Ireland recorded 8905 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, Ireland reported 1711233 Coronavirus Cases. This dataset includes a chart with historical data for Ireland Coronavirus Deaths.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team, except for aggregation of individual case count data into daily counts when that was the best data available for a disease and location. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format. All geographic locations at the country and admin1 level have been represented at the same geographic level as in the data source, provided an ISO code or codes could be identified, unless the data source specifies that the location is listed at an inaccurate geographical level. For more information about decisions made by the curation team, recommended data processing steps, and the data sources used, please see the README that is included in the dataset download ZIP file.

This dataset is about diseases per day in Ireland. It has 1,326 rows. It features 5 columns: disease, country, cases, and deaths.

COVID-19 is a infectious Disease which has infected more than 500 people in UK and many more people world-wide.

Acknowledgements Sincere thanks to Public Health England and Local governments. Source of Data: UK Government and Public Health UK

****Notes on the methodology**** This service shows case numbers as reported to Public Health England (PHE), matched to Administrative Geography Codes from the Office of National Statistics. Cases include people who have recovered.

Events are time-stamped on the date that PHE was informed of the new case or death.

The map shows circles that grow or shrink in line with the number of cases in that geographic area.

Data from Scotland, Wales and Northern Ireland is represented on the charts, total indicators and on the country level map layer.

Contains Ordnance Survey data © Crown copyright and database right 2020. Contains National Statistics data © Crown copyright and database right 2020.

Terms of Use No special restrictions or limitations on using the item’s content have been provided.

The Children’s Health Act 2018 came into effect on 1st January 2019. Under this act, Children’s Health Ireland was established and all assets, liabilities and records were transferred from Our Lady’s Children’s Hospital Crumlin, Temple Street Children’s University Hospital and National Children's Hospital at Tallaght University Hospital to the new body. From 1st January 2019, all NTPF reports reflect this change and data from the three sites of Children’s Health Ireland are reported as one entity. On the 31st of July 2019 Children's Health Ireland opened a new Paediatric Outpatient Department and Urgent Care Centre at CHI Connolly in Blanchardstown. The waiting lists for this site are incorporated into the Children's Health Ireland figures. Please note that NTPF does not collect activity data, i.e., numbers treated or removed. A snapshot of the number of patients waiting in each hospital is collected and published, monthly, on the NTPF website. Boards and management of individual public hospitals are responsible for the accuracy and the integrity of patient data submitted to NTPF. The NTPF commenced publishing separate Adult and Child Waiting List Reports in April 2021. The Open Data report formats reflect this change from that date forward.

Please see FAQ for latest information on COVID-19 Data Hub Data Flows: https://covid-19.geohive.ie/pages/helpfaqs. Notice: See the section What impact has the cyber-attack of May 2021 on the HSE IT systems had on reporting of COVID-19 data on the Data Hub? in the FAQ for information about issues in data from May 2021.** Due to the ongoing disruption to the HSE IT systems, until further notice the updates for this Open Data service will be provided once daily. These fields are 'nCovidConf' (ncovidconf), 'nDischCovidConf' (ndischcovidconf) and 'nAdmitCovidConf' (adcconf). **Dataset on COVID-19 patients in Intensive Care Units in Ireland. Data is provided for Confirmed COVID-19 cases and the number of new admissions and discharges. Data is based on an aggregate of all public and private ICU/HDU hospitals (including surge units). Data has been provided by the National Office of Clinical Audit (NOCA) ICU Bed Information System (ICU-BIS).This service is used in Ireland's COVID-19 Data Hub, produced as a collaboration between Tailte Éireann, the Central Statistics Office (CSO), the Department of Housing, Planning and Local Government, the Department of Health, the Health Protection Surveillance Centre (HPSC), and the All-Island Research Observatory (AIRO). This service and Ireland's COVID-19 Data Hub are built using the GeoHive platform, Ireland's Geospatial Data Hub.

Datasets in this publication report the number of diagnoses with coronavirus disease (COVID-19) as reported by the Department of Health in Ireland. This includes new cases diagnosed per day and cumulative cases, as well as cases across age groups. The latter also include population estimates by age group for 2019 from Ireland's Central Statistics Office, in order to express cases per million population.

