BUSCADOR RECOLECTA

[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

1 table. -- Excel file includes multiple sheets. -- Original file: contains the originally extracted articles from the OA-ICC database. it contains 564 items with many duplications and articles not related to the Mediterranean. -- Edited file: Missing items from the OA-ICC database (without key) were added. The duplications, non-Mediterranean and non-existent items were removed. We have 534 items as a final outcome., Ocean acidification (OA) is a serious consequence of climate change with complex organism-to-ecosystem effects that have been observed through field observations but are mainly derived from experimental studies. Although OA trends and the resulting biological impacts are likely exacerbated in the semi-enclosed and highly populated Mediterranean Sea, some fundamental knowledge gaps still exist. These gaps are at tributed to both the uneven capacity for OA research that exists between Mediterranean countries, as well as to the subtle and long-term biological, physical and chemical interactions that define OA impacts. In this paper, we systematically analyzed the different aspects of OA research in the Mediterranean region based on two sources: the United Nation’s International Atomic Energy Agency’s (IAEA) Ocean Acidification International Coordination Center (OA-ICC) database, and an extensive survey. Our analysis shows that 1) there is an uneven geographic capacity in OA research, and illustrates that both the Algero-Provencal and Ionian sub-basins are currently the least studied Mediterranean areas, 2) the carbonate system is still poorly quantified in coastal zones, and long-term time-series are still sparse across the Mediterranean Sea, which is a challenge for studying its variability and assessing coastal OA trends, 3) the most studied groups of organisms are autotrophs (algae, phanerogams, phytoplankton), mollusks, and corals, while microbes, small mollusks (mainly pteropods), and sponges are among the least studied, 4) there is an overall paucity in socio-economic, paleontological, and modeling studies in the Mediterranean Sea, and 5) in spite of general resource availability and the agreement for improved and coordinated OA governance, there is a lack of consistent OA policies in the Mediterranean Sea. In addition to highlighting the current status, trends and gaps of OA research, this work also provides recommendations, based on both our literature assessment and a survey that targeted the Mediterranean OA scientific community. In light of the ongoing 2021-2030 United Nations Decade of Ocean Science for Sustainable Development, this work might provide a guideline to close gaps of knowledge in the Mediterranean OA research., Systematic Review Registration https://www.oceandecade.org/, Peer reviewed

[Description of methods used for collection/generation of data] For the study, environmental data were measured by sensors located in both the water column and the benthic compartment (at 4 m and 8 m, respectively). Temperature, salinity and dissolved oxygen (DO) from the water column were measured from October 2019 to October 2021 by a sensor attached to the mooring line. Data were recorded with a CT SBE37 (Conductivity, Temperature) sensor (SBE37SMP-ODO-RS232, Sea-Bird Scientific©) coupled with an SBE 63 (Sea-Bird Scientific©) dissolved oxygen (DO) sensor with accuracies of ± 0.002 °C for temperature, ± 0.002 mS cm-1 for conductivity and ± 2 % for DO. Measurements were taken with a resolution of 0.0001 ºC for temperature, 0.0001 mS cm−-1 for conductivity and 0.2 µmol kg-1 for DO. Multiparametric Hydrolab HL4 probes (OTT HydroMet) were deployed during 8 different periods covering all seasons following the procedure by Hendriks et al. (2021). Accuracy for the multiparametric probe sensors is ± 0.10 ºC for temperature and ± 0.5 % of reading + 0.001 mS cm−1 for conductivity, with resolutions of 0.01 ºC and 0.001 mS cm-−1, respectively. The DO sensor presents an accuracy of ± 0.1 mg L−1 for values lower than 8 mg L−1, and ± 0.2 mg L−1 for values higher than 8 mg L−1, and a resolution of 0.01 mg L−1. Two benthic chambers were installed during May and July 2021 using a design previously described in Barrón et al. (2006). MiniDOT sensors (PME, Inc. ©) were used for temperature and DO measurements every 15 minutes, with accuracies of ± 0.1 ºC and ± 5 %, respectively. DO sensor data were validated against water samples analysed with the Winkler method.. Three chamber replicates were installed during each deployment. Wind speed (m s−1) values at Cabrera NP Station were obtained from data provided by the Organismo Autónomo de Parques Nacionales (OAPN, Spain). For the benthic chambers, night respiration was estimated from changes in DO between one hour after sunset and one hour before sunrise. The same procedure was followed for the calculation of the net community production (NCP) during daylight hours, and the two values were summed for GPP. NCP was used along with the total meadow area coverage and residence time of water in Sta. María Bay to determine the total O2 exported by the meadow to the water column. For the metabolic rate calculation, only oxygen data from the first 24 hours were used., [Methods for processing the data] Seasonal variations in the metabolic rates were analysed with a one-way ANOVA test using the Statistics and Machine Learning ToolboxTM in Matlab® (https://mathworks.com). For this purpose, daily metabolic rates from water column sensors and multiparametric sensors were grouped by season . The same statistical analysis was performed to analyse disparities between sensors. Since benthic chamber data were only available for one day in May and one day in July, differences between deployments were tested using a Student t-test., readme provides background information for csv datafiles. Csv datafiles are processed data of oxygen concentrations used as input for the model, with a frequency of 10 minutes for hydrolab (HL) measurements and hourly for the CT measurements, and a frequency of 15 minutes for MiniDot measurements., Spanish Ministry of Science (SumaEco, RTI2018–095441-B-C21), the Government of the Balearic Islands through la Consellería d'Innovació, Recerca i Turisme (Projecte de recerca científica i tecnològica SEPPO, PRD2018/18), the Posi-COIN Project from the 2018 BBVA Foundation “Ayudas a equipos de investigación científica” call. STARTER research project funded by the 2021 call of the Càtedra de la Mar, Iberostar Foundation. This work is a contribution to CSIC's Thematic Interdisciplinary Platform PTI OCEANS+. The present research was carried out within the framework of the activities of the Spanish Government through the "Maria de Maeztu Centre of Excellence" accreditation to IMEDEA (CSIC-UIB) (CEX2021-001198)., With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2021-001198)., 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

