dataesr/scientific-paragraphs-categorization huggingface dataset:
According to the scan result, the system sets up the new frequency through an infrared link, and GNSS signal collection is initialized. Then, the system loads the calibration data of the relevant radiosonde stored during the preparation phase and checks consistency with PTU criteria. The Meteomodem ARL GC is a standard Meteomodem GC which consists of a sealed box enclosing a reference and a fan which homogenizes the inside temperature and relative humidity. It is recommended to return the Meteomodem GC every 3 years for calibration. The calibration is made with a certified Rotronic HC2A-S probe (https://www. rotronic. com/en/hc2a-s. html, last access: 3 July 2020). Then, the ARL records the ground-check data and the metadata. Balloon inflation starts accordingly: the system monitors a flowmeter to inflate the balloon to the specified volume.
The new ARL version can also recover from balloon bursts without human intervention at the site by using a balloon from another canister. In the previous version, an operator had to visit the ARL to remove broken balloons and restart the ARL during the observation window in such cases. The new system is also equipped with a new simplified wind shield for launches in strong wind conditions. All information and data are stored in a database available for each ARL. Various central monitoring/control functions are provided by using application software and a web browser to access the database on the workstation installed in the ARL. The Meisei ARL GC consists of a temperature and humidity reference sensor and an inspection box. The GC is performed before the sonde loading. The results from the GC are not used in the data processing but only to check if there are anomalies in the radiosondes.
In Table  3 , the Meisei automated radiosonde system specifications are provided. Figure  4  shows a photo of the system along with a sketch of the interior of the system container. For more details on the Meisei ARL experimental setup, visit the Meisei website (http://www. meisei. jp/ars, last access: 3 July 2020). The Japan Meteorological Agency (JMA) has used Meisei ARL data since 2006. Parallel radiosoundings of auto launch and manual launch have not been done yet. This is the reason why this paper does not show additional datasets or comparisons involving Meisei ARL; therefore, the description of the Meisei ARL is the only information which can be shared with readers, according to recommendations provided by Meisei.
" (Ray McGrath, personal communication, 2015). The issue described above does not affect the profile at higher levels. A similar issue has also been reported for data taken during the first few seconds with Meisei ARL, and this is suspected to be due again to the influence of the air inside the launcher. The Meteomodem has recently implemented a new software, EOSCAN, not yet implemented at all the stations, which improves the ARL dataset quality with a number of corrections such as 1. eliminating the GPS disturbances at the end of the tube that can persist in the first 20 s after the release; and 2. adjusting for the systematic bias introduced by the fact that the ARL Meteomodem is air conditioned and affecting the first 150 m of the radiosounding profiles. The performance of the Vaisala ARL has been evaluated through the analysis of a dataset collected at Sodankylä station.
Performance of the Meteomodem ARL The performance of the Meteomodem ARL ground check has been evaluated through the analysis of a dataset collected at the Météo-France Trappes station, where M10 radiosondes have been launched regularly at 11:30 and   23:30 UTC since 2016. The availability of a long time series for the comparison between M10 temperature and humidity sensor and a reference temperature/humidity sensor (Vaisala HMP110; https://www. vaisala. com/sites/default/ files/documents/HMP110-Datasheet-B210852EN_1. pdf, last access: 3 July 2020) at ambient conditions, inside a meteorological shelter for the Trappes station, permits the investigation of the system performance also in the pre-launch phase. Since June 2018, this comparison has been carried out during the 5 min before each automatic sounding.
Vertical velocity and balloon burst altitude for Meteomodem technology A more interesting comparison to show the eventual positive influence of automation on the burst altitude is those related to the dataset discussed in Sect. 3 and summarized in Table 5, shared by Météo-France for Trappes station (Fig. 14 ). In terms of vertical velocity (Fig. 14a ), both the ARL and the manual launches have a quasi-symmetric frequency distribution peaked around 5. 1 and 5. 5 m s -1 , respectively, with a similar spread of about 1. 0 m s -1. For the burst altitude (Fig. 14b ), we have for both the datasets a negatively skewed distribution with an evident peak around 33 km for the manual launches and 35 km for the ARL. The comparison reveals that the burst altitude (Fig. 14b ) is generally higher for the ARL than for the manual launches, likely due to use of different balloons and the more limited human contact with the balloon which hence likely retains greater structural integrity.
