BUSCADOR RECOLECTA

This dataset encompasses data on freshwater sites sampled for environmental data and algae in Madrid County in the 2017-2022 period., Madrid Sampling Sites (2017-2022).txt, Peer reviewed

[Description of methods used for collection/generation of data] IGC measurements at infinite dilution (IGC-ID) and finite dilution (IGC-FD) were carried out using a PerkinElmer Clarus 400 chromatography meter coupled with an Agilent AMD 1000 flowmeter to control the gas injections. The analysis of recorded chromatograms to obtain the isotherms was conducted using the CGI-CF V 709G software from Adscientis. For the determination of energy distribution functions, the FDRJ 07.5.F software from Adscientis was used. For the measurements at infinite dilution (IGC-ID), C3S-I, C3S-II and C3A samples were placed in a 6.4 mm (1/4 in.) measuring column, with an internal diameter of 4 mm and a length of 10 cm. Approximately 2 g of powder were placed in the columns for measurement. Due to the poor handling properties of the kaolinite and metakaolin powders, compaction, crushing and sieving was carried out beforehand. This procedure ensured a suitable gas flow rate in the IGC column. To do this, kaolinite and metakaolin samples were compacted in a pelletizing mold under a pressure of 1 ton for 2 minutes. Afterwards, the pellets were crushed and sieved. A 3.2 mm (1/8 in.) measuring column, with an internal diameter of 2 mm and a length of 10 cm, was filled with pieces with a size between 250 to 450 m. Approximately 0.35 grams of kaolinite and metakaolinite were used in each case. Before conducting the measurements, the surface of the samples inside the column was conditioned for the tests using a flow of dry helium at 5 mL/min during 16 h at 30°C. Measurements were conducted at 30°C for all samples, except kaolinite. In this case, the retention times observed were extremely long, and the measurement temperature was increased to 80°C on this sample to achieve full elution over a reasonable timeframe. Hexane, heptane, octane, nonane, isooctane, 2,2-dimethyl-hexane (2,2-DHM) and cyclooctane were used as n-alkane probes to determine the dispersive surface energy. Isooctane, 2,2-dimethyl-hexane (2,2-DMH) and cyclooctane were used as branched and cyclic probes to measure the surface nanoroughness. Acetone, acetonitrile (CH3CN), tetrahydrofuran (THF), ether, chloroform (CHCl3) and benzene were also used as polar probes to determine the acid-base character of the different cementitious materials. For IGC-FD measurements, water and isopropanol were used as probes over the set of samples with helium as carrier gas in both cases. Water was initially used over C3A and C3S-I samples. In this case, P/P0 values from 0.15 to 0.64 were explored. A strong interaction of water with the surfaces was observed, and consequently isopropanol was selected as a more suitable adsorbate to conduct further experiments. Measurements with isopropanol were done for C3A, C3S-I, C3S-II, C3S-II Ann, C3S-II-TEA, Kaolinite, MK and MK-TEA. For these measurements, the injected amount of isopropanol was fixed so to achieve a P/P0 up to 0.2, with a minimum of 3 injections. All isopropanol injections were preceded by injection pulse of methane (CH4), used as a reference to compute the retention times (TR) at each point of the chromatogram. All measurements were conducted at 30°C. For all cement phases, a 6.4 mm (1/4 in.) measuring column, with an internal diameter of 4 mm and a length of 10 cm was used. The columns were filled with 1.6-2 g of sample. In the case of C3S-II Annealed, a shorter column (3 cm, filled with 0.6 g of sample) with the same diameter was used to decrease the observed retention time. In the case of clay minerals, a 3.2 mm (1/8 in.) measuring column, with an internal diameter of 2 mm and a length of 5 cm was used, filled with 0.13 to 0.18 g of sample. The clay powders were pelletized in the same way as for the IGC-ID measurements. Isothermal calorimetry was used to characterize the reactivity of the different cement phases. Paste samples were prepared by mixing 15 g of powder with distilled water at a water-to-solids ratio of 0.35. A high shear mixer was used at 1600 rpm for 2 minutes. Afterwards, 10 grams of sample were placed in a glass ampoule and sealed. A TAM Air isothermal calorimeter was used to monitor the heat release of the samples for 7 days at 20°C. Isothermal calorimetry was also used to assess the reactivity of the different clay minerals. For this purpose, the R3 test (ASTM C1897) [16] was selected due to its reliability and ease of interpretation. In this procedure, the different clays are mixed with portlandite, calcium carbonate, potassium sulphate, potassium hydroxide and water at 40 °C, and put into glass ampoules inside the calorimeter at 40 °C. The total heat evolved is taken as a measure of the reactivity of the clays., This paper presents a preliminary study of the characterization of the surface energy properties of clinker phases (C3S and C3A), kaolin and metakaolin by Inverse Gas Chromatography (IGC). For this, a reliable measurement methodology was developed. By looking at changes in the whole series of results (dispersive surface energy, specific polar interaction parameter, acid and base constants, morphology index, nanoroughness and adsorption energy distribution function), it is possible to discern changes between the same powders with different surface treatments. A promising correlation between surface properties and the reactivity of studied materials have been found. However, based on the IGC characterization, the increased reactivity of metakaolin compared to the raw kaolinite seems to be strongly linked to the change in local order rather than significant changes in the surface energetics, although a change in the acid/base nature of the surface has been observed., Swiss Agency of Development and Cooperation (SDC) grant 81026665; Swiss National Science Foundation (SNSF) through an Ambizione fellowship (grant 208719); Consejería de Educación e Investigación (Comunidad de Madrid) which funded the 2016-T1/AMB-1434 project in the frame of “Ayudas de Atracción de Talento Investigador”; Ministry of Science and Innovation through funding PID2020-115797RB-I00/AEI/10.13039/501100011033 project., File List: DRX C3S; PSD_C3SC3A; Calculation KaKb; Data_DI&FC; Data_IGC-FC; Water_AEDF_norm; Water_AEDF, Peer reviewed

