BUSCADOR RECOLECTA

The authors acknowledge institutional support of the “Severo Ochoa Centre of Excellence” accreditation to ICM-CSIC (#CEX2019-000928-S). [...] V.S. is supported by a “Ramón y Cajal” research fellowship (RYC2021-033065-I) granted by the Spanish Ministry of Science and Innovation. This study is a contribution to the European Union's Horizon 2020 research and innovation programmes under grant agreement no. 869300 (FutureMARES project) and no. 101059877 (GES4SEAS). The authors also acknowledge the support from the Catalan government through the iMARES research group of quality from the Institute of Marine Sciences (ICM-CSIC) in Barcelona, - MTC & MTR/ # Rcode, raw data, and results of the Mean Temperature of the Catch (MTC) and the Mean Temperature of the Revenue (MTR) analysis. - RESULTS/ # Results of the MTC and MTR analysis: (1) TXTs summaries of the results of the linear regression models (i.e., MTC vs Year and MTR vs Year); (2) CSVs with MTC and MTR results and the strength of the underlying processes; and (3) CSVs with the temporal rate of change of species abundance and revenue. - Ta preference/ # Rcode of the species temperature of preference calculation and the final results. - MUMIN analysis/ # Rcode, input data, and results of the MUMIN model analysis. - Sea temperature analysis/ # Results obtained from the sea temperature analysis, Peer reviewed

This dataset contains the results of the analysis of standing stocks and activity of prokaryotes during the KOSMOS mesocosm experiment carried out in the framework of the Ocean Artificial Upwelling project. The experiment was carried out in the autumn of 2018 in the oligotrophic waters off Gran Canaria. During the 39 days of experiment nutrient-rich deep water was added to the mesocosms in two modes (singular vs recurring additions), with four levels of intensity. Prokaryotic abundances and viability were quantified with a FACSCalibur flow cytometer (Becton-Dickinson). Prokaryotic activity (as protein synthesis) was assessed by means of BioOrthogonal Non-Canonical Amino acid Tagging (BONCAT), using a Zeiss Axio Imager.Z2m Epifluorescence Microscope (AxioCam MRm, Carl Zeiss MicroImaging). The aim of this dataset was to study the effect of different upwelling regimes on the prokaryotic communities and its potential implications for organic matter cycling, This study is a contribution to the Ocean Artificial Upwelling project (Ocean artUp), funded by an Advanced Grant of the European Research Council (No. 695094) awarded to UR. Additional support was provided through projects FLUXES (CTM2015-69392-C3-1-R) and e-IMPACT (PID2019-109084RB-C21) funded by the Spanish National Science Plan. MGL was supported by the Ministerio de Universidades, Gobierno de España (FPU17-01435) during their PhD. MS was supported by the Project MIAU (RTI2018-101025-B-I00), MICOLOR (PID2021-125469NB-C31), the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S), and by a Viera y Clavijo contract funded by the ACIISI and the ULPGC. JA was supported by a Helmholtz International Fellow Award, 2015 (Helmholtz Association, Germany) and by the United States National Science Foundation grant OCE-1840868 to the Scientific Committee on Oceanic Research (SCOR, United States) WG 155, Peer reviewed

Whether restoration actions achieve full ecological recovery is still debated. This is particularly controversial in the marine realm, where the success of restoration is mostly evaluated in terms of the short-term survival of transplanted organisms. In view of this, we combined population and trait-based approaches to explore the long-term effectiveness of active restoration of a key Mediterranean octocoral. For this purpose, an assemblage with restored Corallium rubrum colonies was monitored over ten years and compared with a nearby reference site. Our results revealed growth of the transplanted colonies followed by a change in the functional structure (i.e., dominance and diversity of traits) of the restored assemblage. Interestingly, this change was related not only to the development of the coral but also to the arrival and/or increase of species with different traits. Overall, our findings provide an example of how active restoration of long-lived octocorals can be an effective tool for recovering highly high-diverse coralligenous assemblages at decadal timescales, This work was supported by grant RTI2018-095346-B-I00 HEATMED funded by MCIN/AEI/10.13039/501100011033 and by” ERDF/UE;” grant TED2021-131622B-I00 CORFUN funded by MCIN/AEI/10.13039/501100011033 and by “European Union NextGenerationEU”/PRTR;” the Public agreement PTOP- 2017-130 Seguiment de la Biodiversitat marina funded by Generalitat de Catalunya; the Public agreement PTOP- 2021-3 Seguiment de la Biodiversitat marina funded by Generalitat de Catalunya public agreement; the grant SEP-210597628 Futuremares funded by UE H2020; grant MED0200736 MPA4Change funded by Interreg EuroMED Euro and by UE Grant ICREA Academia 2019 funded by Institució Catalana de Recerca i Estudis Avançats (to C.L.); grant Severo Ochoa Centre of Excellence CEX2019-000928-S funded by MCIU/AEI/10.13039/501100011033 (to J.G.); grant FPU20/03574 funded by MCIU/AEI/10.13039/501100011033 (to Y.Z.); and Grant 2021 SGR 01073 funded by Departament de Recerca i Universitats, Generalitat de Catalunya, Peer reviewed

