BUSCADOR RECOLECTA

Data acquisition: Satellite: ESA SMOS mission (Soil Moisture and Ocean Salinity). Filenames: BEC_SM____SMOS__GLO_L3__X_YYYYMMDDTHHMMSS_025km_TT_____v4.0.nc, being: - X the half-orbit type (A for ascending and D for descending), - YYYYMMDDTHHMMSS the central date (year, month, day, hour, minute and second) in Coordinated Universal Time (UTC) of the period covered by the file, - TT: indicates the temporal coverage of the data file (1d, 3d, 9d, 1m and 1y for daily, 3 days, 9 days, 1 month and 1 year, respectively). Sensor: Satellite SMOS / MIRAS. Spatial resolution: 25 km x 25 km. Spatial grid: WGS_84 / EASE2_M25km, Improvement of the current SMOS soil moisture products produced by the Barcelona Expert Centre (BEC) and development of new added-value products and/or applications over land, INTERACT. Enfoques sinergéticos para una nueva generación de productos y aplicaciones de observación de la Tierra (PID2020-114623RB-C31). Ministerio de Ciencia e Innovación a través de PN2020 - PROY I+D+I – Programa Estatal de I+D+I Orientada a los Retos de la Sociedad – Plan Estatal de Investigación Científica Técnica y de Innovación 2017-2020, · Surface soil moisture (SM) · Data quality index of surface soil moisture (SM_DQX) · Variance of surface soil moisture (SM_VARIANCE) · Number of L2 soil moisture measures (N_SM) · Vegetation optical depth at nadir (VOD) · Data quality index of vegetation optical depth at nadir (VOD_DQX) · Variance of vegetation optical depth at nadir (VOD_VARIANCE) · Number of L2 vegetation optical depth measures (N_VOD) · Time (time) · Latitude (lat) · Longitude (lon) · Coordinate reference system (crs), Peer reviewed

Data acquisition: Satellite: ESA SMOS mission (Soil Moisture and Ocean Salinity), ECMWF skin temperature at 12 UTC and 16-dayTerra MODIS NDVI collection 6. Filenames: BEC_SM____SMOS__EUM_L4__X_YYYYMMDDTHHMMSS_001km_TT_REP_v6.0.nc, being: - X the half-orbit type (A for ascending and D for descending), - YYYYMMDDTHHMMSS the central date (year, month, day, hour, minute and second) in Coordinated Universal Time (UTC) of the period covered by the file, - TT: indicates the temporal coverage of the data file (1d for daily and 3d for 3 days). Sensor: Satellite SMOS / MIRAS. Spatial resolution: 1 km x 1 km. Spatial grid: WGS_84 / EASE2_M01km, Improvement of the current SMOS soil moisture products produced by the Barcelona Expert Centre (BEC) and development of new added-value products and/or applications over land, INTERACT. Enfoques sinergéticos para una nueva generación de productos y aplicaciones de observación de la Tierra (PID2020-114623RB-C31). Ministerio de Ciencia e Innovación a través de PN2020 - PROY I+D+I – Programa Estatal de I+D+I Orientada a los Retos de la Sociedad – Plan Estatal de Investigación Científica Técnica y de Innovación 2017-2020, · Surface soil moisture (SM) · Quality flag of surface soil moisture (quality_flag) · Number of L4 measures (N) · Time (time) · Latitude (lat) · Longitude (lon) · Coordinate reference system (crs), Peer reviewed

Data acquisition: Satellite: ESA SMOS mission (Soil Moisture and Ocean Salinity), ECMWF skin temperature at 12 UTC and NRT 8-rolling day Terra MODIS NDVI collection 6. Filenames: BEC_SM____SMOS__EUM_L4__X_YYYYMMDDTHHMMSS_001km_TT_NRT_v6.0.nc, being: - X the half-orbit type (A for ascending and D for descending), - YYYYMMDDTHHMMSS the central date (year, month, day, hour, minute and second) in Coordinated Universal Time (UTC) of the period covered by the file, - TT: indicates the temporal coverage of the data file (1d for daily and 3d for 3 days). Sensor: Satellite SMOS / MIRAS. Spatial resolution: 1 km x 1 km. Spatial grid: WGS_84 / EASE2_M01km, Improvement of the current SMOS soil moisture products produced by the Barcelona Expert Centre (BEC) and development of new added-value products and/or applications over land, INTERACT. Enfoques sinergéticos para una nueva generación de productos y aplicaciones de observación de la Tierra (PID2020-114623RB-C31). Ministerio de Ciencia e Innovación a través de PN2020 - PROY I+D+I – Programa Estatal de I+D+I Orientada a los Retos de la Sociedad – Plan Estatal de Investigación Científica Técnica y de Innovación 2017-2020, · Surface soil moisture (SM) · Quality flag of surface soil moisture (quality_flag) · Number of L4 measures (N) · Time (time) · Latitude (lat) · Longitude (lon) · Coordinate reference system (crs), Peer reviewed

