dataesr/scientific-paragraphs-categorization huggingface dataset:
We could not directly perform multivariate analyses, as the sensiPhy package 215 cannot deal with multivariate data, and as there is currently no satisfactory method for multivariate PGLS -there are some methods for highly correlated variables that are landmark coordinates, but as yet not for putatively independent ones (e. g. , Adams 216 and Clavel and Morlon 217 ). Therefore, we performed a PGLS regression for each semicircular canal separately (i. e. , natural logarithms of semicircular canal radius against cochlea size), and PGLS residuals were then used as the basis for further interpretation. It should be noted that these residuals are not phylogeny-corrected residuals; they are simply more appropriate to use than residuals derived from OLS regressions. 218  Moreover, subsequent tests on regression residuals are not recommended since they can give erroneous results.
219, 220  Hence, we took an appropriately cautious approach, applying descriptive treatments: we performed discriminant analyses taking into account phylogeny ('phylogenetic flexible discriminant analysis' or pFDA 122 ) or not (traditional linear discriminant analysis, LDA, using the package MASS 221 ) to examine first whether the link between canal and cochlea size proposed by Davies et al. 63  depends on phylogeny (see Data S1I), and second in which group Vielasia sigei falls in each case (see Data S1J). To determine whether Vielasia could be assigned to a particular hearing ability group (same groups as the basilar membrane length vs body mass relationship), we specified the groups as the discriminant factor, Vielasia as the ''test'' group, and all other extant species as the ''training'' set of the discriminant analysis (as their membership of a particular group is already known).
We repeated these analyses for all the phylogenies we created, and we reported average coordinates, reflecting the coordinate variation for each species, for each analysis (Figures 3D and S3B). Numerous R packages allow for performing PGLS regression; here, we chose to use the pgls function of the package caper. 222  The pFDA function (to perform the eponym analysis), as written by  Motani and Schmitz, 122  takes phylogeny into consideration by modifying the inputted phylogeny by transforming its branch lengths (and therefore its variance-covariance matrix) to account for phylogenetic autocorrelation using Pagel's lambda. Motani and Schmitz 122 recommend estimating the lambda parameter from the data, and to provide this estimate of lambda to the pFDA function. However, as well as values of Pagel's lambda, the pgls function of the package caper estimates (using maximum likelihood) values for two other models: Pagel's kappa and delta (see for instance the vignette of the package caper 222 ).
e. , for each regression) or globally (i. e. , for all regressions) optimal parameters, and comparing 2) the parameters value and 95% confidence interval of each PGLS regression with the ''globally optimal'' parameter values. The function can also output the residuals for each PGLS with the ''globally optimal'' parameter values. We then wrote two functions to repeatedly perform pFDA and LDA (pfda_LSchmitz and rep_lda respectively 123 ) using the ''globally optimal'' Pagel's parameter values (and the residuals arising from PGLS regressions using them) for all phylogenetic trees considered. These functions output, for each iteration (= phylogeny), a confusion matrix (a matrix of true and predicted attributions for the training dataset) and various results regarding the training and test datasets (i.
e. , predicted class, probability for each class, discriminant axes scores). To help visualize the variation between all calculated pFDAs and LDAs (i. e. , depending on phylogenies) for each species' score on discriminant axes, we also plotted the morphospaces of each species' points, using both all points of each species ('full' morphospaces) and the 95% points closest to the 'full' morphospace centroid ('95%' morphospaces; i. e. , points below the 95% quantile of Euclidean distance to the full morphospace centroid, the latter being calculated using the sf package 223 ). This procedure has been wrapped in a custom function named plots. variation, 123 taking into account data points (and morphospaces if already computed and saved), average positions, analyses names, axes information (number, percentage of contribution), ecological groups (and colors) if applicable, and options to return the computed morphospaces and to output the produced figures.
