The Consumer Prices Index (CPI) and the Retail Prices Index (RPI) measure the changes from month to month in the cost of a representative 'basket' of goods and services bought by consumers within the UK. This involves weighting together price changes in the indices according to household spending patterns for different categories of goods and services so that each takes its appropriate share. At the beginning of each year the weights used to compile both the CPI and RPI are updated using the latest available information on household spending. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: Updating Weights

RPI, RPI(X), RPI(Y), RPI (pensioners) and RPI (low income) percentage changes and index numbers. The latest report on the Retail Prices index is published here on stats.je.

The 'shopping basket' of items making up the suite of consumer price inflation indices (CPI, CPIH, RPIJ & RPI) are reviewed every year. Some items are taken out of the basket, some are brought in, to reflect changes in the market and to make sure the indices are up to date and representative of consumer spending patterns. This article describes the review process and explains how and why the various items in the inflation baskets are chosen. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: Basket of Goods

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

This article describes the new RPIJ measure of Consumer Price Inflation. RPIJ is a Retail Prices Index (RPI) based measure that will use a geometric (Jevons) formula in place of one type of arithmetic formula (Carli). It is being launched in response to the National Statistician's conclusion that the RPI does not meet international standards due to the use of the Carli formula in its calculation. The accompanying Excel file includes a back series for RPIJ from 1997 to 2012. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: New RPIJ measure of Consumer Price Inflation

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.

Price quote data (for locally collected data only) and consumption segment indices that underpin consumer price inflation statistics, giving users access to the detailed data that are used in the construction of the UK’s inflation figures. The data are being made available for research purposes only and are not an accredited official statistic. From October 2024, private school fees and part-time education classes have been included in the consumption segment indices file. For more information on the introduction of consumption segments, please see the Consumer Prices Indices Technical Manual, 2019. Note that this dataset was previously called the consumer price inflation item indices and price quotes dataset.

Retail Price Index in the United Kingdom decreased to 4.30 percent in October from 4.50 percent in September of 2025. This dataset provides - United Kingdom Retail Price Index YoY- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Inflation Rate in the United Kingdom decreased to 3.60 percent in October from 3.80 percent in September of 2025. This dataset provides - United Kingdom Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Regional Price Index contrasts the cost of a common basket of goods and services at a number of regional locations to the Perth metropolitan area. The RPIs were commissioned to assist with the calculation of the Western Australian State Government’s regional district allowance, and it has been used to assist in policy decision-making. Show full description

Introduction

The 802.11 standard includes several management features and corresponding frame types. One of them are probe requests (PR). They are sent by mobile devices in the unassociated state to search the nearby area for existing wireless networks. The frame part of PRs consists of variable length fields called information elements (IE). IE fields represent the capabilities of a mobile device, such as data rates.
The dataset includes PRs collected in a controlled rural environment and in a semi-controlled indoor environment under different measurement scenarios.
It can be used for various use cases, e.g., analysing MAC randomization, determining the number of people in a given location at a given time or in different time periods, analysing trends in population movement (streets, shopping malls, etc.) in different time periods, etc.

Measurement setup

The system for collecting PRs consists of a Raspberry Pi 4 (RPi) with an additional WiFi dongle to capture Wi-Fi signal traffic in monitoring mode. Passive PR monitoring is performed by listening to 802.11 traffic and filtering out PR packets on a single WiFi channel.
The following information about each PR received is collected: MAC address, Supported data rates, extended supported rates, HT capabilities, extended capabilities, data under extended tag and vendor specific tag, interworking, VHT capabilities, RSSI, SSID and timestamp when PR was received.
The collected data was forwarded to a remote database via a secure VPN connection. A Python script was written using the Pyshark package for data collection, preprocessing and transmission.

Data preprocessing

The gateway collects PRs for each consecutive predefined scan interval (10 seconds). During this time interval, the data are preprocessed before being transmitted to the database.
For each detected PR in the scan interval, IEs fields are saved in the following JSON structure:
PR_IE_data =
{
'DATA_RTS': {'SUPP': DATA_supp , 'EXT': DATA_ext},
'HT_CAP': DATA_htcap,
'EXT_CAP': {'length': DATA_len, 'data': DATA_extcap},
'VHT_CAP': DATA_vhtcap,
'INTERWORKING': DATA_inter,
'EXT_TAG': {'ID_1': DATA_1_ext, 'ID_2': DATA_2_ext ...},
'VENDOR_SPEC': {VENDOR_1:{
'ID_1': DATA_1_vendor1,
'ID_2': DATA_2_vendor1
...},
VENDOR_2:{
'ID_1': DATA_1_vendor2,
'ID_2': DATA_2_vendor2
...}
...}
}

Supported data rates and extended supported rates are represented as arrays of values that encode information about the rates supported by a mobile device. The rest of the IEs data is represented in hexadecimal format. Vendor Specific Tag is structured differently than the other IEs. This field can contain multiple vendor IDs with multiple data IDs with corresponding data. Similarly, the extended tag can contain multiple data IDs with corresponding data.
Missing IE fields in the captured PR are not included in PR_IE_DATA.

