dataesr/scientific-paragraphs-categorization huggingface dataset:
This data set used the reprocessed IGS orbits, clocks, and ERPs produced by JPL/NASA in the framework of the 1st reprocessing campaign organized by IGS. The reprocessed satellite products were generated for the period 1 January 1995 to 31 December 2007, but JPL completed the series until mid-2011 in a consistent way. In this study, we use the ZTD estimates until 31 December 2010 to have an integer number of years. According to the discussion above, the prominent features of the processing procedure are:  GMF mapping functions (hydrostatic and wet). Random Walk model for ZWD and gradient parameters with constraints: 3 mm h -1/2 (ZWD) and 0. 3 mm h -1/2 (gradients). ZWD and gradient sampling: 5 min ZHD and ZWD a priori: 6-hourly ECMWF analysis (provided by TUV). VMF1 mapping functions (hydrostatic and wet).
Ambiguity resolution Float Fixed It should be noted that, due to a flaw in the data file handling, the ZTD files for a number of stations are not available on the FTP repository, and older files were used instead, for which a major difference was the use of the older NMF hydrostatic and wet mapping functions  [36]. The second GNSS data set, referred to as CODE REPRO2015, was processed by the Center for Orbit Determination in Europe (CODE) in 2015  [37]  using the Bernese GNSS software  [38]  in network mode (double-differenced observations). This data set used the reprocessed IGS orbits, clocks, and ERPs produced by CODE in the framework of the 2nd reprocessing campaign organized by IGS. The reprocessed products cover the period from 1 January 1994 to 31 December 2014. For the purpose of this study, we completed the series with the operational CODE products until the end of 2018  [39].
However, the latter underwent a switch in the reference frame to IGS14 and in the antenna cnetwork alibration (double-differenced observations). model to igs14. atx, on 29 January 2017. This change was, thus, included in the metadata for the CODE solution. The prominent features of the CODE processing procedure are: GPS observations  (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001)  and GPS + GLONASS  (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014). According to the discussion above, we see that all the main factors conditioning the accuracy of the ZTD estimates are different between the two data sets. Not discussed above are the elevation cutoff angle and observation weighting, which contribute significantly to the sensitivity of ZTD estimates to the various elevation-dependent error sources (mapping functions, antenna PCO/PCV models, and multipath).
75°× 0. 75°for ERA-Interim and 0. 25°× 0. 25°for ERA5. Reduced representativeness errors are, thus, expected from ERA5 data. Moreover, the IWV values computed from ERA5 are also expected to be of higher quality since this reanalysis used a more recent model and assimilation system, and assimilated a much larger number of observations, especially in recent years  [18]. The third step is the segmentation, i. e. , the detection of change-points in the mean of the IWV difference time series. Here, we use the fast version of the GNSSfast R package published by Quarello et al. [45]. This version is available on https://github. com/arq1 6/GNSSfast. git, accessed on 30 July 2021. The segmentation method estimates K, the number of segments, or the number of change-points K-1, the K means of the segments, and the K -1 positions of the change-points, as well as a periodic bias function f and the monthly variance of the noise (also with annual periodicity), in a unified model for each IWV difference time series.
Conclusions This study investigated the sensitivity of the segmentation method and the IWV trend estimates to different GNSS and reference data properties. It was shown that the GNSS processing methods and the reference data (ERAI versus ERA5) have the strongest impact on the segmentation results (i. e. , number and positions of change-points), while the impact is weaker when the length of the time series (17 versus 25 years) or the auxiliary data (ERAI versus ERA5) are changed. Changing the latter two was shown to achieve 81% and 71% similar change-points, but only 45-49% when the GNSS data set or the reference was changed. These discrepancies in the results indicate that the segmentation is sensitive to small changes in the mean signal, in the noise, and in the periodic bias. These features are determined by several aspects of the GNSS processing procedure, especially: the a priori ZHD correction, the antenna/radome calibration model, the mapping functions, and the elevation cutoff angle.
