Context

This dataset is the result of my study on web-scraping of English Wikipedia in R and my tests on regression and classification modelization in R.

Content

The content is create by reading the appropriate articles in English Wikipedia about Italian cities: I did'nt run NPL analisys but only the table with the data and I ranked every city from 0 to N in every aspect. About the values, 0 means "*the city is not ranked in this aspect*" and N means "*the city is at first place, in descending order of importance, in this aspect* ". If there's no ranking in a particular aspect (for example, the only existence of the airports/harbours with no additional data about the traffic or the size), then 0 means "*no existence*" and N means "*there are N airports/harbours*". The only not-numeric column is the column with the name of the cities in English form, except some exceptions (for example, "*Bra (CN)* " because of simplicity.

Acknowledgements

I acknowledge the Wikimedia Foundation for his work, his mission and to make available the cover image of this dataset, (please read the article "The Ideal city (painting)") . I acknowledge too StackOverflow and Cross-Validated to be the most important focus of technical knowledge in the world, all the people in Kaggle for the suggestions.

Inspiration

As a beginner in data analisys and modelization (Ok, I passed the exam of statistics in Politecnico di Milano (Italy), but there are more than 10 years that I don't work in this topic and my memory is getting old ^_^) I worked more on data clean, dataset building and building the simplest modelization.

You can use this datase to realize which city is good to live or to expand this to add some other data from Wikipedia (not only reading the tables but too to read the text adn extrapolate the data from the meaningless text.)

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

IT: Population in Largest City data was reported at 3,755,830.000 Person in 2017. This records an increase from the previous number of 3,737,750.000 Person for 2016. IT: Population in Largest City data is updated yearly, averaging 3,416,411.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 3,755,830.000 Person in 2017 and a record low of 2,455,581.000 Person in 1960. IT: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

Our analysis focuses on seven metropolitan cities across Italy. Here, we report the number of spatial cells of the mobile phone network and the population (in thousands) of each of these cities split across 6 age groups. Population data is retrieved from the 2011 Italian census and comprises all the census sections within the phone cells considered for each city. It is important to highlight that in each cell of the network there can be several mobile phone users, thus we cannot estimate the fraction of the census population included in our data set. Note that the age groups provided by the Italian census do not perfectly match those of the Telecom Italia dataset.

About this dataset

Context

The dataset tabulates the Italy median household income by race. The dataset can be utilized to understand the racial distribution of Italy income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Italy, TX median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Italy, TX (2021, in 2022 inflation-adjusted dollars)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Italy median household income by race. You can refer the same here

Dataset of the Age-Friendly Cities and Communities Questionnaire - Italian version. The study was conducted in three Italian cities (n = 1,213) on a representative sample of older people, who were asked to rate their life in the city, following the dimensions considered by the World Health Organization essential for the community age-friendliness. More info at: https://extranet.who.int/agefriendlyworld/afp/the-age-friendly-cities-and-communities-questionnaire-afccq/

About this dataset

Context

The dataset tabulates the Italy town household income by gender. The dataset can be utilized to understand the gender-based income distribution of Italy town income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Italy, New York annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Italy, New York annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Italy town income distribution by gender. You can refer the same here

About this dataset

Context

The dataset tabulates the Italy household income by age. The dataset can be utilized to understand the age-based income distribution of Italy income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Italy, TX annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Italy, TX household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Italy income distribution by age. You can refer the same here

This dataset focuses on a sample of 105 Italian cities that have declared a climate emergency by the end of February 2021. It provides key data and information on the selected cities, the contents of their Climate Emergency Declarations (CEDs) and Local Climate Plans (LCPs), both mitigation and adaptation.

It is organised into four spreadsheets containing the following data respectively: 1. General key data on the city sample: List of cities, Province, Region, Latitude, Longitude, Population, Classes of population, Surface area, Adhesion to: C40 / Climate Allliance / Covenant of Mayors / Green City Network 2. Cities by macroregions / regions / provinces and by population classes
3. Climate Emergency Declarations (CEDs) of the sample cities (as of 28 February 2021). It includes the List of cities, CED date, Supporting documents/websites, and outcomes of the content analysis of CEDs, in terms of: references to national petitions, to the Friday for Future movement, to CEDAMIA, to the IPCC Report 2018, and to the Sustainable Development Goals, CO2/GHG targets, links/adhesions to transnational climate networks, references to LCP and their targets, mentions to Adaptation, Local air pollution, and support citizens' initiatives in favour of the climate, requests to local institutions (Regions) and to the government to take climate. 4. Local Climate Plans (LCPs) of the sample cities (as of 19 April 2021). It includes information on the availability of SEAP/SECAP within the Covenant of Mayors, Web source, Name of the MITIGATION plan, Approval date, and the outcomes of the content analysis of LCPs, in terms of: CO2/CO2eq emission target, baseline year, target year, carbon neutrality target and target year, web source, mentions to Local air pollution, adaptation plans (integrated or stand-alone), web source.

