dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the data harvested came in several formats from a simple Word file to an API. Data extraction was only possible thanks to an interdisciplinary team, in the next graphic we show the total amount of values of the two most important categories in SILKNOW (Figure  7 ): materials and techniques. Materials were selected because silk fabrics can be woven in several materials that were used in specific periods of history, to know this is essential when studying and cataloguing a fabric. Techniques, on the other hand, are essential to SILKNOW as we want to act as a digital memory for these historic techniques that nowadays only a few scholars and weavers know. Techniques, on the other hand, are essential to SILKNOW as we want to act as a digital memory for these historic techniques that nowadays only a few scholars and weavers know.
The SILKNOW project aims, among other things, to improve access to this heritage and associated digital information, through various tools. Following a policy of open access to the data we collect. It comes from various sources: mainly internal databases provided by museums and collections, with which we have agreements; and data already in the open, collected through APIs or websites. The result, still under construction, will be a repository where information from many sources will be brought together and made available to users through improved tools: multilingual interface and interrogation, thanks to a specialized thesaurus, organizing information through a data model specific to this type of heritage, based on CIDOC-CRM. SILKNOW also created a Virtual Loom that acts as a digital memory of historical weaving techniques and a spatio-temporal map where these collections will be shown.
Cultural mediation can rely on the use of ICT to support communication between their collections and audiences. In this regard, technology can be used to transmit knowledge but also to attract different categories of the public to museums  [82]. SILKNOW contributes to a bigger understanding of Europe by improving the knowledge and The SILKNOW project aims, among other things, to improve access to this heritage and associated digital information, through various tools. Following a policy of open access to the data we collect. It comes from various sources: mainly internal databases provided by museums and collections, with which we have agreements; and data already in the open, collected through APIs or websites. The result, still under construction, will be a repository where information from many sources will be brought together and made available to users through improved tools: multilingual interface and interrogation, thanks to a specialized thesaurus, organizing information through a data model specific to this type of heritage, based on CIDOC-CRM.
This structure was covered with a GARÍN's silk fabric. The pedagogical motivation is to bring an artistic vision to SILKNOW under the concepts of merging tradition and technology. They work with the idea of giving volume to flat prints and stretching them into a third dimension. The brooches (Figure  9 ) consist of a prototype structure in 3D in black PLA plastic. On this structure, a silk fabric was adapted and on the top as a motif of attachment, a metal piece was engraved with the name of the collection "Like Silk". Several experimental prototypes were made and finally it was decided that the 2nd grade jewelry and object students created a series of eight brooches. The methodology consisted of eight phases: 1. selection of eight silk fabric prints digitized; 2. motifs vectorization, to this end the silhouette of the motifs has been drawn using the Rhinoceros program in its 2D version; 3.
Measurements included in the database Three-dimensional reconstructions of mandibles were obtained from CT scans (computed tomography scan, Scanner General Electric Medical System, model Optima CT660, slice thickness of 0,6mm). The CT scans of patients included in the study were performed at the emergency room of the University Hospital Center of Toulouse Purpan, France, between January 1 st , 2017 and June 1 st , 2017. These images were taken to investigate suspected facial fractures or to assess facial cellulitis. Thirty men and 30 women were included in the analysis  [6, 7]. To be included in the study, patients had to: • Be of adult age, in order to avoid any confounding mandibular developmental phenomena  [8, 9]. • Have undergone a CT scan of the facial bone structure in the presence or absence of contrast medium.
Definition of landmarks The geometry of an object can be quantified using a number of different approaches, including contour curves or surfaces  [10, 11] , but the landmark method was used exclusively for the purposes of the current study. This method relies on the analysis of LM coordinates to capture an object's geometry. It is essential that these reference points are correctly defined to allow different individual conformations to be compared. The "Viewbox Cephalometric" Software was used to import and process previously obtained STL files. This software allows STL files to be visualized and analyzed within the orthonormal reference frame, by placing landmarks (LM) on the surface of the reconstructed mandibles. Eighteen LMs were placed per patient (Figs  1  and 2 ). All measurements were performed by the same observer.
