dataesr/scientific-paragraphs-categorization huggingface dataset:
) in space. In the models we used as examples, the initial spatial configurations can be either flat or heterogeneous, monocentric or polycentric, based on external databases and on internal modelling (generation of synthetic population for instance  (Bhat and Koppelman, 1999) ). In order to test the influence of initial spatial conditions on model outputs, we use a systematic method to compare phase diagrams. Following  Gauvin et al. (2009) , we define a phase diagram as the vector of final aggregated model outputs considered as a function of model parameters. We have as many phase diagrams than we have spatial grids, which makes a qualitative visual comparison not realistic (with around 50 different spatial configurations for each model experiment). A solution is to use systematic quantitative procedures to compare them to a reference case.
The outcome of the model is measured by a combination of three segregation indices: Dissimilarity, Moran's I and Entropy  (Brown and Chung, 2006). We use a ad-hoc implementation of the Schelling model, both in Scala for performance reasons and in NetLogo to ensure visualization of model dynamics. The pseudo-code of the implemented model is available in 5 and source code are available on the repository of the project. In general, the implementations of Schelling models allow only one agent per cell, and their initial distribution is random, therefore following a uniform distribution across the modelled city. In this experiment, we allow more than one agent to be in a given cell. The potential density of a cell is defined by the density grid generated. If the potential density of a cell is not reached at initialisation, more agents can move into the cell during the course of the simulation, otherwise it is deemed full and unavailable for movers.
Results The implementations of the models were done from NetLogo. We modified the sugarscape version with wealth of NetLogo model library (to be able to explore it intensively) and we implemented from scratch the Schelling model. Both pseudo-codes are available in 5, and source code for models, grid classification and simulation results analysis is available on the open repository of the project at https://github. com/AnonymousAuthor2/SpaceMatters. Density grids are also available at this address. Simulation data are available for reproducibility on the dataverse repository at https://doi. org/10. 7910/DVN/A0N5GV.
Reproducibility and Applicability Although the applications we present here are limited by the simplicity of the models, we think that the method could (and should) be applied to larger models including domain mechanisms and more empirical initialisation data, for example synthetic populations. The sensitivity analysis to initial spatial conditions could then be either a replication on the spatial allocation of the synthetic population, or a series of spatial permutations of the empirical spatial inputs. We want to foster this extension of our work by releasing the density grids also generated, as well as the generating work-flow and the model implementation. They are available on the open repository of the project at https://github. com/ AnonymousAuthor2/SpaceMatters. Future work could be done to compare these or generate grids with a larger morphological span, covering other typical urban forms that can be found in the world.
We think that this method has the potential to increase the arsenal of evaluation of geographical models, in order to assess the sensitivity of models to their initial spatial conditions but also to learn about the impact of the urban form on social mechanisms. SpaceMatters. The pseudo-code is in the style of NetLogo code, which is already easily readable. ; e a t t h e s u g a r on t h e patch s e t s u g a r ( s u g a rmetabolism + p s u g a r ) s e t p s u g a r 0.
Acknowledgements The authors acknowledge the funding of their institutions and the  EPSRC  project number  EP/M023583/1. Results obtained in this paper were computed on the vo. complex-system. euvirtual organization of the  European Grid Infrastructure  ( http://www. egi. eu ). We thank the  European Grid Infrastructure  and its supporting  National Grid Initiatives (France-Grilles in particular)  for providing the technical support and infrastructure.
Moreover, in case of a structured population, in accordance with the achieved accuracy, EthAcc showed that the biggest training set is not always better than a smaller and closer training set. We then performed training set optimization with EthAcc and compared it to CDmean. EthAcc outperformed CDmean on real datasets from sugar beet, maize, and wheat. Nonetheless, its performance was mainly due to the use of an optimal but inaccessible set as a start of the optimization algorithm. EthAcc's precision and algorithm issues prevent it from reaching a good training set with a random start. Despite this drawback, we demonstrated that a substantial gain in accuracy can be obtained by performing training set optimization.