Quantifying relative performance In this section, two datasets are investigated to assess the differences in the vertical profiles of temperature and humidity: the set of RS92 parallel (automatic and manual) soundings performed with the automatic radiosonde launchers at Sodankylä, along with a second set of Meteomodem radiosoundings collected at Faa'a station, French Polynesia. These are near-coincident launches but the instruments are on physically distinct balloons which, as they ascend, likely at somewhat different rates if the balloons are not filled identically, will follow subtly distinct pathways leading to offsets in sampling. In the following analysis, given the latitude ϕ, the longitude λ, the Earth's radius R (mean radius of 6371 km), the distance between two balloons (1 and 2) has been calculated using the "haversine" formula  (Sheppard and Soule, 1922 ) which provides the great-circle distance between two points (i.
e. shortest distance over the Earth's surface): d = Rc, where c = 2a tan 2 √ a, (1 -a) a = sin 2 λ 2 + cos (ϕ1) cos (ϕ2) sin 2 λ 2. The haversine formula remains particularly well conditioned for numerical computation even at small distances -unlike calculations based on the spherical law of cosines. The function "atan2" is described in  Glisson (2011). The two datasets are also investigated to show the correlation between the difference in the vertical profiles and the distance between the two flying sondes.
Parallel soundings with Vaisala systems For the same 6-year dataset collected at Sodankylä discussed in Sect. 4, the vertical profiles of the average differences (automatic minus manual) and standard deviations of the temperature and RH measured during parallel soundings are shown in Fig. 15. Systematic differences in the temperature profile are negligible (on average smaller than 0. 01 K) over the entire vertical range up to 25 km a. g. l. , while the standard deviation increases with altitude from values smaller than ±0. 5 K below 15 km to values larger than 1 K above. The result is in agreement with the increase in mean distance between near-simultaneous sonde paths at higher altitudes (Fig. 16 ). A subset of the parallel temperature soundings at Sodankylä has previously been analysed by Sofieva    (2008).
Even though it is hard to separate difference components from non-colocation from those which may arise from instrument-to-instrument differences (e. g. arising from manufacture variations and differences in preparation, storage and launch at the uppermost altitudes), Sofieva et al. found differences in small scale structures in temperature profiles, when the horizontal separation was larger than 20 km. Moreover, to investigate whether the ARL and the manual radiosoundings datasets were selected from populations having the same distribution, i. e. if the calculated mean differences are statistically significant, the Wilcoxon rank sum test has been applied. The test result confirms that the two datasets are samples of the same population, showing a probability larger than 0. 5 for temperature at all the altitude levels below 20 km and larger than 0.
g. stratospheric-tropospheric exchanges). In addition, the analysis was re-run after grouping the ARL flights according to the time a sonde had been loaded to the launcher system (see Sect. 4); variations of time period between sonde loading and actual launch time did not influence the comparison results. Finally, the Wilcoxon rank sum test has been applied to the entire dataset, and the computed probability that the two samples belong to the same population is larger than 0. 35 at all altitude levels. (b)  mean difference between ARL and manual for the 6-year dataset of parallel soundings collected at Sodankylä station at all altitude levels up to 25 km a. g. l. for temperature and up to 15 km a. g. l. for RH. Standard deviation at each pressure level is reported using the grey area. Figure  16.
Parallel soundings at Faa'a with Meteomodem systems A first evaluation of the performance of Meteomodem ARL is provided by the analysis of the datasets collected over 3-14 October 2018 at Faa'a station (French Polynesia, station identifier 0-20000-0-91938; 17. 63 • S, 149. 84 • W; 21 m a. s. l. ), where 21 launches (9 daytime and 12 nighttime) of parallel radiosoundings have been undertaken (a picture is provided in Fig. 17 ) in order both to compare temperature, relative humidity, wind speed and direction, and to study further characteristics of the flights (burst altitude, ascent speed, for example). Météo-France has conducted the intensive operational period, while Institut Pierre Simon Laplace (IPSL) has produced the NetCDF files (data and metadata) for the analysis. Raw data without any correction for temperature and relative humidity have been considered in this paper.
