dataesr/scientific-paragraphs-categorization huggingface dataset:
Heart rate variability (HRV) was measured for the parasympathetic response to assess regulatory changes. This response required more time for activation regulation, so each 4-second gesture video was repeated twice, resulting in an 8-second stimulus duration. Each gesture video was followed by a green screen that matched the background colour in the videos (RGB 86 156 87) and displayed a xation cross ("+") for a duration of 4, 5, or 6 seconds. Within each Gesture condition (meaningless, pantomime, and intransitive), there were two direction blocks consisting of 10 gesture videos each (10 toward the body and 10 away from the body). Each direction block lasted 130 seconds. The order of presentation of the blocks was randomised. The experiment was run on PsychoPy v1. 90. 2 software and began with on-screen instructions in French.
Data analyses Statistica software version 13 was used to perform all analyses, with a signi cance level established at. 05. The data's normal distribution was assessed using Kolmogorov-Smirnov's test, while Mauchley's test was used to verify the assumption of sphericity and the Levene test the equality of variances (homoscedasticity). Results showed that only the pupil dilation data for the Meaningless gestures TB were not normally distributed (K-S d =. 21, p <. 05). However, as the results did not change when non-parametric analyses were used, for each physiological measure (pupil and dilation ΔHF-HRV) we run a Mixed ANOVA with 2 independent Gender groups (males and females) X 3 Gesture Types (meaningless, intransitive and pantomime) X 2 Gesture Directions (TB and AB). Post-hoc Fisher's analyses have been also carried out for signi cant ANOVA effects.
Acknowledgements This study has been conducted in the scope of the  ESTRA International Associated Laboratory (LAI). This research was partly supported by the  Research Federation  ( FR CNRS 2052 )  Visual Sciences and Cultures  and funded by the  ANR-21-ESRE-0030- Equipex + Continuum national grant  from the  Program of Investments of Future. This study was partially conducted at the  Psychology Research Centre  ( PSI/01662 ),  University of Minho , and supported by the  Portuguese Foundation for Science and Technology and the Portuguese Ministry of Science, Technology and Higher Education  ( UID/PSI/01662/2019 ), through the national funds ( PIDDAC).
In line with the IMI Antimicrobial Resistance (AMR) Accelerator program, which overarches the ERA4TB and other IMI consortia, the StWG together with Data Management and Modeling and Simulation teams of the ERA4TB consortium developed data templates for data aggregation, reporting, sharing, and analysis following FAIR principles. 5 n TB, and other infectious diseases where the pathogen and its response to treatment can be studied outside the patient, in vitro experiments are convenient to elucidate drug effects. Among these, time kill assays (TKA) are employed to quantify the antibiotic concentration-effect relationship in a time-dependent manner. In a TKA, bacteria are exposed to different drug concentrations including a negative control or natural growth condition, and the bacterial burden is determined longitudinally at multiple timepoints.
In addition, CFU measurements can be variable due to experimental conditions, number of dilutions plated, methods of colony counting, and the tendency of mycobacterial cells to form aggregates. 8, 9  In order to meet ERA4TB's demand on in vitro analyses, assay capacity needs to be built across partner laboratories. In the case of TKA, these will have to be performed at different locations in parallel, stressing the need for consistency between partners and standardization of a robust and replicable TKA protocol in order to build confidence in data generation for pharmacometric modeling. To address this challenge, the StWG optimized four different CFU plating methodologies to limit variability and increase throughput and capacity. Our objective here was thus to (i) quantify the replicability of a common standardized TKA protocol between six laboratories by determining variability in CFU performed using four plating methods, and (ii) share lessons learnt on how to collaborate between partners in an IMI consortium to harmonize, implement and standardize experimental protocols and data management for consistent reporting and sharing within the AMR Accelerator program.
Partner commitment and fluid communication through e-mail exchanges and frequent videoconferences were critical to achieve a safe working place. Taking TKA as an exemplary methodology, ad hoc data templates were generated to ease experimentalist data reporting load while facilitating the automatization of data transfer into tabular formats required for integration into the AMR Accelerator program. The objective of the template development was thus to create a transparent and traceable dataset containing all information for data analysis, but with limited change in operations by experimentalists. Staying close to the experimentalist' previous data-capturing operations, by basing the standardized template on a typical example of a data acquisition sheet, was expected to result in highest acceptance and adherence to the new standardized template.
