All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

This database that can be used for macro-level analysis of road accidents on interurban roads in Europe. Through the variables it contains, road accidents can be explained using variables related to economic resources invested in roads, traffic, road network, socioeconomic characteristics, legislative measures and meteorology. This repository contains the data used for the analysis carried out in the papers:

1. Calvo-Poyo F., Navarro-Moreno J., de Oña J. (2020) Road Investment and Traffic Safety: An International Study. Sustainability 12:6332. https://doi.org/10.3390/su12166332

2. Navarro-Moreno J., Calvo-Poyo F., de Oña J. (2022) Influence of road investment and maintenance expenses on injured traffic crashes in European roads. Int J Sustain Transp 1–11. https://doi.org/10.1080/15568318.2022.2082344

3. Navarro-Moreno, J., Calvo-Poyo, F., de Oña, J. (2022) Investment in roads and traffic safety: linked to economic development? A European comparison. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-022-22567

The file with the database is available in excel.

DATA SOURCES

The database presents data from 1998 up to 2016 from 20 european countries: Austria, Belgium, Croatia, Czechia, Denmark, Estonia, Finland, France, Germany, Ireland, Italy, Latvia, Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden and United Kingdom. Crash data were obtained from the United Nations Economic Commission for Europe (UNECE) [2], which offers enough level of disaggregation between crashes occurring inside versus outside built-up areas.

With reference to the data on economic resources invested in roadways, deserving mention –given its extensive coverage—is the database of the Organisation for Economic Cooperation and Development (OECD), managed by the International Transport Forum (ITF) [1], which collects data on investment in the construction of roads and expenditure on their maintenance, following the definitions of the United Nations System of National Accounts (2008 SNA). Despite some data gaps, the time series present consistency from one country to the next. Moreover, to confirm the consistency and complete missing data, diverse additional sources, mainly the national Transport Ministries of the respective countries were consulted. All the monetary values were converted to constant prices in 2015 using the OECD price index.

To obtain the rest of the variables in the database, as well as to ensure consistency in the time series and complete missing data, the following national and international sources were consulted:

Eurostat [3]

Directorate-General for Mobility and Transport (DG MOVE). European Union [4]

The World Bank [5]

World Health Organization (WHO) [6]

European Transport Safety Council (ETSC) [7]

European Road Safety Observatory (ERSO) [8]

European Climatic Energy Mixes (ECEM) of the Copernicus Climate Change [9]

EU BestPoint-Project [10]

Ministerstvo dopravy, República Checa [11]

Bundesministerium für Verkehr und digitale Infrastruktur, Alemania [12]

Ministerie van Infrastructuur en Waterstaat, Países Bajos [13]

National Statistics Office, Malta [14]

Ministério da Economia e Transição Digital, Portugal [15]

Ministerio de Fomento, España [16]

Trafikverket, Suecia [17]

Ministère de l’environnement de l’énergie et de la mer, Francia [18]

Ministero delle Infrastrutture e dei Trasporti, Italia [19–25]

Statistisk sentralbyrå, Noruega [26-29]

Instituto Nacional de Estatística, Portugal [30]

Infraestruturas de Portugal S.A., Portugal [31–35]

Road Safety Authority (RSA), Ireland [36]

DATA BASE DESCRIPTION

The database was made trying to combine the longest possible time period with the maximum number of countries with complete dataset (some countries like Lithuania, Luxemburg, Malta and Norway were eliminated from the definitive dataset owing to a lack of data or breaks in the time series of records). Taking into account the above, the definitive database is made up of 19 variables, and contains data from 20 countries during the period between 1998 and 2016. Table 1 shows the coding of the variables, as well as their definition and unit of measure.

Table. Database metadata

Code

Variable and unit

fatal_pc_km

Fatalities per billion passenger-km

fatal_mIn

Fatalities per million inhabitants

accid_adj_pc_km

Accidents per billion passenger-km

p_km

Billions of passenger-km

croad_inv_km

Investment in roads construction per kilometer, €/km (2015 constant prices)

croad_maint_km

Expenditure on roads maintenance per kilometer €/km (2015 constant prices)

prop_motorwa

Proportion of motorways over the total road network (%)

populat

Population, in millions of inhabitants

unemploy

Unemployment rate (%)

petro_car

Consumption of gasolina and petrol derivatives (tons), per tourism

alcohol

Alcohol consumption, in liters per capita (age > 15)

