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Frequently viewed datasets
View Data Catalog
Uniform Fuels QUIC-Fire Simulation Runs Ensemble
Organization:
BurnPro3D
This dataset was generated particularly for the Physics Guided Machine Learning (PGML) research and educational tasks. It is an ensemble of prescribed fire simulations generated by the QUIC-Fire coupled fire-atmospheric modeling tool. Each simulation run is represented by a zarr file, containing the outputs created by QUIC-Fire through the BurnPro3D web interface. To model a burn, users upload the polygon for their burn unit and BurnPro3D uses a 3D fuels model for that location created by FastFuels and an ignition file with a user-defined ignition pattern created in DripTorch. Those files are included here as well. In addition, users define the environmental conditions they would like to model in terms of fuel moisture, wind direction and wind speed. The relevant information about the versions used of BurnPro3D, QUIF-Fire, FastFuels and DripTorch are included in the metadata, along with both presets defined by BP3D and user-defined model inputs. ### Common Parameters Across All Simulation Runs: sim_time: 600 fuel: {'xlen': 600, 'ylen': 600, 'density': 0.7, 'height': 1} output: {'steps_fire': 1, 'steps_wind': 1, 'energy_atmos': True, 'fire_energy': True, 'fuels_moist': True} topo: {'total_startup_iters': 0} ### Varying Parameters: wind_speed: 7 unique values wind_direction: 11 unique values surface_moisture: 3 unique values ignition: 5 unique values ### Total: 1155 simulation runs. ### Ignition Files: Ignite_Aerial.dat Ignite_LongFireline_Inwards.dat Ignite_LongFireline_Outwards.dat Ignite_Strip_Northwards.dat Ignite_Strip_Southwards.dat ### Metadata JSON File: uniform-pgml-success.bp3d.json ### List of Simulation Runs: uniform-pgml-success_list_simulation_runs.csv
Scenario St. Mary Large Ensemble
Organization:
BurnPro3D
This dataset is an ensemble of prescribed fire simulations generated by the QUIC-Fire coupled fire-atmospheric modeling tool. Each simulation run is represented by a zarr file, containing the outputs created by QUIC-Fire through the BurnPro3D web interface. To model a burn, users upload the polygon for their burn unit and BurnPro3D uses a 3D fuels model for that location created by FastFuels and an ignition file with a user-defined ignition pattern created in DripTorch. Those files are included here as well. In addition, users define the environmental conditions they would like to model in terms of fuel moisture, wind direction and wind speed. The relevant information about the versions used of BurnPro3D, QUIF-Fire, FastFuels and DripTorch are included in the metadata, along with both presets defined by BP3D and user-defined model inputs. ### Varying Parameters: wind_speed: 3 unique values wind_direction: 3 unique values surface_moisture: 4 unique values canopy_moisture: 2 unique values ### Total: 72 simulation runs. ### List of Simulation Runs: St._Mary_large_ensemble.csv
EarthScope Stations Dataset
Organization:
EarthScope Consortium
The EarthScope Consortium (https://www.earthscope.org) streams three-dimensional Global Navigation Satellite System (GNSS) high rate (1hz) position time series from nearly a thousand EarthScope and related GNSS stations. These high precision ground-motion time series are used to study a range of geophysical phenomena including earthquakes, volcanos, tsunamis, hydrologic loads, and glaciers. EarthScope is dedicated to supporting transformative global geophysical research and education through operation of the National Science Foundation’s ( NSF) Geodetic GAGE and Seismic SAGE facilities. As part of the National Data Platform (NDP) EarthScope pilot project, the EarthScope GNSS position time series streams are being stored and made available from Data Collaboratory Kafka servers at the University of Utah. This Jupyter Notebook provides tools for access and plotting of sample real time streams and is the foundation for additional services being developed that will facilitate time series analysis including machine learning. Users of EarthScope data agree to follow the EarthScope streaming data policy (https://www.unavco.org/data/policies_forms/data-policy/data-policy-realtime-streaming-gps/data-policy-realtime-streaming-gps.html)
Frequently used OKNs
View OKN CatalogWIFIRE-commons-ontology
The fire science ontology was developed with input from domain experts in fire science with a focus on concepts important for prescribed fires. Datasets in wifire commons are semantically tagged with terms from the ontology which aid in discoverability. As the BurnPro3d platform evolves and new datasets are added to wifire commons, the ontology will need to be augmented to accommodate the new semantic understanding. The ontology can also be visualized under “browse keywords” on the wifire commons front page.
YAGO
YAGO (Yet Another Great Ontology) is an open source[3] knowledge base developed at the Max Planck Institute for Informatics in Saarbrücken. It is automatically extracted from Wikipedia and other sources. As of 2019, YAGO3 has knowledge of more than 10 million entities and contains more than 120 million facts about these entities.[4] The information in YAGO is extracted from Wikipedia (e.g., categories, redirects, infoboxes), WordNet (e.g., synsets, hyponymy), and GeoNames.[5] The accuracy of YAGO was manually evaluated to be above 95% on a sample of facts.[6] To integrate it to the linked data cloud, YAGO has been linked to the DBpedia ontology[7] and to the SUMO ontology.[8] YAGO3 is provided in Turtle and tsv formats. Dumps of the whole database are available, as well as thematic and specialized dumps. It can also be queried through various online browsers and through a SPARQL endpoint hosted by OpenLink Software. The source code of YAGO3 is available on GitHub.
ENVO
A description of the Environment Ontology (ENVO) is published in the Journal of Biomedical Semantics in an article by Buttigieg et al. and a paper describing its development until mid-2016 is available here Our latest releases are described here More information and guides for using ENVO in annotation exercises are available at www.environmentontology.org Please note: ENVO is not an "authority" in itself, but we do try to provide a semantic/ontological expression of existing authoritative classifications alongside project-based or individual knowledge. We aim to create a FAIR compliant space where expressions of this knowledge can co-exist and interoperate.
Host projects and challenges through Educational Gateway
- Launching of class projects and data challenges through National cyberinfrastructure (CI) capabilities
- Access to open learning resources for Data Science, CI, and AI training
- Sample Jupyter Notebooks and Workflows to develop and participate in educational activities
Contact - ndp@sdsc.edu
The National Data Platform was funded by NSF 2333609 under CI, CISE Research Resources programs. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funders.