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This Excel file includes the data and tracked calculations of the manuscript entitled "Sponge contribution to the silicon cycle of a diatom-rich shallow bay". It includes 7 spreadsheets with the following contents: - READ ME - Standing STOCK living sponges - Sponge Si consumption FLUX - Si RESERVOIR in sediments - Sponge Si FLUXES in sediments - DIATOM Si fluxes&stocks (Fig.5) - Calculations for discussion, This research was supported by: - the Spanish Ministry grants CTM2015-67221-R and MICIU: #PID2019-108627RB-I00 to Manuel Maldonado - the grant 12735 – AO2020 of the French National research program EC2CO to Jacques Grall - the ISblue project, Interdisciplinary graduate school for the blue planet (ANR-17-EURE-0015), co-funded by a grant from the French government under the program "Investissements d'Avenir", and the “Xunta de Galicia” postdoctoral grant IN606B-2019/002 to María López-Acosta., Peer reviewed

This Excel file includes the metadata of the survey of the predation activity of the nudibranch Doris verrucosa on the sponge Hymeniacidon perlevis, This research was supported by: - the grant 12735 – AO2020 of the French National research program EC2CO - the ISblue project, Interdisciplinary graduate school for the blue planet (ANR-17-EURE-0015), co-funded by a grant from the French government under the program "Investissements d'Avenir", and the “Xunta de Galicia” postdoctoral grant IN606B-2019/002, No

[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

[Description of methods used for collection/generation of data] Periodically water sampling for dissolved oxygen (DO) and total alkalinity (TA) was done during the sensor maintenance campaigns of BOATS. In two sites, monthly samples were collected from the same depth as the sensors of the BOATS stations, at 1m at the oceanographic buoy, and 4m in PN Cabrera. Samples were taken for dissolved methane (CH4), dissolved oxygen (DO), total alkalinity (TA), dissolved organic carbon (DOC), Chlorophyll a (Chl a), In both stations monthly discrete samples were collected, at 1 m in the Bay of Palma, Surface for Cap ses Salines and at 4 m depth in Cabrera. Samples were collected by submerging a hose connected to a pump at the sensor height of the BOATS stations in Cabrera and the Bay of Palma. At the third site, the lighthouse of Cap ses Salines, a bucket was used to obtain surface water samples., readme provides background information for xlsx datafiles., Methane (CH4) gas is the most important greenhouse gas (GHG) after carbon dioxide, with open ocean areas acting as discreet CH4 sources and coastal regions as intense but variable CH4 sources to the atmosphere. In this database we report measured CH4 concentrations and calculated air-sea fluxes in three sites of the coastal area of the Balearic Islands Archipelago (Western Mediterranean Basin). CH4 levels and related biogeochemical variables were measured in three coastal sampling sites between 2019 and 2021. CH4 concentrations in seawater ranged from 2.7 to 10.9 nM, without significant differences between the sampling sites. Averaged estimated CH4 fluxes during the sampling period for the three stations oscillated between 0.2 and 9.7 μmol m−2 d−1 following a seasonal pattern and in general all sites behaved as weak CH4 sources throughout the sampling period., 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 project SEPPO (PRD2018/18). This work is a contribution to CSIC’s Thematic Interdisciplinary Platform PTI OCEANS+., Peer reviewed

This dataset reports for the classical rosette bottle data for the following Essential Ocean Variables: salinity, dissolved oxygen, total alkalinity, pH, nitrates and silicic acid. It also reports on the measurements of the CTD-O2 probe at the bottle levels: pressure, temperature, salinity and dissolved oxygen, The GEOVIDE cruise was carried out coast to coast between Portugal and Newfoundland via the south tip of Greenland, following the OVIDE line in the eastern part and crossing the Labrador Sea in the western part. The classical hydrographic rosette was cast 163 times at 78 different geographical positions called stations. While the CTD-O2 probe acquired continuous profiles of the “physical” variables (pressure, temperature, salinity and dissolved oxygen), 22 Niskin bottles were closed at different levels during the upcast to provide samples for biogeochemical analysis. After calibration, we find precisions for pressure, temperature, salinity and dissolved oxygen that fit the GO-SHIP international quality requirements. In parallel, but not simultaneously, a trace-metal rosette (TMR) was cast 53 times, also acquiring profiles of physical variables, and equipped with 24 Go-Flo bottles adapted for the sampling of trace metals. Depending on the number of operations, stations were identified as “Short” (one single CTD cast), “Large” (3 CTD casts), “XLarge” (up to 6) and “Super” (up to 11). All along the track of the ship, current magnitude and direction was measured by Ship Acoustic Doppler Current Profilers, down to 1000m depth, No