The GRUAN data processing, which remains under development at the present time for this data stream, has not been applied. The manufacturer Meteomodem IR2010 software was used for both manual and automatic launches. The dataset collected by Météo-France at Faa'a station is not sufficiently large to draw robust statistical inferences. Nevertheless, this dataset is the first ever available to evaluate the performance of the Meteomodem ARL and can provide useful indications of any likely impact upon the data quality of ARL facilities. Before comparing, the T and RH profiles of the parallel sounding dataset have been interpolated to a resolution of 100 m altitude. The difference between the launch time of the ARL and the manual balloons ranges between 1 and 12 s. In Fig. 18 , the horizontal distance between the pairs of parallel soundings at all the altitude levels up to 25 km a.
The reason behind this bias could arise from GC effects or differences in the pre-launch procedures between the two systems affecting the performance of one of the two launches in a quasi-systematic manner throughout the vertical profile. This will be further investigated with the support of the manufacturer. In terms of balloon burst altitude, the ARL proved to be reliable both during the daytime with a burst altitude ranging within 26 688-31 904 m a. g. l. ) versus values within 24 970-30 621 m a. g. l. calculated for the manual launches, while during nighttime the burst altitude ranges within 27 587-30 790 m a. g. l. for the automatic launcher versus values within 27 437-30 139 m a. g. l. for the manual launches. Applying the Wilcoxon rank sum test, the computed probability (0. 05224) for the entire dataset is slightly greater than the 0.
For the Meteomodem ARL at Trappes station, the difference between M10 temperature and humidity sensor and the Vaisala HMP110 housed in the ARL, used as a reference immediately prior to launch shows a few tenths of degree and % RH, respectively. These results need further investigation to understand the underlying reasons and whether manual or ARL operations are closer to the observed atmospheric profiles. -Systematic differences in the temperature profile for both Meteomodem and Vaisala are smaller than ±0. 2 K up to 10 hPa; RH profile differences are smaller than 1 % RH for the Sodankylä Vaisala dataset up to 300 hPa, while it is constantly positive and smaller than 2 % for Faa'a station Meteomodem series. However, the restricted dataset available at Faa'a station means caution should be applied in generalizing these results as representative of all Meteomodem ARL.
This approach cannot directly use traceable calibration standards but it must be based on the comparison with reference thermometers and hygrometers calibrated on a routine and certified basis. In addition, the sonde calibration cannot be monitored at 100 % RH because the airconditioning system within the ARL keeps stable humidity conditions and cannot be modified to avoid an impact on the ARL operation efficiency. For both the approaches above, a customized solution to collect the data and use them in the generation of a GDP must be found given the constraints of the ARL software, which does not allow extra calibration or comparison values to be collected or saved in the main radiosonde launch files. It must be noted that at four JMA stations, not belonging to GRUAN, the Vaisala ARL is used adopting a modified setup of the AS15 system including an additional GC based on reference instruments developed by Vaisala for temperature and humidity, i.
Acknowledgements. Much useful information has been provided by the three manufacturers: Vaisala, Meteomodem and Meisei. Information on which stations use Meteomodem ARLs was provided by  Adrien Ferreira  of Meteomodem in April 2019. Hannu Jauhiainen of Vaisala  provided a list of stations using their Autosonde, including several which were not known from the  WIS reports. Météo-France  and several other national meteorological services have also provided information. Review statement. This paper was edited by  Karin Kreher  and reviewed by two anonymous referees.
ascent speed 6 m s -1 6 m s -1 Min. ascent speed 4 m s -1 4 m s -1 Avg. ascent speed 5 m s -1 5. 4 m s -1 two Vaisala ARL datasets from the Potenza GRUAN station (40. 60 • N, 15. 72 • E; 760 m a. s. l. ) and the Minamidaitōjima station, run by JMA (WIGOS station identifier 0-20000-0- 47945; 25. 79 •.
