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Recent Submissions 

  1. Traffic Simulations for Boston, Lisbon, Los Angeles, Rio de Janeiro, San Francisco 

    Ambühl, Lukas; Menéndez, Mónica; González, Marta C. (2022)
    Files to run traffic simulation of five large-scale cities in the open-source simulator SUMO. Includes: - car network as net.xml - car traffic demand as 24h trip-based demand - configuration file to run the simulations on SUMO
    Model
  2. Vision Transformer model trained on immune cells 

    Hanimann, Gian Jacob; Pfaendler, Ramon; Snijder, Berend (2022)
    Model
  3. Scripts for the article "Drivers of intermodel uncertainty in land carbon sink projections" 

    Padrón Flasher, Ryan; Gudmundsson, Lukas; Liu, Laibao; et al. (2022)
    Scripts for data processing and plotting within the analysis of the drivers of intermodel uncertainty in land carbon sink projections.
    Model
  4. Transcrustal 3-D electrical conductivity model of the Central Main Ethiopian Rift 

    Dambly, Marie Luise Texas; Samrock, Friedemann; Grayver, Alexander; et al. (2022)
    Model
  5. rescoss_logp_ml saved models 

    Isert, Clemens (2022)
    Model
  6. Traffic Model of Mahé, Seychelles 

    Schaniel, Joel; Makridis, Michail; Krütli, Pius; et al. (2022)
    Model
  7. ACSICON ADN model data 

    Fuchs, Alexander; Larsson, Mats; Demiray, Turhan (2022)
    The dataset contains the aggregated distribution network (ADN) models developed in the research project "ACSICON" and published in the paper [1]. The zip files contains data for different penetration levels for grid-forming converters (as defined in [1]), ranging from 5% to 100%. The csv-files, e.g., ABCD_5.csv, contain a 6x6 matrix describing the LTI model in block form [A,B; C, D]. The LTI model has 4 dynamic states (A has dimension ...
    Model
  8. Personalised pose estimation from single-plane moving fluoroscope images using deep convolutional neural networks 

    Vogl, Florian; Schütz, Pascal; Postolka, Barbara; et al. (2022)
    Codes and Model for the publication "Personalised pose estimation from single-plane moving fluoroscope images using deep convolutional neural networks"
    Model
  9. 3D heat transfer model 

    Leith, Kerry; Li, Ying (2022)
    In order to evaluate temperature distribution over elements involved in uniaxial compression assembly and subjected to heating via an increase of air temperature within the climate chamber, we set up a 3D heat transfer model in COMSOL. A cylindrical aluminium specimen was constrained by steel loading plates, of which the top and bottom surfaces are set to room temperature as they are in contact with the air outside the climate chamber, ...
    Model
  10. Bio-economic model on benefits of increasing information accuracy in variable rate technologies 

    Späti, Karin; Huber, Robert; Finger, Robert (2021)
    This bio-economic modeling framework assesses the benefits of different sensor approaches to measuring environmental heterogeneity in the field, ranging from the use of satellite imagery to drones and portable N sensors, in variable rate fertilization. The model is described in detail in the following article: Späti, K., Huber, R., & Finger, R. (2021). Benefits of Increasing Information Accuracy in Variable Rate Technologies. Ecological ...
    Model
  11. Supplementary Material of "Impacts of a Revised Surface Roughness Parameterization in the Community Land Model 5.1" 

    Meier, Ronny; Duveiller, Grégory; Davin, Edouard Léopold; et al. (2021)
    The roughness of the land surface (z0) is a key property for the amount of turbulent activity above the land surface and through that for the turbulent exchange of energy, water, omentum, and chemical species between the land and the atmosphere. Variations in z0 are substantial across different types of land cover from typically less than 1 mm over fresh snow or sand deserts up to more than 1 m over urban areas or forests. In this study, ...
    Model
  12. ROMS+NPZD model data (pt.2): additional data 

    Lovecchio, Elisa (2021)
    These data are derived from the same model run and are intended as complementary to the data stored on: https://www.research-collection.ethz.ch/handle/20.500.11850/278536 (doi: https://doi.org/10.3929/ethz-b-000278536) Files contain surface velocities, fluxes at the edge of the euphotic layer, nutrient content and NCP of the euphotic layer (E.L. = 100 m depth).
    Model
  13. BASEMENT v3, a modular freeware for river process modelling: test cases collection 

    Vanzo, Davide; Bürgler, Matthias; Conde, Daniel; et al. (2021)
    Model

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