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Replication data for : Autoencoder-Based Dimensionality Reduction of Turbulent Channel Flow Under Spanwise Wall Oscillations
No full textThe present dataset contains 2D slices of a turbulent channel flow under spanwise wall oscillations, obtained from simulations with varying actuation amplitudes. The dataset features velocity and temperature fields (u, v, w, and Θ) as well as corresponding latent variables obtained through various dimensionality reduction techniques.
The dimensionless numbers for this case are Reτ = 200 and Pr = 1. The dataset includes ground-truth data, reconstructed fields, and latent variables for multiple models: Convolutional AutoEncoder (CAE), β-Variational AutoEncoder (VAE), and extended Proper Orthogonal Decomposition (POD).
For a detailed description of the dataset structure, file naming conventions, and instructions on how to read and process the data, please refer to the accompanying README file. For a full description of the configuration and methodology, please check the corresponding paper: "Autoencoder-Based Dimensionality Reduction of Turbulent Channel Flow Under Spanwise Wall Oscillations" (not yet published)
04_PICRUSt_results
No full textThe PICRUSt2 program (tool for predicting the abundance of functional genes based on marker gene sequences — typically 16S/18S rRNA and ITS sequence) was used on the described dataset, and the complete output folder was compressed and deposited as it
Data related to the French grapevine breeding program INRAE-ResDur
No full textSequencing, and various data including phenotypic details obtained from the resources generated during the French grapevine breeding program INRAE-ResDu
V.faba.Multi_Cla_Field_LeafFragments
No full textRGB images of faba bean fragments collected in the field and bearing symptoms of plant diseases, annoted by classification at the whole image level (attribution to one causal species
Supplementary Material - NeighborFinder paper
No full textNeighborFinder is an R package that implements a local network inference method designed for microbiome data. It enables the targeted discovery of direct neighbors around a species of interest.
This repository contains the files and code needed to reproduce the figures presented in the article: "NeighborFinder: an R package inferring local microbial network around a species of interest" (Sola, M. et al., 2025).
It also contains the intermediate files needed to evaluate NeighborFinder's performance, as well as the corresponding R script for calculating them from semi-synthetic datasets. The files are organized according to the following tree structure:
│ compute_example_fig1.R
│ compute_execution_time_supplementary.R
│ compute_performance_supplementary.R
├───example
│ ├───network_application_fig1.jpg
│ └───res_all_8_datasets.rds
├───execution_time
│ ├───exec_time_figS3.jpg
│ └───time_high_prev_species_NF.rds
├───graphs
├───performances
│ ├───res_n100
│ │ └───filtering_top_100
│ ├───res_n1000
│ │ └───filtering_top_100
│ ├───res_n250
│ │ └───filtering_top_100
│ ├───res_n50
│ │ └───higher_prevalence
│ │ └───filtering_top_100
│ ├───res_n500
│ │ └───filtering_top_100
│ ├───scores
│ │ ├───n100
│ │ ├───n1000
│ │ ├───n250
│ │ ├───n50
│ │ │ └───higher_prevalence
│ │ └───n500
│ └───truth
│ └───higher_prevalence
└───performance_figures
├───heatmap_100-1000_figS1.jpg
└─── heatmap_50_figS2.jpg
This repository has been organized with an appropriate tree structure to facilitate the execution of the R scripts. These scripts use intermediate files to reproduce the figures. Since generating them takes quite a long time, we have chosen to store all these intermediate files in zip folders.
In practice, users can download the entire repository and unzip the folders so that the paths are correct. To generate figures efficiently, users are invited to execute the part of the R scripts that depends on the files already prepared.
