dataesr/scientific-paragraphs-categorization huggingface dataset:
Radiocarbon ages and use This study uses a regional georeferenced database including 425 sites and 1139 published 14 C dates from archaeological sites in WCA ranging between 5000 and 15 14 C yr BP (Fig. 1 ). Compiled Pennisetum glaucum, Elaeis guineensis, iron metallurgy, and pit features, were either directly dated or we assumed that the age of a given material is equal to ages of nearby dated material. This body of work has already been partially published in a companion paper  (Garcin et al. 2018 ) mainly focused on the respective roles of climate variability and human activity in the Late Holocene Forest crisis 1. Radiocarbon dates and associated errors were 1 The 'late Holocene rainforest crisis' is a vegetation disturbance that is recorded in palaeoenvironmental proxies, mostly pollen, from several sites in Western Central Africa between 2800 and 2000 cal.
If the shape of the SPDs is not overemphasized, this method provides valuable insight into palaeodemographic dynamics  (Timpson et al. 2015; Crema & Bevan 2020). Population dynamics were reconstructed from the SPDs of 14 C dates compared with formal statistical models using the hypothesis-testing approach introduced in  Shennan et al. (2013) , further developed in  Timpson et al. (2014)  and provided in R  (Team 2017) , statistical computing language, by  Crema et al. (2016). These analyses were recently implemented in an R package including more sophisticated features  (Crema & Bevan 2021) , however, we used the original code presented in  Crema et al. (2016). Here, the SPDs of 14 C dates compiled in the database were calculated after correction for oversampling biases (see above). The results of the preparative binning process are shown in Table  S1.
To examine population changes in different geographic sub-regions, a non-parametric extension of this method was performed, specifically a permutation test, allowing the statistical comparison of two or more sets of 14 C dates  (Crema et al. 2016). In this method the null hypothesis is that the SPDs are equal in the regions compared if the sample dates are generated from identically shaped population curves (Fig. 6 ). Three regions were defined (North Coast, South Coast and Hinterland) based on their geographical coordinates and using a hierarchical clustering analysis in Euclidean space (Table  S1  and Fig. 5 ). Had further regions been defined, the number of dates per region would have dropped significantly, (values before and after binning are North Coast:  respectively). This consequent reducing may hamper the statistical significance of 14 C dataset.
SPD deviation of archaeological remains through time In WCA, the SPD deviation of archaeological finds increased from 2800 cal. yr BP until around 1500 cal. yr BP when the curve suddenly fell, confirming the empirical observations of Oslisly (2014) (Fig. 2B ). Based on the SPDs of calibrated 14 C dates for the entire WCA dataset (at 95% confidence interval), a significant change in the archaeological record is inferred, highlighting a major positive deviation in the SPD curve from 2500 to 1500 cal yr BP. This positive deviation is marked by two distinct increases (Fig. 2B ) with maximum values at 2300 cal. yr BP (interval ~2500-2000 cal. yr BP) and 1700 cal. yr BP (2000-1500 cal. yr BP). Conversely, between 1200 and 400 cal. yr BP, the SPD curve shows clearly a global negative deviation, which has been documented in all archaeological contexts as shown in previous studies  (Oslisly et al.
2013; Saulieu et al. 2017). Since the dataset has been binned to avoid over-and under-representation, and since this decrease is documented across the whole region, at whatever scale of analysis, sampling bias can be ruled out. However, it is important to note that the decrease of the values initiated earlier, from 1500 up to 1200 cal. yr BP. After this decline, a stabilisation of the curve occurred between 1200 and 800 cal. yr BP. Finally, values recovered from 800 to 400 cal. yr BP without reaching the exponential null model.
2017)  sites. However, the causes are yet to be elucidated. This study establishes for the first time a plausible picture of the population dynamics and the evolution of cultural traits and technical innovation in WCA over the last five millennia, based on a comprehensive and statistically-binned archaeological database. These results provide a robust archaeological timeframe that may contribute to the refinement of phylogenetic and linguistic models in the area. Together with ancient DNA collected recently data obtained from the Shum Laka site  (Lipson et al. 2020) , aim to also contribute to the debates regarding the cultural and linguistic history of Central African populations as well as the long-term changes in land use that affected this region during the Late Holocene.
