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METRO_REG - "New code list"

Metropolitan regions

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METRO_REG - "New code list"

Metropolitan regions

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Дані і статистика

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METRO_REG - "New code list"

Metropolitan regions

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New package of sanctions against Russia announced

Unia Europejska

1 хв читання

New package of sanctions against Russia announced

President von der Leyen announced the 21st sanctions package against Russia. It focuses on sectors with the highest impact, like energy, financial services and crypto and trade. Also, former Russian combatants would be banned to enter into the European Union.

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European Union

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New package of sanctions against Russia announced

President von der Leyen announced the 21st sanctions package against Russia. It focuses on sectors with the highest impact, like energy, financial services and crypto and trade. Also, former Russian combatants would be banned to enter into the European Union.

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Європейський Союз

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New package of sanctions against Russia announced

President von der Leyen announced the 21st sanctions package against Russia. It focuses on sectors with the highest impact, like energy, financial services and crypto and trade. Also, former Russian combatants would be banned to enter into the European Union.

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Nauka i kosmos

5 хв читання

NASA Uses Machine Learning to Enhance Flash Flood Warnings

The Transient Artifact and Continuous Learning System (TACLS) leverages data from continuously operating satellite networks coupled with machine learning models to help meteorologists at the National Weather Service forecast flash floods more efficiently. This new software is the result of a collaboration between NASA’s Jet Propulsion Laboratory, the University of California, San Diego (UCSD), and the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS). Show downloads TACLS test prediction run TACLS test prediction run (Original) MP4 Close To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video A visual analysis from a TACLS test prediction run using data from flash floods the week of Christmas, 2025. The image shows flash flood warning (FFW) probabilities generated by TACLS (in shades of red) and overlaid on areas that received flash flood warnings from the National Weather Service (in blue). Credit: UCSD Scripps Institution of Oceanography Created with support from NASA’s Earth Science Technology Office (ESTO), TACLS leverages machine learning to automatically locate evidence (unusual increases in atmospheric moisture) of impending flash flooding that meteorologists may otherwise miss as they analyze large amounts of data. TACLS flags that evidence, indicates where flash flooding could likely occur, and displays that information via a user-friendly visualization for human analysts to interpret. Those analysts can then decide whether to issue a flash flood warning or weather advisory. This novel framework for tracking extreme weather events and predicting imminent flash floods operates in near real-time, producing forecasts in as little as fifteen minutes. “That’s really what we wanted to do, to give meteorologists a tool to help decision making for flash flood warnings,” said Yehuda Bock, Distinguished Researcher at the UCSD Scripps Institution of Oceanography and principal investigator for TACLS. In simulations testing, TACLS used data from diverse severe weather events—including atmospheric rivers, monsoonal convection, and tropical cyclone remnants—between 2017 and 2023 and successfully captured 93% of the issued flash-flood warnings. Meteorologists from the National Weather Service are currently working to incorporate TACLS into their existing systems for forecasting flash floods in Southern California. A cyclone makes landfall across the California coast on November 19, 2024. TACLS will help give communities more time to prepare for impending severe weather. Credit: NASA This learning system has two main components. First, an analytic back-end software suite uses machine learning algorithms to process satellite data and determine areas at risk for flooding. Second, user-friendly visualization software highlights those areas for further analysis by humans. The ACLS back-end software analyzes data from satellites in the Global Navigation Satellite System (GNSS), a constellation of satellite networks that drive navigation services around the world. Water vapor in the troposphere delays signals from these satellites as they travel to Earth. This signal delay can be analyzed to calculate the amount of water vapor in the atmosphere over a particular location on Earth. The TACLS analytic back-end software suite features a machine learning model trained using more than 30 years of past GNSS data. This model is an anomaly detector that tracks unusual increases in atmospheric moisture. The model then carefully examines that atmospheric moisture data and determines whether it’s either an artifact (a false feature or distortion in the data) or a transient (a time-sensitive physical event, like heavy precipitation) that requires interpretation by human analysts. If TACLS determines the data represents a transient, such as an extreme weather event that warrants a flash flood warning, it will forward that data to the TACLS visualization software (MGViz) for further evaluation by humans. The analysts use their judgement and experience to interpret these events and determine whether the flagged data indicates a flash flood is likely, and, if necessary, issue a flash flood warning. Several past innovations developed at JPL are leveraged by TACLS to process GNSS data and present the results. The analytic back-end software suite incorporates elements from JPL’s Domain-agnostic Outlier Ranking Algorithms program and the Time-series Forecasting, Evaluation, and Deployment program. The TACLS visualizer is based on the Multi-Mission Geographic Information System, originally developed at JPL for NASA’s Mars missions. The TACLS software binds all these components within a novel system that enhances existing methods to reduce the amount of time it takes for a human analyst to determine whether to issue a flash flood warning. Both the TACLS software and the data used to train it will be open-source, allowing scientists to either tailor this model in response to their unique research needs or create their own model from scratch. For additional details, see the entry for this project on NASA TechPort. Project Lead: Dr. Yehuda Bock, University of California, San Diego. Sponsoring Organization(s): NASA’s Earth Science Technology Office Advanced Information Systems Technology Program; JPL; NOAA; National Weather Service.

