dataesr/scientific-paragraphs-categorization huggingface dataset:
We further add a custom background correction at the group level prior to ramp fitting, and subtract from each column the median of the six pixels at the top and at the bottom of the detector, excluding outliers at more than the 3-σ level. We skip the "photom" step in Stage 2 of the STScI detector pipeline because absolute fluxes are not needed in our analysis. We also skip the "extract1d" step as we perform custom spectral extraction using Eureka!. For 1D spectral extraction, we trim the array to include only columns 14 to 495 in the dispersion direction, as NIRSpec's throughput is negligible beyond this range. We then use the median detector frame to construct the weights used in the optimal extraction based on  52. Pixels are masked if they have an marked data quality flag (i. e. , bad pixels that are flagged by the jwst pipeline as "DO NOT USE" for various reasons) or if they are clipped by two iterations of 10-σ-clipping of the time series.
Reduction Comparison Procedural differences exist across the four main reductions of the dataset, which may account for the subtle qualitative differences between the final reduced spectra. A careful investigation of these nuances is warranted and will be presented in a future paper. Table  2  highlights some key procedural differences between the reductions. We note that despite these differences, the resulting exoplanet spectra are qualitatively in excellent agreement with each other (see Fig. 3 ), owing to the stability of the data and the self-calibrating nature of the transit technique. Table  2 : An overview of the analysis procedures used by the independent data reductions. The spectrophotometric scatter is estimated from the standard deviation of the pre-transit data between 0. 62-5.
The best-fit value of the grey cloud opacity κ cld = 10 -2. 07 cm 2 /g is calculated in a Bayesian framework by post-processing the RCTE model grid with this cloud opacity and comparing these post-processed models with the data. The ATMO grid includes grey cloud decks at multiple pressures between 1 and 50 mbars, but with variable factors 0, 0. 5, 1, and 5 governing cloud opacity with respect to H 2 's scattering cross-section at 0. 35 microns, where a factor 0 implies a cloud-free model spectrum. The PHOENIX grid includes similar cloud decks but between 0. 3-10 mbars with cloud optical depth enhancement factors (identically defined as the ATMO grid) 0 and 10. We find that the cloudy models better fit the data than clear models across all four model grids. The contribution of clouds in limiting the depths of the gaseous features across the entire wavelength range is also shown in Fig.
Acknowledgments This work is based on observations made with the  NASA/ESA/CSA JWST. The data were obtained from the  Mikulski Archive for Space Telescopes at the Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-03127  for JWST. These observations are associated with program  JWST-ERS-01366. Support for program JWST-ERS-01366 was provided by  NASA  through a grant from the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-03127. This work benefited from the  2022 Exoplanet Summer Program  in the  Other Worlds Laboratory (OWL) at the University of California, Santa Cruz , a program funded by the  Heising-Simons Foundation.
Code Availability The codes used in this publication to extract, reduce, and analyse the data are as follows; STScI JWST Calibration pipeline (https://github. com/spacetelescope/jwst), FIREFLy  24  , tshirt  41  , Eureka! 48 (https://eurekadocs. readthedocs. io/en/latest/), and Tiberius  16, 53, 54. In addition, these made use of Exoplanet  44  (https://docs. exoplanet. codes/en/latest/), Pymc3 87 (https://docs. pymc. io/en/v3/index. html), ExoTEP  49, 50, 51  , Batman  33  (http://lkreidberg. github. io/batman/docs/html/index. html), ExoTiC-ISM  55  (https://github. com/Exo-TiC/ExoTiC-ISM), ExoTiC-LD 56 (https://exotic-ld. readthedocs. io/en/latest/), Emcee  32  (https://emcee. readthedocs. io/en/stable/), Dynesty  70  (https://dynesty. readthedocs. io/en/stable/index. html), and chromatic (https://zkbt.
github. io/chromatic/), which use the python libraries scipy  88  , numpy  89  , astropy  90, 91  , and matplotlib  92. The atmospheric models used to fit the data can be found at PICASO  61, 62, 63, 64  (https://natashabatalha. github. io/picaso/), Virga85 (https://natashabatalha. github. io/virga/), ScCHIMERA  59, 60  (https://github. com/mrline/CHIMERA), ATMO  66, 67  , and PHOENIX  68, 69.
