dataesr/scientific-paragraphs-categorization huggingface dataset:
(2021)  and  van Exem et al. (2022). Images have a spatial resolution between 46x46 and 84x84 µm². Normalisation was carried out using the ENVI/IDL 5. 5/8. 2 software. Following van  Exem et al. (2022) , the spectral index Area600-760 was used as an indicator for chloropigments indicating past in-situ productivity. To account for changes in average reflectance induced by changes in carbonate content, Area600-750 was normalised with the Rmean. XRF-scanning was carried out at GEOPOLAR (Geomorphology and Polar Research) at the University of Bremen with an XRF Core Scanner (ITRAX, Cox Analytics) at 2-mm step size with a Mo tube (30 kV, 50 mA, 5 s exposure time). Scanning XRF-derived elemental variations might be influenced by sample geometry, physical properties (e. g. water content, surface roughness, grain size variations) or scanner settings  (Croudace and Rothwell, 2010; Weltje and Tjallingii, 2008).
To reduce such effects, only elements with less than 5 % zero values (Si, K, Ca, Ti, Mn, Fe, Ni, Cu, Zn, Sr) were centre log-ratio (clr) transformed  (Aitchison, 1982)  using the PAST 4 software  (Hammer, 2022). As proposed by  Adolph et al. (2023)  Cu, Ni and Zn are used as a sum parameter ∑(Cu,Ni,Zn)clr for anthropogenic impact.
Measurements were carried out using a Laser Particle Sizer (Fritsch Analysette 22 microtec plus). The first reproducible of nine subsequent runs was used for interpretation. Grain size statistics were calculated using the GRADISTAT 9. 2 software  (Blott and Pye, 2001). Carbonate content was determined on ground and homogenised samples by the Scheibler method on 0. 17 to 0. 55 g sample material. Subtracting carbonate content and LOI550 from the total sample weight, the percentage of siliciclastics, which includes a share of silicious algae as revealed by microscopic analyses on the LOI ash residues, was calculated. Dried and homogenised sediment samples of 1. 8 to 11. 3 mg were used to analyse total carbon (TC) and total nitrogen (TN). Concentrations were obtained using a Euro EA CNS analyser.
Statistics Similar sedimentological and geochemical composition intervals were established using a stratigraphically constrained cluster analysis on clr-transformed XRF data and sedimentological parameters. XRF data were scaled to a 1-cm resolution calculating the mean for each centimetre to account for differences in resolution and noise between XRF scans and sedimentological data. Calculations were carried out using the R package 'rioja' (v. 1. 0. 5)  (Juggins, 2022). As the cluster analysis did not cover some changes or would have led to many clusters, we included an additional unit boundary based on visual inspection (Unit C1 to C2). Pearson's r-values were calculated with the r-package 'Hmisc' (v. 5. 0-1, Harrell Jr (  2023 )) (Supplement S1) and values with p < 0. 001 are considered significant and mentioned in the text.
Acknowledgement MLA received a  Graduate Scholarship  (Landesgraduiertenstipendium) from the  Federal State of Mecklenburg-Western Pomerania  to conduct this research. We want to acknowledge  J. Becker  and  M. Steinich  for their support of the field campaign and  M. Steinich  and  U. Dolgner  for CNS and TIC analyses. Moreover, we want to thank  Kaja Müller ,  Antonia Kühn  and  Rafael Tüllinghoff  for their support during sampling and laboratory analyses. We would also like to thank  Peter Appleby  for carrying out the 137 Cs-and 210 Pb dating and  Christian Ohlendorf  and  Rafael Stiens  for XRF scanning. We want to thank  Roland Zech  for discussions and laboratory and instrument access at the  Physical Geography department  of the  Friedrich-Schiller University Jena. Moreover, we thank  Kevin Jacq  for his support during the Hyperspectral Imaging.
, 2019; Filipiak & Weiner, 2014; Gittings & Giller, 1998; Riley et al. , 2014; Vos et al. , 2013). Therefore, it is likely that substrate energy is a better determinant for explaining shifts in biodiversity in lower trophic levels of decomposer communities  (Yang et al. , 2022). We used saproxylic, i. e. , deadwood-dependent, beetles (Coleoptera) to rank the importance of ambient energy versus substrate energy in determining species richness. The studied taxa allowed us to track energy through an ecosystem, similar to often studied plant-herbivory interactions, through sampling techniques which enabled us to tie sampled individuals to a specific resource object. For this, we compiled data from experimental studies of saproxylic beetles along a latitudinal gradient in Europe covering 2748 individual deadwood objects from 618 sites.
