GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Snowcast Showdown 
3_agency: Department of the Interior
3_abr: DOI
4_bureau: BOR
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Reclamation partnered with Bonneville Power Administration, NASA - Goddard Space Flight Center, U.S. Army Corps of Engineers, USDA - Natural Resources Conservation Service, U.S. Geological Survey, National Center for Atmospheric Research, DrivenData, HeroX, Ensemble, and NASA Tournament Lab to run the Snowcast Showdown Prize Competition. In this competition, particiapnts were asked to develop mehtods to estimate distributed snow information by blending observations from different sources  using machine learning methods that provide flexible and efficient algorithms for data-driven models and real-time prediction/estimation. Winning methods are now being evaluated and folded into a follow-on project with NOAA's River Forecast Centers. 
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 4/14/2023
19_date_acq_dev_began: 12/7/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: Data not reported by submitter and will be updated once additional information is collected
24_hisp_support: No
28_iqa_compliance: Actitivity as proof of concept and did not yield influential scientific information, so IQA not applicable.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Non-sensitive, mission data describing resource conditions relevant to the condition assessment/prediction question being addressed via AI-ML.
34_data_docs: Data not reported by submitter and will be updated once additional information is collected
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: PyForecast 
3_agency: Department of the Interior
3_abr: DOI
4_bureau: BOR
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Pyforecast is a statistical/ML water supply forecasting software developed by Reclamation that uses a range of data-driven methods.  
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 1/1/2020
22_dev_method: Developed in-house.
24_hisp_support: No
28_iqa_compliance: Actitivity yielded forecasting tool that was peer reviewed to the rigor required for influential scientific information, per IQA.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Non-sensitive, mission data describing resource conditions relevant to the condition assessment/prediction question being addressed via AI-ML.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Improved Processing and Analysis of Test and Operating Data from Rotating Machines
3_agency: Department of the Interior
3_abr: DOI
4_bureau: BOR
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: This project is exploring a better method to analyze DC ramp test data from rotating machines. Previous DC ramp test analysis requires engineering expertise to recognize characteristic curves from DC ramp test plots. DC ramp tests produce a plot of voltage vs current for a ramping voltage applied to a rotating machine. By using machine learning/AI tools, such as linear regression, the ramp test plots can be analyzed by computer software, rather than manual engineering analysis, to recognize characteristic curves. The anticipated result will be faster and more reliable analysis of field-performed DC ramp testing.
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
22_dev_method: Developed in-house.
24_hisp_support: No
28_iqa_compliance: Actitivity as proof of concept and did not yield influential scientific information, so IQA not applicable.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Non-sensitive, mission data describing resource conditions relevant to the condition assessment/prediction question being addressed via AI-ML.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Improving UAS-derived photogrammetric data and analysis accuracy and confidence for high-resolution data sets using artificial intelligence and machine learning
3_agency: Department of the Interior
3_abr: DOI
4_bureau: BOR
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: UAS derived photogrammetric products contain a large amount of potential information that can be less accurate than required for analysis and time consuming to analyze manually. By formulating a standard reference protocol and applying machine learning/artificial intelligence, this information will be unlocked to provide detailed analysis of Reclamation's assets for better informed decision making.
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
22_dev_method: Developed in-house.
24_hisp_support: No
28_iqa_compliance: Actitivity as proof of concept and did not yield influential scientific information, so IQA not applicable.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Non-sensitive, mission data describing resource conditions relevant to the condition assessment/prediction question being addressed via AI-ML.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Photogrammetric Data Set Crack Mapping Technology Search 
3_agency: Department of the Interior
3_abr: DOI
4_bureau: BOR
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: This project is exploring a specific application of photogrammetric products to process analysis of crack mapping on Reclamation facilites.  This analysis is time consuming and has typically required rope access or other means to photograph and locate areas that can now be reached with drones or other devices.  By formulating a standard reference protocol and applying machine learning/AI, this information will be used to provide detailed analysis of Reclamation assets for better decision making. 
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
22_dev_method: Developed in-house.
24_hisp_support: No
28_iqa_compliance: Actitivity as proof of concept and did not yield influential scientific information, so IQA not applicable.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Involves use of high resolution imagery of large infrastructure (CUI). 
