GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Cell Phone Application for Oil Spill Detection
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: We would like to develop a model that can be used to interpret cell phone images to predict oil in environmental samples. The tool can be rapidly deployed for use in the field by the oil spill responder community.
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
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: Wave runup and total water level observations from time series imagery at several sites with varying
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: Tool is used for separation (segmentation) of land and water in images. The resulting mask is used to calculate water levels. Tool will be used to compare to forecasted water levels and may be displayed on USGS webpages
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
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: Development of a Ploidy Distinction Application: A Machine Learning Approach for Discriminating Trip
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: A machine learning approach is being researched as a way for fisheries personnel in the field to determine the ploidy of wild-caught invasive carp. The use of machine learning in biomedical imaging accumulates substantial volumes of data via digital images, applies computational tools for processing these data, and can combine images from various microscopes by enhancing and equalizing image resolution. Thus, a computational model was trained with selected brightfield microscopic images of whole blood smears made from known diploid and triploid Grass Carp. Digital images were generated by using two types of microscopes: (1) a laboratory-dedicated, high caliber scope, and (2) a portable microscope meant for use in the field.
12_outputs: see description
16_dev_stage: Initiated
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
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: Storm Induced Erosion Response Network
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: Provide the next generation of Total Water Level and Coastal Change Forecast
12_outputs: see description
16_dev_stage: Initiated
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
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: Coastal Ecosystem Prediction System
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 USGS is partnering with NOAA National Centers for Coastal Ocean Science to co-develop an integrated, national-scale framework for projecting wetland vulnerability to sea level rise. Multi-model ensemble predictions will show a range of possible future conditions and inform the protection of coastal communities, economies and their natural resources.
12_outputs: see description
16_dev_stage: Initiated
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
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: National Wildlife Disease Database
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 National Wildlife Health Center (NWHC) has contracted the Pacific Northwest National Laboratory (PNNL) to build a national wildlife disease database (NWDD).  Funded through the American Rescue Plan Act of 2021 (ARPA), the NWDD will bring together various wildlife health data streams across informational domains (i.e., laboratory results, environmental observations, news media, etc.) to provide situational awareness and advanced analytics to natural resource authorities around the country.  As part of the NWDD, PNNL plans to integrate their Canvas software.  Canvas is a machine learning, AI, and data science tool that can visualize and contextualize information from one or more sources.
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
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: Sediment Transport in Coastal Environments (funded by San Francisco Bay-Delta PES)
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: Machine learning based time-series imputation of oceanographic time-series
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
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: Machine learning based shoreline time-series imputation, classification and forecasting (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: Supports basic data generation and QC/QA procedures, for large scale and short-term forecasting of shoreline trends
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
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: National Oceanographic Partnership Program (NOPP)
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: Machine Learning based coastal sediments assessment and prediction
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
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: RSCC and TCA projects.
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: Machine Learning (ML) methods for identifying, assessing, and quantifying coastal features and habitats and coastal change hazards.
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
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: Machinine Learning based shoreline detection and sea ice dynamics using coastal cameras
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: Supports basic data generation and QC/QA procedures, for large scale and short-term forecasting of shoreline trends and sea-ice dynamics in coastal environments
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
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: Using Machine Learning in USGS StreamStats to make suspended sediment and bedload predictions
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: A tool for resource managers who need estimates of suspended sediment and bedload in Minnesota rivers without sampling data.
12_outputs: Outputs were not documented and DOI will update the use cases once additional data is collected from submitter
16_dev_stage: Operation and Maintenance
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
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: Merbok Supplemental
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: Machine Learning based shoreline detection and mapping, automated data suitability analyses from satellite imagery
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
27_public_info: Yes
28_iqa_compliance: USGS Fundamental Science Practicies Policies
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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.