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2DES data on gold nanorods
No full textdatasets obtained applying 2DES on colloidal suspensions of gold nanorods with different aspect rati
Hatching with Numbers: How Pre-natal Experience Affects Chicks' Left-to-Right Mental Number Line
No full textThe present study aims to explore the role of brain lateralization in Space-number association by testing newborn domestic chicks (Gallus gallus) in an ordinal task. Domestic chicks offer the possibility to manipulate the degree of brain lateralization through light exposure during the last period of incubation: light-incubated chicks are strongly lateralized, and dark-incubated chicks are weakly lateralized brains. In two experiments, 100 male chicks were trained to select the fourth element in a series of 10 identical elements. In each experiment, a group of strongly lateralized chicks and a group of weakly lateralized chicks underwent sagittal and three fronto-parallel tests (one in binocular condition and two in monocular conditions). At test, in Experiment 1, both spatial and numerical cues were available by having constant inter-element distance; in Experiment 2, spatial cues were excluded by altering inter-element distance in each trial. In experiment 1, hatch conditions significantly affected the sagittal test: a higher degree of lateralization led to a higher accuracy. In the fronto-parallel left and right tests, there was a main effect of hatch condition and side preference, with interactions showing stronger side biases in light-hatched chicks. In experiment 2, the degree of lateralization did not influence the accuracy and leftward bias in the sagittal test or in the fronto-parallel binocular test. In the fronto-parallel left and right tests, side preferences remained, with an interaction in the fronto-parallel right test showing a right bias only in light-hatched chicks.
The results indicate that the degree of hemispheric lateralization affects performance in ordinal tasks when both spatial and numerical cues are relevant. In contrast, pre-hatching light stimulation did not affect chicks' performance on purely ordinal information processing. These findings suggest that brain lateralization and spatial cues in the environment jointly contribute to the directional SNA
VREM-FL datasets: a collection of datasets for vehicular federated learning
No full textThis dataset collection includes three files that were used for the paper
L. Ballotta, N. D. Fabbro, G. Perin, L. Schenato, M. Rossi and G. Piro, "VREM-FL: mobility-aware computation-scheduling co-design for vehicular federated learning," in IEEE Transactions on Vehicular Technology, doi: 10.1109/TVT.2024.3479780.
Specifically, each file contains 6 columns: {timestep, vehicle ID, x coordinate in the map, y coordinate in the map, real bitrate, estimated bitrate}. The datasets, obtained from REMs with Gaussian estimation and real (https://ieee-dataport.org/open-access/crawdad-romataxi) or simulated (https://eclipse.dev/sumo/) vehicular mobility, are used in the original paper for optimizing the task of federated learning (client scheduling and resource allocation)
Martian simulant analysis dataset
No full textThis dataset contains data derived from chemical, mineralogical, granulometric and hyperspectral acquisitions of Mars Global (MGS-1) High-Fidelity Martian Dirt Simulant [1], Mojave Mars Simulant MMS-1 and Enhanced Mars Simulant (MMS-2). The instruments used for this work are:
- Laser Diffraction Particle Size Analyzer Malvern Panalytical Mastersizer3000: granulometric analysis;
- Inductively Coupled Plasma Mass Spectrometer (ICP-MS) Perkin-Elmer NexION 350X: chemical analysis;
- X-Ray powder Diffractometer (XRD) Philips X’Pert PRO: mineralogical analysis;
- Scanning Electron Microscope (SEM-EDS) Tescan SOLARIS equipped with Oxford Instruments microanalytical system: mineralogical analysis.
- Headwall Photonics Nano-Hyperspec (400-1000 nm) and Micro-Hyperspec (900-2500 nm) cameras: hyperspectral acquisitions;
The Mastersizer3000 software creates tables and related plots of grainsize ready to use.
The detector of mass spectrometer generates data in CPS (counts per second). Knowing the dilutions of the solutions, the spectrometer software calculates the quantities in terms of weight over volume (µg/l) in a table. Thereafter, knowing the initial weights of the simulant in the solutions, the operator converts data from weight over volume (µg/l) to weight over weight (mg/kg).
