GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: NAL Automated Indexing
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: ARS: Agricultural Research Service
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: This system automatically assigns word tags to agricultural research articles from a controlled list of terms provided by the National Agricultural Library Thesaurus (NALT). The tags can be used to look up and retrieve articles. Using these tags benefits users by making it easier to find the content they are looking for.
12_outputs: The model outputs terms to use as search tags that are specific to the article that the model analyzed.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2011
19_date_acq_dev_began: 1/1/2012
20_date_implemented: 6/1/2012
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 1232SSA1C0014
24_hisp_support: No
27_public_info: Yes
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The data is a collection of text scripts from publishers that have undergone quality assurance and quality control processing.
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: 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: No
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: Predictive Modeling of Invasive Pest Species
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: APHIS: Animal and Plant Health Inspection Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The purpose of the model is to check how likely it is for imported agricultural products from other countries to have pests. Benefits include more reliable discovery and quarantine of invasive pests, preventing pest invasion and making trade safer.
12_outputs: The model outputs are a prediction of whether a product carries an invasive species and what invasive species category the pest belongs to.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/1/2015
19_date_acq_dev_began: 7/1/2015
20_date_implemented: 5/1/2018
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: Inspection data was collected from Plant Protection and Quarantine (PPQ) and Customs and Border Protection (CBP). Quality control of the data was conducted by data analysts. There was no data augmention performed. The data is structured and has several million records with more than 50 numerical and categorical variables. This data is not publicly available. 
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: 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: Not sure
45_compute_request: Other
45_question_type: Not sure
47_timely_resources: Other
47_question_type: Not sure
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.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Detection of Pre-symptomatic HLB Infected Citrus
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 the model is to detect citrus trees infected with Huanglongbing (HLB) disease using images collected by a camera sensor on a small drone. This system would decrease time and cost associated with manual searching for HLB infected trees.
12_outputs: The model outputs GPS Coordinates of potential Huanglongbing (HLB) infected areas.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 9/30/2022
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: High Throughput Phenotyping in Citrus Orchards
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 main purpose of this system is to analyze drone images to locate, count, and categorize citrus trees in an orchard to monitor orchard health. This use case saves thousands of man-hours searching for signs of plant damage and disease in orchards.
12_outputs: The model output flags images containing plant damage or disease.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 9/30/2022
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: Detection of Aquatic Weeds
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 locate and identify aquatic weed species using images from drones. Expected benefits include decreasing time that would have been spent manually reviewing the images.
12_outputs: The model outputs the aquatic weed species contained in the image.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 9/30/2022
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: Automated Detection & Mapping of Host Plants from Ground Level Imagery
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: This system generates maps of specific tree species from ground-level (streetview) images. Expected benefits are decreased time and cost associated with manual collection of the data.
12_outputs: The model outputs GPS coordinates of flagged locations.
16_dev_stage: Retired
17_impact_type: Neither
21_date_retired: 9/30/2022
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: Democratizing Data
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: NASS: National Agricultural Statistics Service
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: This system scans collections of published documents to find how publicly-funded data and evidence are used to serve science and society. This helps the National Agricultural Statistics Service and the Economic Research Service understand who is using their data and why. This improves customer service, helps evaluate programs, and answers important questions for planning and learning.
12_outputs: The model outputs text containing the identified dataset reference information.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 3/8/2021
19_date_acq_dev_began: 6/3/2021
20_date_implemented: 7/30/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: 58-3AEU-3-0003
24_hisp_support: No
27_public_info: Yes
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The model is trained on publicly available peer reviewed research (text data) and known usages of datasets.
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 – source code is publicly available.
39_code_link: https://github.com/democratizingdata/democratizingdata-ml-algorithms
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Other
45_question_type: This was a cooperative agreement on all publicly available data.  No Federal machines were used to work with the model.  
47_timely_resources: Yes
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 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: Land Change Analysis Tool (LCAT)
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FPAC-BC: FPAC Business Center
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: The Land Change Analysis Tool (LCAT) creates high resolution maps to help make land use decisions. For example, it has been used to monitor eastern redcedar for about 40 years in South Dakota and to support wildlife hazard assessments at airports with various organizations. This tool reduced the labor hours needed by the Farm Service Agency (FSA) to review land data accuracy in Georgia by 100 times.
12_outputs: The model outputs land cover maps.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 10/1/2018
19_date_acq_dev_began: 10/1/2018
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The dataset is created from the National Agriculture Imagery Program, which contains images of land taken from aircrafts.