Protocol for the analysis of measurements Previously listed LMs were used to calculate the lengths and angles of interest as defined below (Table  1 ). We have defined a method for calculating these variables with the Viewbox software. Statistical analyses of the metric variables were performed using version 8. 0 of the Graph-Pad Prism software (GraphPad Software, La Jolla, USA). The parametric function of our series was tested with a Shapiro-Wilk test. Quantitative data were analyzed using the ANOVA or Friedman test, based on the distribution of variables around the mean. A p-value less than 5% were considered statistically significant. To test concordance of the LM reference points, 5 repeat measurements were performed on 10 randomly selected patients. The methodical error was assessed by intraclass correlation coefficient (ICC).
Heterogeneity in prepared pigments While in the above discussion samples were considered as homogeneous, heterogeneity was investigated on the heavy-liquid pigments using FF-XANES  (45, 46)  to understand more intricately the influence of preparation method on natural ultramarine pigments. This technique consists of acquiring x-ray radiographs of the sample (in transmission), at a hundred energies below and above the absorption edge of interest (here, sulfur K-edge) (see details below). The full FF-XANES data from each sample therefore provide one XANES spectrum per radiograph pixel; hence, about 1 million to 4 million XANES spectra were collected per radiograph stack in a reasonable time. As a result, data interpretation is not biased by the selection of a few points of interest, as may be the case with standard XANES, thereby allowing statistical consideration of heterogeneity.
Preliminarily, analysis of FF-XANES data was done on few individual pixels or in a qualitative way by measuring integrated intensity over specific energy region of interests. This revealed an increase in heterogeneity with increasing temperature of heat treatment. However, considering the very high number of pixels in FF-XANES, a statistical approach was then preferred. Attempts using principal component analysis (PCA) coupled with k-means clustering were ineffective at describing the preliminary findings. Alternatively, nonnegative matrix factorization (NMF)  (47)  was successfully used to decompose each normalized FF-XANES spectrum into main components (see Materials and Methods for further details). The three NMF components obtained through analysis of H-750 spectra (Fig. 2A ) show spectral patterns similar to those observed in the average spectra of Fig.
Data processing and statistical analysis Data processing was performed using several open-source software packages. For XRF maps and XANES spectra gathered in XRF mode, the PyMca (55) software was used for batch fitting, in particular to separate the S L-line from the Pb M-lines in historical samples supplemented with Python script (https://github. com/woutdenolf/ spectrocrunch). For FF-XANES, radiograph alignment was achieved using Silx (https://github. com/silx-kit/silx). Normalization of all (unfocused, , and FF) XANES spectra [pre-edge to 0 arbitrary units (AU) and post-edge to 1 AU] was accomplished using PyMca (version 5. 4. 0) (55). However, this process led to a loss of some pixels in the instance of FF-XANES: In this mode, spectra are acquired in transmission, leading to decreased sensitivity for sulfur species and less optimum batch normalization results than in XRF mode.
Image manipulation (in particular, production of masks for FF-XANES) was achieved in GIMP (2. 10. 8). All other processing was performed via Python (3. 6. 5) using standard scientific packages (e. g. , pandas, NumPy, Matplotlib, and seaborn), and figures were compiled in Inkscape (0. 92). Of note, subtle differences in energies between beamtimes were corrected by calibrating all lazurite prepeaks to 2469. 00 eV, and FF-XANES average spectra were calculated over all pixels representing pigment using a mask. NMF (47) was used using the Python package scikit-learn  (56). Unlike PCA, NMF provides a parts-based representation of the data through the constraint that the parts (or components) must be nonnegative. While NMF is more applicable to XANES and many other spectral data, it still appears underused in the literature.