When a new MAC address is detected in the current scan time interval, the data from PR is stored in the following structure:

{'MAC': MAC_address, 'SSIDs': [ SSID ], 'PROBE_REQs': [PR_data] },

where PR_data is structured as follows:
{
'TIME': [ DATA_time ],
'RSSI': [ DATA_rssi ],
'DATA': PR_IE_data
}.

This data structure allows storing only TOA and RSSI for all PRs originating from the same MAC address and containing the same PR_IE_data. All SSIDs from the same MAC address are also stored.
The data of the newly detected PR is compared with the already stored data of the same MAC in the current scan time interval.
If identical PR's IE data from the same MAC address is already stored, then only data for the keys TIME and RSSI are appended.
If no identical PR's IE data has yet been received from the same MAC address, then PR_data structure of the new PR for that MAC address is appended to PROBE_REQs key.
The preprocessing procedure is shown in Figure ./Figures/Preprocessing_procedure.png
At the end of each scan time interval, all processed data is sent to the database along with additional metadata about the collected data e.g. wireless gateway serial number and scan start and end timestamps. For an example of a single PR captured, see the ./Single_PR_capture_example.json file.

Environments description

We performed measurements in a controlled rural outdoor environment and in a semi-controlled indoor environment of the Jozef Stefan Institute.
See the Excel spreadsheet Measurement_informations.xlsx for a list of mobile devices tested.

Indoor environment

We used 3 RPi's for the acquisition of PRs in the Jozef Stefan Institute. They were placed indoors in the hallways as shown in the ./Figures/RPi_locations_JSI.png. Measurements were performed on weekend to minimize additional uncontrolled traffic from users' mobile devices. While there is some overlap in WiFi coverage between the devices at the location 2 and 3, the device at location 1 has no overlap with the other two devices.

Rural environment outdoors

The three RPi's used to collect PRs were placed at three different locations with non-overlapping WiFi coverage, as shown in ./Figures/RPi_locations_rural_env.png. Before starting the measurement campaign, all measured devices were turned off and the environment was checked for active WiFi devices. We did not detect any unknown active devices sending WiFi packets in the RPi's coverage area, so the deployment can be considered fully controlled.
All known WiFi enabled devices that were used to collect and send data to the database used a global MAC address, so they can be easily excluded in the preprocessing phase. MAC addresses of these devices can be found in the ./Measurement_informations.xlsx spreadsheet.
Note: The Huawei P20 device with ID 4.3 was not included in the test in this environment.

Scenarios description

We performed three different scenarios of measurements.

Individual device measurements

For each device, we collected PRs for one minute with the screen on, followed by PRs collected for one minute with the screen off. In the indoor environment the WiFi interfaces of the other devices not being tested were disabled. In rural environment other devices were turned off. Start and end timestamps of the recorded data for each device can be found in the ./Measurement_informations.xlsx spreadsheet under the Indoor environment of Jozef Stefan Institute sheet and the Rural environment sheet.

Three groups test

In this measurement scenario, the devices were divided into three groups. The first group contained devices from different manufacturers. The second group contained devices from only one manufacturer (Samsung). Half of the third group consisted of devices from the same manufacturer (Huawei), and the other half of devices from different manufacturers. The distribution of devices among the groups can be found in the ./Measurement_informations.xlsx spreadsheet.

The same data collection procedure was used for all three groups. Data for each group were collected in both environments at three different RPis locations, as shown in ./Figures/RPi_locations_JSI.png and ./Figures/RPi_locations_rural_env.png.
At each location, PRs were collected from each group for 10 minutes with the screen on. Then all three groups switched locations and the process was repeated. Thus, the dataset contains measurements from all three RPi locations of all three groups of devices in both measurement environments. The group movements and the timestamps for the start and end of the collection of PRs at each loacation can be found in spreadsheet ./Measurement_informations.xlsx.

One group test

In the last measurement scenario, all devices were grouped together. In rural evironement we first collected PRs for 10 minutes while the screen was on, and then for another 10 minutes while the screen was off. In indoor environment data were collected at first location with screens on for 10 minutes. Then all devices were moved to the location of the next RPi and PRs were collected for 5 minutes with the screen on and then for another 5 minutes with the screen off.