Acknowledgments We acknowledge the  French National Research Institute for Sustainable Development (IRD)  and the  LMI DYCOFAC  for their support. This study was supported by the  ANR  project  TAPIOCA. Many 14 C dates were carried out by the National Platform LMC14 (LSCE( CNRS-CEA-UVSQ)-IRD-IRSN-MC ) with the support of  IRD  and  ANPN.
Statistical analysis Statistical analysis was performed using the IBM SPSS 20 software. Continuous variables were compared using an independent sample t-test and Mann-Whitney U test. Categorical variables were compared using the chi-square test and Kruskal-Wallis test respectively. Recurrence-free survival (RFS) and OS curves were constructed using the Kaplan-Meier method and compared using the log-rank test. The Cox proportional hazards model was used in a stepwise manner (entry criterion P = 0. 05 and removal criterion P = 0. 1) to explore independent prognostic RFS and OS factors. We performed a propensity score matching (PSM) analysis to decrease selection bias by building a matched group of patients to compare perioperative characteristics, shortterm and long-term outcomes in resection and ablation groups.
REMERCIEMENTS Que Christophe GALKOWSKI soit remercié pour tous les échantillons que j'ai pu lui adresser à identifier. Mes remerciements s'adressent également à Philipe BOURLET pour ses recommandations à la relecture du document et à Laurent COLINDRE pour ses encouragements. Enfin, j'exprime ma reconnaissance à Tanguy JEAN et Benoît GESLIN pour leurs précieux conseils et leur accompagnement patient et pédagogique.
This work presents an accuracy study of the open source OCR engine, Kraken, on the leading Arabic scholarly journal, al-Abhath. In contrast with other commercially available OCR engines, Kraken is shown to be capable of producing highly accurate Arabic-script OCR. The study also assesses the relative accuracy of typeface-specific and generalized models on the al-Abhath data and provides a microanalysis of the "error instances" and the contextual features that may have contributed to OCR misrecognition. Building on this analysis, the paper argues that Arabic-script OCR can be significantly improved through (1) a more systematic approach to training data production, and (2) the development of key technological components, especially multilanguage models and improved line segmentation and layout analysis.
Introduction In late 2017 JSTOR initiated a collaboration with the Open Islamicate Texts Initiative (OpenITI) to run an Optical Character Recognition (OCR) pilot for the JSTOR Arabic digitization feasibility study (funded through a generous grant by the National Endowment for the Humanities). 1 The problem that JSTOR had encountered in their feasibility study was the problem that has long plagued efforts of scholars and librarians to digitize Arabic, Persian, Urdu, and Ottoman Turkish print documents: Arabic-script OCR programs produce notoriously poor results, despite the optimistic claims of some of their marketing materials  (Alghamdi and Teahan 2017). Mansoor  Alghamdi and William  Teahan open their 2017 study by noting that "although handwritten script is significantly more challenging than printed Arabic text for OCR, Arabic printed text OCR still poses significant challenges.
" After evaluating Sakhr, Finereader, RDI Clever Page, and Tesseract (Version 3)-the main options for Arabic-script OCR-on Arabic print works they conclude that "all the evaluated Arabic OCR systems have low performance accuracy rates, below 75 per cent, which means that the time which would take to manually correct the OCR output would be prohibitive. " These results are consonant with the authors' own experience using these OCR engines and those of our colleagues in the field of Islamicate Studies. In addition to these programs' lackluster performance, they also are not ideal systems for academic users for other reasons as well-for example, several are prohibitively expensive (for the average academic) and they offer little out of the box trainability (i. e. , they come only with a generic OCR model and they cannot be trained to recognize new typefaces).
