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The database provides measurements of jellyfish species and abundance on the shores of the whole Canary Current Large Ecosystem conducted over 2009-2019, covering Spanish and Moroccan coasts. The dataset is provided as [space] delimitated plain text file within a compressed folder that also includes a single README file (in text format) containing a detailed description of the data structure., [General Notes] The data are provided under an Attribution-ShareAlike 4.0 International license. However, if you use the data, so as to support the authors, please consider citing the above mentioned article where data collection and analytical techniques are given in detail. Here we only give a brief details and a guide to the contents of the data files. Data files are in UTF8 encoding, plain text format with comma used as the delimiter. All data files have column titles as the first line. One column is written for each measured parameter. Missing data are filled with NaN., [Geographical coordinates of the sampling area] coordinates.txt provides the geographical coordinates of the sampling area. ST.ID represents the code (used to link to other data tables) for each site. long and lat are longitude and latitude, respectively., The dataset is subject to a Creative Commons License Attribution-ShareAlike 4.0 International., This data set includes data collected from ICMAN-CSIC (Spain) and INRH (Morocco) between 2009 and 2019 that have been used to estimate the diversity, seasonality and inter-annual variability of jellyfish abundance in the Canary Current Large Ecosystem., This research was supported by projects P07-RNM-02976 (Junta de Andalucía), CTM2011-22856 (Spanish Ministry of Science and Innovation) and 2019AEP203 (CSIC)., Peer reviewed

The dataset is made available under the Open Database License. Any rights in individual contents of the database are licensed under the Database Contents License. Please, read the full ODbL 1.0 license text for the exact terms that apply. Users of the dataset are free to: Share: copy, distribute and use the database, either commercially or non-commercially. Create: produce derivative works from the database. Adapt: modify, transform and build upon the database. Under the following conditions: Attribution: You must attribute any public use of the database, or works produced from the database. For any use or redistribution of the database, or works produced from it, you must make clear to others the license of the original database. Share-Alike: If you publicly use any adapted version of this database, or works produced from an adapted database, you must also offer that adapted database under the ODbL., List of experimental data:A-Preliminary MS1-potential peptide biomarkers_OFGL-CAL batchFull-MS1 analyses (mzML files) of NORMAL (n=6) and HIGH (n=6) pHu replicates (NORMAL/HIGH-RPL batch).Statistical analysis of results will lead to the obtaining of candidates further studied by SRM assays.1-HIGH 12-HIGH 23-HIGH 34-HIGH 45-HIGH 56-HIGH 67-NORMAL 18-NORMAL 29-NORMAL 310-NORMAL 411-NORMAL 512-NORMAL 6NOTE: inspection of full-MS1 mzML extension was featured by an open-source freeware MZmine 2 (http://mzmine.github.io/download.html). B-SRM_RPL BatchSRM 1 and 2 assays (RAW and mzML files) of NORMAL/HIGH-RPL batch. SRM 1-REPLICATES1-HIGH 12-HIGH 2 3-HIGH 34-HIGH 4 5-HIGH 5 6-HIGH 6 7-Normal 18-Normal 29-Normal 310-Normal 411-Normal 512-Normal 6 SRM 2-REPLICATES1-HIGH 12-HIGH 2 3-HIGH 34-HIGH 4 5-HIGH 5 6-HIGH 6 7-Normal 18-Normal 29-Normal 310-Normal 411-Normal 512-Normal 6 NOTE: inspection of SRM RAW extensions was powered by freely available ThermoScientific FreeStyle application (https://thermo.flexnetoperations.com). Inspection of SRM mzML files can be featured by other freeware solutions (i.e. Skyline, https://skyline.ms/wiki/home/software/Skyline/). C-SRM_CAL batchSRM 1 and 2 analyses (RAW and mzML files) of NORMAL/HIGH-CAL batch. SRM1-CALHIGH-CAL1-A2-B3-C4-D5-ENORMAL-CAL1-A2-B3-C4-D5-ESRM2-CALHIGH-CAL1-A2-B3-C4-D5-ENORMAL-CAL1-A2-B3-C4-D5-ENOTE: inspection of RAW and mzML SRM files was carried out as described above.END, No