Materials and methods The genomic prediction of the genetic value of test individuals was based on GBLUP  [7] , and we define the accuracy of genomic prediction as Pearson's correlation between its phenotype and its BLUP-value for a random test individual and a given training set. We refer to this correlation as the accuracy in the text below. This accuracy is a theoretical quantity that is usually estimated using the mean of empirical Pearson's correlation between phenotypic values and their GBLUP for a random test set. In statistics, this estimate is called an empirical estimate or sample estimate, but we call it the test set (TS) accuracy in the analysis that follows. This TS accuracy can be calculated only if phenotypes are observed in test individuals, which is the case when the TS is randomly drawn within a dataset or obtained by a simulation process.
The panel optimization burden was dealt with by the hill-climbing algorithm with exchange moves that was proposed in  [23]. We did not implement a stopping criterion to always perform a given number of exchange moves. The number of exchange moves was set to 5000. The starting training set of the hill-climbing algorithm for both criteria was the optimal training set found by maximization of the TS accuracy, i. e. , we found the training set that gave a maximum for the mean correlation between GBLUP and the observed phenotypes of the test individuals. This maximization of the TS accuracy was possible because the TS was randomly drawn within the whole dataset, and therefore test individuals had phenotypes. Having this set as a start of the algorithm prevents it from getting stuck in a local maximum that is far from the maximum of the TS accuracy.
Simulation implementations Method comparisons were made by randomly drawing a TS within the whole panel. Unless precise, 20% of the panel served as a TS and the remaining individuals were the candidates or the largest training set when we did not perform training set optimization. To ensure accurate comparisons, the TS accuracy and the accuracy estimates were computed on the same TS. The results were obtained for 30 random TS draws. Predicted genetic values of TS individuals were obtained by estimating SNP effects with the training set by means of the mixed. solve function of R package rrBLUP https://cran. r-project. org/web/packages/rrBLUP/index. html. This function was also applied to compute CD, PEV, and CDmean via our own R code. The R code for computing EthAcc (from a training set that is genotyped and phenotyped and a test set that is only genotyped) is given in S1 File.
We used our own R code, which was rewritten based on Segura's code  [28]  and that is now available on CRAN https://cran. rproject. org/web/packages/mlmm. gwas/index. html, to implement the forward selection MLMM approach. The glmnet R package https://cran. r-project. org/web/packages/glmnet/ index. html was used to perform localization of QTLs by penalized regression methods. The parcor R package https://cran. r-project. org/web/packages/parcor/index. html with the parameter of cross-validation set to 5 was used to perform adaptive LASSO. To avoid computation issues, SNP filtration on minor allele frequency (MAF) was carried out. All code can be provided on request.
Plant material Maize data has been downloaded from http://www. genetics. org/content/suppl/2012/08/03/ genetics. 112. 141473. DC1. Sugar beet, sunflower and wheat data are available at https://lipmbrowsers. toulouse. inra. fr/pub/Mangin2018-PlosOne/ Sugar beet. A panel of 2101 elite lines of diploid sugar beet (Beta vulgaris L. ), which resulted from many different crosses in Florimond Desprez's breeding program, was analyzed. Testcross progenies of these lines were evaluated in unbalanced multienvironment trials for seven traits: the white sugar yield (WSY, t/ha), sugar content (S, %), white sugar content (WS, %), the root yield (RY, t/ha), potassium content (K, meq/100 g), sodium content (Na, meq/100 g), and α-amino nitrogen content (N, meq/100 g). The sugar beet panel was fingerprinted with 836 SNP markers, but the panel was analyzed with 692 SNPs after filtration for MAF and missing data (see details in S1 File).