Folder structure

The root directory contains two files in JSON format for each of the environments where the measurements took place (Data_indoor_environment.json and Data_rural_environment.json). Both files contain collected PRs for the entire day that the measurements were taken (12:00 AM to 12:00 PM) to get a sense of the behaviour of the unknown devices in each environment. The spreadsheet ./Measurement_informations.xlsx. contains three sheets. Devices description contains general information about the tested devices, RPis, and the assigned group for each device. The sheets Indoor environment of Jozef Stefan Institute and Rural environment contain the corresponding timestamps for the start and end of each measurement scenario. For the scenario where the devices were divided into groups, additional information about the movements between locations is included. The location names are based on the RPi gateway ID and may differ from those on the figures showing the

There are a number of differences between the Consumer Prices Index (CPI) and Retail Prices Index (RPI), including their coverage, population base, commodity measurement and methods of construction. Combined, these differences have meant that, for most of its history, the CPI has been lower than the RPI. One of the main reasons to this difference is the method of construction at the lowest level, where different formulae are used in the CPI and RPI to combine individual prices. This difference is usually referred to as the formula effect. This article will investigate similar formula effects present in the inflation measures of other countries, and where necessary will attempt to explain why the magnitude of the formula effect experienced by other countries differs from that of the UK. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: International Comparison

This file contains the minimal derivative dataset required to reproduce the REI and RPI indices presented in the manuscript "A Two-Dimensional Framework for Profiling Online Reviewer Behaviour".The dataset includes cleaned observations and reviewers with at least three ratings.Only the variables strictly necessary for computing the REI and RPI measures are provided (User_id, Title, review/score).The raw Amazon Book Reviews dataset is not included, as it originates from an external public repository (Kaggle – Mohamed Bakhet, 2022) and is not owned by the authors. Users wishing to access the full raw dataset may obtain it directly from the original source:https://www.kaggle.com/datasets/mohamedbakhet/amazon-books-reviews/dataThis derivative dataset is released for the purpose of ensuring reproducibility in compliance with PLOS ONE data policies.

Inflation Rate in Spain decreased to 3 percent in November from 3.10 percent in October of 2025. This dataset provides the latest reported value for - Spain Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The purpose of this article is to give the estimated effects on the Consumer Price Indices and Retail Prices Index resulting from duty and taxation changes announced in the Budget. This article is simply a helpful guide to users of the CPI and RPI. The Office for National Statistics (ONS) accepts no liability whatsoever for losses of any kind arising as a result of reliance on this note. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: Budget

Inflation Rate in Netherlands decreased to 2.90 percent in November from 3.10 percent in October of 2025. This dataset provides - Netherlands Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Construction Output Price Indices (OPIs) from January 2014 to September 2025, UK. Summary

Integrated Ocean Drilling Program (IODP) Expedition 303 to the North Atlantic provided 16 records of the Matuyama-Brunhes polarity transition (MBT), based on u-channel and discrete samples, from holes drilled at three sites (Sites U1304, U1305 and U1306) that have mean Brunhes sedimentation rates of 16-18 cm/kyr. The MBT occurs during the transition from marine isotope stage (MIS) 19c to MIS 18e, with mid-point at ~773 ka, and a transition duration of ~8 kyr. Combining the new MBT records, including one new record for the top Jaramillo, with previously published North Atlantic MBT records (ODP Sites 983, 984 and 1063) yields a total of more than 20 high-sedimentation-rate polarity transition records. The MBT yields a repetitive pattern of transitional field states as virtual geomagnetic poles (VGPs) move from high southern latitudes to loop over the Pacific, group in NE Asia, and transit into the mid-latitude South Atlantic before reaching high latitudes in the Northern Hemisphere. The VGPs for the top Jaramillo transition feature a loop over the Pacific, then a NE Asia group before transit over the Indian Ocean to high southerly latitudes. The North Atlantic MBT records described here contrast with longitudinally-constrained VGP paths for the MBT, indicating that relatively low sedimentation rate (~4 cm/kyr) records of the MBT are heavily smoothed by the remanence acquisition process and do not adequately represent the MBT field. The VGPs at the MBT and top Jaramillo, as measured in the North Atlantic, have similarities with excursion (Iceland Basin) VGP paths, and were apparently guided by maxima in downward vertical flux similar to those seen in the modern non-dipole (ND) field, implying longevity in ND features through time.

Data relating to the price of houses sold in the Glasgow Area from the years 1991 - 2013.Some elements of the dataset are derived from information produced by Registers of ScotlandCLASS Administrative Classification onlySTNO Street NumberSTnu Street NumberFLATPOSN Flat PositionSTNAME Street NamePOSTCODE Post CodeMONTH OF SALE Month of SaleYEAR OF SALE (CALENDAR) YEAR OF SALE (CALENDAR)YEAR OF SALE (BUSINESS) YEAR OF SALE (BUSINESS)MONTH AND YEAR MONTH AND YEARQUARTER_(CALENDAR) QUARTER_(CALENDAR)ACTUAL PRICE AT POINT OF SALE Actual Price RPI Retail Price Index - Published every month and available for the last 20 yearsDEFLATOR Figure used to to determine change in house prices over time - calculated fromthe Retail Price Index and other dataPRICE CONSTANT AT July 2013 Actual Price multiplied by the Deflator. This is the price if RPI is applied to original sale price - How much would the property be valued at now. ORIGINOFBUY Council area or Country where the buyer comes fromOMIT OR USE Oroginal data also included retail and commercial data. - Not reproduced hereNEWBUILD OR RESALE Is it a newbuild house or a resaleLHF Local Housing Forum Area