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Science & Space

5 хв читання

NASA Uses Machine Learning to Enhance Flash Flood Warnings

The Transient Artifact and Continuous Learning System (TACLS) leverages data from continuously operating satellite networks coupled with machine learning models to help meteorologists at the National Weather Service forecast flash floods more efficiently. This new software is the result of a collaboration between NASA’s Jet Propulsion Laboratory, the University of California, San Diego (UCSD), and the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS). Show downloads TACLS test prediction run TACLS test prediction run (Original) MP4 Close To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video A visual analysis from a TACLS test prediction run using data from flash floods the week of Christmas, 2025. The image shows flash flood warning (FFW) probabilities generated by TACLS (in shades of red) and overlaid on areas that received flash flood warnings from the National Weather Service (in blue). Credit: UCSD Scripps Institution of Oceanography Created with support from NASA’s Earth Science Technology Office (ESTO), TACLS leverages machine learning to automatically locate evidence (unusual increases in atmospheric moisture) of impending flash flooding that meteorologists may otherwise miss as they analyze large amounts of data. TACLS flags that evidence, indicates where flash flooding could likely occur, and displays that information via a user-friendly visualization for human analysts to interpret. Those analysts can then decide whether to issue a flash flood warning or weather advisory. This novel framework for tracking extreme weather events and predicting imminent flash floods operates in near real-time, producing forecasts in as little as fifteen minutes. “That’s really what we wanted to do, to give meteorologists a tool to help decision making for flash flood warnings,” said Yehuda Bock, Distinguished Researcher at the UCSD Scripps Institution of Oceanography and principal investigator for TACLS. In simulations testing, TACLS used data from diverse severe weather events—including atmospheric rivers, monsoonal convection, and tropical cyclone remnants—between 2017 and 2023 and successfully captured 93% of the issued flash-flood warnings. Meteorologists from the National Weather Service are currently working to incorporate TACLS into their existing systems for forecasting flash floods in Southern California. A cyclone makes landfall across the California coast on November 19, 2024. TACLS will help give communities more time to prepare for impending severe weather. Credit: NASA This learning system has two main components. First, an analytic back-end software suite uses machine learning algorithms to process satellite data and determine areas at risk for flooding. Second, user-friendly visualization software highlights those areas for further analysis by humans. The ACLS back-end software analyzes data from satellites in the Global Navigation Satellite System (GNSS), a constellation of satellite networks that drive navigation services around the world. Water vapor in the troposphere delays signals from these satellites as they travel to Earth. This signal delay can be analyzed to calculate the amount of water vapor in the atmosphere over a particular location on Earth. The TACLS analytic back-end software suite features a machine learning model trained using more than 30 years of past GNSS data. This model is an anomaly detector that tracks unusual increases in atmospheric moisture. The model then carefully examines that atmospheric moisture data and determines whether it’s either an artifact (a false feature or distortion in the data) or a transient (a time-sensitive physical event, like heavy precipitation) that requires interpretation by human analysts. If TACLS determines the data represents a transient, such as an extreme weather event that warrants a flash flood warning, it will forward that data to the TACLS visualization software (MGViz) for further evaluation by humans. The analysts use their judgement and experience to interpret these events and determine whether the flagged data indicates a flash flood is likely, and, if necessary, issue a flash flood warning. Several past innovations developed at JPL are leveraged by TACLS to process GNSS data and present the results. The analytic back-end software suite incorporates elements from JPL’s Domain-agnostic Outlier Ranking Algorithms program and the Time-series Forecasting, Evaluation, and Deployment program. The TACLS visualizer is based on the Multi-Mission Geographic Information System, originally developed at JPL for NASA’s Mars missions. The TACLS software binds all these components within a novel system that enhances existing methods to reduce the amount of time it takes for a human analyst to determine whether to issue a flash flood warning. Both the TACLS software and the data used to train it will be open-source, allowing scientists to either tailor this model in response to their unique research needs or create their own model from scratch. For additional details, see the entry for this project on NASA TechPort. Project Lead: Dr. Yehuda Bock, University of California, San Diego. Sponsoring Organization(s): NASA’s Earth Science Technology Office Advanced Information Systems Technology Program; JPL; NOAA; National Weather Service.