Species data We compiled a dataset from experimental studies on saproxylic beetles across Europe, ranging from Mediterranean to boreal forests with mean annual temperatures ranging from -0. 1 to 14. 9°C, based on published and unpublished experiments available among co-authors (Figure  2 ; Table  S1 ). We only considered experimental setups that sampled long enough to include the complete adult activity spectrum of all saproxylic beetle species in the respective areas and allowed us to assign emerging species of saproxylic beetles to a specific deadwood object, e. g. , stem-emergence traps or rearing of deadwood objects. Comparability of the sampling methods is ensured, as both trap types yield highly correlated measures of biodiversity  (Hagge, Leibl, et al. , 2019). Furthermore, we only included data without artificial manipulation and experimental treatments of the deadwood objects (e.
Ambient energy Data on climate was extracted on the object level using R 4. 0. 4 (R Core Team, 2020). Temperature, temperature seasonality, and solar radiation were extracted from the WorldClim2 database with a spatial resolution of ~1 km 2 aggregated across a target temporal range of 1970-2000  (Fick & Hijmans, 2017 ) by using the raster package  (Hijmans et al. , 2022). The seasonality of solar radiation and temperature was calculated as the standard deviation of the monthly means. To correct temperature seasonality for influences of temperature, we used the residuals of a linear regression model of both variables. An overview of all variables, including ecological importance, range, and standard deviation is displayed in Table  1  and Table  S3.
Co-variables To obtain precise data on the deadwood, we compiled potentially important co-variables. Those included: (i) the geographic coordinates of the plot, (ii) tree species, (iii) object position (ground, elevated (without contact to soil), snag), (iv) volume calculated as a cylinder using the length of the object for deadwood which was completely enclosed by the trap (rearing) and the length of the trap for objects which were not fully enclosed (emergence traps). Diameters of deadwood objects included in this study ranged from 1 to 85 cm with a mean of 25. 40 ± 13. 6 cm. Distribution of object diameters separated by sampling years is displayed in Figure  S2. Furthermore, we included: (v) decay stage, and (vi) the exposure time in years after the experiment was started. We used the exposure time for each deadwood object as a proxy for decay stage.
For studies that were not set up experimentally, but included deadwood of different decay stages on sites, we predicted the exposure time by a linear model of decay stage and exposure (Table  S3 ). Exposure times included in our study ranged from 0 years (sampling took place in the same year as exposition) to a maximum of 8 years with a mean of 2. 94 ± 2. 0 years. We also gathered the environmental co-variable (vii) canopy closure, which was measured between 0 and 100% with either different laser-, photography-, lidar-, or radar techniques or was visually estimated by the data contributors in 5% steps. Furthermore, we mined data for (viii) precipitation, (ix) precipitation seasonality, and (x) elevation from the elevatr package based on raster data of Amazon Web Services Terrain Tiles  (Hollister et al.
Tree species for which we were not able to measure traits (12% of our tree species) were complemented by the mean of each trait from all tree species within the same genus. Weak collinearity among predictor variables was ensured by using the variance inflation factor, provided by the vifstep function of the udsm package  (Naimi et al. , 2014) , with a threshold of 4. To avoid collinearity in our analyses, we excluded the following variables: anatomy fractions, wood pH-value, gross calorific value, elevation, precipitation, and precipitation seasonality. For an overview of correlation of all initial variables see Figure  S3  in the Supporting information. We used the statistical framework based on Hill numbers with the exponents q = 0 (species richness), q = 1 (the exponential of Shannon's entropy index, hereafter referred to as Shannon diversity), and q = 2 (the inverse of Simpson's concentration index, hereafter referred to as Simpson diversity)  (Chao et al.
, 2014). To disentangle the effects of population sizes from energy availability within the ambient energy-and productivity hypothesis we compared species density and species richness as response variables in our models. We follow the definitions of  Gotelli and Colwell (2001) , where species density is standardized to a specific sampling unit (deadwood area covered by trap) and species richness is standardized to the number of individuals. Possible sampling effects are mediated by using diversity measures such as species richness as a response variable, which are corrected for respective abundances. Calculation of Hill numbers were conducted on object-by-year-level using the estimateD function for abundance data to a sample coverage level of 95% from the iNEXT package  (Hsieh et al. , 2016).
