dataesr/scientific-paragraphs-categorization huggingface dataset:
To remove the remaining vertical offsets, the TDX DEMs are again bi-linearly vertically adjusted to the SRTM elevations and finally mosaicked to one single DEM, including a time stamp for the individual pixels. The SRTM DEM and the resulting TDX DEM mosaics are subtracted to estimate the elevation change rates Δh/Δt for the respective periods. As a time stamp for the SRTM elevation data, the mean date of the SRTM 2000-02-16 is taken. Voids in the SRTM DEM were filled using elevation information from different sources (NASA JPL, 2013) without providing the corresponding date information. Thus, the provided masks used are rejected the void-filling data in our Δh/Δt analyses. Since, the glaciers in Bolivia show in general a retreat pattern  (Cook and others, 2016) , the glacier outlines of the starting year of the respective periods are applied to measure Δh/Δt and consequently ice volume losses on the glacierized areas.
S2 ). A negligible influence on the elevation change measurements by the SAR signal penetration is found (see Section 5. 2 for a detailed discussion). Consequently, no correction of the SAR data is applied. However, we considered a potential influence in the total error quantification of the mass balances, since the surface conditions in 2000 and 2016 might have been different as in 2013 and SRTM data were acquired at a lower SAR frequency. The uncertainty contribution due to an offset in the SAR signal penetration between SRTM and TanDEM-X is estimated according to  Malz and others (2018). Deviations in the elevation change rates caused by differences in the SAR signal penetration of C-and X-band data in snow and ice surfaces in areas below the ELA (estimated at an average elevation of 5144 m a.
The evaluation of the elevation differences on glacier areas between the TDX DEM and Pléiades DEM in 2013 revealed an average offset at regions below and above the ELA of -0. 7 and -0. 1 m, respectively (Fig. S2 ). The total offset due to SAR signal penetration is small considering the temporal difference of 4 months between both acquisitions (end of wet season and beginning of dry season, accumulation in the upper reaches and high melt rates toward the terminus), the average surface-lowering rates (2000-2016) of -1. 0 and -0. 7 m a -1 at areas below and above the ELA, respectively, and the uncertainty of the elevation change measurements. The SAR data (SRTM and TDX) were acquired during the transition and wet season (Sections 2 and 3. 2), when the melt rates are highest and meltwater is most likely present in the snow pack, strongly limiting the signal penetrations.
Moreover, glaciological mass-balance measurements can be biased by the spatial data sampling (see above). Thus, we attribute the offset in the trends to differences in the spatial coverage and sampling as well as to the different mass-balance method used. As mentioned in Section 4. 2, data voids in InSAR-based DEMs can occur due to topographic limitations like SAR shadow and layover. For the study periods 2000-2013 and 2000-2016, the SRTM DEM is used as a reference. Only 63. 3% of the glacierized area in 2000 is covered by SRTM elevation data. In combination with the TanDEM-X coverage in 2013, we retrieve Δh/Δt measurements on 59. 2% of the glacier area. This corresponds to coverage of 93. 5% of the SRTM data by TanDEM-X data. The largest data voids are present in the Δh/Δt fields for 2000-2016 (52.
1 ) is revealed for 2013-2016. Consequently, the achievable data coverage can be increased when comparing TanDEM-X to TanDEM-X DEMs, especially in alpine regions where SRTM data can have large voids. Future studies can benefit from using TanDEM-X DEMs instead of SRTM DEM as a reference since less extrapolation is needed, helping to increase the accuracy (Section 3. 3). Moreover, the amount of individual glacier mass-balance estimates is limited by voids in the Δh/Δt fields (Section 3. 2). Thus, a higher spatial coverage will also increase the amount and quality of glacier-wide mass budget information. Combining TanDEM-X data from ascending and descending orbits is highly recommended if available for the specific region and time in order to obtain optimum data coverage. Moreover, many published studies used SRTM DEMs to compute elevation change rates.
Comparable reduction in glacier meltwater contribution to the runoff of Zongo watershed by 2100 was modeled by  Frans and others (2015). Large-scale and spatially detailed information is needed on the future evolution of glaciers, since the metropolis La Paz/El Alto obtains water from a large catchment area and further extensions are planned (personal communication with local stake holders). Thus, the results obtained in this study provided a consistent database for further regional and detailed analyses of glacier changes in Bolivia.
