Resultados de investigación

Additional file 2: Dataset S2. Spreadsheet containing information from the nanoparticle tracking analysis about the excretory/secretory products nanoparticle concentration according to size at each sampling point (24 h: tab A, 48 h: tab B and 72 h: tab C)., Ministerio de Ciencia e Innovación; Grantová Agentura České Republiky; Ministerstvo Školství, Mládeže a Tělovýchovy Consejo Superior de Investigaciones Cientificas (CSIC), Peer reviewed

This research was financially supported by the Biotechnology Program and Environmental pollution and climate program in the Kuwait Institute for Scientific Research (BT-35/FB149K). MG was supported by a Juan de la Cierva-formacion 2021 fellowship (FJC2021–047494-I/MCIN/AEI/10.13039/501100011033) from the European Union “NextGenerationEU”/PRTR and by the Beatriu de Pinós programme (2022 BP 00209) funded by the Direcció General de Recerca (DGR) del Departament de Recerca i Universitats (REU) of the Government of Catalonia, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

This study is part of the Intramural CSIC Project ‘Opportunistic gulls as sentinel species to monitor urban marine ecosystems’. Infraestructures de la Generalitat de Catalunya S.A.U. funded some of the GPS devices. V.M.-V. a Juan de la Cierva fellowship from the Spanish Government (JDC2022-049638-I). [...] This work acknowledges the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

Additional file 4: Dataset S3. Spreadsheet containing information on the proteome analysis of isolated extracellular vesicles from Sparicotyle chrysophrii. The spreadsheet contains sequences classified as non-enzyme proteins (tab A), unassigned Enzyme Commission number (tab B), oxidoreductase (tab C), transferases (tab D), hydrolases (tab E), lyases (tab F), isomerases (tab G), ligases (H) and translocases (tab I)., Ministerio de Ciencia e Innovación; Grantová Agentura České Republiky; Ministerstvo Školství, Mládeže a Tělovýchovy; Consejo Superior de Investigaciones Cientificas (CSIC), Peer reviewed

This project (SO-FRESH) was supported by the European Space Agency (ITT AO/1-10461/20/I-NB), This work contributes to the Institut de Ciències del Mar "Severo Ochoa Centre of Excellence" accreditation CEX2024-001494-S funded by AEI 10.13039/501100011033 of the Spanish Ministry of Science and Innovation, Peer reviewed

Enlace alternativo para la descarga: https://ss3.scayle.es:443/ule-bibliotecas/d-MedBioclim.rar., d-MedBioclim is a newly developed dataset for the Euro-Mediterranean region. This dataset applies the delta-change method by comparing the values of 25 General Circulation Models (GCMs) for the reference period (1981–2010) with their projections for future periods (2026–2050, 2051–2075, and 2076–2100) under the SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5 scenarios. These anomalies are added to two pre-existing datasets, ERA5-Land and CHELSA, yielding resolutions of 0.1º and 0.01º, respectively. Additionally, this manuscript provides a ranking of GCMs for each major river basin within the study area to guide model selection. d-MedBioclim includes, for all the aforementioned scenarios, monthly mean temperature, total monthly precipitation, and 23 bioclimatic variables, including 9 (biorm1 to biorm9) from the Worldwide Bioclimatic Classification System (WBCS) that are not available in other databases. It also provides two climate classifications: Köppen-Geiger and WBCS. This dataset is expected to be a valuable resource for modeling the distribution of Mediterranean species, which are highly affected by climate change, Peer reviewed

Additional file 2. Table S1. Saltol-linked SSR markers, used for foreground selection on F1 and BC1F1 generations and their physical position on chromosome 1; primer nucleotide sequences, and physical locations within the Saltol QTL are provided., NEURICE project (New commercial European RICE (Oryza sativa) harbouring salt tolerance alleles to protect the rice sector against climate change and apple snail (Pomacea insularum) invasion, Peer reviewed

