The Retail Price Index (RPI) is one of the main measures of inflation used to calculate the change in the price of goods and services within the British economy. In the second quarter of 2025 the index value was 403.2, indicating that the price for a fixed basket of goods had increased by almost more than 300 percent since 1987. The RPI inflation rate for June 2025 was 4.4 percent, up from 3.2 percent in March 2025 Inflation and UK living standards For UK consumers, high inflation is one of the main drivers of the ongoing cost of living crisis. With wages struggling to keep up with the pace of inflation for a long period between 2021 and 2023, UK households saw their living standards fall significantly. In 2022/23, real household disposable income in the UK is estimated to have fallen by 2.1 percent, which was the biggest fall in living standards since 1956. While there have been some signals that the crisis eased somewhat in 2024, such as falling energy and food inflation, an increasing share of UK households have reported increasing living costs since Summer 2024. Additional inflation indicators Aside from the Retail Price Index, the UK also produces other inflation indices such as the Consumer Price Index (CPI) and the Consumer Price Index including owner occupiers' housing costs (CPIH). While these particular indices measure consumer price increases slightly differently, they both provide an overall picture of rising prices. More specific inflation rates, such as by sector, are also produced, while other indices omit certain items, such as core inflation, which excludes food and energy inflation, to provide a more stable measure of inflation.

The inflation rate for the Retail Price Index (RPI) in the United Kingdom was 4.4 percent in June 2025, up from 4.3 percent in the previous month. From 2021 onwards, prices in the UK rose rapidly, with the RPI inflation rate peaking at 14.2 percent in October 2022. Although inflation fell in subsequent months, it wasn't until July 2023 that inflation fell below double digits, and as of late 2024, the RPI rate was still above three percent. The CPI and CPIH While the retail price index is still a popular method of calculating inflation, the consumer price index (CPI) is the current main measurement of inflation in the UK. There is also an additional price index, which includes some extra housing costs, known as the Consumer Price Index including homer occupiers' costs (CPIH) index, which is seen by the UK's Office of National Statistics as the official inflation rate. As of December 2024, the CPI inflation rate stood at 2.5 percent, while the CPIH rate was 3.5 percent. Core inflation down in 2024 Another way of measuring inflation is to strip out the volatility of energy and food prices and look at the underlying core inflation rate. As of December 2024, this was 3.2 percent, slightly higher than the overall CPI rate, but more aligned with the overall figure than it was in 2022 and 2023. When inflation peaked at 11.2 percent in October 2022, for example, core inflation stood at just 6.5 percent. After energy prices in 2023 fell relative to 2022, the overall inflation rate in the UK declined quite rapidly, with core inflation overtaking the overall rate in July 2023. During the most recent period of high inflation, core inflation peaked at 7.1 percent in May 2023, and while taking longer to fall than the overall figure, has generally been declining since then.

Retail Price Index in the United Kingdom increased to 4.40 percent in June from 4.30 percent in May of 2025. This dataset provides - United Kingdom Retail Price Index YoY- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation is an important measure of any country’s economy, and the Retail Price Index (RPI) is one of the most widely used indicators in the United Kingdom, with the rate expected to be 4.1 percent in 2025, compared with 3.6 percent in 2024. This followed 2022, when RPI inflation reached a rate of 11.6 percent, by far the highest annual rate during this provided time period. CPI vs RPI Although the Retail Price Index is a commonly utilized inflation indicator, the UK also uses a newer method of calculating inflation, the Consumer Price Index. The CPI, along with the CPIH (Consumer Price Index including owner occupiers' housing costs) are usually preferred by the UK government, but the RPI is still used in certain instances. Increases in rail fares for example, are calculated using the RPI, while increases in pension payments are calculated using CPI, when this is used as the uprating factor. The use of one inflation measure over the other can therefore have a significant impact on people’s lives in the UK. High inflation falls to more typical levels by 2024 Like the Retail Price Index, the Consumer Price Index inflation rate also reached a recent peak in October 2022. In that month, prices were rising by 11.1 percent and did not fall below double figures until April 2023. This fall was largely due to slower price increases in key sectors such as energy, which drove a significant amount of the 2022 wave of inflation. Inflation nevertheless remains elevated, fueled not only by high food inflation, but also by underlying core inflation. As of February 2025, the overall CPI inflation rate was 2.8 percent, although an uptick in inflation is expected later in the year, with a rate of 3.7 percent forecast for the third quarter of the year.