This difference might be due to the particular penalty function that we used here  [46, 47]. The penalty function might be calibrated differently to achieve a better balance between the probability of detection and the probability of false detection to our particular data  [6]. This study also points to the strong impact of the inhomogeneity correction on the estimated trends. When only the change-points validated with the metadata are used, the impact of the correction is substantial on the global mean trend, on the dispersion, and on the RMS difference with respect to ERA5. When all the detected change-points are used, the mean and dispersion are again modified, but to a lesser extent, and the RMS difference with ERA5 is further reduced. The latter feature is expected when ERA5 or ERAI are used as a reference in the segmentation, especially since a few change-points may be due to the reanalyses (e.
g. , suspected at stations WUHN, GUAM). The uncertainty in the estimated trends from the use of the different reprocessed GNSS data sets or reference reanalysis in the segmentation is about 0. 015-0. 019 kg m -2 year -1 (RMS difference between the tested pairs of data sets) when only the validated change-points are used and 0. 002 to 0. 012 kg m -2 year -1 when all the change-points are used. The auxiliary data has a marginal impact on the trends. The longer time series (25 years) provides higher accuracy in the trend estimates, with a mean standard error of 0. 018 kg m -2 year -1 and a dispersion of ∼0. 03 kg m -2 year -1 throughout the global network. The homogenized GNSS trends and both reanalyses agree in the global mean IWV trend estimate of 0. 027 kg m -2 year -1 , which indicates a global moistening over the past 2.
5 decades at a rate close to the 7% per degree of warming predicted by Clausius Clapeyron equation  [50]. The main limitation of our current homogenization method is the attribution step (Figure  2 ), which could not be used to check all the change-points because of the lack of nearby stations. In future work, we plan to include reprocessed GNSS data from additional stations in regions where dense networks are available (e. g. , Europe, USA, Japan, etc. ). Some limitations of the current segmentation method were highlighted, as well, especially the dependence on the length of the time series, which may be mitigated by careful calibration of the penalty function. The segmentation method is also sensitive to the longterm variations in the noise and bias, which are not specifically modeled here (we assume both are periodic with a fundamental period of 1 year).
At some sites, strong inter-annual or decadal variations in the noise and/or bias were captured by the segmentation. These spurious change-points need to the detected and removed, unless the long-term variations are reduced, e. g. , from the use of a better reference data set, such as a nearby GNSS station, rather than a reanalysis. Finally, this study also helped to better understand the performance and limitations of the GNSS data processing procedures. This knowledge can be helpful in the future to reprocess the GNSS data and produce better "homogenized" daily IWV time series at the processing level.
Acknowledgments: The authors are grateful to AERIS, the  French data  and service center for atmosphere, for providing the  ERA-Interim  and  ERA5  reanalysis data, and hosting the GNSS IWV data sets. The contribution of the fourth author has been conducted as part of the project  Labex MME-DII  ( ANR11-LBX-0023-01 ) and within the  FP2M federation  ( CNRS FR 2036 ).
CODE -ERAI CODE (b) -ERAI CODE (b) -ERA5 CODE (b) -ERA5 CODE (c) -ERA5 CODE time-matched CODE time-limited CODE (a) CODE (b) CODE (c) Raw data Data Availability Statement: The daily IWV data set based on the IGS repro1 ZTD data (120 stations, period 1995-2010) is available from https://doi. org/10. 14768/06337394-73a9-407c-9997-0e380dac5 591, accessed on 30 July 2021 [43]. The daily IWV data set based on the CODE REPRO2015 ZTD data (436 stations, period 1994-2018) is available from https://dx. doi. org/10. 25326/18, accessed on 30 July 2021 [44].
Data volumes collected in many scientific fields have long exceeded the capacity of human comprehension. This is especially true in biomedical research where multiple replicates and techniques are required to conduct reliable studies. Ever-increasing data rates from new instruments compound our dependence on statistics to make sense of the numbers. The currently available data analysis tools lack user-friendliness, various capabilities or ease of access. Problem-specific software or scripts freely available in supplementary materials or research lab websites are often highly specialized, no longer functional, or simply too hard to use. Commercial software limits access and reproducibility, and is often unable to follow quickly changing, cutting-edge research demands. Finally, as machine learning techniques penetrate data analysis pipelines of the natural sciences, we see the growing demand for user-friendly and flexible tools to fuse machine learning with spectroscopy datasets.
