BUSCADOR RECOLECTA

Detailed synthetic description of the molecular precursors of 2-OS and SMe-2OS, and corresponding spectroscopic data; additional STM and STS data: sample after deposition of diol 1, on-surface generation of 2-OS by tip-induced reactions, manipulation experiments on 2-OS, on-surface generation of SMe-2OS, computed derivative of the dI/dV spectra in Figure 4; complementary MFH calculations; DFT study of the adsorption geometry of 2-OS on Au(111) and of the changes due to interactions with the STM tip, and DFT calculations including the effects of temperature., Peer reviewed

The common house mosquito Culex pipiens s.l., widely distributed in Europe, Africa, and North America has two recognized biotypes, Cx. pipiens biotype pipiens and Cx. pipiens biotype molestus, which hybridize. Despite their morphological similarities, these biotypes may exhibit ecological differences. This complex ecological mosaic may affect the interaction of Cx. pipiens with pathogens like avian Plasmodium, which is transmitted to wildlife. Although the vector competence for Cx. pipiens biotype molestus has been well studied, there is a lack of studies comparing the vector competence of Cx. pipiens biotype pipiens and their hybrids for the transmission of avian Plasmodium. Here, we evaluated the vector competence of the Cx. pipiens biotypes pipiens, molestus and their hybrids for the transmission of two avian Plasmodium species. Mosquitoes were allowed to feed on blood of wild infected birds and the presence of DNA of Plasmodium in head-thorax and saliva of mosquitoes was molecularly evaluated at 13 day-post exposure. The transmission rates (i.e., the detection of parasite DNA in mosquito saliva) for Plasmodium cathemerium were similar for the two biotypes of Cx. pipiens and their hybrids while Plasmodium relictum DNA was only found in the saliva of Cx. pipiens biotype pipiens. In addition, P. cathemerium was significantly more prevalent than P. relictum in the saliva of Cx. pipiens biotype pipiens. Our results suggest that avian Plasmodium is transmitted by both Cx. pipiens biotypes and their hybrids although differences could be found depending of the parasite species studied. Differences in the abundance of each biotype and their hybrids within areas characterized by distinct environmental conditions, along with variations in their blood-feeding patterns and the parasites infecting birds, could ultimately determine differences in the relevance of each Cx. pipiens biotype in the transmission of avian Plasmodium., Peer reviewed

Additional DFT data for single layer and hetero-bilayer NbSe2 structures is provided., Peer reviewed

Scattering-type scanning near-field optical microscopy (s-SNOM) and Fourier transform infrared spectroscopy (FTIR) (S1); calculated spectra of the dielectric function and reflectivity for the normal angle of incidence (S2); effect of the finite angle of incidence on the experimental reflectivity spectra (S3); polaritonic wavelength, group velocity, and propagation length: experiment vs theory (S4); temperature-dependent study of the polaritonic lifetime in the elliptical band (S5)., Peer reviewed

Summary of other copolycarbonate systems, synthetic method, 1H NMR and 13C NMR spectra, theoretical estimation of ideal melting enthalpy, WAXS/SAXS, and FT-IR spectra., Peer reviewed

Detailed information on the turning point for the reflected and trapped molecules., Peer reviewed

