GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Articulate Training Develpment AI Add On
3_agency: Department of the Interior
3_abr: DOI
4_bureau: OS - Office of the Secretary of the Interior
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The Office of Emergency Management's Interior Operations Center uses Articulate to create online training courses for OEM staff. Articulate has introduced an AI add on that will expedite course creation and editing. The AI is a realistic text to speech solution but in the future will also assist with course content creation.
12_outputs: create online training courses for OEM staff.
16_dev_stage: Initiated
17_impact_type: Neither
24_hisp_support: No
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Machine Learning Applied to Geotechnical Engineering: Statistical Methods Applied to Seismic Analysis 1
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: Purpose is to create machine learning models that predict soil liquefaction during a seismic event and compare to the geotechnical case history database to gain confidence in the machine learning estimates. The expected benefits are the existence of another tool for geotechnical staff to predict the probability of liquefaction and reducing the amount of more complex approaches to be conducted.
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
18_date_initiated: 10/1/2018
19_date_acq_dev_began: 6/1/2021
20_date_implemented: 1/1/2023
22_dev_method: Developed in-house.
24_hisp_support: No
28_iqa_compliance: Activity 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: Machine Learning Applied to Geotechnical Engineering: Statistical Methods Applied to Seismic Analysis 1
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: Purpose is to create machine learning models to predict crest movement of an embankment dam during a seismic event. The expected benefits are the creation of a screening-level tool to be used prior to large investments in field exploration, lab testing, and large scale modeling efforts.
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
18_date_initiated: 12/19/2016
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: Machine Learning Working Group
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: Description: Perform networking and reach out to Reclamation employees to gauge staff experience and interest in creating machine learning models.  Also document potential applications of machine learning to various problems encountered by staff at Reclamation in their daily work.   
How it helps: This gives us a better idea of the in-house capabilities of staff at Reclamation and of the potential uses that machine learning can play to help solve problems faced by Reclamation.     
Stage of development: Ongoing discussion and information gathering with staff is occurring.  
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
18_date_initiated: 8/1/2020
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: Machine Learning for Chemical Savings at Reverse Osmosis Plants
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: Chemical use for cleaning membranes in reverse osmosis (RO) water treatment facilities can  contribute significant costs to the treatment process. Reclamation’s Desalination and Water  Purification Research Program (DWPR) funded GHD to apply machine learning techniques to optimize Clean In Place chemical usage at RO plants. The techniques analyze water chemistry data to inform application of chemicals and result in less chemical per unit of water produced without sacrificing operational uptime or membrane life. The project was implemented at full scale operational facilities of the Water Replenishment District located in Southern California. The project demonstrated that machine learning can be a useful tool to optimize cleaning frequency of RO ystems.
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
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: Machine Learning Refines Quagga Habitat Suitability
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: Extensive research has been conducted to prevent the spread of quagga mussels, a costly and damaging invasive species. Notably, several detected introductions failed to establish populations in hypothetically suitable waterbodies by pH and calcium levels. To better parameterize quagga habitat suitability, we collected ecological data at 20 stations across four invaded and two uninvaded, connected waterbodies in Arizona during 2021-2023. Data were analyzed by gradient boosted machine, an ensemble machine learning algorithm that aggregates iteratively optimized decision trees. Results identified water conditions and plankton taxa that further differentiated quagga-invaded from uninvaded stations, within a system of pH- and calcium-suitable waterbodies.
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
20_date_implemented: 1/1/2024
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: Generative AI to Improve Visitor Experience on NPS.gov
3_agency: Department of the Interior
3_abr: DOI
4_bureau: NPS
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: This is a proof of concept to us generative AI to extract data from NPS.gov and the NPS API to bring forth relevant content to visitors based on topics of interest allowing for improved trip planning.  This proof of concept enriches structured data without requiring parks to create new content and reduced the time and labor cost of re-creating content.  
16_dev_stage: Initiated
17_impact_type: Neither
28_iqa_compliance: Actitivity as proof of concept and did not yield influential scientific information, so IQA not applicable.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Autonomous Drone Inspections
3_agency: Department of the Interior
3_abr: DOI
4_bureau: BSEE
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: BSEE has entered into an Interagency Agreement with the Massachusetts Institute of Technology (MIT) Lincoln Laboratory (LL) to research autonomous unmanned aerial systems (UAS) and sensor technologies, with the goal of extending BSEE’s inspection capabilities to previously inaccessible environments on the Outer Continental Shelf (OSC). This research aims to build upon and leverage the work conducted by NASA's Advanced Supercomputer Division on corrosion models developed for the Level 1 Survey Report's Corrosion Level Classifications.
