BUSCADOR RECOLECTA

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

Fig A. Dose-response curves for commercial drugs derived from the Larval Migration Inhibition Test (LMIT). Y-axis is the efficacy percentage (%). The standard error is shown by the error bars. Fig B. Dose-response curves for commercial drugs derived from the Larval Mortality Test (LMT). Y-axis is the efficacy percentage (%). The standard error is shown by the error bars. Fig C. Dose-response curves for diamine (AA) derivatives derived from the Larval Migration Inhibition Test (LMIT). Y-axis is the efficacy percentage (%). The standard error is shown by the error bars. Fig D. Dose-response curves for aminoalcohol (AO) derivatives derived from the Larval Migration Inhibition Test (LMIT). Y-axis is the efficacy percentage (%). The standard error is shown by the error bars., Peer reviewed

Peer reviewed

Software, data, code and notebooks for the reproducibility of the results., Peer reviewed

A total of 171 triplets (sire/dam/offspring) were collected from seven Spanish beef cattle populations (Asturiana de los Valles, n=25; Avileña-Negra Ibérica; Bruna dels Pirineus, n=25; Morucha, n=25; Pirenaica, n=24; Retinta, n=24; Rubia Gallega, n=24). The parents were chosen by minimizing the genealogical coancestry among them with the aim of capturing the existing variability within the populations. Individuals were genotyped using the BovineHD 777K BeadChip (Illumina Inc., USA), The SNPs present in the data file belonged to the autosomal chromosomes and those that were in repeated positions were excluded. Additional requirements were Mendelian error rates below 0.05 and call rates over 95% for both, individuals and SNPs. The quality control was made using the PLINK 1.0.7 software. Phasing was performed with BEAGLE with the "trio" option., Peer reviewed