dataesr/scientific-paragraphs-categorization huggingface dataset:
in other instances, the name listed in catalogues is not that of the actual translator; and on occasion it seems possible that the translator's name provided in the book itself is a pseudonym designed to conceal a plagiaristic act of publication. sometimes, much is known about a translator's life; often, nothing at all. it is therefore impossible to engage in any systematic visualization based on translators' lives. this is a particular shame as a translator's location, life-story, or regional identity may be interesting in themselves, and may also leave their marks on the use of language in the translations. the dataset employed for the maps included eighteen possible attributes, of which eight were necessary: place of publication named in the book, city (which sometimes differs from the named place of publication), country, language, date, translator(s) when known, publisher, and geographical coordinates.
the dataset was restructured to compose geoJson files and shapefiles necessary for the visualization of point maps, and polygonal elements corresponding to the countries where Jane Eyre translations have been published. the data were therefore adapted to different types of digital visualization. We worked with the online Javascript-based applications storyMaps Js (storymap. knightlab. com/) and Carto (carto. com/) to create the 'Covers Maps' and 'time Map' (see description below) and, in doing so, to correct the location data in intermediate drafts of the project. the main, synchronous global maps were then developed in the r programming language through the Leaflet package (rstudio. github. io/leaflet/) which allows the use of homonymous Javascript libraries dedicated to spatial analysis (leafletjs.
com/). 2 the numbers in the provisional dataset used to make the maps were: russian 56; spanish 52; Chinese 42; italian 40; turkish 36; dutch 34; persian 30; german 28; Korean 27; greek 23; Japanese 21; portuguese 33; Arabic 16; French 23; hungarian 11; swedish 8; danish 9; hebrew 7; romanian 6; ukrainian 6; Vietnamese 6. Five acts of translation each: estonian, norwegian. Four acts of translation: Catalan, polish, serbian, thai. three acts of translation: Bengali, Croatian, Czech, gujarati, Lithuanian, slovenian. two acts of translation: Albanian, Armenian, Bulgarian, esperanto, Finnish, indonesian, Malay, sinhalese, tagalog. one act of translation: Afrikaans, Amharic, Assamese, Basque, Burmese, hindi, icelandic, Latvian, Macedonian, Malayalam, Mongolian, punjabi, tajik, tamil, tibetan. nB the numbers given in the discussion in the main body of this article represent the current state of our knowledge, so will often differ from the older figures listed in this footnote.
this picture in some ways resembles and in others contrasts 3 these translations were researched by sowon s. park, Kayvan tahmasebian, Yorimitsu hashimoto, emrah serdan, eleni philippou, Céline sabiron, Léa Koves, Vincent thierry, Alessandro grilli, Caterina Cappelli, Anna Ferrari, paola gaudio, and Mary Frank. nB the numbers given in the main text here may differ from those in footnote 2 which describes the old dataset embodied in the maps. with that in the indian subcontinent where the first, abridged, translation into an indian language happened only in 1953, done by K. Appā tturai into tamil, and published in the south indian city of tirunelveā li, tamil nadu (it was perhaps helped into existence by the successful hindi film version of Jane  Eyre, Sangdil, in 1952). A second, even more abridged translation, done by suraiya Akhtar Begum into Bengali followed in 1977, since when interest in translating the novel, and opportunities for doing so, seem to have grown, and sixteen further translations have appeared, into punjabi, Malayalam, gujarati, Assamese, nepali, hindi, Kannaā a, and again tamil and Bengali  (Kragh and Jain).
Sulfur isotope composition of organic tissues is a commonly used tool for gathering information about provenance and diet in archaeology and paleoecology. However, the lack of maps predicting sulfur isotope variations on the landscape limits the possibility to use this isotopic system in quantitative geographic assignments. We compiled a database of 2,680 sulfur isotope analyses in the collagen of archaeological human and animal teeth from 221 individual locations across Western Europe. We used this isotopic compilation and remote sensing data to apply a multivariate machine-learning regression, and to predict sulfur isotope variations across Western Europe. The resulting model shows that sulfur isotope patterns are highly predictable, with 65% of sulfur isotope variations explained using only 4 variables representing marine sulfate deposition and local geological conditions.
