Scotland’s population was estimated to be 5,479,900 at mid-2021 (30 June 2021). The population increased by 13,900 people (0.25%) in the year to mid-2021. The average annual growth in the 5 years before the pandemic was higher than this, at around 23,100 people (0.43%). There have been more deaths than births for the last seven years. In the latest year, deaths outnumbered births by the largest amount on record. Migration was the main driver of population growth over the latest year. More people moved to Scotland than left, as has been the case for the last two decades. The pattern of population change was different to previous years. In the latest year, the population of the largest cities fell, which was a change from growth in previous years. The greatest population growth was in council areas around Edinburgh. In addition, many rural areas which had previously had falling populations saw an increase in population over the latest year.

This data is sourced from the Census 2011 and shows the population and population density by council area. Raw data sourced from http://www.scotlandscensus.gov.uk/en/censusresults/downloadablefiles.html and then manipulated in excel to merge a number of tables. The resulting data was joined to a shapefile of Scottish Council areas from sharegeo (http://www.sharegeo.ac.uk/handle/10672/305). Both sources should be attributed as the sources of the base data. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2012-12-19 and migrated to Edinburgh DataShare on 2017-02-21.

Facts and statistics about different areas of the city Find latest available data for by using this interactive search tool. You can view and compare up to 2 geographies. These include the Council's 17 wards, the 4 new locality areas, Edinburgh and Scotland. How to use this tool: pick the areas you want to view from the drop down list under 'first area / second area' and then click on the tabs at the bottom of the spreadsheet to view your results. There is also a tab showing summary information of the 4 new localities data - 'Locality Comparison' in white. The data in the spreadsheet includes: Population - gender and age Housing Employment Education and professions Income Benefits Health & disability Lifestyle Satisfaction with Services Scottish Index of Multiple Deprivation data Additional metadata: - Licence: http://reference.data.gov.uk/id/open-government-licence

This group of datasets describe the suicides in Scotland for the period 1982-2009. There are 4 separate datasets: All Suicides/Male Suicides/Female Suicides/All Suicide Rate (expressed per 100,000 people). The data is broken down into Local Authority Areas making it easier to investigate any spatial disparity in the suicide figures. A couple of points are worth noting are that it is unclear if the suicide data shows all suicides or just those of Adults. A recent Scottish Government report(http://www.scotland.gov.uk/Publications/2007/03/01145422/20) used deaths of people over 15 years old. Differences in the rates between this data and the results presented in the Scottish Government report may also be due to different population datasets being used. Suicide data sources form the Scottish Public Health Observatory (http://www.scotpho.org.uk/home/Healthwell-beinganddisease/suicide/suicide_data/suicide_la.asp) and the population data used to calculate the rates was sourced from ShareGeo Open (http://hdl.handle.net/10672/95) which uses mid-year estimates downloaded from Nomis (www.nomisweb.co.uk/. Datasets were joined to Local Authority (district, unitary authority and borough) boundaries downloaded from Ordnance Survey OpenData Boundary Line dataset. All spatial analysis was carried out in ArcGIS. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2011-01-13 and migrated to Edinburgh DataShare on 2017-02-21.

The GS:SFHS Data Dictionary is a set of information describing the contents, format, and structure of the phenotype data collected during recruitment (2006-2011) to the Generation Scotland Scottish Family Health Study (GS:SFHS), or derived subsequently from study data collected during recruitment. This dataset replaces the one at https://datashare.is.ed.ac.uk/handle/10283/2724

Generation Scotland (GS) conducted a recontact survey on ~7,000 participants who could be contacted by email. The survey used Survey Monkey Inc. to understand GS participant opinions on health research. A total of 2,316 participants responded. This dataset include the summary level data and anonymised individual level data.

Noise maps showing areas that are relatively louder or quieter, measured in decibels (dB), for industry noise within agglomerations in 2021. The data represents the evening levels (Leve) – the annual average A-weighted long-term sound over the evening period (19:00-23:00). Qualifying agglomerations are urban areas with populations in excess of 100,000 people, i.e. Aberdeen, Dundee, Edinburgh, Falkirk and Glasgow.

Scottish Ministers commissioned strategic noise mapping analysis to meet the requirements of the Environmental Noise Directive (Directive 2002/49/EC), which is legally implemented in Scotland thorough the Environmental Noise (Scotland) Regulations 2006, as amended by the Environmental Noise (Scotland) Amendment Regulations 2018.

Noise maps are used to identify areas where noise levels are high and, by linking population data, estimate how many people are affected. This aids in the production of noise action plans to manage noise and reduce noise levels where appropriate.

Orkney and Shetland, the population isolates which make up the Northern Isles of Scotland, are of particular interest to multiple sclerosis (MS) research. While MS prevalence is high in Scotland, Orkney has the highest global prevalence, higher than more northerly Shetland. Many hypotheses for the excess of MS cases in Orkney have been investigated, including vitamin D deficiency and homozygosity: neither was found to cause the high prevalence of MS. It is possible that this excess prevalence may be explained through unique genetics. We used polygenic risk scores (PRS) to look at the contribution of common risk variants to MS. Analyses were conducted using ORCADES (97/2118 cases/controls), VIKING (15/2000 cases/controls) and Generation Scotland (30/8708 cases/controls) datasets. However, no evidence of a difference in MS associated common variant frequencies was found between the three control populations, aside from HLA-DRB1*1501 tag SNP rs9271069. This SNP had a significantly higher risk allele frequency in Orkney (0.23, p-value = 8 x 10-13) and Shetland (0.21, p-value = 2.3 x 10-6) than mainland Scotland (0.17). This difference in frequency is estimated to account for 6 (95% CI 3, 8) out of 150 observed excess cases per 100,000 individuals in Shetland and 9 (95% CI 8, 11) of the observed 257 excess cases per 100,000 individuals in Orkney, compared with mainland Scotland. Common variants therefore appear to account for little of the excess burden of MS in the Northern Isles of Scotland.