This dataset encompasses data on freshwater sites sampled for environmental data and algae in Madrid County in 2019, May., Madrid Sampling Sites (2019, May).txt, Peer reviewed

The files contain the dataset of the figures in the article "Description of ultrastrong light-matter interaction through coupled harmonic oscillator models and their connection with cavity-QED Hamiltonians", written by Unai Muniain, Javier Aizpurua, Rainer Hillenbrand, Luis Martín-Moreno and Rubén Esteban. The article will be published in the journal Nanophotonics (DOI: 10.1515/nanoph-2024-0528), MICIU/AEI/10.13039/ 501100011033 and ERDF, EU (grants PID2022-139579NB-I0 and PID2021-123949OB-I00);, MICIU/AEI/10.13039/ 501100011033 (grants Grant CEX2020-001038-M, PID2020-115221GB-C41 and CEX2023-001286-S);, Basque Government (grant IT 1526-22 for consolidated groups of the Basque University);, Department of Economy of the Basque Government (project 4usmart Elkartek);, Government of Aragon (Project Q-MAD);, Peer reviewed

Additional characterization data including NMR, FTIR, ESI HRMS, MALDI-ToF MS, DSC, rheology, and MIP results., Peer reviewed

Experimental details for the synthesis of 1, summary of crystallographic data for [1]Cl and 1 (at 173 and 296 K), comparison of the experimental and computed PXRD patterns and EPR spectra for 1 as well as additional magnetic measurements and computational studies on 1., Peer reviewed

Additional experimental details on ferroelectric poling of the PZT film; electrical characterization of the nominally neutral 180° DWs by conductive AFM; PFM images of the reorganization of the a domains after poling; STEM images illustrating the interaction between 180° and 90° DWs; details of the determination of atomic displacements around the DWs; STEM analysis of a twisted DW., Peer reviewed

Further tables of refinement parameters, fits to magnetometry data, and depictions of magnetic exchange interactions., Peer reviewed