Data managers and data analysist worked on function-and macro-based exports of the relevant data for data storage and analysis. Data templates included essential information up to regulatory submission standards (Supplemental information -Raw data template. xlm & Guidelines), allowing for data provenance including audit trails; for this, an electronic Laboratory Notebook was implemented in the different laboratories. The standardized data template is subject to data transfer to the AMR repository, with data availability to all AMR Accelerator consortia. Data availability will improve collaboration within and across consortia. The standardized data template was developed for TKA data, but is currently being expanded to include all experimental procedures performed in ERA4TB, including assays such as artificial caseum, efflux pump inhibition, resistance evolution assays, frequency of resistance, granuloma-like structure, hERG safety, hollow fiber system for TB, growth inhibition assays, or in vitro pharmacokinetics.
In summary, here we share lessons learnt on how to build a collaborative space between partners in an IMI consortium to harmonize, implement and standardize experimental protocols and data management for consistent reporting and sharing within the AMR Accelerator program, and described how different areas of expertise come together to rise to the challenge. Standardization to achieve robust and replicable results is the first step to improve and streamline the elements in the developmental process to bring new combination therapies into the clinic for the treatment of all TB forms.
Limitations of the study Despite protocol standardization and shared Mtb strain, baseline bacterial burden was variable between laboratories. Baseline bacterial burden can impact TKA data, and consequently the interpretations of drug effects. The slow growth rate of Mtb implies that, in TKA, CFU data of the first timepoint is counted almost after the last timepoint is taken (typically 21 days), preventing pre-or mid-experiment adaptations. Only two dose levels per drug were utilized in these experiments, limiting the possibilities to extract a reliable exposure-response relationship of the drug tested; although this was not the scope of this exercise since the activity of both drugs is well characterized against M. tuberculosis. As such, drugs and dose levels tested here were selected to reflect bactericidal and sterilizing effects in the context of a TKA.
Standardized bacterial strain & growth conditions Experiments were carried out with the Mtb strain H37Rv (GenBank ID: NC_000962. 3) from standardized stocks distributed by FZB to all six partner laboratories involved in the TKA. 27 The Mtb strain has no sex assigned. Bacterial stocks were further sub-cultured following a common agreed sub-cultivation procedure and stored at -80 C. After 14 days at -80 C, CFU titers were determined by selecting three stock tubes per box (n= 81). CFU enumeration is performed every six months to ensure that freeze stocks do not lose cell viability. Difco TM Middlebrook 7H9 broth supplemented with 0. 5% glycerol, 10% BBL TM Middlebrook OADC Enrichment and 0. 025% tyloxapol was used for bacterial expansion and stock generation. 7H9 broth supplemented with 10% OADC and 0.
The ERA4TB consortium falls within the broader scope of the larger IMI AMR Accelerator program, which comprises three pillars: Capability Building Network, Tuberculosis Drug Development Network; and Portfolio Building Networks. Under one structure, the AMR Accelerator addresses many of the scientific challenges of AMR, contributing to the European action plan on AMR 26  and supporting the development of new ways to prevent and treat AMR. Projects in the AMR Accelerator are bound to the development of new pre-clinical tools and methods to assist in the translation of novel anti-infective agents' pre-clinical data to the clinic. To this end, data management guidelines and an IT infrastructure enable the collection, aggregation, storage, sharing, and analysis of datasets generated by AMR Accelerator projects.
ACKNOWLEDGMENTS The project leading to this publication has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking (JU)  under grant agreement No  853989. The JU receives support from the  European Union 's  Horizon 2020 Research and Innovation Program  and  EFPIA and Global Alliance for TB Drug Development Non-Profit Organisation, Bill & Melinda Gates Foundation ,  University of Dundee. This work reflects only the author's views, and the JU is not responsible for any use that may be made of the information it contains. We would especially like to thank the technical support of  Larissa Mohr ,  Silvia Maass ,  Tanja Niemann , and  Vanessa Mohr  from the  Research Center Borstel Leibniz Lung Center, Begon ˜a Gracia Dı ´az  and  Ana Pico Ḿarco  from the  University of Zaragoza  and  Jason Chhen  from the  University of Cologne.