DALIA dataset (Campobasso, Italy). DOI: 10.5281/zenodo.15395163

de Francesco M.C., Carranza M.L., Capotorti G., Del Vico E., D’Angeli C., Montaldi A., Paura B., Santoianni L.A., Varricchione M., Stanisci A. (2025).

In the framework of the National Biodiversity Future Centre (NBFC), specifically within the “Urban Biodiversity” working group (Spoke 5), we developed the DALIA relational database, which contains records of tree, shrub, and liana taxa recorded in the Functional Urban Area of Campobasso (Southern Italy). The DALIA database includes 170 species and subspecies (126 native and 44 alien) belonging to 46 taxonomic families (35 natives, 23 aliens). Each taxon, whether native or alien, was classified according with multiple ecological, functional, and biogeographic groups.

The database contains 6 tables described below:

Table “Taxonomy” including the scientific names according to WFO (WFO, 2024) and Flora d'Italia (Portal to the Flora of Italy, 2024) with the relative authors, and the common name in English and Italian languages (Portal to the Flora of Italy, 2024); the taxonomic family (Bartolucci et al. 2024), the classification in natives, archeophytes, neophytes and their status (invasive, casual, naturalized) (Galasso et al. 2024).

Table “Chorology” including the geographic distribution of the native species (Pignatti 1982) and the origin area of the archaeophyte and neophyte species (WFO, 2024).

Table “Traits” including life form and growth form categories according to the Raunkiær system (Pignatti et al. 2017; Raunkiær 1934); the plant growth habits, differentiating plants in tree, shrub, and liana (Diaz et al. 2022); the maximum height reached by the plants (Diaz et al. 2022); the leaf types based on leaf morphology, anatomy, and persistence (Chytry et al. 2024).

Table “Bloom & Dispersion” including flowering periods expressed as bloom months, total flowering length, and seasons (Pignatti et al. 2017); the generative diaspores, the classification of seeds, fruits, and any appendages serving a role in dispersal (fleshy, non-fleshy indehiscent, pappose, winged, unspecialized) (Sádlo et al. 2014); the dispersion modes (Lososová et al. 2023).

Table “Indicators” including the values for the EIVE’s - Ecological Indicator Values for Europe (Dengler et al. 2023); for the Disturbance Indices - Disturbance indicator values for European plants (Midolo et al. 2023); the GRIME values for the CSR strategies in plants (Pierce et al. 2016).

Table “Conservation status” including the possible diagnostic role for Habitat Directive (92/43/EEC) (Habitat Directive 92-43-CEE, 2024); for EUNIS habitats (Chytrý et al., 2020); the IUCN Status and Trend Population for Europe (IUCN, 2024).

The DALIA database reveals a high woody plant diversity for the FUA of Campobasso when compared with other similar studies (Roma-Marzio et al. 2016), with a high percentage of native species (Quaranta et al. 2025). This insight greatly differs from what has been recorded in large cities where aliens in the urban floras make up 40% of the total number of taxa (Pyšek 1998; Ricotta et al. 2009; Lososová et al. 2012).

DALIA is expected to act as a useful pilot tool for Nature-based Solutions (NBS) and environmental restoration actions in cities of Italian and Mediterranean inner territories. It also provides valuable ecological information that can be utilized in urban greenery projects, emphasizing the added value of avoidance of invasive and competitive alien plants while favoring native species found within the EU forest habitats of the nearby Natura 2000 areas (Capotorti et al. 2016; Resemini et al. 2025; EC2023).

References

Bartolucci F, Peruzzi L, Galasso G, Alessandrini A, Ardenghi NMG, Bacchetta G, Banfi E, Barberis G, Bernardo L, Bouvet D, Bovio M, Calvia G, Castello M, Cecchi L, Del Guacchio E, Domina G, Fascetti S, Gallo L, Gottschlich G, Guarino R, Gubellini L, Hofmann N, Iberite M, Jiménez-Mejías P, Longo D, Marchetti D, Martini F, Masina RR, Medagli P, Peccenini S, Prosser F, Roma-Marzio F, Rosati L, Santangelo A, Scoppola A, Selvaggi A, Selvi F, Soldano A, Stinca A, Wagensommer RP, Wilhalm T, Conti F (2024) A second update to the checklist of the vascular flora native to Italy. Plant Biosystems - An International Journal Dealing with All Aspects of Plant Biology 158(2): 219–296. https://doi.org/10.1080/11263504.2024.2320126

Capotorti, G, Del Vico, E, Anzellotti, I, Celesti-Grapow, L (2016). Combining the conservation of biodiversity with the provision of ecosystem services in urban green infrastructure planning: Critical features arising from a case study in the metropolitan area of Rome. Sustainability, 9(1), 10. https://doi.org/10.3390/su9010010

Ceralli D, D’Angeli C, Laureti L (2021) The “Carta della Natura” project: the case study of Molise region. Proceedings of the International Cartographic Association 4: 1–7. https://doi.org/10.5194/ica-proc-4-18-2021