• Visual programming: Orange does not impose a preset order of operations. Instead, it offers components-widgets-that either process, visualize or model inputs. Users can connect them as they see fit as long as they share connection types. This approach allows the creation of flexible workflows; • Immediate feedback: Orange follows the general principle that being able to observe the effects of actions, and adapt immediately, improves efficiency. The option to inspect the results at every step of the analysis helps users gain confidence in results and familiarity with the analysis procedures  [14]   [15] , image analytics  [14] , and time series analysis. Quasar is a packaged distribution of Orange, extended with specific, preselected addons, that we provide as a single installer. Currently, it extends Orange with components specific to spectral data analysis.
The spectroscopy add-on, Orange Spectroscopy, evolved from the Infrared Orange add-on  [16]  and shares the same basic features with a plethora of improvements and new functionality. Compared to Infrared Orange, Orange Spectroscopy adds new spectral preprocessing methods along with many user interface and general stability improvements. Since our first report in 2017  [16] , we added more than 30,000 lines of code into the orange-spectroscopy GitHub repository alone. Quasar's new components for spectral data were designed for maximum interoperability with the rest of Orange. Therefore, all machine learning capabilities of Orange are available to the spectroscopist: techniques such as clustering or classification can be directly applied to spectral data. We achieve this by embracing Orange's Table data structure and conforming to its ways of data processing.
Case Study 1: Identification and Localization of the Medullas in Human Hair Sections To showcase unsupervised learning on spectral data with Quasar, we will use an FTIR hyperspectral dataset of multiple hair sections measured in transmission mode as described in detail in the study of Sandt and Borondics  [17]. The dataset contains absorbance values in the 4000-800 cm -1 range. Figure  2  shows how Quasar allows identification of interesting groups of spectra: in this case, detecting the hair medulla. In the workflow, we used unsupervised learning-clustering-twice: first to remove physical variations such as the background or scattered edges, and then, to detect variations in the chemical composition inside the hair sections. Typically, we would use thresholds according to the intensity (the Select Rows widget in Quasar) to remove the background.
On this dataset, k-Means clustering provided the separation we required. To achieve a useful identification of groups of similar spectra, we clustered second derivatives of original spectra. Therefore, the output-spectral graph in the last Hyper-Spectra widget in the Figure  2c -is shown in the derivative space. Spectra in the derivative space are often harder to interpret. Thus, to investigate the selected spectrum of the medulla in the space of original data, we used a Spectra with the selected spectrum as its subset input; there, we were able to observe the selected spectrum before any processing. The spectrum reveals a characteristic lipid signature. Even though spectra in the dataset are saturated in the Amide I and II regions, the methods we employed were robust enough to identify a known biological structure, i.
e. , the medulla. The case study showcases how immediate feedback influences the analysis. If we did not preprocess the data before the second k-Means clustering, the clusters obtained are not meaningful. On these data, seeing clustered display (in Hyperspectra) while searching for appropriate preprocessing proved invaluable: an interesting clustering result steered the analysis in a particular direction. We used k-means clustering to find groups of similar spectra. Straightforward alternatives for grouping, such as hierarchical clustering or DBSCAN, would likely yield similar results, but k-means was computationally the most efficient. Furthermore, dimensionality reduction techniques such as principal component analysis, multidimensional scaling (MDS), or t-distributed stochastic neighbor embedding (t-SNE) applied before grouping could help better understand the structure within groups.
Quasar offers all listed techniques. One of the design principles of Quasar is that anything displayed can be analyzed further. In the workflow shown in Figure  3 , we applied the principal component analysis (PCA) to clusters obtained in Figure  2. We needed to apply PCA to the original spectra, not the derivatives used for clustering, to understand the cluster's spectral characteristics. We thus merged the resulting clusters with the original data and, after preprocessing, applied PCA. In Figure  3c , a Scatter Plot shows that the first principal component easily identifies the cluster, and the Spectra shows the corresponding loadings with the lipid signature.
