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README.md

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+# MultiSource-ESCO-Skills: A Unified Dataset for Skill Extraction
+
+This dataset aggregates data from multiple sources—course descriptions, CV content, and job descriptions—all linked to ESCO skills. It is designed to help researchers and practitioners develop and fine-tune NLP models (e.g., BERT or SentenceTransformer-based models) for automated skill extraction.
+
+## Dataset Overview
+
+- **Name:** MultiSource-ESCO-Skills
+- **Sources:**
+  - **Course Content:** Educational course materials
+  - **CV Content:** Curriculum vitae and resumes
+  - **Job Descriptions:** Listings and descriptions of job roles
+- **Data Format:** CSV file
+- **Structure:** Each row in the CSV represents a single sentence extracted from the original JSON files. The key fields include:
+  - **escoid:** The unique identifier linking to the ESCO skill (URI).
+  - **preferredLabel:** The standardized label or name of the ESCO skill.
+  - **description:** Detailed information about the ESCO skill.
+  - **sentence:** An individual sentence extracted from the source text.
+  - **sentence_type:** Indicates whether the sentence is from the "explicit" or "implicit" category.
+  - **extract:** A categorical label indicating the source of the data (`course`, `cv`, or `job`).
+
+## Data Creation Process
+
+The dataset is generated by merging three JSON files (one for courses, one for CVs, and one for jobs). Each JSON file contains an array of objects that include:
+- **ESCO Skill Metadata:** `escoid`, `preferredLabel`, and `description`
+- **Sentence Examples:** Two lists of sentences, one under `"explicit"` and one under `"implicit"`
+
+For each object, every sentence from both the explicit and implicit lists is extracted into individual rows, while retaining the associated ESCO metadata and adding an `extract` field to indicate the original data source.
+
+## Intended Use Cases
+
+- **Skill Extraction:** Fine-tune models to extract and map relevant skills from various textual inputs.
+- **Semantic Similarity:** Develop embedding-based models to compare free-form text with standardized ESCO skill descriptions.
+- **NLP Research:** Serve as a resource for studying language understanding and information extraction in vocational domains.
+
+## Example Usage
+
+Below is a simple Python snippet to load and inspect the dataset:
+
+```python
+import pandas as pd
+
+# Load the merged CSV dataset
+df = pd.read_csv("merged_dataset.csv")
+print(df.head())