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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

Supplementary file1, Peer reviewed

Excel file including: 1st excel sheet: physico-chemical data (pH, water activity aw, moisture (%), Fat (%), Protein (%), color parameters (L*, a*, b*), texture parameters (Hardness (N), Springiness, Cohesiveness, Chewiness) and cooked loss (%) of analyzed samples. 2nd excel sheet: fatty acid methyl esters (FAME) data expressed as percentage (g/100 g total fatty acids) and content (mg/100 g product-patty) of analyzed samples, and 3rd excel sheet: Sensory analysis by free choice profile (FCP)., Financial support from MCIU/AEI/10.13039/501100011033 Grant reference PID2021-122581OB-100 and CEX2021-001189-S, and by “ERDF A way of making Europe”, With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2021-001189-S), Peer reviewed

Experimental procedures, synthesis, NMR spectra, crystallographic and photophysical data, compound stability, conductance and Seebeck measurements, XPS data and theoretical data, crystallographic data for compounds 1a and 1b (CSD 2301298 and 2301299, respectively)., Peer reviewed

Esta es la v 1 del dataset. La segunda versión puede verse en este mismo repositorio http://hdl.handle.net/10261/409397, Los datasets del proyecto DeQuDy son datos en brutos que permiten el análisis de las características codicológicas y textuales del libro sagrado musulmán en la península ibérica. Este conjunto de datos está formado por las características más relevantes de cada obra coránica. Estos datos pueden verse en relación a los datos del proyecto ERC Synergy Grant, The European Qur’an, liderado por Mercedes García-Arenal, ya que son Aquí publicamos cinco tablas. Cada una representa bloques de características de los manuscritos estudiados. Las tablas siguientes propiedades: Typology, Current location, Institution, Collection, Present shelfmark /signature, Former shelfmarks/signature, Total pages/fols, Folio measures, Material composition, Codicological unit I, Writing support, Textual composition, Textual interval, Title on the manuscipt specimen, Title position, Attributed title, Language (Main), Script Incipit, Incipit position, Explicit, Explicit position, State of preservation, Released date, Released place, Other dates, Other places, Colophon, Colophon position, General decoration, Quranic structure decoration, Quranic reading, Illustrations, Marginalia, Bibliographical references, Link to library catalogue, Descriptive card, Type of relationship, Bookbinding type, Origin of the bookbinding, Bookbinding measures, Covering material Claps, Covering decoration, Writing support colour Watermark, Watermark identification, Catchwords, Presence of numeration, Numeration system, Origin of the numeration, Writing area measures, Number columns, Number of lines, Hands, Position, Text calligraphic style, Text vocalisation, Text ductus colour, Text vowels colour, Text alif elongation (madd colour), Text sukun colour, Text tashid colour, Text wasla colour, Text hamza colour, Headings calligraphic style, Headings vocalisation, Headings ductus colour, Headings vowels colour, Headings alif elongation colour, Headings sukun colour, Headings tashdid colour, Headings hamza colour, Colophon calligraphic style, Colophon vocalisation, Colophon ductus colour, Colophon vowels colour, Colophon alif elongation madd colour, Colophon sukun colour, Colophon tashdid colour, Decorations, Decoration position, Decoration colours, Illustrations, Illustration position, Marginalia, About the text, Hands of marginalia, Location, Position, Released date, Released place, Language, Script, Calligraphic style, Decoration Colour, Writing baseline orientation, Transcription, About the use and transmission of the specimen, Hands of marginalia, Location, Position, Date, Place, Language, Script, Calligraphic style, Decoration Colour, Writing baseline orientation, Transcription, Initial formula, Fol., 1 ayat division, Numbered, 1 ayat markers, 1 ayat calligraphic style, 1 ayat colour, 5 ayats division, 5 ayats markers, 5 ayats calligraphic style, 5 ayats colour, 10 ayats division, 10 ayats markers, 10 ayats calligraphic style, 10 ayats colour, Ayat al-arsh (throne) mark, Ayat al-arsh marker, Ayat al-arsh calligraphic style, Ayat al-arsh colours, Other ayats marks, Ayat number, Other ayats marker, Other ayats mark calligraphic style, Other ayats mark colour, Recitation marks, Recitation marks colour, Surah headings decoration, Surah headings deco colour, Surah headings deco remarks, HIZB location, HIZB decoration, HIZB decoration calligraphic style, HIZB decoration colours, Hizb subdivision location, Hizb subdivision deco, Hizb subdivision calligraphic, Hizb subdivision colour, 30 YUZ location, 30 YUZ decoration no-tipo, 30 YUZ decoration calligraphic, 30 YUZ decoration colours, 27 YUZ location , 27 YUZ decoration, 27 YUZ decoration calligraphic 27 YUZ decoration colours, Others divisions location, Others division decoration, Others deco calligraphic style, Others deco colours, SAYDA location, SAYDA decoration SAYDA deco calligrapic style, SAYDA deco colours, [EN] The project “Deciphering Qur’anic Dynamics in Spain" (DeQuDy) is a technological development proposal whose objective is the creation, analysis and management of the data extracted from the cataloguing, description and analysis of the more than two hundred manuscripts that can be considered as the Qur’anic corpus of the Iberian Peninsula. The processing and management of these data will be developed as a behavioural propagation model of information regarding the transmission of the Qur’an in the Iberian Peninsula, [ES] Deciphering Qur’anic Dynamics in Spain (DeQuDy) es una propuesta de desarrollo tecnológico cuyo objetivo es la creación, análisis y gestión de los datos extraídos de la catalogación, descripción y análisis de los más de doscientos manuscritos que pueden considerarse como el corpus coránico de la Península Ibérica. El tratamiento y gestión de estos datos se desarrollará como un modelo de propagación de la información relativa a la transmisión del Corán en la Península Ibérica., Peer reviewed

Additional information about the preparation and structural characterization of the samples, as well as about the thermal conductivity measurements and analysis., Peer reviewed

Movies displaying dynamic visualizations for the growth of the coral structures collected in Fig. 3 of the main text of “A generalized numerical model for clonal growth in coral colonies”., Peer reviewed

Norges Forskningsråd H2020 Industrial Leadership H2020 Spreading Excellence and Widening Participation L. Meltzers Høyskolenfond 2022, Peer reviewed