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FraudZen Dataset: Realistic Ground Truth CDRs of Bypass Fraud Techniques in Mobile Networks
No full textThis dataset contains synthetic call‑detail records (CDRs) generated with the open‑source FraudZen simulator. FraudZen models legitimate mobile subscribers and SIMBox fraudsters under multiple threat strategies (mobility, traffic, social, and their combinations, in both naïve and advanced variants). The traces cover different fraud–prevalence levels (3 % and 12 % of the country's incoming international calls) and varying numbers of fraudulent SIM cards (from 5 to 200) per simulation run, offering a rich test‑bed for benchmarking anomaly‑ and fraud‑detection algorithms in cellular networks
Data associated to the paper "Analyzing resilience of European beech tree to recurrent extreme drought events through ring growth, wood anatomy and stable isotopes "
No full textThis dataset is provided as supplementary material of the following article: Zhang G, Breda N, Steil N, Gaertner PA, Levillain J, Ruelle J, Massonnet C. (2025) Analyzing resilience of European beech tree to recurrent extreme drought events through ring growth, wood anatomy and stable isotopes. Journal of Ecology.
The study investigates the physiological processes linked to water and carbon constraints involved in the resilience of beech cambial growth to the recent drought event in Central Europe. The dataset includes a dendrometric description of the 56 trees and annual soil water deficit data of 4 stands in the NorthEastern France where the study was conducted. We also provide tree ring data from 2013 to 2022 period (ring width, anatomical parameters of vessels, stable isotope 13C and 18O and water use efficiency claculation). Long term data of tree ring width and basal area increment for the 56 trees are also provided
Supplementary table 1. Milk fatty acid composition of cows submitted to different milking frequencies in interaction with their susceptibility to lipolysis
No full textSupplementary table : Milk fatty acid composition of cows submitted to different milking frequencies in interaction with their susceptibility to lipolysis related to the article "Milking frequency and dairy cow susceptibility to lipolysis interact to alter milk lipolysis and composition
Accompanying Dataset and Python Code for Reproducibility and Implementation of the IR-TDIBC Method
No full textSupporting Dataset and Python Code for TDIBC Method
This repository provides the dataset and Python codes necessary to regenerate the figures presented in the manuscript "Revisiting Nonlinear Impedance in Acoustic Liners" (https://hal.science/hal-04810729v1) and to facilitate the proper implementation of the Impulse-Response Time-Domain Impedance Boundary Condition (IR-TDIBC) method. The materials aim to promote transparency, reproducibility, and accessibility for researchers working with nonlinear impedance models and acoustic liners in time domain.
Contents
Dataset: Includes data used in the manuscript, covering experimental measurements obtained in an impedance tube and flow noise obtained in the B2A bench at ONERA.
Python Scripts: Scripts designed to:
Recreate the figures from the paper
Demonstrate the IR-TDIBC implementation step-by-step
Features
Scripts for generating plots and verifying results from the manuscript.
Clear examples to help users adapt the IR-TDIBC method to their specific setups.
Annotations and explanations within the code for ease of understanding and modification.
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Measured and estimated tree dimensions of maritime pine (Pinus pinaster) and birch (Betula pendula) in the tree diversity experiment ORPHEE at the end of 2014
No full textThe ORPHEE experiment is located 40km south of Bordeaux (44°440 N, 00° 460 W) and belongs to the worldwide Tree Diversity Network (TreeDivNet). The experimental plantation was established in January 2008 on a clear cut of former maritime pine stands on a sandy podzol. Eight blocks were established, with 32 plots in every block. Each plot contained 10 rows of 10 trees planted 2 m apart, resulting in 100 trees within a plot of 400 m². The total initial stand density was therefore 2500 trees per hectare in each plot. Each plot was planted with one to five species. Only maritime pine (Pinus pinaster/Ppin) and silver birch (Betula pendula/Bpen) were considered in this data set.
For this study, we have selected 14 plots per blocks. The present data set is composed by data collected in 2014 (7 years-old) on the target trees at the center of the plots in order to avoid edge effects (measured planting locations = 36).