mot_index

Motorization index, in cars per 1,000 inhabitants

den_populat

Population density, inhabitants/km2

cgdp

Gross Domestic Product (GDP), in € (2015 constant prices)

cgdp_cap

GDP per capita, in € (2015 constant prices)

precipit

Average depth of rain water during a year (mm)

prop_elder

Proportion of people over 65 years (%)

dps

Demerit Point System, dummy variable (0: no; 1: yes)

freight

Freight transport, in billions of ton-km

ACKNOWLEDGEMENTS

This database was carried out in the framework of the project “Inversión en carreteras y seguridad vial: un análisis internacional (INCASE)”, financed by: FEDER/Ministerio de Ciencia, Innovación y Universidades–Agencia Estatal de Investigación/Proyecto RTI2018-101770-B-I00, within Spain´s National Program of R+D+i Oriented to Societal Challenges.

Moreover, the authors would like to express their gratitude to the Ministry of Transport, Mobility and Urban Agenda of Spain (MITMA), and the Federal Ministry of Transport and Digital Infrastructure of Germany (BMVI) for providing data for this study.

REFERENCES

1. International Transport Forum OECD iLibrary | Transport infrastructure investment and maintenance.

2. United Nations Economic Commission for Europe UNECE Statistical Database Available online: https://w3.unece.org/PXWeb2015/pxweb/en/STAT/STAT_40-TRTRANS/?rxid=18ad5d0d-bd5e-476f-ab7c-40545e802eeb (accessed on Apr 28, 2020).

3. European Commission Database - Eurostat Available online: https://ec.europa.eu/eurostat/data/database (accessed on Apr 28, 2021).

4. Directorate-General for Mobility and Transport. European Commission EU Transport in figures - Statistical Pocketbooks Available online: https://ec.europa.eu/transport/facts-fundings/statistics_en (accessed on Apr 28, 2021).

5. World Bank Group World Bank Open Data | Data Available online: https://data.worldbank.org/ (accessed on Apr 30, 2021).

6. World Health Organization (WHO) WHO Global Information System on Alcohol and Health Available online: https://apps.who.int/gho/data/node.main.GISAH?lang=en (accessed on Apr 29, 2021).

7. European Transport Safety Council (ETSC) Traffic Law Enforcement across the EU - Tackling the Three Main Killers on Europe’s Roads; Brussels, Belgium, 2011;

8. Copernicus Climate Change Service Climate data for the European energy sector from 1979 to 2016 derived from ERA-Interim Available online: https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-european-energy-sector?tab=overview (accessed on Apr 29, 2021).

9. Klipp, S.; Eichel, K.; Billard, A.; Chalika, E.; Loranc, M.D.; Farrugia, B.; Jost, G.; Møller, M.; Munnelly, M.; Kallberg, V.P.; et al. European Demerit Point Systems : Overview of their main features and expert opinions. EU BestPoint-Project 2011, 1–237.

10. Ministerstvo dopravy Serie: Ročenka dopravy; Ročenka dopravy; Centrum dopravního výzkumu: Prague, Czech Republic;

11. Bundesministerium für Verkehr und digitale Infrastruktur Verkehr in Zahlen 2003/2004; Hamburg, Germany, 2004; ISBN 3871542946.

12. Bundesministerium für Verkehr und digitale Infrastruktur Verkehr in Zahlen 2018/2019. In Verkehrsdynamik; Flensburg, Germany, 2018 ISBN 9783000612947.

13. Ministerie van Infrastructuur en Waterstaat Rijksjaarverslag 2018 a Infrastructuurfonds; The Hague, Netherlands, 2019; ISBN 0921-7371.

14. Ministerie van Infrastructuur en Milieu Rijksjaarverslag 2014 a Infrastructuurfonds; The Hague, Netherlands, 2015; ISBN 0921- 7371.

15. Ministério da Economia e Transição Digital Base de Dados de Infraestruturas - GEE Available online: https://www.gee.gov.pt/pt/publicacoes/indicadores-e-estatisticas/base-de-dados-de-infraestruturas (accessed on Apr 29, 2021).