The data stem from an ATII survey fielded in 2016 in the framework of PACIS, a probability-based mixed-modes panel run by the Spanish Research Council’s Institute for Advanced Social Studies (IESA-CSIC). PACIS comprises people aged 16 or more residing in private households in Andalusia, Spain’s largest and most populous region. The panel was recruited by off-line probability sampling and is conceived as a pool of respondents that are periodically invited to participate in different cross-sectional surveys. The one on ATII targeted Spanish nationals only and achieved a 44.2% response rate (n=1,232), 61% (n=753) of which via CAWI (default mode) and 39% (n=479) via CATI (backup mode). Non-response bias (Groves 2002; Groves et al. 2001) was corrected by recruiting new panelists via CATI interviews with ages 60 and more and with post-hoc adjustment with raking ratio estimation weights based on official population statistics. The questionnaire took about 18 minutes to complete on average (18.75 for CATI vs. 17.46 for CAWI)., Descriptive results were distributed under the title “¿Qué opinamos los andaluces sobre la inmigración?” (What do we Andalusians think about immigration?), The initial dataset was created on 15/09/2016; some additional variables (e.g. for weighting list experiment groups) were included later. In October 2020, some response options were recoded to make the published version more intelligible to third parties; this mostly affected divergent DK/NA options in the CAWI and CATI data collection modes, which were unified as one single code., The study focuses on migration attitudes in the Southern Spanish region of Andalusia, aiming to clarify three main interrogatives: (1) How did attitudes toward immigration and immigrants evolve amidst the context of unprecedented immigration flows across the Mediterranean? (2) Do attitudes toward immigrants evolve in parallel to opinions and attitudes regarding immigration as such? (3) How widespread is anti-immigrant sentiment, net of the distortions caused by social desirability pressures? These objectives were derived from prior research, including various surveys on migration attitudes in Andalusia which serve as benchmarks regarding time trends. The data suggest, firstly, that the so-called refugee crisis did not alter Andalusians’ migration attitudes substantively; secondly, that the (inter)personal component of immigration attitudes is largely disconnected from general impact assessments and policy preferences; and thirdly, that albeit estimates of anti-immigrant sentiment increase in non-obtrusive measurement, generalized antipathy toward immigrants appears to be a minority proposition in Andalusia. The dataset contains 77 columns (=variables) and 1,232 lines(=cases)., Self-financed by IESA-CSIC in the framework of the PACIS Project (PIE 201410E030)., The questionnaire includes the following ítems E0. Nationality (filter) P1. Most important problems (open-ended, multiple answer) P2. List experiment 1 (uneasy situations) P3. Perception on the protective role of the state toward different social groups (5 items) P4. Preferred prioritization in state protection of social groups (1º and 2º) P5. List experiment 2 (antipathetic groups) P6. Perceived positive effects of immigration (open-ended, multiple answers) P7. Perceived negative effects (open-ended, multiple answers) P8. Evaluation of effects P9. Rights from legal immigrants (3 items) P10. Policy to be adopted towards legal immigrants P11. Policies to be adopted towards immigrant workers and refugees (2 items) P12. Attitudes toward different immigration policies (6 items) P13. Sympathy toward immigrant people P14. Social ties with immigrants (2 items, the first one filter) P15. Preference on the place of residence P16. Trust in immigrants in general P17. Perception on the most common migrant status in Spain E1. Gender E2. Age E3. Place of Birth E4 Religious practice E5. Educational level E6. Unemployment vulnerability E7. Employment status E8. Subjective social class E9. Political ideology E10. Social trust, Peer reviewed

Este estudio fue realizado a través de un cuestionario en Google Docs, enviado principalmente a través de WhatsApp para fomentar la viralidad. Los datos publicados en este dataset se corresponden con la descarga estándar del documento, si bien se trabajaron para realizar algunas estadísticas de interés., [ES] Conjunto de datos e informe sobre el estudio realizado para evaluar la incidencia de la oferta arqueológica en formato virtual durante las primeras semanas de confinamiento por Covid-19 en España., [EN] Dataset and report about the study undertook to evaluate the impact of the digital archaeological offer during the first weeks of lockdown due to the Covid-19 epidemic in Spain., Se agradece el apoyo de la Asociación para la Investigación y Difusión de la Arqueología Pública, JAS Arqueología., 1. Informe final en español [Informe_final_ES.pdf]; 2. Executive report in English [Executive_report_EN.pdf]; 3. Documento excel con la tabla de respuestas recibidas sin procesar [Respuestas_Covid_VV_295_300420.xlsx]; 4. Descarga del cuestionario en Google Docs enviado [Cuestionario_Covid.pdf]., No