However, to obtain precise and unbiased provenance information, isotopes analysed in a tissue of interest needs to be compared to an isotopic baseline  [7]. Without these baselines, isotope data from individuals can only be compared with each other or with existing databases  [8]. While these points-to-points comparisons can be useful, they are spatially biased, limited, and often ambiguous  [8]. A more quantitative approach to isotope provenancing is to use continuous-surface geographic assignment by comparing the measured isotope data in animal tissue to a map predicting isotope variations across the landscape, also called an isoscape  [8]. This approach incorporates uncertainty and produces continuous-probability surfaces to visualize potential location of origin over an entire study area.
Data compilation. To characterize the δ 34 S baseline in human collagen across Western Europe, we compiled all the δ 34 S data published in human and animal collagen across Europe from the Mesolithic to the XXth century (S1 Dataset). During the Holocene, changes in the geology and climate of Western Europe were small, likely leading to stable δ 34 S patterns on the landscape  [43]. We recorded the country, locality name, and species. We also compiled the latitude, longitude and reported the archeological period for each individual sample. In a few cases, geographic coordinates were reported by the authors in the publication. Geographical coordinates of numerous sites were found on GeoHack and, for a few occasions, on Wikipedia. When these were not included, we used Google Earth to georeference reported geographic information (e.
g. , maps or locality names). When necessary, authors of publications were contacted to clarify locality information. The distance to the coast was then obtained using the NASA dataset (https://oceancolor. gsfc. nasa. gov/docs/distfromcoast/) or, for Great Britain, the Doogal website (https://www. doogal. co. uk/DistanceToSea. php). We then screened all the δ 34 S data from poorly preserved collagen based on the following criteria C/S<300 and N/S<100  [44]. When only S data were available, we did not screen data. We also screened a few datasets for which diagenesis or contamination is explicitly mentioned in the publication (e. g. ,  [45] ). We also eliminated individuals that were explicitly designated as nonlocal in publication based on archeological evidences (i. e. , we define non-locals as individuals coming from a distant locality >100 km).
We did not consider data from aquatic animals or fish consumers to minimize dietary effect on δ 34 S variations. After screening, we calculated the average δ 34 S value for all individuals. 2. 1. 2. Auxiliary variables. We assembled data on selected covariates that represent the main factors that impact variability in δ 34 S values. The variables include geology, climate (e. g. , precipitation, temperature), soil proprieties (e. g. , pH, clay content, organic matter), aerosol deposition (e. g. , sea salt) and distance to the coast (Table  1 ). We resampled and reprojected all the selected environmental geospatial products into WGS84-Eckert IV 1km 2 resolution and used latitude and longitude of each sampling site to extract the local values for each raster. We assumed that, in archaeological times, humans and animals mostly consumed local food (within 20 km), and their δ 34 S values likely reflected that of local food systems.
Machine-learning multivariate regression. We combined the δ 34 S compilation and the extracted covariate into a regression matrix. We then tested two main approaches to predict δ 34 S variations across the landscape using the GSIF package in R  [46]. Briefly, the GSIF package automatically fits multivariate regression-kriging model for a given set of points and covariates. It first fits a regression model (e. g. Generalized Linear Model, random forest model) and then fits variogram for the residuals. We tested the potential of generalized linear model and random forest model to predict δ 34 S variations. For conciseness, we only describe the results obtained from random forest regression kriging, which performed better than other models. The R script for applying this modelling approach, including the generalized linear model, is available in S1 Script.
Random forest is a machine-learning algorithm trained by bootstrap sampling and random feature selection. Random forest creates multiple decision trees on different data samples where sampling is done with replacement to prevent overfitting. To make fair use of all potential predictors, the number of features split at each node of a tree is limited to some user-defined threshold. Ultimately, random forest aggregates the results of these decision trees to predict the mean value of the response variable. In our approach, the models were optimized using root mean squared error (RMSE) as the primary metric and a 10-fold repeated cross-validation scheme with 5 repetitions using 80% of the data for training at each iteration. To maximize model performance while minimizing the number of predictors included, we used the Variable Selection Under Random Forest (VSURF) package  [47] , which helps eliminate irrelevant and redundant variables.