Case Study 2: Building a Drug Resistance Prediction Model Here, we apply supervised learning on FTIR spectra obtained from three different cell types, aiming to develop a statistical model for predicting resistance to all tyrosine kinase inhibitors (drugs aimed for treating chronic myeloid leukemia) conferred by the T315I mutation in the BCR-ABL gene  [18]. The spectra in the dataset correspond to individual cells measured with synchrotron radiation FTIR microspectroscopy in transflection mode, each described by absorbance values in the 1800-900 cm -1 range. Figure  4  shows the workflow and its most important components. After preprocessing, we evaluated two supervised prediction methods, logistic regression and random forests, using five-fold cross-validation. Looking at the Test and Score widget, it is clear that random forests performed better for all calculated metrics.
Case Study 3: Protein Secondary Structure Peak Fitting Next, we highlight how peak fitting capabilities  [19]  are integrated into a larger workflow in Quasar using spectra from a study on human cirrhotic liver tissue  [20]  measured with FTIR and annotated according to the majority presence of a chemical compound (collagen, glycogen, lipids, or DNA) in that part of the cell. In a recent paper, Stani et al. [21]  established a correlation between two distinct protein spectral regions (Amide I and Amide III) for collagen during thermal degradation. In their analysis, they use spectral preprocessing and peak fitting to extract secondary structure characteristics. Figure  5  shows the workflow which implements their described peak fitting method in Quasar. After filtering the dataset to select rows labelled as "collagen", we split the spectra into two sub-regions, Amide I (1750-1580 cm -1 ) and Amide III (1310-1175 cm -1 ), and identically preprocessed them with rubber band baseline correction and min-max normalization.
We configured peak fitting, bands, components, and fitting constraints as described in  [21] , and obtained starting parameters from an initial fit performed on the average spectrum for the final full-dataset fit. The Peak Fit widget (Figure  5a ; Amide III) shows the resulting fits for some example spectra, while fit result parameters, total fits, and residuals are output for the entire dataset. Next, we can analyze the resulting fit parameters as any other feature in Quasar. Stani et al. [21]  found a positive correlation between the Amide I 1690 cm -1 and the Amide III 1284 cm -1 components, assigning both to the carbonyls which comprise an intra-strand hydrogen bond network stabilizing the triple-helix fibrils. Despite the lack of perturbation in the dataset used here, we also observe a positive correlation between these component areas (Figure  5b ) within the natural variation of the protein.
Limitations and Further Development Quasar, similarly to most general-purpose scientific software such as R, Matlab, or SciPy, requires the complete dataset in memory. Big data require specialized libraries that, instead of loading the entire dataset into memory, analyze the data while passing through it directly on a disk. The authors of the memory-efficient SIproc library  [1]  report that the rate of reading through the data is commonly their libraries' speed-limiting factor. Still, their library only provides a programmatic interface excluding non-experts. Following the same philosophy, we are currently changing Orange and Quasar extensions to support out-of-memory data handling.
Current Project Reach Currently, we know of 35 published scientific publications that successfully used Quasar or the Orange Spectroscopy add-on for data analysis (an up-to-date list is available at https://quasar. codes/publications/, accessed on 1 September 2021). Workshops about spectral data analysis with Quasar took place in Italy, Slovenia, Norway, France, Belarus, Canada, Australia, and, since COVID-19, online with participants from the UK, Sweden, Germany, and Egypt.
Conclusions Here, we introduced Quasar, a software based on Orange, to reform data analysis in the natural sciences. Currently, Quasar provides tools and functionality aimed at spectroscopy data analysis and combines them with powerful machine learning methods. Thanks to the visual programming approach of Orange, scientists without any coding background can build flexible data processing workflows and explore various analysis ideas interactively. With the expansion of the Quasar community, and contributions from expert developers to the open-source code, functionality can be extended towards new fields, measurement and data analysis techniques. The free availability of Quasar addresses bottlenecks in adoption. In conclusion, we strongly believe that this approach will empower scientists to better and easier understanding of experimental data.