Chytrý M, Řezníčková M, Novotný P, Holubová D, Preislerová Z, Attorre F, Biurrun I, Blažek P, Bonari G, Borovyk D, Čeplová N, Danihelka J, Davydov D, Dřevojan P, Fahs N, Guarino R, Güler B, Hennekens SM, Hrivnák R, Kalníková V, Kalusová V, Kebert T, Knollová I, Knotková K, Koljanin D, Kuzemko A, Loidi J, Lososová Z, Marcenò C, Midolo G, Milanović D, Mucina L, Novák P, von Raab-Straube E, Reczyńska K, Schaminée JHJ, Štěpánková P, Świerkosz K, Těšitel J, Těšitelová T, Tichý L, Vynokurov D, Willner S, Axmanová I (2024) FloraVeg.EU — An online database of European vegetation, habitats and flora. Applied Vegetation Science 27 (3): e12798. https://doi.org/10.1111/avsc.12798

Chytrý M, Tichý L, Hennekens SM, Knollová I, Janssen JAM, …, Schaminée JHJ (2020) EUNIS Habitat Classification: Expert system, characteristic species combinations and distribution maps of European habitats. Applied Vegetation Science 23: 648–675. https://doi.org/10.1111/avsc.12519

Dengler J, Jansen F, Chusova O, Hüllbusch E, Nobis MP, Van Meerbeek K, Axmanová I, Bruun HH, Chytrý M, Guarino R, Karrer G, Moeys K, Raus T, Steinbauer MJ, Tichý L, Tyler T, Batsatsashvili K, Bita-Nicolae C,

Díaz S, Kattge J, Cornelissen JHC et al. (2022) The global spectrum of plant form and function: enhanced species-level trait dataset. Scientific Data 9: 755. https://doi.org/10.1038/s41597-022-01774-9

Díaz S, Kattge J, Cornelissen JHC et al. (2022) The global spectrum of plant form and function: enhanced species-level trait dataset. Scientific Data 9: 755. https://doi.org/10.1038/s41597-022-01774-9

EC 2023. Guidelines on Biodiversity-Friendly Afforestation, Reforestation and Tree Planting (https://environment.ec.europa.eu/publications/)

Galasso G, Conti F, Peruzzi L, Alessandrini A, Ardenghi NMG, Bacchetta G, … Bartolucci F (2024) A second update to the checklist of the vascular flora alien to Italy. Plant Biosystems - An International Journal Dealing with All Aspects of Plant Biology 158(2): 297–340. https://doi.org/10.1080/11263504.2024.2320129

Habitat Directive 92-43-CEE (2024) (http://vnr.unipg.it/habitat/)

IUCN (2024) (https://www.iucnredlist.org/)

Lososová Z, Axmanová I, Chytrý M, Midolo G, Abdulhak S, Karger DN, Renaud J, Van Es J, Vittoz P, Thuiller W (2023) Seed dispersal distance classes and dispersal modes for the European flora. Global Ecology and Biogeography 32(9): 1485–1494. https://doi.org/10.1111/geb.13712

Midolo G, Herben T, Axmanová I, Marcenò C, Pätsch R, Bruelheide H, Karger DN, Aćić S,

The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.

The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.

Description of the containing files inside the Dataset.

The ECFAS Coastal Dataset represents a single access point to publicly available Pan-European datasets that provide key information for studying coastal areas. The publicly available datasets listed below have been clipped to the coastal area extent, quality-checked and assessed for completeness and usability in terms of coverage, accuracy, specifications and access. The dataset was divided at European country level, except for the Adriatic area which was extracted as a region and not at the country level due to the small size of the countries. The buffer zone of each data was 10km inland in order to be correlated with the new Copernicus product Coastal Zone LU/LC.

Specifically, the dataset includes the new Coastal LU/LC product which was implemented by the EEA and became available at the end of 2020. Additional information collected in relation to the location and characteristics of transport (road and railway) and utility networks (power plants), population density and time variability. Furthermore, some of the publicly available datasets that were used in CEMS related to the above mentioned assets were gathered such as OpenStreetMap (building footprints, road and railway network infrastructures), GeoNames (populated places but also names of administrative units, rivers and lakes, forests, hills and mountains, parks and recreational areas, etc.), the Global Human Settlement Layer (GHS) and Global Human Settlement Population Grid (GHS-POP) generated by JRC. Also, the dataset contains 2 layers with statistics information regarding the population of Europe per sex and age divided in administrative units at NUTS level 3. The first layer includes information for the whole of Europe and the second layer has only the information regarding the population at the Coastal area. Finally, the dataset includes the global database of Floods protection standards. Below there are tables which present the dataset.

