dataesr/scientific-paragraphs-categorization huggingface dataset:
Results During the phase II trial, 56 patients were enrolled. Data from all included patients were made available by the investigators from the recruiting institutions. Six patients (10. 7%) discontinued treatment before the primary endpoint at 12 weeks and two patients (3. 6%) had a follow-up 12 months and were not included in the long-term analysis. A total of 48 of 56 patients (85. 7%) were included in this long-term analysis (Fig. 1 ). Two of the eight patients not included for long-term analysis had progressive disease as the best objective response within 12 weeks of treatment. Of the 56 patients enrolled during the phase II trial, 29 patients (51. 8%) had tumour progression and the median PFS was 77 (IQR 12. 0e97. 0) months. The 48 patients included for the long-term analysis had a mean age of 37 years (SD AE 13.
On the other hand, many patients had ongoing disease control after treatment discontinuation. This suggests that TKI discontinuation in TGCT is not inevitably linked to progression, as seen in other diseases such as advanced gastrointestinal stromal tumours, possibly justifying treatment breaks  [42]. Because nilotinib's primary effect in TGCT is considered to target non-neoplastic cells, it is unlikely that treatment breaks promote resistance. Therefore, effective and welltolerated CSF1R inhibitors could be given for longer durations. We encourage future studies to collect longterm data regarding different treatment durations, retreatment or alternative treatments. This study contains several limitations. Although the nilotinib phase II study was a prospective trial, current data were retrospectively collected for this long-term study.
This resulted in some missing data and follow-up regimes were performed according to local schedules. Secondly, radiological progression, clinical worsening and adverse events were not assessed by validated criteria such as RECIST, patient-reported outcome measurements or CTCAE because this was no longer performed after study completion. Additionally, the criteria such as radiological progression, clinical worsening and tumour operability were assessed by local teams and not centrally. This could possibly introduce assessment bias. However, these criteria were assessed by experts from reference sarcoma centres, which might decrease heterogeneity. Also, all expert centres provided their data and we were able to include all patients from the original study in the current analysis. Finally, since this is a single-arm study, there is an inability to distinguish between treatment effect and natural behaviour of TGCT, which is not well understood to date.
CATS cloud profile data Based on the International Space Station (ISS), the  Cloud-Aerosol Transport System (CATS, McGill et al. , 2015)  performed elastic-backscatter measurements at 1064 nm from February 2015 to October 2017  (Pauly et al. , 2019). In contrast to CALIPSO, the orbit of the ISS has an inclination of 51. 6 • and is not sun-synchronous. Therefore, CATS observations are restricted to latitudes lower than 51 • but allow for covering of different times of the day. This latter capability is used here to assess the representativeness of cloud observations at the fixed CALIPSO Equator pass times of 01:30 and 13:30 local time. Two Level-2 CATS-ISS products are used to document the diurnal variability of clouds. The first is the L2O Profile product (05kmPro. v3. 01, Yorks et al. , 2016a)  which documents profiles of various atmospheric properties at 60 m vertical resolution in 5 km intervals along the ISS ground track.
, 2005) : a non-opaque cloud layer with a top pressure between 440 and 680 hPa. For both products, it is required that the quality scores, which range between 0 and 10, have to be larger than 5 for cloud identification. A comparison between the fractions of cirrus and altocumulus clouds as inferred from the data sets of CALIPSO and CATS following the approaches described above is presented in the Appendix.
This scenario will be referred to as isolated cirrus. The second setup refers to the possibility of detecting mid-level clouds with a ground-based lidar by screening the data set for profiles in which transparent altocumulus is identified without the presence of any of the four low-level cloud The number of profiles selected in this way is mapped on a grid of 2. 50 • longitude by 1. 25 • latitude to assess the occurrence rate of conditions for which a ground-based instrument could observe mid-level and high clouds without attenuation of the laser beam by clouds at lower levels. The day-night flag is used to assess changes in cloud occurrence for CALIPSO overpasses during day and night. The annual variation is studied by compiling maps for the months December, January, and February (DJF), March, April, and May (MAM), June, July, and August (JJA), and September, October, and November (SON).
