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--- |
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language: |
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- fr |
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tags: |
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- france |
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- data-catalog |
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- data-gouv |
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- metadata |
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- open-data |
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- government |
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- etalab |
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- embeddings |
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pretty_name: Data.gouv.fr Datasets Catalog |
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size_categories: |
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- 10K<n<100K |
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license: etalab-2.0 |
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configs: |
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- config_name: latest |
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data_files: "data/data-gouv-datasets-catalog-latest/*.parquet" |
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default: true |
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--- |
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# 🇫🇷 Data.gouv.fr Datasets Catalog |
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This dataset contains a processed and embedded version of the **catalog of datasets published on [data.gouv.fr](https://www.data.gouv.fr)**, the French open data platform. |
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The dataset was published by data.gouv.fr on the [dedicated dataset page](https://www.data.gouv.fr/datasets/catalogue-des-donnees-de-data-gouv-fr/). |
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It includes rich metadata about each public dataset: title, URL, publisher organization, description, tags, licensing, update frequency, usage metrics, and more. |
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The dataset provides semantic-ready and structured for semantic indexing and retrieval. |
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Each chunk has been embedded using the [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) model, making this catalog ready for search, classification, or retrieval-augmented generation (RAG) pipelines. |
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## 🗂️ Dataset Contents |
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The dataset is provided in **Parquet format** and contains the following columns: |
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| Column Name | Type | Description | |
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|------------------------------|-------------------|-----------------------------------------------------------------------------| |
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| `chunk_id` | `str` | Unique source based identifier of the chunk | |
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| `doc_id` | `str` | Document identifier from the source site (slug). | |
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| `chunk_xxh64` | `str` | XXH64 hash of the `chunk_text` value. | |
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| `title` | `str` | Title of the dataset. | |
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| `acronym` | `str` | Acronym of the dataset (if available). | |
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| `url` | `str` | URL of the dataset on data.gouv.fr. | |
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| `organization` | `str` | Name of the associated organization. | |
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| `organization_id` | `str` | Unique ID of the organization. | |
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| `owner` | `str` | Name of the dataset owner. | |
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| `owner_id` | `str` | Unique ID of the dataset owner. | |
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| `description` | `str` | Full description of the dataset. | |
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| `frequency` | `str` | Update frequency of the dataset. | |
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| `license` | `str` | License type (e.g. Etalab-2.0). | |
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| `temporal_coverage_start` | `str` | Start of the temporal coverage (if applicable). | |
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| `temporal_coverage_end` | `str` | End of the temporal coverage (if applicable). | |
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| `spatial_granularity` | `str` | Spatial granularity level (e.g. country, region, etc.). | |
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| `spatial_zones` | `str` | Names of the spatial zone covered. | |
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| `featured` | `bool` | Whether the dataset is featured. | |
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| `created_at` | `str` | Dataset creation date (standard ISO 8601). | |
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| `last_modified` | `str` | Last modification date (standard ISO 8601). | |
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| `tags` | `str` | Comma-separated list of tags associated with the dataset. | |
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| `archived` | `str` | Whether the dataset is archived. | |
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| `resources_count` | `int` | Total number of attached resources. | |
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| `main_resources_count` | `int` | Number of primary resources. | |
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| `resources_formats` | `str` | Formats used by the dataset's ressources (e.g., CSV, JSON, PDF) | |
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| `harvest_backend` | `str` | Name of the harvest backend. | |
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| `harvest_domain` | `str` | Domain source of the harvested dataset. | |
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| `harvest_created_at` | `str` | Harvest creation date (standard ISO 8601). | |
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| `harvest_modified_at` | `str` | Harvest last update date (standard ISO 8601). | |
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| `harvest_remote_url` | `str` | Remote source URL of the harvested dataset. | |
