dataesr/scientific-paragraphs-categorization huggingface dataset:
The final prediction was then multiplied by the optimized scaling factor. Note, this post processing does not affect the Pearson coefficient we used for all the quantifications of the study. Custom made scripts to compute the TFs. A simple graphical user interface version of our scripts is available on GitLab (https://gitlab. com/AliK_A/buildtf), as well as some data examples in order to give the opportunity to the reader to compute the TFs. Statistical tests. All statistical analyses are reported in the manuscript, with p values. A confidence level of 5% was chosen, a priori, as threshold for significant difference between samples, however higher confidence levels are reported in some figures, with graphical indicators of p values. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements Financial support was provided by the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  European Research Council  ( ERC-2013-AD6 ;  339513 ), the  Agence Nationale de la Recherche  ( ANR/NSF 15-NEUC-0003-02  and  NR-16-RHUS-0004  [ RHU TRT_cSVD ]), the  Fondation Leducq Transatlantic Networks of Excellence program  ( 16CVD05 , Understanding the role of the perivascular space in cerebral small vessel disease) and the  IHU FOReSIGHT  [ ANR-18-IAHU-0001 ] supported by  French  state funds managed by the  Agence Nationale de la Recherche  within the  Investissements d'Avenir program. R. L. R. had a postdoctoral fellowship award from  EMBO  ( ALTF 384-2015 ). We thank  Manon Omnes  for mice surgeries and  Christophe Tourain  for technical help.
Code availability The whole code used to analyze the datasets and get the results and figures is available from the corresponding authors on reasonable request. As described in the "Methods" section, a simple version of our scripts, with a graphical user interface, and a subset of our data is available on GitLab (https://gitlab. com/AliK_A/buildtf) and on Zenodo (https:// doi. org/10. 5281/zenodo. 3773863).
, 2013)  in situ measurements confirm the improvements in the Baseline-D freeboard product quality. Relative to OIB, the Baseline-D freeboard mean bias is reduced by about 8 cm, which roughly corresponds to a 60 % decrease with respect to Baseline-C. The BGEP data indicate a similar tendency with a mean draft bias lowered from 0. 85 to -0. 14 m. For the Published by Copernicus Publications on behalf of the European Geosciences Union. two in situ datasets, the root mean square deviation (RMSD) is also well reduced from 14 to 11 cm for OIB and by a factor of 2 for the BGEP. Observations over inland waters show a slight increase in the percentage of good observations in Baseline-D, generally around 5 %-10 % for most lakes. This paper provides an overview of the new Level 1 and Level 2 (L2) CryoSat Ice Baseline-D evolutions and related data quality assessment, based on results obtained from analyzing the 6-month Baseline-D test dataset released to CryoSat expert users prior to the final transfer to operations.
In this regard, the new CryoSat Ice Baseline-D processors have been developed and tested. An Ice Baseline-D test dataset (TDS) covering three different time periods (September-November 2013, February-April 2014 and April 2016 (only SARIn)) was made available to the CryoSat Quality Working Group (QWG) and scientific experts in order to opportunely validate and check the quality of the new products. This paper provides an overview of the CryoSat Ice Baseline-D evolutions of the processing algorithms and focuses on the in-depth validation performed on the TDS over land ice, sea ice and inland waters. The transfer to operations of the new CryoSat Ice Baseline-D processors was performed on 27 May 2019, and a complete mission data reprocessing is ongoing in order to provide users with homogeneous and coherent CryoSat Ice products for proper data exploitation and analysis.
3. 3. 1). This method improves the capability of the algorithm to reject waveforms contaminated by off-nadir specular reflections (as described in https://earth. esa. int/documents/10174/ 125272/CryoSat-L2-Design-Summary-Document, last access: January 2020). Some tuning of the thresholds for the other metrics has also been performed, based on analysis of the test datasets. For the land ice domain, new slope models have been generated, using the digital elevation models (DEMs) of Antarctica and Greenland described in  Helm et al. (2014). These models were created with more recently acquired data and therefore better represent the slope of the surface during the period of the CryoSat mission. The DEMs were sampled at a high resolution to derive the surface slope correction. Lastly, several improvements have been made to the contents of the L2 products.
