Name: Norsk Regnesentral (Norwegian Computing Center)
Type: Organization
Bio: Norwegian Computing Center is a private foundation performing research in statistical modeling, machine learning and information/communication technology
Location: Oslo, Norway
Blog: https://www.nr.no/
Norsk Regnesentral (Norwegian Computing Center)'s Projects
Estimation of snow-parameters from sentinel-3 data using deep learning
Channel parameter estimation for ResQml models (related to nrresqml)
Various tools and techniques for evaluating climate models
Demo project for showing code review and project management
eXplego is a decision tree toolkit that provides developers with interactive guidance to help select an appropriate XAI-method for their particular use case.
Python framework for graph-based dialogue management
Updated biological data, pup production estimates and priors used in modelling the Harp and Hooded seal populations in the West Ice.
Functions to calculate the integrated qudratic distance (IQD)
The Color Contrast Checker for WCAG 2.1 to measure contrast of 2+ colors to comply with the WCAG 2.1 guidelines for improved usability and accessibility. This work has been supported by the UnIKT program of the Norwegian Directorate for Children, Youth and Family Affairs (Bufdir).
Python package to generate counterfactuals using Monte Carlo sampling of realistic counterfactual explanations
data interface to met Norway
Neural models for detecting and masking personal information from texts
Recipe for using a virtual machine.
Python package for converting Delft3D models (netcdf) to ResQml
Tools used by Alex Lenkoski at the Norwegian Computing Center to develop R packages
R package for analyzing survey aerial photos and estimating the pup production of harp and hooded seals.
R Connector for the Meteomatics Weather API
r-package for Statistical Assessment of Marine Mammals
R package for a prediction model for significant wave height (SWH) based on sea level pressure (SLP)
Stochastic Assessment Model for Harp Seals
Explaining the output of machine learning models with more accurately estimated Shapley values
skchange provides sktime-compatible change detection and changepoint-based anomaly detection algorithms