16. Ministerio de Fomento. Dirección General de Programación Económica y Presupuestos. Subdirección General de Estudios Económicos y Estadísticas Serie: Anuario estadístico; NIPO 161-13-171-0; Centro de Publicaciones. Secretaría General Técnica. Ministerio de Fomento: Madrid, Spain;

17. Trafikverket The Swedish Transport Administration Annual report: 2017; 2018; ISBN 978-91-7725-272-6.

18. Ministère de l’Équipement, du T. et de la M. Mémento de statistiques des transports 2003; Ministère de l’environnement de l’énergie et de la mer, 2005;

19. Ministero delle Infrastrutture e dei Trasporti Conto Nazionale delle

Global Sanction Screening and Access Options

Our Global Sanction Lists Database is a powerful tool designed for quick and easy global sanction screening and verification of both individuals and organizations listed on international sanction lists. This service emphasizes the fight against money laundering and terrorism financing (AML-CFT), ensuring your business stays in line with global regulations. We keep our database up to date every day, processed into a professional and secure system, giving you access to the most current information.

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EU-Level PEP Screening and Access Options

Our service provides exclusive access to a database for EU-level PEP screening of Politically Exposed Persons at the European Union level. It empowers obligated entities to efficiently identify individuals with significant public roles within EU institutions and bodies. This database provides insights into persons currently in or those who have held significant public positions in Brussels and other EU institutions in the last 12 months. It spans not only individuals in key positions but also their relatives, broadening the scope for risk assessment. With daily updates from diverse public sources and careful manual processing, our database aids organizations in effectively navigating compliance and mitigating PEP-related risks.

PEP Group 1: Significant Public Functions

Includes individuals currently in or who have in the last 12 months held function of significant public role as defined by the Directive of the European Parliament and of the Council EU 2015/849 and further detailed in the Commission Decision C/2002/3105. Profiles generally include the exact date of birth and usually the domicile. In cases where the full date of birth is not available, the indication "Partially Identified PEP" is displayed. Individuals with enduring risks are recorded for up to five years after ending their function, especially for positions of pan-European significance or extended duration.

Specific Positions within PEP Group 1: Executive Authority Leaders Legislative Members Judges Members of the European Central Bank Bodies Members of the Court of Auditors Ambassadors and Chargés d’Affaires

PEP Groups 2 - 4 and 7: Family and Close Associates

Includes spouses/partners, children (including sons-in-law and daughters-in-law), and parents of individuals in Group PEP1, as well as individuals in a familial or similar relationship.

Specification PEP2: Spouse/partner PEP3: Child/son-in-law/daughter-in-law PEP4: Parent PEP7: Individuals in a long-term familial or similar relationship

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on pressure levels from 1940 to present".

This dataset provides a gridded modelled time series of river discharge forced with seasonal range meteorological reforecasts. The data is a consistent representation of a key hydrological variable across the global domain, and is a product of the Global Flood Awareness System (GloFAS). It is accompanied by an ancillary file for interpretation that provides the upstream area (see the related variables table and associated link in the documentation). This dataset was produced by forcing the open-source LISFLOOD hydrological model with input from the European Centre for Medium-range Weather Forecasts (ECMWF) ensemble seasonal forecasting system, SEAS5. For the period of 1981 to 2016 the number of ensemble members is 25, whilst reforecasts produced for 2017 onwards use a 51-member ensemble. Reforecasts are forecasts run over past dates, with those presented here used for producing the seasonal river discharge thresholds. In addition, they provide a suitably long time period against which the skill of the seasonal forecast can be assessed. The reforecasts are initialised monthly and run for 123 days, with a 24-hourly time step. For more specific information on the how the seasonal reforecast dataset is produced we refer to the documentation. Companion datasets, also available through the Early Warning Data Store (EWDS), include the seasonal forecasts, for which the dataset provided here can be useful for local skill assessment and post-processing. For users looking for shorter term forecasts there are also medium-range forecasts and reforecasts available, as well as historical simulations that can be used to derive the hydrological climatology. For users looking specifically for European hydrological data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS), which is managed, technically implemented and developed by the European Commission’s Joint Research Centre.

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. ERA5-Land uses as input to control the simulated land fields ERA5 atmospheric variables, such as air temperature and air humidity. This is called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate correction'.
The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. The temporal and spatial resolutions of ERA5-Land makes this dataset very useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states.

LifeSnaps Dataset Documentation

Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the European H2020 RAIS project. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction.

The following instructions will get you started with the LifeSnaps dataset and are complementary to the original publication.

Data Import: Reading CSV

For ease of use, we provide CSV files containing Fitbit, SEMA, and survey data at daily and/or hourly granularity. You can read the files via any programming language. For example, in Python, you can read the files into a Pandas DataFrame with the pandas.read_csv() command.