* Adriatic folder contains the countries: Slovenia, Croatia, Montenegro, Albania, Bosnia and Herzegovina

* Malta was added to the dataset

Copernicus Land Monitoring Service:

Coastal LU/LC

Scale 1:10.000; A Copernicus hotspot product to monitor landscape dynamics in coastal zones

EU-Hydro - Coastline

Scale 1:30.000; EU-Hydro is a dataset for all European countries providing the coastline

Natura 2000

Scale 1: 100000; A Copernicus hotspot product to monitor important areas for nature conservation

European Settlement Map

Resolution 10m; A spatial raster dataset that is mapping human settlements in Europe

Imperviousness Density

Resolution 10m; The percentage of sealed area

Impervious Built-up

Resolution 10m; The part of the sealed surfaces where buildings can be found

Grassland 2018

Resolution 10m; A binary grassland/non-grassland product

Tree Cover Density 2018

Resolution 10m; Level of tree cover density in a range from 0-100%

Joint Research Center:

Global Human Settlement Population Grid
GHS-POP)

Resolution 250m; Residential population estimates for target year 2015

GHS settlement model layer
(GHS-SMOD)

Resolution 1km: The GHS Settlement Model grid delineates and classify settlement typologies via a logic of population size, population and built-up area densities

GHS-BUILT

Resolution 10m; Built-up grid derived from Sentinel-2 global image composite for reference year 2018

ENACT 2011 Population Grid

(ENACT-POP R2020A)

Resolution 1km; The ENACT is a population density for the European Union that take into account major daily and monthly population variations

JRC Open Power Plants Database (JRC-PPDB-OPEN)

Europe's open power plant database

GHS functional urban areas
(GHS-FUA R2019A)

Resolution 1km; City and its commuting zone (area of influence of the city in terms of labour market flows)

GHS Urban Centre Database
(GHS-UCDB R2019A)

Resolution 1km; Urban Centres defined by specific cut-off values on resident population and built-up surface

Additional Data:

Open Street Map (OSM)

BF, Transportation Network, Utilities Network, Places of Interest

CEMS

Data from Rapid Mapping activations in Europe

GeoNames

Populated places, Adm. units, Hydrography, Forests, Hills/Mountains, Parks, etc.

Global Administrative Areas

Administrative areas of all countries, at all levels of sub-division

NUTS3 Population Age/Sex Group

Eurostat population by age and sex statistics interescted with the NUTS3 Units

FLOPROS

A global database of FLOod PROtection Standards, which comprises information in the form of the flood return period associated with protection measures, at different spatial scales

Disclaimer:

ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.

This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211

ATENA dataset (Italy). DOI:10.5281/zenodo.15861218

The ATENA database (dATabase of ornamEntal non-NAtive trees of seven Italian cities) was developed within the framework of the National Biodiversity Future Center project aimed at improving knowledge and management of biodiversity in Italy. ATENA compiles comprehensive taxonomic, ecological, and trait data on non-native ornamental trees occurring in public green spaces of seven representative Italian cities: Milan, Turin, Pavia, Asti, Rome, Campobasso, and Palermo.

Ornamental non-native trees contribute significantly to urban landscapes by enhancing aesthetic appeal and providing multiple ecosystem services. The database currently includes 317 taxa. ATENA is designed as an essential resource to make freely available taxonomic and ecological features of urban trees in public spaces, and to support urban forestry, green space planning, and biodiversity management, helping prevent the introduction and spread of problematic non-native tree species in urban environments.

The dataset consists of four interlinked tables:

• TAXONOMY: This table contains taxonomic information for each species, including scientific names and nomenclature following World Flora Online (WFO 2024) and Plants of the World Online (POWO 2024), authorship details, accepted names, and family assignments according to the Italian checklist of alien flora (Galasso et al. 2024), along with subsequent updates as continuously integrated into the Portal of the Flora of Italy (2025). It also indicates whether each species is listed in European, national, or regional regulations for non-native plants in Italy (Brundu et al. 2020).
• ECOLOGICAL_INDICATORS: This table provides ecological indices for each taxon, including Ecological Indicator Values for Europe (EIVEs; Dengler et al. 2023), and additional Ellenberg Indicator Values specifically compiled for the alien vascular flora of Italy (Domina et al. 2018).
• TRAITS: This table includes a comprehensive set of functional and biological traits for each species. It records plant growth form, dispersal mode, diaspore type, plant height, leaf type, and leaf phenology, as well as leaf area, leaf nitrogen content per mass (Nmass), leaf mass per area (LMA), diaspore mass, and stem specific density (SSD), following Díaz et al. (2022).
• DISTRIBUTION: This table reports the presence and frequency of each taxon across seven Italian cities (Milan, Turin, Pavia, Asti, Rome, Campobasso, and Palermo), with individual columns for each city. It also includes information on whether the species is considered an archaeophyte or neophyte in Italy (based on Celesti‐Grapow et al. 2009; Galasso et al. 2024), as well as its area and biome of origin (POWO 2024; WFO 2024).

