Manufacturing Production in France increased 1.50 percent in September of 2025 over the same month in the previous year. This dataset provides the latest reported value for - France Manufacturing Production - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

GDP from Manufacturing in France increased to 59664 EUR Million in the third quarter of 2025 from 59188 EUR Million in the second quarter of 2025. This dataset provides - France Gdp From Manufacturing- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Manufacturing Payrolls in France increased by 2841 thousand in June of 2025. This dataset provides - France Payroll Employment in Manufacturing- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Industrial Production in France increased 1.30 percent in September of 2025 over the same month in the previous year. This dataset provides the latest reported value for - France Industrial Production - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Monthly and long-term France Industry data: historical series and analyst forecasts curated by FocusEconomics.

Italy and France are historically among the countries that produce the most prestigious wines worldwide. In Europe, these two countries together produce more than half of the wines classified under the Protected Designation of Origin (PDO) label, the strictest quality mark of food and wines in the European Union. Due to their long tradition in wine protection, Italy and France include highly detailed regulatory information in their wine PDO regulatory documents that are usually not available for other countries, such as specific information about the main cultivars that must be used to make each wine product or the related required planting density in the vineyards. However, this information is scattered throughout the documents of each wine production area and has never been extracted and homogenised in a unique dataset. Here, we present the first dataset that characterizes the PDO wines produced in Italy and France at very high detail based on the documents from the official EU geographical indication register. It includes, for each country, a standardized list of the PDO wine names, linked with their specific regulatory requirements, including the wine colour, type, cultivars used and maximum allowed yields. The unprecedent level of detail of this dataset allows for the first time the analysis of more than 5000 traditional wines and their legal and agronomic specifications. This gives insights into the interplay between the European Union quality regulation policy, the wine sector and agronomic practices, enabling researchers and practitioners to analyze wine production in the context of specific regulations or economic scenarios.

⚡️**About The Dataset**⚡️ This dataset consists of wind 💨 and solar ☀️ energy production (in MW)⚡️ records on an hourly basis for the French grid since 2020. Its primary purpose is to enable the Commission de Régulation de l'Énergie (CRE) to calculate the reference price used in the calculation of additional remuneration for the wind and solar sectors.

The additional remuneration is a support mechanism for wind and solar energy producers, as defined in Articles L. 314-18 to L. 314-27 of the Energy Code. This mechanism was introduced by the Law on Energy Transition for Green Growth (LTECV). It allows renewable energy producers who directly market their electricity to receive a premium that compensates for the difference between their income from sales and a reference remuneration level. The reference remuneration is fixed by public authorities through a tariff decree or by the producer through a competition procedure, depending on the type of installation.

This additional compensation is generally classified as a variable bonus, or ex post, as its amount is adjusted to account for the variance between the reference compensation and the actual income derived from the market. The system's main objective is to expose producers to short-term market price signals while ensuring they receive reasonable remuneration for their renewable energy production.

⚡️**Dataset Loading**⚡️

You can simply load the dataset by running the following script :

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')

class CONFIG:

  NAMES_DTYPES = {
    "Source" : str,
    "Production" : np.float32
  }

data = pd.read_csv(
  "/kaggle/input/intermittent-renewables-production-france/intermittent-renewables-production-france.csv",
  index_col="Date and Hour",
  parse_dates=["Date and Hour", "Date"],
  infer_datetime_format=True,
  dtype=CONFIG.NAMES_DTYPES
  )

⚡️**Dataset Usage for Machine Learning**⚡️ The dataset offers valuable opportunities for various machine learning applications in the domain of renewable energy and power market analysis. Here are some potential use cases:

1. Time Series Forecasting: Machine learning models can be trained on the hourly production records to forecast future wind and solar energy production levels. These predictions are crucial for grid operators, energy traders, and policymakers to plan and optimize energy distribution and utilization efficiently.

2. Anomaly Detection: By employing machine learning algorithms, anomalies in energy production patterns can be detected. Anomalies may indicate equipment malfunctions, weather-related issues, or other irregularities that require attention.

3. Price Signal Analysis: Using the reference price data, machine learning models can analyze market price signals and their impact on renewable energy producers. This analysis can assist stakeholders in making informed decisions about energy selling strategies and profit optimization.

