dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis For each experimental series, data are presented as means ± standard deviation. For data from longitudinal follow-up of in vivo imaging, each animal is its own control, and values were normalized to the animal's baseline values. Statistical analysis was performed with GraphPad Prism 9. 2. 0 (GraphPad Software, San Diego, CA, USA). Statistical significance (p < 0. 05) for each variable was estimated by one-way or twoway ANOVA when group variances were equal (Bartlett test); if not the non-parametric Kruskall-Wallis test, and the Holm multiple comparisons test was used to execute simultaneous t-tests.
Acknowledgements We are grateful to  Corinne Lesaffre  from the  PARCC Histology Core Facility ,  Florence Marliot  from the  HEGP Histology Core Facility ,  Dr Hana Manceau  and  Nicolas Sorhaindo  from the  Bichat Biochemistry Core Facility , to  Maryline Favier  and  Rachel Onifarasoaniaina  from the  Cochin Histology Core Facility , and to  Omar Zenteno  for Supplementary Fig. 7a. In vivo imaging was performed at the  Imaging Facility (Plateforme d'Imageries du Vivant) of the University Paris Cité  supported by  France Life Imaging  ( ANR-11INBS-0006 ), by  Infrastructures Biologie-Santé (IBiSa) , by Aviesan grants # ASC20001SSP ,  ASC16025KSA  and  ASC20031KSA , and by the  Région Ile-de-France SESAME funding program. V. N. was funded by  H2020 MAESTRIA #965286. We warmly thank  Philippe Chafey  for his contribution to sample preparation, proteomics data processing, and for his long career of contributions to protein analysis for the scientific community.
Many thanks to  Tony Lefebvre  from  University of Lille  for his expert advice on O-and N-acetylglycosylation. This work was supported by the  DIM Thérapie Génique Paris Ile-de-France Region , by  IBiSA , by the  Labex GR-Ex , and by a grant from  ANR   PACIFIC  ( ANR-18-CE14-0032 ). T. Y. was supported by the  French Ministry of Research and Higher Education , and by the  ANR PACIFIC  grant. M. P. -L. received funding from the  European Union 's  Horizon 2020 research and innovation Program  under the  Marie Sklodowska-Curie Grant  Agreement no. 101030046 , and by the  Programme Ramón y Cajal   RYC2021-032739-I , funded by  MCIN/AEI/ 10. 13039/501100011033  and the  European Union "NextGenerationEU"/ PRTR. Part of the technology developed for this study was supported by  SIRIC CARPEM  grants to B.
To improve experimental ecological validity and to overcome the limitations of previous research online platforms, a framework for programming remote and browserbased experiments, ReActLab (Remote Action Laboratory), has been recently developed and described here in Balestrucci et al. A platform run via open-source JavaScript libraries offers the possibility to run experiments entirely within the browser environment, enabling portability to any operating system and devices such as the Android tablets used in the case study of the paper. Results from visuomotor adaptation and visual localization experiments are compared to data obtained from similar tasks in laboratory settings, highlighting the advantages and limitations of the approach. This contribution prepares the field for new research paradigms and advanced computational models of cognition, highlighting the potential of logical derivation scripts and network graph visualization based on open-source R language.
This information enables educators and administrators to intervene and provide targeted support, thereby increasing student retention rates and fostering a more inclusive and supportive learning environment  (Schofield, 2021). Another area where EDS proves valuable is in the field of educational policy and decision-making. By analyzing large-scale educational data, such as national or international assessments, EDS can provide policymakers and educational leaders with evidencebased insights. These insights can inform the development of targeted interventions, curriculum improvements, and resource allocation strategies to enhance overall educational quality and equity (Fig. 4 ). (Kelleher et al. , 2020; Martínez-Plumed et al. , 2019)  The Education Data Science lifecycle can be divided into six main steps: Problem identification and understanding The first step is to identify and understand the problem that you are trying to solve.