The surface-type mask model used to discriminate different types of targets has been updated (as described in the Baseline-D product handbook available at https://earth. esa. int/documents/ 10174/125272/CryoSat-Baseline-D-Product-Handbook, last access: February 2020). Variables have been added to the netCDF to explicitly cross-reference the 1 and 20 Hz data. Finally, the retracker-corrected range to the surface has been added to the product. Table  1  summarizes the major differences between Baseline-D and Baseline-C. IDEAS+ performed QC of a 10 d sample of L1B and L2 data to assess data quality and check for major anomalies. Following these QC checks, this 6-month TDS was made available to the CryoSat QWG for more detailed scientific analysis. The content of the product header files (HDR format) was checked to confirm that all dataset descriptors (DSDs) were present and correct and all header fields were correctly filled.
Similarly, the global-attributes section of the netCDF has been checked to ensure data files were consistent and complete. The CryoSat data products contain many data flags which provide information and warnings about any inconsistencies present in the data products. These flags have been checked for any unexpected values that may indicate processing anomalies, and all external geophysical corrections were checked to ensure that they were computed correctly. Some minor unexpected changes to the configuration of particular flags were observed as well as the incorrect scaling of the altimeter wind speed values. These minor issues have been resolved in the final Baseline-D release, which has been put into operation. 3. 2 Land ice.
Impact of algorithm evolution on land ice products CryoSat L1B and L2 products generated using the Baseline-C processors are the primary input to obtain elevation change time series of the large ice sheets. As those time series are the primary dataset to obtain ice-sheet-wide mass balance and therefore the contribution to sea level change, a consistent high-quality CryoSat L1B-L2 product is essential. To derive mass balance estimates the Alfred Wegener Institute (AWI) processing chain was used, introduced by  Helm et al. (2014) , including TFMRA (threshold first-maximum retracker algorithm) retracking and the refined slope correction  (Roemer et al. , 2007)  for LRM as well as an interferometric processing using phase and coherence for the SARIn mode L1B data products. In addition, several other groups rely on high-quality L1B and L2 data products to generate time series of elevation and mass change (e.
This has been implemented in Baseline-D, where a new star tracker processor was developed to create files containing the most appropriate star tracker data. In addition, new fields were added to the L1B products to include the antenna bench angles (roll, pitch and yaw), and https://doi. org/10. 5194/tc-14-1889-2020 The Cryosphere, 14, 1889-1907, 2020   the sign conventions of these fields were updated. To estimate the impact of the algorithm evolution of the CryoSat Ice processor to Baseline-D on land ice data records, L2type products for Baseline-C and Baseline-D were computed using the AWI processing chain. In addition, a Level 2 indepth (L2I; https://earth. esa. int/documents/10174/125272/ CryoSat-Baseline-D-Product-Handbook, last access: February 2020) product retracker and slope corrections were implemented in the individual datasets to be compared.
Baseline-D SARIn swath data over Antarctica Standard radar altimetry relies on the determination of the point of closest approach (POCA), sampling a single elevation beneath the satellite. Using the CryoSat interferometric mode (SARIn), it is possible to resolve more than just the elevation at the POCA. If the ground terrain slope is only a few degrees, the CryoSat altimeter operates in a manner such that the interferometric phase of the altimeter echoes may be unwrapped to produce a wide swath of elevation measurements across the satellite ground track beyond the POCA. Swath processing also provides a near-continuous elevation field, making it possible to form digital elevation models and to map rates of surface elevation change at a true resolution of 500 m, an order of magnitude finer than is the current state of the art for the continental ice sheets  (Gourmelen et al.
, 2018). To assess the performance of swath data derived from Baseline-C and Baseline-D CryoSat L1B data, a point-to-point comparison was performed over the Siple Dome, Antarctica. This comparison gave a measure of the precision of swath elevation measurement and allowed for a comparison of each baseline. The Siple Dome region has been chosen as it is a relatively stable area with large areas of constant sloping terrain, ensuring a high sampling density of swath data. The Baseline-D TDS from February to April 2014 and the Baseline-C data from the same time period were used in this assessment. Baseline-C data were used with both the original star tracker measurements and revised measurements provided by ESA. These were supplied as a result of an incorrect mispointing angle for the aberration of light being implemented in Baseline-C, which led to an error in the calculation of the roll of the satellite.