Acknowledgments: The development of Quasar is a real community effort from around the world and the authors of this paper gratefully acknowledge these contributions. We would like to extend our special thanks to the members of the  Bioinformatics laboratory at the University of Ljubljana (Slovenia)  for their continuous effort in improving Orange;  Lisa Vaccari  and  Giovanni Birarda  from the  ELETTRA Synchrotron (Italy)  for contributing to the  Infrared Orange  project in 2016;  Scott M. Rosendahl  from the  Canadian Light Source (Canada)  for dedicating resources to  Infrared Orange and Quasar ,  Raul O. Freitas  and  João E. Levandoski  from  LNLS (Brazil ) for developing s-SNOM related functionality;  Achim Kohler ,  Johanne H. Solheim  and  Vladislav Blazsko  from the  NMBU University (Norway ) for implementing the EMSC, ME-EMSC preprocessors and visual image overlays;  Noah A.
STACKS, however, requires substantial computational resources to run on large datasets and depends on multiple parameters that can greatly influence the outcome of the analysis  (Paris, Stevens, & Catchen, 2017; Rodríguez-Ezpeleta et al. , 2016; Shafer et al. , 2018)  , for instance, in the context of sex determination, the classification of a marker as a sex-specific sequence ( e. g. from a sex-specific insertion) or as a sex-specific allele ( e. g. an allele specific to one sex in a polymorphic locus)  (Utsunomia et al. , 2017). In addition, the lack of visualization tools for RAD-Seq data makes the interpretation of results from some datasets difficult, for instance, datasets containing individuals with mis-assigned sex, strong population structure, or sex-bias in sequencing depth. To overcome these limitations, we developed RADSex, a RAD-Seq data analysis computational workflow specifically designed to study sex determination.
RADSex is simple to use, makes no assumptions whether sex-biased markers are sex-biased sequences or sex-biased alleles in a polymorphic locus, requires few resources, and offers visualization tools in the form of an R package. To demonstrate the relevance of RADSex, we analyzed a previously published dataset from the Japanese medaka, Oryzias latipes , for which we replicated previous findings, including the identification of two sex-reversed individuals, and we uncovered a previously overlooked Y-specific polymorphism in the sequenced population. We then used RADSex to analyze new datasets that we generated from 15 fish species spanning multiple systematic orders and compared the runtime and memory usage to that of STACKS. In six of these species, we identified the sex-determination system as well as multiple sex markers, and we identified the sex chromosome in one species for which a reference genome was available.
Overview of the RADSex analysis workflow The underlying principle of RADSex is to group identical RAD-Seq reads from all individuals in a dataset into non-polymorphic markers, and then consider the presence or absence of each marker in each individual. Markers created by RADSex thus differ from that of other software such as Stacks (J. M. Catchen et al. , 2011)  which typically attempt to reconstruct genotypes from polymorphic markers. The RADSex workflow ( Fig. 1   The next step is to identify markers significantly associated with sex. The distrib command uses the table of marker depths generated with process to compute the distribution of markers between males and females. Phenotypic sex for each individual is given by a user-supplied tabulated file with the identifier of each individual in the first column and its sex in the second column.
Using the output of radsex map ( Supplementary table  3  ), the radsex_map_circos() function of sgtr generates a circular plot in which sex-bias, on the top track, and -log 10 (p association with sex ), on the bottom track, are plotted against genomic position using R's circlize package (Z. Gu, Gu, Eils, Schlesner, & Brors, 2014). Each sector of the plot represents a linkage group, with unplaced scaffolds, which are important to include due to frequent assembly problems with sex chromosomes, appearing in the last sector. Each metric can also be displayed in a Manhattan plot using the radsex_map_manhattan() function; in addition, a linear plot showing both metrics for a given genomic region can be generated using the radsex_map_region() function from sgtr. Finally, radsex provides two commands to assess the distribution of marker depths in the entire dataset.