About this dataset

Context

The dataset tabulates the population of Italy town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Italy town. The dataset can be utilized to understand the population distribution of Italy town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Italy town. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Italy town.

Key observations

Largest age group (population): Male # 60-64 years (70) | Female # 60-64 years (50). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Italy town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Italy town is shown in the following column.
• Population (Female): The female population in the Italy town is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Italy town for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Italy town Population by Gender. You can refer the same here

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

The total population in Italy was estimated at 59.0 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides the latest reported value for - Italy Population - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Italy, New York, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 15,189, while the mean income for the highest quintile (20% of households with the highest income) is 194,470. This indicates that the top earners earn 13 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 308,510, which is 158.64% higher compared to the highest quintile, and 2031.14% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Italy town median household income. You can refer the same here

Context

This is the Italian Coronavirus data repository from the Dipartimento della Protezione Civile . This dataset was created in response to the Coronavirus public health emergency in Italy and includes COVID-19 cases reported by region

Sample Queries

Dati Italia COVID-19: Which provinces in Italy have the most confirmed cases? Find which Italian provinces have the highest number of confirmed COVID-19 cases as of yesterday. SELECT covid19.province_name AS province, covid19.region_name AS region, confirmed_cases FROM bigquery-public-data.covid19_italy.data_by_province covid19 WHERE EXTRACT(date from DATE) = DATE_SUB(CURRENT_DATE(),INTERVAL 1 day) ORDER BY confirmed_cases desc

Sample Query 2

What percentage of tests performed have resulted in confirmed cases by region? This query determines what percent of tests performed are made up by confirmed cases. SELECT covid19.region_name AS region, total_confirmed_cases, tests_performed, ROUND(total_confirmed_cases/tests_performed*100,2) AS percent_tests_confirmed_cases FROM bigquery-public-data.covid19_italy.data_by_region covid19 WHERE EXTRACT(date from DATE) = DATE_SUB(CURRENT_DATE(),INTERVAL 1 day) ORDER BY percent_tests_confirmed_cases desc

Data come from the follow-up of the main study “Inclusive ageing in place” (IN-AGE), regarding frail older people aged 65 years and over (males and females). The main study was a cross-sectional qualitative survey carried out in 2019 by face-to-face interviews to frail older people without cognitive impairment, and living at home, alone or with a private personal care assistant (PCA), in three Italian Regions: Lombardy (North), Marche (Centre) and Calabria (South). Both peripheral/degraded areas of urban sites and fragile rural locations were included, with regard to social and material vulnerability aspects (e.g. high presence of frail older people living alone, poor provision of services). The follow up was carried out in July-September 2020, and it was aimed to explore and compare effects of lockdown, due to the first wave of the COVID-19 pandemic (February-May 2020), on frail older people living alone at home in Brescia and Ancona, two urban cities located respectively in the Northern and Central Italy. This country was the Western epicenter of the first wave of the pandemic, that differently affected the two cities as for infections, with a more severe impact on the former one. The dataset (41 respondents, vs 48 in the main survey) regards available care arrangements, both informal (family members) and formal (public services), to support the performing of daily living activities (ADLs and IADLs), especially in the presence of functional limitations. The use of/access to health services (General Practitioner, Medical Specialist and other health services) was also explored. A semi-structured interview was administered by telephone due to social distancing imposed by the pandemic. Participants were asked to report possible worsening/improving (or no change/not affected) due to the pandemic. A simple quantitative analysis (frequency distribution/bivariate analysis) of closed responses was carried out by using Microsoft Excel software 2019. Analyses suggested how the lockdown and social distancing overall negatively impacted on frail older people living alone, to a different extent in Ancona and Brescia, with a better resilience of home care services in Brescia, and a greater support from the family in Ancona, where however major problems in accessing health services also emerged. Even though the study was exploratory only, also due to the small sample, that cannot be considered as representative of the target population, findings suggested that enhancing home care services, and supporting older people in accessing health services, could allow ageing in place, especially in emergency time. The dataset is provided in open format (xlsx) and includes the following: a “numeric” dataset regarding the unlabelled dimensions used for statistics elaboration; a codebook with both the complete variables list and variables labels we used. The dataset was produced within the framework of the IN-AGE project, funded by Fondazione Cariplo, Grant N. 2017-0941.