We used generalized additive mixed models with the gamm function of the mgcv package  (Wood et al. , 2016)  with a Gaussian error distribution for species diversity measures (q0-q2), and Poisson error distribution for species density as response variables. We used two spline-based smooths (latitude, longitude), which allowed us to account for unmeasured sampling site specific differences to reveal effects of energy controlled by co-variables. We tested for effects of ambient and substrate energy variables as predictors (Table  1 ). Furthermore, we included important co-variables which either indirectly affect energy measures (e. g. , decay stage or physical wood traits) or which control for the resource and energy amount present in each object (e. g. , object volume, dry density, decay stage, see Table  1 ).
R E SU LT S We compiled 21 datasets from 8 European countries containing 49 tree species (Figures  2  and 3 ) and 332,557 saproxylic beetles of 901 species. Divided by feeding guild, we accumulated 192,728 (58%) xylophagous, 93,363 (28%) mycetophagous, and 46,383 (14%) zoophagous beetle individuals, excluding detritivorous species. The three most frequent tree species in our data were Fagus sylvatica (926 objects; 34%), followed by Picea abies (290 objects; 11%), and Abies alba (100 objects; 4%). Results of PCA showed high variation of wood traits between broadleaf tree species, but more narrow ranges for coniferous tree species (Figure  S5 ). Ambient energy was the main driver of overall species diversity of saproxylic beetles (Figure  4 ; Tables  S8-S10 ). Increasing solar radiation led to higher species richness (q = 0), Shannon diversity (q = 1), and Simpson diversity (q = 2).
Distribution of dataset origins. Sampling methods are marked with 'e' (emergence traps in situ), 'r' (rearing of deadwood objects ex situ) or 'e/r' for both within one dataset. Background shows European forest cover taken from CORINE Land Cover 2018 (EEA, 2018). T A B L E 1 Variables describing ambient energy, substrate energy, and co-variables accounting for differences in environmental conditions, resource amount and evolutionary differences, including their units and ecological importance.
F Distribution of dataset origins. Sampling methods are marked with 'e' (emergence traps in situ), 'r' (rearing of deadwood objects ex situ) or 'e/r' for both within one dataset. Background shows European forest cover taken from CORINE Land Cover 2018 (EEA, 2018). T A B L E 1 Variables describing ambient energy, substrate energy, and co-variables accounting for differences in environmental conditions, resource amount and evolutionary differences, including their units and ecological importance.
An ab initio theoretical calculation performed with the complete active space self-consistent field (CASSCF)/ N-electron valence perturbation theory to second order (NEVPT2) method and def2-TZVP basis-sets using ORCA software  56, 57  and crystallographic atom coordinates give values of D = -118. 1 cm -1 (E/D = 0. 002) and g x = 1. 94, g y = 1. 95, g z = 3. 3, in excellent agreement with experiment (only the anion [L 2 Co] 2-and the major fraction of its disordered part was included in the calculations; details are provided in the SI). To explore the magnetization relaxation dynamics, zero fieldcooled (ZFC) and field-cooled (FC) magnetic susceptibility measurements were carried out at a sweep rate of 2 K/min, revealing a blocking temperature (T B ) of 3. 5 K (Figure  2  inset). Temperature-dependent hysteresis measurements carried out at a sweep rate of 30 Oe/s show a butterfly-shaped open hysteresis loop up to 3.
Instruments for Spectroscopic and Analytical Characterization 1 H NMR and  13  C NMR spectra were recorded on Bruker Avance 400 spectrometers at room temperature. Chemical shifts are reported in parts per million (ppm) relative to residual proton and carbon signals of the solvent. IR spectra were recorded on a Cary 630 FTIR spectrometer equipped with Dial Path Technology and analyzed by FTIR MicroLab software. Vis-NIR (visible-near-IR) spectra of solutions and solid material were recorded with a Varian Cary 5000 spectrophotometer. ESI mass spectra were collected using a Bruker HCT ultra spectrometer. Elemental analyses were performed by the analytical laboratory of the Institute of Inorganic Chemistry at the University of Goẗtingen using an Elementar Vario EL III instrument.
Magnetic Measurements Magnetic measurements were carried out with a Quantum-Design MPMS3 SQUID magnetometer equipped with a 7 T magnet. dc magnetic susceptibility measurements were performed under an applied dc field of 0. 5 T with powdered polycrystalline samples in the range from 200. 0 to 2. 0 K. The powdered samples were packed in a polycarbonate or gelatine capsule and covered with low-viscosity perfluoropolyether-based inert oil Fomblin Y45 in a non-magnetic sample holder. Each raw data for the measured magnetic moment was corrected for the diamagnetic contribution of the capsules (including the inert oil) according to M dia (capsule) = χ g •m•H, with an experimentally obtained gram susceptibility of the capsules including the inert oil. The diamagnetic contribution of the compounds was corrected using Pascal's constants.
