GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: SERVIR Water Security: SERVIR is applying deep learning on in situ and global/regional hydrologic forecasts to fill gaps in historic and recent streamflow and river elevation estimates, and improve streamflow forecasts, to a vertical precision of meters to centimeters, potentially for thousands to millions or river reaches worldwide.
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Resilience, Environment, and Food Security
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: Fill gaps in river elevation and streamflow estimates; to more accurately forecast streamflow in ungauged rivers.
12_outputs: estimates and forecasts that can inform water security decisions
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/14/1905
19_date_acq_dev_began: 7/14/1905
20_date_implemented: 7/15/1905
22_dev_method: Developed in-house.
24_hisp_support: No
31_data_catalog: Yes
33_agency_data: Free-and-open satellite data from NASA and GEOGLOWS streamflow model
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: 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: 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: SERVIR Ecosystem and Carbon Management: Automation of land cover and change mapping, including forest disturbance due to roads and mining. SERVIR is applying machine learning and deep learning techniques on satellite data to automate the classification and identification of land surface activities at the scale of 10s of meters.
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Resilience, Environment, and Food Security
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: Improve detection of land cover changes to inform resource management, carbon and greenhouse gas emissions estimates.
12_outputs: geospatial information that can inform ecosystem and carbon management monitoring systems and decisions
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 7/12/1905
19_date_acq_dev_began: 7/14/1905
20_date_implemented: 7/15/1905
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: documentation, peer review, and open source methods
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Free-and-open satellite data from NASA and ESA
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 – source code is publicly available.
39_code_link: https://sites.google.com/uah.edu/2020-tensorflow-technical-exch
40_has_ato: No
42_dev_tools_wait: 6-12 months
43_infra_provisioned: No
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: 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: SERVIR Weather & Climate Resilience: Short-term and seasonal weather prediction and hazard mapping. SERVIR is mapping floods and landslides with machine learning classification techniques, on the order of 10m to 10km. SERVIR is exploring the performance of weather foundational models versus numerical weather predictions on the 4-10km scale.
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Resilience, Environment, and Food Security
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: Improve detection and mapping of hazards and more accurately predict severe weather, to ultimately reduce impacts of climate- and weather-related events.
12_outputs: estimates and forecasts that can inform short-term weather- and subseasonal climate decisions, and climate-related disaster risk reduction
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/14/1905
19_date_acq_dev_began: 7/14/1905
20_date_implemented: 7/15/1905
22_dev_method: Developed in-house.
24_hisp_support: No
31_data_catalog: Yes
33_agency_data: Free-and-open satellite data from NASA and ECMWF weather predictions
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: 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: 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: SERVIR Air Quality and Health: Processing modeled air quality forecasts and assimilating large volumes of data from satellite instruments. SERVIR is downscaling satellite- and model-derived air quality predictions with neural networks, down to 5km spatial resolution.
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Resilience, Environment, and Food Security
8_topic_area: Science & Space
10_commercial_ai: None of the above.
11_purpose_benefits: More accurately track and monitor air quality events, their sources, and their impact areas.
12_outputs: geospatial information that can inform air quality and related health monitoring and decisions
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 7/14/1905
19_date_acq_dev_began: 7/15/1905
20_date_implemented: 7/15/1905
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: documentation, peer review, and open source methods
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Free-and-open satellite data from NASA and ESA
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: No
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: 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: Amazonía Mia - AI for environmental law enforcement in Colombia
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Colombia
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: Strengthen the capabilities of the Attorney General's Office for the investigation and prosecution of environmental crimes, with emphasis on deforestation in the Colombian Amazon, by improving technical, technological and human capabilities, facilitating an effective approach to criminal types and identification and prosecution of major drivers of these behaviors.
12_outputs: Reports, visualizations
16_dev_stage: Operation and Maintenance
17_impact_type: Rights-Impacting

18_date_initiated: 7/15/1905
19_date_acq_dev_began: 7/15/1905
20_date_implemented: 7/16/1905
22_dev_method: Developed with contracting resources.
23_contract_piids: 72051421D00005
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
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: Yes
38_code_access: No – agency does not have access to source code.
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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.
52_impact_assessment: No
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
2_use_case_name: Knowledge management and situational awareness for civil society in Colombia
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Colombia
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: Development of an application that allows social organizations to manage the knowledge they produce in their activities and projects. It seeks, on the one hand, to facilitate access to advanced digital solutions, which enhance the use of information through an intuitive natural language interface, to organizations that require information on security and violence in Colombia that are part of the Suma Social Program. On the other hand, to strengthen organizations that need to monitor security contexts and conditions to support their decision-making processes, with a new digital solution that not only provides, but also facilitates the efficient and effective use of crucial information.
