dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This project was supported by a grant from the  Fond Recherche of the ENS de Lyon. We are grateful to the  Pôle Scientifique de Modélisation Numérique (Lyon, France)  for computer time, and the  SFR Biosciences Gerland-Lyon Sud  ( UMS344/US8 ) for the access to the MacsQuantVYB flow cytometer. We thank  Dr Francesca Palladino  and  Matthieu Caron  for technical support on the Western blots and  Dr Gaël Yvert  for helpful comments on the manuscript.
Data Availability Our Python-based program for the automated generation of the qY2H affinity ladder (with a graphical user interface) can be downloaded here: http://github. com/LBMC/qY2H-Affinity-Ladder. The minimal requirements and installation instructions are given in the user guide (see Suppl. Material). The flow cytometry files of the experiment shown in Fig. 4  and Fig. 5A can be downloaded from http://flowrepository. org under accession number FR-FCM-ZYUL and FR-FCM-Z25G. Access links for reviewers: https://flowrepository. org/id/RvFrO0jCGA0pYODU9QMfnh1NfIktWPLCUjhAQoTTrn M8IpypLwYkSY0lBJ8HT39h https://flowrepository. org/id/RvFrN8vk22YkHXo2ApiVgNgWV47RREYocQ64LQg0bH u88yVTv4lVVbfMN5gxM4Zr Other experimental data can be provided upon request.
Highly-and retro-reflective materials have recently been investigated and proposed as a new urban coating solution to reduce the so-called urban heat island effect. The present study aims at providing a numerical model for assessing inter-buildings solar reflections when these materials are applied to urban canyon's surfaces. The proposed model includes a function that accounts for sunray angle dependency of the solar reflectance, which is specifically important with regard to retro-reflective behavior. The novelty of this numerical model based on a Monte Carlo simulation approach implemented in the Matlab simulation environment is to conduct full raytracing solar analyses which can reproduce the energy exchange phenomena and simulate optical material properties. Experimental validation and inter-software comparison are carried out with measured data collected in an experimental facility in La Rochelle, France, in addition to simulation results from the Radiance-based Diva for Rhino tool.
However, their impact on urban microclimates and mutual solar reflections between buildings has only been investigated in preliminary studies  [27]. The main barrier to achieving a deeper understanding of the potential of RR materials is the lack of adequate tools to accurately model their optical properties. As reported in Table  1 , existing software and urban canyon models (UCMs) present some limitations both in describing the complete energy exchange phenomena (i. e. Diva for Rhino can only assess short-wave solar irradiation) and in the assumptions used to simulate the material's optical properties (i. e. UCMs considers every material as optically diffuse). The different software used for solar analyses (e. g. Diva for Rhino, Ladybug, Honeybee, Dragonfly, Radiance, and Daysim) need to be coupled with other simulation engines to obtain a complete evaluation of the UC energy balance.
Nomenclature and acronyms ✓ - - - - ✓ ✓ - Ladybug software ✓ - - - - ✓ ✓ - Honeybee software ✓ ✓ - ✓ - ✓ ✓ - Dragonfly software ✓ ✓ - ✓ - ✓ ✓ - Radiance software ✓ - - - - ✓ ✓ - Daysim software ✓ a - - - - ✓ ✓ - [28] numerical model ✓ ✓ ✓ ✓ ✓ - ✓ - [29] numerical model ✓ ✓ ✓ ✓ ✓ - ✓ - [30] numerical model ✓ ✓ ✓ ✓ ✓ - ✓ - [31] numerical model ✓ ✓ ✓ ✓ ✓ - ✓ - Proposed model numerical model ✓ potential future development potential future development potential future development potential future development ✓ ✓ ✓ a Daysim assesses the short-wave irradiation only in the indoor environment. canyon (UC). Furthermore, the Monte Carlo-based routine implemented to model short-wave solar radiation can be applied (with some modifications) to the assessment of long-wave solar radiation as well, although this application has not been evaluated in this study.
