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08 - IDW and TPS correction
No full textSpatial IDW and TPS correction of the hourly radar precipitation values for each centroid of the study area.
Once the residuals at the rain gauge locations were calculated with respect to the radar, the error was interpolated exploiting the Inverse Distance Weighting (Thin Plate Spline) method and added to the radar values in each 1 km x 1 km cell (radar considered as the trend).
Naming conventions:
P_R_idw_n°.csv and P_R_tps_n°.csv
for example, P_R_idw_2.csv, number varies based on the centroid which it refers to
00 - PhD Data Management Plan - Citrini
No full textData Management Plan of the PhD project "Modelling current and future water resources availability in an Alpine catchment exploited for hydropower production" and its attachment
01 - Radar Precipitation extraction 2005 - 2020
No full textHourly precipitation values extracted from raster related to MeteoSWISS radar outputs. The values represent the average, minimum and maximum hourly precipitation in the cell (1km X 1km) corresponding to the ARPA station of Grosio. After extracting the data from the raster cell corresponding to the weather station (values from 0 to 255), they are reclassified according to a conversion table whose result is the mean value, minimum and maximum expressed in mm/h.
Naming conventions:
for the extraction:
combPointValue_yyyy.csv
for example, combPointValue_2005.csv
for the conversion:
Type_Value_intensity.csv
for example, Stations_Value_min.csv or Centroids_Value_min.cs
ResPOnsE COVID-19. Cumulative file: Wave 1 to Wave 6 (Italian version)
No full textWhat 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 variables capturing daily information on the health emergency, such as the number of people infected by COVID-19 and the number of deaths due to COVID-19 at the national and regional level on the day of the interview.
The macro-level variables were extracted here: https://github.com/pcm-dpc/COVID-19/tree/master/dati-andamento-nazionale
Team
The research team is coordinated by Cristiano Vezzoni and Antonio Chiesi and includes all members of the spsTREND Laboratory, who contributed in various ways to the successful outcome of the survey.
The questionnaire is the result of a discussion among all research team members.
As for the thematic modules, the design was entrusted to one or more members of the team based on expertise on the topic: Gender inequalities (Giulia Dotti Sani), Religion (Ferruccio Biolcati, Francesco Molteni, Riccardo Ladini), Political-electoral (Paolo Segatti, Nicola Maggini), Transformations of democracy (Marco Maraffi, Andrea Pedrazzani), State and market (Antonio Chiesi, Paolo Segatti, Cristiano Vezzoni), Europe and solidarity between countries (Simona Guglielmi, Paolo Segatti), Vaccines (Cristiano Vezzoni, Riccardo Ladini and Ferruccio Biolcati), Schooling (Giulia Dotti Sani, Simona Guglielmi, Nicola Maggini), Social capital (Antonio Chiesi), War in Urkaine (Paolo Segatti, Simona Gugliemi, Simone Sarti), Climate Change (Riccardo Ladini, Marta Moroni) Biodiversity (Simona Guglielmi, Marta Moroni, Riccardo Ladini), AI (Cristiano Vezzoni), Conspiracy Theories (Riccardo Ladini, Cristiano Vezzoni).
The management and validation of the dataset, as well as the preparation of the graphs for the periodic reports, were taken care of by Francesco Molteni, with contributions from Giulia Dotti Sani and Marta Moroni. Giulia Dotti Sani, Nicola Maggini, and Riccardo Ladini contributed to the construction of the weights for reporting to the population
SENSE_WP1_T1.5_Evaluate the effect of flavonoids in bacteria phenotypes_swimming_v.02
No full textThis dataset describes the flavonoid-induced effect on swimming motility of the PCB-degrading strain Paraburkholderia xenovorans LB40
09 - Kriging OK and DK correction
No full textSpatial Kriging OK and DK correction of the hourly radar precipitation values for each centroid of the study area.
Once the residuals at the rain gauge locations were calculated with respect to the radar, the error was interpolated exploiting the Ordinary (OK) and Detrended (DK) kriging techniques and added to the radar values in each 1 km x 1 km cell (radar considered as the trend).
Naming conventions:
P_R_kr_ord_n°.csv and P_R_kr_det_n°.csv
for example, P_R_kr_ord_2.csv, number varies based on the centroid which it refers to
UMILRawData_Deliverable 5.2
No full textThis dataset collects all the raw data explained of the deliverable 5.
Replication Data for: Transcriptomics of "Glucagon-like peptide 1 receptor is a T cell-negative costimulatory molecule"
No full textData related to the paper by Ben Nasr M in Cell Metabolism 202
Maps of estimated date per cell, one for each combination of parameters, for "Modelling the timing of migration of a partial migrant bird using ringing and observation data: a case study with the Song Thrush in Italy"
No full textThis dataset includes the maps of the estimated date per cell when a given proportion of individuals was on the move. These maps are based on model interpolation and were used for creating the interpolated maps. One map is created for each combination of parameters used in the model
12 - Forecasted dataseries of precipitation and temperature
No full textHourly forecasted dataseries of precipitation and temperature extracted from MOLOCH output.
Data were extracted from the outputs of the MOLOCH model by transforming them into netCDF and extracting the values in the location of subbasins from the temperature and precipitation layer in R environment.
Naming conventions:
T_YYYYMMDD.csv
P_YYYYMMDD.csv
for example, T_20180503.csv, number varies based on the date and the sub-basin which it refers to