4. Optimization of Renewable Energy Production: Machine learning models can optimize the operation of renewable energy installations, considering factors such as weather forecasts, market prices, and production costs. This will help determine the most favorable times for energy production.

5. Performance Comparison of Energy Installations: Machine learning can be used to compare the performance of different types of renewable energy installations. This analysis can provide insights into the efficiency of various technologies under different conditions and market dynamics.

6. Policy Impact Assessment: The dataset's information on the additional remuneration support mechanism can be leveraged to assess the effectiveness of policies promoting renewable energy. Machine learning models can help identify the influence of such policies on energy production, market participation, and financial outcomes for producers.

7. Energy Market Price Prediction: By integrating this dataset with other relevant market data, machine learning models can predict energy market prices. Such predictions can assist renewable energy producers in making informed decisions about when and how much energy to sell.

To achieve optimal results for these tasks, data preprocessing, handling of missing values, and potential integration with other datasets or external factors (e.g., weather data, economic indicators) are crucial for enhancing the performance and accuracy of machine learning models.

Feeds contribute highly to environmental impacts of livestock products. Therefore, formulating low-impact feeds requires data on environmental impacts of feed ingredients with consistent perimeters and methodology for life cycle assessment (LCA). We created the ECOALIM dataset of life cycle inventories (LCIs) and associated impacts of feed ingredients used in animal production in France. It provides several perimeters for LCIs (field gate, storage agency gate, plant gate and harbour gate) with homogeneously collected data from French R&D institutes covering the 2005–2012 period. The dataset of environmental impacts is available as a Microsoft® Excel spreadsheet on the ECOALIM website and provides climate change, acidification, eutrophication, non-renewable and total cumulative energy demand, phosphorus demand, and land occupation. LCIs in the ECOALIM dataset are available in the AGRIBALYSE® database in SimaPro® software. The typology performed on the dataset classified the 149 average feed ingredients into categories of low impact (co-products of plant origin and minerals), high impact (feed-use amino acids, fats and vitamins) and intermediate impact (cereals, oilseeds, oil meals and protein crops). Therefore, the ECOALIM dataset can be used by feed manufacturers and LCA practitioners to investigate formulation of low-impact feeds. It also provides data for environmental evaluation of feeds and animal production systems. Included in AGRIBALYSE® database and SimaPro®, the ECOALIM dataset will benefit from their procedures for maintenance and regular updating. Future use can also include environmental labelling of commercial products from livestock production.

Economic data on forestry and logging, physical and monetary data on supply and use of wood, and employment data. 2. Aggregates include output, intermediate consumption, gross value added, fixed capital consumption, gross fixed capital formation and different measures of income of forestry and logging. The data are in current basic prices and use the concepts and definitions of National Accounts. 3. EU Member States, EFTA countries, the UK and selected candidate countries. Data for France cover only mainland France without the overseas territories and dominions French Guyana, Guadeloupe, Martinique, Réunion or Mayotte. They are collected as part of European Forest Accounts (EFA), which also covers wooded land, timber, output of the forestry industry by type, and labour input in annual work units (AWU). Employment data from Eurostat's Labour Force Survey (LFS) are presented as well, covering estimates of the number of employees in forestry and logging, the manufacture of wood and products of wood and cork, the manufacture of paper and paper products, and the manufacture of furniture. There are two separate tables because of the change in the EU's classification of economic activities from NACE Rev. 1.1 to NACE Rev. 2 in 2008. Sources: questionnaire on European Forest Accounts (EFA) and LFS . The questionnaire and its explanatory notes can be found on our open-access communication platform under the interest group 'Forestry statistics and accounts'.

Dear Scientists,

I am sharing with you this gold mine, a descriptive listing of all the Real Estate sales in France for 2022. The dataset comes from Gouvernemental website data.gouv.fr where you can access for free the past 5 years of sales of the Real Estate market.

I removed dead columns with 99% missing values and did not apply any kind of features engineering. Some columns have missing values but not worth dropping since the rows has valuable information.

Feel free to ask in comments if you need additional information concerning the French RE market, or about features meanings.

To give you some context, with the data available you can find out: - The real address of sold properties in France - The price of sold properties - The date the transaction occured - The description of sold properties (type, size, number of rooms) - The nature of the mutation (sale, swap, VEFA (Vente en l'état futur d'achèvement) etc..)

