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[EN] Three files in .csv format ('Infogeo_Spain10.csv', 'MOTEDAScentury_TMAX_1916-2015.csv', 'MOTEDAScentury_TMIN_1916-2015.csv' and a fourth in netCDF format 'MOTEDAS_century.nc'). The first one contains the metadata of the grid points, the first column is the ID (Point), the second is the latitude (lon), the third is the latitude (lat) and the fourth is the elevation of the point with respect to the sea level (alt.). In the second and third file the climatic information is collected, in both, the first column is the ID (Point), in the second the year to which the data refer (Year), the following 12 columns show the monthly temperatures (maximum and minimum daily respectively) from January to December (Jan to Dec), all of them expressed in degrees Celsius. The fourth file contains the above-mentioned variables in netCDF format. [ES] Tres archivos en formato .csv ('Infogeo_Spain10.csv', 'MOTEDAScentury_TMAX_1916-2015.csv', 'MOTEDAScentury_TMIN_1916-2015.csv' y un cuarto en formato netCDF 'MOTEDAS_century.nc'). El primero de ellos contiene los metadatos de los puntos del grid, la primera columna es el ID (Point), la segunda es la latitud (lon), la tercera es la latitud (lat) y la cuarta es la elevación del punto respecto al nivel del mar (alt.). En el segundo y tercer archivo queda recogida la información climática, en ambos, la primera columna queda recogido el ID (Point), en la segunda el año al que van referidos los datos (Year), las 12 columnas siguientes se muestran las temperaturas mensuales (máximas y mínimas diarias respectivamente) de enero a diciembre (Jan a Dec), expresadas todas ellas en grados Celsius. En el cuarto archivo quedan recogidas las variables anteriormente mencionadas en formato netCDF., [EN] A monthly temperature (mean daily maximum and minimum) gridded data set over mainland Spain between December 1915 and December 2020. The dataset combines ground observations from the National Climate Data Bank (NCDB) of the Spanish national climate and weather service (AEMET) and new data rescued from meteorological yearbooks published prior to 1951 that was never incorporated into the NCDB. The yearbooks data represented a significant improvement of the dataset, as it almost doubled the number of weather stations available during the first decades of the 20th century. The station data were interpolated on a 0.1° × 0.1° regular grid using the angular distance weighting (ADW) method., [ES] Conjunto de datos en rejilla de temperatura mensual (promedio de los valores máximos y mínimos diarios) en la España peninsular entre diciembre de 1915 y diciembre de 2020. El conjunto de datos utilizado combina observaciones del Banco Nacional de Datos de AEMET y nuevos datos rescatados de los anuarios climáticos publicados con anterioridad a 1951, y que casi duplican la información existente sobre la primera mitad del siglo 20. El conjunto final contiene información de un total de 5.259 observatorios. Las observaciones se interpolaron a una rejilla regular de 0.1° × 0.1° mediante el método de ponderación inversa a la distancia angular (ADW)., Project CGL2017-83866-C3-3-R (CLICES: Climate of the last Century in the Spanish mainland), funded by the Spanish Ministry of Science., Infofile, grid., No