For the files YYYYMMDD_covid_ie_age_groups.csv, variable descriptions are as follows:

• age_group: Age groups, in years
• cases: Total cases of COVID-19 diagnosed in Ireland by age group, as per the Department of Health
• pop_estimate: National population estimates by age group for 2019 in Ireland, as per the Central Statistics Office (Table 7 https://www.cso.ie/en/releasesandpublications/er/pme/populationandmigrationestimatesapril2019/), expressed in thousands.
• cases_per_million: Cases of COVID-19 diagnosed in Ireland by age group, expressed per 1 million individuals

For the files YYYYMMDD_covid_ie_daily_cases, variable descriptions are as follows:

• date: Date, in DD-MM-YYYY format
• daily_cases: New cases of COVID-19 diagnosed per day in Ireland, as per the Department of Health (https://www.gov.ie/en/news/7e0924-latest-updates-on-covid-19-coronavirus/)
• cumulative_cases: Cumulative number of COVID-19 cases in Ireland
• percent_daily_increase: New cases of COVID-19 diagnosed per day in Ireland as a percentage of cumulative number of cases up to that date.

Analysis of ‘COVID-19 HPSC HIU Latest Local Electoral Area Mapped’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/f5b674f7-3bd6-4a2d-9f35-2d3c5472b71e on 13 January 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

The number of final orders made against mortgage cases disposed in the High Court. Datasets are produced on an annual year basis. The dataset is entered onto ICOS, the Integrated Courts Operations System. The data are then extracted and merged with the Central Postcode Directory, and aggregated information uploaded to this portal. Northern Ireland Courts and Tribunals Service collects information on writs and originating summonses issued in respect of mortgages in Chancery Division of the Northern Ireland High Court. This covers both Northern Ireland Housing Executive and private mortgages, and relates to both domestic and commercial properties. A mortgage case may involve more than one address or a land property. In such cases, the first postcode address entered onto ICOS is used. Not all writs and originating summonses lead to eviction. A plaintiff begins an action for an order for possession of property. The court, following a judicial hearing, may grant an order for possession. This entitles the plaintiff to apply for an order to have the defendant evicted. However, even where an order for eviction is issued the parties can still negotiate a compromise to prevent eviction. When a case is disposed of, it may have more than one final order made. This database contains the last final order made. A description of the orders is below: Possession: The court orders the defendant to deliver possession of the property to the plaintiff within a specified time. If the defendant fails to comply with the court order the plaintiff may proceed to apply to the Enforcement of Judgements Office to repossess the property and give possession of it to the plaintiff. Sale and Possession: If the plaintiff seeks possession of property which is subject to an ‘equitable mortgage’ (i.e. normally one created informally by the deposit of deeds rather than the execution of a mortgage deed) the court may order a sale of the property to enable enforcement of the equitable mortgage and that the defendant give up possession for that purpose. The sale price is subject to approval by the court. Suspended Possession: The court may postpone the date for delivery of possession if it is satisfied that the defendant is likely to be able, within a reasonable period, to pay any sums due under the mortgage, or to remedy any other breach of the obligations under the mortgage. A suspended possession order cannot be enforced by the plaintiff without the permission of the court, which will only be granted after a further hearing. Other: other orders include strike out, dismiss action, and other less common orders. Strike out: This occurs when the moving party does not wish to proceed any further, or when the court rules that there is no reasonable ground for bringing or defending the mortgage action. Dismiss action: The mortgage action is dismissed by the courts. Other orders: These include: (a) Declaration of possession coupled with an order for sale in lieu of partition and (b) Stay of Eviction - after a Possession Order is granted but prior to actual repossession, the Defendant may apply to Court to seek a stay of eviction which, if granted, prevents repossession for a certain defined period. Users of this data may have been able to self-identify themselves due to the low values in some cells. Primary and secondary disclosure control methods have been applied to this data, denoted by cells with missing data in the tables. Values of less than four, but not zero, were initially suppressed, but some of these values could have been calculated using some row and column totals and thus secondary suppression was applied to the next lowest value in the row and column. The data contain the number of final orders made against cases disposed by each Local Government District and have the following proportions of postcode coverage: 2012, 97.7%; 2013, 96.5%; 2014, 96.0%; 2015, 94.8%; 2016, 95.5%; 2017, 95.1%; 2018, 94.8%; 2019, 93.8%; 2020, 95.6%; 2021, 93.6%; 2022, 95.3%; 2023 97.5%