3D geometric morphometric analysis We included the composite 3D cranial and mandibular models of Vielasia sigei in the 3D geometric morphometric dataset of  Arbour et al. , 65 ESM3  capturing shape of bat crania (with 36 landmarks and 5 curves represented by equidistant semi-landmarks) and mandibles (with 20 landmarks and 4 curves). Using the same protocol, we collected these data from Vielasia sigei with  Landmark 3. 6 114 (Data S1N and S1O). This allowed us to compare Vielasia with 202 extant bat species for the cranium and 191 species for the mandible, using the dietary and echolocation emission categories assembled by Arbour et al. 65  (Data S1P). Processing of the cranium and mandible landmark data (i. e. , inputting the data, flipping the data when the left or right landmark was missing, estimating missing data, performing the generalized Procrustes alignment, averaging Procrustes coordinates for each species, and averaging paired landmarks) was performed in R 4.
1. 1, 121  following the approach of Arbour et al. ,  65  and was implemented as a custom R function (Arbour_et_al_treatment; available at  Maugoust et al. 123  ); this function uses functions from the packages geomorph  224, 225  and LOST. 226  We then performed phylogenetic Principal Component Analyses (pPCA) on these aligned datasets using the package phytools. In order to examine variation in pPCA results implied by phylogenetic variation (in terms of branch lengths), pPCAs were performed for all 1103 trees corresponding to the Shi and Rabosky 83 phylogeny (see ''phylogenetic framework'' above), and so there were a total of 1103 pPCAs for the cranial and the mandibular datasets. To reproduce Arbour et al. 's 65 approach as closely as possible (and so ensure meaningful comparison between our results and theirs), we did not use the set of phylogenies corresponding to the later phylogeny of Amador et al.
QUANTIFICATION AND STATISTICAL ANALYSIS The statistical treatment of morphometric data was performed using R 4. 1. 1 121 and PAST 4. 05 119 (see key resources table ). It can be re-performed using the source data (also available in Data S1G, S1K, S1M-S1P), with the R script and the entire results of its execution (a RData file) being available at. 123  The R script contains original R functions written for our analyses (as detailed above) and existing R functions (with minor modification) to perform phylogenetic flexible discriminant analysis (pFDA 122 ; retrieved from the Github page of Lars Schmitz). The phylogenetic analyses were carried out in TNT 1. 5 118 (maximum parsimony analyses of the morphological matrices) and MrBayes 3. 2. 7a 77 (undated and dated Bayesian analysis of the total evidence matrix), with Templeton tests of alternative topologies and mapping of synapomorphies in PAUP* 4.
Vielasia sigei holotype and paratypes This paper; Legendre et al. 50  ; Sig e 58  UM-VIE-250-255, UM-VIE-614, 669-677, UM-VIE-02, 17, 19, 20   Vielasia sigei 3D models of cranium and bony labyrinth and the trees output by analyses of these matrices, are available on a public GitHub repository: https://github. com/ jacobmaugoust/vielasia-sigei_hand-et-al and as a Zenodo DOI: https://doi. org/10. 5281/zenodo. 8176111; all results, trees, and statistics are included in supplemental information (Data S1, S2, and FiguresS1-S5). d All original code has been deposited on a public Github repository: https://github. com/jacobmaugoust/ vielasia-sigei_hand-et-al and is available as a Zenodo DOI: https://doi. org/10. 5281/zenodo. 8176111 as of the date of publication. DOIs are listed in the key resources table.
Experiments are essential to understand the behaviour and performance of energy storage systems. In this field, a considerable amount of experimental data is generated and data processing is a tedious task. To date, research teams working in the field of energy storage tend to focus on developing their own analysis tools rather than using existing open source software. This strategy can be detrimental to the quality and reproducibility of the research. This paper presents DATTES, a free and open source software for analysing experimental battery data. The software provides a comprehensive and customizable toolkit for extracting, analysing and visualizing experimental data. It also creates gateways to other open software and tools. In this way, DATTES enables users to get the most out of their experimental data and engage in open and reproducible science.