As detailed above, heterogeneity increases with increasing temperature of heat treatment; therefore, decision was taken to perform NMF on H-750 spectra, from 2467. 60 to 2475. 60 eV, using three components; the number of components was chosen to be the lowest that described heterogeneities observed with qualitative region-of-interest measurements. The FF-XANES spectra for all samples were then decomposed on the basis of these three components, and false-colored maps were produced of their absolute and relative proportions. Details of data processing are provided in Jupyter Notebook sheets available on GitHub at https://github. com/alessaan/rhapsody-in-blue, and all raw, preprocessed, and processed XANES data files are available at doi:10. 15151/ESRF-DC-186933507.
To meet this need, the studies gathered in this book aim to shed light on navigation in the Eastern Mediterranean during the Hellenistic period. They deal with all the parts of the Eastern Mediterranean: not only the Aegean Sea, but other seas between Asia Minor and Egypt. They use all kinds of data -literary, epigraphical, papyrological, iconographic and archaeological. The goal of the book is not to give an exhaustive analysis of maritime commerce; it is to set up the initial framework to help future scholars in their research, as more and more archaeological shipwrecks continue to be discovered and made public in the next decades. This chapter is followed by a study, conducted by Jean-Marie Kowalski, about the role of Cyprus in the network of maritime routes (chapter 2). He demonstrates the differences between the distances given by the literary sources.
Representation of the Eastern Mediterranean The two main editions in use nowadays derive from one tenth-century manuscript (Matritensis 121), in which the text of the Stadiasmus comes immediately after the Chronicle of Hippolytus. 19 This unique manuscript is badly damaged and can hardly be deciphered. That is why Müller's edition of the Stadiasmus in the Geographi Graeci Minores contains many corrections and much additional information. Helm's edition is more recent (1929) but it does not contain as many corrections of Müller's edition. The manuscript history of the Stadiasmus means that the reader must be very careful when applying the quantitative data given by the text about the different itineraries. These factors will not be thoroughly discussed, but one can see, for example, that according to the Stadiasmus, Kargaia is supposedly only 40 stadia 20 away from Kouriakos, while the true distance is approximately 13 nautical miles.
Influence of Weather Conditions Over Navigation Even if climatology has changed within a timespan of two millennia, the lack of statistics before the second half of the twentieth century made it acceptable to use NOAA's data. We have decided here (table  2. 1) to rate this data according to the average winter (end of December) and summer conditions (end of June) according to the angle between the wind and the route supposedly followed by the ships along the itineraries mentioned (from 180 to 135 degrees: 3 -fair conditions -, from 135 to 90 degrees: 4 -highly favourable conditions -, around 90 degrees: 2 -average conditions -, from 90 to 45 degrees: 1 -poor conditions -, and from 45 to 0 degrees: 0 -unfavourable conditions -). If this assessment can be applied to the capacities of the ancient ships, two further conclusions can be drawn.
29 Many ships subsequently wrecked, especially the Madrague de Giens, were involved in that trade. In fact, even the smallest ships carried wine. The vessel that sank off Cap Bénat carried no more than three pithoi, i. e. 3. 3 tonnes, 30 but the wine trade in that area was so successful that Roman merchants used large vessels, called dolia, to carry larger containers than amphorae. 31 lthough the data are patchy, there might have been more of these routes. For instance, the hulls found in Caesarea 32 and Antirhodos 33 almost certainly belonged to medium-sized or, perhaps, even large ships. They can likely be dated to the first century CE. However, we cannot totally rule out the possibility that both of these shipwrecks were from earlier or later. No definite date can be asserted as long as no dendrochronological analysis has been published.