Qualitative phase analysis on powder diffraction data has been run through a search-match algorithm, in order to identify the minerals species. Quantitative phase analysis has been performed using the Rietveld method with internal standard addition, as implemented in Profex-BGMS v. 5.2.3.
The SEM products are photos of the sites, where the mineralogical analysis is performed, and graphs with peaks. The peaks are associated with specific chemical elements through the SEM software, that allows to determine the minerals present in the simulant. It was carried out only on the largest grains of MGS-1 simulant.
The hyperspectral cameras acquire hyperspectral cubes that the operator can open in ENVI software [3] or similar ones for hyperspectral imaging data-sets to extrapolate the spectrum of the simulants. The spectrum is saved in table format (ASCII) and can be opened in Origin software [4] or any software dedicated to graph and table management (e.g., Excel). In Origin, the operator produces the spectral plot, where he/she can continue with direct interpretations of absorption peaks, characteristic of particular minerals
Data for Periodic protected agriculture mapping at continental scales with Sentinel-2 imagery within the Google Earth Engine platform
No full textThe collection contains:
1) The script as a .txt file to run the classifier OPAC and its whole workflow in the Google Earth Engine platform;
2) The 115 output raster files with a spatial resolution of 20 m for the year 2019 produced and analysed in this study in geotiff format. The self-explanatory file names are composed by the abbreviated name of the country, the string “GEE_OPAC_Corine-”, followed by a string generated by the GEE platform during export. The size of each raster depended on the size of the tiles automatically produced by the GEE platform during export.
The value of each pixel corresponds to one of the following classes:
1 - Forest, 2 - Protected Agriculture (classes 2, 3 and 6 were grouped together), 4 and 5 - Shadow, 7 - Vegetated, 8 - Water, 9 - Snow, 10 - Baresoil.
3) The ESRI shapefile “Validation_all_shape.shp” and related files (in epsg: 4326) locating the 450 points used for validation. The attribute table consists of:
“ID” - Numeric increasing value for ordering, “Label” - True label assigned from visual inspection of the true color image from Sentinel 2 and the very high resolution RGB satellite images available in the GEE platform, “Country” – Country where the pixel belong, “Long” – Longitude, “Lat” – Latitude, “OPAC” – class assigned by the classifier
TP and OJP xylem anatomical chronologies
No full textTurkey Point, Ontario, Canada
Old Jack Pine, Saskatchewan, Canada
1970-201
Cancer-on-a-Chip Platform to Study Metastatic Microenvironments
No full textOriginal data and manuscript. Additive Manufacturing (AM) is a very advantageous technology to produce inserts for the Injection Molding (IM) of thermoplastic devices. Data concern a cancer-on-a-chip platform mimicking Neuroblastoma (NB) progression
Proximate, Ultimate analysis and Emission Factors
No full textData set including lump charcoal and charcoal briquettes samples qualitative characteristics and relative emission dat
Spring-water temperature from rock glaciers in the Val di Sole catchment (Easter Italian Alps)
No full textSpring-water temperature was measured at 131 springs with elevation ranging between 1698 and 3039 m a.s.l. in the Val di Sole catchment (Eaastern Italian Alps). Measurements were taken from mid-August to mid-October, after the end of the snowmelt. Most springs have been measured once per year from 2018 to 2020, and a small group of them was also measured in 2021. Spring-water temperature was used to investigate the spatial distribution of mountain permafrost in the study area, and in particular its occurrence in rock glaciers classified as relict
Modelling wood skidding extraction in mountainous forest though engine data acquisition and analysis: dataset
No full textThe dataset presented forms the foundation of a study, which explores the use of standardized SAE J1939 data from forestry machines to improve decision-making and ensure sustainable forest operations. Conducted in alpine forests between January and September 2023, the research focused on a clam-bunk skidder, aiming to identify work phases, calculate fuel consumption, and model production per work cycle. The study also tested the feasibility of the Automated Time Study (ATS) in mountainous regions, achieving over 82% accuracy in identifying work cycles