dataesr/scientific-paragraphs-categorization huggingface dataset:
The research of the second author was made possible thanks to the financial support of the From Dust to Dawn:  Multilingual Grammar Extraction from Grammars project  funded by  Stiftelsen Marcus och Amalia Wallenbergs Minnesfond   2017. 0105  and the  Dictionary/Grammar Reading Machine: Computational Tools for Accessing the World's Linguistic Heritage (DReaM)  Project awarded 2018-2020 by the  Joint Programming Initiative in Cultural Heritage and Global Change, Digital Heritage and Riksantikvarieämbetet, Sweden. The third author is thankful for the support of grants from the  Université de Lyon  ( ANR-10-LABX-0081 ,  NSCO ED 476 ), the  IDEXLYON  Fellowship ( 2018-2021 ,  16-IDEX-0005 ), and the  French National Research Agency  ( ANR-11-IDEX-0007 ). Data availability statement and supplemental data: The content of the database will be available under the CLDF format and stored in a Github repository.
It was made clear to the students that participation was voluntary and that the raw data would not be seen either by their lecturers or programme administrators. Two-hundred and two usable responses were received from across the five campuses. Table  1  shows a range of demographic detail from the sample. The data were analysed with the Statistical Package for the Social Sciences (SPSS) using crosstabulation, Chi-square tests of association, correlation and principal component analysis (PCA) to evaluate how the students' expectations were met across the five cohorts. The data analysis started from the assumption that each franchisor would intend to meet their students' expectations in each campus; these assumptions were compared with the actual student response data.
Parental funding was 67. 3 per cent of the sample with 55 per cent studying at the undergraduate level. The test of association with the level of study is significant (χ 2 = 18. 233, d. f. = 2; p < 0. 001). The students were asked in multiple response questions who they believed had been the main influencer/s of their decision to pursue their academic studies per se. The options included consulting with parents, deciding by oneself, past teachers, friends and other students. Unsurprisingly, the crosstabulation and Chi-squared test of association (Tables  2  and 3 ) show that consultation with parents played a significant role. To investigate the influence of parents in the decision-making process more deeply, the data were further analysed by undergraduate and postgraduate IAF programme. Deciding with parents (52.
Sample selection criteria The PSID-CDS offers a total of 6,915 observations (2,904 in wave 1, 2,569 in wave 2, 1,442 in wave 3) from children who filled in a time-use diary 9. Not all returned both weekend and weekday diaries, and I exclude such cases to avoid classical measurement error. I further restrict the sample to observations providing information on relevant variables. This leads to a sample of 5,264 observations (2,488 in wave 1, 1,776 in wave 2, 1,000 in wave 3). I also drop outliers where the child was declared as spending the whole day in passive leisure or less than 50 hours a week sleeping, or where no information is 7 A more detailed description of attrition is given in Section S. 3. 8 The only other panel data using time-use diaries appears to be the Longitudinal Study of Australian Children (LSAC).
However, although the LSAC is based on a larger sample and is biannually surveyed, there is no distinction between parent and step-parent in the time-use diary. This makes the PSID-CDS a more appropriate data set for studying the effect of parental separation on both children's time-use and parental time investments  (Arbor, 2017). 9 The response rates are greater than 80%. No explanation for failure to respond is provided in the user guides, but it could be due to the long time required to fill in a time-use diary  (Juster, Ono and Stafford, 2003)  provided on who was present for more than 130 hours a week. 10 This yields a sample of 5,153 observations (2,478 in wave 1, 1,730 in wave 2, 945 in wave 3). Also excluded from the sample are those families whose status as single-mother families (with or without a step-parent) or single-father families does not change throughout the three waves 11.
At each site, three replicate 25m transects were conducted at each of the two reef types (barrier reef: 1-2 m depth; and outer slope: 10-12m depth) with two passes conducted per transect  17. Mobile fish were recorded during the first pass as they can quickly leave the survey area, and more cryptic fishes were the focus of the second pass. Transects at a given location were conducted with a 25m gap between each replicate. During each transect, all adult fishes were identified to species, which were then classified as either harvested by fishers or not harvested by fishers  17. We included data from 2011-2020 in our analysis. Data from previous years were not included as Moorea experienced a crown-of-thorn starfish (Acanthaster planci) outbreak between 2006 -2009, and a cyclone in 2010, which comprise disturbances not relevant to this study  14, 18.