The electron density values listed in this file are derived from the IMAGE Radio Plasma Imager (B.W. Reinisch, PI) data using an automatic fitting program written by Phillip Webb with manual correction. The electron number densities were produced using an automated procedure (with manual correction when necessary) which attempted to self-consistently fit an enhancement in the IMAGE RPI Dynamic Spectra to either 1) the Upper Hybrid Resonance band, 2) the Z-mode or 3) the continuum edge. The automatic algorithm works by rules determined by comparison of the active and passive RPI data [Benson et al., GRL, vol. 31, L20803, doi:10.1029/2004GL020847, 2004]. The manual data points are not from frequencies chosen freely by a human. Rather the human specifies that the computer should search for a peak or continuum edge in a certain frequency region. Thus even the manual points are determined, in part, by the automatic algorithms. Of course that does not guarantee that the data points are right, but it does eliminate some human bias.

The electron density values listed in this file are derived from the IMAGE Radio Plasma Imager (B.W. Reinisch, PI) data using an automatic fitting program written by Phillip Webb with manual correction. The electron number densities were produced using an automated procedure (with manual correction when necessary) which attempted to self-consistently fit an enhancement in the IMAGE RPI Dynamic Spectra to either 1) the Upper Hybrid Resonance band, 2) the Z-mode or 3) the continuum edge. The automatic algorithm works by rules determined by comparison of the active and passive RPI data [Benson et al., GRL, vol. 31, L20803, doi:10.1029/2004GL020847, 2004]. The manual data points are not from frequencies chosen freely by a human. Rather the human specifies that the computer should search for a peak or continuum edge in a certain frequency region. Thus even the manual points are determined, in part, by the automatic algorithms. Of course that does not guarantee that the data points are right, but it does eliminate some human bias.

Measures of monthly UK inflation data including CPIH, CPI and RPI. These tables complement the consumer price inflation time series dataset.

This dataset provides values for RETAIL PRICE INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The inflation rate for the Retail Price Index in the United Kingdom in the fourth quarter of 2022 reached **** percent, before falling in the subsequent quarters. The RPI rate in the fourth quarter of 2024 was *** percent, with an uptick expected in RPI inflation expected in 2025, peaking at *** percent in the third quarter of 2025.

Average RPI values for stocks in different market-cap categories and average RPI values for market indexes (M = 350).

The file is the dataset for the manuscript entitled "Assessing the reliability of the pseudo-Thellier estimates in fired ceramics and source clays" by Poletti et al. including all parameters used as selection criteria, as well as the values of relative palaeointensity, remanence anisotropy, calibration factors, and the normalisers data shown in the manuscript.

Repository includes maps of available water capacity as defined by United States soil survey program (1/3 to 15 bar).

The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.

Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal.

File Name Details:

ACCURACY!! Please see manuscript and Github repository for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (NRCS field pedons plus NRCS laboratory pedons; file ending _CV_plots.tif) and for just the CV results at laboratory pedons (file ending _CV_SCD_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000).

Elements are separated by underscore (_) in the following sequence:

property_r_depth_cm_geometry_model_additional_elements.extension

Example: awc_r_0_cm_2D_QRF.tif

Indicates available water content (awc) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions.

The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).

_95PI_h: Indicates the layer is the upper 95% prediction interval value.

_95PI_l: Indicates the layer is the lower 95% prediction interval value.

_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI.

References

Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma.