Acknowledgements. This work was financially supported with the  DLR/ BMWi  grant  GEKKO  ( 50EE1544 ), the grant  BR2105/14-1  within the  DFG Priority Program  ' Regional Sea Level Change and Society ' as well as the  HGF Alliance Remote Sensing & Earth System Dynamics. We thank the  German Aerospace Center  for providing TanDEM-X data free of charge under AO XTI_GLAC0264. Landsat data were kindly provided via USGS Earth Explorer; SRTM by  NASA. In situ glaciological and meteorological measurements at Zongo and  Charquini Sur  glaciers were provided through the  French Service National d'Observation GLACIOCLIM  (https://glacioclim. osug. fr/) and the  Laboratoire Mixte International GREAT-ICE (a joint initiative between the French IRD and national counterpart in Ecuador, Bolivia, Peru and Colombia ).
The Pléiades DEM was obtained through the  CNES/ SPOT-Image ISIS program  (contract # FC18473 , led by  A. Rabatel ). A. Rabatel acknowledges the support of the  Labex OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ). We thank the scientific editor  Hamish Pritchard  and four anonymous reviewers.
Analytical instruments and conditions for trace metals For analysis of trace metals, atomic absorption spectrometry (AAS) was used. Fe, Sn, Zn and Cu were analyzed on flame AAS (FAAS), while the analysis of Pb, Cd, Cr and Ni was carried on graphite furnace AAS (GFAAS). The FAAS system used was a Thermo Electron Corporation atomic absorption spectrometer with Thermo Solaar (version 10. 11) data acquisition software, and the GFAAS system was a Shimadzu AA-6800 atomic absorption spectrometer with WizAArd data acquisition software. The instrumental conditions and matrix modifiers were inspired from the instrument supplementary cookbook and are shown in Supplementary material Table  S2. Quantification was performed with external calibration of five points in ranges appropriate for each element (see Supplementary material Table  S3 ).
7 ). From coccospheres to coccoliths. Individual coccoliths were extracted from 3D matrices of the coccospheres using manual segmentation of the 3D array via the ImageJ software (Supplementary Fig. 3 ). Top distal views show the elliptical shape of coccoliths with shield excentricities ranging from 0. 48 to 0. 65, whereas central area eccentricities range from 0. 66 to 0. 81 (Fig. 2a , Supplementary Fig. 8 , Supplementary Table  1 ). These eccentricities may originate from the elliptical shape of the OBPS on/around which the nucleation of the coccolith took place. In addition, cross-sections show that both shields of the coccoliths are out-of-plane inclined by about α ~30 ± 5°along the major axis and α ~25 ± 5°along the minor axis (Fig. 2b, c ). These inclinations likely correspond to the curvature of the nuclear membrane to which the coccolith vesicle is apposed during intracellular formation of the coccolith  40, 41.
3  (black dots). The mass of coccoliths significantly varied in single coccospheres. For instance, the coccosphere of G. oceanica analyzed by 3D-CXDI was composed of 10 coccoliths, the mass of which varied more than threefold (m = 7. 2-23. 1 pg). A similar variability was observed for E. huxleyi RCC1212 (m = 1. 5-5. 1 pg). 3D-CXDI data were compared to mass estimates obtained by PLM on a coccolith population originating from a large number of coccospheres obtained from the same cultures (colored dots in Fig. 3 ). The coccolith mass variability obtained by PLM was only slightly greater than that obtained by 3D-CXDI. This shows that the distribution of coccolith masses within a coccosphere is indicative of the distribution of masses within a species. Role of organic base plate scale (OBPS) size.
After subtracting the 3D Gaussian function, voxels with negative density values were set to zero. Examples of the flattening corrections are shown in Supplementary Fig. 1C. Chimera software was used for the visualization of the surface of the 3D volume obtained by CXDI. For each species, the tomographic slice images before the Gaussian subtraction are available in figShare (https://figshare. com/). PRTF and real resolution of CXDI measurements. The real resolution of the average images was estimated using the phase retrieval transfer function (PRTF)  62. The intersection at 0. 5 threshold was used to estimate the real resolution (see for instance ref. 31  ). The PRTFs were calculated for R. parvula, E. huxleyi RCC1216, and G. oceanica (Supplementary Fig. 2 ). We found that the PRTFs for R.