2 , Supplementary Fig. S1 ). This interval is bracketed by the dominance of semidesert landscapes and heathlands indicating cold-dry and cold-humid climates during MIS 18e and MIS 18a, respectively. The forest pollen percentage reached 60 % at c. 730 ka, a similar value characterising other interglacials in South-West Iberia such as MIS 13 (~500 ka  19  ), the last interglacial (MIS 5, ~130 ka  20  ) and the Holocene  21. Moreover, our data record all the phases characterising the succession of vegetation during interglacial periods in the Mediterranean region (Juniperus and Betula pioneer woodlands/deciduous and evergreen Quercus-Olea-Phillyrea-Pistacia forest/deciduous and evergreen Quercus woodlands) (Fig. S1 )  22. Site U1385 indicates that the expansion of the deciduous oaks parallels the development of the Mediterranean sclerophyllous taxa, with a synchronous maximum expansion.
3. 0 package (Package "PropCIs"). The calculation of CIs is based on the 'exact' Clopper-Pearson method assuming a binomial proportion  55, 56. The average uncertainty of the calculated pollen percentage of the Mediterranean forest values in our analysis is less than 8% (Fig. S2 ). Total spores and pollen concentrations oscillate between 2000 and 28,000 grains. cm -  (Fig. 2 ). Changes in grain concentrations do not parallel changes in pollen percentages and, therefore, these latter changes indicate actual variations in forest cover and composition. The interpretation of the pollen diagram was assisted by a constrained hierarchical cluster analysis based on Euclidean distance between samples (Fig. S1 ). Analysis was performed in the R environment v. 3. 6. 3 using the "chclust" function from the Rioja package  57.
Data analyses To correlate the paleoclimate records, we used first the binned correlation technique  36, 37  to convert the unevenly spaced paleoclimate time series under analysis to evenly spaced time series. This technique is based on a novel estimation approach proposed by ref. 36 for estimating the correlation between two paleoclimate time series with different timescales. The idea is that autocorrelation means that memory enables values obtained on different time points to be correlated. Binned correlation is performed by resampling the unevenly spaced paleoclimate time series under analysis into time bins on a regular grid, assigning the mean values of the variable under scrutiny within those bins. This method was recently implemented in the R package BINCOR  63  , which is freely available on CRAN.
To estimate the time-dependence of the strength of association between the largescale reconstructed time series and the Mediterranean forest pollen (MF), we used a robust correlation approach that takes into account the serially dependence of the time series under study. We have followed the method developed by  38  to estimate the Pearson's correlation coefficients that takes into account the serially dependence. This method also includes an estimation of a confidence interval (95%) obtained through a nonparametric stationary bootstrap technique with an average block length proportional to the maximum estimated persistence time of the data under analysis. This statistical technique is implemented in the software PearsonT3, which is freely available from http://www. climate-risk-analysis. com.
iLOVECLIM model We used the intermediate complexity climate model iLOVECLIM in version 1. 1. 5 (ref. 64), which is a development branch of the LOVECLIM model in version 1. 2 described in Goosse et al. 65. iLOVECLIM does not present major differences in the physics of the atmosphere and ocean compared to LOVECLIM. The based components of this version are the atmosphere ECBilt 66 , the ocean CLIO  67  and the vegetation and land surface VECODE  68. The modelled tree fraction in VECODE includes shrub plants such as Ericaceae  68. Oxygen isotopes ( 18 O and 16 O) have been implemented in the coupled model and evaluated against observed data in water samples and carbonates  69. An equilibrium run was first performed for 5000 years under climatic conditions at 800 ka. We then ran a transient run between 800 and 670 ka.
Acknowledgements This research used samples collected during the Expedition no. 339  " Mediterranean Outflow " of the  Integrated Ocean Drilling Program  (IODP). M. F. S. G. acknowledges funding from the  GPR Human Past (University of Bordeaux ). A. B. thanks  Deutsche Forschungsgemeinschaft (DFG) , project  BA 3809/8. C. Z. acknowledges funding from  IODP France  and  J. M. P. -M. from the  Junta de Castilla y León  and the  European Regional Development Fund  (Grant  CLU-2019-03 ). T. R. acknowledges funding from  FCT through projects Hydroshift  ( PTDC/CTA-CLI/4297/ 2021 ),  WarmWorld  ( PTDC/CTA-GEO/29897/2017 ),  UIDB/04326/2020 ,  UIDP/04326/2020 ,  LA/P/0101/2020  and  EMSO-PT (POCI-01-0145-FEDER-022157 ). We thank  Vincent Hanquiez  for drawing Fig. 1 and  Ludovic Devaux  for pollen sample preparation.