The development of single-cell transcriptomic technologies yields large datasets comprising multimodal informations, such as transcriptomes and immunophenotypes. Despite the current explosion of methods for pre-processing and integrating multimodal single-cell data, there is currently no userfriendly software to display easily and simultaneously both immunophenotype and transcriptomebased UMAP/t-SNE plots from the pre-processed data. Here, we introduce Single-Cell Virtual Cytometer, an open-source software for flow cytometry-like visualization and exploration of pre-processed multiomics single cell datasets. Using an original CITEseq dataset of PBMC from an healthy donor, we illustrate its use for the integrated analysis of transcriptomes and epitopes of functional maturation in human peripheral T lymphocytes.
INTRODUCTION The recent development of techniques for single cell RNA sequencing (scRNAseq) has resulted in an accrual of scR-NAseq datasets comprising thousands of cells from many lineages, tissues, physiological conditions and species. The classical representation of such datasets is based on their dimensionality reduction e. g. by t-stochastic neighborhood embedding (t-SNE) or uniform manifold approximation and projection (UMAP). In such steps, all cells are plotted according to their transcriptomic similarity with immediate neighbours and the overall community of cells, forming separate groups or clusters. The lineage, status and hallmarks of such cells and clusters are then identified by their expression levels of single genes, chosen for their hallmark expression patterns, e. g. expression of the CD14 gene for monocytes, or of the MKI67 gene for proliferating cells.
Nevertheless, both technical noise from the data acquisition process, and massive gene dropouts impair detection of many genes in scRNAseq datasets. Consequently, mapping the expression level of a single gene in a t-SNE map is generally less informative than mapping the enrichment of a corresponding multi-gene signature. We recently developed Single-Cell Signature Explorer, a tool which scores gene signatures by their UMI to total cell UMI ratio in each single cell from large datasets  (1). This tool further overlays UMAP or t-SNE maps of the dataset with heatmap-encoded single cell scores of any multigene signature. By allowing the user to see how these scores vary across all cells, it provides a visualization of any transcriptomic hallmark in the dataset. For example, this tool returns scores interpreted by user for identifying B versus non-B cells among peripheral blood mononuclear cells (PBMC).
In addition, Seurat not only allows analysis of single cell transcriptomes, but also of multimodal datasets through R-based command lines (https://satijalab. org/seurat/v3. 1/multimodal vignette. html). So currently, there are several existing methods that are able to pre-process raw CITE-seq datasets from read alignments up to production of tables of cells, genes, epitopes and UMAP/t-SNE coordinates. These methods can also be used to analyze such table's transcriptome and phenotype data together. But although the pre-processing performed by skilled bioinformatics analysts will remain a prerequisite, there is a growing demand from a broader scientific audience for user-friendly and open source analytic tools to finally explore any pre-processed CITE-seq datasets with simple tools. In this aim here, we introduce Single-Cell Virtual Cytometer, a small software to analyze pre-processed CITE-seq tables with user-friendly and flow cytometrylike interface.
From any table of pre-processed CITE-seq dataset, Single-Cell Virtual Cytometer allows to visualize both cell transcriptomes and epitopes in t-SNE or UMAP, through flow-cytometry-like gatings, quadrants and selection of subsets of cells. We examplify its use to characterize the gene signatures for stages of functional maturation of peripheral T lymphocytes using an original CITE-seq datasets of PBMC from healthy individual. Our tool, implemented as freely available open-source software, represents a unvaluable resource to fully exploit the expanding universe of multi-omics datasets necessary to cancer research and care.
