Time series data for the statistic Consumer Price Index, All items, Percentage change, Previous period and country Dominica. Indicator Definition:Consumer Price Index, All items, Percentage change, Previous periodThe indicator "Consumer Price Index, All items, Percentage change, Previous period" stands at 0.0082 as of 1/31/2025. Regarding the One-Year-Change of the series, the current value constitutes an increase of 0.185 compared to the value the year prior.The Serie's long term average value is 0.143. It's latest available value, on 1/31/2025, is -0.135 lower, compared to it's long term average value.The Serie's change from it's minimum value, on 12/31/2017, to it's latest available value, on 1/31/2025, is +4.04 .The Serie's change from it's maximum value, on 7/31/2003, to it's latest available value, on 1/31/2025, is -2.56 .

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.

This data set contains annual and monthly data for exchange rates important to U.S. agriculture. It includes both nominal and real exchange rates for 79 countries, plus the European Union (EU), as well as real trade-weighted exchange rate indexes for many commodities and aggregations.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with links to Excel files For complete information, please visit https://data.gov.

Time series data for the statistic Consumer Price Index, All items, Percentage change, Previous period and country Bhutan. Indicator Definition:Consumer Price Index, All items, Percentage change, Previous periodThe indicator "Consumer Price Index, All items, Percentage change, Previous period" stands at 0.2107 as of 3/31/2025. Regarding the One-Year-Change of the series, the current value constitutes an increase of 0.1419 compared to the value the year prior.The Serie's long term average value is 0.416. It's latest available value, on 3/31/2025, is -0.206 lower, compared to it's long term average value.The Serie's change from it's minimum value, on 5/31/2024, to it's latest available value, on 3/31/2025, is +3.17 .The Serie's change from it's maximum value, on 7/31/2020, to it's latest available value, on 3/31/2025, is -3.06 .

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 benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Slope-Area Index (SAI) is used to predict erosion along a stream channel. It is a function of channel slope, and drainage area upstream raised to the exponent used in equations for flood frequency of 2-percent exceedance floods. The guidelines for use of the coefficient from 2-percent exceedance came from meetings with cooperators describing bankfull discharge as a 2-percent exceedance. The Slope-Area Index, as defined by Cartwright and Diehl, 2017, is calculated as: SAI = S * A^b (1) where SAI is the slope-area index, S is the channel slope, A is the drainage area (the number of cells draining into the target cell), and b is a user-specified exponent. The flood frequency report for NC (Weaver and others, 2009) defines the regional regression equations for exceedance flows for rural basins in the Southeast. The 2-percent chance exceedance flow raises the Drainage Area uses a 0.60 coefficient for all the physiographic provinces other than Small Urban basins in the Piedmont. The Urban equations drainage area coefficients for 2-percent exceedance flow (Feaster and others, 2014) were used for Piedmont (HLR2) urban basins. Urban basins were defined as catchments which were greater than 10% developed (urban). For piedmont (HLR2, small urban le 3 dasqmi and pcturb gt 10%) SAI = slope*da^0.8 For piedmont (HLR2, small urban dasqmi gt 3 and le 436 and pcturb ge 10%) SAI = slope * da^.5

