Last updated: 2020-06-30
June, 2020 #
- Release of B-Clean API v0.1 to support automatic data cleaning
- Release of Semantic Modeling API v0.1 to support automatic data modeling
- Release of DAME 5.2.0
- Added changes so users can specify their own parameter values instead of defaults.
- Release of MIC 1.0.0
- Now users can start from their model folder (no requirements for data organization)
- Automatic detection the framework/language used by the model component (python, conda, java, general)
- Automated extraction of dependencies according to the framework/language (pip is supported)
- Creation a Linux environment using Docker
- MIC can now trace commands and automatically detect configuration files, inputs and outputs
- Release of riverwidthEO version 1.2
- updated the classification model by adding ~3,000 labeled and 90,000 unlabeled images.
- Release of River Segment Surface Area Dataset version 1.2 for Ethiopia
- updated the results for all of Ethopia using the updated model.
- Release of two new Jupyter Notebooks for the TopoFlow model, and updates to several existing notebooks:
May, 2020 #
- Release of DSI v1.1.0
- Configuration of the model using a JSON file
- Add support to ECMWF
- Add support to run globally
- Improve compatibility with xarray (performance improvement)
- Release of DAME 5.1.1
- Run models using local data
- Configure with custom ModelCatalog or user
- Support Docker on Windows, Linux and macOS (Beta)
- Validation of URLs
- List and show model configurations and setups
- Integration of data transformations for TopoFlow Data (Weather)
- New release of MIC 0.4.1 - ALPHA
- End-to-end encapsulation of model components.
- Provides templates for creating components.
- Enables testing through Docker images (locally)
- Create a snapshot of a model component, saving code in GitHub and Docker image in DockerHub.
- After testing, pushes changes to model catalog.
- Release of Data Transformation service v1.1
- Support data streaming
- Add new pipeline/adapters to support GLDAS2Cycles transformation
- Add new pipeline/adapters to support variable aggregation in GLDAS data by both time and woreda
- Design a procedure to quickly import external transformation libraries (e.g. Topoflow transformation notebooks)
- Release of riverwidthEO version 1.1
- updated the methodology to detect cloudy pixels.
- updated the methodology to use clustering and classification together to handle hazy images (that are missed by cloud filters)
- updated the classification model by adding more training images.
- Release of River Segment Surface Area Dataset version 1.1 for Ethiopia
- updated the results for 5 basins in Ethopia using the updated model.
- Release of three new Jupyter Notebooks for the TopoFlow model, and three updates to existing notebooks:
- Release of notebook and Python tools for performing scientific variable exploration and grounding to Scientific Variables Ontology (SVO) variables and entries in the World Modelers Indicators (WMI) list:
April, 2020 #
- Release of DAME 4.1.3
- 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
- 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 #
- Release of MINT-UI 4.3.4
- Users can run their ModelConfigurations
- Release of DAME 3.3.0
- Execute models from MINT on Desktop/Server
- Release of Model Catalog API 1.4.0
- Users can insert their ModelConfigurations
February, 2020 #
- Release of MINT-UI 4.3.0 (Feb 26)
- Release of MINT-UI 4.2.1) (Feb 19)
- Release of MINT-UI 4.2.0) (Feb 14)
- Release of MINT-UI 4.1.0(https://mint.isi.edu/)
- Fixing bugs and usability improvements Release Release 4.1.0 · mintproject/mint-ui-lit
January, 2020 #
- Release of MINT-UI 4.0.0
- Bug fixes and usability improvements Release Release 4.0.0-0 · mintproject/mint-ui-lit
December, 2019 #
MINT Data Catalog #
- Releases of MINT Data Catalog UI
- MINT Data Catalog Github repository. Specifically,
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
- Release of MINT-GeoViz
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.