dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: We thank all the people and institutions that collaborated in the fieldwork research at  Cova Foradada , especially the city council of Calafell. We thank  A. Bucchi ,  H. -M. Berg ,  L. Cobo ,  D. Csermely ,  A. Gómez-Olivencia ,  N. Gregorev ,  B. Kelly ,  V. Laroulandie ,  C. Lefevre ,  E. Maldonado ,  C. Núñez-Lahuerta ,  Ó. Pérez-Parque ,  J. Quesada ,  Y. Quintino ,  A. J. Romero ,  R. Sáez ,  G. A. Tsoukalos ,  P. Vallverdú , and  D. Wiist  for providing different data, references, comments, and help that have improved this work. We thank  E. Moreno  for restoring of the Foradada specimens. We thank our colleagues at the NeanderArt conference (celebrated in August 2018 in Torino, Italy) for their feedback. L. A. C. would like to thank the team, especially  J. Yravedra , for their support and help with the studies.
-M. F. as the principal investigator). The research work of A. R. -H. was financed by the  Spanish Ministry of Science, Innovation and Universities  ( FJCI-2015-24144 ,  Subprograma Juan de la Cierva ) and  CGL2015-65387-C3-1-P (MINECO/FEDER ). The research of J. I. M. was financed by the  Juan de la Cierva-Incorporación  ( IJCI-2017-31445 )  MICINN fellowship. The research of G. G. -A. was financed by the  Universitat de Barcelona   APIF 2018  fellowship. The research work of J. L. F. -M. was financed by  FPI program  of the  Spanish Ministry of Economy and Competitiveness MINECO/FSE  ( BES-2015-074931 ). The research work of J. -M. T. was funded by  CNRS UMR 7041 ArScAn équipe Ethnologie préhistorique (P. Bodu). J. M. is the beneficiary of an  Erasmus Mundus Doctorate  scholarship for an  International Doctorate in Quarternary and Prehistory (IDQP).
ACKNOWLEDGEMENTS We would like to thank to  C. Cretin ,  J. -J. Cleyet-Merle ,  P. Jaquement ,  S. Madeleine  ( Musée National de Prehistoire ) for the permission, access and technical support during our stay in Les Eyzies. We would like to further thank to the research groups from  PACEA, University of the Basque Country  and the  Paleoanthropology group  from the MNCN-.
| 168 | Madrid for fruitful discussions, and to  Bruno Maureille  for his comments that helped to improve this paper. Rolf Quam  reviewed the English for the final draft. This work was supported by the Research Groups  IT1418-19  ( Eusko Jaurlaritza-Gobierno Vasco ), and by the  Spanish Ministerio de Ciencia, Innovación y Universidades  (project  PGC2018-093925-B-C33 ). Ago is funded by a  Ramon y Cajal fellowship  ( RYC-2017-22558 ) DGM was funded by the  IdEx University of Bordeaux Investments  for the  Future program  ( ANR-10-IDEX-03-02 ).
Background: While many studies analyze the functions that images can fulfill during humanitarian crises or catastrophes, an understanding of how meaning is constructed in text-image relationships is lacking. This article explores how discourses are produced using different types of text-image interactions. It presents a case study focusing on a humanitarian crisis, more specifically the sexual transmission of Ebola. 2. Methods: Data were processed both quantitatively and qualitatively through a keyword-based selection. Tweets containing an image were retrieved from a database of 210,600 tweets containing the words "Ebola" and "semen", in English and in French, over the course of 12 months. When this first selection was crossed with the imperative of focusing on a specific thematic (the sexual transmission of Ebola) and avoiding off-topic text-image relationships, it led to reducing the corpus to 182 tweets.
This allowed us to search for generic terms indicating the presence of an image in a tweet. Taking into account the time that could be devoted to qualitative analysis with such a huge corpus, an initial sampling target of 6000 tweets was selected that could be widened if the results did not answer our initial interrogation or if they led to new hypotheses. The operative keywords were obtained after a first sampling pass, using these 6000 tweets containing "http" and opening every link: The final list contained the words "photo", "instagram", and "ifunny. " In this first pass, a total of 14,316 tweets containing an image were identified in the 210,600 tweets containing the words "Ebola" and "semen". Because users easily resort to Twitter's image feature, and because this feature was automatically translated in our database, the word "photo" was pervasive (916 tweets containing "photo" out of the 6000 compared to only 11 and 9 tweets for "Instagram" and "ifunny").