This dataset includes agronomic, climatic, thermal and spectral information from field experiments conducted in Aranjuez (Central Spain) with winter wheat (Triticum aestivum L.) in 2018 and 2019. The total number of experiments was two, and each experiment comprises 32 plots with various combinations of water (2 levels) and nitrogen (4 levesl) applications. The agronomic data are summarized in an Excel file and contains the biomass, nitrogen concentration and content in the biomass, the shoot biomass, the shoot biomass nitrogen concentration and content, the spikes biomass, the spikes biomass nitrogen concentration and content, and the nitrogen nutrition index (NNI) at various dates. This crop file also contains the crop yield, protein content and N exported in the wheat grain (N output) at harvest in July. The thermal and spectral file includes the average canopy temperature and the average spectral reflectance from each plot at various dates. The weather data file includes the main climate variables registered in a meteorological station located on the farm., [+ Value of the data:] Data relating agronomic and sensor information from wheat under different nitrogen and water conditions will be useful for understanding crop performance and optimizing irrigation and nitrogen fertilization. The data on nitrogen nutrition index (NNI) are particularly valuable because there is a need to relate solid nutritional indicators with remote sensing data., [+ Detailed description:] The file ‘Crop_agronomic_data_wheat.xlsx’ contains the specific date in which each sample was taken, the N and water level, the dry biomass (kg dm/ha), the N concentration (%) and N content (kg N/ha) of the aerial biomass, the shoot biomass (kg dm/ha) with its N concentration (%) and N content (kg N/ha), the spike biomass (kg dm/ha) with its N concentration (%) and N content (kg N/ha), the NNI adjusted and the NNI calculated following Justes equation. In addition, the dataset contains the wheat grain yield (kg dm/ ha), grain N concentration (%) and grain content or N output (kg N ha/1) at harvest in the two years were recorded. The file ‘Crop_Reflectance_temp_from_Plane.xlsx’ contains the average canopy temperature (ºC) and the average spectral reflectance from each plot at various dates. The values were obtained from the airborne images acquired with thermal and hyperspectral cameras mounted on a plane that flight at 300 m over the experiment in March, April and May. The original images are available under reasonable request to the authors. The ‘Weather_Data_Aranjuez_01_01_2018_31_12_2020’ file includes the main climate variables registered in a meteorological station located on the farm., More detail about these data can be found in the article entitled ' Simultaneous assessment of nitrogen and water status in winter wheat using hyperspectral and thermal sensors’ published in European Journal of Agronomy (vol. 127, p. 126287) by Pancorbo et al. in 2021., [Steps to reproduce] Biomass, N concentration and N content in the different plant components were determined from two samples (0.5 x 0.5 m) collected in each plot at three different growth stages (mid stem elongation, final stem elongation and flowering) both years. At harvest, the central fringe from each plot was harvested with an experimental combine to record grain yield. The N concentration of wheat components (spikes and the rest of the biomass) and grain was determined by the combustion method in a subsample from each plot. The NNI was determined as the ratio between the actual crop N concentration and the critical N concentration for a given biomass (i.e. the N concentration that enables maximum growth). Spectral and thermal data were extracted from images acquired from hyperspectral and thermal sensors onboard an aircraft flying 300 m above ground at 70 knots ground speed with heading on the solar plane. The hyperspectral imager covering the VNIR region (Hyperspec VNIR model, Headwall Photonics, Fitchburg, MA, USA) captured the reflected light between 400 and 850 nm with a spectral resolution of 6.5 nm full-width at half maximum (FWHM) and 50º field of view (FOV) that yielded a spatial resolution of 0.2 m. Reflectance in the SWIR region was obtained with a hyperspectral sensor (NIR-100 model, Headwall Photonics, Fitchburg, MA, USA) from 950 to 1750 nm at 6.05 nm FWHM, with an FOV of 38.6º and 0.6 m spatial resolution. The surface temperature was recorded with a thermal sensor (SC655 model, FLIR Systems, Wilsonville, OR, USA) at a spatial resolution of 0.25 m, 16-bit radiometric resolution, focal length of 13.1 mm, and 45 × 33.7º FOV in each flight. The sensor has ±2 °C of accuracy, and a thermal sensitivity <0.05 °C at 30°C. A single nadir-oriented image was collected from each plot with the thermal sensor, and from the borders to obtain dry and wet bare soil temperature. The surface temperature of each plot was calculated as the average of the pixels in the center of the acquired image. Throughout the sampling, air temperature, radiance and relative humidity were monitored., Ministerio de Ciencia, Tecnología e Innovación PID2021-124041OB-C21/22, Peer reviewed

This dataset contains 24 individual UAV RGB videos in vineyard in Spain. Each flight were collected by mannully controlled with different perceptives (front view, and side view). The video 56 and 61 contains loop closure for detailed SLAM, path planning and 3D reconstruction tasks. The flight record information was saved as .xlsx, which contains flight height, roll, yaw, pitch, speed, distance, and etc., Peer reviewed

These models allow researchers to: Reproduce the prediction results in predictions_2019-2020.zip, Analyze different training strategies (full vs tiled), Fine-tune or reuse the models on new domains or variables, Compare architectures in a standardized setting., This Zenodo repository provides the trained models and prediction outputs used in the study "Transformer-based super-resolution downscaling for regional reanalysis: Full domain vs tiling approaches". The study evaluates and compares the performance of several deep learning-based super-resolution (SR) methods, including Swin2SR, DeepESD, and U-Net, for enhancing the spatial resolution of reanalysis temperature data from ERA5 (0.25º) to CERRA (0.05º) over the Iberian Peninsula., Peer reviewed