With these problems in mind, in 2016 OpenITI began working on the development of open source OCR tools for Arabic-script languages (in print form) in collaboration with the computer scientist 1 OpenITI is a multi-institutional initiative that is focused on building digital infrastructure for the computational study of the texts of the Islamicate world. It is currently led by Dr. Matthew Thomas Miller (Roshan Institute for Persian Studies, University of Maryland, College Park), Dr. Sarah Bowen Savant (Aga Khan University, London), and Dr. Maxim Romanov (University of Vienna). Benjamin Kiessling (University of Leipzig/Université PSL) is one of OpenITI's primary computer science collaborators and he served as the technical lead for the OpenITI JSTOR OCR pilot. More information on JSTOR's NEH-funded project can be found in Kiplinger and Ray 2019.
Kiessling et al: Advances and Limitations in Open Source Arabic-Script OCR Art. 8, page 3 of 30 Benjamin Kiessling. To date our work has primarily focused on Arabic-script OCR for print documents since handwritten text recognition (HTR) for Arabic-script manuscripts presents a series of additional issues (e. g. , even more complex line segmentation and page layout analysis problems, a dizzying array of different script styles and scribal hands). However, we have begun preliminary experiments on Persian and Arabic manuscripts with some promising initial results using distantly supervised methods of training data production and the new line segmentation methods developed by  Kiessling (Kiessling et al. 2019). (See also the experiments on HTR for Arabic manuscripts led by the British Library in  Clausner et al.
2018; Keinan-Schoonbaert 2019; Keinan-Schoonbaert 2020 ). OpenITI's first OCR study with the new open source OCR engine, Kraken, developed by Kiessling, demonstrated that it was capable of achieving Arabic script-only accuracy rates >97. 5% with as little as 800-1,000 lines of training data for that document's typeface  (Kiessling et al. 2017). (Training data, in the context of OCR, consists of pairs of scans of individual lines of text with their digital transcription. ) OpenITI has also replicated these high accuracy rates on Persian texts, with Perso-Arabic script-only accuracy rates ranging from 96. 3% to 98. 62% with typeface-specific models. (This work has not been published yet, but the full CER reports for these tests can be viewed in Open Islamic Texts Initiative 2021b, "OCR_GS_Data".
) In this work, we present the results of our OCR study done in collaboration with JSTOR on the al-Abhath Arabic journal (arguably the most important Arabic language scholarly journal in the Middle East). In contrast with many OCR accuracy reports, in this study we performed both detailed manual and automatic Character Error Rate (CER) accuracy checks, which enabled us to develop a much more fine-grained understanding of where the Kraken OCR engine was failing to properly transcribe the Arabic text. These results confirm Kraken's ability to produce highly accurate Arabic-script OCR, but they also provide new insights into the importance of systematic training data production, the relative accuracy of typeface-specific and generalized models, and the key technological improvements needed for improved Arabic-script OCR output.
OpenITI OCR software: Kraken Kraken is an open-source OCR engine that was developed by Benjamin Kiessling. It utilizes a segmentationless sequence-to-sequence approach  (Graves et al. 2006 ) with a hybrid convolutional and recurrent neural network to perform character recognition  (Kiessling 2019 ) which obviates the need for character-level segmentation-i. e. , the neural network responsible for text extraction from image data recognizes whole lines of text without resorting to smaller subunits like words or characters which can be difficult to accurately compute for languages written in connected script. As an initial preprocessing step page images are converted into black and white through a process called binarization. Layout analysis, i. e. the detection of lines for subsequent steps, is then performed on this binarized image with an algorithm based on traditional computer vision methods.
In a final step, the previously detected rectangular lines are fed into the neural network for character recognition. (More on Kraken's technical details can be found in  Kiessling, n. d. ) The benefit of eliminating fine-grained segmentation in comparison to older character segmenting systems such as tesseract 3, Sakhr, and most likely Abbyy FineReader (as a proprietary software the exact nature of the classifier is unknown) can be seen not only during recognition but also in the streamlined production process of training data for adaptation of the OCR system to new scripts and typefaces. With character-based systems annotators have to manually locate and transcribe single characters while Kraken is trained on full line transcriptions which are faster to annotate and verify, especially in the case of connected scripts.