The interval from zero to one was further divided into four sub-sections in which the boundaries described the probability of the photon being absorbed or reflected in different ways (Eq. (  3 )). These coefficients could be considered either as constants or as a function of other parameters (i. e. angle of incidence) and allowed simulating the behavior of RR materials and their solar reflectance. The exit angle from the reflecting FE was estimated according to the reflection method computed in the Monte Carlo simulation (Fig. 2 ). The Matlab numerical model reiterated this part of the procedure until either the photon was absorbed, exited the canyon, or the maximum number of reflections (ab value) was reached. Thus, the algorithm was able to trace the path of each sun ray and calculate the number of events absorbed and reflected by each FE.
Parameters setting for simulations The influence of the pairs of values set for the simulation variables (ab setting and number of entering photons) on the output data from the Matlab numerical model (Irr out ) is investigated in this sub-section. At first, the number of photons (hereinafter referred to as "number of events") was kept constantand equal to 10 5 -while the ab number was increased until all the events were fully-traced (absorbed before the cutoff). The resulting trends given by the number of fully-traced photons was assessed depending on the selected ab number and a percent of variation (Δ % ) from the plateau's value (amount of entering photons) was then assigned to each ab setting. Next, the ab number was considered equal to the greatest ab number investigated in the first step while the number of events was increased from 10 1 to 10 6 following a logarithmic scale.
The c v was calculated to be 0. 9% by considering the μ Fig. 2. On the left, overview of the different ways a surface interacts with incident photons (thick solid arrow). The photons can be reflected specularly (thin solid arrow), retro-reflected (dotted line), or diffusively reflected (all the arrows within the dashed circle). On the right, the visualization of the ray-tracing process depending on the considered ab values. In this example, the photon leaves the UC after three reflections. and σ equal to 201 W/m 2 and 1. 7 W/m 2 , respectively. Hence, the relative error, which can be associated with the outcomes from Matlab numerical model, was estimated at 3. 2% and calculated as the root sum square of the two aforementioned uncertainties (3. 0% and 0. 9%).
Workflow The workflow followed in this research study was carried out in three steps, which focus on assessing the accuracy of the proposed numerical model developed in Matlab for the simulation of short-wave solar reflection events (Fig. 6 ). In the initial step, the "parameters setting for simulations" was conducted for the proposed numerical model by focusing on the number of ambient bounces (amount of reflections considered by the tool in the ray-tracing analyses for each ray before the cutoff) and the number of photons entering the UC through the top surface. Several pairs of values (a pair consisting in one value for the ab number and one value for the number of events) were assessed to optimize the computational time while maintaining the results' uncertainty below a 5. 0% threshold.
The number of FE defined for the canyon ceiling was not investigated, while the other input data, which referred to the canyon features and boundary conditions, were set to match with the ones from the experimental facility. A Typical Meteorological Year or TMY weather data file obtained through the EnergyPlus weather (. epw) 1 of La Rochelle was used and the time of 11:00 a. m. on the 21st of August was selected during the preliminary research stage as the hour that best represented the most frequent environmental conditions observed during the monitoring campaign. The accuracy of the results was finally quantified according to the calculated Δ % and c v. The second step of the research method aimed at validating the Matlab numerical model against data collected during the monitoring campaign in La Rochelle.
For the input parameters, the pyranometer features and the climate information collected by the weather station in situ were considered along with the input parameters from the preliminary step. At this stage of the research, the weather of the 21st of August (from 9 a. m. to 5 p. m. ) was considered most representative of the climate conditions observed during that month under the monitoring campaign. The fraction of the hourly Irr out , which was measured by the pyranometer (Irr pyr ), was assessed by the Matlab numerical model and results were validated against experimental data. The arithmetic differences (hereinafter referred to as "residues") and the Δ % between the two groups of Irr out amounts (from Matlab numerical model and from the pyranometer) were estimated in order to define the mean value (μ), the standard deviation (σ), and other validation indices such as the Normalized Mean Bias Error (NMBE), the Coefficient of Variation of the Root Mean Square Error (CV(RMSE)), and the coefficient of determination (R 2 ).