University of Cyprus. It was organized by  Stella Demesticha  (University of CyprusIt was supported by the ' Sailing in Cyprus Through the Centuries ' project, the  French Institute in Cyprus (Institut Français de Chypre) , the  CReAAH (Centre de Recherches en Archéologie, Archéosciences ,  Histoire -UMR 6566 ) and the  Scientific Interest Group of Maritime History (Groupement d'Intérêt Scientifique d'Histoire Maritime ). The book has been edited by  Emmanuel Nantet  and improved by further contributions, with sincere thanks to the contribution of the  French Institute in Cyprus, of the CRESEM (Centre de Recherche sur les Sociétés et les Environnements Méditerranéens -EA 7397 ) and the  LabEx Archimède  ( Archéologie et Histoire de la Méditerranée et de l'Égypte Anciennes ). This book has benefited greatly from the advice of  Conor Trainor  ( University of Warwick ).
Although there is a move towards Open Data, with research funding bodies more frequently requiring data management plans and dissemination strategies, the data management challenges inherently linked to virtual exchange research are understudied. Data collection is often reported upon in papers addressing interaction analysis or language development, but little attention has been paid to offering critical discussion of data collection and structuration methods or practical advice to encourage data / corpora dissemination. This paper reports on two phases of the Multimodal Teletandem Corpus project (Aranha & Lopes, 2019) that structured 581 hours of video data from Portuguese-English teletandem sessions, 351 chat logs, 956 written productions exchanged between the partners (original, revised and corrected versions), 91 initial and 41 final questionnaires, and 666 learning diaries.
However, these practices are far from being common and little attention has been paid to offering critical discussion of data collection and structuration methods or practical advice to encourage data / corpora dissemination. With the aim of addressing this gap in the field, this paper describes how data from the Teletandem Brasil: Língua Estrangeira para Todos  (Telles, 2006)  project was collected at São José do Rio Preto campus then structured into a LEarning and TEaching Corpus (LETEC). Teletandem is considered an approach to virtual exchange where the focus is on language learning. Telles defines it as "a virtual, collaborative and autonomous context in which two speakers of different languages use the text, voice, and webcam image resources of VOIP technology to help each other learn their native language" (2015: n.
p. ). This paper takes the form of a case-study of the Teletandem Brasil project and focuses on some of the data management difficulties encountered and the data management planning steps that, consequently, are being introduced for future telecollaboration instantiations. It is hoped that it offers some useful reflections for colleagues who are setting up their own research protocols for telecollaboration projects. The first section of this paper examines data management, firstly within the context of the data lifecycle and, secondly, with respect to LETEC and with reference to the Computer-Assisted Language Learning (CALL) field. We then outline the pedagogical and research contexts of the Teletandem Brasil project before detailing why it was decided to structure data from this project into the Multimodal Teletandem Corpus (MulTeC,  Aranha & Lopes, 2019).
Data management challenges in the processing of data that relate to consent terms, VIRTUAL EXCHANGES AS COMPLEX RESEARCH ENVIRONMENTS corpus metadata and data storage are then discussed in the third section and strategies introduced within the research project to overcome these challenges explained. Based on lessons learnt, we finish by offering colleagues a series of questions for consideration in the data management planning stages of any telecollaborative exchange. These questions might serve as a toolkit for colleagues considering structuring and sharing data from other telecollaboration projects.
13). This definition places the emphasis on primary 'raw' data and research data as situated before the analysis and interpretation processes. However, the movement towards OpenData that is free for anyone to access, use, modify and share, implies that the term also needs to include 'researchready data' or 'compiled data' (i. e. datasets that have already been processed, cleaned, VIRTUAL EXCHANGES AS COMPLEX RESEARCH ENVIRONMENTS annotated, interpreted and made available to a scientific community) and the notion that 'research data' forms part of a data lifecycle and is not simply the focus of analysis and observation.
Research context The Teletandem Brasil project  (Telles, 2006)  has, since its creation, been the focus of research studies in the field of Applied Linguistics (see http://www. teletandembrasil. org/publications. html). Besides being a pedagogical project, whose aim is to develop linguistic and cultural skills, the research staff also study diverse VIRTUAL EXCHANGES AS COMPLEX RESEARCH ENVIRONMENTS aspects of this learning environment. The amount of data generated by learners within the Teletandem Brasil context has been huge but until lately had never been compiled: Data that was the focus of each research study was collected and treated individually, and used solely to fulfil the purposes and needs of specific research questions. Indeed, as each new Master's or PhD student integrated the project, or permanent researchers explored a different research angle, new data was collected.