This research was carried out within the framework of the Horizon Europe projects, REDRESS (N. 101135492; R.D., S.B., C.C., J.C., C.G., A.Gr., T.M., N.P., E.R., P.V.S., C.S., and J.V.T.); with the contribution from the projects CLIMAREST (N. 101093865; RD, C.C., J.C., C.G., and S.F.), FORESCUE (Biodiversa+2021-134; R.D., S.B, S.O., L.M., and S.F), MERCES (N. 689518; R.D., J.A., S.B., C.B., W.C., R.C., C.C., J.G., A.Gr., T.M., N.P., E.R., C.S., P.V.S., J.V.K., J.V.T., and S.F.), Generalitat Valenciana, Conselleria d'Educació PROMETEO (Project R2D, CIPROM/2021/001; J.C-S.) and by the “National Biodiversity Future Center—NBFC (NRRP; R.D., S.B., C.C., P.D., and S.F.; Decree no. 3175 of 18 December 2021 by the Italian Ministry of University and Research, funded by the European Union - NextGenerationEU, Award Number: project code CN_00000033, Concession Decree No. 1034, of 17 June 2022)”, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

[Description of methods used for collection/generation of data] Three different UAV platforms were used for data acquisition, each chosen based on their payload capacity and compatibility with various sensors:: 1) DJI Matrice 300 RTK (M300). This quadcopter supports a maximum payload of 2.5 kg and has a maximum take-off weight (MTOW) of 9 kg. It was equipped with several sensors to capture multispectral imagery, RGB data, and gas concentration measurements: - DJI Zenmuse H20T. A multi-sensor payload featuring a 20 MP RGB zoom camera (1/2.7” CMOS) and a 12 MP wide-angle RGB camera (1/2.3” CMOS). It includes a detachable gimbal and offers a maximum shutter speed of 1/8000 s. Under controlled conditions, the manufacturer specifies an accuracy of 0.2 m plus 0.15% of the target distance. - MicaSense RedEdge-MX. A multispectral dual sensor that captures 10 bands across the visible and near-infrared (NIR) spectrum, including blue (444, 475 nm), green (531, 560 nm), red (650, 668 nm), red edge (705, 717, 740 nm), and NIR (842 nm). The system integrates a Downwelling Light Sensor (DLS) with GPS to account for changes in solar irradiance, along with a radiometric calibration panel (RP04-1924106-0B) to ensure radiometric accuracy. - Sniffer4D V2 RS (Soarability). A multi-gas sensing and mapping system capable of simultaneously detecting total volatile organic compounds (TVOC, ppm), sulfur dioxide (SO₂, μg/m³), ozone and nitrogen dioxide (O₃ + NO₂, μg/m³), particulate matter (PM1.0, PM2.5, PM10, μg/m³), flammable gases (CxHy, %), and hydrogen sulfide (H₂S, μg/m³). It provides real-time visualization in both 2D and 3D. In addition, it was integrated with the Sniffer4D Multi-pass TDLAS Hyper-local Methane Module, enabling quantitative methane detection with a resolution of 1 ppm. 2) DJI Mavic 3E (M3E). A lightweight quadcopter equipped with a 20 MP wide-angle RGB camera (4/3” CMOS) and a 12 MP telephoto RGB camera with 56× hybrid zoom. It has an MTOW of 1,050 g and a maximum flight endurance of 45 minutes. 3) DJI Mavic 2 Enterprise Advanced (M2EA). This UAV features a 48 MP RGB optical sensor with a 1/2” CMOS sensor. It has an MTOW of 1,100 g and a maximum flight duration of 31 minutes., [Methods for processing the data] 1) Structure from Motion (SfM) photogrammetry: Aerial images acquired along pre-programmed flight paths with sufficient overlap were processed using Pix4D Mapper (Pix4D SA, Lausanne, Switzerland, v.4.8.3) to generate high-resolution topographic products. The workflow consisted of image alignment, point cloud generation, digital surface model (DSM) reconstruction, orthomosaic and reflectance maps generation. 