In the third step, the outcomes from the Matlab numerical model were compared to the ones from Diva for Rhino. The input data was the same as the one described for the first stage, with the difference that Diva for Rhino could directly access the climate information without using another tool to visualize and process it (Elements software), unlike the numerical model. The time interval was extended in order to consider a wider range of climate conditions: the 21st of January (from 9 a. m. to 4 p. m. ) and the 21st of August (from 6 a. m. to 7 p. m. ) were chosen. The specific hourly amount of Irr out was estimated by the numerical model and Diva for Rhino: both its spatial distribution on the UC's median and the average value were investigated. Then, the residues were assessed along to the Δ % values in order to define the μ and the σ numbers (outputs).
Inter-software comparison The inter-software comparison was conducted between the Matlab numerical model and the Diva for Rhino tool. The hourly amounts of solar irradiation incident on the UC top surface were evaluated by the Diva for Rhino software and compared with the quantities from the numerical model. The input data referring to the urban canyon features and the boundary conditions were defined according to the experimental facility in La Rochelle and kept constant throughout the study. The grid of test points generated by Diva for Rhino to conduct solar Fig. 6. Overview of the three steps followed in this study. The input data are classified as "simulation variables", "UC features", and "boundary conditions". The "boundary conditions" are grouped in turn depending on the source: "TMY weather file" and "weather station in situ".
analyses on the investigated surface was set at 0. 1 m distance from the canyon ceiling (facing downward) with a cell size equal to 1 m by 1 m. The number of FEs on the top surfaceequivalent to Diva for Rhino's number of test points in the Matlab numerical modelwas considered equal to 12 to guarantee an adequate discretization of the results and the correspondence with the grid of test points set for the geometry in Diva for Rhino. When it comes to the other simulation variables, a complete overview of the 'rtrace' parameters employed in Diva for Rhino is shown in Table  3. The ab number was set equal to five to estimate global irradiance composed by diffuse, direct and up to fifth-reflection contributions. This ab-setting guaranteed a good compromise between computational time and result accuracy since the estimated quantities steadily converged towards the most accurate value (which was found after seven ambient bounces with an increment lower than one percent)  [44].
0% uncertainty, which caused an absolute error ranging from �9 W/m 2 (at 9. 00 a. m. ) to �18 W/m 2 (at 3. 00 p. m. ). Regarding the numerical model, the hourly results from the solar analysesconducted considering the same conditions of the experimental facilityare reported in Table  4. The daily peak of the Irr pyr was found at 3. 00 p. m. (the same as in the distribution of the pyranometer's measurements) and equal to 205 W/m 2. The two distributions of the hourly amounts of Irr pyr (Matlab numerical model and pyranometer's measurements) showed the same trend: they are shown overlapping each other in Fig. 8a  by considering the respective uncertainties (around 3. 0% for the numerical model and 9. 0% for output data from the pyranometer). The graph highlights how the simulated Irr pyr amountsand the respective error bars based on the algorithm's uncertaintywere well within the interval defined by the measured solar irradiation and the respective absolute error.
For instance, the Irr pyr assessed by the numerical model at 11. 00 a. m. was equal to 170 � 5 W/m 2 which is consistent with the interval 180 � 7 W/m 2 determined during the monitoring campaign. The analyses of residues calculated from the nominal values highlighted how these values could vary between À 10 W/m 2 and þ10 W/m 2 , with the μ and the σ equal to À 2 W/m 2 and 7 W/ m 2 , respectively. The uncertainty analysis conducted considering the measured and the simulated values reported in Table  4  highlighted that the NMBE and the CV(RMSE) numbers were equal to À 1% and 4%, respectively; while the R 2 is 0. 97. Hence, the developed numerical model is characterized by a high degree of confidence according to the limits presented in ASHRAE Guideline 14 and discussed in sub-section 3. 4. Finally, the Δ % between the Irr pyr estimated by Matlab numerical model throughout the day of the 21st of August and the measured quantity from the experimental facility were investigated.