References

Brundu G, Minicante SA, Barni E, Bolpagni R, Caddeo A, Celesti-Grapow L, Cogoni A, Galasso G, Iiriti G, Lazzaro L, Loi MC, Lozano V, Marignani M, Montagnani C, Siniscalco C. 2020. Managing plant invasions using legislation tools: an analysis of the national and regional regulations for non-native plants in Italy. Annali di Botanica 10, 1-11. https://doi.org/10.13133/2239-3129/16508

Celesti‐Grapow L, Alessandrini A, Arrigoni P, Banfi E, Bernardo L, Bovio M, Brundu G, Cagiotti M, Camarda I, Carli E, et al. 2009. Inventory of the non‐native flora of Italy. Plant Biosyst. 143(2):386–430. https://doi.org/10.1080/11263500902722824

Dengler J, Jansen F, Chusova O, Hüllbusch E, Nobis MP, Van Meerbeek K, Axmanová I, Bruun HH, Chytrý M, Guarino R, et al. 2023. Ecological Indicator Values for Europe (EIVE) 1.0. Vegetation Classification and Survey. 4:7–29. https://doi.org/10.3897/VCS.98324

Díaz S, Kattge J, Cornelissen JHC, Wright IJ, Lavorel S, Dray S, Reu B, Kleyer M, Wirth C, Prentice IC, et al. 2022. The global spectrum of plant form and function: enhanced species-level trait dataset. Sci Data. 9(1). https://doi.org/10.1038/s41597-022-01774-9

Domina G, Galasso G, Bartolucci F, Guarino R. 2018. Ellenberg Indicator Values for the vascular flora alien to Italy. Flora Mediterranea. 28:53–61. https://doi.org/10.7320/FlMedit28.053

Galasso G, Conti F, Peruzzi L, Alessandrini A, Ardenghi NMG, Bacchetta G, Banfi E, Barberis G, Bernardo L, Bouvet D, et al. 2024. A second update to the checklist of the vascular flora alien to Italy. Plant Biosyst. 158(2):297–340. https://doi.org/10.1080/11263504.2024.2320129

Portal to the Flora of Italy. 2024. Portal to the Flora of Italy. Version 2024.2. Available at http:/dryades.units.it/floritaly [accessed: 05/07/2025].

POWO. 2024. Plants of the World Online. Facilitated by the Royal Botanic Gardens, Kew. Published on the Internet. https://powo.science.kew.org/ Retrieved 30/11/2024.

Raunkiær C. 1934. The life forms of plants and statistical plant geography. https://api.semanticscholar.org/CorpusID:129154685

WFO. 2024. World Flora Online. Version 2024.12. Published on the Internet; http://www.worldfloraonline.org Accessed on: 30112024.

Funding

The work is funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union – NextGenerationEU; Project code CN_00000033, Concession Decree No. 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP H73C22000300001, Hub: Biodiversity, Spoke 5: Urban biodiversity, Project title “National Biodiversity Future Center - NBFC”.

The Project is partially funded by PRIN 2022JBP5F8- PREVALIEN. Enhancing Knowledge on Prevention and Early Detection of the Invasive Alien Plants of (European) Union concern in the Italian Protected Areas. CUP Master: J53D2300657-0006

NB This datasets has restricted access due to GDPR considerations. Anna visited Swansea University, Wales, a recently built institution. Photos of the visit were taken by a friend, not by her. The experience was unusual for them coming from a small city in Italy. They were intrigued by modern dances like the Twist, which she hadn't seen before. They were surprised also by boys with long hair. While they were familiar with the Beatles, Italy had not yet seen young people organizing themselves into musical groups like we observed during our visit. In Italy, this type of musical band trend emerged later, perhaps a year or two after our visit to Swansea. In 1966, there was a second exchange involving the Italian group. The Welsh family she stayed with owned a grocery shop. The living quarters were located on the first floor above the shop, situated on the street. During her third exchange in Esslingen, they were taken to Stuttgart airport were they visited an airplane to explore its interior. This was Anna’s first time being inside an airplane, and everything about it was new and exciting for her. For Anna, art and cultural experiences were more important than discussions about economy and work. However, the most important aspect of these exchanges wasn't the places they visited or the activities we did—it was the opportunity to be together with boys and girls from different nationalities. Building connections and sharing experiences with peers from diverse cultures, speaking different languages, was the most enjoyable and valuable part of the exchange for Anna. 50 years have passed, a long time, according to Anna. She has forgotten many facts but remembers feelings. Remembers the feeling of great fun. Anna remembers how surprised she was about many things different from what she had seen till that moment. Friends in a Cold Climate: After the Second World War a number of friendship ties were established between towns in Europe. Citizens, council-officials and church representatives were looking for peace and prosperity in a still fragmented Europe. After a visit of the Royal Mens Choir Schiedam to Esslingen in 1963, representatives of Esslingen asked Schiedam to take part in friendly exchanges involving citizens and officials. The connections expanded and in 1970, in Esslingen, a circle of friends was established tying the towns Esslingen, Schiedam, Udine (IT) Velenje (SL) Vienne (F) and Neath together. Each town of this so called “Verbund der Ringpartnerstädte” had to keep in touch with at least 2 towns within the wider network. Friends in a Cold Climate looks primarily through the eyes the citizen-participant. Their motivation for taking part may vary. For example, is there a certain engagement with the European project? Did parents instil in their children a a message of fraternisation stemming from their experiences in WWII? Or did the participants only see youth exchange only as an opportunity for a trip to a foreign country? This latter motivation of taking part for other than Euro-idealistic reasons should however not be regarded as tourist or consumer-led behaviour. Following Michel de Certeau, Friends in a Cold Climate regards citizen-participants as a producers rather than as a consumers. A participant may "put to use" the Town Twinning facilities of travel and activities in his or her own way, regardless of the programme. Integration of West-Europe after the Second World War was driven by a broad movement aimed at peace, security and prosperity. Organised youth exchange between European cities formed an important part of that movement. This research focuses on young people who, from the 1960s onwards, participated in international exchanges organised by twinned towns, also called jumelage. Friends in a Cold Climate asks about the interactions between young people while taking into account the organisational structures on a municipal level, The project investigates the role of the ideology of a united West-Europe, individual desires for travel and freedom, the upcoming discourse about the Second World War and the influence of the prevalent “counterculture” of that period, thus shedding a light on the formative years of European integration.