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Наука і космос

5 хв читання

NASA Uses Machine Learning to Enhance Flash Flood Warnings

The Transient Artifact and Continuous Learning System (TACLS) leverages data from continuously operating satellite networks coupled with machine learning models to help meteorologists at the National Weather Service forecast flash floods more efficiently. This new software is the result of a collaboration between NASA’s Jet Propulsion Laboratory, the University of California, San Diego (UCSD), and the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS). Show downloads TACLS test prediction run TACLS test prediction run (Original) MP4 Close To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video A visual analysis from a TACLS test prediction run using data from flash floods the week of Christmas, 2025. The image shows flash flood warning (FFW) probabilities generated by TACLS (in shades of red) and overlaid on areas that received flash flood warnings from the National Weather Service (in blue). Credit: UCSD Scripps Institution of Oceanography Created with support from NASA’s Earth Science Technology Office (ESTO), TACLS leverages machine learning to automatically locate evidence (unusual increases in atmospheric moisture) of impending flash flooding that meteorologists may otherwise miss as they analyze large amounts of data. TACLS flags that evidence, indicates where flash flooding could likely occur, and displays that information via a user-friendly visualization for human analysts to interpret. Those analysts can then decide whether to issue a flash flood warning or weather advisory. This novel framework for tracking extreme weather events and predicting imminent flash floods operates in near real-time, producing forecasts in as little as fifteen minutes. “That’s really what we wanted to do, to give meteorologists a tool to help decision making for flash flood warnings,” said Yehuda Bock, Distinguished Researcher at the UCSD Scripps Institution of Oceanography and principal investigator for TACLS. In simulations testing, TACLS used data from diverse severe weather events—including atmospheric rivers, monsoonal convection, and tropical cyclone remnants—between 2017 and 2023 and successfully captured 93% of the issued flash-flood warnings. Meteorologists from the National Weather Service are currently working to incorporate TACLS into their existing systems for forecasting flash floods in Southern California. A cyclone makes landfall across the California coast on November 19, 2024. TACLS will help give communities more time to prepare for impending severe weather. Credit: NASA This learning system has two main components. First, an analytic back-end software suite uses machine learning algorithms to process satellite data and determine areas at risk for flooding. Second, user-friendly visualization software highlights those areas for further analysis by humans. The ACLS back-end software analyzes data from satellites in the Global Navigation Satellite System (GNSS), a constellation of satellite networks that drive navigation services around the world. Water vapor in the troposphere delays signals from these satellites as they travel to Earth. This signal delay can be analyzed to calculate the amount of water vapor in the atmosphere over a particular location on Earth. The TACLS analytic back-end software suite features a machine learning model trained using more than 30 years of past GNSS data. This model is an anomaly detector that tracks unusual increases in atmospheric moisture. The model then carefully examines that atmospheric moisture data and determines whether it’s either an artifact (a false feature or distortion in the data) or a transient (a time-sensitive physical event, like heavy precipitation) that requires interpretation by human analysts. If TACLS determines the data represents a transient, such as an extreme weather event that warrants a flash flood warning, it will forward that data to the TACLS visualization software (MGViz) for further evaluation by humans. The analysts use their judgement and experience to interpret these events and determine whether the flagged data indicates a flash flood is likely, and, if necessary, issue a flash flood warning. Several past innovations developed at JPL are leveraged by TACLS to process GNSS data and present the results. The analytic back-end software suite incorporates elements from JPL’s Domain-agnostic Outlier Ranking Algorithms program and the Time-series Forecasting, Evaluation, and Deployment program. The TACLS visualizer is based on the Multi-Mission Geographic Information System, originally developed at JPL for NASA’s Mars missions. The TACLS software binds all these components within a novel system that enhances existing methods to reduce the amount of time it takes for a human analyst to determine whether to issue a flash flood warning. Both the TACLS software and the data used to train it will be open-source, allowing scientists to either tailor this model in response to their unique research needs or create their own model from scratch. For additional details, see the entry for this project on NASA TechPort. Project Lead: Dr. Yehuda Bock, University of California, San Diego. Sponsoring Organization(s): NASA’s Earth Science Technology Office Advanced Information Systems Technology Program; JPL; NOAA; National Weather Service.

