Find research datasets worth reusing

opencc-by-4.0Feb 2023View details →

Tea bag index (TBI) for a pot trial with five plant species in Trani (Apulia, Italy)

The file contains mean k and S value per GPS location, with as meta-data the starting date, duration and biome of the study.

opencc-by-4.0Feb 2022View details →

Bathymetry and Sediment thickness distribution of Lago dei Seracchi alpine lake, Rutor basin, Aosta Valley, Italy

Maps of water depth and sediment accumulation in an Italian proglacial lake, done by Ground Penetrating Radar (GPR) in July 2021. Supporting Time domain reflectometry surveys and geotechnical analyses on the sediments are also provided. For details, see the readme file in the dataset folder.

opencc-by-4.0Nov 2022View details →

Pesticide residue monitoring in South Tyrol (Italy)

<ul> <li>Pesticide residue monitoring programm in South Tyrol (Province Bolzano, Italy)</li> <li>The programm is organized and conducted by the Department of Public Health, Servizio Igiene e Sanità Pubblica—Azienda Sanitaria dell’Alto Adige, Bolzano, Italy</li> <li>Further despription and data available at: <ul> <li>https://www.sabes.it/gesundheitsvorsorge/umweltmedizin.asp</li> <li>https://umwelt.provinz.bz.it/pestizide-suedtirol.asp</li> </ul> </li> <li>Residue concentrations of grass samples per sampling site and sampling date can be found in the file "Pesticide_monitoring_SouthTyrol_2018_2021.csv "</li> <li>Available period: 2018 to 2021</li> <li>All sampling sites were public sites</li> <li>Names of active agents are in Italian</li> <li>Residue concentartions are given in "mg kg-1"</li> <li>"n.d." means "not detected"</li> <li>Lower limit of quantification (LOQ) was by default 0.01 mg kg-1</li> <li>GPS coordinates of the sampling sites are can be found in thefile "Coordinates_sampling_sites.csv"</li> </ul>

opencc-by-4.0Dec 2022View details →

Dataset for: "Last century changes in annual precipitation in a Mediterranean area and their spatial variability. Insights from northern Tuscany (Italy)"

The version 1.0 contains the supporting data for the work (still under submission) "Last century changes in annual precipitation in a Mediterranean area and their spatial variability. Insights from northern Tuscany (Italy)". The following files are here available (all file are georeferenced in EPSG: 3003): - AVG_Rainfall_1990-2019.tif -> Raster map of the mean annual precipitation for the northern Tuscany, Italy. It encompasses the portion of the Tuscany region northern of the cities of Livorno - Florence. The interpolation was validated via a leave one out cross-validation procedure. - D3-1_Area2_ApuanAlps.tif -> Raster map of the differences in mean annual precipitation between the two 3-decades periods 1921 to 1950 and 1990 to 2019 for the Apuan Alps mountain ridge (Tuscany, Italy). - D3-2_Area2_ApuanAlps.tif -> Raster map of the differences in mean annual precipitation between the two 3-decades periods 1951 to 1980 and 1990 to 2019 for the Apuan Alps mountain ridge (Tuscany, Italy). - DeltaSHP_Points_AVG_Annual_Rainfall.zip -> Shape file of the raingauges locations with the mean annual precipitation values of the period 1990 to 2019. - RaingaugesSHP_Points_AVG_Annual_Rainfall_1990-2019.zip -> Shape file of the raingauges locations with the following information: differences in the mean annual precipitation values between the two 3-decades periods 1951 to 1980 and 1990 to 2019 (named D3-2); p values of the t-test for significance of the differences between the mean annual precipitation ofthe two 3-decades periods 1951 to 1980 and 1990 to 2019; difference in the mean annual precipitation values between the two 3-decades periods 1921 to 1950 and 1990 to 2019 (named D3-1); p values of the t-test for significance of the differences between the mean annual precipitation ofthe two 3-decades periods 1921 to 1950 and 1990 to 2019.

opencc-by-4.0Apr 2023View details →

ipaast-czo case study OptRX data: Montalcino (SI, Italy)