6 long-term average compared to 62 during lockdown) and Pseudocheilinus hexataenia (1. 2 long-term average compared to 30 during lockdown) (Fig. S1  and summary data available in Table  S1 ). Again, five species (Arothron hispidus [1 recorded during lockdown], Chaetodon trifasciatus [24 recorded during lockdown], Forcipiger brevirostris [2 recorded during lockdown], Ostracion melagris [3 recorded during lockdown] and Thalassoma purpureum [1 recorded during lockdown]) were also recorded for the first time during the lockdown (Fig. S1  and summary data available in Table  S1 ).
Funding: This work was supported by several grants:  French Polynesia government (DRM & DIREN) ,  Fondation de France  ( 2019-08602 ),  LabEx CORAIL  (project 2018  Emul ),  ANR-19-CE34-0006 - Manini ,  ANR-19-CE14-0010-SENSO , the  Rāhui Forum and Resource Center supported by Bloomberg's Philanthropy , and the  Alexander von Humboldt Foundation.
We drew 4,016 German participants from the online access panel of respondi based on German population margins (see the sample composition in Online Appendix Section A8). A power analysis using the R package cjpowR  (Schuessler & Freitag, 2020)  demonstrates that the sample size provides sufficient statistical power for all subgroups (Online Appendix Section A9), including radical left voters that are underrepresented in the crosscountry sample.
Acknowledgements We thank participants at the  Sciences Po Centre for European Studies and Comparative Politics Pre-Publication Workshop  and at the  GESIS Political Science Roundtable ,  Gilles Ivaldi ,  Ralph Schroeder  and  Pu Yan  for their helpful comments on previous drafts of the paper. We are grateful to  Roberto Ulloa  for supporting us with the experimental setup and  Céline Widera  for excellent research assistance. We thank  Evelyne Brie ,  Katjana Gattermann ,  Reinhard Heinisch ,  Kristof Jacobs ,  Alexander Kustov ,  Marcel Lewandowsky ,  Sven-Oliver Proksch ,  Bartek Pytlas ,.
Acknowledgments We thank  Anne Atlan  for her initial contribution to the idea developed in this work and her careful reading of previous versions of this manuscript. We thank  Paul Ode  and  Peter Mayhew  for their comments on a previous version of the manuscript. Also, this work was achieved using the biomed virtual organization of the EGI infrastructure, with the dedicated support of resource centers BEINJING-LCG2, IN2P3-IRES, OBSPM,  INFN-FERRARA, GRIF, INFN-CATANIA ,  INFN-ROMA3 ,  INFN-BARI ,  CREATIS-INSA-LYON, NCG-INGRID-PT, INFN- PISA, CESNET-MCC and CLOUFIN, resource centres in UK hosted by GridPP collaboration, and the additional support of the resource centres listed here: http://operations-portal. egi. eu/vapor/resources/GL2Browser?VOfilter=biomed. This work was supported by the  Israel Institute for Advanced Studies , in Jerusalem, that hosted one of the authors (EW).
The Bighorn Sheep's Case To understand this process of reinterpretation, it is first necessary to define what the natural environment is in the Sasanian era and how humans interacted with it. For this type of analysis, archaeozoological data are valuable. Bone assemblages for the Sasanian period are few, but some have been analyzed, particularly those from the excavations of the wall at Gorgān  (Mashkour et al. 2013) , the Geoktchik Depe site  (Mashkour 1998) , Merw  (Smith 1997) and Siraf (von Driesch & Dockner 2002). It is then necessary to define the natural environment within the glyptic and for this purpose to identify its species as precisely as possible vii. Archaeozoology and iconographic data can then be compared to determine what has been retained from the natural environment in glyptic imagery viii.