The dataset can be used in bundle with other sources to increase the accuracy of existing datasets related to traffic or marketing intelligence. For example to better understand the route users takes inside the cities, or which block surrounding the centre receives the majority of passages. Or they could be interpolated to confirm or obtain brand new trends.

Immagine having a customer asking which city area could be the most desirable for Advertising boards, knowing not only the general traffic conditions but also the passages into hot spots such as limited traffic zone entrances (an hot spot where usually drivers keep low speed to pay attention to the surrouding area) will sure help give a over the top service.

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.

The seismic data acquisition shown in the repository was done in the frame of the HISOPE project, which is intended to provide critical marine geological and archaeological constraints, in support of two currently funded coastal research projects: the IDEX Lyon project Thalassocracies without a port, myth or reality? (2020-2022) and the LabEx-IMU (Institut des Mondes Urbains) URBO project (2020-2023). These projects are carried by the Archéorient laboratory (UMR 5133 CNRS - Université Lyon 2) as part of a scientific consortium associating several other French and Italian laboratories and institutions: UMR 5189 HiSoMA, UMR 5600 EVS (ENS Lyon - Univ. Lyon 1 ), the Italian archaeological superintendencies of Lazio and Tuscany, the Museo civico del mare e della navigazione antica de Santa Severa, the University of Florence, the University of Roma-La Sapienza, the CNR, the WWF, the Municipality of Orbetello. The main objective is to discover ancient port cities along the seafront of the Tyrrhenian Sea. If the Roman ports, dating from the 4th century BC were discovered, the Etruscan ports (dating from the 10th to the 5th century BC) remain untraceable. The Etruscans are one of the only Mediterranean thalassocracies (societies based on maritime commercial and military power) whose archaeologists have never found the ports. The second objective is to determine the environmental characteristics of the sites chosen for the foundation of the Etruscan ports. The study of the evolution of these environments will also make it possible to understand the factors (environmental, political, economic) which led to their disappearance, and to the ex nihilo creation of new port sites in Hellenistic and Roman times. These projects are based on a resolutely interdisciplinary approach and bring together historians, archaeologists, geophysicists, geologists and geoarchaeologists. The main goals of the field campaigns carried out in this context are 1 ° to identify the location of the port basins (underground or at sea), and 2 ° to sample the study sites, in order to characterize their geological environment. For these reasons, the field missions combine terrestrial geophysical prospecting as well as terrestrial coring campaigns and lagoon coring. The main archaeological targets of the 2019 and 2020 campaigns were two Etruscan coastal cities: Pyrgi (Lazio) and Orbetello (Tuscany). In both cases, land surveys (geophysical and coring) have shown that, contrary to the dominant hypotheses, the port basins are not currently located on land. The present HISOPE - IFREMER project therefore aims to use sediment imagery to determine if they are present at sea, and to characterize the evolution of the coastline that has led to this present state. The reasons are very different in each case. In Pyrgi, the coastline has receded sharply since Etruscan times, due to rapid beach erosion. This decline sharply accelerated in the 20th century, threatening this important archaeological site. In Orbetello, the lagoon, in which the ancient city was settled, seems to have become a more restrictive environment over time; even becoming a source of countless environmental problems, against which the inhabitants still struggle today. The present HISOPE - IFREMER project aims to acquire high frequency seismic lines on these two sites. At Pyrgi, in an open sandy marine environment, sediment imagery will help determine the position of ancient shorelines, and determine the ancient positioning of archaeological remains discovered 350 m offshore, in October 2020. In Orbetello, set of measurements, in front of a tombolo that closes the lagoon, aims to document the source of a high-energy event that affected the lagoon after Roman times. In addition to providing answers to archaeologists, these measurements will provide fundamental information on the dynamics of these environments, making it possible to improve the fight against coastal erosion in Pyrgi, and the fight against the progressive confinement of the lagoon in Orbetello. Seismic data were acquired with high resolution 3,5 kHz Ixblue Subbottom Profiler, customized by Ifremer institute to optimize the data acquisition in shallow water; the sonar was hull mounted on board of the Haliotis research vessel; data post processing was performed by QC_Subop software (developed by Ifremer /REM/GEOOCEAN/ANTIPOD laboratories), and Geosuite software (license to CNR of Oristano). The flow processing was based on signal normalization, basic band pass filtering, and gain adjustment by Liner Gain (LG) or Time Varying Gain (TVG), depending on acquisition depth and sea bottom reflectivity . In the repository are provided navigation data (shape and Kmz file) showing navigation points; let's find in the attributes the file and line name, the progressive fix number of each profile (related to the acquisition shot) and the acquisition date (Year, month, day, hour. Minute, second); processed profiles are shown in jpg files with reference to line name, fix/shot (for horizontal scaling) and vertical scale in milliseconds; a conversion table from ms to meters is provided in the repository, assuming 1500 as sound velocity speed in the water.