Mid-level clouds Occurrence rates of transparent mid-level clouds that would be detectable with a ground-based lidar from 15 years of CALIPSO data are presented in Fig. 4. The land regions with the highest fraction of CALIPSO profiles that contain transparent altocumulus clouds without low-level clouds below are the Greenland ice sheet, eastern Antarctica, the western flanks of the Rocky Mountains and Andes, western Australia, southern Africa and the Horn of Africa, large parts of the Middle East, and the area around the Himalayas. Figure  4b  and c reveal that cloudy profiles containing transparent altocumulus are dominant over the continents and the equatorial oceans. A similar finding is presented in  Bourgeois et al. (2016). In contrast, transparent altocumulus generally occurs less frequently over mid-latitude oceans and the stratocumulus decks to the west of the continents, though aircraft measurements have shown that the occurrence rate of thin midlevel clouds is non-negligible in these regions  (Adebiyi et al.
This is corroborated by the zonal means over land in Fig. 7d , which also reveal the smallest anomaly during the morning hours. From this investigation of diurnal variation and the magnitude of the anomaly during day and night, it can be concluded that CALIPSO nighttime observations at 01:30 local time might be more representative for an objective assessment of the ideal regions for observing mid-level and high clouds with ground-based lidar instruments compared to daytime observations at 13:30 local time. The results of an analogous investigation for transparent altocumulus clouds are presented in Fig. 8. Again, the distribution of isolated altocumulus clouds from CATS observations resembles that derived from the CALIPSO time series in Fig. 4a. However, the smaller volume of the CATS data set in combination with the coarser resolution of the spatial gridding leads to a weaker contrast in altocumulus occurrence in Fig.
8a  compared to the CALIPSO data in Fig. 4a. The anomaly in the diurnal variation of the zonal mean occurrence rate of isolated altocumulus is much weaker than for cirrus for observations over all surfaces as well as land only. In fact, the anomaly is negligible when considering all surfaces (Fig. 8b  and d ). Considering only data over land shows a weak positive anomaly around noon and an even weaker negative anomaly between 18:00 and 06:00 local time (Fig. 8c ). Hence, CALIPSO observations at 01:30 local time should be representative for the better part of a day.
Hence, observations with the CATS lidar have been used to investigate the representativeness of the CALIPSO cloud sampling. While the CATS data set flags water and ice clouds, additional screening is needed to identify cloud types such as altocumulus and cirrus as provided in the CALIPSO data set. Scenarios that are comparable to the ones selected from the CALIPSO observations have been compiled by screening the CATS data set for the occurrence of cloud ice and for the location of cloud base and top heights. The analysis of the data sets from the two spaceborne lidars shows a remarkable resemblance in the regional and vertical distribution of isolated cirrus and altocumulus clouds despite the different approaches to extracting the corresponding information from the two data sets. The diurnal variation of cloud occurrence from the CATS data set leads to the conclusion that CALIPSO observations at 01:30 local time are representative for the better part of the day, while the period of the largest anomaly is covered by the overpass at 13:30 local time.
For a fair comparison with the CALIPSO data in Fig. 1a , i. e. clouds that are (i) flagged as cirrus and (ii) are the only cloud type in the profile, the CATS data set has been screened for what is referred to here as isolated cirrus clouds. The cloud subtype classification is not available in the CATS data set. However, when only ice clouds are present in a profile (i. e. they are not the top of a larger system or part of a mixed-phase system) and these clouds are not flagged as opaque, they are most likely to be cirrus. The CALIPSO Atmospheric_Volume_Description variable and the CATS Percent_Opacity_Fore_FOV variable are used to select clouds that are neither opaque nor fully attenuated. Figure  A2  shows that the vertical profile and the zonal average of the fraction of isolated ice clouds as seen by CATS match those seen by CALIPSO very closely.
About 75 % to 95 % of isolated ice clouds between 7. 5 and 17. 5 km height are categorized as cirrus. Moreover, 88 % of profiles containing isolated ice clouds in CALIPSO data between 51 • S and 51 • N are categorized as cirrus (92 % for 30 • S to 30 • N). This fraction decreases towards higher latitudes beyond the      area of CATS data coverage between 51 • S and 51 • N. The findings in Fig. A2  suggest that the majority of isolated ice clouds in non-opaque CATS profiles are equivalent to isolated cirrus in CALIPSO data. In addition, cloud-fraction retrievals for isolated ice clouds in non-opaque profiles give comparable results when using data from the CALIPSO and CATS instruments. As a consequence, the investigation of the diurnal variability of ice clouds considers CATS profiles that contain isolated, non-opaque ice clouds and assumes that these profiles are equivalent to CALIPSO profiles that contain isolated cirrus clouds.