Motivation and significance Data processing plays an increasingly important role in scientific studies. Software such as MATLAB, Python, or R is at the heart of this activity, and according to a study by  Hettrick et al. , 70 % of scientists acknowledge that their research would not be possible without it  [1]. This activity is also time-consuming for scientists, who typically spend 30 percent or more of their research time on it. In the field of energy storage, a wide variety of tools and methodologies are used to generate experimental data. As a result, the experimental data produced is difficult to compare. In addition, most research teams have developed their own data processing tools adapted to their specific needs and data sets. Although this organisation can be explained by the need of researchers to quickly process the data of their projects, such a development strategy limits the reproducibility of the work and tends to significantly increase the time spent by researchers on programming.
Furthermore, since the vast majority of researchers are self-taught developers, they may lack experience with good software development practices. As a result, the quality of the research may be compromised. Comparison between studies from two different laboratories is also hampered because each team is likely to use its own methodology for generating and processing data. This lack of common data processing tools is a major barrier to sharing and comparing experimental results  [2]. Open source software has recently gained increasing interest in the field as it allows for the sharing of programming efforts, best practices, and analysis methodologies. In the current landscape of open source software for energy storage systems, Python dominates. BEEP, cellpy, impedance. py or Pybamm are some examples of popular software in the field of batteries  [3, 4, 5].
They are all written in Python. This monopoly can be explained by the qualities of this versatile programming language. Python is free and open source, it emphasizes the readability of the code and it comes with a very complete library of tools for data analysis, such as Scipy, Numpy or Matplotlib. However, although this language is slowly gaining interest in the energy storage community, MATLAB remains by far the most popular language. For example, Google Scholar has indexed more than 200,000 publications that mention the keywords "battery" and "MATLAB". This is three times more than for "Python". MATLAB's popularity is due to the variety of applications it offers. The use of matrices and arrays makes it easy to use in scientific problem solving. Finally, MATLAB is the basis for Simulink, a block diagram environment for modeling, simulation, and analysis of multiphysics dynamic systems.
dattes structure The second step is to enrich the experimental data with metadata and to structure the information. Metadata allow the enrichment of databases, for example during ageing campaigns, with data about each experiment (experimenter, equipment used, etc. ) or about the battery (nominal characteristics, cell identifier, etc. ). During this step, the function dattes structure allows to divide the experiment into characteristic phases according to the different modes of battery stress: rest, constant current (CC), constant voltage (CV), etc. This function also makes it possible to determine the state of charge (SoC) of the battery at any moment of the experiment.
Illustrative Examples This section illustrates the DATTES workflow over a sample experimental dataset. The code corresponding to this example is available by running the function demo dattes. Data in this dataset correspond to a characterisation experiment described in preceding works  [7, 8, 9]. The experimental raw data is output from a Biologic cycler. The function dattes import allows to convert these raw data into a standard format, i. e. XML. Then the function dattes structure allows to extract the characteristic values of the battery (time, current, voltage, etc. ) and to structure the experiment in phases depending on the cycler mode (CC, CV, rest, etc. ). At this level, metadata can be included in the experimental data using JSON files (*. meta extension). Figure  1  shows each phase number during the experiment from the previously identified modes.
Impact While DATTES stands out as the first MATLAB software to support the entire data processing chain for battery experiments. Some of its features are also unique. The number and variety of input data formats that DATTES can handle is very large. Currently, data produced by the Arbin, Bitrode, Biologic, Digatron, and Neware cyclers are supported by DATTES, and other data formats can be easily adapted. DATTES also provides conversion of raw experimental data into a standard format, XML, following the structure defined in the VEHLIB library. DATTES is thus natively compatible with the energy management software VEHLIB  [6]. To date and at our best knowledge, DATTES is the open source experimental data processing software that offers the largest number of analysis tools. One of the reasons for this ability to handle different types of experiments is the method of experiment segmentation according to battery usage phases.
This facilitates the analysis of patterns suitable for each type of analysis (e. g. impedance, pseudo-OCV or ICA, etc. ). Special attention has also been paid to data traceability. Raw data are first structured and enriched with metadata using *. meta files (dattes structure), e. g. nominal cell properties, experiment date/time/equipment, etc. ). After that, each step of the DATTES workflow (dattes configure, dattes analyse) is traceable. The result delivered by DATTES contains information about each analysis method (current levels, phase selection conditions, signal filtering parameters, etc. ). The DATTES result structure contains raw experiment profiles (date, time, current, voltage, etc. ), metadata, analysis results, and analysis configuration. This data structure is fully documented  [10].
