Italy 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. Italy 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. Italy 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.; ;

Italy IT: Population in Largest City: as % of Urban Population data was reported at 8.953 % in 2017. This records an increase from the previous number of 8.920 % for 2016. Italy IT: Population in Largest City: as % of Urban Population data is updated yearly, averaging 8.946 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 9.181 % in 1972 and a record low of 8.240 % in 1960. Italy IT: Population in Largest City: as % of Urban Population 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 percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted Average;

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.

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 Black Death was the largest and deadliest pandemic of Yersinia pestis recorded in human history, and likely the most infamous individual pandemic ever documented. The plague originated in the Eurasian Steppes, before moving with Mongol hordes to the Black Sea, where it was then brought by Italian merchants to the Mediterranean. From here, the Black Death then spread to almost all corners of Europe, the Middle East, and North Africa. While it was never endemic to these regions, it was constantly re-introduced via trade routes from Asia (such as the Silk Road), and plague was present in Western Europe until the seventeenth century, and the other regions until the nineteenth century. Impact on Europe In Europe, the major port cities and metropolitan areas were hit the hardest. The plague spread through south-western Europe, following the arrival of Italian galleys in Sicily, Genoa, Venice, and Marseilles, at the beginning of 1347. It is claimed that Venice, Florence, and Siena lost up to two thirds of their total population during epidemic's peak, while London, which was hit in 1348, is said to have lost at least half of its population. The plague then made its way around the west of Europe, and arrived in Germany and Scandinavia in 1348, before travelling along the Baltic coast to Russia by 1351 (although data relating to the death tolls east of Germany is scarce). Some areas of Europe remained untouched by the plague for decades; for example, plague did not arrive in Iceland until 1402, however it swept across the island with devastating effect, causing the population to drop from 120,000 to 40,000 within two years. Reliability While the Black Death affected three continents, there is little recorded evidence of its impact outside of Southern or Western Europe. In Europe, however, many sources conflict and contrast with one another, often giving death tolls exceeding the estimated population at the time (such as London, where the death toll is said to be three times larger than the total population). Therefore, the precise death tolls remain uncertain, and any figures given should be treated tentatively.