"DVF" stands for "Demande de Valeur Foncière," which translates to "Request for Property Value" in English. DVF is a system used in France to provide information about real estate transactions, particularly property sales and their associated prices.

The DVF system was established to enhance transparency in the French real estate market and make property transaction data accessible to the public. It allows individuals to inquire about property sale prices in specific areas or regions of France. This information can be valuable for various purposes, including:

Property Valuation: Homebuyers and sellers can use DVF data to get an idea of property values in a particular area, helping them make informed decisions about buying or selling real estate.

Market Analysis: Real estate professionals and analysts use DVF data to assess market trends and fluctuations in property prices. This information can inform investment decisions and market research.

Taxation: Local authorities and tax authorities use DVF data to assess property taxes, as property values are a key factor in determining tax rates.

Urban Planning: Municipalities and urban planners may use DVF data to gain insights into property transactions and trends within their regions, helping them make decisions about development and infrastructure.

It's important to note that DVF data typically includes information about the sale price, the date of the transaction, the property's location, and other relevant details. However, personal information about buyers and sellers is generally not disclosed in the publicly available DVF dataset.

DVF data has become increasingly accessible through online platforms and government websites, making it a valuable resource for those interested in the French real estate market. It provides transparency and aids in making informed decisions related to property transactions and investments.

Features (Columns):

• Date mutation (Mutation Date): The date on which the property mutation or transaction occurred.
• Nature mutation (Mutation Nature): The nature or type of property mutation, such as sale, inheritance, etc.
• Valeur fonciere (Property Value): The value of the property.
• No voie (Street Number): The street number of the property.
• Type voie (Street Type): The type of street (e.g., avenue, boulevard) where the property is located.
• Code voie (Street Code): A code associated with the street where the property is located.
• Code postal (Postal Code): The postal code of the property's location.
• Commune (Town/City): The town or city where the property is located.
• Code departement (Department Code): The code of the department where the property is situated.
• Code commune (Commune Code): A code specific to the commune where the property is located.
• Section (Section): Information about the property section.
• No plan (Plan Number): The plan number associated with the property.
• Nombre de lots (Number of Lots): The total number of lots or portions in the property.
• Type local (Local Type): The type of local or property (e.g., residential, commercial).
• Surface reelle (Actual Built Area): The actual built area of the property.
• Nombre pieces principales (Number of Main Rooms): The number of main rooms in the property.
• Surface terrain (Land Area): The total land area associated with the property.

153 views (2 recent) Economic data on forestry and logging, physical and monetary data on supply and use of wood, and employment data. Aggregates include output, intermediate consumption, gross value added, fixed capital consumption, gross fixed capital formation and different measures of income of forestry and logging. The data are in current basic prices and use the concepts and definitions of National Accounts. EU Member States, EFTA countries, the UK and selected candidate countries. Data for France cover only mainland France without the overseas territories and dominions French Guyana, Guadeloupe, Martinique, Réunion or Mayotte. They are collected as part of European Forest Accounts (EFA), which also covers wooded land, timber, output of the forestry industry by type, and labour input in annual work units (AWU). Employment data from Eurostat's Labour Force Survey (LFS) are presented as well, covering estimates of the number of employees in forestry and logging, the manufacture of wood and products of wood and cork, the manufacture of paper and paper products, and the manufacture of furniture. There are two separate tables because of the change in the EU's classification of economic activities from NACE Rev. 1.1 to NACE Rev. 2 in 2008. Sources: questionnaire on European Forest Accounts (EFA) and LFS . The questionnaire and its explanatory notes can be found on our open-access communication platform under the interest group 'Forestry statistics and accounts'.

185 views (3 recent) Economic data on forestry and logging, physical and monetary data on supply and use of wood, and employment data. Aggregates include output, intermediate consumption, gross value added, fixed capital consumption, gross fixed capital formation and different measures of income of forestry and logging. The data are in current basic prices and use the concepts and definitions of National Accounts. EU Member States, EFTA countries, the UK and selected candidate countries. Data for France cover only mainland France without the overseas territories and dominions French Guyana, Guadeloupe, Martinique, Réunion or Mayotte. They are collected as part of European Forest Accounts (EFA), which also covers wooded land, timber, output of the forestry industry by type, and labour input in annual work units (AWU). Employment data from Eurostat's Labour Force Survey (LFS) are presented as well, covering estimates of the number of employees in forestry and logging, the manufacture of wood and products of wood and cork, the manufacture of paper and paper products, and the manufacture of furniture. There are two separate tables because of the change in the EU's classification of economic activities from NACE Rev. 1.1 to NACE Rev. 2 in 2008. Sources: questionnaire on European Forest Accounts (EFA) and LFS . The questionnaire and its explanatory notes can be found on our open-access communication platform under the interest group 'Forestry statistics and accounts'.