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Nauka i kosmos

2 хв читання

Department of Health and Human Services Digital Stockpile & Manufacturing Response Network Challenge

Alexandre – stock.adobe.com NASA’s Center of Excellence for Collaborative Innovation (CoECI) assists in the use of crowdsourcing across the federal government. CoECI’s NASA Tournament Lab offers the contract capability to run external crowdsourced challenges on behalf of NASA and other agencies. Sponsored by the Administration for Strategic Preparedness and Response (ASPR), a division of the U.S. Department of Health and Human Services (HHS), this prize competition seeks forward-thinking solutions to strengthen the nation’s ability to rapidly produce and distribute critical medical supplies during public health emergencies and supply chain disruptions. Through three challenge phases, participants will develop an innovative conceptual systems design using technologies and frameworks that advance the future of resilient medical manufacturing, logistics, and digital coordination capabilities. Phase 1:  Participants will submit: 8-page submission paper 3-minute Pitch video Blueprint supporting the key capabilities and structure of the solution Submissions will be evaluated per challenge Judging Criteria. Following the Judge evaluation period, up to 8 Finalists will receive a $5,000 prize each and be invited to the hybrid (in-person and virtual) Pitch Event at ASPR headquarters in Washington, DC. Up to 3 Winners from the Pitch Event will receive a $150,000 prize each and be invited to the innovation development phase. Phase 2:  Two developmental milestones will monitor solution development and will include $75,000 additional prizes for each milestone complete (up to $150,000 in total milestone prize payments). Phase 3:  At the end of the development milestone period, up to 3 teams may be invited to the final Live Validation Event to test their solution under applicable real-world simulations and compete for a total prize purse up to $1,100,000. ‍Total Prizes: Up to $2.04 Million Challenge Launch: June 15, 2026 Phase 1 Submissions Due: August 28, 2026 For more information, visit : https://www.expeditionhacks.com/challenges/digital-stockpile-challenge

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Science & Space

2 хв читання

Department of Health and Human Services Digital Stockpile & Manufacturing Response Network Challenge