This item has been replaced by the one which can be found at https://doi.org/10.7488/ds/2602 ## The Generation Scotland: Scottish Family Health Study (GS:SFHS) is a family-based population cohort with DNA, biological samples, socio-demographic, psychological and clinical data from ~24,000 adult volunteers across Scotland. Although data collection was cross-sectional, GS:SFHS became a prospective cohort due to the ability to link to routine Electronic Health Record (EHR) data. Over 20,000 participants were selected for genotyping using a genome-wide array. Genome-wide association studies (GWAS) were used to test the effects of a spectrum of variants, imputed using the Haplotype Research Consortium (HRC) dataset, on medically relevant traits measured directly or obtained from EHRs. The HRC dataset allowed investigation of variants with low minor allele frequencies in the entire GS:SFHS genotyped cohort. Genome-wide associations were run on 20,032 individuals using both genotyped and HRC imputed data. Results replicated known associations and additionally revealed novel findings, mainly with rare variants. The dataset contains genome-wide association summary statistics for this project. The other 11 data files can be found here: http://datashare.is.ed.ac.uk/handle/10283/2789

Noise maps showing areas that are relatively louder or quieter, measured in decibels (dB), for industry noise within agglomerations in 2021. The data represents the day-evening-night levels (Lden) which are annual average noise levels with separate weightings for the evening and night periods. Day, evening and night periods are defined as 07.00 to 19.00, 19.00 to 23.00 and 23.00 to 07.00 respectively. Qualifying agglomerations are urban areas with populations in excess of 100,000 people, i.e. Aberdeen, Dundee, Edinburgh, Falkirk and Glasgow.

Scottish Ministers commissioned strategic noise mapping analysis to meet the requirements of the Environmental Noise Directive (Directive 2002/49/EC), which is legally implemented in Scotland thorough the Environmental Noise (Scotland) Regulations 2006, as amended by the Environmental Noise (Scotland) Amendment Regulations 2018.

Noise maps are used to identify areas where noise levels are high and, by linking population data, estimate how many people are affected. This aids in the production of noise action plans to manage noise and reduce noise levels where appropriate.

The Generation Scotland: Scottish Family Health Study (GS:SFHS) is a family-based population cohort with DNA, biological samples, socio-demographic, psychological and clinical data from ~24,000 adult volunteers across Scotland. Although data collection was cross-sectional, GS:SFHS became a prospective cohort due to the ability to link to routine Electronic Health Record (EHR) data. Over 20,000 participants were selected for genotyping using a genome-wide array. Genome-wide association studies (GWAS) were used to test the effects of variants on the uric acid phenotype from the EHR. Results replicated a known association and the dataset contains genome-wide association summary statistics for this project.

Noise maps showing areas that are relatively louder or quieter, measured in decibels (dB), for industry noise within agglomerations in 2021. The data represents the 16-hour daytime level (LAeq,16h) - the A-weighted equivalent continuous sound level over a 16-hour time period (07:00-23:00). Qualifying agglomerations are urban areas with populations in excess of 100,000 people, i.e. Aberdeen, Dundee, Edinburgh, Falkirk and Glasgow. Scottish Ministers commissioned strategic noise mapping analysis to meet the requirements of the Environmental Noise Directive (Directive 2002/49/EC), which is legally implemented in Scotland thorough the Environmental Noise (Scotland) Regulations 2006, as amended by the Environmental Noise (Scotland) Amendment Regulations 2018. Noise maps are used to identify areas where noise levels are high and, by linking population data, estimate how many people are affected. This aids in the production of noise action plans to manage noise and reduce noise levels where appropriate.

Methods: This was a national, population-based, cross-sectional study of routinely-collected mortality and demographic data pertaining to March-August of 2020 (COVID-19 pandemic) compared to the corresponding periods in 2015-2019. ICD-10-coded causes of death of deceased people of any age were obtained from a national mortality registry of death certificates. The G40-41 ICD-10 codes for epilepsy were used to define epilepsy-related deaths, with or without a U07*1-07*2 ICD-10 code for COVID-19 listed as an additional cause. Deaths unrelated to epilepsy were defined as all remaining Scottish deaths without G40-41 ICD-10 codes listed as a cause. We assessed the number of epilepsy-related deaths in 2020 compared to mean year-to-year variation observed in 2015-2019 (overall, men, women). We assessed proportionate mortality and odds ratios (OR) for deaths with COVID-19 listed as the underlying cause in people with epilepsy-related deaths compared to in deaths unrelated to epilepsy, reporting 95% confidence intervals (95% CIs). Sheet 1 contains a key to the remaining dataset.