Microstructure and morphology characterizations The microstructure of the alloy matrix was characterized by mean of electron backscatter diffraction (EBSD) at 20 kV accelerating voltage with a step size of 150 nm using a field emission scanning electron microscope (FESEM) equipped with an Oxford HKL Fast electron backscatter diffraction detector. Post-processing was carried out using the HKL CHANNEL5 software. Top surface-and cross-sectional morphologies of corrosion products were documented by means of scanning electron microscopy (SEM) using a LEO 1530 instrument with a Gemini column, upgraded to a Zeiss Supra 55 (equivalent). The images were recorded using a backscattered electron (BSE) detector at an accelerating voltage of 15 kV.
Analyses were performed on different areas (between 2 and 6 different locations), approximately sized 700 × 300 μm 2 , collecting overview spectra and high resolution spectra (20 eV) of C 1 s, O 1 s, Cu 2p, Cu KLL, and Sn 3d. The C 1 s peak position at 285. 0 eV was used for binding energy calibration. Information on elemental depth distribution within the patina was provided by means of glow discharge optical emission spectroscopy (GDOES, Leco GDS 850 instrument) combined with elemental analysis (line-, spot-and image-analysis) using energy dispersive spectroscopy (EDS, X-Max SDD, Silicon Drift 50 mm 2 detector, Oxford Instruments). The GDOES measurements, analyzed by the LECO data handling system, reflect information from a circular area (ϕ 4 mm) being continuously sputtered using Ar plasma at a pressure of 6-7.
Alloying with Sn results in a microstructure of reduced grain size and an increased fraction of twin boundaries compared with Cu metal but does not result in any segregation at either grain boundaries or in grains As similar results were obtained for Cu4Sn and Cu6Sn, only data for Cu4Sn will be presented below. Similar to Cu metal, the Cu4Sn alloy forms a single phase of face centered cubic (fcc) solid solution. A major inherent difference between Sn-bronze and Cu metal is that alloying with Sn can result in microstructural differences such as grain boundary segregation, secondary phases and the introduction of residual stresses, as well as local variations in chemical composition, characteristics that may influence the corrosion behavior  [33]. The microstructure and the chemical composition of the bulk material were therefore analyzed by means of EBSD and scanning-AES.
Acknowledgements The  European Copper Institute, Brussels, Belgium  and the  Scandinavian Copper Development Association are highly acknowledged  for financial support, providing material and giving valuable comments. We are also grateful to  Oskar Karlsson  and  Mats Randelius  at  Swerea Kimab ,  Stockholm Sweden  for help with SEM/EDS imaging and GDOES analyses. S. Jafarzadeh ,  J. Brunk  and  J. Sandberg , former PhD-students at  KTH , are highly acknowledged for help with the field exposures. The authors would like to express their sincere gratitude to the  China Scholarship Council  for supporting Tingru Chang's stay at  KTH Royal Institute of Technology, Sweden.
Results Glass thickness. Glass thickness helps ensure window sturdiness, minimising the window weight and in addition optimising light transmission. As light absorption is proportional to chromophore concentration and glass thickness, we have measured the glass thickness prior to each spectroscopy measurement, at the spot analysed by optical spectroscopy, using a minimal-contact ultrasonic gauge. The overall average thickness of the medieval and modern glasses is similar: 3. 03 mm and 2. 96 mm respectively (Fig. 2 ). These values are in agreement with the few data on flat glasses of similar age, in the range 1. 5-3 mm, such as in France  4, 13  , Germany  21  , Italy  22  or Spain  23, 24. Close thickness values are observed for the different colours and suggest the use of high quality, thin glass in agreement with the royal status of this cathedral.
Despite the difficulties raised by their typologies and therefore by their classification, the aim of this work is to make a first approach to understanding the technologies and alloys employed in the production of small gold bells. Fourteen unprovenanced gold miniatures in the collection of the National Museum of Ethnography and Folklore (MUSEF), La Paz, Bolivia, classified as bells, were studied using portable equipment. In order to provide further discussion on the analytical data obtained, the alloys employed in their production are compared to published data on Tiwanaku and Incan gold objects and on native gold from Andean deposits.