Code availability Codes for the statistical analyses (Bincor and Pearson's correlations) are available in the Supplementary Information. The iLOVECLIM source code and developments are hosted at http://forge. ipsl. jussieu. fr/ludus (ref. 77)  but are not publicly available due to copyright restrictions. Access can be granted on demand by request to D. M. Roche (didier. roche@lsce. ipsl. fr) to those who conduct research in collaboration with the iLOVECLIM user group.
Surface Measurements We compare results to the NOAA Global Monitoring Laboratory COS measurements, which were used in the two inversions (Table  1 ) during 2010-2018 & 2019 at 14 sites. Further information is given in Appendix Table  B1. The COS observations have been collected at multiple surface sites around the world as paired flasks one to five times a month since 2000 and have then been analyzed with gas chromatography and mass spectrometer detection. The COS measurements have been kept for this study only if the difference between the pair flasks was less than 6. 3 pmol mol -1. These data are an extension of the measurements first published in  Montzka et al. (2007)  and are regularly updated at https://gml. noaa. gov/hats/gases/OCS. html. In addition, we used measurements from the French sampling site Gif-sur-Yvette (GIF) (48.
The upper altitude that was typically reached was 8 km. This NOAA aircraft platform data set was already evaluated in other studies  (Hu et al. , 2021; Ma et al. , 2021; Remaud et al. , 2022 Remaud et al. , , 2023a)). Note that TOMCAT did not provide the requested model output for the HIPPO, ATom and the NOAA aircraft platforms. As a result, the strong mixing models are represented only by TM5 and TM3 in the comparison to HIPPO, ATom and NOAA aircraft observations.
Post-Processing of the Simulations and Measurements In this analysis we focus on the annual mean and the mean seasonal cycle. To this end, the surface data were processed using the CCGVU curve fitting procedure developed by the (Carbon Cycle Group of the Earth System Research Laboratory [CCG/ESRL]) at NOAA, USA  (Thoning et al. , 1989). The CCGVU procedure is fully described and freely available at http://www. esrl. noaa. gov/gmd/ccgg/mbl/crvfit/crvfit. html. The procedure estimates a smooth function by fitting the time series to a first-order polynomial equation for the growth rate combined with a two-harmonic function for the annual cycle. The seasonal cycle and annual gradient were extracted from the smooth function. In addition, outliers were discarded if their values exceed 3 times the standard deviation of the residual time series.
To calculate the EWSE of model groups, the arithmetic mean was taken. To calculate the statistics of the mean seasonal cycle, the time series were first processed by the CCGVU software to remove the trend and outliers, after which the RMSE and EWSE were calculated for each model. The Pearson correlation coefficient is defined as: 𝜌𝜌𝑚𝑚𝑚𝑚𝑚 = Cov(𝑚𝑚𝑚 𝑚𝑚) 𝜎𝜎𝑚𝑚𝜎𝜎𝑚𝑚 𝑚 (3) where ρ is Pearson correlation coefficient, Cov is the covariance of modeled and observed samples, and σ m and σ o are the standard deviation of modeled and observed samples over certain average (e. g. , averaged in latitudinal bins), respectively. The calculation of Pearson correlation coefficient is performed using the Python module Scipy version 1. 7. 3. Information S1. The general feature is that all the models with both fluxes capture the meridional gradient relatively well, but overestimate the mole fraction at the GIF station.
Evaluation With HIPPO Aircraft Data We use a subset of the HIPPO results from the multi-seasonal aircraft campaigns to evaluate the optimized fluxes as they have not been used in the data assimilation process. HIPPO campaigns 1-2 are not used because model simulations start from 2010, while the HIPPO 1-2 data were collected in 2009. Two aspects are considered in the evaluation: (a) the meridional gradient and (b) the vertical distribution of the COS abundance. Figure  7  shows the meridional distributions over the Pacific Ocean. We averaged the observations over 20° latitude bins and in the vertical in two bins: the boundary layer (below 2 km), and the free troposphere (2-8 km). The prominent feature of simulations compared with HIPPO is that the two fluxes underestimate the observations, specifically over tropical regions in the free troposphere.