These data were collected as part of a case study for the ipaast project. The aim of the survey was to produce datasets interoperable for applications in archaeology and precision agriculture. The OptRx® Crop Sensors (AgLeader Technology, Ames, IO, USA) measure the reflectance in the 630–685 nm (red), 695–750 nm (RE red edge) and 760–850 nm (NIR—Near InfraRed) wavebands. Using those wavebands, NDVI and NDRE indexes are calculated. NDVI and NDRE are vegetative indexes obtained from the red, red-edge and NIR wavebands with formulas 1 and 2:     NDVI = NIR−REDNIR+RED ; NDRE= NIR−RENIR+RE   The two index values range from -1 (bare ground or water) to 1 (highly vigorous vegetation).   To collect data, the sensor was mounted on a ground vehicle, a Kubota B2420 tractor. The sensor was paired with a GNNS receiver, GPS 6500 from AgLeader Technology (Ames, IO, USA). The instrumentation was coupled with the hardware and the rough book (Panasonic ToughPad FG-Z1, Panasonic Core. It was possible to install the sensor facing the ground using a metal bracket positioned on the front of the tractor. The sensor was positioned 1.15 m from the ground, emitting a rectangular footprint of 1.14 m in length and 20cm in width. The data were collected every 30 cm in alternate rows. 12 rows in total were analysed, covering a surface of 1.07 ha.  Data were processed on QGIS. First, the data was interpolated with the Inverse Distance Weighting (IDW) function. The function was set up with a distance coefficient P of 4, with 40 rows and 98 columns. A Gaussian filter with a standard deviation value of 2 and a range of research of 3 was subsequently applied to create a representative raster.

opencc-by-4.0May 2023View details →

Lake Pertusillo reservoir induced seismicity catalog (Southern Italy)

We present a detailed analysis of the small magnitude (ML<3) Reservoir Induced Seismicity associated with the Pertusillo water reservoir located in the high seismic hazard zone of Val d’Agri (Southern Italy).  We apply template-matching detection to a 13-month-long dense passive survey, obtaining a final high-precision double-difference catalog of 5,068 earthquakes (-0.7<ML<2.6, MC=0.2).  The original template dataset is composed of 408 hand-picked earthquakes, recorded during a 13-month-long (2005-2006) passive survey, at a dense network of 46 seismic stations (the average receiver spacing is 5 km) composed by: 22 temporary 3C continuously-recording stations of the temporary experiment described in Valoroso et al., [2009], plus 24 permanent INGV and ENI (the local oil company) stations, and accurately located in a 3D high-resolution VP and VP/VS velocity model [Improta et al., 2017].  The attached file is a plain text with a space as separator.  Here below the header is explained. ID: the unique event identifier LAT: hypocenter latitude expressed in degrees  LONG: hypocenter longitude east of Greenwich, expressed in degrees DEPTH: hypocenter depth expressed in km  OTIME: date of the origin time in the format YYYY-MM-DD[T]hh:mm:ss.msec ML: magnitude (pure number)

opencc-by-4.0Sep 2022View details →

AgrImOnIA: Open Access dataset correlating livestock and air quality in the Lombardy region, Italy