Methods of analysis The gold bells and the miniature kero in the MUSEF collection were studied in-situ using non-invasive techniques and portable equipment  (Guerra, 2008; Guerra and Tissot, 2016; Guerra and Rehren, 2009). The construction details were observed under a high-resolution Flexia video microscope with a zoom reaching x80, built-in LED illumination and Bersoft image measurement software. The composition of the alloys was determined with the handheld XRF spectrometer of the MUSEF, a Niton Thermo Scientific XL3t GOLDD, with 3 mm spot collimation, and with an Ag X-ray tube operating at 50 kV, 200 µA. The objects were positioned on the fixed spectrometer table to ensure constant measurement conditions, and the integrated CCD camera was used for locating the area of interest in order to avoid irregular surfaces and select the flattest areas.
For quantitative analysis, we used the method delivered by Thermo Scientific with the Niton XL3t GOLDD spectrometer, which was operated in precious metals mode. The XRF analyses were carried out with an acquisition time of 240 sec. The majority of the results reported below correspond to averages of 2-3 measurements. A set of both standard reference materials certified for element contents and internal reference gold alloys were analysed with the same experimental conditions as the objects. The data obtained for these alloys, which cover a wide range of Au, Ag and Cu contents, was normalised to 100 wt% and is summarised in Table  1. The results were used to validate the measured amounts and to construct an adjustment curve to be fitted to the elemental data. The use of standard reference materials certified for element contents also allowed a check on any influence of the X-ray tube Ag anode on the Ag concentrations determined for the alloys.
As shown in Table  1 , the analysis of standard CLAL1 containing 50 wt% Au, 50 wt% Cu and no Ag has in fact indicated a very low error in the amount of silver (0. 1 wt%). When three results are excluded from Table  1 , the accuracy of the data is typically better than 5% relative. Precision was checked by the repeated analysis of standard LA1, showing that it is better than 2% relative. The confidence level for data obtained using handheld XRF is in general good  (Hull, 2017) , in our case even for not entirely flat surfaces (see below section 4. 2 and Table  3 ). However, XRF analyses only the surface layers of gold alloys  (Troalen et al. , 2014). When using a specific setup, the penetration depth varies for a particular incident energy according to the composition of the alloys and the energy of the X-ray lines used for elemental quantification  (Troalen and Guerra, 2016).
Acknowledgements The authors gratefully acknowledge  Alfredo C. Basagoitia  and  Primitivo A. Calcina  at the MUSEF for support with the XRF equipment and  Proyecto Titicaca  at  Université libre de Bruxelles , in particular  Dr. Christophe Delaere , for support during this work. We are also grateful to  Mike Cowell , who made valuable suggestions on the manuscript, and to the anonymous reviewers, whose comments improved the quality of this manuscript.
Cochrane systematic reviews, which summarize existing data on a given medical question, are considered the gold-standard in this field. The reviews themselves are published in subscription databases but their abstracts are freely available online and translated by regional Cochrane centers from English into various languages, French among others. Cochrane reviews aim for a factual and objective presentation of research results in order to facilitate their transfer into medical practice. They can thus be considered to represent, according to Reiss's classification  [9] , a purely informative text type without any persuasive objective. In this context, then, distortion is defined as interference with the communicative purpose of the text. This interference can be due to different kinds of elements in the translated text that impact the interpretation of essential characteristics of the systematic review, for instance the effectiveness of the intervention or the authors' level of confidence in their results.
Materials and Methods A corpus study of selected lexico-grammatical patterns was conducted on Cochrane systematic review abstracts pre-translated from English into French by the neural MT engine DeepL and post-edited by second-year master's students in specialized translation at the Université de Paris. The master's in Language Engineering and Specialized Translation (ILTS) is a professionally-oriented master's program within the European Masters in Translation (EMT) network for training future language specialists with skills adapted to market needs. Student selection for the second-year master's program is based on testing of their language and translation skills. Students admitted to the program must have perfect command of the French language, as well as excellent comprehension and writing skills in English.