Information on fish kills have been compiled and reported annually since 1969 by the Inland Fisheries Trust (formerly the Central and Regional Fisheries Boards and now Inland Fisheries Ireland) and Marine Institute (formerly Fisheries Research Centre). The authors collated all available data from published annual reports, fish kills reports and internal unpublished IFI reports. The data used varied in quality throughout the period with no, or sparse records available during certain periods in the 1970’s, 1980’s and 1990’s. Information on the species impacted and the number of mortalities was typically reported to the relevant agencies or gathered by those assessing the fish kills in response to these reports. This data was collated and summarised into four categories, i.e., salmonid, coarse fish, marine and other fish. In a small number (approximately 20) of cases “All species” was reported, we assigned these cases to salmonids as no other information was available. The cause of most fish kill events was recorded at the time of the incident but in many cases, the reason was unreported because the cause was unknown, or suspected but unconfirmed. In addition, many records were described with varying degrees of detail rather than assigned to convenient and discrete categories. In other instances, more than one cause was described. Where data were available the specific cause was classified into eight broad categories, namely agriculture, construction, eutrophication, industrial, mining, municipal, other and unknown. Where a fish kill impacted both a river and a lake, the data was treated as two separate incidents and assigned separate codes, but such instances were rare. Where a fish kill impacted both a tributary and a main channel, the data was treated as one incident. The data lags at least a year behind IFI’s internal reports to allow for prosecutions to be completed. Link to report - Fish and Habitats: Science and Management, Vol. 3 Fish Kills in Ireland – History, Current Status and Recovery Field Name Field Alias Field Type Field Description cdRbd River Basin District Text IFI river basin districts (RBDs), broad geographic delineations. These include the Eastern River Basin District (ERBD), Northwestern River Basin District (NWRBD), Shannon River Basin District (ShRBD), Southeastern River Basin District (SERBD), Southwestern River Basin District (SWRBD) and Western River Basin District (WRBD). Please note that the areas covered by the Loughs Agency are not included in this dataset. EPA_46 EPA 46 Text High level river monitoring and water quality layer consisting of 46 river catchments (see Appendix E). The catchments illustrated here, serve as a guide because they are at a relatively high level (mainly hydrometric area) and therefore, administrative in nature. For example, the “Liffey and Dublin Bay (09)” includes the main River Liffey catchment itself, but also numerous smaller, unconnected, coastal streams. Furthermore, the River Shannon (Hydrometric Areas 25 and 26) has been divided into 11 smaller catchments (Appendix E). Please note that the areas covered by the Loughs Agency are not included in this dataset. indFisheryDistrict Fishery District Text Fishery District Licence areas. Polygon dataset representing the 17 IFI fishery districts. A district licence allows for angling in one fishery district. Please note that the areas covered by the Loughs Agency are not included in this dataset. yrFishKills Year of fish kill Text Year of reported fish kill indWbType Water body type Text River, lake, transitional water, coastal, artificial (pond, reservoir, canal, etc.) County County Text Irelands county layer (excluding the six counties within Northern Ireland). indWbName Waterbody Name Text Water body name indFinalCause Final Cause Text Reported final summary cause of each fish kill. Where data were available the specific cause was classified into eight broad categories, namely agriculture, construction, eutrophication, industrial, mining, municipal, other and unknown. indSalmonidMortality Salmonid Mortality Recorded Text Confirmation if the salmonid fish family group was impacted by the fish kill. Salmonid = freshwater spawning fish that generally have evolved to survive in cold or cool water conditions. (Atlantic salmon, brown trout, sea trout or Arctic char). Y= Salmonid family group impacted and blank (no value) = none or not recorded. indCoarseFishMortality Coarse Fish Mortality Recorded Text Confirmation if the coarse fish group was impacted by the fish kill. Irish coarse fish species includes perch, roach, rudd, bream, dace, chub and tench (but usually not eels or lamprey). For the purposes of this study we also included pike, minnow, stone loach, gudgeon, three and nine/ten spined stickleback in the category. Hybrids of roach, rudd and bream are also included. In many cases the exact species wasn’t reported. Y= Coarse fish group impacted and blank (no value) = none or not recorded. indMarineFishMortality Marine Fish Mortality Recorded Text Confirmation if the marine fish group was impacted by the fish kill (examples included are flounder, bass, mullet and sprat). Y= marine fish group impacted and blank (no value) = none or not recorded. indOtherFishMortality Other Fish Mortality Recorded Text Confirmation if Other fish species (excluding salmonid, coarse and marine) were impacted by the fish kill. The Other fish group includes eel and lamprey (Brook, Freshwater or Sea Lamprey). Y= Other fish group impacted and blank (no value) = none or not recorded. indFamilyAffected Fish Family Affected Text Name of fish family groups affected by the fish kill or Not recorded.