Experimental and Theoretical methods. Figure S1: TEM images. Figure S2: Estimated gap between particles. Figure S3: Fitted curves to experimental absorbance spectra. Figure S4: Color matching functions. Figure S5: Standard iluminant D65. Figure S6: Hue wheel. Figure S7: Experimental absorbance spectra of dispersed and aggregated NPs. Figure S8: Aggregation kinetics of NPs. Figure S9: Change of Hue value for experimental samples. Figure S10: Calculated extinction cross sections of single NPs and NPs clusters. Figure S11: Change of Hue values for the calculated cases. Table S1: DNA sequences used. Table S2: Theoretical and experimental color differences (ΔE76)., Peer reviewed

We include the dataset corresponding to the figures of the paper "Nonlinear Optical Response of a Plasmonic Nanoantenna to Circularly Polarized Light: Rotation of Multipolar Charge Density and Near-Field Spin Angular Momentum Inversion" by M. Quijada, A. Babaze, J. Aizpurua, and A.G. Borisov, published in the journal ACS Photonics, with DOI: 10.1021/acsphotonics.3c00783 . The set includes data to generate: optical spectra, charge density maps, time evolution of multipolar moments, and all the plots in the paper., Peer reviewed

13 pages. -- The PDF file includes: Supporting text. -- Figs. S1 to S1. -- Legends for Movies S1 to S4., Self_organized_appendix.pdf, pnas.2216024120.sm01.mp4, pnas.2216024120.sm02.mp4, pnas.2216024120.sm03.mp4, pnas.2216024120.sm04.mp4, Peer reviewed

Each folder contains .txt files of the data for each of the figures indicated on its name, together with README instructions on each case., The file contains the dataset corresponding to the figures of the article "Characterizing the Backscattered Spectrum of Mie Spheres" written by Martín Molezuelas-Ferreras, Álvaro Nodar, María Barra-Burillo, Jorge Olmos-Trigo, Jon Lasa-Alonso, Iker Gómez-Viloria, Elena Posada, J. J. Miguel Varga, Rubén Esteban, Javier Aizpurua, Luis E. Hueso, Cefe Lopez, and Gabriel Molina-Terriza (DOI: 10.1002/lpor.202300665). The data is organized into different folders, and each folder contains .txt files of the data for each of the figures indicated on its name, together with README instructions on each case., PRE2018-085136. MCIN/AEI/10.13039 /501100011033 through Project Ref. No. FIS2017-87363-P. MCIN/AEI/10.13039/501100011033 and “ESF Investing in your future” through Project Ref. No. BES-2017-080073. MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe” through Project Ref. No. PID2022-139579NB-I00. Department of Education, Research and Universities of the Basque Government through Project Ref. No. IT 1526-22. CSIC Research Platform PTI-001. MCIN/AEI/10.13039/501100011033 through Project Ref. No. MDM-2016-0618. MCIN/AEI/10.13039/501100011033 and the European UnionNextGenerationEU/PRTR through the Juan de la Cierva Fellowship Ref. No. FJC2021-047090-I. MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe” through Project Ref. No. PID-2022-137569NBC43. MCIN/AEI/10.13039/501100011033 through Project Ref. No. PID2021-124814NB-C21., Peer reviewed

S1 Linear-response frequency-domain TDDFT calculations for a planar free-electron metal slab S1.1 Ground-state calculations S1.2 Linear-response calculations S1.3 Reflection coefficient R(ω, kk), surface response function g(ω, kk), and Feibelman parameter d⊥(ω, kk) S2 Real-time TDDFT calculations of the optical response of a cylindrical nanowire S2.1 Ground-state calculations S2.2 Optical response S3 Surface-response formalism (SRF) for the optical response of a cylindrical nanowire S3.1 Induced potential and the Feibelman parameter S3.2 Plasmon resonances sustained by a cylindrical nanowire within the SRF S4 Multipolar polarizabilities of a cylindrical nanowire S4.1 General expressions of the multipolar polarizability and multipole moment S4.2 Multipolar polarizability within the SRF and the classical theory S4.3 Multipolar polarizability from TDDFT calculations S5 Assessing the robustness of the dispersive Feibelman parameter S5.1 Real-time TDDFT calculation of dcyl⊥(ω, m) S5.2 Comparison between the dispersive Feibelman parameter dcyl⊥(ω, m) calculated with TDDFT for different nanowire sizes and d⊥(ω, kk) calculated with TDLDA for the planar metal surface S6 Resonant frequency and width of the plasmon resonances, Peer reviewed