BUSCADOR RECOLECTA

Concentration of regulated pollutants, nutrients (ammonium, nitrate, phosphate) and total organic carbon (TOC) in eighty-nine sampling sites located in sixteen river basins throughout Catalonia (NE Spain), and information related to the sampling sites

Red leaf blotch (RLB) of almond, caused by the fungus Polystigma amygdalinum P.F. Cannon, is one of the most important leaf diseases affecting almond trees (Prunus dulcis (Mill.) D.A. Webb) in the Mediterranean basin and Middle East regions. From 2019 to 2021, airborne ascospores were monitored on a daily scale through Hirst-type air samplers in two almond orchards located in Lleida (NE Spain) and quantified through qPCR methods. In the same locations, red leaf blotch incidence and severity were evaluated weekly in 2021. Weather data (direct and derived variables) is also provided, from two automatic weather stations located near the almond orchards, for potential epidemiologic studies.

STL Files for the 3D Printing of a Customized Spectroelectrochemical Cell for Magnetic Nanostirring Applications on the SPELEC Instrument (Metrohm) This dataset contains STL files for the 3D printing of a customized spectroelectrochemical (SEC) cell specifically designed to enable magnetic nanostirring during SEC measurements using the SPELEC instrument (Metrohm). The original design was created using FreeCAD software. The printed cell used in experimental studies was fabricated in black polylactic acid (PLA) using an XYZPrinter da Vinci 1.0 Pro equipped with a 0.4 mm nozzle. The design includes two modular parts forming the main cell body, incorporating a 4-pillar structure to ensure proper sealing and minimize external light interference during measurements. This configuration allows for the use of magnetic nanostirring with sample volumes as low as 50 µL when placed on a standard magnetic stirrer platform. The closure mechanism in this design provides an alternative to the standard commercial SEC cell, which uses a magnetic coupling closure system that is not compatible with magnetic nanostirring under equivalent experimental conditions. The files are provided in STL format and can be directly used for additive manufacturing using common FDM 3D printers.

The dataset is the one related to the publication Isidro Martín, Gema López, Moisés Garín, Eloi Ros, Pablo Ortega, Joaquim Puigdollers, "Hole selective contacts based on transition metal oxides for c-Ge thermophotovoltaic devices", Solar Energy Materials and Solar Cells 251, 112156 (2023). DOI:https://doi.org/10.1016/j.solmat.2022.112156. The data is used to characterize the contact selectivity of MoOx, VOx and WOx which includes effective lifetime measurements to determine surface passivation, effective surface recombination velocity, contact resistance measurements and internal reflectance of the contact.

Datos de desprendimientos de rocas en España desde 1800 a 2021 extraidos de la prensa nacional y local. Incluye daños materiales y humanos, así como la fuente de datos., Data on rockfalls in Spain from 1800 to 2021 extracted from the national and local press. It includes material and human damages, as well as the data source.

Data from the archaeological intervention as a result of the rehabilitation and adaptation works to the Roman quarry Medol (Tarragona) carried out from April and December 2013. The data are from Roman times (2nd century BC - 2nd century AD) and include archaeological inventory, photographs, plans and drawings.,
Dades de la intervenció arqueològica arran de les obres de rehabilitació i adequació a la Pedrera romana del Mèdol (Tarragona) duta a terme entre abril i desembre de 2013. Les dades són d’època romana (S. II aC-II dC) i inclouen l’inventari arqueològic, fotografies, planimetries i làmines.

JavaScript code to be implemented in Google Earth Engine(c) for large-scale, multi-temporal remote sensing of palaeo-river networks.

This research presents a seasonal multi-temporal approach to the detection of palaeo-rivers over large areas based on long-term vegetation dynamics and spectral decomposition techniques. Twenty-eight years of Landsat 5 data, a total of 1711 multi-spectral images, have been bulk processed using Google Earth Engine© Code Editor and cloud computing infrastructure.

JavaScript code to be implemented in Google Earth Engine(c) for automated machine learning-based potsherd detection using high-resolution drone imagery.

This research presents the first proof of concept for the automated recording of material culture dispersion across large areas using high resolution drone imagery, photogrammetry and a combination of machine learning and geospatial analysis that can be run using the Google Earth Engine geospatial cloud computing platform.

JavaScript code to be implemented in Google Earth Engine(c) for Hybrid MSRM-Based Deep Learning and Multitemporal Sentinel 2-Based Machine Learning Algorithm.

Algorithm for large-scale automatic detection of burial mounds, one of the most common types of archaeological sites globally, using LiDAR and multispectral satellite data. Although previous attempts were able to detect a good proportion of the known mounds in a given area, they still presented high numbers of false positives and low precision values. Our proposed approach combines random forest for soil classification using multitemporal multispectral Sentinel-2 data and a deep learning model using YOLOv3 on LiDAR data previously pre-processed using a multi–scale relief model. The resulting algorithm significantly improves previous attempts with a detection rate of 89.5%, an average precision of 66.75%, a recall value of 0.64 and a precision of 0.97, which allowed, with a small set of training data, the detection of 10,527 burial mounds over an area of near 30,000 km2, the largest in which such an approach has ever been applied. The open code and platforms employed to develop the algorithm allow this method to be applied anywhere LiDAR data or high-resolution digital terrain models are available.

JavaScript code to be implemented in Google Earth Engine(c).

The multi-scale relief model (MSRM) is a new algorithm for the visual interpretation of landforms using DSMs. The significance of this new method lies in its capacity to extract landform morphology from both high- and low-resolution DSMs independently of the shape or scale of the landform under study. This method thus provides important advantages compared to previous approaches as it: (1) allows the use of worldwide medium resolution models, such as SRTM, ASTER GDEM, ALOS, and TanDEM-X; (2) offers an alternative to traditional photograph interpretation that does not rely on the quality of the imagery employed nor on the environmental conditions and time of its acquisition; and (3) can be easily implemented for large areas using traditional GIS/RS software.

The algorithm is tested in the Sutlej-Yamuna interfluve, which is a very large low-relief alluvial plain in northwest India where 10 000 km of palaeoriver channels have been mapped using MSRM.