Acknowledgements We thank  Irina Snigireva  for SEM analysis. We gratefully acknowledge the  European Synchrotron Radiation Facility (Grenoble, France)  for provision of ID10 synchrotron beam time ( EV162  and  CH4936 ). The authors thank  Jerome Kieffer  and  Pierre Paleo  for accelerating the reconstruction code. A. G. would like to thank  A. J. Pryor  for fruitful discussions and advice for the use of Chimera. This software is developed by the  Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco  (supported by  NIGMS   P41-GM103311 ). Funds from the  Agence Nationale de la Recherche  under project  ANR-12-B06-0007  ( CALHIS ) are acknowledged.
6084/m9. figshare. 7413491. v1 for Gephyrocapsa oceanica RCC1314 in Fig. 1a , https://doi. org/10. 6084/m9. figshare. 7413377. v1 for Reticulofenestra parvula RCC4036 in Fig. 1c. In addition, the data with the dimensions of the coccoliths are collected in Supplementary Table  1.
The mean of the adjusted measurement points of a chosen latitude range and season is then used as the bias-free climatological value. When applying the adjustment factor to seasonal averages in 30 • zones, the maximum spatiotemporal sampling bias adjustment was 11 % for OCS mixing ratios at 16 km and 5 % for the stratospheric OCS column. The adjustments were validated against the much denser and more homogeneous OCS data product from the limb-sounding MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) instrument, and both the direction and magnitude of the adjustments were in agreement with the adjustment of the ACE-FTS data.
We introduce these measurements in Sect. 2, together with OCS measurements from Envisat-MIPAS that will be used to evaluate our method. Section 3 describes the method developed to estimate and adjust for spatiotempo-ral sampling biases in detail, which is then evaluated using the much denser and more homogeneous MIPAS data set in Sect. 4. Limitations of the method and its applicability to other tracers and regimes are discussed in Sect. 5.
A quantitative evaluation using MIPAS observations To quantify the sampling bias arising from the sparse ACE-FTS sampling for a chosen latitude-time box, the OCS data product from the MIPAS instrument, with its much denser data coverage, is used. Because of the dense sampling pattern and almost complete latitude coverage (down to 88 • S), the sampling bias of MIPAS is negligible compared to that of ACE-FTS. Figure  4  visualizes how much denser the MIPAS sampling is compared to that of ACE-FTS globally (Fig. 4a ) and in a chosen latitude-time box (Fig. 4b ). Figure  4a  shows that both seasonal evolution and latitudinal variability of OCS mixing ratios at 16 km altitude are much better resolved by MIPAS than by ACE-FTS (cf. Fig. 1a ). Overall, seasonality and mixing ratio distribution agree well with the regression model output in Fig.
Acknowledgements. Measurements used in this study are from the  ACE-FTS  instrument and MIPAS together with the dynamical tropopause data from  ECMWF. The  Atmospheric Chemistry Experiment (ACE) , also known as SCISAT, is a Canadian-led mission mainly supported by the  Canadian Space Agency and the Natural Sciences and Engineering Research Council of Canada. MIPAS spectra used for deriving OCS vertical profiles at the  Karlsruhe Institute of Technology  have been provided by the  European Space Agency. Corinna Kloss  has been supported by the graduate school of  Forschungszentrum Jülich HITEC (Helmholtz Interdisciplinary Doctoral Training in Energy and Climate Research ) and  ANR-17-CE01-0015  ( TTL-Xing ). Marc von Hobe was supported by the  German Federal Ministry of Education and Research  through the project  ROMIC-SPITFIRE  ( BMBF-FKZ: 01LG1205C ).
Data availability. The IMK/IAA-generated (Institute of Meteorology and Climate Research/Instituto de Astrofísica de Andalucía) MIPAS data used in this study are available for registered users at http://www. imk-asf. kit. edu/english/308. php (last access: 29 March 2019). The ACE-FTS level 2 (V3. 6) data used in this study are available upon request at https://databace. scisat. ca/ l2signup. php (last access: 29 March 2019).