Evaluation With ATom Aircraft Data In this section, we use ATom aircraft data to evaluate the model simulations. The ATom data were collected in four different campaigns, across mainly the Atlantic and Pacific oceans. We evaluate the fluxes and model group performance separately over the Atlantic and Pacific oceans and also assess the impact of nearby continents. Figure  9  shows the COS meridional gradient over the Atlantic Ocean (results of the individual models are shown in Figure  S12  in Supporting Information S1). In the lowest 2 km over the Atlantic Ocean, the meridional gradients observed by ATom are reasonably well reproduced by the models. ATom2 observed a drawdown of COS over the Atlantic in the SH, mostly above 2 km. These observations are probably impacted by uptake of the Amazon forest, which is consistent with a recent model study of  Stinecipher et al.
A more realistic approach would be to treat the COS chemistry as a 3-dimensional loss  field. Second, another limitation of this work is that the ATMs started from a zero COS initial state, which made direct comparisons against COS measurements challenging. We solved this by correcting the models for budget imbalances (Table  2 ) and adding 485 pmol mol -1. This adjusts the model simulations to the NOAA surface network as reference, yet the procedure is based on the assumption that the COS abundance does not change over time in the troposphere. The issue is alleviated by applying the standard CCGVU software to filter out inter-annual and synoptic signals. Thus comparing the modeled seasonal cycles to observed cycles is likely reliable. However, this correction procedure may partially explain the offsets between the models with HIPPO and ATom observations.
1. More elaborate data assimilation and model evaluation methods would be helpful. These methods could make use of FTIR and satellite data to further constrain the sources and sinks of COS. Recent studies have used MIPAS satellite data to constrain GPP over the Amazon region  (Ma et al. , 2023; Stinecipher et al. , 2022). Hannigan et al. (2022)  recently presented the extensive COS FTIR Network linked to the Detection of Atmospheric Composition Change (NDACC) network, which could enable a more comprehensive model evaluation and offers possibilities for data assimilation in the future. 2. In general, COS inversion studies are still limited by a lack of COS observations, and more measurement data could advance understanding. Areas of particular interest are some unique and large ecosystems that currently are poorly characterized, such as the Amazon rain forest and data-void regions like Asia.
Data Availability Statement The COS mole fraction time series at station GIF from 2014 to 2019 are described in  Belviso et al. (2020). The COS mole fraction time series at station WIS are provided by Dan Yakir. COS measurements from GIF and WIS sites are calibrated on the same scale as the NOAA observations. The LMDz model is available from http://svn. lmd. jussieu. fr/LMDz/LMDz6/ under the CeCILL v2 free software license. The source codes of NICAM-TM are included in the package of the parent model NICAM, which can be obtained upon request under the general terms and conditions (http://nicam. jp/hiki/?Research+Collaborations). The source code of MIROC4-ACTM is described in  Patra et al. (2022). The sources codes of TM3 and TM5 are availa-Observation Code Location Year Latitude Longitude Ground elevation (m a.
a b s t r a c t Stereo-electroencephalography (SEEG) is the surgical implantation of electrodes in the brain to better localize the epileptic network in pharmaco-resistant epileptic patients. This technique has exquisite spatial and temporal resolution. Still, the number and the position of the electrodes in the brain is limited and determined by the semiology and/or preliminary non-invasive examinations, leading to a large number of unexplored brain structures in each patient. Here, we propose a new approach to reconstruct the activity of non-sampled structures in SEEG, based on independent component analysis (ICA) and dipole source localization. We have tested this approach with an auditory stimulation dataset in ten patients. The activity directly recorded from the auditory cortex served as ground truth and was compared to the ICA applied on all non-auditory electrodes.
e. , the ground truth), and on all the other contacts (without the electrode in the auditory areas) to reconstruct the same activity from purely volume conducted fields  ( Makarov et al. , 2010 ). In both cases, ICA was applied on the continuous data and was set for obtaining as many components as available contacts (  = ;  Artoni et al. , 2018 ). ICA was computed with the Fieldtrip Matlab (Mathworks, Natick MA) toolbox  ( Oostenveld et al. , 2011 ). We used the infomax algorithm, which aims to minimize the mutual information between components  ( Bell and Sejnowski, 1995 ) , and was implemented originally in EEGLAB  ( Delorme and Makeig, 2004 ).