CryoSat Baseline-D freeboard assessment The different physical characteristics of sea ice and leads, which provide the local sea surface height, affect the shape and the power of the reflected radar pulses received by the altimeter, allowing for surface discrimination. Retracking echoes coming from sea ice and leads enables determination of the height of the sea ice and the sea level, respectively. Finally, the freeboard height is obtained by subtracting the local sea surface height from the sea ice elevations. Previous analyses carried out by the Cryo-seaNice ESA project highlighted important overestimations in the free-board values of the ESA CryoSat Baseline-C products relative to in situ data (see the recommendation Rec. 9 in CSEM Report, 2017). Following these conclusions, modifications have been made to develop the new ESA CryoSat Baseline-D freeboard product.
apps. nsidc. org/pub/DATASETS/ ICEBRIDGE/Evaluation_Products/IceBridge_Sea_Ice_ Freeboard_SnowDepth_and_Thickness_QuickLook, last access: October 2019) in situ measurements. To compute the OIB sea ice freeboard, we calculate the difference between the ATM (Airborne Topographic Mapper) mean total freeboard and the snow depth estimated from the snow radar. The freeboard radar is then deduced considering the decrease in radar velocity in the snow pack as follows: FB ice = FB laser -snowdepth, (1) FB radar = FB ice -snowdepth × (1 + 0, 51 × ρ s ) (-1. 5) , (2) with ρ s = 0, 3. To compare with BGEP data, we compute a CryoSat Ice draft from the difference between the gridded sea ice thickness (that integrates the snow load) and ice freeboard data. Note that the ice freeboard is calculated from the radar freeboard taking into account the decrease in radar velocity in the snow pack using the formula specified in Eq.
(  2 ), with the snow depth provided by the Warren99 modified climatology  (Warren et al. , 1999)  and the official OSI SAF (Ocean and Sea Ice Satellite Application Facility) sea-ice-type classification available at the NSIDC. To ensure the consistency between in situ measurements and altimetric observations, all data are projected onto monthly EASE2 500 × 500 grids identical to the one of the altimetric product. Each in situ measurement presented in Fig. 9  is the average of all data in a 12. 5 km × 12. 5 km grid pixel size. Relative to OIB, the Baseline-D freeboard mean bias is reduced by about 8 cm, which roughly corresponds to a 60 % decrease. The BGEP data indicate a similar tendency with a mean draft bias lowered from 0. 85 to -0. 14 m (mean draft is ∼ 1 to 1. 5 m). For the two in situ datasets, the root mean square deviation (RMSD) is also well reduced from 14 to 11 cm for OIB and by a factor of 2 for the BGEP.
Impact of algorithm evolution on sea ice thickness consistency Operational L1B products generated by the CryoSat Baseline-C Ice processor are a primary dataset for observing changes in sea ice thickness in the Northern Hemisphere. Examples of the application of CryoSat L1B products in sea ice climate research are formalized climate data records such as those of the ESA Climate Change Initiative (CCI;  Paul et al. , 2018; Hendricks et al. , 2018b)  and the Copernicus Climate Change Service (C3S;  Hendricks et al. , 2018a, b). In addition, several agencies and institutes generate sea ice data records based on the CryoSat L1B Baseline-C products  (Tilling et al. , 2018; Ricker et al. , 2014; Kurtz et al. , 2014; Kwok et al. , 2015; Guerreiro et al. , 2017). To estimate the impact of the algorithm evolution of the CryoSat Ice processor to Baseline-D on these sea ice data records, we compute sea ice thickness (SIT) for both Baseline-C and Baseline-D primary input datasets with an otherwise identical processing environment.