Single-Cell Virtual Cytometer Single-Cell Virtual Cytometer is a new tool, part of Single-Cell Signature Explorer software package (1) dedicated to high throughput signature exploration in single-cell analysis. It brings the flow cytometry software capabilities to single cell analysis. It is able to define and gate cell populations based on the 2D plot of, for example two antibodies or genes, and to display simultaneously the selected cells on a UMAP or t-SNE map. There is no limit to the number of antibodies, genes or other criteria possibly used to define the plot, including combinations of transcriptomic, proteomic and signature scores  (1). Single-Cell Virtual Cytometer takes a data table as input, using tab-separated text file format, with the cells tag in rows and genes expression, antibodies detection levels, signature scores in columns and at least two columns with (x,y) coordinates for a map, such as t-SNE or UMAP.
Once the data table is loaded, the user can then select two criteria, such as two antibodies. With such criteria, a flow cytometry-like contour plot of the entire dataset is drawn. Using a lasso or a box selection tool, the user can select some cells and immediately see these on a t-SNE/UMAP map. Single-Cell Virtual Cytometer supports an unlimited level of successive gatings. Quadrant gates display the number and % of cells in each quadrant, and trigger their location on the t-SNE/UMAP. For further analyses, it is possible to export separately the codes of cells gated or defined by each quadrant, as well as their main statistics.
CITE-seq counter We developed CITE-seq-counter software to count the UMI of antibodies tags in raw sequencing reads. CITE-seqcounter has been developed for Single Cell CITE-seq samples processed with 10×Genomics technologies. It takes as input fastq files R1 and R2 from the sequencer, the antibodies barcodes, and a white list of cells obtained from Seurat (4). Cell and antibody barcode positions are adjustable as well as UMI positions. Since sequencers can produce errors, one mismatch is allowed in the barcode and in the UMI. PCR duplicates (same UMI + barcode) are excluded from the counts. The software is written in Go, it is fast and the memory usage is as low as possible. Only the result table and two sequences R1 R2 are stored in RAM at the same time.
Single-Cell Virtual Cytometer for analysis of pre-processed CITE-seq datasets Single-Cell Virtual Cytometer is a small software (3Mo) which does not require any complex installation, and can be run immediately in a web browser without any programming skills. The user can immediately explore any preprocessed CITE-seq data without mastering any R command line instructions. Importantly, these input data must consist in csv tables featuring cells in rows and columns for genes, antibodies, signature scores or any other quantitative single cell readout. Indeed, such tables must also have for each cell at least one set of map (x,y) coordinates from any dimensionality reduction method. From such pre-processed CITE-seq data, Single-Cell Virtual Cytometer typically displays both a flow cytometry-like density plot of cell surface markers (left panel, referred below to as phenotype panel) and the corresponding dimensionally reduced map of cells NAR Genomics and Bioinformatics, 2020, Vol.
2, No. 2 3 based on their transcriptomes (right panel). Based on any user-defined criteria, the cells selected by gates or quadrants on left panel's density plot are interactively displayed on the right panel showing the corresponding t-SNE/UMAP (Supplementary data demo video). Setting quadrants in the phenotype panel automatically triggers display of both percentages and counts of cells from each quadrant. Hence from a CITE-seq dataset analyzed with Single-Cell Virtual Cytometer, it is possible to select two antibodies to get their density plot across the entire dataset. Using gates or quadrants from this phenotype plot, the user can select further a subset of cells to visualize on the transcriptomic map. The cell tags of any selected subset of cells can be exported as a txt file. Downstream sub-gating and analysis with other antibodies of selected cells can be reiterated without limits.
Comparison with existing scRNAseq and flow cytometry visualization tools Seurat 3. 0. Seurat 3. 0 (4) is an R package designed for QC pre-processing, analysis and exploration of single cell RNAseq data. The Seurat pipeline enables users to identify and interpret sources of heterogeneity from single-cell transcriptome measurements, and to integrate diverse types of singlecell data, performing the so-called multimodal integration. Seurat has a command line tool able to generate dimensionality reduction maps from t-SNE or UMAP, allowing users to select clusters and subsets of cells. Hence the use of Seurat requires bioinformatics skills that are however no more needed for using Single-Cell Virtual Cytometer, which was rather designed for end users more accustomed to flow cytometry. Loupe cell browser.