Decoding learners' preferences: methodological approach to identifying learning styles in MOOCs In this section, we provide an outline of our proposed approach for identifying learning styles in MOOCs. We first present an overview of our solution and then delve into the detailed phases of our methodology. The main aim of our work is to propose a predictive model for learning styles. Our process is composed of three stages, as shown in Fig. 5. The first phase of our process consists of data collection (dataset). The data that are gathered are related to the behavioral traces of the learners during the learning process. To ensure the reliability and usefulness of the collected data, a pre-processing step that involves cleaning and filtering the raw data was performed. This step helps eliminate any inconsistencies or noise that may affect the accuracy of our model.
Dataset description The dataset used in this research work, was collected from edX course "Statistical Learning" (session Winter 2015 and 2016), via a data-sharing agreement with the Stanford University. The course emphasized the instruction of both supervised and unsupervised learning algorithms, along with the underlying theoretical concepts. This comprehensive curriculum aimed to equip learners with the knowledge and skills necessary to understand and apply these algorithms in practice. The course spanned nine weekly sessions, each consisting of six to eleven lecture videos, readings, and quizzes. To receive a statement of accomplishment, learners have to get an overall course grade of 50% or higher. Table  1  provides an overview of the dataset, highlighting the number of enrolled learners and the events recorded from these learners.
Raw data contains of noisy data, this noise affects the learning ability of the machine-learning model, Therefore, before using the data to train our model, it must be cleaned, formatted, and restructured -this is typically known as preprocessing  (Mazzola & Mazza, 2009). Fortunately, for our dataset, we are not dealing with missing values, however, there are some qualities about certain features that must be adjusted. In this context, we reduced the number of "event_type" from 7550 distinct event_type to 55 after preprocessing. The database was analyzed in order to detect outliers corresponding to learners with anomalous behaviors (values far out of the typical range). These outliers can have a negative impact on the results of our analysis (the model can be trained more slower, augment the complexity of the model, higher risk of overfitting)  (Fayyad et al.
To address this issue and ensure a uniform distribution of feature values, we apply the MinMax scaling method (Eq. 1). This method involves normalizing numerical values to a consistent scale, typically ranging from -1 to 1. where x min and x max are the minimum and maximum values, respectively, of the feature x in the dataset. To carry out this normalization, the MinMaxScaler function provided by sklearn was used. In summary, our data preprocessing pipeline encompasses both feature selection, guided by prior research, and feature scaling using the MinMax method. By selecting relevant features and ensuring uniformity in their values, we aim to enhance the performance and interpretability of our machine learning model in the context of this study. (1) From events logs to insights: a methodology through machine learning for learning style detection x ′ = x -x min x min -x max.
Aggregation process In the Felder-Silverman Learning Style Model (FSLSM), the poles of each dimension are opposed  (Felder & Silverman, 1988; Graf et al. , 2008) , as shown in Fig. 2. Therefore, when there is a strong occurrence of a specific behavior that indicates one pole, a low occurrence of the same behavior indicates the other pole, and vice versa  (Graf & Liu, 2009). For example, learners with a strong active learning style also show a weak preference for a reflective learning style. If the preferences are similar (either strong or weak), the learning style is balanced. In order to label our dataset according to this scale of preferences (strong pole1 , moderate pole1 , balanced, moderate pole2 , strong pole2 ), we propose a grid called the "balance of learning styles" (Fig. 7 ), which consists of adding the two degrees of preferences (preference pole1 , preference pole2 ) relative to each dimension.
Supervised modeling: predictive models for identifying learners' learning styles In this paper, we compare several classifiers to assess their performance for the identification of learning styles: decision tree (DT), random forest (RF), k-nearest neighbor (KN) and neural network (NN). These classifiers were chosen because of their large use and good performance in similar research papers. To conduct the evaluation, the dataset was divided into four separate datasets, each corresponding to a dimension of the Felder-Silverman Learning Style Model (FSLSM). For each dataset, a training set and a testing set were created. In the context of machine learning algorithms, the selection of appropriate hyperparameters is crucial for achieving optimal model performance  (Duong, 2019). Hyperparameters are considered as properties of an algorithm that need to be defined prior to training a model.