S1 Appendix. Full derivation of the mean phase of firing.Provides a detailed solution to the deterministic part of Eq 1, resulting in the “rate-to-phase” transfer function (Eq 3) previously derived in [6]., S2 Appendix. Full derivation of the variance of phase of firing. Explains the use of a first-order Taylor series expansion and the propagation of uncertainty around the spike threshold to derive an analytical approximation of the phase variance., S3 Appendix. Approximation of information rate. Describes the approximation of the entropy of Gaussian mixtures to derive an analytical estimation of the information rate. Additionally, it introduces a correction factor to account for cycle-to-cycle correlations., S4 Appendix. Neuron parameters. Includes: Table A. Default parameters for hippocampal neurons; Table B. Neuron parameters along the hippocampal dorsoventral axis; Table C. Neuron parameters for visual and olfactory cells. -- S5 Appendix. Simulations. Details the numerical integration of Eq 1 used in simulations supporting our theoretical framework., S1 Fig. Oscillatory frequency modulates the tonic input range that makes neurons phase-lock. (A) Phase-locking function for different values of the oscillatory frequency f (color coded). Shaded areas denote the phase-locking range of Is, corresponding to the domain of Is in Eq 3. The parameters are the same ones used in [6] and in Fig 1, to match hippocampal physiology (described in Table A in Appendix). Note that the phase-locking range spans half of a cycle and not the full cycle as previously thought, in agreement with recent studies [7]. (B) Length of Is range (max(Is) − min(Is) = 2AIosc) across frequencies. (C) Average Is as (middle point in Is range) across frequencies. (D) Effective amplitude of the membrane potential oscillation, Vosc produced by the oscillatory input Iosc. Given that the membrane acts as a low-pass filter, determined by τm, the effective oscillation in the membrane potential can be found to be Vosc = RmIoscA. Thus, since the membrane filters the oscillatory input as , the amplitude of the membrane potential Vosc will decrease with f approximately as ∼ 1/f., S2 Fig. Analytical approximation of phase distributions. (A) Phase distributions for a range of frequencies and noise strengths. Histograms denote the simulations whereas solid lines denote the theoretical predictions. For the simulations, first-spike phases are recorded from the beginning of the second cycle (with the trough as ϕ = 0), after initializing the neurons to their expected phase ϕ0 = μϕ to allow them to reach steady-state dynamics (as described in Appendix). The parameters used here are described in Table A in Appendix). (B) Average variance in rad2 (across Is levels) across a wide frequency–noise parameter space, for simulations and the theoretical predictions. (C) Diagonal slices of plots in (B), showing the deviation of the theory from the simulations after a certain level of noise amplitudes at high frequencies, due to the bounded variance of simulated spike phases constrained to the measurable range of [0, 2π] radians., S3 Fig. Effective rhythmic input sampling. (A) An example signal with τs of 100 ms sampled by different oscillation frequencies. (B) Effective frequency for various τs values., S4 Fig. Information rate across frequencies for the range of physiologically realistic noise levels (η)., S5 Fig. Normalized information rate across the frequency–noise parameter space for simulations and theoretical predictions., S6 fig. Normalized information rate across the frequency–noise parameter space for a wide range of input signal time constants τs., S7 Fig. Normalized information rate across the frequency–noise parameter space for a wide range of membrane time constants τm., S8 Fig. Optimal frequency for a wide range of membrane time constants τm and input signal time constants τs. At every point of the τm − τs parameter space (logarithmically discretized in a 200 × 200 grid), we computed rnorm over the frequency–noise space (as in e.g., Fig 4B). Then, the optimal frequency was estimated as an average of the peak frequency between the physiologically-realistic noise range η = [0.1, 0.15]., S9 Fig. Normalized information rate for colored noise with different long-range correlation lengths: White, pink, and brown noise.A value of 100 ms was used here for τs. All plots represent the results of simulations., S10 Fig. Normalized information rate across the dorsoventral axis for simulations and theoretical predictions., S11 Fig. Normalized information rate across the frequency-amplitude space for simulations and theoretical predictions., Peer reviewed