Magnetic measurements of frozen solutions were carried out by dissolving the sample in DMF in an NMR tube and sealing the tube under a vacuum to exclude dioxygen. Experimental data were modeled using a fitting procedure to the spin Hamiltonian: Ä Ç Å Å Å Å Å Å Å Å É Ö Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ = + + + H D S S S E S S BgS 1 3 ( 1) ( ) z x y 2 2 2 B Full-matrix diagonalization of the spin Hamiltonian for ZFS and Zeeman splitting was performed with the julX_2s program. 55  ac susceptibility measurements were carried out in an oscillating ac field of 3. 0 Oe and frequencies ranging from 0. 1 to 1000 Hz.
Single-Crystal Structure Determination In-house X-ray data were collected on an STOE IPDS II diffractometer (graphite monochromated Mo-Kα radiation, λ = 0. 71073 Å) by use of ω scans at -140 °C. To obtain high-quality data necessary for modeling the ED of 1, single crystal X-ray diffraction was measured at BL02B1 at the SPring-8 synchrotron. ω scans were performed from 0 to 180°in steps of 0. 1°, with fixed χ assuming values of 0, 20, and 40°, respectively, and 2θ at 0 and -20°, resulting in 6 scans in total with 1800 frames in each. The scan time was 0. 44 s per frame and the diffracted data was collected with a PILATUS3 X 1M CdTe detector. The crystal was cooled to 25 K and a wavelength of 0. 24830 Å was employed. Further experimental details as well as details of the structure solutions are provided in the Supporting Information.
CCDC 2212249 (1, STOE IPDS), 2212250 (2, STOE IPDS), and 2212086 (1, SPring-8 synchrotron) contain the supplementary crystallographic data for this paper. These data can be obtained free of charge via www. ccdc. cam. ac. uk/data_request/cif, by emailing data_request@ccdc. cam. ac. uk, or by contacting The Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK; fax: +44 1223 336033.
Electron Density Modeling The ED was modeled based on the synchrotron data with the program XD2016 73 using the Hansen-Coppens MM formalism. The z axis for Co was chosen to point in-between N(1) and N(2), and the x-axis in-between N(1) and N(3). Hydrogen atoms have been positioned using tabulated bond distances and described with isotropic ADPs. Hexadecapolar angular functions were used to describe the ED for all non-H atoms, while selected quadropoles were used for hydrogen atoms. Core expansion/contraction parameters were included based on the results of multipole modeling from theoretical structure factors.
Polarized Neutron Diffraction PNPD data were collected at the high-flux powder diffractometer D20 of the Institut Laue-Langevin (ILL)  71  on a powdered sample of 1. Data were measured at 2 K and an applied magnetic field of 1 T, with neutrons of a 2. 41 Å wavelength, using the angular range 2θ = 7. 6-32. 1°. The flipping difference pattern was used to model the atomic site susceptibility tensor for Co using the Python-package Cryspy (v. 0. 5. 9) through Cryspy_Editor (v. 1. 6. 0). 50  Single-crystal PND data were measured at the ORNL HFIR four-circle single-crystal neutron diffractometer HB-3A/DEMAND. 72  Data were measured with a 1. 5424 Å wavelength at 4 K and a field of 0. 48 T. Only one crystal orientation provided data of sufficient quality, and a joint PND/ PNPD model was refined.
Theoretical Calculations Ab initio calculations were performed in ORCA software,  56, 57, 74  using the atom positions obtained from the EED. Prior to the calculation, the two counterions were removed from the structure, so the calculation is only performed on the [L 2 Co] 2-dianion. Furthermore, the disorder within the anion was removed, that is, one of the peripheral 4-Cl-phenyl groups (see Figure  S33a ) was removed and the occupancy of the other group was set to 1. The CASSCF method was used in the calculation with the seven d-electrons in the Co II ion in five active orbitals, together with the NEVPT2 method. In the calculations, all 10 S = 3/2 states and 40 S = 1/2 states were included, and the def2-TZVP basis set was used. The alignment of the molecule was performed according to the pseudo-D 2d symmetry with the z axis for Co between the N(1) and N(2) atoms, and the x axis for Co between the N(1) and N(3) atoms.
