Resultados de investigación

Raw data from which all the analysis and paper results have been generated., Ibero-American scholarship program (2018-2019) of Banco Santander. “Technological innovations for the management and improvement of the quality and health of banana soils in Latin America and the Caribbean” financed by FONTAGRO and coordinated by Bioversity International. .SHui project funded by the European Commission (GA 773903)., Peer reviewed

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 800617., Ver "AriMov_Leeme.txt", Peer reviewed

"AAzCam" es el acrónimo de un juego de datos construido para investigar la morfogénesis de las redes viales, desde la época prehispánica hacia la actualidad, en la cuenca alta de Azapa (región de Arica y Parinacota, Chile)., Programme Altos Arica, Ministère de l'Europe et des Affaires Etrangères de la république francaise. MSCA800617 UE H2020. Fondecyt 11121665. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 800617., ver archivo "AAzCam_Leeme.txt", Peer reviewed

The dataset includes information on little bustard abundance over 10 years in relation to foraging, nesting habitat suitability and urban land, as well as information used to calculate habitat suitablity., Peer reviewed

Data information Meteorological variables, rainfall amount, throughfall amount, rainfall and throughfall isotopic composition and rainfall and throughfall drop size distributions. Open area and Pinus sylvestris L forest in Vallcebre research catchments. Study period: from May 2018 to July 2019. Data structure Date and time format • dd/mm/yyyy: day/month/year. • hh:mm:ss: hour/minutes/seconds Meteorological variables • Temp (°C): Air temperature (°C) • RH (%): Relative humidity (%) • Rn (W m-2): Net radiation (W m-2) • u (m s-1): Wind speed (m s-1) • Wind dir (°): Wind direction (°) Hydrometric variables • Rainfall (mm): Rainfall volume (mm) • Throughfall (mm): Throughfall volume (mm) Isotopic data • ISCO_sample: Sample number recorded by the ISCO sampler • RF_δ18O (‰): Rainfall oxygen-isotopic composition (‰) • RF_δ2H (‰): Rainfall deuterium-isotopic composition (‰) • TF_δ18O (‰): Throughfall oxygen-isotopic composition (‰) • TF_δ2H (‰): Throughfall deuterium-isotopic composition (‰) Drop size distribution data • RF_N drops: Number of recorded rainfall drops • RF_Vel mean: Mean velocity of rainfall drops (m s-1) • RF_nKE: Rainfall kinetic energy (J m-2) • RF-0.1 to RF-10.0: Rainfall volume (mm) for 0.1 mm drop size class to rainfall volume (mm) for 10 mm drop size class. • TF_N drops: Number of recorded throughfall drops • TF_Vel mean: Mean velocity of throughfall drops (m s-1) • TF_nKE: Throughfall kinetic energy (J m-2) • TF-0.1 to TF-10.0: Throughfall volume (mm) for 0.1 mm drop size class to Throughfall volume (mm) for 10 mm drop size class “N/D” indicated no available data and “Discarded samples” indicated the water isotope samples that have been discarded from the analysis because the (rainfall or throughfall) water samples contained mixed water either from pre- or post-event, therefore, they corresponds to the first or last samples of certain events., Throughfall isotopic composition and drop size distributions in a Mediterranean mountain forest (Scots pine stand)., Peer reviewed

