BUSCADOR RECOLECTA

[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

The acquired data comprises 2D/3D and 3C components. The acquisition employed 647 seismic receivers, distributed in a 3D mesh around the target and along six 2D crooked lines sampling the study area. The source employed was a 32 t vibroseis truck, operating at c. 900 points in the pathways along the 2D profiles. Each vibration point was used three times, with frequency sweeps of 10-100 Hz., Fair and sustainable production of raw materials is one of the main challenges faced by our society. Through its RawMaterials Programme, the European Institute of Technology (EIT) encourages research and innovation solutions for mineral exploration to make them safer, sustainable and cost-effective. The SIT4ME project, funded by EIT, addresses these objectives by undertaking seismic mineral exploration methods in crystalline tectonic settings, at a reduced cost. The SIT4Me project will analyse the efficiency of passive seismic methods (i.e. ambient noise interferometry) for subsurface imaging, by comparing active- and passive-source datasets in mining areas. The files in this dataset correspond to the controlled-source acquisition., European Institute of Innovation & Technology, Grant number EIT 17024, SIT4ME project, Peer reviewed

The data acquisition contract was awarded to ENRESA and took place in November and December 2013. Seismic data was successfully collected in the Záncara river basin (Cuenca, Spain). The total amount of seismic reflection data collected was 9.7 km in 4 high-resolution seismic reflection profiles E-W oriented. Technical specifications of the profiles: Seismometer: 10 24-channel GEODE ultra-light seismic recordes, Receiver number: 240, Receiver interval: 2 m, Source: accelerated weightdrop 250 kg and 100 kg, Source interval: 6 m, Sample rate: 1 ms, Record time: 4 s. Contact person: Carbonell, R., rcarbo@ictja.csic.es, A high-resolution 2-D seismic reflection survey was acquired to obtain a seismic image of the geological structure of the Záncara river basin (eastern Spain). The study area consists of lutites and gypsum from a Neogene sedimentary sequence. The project also targeted the geometry of the geological structure and the mechanical properties of the underground materials. In addition, this study allowed for an improvement of the geophysical acquisition technics and protocols that provided with a better resolution seismic imaging. Thus, reducing costs and improving the effectiveness of the seismic acquisition., Convenio Colaboración CSIC-ENRESA. Código CSIC: 20133830, Peer reviewed

The data acquisition contract was awarded to ENRESA and took place in January 2014. Seismic data was successfully collected in the Záncara river basin (Cuenca, Spain). This is a high-resolution seismic tomography survey to obtain a full 3-D P-wave seismic velocity image of the studied area. A regular and dense grid of 676 shots and 1200 receivers was used to image a 500 m x 500 m area of the shallow surface. A 240-channel system and a seismic source, consisting of an accelerated weight drop, were used in the acquisition. Half a million travel-time picks were inverted to provide the 3-D seismic velocity distribution up to 120 m depth. The 3-D survey was acquired in five swaths, each one consisting on five receiver lines, resulting in a total of 3380 shot gathers. Technical specifications of the profiles: Receiver number: 240, Receiver interval: 2 m, Source accelerated weightdrop: 250 kg, Source interval: 6 m, Sample rate: 1 ms, Record time: 4 s., In this repository, in addition to the seismic 3DSurvey, a resistivity model (VICANAS_3D_Res_UTM30), a lithological model (VICANAS_3D_Vp_Res_Lito_UTM30), and a training set (VICANAS_Training_set) are available. In order to improve the geological interpretation of the seismic tomography, we integrated it with the resistivity model to build a 3D lithological model. To this aim, we created a new bi-parameter grid with Vp and Res values at each node. Then, we lithologically classified the nodes using supervised learning according to a training set extracted from the wells., A high-resolution seismic tomography survey was acquired to obtain a full 3-D P-wave seismic velocity image of the Záncara river basin (eastern Spain). The study area consists of lutites and gypsum from a Neogene sedimentary sequence. The project also targeted the geometry of the underground structure with emphasis on defining the lithological contacts but also the presence of cavities and faults or fractures. An extensive drilling campaign provided uniquely tight constraints on the lithology; these included core samples and wireline geophysical measurements. The analysis of the well log data enabled the accurate definition of the lithological boundaries and provided an estimate of the seismic velocity ranges associated with each lithology. The final joint interpreted image reveals a wedge-shaped structure consisting of four different lithological units. The study features the necessary key elements to test the travel time tomographic inversion approach for the high-resolution characterization of the shallow surface. In this methodological validation test, travel-time tomography demonstrated to be a powerful tool with a relatively high capacity for imaging in detail the lithological contrasts of evaporitic sequences located at very shallow depths, when integrated with additional geological and geophysical data., Convenio Colaboración CSIC-ENRESA. Código CSIC: 20133830, Peer reviewed