dataesr/scientific-paragraphs-categorization huggingface dataset:
RT analysis with START-R suite allows robust statistical analysis We developed a software allowing an automatic detection of RT regions and a differential analysis between two conditions. The START-R suite is implemented into an HTML interface for more efficient use and sharing by biologists. START-R is built-in and packaged into a virtual environment with Docker (Figure  1 ). Thus, START-R can be easily deployed on a personal computer or on a server, and can run independently of any library updating. START-R provides as many parameters as possible for a comprehensive analysis of the RT program (Supplementary Figure  S1A-K ). Furthermore, we added new scaling, normalization and smoothing methods (Supplementary Figure  S2 ) and also novel statistical approaches to detect differences between two samples.
A classical differential analysis performed with START-R takes ∼15 min with a standard laptop computer. START-R Analyzer can run data from all organisms with different genome assemblies. This flexibility is one of the new aspects of the START-R suite that allows to analyze RT program in every organism (Supplementary Figure  S1B ). In addition, START-R generates RT profiles in PDF and all the files necessary for a better and customized visualization with START-R Viewer. START-R Analyzer also produces specific files that could be visualized with START-R Viewer, a specific genome browser tool dedicated to START-R suite.
Following TTR detection, START-R Analyzer localizes CTRs: TTRs are subtracted from the genome (Supplementary Figure  S3 ) and the remaining regions are considered as CTRs. At the end of these steps, START-R Analyzer automatically generates a BED file for CTRs and TTRs making easier further bioinformatic analyses and the display of the RT domains via a genome browser (Supplementary Figure  S1F ). A codebook is also generated to ensure the traceability of options chosen for each analysis. For the storage and production steps, the START-R suite was concatenated using Docker in order to install, use and share it easily. These software can be used with different operating systems: Windows, Mac OS and Linux. The source code and the installation procedure are available on GitHub (https://github. com/thomasdenecker/START-R).
To install the START-R suite, users should install Docker and follow the Readme file containing the installation procedure (https://github. com/thomasdenecker/ START-R/blob/master/README. md). Finally, in order to run the START-R suite, users should double-click on the START-R file (Windows) or launch the command line (Mac OS X, Linux), followed by opening an internet browser at the following URLs: http://localhost:3838/ for START-R Analyzer and http://localhost:3839/ for START-R Viewer. We added a step allowing the differential analysis of RT programs from two experiments. Thus, we can now compare RT profiles obtained in different conditions and/or with different cell lines to identify genomic elements that can modify the RT program. Our differential analysis includes three different methods of comparison: the Mean method, the Euclidean method and the Segment comparison method.
When the goal is to identify most regions with strong RT changes, we recommend using the most stringent Mean method. The less stringent Segment and Euclidean methods allow the detection of a larger set of RT changes, while increasing the risk of obtaining false positives. The last major implementation is START-R Viewer (Supplementary Figure  S1K ). This web-based interface allows the visualization of the RT profile generated by START-R Analyzer in dynamic charts obtained with the Plotly library (C. Sievert, C. Parmer, T. Hocking, S. Chamberlain, K. Ram, M. Corvellec and P. Despouy, plotly: create interactive web graphics via 'plotly. js', R package version 4. 7. 1, 2017, https://CRANR-project. org/package=plotly). One can easily identify CTRs, TTRs (Supplementary Figure  S5A ) and significantly advanced or delayed regions (Supplementary Figure  S5B ).
START-R analysis of RT programs during differentiation in mouse: a new analysis of previous data To validate our START-R based-approach without a priori consideration, we decided to re-analyze the data generated by the Gilbert's group concerning the changes of RT program during cell differentiation in mouse D3esc and D3npc9 cell lines  (16). We converted these raw data with the convertPair. R script available in our GitHub project into the correct format for START-R Analyzer. RT profiles generated with START-R Analyzer (Figure  2 ) and molecular signatures (Supplementary Figure  S6 ) are identical to the ones previously described by Gilbert's group. Each modified timing region had a particular molecular signature: Lateto-Early or advanced regions show a GC/LINE-1 density and gene coverage similar to constant Early regions, while Early-to-Late or delayed regions showed GC/LINE-1 density and gene coverage similar to constant Late regions.
