BUSCADOR RECOLECTA

I. STRUCTURAL CHARACTERIZATION II. XANES CHARACTERIZATION III. RESISTIVITY, Peer reviewed

All the experiments have been carried out on thin films of IrO2, the thickness and crystallinity of the each film being specified in each file. X-ray reflectivity (XRR) and X-ray diffraction measurements (XRD) were performed by using Bruker D8 and Rigaku D/max-2500 diffractometers, respectively, by using the Ka radiation line of copper. The electrical resistivity was measured using the four points van der Pauw method by means of a Quantum Design PPMS-9T with no applied magnetic field and with a small electric current (0.1 mA). Magnetization measurements were carried out by using a commercial SQUID magnetometer from Quantum Design. Magnetization versus temperature data were collected from 5 to 350 K with a heating rate of 5 K/min at 1000 Oe. High-energy resolution fluorescence detected X-ray absorption spectroscopy (HERFD-XANES) measurements were carried out at RT by using a beamline I20-Scanning at Diamond Light Source. More informacion can be found in https://doi.org/10.1107/S1600577518008974. X-ray magnetic circular dichroism (XMCD) measurements were carried out at beamline 4-ID-D of the Advanced Photon Source, Argonne National Laboratory. Measurements were done in a cryomagnet with the sample cooled with 4He vapor. A 500 micron thick diamond phase plate was used to generate circularly polarized X-rays and XMCD measurements were carried out in helicity-switching mode. Measurements were done in fluorescence mode using a grazing incidence geometry and an energy dispersive 4-element Si drift diode detector placed at 90 degrees relative to the incident beam direction. XMCD Data were collected at 10 K with the magnetic fields along and opposite the X-ray propagation direction to remove any artifacts of nonmagnetic origin., Spanish MINECO projects MAT2014-54425-R (MINECO/FEDER, UE), MAT2017-82970-C2-R (AEI/FEDER, UE), MAT2017-83468-R (AEI/FEDER,UE), MAT2017-87134-C02-01-R (AEI/FEDER, UE) and MAT2017-87134-C02-02-R (AEI/FEDER, UE); Spanish MICINN project PID2020-115159GB-I00 / AEI / 10.13039/501100011033; Aragon Regional Government (Projects No. E12-20R and E28-20R); European Union’s Horizon 2020 program Marie Sklodowska-Curie grant agreement no. 665919; European Union’s Horizon 2020 Programme project Quantox of QuantERA ERA-NET Cofund of Quantum Technologies (Grant Agreement No. 731473)., XRD_IrO2_001epitaxy.dat XRD_IrO2_100epitaxy.dat XRD_IrO2_110epitaxy.dat XRD_IrO2_110textured.dat AFM_2d2nm_thick_001epitaxy.tif STEM_5d7_nm_thick_001epitaxy.tif XAS_IrO2_001epitaxy_5d7nm.dat XAS_IrO2_001epitaxy_96nm.dat XAS_IrO2_100epitaxy_1d7nm.dat XAS_IrO2_100epitaxy_5d1nm.dat XAS_IrO2_100epitaxy_89nm.dat XAS_IrO2_110epitaxy_5d3nm.dat XAS_IrO2_110epitaxy_92d2nm.dat XAS_IrO2_polycrystalline_films resistivity_IrO2_ 001epitaxy.dat resistivity_IrO2_ 100epitaxy.dat resistivity_IrO2_ 110epitaxy.dat resistivity_IrO2_ 110texture.dat MvsT_IrO2_001epitaxy_1d5nmthick.dat MvsT_IrO2_100epitaxy_1d5nmthick.dat XAS_XMCD_IrO2_100epitaxy_1d5nmthick.dat XAS_XMCD_powderIrO2.dat XRR_IrO2_5nmthick.dat XRR_IrO2_100nmthick.dat XAS_polycrystalline_vs_amorphous.dat rockingcurves_IrO2.dat RSM_IrO2_001epitaxy_5d7nm.dat RSM_IrO2_001epitaxy_96nm.dat RSM_IrO2_100epitaxy_5d1nm.dat RSM_IrO2_100epitaxy_89nm.dat, Peer reviewed