[Description of methods used for collection/generation of data] The models are 2D plane strain including a homogeneous and isotropic granitic reservoir rock ‎intersected by an interconnected fracture network and a distant fault to the wellbore. Generic hydromechanical properties are assumed for the reservoir rock and the intersecting fractures and fault. The fault ‎in the models is either hydraulically isolated from the fracture network, i.e., our "basecase" scenario, or intersected by them, a scenario referred to as "connected". Post-‎injection protocols to control seismicity include well shut-in or bleed-off. During bleed-off, the ‎wellhead is opened to impose hydrostatic pressure conditions along the well over a period of ‎either 24 h or 1 h. The ‎hydrostatic pressure profile is maintained afterwards. Different injection ‎periods are considered to inspect the effect of this parameter on the effectiveness of bleed-off ‎in controlling post-injection seismicity. ‎, [Methods for processing the data] The Code_Bright executables "Cb_v9_3.exe" or "Cb_v9_1_3par4", i.e., the parallel version of the code running on 4 CPU cores, can be used to run the numerical models. Upon running the executables, the code generates several outputs, including "root.post.msh" and "root.post.res". These two outputs include the mesh structure and the simulation results, respectively. Any postprocessing software supporting the output file formats can be used to visualize the data., The Global physics-based database of injection-induced seismicity is licensed under Creative Commons CC BY 4.0 International License. Users are free to copy and redistribute the database material in any medium or format, and remix, transform, and build upon the material for any purpose, even commercially., This dataset comprises the input files for numerical simulation of stimulating an enhanced geothermal system and the associated risk of induced seismicity. Numerical models explore the hydromechanical mechanisms of post-injection induced seismicity and the potential impact of bleed-off to control seismicity. The models are 2D plane strain including a homogeneous and isotropic granitic reservoir rock intersected by an interconnected fracture network and a distant fault to the wellbore. The fault in our numerical models is either hydraulically isolated from the fracture network, i.e., our “basecase” scenario, or intersected by them, a scenario referred to as “connected”. Post-injection protocols to control seismicity include well shut-in or bleed-off. During bleed-off, the wellhead is opened to impose hydrostatic pressure conditions along the well over a period of either 24 h or 1 h. The ‎hydrostatic pressure profile is maintained afterwards. Different injection periods are considered to inspect the effect of this parameter on the effectiveness of bleed-off in controlling post-injection seismicity. Following this brief explanation, the dataset includes seven models provided in seven distinct folders: “Basecase_Inj250h_shutin”, basecase scenario of 250 h injection followed by well shut-in, “Basecase_Inj250h_bleedoff_1h”, basecase scenario of 250 h injection followed by well bleed-off over 1 h, “Basecase_Inj250h_bleedoff_24h”, basecase scenario of 250 h injection followed by well bleed-off over 24 h, “Connected_Inj84h_shutin”, connected scenario of 84 h injection followed by well shut-in, “Connected_Inj84h_bleedoff_1h”, connected scenario of 84 h injection followed by well bleed-off over 1 h, “Connected_Inj200h_shutin”, connected scenario of 200 h injection followed by well shut-in, “Connected_Inj200h_bleedoff_1h”, connected scenario of 200 h injection followed by well bleed-off over 1 h. In each folder, there is a file with the name of the folder ending as “_gen.dat” which contains the input data of the model, including material properties, initial and boundary conditions and the time intervals. There is also a file ending as “_gri.dat” that includes the information on the mesh. The file “root.dat” includes the name of the model. To run the simulation, execute the Code_Bright executables “Cb_v9_3.exe” or “Cb_v9_1_3par4”, i.e., the parallel version of the code running on 4 CPU cores, in each folder., - Project EASY GEO-CARBON ‎‎(www.easygeocarbon.com)‎ funded by MCIN/AEI‎ through grant number PCI2021-122077-2B‎. - The UKRI Postdoc Guarantee ‎Award THMC4CCS funded by the Engineering and Physical Sciences Research Council through grant number EP/X026019/1. - The ‎Starting Grant ‎‎GEoREST (www.georest.eu) funded by the European Research Council (ERC) under ‎the ‎‎European Union’s Horizon 2020 Research and ‎Innovation Program through under Grant ‎agreement No. 801809. ‎‎, This dataset comprises the input files for numerical simulation of stimulating an enhanced ‎geothermal system and the associated risk of induced seismicity. Numerical models explore the ‎hydromechanical mechanisms of post-injection induced seismicity and the potential impact of ‎bleed-off to control seismicity.‎ The dataset includes seven models provided in seven distinct folders: ‎ ‎"Basecase_Inj250h_shutin", basecase scenario of 250 h injection followed by well shut-in,‎ ‎"Basecase_Inj250h_bleedoff_1h", basecase scenario of 250 h injection followed by well bleed-‎off over 1 h,‎ ‎"Basecase_Inj250h_bleedoff_24h", basecase scenario of 250 h injection followed by well ‎bleed-off over 24 h, ‎ ‎"Connected_Inj84h_shutin", connected scenario of 84 h injection followed by well shut-in,‎ ‎"Connected_Inj84h_bleedoff_1h", connected scenario of 84 h injection followed by well bleed-‎off over 1 h,‎ ‎"Connected_Inj200h_shutin", connected scenario of 200 h injection followed by well shut-in,‎ ‎"Connected_Inj200h_bleedoff_1h", connected scenario of 200 h injection followed by well ‎bleed-off over 1 h.‎, Peer reviewed