The value of the probability of origin will also be influenced by the uncertainty of the isoscape at each pixel. The equation above is valid when using single isotopic system geographic assignment. Under the assumption of independence between isotopic systems, the combined probability density is simply the product of the single isotope probability surfaces obtained from the equation above. The script to generate the assignments for teeth is available in S2 Script. 2. 3. 2 Oxygen isoscape. δ 18 O variations in animal tissues are primarily inherited from drinking waters which follow primarily precipitation patterns  [58]. The best approach to generate a well-calibrated δ 18 O with spatially-explicit uncertainty is to use the framework described in the assignR package  [10]. In this package, data from known-origin are used to calibrate a relationship between modeled precipitation δ 18 O values and that of the tissue of interest.
[15]. This uncertainty is added to the uncertainty of the precipitation isoscape used in step 1  [20]. By using this isoscape, we make several assumptions. First, we assumed the δ 18 O w in Europe today are similar to those that occurred at the time these individuals lived ~500-years ago. This assumption is probably reasonable as atmospheric circulation, sea-level, temperature and topography were relatively similar to modern  [43]. Even in cases where climate would be different than modern times (e. g. , Last Glacial Maximum), maps of δ 18 O values in precipitation can be generated  [59]  and calibrated using the assignR package  [10]. Second, climate variations occurring during the period of teeth formation did not impact their δ 18 O c. Third, the water the individuals drank was local and not coming from distant rivers coming from high altitudes or from evaporated lakes.
Strontium isoscape. To model 87 Sr/ 86 Sr variations in human teeth, we used a published model that predicts 87 Sr/ 86 Sr ratio in bioavailable Sr across the world and its associated uncertainty  [13]. Briefly, this model uses a multivariate random forest regression framework, coupling biogeoenvironmental covariates and bioavailable 87 Sr/ 86 Sr data, to predict the average 87 Sr/ 86 Sr at each pixel and its associated uncertainty (1 standard deviation). The model explained more than 60% of the variance after cross-validation over a bioavailable 87 Sr/ 86 Sr dataset across Western Europe. When applying this isoscape built and calibrated on modern data, we make the assumption that the present day bioavailable 87 Sr/ 86 Sr ratio is a good estimate of the bioavailable Sr available to the individuals studied here ~530-years ago.
Sulfur isotope The screened dataset includes 187 averaged values from individual locations published between 2001 and 2020 and are given with the associated references in S1 Dataset. The δ 34 S values are normally distributed (Shapiro Test, p-value<0. 05). δ 34 S values range from -5. 1‰ to 21. 2‰ and average 8. 29‰. Standard deviations for each site are ranging from 0. 07 to 5. 3‰ and average 1. 77‰. Some geographical regions of Western Europe are underrepresented due to the lack of publications on S isotopes in bone collagen: Central Spain, Northern Germany, Denmark and Ireland. δ 34 S values in the compiled database displayed a normal distribution. After VSURF feature selection, sea salt aerosol deposition, dust aerosol deposition (i. e. , the deposition of mineral dust primarily generated in arid regions and transported by atmospheric circulation), and Bouguer anomaly (i.
e. , The remaining value of gravitational attraction after accounting for the theoretical gravitational attraction at the point of measurement which is influenced by geology and topography) were the dominant predictors of the δ 34 S values (Fig  1B ). The resulting model performed well, explaining close to 67% of the variance with a Root Mean Square Error (RMSE) inferior to 2. 8‰ (Fig  1A ). The value of 2. 8‰ represents ~10% of the full range of measured δ 34 S values over the compiled dataset. This uncertainty is relatively uniform across the prediction range (i. e. , residuals are normally distributed). We used partial dependence plots to investigate the relationship between predicted δ 34 S values and VSURF-selected predictors (Fig 1C  -1E ). The random forest regression model produced a δ 34 S isoscape in human teeth that displays strong spatial patterns associated with aerosol deposition and geology.