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Long-term temporal dynamics of an overall annual intensity indicator of grapevine pests and diseases in three French vineyards
No full textHistorically from the beginning of the 20th century, the French agricultural warning service has published weekly reports and annual summaries of key pest and disease pressures. The summaries were based on a large number of plots, notably vineyards including not-treated ones, monitored in different regions, with different local editions for each region. They constitute a highly valuable corpus of literature on pests and diseases presence and overall damage in vineyards. We used this literature to develop a textual analysis and build an integrative grading system for annual pest occurrence and damage intensity over a long period (1941 to 2023) in the Bordeaux, Champagne and Vaucluse regions. To reconstruct the pests and diseases occurrence and intensity over time in the three regions, we then established a long-term database of annual grades. The various grapevine diseases include notably : downy and powdery mildews, black rot, rotbrenner and gray mold and for the phytophagous insects : european vine moth and vine moth. This tool can be very useful for characterizing the epidemiological status of various years or vintages, and analysing long-term trends versus more isolated events. This will allow us to better describe and understand past pests and pathogens temporal dynamics and link them to biotic and/or abiotic contexts. This will be helpful for anticipating the necessary progress in grapevine protection against quantitative and/or qualitative loss and adapting viticulture to global changes and regulatory or marketing evolutions
State of the art on the sustainability of lithium-ion batteries for electric mobility
No full textThis Excel file provides a compilation of articles from the state of the art on the sustainability of lithium-ion batteries. It outlines a range of criteria, including the life-cycle stages analyzed, the methodological approaches employed, and the indicators applied across the reviewed publications. This document encompasses studies on Life Cycle Assessment (LCA), Social Life Cycle Assessment (SLCA), Life Cycle Costing (LCC), and broader sustainability analyses. This work is open-source, and contributions aimed at enriching its content are welcome
Robust large area molecular junctions of self-assembled monolayers of a helical paddlewheel complex
No full textDataset associated with "Robust large area molecular junctions of self-assembled monolayers of a helical paddlewheel complex"
Dataset production context: The CISS effect has attracted significant attention in recent years, though discrepancies between theoretical predictions and experimental results highlight the need for new, adaptable systems to aid theoretical advancements. In this work, we present the preparation of a diruthenium chiral complex and its investigation in self-assembled monolayers and large area junctions. Our findings demonstrate that the SAMs of this racemic compound exhibit reliable electrical properties, laying the groundwork for future studies of the CISS effect using analogous enantiopure compounds.
For more information see the article. </strong
Indicateurs des séries annuelles issus des projections hydrologiques Explore2 pour le modèle MORDOR-SD sous RCP 4.5
No full textIndicateurs des séries annuelles issus des débits journaliers simulés par le modèle hydrologique MORDOR-SD pour l'ensemble des projections climatiques Explore2 sous RCP 4.5. Ces fichiers résultent de l'agrégation temporelle des simulations hydrologiques sous runs historiques (avant 2005) et des projections hydrologiques (post 2005), fichiers NetCDF disponibles au téléchargement dans la collection Explore2 - Projections hydrologiques.
Ce dépôt regroupe un tableau par indicateur et chaîne de simulation, c'est-à-dire, scénario d'émission RCP, couple GCM/RCM, correction de biais BC et modèle hydrologique HM. Ces données sont brutes et contiennent donc des chaînes de projections jugées aberrantes / horsains qu'il est possible de filtrer grâce à des métadonnées supplémentaires. Pour des raisons techniques, ces indicateurs sont regroupés par dossiers compressés selon les différentes phases du régime hydrologique.
La description des chaines de modélisation du climat et celle des modèles hydrologiques sont, respectivement, disponibles dans le rapport https://doi.org/10.57745/PUR7ML et dans les annexes du rapport https://doi.org/10.57745/S6PQXD. Retrouvez le diagnostic des modèles hydrologiques résumé à l'échelle des régions hydrologiques dans les fiches téléchargeables ici : https://doi.org/10.57745/DMFUXW.
Métadonnées supplémentaires : Récapitulatif de l'ensemble des indicateurs hydrologiques : https://doi.org/10.57745/JVNHQL Récapitulatif de l'ensemble des chaînes de simulation : https://doi.org/10.57745/R6HG5X Description de l'ensemble des points de simulation : https://doi.org/10.57745/UTKWR5 Liste des chaînes de modélisation jugées aberrantes / horsains : https://doi.org/10.57745/YZNENQ Récapitulatif des années pivots utilisées pour la TRACC : https://doi.org/10.57745/DCOQM6
Décomposition des chaînes de caractères formant le nom des fichiers parquet, séparées par des "_" : {1} Indicateur : Le nom de l’indicateur, du type de statistique calculée {2} Échantillonnage : Échantillonnage temporel sur laquelle est calculé l’indicateur → {1}_{2} Variable : Variable résultante d'un indicateur temporellement contextualisé {3} EXP : Identifiant de l’expérience historique (post 2005) ou future (post 2005) {4} GCM : Identifiant du GCM forçeur {5} RCM : Identifiant du RCM {6} BC : Identifiant de la méthode de correction de biais statistique {7} HM : Identifiant du modèle hydrologique
Les colonnes des fichiers parquet sont : EXP : Voir ci-dessus GCM : Voir ci-dessus RCM : Voir ci-dessus BC : Voir ci-dessus HM : Voir ci-dessus code : Code à 10 caractères du point de simulation fourni dans la description des points de simulation date : Date du début de la période annuelle d'agrégation (i.e. 2042-05-01 indique que l'année hydrologique commence en mai, plus d'information dans les métadonnées de variable) *Variable* : Voir ci-dessus
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