Table S1: List of models used in the study, including the GCMs and RCMs Figure S1: (Top) Evolution of the recorded crop yields of corn, maize, and grape in Moldova and (Bottom) the annual average anomalies for the current and previous 5 years. Figure S2: Evolution of the anomalies in AED calculated from the FAO56-Penman-Monteith and the Hargreaves equations. Figure S3: Spectral characteristics of precipitation time series recorded during the period 1853–2020 at Chisinau station. (a) Time series of monthly precipitation records; (b) Global wavelet periodogram expressed as percentage of total global power summed over all periods. Time-scales beyond the dominant annual time-scale are identified and magnified in the bottom figures; (c) Seasonal-annual response; (d) Short-term annual response; and (e) Long-term-annual response. Figure S4: Local wavelet power spectrum of the monthly precipitation time series. The vertical axis is the wavelet time-scale and the horizontal axis is the time position during the period 1853–2020 at Chisinau station. The thick white dashed curve depicts the cone of influence below which the edge effects on the amplitude of the local power spectrum are negligible.61 Table S2: Summary of the temporal variance fraction accounted by each dominant time-scale determined based on the local and global power spectra displayed in Figures S1 and S2. The variance fractions were obtained by applying the standard statistical variance formula to each wavelet time series. Percentages in parentheses give incremental percentage of such temporal variance as the sum of the time-scales. Figure S5: Temporal scale-by-scale decomposition of monthly precipitation time-series anomalies (mm/month). Positive and negative anomalies were calculated with respect to the mean values. The three nonoverlapping time-scales bands contain the major power peaks and were determined based on the global power spectra displayed in Figure S1. Seasonal-to-annual time-scales represent a time band of 0.083 to 1.5 years; short-term-annual time-scale depicts a time band of 1.5−10.5 years; and long-term-annual time-scale is related with a time band of 10.5−50 years. Figure S6: Evolution of the SPI in the five meteorological stations used in this study and from the average precipitation series. Figure S7: Monthly correlations between maximum temperature accumulated over different temporal scales and the crop yields of maize, sunflower, and grape in Moldova. Figure S8: Monthly correlations between Penman-Monteith reference evapotranspiration accumulated over different temporal scales and the crop yields of maize, sunflower, and grape in Moldova. Figure S9: Pearson's r correlations between the maize yield and the monthly meteorological variables accumulated over different temporal scales for the period 2000–2020. Figure S10: Pearson's r correlations between the sunflower yield and the monthly meteorological variables accumulated over different temporal scales for the period 2000–2020. Figure S11: Pearson's r correlations between the grape yield and the monthly meteorological variables accumulated over different temporal scales for the period 2000–2020. Figure S12: Evolution of annual precipitation, maximum and minimum temperature from 43 climate models considering the historical period (from 1970 to 2005) and the RCP8.5 scenario (2006−2100). Gray lines represent the individual models and color lines the average of the different models. Black line represents the regression line and dark colors the evolution of observations for Moldova. Figure S13: Distribution of monthly maximum temperature from the different climate models (gray lines) for the periods 1970–2020 and 2070–2100. Dashed black lines represent the average of the models and red line the observed temperature for the period 1970–2020., Peer reviewed

• Online Appendix A: Course structure and screenshots of the exam • Online Appendix B: Nomenclature • Online Appendix C: Subject pool • Online Appendix D: Differences between exam versions • Online Appendix E: Scatter plots of individual data • Online Appendix F: Distribution of answers • Online Appendix G: Cumulative completion times • Online Appendix H: Joint data OLS regression model • Online Appendix I: Informativeness of exam grades • Online Appendix J: Sample exam, Peer reviewed

Raw data obtained at the ALBA synchrotron with SiC diode devices for the paper: "Characterisation of SiC radiation detector technologies with synchrotron X-rays", This work has been funded by the IMB-CNM (CSIC) internal TRIGGER project PlACeD and the laCaixa Health foundation DosiFLASH project HR23-00718. These experiments were performed at BL13-XALOC beamline at ALBA Synchrotron with the collaboration of ALBA staff. The beam time was funded by ALBA with proposal ID 2024028103. This work has also received funding from the EMPIR programme co-financed by the Par-ticipating States and from the European Union’s Horizon 2020 research and innovation program under project 18HLT04-UHDpulse, from project NEWDOSI (PID2021-123484OB-I00), financed by MCIN / AEI / 10.13039/501100011033 / FEDER, UE and from Grant RTC-2017–6369–3 (GRACE) by MCIN/AEI/ 10.13039/ 501100011033 and ERDF ‘A way of making Europe’. Support through the Maria de Maeztu grant CEX2023-001397-M funded by MICIU/AEI/10.13039/501100011033 is acknowledged., List of files: . Fig3-linescan-SiC-Graphene_50x50_2.dat . Fig3-linescan-SiC-Metal3um_50x50_2.dat . Fig3-LineScan_SiC-NoMetal3um_50x50_2.dat . Fig3-LineScan_SiC_metal50um_50x50.dat . Fig4-currenscan_SiC_metal50um_50x50.dat . Fig4-currentscan-SiC-Graphene_50x50_3.dat . Fig4-currentscan-SiC-Metal3um_50x50_3.dat . Fig5-Energyscan-SiC-Graphene_50x50_2.dat . Fig5-Energyscan-SiC-Metal3um_50x50_1.dat . Fig5-Energyscan-SiC-Metal3um_inRing_50x50_2.dat . Fig5-Energyscan-SiliconReference_Hamamatsu-P1_sample1_50x50_3_sorted.dat . Fig5-EnergyScan_SiC_metal50um_50x50_removed_saturated.dat, No