The structure of subpopulations in this panel was then studied. We applied hierarchical clustering to principal components using the Facto-MineR package https://cran. r-project. org/web/packages/FactoMineR/index. html [35]  in R software to assign each individual to a subpopulation after principal component analysis (PCA). The HCPC function of the FactoMineR package implements this calculation after having constructed the hierarchy and suggests an optimal level for division. Sunflower. Sunflower data covered hybrids obtained by crossing 36 restorer of CMS PET1 male sterility lines serving as males to 36 maintainer lines as females in an incomplete factorial as described elsewhere  [36]. Hybrid genotyping data were obtained from the wholegenome sequencing of their parents. A total of 468 194 nonredundant SNPs passed the different filters  [36]  and were used to compute their kinships.
Adaptive LASSO was ranked after penalized regressions that based their choice for the LASSO skrinkage parameter on the criterion of minimum MSE plus one standard error; however, MLMM was the best performer. Accordingly, the theoretical accuracy with QTL locations obtained by MLMM was named EthAcc for Estimated THeoretical ACCuracy. We proposed R code to compute EthAcc that does not involve Henderson's mixed model equations, as opposed to  [23]  and  [33] , but made all the calculations based on the ridge regression matrix H ¼ X train X 0 train þ s 2 ε s 2 b I � � , where X train is a matrix of genomic information of training individuals. The reason is the necessity to have an invertible kinship matrix to use Henderson's equations, which is not the case for VanRaden's kinship matrix. When not invertible, the kinship matrix is generally projected on the cone of positive-definite matrices which create computation approximations.
MLMM is a long CPU software application, especially with a high number of markers but it could be replaced by the best penalized regression which is much faster and therefore allows much more moves. Nevertheless, the crucial point is the precision of EthAcc. Indeed, a MSE of 10 -3 on average is not a guarantee of having a close estimate of the accuracy. During the optimization process, some training sets were mostly overestimated then yielding false positive training sets. The precision of the theoretical accuracy-with known causal-QTL locations and effects-was shown to be much higher  [20, 27]. This finding suggests that the QTL localization should be improved. Adding functional information such as candidate genes, metabolic networks, and transcription factors may help to explore various ways to facilitate the QTL localization by limiting the search to fragments of the genome that are known to be involved in the trait of interest.
Funding: BM, LM, EG-D have been supported by the  French National Research Agency  (ANR: https://www. agence-nationale-recherche. fr/) as part of the  "Investissements d'Avenir" Program :  AKER  project  ANR-11-BTBR-0007 ,  AMAIZING  project  ANR-10-BTBR-03 , and  SUNRISE  project  ANR-11-BTBR-0005. The Florimond  Desprez Veuve & Fils  company provided support in the form of a salary for EG-D. The specific roles of EG-D are articulated in the 'author contributions' section. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The explored solution is a lesser important use of the transcription and an emphasis on student's activity indicators. Currently, post-session access to course content is limited to an export of the text posted, generated and typed. The video recorded can also be watched separately. We plan to develop a browsing system allowing students to retrieve all this content on a single interface, synchronized together. Setting the replay to a particular point in time would display the corresponding pieces of transcription and resources. Due to the large amount of content, readability and browsing can become a tedious task; a thematic segmentation system will be implemented in order to divide the session into smaller chapters, easier to label and to browse individually. As for the teacher, post-session use of traces and generated content is for now limited to the display of the contents of the Moodle database.
In its native range it is beloved by environmentalists. In its introduced range, it is the focus of environmental action by citizen scientists. The USA Pacific coast, for instance, is commonly patrolled by volunteers called "Spartina Watchers" participating in early-detection activities to discover new non-native Spartina populations. This has been repeatedly acknowledged at the "International Spartina Accepted Article. Conference", attended by professional scientists and environmental managers, and held regularly since 1990 in different countries to discuss the problems associated with this plant group, its increasing global distribution, the impacts on human society, and possible solutions. While databases such as GenBank (https://www. ncbi. nlm. nih. gov/genbank/) and Tropicos (https://www.