After the development of the classification models, the subsequent step involved evaluating their performance on unseen test data. To assess the models' effectiveness, we employed four commonly used evaluation metrics: accuracy, precision, recall, and F1-score. Considering the highly unbalanced nature of the dataset, it is crucial to employ evaluation metrics that can effectively handle class imbalance. In this context, the use of macro-precision and micro-precision metrics proves to be valuable for assessing the performance of our models  (Tharwat, 2018). These metrics specifically account for the imbalanced distribution of classes in the data and provide a comprehensive evaluation of the models' predictive capabilities. Having explored the methodology and evaluation process, we now turn our attention to the core of our study: predicting the learning styles of students in a MOOC.
In the subsequent analysis, we will delve into the performance of the developed models across the four datasets. Assessing the performance of the developed models across all four datasets, it is evident that they let to favorable results (Table  7 ). However, upon close examination of the performance metrics showcased in Tables  8, 9 , 10 and 11, it is noted that among all the developed models, the decision tree (DT) classifier consistently demonstrated the highest level of performance. This finding strongly emphasizes the relevance and suitability of the DT classifier for our specific study objectives. Its superior performance underscores its efficacy in accurately predicting and understanding students' learning styles within the context of our research. As mentioned previously, the four datasets are highly unbalanced.
User interaction with MOOCLS MOOCLS is a dynamic web application that is accessible via a browser and does not require prior installation. The overall functions of the platform are realized via PHP and MySQL, and we also used Highcharts JavaScript library, which is a tool for developing interactive charts. MySQL was used as a relational database engine to store the features of the learners and their predicted learning styles. This section presents a brief description of the features of MOOCLS in terms of user interaction. After logging into MOOCLS, users are greeted with a page displaying a comprehensive list of available courses (Fig. 9 ). This empowers teachers and instructors to choose a particular course they are interested in and explore the distribution of learning styles among the enrolled learners.
After the participants had interacted with MOOCLS, they were asked to fill out a questionnaire containing 16 items, using an online Google Form and consisted of 16 items divided into different sections. The first section (Q1 to Q6) of the questionnaire collected personal data about the participants, such as their age, gender, educational background, and prior experience in pedagogical design for online courses. This section aimed to gather information about the participants' profiles and their levels of expertise in the subject matter. The second section (Q7 to Q12) focused on assessing the utility of the MOOCLS tool. Participants were presented with specific questions aimed at evaluating the tool's ability to provide a concise overview of key information. This section aimed to measure participants' perceptions of the tool's effectiveness in presenting relevant information in a clear and concise manner.
Our study is distinctive in the research field, as few works analyzed MOOC databases, especially with such a large number of learners  (Essa et al. , 2023; Raleiras et al. , 2022). To achieve this goal, we proceeded with data pre-processing and feature selection, followed by grouping learners into clusters based on their preferences for each learning style using unsupervised machine learning techniques. Subsequently, to create labeled datasets representing each dimension of learning styles, a unique approach termed the "balance of learning styles" was employed. This method involved merging the two poles (learning styles) within each dimension to quantify the degree of dominance exhibited by each style. By employing this approach, we aimed to establish a comprehensive understanding of learners' preferences and their relative strengths within different learning style dimensions.
Our study reveals that a predominant number of learners exhibited active, visual, sequential, and sensory learning styles. These findings align with the works of  Felder and Silverman (1988). We conducted a comparative analysis of four supervised machine learning algorithms: decision tree, random forest, k-nearest neighbor, and a neural network. The objective was to assess their performance in predicting learning styles based on the collected data. The results of the analysis revealed that the decision tree model exhibited exceptional accuracy, surpassing a threshold of 98% across all four datasets. This finding highlights the effectiveness of the decision tree algorithm in accurately predicting learners' learning styles based on the provided features. The high accuracy achieved by the decision tree model underscores its potential as a valuable tool for educators seeking to understand and cater to the diverse learning preferences of their students.
