dataesr/scientific-paragraphs-categorization huggingface dataset:
Most of the papers were in English, but data were also extracted from papers published in, for example, Spanish and Portuguese (often South American studies), as well as French and German. Due to a large data gap, the database was supplemented by an additional database and literature search featuring studies published in Russian and carried out in a number of former Soviet states. In total, this resulted in 7224 unique studies to be checked, and 1442 studies from which records could be extracted or obtained. A record refers to a row in the database, and represents a known number of soil samples with specified geographical coordinates. Depending on how results were reported in a specific study, a record can represent one or more locations or environments. Each record can also provide one or more commonly recorded seed bank metrics (mainly total species, total number of seeds, seeds m -2 density), which we term observations.
A study can provide multiple observations by reporting more than one metric, and/or by (for example) reporting metrics across several distinct records (i. e. different locations or environments). In total, the 1442 studies provided 8087 observations across 3096 records (Figure  2 , Table  S1 ). The database includes soil seed bank data from 94 countries and all seven continents. Full details regarding which data were extracted and how are available in  Auffret et al. (submitted). Below, we describe only the variables in the database that were used for this synthesis.
Sampling information Soil seed bank studies often involve the collection of multiple samples of soil with a soil corer. These samples are then usually pooled, in order to account for the spatial patchiness of species and seeds in the soil  (Matlack & Good 1990; Plue & Hermy 2012; Thompson 1986). Therefore, different numbers of cores of different sizes can contribute towards reported metrics of richness, density and abundance coming from soil seed bank studies. Our database therefore also contains detailed sampling information, including soil sample sizes, the number of samples taken, and the number of sites sampled for each observation. This allowed us to quantify different aspects of sampling effort across studies with standardised metrics, including the total sampled area and volume, the total number of samples, and the total number of sites that went into each observation.
A site is a distinct location from which soil samples have been taken, usually as a form of replicate in study authors' data collection, but within one record (with one set of geographical coordinates). Where coordinates were not provided by a study's authors, we estimated them from the description of the study location(s). We used total sampled area in m 2 as our measure of sampling scale, calculated as the size of the individual soil samples multiplied by the total number of samples taken for a particular observation. This metric accounts for differences in soil core sizes across studies, and we expect seed bank abundance and species richness to accumulate across sampled areas for plant communities  (Plue et al. 2021; Vandvik et al. 2016). Despite variation among studies, we do not consider sample depth nor volume.
While admittedly an imperfect measure of an observation's geographical extent, we found it to be a parsimonious covariate, as well as the only such metric that was available in all records. More than half of our observations represent samples collected within a single site (n = 4763). Following soil collection and eventual pooling, seeds in the soil seed bank are typically assessed in different ways that can affect estimations of richness and density  (Price et al. 2010). Therefore, we also extracted this information for the database. The different methods are 'Emergence': the most common, by which the soil is planted in a greenhouse and emerging seedlings identified; 'Field': similar to emergence, but soil samples are planted in natural field conditions; 'Extraction': where soil samples are examined under a microscope and individual seeds identified and counted.
Response variables Our database contains three main metrics from the soil seed bank literature that we use as response variables: [1] the total number of species recorded in a reported area soil seed bank sampled (hereafter species richness) ; [2] the total number of seeds recorded in the area sampled (hereafter abundance), and [3] seed density m -2 in the soil (hereafter density). Typically, seed density is back-calculated by a study's authors as the number of seeds in the samples divided by the area of ground covered by the sampling regime. Where only one of either total seeds or seed density were provided by the authors, we calculated the other variable. In addition to these three variables, we calculated (where possible according to available information): [4] the number of seeds per species, as a measure of the soil seed bank richness-abundance relationship.