12_outputs: Reports, visualizations, chat interface
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/16/1905
19_date_acq_dev_began: 7/16/1905
20_date_implemented: 7/16/1905
22_dev_method: Developed with contracting resources.
23_contract_piids: 72051421CA00006
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
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: Yes
38_code_access: No – agency does not have access to source code.
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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: Virtual host for attendees of the Invest for Climate activity
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Colombia
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: Present and interact with the attendees of the Invest for Climate Activity through a virtual host. Objectives include increasing the visibility of the project and facilitating interaction with the attendees.
12_outputs: Interactive "host" avatar
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/16/1905
19_date_acq_dev_began: 7/16/1905
20_date_implemented: 7/16/1905
22_dev_method: Developed with contracting resources.
23_contract_piids: 72051424D00003
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
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: Yes
38_code_access: No – agency does not have access to source code.
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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: Farmer support chatbot in Colombia
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Colombia
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: Chatbot using AI to interpret agroclimatic information and share with producer families recommend practices based on climate information.
12_outputs: Reports
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/16/1905
19_date_acq_dev_began: 7/16/1905
20_date_implemented: 7/16/1905
22_dev_method: Developed with contracting resources.
23_contract_piids: 72051423D00002
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
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: Yes
38_code_access: No – agency does not have access to source code.
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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: LIDA: Instant Messaging System in the Family Commissioner's Office of Montería - Córdoba, Colombia
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Colombia
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The LIDA instant messaging system in the Family Commissioner´s Office of Montería,  seeks to strengthen communication channels with citizens and strengthen the promotion  of rights, based on knowledge of gender-based violence (GBV) referral pathways.
12_outputs: Reports
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/16/1905
19_date_acq_dev_began: 7/16/1905
20_date_implemented: 7/16/1905
22_dev_method: Developed with contracting resources.
23_contract_piids: 72051422D00001
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: Yes
38_code_access: No – agency does not have access to source code.
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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: AI assistant for Colombia Partners for Transparency
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Colombia
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: This tool combines Large Language Models (LLM) with Retrieval-Augmented Generation (RAG) to enhance interaction with the PfT documentation. It allows users to upload documents in word and pdf and ask natural language questions, similar to ChatGPT, providing precise and contextually answers based on the specific content of the documents (https://www.evidencebase.ai/)
12_outputs: Chatbot interface
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/16/1905
19_date_acq_dev_began: 7/16/1905
20_date_implemented: 7/16/1905
22_dev_method: Developed with contracting resources.
23_contract_piids: 72051420CA00001
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: Yes
38_code_access: No – agency does not have access to source code.
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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: Twitter trend analysis in Colombia
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Colombia
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: This system uses artificial intelligence through a control dashboard, presenting a counter of retweets in the last hour with gender classification, topic classifier, georeferencing, sentiment trends, and emotion analysis in tweets. This application is designed as a measure to counteract misinformation and promote transparency on social media. In the framework of promoting transparency, access to information, and open government as paths to combat corruption, the product will develop several Machine Learning algorithms for identifying trending posts, recognizing users' demographic, psychographic, and sociographic characteristics, and detecting fake news.
12_outputs: Reports, visualization
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/15/1905
19_date_acq_dev_began: 7/15/1905
20_date_implemented: 7/15/1905
22_dev_method: Developed with contracting resources.
23_contract_piids: 72051420CA00001
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: Yes
38_code_access: No – agency does not have access to source code.
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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: Project Data Machine: Open governance data in Colombia
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Colombia
8_topic_area: Diplomacy & Trade
10_commercial_ai: None of the above.
11_purpose_benefits: This is a technological tool focused on integrating information related to the Congress of the Republic into a homogeneous and easily downloadable format for data extraction or processing. The benefits of this project include (i) Providing data in open and standard formats that are suitable for reuse by the target audience, (ii) Using agreed-upon schemas and vocabularies and providing a defined data structure so that the information can be correctly interpreted when reused, (iii) Offering a consultation point that includes an inventory with descriptive and technical information about the exposed datasets, (iv) Providing accessible data from persistent and user-friendly web addresses, (v) The project serves as a fundamental catalyst for open data initiatives for public and private entities
12_outputs: Text
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/15/1905
19_date_acq_dev_began: 7/15/1905
20_date_implemented: 7/15/1905
22_dev_method: Developed with contracting resources.
23_contract_piids: 72051420CA00001
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: Yes
38_code_access: No – agency does not have access to source code.
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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.