12_outputs: Multiple outputs for inspections, including decisions on corrosion levels, if a platform is boardable, does it have methane leaks.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 5/13/2022
19_date_acq_dev_began: 10/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: IAA E24PG00019
24_hisp_support: No
27_public_info: No
28_iqa_compliance: IQA not applicable. Project is for research and proof of concept, not in production.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Other
31_question_type: Most of the data needed is cataloged but some data points are not.
33_agency_data: The model development stage has not yet begun. Currently in information gathering stage.
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Project is being developed outside BSEE's IT infastructure.
45_compute_request: Other
45_question_type: Project is being developed outside BSEE's IT infastructure.
47_timely_resources: Other
47_question_type: Project is being developed outside BSEE's IT infastructure.
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: Well Activity Report Classification
3_agency: Department of the Interior
3_abr: DOI
4_bureau: BSEE
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: Researching the use of Masked Language Models and Convolutional deep neural networks to identify classification systems for significant well event using data from Well Activity Reports. 
12_outputs: Decision on what type of siginificant event a well activity report should report.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 8/1/2022
19_date_acq_dev_began: 8/1/2022
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: IQA not applicable. Project is for research and proof of concept, not in production.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Other
31_question_type: Most of the data needed is cataloged but some data points are not.
33_agency_data: Data in oracle tables and unstructured attachment data.
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
40_has_ato: No
42_dev_tools_wait: 6-12 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: Getting access to most tools has been timely but budget constraints make it hard to acquire resources for larger complex AI models that need more computational power.
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: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: Level 1 Survey Report Corrosion Level Classification
3_agency: Department of the Interior
3_abr: DOI
4_bureau: BSEE
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: Offshore operators conduct Level 1 surveys annually to report on platform structural integrity, as mandated by 30 CFR 250.901(a)(7), and submit these surveys to BSEE. Each survey includes a corrosion assessment of the platform with accompanying photos. Each area is assigned a coating grade and are key indicators of a platforms overall structural health. Currently, BSEE manually reviews each report to determine if a platform requires further audits, a process that is both time and labor intensive. To expedite this, BSEE has been collaborating with NASA's Advanced Supercomputer Division to develop a machine learning model designed to flag potential mislabeling of corrosion within the survey reports. The current version of the model has shown promising accuracy in identifying sections with potential misratings, especially in areas with the most severe corrosion. BSEE and NASA will continue to focus on further improving the model's accuracy while also exploring ways to integrate it into BSEE's IT infrastructure and operational workflows for potential future implementation.
12_outputs: Corrosion Level Decision
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2018
19_date_acq_dev_began: 3/24/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: IAA E21PG00001
24_hisp_support: No
27_public_info: No
28_iqa_compliance: IQA not applicable. Project is for research and proof of concept, not in production.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Other
31_question_type: Most of the data needed is cataloged but some data points are not.
33_agency_data: Unstructured Level 1 Surveys
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Project is being developed outside BSEE's IT infastructure.
45_compute_request: Other
45_question_type: Project is being developed outside BSEE's IT infastructure.
47_timely_resources: Other
47_question_type: Project is being developed outside BSEE's IT infastructure.
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
2_use_case_name: Well Risk Assessment
3_agency: Department of the Interior
3_abr: DOI
4_bureau: BSEE
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: NASA's Advanced Supercomputer Division will utilize the work performed in the sustained casing pressure research to explore the development of machine learning models to identify various precursors of risk factors for wells. By identifying these risk factors it would help inform BSEE engineers of potential problems with the well during its various stages of development. Currently, BSEE and NASA are focusing on identifying the precursors to sustained casing pressure and evaluating their contribution to the overall risk of a well.
12_outputs: Well risk score
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 3/21/2024
19_date_acq_dev_began: 3/21/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: IAA E21PG00001
24_hisp_support: No
27_public_info: No
28_iqa_compliance: IQA not applicable. Project is for research and proof of concept, not in production.
29_contains_pii: No
30_saop_review: No
31_data_catalog: Other
31_question_type: Most of the data needed is cataloged but some data points are not.
33_agency_data: Data in oracle tables and unstructured attachment data.
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: Project is being developed outside BSEE's IT infastructure.
45_compute_request: Other
45_question_type: Project is being developed outside BSEE's IT infastructure.
47_timely_resources: Other
47_question_type: Project is being developed outside BSEE's IT infastructure.
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.