The data here were originally posted to facilitate timely and transparent peer review. The final public data release with formal metadata is now available from at the following location:

Nauman, T.W., and Duniway, M.C., 2020, Predictive soil property maps with prediction uncertainty at 30 meter resolution for the Colorado River Basin above Lake Mead: U.S. Geological Survey data release, https://doi.org/10.5066/P9SK0DO2.

Associated publication:

Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194. https://doi.org/10.1002/saj2.20080.

UPDATE: WE FOUND A RENDERING ERROR IN MANY AREAS OF THE 5 CM MAP. WE HAVE RECREATED THE MAP AND INCLUDED IN THIS VERSION OF THE REPOSITORY.

Repository includes maps of organic matter content (% wt) as defined by United States soil survey program.

These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.

This data should be used in combination with a soil depth or depth to restriction layer map (both layers that will be released soon as part of this project) to eliminate areas mapped at deeper depths than the soil actually goes. This is a limitation of this data which will hopefully be updated in future updates.

The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.

Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal.

File Name Details:

ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (_CV_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000). Predictions are also evaluated with the U.S. soil survey laboratory database soil organic carbon (SOC) data. The SOC measurements were coverted to OM matter values using the common 1.724 conversion factor. The converted OM values are compared to predicted OM values using an accuracy plot (OM_SOC_plots.tif).

Elements are separated by underscore (_) in the following sequence:

property_r_depth_cm_geometry_model_additional_elements.extension

Example: om_r_0_cm_2D_QRF_bt.tif

Indicates soil organic matter content (om) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions. The _bt indicates that the map has been back transformed from ln or sqrt transformation used in modeling.

The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).

_95PI_h: Indicates the layer is the upper 95% prediction interval value.

_95PI_l: Indicates the layer is the lower 95% prediction interval value.

_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI.

References

Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma

A thorough rock- and palaeomagnetic investigation of an almost 13-m-thick Upper Pleistocene loessepalaeosol sequence (LPSS) outcropping near the village of Süttö in northern Hungarywas conducted to obtain rock magnetic and palaeomagnetic-based chronostratigraphies. The low-field susceptibility (Klf), the frequency-dependent susceptibility (Kfd), the ARM and the “S”IRM of the LPSS at Sütt}o are shown together in Fig. 3 as a function of the profile depth. The very high values at the top of the profile characterise recent soil (unit 1). With the beginning of the uppermost pure loess layer (unit 2), the susceptibility signal drops considerably and then slowly increases with increasing depth (age). Apart from the clearly higher susceptibility values that unambiguously correlate with the identified soils, there appears to be a quasi-linear increasing susceptibility trend in the uppermost loess section (unit 2), which is also weakly present in the frequency-dependent susceptibility (Kfd) and in the “S”IRM, but is not visible in the ARM. Generally, the primarily concentration-dependent parameters Klf, ARM and “S”IRM show a similar course with depth. The upper part of the sand unit at a depth of approximately 8 m is characterised by high susceptibility, no elevated frequency dependence and missing higher values for ARM but somewhat higher “S”IRM. The Max/Min variation in Klf and “S”IRM is less than one order of magnitude incontrast to the ARM, which has variations of more than one order of magnitude (Fig. 3).

Objective: The stratification of neuroblastoma (NBL) prognosis remains difficult. RNA-based signatures might be able to predict prognosis, but independent cross-platform validation is still rare.Methods: RNA-Seq-based profiles from NBL patients were acquired and then analyzed. The RNA-Seq prognostic index (RPI) and the clinically adjusted RPI (RCPI) were successively established in the training cohort (TARGET-NBL) and then verified in the validation cohort (GSE62564). Survival prediction was assessed using a time-dependent receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Functional enrichment analysis of the genes was conducted using bioinformatics methods.Results: In the training cohort, 10 gene pairs were eventually integrated into the RPI. In both cohorts, the high-risk group had poor overall survival (OS) (P < 0.001 and P < 0.001, respectively) and favorable event-free survival (EFS) (P = 0.00032 and P = 0.06, respectively). ROC curve analysis also showed that the RPI predicted OS (60 month AUC values of 0.718 and 0.593, respectively) and EFS (60 month AUC values of 0.627 and 0.852, respectively) well in both the training and validation cohorts. Clinicopathological indicators associated with prognosis in the univariate and multivariate regression analyses were identified and added to the RPI to form the RCPI. The RCPI was also used to divide populations into different risk groups, and the high-risk group had poor OS (P < 0.001 and P < 0.001, respectively) and EFS (P < 0.05 and P < 0.05, respectively). Finally, the RCPI had higher accuracy than the RPI for the prediction of OS (60 month AUC values of 0.730 and 0.852, respectively) and EFS (60 month AUC values of 0.663 and 0.763, respectively) in both the training and validation cohorts. Moreover, these differentially expressed genes may be involved in certain NBL-related events.Conclusions: The RCPI could reliably categorize NBL patients based on different risks of death.