[EN] Three files in .csv format ('Infogeo_Spain10.csv', 'MOTEDAScentury_TMAX_1916-2015.csv' and 'MOTEDAScentury_TMIN_1916-2015.csv'). The first one contains the metadata of the grid points, the first column is the ID (Point), the second is the latitude (lon), the third is the latitude (lat) and the fourth is the elevation of the point with respect to the sea level (alt.). In the second and third file the climatic information is collected, in both, the first column is the ID (Point), in the second column the year to which the data refer (Year), the following 12 columns show the monthly temperatures (maximum and minimum daily respectively) from January to December (Jan to Dec), in the next 4 columns the seasonal averages (Win to Aut) and in the last one the annual average (Ann); all of them expressed in 1/10 Celsius degrees. [ES] Tres archivos en formato .csv ('Infogeo_Spain10.csv', 'MOTEDAScentury_TMAX_1916-2015.csv' y 'MOTEDAScentury_TMIN_1916-2015.csv'). El primero de ellos contiene los metadatos de los puntos del grid, la primera columna es el ID (Point), la segunda es la latitud (lon), la tercera es la latitud (lat) y la cuarta es la elevación del punto respecto al nivel del mar (alt.). En el segundo y tercer archivo queda recogida la información climática, en ambos, la primera columna queda recogido el ID (Point), en la segunda el año al que van referidos los datos (Year), las 12 columnas siguientes se muestran las temperaturas mensuales (máximas y mínimas diarias respectivamente) de enero a diciembre (Jan a Dec), en las 4 columnas siguientes se muestran los promedios estacionales (Win a Aut) y en la última el promedio anual (Ann); expresadas todas ellas en 1/10 grados Celsius., [EN] A monthly temperature (mean daily maximum and minimum) gridded data set over mainland Spain between December 1915 and December 2015. The dataset combines ground observations from the National Climate Data Bank (NCDB) of the Spanish national climate and weather service (AEMET) and new data rescued from meteorological yearbooks published prior to 1951 that was never incorporated into the NCDB. The yearbooks data represented a significant improvement of the dataset, as it almost doubled the number of weather stations available during the first decades of the 20th century. The final dataset contains records from 5,259 stations. The station data were interpolated on a 0.1° × 0.1° regular grid using the angular distance weighting (ADW) method., [ES] Conjunto de datos en rejilla de temperatura mensual (promedio de los valores máximos y mínimos diarios) en la España peninsular entre diciembre de 1915 y diciembre de 2015. El conjunto de datos utilizado combina observaciones del Banco Nacional de Datos de AEMET y nuevos datos rescatados de los anuarios climáticos publicados con anterioridad a 1951, y que casi duplican la información existente sobre la primera mitad del siglo 20. El conjunto final contiene información de un total de 5.259 observatorios. Las observaciones se interpolaron a una rejilla regular de 0.1° × 0.1° mediante mediante el método de ponderación inversa a la distancia angular (ADW)., Project CGL2017-83866-C3-3-R (CLICES: Climate of the last Century in the Spanish mainland), funded by the Spanish Ministry of Science., Infofile, grid., No

[EN] Three files in .csv format ('Infogeo_Spain10.csv', 'MOTEDAS_TMAX_1950-2010.csv' and 'MOTEDAS_TMIN_1950-2010.csv'). The first one contains the metadata of the grid points, the first column is the ID (Point), the second is the latitude (lon), the third is the latitude (lat) and the fourth is the elevation of the point with respect to the sea level (alt.). In the second and third file the climatic information is collected, in both, the first column is the ID (Point), in the second column the year to which the data refer (Year) and the following 12 columns show the monthly temperatures (maximum and minimum daily respectively) from January to December (Jan to Dec) and expressed in 1/10 Celsius degrees. [ES] Tres archivos en formato .csv ('Infogeo_Spain10.csv', 'MOTEDAS_TMAX_1950-2010.csv' y 'MOTEDAS_TMIN_1950-2010.csv'). El primero de ellos contiene los metadatos de los puntos del grid, la primera columna es el ID (Point), la segunda es la latitud (lon), la tercera es la latitud (lat) y la cuarta es la elevación del punto respecto al nivel del mar (alt.). En el segundo y tercer archivo queda recogida la información climática, en ambos, la primera columna queda recogido el ID (Point), en la segunda el año al que van referidos los datos (Year) y las 12 columnas siguientes se muestran las temperaturas mensuales (máximas y mínimas diarias respectivamente) de enero a diciembre (Jan a Dec) y expresadas en 1/10 grados Celsius., [EN] A monthly temperature (mean daily maximum and minimum) gridded data set over mainland Spain between December 1950 and December 2010. The dataset was developed from surface observations from the National Climate Data Bank (NCDB) of the Spanish national climate and weather service (AEMET), comprising data from 1358 complete series. The station data were interpolated on a 0.1° × 0.1° regular grid using the angular distance weighting (ADW) method., [ES] Conjunto de datos en rejilla de temperatura mensual (promedio de los valores máximos y mínimos diarios) en la España peninsular entre diciembre de 1950 y diciembre de 2010. El conjunto de datos se basa en observaciones del Banco Nacional de Datos de AEMET. El conjunto final contiene información de un total de 1358 series completas. Las observaciones se interpolaron a una rejilla regular de 0.1° × 0.1° mediante el método de ponderación inversa a la distancia angular (ADW)., Project HIDROCAES (CGL2011-27574-C02-01), funded by the Ministry of Science and Innovation (Spanish Government)., Infofile, grids., No