Retention indices based on n-alkanes are a relative measure of the retention of chemical compounds in gas chromatography. Unlike retention times, retention indices are independent of most experimental conditions (flow rate, temperature, etc.) and depend mainly on the type of stationary phase and the structure of the compound. This is a small free data set made by us containing times and retention indices of 200+ organic molecules. The data were obtained under gas chromatography conditions.A set of 240 organic compounds of various chemical natures was used. Most of the compounds were purchased from commercial suppliers. All standard samples were tested by GC-MS (electron ionization) using the NIST 17 database. A standard mixture of C7-C40 n-alkanes was used (1000 μg/ml of each component in hexane, Sigma-Aldrich). Acetonitrile (UHPLC-Supergradient PAI-ACS, Panreac) was used to dissolve standard compounds. A sample of 1.5 mg of solid or 1 μL of liquid of each substance was dissolved in 1 mL of acetonitrile. Analyzes were carried out on a gas chromatograph-mass spectrometer Shimadzu GCMS-TQ8040 with a quadrupole mass analyzer. Up to 10 compounds (~1.5 mg/ml each) were mixed in one batch. When peak annotation was ambiguous, measurements of individual compounds were performed.The following chromatographic columns were used: ZB-5MS (60 m x 0.25 mm x 0.25 μm, Agilent) and SH-Stabilwax (30 m x 0.25 mm x 0.1 μm, Shimadzu). Length, inner diameter, and thickness of the layer are given.Measurements on the ZB-5MS column were performed under the following conditions: temperature of injector: 250 °C; temperature of ion source: and 200 °C; carrier gas: He; flow control mode: constant linear velocity; column flow rate: 1.47 ml/min; injection split ratio: 1:50; temperature programming: the temperature was kept at 30 °C during 5.5 min, then raised from 30 °C to 320 °C at 4 °C/min rate and then was kept constant during 10 min; the mass detector was used in electron ionization mode at 70 eV, scan rate: 1666 units/s, mass range: 44 - 500 m/z.Measurements on the SH-Stabilwax column were performed under the following conditions: temperature of injector: 250 °C; temperature of ion source: and 200 °C; carrier gas: He; flow control mode: constant linear velocity; column flow rate: 1.0 ml/min; injection split ratio: 1:50; temperature programming: the temperature was raised from 50 °C to 240 °C at 8 °C/min rate and then was kept constant during 30 min; the mass detector was used in electron ionization mode at 70 eV, scan rate: 2500 units/s, mass range: 45 - 500 m/z.The data set can be used in chemical analysis and as a benchmark for machine learning. The data set contains 240 entries for the non-polar stationary phase (ZB-5MS) and 202 for the polar stationary phase (SH-Stabilwax).Predictions of retention indices for a range of molecules made using seven machine learning models published in the literature are given in predictions.xlsxThis research was supported by the Ministry of Science and Higher Education of the Russian Federation (№ 124041900012-4).

High-resolution transmission electron microscopy (HRTEM) is an important technique for investigating nanoparticles at atomic resolution. One drawback is that the intense electron beam required for sufficient electron signal can be harmful for the sample. Lowering the electron beam intensity (electron dose rate) can cause less damage to the sample. However, the latter can result in images drowning in noise. Novel techniques applying neural networks in machine learning can be applied to detect events during a series of HRTEM images recorded at low electron dose rate.To assist the machine learning approach, novel signal-to-noise ratio (SNR) models are applied to series of HRTEM images at varied electron dose rates. Furthermore, a novel approach of structural similarity index measurement (SSIM), where each frame is compared to an adjacent frame, is applied to the HRTEM image series.The HRTEM image series consist of .dm4 files for each point in time. All files are compressed into a folder (.zip). The dataset provided is an example of 50 frames, each recorded at 0.2 s exposure time, at a fixed dose rate.Obtaining SNR and SSIM values of the HRTEM image series is done using Jupyter notebooks. An example of such is obtainable at a GitLab repository: https://gitlab.com/wibang_dtu_91dk/doserate-snr-ssim/The Jupyther notebook loads the individual .dm4 files into a stack. Using a specific package called HyperSpy, the user can browse through the series and subsequently extract data from selected areas used for the SNR and SSIM. The Jupyter notebook exports the data into data sheets (.csv), which can be loaded into other scripts for further treatments. Finally, the script can also export the data as a video (.mp4 and .gif) at a specified frame rate.The main work referring to the Jupyter notebook and dataset is a recently accepted article: W. B. Lomholdt, M. H. L. Larsen, C. N. Valencia, J. Schiøtz and T. W. Hansen, "Interpretability of high-resolution transmission electron microscopy images", Ultramicroscopy, vol. 263, 2024, pp. 113997, https://doi.org/10.1016/j.ultramic.2024.113997

Time series data for the statistic Consumer Price Index, All items, Percentage change, Previous period and country Rwanda. Indicator Definition:Consumer Price Index, All items, Percentage change, Previous periodThe indicator "Consumer Price Index, All items, Percentage change, Previous period" stands at -0.0155 as of 6/30/2025. Regarding the One-Year-Change of the series, the current value constitutes an increase of 0.5963 compared to the value the year prior.The Serie's long term average value is 0.597. It's latest available value, on 6/30/2025, is -0.612 lower, compared to it's long term average value.The Serie's change from it's minimum value, on 12/31/2023, to it's latest available value, on 6/30/2025, is +4.39 .The Serie's change from it's maximum value, on 10/31/2022, to it's latest available value, on 6/30/2025, is -5.66 .

Abstract

The purpose of the SEPHER data set is to allow for testing, assessing and generating new analysis and metrics that can address inequalities and climate injustice. The data set was created by Tedesco, M., C. Hultquist, S. E. Char, C. Constantinides, T. Galjanic, and A. D. Sinha.