Introduction: The Italian peninsula is in the center of the Mediterranean area, and historically it has been a hub for numerous human populations, cultures, and also animal species that enriched the hosted biodiversity. Horses are no exception to this phenomenon, with the peculiarity that the gene pool has been impacted by warfare and subsequent “colonization”. In this study, using a comprehensive dataset for almost the entire Italian equine population, in addition to the most influential cosmopolitan breeds, we describe the current status of the modern Italian gene pool.Materials and Methods: The Italian dataset comprised 1,308 individuals and 22 breeds genotyped at a 70 k density that was merged with publicly available data to facilitate comparison with the global equine diversity. After quality control and supervised subsampling to ensure consistency among breeds, the merged dataset with the global equine diversity contained data for 1,333 individuals from 54 populations. Multidimensional scaling, admixture, gene flow, and effective population size were analyzed.Results and Discussion: The results show that some of the native Italian breeds preserve distinct gene pools, potentially because of adaptation to the different geographical contexts of the peninsula. Nevertheless, the comparison with international breeds highlights the presence of strong gene flow from renowned breeds into several Italian breeds, probably due to historical introgression. Coldblood breeds with stronger genetic identity were indeed well differentiated from warmblood breeds, which are highly admixed. Other breeds showed further peculiarities due to their breeding history. Finally, we observed some breeds that exist more on cultural, traditional, and geographical point of view than due to actual genetic distinctiveness.

What’s Included

This training dataset comprises more than 10,000 conversational text data between two native Italian people in the general domain. We have a collection of chats on a variety of different topics/services/issues of daily life, such as music, books, festivals, health, kids, family, environment, study, childhood, cuisine, internet, movies, etc., and that makes the dataset diverse.

These chats consist of language-specific words, and phrases and follow the native way of talking which makes the chats more information-rich for your NLP model. Apart from each chat being specific to the topic, it contains various attributes like people's names, addresses, contact information, email address, time, date, local currency, telephone numbers, local slang, etc too in various formats to make the text data unbiased.

These chat scripts have between 300 and 700 words and up to 50 turns. 150 people that are a part of the FutureBeeAI crowd community contributed to this dataset. You will also receive chat metadata, such as participant age, gender, and country information, along with the chats. Dataset applications include conversational AI, natural language processing (NLP), smart assistants, text recognition, text analytics, and text prediction.

This dataset is being expanded with new chats all the time. We are able to produce text data in a variety of languages to meet your unique requirements. Check out the FutureBeeAI community for a custom collection.

This training dataset's licence belongs to FutureBeeAI!

Context

Due to the coronavirus epidemic, and following the measures adopted by the Government to contain it, Istat has implemented a series of actions to ensure the continuity and quality of statistical production even in the emergency situation.

Content

TheThe Italian National Institute of Statistics has reorganized data collection by sustainable acquisition techniques, innovative methodologies and use of data sources; it also provided most appropriate solutions to support statistical production processes, in full protection of workers’ health.

Official statistics are fundamental for measuring the evolution of economy and society; their production and dissemination at the service of institutions, policy-makers, families and businesses, therefore, cannot be stopped, but need to be rethought to be ready to provide the country with all necessary answers, and above all to support and monitor the future country’s recovery.

Reference period: 01/01-04/04, Years 2015-2020. Collected data thanks to Istat Survey Deaths of resident population, that uses administrative source to collect main individual characteristics of deaths, and to processing ANPR (National Resident Population Register) source data for deaths referring to the 2020 year.

Processing data of municipalities (1,689) where ANPR data are considered reliable and migrated in ANPR database before January 1st, 2020.

