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Consumer perception of "artificial meat" in the educated young and urban population of Africa.
No full textSurveys were designed to explore the responses of African consumers of the next generations (i.e., mainly urban, more educated and younger consumers) to relevant questions in order to investigate their attitudes, outlook, potential acceptance, and willingness to engage with “artificial meat,” and to provide insight into the factors that may lead to the acceptance of “artificial meat” in the general context of Africa
Food product descriptions including OQALI thesaurus categorization
No full textThis dataset comprises three files:
File 1 contains 60,400 food products described in French with 4 textual variables: name of product, denomination, precision (flavor...), method of conservation. There are the 4 textual variables selected by the OQALI experts to preserve categorisation during evolutions of OQALI thesaurus. The set of 60,400 food products is also categorized into the sectors and families of the OQALI thesaurus.
File 2 contains the OQALI thesaurus (32 sectors divided into 643 families). By example, in OQALI, “Dairy products and fresh desserts” is one of the largest sectors, which contains several families such as “Classic yogurts and sweetened fermented milks”, “Classic sweet fresh cheeses”.
File 3 explains how brands have been anomized in File 1
De novo assembly of the transcriptome from allis shad (Alosa alosa) larvae
No full textVersion: 0.1 (2025-12-11)
This README file have been created on 2025-12-11 by Davide Degli Esposti
Last up-date : [2025-12-11].
# GENERAL INFORMATION
## Title : De novo assembly of the transcriptome from allis shad (Alosa alosa) larvae
The de novo assembly transcriptome and derived ORF of the Alosa alosa are provided in this database. Sequencing raw-data have been submitted, as requested, to NCBI and are freely available at
https://www.ncbi.nlm.nih.gov/bioproject/PRJNA809144
and
https://www.ncbi.nlm.nih.gov/sra?LinkName=biosample_sra&from_uid=26186915
## DOI:
## Adresse de contact : [email protected]
Streamflow monitoring on the Yzeron watershed (since 1997)
No full textContinuous monitoring of stream water level and streamflow at 4 gauging stations on the Yzeron watershed, since 1997, as part of the Field Observatory for Urban Water Management (OTHU). Watershed areas are 3 to 22 km2
French hospital dataset 2013-2021
No full textThis Dataset provides agregated data on 1111 hospitals in France over the years 2013-2021. It uses the SNDS/PMSI-MCO, SAE and ENC as data sources.
It can be used to replicate: "Does Virtual Integration Improve Care Coordination?
Assessing the Impact of the 2016 Public Hospitals Reform in France
Data and codes from: Comparison of Solar Imaging Feature Extraction Methods in the Context of Space Weather Prediction with Deep Learning-Based Models
No full textThis dataset contains replication data for the paper "Comparison of Solar Imaging Feature Extraction Methods in the Context of Space Weather Prediction with Deep Learning-Based Models".
It includes files stored into HDF5 (Hierarchical Data Format) file using HDFStore. One file contains the extracted features using the 6 different techniques for the wavelength 19.3 nm named solar_extracted_features_v01_2010-2020.h5 and the second the SERENADE outputs named serenade_predictions_v01.h5.
Both files contain several datasets labeled with ‘keys’. The latter correspond to the extraction method. Here is a list of the key names:
gn_1024: corresponding to the GoogLenet extractor with 1024 components.
pca_1024: corresponding to the Principle Component Analysis technique leaving 1024 components.
ae_1024: corresponding to the AutoEncoder with a latent space of 1024.
gn_256 (only in solar_extracted_features_v01_2010-2020.h5): corresponding to the GoogLenet extractor with 256 components.
pca_256: corresponding to the Principle Component Analysis technique leaving 256 components.
ae_256: corresponding to the AutoEncoder technique with a latent space of 256.
vae_256 (only in solar_extracted_features_v01_2010-2020.h5): corresponding to the Variational AutoEncoder technique with a latent space of 256.
vae_256_old (only in serenade_predictions_v01.h5): the output predictions of SERENADE using the VAE extracted features using the hyperparameters optimized for GoogLeNet.
vae_256_new (only in serenade_predictions_v01.h5): the output predictions of SERENADE using the VAE extracted features with the alternative architecture.
All the above-mentioned models are explained and detailed in the paper.
In order to read the files, the user can do it with the Pandas package for Python as follows:
import pandas as pd
df = pd.read_hdf('file_name.h5', key = 'model_name')
and replace file_name by either solar_extracted_features_v01_2010-2020.h5 or serenade_predictions_v01.h5 and model_name by one of the models in the list above.
The extracted features dataset will output a pandas DataFrame indexed by datetime and either 1024 or 256 columns of features. An additional column indicates to which subset (train, validation and test) the corresponding row belongs. The SERENADE outputs dataset will output a DataFrame indexed by datetime and 4 columns:
Observations: the first column contains the true daily maximum of the Kp index.
Predictions: the second column contains the predicted mean of the daily maximum of the Kp index.
Standard Deviation: the third column contains the standard deviation as the predictions are probabilistic.
Model: this column specifies from which feature extractor model the inputs were used to generate the predictions.
We add the feature extractors AE and VAE class codes as well as their weights in the AEs_class.py and VAE_class.py codes and best_AE_1024.ckpt, best_AE_256.ckpt and best_VAE.ckpt checkpoints respectively.
The figures in the manuscript can be reproduced using the codes named after the corresponding figure. The files 6_mins_predictions and seed_variation contain the SERENADE predictions to reproduce figures 7, 8, 9 and 10
Hydrological reconstruction (1960-2021) of the Lepsämänjoki DRN (Finland)
No full textHydrological model outputs at daily time step for the reconstruction period (1960-2021) in the Lepsämänjoki DRN (Finland).
Simulated discharge, baseflow an state of flow (flowing/dry) is spatially distributed at the reach scale.
Other hydroclimatic variables (temperature, precipitation, rainfall, snowfall, potential evapotranspiration, actual evapotranspiration, vegetation interception, snow water equivalent, saturation of the soil layer, saturation of the grounwater layer) are spatially aggregated at the catchment scale
CLIMAE bibliometric dataset 2010-2021
No full text4230 WOS scientific articles and reviews matching the CLIMAE bibliometric query (see https://hal.science/hal-04762738) , identified with the following fields : DOI, Authors, Title, date, journal, authors affiliation, authors keywords, abstract…
Articles dealing both with climate adaptation and climate mitigation in the fields of forest, agriculture, food, and water management, agro-ecology, food and global health, biodiversity, bioeconomy, climate change and risks, societies and territories.
These areas correspond to the research fields of the French National Research Institute for Agriculture, Food and the Environment (INRAE)
PLAN DE GESTION DE DONNÉES DE L'UNITÉ EXPÉRIMENTALE FOURRAGES RUMINANTS ENVIRONNEMENT DE LUSIGNAN
No full textPlan de gestion de données de structure de l'unité expérimental Fourrages Ruminants Environnement de Lusignan (FERLUS