An onshore-offshore network of 24 Ocean-Bottom Seismometers or Hydrophones (12 OBS and 8 OBH) and 35 land stations deployed in 46 different sites. The OBS/OBH network remained fixed during all the experiment and covered on place all the profile intersections, with an average spacing of 30–40 km. The land seismic stations were deployed with an average spacing of 10–15 km extending the N-S profiles onshore, with some additional instruments following the shoreline. The seismic source consisted of a Bolt airgun array fired every 40 s, resulting in a shot spacing of 100 m. The shooting pressure was set constant at 140 bars., Ruiz Fernández, Mario; mruiz@geo3bcn.csic.es, MARCONI (MARgen COntinental Nord Iberico - North Iberian Continental Margin) is a deep seismic reflection survey carried out in the southeastern part of the Bay of Biscay in September 2003. It included the acquisition of 11 multichannel (http://dx.doi.org/10.20350/digitalCSIC/8972) and wide-angle deep seismic reflection profiles covering a total length of 2000 km., MARCONI (MARgen COntinental Nord Iberico - North Iberian Continental Margin) is a deep seismic reflection survey carried out in the southeastern part of the Bay of Biscay in September 2003. It included the acquisition of 11 multichannel (http://dx.doi.org/10.20350/digitalCSIC/8972) and wide-angle deep seismic reflection profiles covering a total length of 2000 km, Ministerio de Ciencia y Tecnología, REN2001-1734, MARCONI; CDS2006-0041, Consolider-Ingenio 2010 Programme Topo-Iberia; Ministerio de Ciencia e Innovación, CGL2008-03474-E/BTE, TopoMed; Ministerio de Economía y Competitividad, CGL2013-48601-C2-2-R, MISTERIOS, Peer reviewed

Sergio Rodríguez Jesús de la Rosa y Jessica López-Darias. Base de datos del Estudio sobre la meteorología, niveles y composición de las partículas en suspensión durante la SuperCalima de febrero de 2020 en Canarias. Informe del CSIC, la Universidad e Huelva y la Universidad de La Laguna para la Consejería de Transición Ecológica, Lucha contra el Cambio Climático y Planificación Territorial del Gobierno de Canarias., This dataset contains observations of chemical composition of PM10 airborne particles during February 2020 at El Rio site (Tenerife, Canary Islands, 28.145140oN, 16.523732oW, 500 meters above sea level), including the period 22-24 February, when the Canary Islands were impacted by a severe dust storm. Elemental composition was determined by IPC-OES and IPC-MS (after acid HNO3 : HF : HClO4 digestion), ions and cations by ion chromatography (after leaching in ultrasound bath) and elemental and organic carbon by Thermo-Optical Transmittance., Estudio financiado por el Gobierno de Canarias. Consejería de Transición Ecológica, Lucha contra el Cambio Climático y Planificación Territorial., Peer reviewed

This dataset contains data of aerosol chemistry and soluble iron collected in Tenerife, Barbados and Miami in summer 2015 within the frame of the project AEROATLAN (reference CGL2015-66299-P), funded by the Minister of Economy and Competitiveness of Spain and by the European Regional Development Fund., This dataset was obtained within the frame of the project AEROATLAN (reference CGL2015-66299-P), funded by the Minister of Economy and Competitiveness of Spain and by the European Regional Development Fund., No

The ‘Scientific Culture at Enterprises’ (SCe) project aims to identify the different factors that characterize the image of science held by entrepreneurs and business managers, explore the relationships among these factors, and shed light on the role they play in defining this image and ultimately in developing a culture of science in the business sector. This dataset includes the raw data of the survey 'Scientific Culture, Perception and Attitudes towards Science and Innovation in the Spanish Business Sector', carried out with a specially designed telephone survey questionnaire of a representative sample of Spanish companies., El proyecto 'Cultura Científica Empresarial' tiene como objetivo identificar los factores que caracterizan la imagen de la ciencia de los empresarios y directivos de empresas, explorar las relaciones entre estos factores e investigar su papel en la definición de una cultura científica en el sector empresarial. El presente conjunto de datos procede de la encuesta 'Cultura Científica, Percepción y Actitudes ante la Ciencia y la Innovación en el Sector Empresarial Español', realizada telefónicamente a una muestra representativa de las empresas españolas., Survey work funded by the Spanish Ministry of Economy, Industry and Competitiveness. Spanish National Plan for Scientific and Technical Research and Innovation. Research Project CSO2014-53293-R, Encuesta financiada por el Ministerio de Economía y Competitividad. Plan Estatal de Investigación Científica y Técnica y de Innovación. Proyecto CSO2014-53293-R, - Survey technical details (Ficha técnica de la encuesta): File ‘TechnicalDetails_Español+English': - Codebook and coding standards followed for all variables (Libro de códigos y codificación utilizada para todas las variables): Files ‘Codebook' and ‘LibroCodigos'; - Syntax (Sintaxis): Files 'Syntax' and 'Sintaxis'; - Microdata ASCII (Microdatos ASCII): File 'MicrodataASCII'; - Questionnaire in Spanish and English (Cuestionario en español e inglés): File ‘Questionaire_Español+English’, No