SAMIpH_Database_2012_2017.csv provides data for the years 2012 and 2017.DATE_UTC and TIME_UTC are the day and time at which the measurement was taken, in UTC. pHConstSal35 is the pH at a constant salinity of 35. TEMPERATURE and SALINITY are the water temperature (in degrees Celsius) and the water salinity (practical salinity units, i.e., no units) respectively. SAMICO2_Database_2013_2017.txt provides data between the years 2013 and 2017. DATE are the day and time at which the measurement was taken, in UTC and CO2 is the in situ pCO2 value (in uatm) recorded by the SAMI device, The database provides measurements of the carbon system parameters pH and pCO2 obtained with SAMI sensors (Sunburst Sensors, LLC) attached to a mooring line deployed in the Strait of Gibraltar between the years 2012 and 2017. Temperature and salinity data were obtained with a Conductivity-Temperature probe (CT Seabird SBE37-SMP) also installed in the line. Sampling interval was initially set to 60 min, but a battery run off happened in summer 2013 (which caused a six-month data gap) advised changing the interval to 120min to extend the battery life., This research was supported by the COMFORT project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 820989 (project COMFORT, "Our common future ocean in the Earth system – quantifying coupled cycles of carbon, oxygen, and nutrients for determining and achieving safe operating spaces with respect to tipping points).” Funding was also provided by grant EQC2018-004285-P funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”., Peer reviewed

[Method] Splicing functional assays of PALB2 variants by hybrid minigenes. Protocol Workflow: 1) Minigene construction. 2) Site-directed mutagenesis. 3) Cell Transfection. 4) RNA Isolation. 5) RT-PCR. 6) Transcript characterization (Sanger sequencing and fluorescent fragment electrophoresis)., This dataset corresponds to a comprehensive splicing analysis of splice-site variants of exons 1 to 3 of the breast cancer susceptibility gene PALB2. These variants were reported at the ClinVar database. Loss-of-function variants at the PALB2 gene are known to confer risk to breast and ovarian cancers. A total of 31 PALB2 variants at the intron/exon boundaries were analyzed with MaxEntScan. Sixteen variants were selected and genetically engineered into a PALB2 splicing reporter minigene. We found that 12 variants disrupted splicing and six of them could be classified as likely pathogenic. Hence, they are clinically actionable findings so variant-carriers may benefit from tailored prevention protocols and therapies., This work was supported by grants from the Spanish Ministry of Science and Innovation, Plan Nacional de I+D+I 2013-2016, ISCIII (PI20/00225) co-funded by FEDER from Regional Development European Funds (European Union) and from the Consejería de Educación, Junta de Castilla y León, ref. CSI242P18 (actuación cofinanciada P.O. FEDER 2014-2020 de Castilla y León)., Peer reviewed

File1: VERDI_samples_parameters.xlsx - Physico-chemical variables obtained from CTD profile - Inorganic nutrients concentrations - Chlorophyll-a concentrations - Organic carbon and nitrogen concentrations - Phytoplankton abundances - Detections of chytrids File 2: VERDI_asv_table.tbl: ASV abundances from natural samples and incubations File 3: VERDI_tax_table.tbl: Taxonomic assignments of ASVs File 4: VERDI_asv_seqs.fa: Sequences of ASVs File 5: VERDI_incubations_images.zip - Compilation of images taken during incubations with diatoms, The presence of phytoplankton parasites along the water column was explored at the Long Term Ecological Station MareChiara (LTER-MC) in the Gulf of Naples (Mediterranean Sea) in October 2019. Microscopy analyses showed diatoms dominating the phytoplankton community in the upper layers (0-20 m). Metabarcoding data from the water column showed the presence of Chytridiomycota predominantly in the upper layers coinciding with the vertical distribution of diatoms. Laboratory incubations of natural samples enriched with different diatom cultures confirmed parasitic interactions of some of those chytrids – including members of Kappamyces – with diatom taxa. The temporal dynamics of diatoms and chytrids was also explored in a three-year metabarcoding time-series (2011-2013) from surface waters of the study area and in sediment samples. Chytrids were recurrently present at low relative abundances, and some taxa found to infect diatoms in the incubation experiments were also identified in the ASV time-series. However, co-occurrence analyses did not show any clear or recurrent pairing patterns for chytrid and diatom taxa along the three years. The chytrid community in the sediments showed a clearly different species composition compared to the recorded in the water column samples, with higher diversity and relative abundance. The combination of observations, incubations and metabarcoding confirmed that parasites are a common component of marine protist communities at LTER-MC. Host-parasite interactions must be determined and quantified to understand their role and the impact they have on phytoplankton dynamics, - European Union’s Horizon 2020 research and innovation programme under grant agreement No 730984, ASSEMBLE Plus project. - Spanish MICINN Project SMART (PID2020-112978GB-I00) - The research program LTER-MC is funded by the Stazione Zoologica Anton Dohrn, - Physico-chemical variables obtained from CTD profile - Inorganic nutrients concentrations - Chlorophyll-a concentrations - Organic carbon and nitrogen concentrations - Phytoplankton abundances - Detections of chytrids - Metabarcoding ASV abundances from natural samples and incubations - Metabarcoding Taxonomic assignments of ASVs - Metabarcoding Sequences of ASVs - Compilation of images taken during incubations with diatoms, Peer reviewed