The GEOVIDE cruise was carried out coast to coast between Portugal and Newfoundland via the south tip of Greenland, following the OVIDE line in the eastern part and crossing the Labrador Sea in the western part. The classical hydrographic rosette was cast 163 times at 78 different geographical positions called stations. While the CTD-O2 probe acquired continuous profiles of the “physical” variables (pressure, temperature, salinity and dissolved oxygen), 22 Niskin bottles were closed at different levels during the upcast to provide samples for biogeochemical analysis. After calibration, we find precisions for pressure, temperature, salinity and dissolved oxygen that fit the GO-SHIP international quality requirements. In parallel, but not simultaneously, a trace-metal rosette (TMR) was cast 53 times, also acquiring profiles of physical variables, and equipped with 24 Go-Flo bottles adapted for the sampling of trace metals. Depending on the number of operations, stations were identified as “Short” (one single CTD cast), “Large” (3 CTD casts), “XLarge” (up to 6) and “Super” (up to 11). All along the track of the ship, current magnitude and direction was measured by Ship Acoustic Doppler Current Profilers, down to 1000m depth. This dataset reports for the water mass proportions (from 0 to 1, i.e., from 0 to 100%) for the classical rosette for the following water masses: East North Atlantic Central Water of 16ºC (ENACW16) and of 12 ºC (ENACW12); Subpolar Mode Water of 8ºC (SPMW8), of 7ºC (SPMW7) and of the Irminger Sea (IrSPMW); Labrador Sea Water (LSW); Subarctic Intermediate Water of 6ºC (SAIW6) and of 4ºC (SAIW4); Mediterranean Water (MW); Iceland–Scotland Overflow Water (ISOW); Denmark Strait Overflow Water (DSOW); Polar Intermediate Water (PIW); and North-East Atlantic Deep Water lower (NEADWL). The dataset also contains the changes in oxygen due to the remineralisation of organic matter (DO2bio; in µmol kg-1)., No

6 files, This dataset consists of the estimated decadal changes in the oceanic content of anthropogenic CO2 (∆Cant) between 1994, 2004 and 2014 as described in detail in Müller et al. (2023, in press, AGU Advances). These estimates have been derived from the GLODAPv2.2021 product (Lauvset et al., 2021) using the eMLR(C*) method developed by Clement & Gruber (2018). The datasets contain in addition to the standard estimate also 10 sensitivity cases, which are intended to assess the robustness of the standard estimates to different changes in the estimation procedure. All estimates are provided on a horizontal grid with 1° x 1° resolution. Two primary files are provided: one containing the full three-dimensional distribution of ∆Cant and one containing the vertically integrated values, i.e., the column inventories, 821003 - Climate-Carbon Interactions in the Coming Century (EC) 821001 - Southern Ocean Carbon and Heat Impact on Climate (EC), Peer reviewed

-Dataset-/n/n The dataset is a collection of 133 audio percussion patterns spanning five different pattern classes as described below. The scores for the patterns and additional details about the patterns are at: http://compmusic.upf.edu/bo-perc-patterns/n/n-Audio Content-/n/nThe audio files are short segments containing one of the above mentioned patterns. The audio is stereo, sampled at 44.1 kHz, and stored as wav files. The segments were chosen from the introductory parts of arias. The recordings of arias are from commercially available releases spanning various artists. The audio and segments were chosen carefully by a musicologist to be representative of the percussion patterns that occur in Jingju. The audio segments contain diverse instrument timbres of percussion instruments (though the same set of instruments are played, there can be slight variations in the individual instruments across different ensembles), recording quality and period of the recording. Though these recordings were chosen from introductions of arias where only percussion ensemble is playing, there are some examples in the dataset where the melodic accompaniment starts before the percussion pattern ends. /n/n-Annotations-/n/nEach of the audio patterns has an associated syllable level transcription of the audio pattern. The transcription is obtained from the score for the pattern and is not time aligned to the audio. The transcription is done using the reduced set of five syllables described in Table 1 of [1] and is sufficient to computationally model the timbres of all the syllables. The annotations are stored as Hidden Markov Model Toolkit (HTK) label files. There is also a single master label file provided for batch processing using HTK (http://htk.eng.cam.ac.uk/). /n/n-Dataset organization-/n/nThe dataset has wav files and label files. The files are named as /n./nThe pID is as in Table 1, instID is a three digit identifier for the specific instance of the pattern, and extension can be .wav for the audio file or .lab for the label file. pID ϵ {10, 11, 12, 13, 14}, InstID ϵ {1, 2, ..., NpID}. e.g. The audio file and the label file for the fifth instance of the pattern duotuo is named 12005.wav and 12005.lab, respectively. The master label file is called masterLabels.lab/n/n-Availability of the Dataset-/n/n The annotations are publicly shared and available to all. The audio is from commercially available releases. It cannot be publicly shared but can be made available on request for non-commercial research purposes. In the future, the dataset would be available for viewing and download through an interface in Dunya (http://dunya.compmusic.upf.edu). Beijing Opera Percussion Pattern (BOPP) dataset is a collection of 133 audio percussion patterns covering five pattern classes. The dataset includes the audio and syllable level transcriptions for the patterns (non-time aligned). It is useful for percussion transcription and classification tasks. The patterns have been extracted from audio recordings of arias and labeled by a musicologist.

Beijing Opera percussion dataset is a collection of 236 examples of isolated strokes spanning the four percussion instrument classes used in Beijing Opera. It can be used to build stroke models for each percussion instrument. /nAll the sounds in this pack were played by Ying Wan of the London Jing Kun Opera Association. Recorded by Mi Tian at the Centre for Digital Music, Queen Mary University of London, UK in September 2013 using an AKG C414 microphone under studio conditions.