dataesr/scientific-paragraphs-categorization huggingface dataset:
| THE WORKSHOP AND CRAFTING A COLLECTIVE REFLECTION Poetic inquiry offers a broad range of opportunities to attend to embodied and affective aspects of research (see  van Eck et al. , 2021). In our online workshop at EGOS, we zoomed in on poetic inquiry as a method for data analysis. As preparation for the workshop, participants were asked to bring a text that was relevant to their research projects such as an interview transcript, fieldnotes, and an academic paper. This could be a text that inspired them or sparked emotions in some way (e. g. , anger, frustration, joy, shame). The sub-plenary started with an introduction on the official platform through which all the sub-plenaries were organized. But as this set-up offered only a streaming function, and no opportunities to see participants and exchange thoughts with them, we switched to a Zoom room outside the platform.
Petersburg (Russia) using helical CT (CT; beam collimation, 1 mm; pitch, 1; slice reconstruction increment, 0. 3 to 0. 5 mm)  118. Finally, the RH ilia were scanned at the PLACAMAT platform (UMS 3626, University of Bordeaux, France) using X GE™ V/TOME/X S microtomographic equipment with a resolution of 30 μm. 3D data processing and geometric morphometrics analysis. After importing the image stacks into Avizo ® 7. 0. 1. software (Visualization Sciences Group Inc. ), the ilium surfaces were first segmented using the density based thresholding using the histogram tool, then they isolated using the tool extract subvolume. From these surfaces, the contour of the iliac bone, when oriented in lateral or medial view, and that of the acetabular area were quantified by manually applying a template of 72 landmarks (5 fixed landmarks and 67 semilandmarks) on each ilium according to a protocol available in Supplementary Information, Supplementary Methods.
Using the Avizo ® landmark module, anatomical landmarks were placed on the iliac spines: anterior-superior, posterior-superior and posterior-inferior, and two others were positioned anterior-posteriorly on either side of the acetabulum. Using the Avizo ® B-spline function, the semilandmarks were placed on the outline of the iliac bone through the outline of the acetabular area (Fig. 8  and Table  2 ). The landmark coordinates were then imported and processed in RStudio 128 using the Morpho  129  and Geomorph  130  packages. Missing landmarks were estimated, for some individuals with a slightly eroded iliac crest outline, using the thin-plate spline (TPS) interpolation function (Morpho package v. 2. 8; Ref. 129  ). After mirroring the landmark configurations of the 15 right ilia, due to the higher number of left ilia in our comparative sample, the bending energy was minimized by sliding the semilandmarks which can then be considered homologous  [131] [132] [133].
Acknowledgements To carry out the 3D study of the ilia, AG and AS received financial support from the  Department of Human Evolution  of the  Max Planck Institute for Evolutionary Anthropology (Leipzig, Germany). We are also grateful to the  PACEA laboratory (Bordeaux, France)  and its former director,  A. Delagnes , for having allowed access to its premises for the realization of this work during the Covid-19 lockdown period. We thank the  Musée national de Préhistoire of les Eyzies-de-Tayac (Dordogne, France)  and its former director,  J. J. Cleyet-Merle , for access to the fossil ilia AR-63 and Le Moustier 2. We thank  H. Temming  for providing us with μCT scans of the three fossil ilia. We are grateful to  N. Vanderesse  for the acquisition of the μCT scans of the RH ilia. AG, JH and BM received support from the  Research Program  of the Nouvelle Aquitaine Region: 2021-2026 ADNER, convention n°AAPR2021-2020-11779310.