Except for Bouguer anomalies, no geological or soil variables (i. e. , lithology, age, clay content or soil pH) were selected by the VSURF algorithm. This lack of geological controls is a bit surprising because δ 34 S values are known to be influenced by bedrock geology  [29]. For example, in areas with bedrock containing high sulfide content (e. g. , marine shale), ecosystems usually display low or negative δ 34 S values  [31, 37]. The compiled dataset encompasses only a few sites with negative δ 34 S values and the predicted δ 34 S values at these sites overestimate the observed δ 34 S by several per mile (Fig  1A ). At these sites, sulfur sources are probably dominated by geogenic sulfides with low δ 34 S but this control is not well-represented in our current model. For example, eastern England and northeastern France have a similar geology composed primarily of Jurassic formation rich in organic black shales.
Implications and future improvements We demonstrated that triple δ 34 S -87 Sr/ 86 Sr-δ 18 O geographic assignment can considerably increase the precision of geographic assignment in comparison with single isotope assignment. In this particular study, δ 18 O assignments had the worst precision. This low performance of δ 18 O assignments is associated with the high uncertainty of δ 18 O isoscape in teeth. Analyzing a larger dataset of δ 18 O values in the teeth of individuals with known origin will be critical to further improve the potential precision of δ 18 O assignments. However, as observed in this study and others, δ 18 O values show large variability within teeth and between individuals at a given site. Fractionation associated with specific diet, physiology, or tissue can superimpose δ 18 O variations driven by geography  [15].
This ore belongs to the Co-Mn (1c) group recognized by Gratuze et al. (Table  1 ). Thus, at least five groups of cobalt sources can now be identified. Demirsar Arli et al. [25]  have recently compared the variability of the chemical data measured by pXRF on a selection of Ottoman Iznik tiles. They demonstrated that the variability of the CoO/NiO/MnO ratios measured for a single artefact, on a surface area equal to ~1 cm 2 is significant, being larger than the measurement error. It is important to note that areas that are homogeneously coloured on Iznik tiles are generally larger than 1 cm 2 (Figurec 2a,b,d). A similar conclusion can be made for the CoO/Bi2O3 ratios. Furthermore, the distribution of the data is strongly anisotropic (Figure  6 ) along the lines joining defined CoO/NiO (~3 to 1) and MnO/CoO ratios (~>3).
Additionally, the data obtained from two glazed hexagonal bricks which were unearthed during the Iznik tile kilns excavation in 2019 were distributed outside of the main group. The Zn content has been measured at a small level and seems not to be effective for potential use in the categorization except for the Muradiye tiles (the origin of these tiles with particular décor of Chinese style, i. e. , Figure  2a , has been highly debated for decades) that are quasi zinc-free. The distribution of copper content is larger and the data from the Muradiye tiles form also a specific group as copper-rich. The distribution of manganese is rather limited, but a specific series is formed by the blue colour of the Şah Melek Paşa and Muradiye mihrab tiles and some excavated sherds. Analytical data support the very specific origin of these tiles deduced from their particular décor.
The diagrams seem to be quite different from those drawn with data obtained from the Ottoman tiles (Figure  7 ), but the three groups identified for Asian porcelains, namely the Mn-rich correspond to Groups B and C already identified for other periods and continent (N2 group in Figure  5  and B-C group in Figure  8  closer to N2 group in Figure  5 )  [22, 23]  and Co-rich (Group A, cf. N1 in Figure  5 ). This demonstrates that the ores coming from deposits of the same geological types led to similar classification, whatever the mining places. However, Asian blues are all free of bismuth, and of copper, both of which are very variable in Ottoman glazes. Rare Asian and Iznik artefacts show significant levels of arsenic, most of the data corresponding to Group 3 and more rarely to Group 2. The most significant feature is the comparison of the CoO-Fe2O3-MnO diagram that is in contrast with that drawn from the Iznik data.