This work assessed the significance of the taxonomic composition and/or metabolic activity of obese- microbiota by massive 16S rRNA gene sequencing of the faecal microbiome of obese and normoweight individuals. A total of 26 human faecal samples were analysed in this study. Faeces were collected from 13 normoweight (N) (body mass index = 18 - 25 kg m2) and 13 obese (O) adult volunteers (body mass index > 30 kg m2). All volunteers gave written informed consent to the protocol, which had been approved by the Clinical Ethics Committee of Hospital Ramón y Cajal with the code 394/14 and Spanish Council of Scientific Research (CSIC; Spain). Stool samples were collected under aseptic conditions in sterile and screw-top containers and immediately stored at –80°C. The taxonomic composition of the fecal microbiota of obese (Om) and normal weight (N) individuals was determined by massive sequencing using the Illumina Seq platform. Genomic DNA was extracted from faecal samples previously thawed at room temperature following the protocol described by Moles et al. (2013). DNA samples were subjected for massive 16S rDNA gene V3-V4 amplicon sequencing on the Ilumina MiqSeq platform. The primers were selected from previously described ones (Klindworth et al., 2013). Quality assessment was performed by the use of prinseq-lite program (Schmieder, et al., 2011) applying following parameters: min_length: 50; trim_qual_right: 30; trim_qual_type: mean; trim_qual_window:20; R1 and R2 from Illumina sequencing where joined using FLASH program (Magoc, et al., 2011) applying default parameters., We acknowledge financial support from the Spanish Ministry of Science and Innovation (AGL2016-75951-R)., Sequence Data: Trimmed and cleaned reads for each sample, according to parameters described in Methods section. When a sequence did not fit the minimum length or quality is removed from the dataset. This will generate a singleton file containing those reads without their pair.-- N denotes sequence data of faecal samples from normoweight individuals; OM denotes sequence data of faecal samples from obese individuals., Peer reviewed

(1) Five Fiels with Data obtained by computing numerical simulations. DSA results were performed in a linked code of TRACONF (Carrera et al. 1993) and CHEPROO (Bea et al. 2009). WMA results were performed by a self-made code based on TRACONF_CHEPROO original code. - Data in "FIG_4_half_domain_inj": SHEET 1 "u_data (fig 4b)": cross section of component at diferent node columns SHEET 2 "r_data (fig 4a)": cross section of reaction rates at diferent node columns - Data in "FIG_5a_CAL_equil_accur": Accuracy performance for WMA and DSA for CAL equilibrium case. Check the Numerical dispersion effect between models of just different time steps A3: Data case information A19: DSA data I19: WMA data - Data in "FIG_5b_CAL_kin_accur": Accuracy performance for WMA and DSA for CAL kinetic case. Check the Numerical dispersion effect between models of just different time steps Idem than previous - Data in "TABLE_3a_CAL_equil_cpu": Comparison between CPU performance between DSA and WMA algorithms on CAL EQUILIBRIUM case A4: CPU computed from numerical simulations A15: Obtain the constants of each step calculation A23: CPU calculated analiticaly (NOT shown in this versiona as a Figure) - Data in "TABLE_3b_CAL_kin_cpu": Comparison between CPU performance between DSA and WMA algorithms on CAL KINETIC case Idem than previous (2) Two files with computed models: - Data in "EXAMPLE_of_M": Easy 1D example of 5 nodes to understand the difference between classic (sheet "classic ADE solution") solutions and WMA solution (sheet "WMA solution") One can set the value (between 0 and 1) of B5 cell at first sheet "Data" to see how the matrix M looks (cells M3:Q7) - Data in "APPENDIX_C_RT_Pulse_2d_WMA": Comparison between analytical solution (De Simoni et al., 2005) and WMA results of 2D model after pulse injection of mass M, Five files with data obtained by computing numerical simulations and two files with computed numerical models, Peer reviewed

Data obtained by computing numerical simulations. RW results were performed by a self-made Python code. WMA results were performed by a self-made code modifying the original TRACONF (Carrera et al. 1993) linked with CHEPROO (Bea et al. 2009). MAWMA results were performed by a self-made application of framework platform KRATOS (Dadvand et al. 2010) - Data in "Fig 3": Comparison of acccuracy performances between RW, WMA and MAWMA methods. A1: Information data about the problem A8: Dissipation rate results A23: Dispersion results - Data in "Analytical_solution (Dentz and Carrera 2007)": Calculation of the analytic solution of aparent dispersion, Data obtained by computing numerical simulations. RW results were performed by a self-made Python code. WMA results were performed by a self-made code modifying the original TRACONF (Carrera et al. 1993) linked with CHEPROO (Bea et al. 2009). MAWMA results were performed by a self-made application of framework platform KRATOS (Dadvand et al. 2010), Peer reviewed