[Description of methods used for collection/generation of data] We sampled 168 pedunculate oaks in 2020 (25 sites) and in 2021 (34 sites) across 18 European countries (Figure 1). At each site, consisting of woods or forests of 1 ha or more, we haphazardly selected three adult pedunculate oaks. We selected the focal oaks among those with low-hanging branches that could easily be reached from the ground. In early summer – approximately 10–12 weeks after oak bud break at each site – we selected four low-hanging branches on each tree, pointing north, south, east and west directions, and haphazardly collected 30 developed leaves per branch at a random time of the day, for a total of 120 leaves per tree. Leaves were oven-dried 48 h at 45°C immediately after collection. We quantified flavonoids as rutin equivalents, hydrolysable tannins as gallic acid equivalents, and lignins as ferulic acid equivalents (Moreira et al. 2018). We obtained the quantification of these phenolic compounds by external calibration using calibration curves at 0.25, 0.5, 1, 2 and 5 μg mL-1. Phenolic compound concentrations were expressed in mg·g-1 tissue on a dry weight basis. We also quantified phenolic compound diversity at the individual plant level using two indices: phenolic compound richness, defined as the total number of phenolic compounds, and the Shannon diversity index. We assessed herbivory on a subset of 60 leaves per tree, blindly drawn from the original set of 120 leaves. These 60 leaves, along with the remaining 60 (unassessed) leaves were kept in a sealed plastic box with silica gel until they were processed for leaf phenolics and the feeding trial. We also performed a feeding experiment in 2021 and repeated 2022 with oak leaves sampled in 2020 and 2021, respectively. In total, it included 177 larvae that were reared on an artificial diet prepared from oak leaves from 55 sites (4 of the sites were sampled twice in 2020 and 2021)., [Methods for processing the data] We analyzed the data using the program R., The dataset consists on the variables that resulted of the combination of field observations and feeding trials in controlled environments to investigate the effect of climate on chemical defences and insect herbivory in pedunculate oak (Quercus robur L.) throughout most of its geographic range in Europe, while controlling for physical defences. We measured herbivory and quantified secondary metabolites (phenolics) in 168 pedunculate oaks distributed across its European geographic range. We fed spongy moth larvae (Lymantria dispar L.) with a semi-artificial diet incorporating grounded leaves from the same oaks to isolate chemical traits from physical traits. We measured a total of 18 variables (Site_ID; Year_sampling; Latitude and Longitude; MAT; MAP; Flavonoids (mg g⁻¹ ); Lignin (mg g⁻¹ ); Condensed tannins (mg g⁻¹ ); Hydrolysable tannins (mg g⁻¹ ); Total phenolics; Phenolic compound richness (S); Shannon's diverisry index (H); Hydrolysable tannins (mg g⁻¹ ); Herbivory_site; Replicate; Weight_i; Weight_f and RGR)., This study received financial support from the French National Research Agency (ANR) under the Investments for the Future Programme, through the Cluster of Excellence COTE (Continental To Coastal Ecosystems: Evolution, Adaptability, and Governance) (ANR-10-LABX-45). Additional funding was provided by the BNP Paribas Foundation as part of its Climate & Biodiversity Initiative and the citizen science project Tree Bodyguards. The work of EVC was also supported by the Ministry of Science and Innovation (MICINN) (Spain) through a Juan de la Cierva fellowship no. FJC2021-046608-I. The work of AM was supported by the Grant Agency of the Czech Republic PIF Outgoing grant No. 23-07045O and Programme of Support of Promising Human Resources: Postdoctoral Researchers no. L200962302, awarded by the Czech Academy of Sciences. FB was supported by the project PN 23090102. EM was supported by the Grant Agency of the Czech Republic (19-28126X). KS was supported by the Grant Agency of the Czech Republic JUNIOR STAR project No. 22-17593M. XM was supported by grants from the Spanish Ministry of Science and Innovation (PID2022-141761OB-I00 and EUR2023-143463 projects). ZV was supported by the María Zambrano Programme of the Spanish Ministry of Universities. MdG was supported by the core research group ‘Forest biology, ecology and technology’ (P4-0107) of the Slovenian Research Agency. SG was supported by a Royal Society University Research Fellowship. MB was supported by the Forest Research Centre, a research unit funded by FCT, Portugal (UIBD/00239/2020) and the Laboratory for Sustainable Land Use and Ecosystem Services-TERRA (LA/P/0092/2020)., File List: This file corresponds to the dataset that has being used in the article “Effects of climate on leaf phenolics, insect herbivory, and their relationship in pedunculate oak (Quercus robur) across its geographic range in Europe” by Valdés-Correcher et al. 2025 (Oecologia). Table including the variables present in the dataset:, Peer reviewed