Annual descriptive price statistics for each calendar year 2005 – 2024 for 11 Local Government Districts in Northern Ireland. The statistics include: • Minimum sale price • Lower quartile sale price • Median sale price • Simple Mean sale price • Upper Quartile sale price • Maximum sale price • Number of verified sales Prices are available where at least 30 sales were recorded in the area within the calendar year which could be included in the regression model i.e. the following sales are excluded: • Non Arms-Length sales • sales of properties where the habitable space are less than 30m2 or greater than 1000m2 • sales less than £20,000. Annual median or simple mean prices should not be used to calculate the property price change over time. The quality (where quality refers to the combination of all characteristics of a residential property, both physical and locational) of the properties that are sold may differ from one time period to another. For example, sales in one quarter could be disproportionately skewed towards low-quality properties, therefore producing a biased estimate of average price. The median and simple mean prices are not ‘standardised’ and so the varying mix of properties sold in each quarter could give a false impression of the actual change in prices. In order to calculate the pure property price change over time it is necessary to compare like with like, and this can only be achieved if the ‘characteristics-mix’ of properties traded is standardised. To calculate pure property change over time please use the standardised prices in the NI House Price Index Detailed Statistics file.

The subject IDs and effect sizes for the top quartile of participants as selected by their adjusted and unadjusted facilitation effectsAverage from the group experiment.

Annual descriptive price statistics for each calendar year 2005 – 2024 for 462 electoral wards within 11 Local Government Districts. The statistics include: • Minimum sale price • Lower quartile sale price • Median sale price • Simple Mean sale price • Upper Quartile sale price • Maximum sale price • Number of verified sales Prices are available where at least 30 sales were recorded in the area within the calendar year which could be included in the regression model i.e. the following sales are excluded: • Non Arms-Length sales • sales of properties where the habitable space are less than 30m2 or greater than 1000m2 • sales less than £20,000. Annual median or simple mean prices should not be used to calculate the property price change over time. The quality (where quality refers to the combination of all characteristics of a residential property, both physical and locational) of the properties that are sold may differ from one time period to another. For example, sales in one quarter could be disproportionately skewed towards low-quality properties, therefore producing a biased estimate of average price. The median and simple mean prices are not ‘standardised’ and so the varying mix of properties sold in each quarter could give a false impression of the actual change in prices. In order to calculate the pure property price change over time it is necessary to compare like with like, and this can only be achieved if the ‘characteristics-mix’ of properties traded is standardised. To calculate pure property change over time please use the standardised prices in the NI House Price Index Detailed Statistics file.

WHO age-standardized and age-specific multimorbidity and dual long-term conditions combinations prevalence estimates: Malawi, The Gambia and Uganda.

AL refers to the axial length, CCT to the central corneal thickness, ACD to the external phakic anterior chamber depth measured from the corneal front apex to the front apex of the crystalline lens, LT to the central thickness of the crystalline lens, R1 and R2 to the corneal radii of curvature for the flat and steep meridians, Rmean to the average of R1 and R2, PIOL to the refractive power of the intraocular lens implant, and SEQ to the spherical equivalent power achieved 5 to 12 weeks after cataract surgery.

Sediments undergoing accretion in trench-forearc systems are subjected to conditions of large lateral thrusting. This stress regime controls the mechanism of faulting as well as the yield and strength properties of the sediment. Understanding them is therefore crucial for the construction of quantitative models of sediment dynamics in convergent margin settings. For this purpose triaxial and oedometer tests were performed on six whole-round core samples recovered from Site 808 from depths between 173 and 705 mbsf. Samples from five depth intervals were subjected to a triaxial test program that was primarily designed to define yield and strength behavior. Test specimens were cut parallel and normal to the core axis. Additional five oedometer tests with similarly prepared specimens were performed on samples from four depth intervals to evaluate the directional state and degree of sediment compaction. Test results show that the degree of sediment compaction is higher than expected from overburden. This overcompaction increases with depth. A well-developed mechanical anisotropy is evident in all samples tested, regardless of their depth and lithology. Values of yield limit, stiffness, and shear strength are up to 40% higher in the horizontal direction compared to the vertical direction. In addition the test data demonstrate that the axis of the volumetric yield loci have rotated into extensional stress field. This verifies that the mechanical state of sediment in the accretionary wedge is controlled by in-situ stress conditions of extensional nature. The coefficients of lateral stress inferred suggest that the extensional stress regime becomes increasingly effective with depth.

This dataset accompanies the conference submission 'Journal metrics as predictors of Research Excellence Framework 2021 results: Comparison of impact factor quartiles and Finnish expert-ratings'. It contains the data in CSV format, one file per unit of analysis (Units of Assessment, Higher Education Institutions, and Subject Areas).

The format of the UoA file is as follows (the other two files are analogous):

• institution_name: name of higher education institution (e.g. university)

• unit_of_assessment_name: UoA name in REF (https://www.ref.ac.uk/panels/units-of-assessment/)

• main_panel: main panel in REF

• multiple_submission_letter: blank unless submitted to multiple panels.Exceptionally HEIs may have requested permission to make two submissions from the same UoA to different panels.

• multiple_submission_name: blank unless submitted to multiple panels

• non_english: number of articles in language other than English

• jufo_score_uoa: JUFO score (see paper for calculation details) of UoA

• jif_score_uoa: JIF score (see paper for calculation details) of UoA

• ref_score_uoa: REF score (see paper for calculation details) of UoA

In 2024, the average annual full-time earnings for the top ten percent of earners in the United Kingdom was 72,150 British pounds, compared with 22,763 for the bottom ten percent of earners. As of this year, the average annual earnings for all full-time employees was 37,430 pounds, up from 34,963 pounds in the previous year. Strong wage growth continues in 2025 As of February 2025, wages in the UK were growing by approximately 5.9 percent compared with the previous year, with this falling to 5.6 percent if bonus pay is included. When adjusted for inflation, regular pay without bonuses grew by 2.1 percent, with overall pay including bonus pay rising by 1.9 percent. While UK wages have now outpaced inflation for almost two years, there was a long period between 2021 and 2023 when high inflation in the UK was rising faster than wages, one of the leading reasons behind a severe cost of living crisis at the time. UK's gender pay gap falls in 2024 For several years, the difference between average hourly earnings for men and women has been falling, with the UK's gender pay gap dropping to 13.1 percent in 2024, down from 27.5 percent in 1997. When examined by specific industry sectors, however, the discrepancy between male and female earnings can be much starker. In the financial services sector, for example, the gender pay gap was almost 30 percent, with professional, scientific and technical professions also having a relatively high gender pay gap rate of 20 percent.

Baseline lifestyle factor prevalence estimates: Malawi and Uganda.

WHO age-standardized prevalence estimates for single long-term conditions: Malawi, The Gambia and Uganda.

Baseline socio-demographic factor prevalence estimates: Malawi, The Gambia and Uganda.

Estimates of the association between sociodemographic and lifestyle factors and multimorbidity: Malawi, The Gambia and Uganda.

Data are summarized as median [inter quartile range].

Subgroup analysis of the cardiovascular disease incidence according to the quartiles of neutrophil count.