Observations Our selected sample consists of 36 transition discs, chosen from the sample of  Francis & van der Marel (2020)  where sufficient CO data are available. Except for TW Hya, these discs show large (> 25 au) inner dust cavities and therefore represent the ideal candidates to search for planet-disc interactions. It comprises different star-disc system architectures, including a range of spectral types (M2 to A0; primary) and stellar masses (0. 4 M to 2. 6 M , primary) counting 23 TTauri and 13 Herbig stars. Some stellar and disc properties of our targets are listed in Table  1. For our analysis we collect either Band 6 or Band 7 archival CO line data, observed with ALMA. For most sources (two third) we use reimaged/self-calibrated data cubes that are either public or were obtained via private communication, for the remaining sources we use archival data products.
This is indicated in Table  2 , where some characteristics of the data cubes are listed for the main lines used in our study. Typical spectral resolutions of the data are a few 100 m s -1 and spatial resolutions lie between ∼ 6 -135 au (median: 31 au). RMS values lie between ∼0. 5-47 K (median: 3. 4 K) when scaled for a channel width of 100 m s -1. To assess if combining reimaged and archival products affects our results we compare the reimaged data sets with the archival products for the same data set. We find that the Keplerian fit (see Sect. 4. 2) is not significantly affected. The detection Several discs in our sample are affected by cloud absorption, namely AB Aur, HD 142527, HD 97048, HP Cha, IP Tau, IRS 48, PDS 99, SR 21, Sz 91 and SZ Cha. We mask the regions affected by this in the calculations of radial profiles (Sect.
Brightness temperature In Fig. 1  we present the peak brightness temperature maps (continuum subtracted) for the main CO lines (mostly 12 CO, some 13 CO). Maps for the additional lines can be found in shown again in Fig. A. 1 with overlaid continuum images, illustrating that for most targets the dust disc (mm-sized grains, B6 and B7) is substantially smaller than the gas disc. This can readily be explained by radial drift processes and/or by the difference between dust and gas opacities  (Facchini et al. 2017; Trapman et al. 2019 ). To compute the peak intensity maps we use the standard moment 8 implementation in the bettermoments code  (Teague & Foreman-Mackey 2018)  and then convert from flux density units to units of Kelvin with the Planck law. No masking is applied in the computation of the maps.
Radial profiles In Fig. 4  the radial peak intensities are displayed for the different CO lines (colored lines) as well as the mm-continuum (black lines). These curves are calculated by azimuthally averaging the peak intensities for radial annuli of equal width, using the GoFish package  (Teague 2019). By default, the widths of the annuli in this package are given as 1/4 of the beam major axis. For the computation we assume the geometries (thin or thick disc) obtained from the fitting of the rotation maps (see Sect. 4. 2). For both geometries we recover similar radial profiles (due to similar fits, see discussion in Sect. 4. 2) and thus the curves are only shown for the thin disc geometry in Fig. 4. All profiles are normalised to the peak value. The uncertainties, shown as shaded regions, correspond to the standard deviation per annulus divided by the square root of the number of independent beams in the annulus.
SZ Cha 12 CO 3-2 [arcsec] T Cha 12 CO 2-1 [arcsec] TW Hya 12 CO 3-2 [arcsec] UX Tau A 12 CO 3-2 [arcsec] V1247 Ori 12 CO 3-2 note that artificial cavities can be created in the peak intensity, depending on the beam size. Near the star the emission is less extended than the beam size and during the beam convolution the intensity gets diluted over the full extent of the beam, hence the peak intensity decreases. When studying the inner disc it is therefore important to look at the velocity integrated intensity, which is less affected by this issue. However, for the integrated intensity there may be contributions from the back side of the disc  (Rab et al. 2019 ). The radial profiles for the integrated intensity are shown in the Appendix in Fig. F. 1. The measured brightness temperature will also be affected by the subtraction of continuum (except for HD 97048 all data are continuum subtracted).