Data Import: Setting up a MongoDB (Recommended)

To take full advantage of the LifeSnaps dataset, we recommend that you use the raw, complete data via importing the LifeSnaps MongoDB database.

To do so, open the terminal/command prompt and run the following command for each collection in the DB. Ensure you have MongoDB Database Tools installed from here.

For the Fitbit data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c fitbit 

For the SEMA data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c sema 

For surveys data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c surveys 

If you have access control enabled, then you will need to add the --username and --password parameters to the above commands.

Data Availability

The MongoDB database contains three collections, fitbit, sema, and surveys, containing the Fitbit, SEMA3, and survey data, respectively. Similarly, the CSV files contain related information to these collections. Each document in any collection follows the format shown below:

{
  _id: 

This dataset provides global ocean and sea-ice reanalysis (ORAS5: Ocean Reanalysis System 5) monthly mean data prepared by the European Centre for Medium-Range Weather Forecasts (ECMWF) OCEAN5 ocean analysis-reanalysis system. This system comprises 5 ensemble members from which one member is published in this catalogue entry. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset taking into account the laws of physics. The reanalysis provides information without temporal and spatial gaps, i.e. the data are continuous in time, and the assimilation system provides information on every model grid point independently of whether observations are available nearby or not. The OCEAN5 reanalysis system uses the Nucleus for European Modelling of the Ocean (NEMO) ocean model and the NEMOVAR ocean assimilation system. NEMOVAR uses the so-called 3D-Var FGAT (First Guess at Appropriate Time) assimilation technique, which assimilates sub-surface temperature, salinity, sea-ice concentration and sea-level anomalies. The ORAS5 data is forced by either global atmospheric reanalysis (for the consolidated product) or the ECMWF/IFS operational analysis (for the operational product) and is also constrained by observational data of sea surface temperature, sea surface salinity, sea-ice concentration, global-mean-sea-level trends and climatological variations of the ocean mass. The consolidated product (referred to as "Consolidated" in the download form) uses reanalysis atmospheric forcing (ERA-40 until 1978 and ERA-Interim from 1979 to 2014) and re-processed observations. The near real-time (referred to as "Operational" in the download form) ORAS5 product is available from 2015 onwards and is updated on a monthly basis 15 days behind real time. It uses ECMWF operational atmospheric forcing and near real time observations. The consolidated data benefits from atmospheric forcing consistency. The operational data benefits from near real-time latency. ORAS5 data are also available at the Copernicus Marine Environment Monitoring Service (CMEMS) and at the Integrated Climate Data Centre (ICDC), Hamburg University. The present dataset, at the time of publication, provides more variables than the others and has regular updates with near real-time data. For the period from 2015 to the present, the operational ORAS5 data provided in the CDS is different from the dataset provided by CMEMS, because different atmospheric forcings and ocean observation data were used in the generation of the two products. The ORAS5 dataset is produced by ECMWF and funded by the Copernicus Climate Change Service (C3S).

Please provide the following information under FOI law full schedule of uk databases used to check eligibility for Health Insurance Card eg NI, passport, register of births number of applications for HI Card received april 22-april 23 number of applications rejected due to lack of proof of eligibility april 22-april 23 number of people required to provide further proof following application NHS definition of legal criteria for eligibility for Health Insurance Card Your request was received on 16 August 2023 and I am dealing with it under the terms of the Freedom of Information Act 2000. On 3 December 2023 you clarified the following: 1) When assessing UK Global Health Insurance Card applications does the Authority have access to UK Government records? For example Registration of Births, National Insurance, EU Settlement Scheme records, UK Passport Office Records, DVA Records of Driving Licences? 2) Please give me the number of applications for UK Global Health Insurance Card applications in the last financial year. Please also indicate the number that were approved and the number rejected due to insufficient proof of residency. On 27th December 2023 you clarified the following: 5) I can confirm I want the information for EHIC, UK EHIC and UK GHIC. Response Question 1 When assessing UK Global Health Insurance Card applications, the NHSBSA has access to some UK Government records, such as EU settlement Scheme records. The NHSBSA does not have access to National Insurance records, Registration of Births, UK Passport Office Records or DVA Records. UK Global Health Insurance Card applications are based on a residency system and the NHSBSA will use third party data provider Equifax to establish UK residency. This is stated in our Privacy Notice. https://www.nhsbsa.nhs.uk/our-policies/privacy/overseas-healthcare-services-privacy-notice#:~:text=You%20have%20the%20right%20to,it%20for%20longer%20than%20necessary Question 2 There were 6,510,849 UK Global Health Insurance Card applications in the last financial year. Question 3 and 4 6,016,310 applications were approved and 145,876 were rejected because we were unable to establish proof of residency. The remaining applications were either rejected for other reasons, or we have not yet finished dealing with them. Question 5 The following links provide definitions of legal criteria for eligibility for UK GHIC and UK EHIC: • https://faq.nhsbsa.nhs.uk/knowledgebase/article/KA-26813 • https://www.nhs.uk/using-the-nhs/healthcare-abroad/apply-for-a-free-uk-global-health-insurance-card-ghic/ Please note that we do not issue EHIC anymore as that card has been replaced by the UK EHIC.