Accepted Article. were peer reviewed and published by respected journals focused on plant systematics. Similarly, the "New Flora of the British Isles" published in 2019 retains the name Spartina after considering the case  (Stace 2019). It is of no small concern that globally popular (digital open access websites) databases are likely to induce the acceptance of replacing Spartina for Sporobolus, while potentially restraining any further nomenclatural discussions and sound open debate. Spartina is a deeply-nested, strongly supported clade  (Peterson et al. 2014a,b). Peterson et al. (2014b)  proposed retaining Spartina as a subgenus of Sporobolus ("A molecular phylogeny and new subgeneric classification. " (title of paper) and "A proposed classification of the Sporobolinae and subgeneric classification of Sporobolus" (Table  2 legend) ).
Acknowledgments We are grateful to the many colleagues, students and eight anonymous expert taxonomists from  Argentina, USA, Spain, UK  and  Uruguay  for sharing their opinions, perspectives and ideas, improving our reasoning and encouraging us to initiate this debate. The authors' positions are personal, and do not necessarily reflect the organizations or networks they represent or with which they are affiliated. We are also deeply grateful to two anonymous reviewers as well as to the Editor-in-Chief who supplied excellent insight that truly improved our work.
ABSTRACT We present here a software program dedicated to the fitting of experimental dose-response data, which integrates seamlessly with Excel and allows curve fitting plots and results to reside alongside data within Excel spreadsheets. The program, named eeFit, for Excel-Embedded Fitting software, requires no advanced knowledge of Excel or nonlinear leastsquares fitting. Any experimental data present in an Excel file, such as dose-effect data obtained with membrane receptor or ion channel ligands, can be graphed and fitted interactively with standard Hill models for activation or inhibition, or with more complex models for biphasic effects resulting from combinations of activation and inhibition. When benchmarked against the commercial program Origin, eeFit yielded equivalent or better results, in terms of accuracy and convergence, and proved much easier to learn and use.
METHOD SUMMARY The program eeFit is an Excel add-in designed to fit Hill models to experimental dose-response data using nonlinear least-squares regression by means of the Solver add-in. It provides most features of commercial software but is simpler to operate because of its advanced user interface and its complete integration within Excel. A large part of biological and medical research relies on the measurement of the effects of molecules on biological systems and on the quantification of these effects using receptor-ligand mathematical models (e. g. ,  [1] [2] [3] [4] [5] [6] [7] [8] ). These models are generally formulated using nonlinear Hill equations  [9]. Fitting of Hill equations to experimental doseresponse data to extract descriptors, such as dissociation constant and Hill coefficient, requires nonlinear least-squares analysis  [10, 11].
This is usually achieved using dedicated commercial programs, such as Origin (OriginLab Corp. ), SigmaPlot (Systat Software, Inc. ) or GraphPad Prism (GraphPad Software). These programs, beside their costs, aim to cater to the needs of a wide range of users, leading to an inflation of features and higher complexity. They cannot provide the same level of comfort as a program dedicated to a specific problem such as dose-response fitting. Here, we present the software program eeFit that is dedicated to doseresponse fitting and provides most features of commercial programs while being seamlessly integrated within the Microsoft Excel spreadsheet software, which is a popular environment for storing, visualizing and analyzing data. Using Excel for regression analysis of dose-response data has been described previously in several articles.
Most articles are restricted to more-or-less detailed instructions for using Solver  [12] [13] [14] [15] [16] [17]. A few publications describe programs to perform curve fitting in Excel. Gadagkar and Call  [18]  introduced two tools: an Excel template for Hill equation fitting that resembles eeFit in spirit but offers only a rudimentary user interface, and the stand-alone program HEPB, which is not integrated with Excel. Another short report  [19]  presented a similar template-based tool. Zhang et al. [20]  described the add-in PKSolver written for earlier versions of Excel, which has some design similarities with eeFit but is limited to pharmacokinetic models and is less integrated in Excel. Overall, eeFit constitutes significant progress over other freely-available Excel tools. It should be noted that a more complex tool, Dr-Fit, is also available  [21].