The dataset contains all the chemical data collected for the work “Mechanisms controlling explosive-effusive transition of Teide-Pico Viejo dome eruptions”. The samples were taken from Pico Cabras dome (Tenerife), including deposits of the sub-plinian fallout (explosive phase) and the lava flow and dome (effusive phase). The samples were analysed with a Scanning Electron Microscope (SEM) Merlin Compact ZEISS with an EDS Bruker detector (QUANTAX-XFlash6-30mm2-129eV) at the Institut des Science de la Terre d’ Orleans (ISTO) Measures Physiques laboratory (Centre National de la Recherche Scientifique, Orleans, France). Additionally, we performed a micro X-Ray Flourescence (Micro-XRF) analysis using the XTRACE detector (Tube Rh – filtres Al, Ti, Ni) connected to the SEM, which was calibrated against inner Br glassy standards containing different amounts of Br (Faranda et al., 2019) in order to analyse Br in sodalite and glass in the pumice samples. Mineral phase compositions were obtained with an electron microprobe SX-FIVE, also at the ISTO. An acceleration voltage of 15kV, a sample current of 6nA, and a counting time of 10s were used. For the glasses, a defocused beam of 10μm and 20μm was used, whereas for minerals a focused beam was employed. Whole-rock data was obtained by analysing two glass samples resulting from the two-step melting (with grinding in between) of lava and pumice samples at 1400ºC for 4 hours in total time, respectively. The aim of the study was contributing to the understanding of the mechanisms that control the transition between explosive and effusive dynamics in dome-forming eruptions at T-PV stratovolcanoes and propose a conceptual model for the magmatic reservoir of Pico Cabras prior to eruption. In this dataset we collect all de geochemical data obtained in the study. The Excel file consists of seven sheets, each one corresponding with one of the principal phases found in the samples: feldspars, pyroxenes, biotites, magnetites, ilmenites, sodalites and glasses (including the whole-rock analysis resulting from the obsidian obtained in the melting of the samples). The four analysed samples were given a International GeoSample Number (IGSN) for their correct identification. IEODG0001, IEODG0002 and IEODG0004 are samples from the effusive phase of Pico Cabras dome eruption (the later corresponding to an obsidian sample from the dome itself). IEODG0003 are pumices samples from the explosive phase in which minerals and glass fragments were selected for analysis., The dataset contains all the chemical data collected for the work “Mechanisms controlling explosive-effusive transition of Teide-Pico Viejo dome eruptions”. The samples were taken from Pico Cabras dome (Tenerife), including deposits of the sub-plinian fallout (explosive phase) and the lava flow and dome (effusive phase). The samples were analysed with a Scanning Electron Microscope (SEM) Merlin Compact ZEISS with an EDS Bruker detector (QUANTAX-XFlash6-30mm2-129eV) at the Institut des Science de la Terre d’ Orleans (ISTO) Measures Physiques laboratory (Centre National de la Recherche Scientifique, Orleans, France). Additionally, we performed a micro X-Ray Flourescence (Micro-XRF) analysis using the XTRACE detector (Tube Rh – filtres Al, Ti, Ni) connected to the SEM, which was calibrated against inner Br glassy standards containing different amounts of Br (Faranda et al., 2019) in order to analyse Br in sodalite and glass in the pumice samples. Mineral phase compositions were obtained with an electron microprobe SX-FIVE, also at the ISTO. An acceleration voltage of 15kV, a sample current of 6nA, and a counting time of 10s were used. For the glasses, a defocused beam of 10μm and 20μm was used, whereas for minerals a focused beam was employed. Whole-rock data was obtained by analysing two glass samples resulting from the two-step melting (with grinding in between) of lava and pumice samples at 1400ºC for 4 hours in total time, respectively. The aim of the study was contributing to the understanding of the mechanisms that control the transition between explosive and effusive dynamics in dome-forming eruptions at T-PV stratovolcanoes and propose a conceptual model for the magmatic reservoir of Pico Cabras prior to eruption. In this dataset we collect all de geochemical data obtained in the study. The Excel file consists of seven sheets, each one corresponding with one of the principal phases found in the samples: feldspars, pyroxenes, biotites, magnetites, ilmenites, sodalites and glasses (including the whole-rock analysis resulting from the obsidian obtained in the melting of the samples). The four analysed samples were given a International GeoSample Number (IGSN) for their correct identification. IEODG0001, IEODG0002 and IEODG0004 are samples from the effusive phase of Pico Cabras dome eruption (the later corresponding to an obsidian sample from the dome itself). IEODG0003 are pumices samples from the explosive phase in which minerals and glass fragments were selected for analysis., This study has been partially funded by EC Grant EVE (ref: DG ECHO H2020 826292) and was financially support by the Equipex-Planex ANR-11-EQPX-0036 and Labex-Voltaire from Orléans., Excel file: Dorado et al., (2020). Pico Cabras products chemistry. 7 sheets: Feldspar, Piroxene, Biotite, Magnetite, Ilmenite, Sodalite, Glass., No