Previously, no standardized framework for the collection of visual, spoken and written data was established and multimodal data analysis procedures were not agreed upon within the overarching project. Therefore, one may find a variety of procedures for data collection, processing and analysis in the different academic publications that relate to the project. Long-term data planning was not considered prior to each research project. This practice underlines that preparing data for reuse was perceived by these young researchers as time-consuming and, sometimes, unviable. Permanent researchers were then pushed to consider a more systematic data collection approach for teletandem instantiations that took place between 2012 and 2015 in order to make data available, via initially a databank  (Aranha, Luvizari-Murad & Moreno, 2015)  for the on-site researchers involved in the project.
Data collection Data from 16 different groups (11 integrated and 5 semi-integrated) were systematically collected between 2012 and 2015. Figure  5  describes the type of data that comprises the MulTeC corpus. Questionnaires were initially administered using paper-based versions but nowadays online questionnaires are used. This centralised data collection was then structured into the MulTeC databank for use by Master's and Doctorate students as well as permanent researchers.
Data Processing: The MulTeC Corpus Given the positive response from the research team to the teletandem databank  (Aranha, Luzivari-Murad & Moreno, 2015) , in 2016 the permanent researchers involved in the project decided to take the data management process one step further and transform the databank into VIRTUAL EXCHANGES AS COMPLEX RESEARCH ENVIRONMENTS a corpus by following LEarning and TEaching Corpora principles  (Chanier & Wigham, 2016). The objectives behind this transformation were (i) to encourage collaboration among other researchers interested in investigating telecollaborative learning practice who could not come to the teletandem lab, (ii) incite research studies that are cumulative, contrastive and longitudinal, (iii) promote internationalization of data and (iv) be able to publish the data alongside research studies.
The MulTeC corpus  (Aranha & Lopes, 2019) , built from the teletandem databank, structures the data of 282 teletandem participants from both the integrated teletandem and semiintegrated modalities. Representing 145 GB of data, it comprises 581 hours 19 minutes of video data from TOSs, 666 learning diaries, 351 chat conversations used during TOS, 956 texts (both in Portuguese, written by L2 students and commented upon by L1 native speakers and in English, written by L2 students and commented by a proficient English speaker), 91 initial questionnaires and 41 final ones. The path to build the MulTeC corpus was not a smooth one; however, it taught us many lessons that will be used to collect, compile and organize other corpora. We now illustrate three areas of data management for which our procedures changed.
Consent terms As data was to be collected for a corpus that was, in turn, to be shared with third-party researchers, consent was a delicate yet important issue. The MulTeC database comprised data from UGA (University of Georgia at Athens) and UNESP/IBILCE (São Paulo State University at São José do Rio Preto). When collection started in 2011, both universities created locally their consent forms. UGA had their consent terms approved by their Institutional Review Board whilst UNESP had theirs approved by the local ethics committee. The result was that, depending on the institution in which they were enrolled, students signed different documents and consented to different terms. Consequently, we faced several issues when trying to track, for each teletandem pair, whether both students had agreed for their data to be included in MulTeC.
These included: a) The problem of tracking students one by one; b) The difficulty that if one student refused to sign the consent form, the use of the teletandem partnership's data was impaired even if the student from the other institution had agreed to the consent terms. As all data was recorded, this required researchers to find and delete captured data for which consent had not been given; c) The students' interpretations of the statements and their wording created odd consent terms, e. g. a student allowing the use of his/her video recordings for research purposes but not his/her image. To facilitate the integration of data into the MulTeC corpus, adjustments were made. Written consent procedures are now discussed by both partners and students from both institutions are asked to agree to the consent terms as approved by the UNESP Ethics Committee.