However, it should be added that the lion was entering the "economy of salvation" (S. Azarnouche): killing a lion could help tip the balance of the afterlife in your favour! (Based on the text of Pahlavi Rivayat, Williams Edition and Translation, 1990. ) Without necessarily applying this economy, it can be assumed that the Zoroastrians in Iran in late antiquity were then aware of the lion's bad status within their own religion. xxi Analysis carried out on the basis of 3302 iconographies from seals and bullae and collected in a database during the doctoral project. xxii I thank Frantz Grenet for that suggestion xxiii The use of the term "model" seems to imply a fairly strictly controlled production of glyptic from an iconographic point of view. In the current state of the sources, it is difficult to know how they were produced, and whether they were produced, in whole or in part, by royal workshops, for example.
Hiding knowledge engenders economic losses. Customer knowledge is an external source praised by knowledge management scholars. Multi-sided Internet platforms become knowledge flow vehicles, nurturing customer knowledge. Knowledge hiding is widespread in services contexts impairing knowledge sharing. Firms need to integrate the knowledge-based customer experience to attain performance goals. Using social exchange theory and customer experience theory, we adopt a customer perspective to uncover the type of knowledge hidden and the motivations for customers to hide it on service multi-sided platforms. Combining narrative inquiry and critical incident technique we collected an original dataset of 51 narrative accounts of multi-sided platform users (Uber, Airbnb, and BlaBlaCar). We identify 10 major categories of hidden knowledge building on the identified critical incidents and five motivations to hide knowledge.
The research questions are as follows: What is the content of tacit hidden knowledge, and what are the motivations for end-users in the multi-sided platforms context to hide knowledge? Using the narrative inquiry approach combined with the critical incident technique, we collected an original dataset of 51 narrative accounts. Through these contextualized data, we obtain a complete narrative of participants' experience on MSPs and we unveil the drivers of hidden knowledge. The paper contributes to literature in four distinct ways. First, through adopting a bottom-up approach starting from the customer lived experience and perceptions as sources of tacit knowledge  (Jaziri, 2019) , we aim to expand the knowledge hiding literature by focusing on understanding why and when customers hide knowledge  (Singh, 2019).
Literature Review We searched on PubMed and Google Scholar for articles responding to the following keywords:  (  OR sickness impact profile[MeSH Terms]). The selected results were based on NLP, social media, patients' messages, QoL, diseases, and side effects. About 40 articles were found and used with the aim of establishing the best method and modeling to adopt (Multimedia Appendix 1). A focus was made on articles that developed machine learning techniques over neural network because of their lower cost in resources and correspondence with our database. The takeaway from literature review is that some machine learning methods, tools, or approaches were highlighted for their good performance in the literature review, such as Naive Bayes, Max Entropy, Decision Tree (10 folds), and MaxVote.
Data Sources and Manual Treatment The sources of data were 19 online general or health-related community forums in France, which are as follows: Atoute  [19] , Doctissimo  [20] , AuFeminin  [21] , Journal des femmes  [22] , Psychoactif  [23] , Forum. hardware [24] , Lesimpatientes  [25] , Laxophobie  [26] , Magic maman  [27] , thyroide  [28] , forum ado/public. fr [29] , Onmeda  [30] , Psychologies  [31] , MeaMedica  [32] , Futura-sciences  [33] , Allodocteurs  [34] , Vulgaris Medical  [35] , Lymphome espoir  [36] , and Maman pour la vie  [37]. Facebook and Twitter were not included because tweets are limited to 240 characters, which limited the probability of disease history development and impact testimonies. Facebook was also discarded for data privacy questions and difficulties of access.
Messages were extracted using a web crawler technology  [38, 39]. Health-related messages were selected based on a named entity recognition (NER) module. NER is a process where a sentence is parsed through to find entities (names, organizations, locations, and quantities). The NER module was used here to identify drug or disease mentions using an approximate matching algorithm. These messages were then preprocessed and stored. The metadata extracted along with the text were the date and hour of post. Raw data sets were composed of randomly selected health-related messages according to the presence of treatment or disease in it. Preprocessing of the extracted data included a code attribution to every message as identifier, the detection of sentences, normalization, and deduplication; since the extracted data were unstructured, this was a necessary first step to process patients' posts.