Record: 1. CODES NUTS2 = Istat code of NUTS2
2. CODES NUTS3 = Istat code of NUTS3 3. CODES_NUTS3_LAU2 = Istat code of LAU2 4. NUTS 2 = Region of residence 5. NUTS 3 = Province of residence 6. LAU 2 = Municipality of residence 7. DATA_INIZIO_DIFF = Date of first dissemination of data in 2020 8. WEEK= Week of death (first considered period, from January 1st to January 11th, is 11 days) 9. AGE CLASS = Age class at the time of death 10. MALES_2015: total male deaths in 2015 11. MALES_2016: total male deaths in 2016 12. MALES_2017: total male deaths in 2017 13. MALES_2018: total male deaths in 2018 14. MALES_2019: total male deaths in 2019 15. MALES_2020: total male deaths in 2020 16. FEMALES_2015: total female deaths in 2015 17. FEMALES_2016: total female deaths in 2016 18. FEMALES_2017: total female deaths in 2017 19. FEMALES_2018: total female deaths in 2018 20. FEMALES_2019: total female deaths in 2019 21. FEMALES_2020: total female deaths in 2020 22. TOTAL_2015: total deaths in 2015 23. TOTAL_2016: total deaths in 2016 24. TOTAL_2017: total deaths in 2017 25. TOTAL_2018: total deaths in 2018 26. TOTAL_2019: total deaths in 2019 27. TOTAL_2020: total deaths in 2020

Acknowledgements

https://www.istat.it

https://www.istat.it/en/archivio/240106

Inspiration

These data also provides most appropriate solutions to support statistical production processes, in full protection of workers’ health.

The dataset provides data on Municipal Unions (MUs) in Italy for the 1996-2015 period. Municipal Unions are forms of intermunicipal cooperation aimed at delivering municipal services and characterized by a high degree of formalization and institutionalization. The following 15 variables/indicators are taken into account: Year of establishment of the Municipal Union (Year); number of founding members (N founding members); number of members (N members 2015); number of member municipalities subject to compulsory cooperation by the Italian law (N C members); belonging (Y) or not (N) of all MU member municipalities to a unique Socio-sanitary District (Unique SSD); belonging (Y) or not (N) of all MU member municipalities to a unique Local Work System (Unique LWS); total inhabitants (Inh.N); inhabitants of the smallest member municipality (Inh.S); inhabitants of the largest member municipality (Inh.B); ratio smallest/largest population (Inh.R); average population (Inh.Av.); surface area (sq Km); average surface area (Av. Sur); Territorial Accessibility Index (TAI); Demographic Balance Index (DBI).
The dataset gives systematic information on Municipal Unions established in Italy from 1996 to 2015. Data relate to 2015, unless differently specified. A total number of 462 Municipal Unions is taken into account, that is all those present in the country at the year of data collection.

Objectives: One of the largest clusters of Covid-19 infections was observed in Italy. The population was forced to home confinement, exposing individuals to increased risk for insomnia, which is, in turn, associated with depression and anxiety. Through a cross-sectional online survey targeting all Italian adult population (≥18 yrs), insomnia prevalence and its interactions with relevant factors were investigated.Methods: The survey was distributed from 1st April to 4th May 2020. We collected information on insomnia severity, depression, anxiety, sleep hygiene behaviors, dysfunctional beliefs about sleep, circadian preference, emotion regulation, cognitive flexibility, perceived stress, health habits, self-report of mental disorders, and variables related to individual difference in life changes due to the pandemic's outbreak.Results: The final sample comprised 1,989 persons (38.4 ± 12.8 yrs). Prevalence of clinical insomnia was 18.6%. Results from multivariable linear regression showed that insomnia severity was associated with poor sleep hygiene behaviors [β = 0.11, 95% CI (0.07–0.14)]; dysfunctional beliefs about sleep [β = 0.09, 95% CI (0.08–0.11)]; self-reported mental disorder [β = 2.51, 95% CI (1.8–3.1)]; anxiety [β = 0.33, 95% CI (0.25–0.42)]; and depression [β = 0.24, 95% CI (0.16–0.32)] symptoms.Conclusion: An alarming high prevalence of clinical insomnia was observed. Results suggest that clinical attention should be devoted to problems of insomnia in the Italian population with respect to both prevention and treatment.

Employment Rate in Italy increased to 62.90 percent in May from 62.70 percent in April of 2025. This dataset provides - Italy Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