Inter-software comparison The reliability of the results (Irr out ) carried out by the numerical model is here discussed through a comparison with outcomes assessed by the Radiance-based software Diva for Rhino, for summer (21st of January) and winter (21st of July) conditions. The solar analyses conducted for the 21st of January from 9. 00 a. m. to 4. 00 p. m. showed the minimum overall difference between the hourly outcomes from the two tools. The residues ranged from À 5 W/m 2 (12. 00 p. m. and 2. 00 p. m. ) to 5 W/m 2 (4. 00 p. m. ), with a null value at 9. 00 a. m. (when both Matlab and Diva for Rhino numerical models quantify the Irr out equal to 84 W/m 2 ) (Fig. 9b ). The daily trends of the Irr out are depicted in Fig. 9a : the numerical model estimated the peak (124 W/m 2 ) at 1. 00 p.
m. , while Diva for Rhino software considered it to happen at 12. 00 p. m. (125 W/m 2 ). Moreover, the Δ % among the two series of values ranged from À 4. 0% (12. 00 p. m. , 2. 00 p. m. , and 3. 00 p. m. ) to þ9. 0% (4. 00 p. m. ), while their μ was null and the respective σ equal to 5. 0%. Finally, the Irr out estimated throughout the day was 736 Wh/m 2 according to Matlab numerical model and 745 Wh/m 2 according to Diva for Rhino, with Δ % of around À 1. 0%. On Fig. 10  the fraction of Irr out impinging on each FE (or test point, in Diva for Rhino environment) of the top surface are reported for the three hours (10. 00 a. m. , 1. 00 p. m. , and 4. 00 p. m. ) representing several different moments of the day (early morning, midday, and late afternoon). On this figure, it is possible to observe that the quantities estimated by the numerical model for the FEs which are close to the two façades (ID_FE values are 1, 2, 11, and 12) are the ones that diverged the most from Diva for Rhino's values (residues achieve 65 W/m 2 ).
Conversely, the same trends (monotone decay at 9. 00 a. m. , almost constant at 1. 00 p. m. , and monotone growth at 5. 00 p. m. ) were seen for the remaining elements of the distributions (Fig. 12 ). The comparison between results from the numerical model and the ones from Diva for Rhino permitted verifying the reliability of the proposed script both in summer and in winter conditions. The accuracy of the results and the appropriateness of the Monte Carlo method to simulate solar short-wave reflections events was confirmed at this stage by the low Δ % amounts and residues observed in the analyses of the hourly Irr out as well as of the amounts of solar irradiation impinging on FEs during three representative hours of the day.
Considerations on the uncertainty of the Monte-Carlo based model The double validation process (experimental validation and intersoftware comparison) of outputs given by the Matlab numerical model is here summarized based on the parameters reported in Table  5. The validation process against experimental data showed that the Matlab numerical model was quite accurate when summer boundary conditions were simulated, although it tended to slightly under-predict solar irradiation reflected in the UC (μ calculated for residues equals À 2. 0%) with a low dispersion of the results (σ calculated for residues is 4. 0%). The degree of confidence demonstrated by the numerical model was also confirmed by the evaluated validation indices: the NMBE and the CV(RMSE) numbers, and the R 2 coefficient complied with the acceptability criteria from ASHRAE Guideline 14 (Table  6 ).
When it comes to the inter-software comparison, the solar analyses conducted by the numerical model and the ones from Diva for Rhino tool for summer season highlighted that the former over-estimates the solar  irradiation (μ achieves 8. 0%) with a negligible result's dispersion (σ is 4. 0%). However, when winter conditions were considered, the simulation results from the two tools did not significantly differ (μ is null and σ equals 5. 0%).
Conclusions and future developments An innovative algorithm for conducting full ray-tracing solar analyses in the Matlab environment by implementing a Monte Carlo-based routine has been described and validated in the present research study. The numerical model represents an important step in the enhancement of tools for conducting full ray-tracing solar analyses as well as in the development of RR technologies and their application as urban coatings. Its main strength is that it allows assessing the influence of RR materials on the energy balance of UCs by accessing all the physical variables which regulate reflection events. The high accessibility of the material properties, the full ray-tracing assessment, and the possible introduction in the calculation of other physical phenomena describing energy exchanges make this model more advantageous than other existing tools which present some simplifications (i.