SI analyses and data treatment SI were analysed and treated according to the protocol proposed by Dillmann and L'Heritier  [30]  and adapted by Disser et al  [31]. To enhance the representativeness of the average slag produced and to obtain information on metallurgical processes, several hundreds to thousands of SI were analysed per sample by SEM-EDS (Scanning Electron Microscope coupled with Energy Dispersive Spectroscopy). The position on the cross section of each SI was detected, with special attention being given to those located along welding lines. Major elemental composition analyses of the SI were performed using EDS with a silicon drift detector (SDD) with a large window (about 90 000 cps) coupled with a SEM (FEG JEOL 7001-F) at 15 kV accelerating voltage, and a probe current of about 8 nA.
e. ore, charcoal, furnace lining) the respective ratio of these elements (trace and major) should be relatively constant and consequently their Xij too. Different statistical data treatments were used: • Hierarchical Clustering Analysis (HCA) on the Xij using the Ward method to discriminate composition clusters; • for a low dimensional representation of the data, the t-SNE (t-distributed stochastic neighbor embedding) algorithm  [39]  was used (package Rtsne of the R software). The t-SNE algorithm was run with a perplexity of 30. For each dataset, the algorithm was run about 10 times. The result with the lowest Kullback-Leibler divergence (itercost in R package) was chosen.
Dating of the staples In the most carburized areas of six staples, local sampling of metal was performed for radiocarbon dating. Two of the dated staples come from the tribunes and four from the top walls. Two or three 14 C measurements were obtained per sample and their good consistency allowed us to group them with the R_Combine operation of the OxCal program to give a more accurate dating (Table  4  and  Fig 13). The results show that all samples are contemporaneous with the construction phases of Notre-Dame in the middle of the 12 th or beginning of the 13 th century, thus indicating that they were probably produced and implemented coeval to the erection of the cathedral. The results also show that both samples from the tribunes are somewhat older than those from the top walls. Despite two slightly different dates obtained on staple CH1, the combination of both dates seems coherent with this pattern.
Could this be linked to changes in the management and financing of the construction? It is well known that Bishop Maurice de Sully was the initiator and commissioner of the construction of Notre-Dame in the second half of the 12 th century, but what was the exact role of the chapter? The canons appear to have played a major role in the cathedral's construction, maybe from the earliest construction stages and especially after Maurice de Sully's death in 1196  [87]. Did iron supply partly rely on the Bishop's or the chapter's domains? On another level, major changes also occurred at that time in Paris, which became a true capital city during the reign of Philippe Auguste (1180-1223) where bourgeois and merchants played a essential role from the late 1190s. Could the drastic shift observed in iron supply be linked to these social changes and a new organization in the supply of the city and/or the building yard? And where did the welding and mixing of iron from different provenances take place: in the blacksmith's workshop or on the building yard, or prior to that, before the iron reached Paris? All these hypotheses and questions could be explored with the analysis of additional staples and crossing these data with slag analyses from identified archaeological iron production sites to link the iron armature of Notre-Dame with regional or more distant bloomeries.
The discovery of numerous welds and multiple provenances thanks to a metallographic study and SI chemical analyses sheds light on the activity of the iron market in a major medieval European city and on the nature of the goods that circulated. These results need to be reinforced with the opening of the Notre-Dame restoration yard and accessibility to new structures thanks to the dismantling of stone and repair of the framework, but also by implementing a comprehensive database of regional iron productions to better trace its circulation.
Acknowledgments This research was organised by the  CNRS (Mission pour les initiatives transverses et interdisciplinaires)  and the  Ministry of Culture  as part of the " Notre-Dame scientific project ". It was designed as a result of collaborative work involving all the members and laboratories of the Metal Workgroup (https://www. notre-dame. science/gt-metal-membres/). The authors would like to thank the  Regional Archaeological Service (SRA I ˆle-de-France) , the  Regional Conservation of Historic Monuments (CRMH I ˆle-de-France)  and its curator  Marie-Helène Didier , the  Chief Architects of Historic Monuments  ( Philippe Villeneuve, Remi Fromont, Pascal Prunet ) and the  Notre-Dame Public Establishment (Rebatir Notre-Dame de Paris)  for their support (authorisations to access the monument and for sampling).