Methodology

SEPHER draws upon four major source datasets: CDC Social Vulnerability Index, FEMA National Risk Index, Home Mortgage Disclosure Act, and Evictions datasets. The data from these source datasets have been merged, cleaned, and standardized and all of the variables documented in the data dictionary.

CDC Social Vulnerability Index

CDC Social Vulnerability Index (SVI) dataset is a dataset prepared for the Centers for Disease Control and Prevention for the purpose of assessing the degree of social vulnerability of American communities to natural hazards and anthropogenic events. It contains data on 15 social factors taken or derived from Census reports as well as rankings of each tract based on these individual factors, groups of factors corresponding to four related themes (Socioeconomic, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation) and overall. The data is available for the years 2000, 2010, 2014, 2016, and 2018.

FEMA National Risk Index

The National Risk Index (NRI) dataset compiled by the Federal Emergency Management Agency (FEMA) consists of historic natural disaster data from across the United States at a tract-level. The dataset includes information about 18 natural disasters including earthquakes, tsunamis, wildfires, volcanic activity and many others. Each disaster is detailed out in terms of its frequency, historic impact, potential exposure, expected annual loss and associated risk. The dataset also includes some summary variables for each tract including the total expected loss in terms of building loss, human loss and agricultural loss, the population of the tract, and the area covered by the tract. It finally includes a few more features to characterize the population such as social vulnerability rating and community resilience.

Home Mortgage Disclosure Act

The Home Mortgage Disclosure Act (HMDA) dataset contains loan-level data for home mortgages including information on applications, denials, approvals, and institution purchases. It is managed and expanded annually by the Consumer Financial Protection Bureau based on the data collected from financial institutions. The dataset is used by public officials to make decisions and policies, uncover lending patterns and discrimination among mortgage applicants, and investigate if lenders are serving the housing needs of the communities. It covers the period from 2007 to 2017.

Evictions

The Evictions dataset is compiled and managed by the Eviction Lab at Princeton University and consists of court records related to eviction cases in the United States between 2000 and 2016. Its purpose is to estimate the prevalence of court-ordered evictions and compare eviction rates among states, counties, cities, and neighborhoods. Besides information on eviction filings and judgments, the dataset includes socioeconomic and real estate data for each tract including race/ethnic origin, household income, poverty rate, property value, median gross rent, rent burden, and others.

Time series data for the statistic Consumer Price Index, All items, Percentage change, Previous period and country Uganda. Indicator Definition:Consumer Price Index, All items, Percentage change, Previous periodThe indicator "Consumer Price Index, All items, Percentage change, Previous period" stands at 0.0977 as of 6/30/2025. Regarding the One-Year-Change of the series, the current value constitutes an increase of 0.0643 compared to the value the year prior.The Serie's long term average value is 0.447. It's latest available value, on 6/30/2025, is -0.349 lower, compared to it's long term average value.The Serie's change from it's minimum value, on 6/30/2012, to it's latest available value, on 6/30/2025, is +1.12 .The Serie's change from it's maximum value, on 9/30/2011, to it's latest available value, on 6/30/2025, is -5.07 .

Time series data for the statistic Consumer Price Index, All items, Percentage change, Previous period and country Suriname. Indicator Definition:Consumer Price Index, All items, Percentage change, Previous periodThe indicator "Consumer Price Index, All items, Percentage change, Previous period" stands at 0.4639 as of 2/28/2025. Regarding the One-Year-Change of the series, the current value constitutes an increase of 0.0853 compared to the value the year prior.The Serie's long term average value is 1.41. It's latest available value, on 2/28/2025, is -0.949 lower, compared to it's long term average value.The Serie's change from it's minimum value, on 11/30/2008, to it's latest available value, on 2/28/2025, is +3.64 .The Serie's change from it's maximum value, on 11/30/2015, to it's latest available value, on 2/28/2025, is -15.12 .

1) data content: social and economic data of major countries and regions in the pan third polar region, including four categories: urbanization index, economic and industrial index, population index and social index, including urbanization rate, total population, population in the largest city, population, GDP, life expectancy and other indicators in the urban agglomeration with population over 1 million; 2) data source and processing method: data source World Bank, 65 countries and regions of Pan third pole are extracted, others are not processed; 3) data quality description: some data are missing from 1960-1992; 4) data application results and prospects: it can be used for urbanization and other socio-economic analysis.

This table contains 13 series, with data from 1949 (not all combinations necessarily have data for all years). Data are presented for the current month and previous four months. Users can select other time periods that are of interest to them.