Inflation Rate in France remained unchanged at 1 percent in July. This dataset provides the latest reported value for - France Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset contains the known hydrocarbon reservoirs within the study area of the Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB) as part of Phase 1, Natural Reservoirs Quality Analysis. The final values for Reservoir Productivity Index (RPI) and uncertainty (in terms of coefficient of variation, CV) are included. RPI is in units of liters per MegaPascal-second (L/MPa-s), quantified using permeability, thickness of formation, and depth. A higher RPI is more optimal. Coefficient of Variation (CV) is the ratio of the standard deviation to the mean RPI for each reservoir. A lower CV is more optimal. Details on these metrics can be found in the Reservoirs_Methodology_Memo.pdf uploaded in the associated "Natural Reservoir Analysis" dataset linked below.

Consumer Price Index CPI in Malta increased to 129.43 points in June from 127.92 points in May of 2025. This dataset provides - Malta Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

UPDATE: AN ERROR WAS FOUND IN THE TRAINING DATA PREPARATION. AN UPDATED VERSION HAS BEEN CREATED. THIS VERSION SIGNIFICANTLY UNDERESTIMATES VALUES, BUT REPRESENTS TRENDS DECENTLY, PLEASE SEE THE UPDATED VERSION OF THIS REPOSITORY FOR BETTER PREDICTIONS.

Repository includes maps describing the depth (cm) to the top of any water or root soil restrictive layer (resdept) as defined by United States soil survey program.

These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.

The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.

Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal.

File Name Details:

ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for the training sample (file ending _CV_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000).

Elements are separated by underscore (_) in the following sequence:

property_r_depth_cm_geometry_model_additional_elements.extension

Example: resdept_r_cm_2D_QRF.tif

Indicates depth to top of restriction (resdept; in cm) using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions.

The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).

_95PI_h: Indicates the layer is the upper 95% prediction interval value.

_95PI_l: Indicates the layer is the lower 95% prediction interval value.

_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI.

References

Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma

The files included in this submission contain all data pertinent to the methods and results of this task's output, which is a cohesive multi-state map of all known potential geothermal reservoirs in our region, ranked by their potential favorability. Favorability is quantified using a new metric, Reservoir Productivity Index, as explained in the Reservoirs Methodology Memo (included in zip file). Shapefile and images of the Reservoir Productivity and Reservoir Uncertainty are included as well (hover over file display names to see actual file names in bottom-left corner of screen). This shapefile contains the data associated with the GPFA-AB Phase 1 Task 2, Natural Reservoirs Quality Analysis, in a format that can be uploaded into any GIS software. The final values for Reservoir Productivity Index (RPI) and uncertainty (in terms of coefficient of variation, CV) are held in columns "RPI" and "RPI CV". RPI is in units of liters per MegaPascal-second (L/MPa-s), quantified using permeability, thickness of formation, and depth. A higher RPI is more optimal.Coefficient of Variation (CV) is the ratio of the standard deviation to the mean RPI for each reservoir. A lower CV is more optimal. Details on these metrics can be found in the Reservoirs_Methodology_Memo.pdf. *Newer version exists - see link below