Recherche Data Gouv
Not a member yet
4753 research outputs found
Sort by
Insights into the genomic and phenotypic diversity of Monosporozyma unispora strains isolated from anthropic environments (supporting data)
No full textSupporting data from the study of the genomic and phenotypic diversity of a collection of 53 Monosporozyma unispora strains, mainly from cheese, kefir and sourdough
D7.6 Report on Dissemination and Networking Activities on Innovation Actions
No full texthis deliverable reports on the dissemination and networking activities carried out to promote Innovation Actions (IAs) within the project. It describes the development and communication of Innovation Cards and webinars to enhance IA visibility, as well as the tools developed to maximise the outreach of the IAs, including the organisation of key events. It also provides an overview of the dissemination efforts conducted by other project beneficiaries. Together, these activities strengthened knowledge exchange, stakeholder engagement, and the uptake of the project’s Innovation Actions
integration DeepLabv3+ applied to RGB images and vegetation indices for nitrogen status in cereal-legume inter-cropping system
No full textThis RGB image dataset was acquired with 3 smartphones (Samsung Galaxy A12, Xiaomi Redmi Note 4, Xiaomi Redmi Note 11) on 3 dates (2024/03/21 ; 2024_04_08 and 2024_04_22) at the INRAE experimental site in Bretenière 21110, France. The plots are 9m x 1.5m.
The data concerns an intercropping of Triticale (variety Ramdam) mixed with Fava bean (variety Irena), sown either in rows or as a mixture. Segmentation done with DeepLabv3+ (see publication: https://doi.org/10.1163/9789004725232_060
Indicateurs des changements par horizons temporels issus des projections hydrologiques Explore2 pour le modèle MORDOR-SD sous RCP 8.5 (référence 1976-2005)
No full textIndicateurs des changements par horizons temporels issus des débits journaliers simulés par le modèle hydrologique MORDOR-SD 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, horizon temporel 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} HX : Horizon futur (H[123]) → {1}_{2}_{3} Changement : Changement d'une variable pour un horizon temporel par rapport à une période de référence, défini dans le récapitulatif des indicateurs hydrologiques {4} EXP : Identifiant de l’expérience historique (post 2005) ou future (post 2005) {5} GCM : Identifiant du GCM forçeur {6} RCM : Identifiant du RCM {7} BC : Identifiant de la méthode de correction de biais statistique {8} HM : Identifiant du modèle hydrologique {9} Référence : Période de référence (ref-YYYYMMDD-YYYYMMDD) {10} Futur : Période futur (fut-YYYYMMDD-YYYYMMDD)
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 *Changement* : Voir ci-dessus
Retrouvez des scripts d'aide pour utiliser ces données parquet
A new biofunctionalized and micropatterned PDMS is able to promote stretching induced human myotube maturation.
No full textThe data supporting the findings of this study are presented. All biological data have been consolidated into a single Excel file. The raw spectrometry data from the analyses are accessible in the manufacturer's format, along with the numerical data in an Excel file. Atomic force microscopy (AFM) data have been extracted and are available in an Excel format.
Funding came in the form of a PhD scholarship from the CNRS GDR AAP 2020. This work was partly supported by the French RENATECH network and its FEMTO-ST technological facility for Alpha 110 XPS analyses. Peptide purifications were performed using the facilities of SynBio3 IBISA platform supported by IBMM and ITMO cancer
Voltammograms obtained during on-site monitoring of Co and Ni
No full textThis dataset is the result of the development of an analytical flow system for on-site monitoring of trace metals in a river. The system was deployed on site over a one-month period. Here is an example of the data obtained in 24 hours
UnderPressure - A motion capture dataset synchronised with foot pressure insoles data for foot contact detection, ground reaction force estimation and footskate cleanup
No full textWe propose a novel database of human motion sequences captured together with pressure insoles data. For further details please refer to the research article describing the capture process
FaultDeform : Fast and Accurate Sub-pixel Displacement Estimation From Optical Satellite Images based on Deep Learning
No full textContains the FaultDeform train, validation and test sub-datasets (10,000 samples). The three sub-datasets contain samples, built with pairs of large 1024x1024 windows that contain realistic synthetic faults, and their displacement maps associated
SANI-TTERR Project: Map of a spatially explicit characterisation factor (CF) for Listeria monocytogenes impact assessment
No full textThe dataset is a spatially explicit characterisation factor (CF) for Listeria monocytogenes impact assessment. It results from the research project SANI-TTERR, funded by ADEME (APR GRAINE grant number 2307D0040).
An innovative approach was proposed to include pathogenic impact in Life Cycle Assessment (LCA) through a new impact category, “microbial pathogenic potential”. To demonstrate the feasibility of its calculation, a “proof of concept” was developed for the construction of the characterisation factors (CF) for this new category. The study focuses on a model bacterial pathogen – Listeria monocytogenes – emitted on agricultural soils during application of organic fertilisers in metropolitan France.
CF is expressed as the product of three factors: the fate factor (FF), the exposure factor (XF), and the effect factor (EF). FF was spatially explained in the native resolution of the agricultural plot and based on a fuzzy logic approach. Two distinct models were developed to introduce the impact of soil conditions (FFB) and the physical characteristics of agricultural plots (FFP) on the persistence and survival of the pathogen after landspreading. Data from the INCA3 survey on the French dietary habits were collected to calculate XF. Only one category of food potentially contaminated was considered: cheese. XF is therefore the share of cheese in the daily diet of French people. EF is calculated based on the probability of developing diseases for vulnerable populations.
The CF equation is: CF [L.monocytogenes] = (FFP × FFB ) × (BE × DFI) × (PDL × DALY)
Where CF [L.monocytogenes] is the characterisation factor, FFP the fate related to physical properties, FFB the fate related to biogeochemical properties, BE the basic exposure, DFI the daily food intake, PDL the probability of developing invasive listeriosis and DALY as disability-adjusted life years.
The CF is calculated at the scale of agricultural parcels provided by the RPG (RPG Explorer 2015-2021) and aggregated at a regular grid of 10×10km. The dataset is published in OGC GeoPackage format and in Lambert 93 (EPSG:2154).
Dataset fields:
- id (Integer): grid id
- wa_ffp (float): physical characteristics of agricultural plots (weighted average of agricultural plot values)
- wa_ffb (float): soil conditions (weighted average of agricultural plot values)
- wa_ff (float): Fate factor (weighted average of agricultural plot values)
- xf (float): exposure factor (XF), and the effect facto
- ef (float): effect factor
- cf (float): characterisation facto
River metabolism productivity data in the DRYvER project
No full textDaily estimates of gross primary production (GPP, g O₂ m⁻² d⁻¹) and ecosystem respiration (ER, g O₂ m⁻² d⁻¹) calculated from inverse Bayesian modelling. Estimates obtained from flowing reaches distributed across the six European DRNs and three campaigns. Environmental variables included: water temperature, discharge and photosynthetic photon flux density (µmol m-2 s-1)