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: Yes
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: 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: 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: OCIO/CDO Council Comment Analysis Tool
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 prototype helps reviewers identify the main topics and themes of comments, and then group similar comments together. This makes the comment review process more efficient by providing new insights and speeding up comment processing. Benefits include reducing repeated development efforts across the government and saving costs.
12_outputs: The model outputs groups of comments categorized by topic and similarity.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 12/1/2020
19_date_acq_dev_began: 1/1/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: 12314418F0020/P00075
24_hisp_support: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The training data contains publicly available comment data exported from Regulations.gov. The data largely contains text; some data samples could contain images or data tables. 
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.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: Yes
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: Retailer Receipt Analysis
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FNS: Food and Nutrition Service
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: Inputting large amounts of data from paper forms into a digital system using AI.
11_purpose_benefits: This system uses optical character recognition (OCR) to convert physical inventory documentation into digital text. This makes the review of inventory documents more efficient and consistent.
12_outputs: The model outputs digital text of inventory documentation and distinguishes food items and categories.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2021
19_date_acq_dev_began: 10/1/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: Contract number: 47QTCA18D0081
24_hisp_support: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The dataset is a collection of Supplemental Nutrition Assistance Program (SNAP) retailer inventory documentation submitted in response to charges of program violations. 
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: More than 12 months
43_infra_provisioned: Yes
45_compute_request: No
47_timely_resources: Yes
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 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: Ecosystem Management Decision Support System (EMDS)
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: This system provides decision support for environmental analysis and planning by using AI-powered tools in ArcGIS and QGIS. Use of this system empowers stakeholders to make more informed and effective decisions about natural resource management.
12_outputs: Outputs from this system include the identification of landscapes in need of management/maintenance, along with suggested management actions based on considerations such as cost, efficacy, and policy.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/1994
19_date_acq_dev_began: 6/1/1995
20_date_implemented: 2/1/1997
22_dev_method: Developed with contracting resources.
23_contract_piids: Many contracts dating from 1995
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Primary data sources included observed insect and pathogen-induced mortality, key critical loads for soil and the atmosphere, long term seasonal departures in temperature and precipitation, road densities, uncharacteristic wildfires, historical fire regime departure, wildfire potential, insect and pathogen risk, and vegetation departure from natural range of variability.
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: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Ecosystem Management Decision Support System
42_dev_tools_wait: 6-12 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: Cross-Laminated Timber (CLT) Knowledge Database
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: This system enables researchers, practitioners, and the public to find specialized information about timber products. Benefits include faster information sharing and less time spent on manual searches.
12_outputs: System outputs are webpage links from the timber knowledge database.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 12/1/2017
19_date_acq_dev_began: 12/1/2017
20_date_implemented: 6/1/2018
22_dev_method: Developed with contracting resources.
23_contract_piids: This was done through a Partnership with the University of Minnesota. Question 35 does not include this option, no contracts were used.
24_hisp_support: No
27_public_info: Yes
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Public domain agency publications were used for the dataset. This includes station papers, research, and journal papers by US Forest Service and other scientists.
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: No
45_compute_request: Other
45_question_type: Computing done with partner university
47_timely_resources: Yes
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.
51_extension_request: No – Agency did not request an extension for this use case.
2_use_case_name: Raster Tools
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: This system will make machine learning techniques available for geospatial applications. Benefits include standardization of methods, improved work quality, and increased user productivity.
12_outputs: The system API (Application Programming Interface) provides various AI outputs, usually in the form of raster images and data tables.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 8/1/2021
19_date_acq_dev_began: 8/1/2021
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
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 – source code is publicly available.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: Yes
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: TreeMap and FuelMap (all versions)
3_agency: Department of Agriculture
3_abr: USDA
4_bureau: FS: Forest Service; Forest Service Research & Development
8_topic_area: Energy & the Environment
10_commercial_ai: None of the above.
11_purpose_benefits: TreeMap provides a detailed model of the forests in the US. It is used for measuring carbon, planning fuel treatments, starting landscape vegetation models, assessing fire effects, and more. Users include the US Forest Service, private companies, and state governments.
12_outputs: TreeMap produces a detailed map of a plot of forest and a database table listing individual tree records or fuel characteristics for each plot.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2010
19_date_acq_dev_began: 10/12/2016
20_date_implemented: 10/1/2018
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: TreeMap is validated using Forest Inventory and Analysis field plot data, consisting of percent forest cover, height, vegetation type, topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape around 2016.
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 – source code is publicly available.
39_code_link: https://github.com/firelab
40_has_ato: No
42_dev_tools_wait: 6-12 months
43_infra_provisioned: No
45_compute_request: Yes
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: 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.