All the registered buildings of this quarter were therefore identi ed and studied on the ground, each line of wall discernible on the surface was cleared, the plans were corrected and completed by hand as much as possible. A further cartographical survey was then carried out in 2016, to enter these new data on the database and correct the preliminary plans and the general map of the quarter, a work which unfortunately could not be fully completed before the end of the project. But the main objective of the Cartographic Project during the QDP was to carry out photogrammetric surveys to produce 3D models that could be used as a support for archaeological study, archival conservation, publication and development, particularly in the perspective of the archaeological park and a UNESCO candidature.
ree eld seasons (2013a, 2014a, 2017b) were carried out by the French company Iconem. e entire surface of the site and its surroundings was scanned using a drone (Figure  1. 4C), with a de nition of 3 to 5cm/pixel, and all main features and excavated buildings were digitalized by drone and pole (Figure  1. 4D), with a de nition of 0. 5 to 1cm/pixel, before and a er conservation work, either by Iconem or by the topographers of the Eveha archaeological team. Related studies e archaeological material unearthed during excavations has been recorded in dedicated databases and studied by specialists; apart from the  An extensive analyse of the material, and especially, of the huge number of ceramics brought to light during excavations, was carried out all along eld work since 2008 (H. Renel, CNRS) in order to build a chrono-typology of this material, an essential tool for the dating and interpretation of the excavated buildings and the chronology of the site (Figure  1.
Background Clinical staging has been widely used to predict and optimize the treatment of medical disorders. Different models have been proposed to map the development, progression, and extension of psychiatric disorders over time, mainly for schizophrenia. The primary objective of this study was to classify patients with psychosis according to the McGorry staging model and compare factors between the different stages. Methods This was a cross-sectional study, collecting data from 158 patients hospitalized for schizophrenia/psychosis. The survey included the Mini International Neuropsychiatric Interview (MINI), Positive and Negative Symptom Scale (PANSS), Montgomery-Asberg Depression Rating Scale (MADRS), Yong Mania Rating Scale (YMRS), Clinical Global Impression (CGI) scale, and the McGorry staging model.
Questionnaire The questionnaire was in Arabic, with an average response time of 50 min. Data was collected by a trained person through a personal interview. The first section of the questionnaire inquired about socio-demographic variables (age, gender, marital status, and educational attainment). The second section included questions about medical history, duration and onset of illness, duration of hospitalization in psychiatry, number of admissions to the psychiatric hospital, family history of psychiatric condition, and chronic medical condition. Ongoing treatments including antipsychotics, antidepressants, mood stabilizers, lithium, anxiolytics, and hypnotics were also recorded. Chlorpromazine equivalent doses were calculated according to the minimum effective dose method  [10]. Moreover, different scales were included in the questionnaire as follows: The Mini International Neuropsychiatric Interview (MINI)  [11]  is a structured diagnostic interview, developed jointly by psychiatrists and clinicians in the United States and Europe, for the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-4) and International Classification of Diseases, 10th revision (ICD-10) psychiatric disorders.
Statistical analysis The SPSS software version 25 was used to perform data analysis. The quantitative variables were considered as normally distributed as verified by the visual inspection of the histogram, while the skewness and kurtosis were within |1. 96  [19]  except for the total PANSS, general psychopathology PANSS subscale, and MADRS scores. For these scales, the median and interquartile range were reported. For the remaining quantitative scales, the means and standard deviation were reported, whereas categorical variables were expressed as absolute frequencies and percentages. The Chi-square and Fisher exact tests were used to test the association between categorical variables, whereas the ANOVA test was used to compare three or more means. P <. 05 was considered significant.
The data was collected at a specific point in time. The follow-up of the patients was not evaluated. Data was collected from one hospital in Lebanon. Finally, rehabilitation and psychotherapeutic approaches may have had an impact on the prognosis of psychosis, hence on the clinical stage classification. In our study, all included subjects are considered as long-term inpatients (hospitalization for at least one year), and benefited from non-specific supportive group therapy, along with medical and psychiatric care. This may be a limitation, even though no specific individual psychotherapy or rehabilitation program was implemented for specific participants.