Annotation The corpus (n=1399 posts), with 1000 (71%) posts with disease mentions and 399 (29%) posts with treatment mentions, was first manually annotated. Manual annotation was performed by 2 individuals: a health-specialized data scientist and a health care professional specialized in social media listening, both sensitized and trained about the medical field of QoL, following guidelines in accordance with the methodology of HRQoL. The 2 annotators' profiles worked in synergy in the approach of data annotation with a medical finality. Medical insight toward patients' testimonies was brought by one of the annotators, with an expert eye toward the variables to be included in the future models by the other. The 1399 health-related messages extracted from forums were split into 2 sets for labelling; respectively, 900 and 499 messages for the 2 annotators.
Model Selection We used data mining and machine learning technologies to categorize and analyze retrieved data of our final corpus according to our predefined objective. As our features do not exhibit negative values, we normalized our data by dividing all feature values by their respective maximum so that all values would be somewhere between 0 and 1, thus minimizing interclass and intraclass variances. All the missing values were replaced by the median so as not to influence intraclass variance. We obtained a first classification algorithm to determine if there was an impact on HRQoL (corresponding to the first step of manual annotation). Subsequently, we created a classification algorithm for each dimension to assess whether the impact concerned the related dimension (second step). We used a 5-fold sequential forward floating selector with an extreme gradient boosting algorithm to select the best features combination.
71 for psychic, 0. 76 for activity-related, 0. 717 for relational, and 0. 765 for financial). Compared to other studies  [44, 45] , these indicators were high and robust; for example, with Twitter and Facebook data, the area under the curve of Caster et al  [44]  varied between 0. 43 and 0. 67. For patient forum posts, sensitivity was 0. 14 (and specificity was 0. 88); and for Twitter and Facebook, sensitivity was 0. 08 or lower. However, the objectives and approaches of these studies were different from ours, and it is thus quite difficult to compare the results. Performance might vary according to the data source. Considering that we were able to access a large data set and to use a satisfying training subset, this might explain our better performance. Nonetheless, Facebook and Twitter were discarded from our extracted sources due to the short messages of Twitter and the difficulties of access to Facebook data.
Limitations also include the data sources. More analysis is needed to prove that insights from social media are complementary to a patient-centric repository. Furthermore, Twitter and Facebook were discarded as sources due to short message format and accessibility issues; however, this does not mean that these social platforms are irrelevant resources for analyzing health testimonies from patients. Our data were randomly extracted from a large sample of French messages coming from French forums and social media. The fact that our sample selection was random should ensure a certain representativity of the internet message population. The proportion of women speaking about their health in forums is higher than the proportion of men (difference of 6%)  [48] , which introduces a possible bias when exploring HRQoL.
| Statistical analysis Statistical analyses were performed using IBM SPSS Statistics version 28. 0. 0 (Inc. , IL. , USA). Continuous variables were presented as mean ± standard deviation. Categorical variables are presented as numbers and percentages. The relationship between categorical variables was assessed using the Chi-square test of Pearson or Fisher exact test if the theoretical numbers were below 5. The connection between a qualitative and a quantitative variable was evaluated using the Student's t test or the ANOVA test. The variables that had a p value <. 20 in univariate analysis and those that had clinical relevance have been introduced in the multivariate analysis. The binary logistic regression model was used to identify independent predictive factors influencing the serologic response after the COVID vaccine.
Data acquisition All flakes were scanned with an Academia 20 structured light surface scanner (Creaform 3D) at a 0. 2 mm resolution. Flakes were scanned in two parts and automatically aligned (or manually aligned in case automatic alignment failed) and exported in STL formats. Cloudcompare 2. 11. 3 (https://www. danielgm. net/cc/) free software was employed to perform additional cleaning, mesh sampling, surface reconstruction, and transformation into PLY files. Finally, all files were decimated to a quality of 50,000 faces using the Rvcg R package v. 0. 21  (Schlager, 2017). The protocol for the digitalization of landmarks on flakes was based on previous studies  (Archer et al. , 2021 (Archer et al. , , 2018)). This included the positioning of a total of 3 fixed landmarks, 85 curve semi-landmarks, and 420 surface semi-landmarks  (Bookstein, 1997a (Bookstein, , 1997b;; Gunz et al.