- Data acquisition Satellite: ESA SMOS mission (Soil Moisture and Ocean Salinity). - Time coverage: 1 January, 2011 - 31 December, 2019. - Time resolution 1 day. - Maps frequency generation: Daily.- Spatial coverage. - Latitude range: 90ºS-90ºN Longitude range: 180ºW-180ºE. - Spatial resolution: 0.05ºx0.05º. - Spatial projectionWGS 84: Latitude-longitude projection WGS84. - Spatial grid: Latitude and longitude regular grid at 0.05 degrees. - Sensor: Satellite SMOS / MIRAS. - Source product: SMOS Level 1B (brightness temperature Fourier coefficients) v 621 provided by ESA. - Format NetCDF Climate and Forecast (CF) Metadata Conventions version 1.6. - Size 303 Gb, BEC ftp service: We serve netCDF data by means of a secure ftp server. NetCDF files from which the maps were made (and other additional data) can be downloaded from sftp address becftp.icm.csic.es at port 27500. If your browser is sftp compatible you can browse directly from sftp://becftp.icm.csic.es:27500 address. In order to download data you should be registered in our BEC ftp service. Registration is free, you can register just by filling the following form: https://bec.icm.csic.es/data-access-ftp/ If you need a dedicated ftp client (for instance FileZilla https://filezilla-project.org/) you should use the following configuration: Host: sftp://becftp.icm.csic.es username: your username password: your password Port: 27500, Develop a new algorithm and novel approaches with the aim of producing the best quality global satellite SSS product using SMOS data, Project LAMBDA 66-SE-CMEMS-Call2 - LOT3. Mercator Ocean under contract number 5173 from 01/04/2018 66-SELAMBDA LOT3, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Level 4 sea surface salinity, Peer reviewed

- Data acquisition Satellite: ESA SMOS mission (Soil Moisture and Ocean Salinity). - Time coverage: 1 January, 2011 - 31 December, 2019. - Time resolution 9 days. - Maps frequency generation: Daily.- Spatial coverage. - Latitude range: 90ºS-90ºN Longitude range: 180ºW-180ºE. - Spatial resolution: 0.25ºx0.25º. - Spatial projectionWGS 84: Latitude-longitude projection WGS84. - Spatial grid: Latitude and longitude regular grid at 0.25 degrees. - Sensor: Satellite SMOS / MIRAS. - Source product: SMOS Level 1B (brightness temperature Fourier coefficients) v 621 provided by ESA. - Format NetCDF Climate and Forecast (CF) Metadata Conventions version 1.6. - Size 52.5 Gb, BEC ftp service: We serve netCDF data by means of a secure ftp server. NetCDF files from which the maps were made (and other additional data) can be downloaded from sftp address becftp.icm.csic.es at port 27500. If your browser is sftp compatible you can browse directly from sftp://becftp.icm.csic.es:27500 address. In order to download data you should be registered in our BEC ftp service. Registration is free, you can register just by filling the following form: https://bec.icm.csic.es/data-access-ftp/ If you need a dedicated ftp client (for instance FileZilla https://filezilla-project.org/) you should use the following configuration: Host: sftp://becftp.icm.csic.es username: your username password: your password Port: 27500, Develop a new algorithm and novel approaches with the aim of producing the best quality global satellite SSS product using SMOS data, Project LAMBDA 66-SE-CMEMS-Call2 - LOT3. Mercator Ocean under contract number 5173 from 01/04/2018 66-SELAMBDA LOT3, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), High resolution sea surface salinity; Low resolution sea surface salinity; Sea surface salinity uncertainty, Peer reviewed