GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: IRIS
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: APHIS: Animal and Plant Health Inspection Service
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this system is to make literature searches more effective for Biotechnology Regulatory Services. This increases work efficiency with our regulatory tasks.
12_outputs: The model outputs recommended literature list for scientists.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/9/2023
19_date_acq_dev_began: 2/5/2024
20_date_implemented: 5/15/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 12639524P0188  
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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.
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: No
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.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Ticket Resolution Categorization (Incident/Change)
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: OCIO: Office of the Chief Information Officer
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this model is to classify the resolution type and tier of all support desk tickets after they have been closed. This model allows the support team to spend more time identifying process inefficiencies and plan solutions rather than categorizing tickets. This will lead to process improvement, automation of repetitive tasks, increased productivity, and higher performance.
12_outputs: The model outputs the classification category of support ticket resolutions.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2023
19_date_acq_dev_began: 6/1/2023
20_date_implemented: 6/1/2023
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: ITSS Contracts
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The dataset is composed of Remedy data, containing inventory data of all of assets at DISC: hardware, software, incident tickets, and change tickets. Data contains numerical, categorical, text, and date data types.
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: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: SD105, disc employee or customer
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Re-use production level code from a different use-case: This use case re-uses production level code and/or internally developed code libraries for model development from existing AI systems or repositories inside 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.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Ticket Templatization
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: OCIO: Office of the Chief Information Officer
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: This model is a non-production exploratory model, meaning it does not make predictions but is used to explore trends within data to gain insights that can help in making data-driven decisions. It is designed to explore and analyze service and change requests submitted through the 105 general form or without templates. This model helps to identify subcategories within the larger dataset that could be candidates for standardization and automation, potentially leading to improved operational efficiency, cost savings, and customer satisfaction.
12_outputs: The model outputs trends within data to assist in decision making.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 1/1/2024
19_date_acq_dev_began: 1/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: ITSS Contracts
24_hisp_support: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The dataset is composed of Remedy data, containing inventory data of all of assets at DISC: hardware, software, incident tickets, and change tickets. Data contains numerical, categorical, text, and date data types.
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: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Re-use production level code from a different use-case: This use case re-uses production level code and/or internally developed code libraries for model development from existing AI systems or repositories inside 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.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: File Rename Automation
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FSA: Farm Service Agency
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this tool is to rename thousands of documents converted from physical to digital records that were given a generic file name. This tool can grab text from page 1 of each document and apply a correct file rename instead of employees having to spend hours manually renaming documents.
12_outputs: The model outputs renamed files.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 11/6/2023
19_date_acq_dev_began: 11/6/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data is created from agency records and were used to train Microsoft Power Automate to extract data from records and use in the file renaming process.
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.
37_custom_code: No
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: Other
47_question_type: Sometimes
49_existing_reuse: Re-use production level code from a different use-case: This use case re-uses production level code and/or internally developed code libraries for model development from existing AI systems or repositories inside 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.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Rapid Drafting of ARS Research Summaries
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: ARS: Agricultural Research Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this tool is to summarize ongoing research from internal Agricultural Research Service (ARS) documents to allow program staff to quickly create accurate and timely summary documents, such as briefing papers, talking points for leadership, and speeches. This will give staff more time for other duties, and leadership will be able to confidently answer questions, justify budget requests, and ensure that our research is innovative and relevant.
12_outputs: The tool outputs talking points and short briefing papers.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 3/18/2024
30_saop_review: No
31_data_catalog: Yes
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: DS Hub Geo-metadata generation
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: NRCS: Natural Resources Conservation Service
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this AI use case is to generate metadata for Natural Resources Conservation Service (NRCS) datasets, ensuring consistency, accessibility, and compliance through generative AI. Expected benefits include increased data accessibility, reduced manual workload, minimized errors, better dataset understanding, and fast data retrieval for stakeholders.
12_outputs: The model outputs accurate, consistent, and compliant metadata appropriate for the existing geospatial data that it analyzed.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 9/20/2024
30_saop_review: No
31_data_catalog: Yes
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Dynamic Soils Hub 
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: NRCS: Natural Resources Conservation Service
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: The Dynamic Soils Hub (DS Hub) under the Natural Resources Conservation Service (NRCS) is a tool designed to help both government workers and the public understand and analyze soil information. The DS Hub links different soil and conservation databases, making it easier to evaluate the environmental benefits of conservation programs by accessing previously separate data and models. This enhances the USDA’s ability to study and report on how soil properties change with conservation efforts over time.