The processing chain for this experiment has been developed at the Alfred Wegener Institute (AWI;  Ricker et al. , 2014) , and we utilize the most recent algorithm version 2. 1  (Hendricks et al. , 2019). The AWI processor is implemented in the python sea ice radar altimetry library along with the climate data records of the ESA CCI and C3S. Processing steps consist of a L2 processor for the estimation of sea ice freeboard and thickness at full along-track resolution and a L2 processor for mapping data on a space-time grid for a monthly period with a resolution of 25 km in the Northern Hemisphere. For a full description of the algorithm and processing steps we direct the reader to  Hendricks et al. (2019). The CryoSat Baseline-D input data are processed with an identical processor configuration to the current Baseline-Cbased AWI reprocessed product line.
64 m, and we therefore conclude that a maximum SIT of -0. 015 m in the period of the TDS is insignificant for the stability of sea ice data records. This bias also includes an issue in the Barents and Kara seas, where the number of orbits in the Baseline-D test dataset was less than in the Baseline-C data, and minor thickness differences can be observed in Fig. 11  due to this selection bias. This impact analysis however does not provide any insights into the specific algorithm changes that are causing the observed SIT. We therefore speculate that the change in power scaling of L1B SIN waveforms, https://doi. org/10. 5194/tc-14-1889-2020 The Cryosphere, 14, 1889-1907, 2020 which was twice the expected waveform in Baseline-C and now corrected in Baseline-D, is the reason for the larger impact on SIN data as the AWI surface-type classification depends partly on total waveform backscatter.
Specifically, we observed that fewer Baseline-D waveforms are classified as lead or sea ice (not shown) with a classification algorithm previously used for Baseline-C. Therefore, the gridded thicknesses in both baselines in SIN mode areas are based on a different subset of input waveforms, which is far less the case in SAR mode areas. An update to the surface-type classification that includes the additional stack peakiness information in Baseline-D has the potential to further improve surface-type classification and consequently sea ice freeboard and thickness. The AWI processing chain is based on the python sea ice radar altimetry processing library (pysiral). The source code is available under a GNU General Public License v3. 0 (https: //github. com/shendric/pysiral, last access: June 2019).
Conclusions In conclusion, validation activities presented in this paper confirm that the new Baseline-D Ice L1B and L2 data show significant improvements with respect to Baseline-C over land ice, sea ice and inland-water domains, while the migration to netCDF makes these new products more userfriendly than the previous EEF products. The assessment of a 6-month TDS by multithematic CryoSat expert users was instrumental in confirming data quality and providing an endorsement from the scientific community before the transfer of the Baseline-D Ice processors to operational production on 27 May 2019. The Baseline-D algorithms show significant improvements over all kinds of surfaces. Most notably, freeboard is less noisy, is no longer overestimated and scatter comparisons with in situ measurements confirm the improvements of the Baseline-D freeboard product quality with a reduction in mean bias by about 8 cm, which roughly corresponds to a 60 % decrease with respect to Baseline-C.
For the two in situ datasets considered (OIB and BGEP) the RMSD is also well reduced from 14 to 11 cm for OIB and by a factor of 2 for the BGEP. In addition, freeboard no longer shows discontinuities at SAR-SARIn interfaces. Over land ice, the main improvements are due to the increased accuracy in the roll angle. This has provided better results with respect to the previous baseline when comparing the data to a reference DEM over the Austfonna ice cap region and improved the ascending and descending crossover mean from 1. 9 to 0. 1 m. Inland-water users also reported significant improvements including a reduction in previously observed measurement outliers and an increased percentage of good observations, generally around 5 %-10 % for most lakes. Overall, this new CryoSat processing Baseline-D will maximize the uptake and use of CryoSat data by scientific users since it offers improved capability for monitoring the complex and multiscale changes in the thickness of sea ice, the elevation of ice sheets and mountain glaciers, and their effect on climate change.
CryoSat Ice Baseline-D validation of test dataset results 3. 1 Data quality -Ice Baseline-D test data verification by IDEAS+ All CryoSat data products are routinely monitored for quality control by the ESA ESRIN (European Space Research INstitute) Sensor Performance, Products and Algorithms (SPPA) office with the support of the Instrument Data quality Evaluation and Analysis Service (IDEAS+). In preparation for the Ice Baseline-D, IDEAS+ performed quality control (QC) checks on test data generated with the new Ice Baseline-D processors (IPF1 vN1. 0 and IPF2 vN1. 0). For testing and validation purposes a 6-month TDS was generated at ESA in a dedicated processing environment for two periods: September-November 2013 and February-April 2014.