Validation of START-R with Early-Late Repli-seq data from mouse Nowadays, many data of RT program are generated with Repli-seq experiments, but their analysis is time-consuming and often requires bioinformatics skills. We analyzed the Early/Late Repli-seq data from Marchal et al. (12). We specifically developed a supplemental script to convert the BAM coverage file to a log Early/Late file (convert bamcoverage file. R) to be sure that the integration into the START-R pipeline was correct. Then, we compared the RT smooth profiles generated from Early/Late Repli-seq data with those generated by the same group using microarrays (Figure  3A ). Profiles are almost identical to the ones described by Marchal et al. (12). Thus, START-R Analyzer and START-R Viewer can be easily used to analyze Early/LateIDEX Repli-seq data, showing their versatility and their simplicity of use.
It also automatically generates files with different output formats essential for further molecular characterizations and compatible for classical bioinformatic tools and/or for GALAXY genomic tools  (21). Then, START-R Viewer produces a nice interface to visualize all the data generated by START-R Analyzer. Furthermore, START-R suite freeware are available on GitHub and their source codes are open to anyone who wants to improve them, as, for example, for studies of allelic changes of RT. In conclusion, it is now possible for any biologist or laboratory to readily explore new or previous RT data simply and quickly. Thus, a large number of laboratories can today use our software to find out whether their experimental conditions are affecting the RT process or are correlated with other molecular mechanisms.
5281/zenodo. 3251905). For the storage and production steps, the START-R suite was concatenated using Docker in order to install, use and share it easily. These software can be used with different operating systems: Windows, Mac OS and Linux. The source code and the installation procedure are available on GitHub (https://github. com/thomasdenecker/START-R). To install the START-R suite, users should install Docker and follow the Readme file containing the installation procedure (https://github. com/thomasdenecker/ START-R/blob/master/README. md). Finally, in order to run the START-R suite, users should double-click on the START-R file (Windows) or launch the command line (Mac OS X, Linux), followed by opening an internet browser at the following URLs: http://localhost:3838/ for START-R Analyzer and http://localhost:3839/ for START-R Viewer.
ACKNOWLEDGEMENTS We thank  Gaëlle Lelandais  for helpful discussions. We also acknowledge the  ImagoSeine core facility of the Institut Jacques-Monod , member of the  France BioImaging  ( ANR-10-INBS-04 ) supported by the  Region Île-de-France  ( E539 ). This project was supported by the generous legacy from  Ms Suzanne Larzat  to our group.
This limits the detection of shallow layers within the ABL, which often occur with stable atmospheric stratification at night. Other ALC have a near-complete optical overlap, allowing shallow layers (< 100 m above ground level, agl) to be derived. Commonly deployed ALC can be split into low-and high-SNR sensors, with varying optical overlap and near-range uncertainties. To obtain spatially consistent data products, the EUMETNET E-PROFILE program  [27]  is developing tools for application to ground-based profiling networks (https://e-profile. eu). To best exploit the respective capabilities of high-SNR and low-SNR ALC types, tailored algorithms (Section 2) are proposed for application in a network setting such as E-PROFILE. STRATfinder, a high-SNR ALC algorithm, combines advantages from both STRAT-2D/STRAT+  [14] [15] [16]  and pathfinderTURB  [22].
The CABAM algorithm  [17, 28]  for low-SNR ALC has been made more flexible for network application. The algorithms are applied to long-term datasets gathered by high-SNR and low-SNR ALC sensors co-located at two sites with differing ABL characteristics. Results are evaluated against thermodynamic ABL indicators, before their agreement is assessed for individual measurement periods and based on their ability to capture diurnal and seasonal average ABL heights (Section 3). Applicability of the algorithms to other ALC models is discussed and site and sensor specific considerations for the implementation of automatic MLH detection across a diverse network are identified (Section 4) before overall conclusions are summarised (Section 5).
Materials and Methods Two tailored algorithms are evaluated for application in a network setting: STRATfinder for high-SNR (Section 2. 1) and CABAM for low-SNR ALC data processing (Section 2. 2). Both rely solely on ALC observations, i. e. , profiles of attenuated backscatter and CBH. Additional quality control steps are implemented to flag physically unreasonable MLH results obtained by either of the two methods (Section 2. 3). For independent reference, thermodynamic ABL heights are determined from Aircraft Meteorological Data Relay (AMDAR) profile observations (Section 2. 4). The evaluation is performed based on observations at two E-PROFILE stations (Section 2. 5).