The total height was measured on living trees using a graduate pole. We also measured circumferences at 1.30m height on 7 randomly chosen pines and 7 randomly chosen birches per plot. We estimated missing tree heights and circumferences and tree volumes by:
- Using height-circumference relationships to estimate the circumferences of trees that have not been measured in 2014 by mean of separate generalized additive models for each stand composition and tree density.
- Using the generic model developed by Deleuze et al. (2014) to aboveground tree volumes. We assigned a minimum volume of 0.01m3/ha to the few trees below 1.30m in height (corresponding to the minimum volume found in the data set).
- Eventually we estimated dimensions of dead trees by averaging diameter, height and volume of living trees in the plot
Physico-chemical parameters of river water in Scorff and its tributaries (Morbihan county - France)
No full textThe data consist of physico-chemical parameters of river raw water in Scorff and its tributaries, collected since 1999. The parameters are water temperature (°C), hydrogen potential (pH), conductivity, turbidity, nitrates (NO3-), ammonium (NH4+) and ortophosphates (PO4 ---). This data is collected as part of the French Environmental Research Observatory (ERO) on Diadromous Fish in Coastal rivers (DiaPFC). ORE DiaPFC is a research infrastructure dedicated to the study of diadromous fish populations under the influence of anthropic pressure (e.g. agriculture) and environmental changes (e.g. climate). The Scorff River is a small coastal river in southern Brittany (France). The main river is 78.6km long, including a 15km estuary. The mean gradient is 3.6%, annual mean discharge is 5m3/s and the drainage basin area has an area of 480km
Sylvaccess
No full textSylvaccess est un logiciel permettant de cartographier automatiquement l’accessibilité des forêts en fonction de différents modes d’exploitation : tracteur forestier, porteur forestier et débardage par câble.
Le modèle s’appuie sur des sources d’information spatiale (a minima : modèle numérique de terrain (MNT), couche de desserte forestière, contours des forêts) et des paramètres techniques propres à chaque système de débardage.
Il offre aussi la possibilité d’intégrer des obstacles physiques ou environnementaux dans l’analyse ainsi que des données sur les volumes de bois sur pied. Les résultats du modèle sont utilisables pour de nombreuses applications forestières allant de l’aménagement et de la planification des opérations d’exploitation jusqu’à la comparaison et la sélection de projets de desserte.Sylvaccess is a model allowing automatic mapping of forest accessibility for the following forest operation systems : skidder, forwarder and cable yarding.
The model is based on spatial information (Compulsory: digital terrain model (DTM), shapefile of forest road network, shapefile of forest contours) and specific parameters of each logging technique.
It can also integrate physical or environmental obstacles in the analysis as well as wood volume information. The outputs of the model can be used for many applications ranging from forest management and planning of logging operations to the comparison and selection of new forest roads projects.</p
Vitis Trait Ontology
No full textTheses files contain the desciptors of variables used to describe phenotypic data recorded on grapevine plants, grape musts or wines. Variables are structured according to the crop ontology recommandations (https://cropontology.org).
Text files can be used to build a SQL database
Indicateurs des séries annuelles issus des projections hydrologiques Explore2 pour le modèle ORCHIDEE sous RCP 8.5
No full textIndicateurs des séries annuelles issus des débits journaliers simulés par le modèle hydrologique ORCHIDEE pour l'ensemble des projections climatiques Explore2 sous RCP 8.5. 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
Retrouvez des scripts d'aide pour utiliser ces données parquet
Phenotypic data for 206 peach accessions and 150 apricot accessions evaluated for respectively 5 and 2 disease incidence traits in several environment-trials in France (2020-2023) and their respective genotyping information
No full textThe dataset contains raw phenotypic and genotypic dataset as well as the link to the GitLab Repository with the R scripts associated to the publication "Multi-environment GWAS uncovers markers associated to biotic stress response and genotype-by-environment interactions in stone fruit trees", Marie Serrie, Vincent Segura, Alain Blanc, Laurent Brun, Naïma Dlalah, Frédéric Gilles, Laure Heurtevin, Mathilde Le-Pans, Véronique Signoret, Sabrina Viret, Jean-Marc Audergon, Bénédicte Quilot, Morgane Roth.