Although the students were familiar with comparable interfaces and some had been previously introduced to Memsource within another class, most were novices to both post-editing and medical translation at the beginning of the class. Experience in both was acquired over the duration of the class, and theoretical teachings and practical exercises were used in support of the post-editing work done by students. The importance of lexico-grammatical patterns in specialized languages and the potential for bias in their translation were specifically discussed approximately half-way through the course. The fact that the study corpus is derived from an actual production context means that various parameters that may have influenced the results and their interpretation could not be controlled. This is often the case for corpus studies, which rely on observations drawn from real-life data instead of strictly limited experiments with a controlled set of parameters.
Corpus Characteristics Data was collected directly from the online platform Memsource in MXLIFF and TMX formats and converted to Excel for uploading to Sketch Engine. The study corpus is available upon request in MS Excel format. The corpus was automatically tagged with TreeTagger when uploaded onto Sketch Engine. The corpus consists of 109 different texts (Cochrane abstracts and plain language summaries) in three versions: English source texts, corresponding NMT output in French, and initial student post-editions of NMT output. The three sub-corpora are aligned on sentence level. Table  1  presents token counts for each sub-corpus as well as the translation coefficient, i. e. , the token count ratio, for the two French-language sub-corpora. The coefficient is somewhat higher than the usually quoted expansion rate for this language pair, i.
Statistical analyses. To compare species diversity between ADE and REF, we plotted rarefaction and extrapolation curves using the iNEXT  64  package for total macroinvertebrate, ant, termite and earthworm species diversity, using the number of TSBF monolith samples as a measure of sampling effort intensity. The same procedure was used for all earthworm data (9 samples per site), termite data obtained from both the 10-m 2 plots and TSBF monoliths, and ant data obtained from both pitfall traps and TSBF monoliths. We used the betapart package  65  in R to decompose β-diversity (calculated using the Sørensen dissimilarity index) into its Turnover (Simpson index of dissimilarity) and Nestedness components using all soil+litter macroinvertebrate, ant, termite and earthworm data from monolith samples.
The average β-diversity was calculated to highlight LUS effect, by comparing all LUS (OF, YF and AS) within each soil type (REF and ADE) and region. The soil type effect was assessed comparing the diversity between REF and ADE soils within each LUS in each region. To identify the effect of geographical distance on species turnover we also calculated the average β-diversity among the three replicates of each LUS within each soil type. Due to non-normal distribution of both the faunal variables (i. e. , density and biomass of invertebrates collected using the TSBF method) and soil properties, we used General Linear Models (GLM) to adjust data to other probability distributions. The best adjustment was quasi-Poisson (overdispersion) and Gamma for invertebrate density and biomass, respectively.
Soil chemical properties were adjusted in Gamma distribution but particle size fractions could not be adjusted. ANOVA tests were performed with the multcomp package  66  of R, adopting a factorial design with the following factors: soil type (ADE and REF) and LUS (old forests, young forests and agricultural systems). When factor interactions were significant (P < 0. 05), the data were analysed comparing the effects of soil type within the LUS and the effects of LUS within each soil type. Significant differences were tested using Tukey's test at 95% probability (P < 0. 05) for GLM, or with non-parametric Kruskal-Wallis tests when data could not be adjusted with GLM. A Principal Component Analysis (PCA) was performed using the density of earthworms, termites, ants and overall (total) soil fauna density and biomass, together with the results of five variables from soil micromorphology (non-aggregated soil, pottery shards and fauna, root and physical aggregates) and ten variables from soil chemical and textural analyses (pH, Al 3+ , P, SB, T, TC, TN, and sand, silt and clay fractions).