Switzerland is leading the ranking by population share with mobile internet access , recording 95.06 percent. Following closely behind is Ukraine with 95.06 percent, while Moldova is trailing the ranking with 46.83 percent, resulting in a difference of 48.23 percentage points to the ranking leader, Switzerland. The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The Global Living Arrangements Database (GLAD), is a global resource designed to fill a critical gap in the availability of statistical information for examining patterns and changes in living arrangements by age, sex, marital status and educational attainment. Utilizing comprehensive census microdata from IPUMS International and the European Labour Force Survey (EU-LFS), GLAD summarizes over 740 million individual records across 107 countries, covering the period from 1960 to 2021. This database has been constructed using an innovative algorithm that reconstructs kinship relationships among all household members, providing a robust and scalable methodology for studying living arrangements. GLAD is expected to be a valuable resource for both researchers and policymakers, supporting evidence-based decision-making in areas such as housing, social services, and healthcare, as well as offering insights into long-term transformations in family structures. The open-source R code used in this project is publicly available, promoting transparency and enabling the creation of new ego-centred typologies based in interfamily relationships

The repository is composed of the following elements: a Rda file named CORESIDENCE_GLAD_2025.Rda in the form of a List. In R, a List object is a versatile data structure that can contain a collection of different data types, including vectors, matrices, data frames, other lists, spatial objects or even functions. It allows to store and organize heterogeneous data elements within a single object. The CORESIDENCE_GLAD_2025 R-list object is composed of six elements:

1. SINGLE AGES: a data frame where data is aggregated by single ages, marital status, educational attainment and living arrangement types. Source of the original data: IPUMS-I
2. AGE GROUPS IPUMS: a data frame where data is aggregated by five-year age groups, marital status, educational attainment and living arrangement types. Source of the original data: IPUMS-I.
3. AGE GROUPS LFS: a data frame where data is aggregated by five-year age groups, marital status, educational attainment and living arrangement types. Source of the original data: EU-LFS.
4. HARMONIZED: a data frame where data is aggregated by five-year age groups, marital status, educational attainment and living arrangement types. The categories of marital status and educational attainment have been harmonized between the two data sources. Source of the original data: IPUMS-I and EU-LFS
5. CODEBOOK: a data frame with the complete list of variables included, their names description and categories.
6. LABELS LAT: A R function to add the qualitative labels to Living Arrangement Types (LAT).
7. ATLAS LIVING ARRANGEMENTS: The url of the folder with leaflet of living arrangements for each sample included in GLAD.

European Union recorded a trade surplus of 13128.90 EUR Million in May of 2025. This dataset provides the latest reported value for - European Union Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

These datasets were obtained from ECMWF/GloFAS on November 13, 2017, to include the flood forecast (area grid) for Hurricanes Harvey and Irma in the USA from August 15 - September 15, 2017. These are contained in netCDF files, one per day.

Note that while folders and files may have the words "areagrid_for_Harvey" in the name, all the data here are for the southeast USA, encompassing both Harvey and Irma impact areas.

Dataset variables: - dis = forecasted discharge (for all forecast step 1+30 as initial value and 30 daily average values, with ensemble members as 1+50 where the first is the so-called control member and the 50 perturbed members) - ldd = local drainage direction within routing model - ups = upstream area of each point within routing model - rl2,rl5,rl20 = forecast exceedance thresholds for 2-, 5- and 20-year return period flows, based on gumbel distribution from ERA-interim land reanalysis driven through the lisflood routing.

