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[Methods] Data obtained from an acoustic telemetry experiment. The acoustic detection sequence was imported to R software to apply a Hidden Markov Model to separate the active and rest states and obtain the circadian-related traits., This dataset contains the necessary data to replicate the work entitled 'Circadian-related behavioural types in free-living marine fish revealed by high-throughput telemetry'. The data were obtained through a high-resolution acoustic telemetry experiment tracking a population of pearly razorfish between April and September 2019. The time series of detections were imported into the R computing environment. We discretized the detections generated by the individuals into bins of 5 minutes (time-steps). We fitted a Hidden Markov Model (HMM) to probabilistically assign two behavioural states to each temporal bin: rest (R) or active (A). We used a zero-inflated Poisson HMM implemented in the ziphsmm package. During these months, two different periods in the reproduction of this species were included: the pre-spawning period and the spawning period. For this purpose, the data were separated into two different datasets: the pre-spawning period dataset, which contains all individuals tracked for at least seven days between April 30 and May 31, and the spawning period dataset, which includes all individuals tracked for at least seven days between June 15 and July 31. The data between June 1 and June 15 were discarded due to maintenance tasks on the acoustic receivers. The data from August and September were discarded due to low data yields. Finally, a third dataset was created, which includes individuals tracked for at least seven days in each period. The three datasets are configured in the same manner, with ID as the identifier for each individual, Day as the tracking date, Dayn as the day of the trial, Awakening Time as the activity onset time in minutes relative to sunrise, Rest Onset as the rest onset time in minutes relative to sunset, RelActivityDuration as the active hours (calculated as the difference between the awakening time and rest onset) relative to daylight hours (calculated as the difference between sunrise and sunset), RelRestDuration as the resting hours (calculated as the difference between the rest onset time and awakening time of the next day) relative to night hours (calculated as the difference between sunset and sunrise of the next day), RelRestMidpoint as the midpoint of the rest relative to the middle of the night, Sex, Size (cm), Period, CHL as the concentration of chlorophyll (Relative Fluorescence Units, RFU), CurrentDirection as the direction in degrees of the surface current, CurrentSpeed as the speed in m/s of the surface current, Light as the daily mean light (lux), O2 as the concentration of dissolved oxygen in water (mV), Salinity (PSU), Temperature as the daily mean temperature (ºC), WavesHeight as the daily mean wave height (m), and WindSpeed as the speed in m/s of the wind., The 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). The CLOCKS I+D+I project funded this work (grant no. PID2019-104940GA-I00) funded by MCIN/AEI/10.13039/501100011033 and the FSE invierte en tu futuro. The telemetry system was financed by the German Federal Ministry of Education and Research (Grant No. #033W024A)., With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2021-001198)., Pre-Spawning_Dataset, Spawning_Dataset, BothPeriods_Dataset., Peer reviewed

[Description of methods used for collection/generation of data] The data derived from the laboratory were obtained through various standardised tests, and recorded to gather behavioural data. For exploration and activity, positional data were acquired via a deep-learning object detection algorithm /YOLOv5). In the case of boldness and aggression, data were obtained by subsequently reviewing the videos. Regarding chronotypes, data were obtained from an acoustic telemetry experiment, but here we present only the scores obtained in a previous study., This dataset encompasses all necessary data required to replicate the study, `Chronotypes-Personality behavioural syndromes in wild fish’. The data were obtained through standardised behavioural tests conducted under laboratory conditions on 63 Pearly Razorfish (Xyrichtys novacula) individuals between April and July of 2019. Over a week, the fish were maintained in isolated aquariums to test their behaviours, including exploration, activity, boldness, and aggression, conducted daily. A Raspberry Pi system, equipped with the YOLOv5 deep-learning automatic tracking algorithm, was used to record these tests and calculate the fish's minute-by-minute position, providing essential data for evaluating exploration and activity. This system also stored videos to retrospectively obtain boldness and aggression data. Each test included only those individuals with at least two measurements. After the laboratory period, the fish were tagged with acoustic tags and returned to the sea to measure their chronotypes; only individuals with at least seven consecutive days of data were considered. The chronotype data, obtained from a previous study, are represented here through the previously derived scores. These laboratory-based experimental data were analysed using R software. In the exploration context, positional data were translated into total active time (TimeOut), minimum distance to the toy (MinDistance), and time spent near the toy (TimeToy). For activity, the data were converted into total active time (TimeOut), total distance covered (Distance), areas (CoreArea and Area), and direction angles (MeanAngle and KappaAngle). A Principal Component Analysis (PCA) was conducted to obtain the scores for exploration, activity, and aggressiveness. Upon acquiring these scores, trait repeatability was computed using a Linear Mixed-Effects Model, fitting the experimental day (Day), the total length of the individual (Size), and the internal condition (Condition) as fixed factors, and the individual (ID) and the experimental week (Week) as random factors. The chronotype scores (Awakening Time and Rest Onset) were subsequently included in each dataset and refitted into the Linear Mixed-Effects Model, including chronotypes as fixed factors. Lastly, a Multivariate Generalised Linear Mixed Model was fit to each pair of laboratory-based traits to derive their correlations., The 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). The CLOCKS I+D+I project funded this work (grant no. PID2019-104940GA-I00) funded by MCIN/AEI/10.13039/501100011033 and the FSE invierte en tu futuro. The telemetry system was financed by the German Federal Ministry of Education and Research (Grant No. #033W024A). This work is a contribution of the Joint Researcher Unit IMEDEA-LIMIA., With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2021-001198)., Peer reviewed