Learners can benefit from model-driven recommendations ensuring they engage with materials and methods best suited to their individual styles. The model can also facilitate the personalization of learning contents and resources, allowing for more targeted and effective instruction. Furthermore, our model cas offers a promising direction for enhancing the MOOC experience through personalized learning paths. Finally, we highlight that the data analyzed in this research was specifically obtained from the edX course "Statistical Learning," which focuses on Supervised Machine Learning and targets learners with a scientific background. While the findings and insights derived from this dataset are valuable, it's important to acknowledge that the underlying methodology was designed with genericity in mind, it can thus be easily applied to analyze the log of many other courses to enable deep understanding of student behaviors.
To realize these benefits, the Fig. 15  illustrates a generic solution based on the proposed model. This solution can seamlessly integrate with diverse MOOC datasets, facilitating educational research and assisting instructors in improving course design. To ensure the broader applicability of MOOCLS, it would be beneficial to apply the solution to datasets from MOOCs covering various themes, such as art & culture, literature, medicine, and more. By examining diverse datasets, we can gain a better understanding of how the tool performs across different subject areas and learner populations.
Instrument The BREAST-Q scale measurement was the tool chosen to support the data collection following the OECD guidelines for the international data collection on breast cancer PROMs  [11]. The BREAST-Q scale was developed and validated to assess the impact and effectiveness of breast surgery to treat breast cancer from the perspective of the patient themselves and is an important measure of the health-related quality of life  [15]. The BREAST-Q scale satisfaction with breast following BCT (lumpectomy) module has 11 items over four response hypotheses (1-very dissatisfied; 2-somewhat dissatisfied; 3-somewhat satisfied; 4-very satisfied). These items cover breast appearance in terms of size, symmetry, softness, implant placement, cleavage, and satisfaction with breasts in relation to how a bra fits and how the breasts look when clothed or unclothed.
Procedure The data collection procedure followed the OECD PaRIS Breast Cancer PROMs Working Group protocol, allowing subsequent comparability with data from other European and OECD countries  [11]. Hospitals were invited to participate in the study, awareness-raising meetings were held with the involvement of hospitals, and doubts were clarified with the research team. A total of 10 participating hospitals submitted the project to the respective ethics committees, and the data collection process began after the necessary approvals. The questionnaire was distributed via an online survey sent by the research team to each participating hospital. Data were collected during a clinical visit by the health care team of each hospital, using the online survey or the paper questionnaire that was thereafter submitted online.
This paper presents an analysis of the saltwater intrusion in the coastal plain of Annaba using the MODFLOW code  (Harbaugh, 2005)  coupled to the MT3DMS model  (Zheng et al. , 2012). MODFLOW allowed to analyze the effects of the intensification of pumping rates in the plain on the aquifer. Steady-state calibration of the model was used to check the boundary conditions assumptions. Transient simulations allowed to improve knowledge about the distribution of the aquifer storage coefficients and to analyze the hydrodynamic evolution of the aquifer. The use of the MT3DMS model involved calibrating the salinity of the groundwater (expressed as Cl -concentration) in steady-state and then in transient conditions over a period of 12 years.
Fig. 2  -Cross section S-N through the lower Seybouse aquifer system (the profile of the cross section can be inferred from the item 5 in Fig. 1 ). Piezometric level measurements and geochemical analysis were carried out in 2009-2012 over a network of 45 water points in the deep Seybouse gravel aquifer. Electrical conductivity (EC) was measured using a WTW multiparameter (Multiline P3 PH/LF SET). Chloride and sodium concentrations were determined by the volumetric method  (AFNOR, 1987). The strontium contents were measured by ICP-MS with an accuracy of 5%. Results are presented in section "Evolution of the groundwater mineralization as a function of distance to the sea". The collected data was used in the groundwater flow and mass transport model for the assessment of the impact of abstractions on the Seybouse coastal aquifer (sections "Groundwater flow model" and "Simulation of saltwater intrusion").