A data set of cross-nationally comparable microdata samples for 15 Economic Commission for Europe (ECE) countries (Bulgaria, Canada, Czech Republic, Estonia, Finland, Hungary, Italy, Latvia, Lithuania, Romania, Russia, Switzerland, Turkey, UK, USA) based on the 1990 national population and housing censuses in countries of Europe and North America to study the social and economic conditions of older persons. These samples have been designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. A common set of nomenclatures and classifications, derived on the basis of a study of census data comparability in Europe and North America, was adopted as a standard for recoding. This series was formerly called Dynamics of Population Aging in ECE Countries. The recommendations regarding the design and size of the samples drawn from the 1990 round of censuses envisaged: (1) drawing individual-based samples of about one million persons; (2) progressive oversampling with age in order to ensure sufficient representation of various categories of older people; and (3) retaining information on all persons co-residing in the sampled individual''''s dwelling unit. Estonia, Latvia and Lithuania provided the entire population over age 50, while Finland sampled it with progressive over-sampling. Canada, Italy, Russia, Turkey, UK, and the US provided samples that had not been drawn specially for this project, and cover the entire population without over-sampling. Given its wide user base, the US 1990 PUMS was not recoded. Instead, PAU offers mapping modules, which recode the PUMS variables into the project''''s classifications, nomenclatures, and coding schemes. Because of the high sampling density, these data cover various small groups of older people; contain as much geographic detail as possible under each country''''s confidentiality requirements; include more extensive information on housing conditions than many other data sources; and provide information for a number of countries whose data were not accessible until recently. Data Availability: Eight of the fifteen participating countries have signed the standard data release agreement making their data available through NACDA/ICPSR (see links below). Hungary and Switzerland require a clearance to be obtained from their national statistical offices for the use of microdata, however the documents signed between the PAU and these countries include clauses stipulating that, in general, all scholars interested in social research will be granted access. Russia requested that certain provisions for archiving the microdata samples be removed from its data release arrangement. The PAU has an agreement with several British scholars to facilitate access to the 1991 UK data through collaborative arrangements. Statistics Canada and the Italian Institute of statistics (ISTAT) provide access to data from Canada and Italy, respectively. * Dates of Study: 1989-1992 * Study Features: International, Minority Oversamples * Sample Size: Approx. 1 million/country Links: * Bulgaria (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02200 * Czech Republic (1991), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06857 * Estonia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06780 * Finland (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06797 * Romania (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06900 * Latvia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02572 * Lithuania (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03952 * Turkey (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03292 * U.S. (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06219

Hospital Beds in Italy decreased to 3.09 per 1000 people in 2022 from 3.12 per 1000 people in 2021. This dataset includes a chart with historical data for Italy Hospital Beds.

Project Summary This study compares the health behaviors of university students in France and Italy, examining how their choices and lifestyles were affected by the COVID-19 pandemic. The aim of the study is to contribute to the development of adequate public health interventions. The survey methodology employed an online questionnaire administered to French and Italian cohorts of university students. It was found that the pandemic mainly affected the mental health and sense of well-being of young people in both countries. The pandemic also altered dietary habits, alcohol consumption, sleep quality, and physical activity levels, all of which strongly affect overall health. More critical values were generally found among the Italian students. The study underscores the need to recognize the impact that the pandemic has had on the young Italian and French student populations. Data Description and Collection Overview The aim of the study was to survey some aspects of life (secondary effects of the pandemic) related to university student cohorts. Specifically, using a comparative approach, the authors investigated possible differences between the two groups residing in different countries in terms of how those groups coexisted with the pandemic. The target populations were university students enrolled in para-medical health degree programs (Nursing, Physiotherapy, etc.) and degree programs in Physical Education for Health and Prevention. Information was collected in the post-acute phase of the third cycle of the pandemic. Specifically, the French cohort was surveyed between January and February 2022, while the Italian cohort was examined between March and April 2022. Based on investigations carried out in France, a suitable number of behaviors likely to be conditioned by the pandemic were selected from the literature. Data was collected for these behaviors using standardized tools, validated and recovered in full or partial form. The tool used in the Italian context, which is part of a larger French data set, consisted of three specific sections, to which a fourth, dedicated to describing the sociographic picture of the respondents, was added. The first section examined the general experience of the students before and during the pandemic, seeking to provide an initial picture of students’ habitual daily behaviors. The second section focused on eating habits without neglecting possible deviations related to eating disorders (SCOFF questionnaire - Sick Control One Stone Fat Food) and the possible use or abuse of alcohol and cigarettes. The third framework, based on a scale known in the literature as the IFIS questionnaire (International Fitness Scale), was used to assess the level of physical activity that characterized the subjects’ daily lives. In both contexts, data were collected through web surveys using institutional directories of university degree programs. Students were invited to complete the survey by a message explaining the purposes of the initiative. This message was followed a week later by a reminder message, whose aim was to boost the participation rate. The Italian participation rate of 25.7% was much higher than the French rate, which was just over 10%. A total of 567 participant responses were collected. Of those, 70.5% were from the French cohort and the remaining 29.5% from the Italian one, percentages which reflected the general populations in the two contexts under investigation (approximately 73% and 27%). The national cohort a respondent belongs to is reflected with the variable “Nazione” (Nationality) in the data spreadsheet, with “1” denoting French and “2” denoting Italian. Selection and Organization of Shared Data The data file included contains all de-identified questionnaire responses. This file contains both the Italian and French responses to the same survey questions, but with slightly different answer formats. For example, for variable “covid_1” (Sei risultato positivo al COVID (una o più volte)? / Have you tested positive for COVID (one or more times)?), the responses of the Italian students are captured as “Si” (Yes) and “No” (No), while those of the French students are captured as “1” and “2”. The correspondent categories can be found in the full questionnaire, which is shared (in Italian) as a documentation file. In the spreadsheet, lack of answers because of skip pattern is distinguished from answers that the respondent chose not to provide. The other documentation file included is the recruitment email / consent script used (in English, French and Italian).