2) Gas Sniffer data monitoring: The gas detection sensors were directly connected to the PSDK port of the DJI M300, which provided power and allowed real-time data transmission to the UAV’s remote controller. Measurements were logged at one-second intervals and simultaneously stored on the sensor’s internal SD card. To ensure stable sampling, the device incorporated a rear-mounted active air intake fan, drawing ambient air at a flow rate of roughly 10 L min⁻¹., [Environmental/experimental conditions] UAV flights were conducted in strict accordance with Spanish civil aviation regulations for emergency operations, under the supervision of the Spanish Aviation Safety Agency (AESA). Moreover, missions carried out following the Valencia cut-off low were undertaken within the framework of the Scientific-Technical Advisory Group for Emergency Crises (GADE) of the Spanish National Research Council (CSIC). For field operations, flight plans were pre-programmed using the DJI Pilot application (Shenzhen Dà-Jiang Innovations Sciences and Technologies Ltd., Guangdong, China) installed on the UAV controller. These plans were configured with fixed parameters, including flight altitude, speed, duration, ground sampling distance (GSD), and image overlap., This repository contains the original raw data, along with some of the topographic products generated after the initial data processing, captured immediately after the 2024 Valencia cut-off low event by a drone equipped with RGB, thermal, multispectral, and gas-sniffer sensors. These datasets were used to obtain the results published in Román et al. 2025 (DOI: available soon). The data, together with their rapid and preliminary processing, were also employed in real time during the emergency to support decision-makers in tasks related to prevention, evacuation, and reconstruction. Furthermore, access to these data makes the methodologies described in the manuscript reproducible, thereby contributing to the development of new tools that can enable faster, more efficient, and more accurate management during future emergencies., This work was financially supported by funds from PIE202430E238 and PIE202460E236. Sensors and UAV were funded by Spanish Government Infrastructure Project EQC2018-004275-P and EQC2019-005721. The Accreditation of IATA as a Center of Excellence, Severo Ochoa CEX2021-001189-S funded by MCIU/AEI/10.13039/501100011033 is fully acknowledged. A. Román, staff hired under the Generation D initiative, promoted by Red.es, an organisation attached to the Ministry for Digital Transformation and the Civil Service, for the attraction and retention of talent through grants and training contracts, financed by the Recovery, Transformation and Resilience Plan through the European Union's Next Generation funds. This study also represents a contribution to the CSIC Scientific-Technical Advisory Group for Emergency Crises (GADE-CSIC, Spain)., With funding form the Spanish government through the "Severa Ochoa Centre of Excellence" accreditation (CEX2021-001189-S)., File List: 6 folders: Dataset 1 - Object Detection Vehicle Yard; Dataset 2 - Object Detection Trash Items; Dataset 3 - Gas Sensors Paiporta; Dataset 4 - Damage Assessment Picanya; Dataset 5 - Damage Assessment Pont Nou; Dataset 6 - Sludge Characterization, Peer reviewed

We have investigated how the dynamics of host replacement affects the evolution of a plant RNA virus. Specifically, we have examined how sudden vs. gradual transitions from more susceptible to less susceptible Arabidopsis thaliana genotypes influenced turnip mosaic virus’ virulence and population genomic diversity. Our results show that the evolution of virulence and the complexity of the mutant swarm is linked to both the type of host succession and the genetic basis of the host resistance. Faster changes in virulence are observed after sudden transitions, with an evolutionary trend towards more severe symptoms that appear latter during infection. Gradual transitions resulted in greater population mutational load and higher genetic polymorphism compared to sudden transitions. In contrast, beneficial mutations associated with sudden transitions had stronger fitness effects than those linked to gradual transitions. This research highlights the importance of considering the rate of environmental changes in virus evolution and provide insights into predicting how viruses adapt and evolve in temporally changing environments, with implications for agriculture and public health., Ministerio de Ciencia, Innovación y Universidades PID2022-136912NB-I00 Conselleria de Cultura, Educación y Ciencia, Generalitat Valenciana CIPROM/2022/59, Peer reviewed