Finally, DATTES facilitates the reuse of the analysis data produced. The function dattes export allows to convert the analysis results into different formats such as *. csv, *. json or *. m. This software has been developed according to Free and Open Source Software (FOSS) and FAIR  [11]  principles, and considerable effort has been made to write clear and complete documentation. The developers hope that the energy storage community will take advantage of the software, as new users are seen as potential reviewers who can catch bugs, ask for new features, and accelerate the rise of computing standards. Finally, DATTES is designed to be modular and easily coupled with other existing software. It can enable the energy storage community to design better batteries, build robust models, and make more accurate predictions with standardized, peer-reviewed methodologies.
Hocking et al. [2017]  described a graph framework to encode many examples of such prior information and a generic algorithm to infer the optimal model parameters, but implemented the algorithm for just a single scenario. We present the gfpop package that implements the algorithm in a generic manner in R/C++. gfpop works for a user-defined graph that can encode the prior information of the types of change and implements several loss functions (Gauss, Poisson, Binomial, Biweight and Huber). We then illustrate the use of gfpop on isotonic simulations and several applications in biology. For a number of graphs the algorithm runs in a matter of seconds or minutes for 10 5 datapoints.
Contribution Hocking et al. [2017]  described a graph-framework to encode many prior constraints on how parameters change at each changepoint, and a generic algorithm to infer the optimal model parameters. However, they implemented the algorithm in a single scenario: where the mean alternately increases then decreases and a Poisson loss. The gfpop package implements their algorithm in a generic manner in R/C++, and works for a user-defined graph and implements several loss functions. The constraints in gfpop are modelled by a graph. The graph can be viewed as a representation of the transition kernel of a specific continuous-state Hidden Markov Model (HMM). At each time t the signal can be in a number of states. These states are the nodes of the graph. Possible transitions between states at time t and t+1 are represented by the edges of the graph.
Outline In Section 2 we formally define the graphs and explain their connection to HMM. We also provide numerous graph examples to illustrate the versatility of our framework. In Section 3 we present the optimization problem solved by our package. In Section 4 we go through the main functions of the package. We illustrate in Section 5 the use of our package on various real datasets. Finally, using simulations we compare in Section 6 the result of our package with those of standard isotonic packages and show the benefit of using robust losses and penalising the number of segments.
The gfpop package 4. 1 Graph construction Our gfpop package deals with collapsed graphs for which all the cost functions γ have the same decomposition (L2, Lin-log or Log-log). All other characteristics are local and fixed per edge. The graph G n (see Subsection 2. 2) is defined in the gfpop package by a collection of edges. Edge parameters. An edge is a list of four main elements: • state1 : the starting node defined by a string; • state2 : the arrival node defined by a string; • type : a string equal to "null", "std", "up", "down" or "abs" defining the type of constraints between successive nodes respectively corresponding to indicators I µt=µ t+1 , I µt =µ t+1 , I µt+c≤µ t+1 , I µt≥µ t+1 +c and I |µ t+1 -µt|≥c ; • penalty : the penalty β e associated to this edge (it can be zero); and some optional elements: • decay : a number between 0 and 1 for the mean exponential decay (in case type is "null") corresponding to the constraint I µ t+1 =αµt ; • gap : the gap c between successive means of the "up", "down" and "abs" types; • K : the threshold for the Biweight and Huber losses (K > 0); • a : the slope for the Huber robust loss (a ≥ 0).
An example of edge. We can define an edge e1 with the function Edge as: e1 <-Edge ( state1 = " Dw " , state2 = " Up " , type = " up " , penalty = 10 , gap = 0. 5) which is an edge from node Dw to node Up with an up constraint, penalty β = 10 and a minimal jump size of 0. 5. An example of graph. We provide an example of graph for collective anomalies detection with the gfpop package given in Figure  24  (see  Fisch et al. [2018] ): > graph ( + Edge ( state1 = " mu0 " , state2 = " mu0 " , penalty = 0 , K = 3) , + Edge ( state1 = " mu0 " , state2 = " Coll " , penalty = 10 , type = " std " ) , + Edge ( state1 = " Coll " , state2 = " Coll " , penalty = 0) , + Edge ( state1 = " Coll " , state2 = " mu0 " , penalty = 0 , type = " std " , K = 3) , + StartEnd ( start = " mu0 " , end = c ( " mu0 " , " Coll " ) ) , + Node ( state = " mu0 " , min = 0 , max = 0) Notice that the graph is encoded into a data-frame.