For clearing, samples were dehydrated in successive PBS/methanol baths (20% methanol overnight, then 40%, 60%, 80%, 2 × 100%, each 2 h), then put overnight in a 2/3 dichloromethane (DCM) (270997-1 L, Merck) 1/3 methanol solution. Samples were then immersed in 100% DCM for 45 min, and then in BenZyl Ether (DBE) (108014, Merck). DBE baths (2 h) were changed until no swirls were seen in the solution following mild agitation of the tube. Cleared samples were then stored in DBE in darkness, where transparency and immunofluorescence were stable for at least a year. Imaging. For imaging, cleared human eyes (macroscopic views shown in Fig. 1c ) were placed in an immersion quartz cuvette (3 cm × 3 cm × 4 cm) filled with the imaging medium DBE. Imaging was performed using a custom made mesoscale selective plane-illumination microscope mesoSPIM  21  system at the Wyss Center in Geneva, which allowed imaging of a human eye in its entirety (travel range 44 × 44 × 100 mm) and provided near-isotropic resolution 3D datasets.
8 µm. Data analysis. After data acquisition z-sub-stacks were concatenated using Image J and the tiles were stitched in 3D using the Grid Collection Stitching plugin tool in TeraStitcher (BMC Bioinformatics, Italy). The resulting HdF5 files were then converted to. ims format and analyzed using the ImarisBitplane software or the virtual reality software Syglass. Imaris software (versions 9. 5-9. 7, Bitplane) was used to render and study samples in 3D. To isolate target ocular structures and vascular beds, builtin tools of slicers or manual segmentation and 3D rendering are used (see Supplementary Fig. 1b ). Built-in Imaris animation and snapshot tools were used for video and image acquisitions. FIJI's plugin ScientiFig was used to create scientific figures and iMovie© (v10. 1. 11) was used for video editing.
Acknowledgements Kaitryn Ronning  for the english revisions and useful advice. Funded by the  Institut Hospitalo-Universitaire FOReSIGHT  ( ANR-18-IAHU-01 ),  Region Ile-de-France  ( EX047007 -SESAME 2019 ) and  Association Contre l'OVR. The funding organizations had no role in the design or conduct of this research.
Data availability The histological images supporting Fig. 1  and Fig. 2  are publicly available in the figshare repository, at the following addresses : https://doi. org/10. 6084/m9. figshare. 24162243. v2  36  and https://doi. org/10. 6084/m9. figshare. 24162237. v2  35. Any remaining information can obtained from the corresponding author upon reasonable request.
Physiological Recording The PPG technique was used to record the arterial pulse wave in the first finger of the lower right limb using the ANGIDIN ® 18 digital plethysmograph (Biofísica Médica, Santiago de Cuba, Cuba), which captures the volume variations of the pulse wave via the reflection method (at 850 nm with a 3. 5-mA photodiode). The PPG signal was then passed to an analog band pass filter from 0. 1 to 15 Hz (Butterworth 5th order filter) and digitized at a resolution of 8 bits (106 samples per second). The recorded data were transmitted, processed and archived using the VAPLET ® auxiliary software (Biofísica Médica, Santiago de Cuba, Cuba). This software allows for the exportation of the signal in a beat-by-beat manner, such that it can then be processed by the Octave software 2019 (version 5.
Statistical Analysis For the comparison of the groups, a t-test of independent samples was applied for the variables that presented a normal distribution and the Mann-Whitney U test for those that did not. The data are expressed as the mean ± standard deviation (SD). Mathematical analyses and dynamic graphs (Figures  2  and 3 ) were performed and generated, respectively, using the Octave software 2019 (version 5. 1. 0; https://www. gnu. org/software/octave/ download. html (accessed on 15 January 2021)). The average of the LF/HF matrices for the PRV was calculated from the CWT-Morlet analysis during the 2 min period after cuff deflation and interpolated at 2 Hz (0. 5 s). Statistical analyses were performed with the JASP software (version 0. 16; https://jasp-stats. org (accessed on 31 January 2021)).
Statistical Analysis For the comparison of the groups, a t-test of independent samples was app the variables that presented a normal distribution and the Mann-Whitney U test f that did not. The data are expressed as the mean ± standard deviation (SD). Math analyses and dynamic graphs (Figures  2  and 3 ) were performed and generated tively, using the Octave software 2019 (version 5. 1. 0; https://www. gnu. org/softw tave/download. html (accessed on 15 January 2021)). The average of the LF/HF for the PRV was calculated from the CWT-Morlet analysis during the 2 min peri cuff deflation and interpolated at 2 Hz (0. 5 s). Statistical analyses were performed JASP software (version 0. 16; https://jasp-stats. org (accessed on 31 January 2021)) cance was defined as p < 0. 05.