Objective Umbrella reviews are a new form of literature review that summarises the strength and/or quality of the evidence from all systematic reviews and meta-analyses conducted on a broad topic. This type of review thus provides an exhaustive examination of a vast body of information, providing the highest synthesis of knowledge. A critical strength of umbrella reviews is recalculating the meta-analytic estimates within a uniform framework to allow a consistent evidence stratification. To our best knowledge, there is no comprehensive package or software to conduct umbrella reviews. Methods The R package metaumbrella accomplishes this aim by building on three core functions that (1) automatically perform all required calculations in an umbrella review (including but not limited to pairwise meta-analyses), (2) stratify evidence according to various classification criteria and (3) generate a visual representation of the results.
In the following sections, we present the use of the R package metaumbrella to analyse the data generated by an umbrella review that contains metaanalyses with dependent effect sizes. We also present complete tutorials for conducting the same data analysis but using two graphical user interface (GUI) platforms: a JAMOVI module and a browser-based application (https://metaumbrella. org/). These tutorials on the JAMOVI module and the browser-based app are available online at https://corentinjgosling. github. io/BMJ_MH_ METAUMBRELLA/).
METHODS To achieve automation in the calculations, the metaumbrella package (as well as its companion JAMOVI module and browserbased app) requests that users build a data set that follows fixed rules. (A detailed description of how building such a wellformatted data set is beyond the scope of this paper. A step-bystep tutorial with a concrete example is provided as a vignette of the package at https://cran. r-project. org/web/packages/metaumbrella/vignettes/format-dataset. html. ) Therefore, to guide users in this formatting, the package also proposes a function that specifically checks the formatting of the data set and provides guidance on formatting the issues detected (figure  1 ). (Details on calculations conducted by the R package metaumbrella can be found in a dedicated vignette at https://cran.
First steps with the metaumbrella package To use the metaumbrella package in R, it must first be installed and loaded using the following respective commands: ► install. packages("metaumbrella") ► library(metaumbrella) Once these R commands have been run, all functions of the package and several example data sets become available. In this paper, we will use one of these data sets stored under the name df. radua2019. This data set is directly inspired from an umbrella review on the putative risk factors for post-traumatic stress disorder collected from 155 studies and synthesised in seven meta-analyses. 23 he following R command allows visual exploration of this data set: ► View(df. radua2019).
Check data set formatting The view. errors. umbrella() function has been specifically designed to guide users in the formatting of their data set. By default, this function allows users to obtain information on formatting problems of their data set by (1) adding two columns to the original data set (column_type_errors and column_errors) describing formatting issues encountered in each row of the data set and (2) generating messages listing all the formatting issues encountered. Two types of formatting issues are identified. Formatting errors (such as negative sample sizes or SEs, too little information to run the analyses, etc) are issues that prevent running calculations. Formatting errors are associated, for each problematic row, with an 'ERROR' value in the column_type_errors column and a description of the problem encountered in the  Open access column_errors column.
In contrast, formatting warnings (such as non-symmetric CI around the effect size, empty rows, etc) are potential inconsistencies that do not prevent running calculations but that should be carefully reviewed by users before relying on the results generated by the metaumbrella package. Formatting warnings are associated, for each suspicious row, with a 'WARNING' value in the column_type_errors column and with a description of the problem encountered in the column_ errors column. To use this function, it only requires applying it on a dataframe. ► view. errors. umbrella( x= df. radua2019) In the JAMOVI module and in the browser-based app, this function is automatically run.
Conduct main calculations To replicate the pairwise meta-analyses and to run additional calculations needed to stratify evidence in an umbrella review, the metaumbrella package relies on the umbrella() function. This function performs the following: ► Fixed-effect or random-effects meta-analyses. ► Assessment of inconsistency/heterogeneity (I²). ► Tests for small-study effects. ► Tests for excess statistical significance. ► Jackknife leave-one-out analysis. The advantage of this function over standard R packages only designed for fitting a single meta-analysis lies in the possibility of (1) automatically fitting several pairwise meta-analyses when input information differs (eg, eight different combinations of input information are possible to conduct an umbrella review with the OR as effect size measure); (  2.