CABAM for Low-SNR ALC The CABAM algorithm, developed for low-SNR ALC observations [17], tracks MLH and additional layers forming the RL, while taking into account the presence of clouds. Following the detection of significant vertical gradients in the attenuated backscatter profiles, layers are connected using a dynamic decision-tree with rules varying through the day. As precipitation hinders MLH detection, affected periods are filtered out automatically, using indicators derived from attenuated backscatter profiles. A first-order estimate of ABLH is calculated as the median of all layers recorded at any given timestep. Automatic layer detection and attribution are performed at the raw resolution of the data, and final results are block-averaged to 15 min (time ending). The code is written in R  [30].
To create a homogeneous dataset, cubic splines with a 50 m resolution starting from the first available observation agl are fit through the temperature profiles. Only flights reporting data below 1500 m agl are included. Each flight is assigned to an airport based on the lowest observation in the profile and to a 15 min time interval based on measurement time closest to 1000 m agl. Pressure heights are converted to heights agl  [34]  using the surface pressure observations reported at the respective airport site. Flights are separated by flight number and reporting time. For each time interval, the flight with the most data points is selected. To reduce outliers in the layer heights derived from AMDAR profiles, z ∆T values are removed if they exceed a seasonally dependant threshold (winter-2000 m, spring-3500 m, summer-3500 m, and autumn-2500 m).
Observations and Measurement Sites The two ABL algorithms are assessed at two E-PROFILE sites (Payerne, Switzerland, and Palaiseau, France) with ALC data analysed for nearly 3 years (Jan 2017-Nov 2019) and 4. 5 years (June 2015-Nov 2019), respectively. Rural Payerne (46 • 48 53 N, 6 • 56 33 E; 490 m above sea level) differs from suburban Palaiseau at the SIRTA site  [35]  (48 • 43 5 N, 2 • 12 27 E; 160 m above sea level) south of Paris, as aerosol concentrations are generally higher at the latter. During the periods analysed here, annual average precipitation ranged from 647 mm (2017) to 864 mm (2019) mm at Payerne and 571 mm (2019) to 713 mm (2016) at Palaiseau. Previous ABL studies at these locations provide valuable insights regarding ABL heights (e. g. ,  [5, 22, 36] ) that were considered when choosing the site-specific settings (Appendix A).
At both sites, two of the most commonly deployed ALC are operated simultaneously: the Lufft CHM15k with a Nd:YAG solid-state laser and the Vaisala CL31 with a laser-diode. At Payerne (Palaiseau), the distance between the two ALC is about 220 m (10 m), while they probe an area of about 2-3 m -2 at 2000 m range given a beam divergence of < 0. 5 mrad. Palaiseau CL31 data are unavailable for July-Nov 2016 because of laser repairs. Instrument characteristics such as the laser pulse energy and receiver aperture affect the recorded backscattered signal power. Given the emitted power of the CHM15k is much greater than for the CL31 (Table  1 ), the two ALC models are classified as high SNR and low SNR, respectively. As a combination of instrument characteristics affect the SNR  [26] , it can be determined based on observations using different approaches for photon counting sensors  [37] , such as the CHM15k, and analogue detectors  [24] , such as the CL31.
The main difference affecting MLH detection is that the low SNR of the CL31 does not allow for variance fields used by STRATfinder (Section 2. 1) to be derived. Hence, data from the CHM15K are processed with STRATfinder and from the CL31 with CABAM, respectively. Prior to applying the ABLH detection algorithms, corrections are performed on the field of attenuated backscatter from both ALC types. CHM15k data are corrected for incomplete optical overlap using a dynamic model to account for effects induced by internal temperature variations  [25]. Following  [22] , attenuated backscatter is considered applicable for MLH detection where the overlap function exceeds 5% (Appendix A, Table  A1 ). The optical overlap functions provided by the manufacturer for the two CHM15k both exceed this threshold above ~240 m range.