The AgrImOnIA dataset is a comprehensive dataset relating air quality and livestock (expressed as the density of bovines and swine bred) along with weather and other variables. The AgrImOnIA Dataset represents the first step of the <a href="http://www.agrimonia.net">AgrImOnIA project</a>. The purpose of this dataset is to give the opportunity to assess the impact of agriculture on air quality in Lombardy through statistical techniques capable of highlighting the relationship between the livestock sector and air pollutants concentrations. The building process of the dataset is detailed in the companion paper: A. Fassò, J. Rodeschini, A. Fusta Moro, Q. Shaboviq, P. Maranzano, M. Cameletti, F. Finazzi, N. Golini, R. Ignaccolo, and P. Otto (2023). Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy. SCIENTIFIC DATA, 1-19. available <a href="https://rdcu.be/c7T9H">here</a>. This dataset is a collection of estimated daily values for a range of measurements of different dimensions as: air quality, meteorology, emissions, livestock animals and land use. Data are related to Lombardy and the surrounding area for 2016-2021, inclusive. The surrounding area is obtained by applying a 0.3° buffer on Lombardy borders. The data uses several aggregation and interpolation methods to estimate the measurement for all days. The files in the record, renamed according to their version (es. .._v_3_0_0), are: <ul> <li> Agrimonia_Dataset.csv(.mat and .Rdata) which is built by joining the daily time series related to the AQ, WE, EM, LI and LA variables. In order to simplify access to variables in the Agrimonia dataset, the variable name starts with the dimension of the variable, i.e., the name of the variables related to the AQ dimension start with 'AQ_'. This file is archived also in the format for MATLAB and R software.  </li> <li> Metadata_Agrimonia.csv which provides further information about the Agrimonia variables: e.g. sources used, original names of the variables imported, transformations applied. </li> <li> Metadata_AQ_imputation_uncertainty.csv which contains the daily uncertainty estimate of the imputed observation for the AQ to mitigate missing data in the hourly time series.   </li> <li> Metadata_LA_CORINE_labels.csv which contains the label and the description associated with the CLC class.   </li> <li> Metadata_monitoring_network_registry.csv which contains all details about the AQ monitoring station used to build the dataset. Information about air quality monitoring stations include: station type, municipality code, environment type, altitude, pollutants sampled and other. Each row represents a single sensor. </li> <li> Metadata_LA_SIARL_labels.csv which contains the label and the description associated with the SIARL class. </li> <li> AGC_Dataset.csv(.mat and .Rdata) that includes daily data of almost all variables available in the Agrimonia Dataset (excluding AQ variables) on an equidistant grid covering the Lombardy region and its surrounding area.  </li> </ul> The Agrimonia dataset can be reproduced using the code available at the GitHub page: <a href="https://github.com/AgrImOnIA-project/AgrImOnIA_Data">https://github.com/AgrImOnIA-project/AgrImOnIA_Data</a> UPDATE 31/05/2023 - NEW RELEASE - V 3.0.0 A new version of the dataset is released: Agrimonia_Dataset_v_3_0_0.csv (.Rdata and .mat), where variable WE_rh_min, WE_rh_mean and WE_rh_max have been recomputed due to some bugs. In addition, two new columns are added, they are LI_pigs_v2 and LI_bovine_v2 and represents the density of the pigs and bovine (expressed as animals per kilometer squared) of a square of size ~ 10 x 10 km centered at the station localisation. A new dataset is released: the Agrimonia Grid Covariates (AGC) that includes daily information for the period from 2016 to 2020 of almost all variables within the Agrimonia Dataset on a equidistant grid containing the Lombardy region and its surrounding area. The AGC does not include AQ variables as they come from the monitoring stations that are irregularly spread over the area considered. UPDATE 11/03/2023 - NEW RELEASE - V 2.0.2 A new version of the dataset is released: Agrimonia_Dataset_v_2_0_2.csv (.Rdata), where variable WE_tot_precipitation have been recomputed due to some bugs. A new version of the metadata is available: Metadata_Agrimonia_v_2_0_2.csv where the spatial resolution of the variable WE_precipitation_t is corrected. <ul> </ul> UPDATE 24/01/2023 - NEW RELEASE - V 2.0.1 minor bug fixed UPDATE 16/01/2023 - NEW RELEASE - V 2.0.0 A new version of the dataset is released, Agrimonia_Dataset_v_2_0_0.csv (.Rdata) and Metadata_monitoring_network_registry_v_2_0_0.csv. Some minor points have been addressed: <ul> <li>Added values for LA_land_use variable for Switzerland stations (in Agrimonia Dataset_v_2_0_0.csv)</li> <li>Deleted incorrect values for LA_soil_use variable for stations outside Lombardy region during 2018 (in Agrimonia Dataset_v_2_0_0.csv)</li> <li>Fixed duplicate sensors corresponding to the same pollutant within the same station (in Metadata_monitoring_network_registry_v_2_0_0.csv)</li> </ul>

opencc-by-4.0Jun 2023View details →

Geochemical data of bottom sediments from a network of drainage canals located in the low-lying coastal area of Ravenna, Italy.