Acknowledgements The study was supported by the  Newton Fund and Fundação Araucária  (grant Nos. 45166. 460. 32093. 02022015 , NE/N000323/1 ),  Natural Environment Research Council (NERC) UK  (grant No. NE/M017656/1 ), a  European Union Horizon 2020 Marie-Curie  fellowship to LC ( MSCA-IF-2014-GF-660378 ) and  DWGS  (No. 796877 ), by  CAPES  scholarships to WCD, ACC, TF, RFS, AF, LM, HSN, TS, AM and  RSM  ( PVE A115/2013 ),  Araucaria Foundation  scholarships to LM, AS,  ACC  and  ES , Post-doctoral fellowships to DWGS ( NERC grant   NE/M017656/1 ) and  ES (CNPq No. 150748/2014-0 ),  PEER (Partnerships for Enhanced Engagement in Research Science Program) NAS/USAID  award number  AID-OAA-A-11-0001 -project 3-188  to RMF and by  CNPq  grants, scholarships and fellowships to ACF, GGB, RF, SWJ,  CRC , EGN and PL (Nos.
Pre-registrations We kept a complete research diary on the Open Science Framework ([removed for blind review]) where all analyses carried out were pre-registered and described. Pre-registration is an open research practice that consists in describing the research design and analysis plan as independently as possible from data collection  [57]. The methods and analyses of this paper were pre-registered (recorded) in several waves. Visual complexity metrics derivation. Our complexity measures were obtained in the following way: (1) three arms were selected for each motif from our reference armorial  [56] , (2) pictures of shields were edited to obtain a picture of the motif on its own, (3) the edited pictures went through the Potrace algorithm to improve their quality (through vectorization), (4) they were zipped and the zip file's size served as our measure of descriptive complexity, while their perimetric complexity was calculated using Mathematica.
Sources Our primary materials were Renesse's Dictionnaire des figures héraldiques  [50] , the Armoiries des familles contenues dans l'Armorial Général de J. B. Rietstap, by Victor and Henri Rolland, and Steen Clemmensen's Armorial (  [49] , armorial. dk). Renesse  [50]  provides a motif-by-motif index of over 100 000 arms, indexing Rietstap's Armorial Général  [39] , while the Rollands' compendium of arms  [55]  provides illustrations for over two thirds of those. Renesse provided a classification of motifs, which was used for both corpora, and frequency data for the Renesse corpus. Rolland provided the pictures of motifs we needed for our visual complexity measures (for both corpora). Finally, Clemmensen's armorial provided us with frequency data for the Clemmensen corpus.
Inventory constitution A list of motifs corresponding to Renesse's classification was built taking Renesse's own subdivisions of his material as guide. Two aspects of motifs that were relevant for the author were not taken into account for classification: the orientation of a motif (i. e. whether the same motif is presented facing the left side or the right side of the arms), and the number of times that it is repeated. In other respects, we stuck as close as possible to Renesse's own descriptions. All further details and steps of sample constitution are reported in S1 File. Information on frequency and information on complexity were collected independently-i. e. , the researcher and research assistants who collected the frequency data did not observe the visual complexity data, and vice versa.
It is defined as two standard deviations above the mean of standard deviations (calculated for each motif on the basis of three pictures, see Eq 1) t ¼ MðSDðdÞÞ þ 2 � SDðSDðdÞÞ Eq ð1Þ with d being the distribution of complexity scores in our dataset of motifs. Applying this exclusion criterion did not change our results: the results obtained without applying the exclusion criteria are available in S1 File, and are very similar to the ones reported in the main text.
B. Rietstap's armorial  [39]  as indexed by T. de Renesse's dictionary  [50]  (here called "Renesse"). That second corpus covers a longer period, until 1880, although most of the arms that are dated occur between 1600 and 1850. Both corpora concern themselves chiefly with the arms of families and individuals, with little to no coverage of civic heraldry, and cover a wide range of European territories. We do not know to what extent the two corpora overlap: some arms are likely to be present in both. In other respects the corpora differ widely, and do not provide the same metadata. Although they do not classify heraldic motifs in identical ways, and have different ways of counting arms and families, our inventory of motifs was applicable to both. Details on how frequency measures were obtained for each dataset are available in S1 File.
Statistical analyses None of the measures we analyzed (for both Clemmensen and Renesse datasets, and all variables, i. e. , frequencies and both measures of complexity) were normally distributed (all ps p <. 01 on Shapiro-Wilk tests). Hence, all statistical tests presented here are non-parametric (Kendall rank correlation tests, because of the presence of ties, which leads to inexact p-values in Spearman's rank correlation test). All analyses were run in R  [62].