This dataset provides a list of surnames that are reliably Irish and that can be used for identifying textual references to Irish individuals in the London area and surrounding countryside within striking distance of the capital. This classification of the Irish necessarily includes the Irish-born and their descendants. The dataset has been validated for use on records up to the middle of the nineteenth century, and should only be used in cases in which a few mis-classifications of individuals would not undermine the results of the work, such as large-scale analyses. These data were created through an analysis of the 1841 Census of England and Wales, and validated against the Middlesex Criminal Registers (National Archives HO 26) and the Vagrant Lives Dataset (Crymble, Adam et al. (2014). Vagrant Lives: 14,789 Vagrants Processed by Middlesex County, 1777-1786. Zenodo. 10.5281/zenodo.13103). The sample was derived from the records of the Hundred of Ossulstone, which included much of rural and urban Middlesex, excluding the City of London and Westminster. The analysis was based upon a study of 278,949 adult males. Full details of the methodology for how this dataset was created can be found in the following article, and anyone intending to use this dataset for scholarly research is strongly encouraged to read it so that they understand the strengths and limits of this resource:

Adam Crymble, 'A Comparative Approach to Identifying the Irish in Long Eighteenth Century London', _Historical Methods: A Journal of Quantitative and Interdisciplinary History_, vol. 48, no. 3 (2015): 141-152.

The data here provided includes all 283 names listed in Appendix I of the above paper, but also an additional 209 spelling variations of those root surnames, for a total of 492 names.

