The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The U.S. federal funds effective rate underwent a dramatic reduction in early 2020 in response to the COVID-19 pandemic. The rate plummeted from 1.58 percent in February 2020 to 0.65 percent in March, and further decreased to 0.05 percent in April. This sharp reduction, accompanied by the Federal Reserve's quantitative easing program, was implemented to stabilize the economy during the global health crisis. After maintaining historically low rates for nearly two years, the Federal Reserve began a series of rate hikes in early 2022, with the rate moving from 0.33 percent in April 2022 to 5.33 percent in August 2023. The rate remained unchanged for over a year, before the Federal Reserve initiated its first rate cut in nearly three years in September 2024, bringing the rate to 5.13 percent. By December 2024, the rate was cut to 4.48 percent, signaling a shift in monetary policy in the second half of 2024. In January 2025, the Federal Reserve implemented another cut, setting the rate at 4.33 percent, which remained unchanged throughout the following months. What is the federal funds effective rate? The U.S. federal funds effective rate determines the interest rate paid by depository institutions, such as banks and credit unions, that lend reserve balances to other depository institutions overnight. Changing the effective rate in times of crisis is a common way to stimulate the economy, as it has a significant impact on the whole economy, such as economic growth, employment, and inflation. Central bank policy rates The adjustment of interest rates in response to the COVID-19 pandemic was a coordinated global effort. In early 2020, central banks worldwide implemented aggressive monetary easing policies to combat the economic crisis. The U.S. Federal Reserve's dramatic reduction of its federal funds rate - from 1.58 percent in February 2020 to 0.05 percent by April - mirrored similar actions taken by central banks globally. While these low rates remained in place throughout 2021, mounting inflationary pressures led to a synchronized tightening cycle beginning in 2022, with central banks pushing rates to multi-year highs. By mid-2024, as inflation moderated across major economies, central banks began implementing their first rate cuts in several years, with the U.S. Federal Reserve, Bank of England, and European Central Bank all easing monetary policy.

Graph and download economic data for FOMC Summary of Economic Projections for the Fed Funds Rate, Median (FEDTARMD) from 2025 to 2027 about projection, federal, median, rate, and USA.

The benchmark interest rate in Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

View data of the Effective Federal Funds Rate, or the interest rate depository institutions charge each other for overnight loans of funds.

The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Mexico was last recorded at 8 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Effective Federal Funds Rate in the United States remained unchanged at 4.33 percent on Wednesday July 2. This dataset includes a chart with historical data for the United States Effective Federal Funds Rate.

The benchmark interest rate in Indonesia was last recorded at 5.25 percent. This dataset provides - Indonesia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to May 2025 about savings, personal, rate, and USA.

About this dataset

Context

The dataset tabulates the Federal Dam population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Federal Dam across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Federal Dam was 124, a 0.80% decrease year-by-year from 2022. Previously, in 2022, Federal Dam population was 125, an increase of 0.81% compared to a population of 124 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Federal Dam increased by 20. In this period, the peak population was 125 in the year 2022. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Federal Dam is shown in this column.
• Year on Year Change: This column displays the change in Federal Dam population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Federal Dam Population by Year. You can refer the same here

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://ngda-cadastre-geoplatform.hub.arcgis.com/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g., 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. PAD-US provides a full inventory geodatabase, spatial analysis, statistics, data downloads, web services, poster maps, and data submissions included in efforts to track global progress toward biodiversity protection. PAD-US integrates spatial data to ensure public lands and other protected areas from all jurisdictions are represented. PAD-US version 4.0 includes new and updated data from the following data providers. All other data were transferred from previous versions of PAD-US. Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in regular PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. Revisions associated with the federal estate in this version include updates to the Federal estate (fee ownership parcels, easement interest, management designations, and proclamation boundaries), with authoritative data from 7 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), and the U.S. Forest Service (USFS). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://ngda-gov-units-geoplatform.hub.arcgis.com/pages/federal-lands-workgroup/ ). This includes improved the representation of boundaries and attributes for the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. Additionally, National Cemetery boundaries were added using geospatial boundary data provided by the U.S. Department of Veterans Affairs and NASA boundaries were added using data contained in the USGS National Boundary Dataset (NBD). State Updates - USGS is committed to building capacity in the state data steward network and the PAD-US Team to increase the frequency of state land and NGO partner updates, as resources allow. State Lands Workgroup ( https://ngda-gov-units-geoplatform.hub.arcgis.com/pages/state-lands-workgroup ) is focused on improving protected land inventories in PAD-US, increase update efficiency, and facilitate local review. PAD-US 4.0 included updates and additions from the following seventeen states and territories: California (state, local, and nonprofit fee); Colorado (state, local, and nonprofit fee and easement); Georgia (state and local fee); Kentucky (state, local, and nonprofit fee and easement); Maine (state, local, and nonprofit fee and easement); Montana (state, local, and nonprofit fee); Nebraska (state fee); New Jersey (state, local, and nonprofit fee and easement); New York (state, local, and nonprofit fee and easement); North Carolina (state, local, and nonprofit fee); Pennsylvania (state, local, and nonprofit fee and easement); Puerto Rico (territory fee); Tennessee (land trust fee); Texas (state, local, and nonprofit fee); Virginia (state, local, and nonprofit fee); West Virginia (state, local, and nonprofit fee); and Wisconsin (state fee data). Additionally, the following datasets were incorporated from NGO data partners: Trust for Public Land (TPL) Parkserve (new fee and easement data); The Nature Conservancy (TNC) Lands (fee owned by TNC); TNC Northeast Secured Areas; Ducks Unlimited (land trust fee); and the National Conservation Easement Database (NCED). All state and NGO easement submissions are provided to NCED. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/programs/gap-analysis-project/science/protected-areas . For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/programs/gap-analysis-project/science/protected-areas . For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-history/ for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B 9) Revised - April 2024 (Version 4.0) https://doi.org/10.5066/P96WBCHS Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.