Biomes We overlaid each record's geographical coordinates with the WWF terrestrial ecoregion map  (Olson et al. 2001)  to assign each observation to an ecoregion (hereafter 'biome'). The 14 biomes that included a soil seed bank observation were merged into 10 'broad' categories (Table  S2 ), to reduce the number of categories with very few observations (Figure  2 , S3, see below). Because the biome map contains categories of terrestrial ecoregions, and because arable systems are very different to natural and semi-natural systems, we also used the seed bank database's habitat classification (five broad categories interpreted from study descriptions) to create two further 'biomes' for aquatic and arable habitats. That is, all observations classified as aquatic (including soil and sediment from both fresh and saltwater water bodies and courses, but not wetlands) and arable habitats (including croplands, orchards and vineyards, but not meadow, pasture or forest plantations) were put into these categories, regardless of their geographical location.
The richness-abunance model contained 2357 observations. Across all statistical models, random intercepts were allowed to vary, with unique study (i. e. publication, some of which provided multiple records/distinct locations), nested within detection method (emergence, extraction, field), included as random effects. We visualised all model estimates on a linear scale. For Bayesian inference and estimates of uncertainty, we fit models using the Hamiltonian Monte Carlo (HMC) sampler Stan  (Carpenter et al. 2017 ) and interfaced through the brms package in R version 4. 3. 1  (Bürkner 2018). We fit all models with four chains, differing iterations and differing assumed distributions (Supplementary Information, Model Details Table  S4 ). We used weakly regularising priors, visually inspected HMC chains for convergence, and conducted posterior predictive checks to ensure the model represented the distribution of the data for each (Supplementary Information, Table  S4  Figure  S1 ).
Predicting global patterns of soil seed banks Geographic patterns of species richness in small areas (alpha diversity) can comparatively differ across habitats or global change drivers from patterns in large areas (gamma diversity). This indicates spatial variation in beta diversity (since beta = gamma / alpha). To investigate this divergence between scales, we predicted the overall average species richness at two spatial scales for every biome using the richness-area model to keep estimates for every biome consistent. We predicted richness at 0. 01 m 2 (alpha diversity), and larger scale, gamma diversity at 15 m 2 , assuming the same sampling depth. These are arbitrary values, but we consider 0. 01 m 2 to be a straightforward value that people can visualise, that falls within the interquartile range of seed bank sampling in our database.
Additionally, this is a common soil sample size in soil seed bank studies, that has been previously used for prediction of soil seed bank richness and density  (Yang et al. 2021). Gamma diversity at 15 m 2 was chosen as the largest scale that we felt confident predicting across biomes, given the input data and model outputs. We predicted diversity at our alpha scale and gamma scale assuming sampling for each observation was carried out at one site, twenty sites and 100 sites. In the main results we report diversity at both alpha and gamma scales for one site, as this represents the majority of our values, and acts as our baseline estimates for diversity. We then estimate beta diversity from these predicted values at our fixed scales to complement slope estimates within our richness-area model.
Discussion We have used a global database of published soil seed bank studies to estimate richness-area relationships and average seed density across the world's biomes. Unsurprisingly, we found that with the exception of aquatic systems, species richness in the seed bank accumulates with spatial scale, both in terms of sampled area and geographic extent (number of sites sampled). Despite large variation within biomes, differences in the slopes of richness-area relationships result in diverging patterns of estimated richness and seed abundance between biomes at different spatial scales, which has consequences for how we compare biodiversity across regions.
Biogeographical patterns of seed bank density Our estimated global average of 4458 seeds m -2 means that in the broadest sense, if you put your finger to the earth, you are likely to be pointing at a seed. The high density of seeds in the soil is a testament to the formation of soil seed banks as an important component of plant population and community dynamics, and a valuable strategy for many plant species to facilitate establishment following disturbance  (Alexander et al. 2012; Chesson 2000; Plue et al. 2017). Perhaps unsurprisingly, arable soils contained the highest number of seeds per square metre (Figure  6 ). These highly and regularly-disturbed systems accumulate the persistent seeds of opportunistic ruderal "weed" species that can be problematic for agricultural production. Indeed, many of the 202 studies contributing to the 769 arable observations in the global seed bank database were investigations of the effects of agricultural management on the weed seed bank (e.