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

This dataset contains a selection of six socioeconomic indicators of public health significance and a “hardship index,” by Chicago community area, for the years 2008 – 2012. The indicators are the percent of occupied housing units with more than one person per room (i.e., crowded housing); the percent of households living below the federal poverty level; the percent of persons in the labor force over the age of 16 years that are unemployed; the percent of persons over the age of 25 years without a high school diploma; the percent of the population under 18 or over 64 years of age (i.e., dependency); and per capita income. Indicators for Chicago as a whole are provided in the final row of the table.

Under the background of global warming, the frequency and intensity of drought are increasing. The lack of water resources, food crisis and ecological deterioration (such as desertification) caused by drought disasters directly threaten the national food security and social and economic development. The technical level of drought disaster risk assessment and emergency management needs to be improved. One belt, one road area has one belt, one road area is fragile, agricultural land is concentrated and drought is frequent. Monitoring the drought level and its temporal and spatial changes in large areas by using remote sensing satellites is of great scientific and practical significance for scientifically grasping the drought pattern, regional differentiation characteristics and its impact on agricultural land in the "one belt and one road" area. The percentage of precipitation anomaly reflects the deviation degree between the precipitation of a certain period and the average state of the same period, expressed as a percentage. Based on the daily rainfall data of GPM imerg final run (GPM), the precipitation of corresponding area is calculated. The distribution characteristics of drought of different grades are analyzed by using the grade evaluation index of precipitation anomaly percentage. The spatial resolution is 200m. The data area is 34 key nodes of Pan third pole (Abbas, Astana, Colombo, Gwadar, Mengba, Teheran, Vientiane, etc.).

This measure answers the question of what number and percentage of residents are living below the federal poverty level, which means they meet certain threshold set by a set of parameters and computation performed by the Census Bureau. Following the Office of Management and Budget's (OMB) Statistical Policy Directive 14, the Census Bureau uses a set of money income thresholds that vary by family size and composition to determine who is in poverty. If a family's total income is less than the family's threshold, then that family and every individual in it is considered in poverty. The official poverty thresholds do not vary geographically, but they are updated for inflation using the Consumer Price Index (CPI-U). The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). Data collected from the U.S. Census Bureau, American Communities Survey (1yr), Poverty Status in the Past 12 Months (Table S1701). American Communities Survey (ACS) is a survey with sampled statistics on the citywide level and is subject to a margin of error. ACS sample size and data quality measures can be found on the U.S. Census website in the Methodology section.

This CSV file contains cumulative fish habitat condition index (HCI) scores generated for river reaches of the conterminous United States as well as indices generated specifically for four spatial units including local and network catchments and 90 m local and network buffers of river reaches. Note that the cumulative HCI score is determined from limiting index scores generated for the four spatial units listed above. Detailed methods for calculating cumulative fish habitat condition index scores as well as the indices for each spatial extent can be found on the following website: http://assessment.fishhabitat.org/: The variables used to create indices in catchments vs. buffers differ due to differences in resolution of datasets. The following anthropogenic disturbance variables were used to create local and network catchment indices: Percent of urban land use, percent of impervious surface, human population density, road density, percent of pasture/hay, percent of cultivated crops, density of point source pollution sites (National Pollution Discharge Elimination, Toxic Inventory Release and National Superfund), nutrient and sediment loading to watersheds, habitat fragmentation metrics (density of dams and road crossings), density of mines and water withdrawals. The following anthropogenic disturbance variables were analyzed to create the local and network buffer indices: percent of urban land use, percent of agriculture, percent of pasture/hay and percent of impervious surface. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment and buffer units. Also included in this CSV file are the most limiting and severe disturbances to stream reaches operating within each of the four spatial extents. Limiting disturbances are defined as those disturbances that result in a stream reach not being in the best available condition determined for the region. Severe disturbances are a subset of limiting disturbances that are associated with stream reaches in a given region that were scored as having high or very high risk of habitat degradation (red and orange color groups). In this data set, indices as well as limiting and severe disturbances are linked to the stream reaches, catchments and buffers created for the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. It is important to recognize that these broadly-defined disturbance variables often act together with other measured or unmeasured threats to degrade habitat. Thus, while we may identify “urbanization” as a major threat to fish habitat in some regions, “urbanization” represents an umbrella term that describes many facets of urban development that could cause degradation to habitats. Fields in this dataset that begin with the "L_" prefix represent the local catchment whereas network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix. Like the catchment variables the buffer variables are labeled using a "LB_" and "NB_" prefix for local buffer and network buffer variables, respectively. More information about the processes used to create scores can be found in the processes section. Version 2.0 includes the addition of severe disturbances for each spatial scale and fixes errors documented in the change log.