This dataset contains all raw geochemical data of bottom sediments from a network of drainage canals located in the low-lying coastal area of Ravenna. The dataset is divided in three separated excel worksheets:  - Focus Area. Sediment composition of the 21 sediment samples collected in 2022 in the Focus Area. Refer to Figs. 1 and 2 in the manuscript Giambastiani et al., 2024 for the sample locations. Listed are also other information related to sampling, such as depositional facies (BR: beach ridge deposits; IF: Interfluvial floodplain deposits), distance from the sea, altimetry, amount of fertilizer applied based on the land use, and EC of drainage water.  The sediment samples were collected in March 2022 along the drainage system of the lowlying coastal aquifer of Ravenna (Italy) by the authors. - LRC, Land Reclamation Consortium. PTEs composition of the sediment samples of the Land Reclamation Consortium dataset. Refer to Fig. 1 and 2 in the manuscript Giambastiani et al., 2024 for the location. Listed are also other information related to sampling, such as distance from the sea, altimetry, and amount of fertilizer applied based on the land use.  The sediment samples were collected since 2010 along the drainage system of the lowlying coastal aquifer of Ravenna (Italy) by The Land Reclamation Consortium of Romagna (Italy). No other uses apart from scientific purpose is allowed without notice to the authors. - Wells. Physical and chemical groundwater parameters of 4 wells localted within the Focus Area. Refer to Fig.2  in the manuscript Giambastiani et al., 2024 for the location.  Data were collected during previous studies by Greggio et al. (2020) and reprocessed to obtain vertical profiles of EC, pH, Eh, and chemical concentrations along the coastal aquifer depth. More informations regarding the source, ownership, collection methodologies and analytical techniques are in Giambastiani et al., 2024.

opencc-by-4.0Jan 2023View details →

Shear modulus reduction and damping ratio curves collected from multiple literature sources available in Italy

Data are 485 G/G0 (γ) and damping ratio, D(γ) curves collected from multiple literature sources available in Italy. Each curve was associated with the related engineering geological units considered in seismic microzonation studies. The data focus on providing reference information that can serve as key data for large-scale hazard assessments worldwide and to guide the translation of information from the laboratory scale to the scale of geological-engineering cross-sections.

opencc-by-4.0Oct 2023View details →

Aerosol absorption data in Modena, Italy published in the journal article by Bigi et. al. (2023)

Dataset of aerosol absorption coefficient at 1 hour time resolution collected at a urban traffic and a urban background site in Modena, Italy. Data is presented in the scientific article:  Bigi, A., Veratti, G., Andrews, E., Collaud Coen, M., Guerrieri, L., Bernardoni, V., Massabò, D., Ferrero, L., Teggi, S., Ghermandi, G: Black Carbon and Brown Carbon absorption by in-situ filter-based photometer and ground-based sun-photometer in an urban atmosphere. Atmos. Chem. Phys., 2023,   Data is freely available but the acknowledgement of the data originator is required: please contact the originator for details.

opencc-by-4.0Jan 2020View details →

Renewable and consumption data in DK and Italy

The wind and solar power, and electricity consumption hourly data  in Denmark and Italy  for 2018  & 2030 scenarios.

opencc-zeroJan 2019View details →

FIG. 2. — A, B in Two new oligohaline Hydrobia (s.l.) (Caenogastropoda) from the transitional Upper Oligocene Galatone Fm (Apulia, Southern Italy)

FIG. 2. — A, B, Hydrobia (s.l.) galatoniana n. sp., Galatone (Lecce, Apulia), Late Oligocene, paratype MPUR7-4198: A, apical view of the shell; B, detail of the protoconch; C, D, Hydrobia (s.l.) ionica n. sp., Galatone (Lecce, Apulia), Late Oligocene, paratype MPUR7-4199: C, apical view of the shell; D, detail of the protoconch. SEM image. Scale bars: A, C, 500 µm; B, D, 200 µm.

opencc-by-4.0Feb 2020View details →

Numerical modeling of the seismic cycle for normal and reverse faulting earthquakes in Italy