The recent development of functional approaches may allow evaluating other aspects of ecosystem quality, functions and interactions with abiotic parameters or other communities. Here, our aim was to create a phytoplankton trait database at the national scale for France. This database will be relevant to analyze phytoplankton communities for a better understanding of phytoplankton functional ecology in lakes of France and other European countries with similar biological communities. For this purpose, we used a French national database of phytoplankton occurrences sampled from lakes over the entire French metropolitan territory. The observed taxa list was used to compile 53 morpho-functional traits associated with phylogeny information. The morpho-functional traits encompassed variables such as shape, biovolume, motility, toxin production and Reynolds groups.

175 views (3 recent) Economic data on forestry and logging, physical and monetary data on supply and use of wood, and employment data. Aggregates include output, intermediate consumption, gross value added, fixed capital consumption, gross fixed capital formation and different measures of income of forestry and logging. The data are in current basic prices and use the concepts and definitions of National Accounts. 3. EU Member States, EFTA countries, the UK and selected candidate countries. Data for France cover only mainland France without the overseas territories and dominions French Guyana, Guadeloupe, Martinique, Réunion or Mayotte. They are collected as part of European Forest Accounts (EFA), which also covers wooded land, timber, output of the forestry industry by type, and labour input in annual work units (AWU). Employment data from Eurostat's Labour Force Survey (LFS) are presented as well, covering estimates of the number of employees in forestry and logging, the manufacture of wood and products of wood and cork, the manufacture of paper and paper products, and the manufacture of furniture. There are two separate tables because of the change in the EU's classification of economic activities from NACE Rev. 1.1 to NACE Rev. 2 in 2008. Sources: questionnaire on European Forest Accounts (EFA) and LFS . The questionnaire and its explanatory notes can be found on our open-access communication platform under the interest group 'Forestry statistics and accounts'.

Introduction

Welcome to the French Language In-car Speech Dataset, a comprehensive collection of audio recordings designed to facilitate the development of speech recognition models specifically tailored for in-car environments. This dataset aims to support research and innovation in automotive speech technology, enabling seamless and robust voice interactions within vehicles for drivers and co-passengers.

Speech Data

This dataset comprises over 5,000 high-quality audio recordings collected from various in-car environments. These recordings include scripted wake words and command-type prompts.

Participant Diversity:

- Speakers: 50+ native French speakers from the FutureBeeAI Community.

- Regions: Ensures a balanced representation of France1 accents, dialects, and demographics.

- Participant Profile: Participants range from 18 to 70 years old, representing both males and females in a 60:40 ratio, respectively.

Recording Nature: Scripted wake word and command type of audio recordings.

- Duration: Average duration of 5 to 20 seconds per audio recording.

- Formats: WAV format with mono channels, a bit depth of 16 bits. The dataset contains different data at 16kHz and 48kHz.

Dataset Diversity

Apart from participant diversity, the dataset is diverse in terms of different wake words, voice commands, and recording environments.

Different Automobile Related Wake Words: Hey Mercedes, Hey BMW, Hey Porsche, Hey Volvo, Hey Audi, Hi Genesis, Hey Mini, Hey Toyota, Ok Ford, Hey Hyundai, Ok Honda, Hello Kia, Hey Dodge.

Different Cars: Data collection was carried out in different types and models of cars.

Different Types of Voice Commands:

- Navigational Voice Commands

- Mobile Control Voice Commands

- Car Control Voice Commands

- Multimedia & Entertainment Commands

- General, Question Answer, Search Commands

Recording Time: Participants recorded the given prompts at various times to make the dataset more diverse.

- Morning

- Afternoon

- Evening

Recording Environment: Various recording environments were captured to acquire more realistic data and to make the dataset inclusive of various types of noises. Some of the environment variables are as follows:

- Noise Level: Silent, Low Noise, Moderate Noise, High Noise

- Parking Location: Indoor, Outdoor

- Car Windows: Open, Closed

- Car AC: On, Off

- Car Engine: On, Off

- Car Movement: Stationary, Moving

Metadata

The dataset provides comprehensive metadata for each audio recording and participant:

Participant Metadata: Unique identifier, age, gender, country, state, district, accent, and dialect.