BmpA and BBK32 overlapping peptides identified as reactive in each of the study group; production of chimeric proteins; Western blot using anti-His-tag antibodies; and potential sources of nonspecific reactions (PDF). Z-score and Z-ratio for particular peptides., Peer reviewed

Table S1: Mean distances in meters between nest-boxes during the study period, all study areas pooled; Table S2: Results of the final GLMM models (using p-values as selection criterion, dropping those variables with p-value > 0.05) for vole activity based in the indirect abundance index (IAI) and different variables potentially affecting vole abundance, considering exclusively alfalfa fields; Table S3: Results of the final GLMM models (using p-values as selection criterion, dropping those variables with p-value > 0.05) for vole activity based on the indirect abundance index (IAI) and different variables potentially affecting vole abundance, considering all habitats except alfalfa fields; Figure S1: Study area of Villalar de los Comuneros (Valladolid province; VC); Figure S2: Study area of Boada de Campos/Capillas (Palencia province; BC); Figure S3: Study area of San Martín de Valderaduey (Zamora province; SMV); Figure S4: Boxplot of average number of fledglings in each study area and by habitat, after breeding season (summer season), considering a circular buffer of 180 m radius around every sampled quadrat, pooling years; Figure S5: Boxplot of average number of fledglings in each study area and by habitat, after breeding season (summer season), considering a circular buffer of 540 m radius around every sampled quadrat, pooling years; Figure S6: Common vole abundance (percentage of quadrats with presence) in alfalfa crops, considering distances to nest-boxes (occupied and unoccupied) and year, all study areas and seasons pooled, using an indirect abundance index (IAI); Figure S7: Common vole abundance (percentage of quadrats with presence) in alfalfa crops, considering distances to nest-boxes and season, all study areas and years pooled, using an indirect abundance index (IAI); Figure S8. Synthetic figure showing the average distance to occupied nest-boxes of sampled quadrats within alfalfa fields with absence (0) or presence (1), split by study area, year, and season; Figure S9. Common vole abundance (percentage of quadrats with presence) in all habitats except alfalfa crops, considering distances to nest-boxes and study areas, all seasons and years pooled, using an indirect abundance index (IAI); Figure S10. Synthetic figure showing the average distance of sampled quadrats within all habitats pooled, excluding alfalfa fields, with absence (0) or presence (1) of vole signs, split by study area, year, and season., Peer reviewed

[Description of methods used for collection/generation of data] Revision of written sources and testing of geographical information., This database will be accessible under request, if permission is granted by the owners. Please contact maria.cruz-berrocal@incipit.csic.es, EUForts is a database of European forts founded in Asia-Pacific between the 16th and 19th centuries. It includes the information about founding and successive cultural attributions, and exact locations of the forts produced specifically for this data set., Grant PID2020-116196GB-I00 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. PI María Cruz Berrocal., Peer reviewed

Table S1: Results of the literature survey to study WNV predominance over other Flavivirus spp.; Table S2: Summary of mosquito captures in the study sites; Table S3: Detailed bELISA and rRT-PCR findings throughout bird Family, study site and area; Table S4: Bird abundance indices per site and area throughout bird Order and Family. References [43,44,45,46,47,48,49,50] are cited in the Supplementary Materials., Peer reviewed

Additional file 1. Figure 1. Representing images of (A) a Control TUNEL-positive sheep oocyte stained with (a) Hoechst 33342 and (b) fluorescein; and (B) a Control TUNEL-negative sheep oocyte stained with (a) Hoechst 33342 and (b) fluorescein., Additional file 2. Table 1. Details of primers used for qPCR., Additional file 3. Figure 2. Depicted images of the cumulus cell populations in terms of (A) viability, apoptosis, and necrosis, (B) active mitochondria, and (C) intracellular levels of ROS and GSH using flow cytometry and analyzed in IDEAS software., Additional file 4. Figure 3. Depicted images of (A) a Control TUNEL-positive expanded sheep blastocyst stained with (a) Hoechst 33342 and (b) fluorescein; and (B) a Control TUNEL-negative expanded sheep blastocyst stained with (a) Hoechst 33342 and (b) fluorescein., Peer reviewed