The OpenITI JSTOR OCR pilot OpenITI began the JSTOR OCR pilot by performing a randomized review of the Arabic typefaces used in each year of the al-Abhath journal. The page images of the journal were obtained from the Arabic and Middle Eastern Electronic Library (Project AMEEL) of Yale University Library. It was determined that there were two basic typefaces in the al-Abhath journal archive, with the first typeface being much more prevalent than the second (typeface #1: volumes  1-33, 36-39, 48-50; typeface #2: volumes 34-35, 40-47). Examples of these two typefaces can be seen in Figures  1 2 (for comparison sake, the last word in both lines-on the left of the page-is the same word). Both typefaces had some internal font differences and other minor character/ script variations (e. g. , patterns of use of alef hamza, slight shifts in placement of dots, slight differences in the degree of curvature of lines in a couple of instances, and minor ligature differences).
This intra-typeface variation was especially apparent in typeface #1, which had a long run as al-Abhath's typeface. To address this issue it was decided that the best approach would be to produce approximately 5,000 lines of training data for the first typeface and 2,000 lines of training data for the second typeface. After a randomized sample of the pages representing each typeface were selected, research assistants working with the OpenITI team produced the training data for these 7,000 lines using CorpusBuilder 1. 0-a new OCR postcorrection platform produced through the collaboration of OpenITI and Harvard Law School's SHARIAsource project. (For more on CorpusBuilder 1. 0, please see Open Islamicate Texts Initiative, "CorpusBuilder 1. 0," 2021a) After these 7,000 lines of training data were double checked for accuracy, a final spot review was conducted.
e. , that Kraken achieves significantly higher accuracy rates on Arabic texts than the commercial OCR solutions for Arabic), the OpenITI team discovered upon further review that there were several problems with the contractor's study. (For OpenITI's previous study on Kraken, please see:  Kiessling et al. 2017. ) First, Page #2-by far the most disappointing result-is a highly atypical page of al-Abhath data. It only contains 37 characters total and much of these are contained in a large header that is in a highly calligraphic script and is heavily vocalized (see Figure  3 ). It is noteworthy that Abbyy performed better on this script, but this page is an extreme outlier in the data. The second issue that we identified in the contractor's review was that they were marking certain differences between the original scans and OCR output as errors which were not true errors, and, in some cases, even marked some characters in the OCR output as errors that were not errors at all.
OpenITI accuracy study The OpenITI team began by generating automatic character error rate (CER) reports for the al-Abhath data (see typeface #2 to use as validation sets, we then trained the model on the remaining lines and tested these models' accuracy using the validation sets. This method of isolating a fixed number of lines of the training data as a validation set for automatic accuracy testing is a standard procedure when evaluating machine learning models. These accuracy results can be found in rows 2-3 in Table  2. These typeface-specific models were the ones used to produce the OCR output that was transferred to JSTOR and that the contractor reviewed for their accuracy study. In the time between the delivery of the ** "Inherited characters" are characters, such as vocalization, that can be used on multiple languages and they only come to be defined in reference to the character with which they are combined (i.
e. , they "inherit" the script of the base character with which it is used). a validation set. The generalized model's total character accuracy rate was 97. 41%-a 2. 57% improvement over the typeface #2-only model (i. e. , a ~50% improvement rate) and 1. 45% improvement over the typeface #1-only model-and its Arabic scriptonly accuracy went up to a respectable 98. 46%. The generalized model performed better than the typeface-specific models in all categories, but its most significant gains were in the category of "inherited" characters. According to the CER reports, the most significant source of errors in both the typeface #2 model and the generalized model were whitespace (spacing) errors and the Arabic vocalization marker, fatḥa tanwīn (unicode codepoint: Arabic fatḥatan). In the case of the typeface #1 model, whitespace errors were again the most significant source of errors, followed by kāf ‫,)ك(‬ yā' ‫,)ي(‬ and then fatḥa tanwīn errors.