DETAILED METADATA:
- Raw phenotypic data associated to the 206 accessions constituting the peach core collection, grown in 3 environment-trials (France, 2021 to 2023). It contains assessments of damages caused 1 pest (Leafhopper) and 3 diseases (Leaf curl, Rust, Powdery mildew and Shot hole) : Peach_CC_Pheno_data.csv
- Raw phenotypic data associated to the 150 accessions constituting the apricot core collection, grown in 2 environment-trials (France, 2020 to 2023). It contains assessments of damages caused by 2 diseases (Rust and blossom blight) : Apricot_CC_Pheno_data.csv
- Raw genotypic data of the 192 genotyped accessions among the peach core collection, characterized for 15,692 SNP markers obtained with the higher density IRSC 16K SNP array, after filtering to retain SNPs with call rate per marker > 90% and with a missingness per individual < 50% : Peach_CC_Geno_data.Rdata
- Raw genotypic data of the 149 genotyped accessions among the apricot core collection, characterized for 588,790 SNP markers obtained with the Illumina HiSeq 2000 NGS technique, after filtering to retain only biallelic SNPs, with more than 10 reads deep/SNP, with a missingness per individual 95% : Apricot_CC_Geno_data.Rdata
- Genetic map data for peach obtained with the higher density IRSC 16K SNP array,
with the 15,692 SNP markers names, their corresponding chromosome and position : Peach_CC_Map.txt
- Genetic map data for apricot obtained with the Illumina HiSeq 2000 NGS technique,
with the 588,790 SNP markers names, their corresponding chromosome and position : Apricot_CC_Map.txt
- Vcf file of genotypic dataset for the 192 genotyped accessions among the peach core collection, characterized with the higher density IRSC 16K SNP array. The genotyping dataset was filtered to retain SNPs with call rate per marker > 90%, and missingness per individual < 50% which resulted in a final set 15,691 markers : Peach_CC.vcf.gz
- Vcf file of genotypic dataset of the 149 genotyped accessions among the apricot core collection obtained with the Illumina HiSeq 2000 NGS technique, with an alignment performed on the third version of the ‘Marouch’ genome. This dataset was first filtered to retain only SNPs with a missingness per individual <50% and only biallelic SNPs with more than 10 reads deep/SNP have been conserved. This dataset consisted in 584,790 markers : Apricot_CC.vcf.gz
- Links to the GitLab repository with the R scripts used for data analysis : Scripts_availability.txt
ABSTRACT:
While breeding for improved immunity is essential to achieve sustainable fruit production, it also requires to account for genotype-by-environment interactions (G × E), which still represent a major challenge. To tackle this issue, we conducted a comprehensive study to identify genetic markers with main and environment-specific effects on pest and disease response in peach (Prunus persica) and apricot (Prunus armeniaca). Leveraging multienvironment trials (MET), we assessed the genetic architecture of resistance and tolerance to seven major pests and diseases through visual scoring of symptoms in naturally infected core collections, repeated within and between years and sites. We applied a series of genome-wide association models (GWAS) to both maximum of symptom severity and kinetic disease progression. These analyses lead to the identification of environment-shared quantitative trait loci (QTLs), environment-specific QTLs, and interactive QTLs with antagonist or differential effects across environments. We mapped 60 high-confidence QTLs encompassing a total of 87 candidate genes involved in both basal and host-specific responses, mostly consisting of the Leucine-Rich Repeat Containing Receptors (LRR-CRs) gene family. The most promising disease resistance candidate genes were found for peach leaf curl on LG4 and for apricot and peach rust on LG2 and LG4. These findings underscore the critical role of G × E in shaping the phenotypic response to biotic pressure, especially for blossom blight. Last, models including dominance effects revealed 123 specific QTLs, emphasizing the significance of non-additive genetic effects, therefore warranting further investigation. These insights will support the development of marker-assisted selection to improve the immunity of Prunus varieties in diverse environmental conditions