In this study, we conducted a field experiment manipulating the presence of EFN and trophobiotic insects (treehoppers or myrmecophilous caterpillars) in the tropical shrub Banisteriopsis malifolia and used a structural equation modelling approach to assess the direct and indirect effects of these ant food resources on leaf herbivory, pollinator activity, and plant fitness., Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Aperfeiçoamento de Pessoal de Nível Superior C01 (CAPES) Spanish National Research Council (COOPA20477), Peer reviewed

Dataset used of relative abundance of bacteria and archaea for the research paper "Vascular macrophytes versus charophytes: how the macrophyte type and warming affect the sediment microbial community and the production of greenhouse gases". Currently under peer-review., Ministerio de Ciencia, Innovación y Universidades Project PID2020-116147GB-C22 Ministerio de Ciencia, Innovación y Universidades Project PID2020-116147GB-C21, Peer reviewed

The GAIT2CARE database contains socio-demographic information, functional assessments, and gait data of older adults, collected before and after an 8-week multicomponent physical exercise intervention, with the goal of evaluating health status and temporal evolution. The dataset includes information from 127 participants, consisting of 85 women (67%) and 42 men (33%), aged between 70 and 93 years (82.36 ± 5,34 years). Participants were divided into two groups according to the type of exercise program followed: Group A (on-site): group-based exercise guided by a specialist at the hospital’s setting (n=63). Group B (app-guided): home-based multicomponent exercise program implemented with remote supervision via the VIVIFIL App providing video instructions, monitored adherence, and allowing chat communication with the healthcare supervisor (n=64). The study followed a pre-post design, with functional and gait assessments performed at two-time visits: at week 0 (before intervention) and at week 8 (after intervention). The level of compliance/adherence at week 8 with the exercise program is also included: null (<20%), poor (20-50%), medium (50-70%) and remarkable (>70%). Functional assessment was collected by 4-meter walking test, Time Up and Go (TUG) test, Short Physical Performance Battery (SPPB), the Fried Frailty Criteria and Falls Efficacy Scale – International (FES-I). Gait data were captured by inertial sensors (IMUs) placed on the feet during walks of 13.07±5.15 minutes duration, which have been analyzed to estimate characteristic gait parameters. Inertial data from foot-mounted IMUs (acceleration (m/s2), angular velocity (rad/s) and timestamps (s)) are included in the database in .csv files for each participant, trial (week 0 and week 8) and for each foot (right foot (RF) and left foot (LF)), to allow researchers to perform other approaches for gait analysis. The complete gait analysis is also included for each participant and trial in .csv files, including the gait parameters estimated for all individual steps. The gait parameters included are: cycle duration (CD) (s), cadence (steps/min), stride length (SL) (m), path length 3D (%SL), path length 2D (%SL), stride velocity (m/s), percentage of swing (%CD), percentage of stance (%CD), percentage of stance subphases (loading, foot-flat, and pushing) (%stance), heel strike pitch (degrees), toe-off pitch (degrees), peak angle velocity (degrees/s), turning angle (degrees), heel range of motion (RoM) (degrees), double support (%CD) and stride length normalized (SL/height). The gait analysis has been conducted following the methodology described in [1]. Of the 127 participants initially registered for the GAIT2CARE project, five participants abandoned the exercise program before it was completed and inertial data from several others were lost due to IMU recording failures, or technical/human problems. Consequently, the inertial and gait analysis files include 93 participants for whom complete and valid inertial data were available, 44 for group A (on-site) and 49 for group B (application-guided). GAIT2CARE is designed to support research on the effectiveness of exercise interventions in older adults, particularly in relation to gait (inertial analysis) and functional status. However, this dataset is also appropriate for extended research on mobility, frailty, fall risk, aging and gait analysis based on foot-mounted inertial sensors. The study was approved by the Research Ethics Committee on Medicinal Products (CEIm) of the Hospital Universitario de Albacete on June 27, 2023 (Reference code No. 2023-071) and has been prospectively registered on ClinicalTrials.gov with identifier NCT06936865 (https://clinicaltrials.gov/study/NCT06936865). [1] L. Ruiz-Ruiz, J. J. García-Domínguez and A. R. Jiménez, "A Novel Foot-Forward Segmentation Algorithm for Improving IMU-Based Gait Analysis," in IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-13, 2024, Art no. 4010513, doi: 10.1109/TIM.2024.3449951., Peer reviewed

This repository contains the source data from all experiments presented in this paper. Each Excel file is named after the corresponding figure number and is organized into sheets, with each sheet representing a specific panel of the figure. The sheets are labeled with the figure and panel names., Peer reviewed