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| `quality_score` | `float` | Quality score assigned by data.gouv.fr. | |
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| `metric_discussions` | `int` | Number of discussions related to the dataset. | |
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| `metric_reuses` | `int` | Number of declared reuses. | |
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| `metric_reuses_by_months` | `str` | Monthly reuse statistics (as JSON string). | |
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| `metric_followers` | `int` | Number of users following the dataset. | |
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| `metric_followers_by_months` | `str` | Monthly follower statistics (as JSON string or number). | |
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| `metric_views` | `int` | Number of views. | |
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| `metric_resources_downloads` | `float` | Number of resource downloads. | |
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| `chunk_text` | `str` | Text used for semantic embedding (title + organization + cropped description).| |
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| `embeddings_bge-m3` | `str` (stringified list) | Embedding of `chunk_text` using `BAAI/bge-m3`. Stored as JSON array string. | |
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--- |
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## 🛠️ Data Processing Methodology |
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### 📥 1. Field Extraction |
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The original dataset was retrieved directly from the official [dedicated dataset page](https://www.data.gouv.fr/datasets/catalogue-des-donnees-de-data-gouv-fr/). |
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This dataset only includes data.gouv.fr datasets that have at least a 100 characters description to remove as much noise as possible from incomplete datasets. |
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### ✂️ 2. Text Chunking |
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The `chunk_text` field was created by combining the `title`, `organization` name, `description`. |
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The `description` was here cropped to a maximum length of +- 1000 characters. |
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The Langchain's `RecursiveCharacterTextSplitter` function was used to crop the description. |
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The parameters used are : |
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- `chunk_size` = 1000 (in order to limit as much noise as possible) |
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- `length_function` = len |
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Then, only the first splitted text was keeped. Which leads to have a cropped description of a maximum of +- 1000 characters. |
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### 🧠 3. Embedding Generation |
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Each `chunk_text` was embedded using the [**`BAAI/bge-m3`**](https://huggingface.co/BAAI/bge-m3) model. The resulting embedding vector is stored in the `embeddings_bge-m3` column as a **string**, but can easily be parsed back into a `list[float]` or NumPy array. |
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## 📌 Embeddings Notice |
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⚠️ The `embeddings_bge-m3` column is stored as a stringified list (e.g., `"[-0.03062629,-0.017049594,...]"`). |
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To use it as a vector, you need to parse it into a list of floats or NumPy array. For example, if you want to load the dataset into a dataframe by using the `datasets` library: |
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```python |
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import pandas as pd |
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import json |
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from datasets import load_dataset |
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# The Pyarrow library must be installed in your Python environment for this example. By doing => pip install pyarrow |
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dataset = load_dataset("AgentPublic/data-gouv-datasets-catalog") |
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df = pd.DataFrame(dataset['train']) |
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df["embeddings_bge-m3"] = df["embeddings_bge-m3"].apply(json.loads) |
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``` |
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Otherwise, if you have already downloaded all parquet files from the `data/data-gouv-datasets-catalog-latest/` folder : |
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```python |
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import pandas as pd |
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import json |
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# The Pyarrow library must be installed in your Python environment for this example. By doing => pip install pyarrow |
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df = pd.read_parquet(path="data-gouv-datasets-catalog-latest/") # Assuming that all parquet files are located into this folder |
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df["embeddings_bge-m3"] = df["embeddings_bge-m3"].apply(json.loads) |
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``` |
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You can then use the dataframe as you wish, such as by inserting the data from the dataframe into the vector database of your choice. |
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## 🐱 GitHub repository : |
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The project MediaTech is open source ! You are free to contribute or see the complete code used to build the dataset by checking the [GitHub repository](https://github.com/etalab-ia/mediatech) |
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## 📚 Source & License |
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### 🔗 Source : |
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- [Data.gouv.fr : Catalogue des données de data.gouv.fr](https://www.data.gouv.fr/datasets/catalogue-des-donnees-de-data-gouv-fr/) |
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### 📄 Licence : |
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**Open License (Etalab)** — This dataset is publicly available and can be reused under the conditions of the Etalab open license. |