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Effects of vehicle traffic on space use and road crossings of caribou in the Arctic
3_agency: Department of the Interior
3_abr: DOI
4_bureau: USGS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Assessing the effects of industrial development on wildlife is a key objective of managers and conservation practitioners. However, wildlife responses are often only investigated with respect to the footprint of infrastructure, even though human activity can strongly mediate development impacts. In Arctic Alaska, there is substantial interest in expanding energy development, raising concerns about the potential effects on barren-ground caribou (Rangifer tarandus granti). While caribou generally avoid industrial infrastructure, little is known about the role of human activity in moderating their responses, and whether managing activity levels could minimize development effects. To address this uncertainty, we examined the influence of traffic volume on caribou summer space use and road crossings in the Central Arctic Herd within the Kuparuk and Milne Point oil fields on the North Slope of Alaska.
12_outputs: Prediction of liquefaction potential at input site, as it compares to existing case history dataset. 
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
20_date_implemented: 10/3/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Non-sensitive, mission data describing resource conditions relevant to the condition assessment/prediction question being addressed via AI-ML.
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Science Support System (SSS)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
51_extension_request: No – Agency did not request an extension for this use case.
59_question_type: No
62_disparity_mitigation: None of the above
63_question_type: No – it is not operationally practical to offer this. (Please explain.)
66_no_appeal_reason: No – This AI use case is not subject to the opt-out requirement because the AI functionality is solely used for the prevention, detection, and investigation of fraud or cybersecurity incidents, or the conduct of a criminal investigation.
2_use_case_name: Automated Walrus Haulout Monitoring
3_agency: Department of the Interior
3_abr: DOI
4_bureau: USGS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this software is to provide a framework for using pre-trained image classification convolutional neural network CNN models to (1) make predictions on unlabled image datasets to provide data for further analysis of walrus (Odobenus rosmarus) coastal haulout occupation and (2) make predictions on image datasets where keywords have been manually (i.e. by human reviewers) added to the image ITPC metadata indicating the content of the images, including the presence and absence of walruses so that the model's performance can be evaluated by comparing its predictions to the human-assigned class labels using confusion matrices and standard classifier evaluation metrics such as accuracy, precision, recall, F1-score (a composite of precision and recall).
12_outputs: screening-level prediction of deformation of embankments, dams, or other facilities due to seismic loading. 
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
20_date_implemented: 2/29/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Non-sensitive, mission data describing resource conditions relevant to the condition assessment/prediction question being addressed via AI-ML.
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Science Support System (SSS)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Forecasting Earthquake Ground Motion Time Series
3_agency: Department of the Interior
3_abr: DOI
4_bureau: USGS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Development of a deep learning models to generate earthquake ground motion time series for potential application to Earthquake Early Warning, Operational Aftershock Forecasting, and the National Seismic Hazard Model.
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Non-sensitive, mission data describing resource conditions relevant to the condition assessment/prediction question being addressed via AI-ML.
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Science Support System (SSS)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: CONUS EcoFlows Planning & Prototype
3_agency: Department of the Interior
3_abr: DOI
4_bureau: USGS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: To support development of national-scale ecological-flow response models, a benchmark estimate of typical, long-term unaltered flow conditions is needed as a reference benchmark to evaluate flow departures from "normal". This model uses unaltered, near-reference USGS gages from across the US, and associated natural landscape datasets, to predict long-term average monthly flows. These models, trained in near-reference sites, are then used to predict the hypothetical near-reference long-term flow conditions of sites with anthropogenic influences, where we have paired biological datasets. By comparing observed flows to these 'natural' expectations, we calculate flow departures from a 'natural normal' and which is used in later ecological modeling.
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
20_date_implemented: 10/3/2022
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Non-sensitive, mission data describing resource conditions relevant to the condition assessment/prediction question being addressed via AI-ML.
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Science Support System (SSS)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Prioritized Constituents: Sediment
3_agency: Department of the Interior
3_abr: DOI
4_bureau: USGS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Regional prediction of suspended sediment concentration in unmonitored rivers to characterize sediment transport in the Delaware, Illinois, and Colorado River Basins.
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
20_date_implemented: 10/2/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Non-sensitive, mission data describing resource conditions relevant to the condition assessment/prediction question being addressed via AI-ML.
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Science Support System (SSS)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
51_extension_request: No – Agency did not request an extension for this use case.