Data availability. CryoSat Baseline-D L1b and L2 data are publicly available from the CryoSat science server https://science-pds. cryosat. esa. int (last access: January 2020, ESA, 2020). https://doi. org/10. 5194/tc-14-1889-2020 The Cryosphere, 14, 1889-1907, 2020.
3 CryoSat Ice Baseline-D validation of test dataset results 3. 1 Data quality -Ice Baseline-D test data verification by IDEAS+ All CryoSat data products are routinely monitored for quality control by the ESA ESRIN (European Space Research INstitute) Sensor Performance, Products and Algorithms (SPPA) office with the support of the Instrument Data quality Evaluation and Analysis Service (IDEAS+). In preparation for the Ice Baseline-D, IDEAS+ performed quality control (QC) checks on test data generated with the new Ice Baseline-D processors (IPF1 vN1. 0 and IPF2 vN1. 0). For testing and validation purposes a 6-month TDS was generated at ESA in a dedicated processing environment for two periods: September-November 2013 and February-April 2014.
This optimal PCR cycle number has been evaluated using the Cycler Correction Factor method as previously described  (Atger et al. 2015). Purified libraries were quantified with Picogreen (Life Technologies) and the size pattern was controlled using the DNA High Sensitivity Reagent kit on a LabChip GX (Perkin Elmer). Libraries were then pooled and clustered at a concentration of 8 pmol on a high output paired end sequencing flow cell (Illumina). Sequencing was performed for 2 × 125 cycles on a HiSeq 2500 (Illumina) with V4 chemistry strictly following Illumina's recommendations. Demultiplexing of the sequence data was done using Cassava (http://cancan. cshl. edu/labmembers/gordon/fastq_illumina_ filter/#contact). Ribosomal RNA was filtered using sortmerna v 2. 1b  (Kopylova et al. 2012). Reads were cleaned with trimmomatic v 0.
36  (Bolger et al. 2014)  with the following parameters: ILLUMINACLIP:TruSeq3-PE. fa:2:30:10 LEADING:5 TRAILING:5 SLIDINGWINDOW:4:15 MINLEN:50. Cleaned reads were mapped to the indexed reference genome sequence of Coffea canephora (Denoeud et al. 2014) using hisat2  (Kim et al. 2015). A counts table was generated from the mapped read files using stringtie  (Pertea et al. 2015)  and prepDE. py script with the reference genome annotation file. Differential gene expression analysis was performed using an FDR of <0. 05 with edgeR  (Robinson et al. 2010 ) using an R script (https://github. com/bcbc-group/manuscripts/blob/master/ McCarthy_2018/Jim_edge. R). All time points were compared to T = 0. Venn diagrams were created (http:// bioinformatics. psb. ugent. be/webtools/Venn/) to illustrate overlapping sets of filtered, differentially expressed genes between T = 0 and the different time points.
Acknowledgments We would like to thank  V. Berry  and  O. Darracq  for assistance growing the plants used,  O. Darracq  and  J. -F. Bouquet  for technical help with the growth chambers and the  BTI Computational Biology Center (BCBC)  for bioinformatics assistance. We also thank  V. Berry ,  O. Darracq ,  M. Lepelley ,  L. Mueller  and  V. Petiard  for their support and helpful comments during the project. This research was supported by  Nestlé R&D Tours , and by a PhD student fellowship from  Nestlé  to E. -H. T.
The grid squares for ERA5 data presented are also shown (green and purple rectangles). Additional site location information is available in Table  S5. [asl]); however, it is not clear how regionally representative these findings may be. Considerable spatial variability in precipitation totals is similarly evident,  29  with positive altitudinal precipitation gradients on slopes with southerly exposure up to $3,000 m  30, 31  and generally negative gradients above this elevation,  21  albeit with considerable seasonal dependence,  32  and some uncertainty between 3,600 and 5,000 m asl in the Khumbu region. 27 A pronounced altitude-independent south-north precipitation gradient of up to À21 mm km À1 in the Khumbu region  33  and nearby Langtang  34  has been attributed to the shadowing effects of higher ridges and peaks.