Link to Dataset related to article "Interpreting Non-coding Genetic Variation in Multiple Sclerosis Genome-Wide Associated Regions"

Multiple sclerosis (MS) is the most common neurological disorder in young adults. Despite extensive studies, only a fraction of MS heritability has been explained, with association studies focusing primarily on protein-coding genes, essentially for the difficulty of interpreting non-coding features. However, non-coding RNAs (ncRNAs) and functional elements, such as super-enhancers (SE), are crucial regulators of many pathways and cellular mechanisms, and they have been implicated in a growing number of diseases. In this work, we searched for possible enrichments in non-coding elements at MS genome-wide associated loci, with the aim to highlight their possible involvement in the susceptibility to the disease. We first reconstructed the linkage disequilibrium (LD) structure of the Italian population using data of 727,478 single-nucleotide polymorphisms (SNPs) from 1,668 healthy individuals. The genomic coordinates of the obtained LD blocks were intersected with those of the top hits identified in previously published MS genome-wide association studies (GWAS). By a bootstrapping approach, we hence demonstrated a striking enrichment of non-coding elements, especially of circular RNAs (circRNAs) mapping in the 73 LD blocks harboring MS-associated SNPs. In particular, we found a total of 482 circRNAs (annotated in publicly available databases) vs. a mean of 194 ± 65 in the random sets of LD blocks, using 1,000 iterations. As a proof of concept of a possible functional relevance of this observation, we experimentally verified that the expression levels of a circRNA derived from an MS-associated locus, i.e., hsa_circ_0043813 from the STAT3 gene, can be modulated by the three genotypes at the disease-associated SNP. Finally, by evaluating RNA-seq data of two cell lines, SH-SY5Y and Jurkat cells, representing tissues relevant for MS, we identified 18 (two novel) circRNAs derived from MS-associated genes. In conclusion, this work showed for the first time that MS-GWAS top hits map in LD blocks enriched in circRNAs, suggesting circRNAs as possible novel contributors to the disease pathogenesis.

GEO database

URL: https://www.ncbi.nlm.nih.gov/geo/

Numero di accesso del dataset: GSE110525

Italy is one of the first European epicenters of the COVID-19 pandemic. In attempts to hinder the spread of the novel coronavirus disease, Italian government hardened protective measures, from quarantine to lockdown, impacting millions of lives dramatically. Amongst the enacted restrictions, all non-essential activities were prohibited as well as all outdoor activities banned. However, at the first spur of the outbreak, for about a dozen of days, physical and sports activities were permitted, while maintaining social distancing. In this timeframe, by administering measures coming from self-determination theory and theory of planned behavior and anxiety state, in an integrated approach, we investigated the prevalence of these activities by testing, via a Structural Equation Model, the influence of such psychosocial variables on the intention to preserve physical fitness during the healthcare emergency. Through an adequate fit of the hypothesized model and a multi-group analysis, we compared the most COVID-19 hit Italian region – Lombardy – to the rest of Italy, finding that anxiety was significantly higher in the Lombardy region than the rest of the country. In addition, anxiety negatively influenced the intention to do physical activity. Giving the potential deleterious effects of physical inactivity due to personal restrictions, these data may increase preparedness of public health measures and attractiveness of recommendations, including on the beneficial effects of exercise, under circumstances of social distancing to control an outbreak of a novel infectious disease.