Assuming that only equilibrium controlled linear or nonlinear sorption and first order irreversible rate reactions are involved in the chemical reactions. The chemical reaction term can be expressed as  (Grove & Stollenwerk, 1984) : 1 N b k K C R C C t r r l θ θ = ∂   = - +   ∂   ∑ (4) where: -C is the concentration of contaminants sorbed on the porous medium; --l is the first-order reaction rate (1/T); --r b is the bulk density of the porous medium (M/L 3 ). Rewriting the first term on the right side of Equation (  4 ) as:  3 ) can be rewritten by substituting Equations (  4 ) and (5) as: C C t C t C b b ∂ ∂ ∂ ∂ = ∂ ∂ θ r θ r - (5) Equation ( ( )       + - ∂ ∂ ∂ ∂ - + ∂ ∂ -         ∂ ∂ ∂ ∂ = ∂ ∂ C C C C t C C q C v C Dij t C b b s s i i j i θ r l θ r θ χ χ χ Rearranging the terms, the governing equation of mass transport model is obtained: ( )       + - + ∂ ∂ -         ∂ ∂ ∂ ∂ = ∂ ∂ C C C q C v C Dij t C R b s s i i j i θ r l θ χ χ χ (7) Where R is the retardation factor, defined as: C C R b ∂ ∂ + = θ r 1 (8) In this paper, the MODFLOW software  (Harbaugh, 2005)  was used to analyze the effects of withdrawals on the Seybouse coastal aquifer system.
The modeled area extends for 264 km 2. It was discretized horizontally into 1056 square cells of 500 m side and vertically into four layers representing the superficial aquifer, the semi-permeable horizon, the deep gravel aquifer and the Numidian clay bedrock. The elevations of the ground surface and of the bottoms of each horizon were determined from the analysis of the available drilling data. The permeabilities of each layer were determined using the transmissivity values resulting from the available pumping tests carried out in the region. Effective rainfall was calculated by the Thornthwaite's formula using monthly rainfall and temperatures observed at Salines station over a 32 years period (1985 to 2017). The main rivers of the region were represented through the River MODFLOW package  (Harbaugh, 2005) , using the gauging carried out by the National Agency for Water Resources upstream of the plain.
The MODFLOW code was coupled to the MT3DMS model  (Zheng & Wang, 1999)  for the following scopes: i) simulation of saltwater intrusion stabilization up to 2003, ii) simulation of the evolution of salinity from 2003 to 2015, integrating hydroclimatic data acquired during this period and iii) 30-years forecast simulation of salinity from 2015 to 2045. To simulate the mineral load of the Mediterranean Sea, a chloride concentration (Cl -) of 25 g/L was imposed at the Northern limit, based on the analysis of samples of seawater periodically collected during the campaigns carried out on the wells of the plain. In addition, a longitudinal dispersion value varying from 10 to 20 m and a porosity of 20% were applied to all the cells of the model. The transverse dispersion was fixed between 0. 1 and 0.
PICMI-like elements are broadly distributed in the Vibrionaceae Our description of the PICMI minimalism was based on a single model system identified in our collection, raising questions about the size, distribution, and diversity of PICMI-like elements in bacterial genomes. We thus built a MacSyFinder model 31 to allow the automatic identification of this element in bacterial genomes. Our PICMI115 prototype is integrated in the V115 host genome at the end of the fis regulator gene and contains only six genes. Among those, we showed that int, alpA, and prim are necessary for PICMI115 lifestyle. In accordance with the experimental data, we set the presence and colocalization of int, alpA, prim, and fis genes as mandatory in the model. We then used it to search for PICMI-like elements in all Genbank bacterial complete genomes (v243, 05/26/2021) and identified 135 elements (Table  S2 ).