12_outputs: The system outputs the class of soil based on the supplied soil information.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 11/11/2020
19_date_acq_dev_began: 11/11/2020
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
37_custom_code: No
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Dynamic Soils Hub 
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for within the agency.
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: U.S. Poultry Operations and Populations Dataset
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: APHIS: Animal and Plant Health Inspection Service
8_topic_area: Emergency Management
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this case is to develop a dataset that addresses the problem of not having complete information about where poultry farms are located and how many birds they have. Filling this gap provides detailed data on poultry farm locations and populations, which is essential for planning animal health emergencies and predicting the spread of diseases.
12_outputs: Output is a national-level dataset of domestic poultry operations and estimated populations. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 5/15/2016
19_date_acq_dev_began: 5/15/2016
20_date_implemented: 6/3/2019
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data consists of a subset of farm locations that were validated using manually digitized poultry operations in 41 counties. Data is not publicly available.
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: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Other
45_question_type: Used existing lab machines and enterprise licenses with Esri(GIS)
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: 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: Equine Operations and Populations Dataset for the U.S.
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: APHIS: Animal and Plant Health Inspection Service
8_topic_area: Emergency Management
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this case is to develop a dataset that addresses the problem of not having complete information about where horse farms are located and how many horses they have. Filling this gap provides detailed data on horse farm locations and populations, which is essential for planning emergencies and predicting the spread of diseases.
12_outputs: Output is a national-level dataset of domestic horse operations and estimated populations.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 1/2/2023
19_date_acq_dev_began: 9/1/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: NADPRP / Farm Bill funds , cooperative agreement with Colorado State University
24_hisp_support: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data is created from National Agriculture Imagery Program (NAIP) images and the National Agricultural Statistics Service Census of Agriculture, which includes number of farms by size and type, inventory and values for crops and livestock, producer characteristics, and much more. Data is not publicly available.
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: No
45_compute_request: Other
45_question_type: In process with MRP GIS Coordinator
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: 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.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: NASS - Naggle 2.0 Automated Editing Tool
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: NASS: National Agricultural Statistics Service
8_topic_area: Other
8_question_type: Agricultural Statistics
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this model is to determine if an answer on a survey is valid or invalid. If an answer is classified as invalid, a regression model will then suggest a corrected value. This approach will help reduce errors and improve the accuracy of survey forms, saving time and reducing the number of labor hours spent on editing.
12_outputs: The classification model outputs an excel sheet with the survey, person, variable, and whether the variable is valid or invalid. The regression model outputs an excel sheet containing the invalid records, which includes the survey, person, variable, original value, and new predicted value. 
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 6/3/2024
30_saop_review: No
31_data_catalog: Yes
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: County-level remotely-sensed corn and soybean yield estimation
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: NASS: National Agricultural Statistics Service
8_topic_area: Other
8_question_type: Agricultural Statistics
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of this tool is to estimate yearly corn and soybean yields for each county using satellite images. More details can be found in the paper titled “An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States” (https://doi.org/10.1016/j.rse.2013.10.027). Benefits of providing county-level crop yield statistics allow stakeholders to make more informed planning and decisions.
12_outputs: The model outputs county-level crop yield estimates for corn and soybeans in the amount of bushels per acre.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2007
19_date_acq_dev_began: 1/1/2009
20_date_implemented: 1/1/2014
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Historical National Agricultural Statistics Service county-level crop yields are used as the dataset. The foundation of the data is images from NASA Moderate Resolution Imaging Spectroradiometer (MODIS) satellites.
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.
37_custom_code: No
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Yes
45_compute_request: No
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: 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.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: NASSportal Intranet Bot
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: NASS: National Agricultural Statistics Service
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: This model will assist National Agricultural Statistics Service (NASS) staff in finding answers to questions on how to administer programs. This will decrease labor hours and increase efficiency of NASS staff in program administration.
12_outputs: The chatbot will provide text outputs.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/1/2024
30_saop_review: No
31_data_catalog: Yes
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: XyloTron/XyloPhone Wood Identification System
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of these tools is to identify different types of wood based on their cross-section. These tools will help industries follow laws and support law enforcement in meeting national (e.g. Lacey Act) and international (e.g. CITES) regulations.
12_outputs: The tools will output a prediction of the type wood.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 1/1/2016
19_date_acq_dev_began: 1/1/2016
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data contains images of the cross-section of polished wood surfaces. As of July 2024, there are 139,639 images of 10,888 wood specimens.
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
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: No
45_compute_request: No
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: 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.