Models used (see [1] for further details): - Hydrology: River discharge is simulated by the Lisflood hydrological model (van der Knijff et al., 2010) for the flow routing in the river network and the groundwater mass balance. The model is set up on global coverage with horizontal grid resolution of 0.1° (about 10 km in mid-latitude regions) and daily time step for input/output data. - Meteorology: To set up a forecasting and warning system that runs on a daily basis with global coverage, initial conditions and input forcing data must be provided seamlessly to every point within the domain. To this end, two products are used. The first consists of operational ensemble forecasts of near-surface meteorological parameters. The second is a long-term dataset consistent with daily forecasts, used to derive a reference climatology.

Suggestions for usage: - Selected software: ArcGIS or QGIS - Select dis for example, then any of the bands (51*31 in total), then set the range manually to 0-1000 or something like that.

Agency: GloFAS [1] From its public website: "The Global Flood Awareness System (GloFAS), jointly developed by the European Commission and the European Centre for Medium-Range Weather Forecasts (ECMWF), is independent of administrative and political boundaries. It couples state-of-the art weather forecasts with a hydrological model and with its continental scale set-up it provides downstream countries with information on upstream river conditions as well as continental and global overviews. GloFAS produces daily flood forecasts in a pre-operational manner since June 2011."

References [1] GloFAS home page [http://www.globalfloods.eu/]

This catalogue entry provides Regional Climate Model (RCM) data on single levels from a number of experiments, models, domains, resolutions, ensemble members, time frequencies and periods computed over several regional domains all over the World in the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX). The term "single levels" is used to express that the variables are 2D-matrices computed on one vertical level which can be surface (or a level close to the surface) or a dedicated pressure level in the atmosphere. Multiple vertical levels are excluded from this catalogue entry. High-resolution Regional Climate Models (RCMs) can provide climate change information on regional and local scales in relatively fine detail, which cannot be obtained from coarse scale Global Climate Models (GCMs). This is manifested in better description of small-scale regional climate characteristics and also in more accurate representation of extreme events. Consequently, outputs of such RCMs are indispensable in supporting regional and local climate impact studies and adaptation decisions. RCMs are not independent from the GCMs, since the GCMs provide lateral and lower boundary conditions to the regional models. In that sense RCMs can be viewed as magnifying glasses of the GCMs. The CORDEX experiments consist of RCM simulations representing different future socio-economic scenarios (forcings), different combinations of GCMs and RCMs and different ensemble members of the same GCM-RCM combinations. This experiment design through the ensemble members allows for studies addressing questions related to the key uncertainties in future climate change. These uncertainties come from differences in the scenarios of future socio-economic development, the imperfection of regional and global models used and the internal (natural) variability of the climate system. This experiment design allows for studies addressing questions related to the key uncertainties in future climate change:

what will future climate forcing be? what will be the response of the climate system to changes in forcing? what is the uncertainty related to natural variability of the climate system?

The term "experiment" in the CDS form refers to three main categories:

Evaluation: CORDEX experiment driven by ECMWF ERA-Interim reanalysis for a past period. These experiments can be used to evaluate the quality of the RCMs using perfect boundary conditions as provided by a reanalysis system. The period covered is typically 1980-2010; Historical: CORDEX experiment which covers a period for which modern climate observations exist. Boundary conditions are provided by GCMs. These experiments, that follow the observed changes in climate forcing, show how the RCMs perform for the past climate when forced by GCMs and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1950-2005; Scenario: Ensemble of CORDEX climate projection experiments using RCP (Representative Concentration Pathways) forcing scenarios. These scenarios are the RCP 2.6, 4.5 and 8.5 scenarios providing different pathways of the future climate forcing. Boundary conditions are provided by GCMs. The period covered is typically 2006-2100.

In CORDEX, the same experiments were done using different RCMs (labelled as “Regional Climate Model” in the CDS form). In addition, for each RCM, there is a variety of GCMs, which can be used as lateral boundary conditions. The GCMs used are coming from the CMIP5 (5th phase of the Coupled Model Intercomparison Project) archive. These GCM boundary conditions are labelled as “Global Climate Model” in the form and are also available in the CDS. Additionally, the uncertainty related to internal variability of the climate system is sampled by running several simulations with the same RCM-GCM combination. On the forms, these are indexed as separate ensemble members (the naming convention for ensemble members is available in the documentation). For each GCM, the same experiment was repeatedly done using slightly different conditions (like initial conditions or different physical parameterisations for instance) producing in that way an ensemble of experiments closely related. More details behind these sequential ensemble numbers is available in the detailed documentation. The data are produced by the institutes and modelling centres participating in the different CORDEX domains with partial support from different international and national contributions including support from COPERNICUS for some of the EURO-CORDEX runs. The data can be used for commercial purposes (unrestricted use) with the exception of the simulations from the following RCMs: BOUN-RegCM4-3 model (for Central Asia and Middle East and North Africa domains) and RU-CORE-RegCM4-3 model (for South-East Asia domain). Precise terms of use are provided in the CORDEX licence.

"Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.This dataset includes demographic data of 22 countries from 1960 to 2018, including Sri Lanka, Bangladesh, Pakistan, India, Maldives, etc. Data fields include: country, year, population ratio, male ratio, female ratio, population density (km). Source: ( 1 ) United Nations Population Division. World Population Prospects: 2019 Revision. ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations Statistical Division. Population and Vital Statistics Reprot ( various years ), ( 5 ) U.S. Census Bureau: International Database, and ( 6 ) Secretariat of the Pacific Community: Statistics and Demography Programme. Periodicity: Annual Statistical Concept and Methodology: Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models. Errors and undercounting occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census. The quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data. The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census. The United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems. International migration is the only other factor besides birth and death rates that directly determines a country's population growth. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect. Population projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant."

The Fiscal Monitor surveys and analyzes the latest public finance developments, it updates fiscal implications of the crisis and medium-term fiscal projections, and assesses policies to put public finances on a sustainable footing.

Country-specific data and projections for key fiscal variables are based on the April 2020 World Economic Outlook database, unless indicated otherwise, and compiled by the IMF staff. Historical data and projections are based on information gathered by IMF country desk officers in the context of their missions and through their ongoing analysis of the evolving situation in each country; they are updated on a continual basis as more information becomes available. Structural breaks in data may be adjusted to produce smooth series through splicing and other techniques. IMF staff estimates serve as proxies when complete information is unavailable. As a result, Fiscal Monitor data can differ from official data in other sources, including the IMF's International Financial Statistics.

The country classification in the Fiscal Monitor divides the world into three major groups: 35 advanced economies, 40 emerging market and middle-income economies, and 40 low-income developing countries. The seven largest advanced economies as measured by GDP (Canada, France, Germany, Italy, Japan, United Kingdom, United States) constitute the subgroup of major advanced economies, often referred to as the Group of Seven (G7). The members of the euro area are also distinguished as a subgroup. Composite data shown in the tables for the euro area cover the current members for all years, even though the membership has increased over time. Data for most European Union member countries have been revised following the adoption of the new European System of National and Regional Accounts (ESA 2010). The low-income developing countries (LIDCs) are countries that have per capita income levels below a certain threshold (currently set at $2,700 in 2016 as measured by the World Bank's Atlas method), structural features consistent with limited development and structural transformation, and external financial linkages insufficiently close to be widely seen as emerging market economies. Zimbabwe is included in the group. Emerging market and middle-income economies include those not classified as advanced economies or low-income developing countries. See Table A, "Economy Groupings," for more details.

Most fiscal data refer to the general government for advanced economies, while for emerging markets and developing economies, data often refer to the central government or budgetary central government only (for specific details, see Tables B-D). All fiscal data refer to the calendar years, except in the cases of Bangladesh, Egypt, Ethiopia, Haiti, Hong Kong Special Administrative Region, India, the Islamic Republic of Iran, Myanmar, Nepal, Pakistan, Singapore, and Thailand, for which they refer to the fiscal year.

Composite data for country groups are weighted averages of individual-country data, unless otherwise specified. Data are weighted by annual nominal GDP converted to U.S. dollars at average market exchange rates as a share of the group GDP.

In many countries, fiscal data follow the IMF's Government Finance Statistics Manual 2014. The overall fiscal balance refers to net lending (+) and borrowing ("") of the general government. In some cases, however, the overall balance refers to total revenue and grants minus total expenditure and net lending.

The fiscal gross and net debt data reported in the Fiscal Monitor are drawn from official data sources and IMF staff estimates. While attempts are made to align gross and net debt data with the definitions in the IMF's Government Finance Statistics Manual, as a result of data limitations or specific country circumstances, these data can sometimes deviate from the formal definitions.