Alexandre – stock.adobe.com NASA’s Center of Excellence for Collaborative Innovation (CoECI) assists in the use of crowdsourcing across the federal government. CoECI’s NASA Tournament Lab offers the contract capability to run external crowdsourced challenges on behalf of NASA and other agencies. Sponsored by the Administration for Strategic Preparedness and Response (ASPR), a division of the U.S. Department of Health and Human Services (HHS), this prize competition seeks forward-thinking solutions to strengthen the nation’s ability to rapidly produce and distribute critical medical supplies during public health emergencies and supply chain disruptions. Through three challenge phases, participants will develop an innovative conceptual systems design using technologies and frameworks that advance the future of resilient medical manufacturing, logistics, and digital coordination capabilities. Phase 1:  Participants will submit: 8-page submission paper 3-minute Pitch video Blueprint supporting the key capabilities and structure of the solution Submissions will be evaluated per challenge Judging Criteria. Following the Judge evaluation period, up to 8 Finalists will receive a $5,000 prize each and be invited to the hybrid (in-person and virtual) Pitch Event at ASPR headquarters in Washington, DC. Up to 3 Winners from the Pitch Event will receive a $150,000 prize each and be invited to the innovation development phase. Phase 2:  Two developmental milestones will monitor solution development and will include $75,000 additional prizes for each milestone complete (up to $150,000 in total milestone prize payments). Phase 3:  At the end of the development milestone period, up to 3 teams may be invited to the final Live Validation Event to test their solution under applicable real-world simulations and compete for a total prize purse up to $1,100,000. ‍Total Prizes: Up to $2.04 Million Challenge Launch: June 15, 2026 Phase 1 Submissions Due: August 28, 2026 For more information, visit : https://www.expeditionhacks.com/challenges/digital-stockpile-challenge

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Наука і космос

2 хв читання

Department of Health and Human Services Digital Stockpile & Manufacturing Response Network Challenge

Alexandre – stock.adobe.com NASA’s Center of Excellence for Collaborative Innovation (CoECI) assists in the use of crowdsourcing across the federal government. CoECI’s NASA Tournament Lab offers the contract capability to run external crowdsourced challenges on behalf of NASA and other agencies. Sponsored by the Administration for Strategic Preparedness and Response (ASPR), a division of the U.S. Department of Health and Human Services (HHS), this prize competition seeks forward-thinking solutions to strengthen the nation’s ability to rapidly produce and distribute critical medical supplies during public health emergencies and supply chain disruptions. Through three challenge phases, participants will develop an innovative conceptual systems design using technologies and frameworks that advance the future of resilient medical manufacturing, logistics, and digital coordination capabilities. Phase 1:  Participants will submit: 8-page submission paper 3-minute Pitch video Blueprint supporting the key capabilities and structure of the solution Submissions will be evaluated per challenge Judging Criteria. Following the Judge evaluation period, up to 8 Finalists will receive a $5,000 prize each and be invited to the hybrid (in-person and virtual) Pitch Event at ASPR headquarters in Washington, DC. Up to 3 Winners from the Pitch Event will receive a $150,000 prize each and be invited to the innovation development phase. Phase 2:  Two developmental milestones will monitor solution development and will include $75,000 additional prizes for each milestone complete (up to $150,000 in total milestone prize payments). Phase 3:  At the end of the development milestone period, up to 3 teams may be invited to the final Live Validation Event to test their solution under applicable real-world simulations and compete for a total prize purse up to $1,100,000. ‍Total Prizes: Up to $2.04 Million Challenge Launch: June 15, 2026 Phase 1 Submissions Due: August 28, 2026 For more information, visit : https://www.expeditionhacks.com/challenges/digital-stockpile-challenge

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Nauka i kosmos

2 хв читання

Metrics

2 Min Read Metrics Services Catalog Click here to view the FY26 Services Catalog The catalogs provide service description, chargeback rate, unit of measure, and service level indicators for each NSSC service. Service Level Agreement (SLA) Click here to view the Service Level Agreement The SLA provides information about roles, responsibilities, rates, and service level indicators for all NASA Centers. The SLA is negotiated on an annual basis in line with the fiscal year. A single SLA is shared by all NASA Centers and signed by the Associate Administrator, Chief Financial Officer, Chief Information Officer, and the Office of Inspector General. The SLA provides for the delivery of specific services from the NSSC to NASA Centers and Headquarters Operations in the areas of: Financial Management Procurement Human Resources Information Technology Agency Business Services NSSC Bill (Formerly know as Performance and Utilization Report (PUR)) *** On-Line Course Management and Training Purchases have been realigned to the OLC &Training Purchases section of the bill in accordance with the realignment of training funds. Center Special Projects have been consolidated into one Special Projects bill with the funding Center identified for each project.*** FY 2026 – Utilization Reports October 2025 November 2025 December 2025 January 2026 February 2026 March 2026 April 2026 FY 2025 – Utilization Reports September 2025 August 2025 July 2025 June 2025 May 2025 April 2025 March 2025 February 2025 January 2025 December 2024 November 2024 October 2024 FY 2024 – Utilization Reports September 2024 August 2024 July 2024 June 2024 May 2024 April 2024 March 2024 February 2024 January 2024 December 2023 November 2023 October 2023  