5 , bottom;  Bookstein, 1997a, b). The entire workflow of landmark and semilandmarks digitalization and relaxation to minimize bending energy was done in Viewbox version 4. 1. 0. 12 (https://www. dhal. com/viewbox. htm), and resulting point coordinates were exported into. xlsx files. Procrustes superimposition  (Kendall, 1984; Mitteroecker and Gunz, 2009; O'Higgins, 2000)  was performed using the package "Morpho" v. 2. 9  (Schlager, 2017)  on RStudio IDE (R Core Team, 2019; RStudio Team, 2019). After performing Procrustes superimposition and obtaining a new set of coordinates, PCA was performed to reduce the dimensionality of the data  (James et al. , 2013; Pearson, 1901). There are multiple reasons to use dimensionality reduction when dealing with high dimensional data on classification, including to avoid having more predictors than observations (p > n), to avoid the collinearity of predictors, to reduce the dimensions of the feature space, and to avoid overfitting due to an excessive number of degrees of freedom (simple structure with lower number of variables).
PCA achieves dimensionality reduction by identifying the linear combinations that best represent the predictors in an unsupervised manner. The principal components (PCs) of a PCA are aimed to capture as high a variance as possible of the complete data  (James et al. , 2013) , and PCs that capture a higher variance do not necessarily need to be the best for classification. For the present work, PCs that represent 95 % of the variance were selected as predictors for training the machine learning models. The threshold of 95 % of the variance was arbitrarily selected since it balances retaining most of the dataset variance on a reduced number of variables. The identification of best PCs for classification was automatically done by the machine learning models using the caret v. 6. 0. 92 package  (Kuhn, 2008).
The present study tests SVM with linear, radial, and polynomial kernels. • Artificial neural network (ANN): with multi-layer perception, uses a series of hidden layers and error backpropagation for model training  (Rumelhart et al. , 1986 ). • Naïve Bayes: computes class probabilities using Bayes' rule  (Weihs et al. , 2005). All models are evaluated using 10 × 50 k-fold cross validation (10 folds and 50 cycles), providing measures of accuracy. Using a 10-fold division, each fold will have 14 data points (with the exception of the last fold, which will have 13 data points). Each fold serves subsequently as test set for a trained model. Although computationally more expensive, this guarantees that all data points will serve as test sets. The 50 cycles provide a random shuffling of the dataset prior to fold division, thus ensuring that the composition of the folds varies in each cycle and it does not play a significant role in the evaluation of the models.
The management of the data and the generation of graphs was done  using the tidyverse v. 1. 3. 1 package  (Wickham et al. , 2019). The training of LDA and KNN was done with MASS v. 7. 3. 57 (Venables and Ripley, 2002). The training of random forest was done using the "ranger" v. 0. 13. 1 package  (Wright and Ziegler, 2017). The training of SVM was done using the e1071 v. 1. 7. 9 package  (Karatzoglou et al. , 2006 (Karatzoglou et al. , , 2004)). The RSNNS v. 0. 4. 14 (Bergmeir and Benítez, 2012)  package was employed to train multi-layer ANN with backpropagation. The klaR v. 1. 7. 0 package was employed to train the naive Bayes classifier  (Weihs et al. , 2005). The k-fold cross validation of all models, precision metrics, and confusion matrix were obtained using the caret v. 6. 0. 92 package  (Kuhn, 2008).
Machine learning models also provide insights into the variable importance for classification. The caret package was employed to extract variable importance after each k-fold cross validation. Package pROC v. 1. 18. 0 is employed to obtain ROC curve and AUC data. All 3D models, original coordinates, data, code, models and complete workflow is freely available at the corresponding Zenodo repository (https://doi. org/10. 5281/zenodo. 7085139).
PCA and model performance The PCA results (Fig. 7 ) show that the first 25 PCs account for 95 % of the variance of the dataset, with PC1 accounting for 21. 39 % of the variance and PC25 accounting for 0. 36 % of the variance. This is an important reduction from the original number of variables (1,524) and is substantially lower than the sample (1 3 9). Fig. 8  presents the performance metrics for each of the models. In general, all models performed with accuracy values higher than 0. 7 with the exception of KNN, Naïve Bayes, and the decision tree with C5. 0 algorithm. When considering the two measures of overall model performance (F1 and accuracy), SVM with polynomial kernel presents the highest performance values (F1 = 0. 75 and accuracy = 0. 757). Additionally, SVM with polynomial kernel also provides the highest values of precision.