Reconstruction of remote activity with ICA To retrieve the auditory source from remote contacts, i. e. , the component identified in the auditory cortex (IC-Aud), we applied ICA on the continuous data (concatenating all the trials) of all the contacts except the electrode in the auditory cortex ( Fig. 1 e ). We computed the zero-lag Pearson correlation coefficient between the obtained components and IC-Aud (i. e. , ground truth) and selected only the component with the highest correlation value ( Fig. 2 f ). We considered it as the putative auditory source, representing the activity from the auditory cortex, but recorded by remote contacts (IC-remAud). The correlation coefficient was used as an indicator of the quality of the temporal reconstruction. We explored how the contact location affected the reconstruction of the source by computing multiple ICAs.
Detection of trend changepoints The visual inspection of the ICA results for individual patients when removing the closest contact at a time revealed the presence of a changepoint that modified the general trend of the data ( Fig. 3  b;  Sharma et al. , 2016 ). To better characterize this changepoint, we applied the Matlab (Mathworks, Natick, MA) function findchangepts. This function identifies the points where the mean and the slope of the signal change most abruptly  ( Haynes et al. , 2017 ). Thus, it detects at which distance the accuracy of the identification of the auditory activity decays, which can be used as an estimation of the maximal distance from the source for a successful time-course reconstruction. We further corroborated the presence of a changepoint by testing the improvement of the linear fit if the data was divided into two sections (before and after the changepoint) or if all points were included in a single regression.
Data and code availability statement The conditions of our ethics approval do not permit public archiving of anonymized study data. Readers seeking access to the data should contact the lead author. Access will be granted to named individuals in accordance with ethical procedures governing the reuse of clinical data, including completion of a formal data sharing agreement. All the code necessary to produce the results here were performed in MATLAB software, using FieldTrip Toolbox (http://www. fieldtriptoolbox. org, Oostenveld et al. , 2011 ) , Brainstorm (https://neuroimage. usc. edu/brainstorm/, Tadel et al. , 2011 )  and Anywave (https://meg. univ-amu. fr/wiki/AnyWave, Colombet et al. , 2015 ) , and are available upon request.
Acknowledgments Research supported by a  FLAG ERA/HBP grant  from  Agence Nationale de la recherche  " SCALES "  ANR-17-HBPR-0005  ). This work was performed on a platform member of  France Life Imaging network  (grant  ANR-11-INBS-0006  ), and it has received support from the  French government  under the  Programme « Investissements d'Avenir », Initiative d'Excellence d'Aix-Marseille Université  via  A * Midex  funding (  AMX-19-IET-004  ), and  ANR  (  ANR-17-EURE-0029  ), as well as the  Recherche hospitalo universitaire (RHU)  project  Epinov  ( ANR-17-RHUS-0004 ). VJL was supported by a postdoctoral fellowship from the  Institute of Language, Cognition  and the  Brain (ILCB , grant  ANR-16-CONV-0002  ).
LG and AQ applied to digital images of archaeological lustrous and non-lustrous gravels as well as gravels collected at natural sites, the Particle Analysis plug-in of the image processing software ImageJ  [59]  to measure length, width, area, perimeter, and calculate circularity (4π*area/perimeter^2) and rounding indexes (4*area/(π*major axis^2). Thickness was measured manually using a digital caliper. LG and AQ extracted colorimetric values, expressed in RGB, by averaging the color of each gravel thanks to the Color Inspector 3D plug-in, and converted into L*a*b* with the grDevices package  [60]. Inter-group comparison of the distribution of those quantitative data was performed through the Kruskal-Wallis non-parametric statistical test with the pgirmess package  [61].
Acknowledgments We thank  Celia-Fat-Chung  for collecting and conditioning the Landry gravels during sediment sieving,  Catherine Cretin ,  Peggy Jacquement ,  Stephane Madeleine  and  Bernard Nicolas  for facilitating our analysis of archaeological material kept at the Musee National de Prehistoire,  Les-Eyzies-de -Tayac ,  Malvina Baumann  and  Luc Detrain  for kindly communicating information on the gravels from  Fourneau du Diable  and  Abri Casserole ,  Veronique Laroulandie  for giving access to gastroliths. We would also like to Frederic  Santos  (FS) for coding the Berry's permutation function and  Anthony Se  ´cher for assisting us during statistical analyses. We are grateful to  Morgan Collard  for his help with tumbling experiments. The archaeological.