The seed bank's role in vegetation recovery following disturbance is not only about seed density, but also the identity of the species in the seed bank. Although the global seed bank database does not contain community information, we can use two commonly-reported seed bank metrics: species abundance and species richness, whose ratio can tell us something about seed bank community structure. The high ratio in arable systems indicates that although agricultural fields have been shown here to have a higher seed density than most natural and semi-natural systems, soil seed bank communities are usually dominated by relatively few common species. Interestingly, the two biomes with seed/species ratios consistently lower than the global average were the biomes with the highest predicted gamma richness (tropical and subtropical forests) and tundra, which had the lowest predicted richness of all terrestrial biomes at both spatial scales.
Global insights into seed bank sampling The large number of soil seed bank studies that have been carried out across the world has allowed our synthesis to be more 'global' than many large reviews of biodiversity, which often suffer from biases relating to the absence or restricted availability of data from some regions  (Boakes et al. 2010; Hughes et al. 2021; Nuñez & Amano 2021). However, our study may still suffer from other biases, originating from the fact that seed banks have not been sampled randomly across the world, but in places where scientists have been interested in finding and identifying seeds in the seed bank  (Boyd et al. 2023). Within the biomes studied here, there are a range of different habitat types, not all of which are likely to have been sampled equally. Nonetheless, the global seed bank database contains samples from many different environments across the world's biomes.
The growth of soil seed bank synthesis What can we learn from studying the soil seed bank? In addition to the continual publication of studies investigating soil seed banks at local scales, there has been an upswing in the synthesis of large datasets to understand broad spatial or ecological patterns relating to soil seed banks. This includes the macro-environmental determinants of small-scale richness and density  (Yang et al. 2021) , and the evolutionary history of seed bank persistence  (Gioria et al. 2020). Here, we complement such findings with an understanding of the spatial scaling of species richness in the soil seed bank, and broad biogeographical patterns in seed bank richness and density. Other large-scale work considers the important role that soil seed banks may play in species and community responses to global changes, related to the trade-off related to seeds being able to persist or disperse in space  (Chen et al.
2020) , or the role of soil seed banks in facilitating biological invasions  (Gioria et al. 2021). We believe that the future of soil seed bank synthesis lies beyond simple measures of community richness and density, or categorisation of species as having or not having persistent seeds. Collating a large number of datasets including community data from the soil seed bank and established vegetation across biomes and systems (see  Plue et al. 2021  for a less-than-comprehensive European study) would allow us to delve deeper into a mechanistic understanding of the role of soil seed banks, biodiversity, restoration and responses to global change.
Acknowledgements Many thanks to all the researchers, students and assistants who have spent their time in the field, lab and greenhouse carrying out investigations of the soil seed bank. Thanks also to those who clearly state their study design and summary results in their publications, as well as those who responded to our requests for further information. AGA is supported by the  Swedish Research Council  ( 2020-04276 ). EL is supported by the  German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig , funded by the  German Research Foundation  ( DFG-FZT 118 ,  202548816 ). VO thanks  RSF  (#  19-14-00038p ). We thank  Christian Krause  and the  UFZ  administrative and support staff of the  High-Performance Computing Cluster EVE , a joint effort of the  Helmholtz Centre for Environmental Research (UFZ)  and  iDiv , for access to, and support associated with, EVE.
Participants Twenty adult participants (12 men and 8 women) were recruited for this study. The lowest sample size to reach a power of 80 % and detect significant time effect of moderate effect size using an α error of 5 % was n = 19 for a repeated measure ANOVA (calculated with G*Power 3. 1. 9 software). Participants were excluded if they experienced one or more low back pain episodes in the past year. Moreover, any pain that prevented the task from being performed was considered an exclusion criterion. An experienced clinician (EMB) screened and established eligibility to participate in the study for each participant. Participants mean for age, weight, height, and BMI were respectively: 28. 2 ± 6. 1 years, 71. 5 ± 12. 5 kg, 170. 8 ± 8. 2 cm and 24. 4 ± 2. 9 kg/m 2. Participants were recruited within the university community and via social media.
