermore, each event is enriched with extensive multimodal data, including 21-day weather time series and multispectral satellite imagery availability, making it a unique resource for bridging Natural Language Processing (NLP) and Earth Observation (EO).
+
+### Supported Tasks and Applications
+
+- **Causal Reasoning & Event Extraction:** Training models to identify cascading effects in unstructured text;
+- **Multimodal Disaster Assessment:** Combining satellite imagery (Sentinel-1, Sentinel-2), weather data, and text to predict physical damage or human casualties;
+- **Climate Impact Analysis:** Analyzing the socio-economic downstream impacts of specific weather triggers across different global regions.
+
+## Dataset Structure
+
+The dataset is provided in `JSON` format. The core JSON structure contains the following nested objects for each disaster event:
+
+### 1. Root Metadata & Impact Data
+Standardized identifiers, geographical coordinates, dates, and quantitative human impact metrics (deaths, affected population, financial damage) sourced from EM-DAT.
+
+### 2. `weather_data`
+Retrieved via the Open-Meteo API, containing:
+- Pre-event and post-event statistical summaries (mean rainfall, snowfall, min/max temperatures);
+- A **21-day daily time series** capturing the meteorological evolution before, during, and after the event.
+
+### 3. `satellite_data`
+Sourced via Copernicus Data Space Ecosystem (CDSE) / Sentinel Hub:
+- Precise bounding boxes (`bbox`) for the affected areas;
+- Temporal availability arrays for Sentinel-1 (SAR), Sentinel-2 (Optical/False Color), and Sentinel-3 (Thermal);
+- Integration with ESA WorldCover land use data.
+
+### 4. `news_data`
+Metadata regarding the information retrieval process:
+- Sources queried (Google News, ReliefWeb, IFRC, Wikipedia);
+- Relevance scores and filtering penalties ensuring only high-quality, event-specific journalistic text is retained.
+
+### 5. `summary` & `causal_chain` (The LLM Reasoning Layer)
+- **Summary:** A concise, factual narrative of the event generated by Llama 3 70B.
+- **Causal Chain:** Generated by Qwen 2.5 72B-Instruct. A normalized JSON array containing the chronological steps of the disaster. Each step includes:
+  - `type_event`: A semantically normalized category (e.g., *Flood*, *Casualties*, *Infrastructure Damage*);
+  - `description`: The specific manifestation of the impact;
+  - `supporting_quote`: An exact or fuzzy-matched quote from the retrieved news context, ensuring a **zero-hallucination rate** in the causal extraction;
+  - `token_usage`: Computational footprint tracking.
+
+## Methodology and Data Pipeline
+
+The construction of this dataset followed a rigorous, multi-stage pipeline designed to eliminate LLM hallucinations and ensure physical consistency:
+
+1. **Event Anchoring:** Sourcing base events from EM-DAT (post-2014) and geocoding locations via Nominatim;
+2. **Multimodal Retrieval:** Automated fetching of historical weather data and satellite imagery footprints;
+3. **Textual Context Gathering:** Scraping, parsing, and algorithmically scoring news articles for relevance;
+4. **Causal Extraction:** Using Qwen-72B to extract causal graphs. A strict Python-based fuzzy-matching algorithm (threshold ≥ 0.80) was applied to cross-reference every LLM-generated quote against the raw news text. Any unverified causal step was systematically dropped;
+5. **Semantic Normalization:** Over 500 raw event types were algorithmically normalized into a clean taxonomy of ~390 standardized categories.
+
+## Bias, Risks, and Limitations
+
+- **Media Coverage Bias:** The richness of the causal chains and the availability of news data are inherently biased toward events that received significant international or English-language media coverage. Events in remote areas may have shorter causal chains.
+- **Satellite Data Availability:** Cloud cover heavily impacts the usability of Sentinel-2 optical imagery during severe weather events (e.g., hurricanes). Sentinel-1 SAR is provided to mitigate this.
+
+## Citation
+
+If you use this dataset in your research, please cite our paper:
+
+```bibtex
+Coming soon...