Brightness temperature structure To uncover possible substructures in the brightness temperature we construct an azimuthally symmetric model with GoFish and subtract this model from the data. The package computes an azimuthally averaged radial profile for a given geometry (compare Sect. 3. 3) and then projects it onto the sky to create an azimuthally symmetric model. For the disc geometry we use the results for the thin and (if available) the vertically extended thick disc from the kinematics modelling described in Sect. 4. 2. The  angles affected by cloud absorption are again excluded from the calculation. The resulting residuals are presented in Fig. 5  for the geometrically thin disc and in the Appendix for the geometrically thick disc models (Fig. B. 1). The color scale is adapted such that regions with hotter temperatures than the model are highlighted.
Velocity structure To analyse the gas kinematics of our sample we use the eddy code (Teague 2019) to fit a Keplerian profile v rot (r, φ) = GM * r • cos φ • sin i + v LSR , (1) with (r, φ) being the deprojected cylindical coordinates, i the inclination of the disc and v LSR the systemic velocity, to the rotation maps shown in Fig. 3. To deproject the sky-plane coordinates (x, y) into the midplane cylindrical coordinates (r, φ), the disc centre (x 0 , y 0 ), i, and the disc position angle PA are used. The latter is measured between the north and the redshifted semi-major axis in an easterly direction. As a first step, the starting positions of the free fit parameters are optimised with scipy. optimize and their posterior distributions are estimated using the MCMC sampler. Besides the model for a geometrically thin disc, eddy also includes the possibility to fit for the vertical structure of the disc.
Conclusive remarks Within our sample, eight discs are marked by clear substructures. In all cases features are seen in the gas temperature and the kinematics -indicating a connection -alongside other substructures in the gas and dust. Ten other discs show tentative features, of which half present signatures in the brightness temperature and kinematics simultaneously. Half of our sample is not showing any substructures in the ALMA data besides a deep gas cavity. Except for MWC 758 and SR 21, all targets that exhibit clear spirals in NIR scattered light show at least tentative features in the brightness temperature and /or kinematics. Scattered light is tracing the hot upper disc layers, most likely to show spirals, and since 12 CO is also tracing the disc surface we expect to pick up these features there as well  (Law et al.
2021). However, many of the tentative features in our sample are not spirals, which is likely related to poor spatial resolutions as well as cloud absorption in some cases. To resolve the spirals expected from the NIR, high sensitivity and spectral and spatial resolution ALMA data is crucial. For the targets showing cloud absorption, deep observations of the less affected 13 CO may help to pick up clear substructures. Two targets (HP Cha and TW Hya) show clear spiral structures without a counterpart having yet been observed in the NIR. It is expected that substructures such as spirals are more likely found in systems with high luminosity and a wide cavity, where the upper disc layers can reach higher temperatures, making it much easier to observe them (e. g. Garufi et al. 2018; van der Marel et al.
Connected to that, the lack of features can not be seen as an indication of missing embedded planets. PDS 70 for example hosts two confirmed massive planets, that have been directly imaged, but does not show any other features in our data despite a deep gas cavity. This is likely because the planets are located further inside the cavity in this system and higher sensitivity plus resolution observations are needed to reveal substructures in the temperature and the kinematics, related to dynamical interactions. Furthermore, as shown by  Izquierdo et al. (2021)  an embedded planet has to be rather massive to excite strong observable signatures in the kinematics: From simulations the authors find strong perturbations for planets more massive than 1 M J , however to pick these up in ALMA data, very high spectral and spatial resolution is essential.
Acknowledgements We greatly thank the referee  Ruobing Dong  for his helpful feedback that improved the quality of this work. We also would like thank all the people that kindly provided the reimaged/selfcalibrated data sets. This paper makes use of different ALMA data sets, detailed in Table 2 andTable E. 1. ALMA is a partnership of ESO (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada)  and  NSC  and  ASIAA (Taiwan)  and  KASI ( Republic of Korea) , in cooperation with the  Republic of Chile. The  Joint ALMA Observatory  is operated by  ESO ,  auI/NRAO  and  NAOJ.
