Resultados de investigación

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

Dataset corresponding to the figures of the article: Influence of vacuum thermal annealing and air exposure on the performance of single-layer MoS2 devices. Pablo Bastante et al 2024 Nanotechnology 35 485201 DOI 10.1088/1361-6528/ad77dc, Peer reviewed

[Data Access] The data processed in version 1.5.1 was a result of research undertaken in two separate projects. The processing and creation of new aerosol optical property products was done within the FOCI project, and the processing and creation of precipitation chemistry and wet deposition products was funded by the World Meteorological Organization for the Measurement-Model Fusion for Total Global Atmospheric Deposition WMO Initiative. The processed data is designed to be complementary to the data provided in version 1.5 of GHOST. The data is separated out per network, per temporal resolution, per component, and is saved as netCDF4 files, per year and month. There is additionally one synthetic network entitled "GHOST", which aggregates data across all networks. The dataset is compressed as .zip files per network. Beneath each network, collections of files per temporal resolution, per component, are compressed as tar.xz files. Each network .zip file can be decompressed via the following syntax: unzip [network].zip Component tar.xz files can be decompressed via the following syntax: tar -xf [component].tar.xz, [How to Use] Inside the GHOST dataset are a plethora of variables, thus it can difficult to fully exploit the extent of the available information. For this reason a companion publication has been written, detailing every aspect of the GHOST dataset: https://doi.org/10.5194/essd-2023-397 If you have any other doubts of queries regarding the dataset, please email: dene.bowdalo@bsc.es, [How to Cite] If you plan to use this work please kindly cite both this dataset and the describing publication: Bowdalo, D.: GHOST: A globally harmonised dataset of surface atmospheric composition measurements, Zenodo [data set], https://doi.org/10.5281/zenodo.10637449, 2024. Bowdalo, D., Basart, S., Guevara, M., Jorba, O., Pérez García-Pando, C., Jaimes Palomera, M., Rivera Hernandez, O., Puchalski, M., Gay, D., Klausen, J., Moreno, S., Netcheva, S., and Tarasova, O.: GHOST: A globally harmonised dataset of surface atmospheric composition measurements, Earth Syst. Sci. Data, 16, 4417–4495, https://doi.org/10.5194/essd-16-4417-2024, 2024., [GHOST] Globally Harmonised Observations in Space and Time, represents one of the biggest collection of harmonised measurements of atmospheric composition at the surface. In total, ~10 billion measurements from 1970-2025, of ~600 different components, from ~40 reporting networks, are compiled, parsed, and standardised. Components processed include gaseous species, total and speciated particulate matter, and aerosol optical properties. The main goal of GHOST is to provide a dataset that can serve as a basis for the reproducibility of model evaluation efforts across the community. Exhaustive efforts have been made towards standardising almost every facet of provided information from the major public reporting networks, saved in 21 data variables, and 163 metadata variables. Extensive effort in particular is put towards the standardisation of measurement process information, and station classifications. Extra complementary information is also associated with measurements, such as metadata from various popular gridded datasets (e.g. land use), and temporal classifications per measurement (e.g. day / night). A range of standardised network quality assurance flags are associated with each individual measurement. GHOST own quality assurance is also performed and associated with measurements. Measurements prefiltered by some default GHOST quality assurance are also provided., We gratefully acknowledge all data providers for the substantial work done in establishing and maintaining the measuring stations that provide the data contained in this dataset. We would also like to warmly thank all data providers who met with GHOST authors through this work, and for all support given, from helping resolve data rights issues, to giving suggestions for improvements. We acknowledge the computing resources of MareNostrum, and the technical support provided by the Barcelona Supercomputing Center (AECT-2020-1-0007, AECT-2021-1-0027, AECT-2022-1-0008, and AECT-2022-3-0013). We also acknowledge the Red Temática ACTRIS España (CGL2017-90884-REDT), and the H2020 project ACTRIS IMP (\#871115). The processing and creation of new aerosol optical property products was funded by EU HORIZON EUROPE under grant agreement no. 101056783 (FOCI project), and the processing and creation of precipitation chemistry and wet deposition products was funded by the World Meteorological Organization for the Measurement-Model Fusion for Total Global Atmospheric Deposition WMO Initiative. The research leading to the creation of this dataset has also received funding from the grant RTI2018-099894-BI00 funded by MCIN/AEI/ 10.13039/501100011033 (BROWNING), the EU H2020 Framework Programme under grant agreement No. GA 821205 (FORCES), the European Research Council under the Horizon 2020 research and innovation programme through the ERC Consolidator Grant grant agreement No. 773051 (FRAGMENT), the AXA Research Fund (AXA Chair on Sand and Dust Storms at the Barcelona Supercomputing Center), and the Department of Research and Universities of the Government of Catalonia through the Atmospheric Composition Research Group (code 2021 SGR 01550)., Peer reviewed

Supplementary Material 3: Supplementary Table 2. List of conserved motifs on P. vulgaris v1.0 promoters., NSF, Peer reviewed

[List of Starch Biosynthesis Genes Analyzed] Phvul.004G029100 Phvul.009G052100 Phvul.003G078800 Phvul.008G033800 Phvul.011G044000 Phvul.007G139400 Phvul.003G069200 Phvul.002G096000, The reference genome used was Phaseolus vulgaris v2.1 (Phytozome genome ID: 442, NCBI taxonomy ID: 3885). Raw whole genome sequences of 126 common bean individual plants (NCBI, PRJNA471678) were processed using Trimmomatic and mapped to the v2.1 common bean reference genome with the BWA aligner. The alignments were then subjected to GATK MarkDuplicates to tag duplicated reads. Promoter regions of starch biosynthesis genes (2000 bp downstream to 200 bp upstream of the transcription start site) were extracted, and variants were called using BCFtools mpileup and call functions., Peer reviewed

[Figures S2:] Average contours of each cultivar classified by aspect ratio intwo groups and by solidity inside each group., Peer reviewed

[Figures S1:] Photographs of the cultivars. Please see also: https://zenodo.org/uploads/10628169 (for seeds of Vitis species other than V. vinifera) and https://zenodo.org/records/14610754 (for seeds harvested in 2024). [] Figures S2: Average contours of each cultivar classified by aspect ratio., Peer reviewed