Other Metadata: Recording transcript, recording environment, device details, sample rate, bit depth, file format, recording time.

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of French voice assistant speech recognition models.

License

This French In-car audio dataset is created by FutureBeeAI and is available for commercial use.

The production of dehydrated alfalfa for animal feed requires substantial energy-consuming processes, which raises questions about the environmental sustainability of this sector; less energy-intensive processes and a transition to renewable energy are needed. In this study, we quantified the environmental footprint of dehydrated alfalfa production in France and its evolution over time using data collected over two periods (2006 to 2009 and 2016 to 2019). This study was based on twelve production units spread throughout the main alfalfa production areas in France, which represent more than 50% of the national production of dehydrated alfalfa. A life cycle assessment was performed for the three steps of this production: production in the field, transportation from the field to the factory and the dehydration process. This dataset contains the life cycle inventory data of these two periods, for the field and factory parts, as well as the data used for preparing the figures presented in the related article.

This dataset includes installed capacity of power generation of Germany, France, Spain and Italy. There are also average hourly generation data per production type for each country as well. The data is retrieved from ENTSO-E (European Network of Transmission System Operators) Transparency Platform.

InstalledCapacity: It is the installed capacity of the corresponding country per production type for years between 2015-2022 Generation: It is the average hourly generation of the corresponding country for each year between 2015-2022 InstalledCapacity_Aggregated: It is the the aggregated data of installed capacity. Some of the categories are aggregated together. Generation_Aggregated: It is the aggregated data of average hourly generation data. some of the categories are aggregated together.

France FR: Access to Clean Fuels and Technologies for Cooking: % of Population data was reported at 100.000 % in 2016. This stayed constant from the previous number of 100.000 % for 2015. France FR: Access to Clean Fuels and Technologies for Cooking: % of Population data is updated yearly, averaging 100.000 % from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 100.000 % in 2016 and a record low of 100.000 % in 2016. France FR: Access to Clean Fuels and Technologies for Cooking: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s France – Table FR.World Bank: Energy Production and Consumption. Access to clean fuels and technologies for cooking is the proportion of total population primarily using clean cooking fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels.; ; World Bank, Sustainable Energy for All (SE4ALL) database from WHO Global Household Energy database.; Weighted average;

1. Dataset presentation.

This is the dataset used to produce the HTR models applied to documentary Latin and French manuscripts presented in the paper: Sergio Torres Aguilar, Vincent Jolivet. Handwritten Text Recognition for Documentary Medieval
Manuscripts. 2022. https://hal.science/hal-03892163

The dataset contains mostly charters and registers from the Late-medieval period (12th-15th). The training and evaluation, entailing 1855 pages, 120k lines of text and almost 1M tokens, were conducted using three freely available ground-truth corpora :

The Alcar-HOME database : https://zenodo.org/record/5600884

The e-NDP corpus : https://github.com/chartes/e-NDP_HTR

The Himanis project : https://zenodo.org/record/5535306

The final model operates in a multilingual environment (Latin and French) and it is able to recognize several Latin script families (mostly Textualis and Cursiva) in documents produced in ca. 12th - 15th centuries. During the evaluation the models shows an accuracy of 94.01% on the validation set and a CER (character error ratio) of about 0.12 to 0.17 on four external unseen datasets. A fine-tuning exercise using 10 ground-truth pages can raise these results to a CER between 0.06 to 0.10 respectively.

2. Dataset contents .

a) GT_list : List containing the GT file names which constitute the training, evaluation and test sets. The images and transcriptions can be downloaded from their original repositories.

b) Training : Contains the training and testing results (evaluation and prediction files) presented in the original paper for the two training phases: Regular (Textualis and Cursiva separated training) and Quartiles (mixed training by quartiles).

c) Useful_scripts : Scripts to produce the HTR metrics (CER, WER, SER) and plot the model's accuracy.

d) Best_model : Contains the best multilingual and multi-script model.

Steel Production in France increased to 1154.61 Thousand Tonnes in December from 1149.15 Thousand Tonnes in November of 2020. This dataset has Steel Production values for France.