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Data visualization web application interface for the BENEFIT-MED project
No full textHere is the link for the web application developped fir the data analysis embedded to the BENEFIT-MED projec
Optimized datasets for syndrome-based neural decoders training
No full textSome of the datasets used to produce the results presented in the IEEE ICMLCN 2025 paper “Doing More With Less: Towards More Data-Efficient Syndrome-Based Neural Decoders“ (available here). Owing to storage limitations, only selected datasets for the (31,21,5) and (63,45,7) BCH codes are provided, in the form of MATLAB MAT files
Tree Crown Data - MONTANE OSUG
No full textThis repository presents data on tree and crown dimension collected on four plots along an elevation gradient in the French Alps. These data have been collected in the framework of the OSUG project MONTANE : Multi-trophic biodiversity and multi-functionality across Alpine Environments.
The objective of this project was to describe the forest three-dimensional structure and tree species diversity on several selected Orchamp gradients (see more details in [link](https://orchamp.osug.fr/)). In second step this field data were used to calibrate remote sensing methods to describe forest structure and composition at large scale at the individual level with lidar and hyperspectral data (see Tusa et al. 2020). See more information about the protocol at https://forgemia.inra.fr/georges.kunstler/montane_data
Untargeted metabolomics raw data
No full textSemi-polar compounds were extracted, including primary and secondary metabolites, using automated high-throughput ethanol extraction procedures at the MetaboHUB-Bordeaux Metabolome (https://metabolome.u-bordeaux.fr/) from 35 mg of fresh powder, following previously established protocols (Luna et al., 2020). All samples were randomised and injected alternately with extraction blanks (prepared without plant material and used to rule out potential contaminants detected by untargeted metabolomics), and 13 Quality control samples that were prepared by mixing 10 µL from each sample. Quality control samples were injected every 8 runs and used for the correction of signal drift during the analytical batch, and the calculation of coefficients of variation for each metabolomic feature so only the most robust ones are retained for chemometrics (Broadhurst et al., 2018).
Untargeted analysis was performed on a UHPLC Vanquish (Thermo Fisher Scientific) coupled to a Q-Exactive Plus mass spectrometer (Thermo Fisher Scientific). One µL of sample was injected on a Phenomenex Luna® Omega Polar C18 column (50 x 2.1 mm, 1.6 µm) at 40°C and a gradient of solvent A (milliQ water – 0.1 % formic acid) and solvent B (acetonitrile – 0.1% formic acid) with a flow of 0.5 mL min-1 was used. The gradient elution was set as follows: 0-11.5 min: 1-40% solvent B; 11.5-12.5 min: 40-95% solvent B; 12.5-14 min: 95% solvent B; 14.5-16 min: 1% solvent B.
The mass spectrometry data was acquired in negative polarity at 140.000 FWHM resolution with an automatic gain target at 3e6 and maximum IT of 100 ms. The source conditions were as follow: Spray voltage: 3000 V; Sheath gas: 45 a.u; Auxiliary gas: 15 a.u; Capillary temperature: 320°C; Probe heater temperature: 250°C; S-lens RF level: 100. The experiments were in full scan (mass range: 70-1050 m/z) – data depending MS2 with top three precursors and normalized collision energies of 15, 30, 45 using a dynamic exclusion of 5 s
A benchmark for elasto-plasticity in finite strain
No full textOverview
Finite strain elasto-plastic simulations are critical in fields such as materials science (metal forming, forging, additive manufacturing) and automotive engineering (crash simulations). These simulations are traditionally carried out using computationally intensive finite element analysis (FEA), which limits their use in optimization tasks (e.g., optimal control, design processes) and real-time applications (e.g., tele-operation, personnel training). In this work, we introduce a benchmark dedicated to highly non-linear elasto-plastic simulations, designed to evaluate and develop neural network models tailored for solving elasto-plastic problems under finite strain conditions, ultimately unlocking the potential for real-time optimization and interactive simulations. The datasets include simulations of 1D and 3D elements, featuring quasi-static sequences of applied loads on complex geometries, and the resulting computed quantities: displacements fields, plastic flow coefficient field, stresses. To specifically evaluate the impact of plasticity on different neural networks, the datasets also feature simulations with identical inputs but employing a purely elastic constitutive law.