e. considering all the surfaces as diffuse reflectors) or limitations (i. e. evaluating only short-wave irradiation). Furthermore, the developed model includes a function that allows varying the coefficients of reflection (ρ d , ρ s , and ρ rr ) -defining both the typology (Lambertian diffuse, specular, retro-reflection) and the magnitude of the phenomenondepending on the angle of incidence of the sun rays. This function permits to replicate the advanced engineered retro-reflective materials and to create a virtual clone whose properties are defined according to the several protocols found in the Literature  [32] [33] [34] [35]. This guarantees a virtual clone extremely close to reality. The reliability of the Monte Carlo method to simulate short-wave reflections events was demonstrated through a double validation process during which simulation outcomes were compared to data measured in the physical urban canyon model in La Rochelle and processed through an inter-software comparison carried out using the Diva for Rhino tool.
The numerical model was also proven to be coherent with the physical model in La Rochelle (France) within the boundary conditions defined in the present work. As far as the future developments are concerned, the Matlab numerical model can be applied (i) to investigate optical issues (glare) related to the exploitation of high-and retro-reflective materials as surface treatment within the built environment, (ii) to evaluate the potentials of retro-reflective technologies in different climate contexts (with different solar geometries), and (iii) to assess the influences of parameters such as the aspect height-to-width ratio and the surface solar reflectance on the amount of solar irradiation reflected beyond the urban canyon when comparing several scenarios. Moreover, the Matlab numerical model can be further enhanced by (iv) including in the calculation other energy exchange phenomena such as long-wave radiation assessment, convective exchanges, and conduction heat transfer.
Acknowledgments The authors wish to thank the  Norwegian University of Science and Technology (Trondheim, Norway) , the  University of Perugia (Perugia, Italy)  and the  CIRIAF -Interuniversity Research Center on Pollution and Environment "Mauro Felli " for supporting the collaboration between the two universities in this work, framed in the  EU program  for education, training, youth and sport -'ERASMUSþ'. The authors also wish to thank the research group coordinated by professor  Emmanuel Bozonnet  for having agreed to share the data collected during the monitoring campaign in  La Rochelle.
0 [24. 4e41. 1] vs 20. 3 [15. 1e29. 2]; P<0. 0001 in the FA group; 39. 3 [34. 3e43. 4] vs 35. 7 [30. 3e39. 5], P<0. 01 in the LA group; and 41. 5 [35. 6e45. 7] vs 36. 8  [28. 1e42. 7], P<0. 01 in the prolific group). In all groups, women were significantly less followed on ResearchGate than men. In the three groups, the Twitter (22. 7%, 25. 0%, and 23. 6%, respectively) and LinkedIn (59. 2%, 56. 5%, and 62. 3%, respectively) usage rate were similar with no difference between men and women in each group. Of the 260 articles included, 94 (36. 2%) manuscripts had female FA, whereas 41 (15. 8%) had female LA. Conclusion: In anaesthesia, the visibility of female researchers on the social network dedicated to scientific research is lower than that of male researchers.
e. at least seven citations in 2016e2017). 8 he second part was an analysis of the most prolific authors. Among the journals selected in the first part, we focused on the so-called 'prolific' authors over the past 6 yr (i. e. publishing on average more than one article per year: at least seven publications in these journals over the 2013e2018 period). To identify the authors, we queried the Web of Science Database for the considered journals separately using their respective International Standard Serial Number (ISSN). We restricted the results to the period 2013e2018 and to the document types 'article' and 'review'. The lists of most productive authors for each journal were retrieved through the results analysis tool provided by the database. The list obtained only gave the surname and initials of the first name, the full first name was found on the sites of the studied journals.
Statistical analysis The values are presented as number and percentage (n, %) for qualitative variables, and as median (inter-quartile range) for quantitative variables. After performing D'Agosti-noePearson normality tests, we found that the distributions of the data analysed were not normal and the quantitative variables were compared using a ManneWhitney test. The qualitative variables were analysed using a c 2 test or a Fisher's exact test if there were few observations (less than 15 for individual cells). Tests were two-sided, and the significance threshold was set at 0. 05, all statistics were produced using GraphPad PRISM software (v 5. 0; GraphPad Software Inc. , San Jose, CA, USA).