Table of contents: zip file containing 7 folders: Figure 2 folder [Figure 2_Blots Report.pdf; Figure_2C_qPCR.xlsx; Figure_2D_qPCR.xlsx; Figure_2E&G_Data&Analysis.xlsx; Figure_2F_Data&Analysis.xlsx] Figure 3 folder [Figure 3_Blots Report.pdf; Figure_3B_Data&Analysis.xlsx; Figure_3C_Data&Analysis.xlsx] Figure 4 folder [Figure 4_Blots Report.pdf;Figure_4A_Data&Analysis.xlsx; Figure_4B_Data&Analysis.xlsx; Figure_4C&D_Data&Analysis.xlsx] Figure 5 folder [Figure 5_Blots Report.pdf; Figure_5A_Data&Analysis.xlsx; Figure_5B_Data&Analysis.xlsx; Figure_5C-D_Data&Analysis.xlsx; Figure_5E-F_Data&Analysis.xlsx] Figure 6 folder [Figure 6_Blots Report.pdf; Figure_6B_Data&Analysis.xlsx; Figure_6C_Data&Analysis.xlsx; Figure_6D_Data&Analysis.xlsx] Figure 7 folder [Figure 7_Blots Report.pdf; Figure_7B_Oxyblot_Data&Analysis.xlsx; Figure_7B_qPCR_Data&Analysis.xlsx; Figure_7B_WesternBlotting_Data&Analysis.xlsx; Figure_7C_Data&Analysis.xlsx; Figure_7D_Data&Analysis.xlsx; Figure_7E_Data&Analysis.xlsx] Supplementary Material folder [Figure_S2_Data&Analysis.xlsx], MCIN/AEI/10.13039/ 501100011033 THEARPY-PID2020-115274RB-I00; 0551_PSL_6_E POCTEP FGCSIC/ PSL-INTERREG/FEDER NITROPROHEAR, Peer reviewed

Data file includes information about identity of each sampled individual, the species id, nest type, age, biometric measurements, the mean of the antagonistic halos shown against each 9 indicator bacteria, the antagonistic activity (average values of the width of antagonistic halos (mm) when tested against each of the nine indicator bacteria), the antagonistic range (Shannon index of the antagonistic activity) and the total density of bacteria on the gland., Ester Martínez Renau was financed by a predoctoral contract PRE2018-085378) while the whole research group received funds from the projects CGL2017-83103-P, PID2020-117429GB-C21 and PID2020-117429GB-C22, funded by the Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación/10.13039/501100011033 and by “Fondo Europeo de Desarrollo Regional, a way of making Europe, Peer reviewed