The dataset contains data from the 14th General Population Census relating to housing. A dwelling is defined as accommodation consisting of a single room or a set of rooms (rooms and ancillary rooms): * built with those requirements that make it suitable as a permanent residence for one or more people, even if one part is used as an office (professional studio, etc.), * equipped with at least one independent access from the outside (road, courtyard, etc.), which does not involve passing through other houses, or from common hallway spaces (landings, galleries, terraces, etc.), * separated from other housing units by walls, * inserted in a building. The following support tables are also available in the dataset: * Descriptions of the columns * Decodes of the codes contained in the columns. Other supporting tables are available at the following links: * Codes of the Italian Municipalities https://dati.comune.milano.it/dataset/ds1527-censimento-2001-comuni-italiani * Codes of the Italian Provinces https://dati.comune .milano.it/dataset/ds1528-censimento-2001-province-italiane * Connection between Census Sections and territorial divisions https://dati.comune.milano.it/dataset/ds1529-censimento-2001-sezioni

What impact has the COVID-19 pandemic had on Italians' attitudes, opinions, and behaviors? From this question, the ResPOnsE COVID-19 project (Response of Italian Public Opinion to the COVID-19 Emergency) was developed starting in March 2020, with the aim of building a research infrastructure for the daily monitoring of public opinion during the COVID-19 emergency. The collection of daily information through online interviews (CAWI) to a sample reflecting the distribution of the Italian population by gender and area of residence was divided into four surveys that took place between April 2020 and July 2023, for a total of more than 40,000 interviews. The infrastructure was designed by the spsTREND "Hans Schadee" laboratory in collaboration with the SWG institute, as part of the "Departments of Excellence 2018-2022" project promoted by the Ministry of University and Research and is supported by funding from the Cariplo Foundation. Overall Research Design The research design included six surveys (waves) following a repeated cross-sectional design, consistent with the dynamic nature of the pandemic phenomenon. The six waves of ResPOnsE COVID-19 are distributed as follows. First wave: from April 6 to July 6, 2020 (~15000 cases) Second wave: from December 21, 2020 to January 2, 2021 (~3000 cases) Third wave: from March 17 to June 16, 2021 (~9300 cases) Fourth wave: from November 10 to December 22, 2021 (~3000 cases) Fifth wave: from November 7 to December 22, 2022 (~9000 cases) Sixth wave: from June 6 to July 6, 2023 (~3000 cases) Rolling Cross-Section and Panel Design The first, third, fourth, fifth and sixth waves collect interviews through a Rolling Cross-Section (RCS) design, that is consecutive daily samples for a relatively long period (in this case 1 to 3 months). In addition, about 60% of subjects were interviewed twice between the first and the sixth wave, thus allowing longitudinal analysis of intra-individual variations that occurred between 2020 and 2023. An RCS survey can be viewed as a cross-sectional survey of a single sample that is, however, "sliced" into many equivalent small subgroups that are released on consecutive days. On the day of release, individuals belonging to a particular sub-group are invited to participate in the survey. The distinguishing feature of the RCS design, however, is that these individuals can also respond in the days following the delivery of the invitation. Hence comes the term "rolling" meaning that the overall sample "rolls" through the days of the survey, making time (days) a random variable. The daily samples are mutually independent and the estimates derived for each are comparable. In this way, the RCS design is optimal for studying trends in the case of time-varying phenomena. For details, see the articles by Vezzoni et al. (2020) and Biolcati et al. (2021). Questionnaire structure The questionnaire administered in the ResPOnsE COVID-19 survey consists of a main questionnaire, containing a core set of questions repeated in each of the six surveys, and one or more thematic modules that may change with each survey. The main questionnaire consists of eleven thematic sections covering the entire survey period. Most of the questions in the questionnaire were repeated in the six surveys, while some questions were eliminated/changed or new ones were introduced in the transition to a new survey. Covering the entire survey period, the basic module is particularly suitable for diachronic analysis, while the structure of the thematic modules, usually collected over a few weeks, suggests an analysis of them with a cross-sectional approach. Source questionnaires in Italian are available for download. The sample The target population consists of Italian residents aged 18 years and older. In the RCS waves, on average, between 100 and 150 interviews were conducted each day, corresponding to about 1,000 interviews per week for the first and the two last surveys and about 700 for the third and fourth surveys (the interviews in the second survey were actually concentrated in a single week), for a total of 42,860 interviews. Given time and resource constraints, probabilistic sampling could not be used. Instead, the samples are drawn from an online community of a commercial research institute (SWG SpA). To correct against expected bias, the sample is stratified by ISTAT macro-area of residence and composed of quotas defined by gender and age. Weights have also been created for carryover to the population. Detailed instructions on using the weights can be downloaded together with the data files. The survey also includes a panel component: about 60 percent of subjects (n = 12,801) were interviewed at least twice between the first, third, fourth, fifth and sixth waves. Over-sampling was also conducted for the Lombardy region, for which 1124 additional cases are available in the third wave Macro level data The cumulative data file also includes official macro-level...