This item is made of 2 files, of which 1 is the dataset in matlab format and the other (Readme .txt) include a small description of the computed variables. Dataset contributed to the Projects CAIBEX and RAIA.-- Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). The authors appreciate that users of these data: 1) Contact Des Barton (e.d.barton@iim.csic.es; barton.des@gmail.com ) to follow the uses of the data, and 2) Include the requested acknowledgment (cite using the DOI of this dataset) in any presentations or publications, From November 2008 to April 2010, an upward-looking Sontek 500 ADP (configured for 3 m cell size and 5 minute sampling interval) was moored on the seabed in 75 m water depth at 42.083°N and 8.933ºW (RAIA station) near Cape Silleiro (NW Iberia, Atlantic Ocean). Each of the 25 current-velocity vertical levels were averaged every 10 minutes, This work has been funded by the Spanish Ministry of Education project “CAIBEX Shelf–ocean exchanges in the Canaries–Iberian Large Marine Ecosystem” (CTM2007–66408–C02–01/MAR, CTM2007–30809–E/MAR, CTM2008–05305–E/MAR); RAIA: ‘Observatorio oceánico del margen Ibérico’ (INTERREG 2009/2011; 0313/RAIA/E) ; and RAIA.co: ’Observatorio marino del margen ibérico y del litoral’ (INTERREG 2011/2013; 052/RAIA.co/1E), Peer reviewed

This item is made of 2 files, of which 1 is the dataset in matlab format and the other (Readme .txt) include a small description of the computed variables. Dataset contributed to the Projects CAIBEX and RAIA.-- Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). The STRAMIX team appreciates that users of these data: 1) Contact Des Barton (e.d.barton@iim.csic.es; barton.des@gmail.com ) or Carmen G. Castro (cgcastro@iim.csic.es) to follow the uses of the data, and 2) Include the requested acknowledgment (cite using the DOI of this dataset) in any presentations or publications, From January to November 2009, 4 hydrographic cruises were carried out along a 42.1°N across-shelf section, near Cape Silleiro (NW Iberia, Atlantic Ocean). These cruises sampled along seven equally spaced stations from the position of the coastal Silleiro buoy at 8.93 °W (75 m depth) to a station located at 9.44°W (~580 m depth, Figure 1). A CTD (Seabird 25) equipped with fluorescence, turbidity and transmittance sensors was used for these hydrographic cruises. The cruises started at the shallow station between 08:00 to 11:00, and ended at the deepest station between 12:00 to 15:00, This work has been funded by the Spanish Ministry of Education project “CAIBEX Shelf–ocean exchanges in the Canaries–Iberian Large Marine Ecosystem” (CTM2007–66408–C02–01/MAR, CTM2007–30809–E/MAR, CTM2008–05305–E/MAR); RAIA: ‘Observatorio oceánico del margen Ibérico’ (INTERREG 2009/2011; 0313/RAIA/E) ; and RAIA.co: ’Observatorio marino del margén ibérico y del litoral’ (INTERREG 2011/2013; 052/RAIA.co/1E), No