[Description of methods used for collection/generation of data] In both stations a SAMI-pH (Sunburst Sensors LCC) was attached, at 1 m in the Bay of Palma and at 4 m depth in Cabrera. The pH sensors were measuring pH, in the total scale (pH𝑇T), hourly since December 2018 in the Bay of Palma and since November 2019 in Cabrera. The sensor precision and accuracy are < 0.001 pH and ± 0.003 pH units, respectively. Monthly maintenance of the sensors was performed including data download and surface cleaning. Temperature and salinity for the Cabrera mooring line was obtained starting November 2019 with a CT SBE37 (Sea-Bird Scientific©). Accuracy of the CT is ± 0.002 ∘C for temperature and ± 0.003 mS cm−1−1 for conductivity. Additionally, oxygen data from a SBE 63 (Sea-Bird Scientific ©) sensor attached to the CT in Cabrera were used. Accuracy of oxygen sensors is ± 2% for the SBE 63., [Methods for processing the data] Periodically water samplings for dissolved oxygen (DO), pH in total scale at 25 ∘C (pH𝑇25) and total alkalinity (TA) were obtained during the sensor maintenance campaigns. DO and (pH𝑇25) samples were collected in order to validate the data obtained by the sensors. DO concentrations were evaluated with the Winkler method modified by Benson and Krause by potentiometric titration with a Metrohm 808 Titrando with a accuracy of the method of ± 2.9 μmol kg−1μmol kg−1 and with an obtained standard deviation from the sensors data and the water samples collected of ± 5.9 μmol kg−1μmol kg−1. pH𝑇25T25 data was obtained by the spectrophotometric method with a Shimadzu UV-2501 spectrophotometer containing a 25 ∘C-thermostated cells with unpurified m-cresol purple as indicator following the methodology established by Clayton and Byrne by using Certified Reference Material (CRM Batch #176 supplied by Prof. Andrew Dickson, Scripps Institution of Oceanography, La Jolla, CA, USA). The accuracy obtained from the CRM Batch was of ± 0.0051 pH units and the precision of the method of ± 0.0034 pH units. The mean difference between the SAMI-pH and discrete samples was of 0.0017 pH units., Funding for this work was provided by the projects RTI2018-095441-B-C21 (SuMaEco) and, the BBVA Foundation project Posi-COIN and the Balearic Islands Government projects AAEE111/2017 and SEPPO (2018). SF was supported by a “Margalida Comas” postdoctoral scholarship, also from the Balearic Islands Government. FFP was supported by the BOCATS2 (PID2019-104279GB-C21) project funded by MCIN/AEI/10.13039/501100011033.This work is a contribution to CSIC’s Thematic Interdisciplinary Platform PTI WATER:iOS., Peer reviewed

[Description of methods used for collection/generation of data] Data was acquired in both stations using a SAMI-pH (Sunburst Sensors LCC) was attached, at 1 m in the Bay of Palma and at 4 m depth in Cabrera. The pH sensors were measuring pH, in the total scale (pH𝑇), hourly since December 2018 in the Bay of Palma and since November 2019 in Cabrera. The sensor precision and accuracy are < 0.001 pH and ± 0.003 pH units, respectively. Monthly maintenance of the sensors was performed including data download and surface cleaning. Temperature and salinity for the Cabrera mooring line was obtained starting November 2019 with a CT SBE37 (Sea-Bird Scientific©). Accuracy of the CT is ± 0.002 ∘C for temperature and ± 0.003 mS cm−1−1 for conductivity. Additionally, oxygen data from a SBE 63 (Sea-Bird Scientific ©) sensor attached to the CT in Cabrera were used. Accuracy of oxygen sensors is ± 2% for the SBE 63., [Methods for processing the data] Once data (available at https://doi.org/XXX/DigitalCSIC/XXX) was validated, several processing steps were performed to ensure an optimal training process for the neural network models. First, all the data of the time series were re-sampled by averaging the data points obtaining a daily frequency. Afterwards, a standard feature-scaling procedure (min-max normalization) was applied to every feature (temperature, salinity and oxygen) and to pHT. Finally, we built our training and validations sets as tensors with dimensions (batchsize, windowsize, 𝑁features), where batchsize is the number of examples to train per iteration, windowsize is the number of past and future points considered and 𝑁features is the number of features used to predict the target series. Temperature values below 𝑇=12.5T=12.5 °C were discarded as they are considered outliers in sensor data outside the normal range in the study area. A BiDireccional Long Short-Term Memory (BD-LSTM) neural network was selected as the best architecture to reconstruct the pHT time series, with no signs of overfitting and achieving less than 1% error in both training and validation sets. Data corresponding to the Bay of Palma were used in the selection of the best neural network architecture. The code and data used to determine the best neural network architecture can be found in a GitHub repository mentioned in the context information., Funding for this work was provided by the projects RTI2018-095441-B-C21, RTI2018-095441-B-C22 (SuMaEco) and Grant MDM-2017-0711 (María de Maeztu Excellence Unit) funded by MCIN/AEI/10.13039/501100011033 and by the “ERDF A way of making Europe", the BBVA Foundation project Posi-COIN and the Balearic Islands Government projects AAEE111/2017 and SEPPO (2018). SF was supported by a “Margalida Comas” postdoctoral scholarship, also from the Balearic Islands Government. FFP was supported by the BOCATS2 (PID2019-104279GB-C21) project funded by MCIN/AEI/10.13039/501100011033.This work is a contribution to CSIC’s Thematic Interdisciplinary Platform PTI WATER:iOS (https://pti-waterios.csic.es/)., Peer reviewed