Under a Creative Common license CC BY 3.0 Deed., Figure S1: Pore size distributions of all PDC-T samples. Table S1: Atomic percentages (at.%) of carbon (C), oxygen (O) and phosphorus (P), obtained by XPS, and ratios of P-C bonds to total P (PT) content for all PDC-T materials. Figure S2: XPS spectra of (a) C 1s and (b) O 1s of the PDC-T series. Table S2: Literature comparison of the performance of materials similar to PDC-900 for the ORR shown in Fig. 4f., This study was partially supported by the French PIA project “Lorraine Université d’Excellence”, reference ANR-15-IDEX-04-LUE, and the TALiSMAN and TALiSMAN2 projects funded by ERDF. SGD thanks the Ministerio de Universidades, the European Union, and the University of Oviedo for their financial support (MU-21-UP2021-030 3026715)., Peer reviewed

Simulated data: For all the studies protein families (DNAK, DDL, TPIS, TRPA, TRXB, SH2). - The folder “*SimulatedANDinferredDATA” includes all simulated (true) protein sequence alignments and inferred with ProtASR under MF and the empirical JTT model (both for joint and marginal ASR) - The folder “*ComputedEnergiesFromData” includes the calculated energies of sequences of every MSA (files *.dat)., Real data: Files of the analysis based on real data are shown in the folder “RealData”, for the studied protein families (DNAK, DDL, TPIS, TRPA, TRXB). MSA and phylogenetic tree are in .fas/.nex formats and Newick format, respectively. Energies are shown in the .dat file and printed on the tree in files *Energies.tre (we recommend open them with FrigTree)., The computational reconstruction of ancestral proteins provides information on past biological events and has practical implications for biomedicine and biotechnology. Currently available tools for ancestral sequence reconstruction (ASR) are often based on empirical amino acid substitution models that assume that all sites evolve at the same rate and under the same process. However, this assumption is frequently violated because protein evolution is highly heterogeneous due to different selective constraints among sites. Here, we present ProtASR, a new evolutionary framework to infer ancestral protein sequences accounting for selection on protein stability. First, ProtASR generates site-specific substitution matrices through the structurally constrained mean-field substitution model (MF), which considers both unfolding and misfolding stability. We previously showed that MF models outperform empirical amino acid substitution models, as well as other structurally constrained substitution models, both in terms of likelihood and correctly inferring amino acid distributions across sites. In the second step, ProtASR adapts a well-established maximum-likelihood (ML) ASR procedure to infer ancestral proteins under MF models. A known bias of ML ASR methods is that they tend to overestimate the stability of ancestral proteins by under-estimating the frequency of deleterious mutations. We compared ProtASR under MF to two empirical substitution models (JTT and CAT), reconstructing the ancestral sequences of simulated proteins. ProtASR yields reconstructed proteins with less biased stabilities, which are significantly closer to those of the simulated proteins. Analysis of extant protein families suggests that folding stability evolves through time across protein families, potentially reflecting neutral fluctuation. Some families exhibit a more constant protein folding stability, while others are more variable. ProtASR is freely available from https://github.com/miguelarenas/protasr and includes detailed documentation and ready-to-use examples. It runs in seconds/minutes depending on protein length and alignment size., Peer reviewed