Executive Summary: Artificial intelligence (AI) is a transformative technology that holds promise for tremendous societal and economic benefit. AI has the potential to revolutionize how we live, work, learn, discover, and communicate. AI research can further our national priorities, including increased economic prosperity, improved educational opportunities and quality of life, and enhanced national and homeland security. Because of these potential benefits, the U.S. government has invested in AI research for many years. Yet, as with any significant technology in which the Federal government has interest, there are not only tremendous opportunities but also a number of considerations that must be taken into account in guiding the overall direction of Federally-funded R&D in AI. On May 3, 2016,the Administration announced the formation of a new NSTC Subcommittee on Machine Learning and Artificial intelligence, to help coordinate Federal activity in AI.1 This Subcommittee, on June 15, 2016, directed the Subcommittee on Networking and Information Technology Research and Development (NITRD) to create a National Artificial Intelligence Research and Development Strategic Plan. A NITRD Task Force on Artificial Intelligence was then formed to define the Federal strategic priorities for AI R&D, with particular attention on areas that industry is unlikely to address. This National Artificial Intelligence R&D Strategic Plan establishes a set of objectives for Federallyfunded AI research, both research occurring within the government as well as Federally-funded research occurring outside of government, such as in academia. The ultimate goal of this research is to produce new AI knowledge and technologies that provide a range of positive benefits to society, while minimizing the negative impacts. To achieve this goal, this AI R&D Strategic Plan identifies the following priorities for Federally-funded AI research: Strategy 1: Make long-term investments in AI research. Prioritize investments in the next generation of AI that will drive discovery and insight and enable the United States to remain a world leader in AI. Strategy 2: Develop effective methods for human-AI collaboration. Rather than replace humans, most AI systems will collaborate with humans to achieve optimal performance. Research is needed to create effective interactions between humans and AI systems. Strategy 3: Understand and address the ethical, legal, and societal implications of AI. We expect AI technologies to behave according to the formal and informal norms to which we hold our fellow humans. Research is needed to understand the ethical, legal, and social implications of AI, and to develop methods for designing AI systems that align with ethical, legal, and societal goals. Strategy 4: Ensure the safety and security of AI systems. Before AI systems are in widespread use, assurance is needed that the systems will operate safely and securely, in a controlled, well-defined, and well-understood manner. Further progress in research is needed to address this challenge of creating AI systems that are reliable, dependable, and trustworthy. Strategy 5: Develop shared public datasets and environments for AI training and testing. The depth, quality, and accuracy of training datasets and resources significantly affect AI performance. Researchers need to develop high quality datasets and environments and enable responsible access to high-quality datasets as well as to testing and training resources. Strategy 6: Measure and evaluate AI technologies through standards and benchmarks. . Essential to advancements in AI are standards, benchmarks, testbeds, and community engagement that guide and evaluate progress in AI. Additional research is needed to develop a broad spectrum of evaluative techniques. Strategy 7: Better understand the national AI R&D workforce needs. Advances in AI will require a strong community of AI researchers. An improved understanding of current and future R&D workforce demands in AI is needed to help ensure that sufficient AI experts are available to address the strategic R&D areas outlined in this plan. The AI R&D Strategic Plan closes with two recommendations: Recommendation 1: Develop an AI R&D implementation framework to identify S&T opportunities and support effective coordination of AI R&D investments, consistent with Strategies 1-6 of this plan. Recommendation 2: Study the national landscape for creating and sustaining a healthy AI R&D workforce, consistent with Strategy 7 of this plan.