Our algorithms are automatic and allow 3D visualizations. Results: We validate these algorithms on various databases with different imaging modalities (Magnetic Resonance Imaging (MRI) and Computed Tomography (CT)). Experimental results are presented on diseased livers, which pose complex challenges because both the overall organ shape and the vessels can be severely deformed. A mean DICE score of 0. 915 is obtained for the liver segmentation, and an average accuracy of 0. 98 for the vascular network. Finally, we present an evaluation of our method for performing the Couinaud segmentation thanks to medical reports with promising results. Conclusions: We were able to automatically reconstruct 3-D volumes of the liver and its vessels on MRI and CT scans. Our goal is to develop an improved method to help radiologists with tumor localization.
Introduction Medical imaging systems such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) provide useful information on the liver. Extraction of the organ and its vessels from images is essential for analysing disease and planning surgical operations. A scheme developed by Couinaud allows the division of the liver into eight functionally independent segments by identification of the main blood vessels (Figure  1 ). [1]  This analysis is most frequently performed by a radiologist This research has been supported by Région Auvergne with the grant "Nouveau chercheur" 1440NCAA and French National Agency for Research (ANR) with the reference ANR-18-CE45-0018 (R-Vessel-X project). via visual inspection and manual segmentation. It is a timeconsuming task, performed slice-by-slice, and the result depends on the skill and experience of the medical expert performing it.
Segmentation of the largest vessels To achieve this, we first erode the vessel segmentation to retrieve the largest vessels. This process allows us to erase the smallest vessels detected in the previous section. The liver receives its blood supply from two large vessels, the portal vein and the hepatic artery. The latter carries blood from the liver to the inferior cava vein, while the first carries blood from the stomach, spleen, intestines and gallbladder to the liver. Detoxified blood leaves the liver through another large vessel, the hepatic vein. The hepatic and portal veins are not connected. After erosion, and thanks to this disconnection, we extract the two principal components, as shown on two MR images from our database in Figure  8. Our method works on the livers of patients with advanced cirrhosis, as shown in Figure  8.
Data and parameters To evaluate our automatic method, we use CT images from two freely available databases: 20 from the SLIVER database  [43]  and 20 from the IRCAD database  [42]. Both databases are widely used in research because some manual segmentations (of liver or/and vessels) are available and they are, therefore, directly comparable. We do not use Shape 2015 database, here, because medical examinations are not available in this dataset. Then, during the shape model construction (see Section 2. 1), some of those testing data are used. To avoid bias, each of the set used in the shape model chosen are removed. The image sizes are 512 × 512 × 64-394, with a voxel size of 0. 56-0. 87 × 0. 56-0. 87 × 0. 7-5 mm 3. We also use 40 MRI volumes from different local hospitals (CHU Clermont Ferrand, France and CH Emile Roux, France).
Image sizes are 250 -512 × 320 -512 × 56-112, with a voxel size of 0. 82-1. 40 × 0. 82-1. 40 × 1. 8-3 mm 3. The 40 liver images in our MRI database come from patients with diseases such as advanced cirrhosis and cancers (including hepatocellular carcinoma). In these cases, segmentations are a challenging task, because the image quality decreases considerably with more heterogeneous liver tissue density; moreover, as mentioned above, livers are severely deformed by the progression of these diseases and there is a decrease in the liver-vessel contrast. Parameters used in the method described in Section 2 are presented in Table  3. Some parameters are chosen based on the literature, including those for RORPO  [28] , and Sato's filter  [23]. Others were derived from the anisotropic diffusion filter in the liver segmentation (n it1 , ts, ξ).
We have modified the conductance ξ to adapt the process to our dataset. Then, some parameters are determined empirically. These parameters are sufficiently generic to work with all images, like t, that allows us to consider intensity values on the results of the Sato's filters; ω 1 and ω 2 , which reflect a connection between two vessels close to a right angle; and n it2 , which limits the size of the centerline extension. We spent time manually optimizing other parameters, like the percentages γ 1 and γ 2 in the liver segmentation; the number of components q to be detected, and the direction of the component λ in the vessel segmentation. In the future we may consider developing automatic calculation processes to pick the best parameters (optimization) using training datasets.