This survey is a not up-to-date version. Please, use the updated version included in the EVS integrated data files. This national dataset is only available for replication purposes and analysis with additional country-specific variables (see ´Further Remarks´).

Two online overviews offer comprehensive metadata on the EVS datasets and variables.

The extended study description for the EVS 2008 provides country-specific information on the origin and outcomes of the national surveys The variable overview of the four EVS waves 1981 1990 1999/2000 and 2008 allows for identifying country specific deviations in the question wording within and across the EVS waves.

These overviews can be found at: Extended Study Description Variable Overview

Moral, religious, societal, political, work, and family values of Europeans.

Topics: 1. Perceptions of life: importance of work, family, friends and acquaintances, leisure time, politics and religion; frequency of political discussions with friends; happiness; self-assessment of own health; memberships and unpaid work (volunteering) in: social welfare services, religious or church organisations, education, or cultural activities, labour unions, political parties, local political actions, human rights, environmental or peace movement, professional associations, youth work, sports clubs, women´s groups, voluntary associations concerned with health or other groups; tolerance towards minorities (people with a criminal record, of a different race, left/right wing extremists, alcohol addicts, large families, emotionally unstable people, Muslims, immigrants, AIDS sufferers, drug addicts, homosexuals, Jews, gypsies and Christians - social distance); trust in people; estimation of people´s fair and helpful behaviour; internal or external control; satisfaction with life.

1. Work: reasons for people to live in need; importance of selected aspects of occupational work; employment status; general work satisfaction; freedom of decision-taking in the job; importance of work (work ethics, scale); important aspects of leisure time; attitude towards following instructions at work without criticism (obedience work); give priority to nationals over foreigners as well as men over women in jobs.

2. Religion: Individual or general clear guidelines for good and evil; religious denomination; current and former religious denomination; current frequency of church attendance and at the age of 12; importance of religious celebration at birth, marriage, and funeral; self-assessment of religiousness; churches give adequate answers to moral questions, problems of family life, spiritual needs and social problems of the country; belief in God, life after death, hell, heaven, sin and re-incarnation; personal God versus spirit or life force; own way of connecting with the divine; interest in the sacred or the supernatural; attitude towards the existence of one true religion; importance of God in one´s life (10-point-scale); experience of comfort and strength from religion and belief; moments of prayer and meditation; frequency of prayers; belief in lucky charms or a talisman (10-point-scale); attitude towards the separation of church and state.

3. Family and marriage: most important criteria for a successful marriage (scale); attitude towards childcare (a child needs a home with father and mother, a woman has to have children to be fulfilled, marriage is an out-dated institution, woman as a single-parent); attitude towards marriage, children, and traditional family structure (scale); attitude towards traditional understanding of one´s role of man and woman in occupation and family (scale); attitude towards: respect and love for parents, parent´s responsibilities for their children and the responsibility of adult children for their parents when they are in need of long-term care; importance of educational goals; attitude towards abortion.

4. Politics and society: political interest; political participation; preference for individual freedom or social equality; self-assessment on a left-right continuum (10-point-scale); self-responsibility or governmental provision; free decision of job-taking of the unemployed or no permission to refuse a job; advantage or harmfulness of competition; liberty of firms or governmental control; equal incomes or incentives for indivi...