SIS SOIL:The new Irish Soil Information System concludes a 5 year programme, supported by the Irish Environmental Protection Agency (STRIVE Research Programme 2007-2013) and Teagasc, to develop a new 1:250,000 scale national soil map (http://soils.teagasc.ie). The Irish Soil Information System adopted a unique methodology combining digital soil mapping techniques with traditional soil survey application. Developing earlier work conducted by An Foras Talúntais, the project generated soil-landscape models for previously surveyed counties. These soil-landscape (‘soilscape’) models formed the basis for training statistical ‘inference engines’ for predicting soil mapping units, checked during field survey. 213 soil series are identified, each with differing characteristics, having contrasting environmental and agronomic responses. Properties were recorded in a database able to satisfy national and EU policy requirements. The Irish soil map and related soil property data will also serve public interest, providing the means to learn online about Irish soil resources. Use the Symbology layer file 'SOIL_SISNationalSoil.lyr' based on Value Field 'Association_Unit'. SIS SOIL DRAINAGE:In Ireland, soil drainage category is considered to have a predominant influence on soil processes (Schulte et al., 2012). The maritime climate of Ireland drives wet soil conditions, such that excess soil moisture in combination with heavy textured soils is considered a key constraint in relation to achieving productivity and environmental targets. Both soil moisture content and the rate at which water drains from the soil are critical indicators of soil physical quality and the overall functional capacity of soil. Therefore, a natural extension to the Irish Soil Information System included the development of an indicative soil drainage map for Ireland. The soil subgroup map was used to develop the indicative drainage map, based on diagnostic criteria relating to the subgroup categorization. Use the Symbology layer file 'SOIL_SISSoilDrainage.lyr' based on Value Field 'Drainage'. SIS SOIL DEPTH: Soil depth is a measure of the thickness of the soil cover and reflects the relationship between parent material and length of soil forming processes. Soil depth determines the potential rooting depth of plants and any restrictions within the soil that may hinder rooting depth. Plants derive nearly 80 per cent of their water needs from the upper part of the soil solum, i.e. where the root system is denser. The rooting depths depend on plant physiology, type of soil and water availability. Generally, vegetables (beans, tomatoes, potatoes, parsnip, carrots, leek, broccoli, etc.) are shallow rooted, about 50–60 cm; fruit trees and some other plants have medium rooting depths, 70–120 cm and other crops such as barley, wheat, oats, and maize may have deeper roots. Furthermore, rooting depths vary according to the age of the plants. The exact soil depth is difficult to define accurately due to its high variability across the landscape. The effective soil depth can be reduced by the presence of bedrock or impermeable layers. Use the Symbology layer file 'SOIL_SISSoilDepth.lyr' based on Valued Field 'Depth'. SIS SOIL TEXTURE:Soil texture is an important soil characteristic that influences processes such as water infiltration rates, rootability, gas exchanges, leaching, chemical activity, susceptibility to erosion and water holding capacity. The soil textural class is determined by the percentage of sand, silt, and clay. Soil texture also influences how much water is available to the plant; clay soils have a greater water holding capacity than sandy soils. Use the Symbology layer file 'SOIL_SISSoilTexture.lyr' based on Value Field 'Texture'. SIS SOIL SOC:In the previous national soil survey conducted by An Foras Taluntais, 14 counties were described in detail with soil profile descriptions provided for the representative soil series found within a county. Soil samples were taken at each soil horizon to a depth of 1 meter and analyses performed for a range of measurements, including soil organic carbon, texture, cation exchange capacity, pH; however in most cases no bulk density measurements were taken. This meant that while soil organic carbon concentrations were available this could not be related to a stock for a given soil series. In 2012/2013, 246 profile pits were sampled and analysed as part of the Irish Soil Information System project to fill in gaps in the description of representative profile data for Ireland. Use the Symbology layer file 'SOIL_SISSoilSOC.lyr' based on Value Field 'SOC'.

This dataset is about book subjects. It has 6 rows and is filtered where the books is The Casement trials : a case study on the right to a fair trial in Northern Ireland. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

See the section What impact has the cyber-attack of May 2021 on the HSE IT systems had on reporting of COVID-19 data on the Data Hub? in the FAQ for information about issues in data from May 2021.** Between 14th May 2021 and 29th July 2021 only the fields 'Number of confirmed COVID-19 cases Admitted on site' (SUM_number_of_confirmed_covid_19_ca) and 'Number of new COVID-19 cases confirmed in the past 24 hrs' (SUM_number_of_new_covid_19_cases_co) in this service were updated.The fields 'Number of New Admissions COVID-19 Positive previous 24hrs' (SUM_no_new_admissions_covid19_p) and 'Number of Discharges COVID-19 Positive previous 24hrs' (SUM_no_discharges_covid19_posit) have no data during this period of time. **Detailed dataset containing a range of COVID-19 related indicators for Acute Hospitals in Ireland. Data is provided for Confirmed COVID-19 cases and the number of new admissions and discharges. Data is based on an aggregate of 29 Acute Hospitals. Data has been provided by the HSE Performance Management Improvement Unit (PMIU).This service is used in Ireland's COVID-19 Data Hub, produced as a collaboration between Tailte Éireann, the Central Statistics Office (CSO), the Department of Housing, Planning and Local Government, the Department of Health, the Health Protection Surveillance Centre (HPSC), and the All-Island Research Observatory (AIRO).