Missed fatḥa tanwīn The exception to our traditional treatment of vocalization discussed in the previous section is the case of the Arabic diacritic fatḥa tanwīn ‫. )ًا(‬ As observed in the CER reports, missed fatḥa tanwīns were a significant source of errors. We also observed this in the manual review of the OCR output (see Figure  16 ). Although in the past we have not transcribed fatḥa tanwīns in the training data production process, we did include fatḥa tanwīns in the JSTOR pilot training data. In many cases the fatḥa tanwīns were transcribed correctly (see Figure  17  for comparison sake). However, as both the CER reports and manual review showed, they still remained a relatively common source of errors. The reason(s) that fatḥa tanwīn      The second type of error that occurred in the context of non-Arabic script was the inverse: that is, poor transcription of Arabic text on a page that is predominantly composed of a non-Arabic language (see Figure  26 ).
Figures 29-31). This type of error was not commonly found in the OpenITI manual accuracy assessment (Figures 29-30 were errors identified in the outside contractor's review of the OCR output), but there were a few cases in which the line segmenter missed a section or a word of a line. Typically this would occur in atypical text presentation formats, such as the table seen in Figure  31. future Arabic-script OCR projects need to study the particularities of the documents they plan to OCR and make sure that the pages selected for training data production contain a sufficient number of the less common ligatures, headers, vocalization marks, footnote texts, numbers, and other particularities of the works to be OCR'd. We followed this randomized training data generation approach in the past  (Kiessling et al.
2017). (See Springmann et al. 2018  for an example of a dataset resulting from haphazard convenience sampling, i. e. harvesting data from sources on which existing methods already produce near-perfect results. ) This more systematic approach to training data production will require more time upfront. But the models produced in this manner could potentially achieve much higher baseline accuracy and reduce the burden of postcorrection. 2. Generalized models. One of the most exciting results from this study was the significant improvements in accuracy achieved with the generalized Arabic model. The success of this approach tentatively suggests that if we continue to add training data sets to this generalized model we can anticipate achieveing higher levels of accuracy on both typefaces on which we have already trained models and new typefaces for which we have no training data yet.
If this pattern holds true in future studies, we would be able to gradually reduce the time and resources necessary to achieve high level accuracy (>97%) on new typefaces in the future. However, more research on generalized models is needed as both the optimal training data selection, including artificial data produced by methods such as  (Milo and Gonzalez, 2019) , for such models and the actual variance on an open text corpus is currently unknown. 3. There are a range of technical improvements-e. g. , multi-language models, improved line segmentation and layout analysis-that could significantly improve OCR accuracy numbers. Efforts are currently underway in both the eScripta project (of which Kiessling is a team member) and OpenITI's Arabic-script OCR Catalyst Project (OpenITI AOCP) to address each of these technical issues.
Analyse statistique L'analyse statistique a été réalisée à l'aide du logiciel Stata 11. 0 (Stat Corporation). Les variables qualitatives ont été exprimées sous la forme des nombres et des pourcentages, et les variables continues sous la forme des moyennes et des écarts-type. La comparaison a été effectuée en utilisant le test exact de Fisher pour les variables qualitatives et le test ANOVA pour les variables continues. Un modèle de régression logistique a été utilisé pour identifier des relations indépendantes entre les facteurs de risque et le critère de jugement principal. Nous avons introduit dans le modèle de régression uniquement les variables qui ont été enregistrées pour toutes les patientes, pendant les 15 années consécutives : la technique employée pour traiter l'ECR, la période, l'existence d'une infiltration vaginale nécessitant une exérèse concomitante du vagin, l'utilisation de stomie préventive, la réalisation d'autres procédures chirurgicales complexes.
Néanmoins, chez les femmes présentant une atteinte concomitante des racines sacrées/nerfs sciatiques, l'infiltration basse ≤ 7 cm au-dessus de la marge anale était notée chez 90,7 % des patientes (39/43 femmes pour lesquelles la hauteur du nodule rectal a été enregistrée dans la base de données), tandis que le vagin était infiltré dans 88,9 % des cas.