Acknowledgements. This work was supported by the  Franco-German Fellowship Programme on Climate, Energy , and  Earth System Research (Make Our Planet Great Again -German Research Initiative ,  MOPGA-GRI ) of the  German Academic Exchange Service (DAAD) , funded by the  German Ministry of Education and Research. VN was funded by  CNES  and  CNRS  and would like to thank the IPSL ESPRI and AERIS computation facilities for data storage and analysis capacity. We thank the AERIS/ICARE Data and Services Center (https://www. icare. univ-lille. fr/,last access:  18 July 2021 ) for providing access to the data used in this study. Financial support. This research has been supported by the  Deutscher Akademischer Austauschdienst  (grant no. 57429422 ). Review statement. This paper was edited by  Edward Nowottnick  and reviewed by two anonymous referees.
Data availability. All spaceborne lidar data used in this study are openly available, e. g. through the ICARE Data and Services Center at https://www. icare. univ-lille. fr/ (last access: 18 July 2021). More specifically, the CALIPSO Cloud profile data (NASA, 2022a) used in this study are located at https://www-calipso. larc. nasa. gov/ resources/calipso_users_guide/data_summaries/profile_data. php (last access: 10 May 2022a), while the CATS data can be found at https://doi. org/10. 5067/ISS/CATS/L2O_N-M7. 2-V3-01_05kmLay (NASA, 2022b) and https://doi. org/10. 5067/ISS/CATS/L2O_N-M7. 2-V3-01_05kmPro (NASA, 2022c).
Field instrumentation The six wells were equipped with a water level data logger U20-001-04 in 2018 and replaced with a U20l-04 in 2019 (HOBO, Onset, USA) for continuous measurements of the water table depth (WTD) and temperature from June 2018 to October 2020. Water temperature was recorded hourly in pools M11 to M15 using HOBO TMC50 probes coupled 135 with a HOBO U12-008 data logger (Onset, USA) from June 2019 to August 2020. A water level data logger was installed in pool M11 (Fig. 1 ) and water level variations were measured from May 20 th to August 28 th 2020. Height variations (in cm) between the peatland surface at wells P5 and P6 and adjacent pool M11 were measured using a Zip Level Pro-2000 (Technidea, USA). Those measurements allowed the water levels in the pool to be compared with those in the two wells (Fig.
Statistical analyses All statistical tests were performed on R (CRAN-Project) through the RStudio interface (RStudio inc. , USA) and all 270 figures were realised with the package ggplot2  (Wickham, 2016). Comparisons of variance tests were performed and in the next sections, the mention of significant differences refers to statistical tests using the following method. First, normal distribution was tested using the Shapiro and Wilk test, and normal distribution was considered true when the p-value was >0. 05. If the distribution was not normal, a Kruskal and Wallis test was performed to compare the averages and significant differences were considered true when the p-275 value was <0. 05. Dunn tests were performed as post-hoc pairwise comparison tests to determine which group was significantly different (when the p-value <0.
Acknowledgments The funding for this research was provided by the  Natural Sciences and Engineering Research Council of Canada and Hydro-Quebec  to  Michelle Garneau  ( RDCPJ 514218-17 ). We thank  Katherine Velghe  and  Alice Parks , from  GRIL , for their laboratory training and assistance in absorbance and fluorescence analyses, and  Paul Del Giogio  for access to his laboratory. Marine Liotaud, from  Geosciences Rennes , is acknowledged for performing TMH-GC-MS analyses 555 and  Frederic Julien ,  Virginie Payre-Suc , and  Didier Lambrigot , from  Laboratoire Ecologie Fonctionnelle et Environnement , for performing DOC/TN and cations/anions analyses. Thanks to  Charles Bonneau ,  Charles-Élie Dubé-Poirier ,  Camille Girard ,  Pénélope Germain-Chartrand ,  Léonie Perrier ,  Guillaume Primeau ,  Khawla Riahi ,  Roman Teisserenc , and  Karelle Trottier  for their assistance in the field.
