MINT Project Releases

Last updated: 2020-06-30

June, 2020 #

May, 2020 #

April, 2020 #

  • Release of DAME 4.1.3
    • Changelog
    • Additional testing and bug fixes (Testing in OSX and Unix). DAME will ask for missing parameters and inputs, using defaults when provided.
    • Improvements to messages and logging in the UI. Now the singularity commands, inputs and Docker images are displayed, in case users want to execute models with their own means.
    • Improved documentation and examples
  • Initial release of MIC 0.2.0 - ALPHA
  • Release of Data Transformation service v1.0
    • Users can run the transformation pipeline through CLI, web service or Docker.
    • Release predefined pipelines in form of configuration files for:
      • Model-specific transformations: Topoflow
  • Release of MINT-Data-Sync system
    • Monitor when new GLDAS data files become available, upload them to MINT Data Server, and register them in MINT Data Catalog
  • Release of River Segment Surface Area Dataset version 1.0 for Ethiopia
    • Processed 8710 river segments (covering all of Ethiopia) using machine learning algorithms and satellite imagery to create surface area timeseries.
    • Uses Sentinel-2 imagery available from December 2015 until March 2020.
    • A csv with surface area timeseries for each river segment is available for download from the MINT Data Catalog.
  • Release of riverwidthEO version 1.0
    • A python package that processes river segments using machine learning algorithms and satellite imagery (Sentinel-2) to create surface area timeseries (delivered as a csv file).
    • Uses descarteslabs API to download data for any given segment.
    • Provides user with options to provide points on a river as input or just provide a region or country to select predefined points on the river. These predefined points are available for rivers (>100 meters in width) across the globe.
  • Release of a Jupyter Notebook for the TopoFlow model with an overview and introduction to new users

March, 2020 #

February, 2020 #

January, 2020 #

December, 2019 #

MINT Data Catalog #

MINT Model Catalog: #

Ensemble Manager API #

  • Release 1.0.0:
    • Execute models using singularity
    • Include parallelism option in config to do multiple model runs
    • Adding DELETE request to executions
    • Local delete for cache of execution

Ingestion API: #

  • Release 1.1.0:
    • Take model execution data and ingest into a database to enable interactive dashboards
    • Gather model ensemble execution results

November, 2019 #

MINT NetCDF Convention #

  • Release of MINT NetCDF convention
    • We propose a self-describing data format for structured gridded datasets for MINT data catalog and visualization based on the NetCDF and the CF convention. Check here for the lastest document. Please open new issues on GitHub or on Google doc for comments.
  • Release of MINT-GeoViz
    • We are releasing the v1 of MINT-GeoViz, an interactive visualization library for large geospatial datasets that follow MINT NetCDF convention. Check out this GitHub repo for more details.
    • Check out our full demo and notebook examples on how to use the library

October, 2019 #

Data Catalog #

  • Release of D-REPR: a library for reading heterogeneous datasets into common representations. Check its GitHub for more information.

May, 2019 #

Model Catalog #

  • New content: The MINT model catalog has been expanded to include a semantic representation of units, scientific variables and links to Wikidata. Check the release on GitHub for more information.
  • New APIs for adding content: We have expanded our APIs with a new client based on Open API that allows anyone to insert models in the model catalog. The API is accessible in the following link: https://api.models.mint.isi.edu/v0.0.2/ui/#/
  • New APIs for accessing content: 3 new methods have been added to our GRLC API. The first one, to obtain additional information of a Scientific Variable given its label (getStandardVariableMetadata). The other two (getInputCompatibleConfig) and (getOutputCompatibleConfig) retrieve those models or software compatible with a target model. This facilitates understanding which software has variables that may interoperate with other software.
  • A new client for accessing content: With our new Python client, now it is possible to access the content in the model catalog without having to issue SPARQL queries or API queries. Check it out here.
  • New examples: Check out our notebook for examples on how to use the client.
  • The Model Explorer has been updated to show the contents of models in a more user-friendly manner. Check here the latest version. Please open new issues on GitHub if you find bugs.