About this dataset

Context

The dataset tabulates the Federal Way population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Federal Way across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Federal Way was 97,701, a 0.18% decrease year-by-year from 2022. Previously, in 2022, Federal Way population was 97,881, a decline of 1.45% compared to a population of 99,324 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Federal Way increased by 12,421. In this period, the peak population was 100,988 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Federal Way is shown in this column.
• Year on Year Change: This column displays the change in Federal Way population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Federal Way Population by Year. You can refer the same here

Household Saving Rate in the United States decreased to 4.50 percent in May from 4.90 percent in April of 2025. This dataset provides - United States Personal Savings Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

US Forest Service Forest Inventory and Analysis National Program.

The Forest Inventory and Analysis (FIA) Program of the U.S. Forest Service provides the information needed to assess America's forests.

https://www.fia.fs.fed.us/

Content

As the Nation's continuous forest census, our program projects how forests are likely to appear 10 to 50 years from now. This enables us to evaluate whether current forest management practices are sustainable in the long run and to assess whether current policies will allow the next generation to enjoy America's forests as we do today.

FIA reports on status and trends in forest area and location; in the species, size, and health of trees; in total tree growth, mortality, and removals by harvest; in wood production and utilization rates by various products; and in forest land ownership.

The Forest Service has significantly enhanced the FIA program by changing from a periodic survey to an annual survey, by increasing our capacity to analyze and publish data, and by expanding the scope of our data collection to include soil, under story vegetation, tree crown conditions, coarse woody debris, and lichen community composition on a subsample of our plots. The FIA program has also expanded to include the sampling of urban trees on all land use types in select cities.

For more details, see: https://www.fia.fs.fed.us/library/database-documentation/current/ver70/FIADB%20User%20Guide%20P2_7-0_ntc.final.pdf

Fork this kernel to get started with this dataset.

Acknowledgements

https://www.fia.fs.fed.us/

https://cloud.google.com/blog/big-data/2017/10/get-to-know-your-trees-us-forest-service-fia-dataset-now-available-in-bigquery

FIA is managed by the Research and Development organization within the USDA Forest Service in cooperation with State and Private Forestry and National Forest Systems. FIA traces it's origin back to the McSweeney - McNary Forest Research Act of 1928 (P.L. 70-466). This law initiated the first inventories starting in 1930.

Banner Photo by @rmorton3 from Unplash.

Inspiration

Estimating timberland and forest land acres by state.

https://cloud.google.com/blog/big-data/2017/10/images/4728824346443776/forest-data-4.png" alt="enter image description here"> https://cloud.google.com/blog/big-data/2017/10/images/4728824346443776/forest-data-4.png

View economic output, reported as the nominal value of all new goods and services produced by labor and property located in the U.S.

This hosted feature layer has been published in RI State Plane Feet NAD 83.The RI Neighborhood Stabilization Program (NSP) Mapping analysis was performed to assist the Office of Housing and Community Development in identifying target areas with both a Foreclosure Rate (Block Group Level) >=6.5% and a Subprime Loan percentage rate >= 1.4% (Zip Code Level). Based on these criteria the following communities were identified as containing such target areas: Central Falls, Cranston, Cumberland, East Providence, Johnston, North Providence, Pawtucket, Providence, Warwick, West Warwick, and Woonsocket. Federal funding, under the Housing and Economic Recovery Act of 2008 (HERA), Neighborhood Stabilization Program (NSP), totaling $19.6 will be expended in these NSP Target Areas to assist in the rehabilitation and redevelopment of abandoned and foreclosed homes, stabilizing communities.The State of Rhode Island distributes funds allocated, giving priority emphasis and consideration to those areas with the greatest need, including those areas with - 1) Highest percentage of home foreclosures; 2) Highest percentage of homes financed by subprime mortgage loans; and 3) Anticipated increases in rate of foreclosure. The RI Office of Housing and Community Development, with the assistance of Rhode Island Housing, utilized the following sources to meet the above requirements. 1) U.S. Department of Housing & Urban Development (HUD) developed foreclosure data to assist grantees in identification of Target Areas. The State utilized HUD's predictive foreclosure rates to identify those areas which are likely to face a significant rise in the rate of home foreclosures. HUD's methodology factored in Home Mortgage Disclosure Act, income, unemployment, and other information in its calculation. The results were analyzed and revealed a high level of consistency with other needs data available. 2) The State obtained subprime mortgage loan information from the Federal Reserve Bank of Boston. Though the data does not include all mortgages, and was only available at the zip code level rather than Census Tract, findings were generally consistent with other need categories. This data was joined to the Foreclosure dataset in order to select areas with both a Foreclosure Rate >=6.5% and a Subprime Loan Rate >=1.4%. 3) The State also obtained, from the Warren Group, actual local foreclosure transaction records. The Warren Group is a source for real estate and banking news and transaction data throughout New England. This entity has analyzed local deed records in assembling information presented. The data set was normalized due to potential limitations. An analysis revealed a high level of consistency with HUD-predictive foreclosure rates.

Inflation Rate in the United States increased to 2.70 percent in June from 2.40 percent in May of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.