The data in this dataset was extracted from the Information Wanted online database. It represents a unique data archive based on the numerous advertisements from the Boston-based Pilot newspaper placed by immigrants (and others) looking for lost friends and relatives from 1831 to 1920. The richness of the data includes demographic data on a large number of Irish immigrants gleaned from the ads such as place of origin in Ireland, occupation, port of entrance in the U.S. and in some cases several migrations in the U.S. and around the world. The current scope of the archive, which represents over 41,000 records, reflects interesting and tumultuous times, such as the Great Irish Famine and the United States Civil War. The database includes records primarily from 1831 through 1869. The author of the dataset, Ruth-Ann Harris, was a professor of history who served as head of the Irish Studies Department at Northeastern University before coming to Boston College as an adjunct professor of history in the Irish Studies Program. In 2005, Harris gave her database to Boston College, and the Office of Marketing and Communications developed the database and hosted it on the website, Information Wanted. Distribution represents the date on which the CSV data was downloaded from the web database.

INFOMAR Shipwrecks. Published by Geological Survey Ireland. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).This dataset shows the point locations of shipwrecks in Irish Waters surveyed by INSS and INFOMAR national seabed mapping projects. Over 480 surveyed shipwrecks have been recorded in the INFOMAR shipwreck inventory. The database accurately records the location of shipwrecks (known, unknown and in some cases uncharted), and contains detailed information regarding each wreck's condition on the seafloor, its extent, dimensions and water depth. This is possible by means of high resolution multibeam data acquired over the site of the wreck. None...

This is a complete dataset of all outputs produced from a survey assessing environmental knowledge (and knowledge gas) across local communities in the Iveragh peninsula, Co. Kerry, Ireland, during the first months of 2021. The dataset includes chart and figures, maps produced using GIS, mind maps, spreadsheets, and a supporting document containing all relevant metadata.