Business Tendency Surveys (BTS) – also called business opinion or business climate surveys – are economic surveys used to monitor and forecast business cycles. Covering 4 different economic sectors (manufacturing, construction, retail trade and services), they are designed to collect qualitative information useful in monitoring the current business situation and forecasting short-term developments by directly asking company managers about the pulse of their businesses. They are well known for providing advance warning of turning points in aggregate economic activity as measured by GDP or industrial production. As respondents provide answers on a 3 scale options (up, same, down, or above normal, normal, below normal), data are summarised in net balances corresponsing to the difference in % of positive over negative replies. Because of their collection mode, timeliness and immediate availability, they have proved to be a cost‑effective mean of generating timely information especially during crises. In the late '90, in collaboration with the European Commission, the OECD has developed a system of harmonised business tendency surveys in order to collect and compare data across countries. The EC Directorate General for Economic and Financial Affairs is since then in charge of running the program and collecting data across EU members, while the OECD helped the adoption and implementation of the same harmonised framework in non-EU OECD countries and BRIICS. By construction, BTS questions are formulated in order to exclude seasonal factors. Nevertheless all series are tested for seasonality by both the OECD (using x12) and by the EC for the EU member data (using DAINTIES). This dataset comprises a set of harmonised target indicators available across OECD and BRIICS countries, any departure from target definitions are documented in the metadata.

Climate Index: Tn10p

Definition: Average number of days that the daily minimum temperature is below the 10th percentile of daily minimum temperatures of a five day window.

Additional information: The dataset is based on an ensemble of EURO-CORDEX model simulations of daily near-surface maximum temperature. All ensemble members are bias-corrected against the gridded daily observational dataset E-OBS.

Results (ensemble mean and standard deviation) are available for historical (1971-2000) and future (2011-2040, 2041-2070, 2071-2100) climate periods and for the representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.

The bias-corrected EURO-CORDEX climate model simulations used are:

• CLMcom-CCLM4-8-17/ICHEC-EC-EARTH, CLMcom-CCLM4-8-17/MOHC-HadGEM2-ES
• DMI-HIRHAM5/ICHEC-EC-EARTH
• KNMI-RACMO22E/ICHEC-EC-EARTH, KNMI-RACMO22E/MOHC-HadGEM2-ES
• SMHI-RCA4/ICHEC-EC-EARTH, SMHI-RCA4/MOHC-HadGEM2-ES

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.

Sample images were collected by SCUBA divers during the austral spring between September and November each year from 2002 to 2014 in McMurdo Sound. Organisms visible in the images were identified to the lowest possible taxonomic category and enumerated.

Related datasets: McMurdo sediment: https://www.bco-dmo.org//dataset/746035 McMurdo epifauna species list: https://www.bco-dmo.org//dataset/746999

Acquisition Description: Sample images were collected by SCUBA divers during the austral spring between September and November each year. To quantify common species we used ten replicate still images, and cropped each image to cover 1 m2. To quantify rare species we used three replicate transects encompassing 10 m2 each. For the cryptic species Laternula elliptica P. P. King, we made in situ counts in six replicate 0.25 m2 areas. Species that could not be counted as individuals were not quantified (e.g. some hydroids, bryozoans, and sponges). Organisms visible in each quadrat or transect were identified to the lowest possible taxonomic category and enumerated. Taxonomy follows that of the World Register of Marine Species (WoRMS, http://www.marinespecies.org/about.php). Individual taxa were counted in either quadrats or transects, depending on abundance. The counts were area-adjusted and combined into a single megafaunal data set.

Two 4 cm diameter, 5 cm deep cores were collected, one for grainsize analysis which was refrigerated until processing, and one for carbon and nitrogen analysis that was frozen until analysis. Results and methodology of grainsize, carbon and nitrogen analysis can be found in the dataset "McMurdo sediment" https://www.bco-dmo.org//dataset/746035.

BCO-DMO Data Manager Processing Notes: * added a conventional header with dataset name, PI name, version date * modified parameter names to conform with BCO-DMO naming conventions * Dataset transposed rows to columns * World Register of Marine Species taxa match tool used to find misspellings and unaccepted names (2018-09-10). No misspellings but three unaccepted names found. Name changes to use the accepted species name reviewed and accepted by the data contributor. ** Tetilla leptoderma -> Antarctotetilla leptoderma (aphiaID: 885825) ** Margarites antarctica -> Margarella antarctica (aphiaID: 197257) ** Corymorpha parvula -> Zyzzyzus parvula (aphiaID: 231614)