Data collection Superficial lumbar extensor muscle activity was recorded bilaterally with two grids of 64 electrodes arranged in 8 columns and 8 rows (10 mm inter-electrode distance; 8 cm by 8 cm; material Cu + chemical gilding; semi disposable adhesive matrix; model ELSCH064, OTBioelettronica, Torino, Italy) which cover approximately L1 to L4-L5 region. The electrodes remained in place between the two fatigue tests. All edges of the grids were taped to avoid any movements of the electrodes on the skin. Prior to the application of electrodes, the recording sites were cleaned with fine-grade sandpaper (Red Dot Trace Prep; 3 M, St. Paul, MN), and shaved. Both grids were placed along the approximate fiber orientation of the superficial lumbar muscles. The medial edge of the grid was at ~ 1 cm from the lumbar spinous process and the center was located at L3 level identified by palpation.
This median location was chosen because the size of the grid was the same for all participants while participants' height was different. The reference electrode was placed over the left posterior superior iliac spine. During the data collection, vertical differential EMG signals were sampled at 2048 Hz, amplified by a factor of 2000 or 5000 and converted to digital (12-bit A/ D converter; 128-channel EMG-USB; OTBioelettronica; -3 dB, bandwidths 10-500 Hz).
Data analysis The last row of each grid was removed because differential signals were collected, providing two final grids of 56 electrodes; 8 columns and 7 rows. First, a band-pass filtered was applied to all EMG signals (frequency bandwidth 20-400 Hz; 4th order Butterworth filter). Second, notch filters were also applied to all signals to eliminate the 60 Hz power line interference and its harmonics (2nd order Butterworth filter). Third, visual inspection of all EMG signals was performed by the same assessor to identify electrodes with low signal-to-noise ratio. These electrode signals were reconstructed by the interpolation of the neighbouring electrodes. If these electrode artifacts were excessive (≥6 electrodes (10 %)), the entire recording was removed from the data analysis  (Gallina et al.
From long-range order to short-range correlations In between 1/8 and optimal doping, our longitudinal scans show a decreasing elastic scattering intensity as doping is gradually increased. The evolution in doping shows a distinct transition in the nature of the charge correlation when the temperature dependence is taken into account. In Fig. 3a-e , we display the temperature dependence for the LSCO x = 0. 138, 0. 145, 0. 16, and 0. 20 compounds. For x > 0. 15, essentially no temperature dependence is observed up to 150 K. We note that these two compounds have been recorded with different energy resolution. Supplementary Fig. 5  therefore the two compounds directly for medium-and high-resolution measurements. Together with the data for LSCO 0. 145 in Figs. 1c-f and 2g it is revealed that the phonon contribution in the medium resolution k scans is only leading to a broadening of the peak while leaving the amplitude the same.
ACKNOWLEDGEMENTS We thank  D. LeBeouf  and  M. -H. Julien  for discussions and comments to the manuscript. Part of the experiments have been performed at the  ADRESS beamline  of the  Swiss Light Source  at the  Paul Scherrer Institut (PSI). We acknowledge  Eugenio Paris  for support at the beamline. K. v. A. , Q. W. , M. H. , T. S. , Y. T. , and J. C. acknowledge support by the  Swiss National Science Foundation  through Grant Numbers  BSSGI0-155873 ,  200021-188564 ,  CRSII2_160765/1  and  200021-178867. K. v. A. is grateful for the support from the  FAN Research Talent Development Fund -UZH Alumni. K. v. A. and Q. W. thank the  Forschungskredit of the University of Zurich , grant no. [ FK-21-105 ] and [ FK-20-128 ]. Y. S. thanks the  Chalmers Area of Advances-Materials Science  and the  Swedish Research Council  (VR) with a starting Grant ( Dnr.