This dataset corresponds to the article: "Predicting the photocurrent-composition dependence in organic solar cells" Xabier Rodríguez-Martínez, Enrique Pascual-San-José, Zhuping Fei, Martin Heeney, Roger Guimerà and Mariano Campoy-Quiles The article was first published on 07 Jan 2021 Energy Environ. Sci., 2021 DOI: 10.1039/D0EE02958K The dataset is divided into five main folders, which are briefly described below. - The "Descriptors" folder contains the detailed lists of optoelectronic descriptors used for the different training procedures performed throughout the work, i.e., a random selection of parameters, a hand-picked selection of parameters and a median of their corresponding distributions. The database of HOMO/LUMO levels and mobilities as retrieved from literature is included as well in an Excel spreadsheet. - The "Discrete devices" folder contains the photovoltaic figures-of-merit (open-circuit voltage, short-circuit current density, fill factor and power conversion efficiency) of the devices prepared at controlled donor:acceptor ratios. In these samples, the active layer thickness was screened in a high-throughput fashion using lateral gradients. Therefore, the variations observed at a given donor:acceptor ratio are solely due to the change in active layer thickness. - The "Graded devices" folder contains Raman mapping and Light-Beam Induced Current (LBIC) data for the 2D combinatorial devices, i.e. those including and orthogonal arrangement of active layer thickness and donor:acceptor ratio gradients on a single large area substrate. - The "Figures" folder contains a Jupyter Notebook file and, alternatively, a Python script, whose full execution in a row generates the figures of the main text and its Electronic Supplementary Information. These codes are designed to run with Python 3.7.3 and Scikit-Learn v0.22.2, while accessing data from other main folders (including "Descriptors", "Discrete devices" and "Graded devices") and subfolders ("Pools" and "Supplementary data"). For this reason, either code must be executed right from their original location within the "Figures" folder while keeping the original folder labels as well. - Finally, the "Raw data" folder contains the binary files generated as per the optoelectronic characterization of the 2D combinatorial devices. These were extracted employing our WITec alpha 300 RA+ confocal Raman setup in combination with LBIC and White-Beam Induced Current (WhiteBIC) measurements. We include as well scripts compatible with GNU Octave (*.m) to display the raw data., This dataset corresponds to the open-source article with DOI 10.1039/D0EE02958K. The package contains (i) the detailed lists of optoelectronic descriptors used to feed machine-learning algorithms; (ii) the photovoltaic figures-of-merit of the organic solar cells therein fabricated; (iii) the photocurrent-composition dependence obtained in 15 different donor:acceptor organic photovoltaic blends using high-throughput combinatorial experimentation; (iv) a set of Python scripts to reproduce the figures of the main text of the article and its supplementary information; and (v) the raw data as extracted from confocal Raman imaging and photocurrent mapping., The dataset is divided into five main folders, which are briefly described in the following lines. The "Descriptors" folder contains the detailed lists of optoelectronic descriptors used for the different training procedures performed throughout the work. The "Discrete devices" folder contains the photovoltaic figures-of-merit (open-circuit voltage, short-circuit current density, fill factor and power conversion efficiency) of the devices prepared at controlled donor:acceptor ratios. The "Graded devices" folder contains Raman mapping and Light-Beam Induced Current (LBIC) data for the 2D combinatorial devices. The "Figures" folder contains a Jupyter Notebook file and, alternatively, a Python script, whose full execution in a row generates the figures of the main text and its supplementary information. Finally, the "Raw data" folder contains the binary files generated as per the optoelectronic characterization of the 2D combinatorial devices., Peer reviewed