Detection Rates and Quality Control High data availability (attenuated backscatter at Payerne (Palaiseau) observed 98% (89-94%) of the time) allowed for MLH detection to be performed on large datasets. After all quality control flags are applied (Figure  2 ), more MLH results are available from STRATfinder (82% at Payerne, 73% at Palaiseau) compared to CABAM (51% at Payerne, 56% at Palaiseau). Of these, MLH is detected by both algorithms simultaneously 44% (46%) of the time at Payerne (Palaiseau). Data availability by season and time of day at both sites are sufficient for analysis (37-54%; Table  2 ). no suitable layer assigned to MLH, the automatic algorithms considered the profile too complex for MLH detection due to precipitation, or results were found inconsistent during post-processing quality control (Section 2.
3) due to precipitation, insufficient layer height variability in the near-range, temporal discontinuity, daytime underestimation, or nocturnal overestimation. In a few complex cases (2% at both sites), no layer was detected by STRATfinder. CABAM does not assume a continuous layer throughout the day so that layer heights may be interrupted when no significant gradient is present in the attenuated backscatter profile at a consistent height. These layer gaps and insufficient SNR explain the higher data fraction, with no MLH detected by the low-SNR ALC (12% at Payerne, 8% at Palaiseau). At Payerne, CABAM flags more periods with complex precipitation patterns (16%) compared to STRATfinder (2%), while the internal precipitation flag is more consistent between algorithms (7-9%) at Palaiseau. Further refinement of the respective modules implemented for detection of precipitation could help increase agreement of this quality flag between algorithms.
As the quality control procedure (Section 2. 3) is not able to capture all such instances, these over-estimations bring the MBE closer to zero for CABAM (Table  3 ). Assessed against AMDAR thermodynamic layer heights, the performance of CABAM at Palaiseau (Table  3 ) is markedly better than reported for the long-term dataset from London, UK  [17]. Improvements of the CABAM MLH by application of the automatic quality control is unlikely responsible for the smaller errors reported here, given supervised layer detection was used in London to minimise false layer attribution. However, z PM is a more appropriate MLH reference than the temperature inversion height used to evaluate the London MLH, with the latter often marking the top of the residual layer  [17]. As synoptic conditions and cloud dynamics modify ABL complexity and sublayer configurations, they may also influence the performance statistics at the two sites.
• The auxiliary data used by STRAT+ to derive MLH may help reduce uncertainty in aerosol-based layer detection. Further, application to a high-power lidar [15] rather than an ALC likely decreases false layer detection. Still, the AMDAR dataset provides a beneficial reference for the evaluation the ALC methods as radiosonde ascents  [44]  do not allow full diurnal evolution of the ABL heights to be assessed. It is further evaluated how the average seasonal diurnal evolution of the ABL heights compares between the aerosol-based methods and the thermodynamic approach (Figure  4 ). More simultaneous data are available in spring and summer (Table  3 ) when unstable atmospheric conditions are more frequent. Here, the MLH is the median z PM from sunrise + 3 h until sunset, the 10 th percentile of z ∆T for the night and early morning.
For this, STRATfinder is used with Lufft CHM8k data and CABAM with Vaisala CL51 data (Supplementary Materials S. 3). Applying the algorithms to the different ALC models is straight forward and without requiring software alterations. For reference, the CHM15k STRATfinder MLH is used. Both the STRATfinder CHM8k and CABAM CL51 data are affected by the SNR. Given the signal strength of the CHM8k is lower than for the CHM15k (Table  1 ), uncertainties increase during daytime peak convection, especially when the ABL configuration is complex. Occasionally an overestimation of CHM8k MLH (cf. CHM15k) occurs when the pathfinding algorithm has difficulties distinguishing between the signal in the ABL and either the noise in the free atmosphere, elevated aerosol, or cloud layers aloft. Case studies (Supplementary Materials S.
3. 1) highlight the importance of absolute calibration of attenuated backscatter  [46, 47] , as this would allow consistent parameter settings across a diverse network. CL51 data are less noisy than CL31 observations (Table  1 ) leading to slightly better agreement with the STRATfinder CHM15k results (Supplementary Materials S. 3. 2), even without application of the deep convection module. These first comparisons suggest that the two automatic algorithms are flexible for application in a network setting and further highlight that algorithm performance depends on the quality of the input data provided. Contrasting SNR characteristics of commonly deployed ALC (CHM15k, CL31) motivated the classification into high-and low-SNR categories. As the low-SNR CL31 does not permit good quality variance fields to be derived, STRATfinder is not suitable to use for this model.