Observations and data reduction The target system, HIP 81208, was observed twice with the SPHERE instrument at the Very Large Telecsope (VLT), both in the context of BEAST. The first observation was acquired on 6 Aug 2019 (MJD 58701. 02), and a second-epoch followup observation was acquired on 5 April 2022  (MJD 59674. 35 ). Both observations used the standard settings for BEAST  (Janson et al. 2021a ) in the IRDIFS-EXT mode  (Zurlo et al. 2014) , which allows simultaneous low-resolution Y JH-band spectroscopy using the Integral Field Spectrograph (IFS;  Beuzit et al. 2019 ) and dual-band imaging in the K band with the K12 filter-pair  (Vigan et al. 2010 ) using the Infra-Red Dual-band Imager and Spectrograph (IRDIS;  Beuzit et al. 2019). The main observational sequence was executed in pupilstabilised mode -which facilitates angular differential imaging (ADI, see  Marois et al.
The total field rotations during the observations were 51. 9 deg in the 2019 epoch and 57. 5 deg in the 2022 epoch. Data reduction was performed according to the regular BEAST procedure  (Janson et al. 2021a) , with the SPHERE Data Center 1  (Delorme et al. 2017 ) software based on the SPHERE pipeline  (Pavlov et al. 2008 ) for all basic calibration steps including dark and sky subtraction, flat fielding, and spectral extraction. For pixel scale, true north orientation and pupil offset, we used the long-term values given in  Maire et al. (2021) , 1 https://sphere. osug. fr/spip. php?article45 which are based on a 5-yr analysis of the astrometric stability of SPHERE that relies on observations of star clusters taken at regular intervals. Accordingly, for both epochs, the pixel scale adopted for IRDIS was 12.
Constraining the orbital parameters Given the astrometric information of the two companions obtained in two different epochs, it is possible to derive constraints on their orbital parameters. We do this using the Python package orbitize!  (Blunt et al. 2020) , which is designed to fit the orbits of directly imaged planets. Orbitize! offers two choices of algorithm for fitting orbits: Orbits for the Impatient (OFTI, see  Blunt et al. 2017)  and Markov chain Monte Carlo (MCMC, see  Ford 2005; Vousden et al. 2016 ). The disadvantage with using MCMC for orbital fitting of long-period orbits is that for cases with less constraints from observations on the orbital parameters, MCMC take a very long time to converge. As the relative displacement between the two epochs for B and C were only 7. 8 mas and 3.
Conclusions In this work, we describe our observations of the B9V star HIP 81208 using the SPHERE instrument at VLT as part of the BEAST survey in two different epochs, obtaining both IFS Y JH spectroscopy and IRDIS K1 and K2 imaging data for the target. We report the discovery of two lower-mass companions to the star; an inner companion HIP 81208B at a projected separation of 0. 325 ± 0. 001 ′′ and an outer companion HIP 81208C at a projected separation of 1. 492 ± 0. 001 ′′. Using its spectrum over the Y JH -K1 -K2 wavelength range, we determine that HIP 81208B is of spectral type M5. Analysing the obtained photometry, we estimate the masses of HIP 81208B and C to be 67 +6 -7 M J and 0. 135 +0. 010 -0. 013 M ⊙ , respectively, indicating that B is most likely a brown dwarf and C a low-mass star.
Acknowledgements. M. J. gratefully acknowledges funding from the  Knut and Alice Wallenberg Foundation. A. V. and P. D. acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 757561 ,  COBREX ; grant agreement no. 885593 ). This publication makes use of CDS and  NASA /ADS services, as well as VOSA, developed under the  Spanish Virtual Observatory  (https://svo. cab. inta-csic. es) project funded by  MCIN/ AEI/10. 13039/501100011033/  through grant  PID2020-112949GB-I00. VOSA has been partially updated by using funding from the  European Union 's  Horizon 2020 Research and Innovation Programme , under Grant Agreement no. 776403  ( EXOPLANETS -A ). This work has also made use of the  SPHERE Data Centre , jointly operated by  OSUG/IPAG (Grenoble) ,  PYTHEAS/LAM/CeSAM (Marseille) ,  OCA/Lagrange (Nice) ,  Observatoire de Paris/LESIA (Paris) , and  Observatoire de Lyon (OSUL/CRAL).