Este dataset está compuesto por 2 archivos, de los cuales el primero es el conjunto de datos con 191 análisis de muestras de agua de temperatura, salinidad, oxígeno, nutrientes, pH, alcalinidad y clorofila, y el otro (Readme.txt) incluye una pequeña descripción de las variables calculadas, El proyecto CAIBEX es un estudio coordinado para comparar la dinámica y actividad biogeoquímica contrastada que se da entre la zona costera y el océano adyacente en la zona de estudio. Es un programa de observación y modelización multidisciplinar que abarca diferentes escalas espaciales y temporales, incluyendo un estudio del papel de los filamentos en el afloramiento estival del caladero de Cabo Silleiro, Galicia (CAIBEX I) y el afloramiento perenne del caladero de Cabo Guir, Marruecos (CAIBEX III) con especial referencia a sus repercusiones en el reclutamiento/dispersión de paralarvas de pulpo común y a las transformaciones biogeoquímicas experimentadas por el material biogénico producido en la zona costera. La campaña CAIBEX I, realizada a bordo del buque Sarmiento de Gamboa, está enfocada al estudio de las estructuras y procesos a mesoescala del sistema de Cabo Silleiro, y tiene como meta principal la definición de la estructura tri-dimensional física, biológica y biogeoquímica de un filamento de afloramiento y su evolución en el tiempo como función del forzamiento por el viento. Para alcanzar estos objetivos, el plan de campaña se planteó tres modos de operación: 1) un mapeado rápido al principio y al fin de la campaña para localizar el filamento y observar los cambios en el contexto general durante el estudio; 2) varios experimentos de deriva en el filamento para ver el desarrollo de una parcela de agua mientras se traslada de la costa al océano; 3) cortes repetidos a través del filamento para estudiar su desarrollo in situ. En cada estación se realizaron perfiles verticales de temperatura, salinidad y oxígeno disuelto con un SBE911plus CTD. Se realizaron tiradas con una roseta con botellas de 10 L de PVC Niskin para obtener muestras de agua para análisis oxígeno disuelto y nutrientes inorgánicos disueltos. El oxígeno disuelto se determinó por titulación potenciométrica de Winkler. El error estándar analítico estimado fue de 1 μmol/kg. Las muestras de nutrientes se determinaron mediante análisis de flujo segmentado con el autoanalizador Alliance Futura siguiendo a Grashoff et al. (1999) con algunas mejoras (Mouriño y Fraga 1985). Los límites de detección fueron 0,02 μmol/L para nitritos, 0,05 μmol/L para nitratos, amonio y silicato y 0,01 μmol/L para fosfatos. El pH del agua de mar se midió espectrofotométricamente siguiendo a Clayton y Byrne (1993) aplicándose una adición de 0,0047 (DelValls & Dickson, 1998). Las muestras de pH (escala de concentración total de hidrógeno, 25°C) se recogieron directamente en cubetas ópticas cilíndricas de 100 mm de longitud en las que se realizan directamente las medidas. La precisión fue 0,003 unidades de pH. Las muestras de alcalinidad total (TA) se recogieron en frascos de vidrio de 500 ml. El TA se determinó por titulación a pH 4,4 con HCl, según el método potenciométrico de Pérez y Fraga (1987) con una precisión de ±2 micromol/kg, CSIC, 1 data csv ‘29AH200901706_hy1.csv’ file and 1 readme.txt file, Peer reviewed

Este dataset está compuesto por 2 archivos, de los cuales el primero es el conjunto de datos con 72 análisis de muestras de agua de temperatura, salinidad, oxígeno, nutrientes, pH, alcalinidad y clorofila, y el otro (Readme.txt) incluye una pequeña descripción de las variables calculadas, El proyecto CAIBEX es un estudio coordinado para comparar la dinámica y la actividad biogeoquímica contrastada entre la zona costera y el océano adyacente en la zona de estudio. Se trata de un programa multidisciplinar de observación y modelización que abarca diferentes escalas espaciales y temporales, incluyendo un estudio sobre el papel de los filamentos en el afloramiento estival del Cabo Silleiro. Como parte del CAIBEX, la estación fija de la plataforma continental de LOCO (Laboratorio de Observación Oceánico y Costero), se ha realizado un amarre de fondo (70 m, próximo a Cabo Silleiro) sin marca de superficie para evitar la localización visual que incluye un marco de forma piramidal de Sidmar con dos boyas VINY en su interior y un ADP Sontek 500 kHz mirando hacia arriba en el centro. El sitio fue visitado mensualmente durante un año a bordo del R/V Mytilus, para monitorear perfiles verticales de variables físicas, biológicas y geoquímicas y para estimar la producción primaria, la respiración microbiana y las tasas de sedimentación. Cada mes, se realizaba un perfil vertical de temperatura, salinidad, fluorescencia y oxígeno disuelto con un SBE911plus CTD. Se obtuvieron muestras de agua para análisis de oxígeno disuelto, pH alcalinidad y nutrientes inorgánicos disueltos con una roseta con 12 botellas de PVC Niskin de 10 L. El oxígeno disuelto se determinó por titulación potenciométrica de Winkler. El error estándar analítico estimado fue de 1 micromol/kg. Las muestras de nutrientes se determinaron mediante análisis en flujo segmentado con un autoanalizador Alliance Futura siguiendo las metodologías de Grasshoff et al. (1999). Los límites de detección fueron 0,02 micromol/kg para nitritos, 0,05 micromol/kg para nitratos, amonio y silicato y 0,01 micromol/kg para fosfatos. Las muestras de alcalinidad total (TA) y pH (escala de concentración total de hidrógeno, 25°C) se recogieron en frascos de vidrio de 500 ml y se analizaron en pocas horas en el laboratorio base. El pH del agua de mar se midió espectrofotométricamente siguiendo a Clayton y Byrne (1993) aplicándose una adición de 0,0047 (DelValls & Dickson, 1998). La precisión fue 0,003 unidades de pH. La TA se determinó por titulación a pH 4,4 con HCl, según el método potenciométrico de Pérez y Fraga (1987) con una precisión de ±2 micromol/kg., Plan Nacional de Investigación; CSIC, 1 data csv ‘29MY200801105_hy1.csv’ file and 1 readme.txt file, Peer reviewed