The STRATfinder pathfinding upper boundary is derived from a series of user-defined settings (Section 2. 1), including a nocturnal and daytime maximum and a specific growth rate when transitioning between those limits in the morning. (3) Daytime: Both methods require a physically possible daytime maximum ABLH to be specified (Appendix A). However, it has minimal impact on performance (unless set too low). During daytime deep convection, a low-SNR sensor is clearly inferior to high-SNR observations. False layers induced by noise may lead to both over-and under-estimation of peak MLH values, depending on the growth limit settings. The improved CABAM performance at Palaiseau with CL51 (cf. CL31) data illustrates this source of uncertainty. The new deep convection module generally improves CABAM performance for CL31 input at Palaiseau where fast growing mixed layers occur frequently during spring and summer.
Average seasonal diurnal patterns of the ABL described by MLH from STRATfinder and CABAM are very similar, both for cloudy and cloud-free conditions. The greatest discrepancy occurs during the evening decay of the mixed layer around sunset, when STRATfinder has a delayed decline of the mixed layer (cf. CABAM). The MLH decay is challenging to track using aerosol-based information as the gradients in attenuated backscatter likely depend on aerosol characteristics (e. g. , size distributions). With a low-SNR ALC, layer detection uncertainty increases when MLH rises fast to high peak levels (above 2000 m), as instrument-related noise can cause gradients in attenuated backscatter within the mixed layer that may be falsely assigned to the MLH. The new deep convection module improves performance for such spring and summer days at Palaiseau, but it occasionally leads to over-estimation when detached elevated aerosol layers occur above the ABL.
First-order estimates of total ABLH are compared, with AMDAR and STRATfinder having reasonable agreement in spring and summer. Both confirm the textbook expectation of the mixed layer extending over the whole ABL once full daytime development is reached. CABAM's ABLH is significantly overestimated during the day suggesting a dedicated module to track ABLH is needed. Results demonstrate the full ABL sublayer configuration can be characterised automatically solely based on ALC observations provided careful data analyses and quality control. Being able to analyse not only the mixed layer but also the evolution of the residual layer has distinct advantages for interpreting air quality measurements and modelling. The present analysis shows very good agreement between the MLH from high-and low-SNR ALC can be achieved at two sites with differing ABL dynamics.
Acknowledgments: The authors would like to thank the technical and  IT  staff at the  SIRTA and Payerne observatories  for operation of the ALC and maintenance of the automatic data flow in near real-time. The authors would like to acknowledge  Météo-France  for providing CL31 raw data. The Vaisala CL51 data are kindly provided by  Dominique Demengel , EDF R&D. We thank the  European Cooperation in Science and Technology (COST)  actions  "TOPROF" (ES1303) and "PROBE"  ( CA18235 ) which fostered collaborations that led to this work.
Technological Methodology The system is composed of 2 databases: the first one is used to edit the game, developed in XML, and the second database is for data that are sent over the internet, which is available via a Web-based platform (type: Learning Management System [LMS] Modular Object-Oriented Dynamic Learning Environment [Moodle]) developed in MySQL. A programming language known as HTML (HTML JavaScript Cascading Style Sheets [CSS]) was used in conjunction with PHP (Hypertext Preprocessor) to develop the activities included in the Web-based system.
Tools The development of Equit'Game was based on 2 essential elements: app design software and the game engine. The design software we used is called Ludiscape, created and developed by Damien Renou  [24]. Ludiscape is a collection of integrated tools designed to help users create training content, e-learning modules, and serious games. This technology allows users to create a variety of educational content simply by using the Ludiscape resource library. Each object in this library provides quick access to properties and settings, requiring no prior experience in programming (Figure  2 ). Full integration of JavaScript with the Ludiscape engine and its extensions ensures maximum flexibility and production speed. Plugins are invisibly integrated into the educational production environment in such a way that they are easy to use.