Two series of data, 'Blue Print' and the Vietnamese artefacts, are identified plus some outliers that are similar to the Iznik data (Figure  7 ). The origins of the blue-and-white porcelains of the 'Blue Print' program are not formally documented because they were not unearthed from an excavation site or kept in the collection of a museum. Indeed, these sherds belong to the private collection of the Art & Science Endowment Trust Charter (ASET) holdings located in Berlin (Germany). They were assumed to be the fragments of genuine objects produced from the Yuan to Ming dynasties. Identification of outliers is however an important information. For instance, regarding the composition of some of the blues of the Muradiye and Şah Melek Paşa mosque tiles  [23] , a clear distribution of composition is observed along the line drawn between CoO and 3ZnO-NiO.
Bismuth and copper are not observed. The Mn content classifies the data into three groups called A, B and C and the Ni-As-Zn ones (Groups 2, 3 and 4) appears to be pertinent for artefacts made at very different periods and places. This confirms that the compositional classification is related to the geological classification (primary type rich in transition metals like Ni, secondary types enriched in arsenic, etc. ) and different sites may have very similar chemical signatures, at least regarding the ratios for some of the components. Thus, consideration of many elements is mandatory. Comprehensive studies comparing the ores from different mines, in particular isotopic characteristics, are further needed. line. The tiles from the Muradiye mosque form a specific group free of chromium. It is worth noting that pure cobalt was measured for a modern restoration in the Selimiye mosque.
The textual evidence also confirmed that the ore (secondary type) included cobaltite (CoAsS) and erythrite (Co3(AsO4)2. 8H2O) as the main cobalt-bearing minerals. The authors also conducted experimental washing processes of the rocks according to a historical text and found that the ore then became more enriched with erythrite. A significant distribution of elemental ratios was also measured for the pieces of rock collected simultaneously. It is obvious that the variability of the ore composition prevents the establishment of a direct relationship between the specific ratio of companion elements versus cobalt. Furthermore, the statistical view of the compositional distribution of ore samples from other geological sites should be considered. Nevertheless, the comparison of diagrams in Figures  7 8 9 suggests that most of the cobalt ores used for Ottoman and Chinese/Vietnamese artefacts are very different and only rarely are data seen to be similar for the two series (see the data on the right side of the top diagram for CoO-NiO-ZnO and CoO-NiO-MnO).
Optical Spectroscopic Non-Invasive Characterization of Colouring Phases and Glassy Silicate Matrix Elemental analysis identifies the elements present in a coloured area/volume but cannot identify the phases, and whether these are crystalline or amorphous. X-ray diffraction patterns, especially performed with a micro beam, include a series of Bragg peaks, the unit-cell structure, and by comparison with a database identifies the (amount of the different) phase(s) and even the complete structure (crystal symmetry, atom positions, etc. ) after the application of a refinement procedure (Rietveld analysis). Raman microspectroscopy identifies both the crystalline and amorphous phases by comparison with a database or by chemical-based deduction  [4, 6, 53, 54, [219] [220] [221] [222] [223] [224] [225].
Acknowledgments: The authors acknowledge Curators, Keepers and all the staff of the French museums giving the opportunity to studies artefacts from their collection:  Cité de la Céramique Sèvres & Limoges; musée du Louvre (Département Islam et Département des  objets d'art), Paris; musée des arts décoratifs, Paris; musée des arts asiatiques-Guimet, Paris; musée du  Chateau de Fontainebleau, Fontainebleau. Authors are deeply grateful to  Belgin Demirsar Arli  and  Sennur Kaya  for giving the permission of doing on-site measurements at  Iznik Tile Kilns Excavation. The authors also acknowledge the authorities of Edirne City and Selimiye Foundation as well as colleagues involved in the previous studies ( Ozan Unsalan ,  Kutalmis Bayraktar ,  Altay Bayatli ).
Funding: The research in France was funded by the  French Agence Nationale de la Recherche ANR EnamelFC  project -19-CE27-0019-02. Part of the study made in Turkey was supported by the  Scientific and Technological Research Council of Turkey (TÜBİTAK)  through  1001-Scientific and Technological Research Projects Funding Program  (Grant No:  MAG 217M625 ).