Table S1. Published peer reviewed manuscripts on the effects of waterlogging on wheat.-- Figure S1. Probability of excess of water during summer in Europe., Peer reviewed

[Detailed description] The file ‘dataset_crop_VIs.xlsx’ contains treatment factors, agronomic variables of wheat at flowering and harvest (aboveground biomass dry weight, N concentration, yield and others defined in the key spreadsheet), vegetation indices calculated from the hyperspectral airborne imagery, water deficit index, vegetation indices calculated from Sentinel-2 satellite imagery, solar induce fluorescence and Sentinel-2 bands convolved with the hyperspectral airborne information. The file “airborne_reflectance_1nm.xlsx” contains treatment factors and the average spectra of each plot resampled to 1 nm measured extracted from the airborne hyperspectral imagery acquired in two different dates., [Value of the data] The present dataset could help researchers and farmers to identify suitable genotypes and their responses to nitrogen and water stress. +The original images used are available under request to the authors. More detailed information about the dataset and methodology can be found in the article entitled “Airborne hyperspectral and Sentinel imagery to quantify winter wheat traits through ensemble modeling approaches” published in Precision Agriculture by Pancorbo et al. in 2023., The dataset includes agronomical, spectral and thermal information derived from a field experiment sowed with winter wheat (Triticum aestivum L.) at La Chimenea farm station (40º04’N, 03º32’W, 550 m a.s.l.), near Aranjuez (Madrid, Spain) during two consecutive growing seasons: 2017/2018 and 2018/2019. Each year, it was established 32 plots that were randomly divided into 4 N levels (N0, N1, N2, N3) and two water levels (w1 and W2) with four replications. The N levels ranged from non-fertilized (N0) to over-fertilized (N3). The two water levels were established at the beginning of flowering by irrigating half of the plots (W2). Spectral and thermal measurements were collected from a hyperspectral and a thermal sensors onboard an aircraft. The hyperspectral imager covering the VNIR region (Hyperspec VNIR model, Headwall Photonics, Fitchburg, MA, USA) captured the reflected light between 400 and 850 nm., Ministerio de Economía e Innovación, España. Ref.PID2021-124041OB-C21/22; PRE2018-084215; AGL2017-83283-C2-1-R. Ministerio de Educación, España. Ref. FPU17/01251. Comunidad de Madrid. Ref. AGRISOST-CM S2018/BAA-4330. Structural Funds 2014-2020 (ERDF and ESF)., Peer reviewed

Figure S1: Protein expression gels using Western blotting experiments., Peer reviewed

Table S1: Chemical compositions of the control wines without oenological additives (average values); Table S2: Chemical compositions of the oenological additives used in this study., Peer reviewed

Table S1. Columns used for the optimization of the HILIC × RP method used in the present research. Table S2. Identification proposed for the anthocyanins evaluated in the grape samples during ripening for a 10-week period. Figure S1. C18-RP 1DLC analysis of grape W9 (A) and wine (B). Blue: separation acquired at 280 nm; red: separation acquired at 520 nm. Figure S2. Chromatograms (280 nm) obtained for the HILIC separations obtained from a grape sample after optimization in a Zic-HILIC (A), diol (B) and silica (C) columns. Separation conditions are shown in inserts. Figure S3. Chromatograms (280 nm) obtained for the RP-based separations obtained from a grape sample after optimization in a PFP (A) and C18 (B) columns. Separation conditions are: flow rate 2 ml min-1 (Panel A) or 3 ml min-1 (panel B); elution with water (0.1% formic acid, solvent A) and acetonitrile (solvent B) as mobile phases. Specific gradients were shown in the respective chromatogram. Figure S4. 2D contour plots from the polyphenolic fraction of a grape sample (W10) obtained using different gradients and mobile phases composition in the second dimension, as indicated. Figure S5. 2D plots obtained under the optimum separation conditions for the grape samples of different ripening degree (W1 to W10)., Peer reviewed