Prior to PCA, a correlation matrix was performed to identify strong 290 correlations between the variables (Fig. SI. 1). One of the correlated variables was excluded from PCA when the correlation was >0. 90 or <-0. 90, with p-values <0. 05. Therefore, the DOC:DON ratio (DOC:DON ~ DOC, cor = 0. 90, p-value <0. 0001), %CAR_MIC (%CAR-MIC ~ deoxyC6:C5 ratio, cor = 0. 99, p-value <0. 0001), and fMIC (fMIC ~ MIC:VEG ratio, cor = 0. 98, p-value <0. 0001) were excluded from the PCA dataset. The PCA was performed with the package FactoMineR.
Marine particles of different nature are found throughout the global ocean. The term "marine particles" describes detritus aggregates and fecal pellets as well as bacterioplankton, phytoplankton, zooplankton and nekton. Here, we present a global particle size distribution dataset obtained with several Underwater Vision Profiler 5 (UVP5) camera systems. Overall, within the 64 µm to about 50 mm size range covered by the UVP5, detrital particles are the most abundant component of all marine particles; thus, measurements of the particle size distribution with the UVP5 can yield important information on detrital particle dynamics. During deployment, which is possible down to 6000 m depth, the UVP5 images a volume of about 1 L at a frequency of 6 to 20 Hz. Each image is segmented in real time, and size measurements of particles are automatically stored.
All UVP5 units used to generate the dataset presented here were inter-calibrated using a UVP5 high-definition unit as reference. Our consistent particle size distribution dataset contains 8805 vertical profiles collected between 19 June 2008 and 23 November 2020. All major ocean basins, as well as the Mediterranean Sea and the Baltic Sea, were sampled. A total of 19 % of all profiles had a maximum sampling depth shallower than 200 dbar, 38 % sampled at least the upper 1000 dbar depth range and 11 % went down to at least 3000 dbar depth. First analysis of the particle size distribution dataset shows that particle abundance is found to be high at high latitudes and in coastal areas where surface productivity or continental inputs are elevated. The lowest values are found in the deep ocean and in the oceanic gyres.
, 2022)  has been commercially available since 2019. Using the standard settings, the UVP5 images a volume of about 1 L at a frequency of 6 to 20 Hz and can be deployed down to 6000 m depth. Particles on each image are automatically sized. Further data processing of all particles allows for the calculation of the particle size distribution -the particle abundance or biovolume in increasing size intervals. The UVP5 STD version covers the size range from 102 µm to ∼ 50 mm ESD (where ESD denotes the equivalent spherical diameter), whereas the HD version covers the size range from 64 µm to ∼ 50 mm ESD. Via a reduction in the distance between the LED lights and the camera, the resolution can be further increased, but the imaged volume is then reduced. Inter-calibrated UVP5 units are globally in use by several teams.
As the UVP5 is mostly integrated in the CTD (conductivity, temperature and depth) rosette and has its own pressure sensor, the fine-scale vertical distribution of particles and major plankton groups can be related to environmental data obtained with other sensors mounted on the CTD rosette. Most efforts regarding the analyses of UVP particle size spectra (including data from earlier versions such as the UVP4) have been focused on the estimation of particle biomass and flux by comparing them with particulate organic carbon (POC) collected in sediment traps or Niskin bottles (e. g. Guidi et al. , 2008b Guidi et al. , , 2015;; Kiko et al. , 2017; Stemmann et al. , 2002 Stemmann et al. , , 2008a)). Particle abundance data have also been used to estimate aerobic  (Kalvelage et al. , 2015; Thomsen et al.
, 2002; Guidi et al. , 2009)  and zooplankton  (Hauss et al. , 2016; Christiansen et al. , 2018; Stemmann et al. , 2004)  interactions with particles have been assessed, and the introduction of particles at depth via zooplankton diel vertical migration have been reported  (Kiko et al. , 2017 (Kiko et al. , , 2020;; Stemmann et al. , 2000). In recent years, image analysis of large objects has been performed, and plankton organisms have been discriminated from the detrital and abiotic particles in subsets of the dataset presented here, allowing the study of large plankton communities  (Forest et al. , 2012) , the biogeography of specific taxa  (Christiansen et al. , 2018; Biard et al. , 2016) , zooplankton functional traits  (Vilgrain et al. , 2021)  and particle types  (Trudnowska et al. , 2021).