Datasets and Rcodes employed in "A comparative study of resource selection functions and imperfect detection models with which to describe wildlife habitat selection: biologging vs. camera traps as data sources.", Peer reviewed

Raw data available for reviewers., Peer reviewed

[Dataset generated in Subtask 2.3.1 of the H2020 EuroSea project] H2020 EuroSea project: The H2020 EuroSea project aims at improving and integrating the European Ocean Observing and Forecasting System (see official website: https://eurosea.eu/). It has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626). Task 2.3: Task 2.3 has the objective to improve the design of multi-platform experiments aimed to validate the Surface Water and Ocean Topography (SWOT) satellite observations with the goal to optimize the utility of these observing platforms. Observing System Simulation Experiments (OSSEs) have been conducted to evaluate different configurations of the in situ observing system, including rosette and underway CTD, gliders, conventional satellite nadir altimetry and velocities from drifters. High-resolution models have been used to simulate the observations and to represent the “ocean truth”. Several methods of reconstruction have been tested: spatio-temporal optimal interpolation, machine-learning techniques, model data assimilation and the MIOST tool. The planned OSSEs are detailed in this public report Barceló-Llull et al. (2020) and the complete analysis is available here Barceló-Llull et al. (2022). Contributors to Task 2.3 are CSIC (Spain), CLS (France), SOCIB (Spain), IMT-Atlantique (France) and Ocean-Next (France). Subtask 2.3.1: Subtask 2.3.1 aims to evaluate different in situ sampling strategies to reconstruct fine-scale ocean currents (~20 km) in the context of SWOT. An advanced version of the classic optimal interpolation used in field experiments, which considers the spatial and temporal variability of the observations, has been applied to reconstruct different configurations with the objective to evaluate the best sampling strategy to validate SWOT. Where? The analysis focuses on two regions of interest: (i) the western Mediterranean Sea and (ii) the Subpolar North West Atlantic. In the western Mediterranean Sea, the target area is located within a swath of SWOT, while in the North West Atlantic the region of study includes a crossover of SWOT during the fast-sampling phase. [Report with the full analysis] The complete analysis can be found in this report: Barceló-Llull et al. (2022). [Codes for the analysis] The codes generated to develop Subtask 2.3.1 can be found on GitHub: https://github.com/bbarcelollull/EuroSea_subTask_2.3.1, The dataset includes: 1) Model outputs used to simulate the observations in different configurations in both regions of study. The folder "2D_model_outputs" contains 2D data used to simulate SSH observations for the analysis of the temporal correlation scale (Barceló-Llull et al., 2022, p. 28-42). The folder "3D_model_outputs" contains 3D model outputs used to simulate observations of temperature and salinity. Note that eNATL60 outputs have been interpolated onto a new regular grid. 2) Simulated configurations (or sampling strategies) in each region (PKL file format). 3) Observations simulated in each configuration in both regions of study. The observations simulated are temperature and salinity. ADCP horizontal velocities are also simulated, however for eNATL60 they will be corrected in the future to account for the rotated original axes. File format: region_configuration_period_model.nc. The folder "SSH" includes the simulated SSH observations for the analysis of the temporal correlation scale (Barceló-Llull et al., 2022, p. 28-42). 4) Reconstructed fields with the spatio-temporal optimal interpolation. File format: region_configuration_period_model_stOI_Lx_Lt_cd_YYYYMMDDhhmm_var.nc (stOI = spatio-temporal optimal interpolation, Lx = spatial correlation scale, Lt = temporal correlation scale, cd = map on the central date of the sampling, YYYYMMDDhhmm = date and time of the map, var = variable interpolated (temperature and salinity) or the derived variables (dynamic height, geostrophic velocities and the Rossby number)). 5) Compared fields (ocean truth from model outputs vs. reconstructed fields) for each region and model (PKL file format)., EuroSea – Improving and Integrating European Ocean Observing and Forecasting Systems for Sustainable use of the Oceans 862626. European Commission., Peer reviewed