Unzip the data
To unzip the split zip archive run :
zip -F data.zip --out single-archive.zip
and :
unzip single-archive.zip
Data format
The data is stored in the `hd5` format and follows the structure:
data/
paperclip/
├─ meshes/
│ ├─ mesh3d.json //initial 3D mesh
├─ data.hd5 //simulation results
├─ test.hd5 //simulation results (test set)
├─ params.json //parameters used in data generation
car_hood/
├─ meshes/
│ ├─ base.stl //fine mesh, used for simulations.
│ ├─ coarse.stl //coarse mesh, on which the results are recorded.
│ ├─ bloq0.stl //meshes containing the blocked displacement zones.
│ ├─ bloq1.stl
│ ├─ bloq2.stl
│ ├─ fine_to_coarse.json //conversion table from fine to coarse mesh.
├─ data.hd5 //simulation results
├─ test.hd5 //simulation results (test set)
├─ params.json //parameters used in data generation
The HD5 files are organized according to the model:
data.hd5/
├─ data_0/
│ ├─ loads/ //loading
│ ├─ elastic/ //elastic results
│ ├─ plastic/ //elastoplastic results
│ ...
Scripts provided
Some useful scripts are provided in the code.zip archive.
Visualize and use the data
A visualization script is available. It can be run as follows:
python view_data.py path/to/dataset/ --datum 167
Torch dataloaders are provided in paperclip/loader.py and twizy/loader.py
Generate the figures of the paper
The figures of the paper can be easily reproduced by running the scripts paperclip/stats.py and twizy/stats.py.
Generate new data
To generate new data, cast3m is required. The installation can be done here.
Fill in the path to the cast3m executable in the file tools/castem.json.
Run one of the data generation scripts. Many options are available, detailed in the scripts.
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Table de correspondance COICOP - Nova - produits alimentaires selon le degré de transformation
No full textTable de correspondance entre les items de la nomenclature COICOP 1998 (Classification of Individual Consumption by Purpose) telle qu’utilisée dans les enquêtes Budget de famille de 2011 et 2017, à son niveau le plus détaillé (6 positions) et la nomenclature NOVA (Monteiro et al 2018 https://doi.org/10.1017/s1368980017000234) qui catégorise les produits selon leur degré de transformation, d’après leur procédé de production et leur composition
Governance and Financing for Building Forest Resilience
No full textAt a societal level, better governed and financed forests can provide a continuous flow of ecosystem services to urban, peri-urban, and rural communities. This report (Milestone 13) focuses on cross-cutting, innovative governance models for changemaking and forest resilience. Aside from governance innovations the task also looks at new business and financing models and instruments that can help build forest resilience and support ecosystem-based adaptation in both European countries and China. This report presents the results of a review and analysis of promising examples
of innovative forest governance and financing from Europe, China, and elsewhere
Carbon and nitrogen mineralization data of various exogenous organic matter (EOM) under controlled conditions
No full textDataset of carbon and nitrogen mineralization of various exogenous organic matter (EOM) under controlled conditions (laboratory incubation). EOMs are various residual organic matters applied to soil as organic fertilizers or organic amendments. They include animal manures and urban and industrial organic wastes and can be used raw or after treatment (e.g. composting or anaerobic digestion). Carbon and nitrogen mineralization data can be used to estimate the contribution of EOM carbon (C) to soil organic carbon (SOC) and EOM nitrogen (N) to the mineral N supply available to crops
Indicateurs des changements par horizons temporels issus des projections hydrologiques Explore2 pour le modèle SMASH sous RCP 4.5 (référence 1976-2005)
No full textIndicateurs des changements par horizons temporels issus des débits journaliers simulés par le modèle hydrologique SMASH 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, 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