Results of the numerical models expressed in terms of nodal stresses, strains and displacements. Data Set S1. Nodal values of the modelled displacements for the L’Aquila 2009 earthquake. Data Set S2. Nodal values of the modelled strain tensor for the L’Aquila 2009 earthquake. Data Set S3. Nodal values of the modelled stress tensor for the L’Aquila 2009 earthquake. Data Set S4. Nodal values of the modelled displacements for the Norcia 2016 earthquake. Data Set S5. Nodal values of the modelled strain tensor for the Norcia 2016 earthquake. Data Set S6. Nodal values of the modelled stress tensor for the Norcia 2016 earthquake. Data Set S7. Nodal values of the modelled displacements for the Emilia 2012 earthquake. Data Set S8. Nodal values of the modelled strain tensor for the Emilia 2012 earthquake. Data Set S9. Nodal values of the modelled stress tensor for the Emilia 2012 earthquake.

opencc-zeroJul 2019View details →

FIG. 2. — A in Annotated checklist of the Hemiptera Heteroptera of the Site of Community Importance and Special Area of Conservation "Alpi Marittime" (NW Italy)

FIG. 2. — A, La Perla small lake; B, La Perla Valley; C, Pastures of the "Colle dell'Arpione"; D, Palanfrè forest; E, Pastures of Palanfrè; F, Pian della Casa and Gias della Casa small ponds; G, Pian del Valasco; H, Vej del Bouc lake. Photos: M. Norbiato.

FIG. 18. — Alloschizidium labronicum n in New species of subterranean and endogean terrestrial isopods (Crustacea, Oniscidea) from Tuscany (central Italy)

FIG. 18. — Alloschizidium labronicum n. sp., from Maroccone, ♀ paratype: A, adult specimen, lateral view; B, dorsal scale-seta; C, cephalon, frontal view; D, cepha- lon, dorsal view; E, epimeron of pereonite 1, dorsal view; F, telson and uropods, dorsal view; G, antennula; H, antenna. Scale bar: A, 1 mm.

FIG. 16. — Trogleluma pilosa n in New species of subterranean and endogean terrestrial isopods (Crustacea, Oniscidea) from Tuscany (central Italy)

FIG. 16. — Trogleluma pilosa n. sp., from Poggio Capalbiaccio, ♀ paratype: A, left mandible; B, right mandible; C, maxillula; D, maxilla; E, maxilliped; F, left uropod.

FIG. 15. — Trogleluma pilosa n in New species of subterranean and endogean terrestrial isopods (Crustacea, Oniscidea) from Tuscany (central Italy)

FIG. 15. — Trogleluma pilosa n. sp., from Poggio Capalbiaccio, ♀ paratype: A, adult specimen, lateral view; B, dorsal scale-seta; C, cephalon, frontal view; D, cephalon, dorsal view; E, epimeron of pereonite 1, dorsal view; F, telson and uropods, dorsal view; G, antennula; H, antenna. Scale bar: A, 1 mm.

FIG. 13. — Parachizidium ferrarai n in New species of subterranean and endogean terrestrial isopods (Crustacea, Oniscidea) from Tuscany (central Italy)

FIG. 13. — Parachizidium ferrarai n. sp., from Gorgona, ♂ paratype: A, left mandible; B, right mandible; C, maxillula; D, maxilla; E, maxilliped; F, uropod.

FIG. 10. — Typhlarmadillidium occidentale n in New species of subterranean and endogean terrestrial isopods (Crustacea, Oniscidea) from Tuscany (central Italy)

FIG. 10. — Typhlarmadillidium occidentale n. sp., from Grotta del Cane di Uliveto, Monte Pisano, ♂ paratype: A, left mandible; B, right mandible; C, maxillula; D, maxilla; E, maxilliped; F, right uropod.

FIG. 8. — Moserius talamonensis n in New species of subterranean and endogean terrestrial isopods (Crustacea, Oniscidea) from Tuscany (central Italy)

FIG. 8. — Moserius talamonensis n. sp., from Grotta Gianninoni, Uccellina, ♂ paratype: A, pereopod 1; B, pereopod 7; C, genital papilla; D, pleopod 1; E, pleopod 2; F, pleopod 3 exopod; G, pleopod 4 exopod; H, pleopod 5 exopod.