Gaussian nine-state model for electrocardiogram data In the context of monitoring hospital patients with heart problems, electrocardiogram (ECG) analysis is one of the most common non-invasive techniques for diagnosing several heart ar-rhythmia  [Afghah et al. , 2015]. A preliminary and fundamental step in ECG analysis is the detection of the QRS complex that leads to detecting the heartbeat and classifying the rhythms  Mousavi and Afghah [2019]. Here, we utilize the proposed change-point detection method to locate the QRS complex in ECG waveforms. The ECG signals used in this study are extracted from the publicly available Physionet Challenge 2015 database PhysioNet [2015], Clifford et al. [2016] that includes measurements for three physiological signals (including ECG) for 750 patients. The resolution and frequency of each signal are 12bit and 250 Hz, respectively.
Utility of penalised isotonic regression models Our package can be used with robust loss functions which have been shown to be useful in the presence of outliers  [Fearnhead and Rigaill, 2018]  and in particular in the context of isotonic regression  [Bach, 2018]. Here we illustrate this on simulations inspired by those of  Bach [2018]  (see Figure  15  with corrupted data). We compare our package using the isotonic model described in Figure  5  with several implementations of the PAVA algorithm  [Best and Chakravarti, 1990, Mair et al. , 2009]. Relative to the very fast O(n) PAVA, our dynamic programming algorithm is slower. However, PAVA only works for the square loss and the non-penalised model (maximum number of changes). In contrast, gfpop can handle non-convex losses (such as the biweight loss) and can include a positive penalty in order to reduce the number of changes.
Parametrisation gfpop. In all simulations we used gfpop with the graph of Figure  5  and a quadratic (l2) or a biweight loss (bw). We considered two different values for the penalty β: 0 and 2σ 2 log(n), with σ 2 the true variance. Thus, we have 4 different algorithms of the gfpop function: gfpop1 (β = 0, K = 0), gfpop2 (β = 2σ 2 log(n), K = 0), gfpop3 (β = 0, K = 3σ) and gfpop4 (β = 2σ 2 log(n), K = 3σ). Competitors. We compared the output of gfpop with those of 2 isotonic regression package functions: • isoreg function of the stats package which is based on the very fast Pool adjacent violators algorithm for the 2 loss [Best and  Chakravarti, 1990 ]; • reg 1d function developed in package UniIsoRegression which solves the isotonic regression problem for the 2 and 1 losses  [Stout, 2008].
A simple illustration We begin by a simple illustration on a step-wise increasing signal with corrupted data. In Figure  16 , we represent the data and the results of various approaches. We see that using a biweight loss our package in blue is closer to the true signal in black than other approaches. In next Subsections we considered Monte-Carlo simulations to confirm this result: see Subsection 6. 4 for the linear increasing scenario and Subsection 6. 5 for the step-wise increasing scenario. The R code of these simulations can be found on the Github page https://github. com/vrunge/gfpop_Rsimu.
Thus, the implementation of a training system for learners, combining knowledge in cognitive neuroscience, with the presentation of appropriate tools, may make it possible on the one hand to modify this attitude of procrastination toward academic work as well as in the organization of daily life, and could on the other hand have an effect on the executive function of planning. In a previous study, NeuroStratE  (Cherrier et al. , 2020)  was implemented with secondary school students. The results indicated no improvement in school results but better self-knowledge. In the current study we tested the part of the program which related to procrastination in university students. The present study aims to assess the effect of an intervention which focuses on teaching undergraduate students the functioning of the brain and providing practical tools  (NeuroStratE, Cherrier et al.