Using geospatial data of wildlife presence to predict a species distribution across a geographic area is among the most common tools in management and conservation. The collection of high-quality presence-absence data through structured surveys is, however, expensive, and managers usually have access to larger amounts of low-quality presence-only data collected by citizen scientists, opportunistic observations, and culling returns for game species. Integrated Species Distribution Models (ISDMs) have been developed to make the most of the data available by combining the higher-quality, but usually scarcer and more spatially restricted presence-absence data, with the lower quality, unstructured, but usually more extensive presence-only datasets. Joint-likelihood ISDMs can be run in a Bayesian context using INLA (Integrated Nested Laplace Approximation) methods that allow the addition of a spatially structured random effect to account for data spatial autocorrelation. Here, we apply this innovative approach to fit ISDMs to empirical data, using presence-absence and presence-only data for the three prevalent deer species in Ireland: red, fallow and sika deer. We collated all deer data available for the past 15 years and fitted models predicting distribution and relative abundance at a 25 km2 resolution across the island. Models’ predictions were associated to spatial estimates of uncertainty, allowing us to assess the quality of the model and the effect that data scarcity has on the certainty of predictions. Furthermore, we checked the performance of the three species-specific models using two datasets, independent deer hunting returns and deer densities based on faecal pellet counts. Our work clearly demonstrates the applicability of spatially-explicit ISDMs to empirical data in a Bayesian context, providing a blueprint for managers to exploit unexplored and seemingly unusable data that can, when modelled with the proper tools, serve to inform management and conservation policies. Methods Presence absence (PA) data PA data for each species were obtained from Coillte based on surveys performed in a fraction of the 6,000 properties they manage (Table 1) by asking property managers (who visit the forests they manage on a regular basis) whether deer were present and, if so, what species. Properties range in size from less than one to around 2,900 ha, and to assign the PA value to a specific location, we calculated the centroid of each property using the function st_centroid() from the package sf in R (Pebesma 2018). The survey was mainly performed in 2010 and 2013, in addition to further data collected between 2014 and 2016. Some properties were surveyed only once in the period 2010–2016, but for those that were surveyed more than once, the value for that location was considered “absence” if deer had never been detected in the property in any of the surveys, and “presence” in all other cases. In addition to these surveys, Coillte commissioned density surveys based on faecal pellet sampling in a subset of their properties between the years 2007 and 2020. Any non-zero densities in these data were considered “presences”, and all zeros were considered “absences”. These data were also summarised across years when a property had been repeatedly sampled and counted as presence if deer had been detected in any of the sampling years. PA data for NI were obtained from a survey carried out by the British Deer Society in 2016. The survey divided the British territory into 100 km2 grid cells, and deer presence or absence was assessed based on public contributions, which were then reviewed and collated by BDS experts. Since 100 km2 grid cells are quite large, we did not, as with the Coillte properties, calculate the centroid of each cell and assign the PA value of the cell to it. Instead, we randomly simulated positions within each cell and assigned the presence or absence value of the cell to each of them. We performed a sensitivity analysis to calculate an optimal number of positions that would capture the environmental variability within each cell, which was set to 5 random positions per grid cell. After processing, we obtained a total of 920 PA data across NI. 2.2.2 Presence-only (PO) data PO data were collected from various sources, mainly (but not only) from citizen science initiatives. The National Biodiversity Data Centre (NBDC) is an Irish initiative that collates biodiversity data coming from different sources, from published studies to citizen contributions. From this repository, we obtained all contributions on the three species, a total of 1,430 records. To this, we added the 164 records of deer in Ireland downloaded from the iNaturalist site, another citizen-contributed database that collects the same type of data. From the resulting dataset, we (1) removed all observations with a spatial resolution lower than 1 km2; (2) did a visual inspection of the data and comments and removed all observations that were obviously incorrect (i.e. at sea or that the comment specified it was a different species); (3) filtered out all the fallow deer reported in Dublin’s enclosed city park (Phoenix Park) since the population there was introduced and is artificially maintained and disconnected from the rest of populations in Ireland; and (4) filtered duplicate observations by retaining only one observation per user, location, and day. The Centre for Environmental Data and Recording (CEDaR) is a data repository for Northern Ireland (NI) that operates in the same way as the NBDC. They provided 872 records of deer in NI, coming from different survey, scientific, and citizen science initiatives, from which we removed all records provided with a spatial resolution lower than 1 km2. The location and species of 469 deer culled between 2019 and 2021 in NI were obtained from the British Agri-Food and Biosciences Institute. For the observations that did not have specific coordinates, we derived them from the location name or postcode if provided. As part of a nationally funded initiative to improve deer monitoring in Ireland (SMARTDEER), we developed a bespoke online tool to facilitate the reporting of deer observations by the general public and all relevant stakeholders e.g. hunters, farmers, or foresters. Observations were reported in 2021 and 2022 by clicking on a map to indicate a 1 km2 area where deer have been observed. For each user and session, we calculated the area of the surface covered in squares, simulated a number of positions proportional to the size of the polygon, and distributed them within it to generate a number of exact positions equivalent to the area where the user had indicated an observation. In total, the SMARTDEER tool allowed us to collect 4,078 presences across Ireland and NI. 2.3.2 Covariate selection Raster environmental covariates used in the models were obtained from the Copernicus Land Monitoring Service (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency EEA), whereas the vector layers (roads, paths) were obtained from the Open Street Map service (OpenStreetMap contributors, 2017. Planet dump [Data file from January 2022]. https://planet.openstreetmap.org). Vector layers were transformed into distance layers (distance to roads, distance to paths) using the distance() function from the package raster, and into density layers (density of roads, paths) using the rasterize() function of the same package (Hijmans 2021). All raster layers were resampled to the lowest resolution available in the used covariates, resulting in a 1 km2 resolution. A full description of the process of covariate selection (including screening for collinearity) can be found in the supplementary material. The covariates eventually used in the model were elevation (m), slope (degrees), tree cover (%), small woody feature density (%), distances to forest edge (m, positive distances indicate a location outside a forest, negative distances indicate a location within a forest), and human footprint index (Venter et al. 2016, 2018). All covariates were scaled by subtracting the mean and dividing by the standard deviation before entering the model (function scale() from the raster package).