The global marine particle size distribution dataset Here, we provide a dataset that was obtained with several inter-calibrated UVP5 units operated by different laboratories and during different cruises and projects around the world (Table  1 ). This international, collaborative effort resulted in a consistent, inter-calibrated global marine particle size distribution database that contains 8805 particle abundance and biovolume profiles obtained in all major oceans and several marginal seas since 2008. We provide further details about the UVP5, the inter-calibration and quality control procedures, and the dataset structure in Sect. 2. The summarizing statistics, maps on data distribution, a description of global particle distribution, and recommendations for use and further growth of the dataset are provided in Sect.
UVP5 description The UVP5 consists of one downward-facing camera in a titanium pressure case and two sets of red LED lights that illuminate a 0. 88-1. 16 L water volume  (Picheral et al. , 2010). The imaged volume depends on the actual instrument set-up    and was determined experimentally for each set-up. During deployment -usually during the downcast of a CTD profile -the UVP5 takes 5-20 pictures of the illuminated volume of water per second. The particles in each image are counted and sized immediately, and the data are stored in the instrument. Particle area is measured as the number of pixels (Sp) of an imaged object and can be converted to particle crosssectional area (Sm) in square millimetres (mm 2 ) as follows: Sm = Aa • Sp Exp. Here, Aa represents the area of one pixel in square millimetres (mm 2 ), and Exp is a dimensionless adjustment factor.
Aa and Exp need to be calibrated experimentally. To conduct the initial calibration for the dataset provided here, natural plankton and particle objects from the Bay of Villefranche-sur-Mer, France, were imaged in an aquarium with the UVP5 HD serial number 203 and using a stereo microscope during experiments conducted in fall 2016. Optimal values for the parameters Aa and Exp were obtained by minimizing S, which is defined as follows: S = i log Aa • S Exp i,p u -log S i,m µ 2 , where S i,p u is the surface area (in pixels) of object i as seen by the UVP, and S i,m µ is the surface area (in mm 2 ) of the same object i measured under the stereomicroscope. The minimization was performed using the Nelder-Mead simplex algorithm (implemented in the MATLAB fminsearch function). For this calibration experiment, an Aa of 0.
Instrument inter-calibration As several UVP5 units were produced, an inter-calibration procedure was developed to allow the comparability of data from these units. The inter-calibration procedure is based on a comparison between one or several reference units (in particular serial numbers 002 and 203) and the units to be calibrated. The imaged volume of each unit is determined before the instruments are deployed simultaneously at sea, on the same instrument carrier, and the normalized size spectra are calculated. These operations have been performed in the Mediterranean Sea off Nice, France, since 2008. Figure  1a  shows an example of raw data from a parallel deployment of a reference unit (serial number 002) and a unit under calibration (serial number 200). The Aa and Exp of the serial number 200 are then adjusted so that the normalized size spectra of both units coincide after post-processing (Fig.
1b ). The reference units were regularly inter-calibrated against each other to check for possible drifts and improved data consistency. The development of the HD version of the UVP5 in 2016 required a revision of the UVP5 inter-calibration procedure, as the pixel resolution had changed  (Picheral et al. , 2010). The calibration obtained for the serial number 203 HD unit in fall 2016 was propagated to several STD model reference units via simultaneous deployment at sea and the subsequent calculation of correction factors. Thereafter, the corrections obtained for the reference units were digitally propagated to all previously used UVP5 units by reanalysing the earlier calibration experiment data. How the uncertainties of the initial calibration of the serial number 203 HD model propagate to the other UVP5 units and if these uncertainties can be reduced require further investigation.
Data collection, processing, quality control and dataset description Metadata (position and time) of each profile collected in the presented dataset were checked by the respective data owners. All instrument settings and calibration coefficients for all cruises and projects were checked and, if necessary, corrected to match the HD inter-calibration results using automatic routines. Data from all cruises were then reprocessed using Zooprocess (https://sites. google. com/view/ piqv/softwares/uvp5, last access: 1 September 2022) to obtain a coherent and inter-calibrated dataset, based on the HD inter-calibration conducted in fall 2016. For easier access and preliminary sharing, the data were then uploaded to EcoPart (http://ecopart. obs-vlfr. fr, last access: 1 September 2022). To enable the data to be archived at PANGAEA, they were directly downloaded from the EcoPart SQL (Structured Query Language) database using a dedicated Python script and separated into 3-year splits to obtain smaller file sizes and to enable the subsequent addition of further data.