Nonlinear regression analysis Excel incorporates a nonlinear regression analysis engine in the form of the Solver add-in  [22]. Solver is a powerful program that has been validated extensively. Using Solver is, however, cumbersome for simple tasks  [13]  and reserved for the experienced Excel user for more advanced goals  [17]. eeFit shields the user from the complexities of Solver by wrapping it under a user-friendly interface that allows rapid use by the novice user as well as full control of operation by the expert. Solver operation is simple: it is given a range of cells, ByChange cells, containing the initial values of the parameters, and a cell quantifying goodness-of-fit, the Target cell. It then tries to reduce the value in the Target cell by iteratively varying ByChange cells until a minimum is reached.
Calculation of standard errors of best-fit values After a fitting run, standard errors of the parameters may be calculated using the curvature matrix method  [16]  and implemented in eeFit using an algorithm described by Bilo  [15]. This method for determining standard errors is really truly appropriate for linear models and is used for nonlinear models by linearization around the best-fit values  [15, 17]. The pitfalls of this method have been documented and the values obtained are likely to underestimate the real variance of parameters  [17]. Nonetheless, these values are provided as these are the only values reported by most commercial packages. They should be used with caution especially if the dataset is small. More realistic values can be obtained using repetitive fitting of the model with constraints imposed on each parameter  [17] , jackknife method  [16] , and so on.
Validation & persistence of fitting sessions Whenever the user exits a fitting session, the program stores the full configuration of that session in the worksheet where it resides. This enables the user to re-enter the session at the point where it was left. Some details of the session (model and weights used for fitting, addresses of original data, names and units of data, graph scaling, etc. ) are stored as invisible metadata in the worksheet properties. The remaining details (X-Y-Error experimental data, parameter values) are visible, and are stored in cells within the area occupied by session. These cells, as well as plots, are not protected so that the user retains the freedom to edit them. Similarly, a session can be moved by inserting or removing rows to the left or columns above. One drawback of that freedom is that the format of the session can be altered and cause program malfunction if rows and columns are added or removed within the session area.
Graphs If the user opts for a linear scale for the X axis, all data points are shown in plot. If a logarithmic scale is used for the X axis, experimental data points having negative or zero X values are automatically excluded from the plot to avoid recurring Excel error messages. To create a model curve for visual assessment of the validity of the fit, X values are incremented linearly for a linear scale, and logarithmically for a logarithmic scale, so that points are always equally spaced when plotted. The increments are calculated so that the model curve spans the range of experimental X values and uses the number of points (typically 50) specified in the program preferences.
Comparison with a commercial software In order to test the validity and performance of eeFit, representative datasets corresponding to all possible monophasic and biphasic dose-response scenarios were analyzed with eeFit and with the software suite Origin Pro, version 2017 (OriginLab). The data, fits and full summary are shown in supplementary file 'SI_eeFit vs Origin. xlsx'. The datasets used are experimental data on ion channels obtained in our laboratory  [23] , as well as simulated data with noise added. eeFit and Origin were in their default configurations, without changes in the Options or Preferences. All fitting runs with eeFit used, as initial values for the parameters, the estimates calculated automatically for each model from the data set to analyze. Except for Tests A and C, fitting runs with Origin did not converge unless initial values close to the best-fit values were entered manually.
Limitations The software imposes that the user inspects the data and chooses a model that looks reasonable. Although it would not be a hard task to automate model selection, interactivity is a conscious choice to force the Resume fitting of selected session using the previous model and parameters. Remove selected session by deleting all graphs, all columns spanning the session area, and all associated underlying metadata. Start fitting data in selected session using one the models available. In case of erratic behavior, use this to try to restore normal operation. Work can be resumed without data loss where it was left off. Duplicate current worksheet and all eeFit metadata associated with existing fitting sessions Open Preferences form to adjust default program, display, graphics and fitting options.