Dataset supporting publication: “Data collected by coupling fix and wearable sensors for addressing urban microclimate variability in an historical Italian city” (publication available for download: GEOFIT Zenodo)

Datasets resulting from monitoring activities of Sant'Apollinare systems and climatic parameters inside and outside the building (post-intervention monitoring).

The article presents the data collected through an extensive research work conducted in a historic hilly town in central Italy during the period 2016-2017. Data concern two different datasets: long-term hygrothermal histories collected in two specific positions of the town object of the research, and three environmental transects collected following on foot the same designed path at three different time of the same day, i.e. during a heat wave event in summer. The short-term monitoring campaign is carried out by means of an innovative wearable weather station specifically developed by the authors and settled upon a bike helmet. Data provided within the short-term monitoring campaign are analysed by computing the apparent temperature, a direct indicator of human thermal comfort in the outdoors. All provided environmental data are geo-referenced. These data are used in order to examine the intra-urban microclimate variability. Outcomes from both long- and short-term monitoring campaigns allow to confirm the existing correlation between the urban forms and functionalities and the corresponding local microclimate conditions, also generated by anthropogenic actions. In detail, higher fractions of built surfaces are associated to generally higher temperatures as emerges by comparing the two long-term air temperature data series, i.e. temperature collected at point 1 is higher than temperature collated at point 2 for the 75% of the monitored period with an average of þ2.8 [1]C. Furthermore, gathered environmental transects demonstrate the high variability of the main environmental parameters below the Urban Canopy. Diversification of the urban thermal behaviour leads to a computed apparent temperature range in between 33.2 [1]C and 46.7 [1]C at 2 p.m. along the monitoring path. Reuse of these data may be helpful for further investigating interesting correlations among urban configuration, anthropogenic actions and microclimate variables affecting outdoor comfort. Additionally, the proposed dataset may be compared to other similar datasets collected in other urban contexts around the world. Finally, it can be compared to other monitoring methodologies such as weather stations and satellite measurements available in the location at the same time.

BRAINTEASER (Bringing Artificial Intelligence home for a better care of amyotrophic lateral sclerosis and multiple sclerosis) is a data science project that seeks to exploit the value of big data, including those related to health, lifestyle habits, and environment, to support patients with Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) and their clinicians. Taking advantage of cost-efficient sensors and apps, BRAINTEASER will integrate large, clinical datasets that host both patient-generated and environmental data. As part of its activities, BRAINTEASER organized two open evaluation challenges on Intelligent Disease Progression Prediction (iDPP), iDPP@CLEF 2022 and iDPP@CLEF 2023, co-located with the Conference and Labs of the Evaluation Forum (CLEF). The goal of iDPP@CLEF is to design and develop an evaluation infrastructure for AI algorithms able to: better describe disease mechanisms; stratify patients according to their phenotype assessed all over the disease evolution; predict disease progression in a probabilistic, time dependent fashion. The iDPP@CLEF challenges relied on retrospective ALS and MS patient data made available by the clinical partners of the BRAINTEASER consortium. The datasets contain data about 2,204 ALS patients (static variables, ALSFRS-R questionnaires, spirometry tests, environmental/pollution data) and 1,792 MS patients (static variables, EDSS scores, evoked potentials, relapses, MRIs). More in detail, the BRAINTEASER project retrospective datasets derived from the merging of already existing datasets obtained by the clinical centers involved in the BRAINTEASER Project. The ALS dataset was obtained by the merge and homogenisation of the Piemonte and Valle d’Aosta Registry for Amyotrophic Lateral Sclerosis (PARALS, Chiò et al., 2017) and the Lisbon ALS clinic (CENTRO ACADÉMICO DE MEDICINA DE LISBOA, Centro Hospitalar Universitário de Lisboa-Norte, Hospital de Santa Maria, Lisbon, Portugal,) dataset. Both datasets was initiated in 1995 and are currently maintained by researchers of the ALS Regional Expert Centre (CRESLA), University of Turin and of the CENTRO ACADÉMICO DE MEDICINA DE LISBOA-Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa. They include demographic and clinical data, comprehending both static and dynamic variables. The MS dataset was obtained from the Pavia MS clinical dataset, that was started in 1990 and contains demographic and clinical information that are continuously updated by the researchers of the Institute and the Turin MS clinic dataset (Department of Neurosciences and Mental Health, Neurology Unit 1, Città della Salute e della Scienza di Torino. Retrospective environmental data are accessible at various scales at the individual subject level. Thus, environmental data have been retrieved at different scales: To gather macroscale air pollution data we’ve leveraged data coming from public monitoring stations that cover the whole extension of the involved countries, namely the European Air Quality Portal; data from a network of air quality sensors (PurpleAir - Outdoor Air Quality Monitor / PurpleAir PA-II) installed in different points of the city of Pavia (Italy) were extracted as well. In both cases, environmental data were previously publicly available. In order to merge environmental data with individual subject location we leverage on postcodes (postcodes of the station for the pollutant detection and postcodes of subject address). Data were merged following an anonymization procedure based on hash keys. Environmental exposure trajectories have been pre-processed and aggregated in order to avoid fine temporal and spatial granularities. Thus, individual exposure information could not disclose personal addresses. The datasets are shared in two formats: RDF (serialized in Turtle) modeled according to the BRAINTEASER Ontology (BTO); CSV, as shared during the iDPP@CLEF 2022 and 2023 challenges, split into training and test. Each format corresponds to a specific folder in the datasets, where a dedicated README file provides further details on the datasets. Note that the ALS dataset is split into multiple ZIP files due to the size of the environmental data. The BRAINTEASER Data Sharing Policy section below reports the details for requesting access to the datasets.