4 seconds for processing a 512 × 512pixel slice, while ours requires only 5-6 s. For purposes of comparison, a manual expert segmentation generally requires 180-240 s for one slice  [53]. Okada et al. [18]  obtain a Jaccard coefficient of 0. 86 for 8 CT scans; we obtain 0. 87 for the 20 SLIVER images. Farzaneh et al. [14]  obtain a Dice coefficient higher than 0. 89 on 11 similar images in pixel spacing. The mean Dice value in our study is 0. 905 for 40 different datasets. Then, we compare our method with the results of efficient liver segmentations applied to the IRCAD and SLIVER datasets. Linguraru et al. [15]  present an automatic method with a 0. 96 Dice value for the SLIVER database, but their atlas-based method requires 3000-3600 s per slice. Our results do not perform as well as methods described in  [11, 16, 19, 54, 55] ; however, all of these studies concentrate their task only on CT scans.
Vessel segmentation results We first evaluate our method presented in Section 2. 2 on 10 synthetic vascular images from the March 2013 VascuSynth database used by Cheng et al. [60]. Averages with standard deviations of True Positives (TP), False Positives (FP), False Negatives (FN) and Overlap Measure (OM)  [60]  are reported in Table  6. We do not have false positives in our results, because our method of extraction of the main components and our partial skeletonization process efficiently handles synthetic data without noise. Moreover, RORPO is an efficient algorithm for the enhancement of vascular structures. Our algorithm has better results than those of article  [60]  for these specified criteria. Then, we apply our vessel segmentation on CT images from the public IRCAD database  [42].
In this database, different contrast agents are used and therefore different levels of contrast enhancement are present. IRCAD offers manual segmentation sets {artery, portal vein, venous system, vena cava}, which allow for further comparison with other methods. Unfortunately, most of the methods described in the literature were tested using private databases, and did not publish their code, making retrospective comparative studies impossible  [25, 27, 34]. As far as we know, there are no open MRI datasets available for liver vessels. A medical expert working with us has segmented portal and hepatic veins for an MRI volume at the portal and arterial phases (times after contrast agent injection), when vessels are most visible. Using these data we quantitatively compare our method with RORPO and Sato's filter (which were thresholded beforehand) based on several classic metrics: accuracy (ACC), specificity (SPE), sensitivity (SENS) and precision (PRE).
Couinaud segmentation results We present here a new process to evaluate the Couinaud representation. We use MR images, MRI i with i ∈ {1, 4}, from our database for which an expert validated our visual results, and CT scans, CT i with i ∈ {1, 8} from the IRCAD database. Among these four MRI volumes, some of the imaged livers have cirrhosis too advanced (with the presence of tumors in many segments) to allow for surgical planning. The Couinaud scheme is useful for localized, resectable tumors; as explained in Section 2. 3, it uses the horizontal portal vein orientation and the three vertical hepatic veins axes to divide the liver into eight functionally independent segments (see Figure  1 ). Vessel segmentation explained in Section 2. 2 allows us to construct this representation. This segmentation is used in daily clinical practice to indicate the exact positions of tumors, which is of high interest in surgical planning.
Adaptation and innovation 4. 2 The procedure used to extract and retain the best of the 7 MM was to list all of the questions and approaches proposed (plus some related or personal ideas). From this step, it was possible to build a first database of more than 160 questions that lead to approximately 800 possible answers. It was then necessary to make groupings by themes. A number of redundancies then appeared. For maximum efficiency, it appears to be fair to dedicate a tool for craftsmen or the micro and small enterprises sector. In doing so, it is important to consider (and sometimes try to counter) most of the advantages (or deficiencies) found in the literature or evoked during interviews:  Simple scales or multiple-choice questionnaires used in many models are easy to employ, but they are sometimes considered to be too subjective for solutions  [81].
This idealistic vision is an organizational model that we believe in. This approach is the perspective that Questions number 6, 12 and 13 (Appendix A) aim to highlight. The ultimate maturity level proposed in Question 12, for estimation phases, is a process wherein a member of the group would be in charge of gathering a set of digital data that is used to transcribe the existing building and the needs of the client. In this scenario, each participant of the project would be able to constitute synchronously in BIM their own contribution and proposal related to their activity. Additions have also been proposed to the positions suggested by Succar (Question number 13) to accentuate this cooperative model vision. The maturities returned to the company seeking to position themselves within this framework of study are obtained according to the methods of calculation proposed in the following section.