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What is the National Data Platform?

The National Data Platform, or NDP, is a federated and extensible data ecosystem to promote collaboration, innovation, and equitable use of data on top of existing cyberinfrastructure capabilities.

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Explore AI services and data collections

Search through a rich collection of open data and AI services spanning diverse applications.

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Dive straight into the data within your browser and run analyses on scalable computing platforms.

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Join activities hosted by professors and organizations and practice your AI skills using real-world data.

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Frequently viewed datasets

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Scenario St. Mary Large Ensemble



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

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EarthScope Stations Dataset


EarthScope Consortium

### EarthScope Stations Dataset Notes

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Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS) format file containing lidar point cloud data. Compression to an LAZ file was done with the LAStools 'laszip' program and can be unzipped with the same free program (laszip.org). LICENSE: US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map

Frequently used OKNs

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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 (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.


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
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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.