The data in this dataset is a spatial inventory of urban agriculture (UA) carried out in the city of Milan (Italy). UA areas where identified with a multi-step and iterative procedure by using different web-mapping tools, especially multitemporal Google Earth images, and ancillary data such as Google Street View and Bing Maps.

License

Creative Commons CC-BY

Disclaimer

Despite our best efforts to validate the data, some information may be incorrect.

Description of the dataset

Typologies of UA

• Residential garden: Private parcel near single houses (e.g. backyard), villas, buildings, industrial and commercial activities, generally managed by property owners. Cultivation is diversified ranging from leafy vegetables to herbs and fruit trees. Production is intended for self-consumption and/or for hobby purposes.
• Community garden: A large area subdivided into multipleplots managed individually (i.e. allotment) or collectively by a group of people. Crop production is intended for self-consumption. Land is assigned by the Municipality; several cases of land cultivated without authorization are also common.
• Urban farm: Parcel managed by professional farmers with an intensive and an advanced cropping system. The cultivation can be specialized or oriented to high diversity vegetables. The production is intended for market. The mapping procedure focus on arable crops, horticulture, vineyard, olive groves and orchard.
• Institutional garden: Parcel managed by institutions or organizations like schools, religious center, prisons and non-profit organizations. The production is generally intended for self-consumption and less frequently for trade. Several gardens in this category are intended for social purposes (e.g. recreation,education, etc.).
• Illegal garden: Parcel isolated, cultivated without authorization organized and managed individually or by a few people. Localization occurs on unused or abandoned areas owned by public bodies or private subjects. The production is intended for self-consumption.
• Nurseries: A large area subdivided into multiple plots managed for growing ornamental plants and flowers.

Land use typologies

• Horticulture: annual crops generally seed sown in spring or summer (tomatoes, lettuce, zucchini, cucumbers, peppers).
• Vineyard: grape vines grown in order to produce wine or table grape.
• Olive groves: olive trees grown in order to produce olive oil or table olives.
• Orchards: mixed trees such as orange, stone fruit, pome fruit, olive trees.
• Mixed crops: an area grown with a mix of horticulture crops and fruit trees, not divisible.
• Nurseries: ornamental plants, trees, flowers.

Credit

Pulighe G., Lupia F. (2019) Multitemporal Geospatial Evaluation of Urban Agriculture and (Non)-Sustainable Food Self-Provisioning in Milan, Italy. Sustainability 2019, 11(7), 1846

https://www.mdpi.com/2071-1050/11/7/1846

We present a comprehensive dataset of channel measurements, performed to analyze outdoor-to-indoor propagation characteristics in the mid-band spectrum identified for the operation of 5th Generation (5G) cellular systems. The dataset includes measurements of channel power delay profiles from two 5G networks operating in Band n78, i.e., 3.3--3.8 GHz. Such measurements were collected at multiple locations in a large office building in the city of Rome, Italy, by using the Rohde & Schwarz (R&S) network scanner TSMA6 for several weeks in 2020 and 2021. A primary goal of the dataset is to provide an opportunity for researchers to investigate a large set of 5G channel measurements, aiming at analyzing the corresponding propagation characteristics towards the definition and refinement of empirical channel propagation models.