Open access (log) OR, (log) risk ratio, (log) incidence rate ratio [IRR], (log) HR, correlation and Fisher's z [R and Z]) in two common effect measures, equivalent Hedges' g (eG) and equivalent odds ratio (eOR)  24  ; (4) automatically conducting additional calculations such as small-study effects  25  and excess for statistical significance  26  ; and (5) automatically handling multivariate situations where the same study reports multiple effect sizes (due to the presence of multiple outcomes measured in the same participants or due to the presence of multiple independent subgroups). In these multivariate situations, the umbrella() function automatically aggregates all the effect sizes coming from the same studies using the procedures described by Borenstein and colleagues. 24 o use this function, it only requires applying it on a wellformatted data set: ► umbrella(x=df.
radua2019, mult. level= TRUE) The ' mult. level' argument should be set as 'TRUE' if the data set contains at least one meta-analysis with a complex data structure, namely with a dependence between some effect sizes (otherwise, this argument can be discarded). Several customisations are possible (such as the choice of the estimator of the between-study variance, the test of excess statistical significance, etc). All possible customisations of the umbrella function can be obtained using the following command in R: ► help(umbrella) In the JAMOVI module and in the browser-based app, this function is automatically run and can be customised to match the needs of the users.
Open access The first pre-established criteria are those proposed by Professor Ioannidis. 5 These criteria propose to stratify evidence in five ordinal classes: 'Class I', 'Class II', 'Class III', 'Class IV' and 'Class ns', with 'Class I' being the highest class that can be reached. The second pre-established criteria are inspired by the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) classification. 27 28 Importantly, this algorithmic approach should not be taken as an equivalent to the approach underlying the standard GRADE criteria. However, in line with the standard GRADE approach, the GRADE classification used in the metaumbrella package stratifies evidence according to four ordinal classes ('High', 'Moderate', 'Low' and 'Very low') and uses a downgrading procedure.
Users can select any of the 13 criteria (minimum 1 and maximum 13) and must set the threshold scores for each selected criteria to reach the five possible classes. The add. evidence() function requires two core arguments, the object in which the calculations performed by the umbrella() function are stored and the name of the criteria used to stratify evidence ('Ioannidis', 'GRADE' or 'Personalized'). Assuming that the calculations conducted by the umbrella() function have been stored in the object 'umb', the following R command allows stratification of the evidence according to the Ioannidis criteria: ► add. evidence (x=umb, criteria="Ioannidis") All customisations for the personalised criteria can be obtained using the following command in R: ► help(add. evidence) In the JAMOVI module and in the browser-based app, this function is automatically run and can be customised to match the needs of the users.
Checking of the data set formatting As can be seen in figure  2 , the view. errors. umbrella returns only a message explaining that the data set is correctly formatted since no errors were detected. If errors had been detected, the function would have returned a message displaying all errors encountered, as well as a data set containing only the rows of the original data set with formatting issues. In the JAMOVI module and in the browser-based app, the results of this function can be easily accessed (see figures 3B and 4B).
005, the Egger's test for small-study effects is not significant (p>0. 05) and the maximum p value achieved in the jackknife meta-analysis remains statistically significant (p<0. 05). ► A Class III can be achieved if the total number of studies is strictly larger than 15, the p value of the meta-analysis is strictly lower than 0. 01 and the Egger's test for small-study effects is not significant (p>0. 05). ► A Class IV can be achieved if the p value of the meta-analysis is strictly lower than 0. 05. ► A Class V is automatically assigned if the criteria for Classes I-IV are not met. In the JAMOVI module and in the browser-based app, the results of this function are presented in the same table as the results of the umbrella() function (see figures 3D and 4D).