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Science & Space

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Metrics

2 Min Read Metrics Services Catalog Click here to view the FY26 Services Catalog The catalogs provide service description, chargeback rate, unit of measure, and service level indicators for each NSSC service. Service Level Agreement (SLA) Click here to view the Service Level Agreement The SLA provides information about roles, responsibilities, rates, and service level indicators for all NASA Centers. The SLA is negotiated on an annual basis in line with the fiscal year. A single SLA is shared by all NASA Centers and signed by the Associate Administrator, Chief Financial Officer, Chief Information Officer, and the Office of Inspector General. The SLA provides for the delivery of specific services from the NSSC to NASA Centers and Headquarters Operations in the areas of: Financial Management Procurement Human Resources Information Technology Agency Business Services NSSC Bill (Formerly know as Performance and Utilization Report (PUR)) *** On-Line Course Management and Training Purchases have been realigned to the OLC &Training Purchases section of the bill in accordance with the realignment of training funds. Center Special Projects have been consolidated into one Special Projects bill with the funding Center identified for each project.*** FY 2026 – Utilization Reports October 2025 November 2025 December 2025 January 2026 February 2026 March 2026 April 2026 FY 2025 – Utilization Reports September 2025 August 2025 July 2025 June 2025 May 2025 April 2025 March 2025 February 2025 January 2025 December 2024 November 2024 October 2024 FY 2024 – Utilization Reports September 2024 August 2024 July 2024 June 2024 May 2024 April 2024 March 2024 February 2024 January 2024 December 2023 November 2023 October 2023  

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Наука і космос

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Metrics

2 Min Read Metrics Services Catalog Click here to view the FY26 Services Catalog The catalogs provide service description, chargeback rate, unit of measure, and service level indicators for each NSSC service. Service Level Agreement (SLA) Click here to view the Service Level Agreement The SLA provides information about roles, responsibilities, rates, and service level indicators for all NASA Centers. The SLA is negotiated on an annual basis in line with the fiscal year. A single SLA is shared by all NASA Centers and signed by the Associate Administrator, Chief Financial Officer, Chief Information Officer, and the Office of Inspector General. The SLA provides for the delivery of specific services from the NSSC to NASA Centers and Headquarters Operations in the areas of: Financial Management Procurement Human Resources Information Technology Agency Business Services NSSC Bill (Formerly know as Performance and Utilization Report (PUR)) *** On-Line Course Management and Training Purchases have been realigned to the OLC &Training Purchases section of the bill in accordance with the realignment of training funds. Center Special Projects have been consolidated into one Special Projects bill with the funding Center identified for each project.*** FY 2026 – Utilization Reports October 2025 November 2025 December 2025 January 2026 February 2026 March 2026 April 2026 FY 2025 – Utilization Reports September 2025 August 2025 July 2025 June 2025 May 2025 April 2025 March 2025 February 2025 January 2025 December 2024 November 2024 October 2024 FY 2024 – Utilization Reports September 2024 August 2024 July 2024 June 2024 May 2024 April 2024 March 2024 February 2024 January 2024 December 2023 November 2023 October 2023  

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Dane i statystyki

1 хв читання

TEN00136 - "Dataset: updated structure and data"

Environmental protection investments of total economy

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Data & Statistics

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TEN00136 - "Dataset: updated structure and data"

Environmental protection investments of total economy

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Дані і статистика

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TEN00136 - "Dataset: updated structure and data"

Environmental protection investments of total economy

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Dane i statystyki

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TEN00135 - "Dataset: updated structure and data"

National expenditure on environmental protection

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Data & Statistics

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TEN00135 - "Dataset: updated structure and data"

National expenditure on environmental protection

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