Individual tree volumes for each plot were estimated using the general volume table (Supporting Information Table  S2 )  (Liu et al. , 2017) , then summed, and then the result was divided by the plot area to give stand volume (m 3 ha -1 ). The forest area and stand volume for each age class of different forest types were recorded at the field sample plots from national forest inventories. The second dataset was from 149 field plots (Fig. 1 ) with areas ranging from 0. 1 to 1. 05 ha. These plots were sampled from 2015 to 2018 using a systematic random sampling method, and were distributed across the Greater Khingan Mountains (a minimum of 132 field plots was determined to be required for statistical representativeness to estimate the C density of forest ecosystems and forest vegetation, and to examine the potential drivers of forest ecosystem C density changes in the Greater Khingan Mountains; see Supporting Information Appendix 2 for a detailed determination of the minimum sample size).
Similar data to those obtained in the national forest inventory were collected at each plot, including the area, latitude and longitude, and stand age, DBH, H, and tree species for all trees with DBH > 5 cm. The methods used to categorize forest type and age class, and to estimate stand volume in 149 field plots were the same as those used in the national forest inventories. Following a chronosequence approach, we used the 149 field plots to estimate the amount of C stored in the shrub, herb, and litter layers during the periods of  1999-2003, 2004-2008, 2009-2013, and 2014-2018 , because the national forest inventory data did not provide information for these layers. The aboveground and belowground biomass were measured in three quadrats along the diagonal of each field plot for shrubs, herbs, and litter using the harvest method (see Section 2.
Estimation of C storage in understory and litter layers in different forest types and age groups A true year-to-year dataset of C density in shrubs, herbs, and litter for different forest types during the 1999-2018 time span could not be obtained. However, to account for the temporal change in C density in Y. Liu et al. shrubs, herbs, and litter we used a chronosequence approach. We estimated C density in shrubs, herbs, and litter based on age classes representative of forest growth stages for different forest types, similar to the methods of  Pan et al. (2004). These estimates are only accurate at the age class level, rather than at the specific year level. Therefore, the C density in shrubs, herbs, and litter of the same forest types and same age classes was hypothesized to be constant. To calculate C storage in the shrub layer (including aboveground and belowground biomass C), shrubs were harvested from three subquadrats (2 × 2 m) along the diagonal of each field plot.
Estimation of C density in soils and total C density Soil data were taken from the International Soil Reference and Information center (ISRIC) database on world soil properties (WISE30sec), which are estimated at 1 km spatial resolution  (Batjes, 2015)  and can be accessed through the ISRIC's Soil Data Hub (http://www. isric. org/exp lore/wise-databases). Before using the gridded soil data from the WISE30sec, a quality test was carried out by comparing the gridded data with data from 48 actual analyses of soils at the same geographic coordinates (Fig. S1  and Table  S7 ). Soil organic C density (SOCD) values were validated using the Wilcoxon rank-sum test, which is a nonparametric two-sample test. We found that there were no significant statistical differences (p = 0. 126 and correlation coefficient = 0.
76) between actual SOCD measurements from 48 field-based soil studies in the Greater Khingan Mountains and gridded SOCD values extracted from the WISE30sec dataset for the same geographical coordinates (see Appendix 4 in the Supplementary Information for details). Hence, we used the gridded data to calculate the surface SOCD in the upper 20-cm soil layer for different forest types and age classes. We selected the upper 20 cm soil layer because it contains most of the biologically active C  (Crowther et al. , 2016)  and in northern forest soils it can store up to 57% of total soil C  (Jobbágy and Jackson, 2000). SOCD was calculated using Eq. (  10 ): SOCD = ρCD(1 -θ) × 10 -1 (10) where SOCD is the soil organic C density (Mg C ha -1 ) at 0-20 cm depth, ρ is the bulk density (g cm -3 ), C is the organic C content (g kg -1 ), D is the depth of soil layer (cm) (D is equal to 20 cm for this study), and θ is the volume percentage of coarse fragments (>2 mm).