dataesr/scientific-paragraphs-categorization huggingface dataset:
perspicillata and two D. rotundus individuals (Additional file 1: Tables  S3  and S4 ). No more than two transcribed alleles were identified in M. molossus samples (Additional file 1: Table  S5 ). All sequences showed BLAST homology with the HLA-DRB1 locus (accession number: NG_029921), with a maximum of 85 % nucleotide identity and 75 % amino acid identity. The percentage of nucleotide and amino acid identity with other bat species (S. bilineata, M. davidii, C. perspicillata and N. albiventris) was above 87 % and 75 % for all transcripts, respectively.
mtDNA D-loop sequence characterization We amplified the first hypervariable segment (HVI) of the mitochondrial DNA (mtDNA) control region (D-loop) for the 146 bats tested. We obtained a 291-bp alignment for M. molossus, a 340-bp alignment for C. perspicillata and a 359-bp alignment for D. rotundus. Thirty-nine mtDNA D-loop haplotypes were identified for C. perspicillata (45 individuals, Genbank accession numbers: KU896735-KU896779), 29 for M. molossus (42 individuals, Genbank accession numbers: KU896693-KU896734) and only five for D. rotundus despite the high number of sequenced individuals (59 individuals, Genbank accession numbers: KU896634-KU896692) (Table  2 ).
Acknowledgments We are grateful to  François Catzeflis  and  Marguerite Delaval  for useful discussions on the biology, ecology and dynamics of bats. All field volunteers and owners and/or managers of capture sites are warmly acknowledged for their assistance in captures. Funding A. Salmier was supported by a grant from European funds (PO FSE 2007-2013) and " Investissement d'Avenir " managed by  Agence Nationale de la Recherche  ( CEBA, Ref. ANR-10-LABEX-25-01 ). This study was conducted within the  CAROLIA program  supported by  European funds (ERDF/FEDER)  and assistance from  Région Guyane and Direction Régionale pour la Recherche et la Technologie. It also received a  European Commission "REGPOT-CT-2011-285837-STRonGer" grant  within the  FP7  and " Investissement d'Avenir " grants managed by the  Agence Nationale de la Recherche  ( CEBA, Ref.
This chapter gives an overview of one possible staged methodology for structuring LCI data by presenting a new scientific object, LEarning and TEaching Corpora (LETEC). Firstly, the chapter clarifies the notion of corpora, used in so many different ways in language studies, and underlines how corpora differ from raw language data. Secondly, using examples taken from actual online learning situations, the chapter illustrates the methodology that is used to collect, transform and organize data from online learning situations in order to make them sharable through open-access repositories. The ethics and rights for releasing a corpus as OpenData are discussed. Thirdly, the authors suggest how the transcription of interactions may become more systematic, and what benefits may be expected from analysis tools, before opening the CALL research perspective applied to LCI towards its applications to teacher-training in Computer-Mediated Communication (CMC), and the common interests the CALL field shares with researchers in the field of Corpus Linguistics working on CMC.
It presents a new scientific object, the LEarning & TEaching Corpora (LETEC). After having clarified the notion of corpora, used in so many different ways in language studies, the methodology used to collect, transform and organize data in order to make them sharable through open-access repositories is described. We suggest how the transcription of interactions may become more systematic, and what benefits may be expected from analysis tools before opening the CALL research perspective applied to LCI towards its applications to teacher-training in Computer-Mediated Communication (CMC), and the common interests we share with researchers in the field of corpus linguistics working on CMC.
Corpora in Linguistics In many areas of general linguistics or even applied linguistics, building and using a corpus is a tradition. A first definition offered by Biber,  Conrad and Reppen (1998) , following the seminal work of Sinclair (1991) (see  O'Keefe et al. [2007]  for full references), could be as follows: a corpus is a principled collection of texts, written or spoken, available for qualitative or quantitative analysis. The word corpus, however, may be indistinctly used by a graduate student to refer to her/his compilation of a set of language examples or a set of texts, or by a researcher in corpus linguistics. A similar confusion exists inthe Humanities around the word database. Any set of data included in a spreadsheet, or even database software, is often labelled a database, while the second indispensable component of a database, i.
Whereas the previous corpora all captured language used in formal or informal situations only by native speakers, a team of linguists gathered in Belgium around Sylviane Granger to launch a new type of corpora, namely Learner Corpora. Productions (mainly academic essays) of learners of English as a second language were collected  (Granger, 2004). Here again, the team did not confuse the concept of a corpus with a simple set of essays in electronic formats. They developed a framework for learner corpus research where data were collected, structured and, from 2009 onwards, annotated in the same way. They included productions of learners with different mother tongues to allow interlanguage comparisons.
The corpus paradigm Taking into consideration the aforementioned lengthy experiences coming from corpus linguistics (whether general or applied linguistics), as well as international recommendations for the management of research data in all scientific disciplines, the corpus paradigm can be developed as follows: -Systematic Data Collection. Even when an individual researcher has a specific research question in mind, let us say, a specific kind of interaction s/he wishes to consider, the whole data set, including interactions, productions, logfiles (data related to what is called learning analytics) should be collected. It is a prerequisite to allow other researchers to reuse the corpus. It also relates to quality criteria. Often a researcher selects a subset of data from the whole data set in order to analyse it and publish an article.
Quality in the research procedure implies that the researcher is able to explain the extent to which a selected subset of data does not correspond to a simple disconnected episode, but really reflects what happened during the online course. -Detailed Data Description. The context of learning situations encompasses many facets, as detailed later in this section. In regards to language corpora,in general, the detailed descriptions are often referred to as metadata. In the metadata, the researcher not only gives a corpus title, date, list of credits, but also explains how the data have been collected, edited and organized. Sociolinguistic information about the participants is detailed. As an example, let us consider a SMS corpus. Metadata will explain how messages have been collected on the phone network(s) and anonymised.
If a researcher tries to access language corpora which pretend to be open access, s/he may discover free access to only a limited part of the corpus, or that the corpus cannot be downloaded, or, when it is a speech corpus, s/he may only have access to the transcripts but not the accompanying audio files, etc. Under such circumstances, research on the corresponding data is impossible. However, there currently exist more frustrating situations-for example, when a researcher adds an extra level of annotation and wants to publish this, but suddenly realizes that s/he is not allowed to because the license attributed by the original collectors of the corpus forbids any derivative work. Securing open access intertwines several steps of a corpus' lifecycle. Before data are collected, the researcher will consider the question of ethics and rights related to participants and their productions, choose the license under which to release the future corpus, and choose in which repository the corpus will be deposited for archiving, for example, at the European level, DARIAH (2013).
An illustration of the staged methodology for building LETEC The LETEC approach to data collection, structuring and analysis comprises successive phases (Figure  1 ). It has been developed from 2006 onwards by the Mulce project  (Reffay et al. , 2012). Using a case-study approach, this section describes these phases in turn, referring to the example of the online English for Specific Purposes course, Copéas, and its associated LETEC (see  Chanier et al. , 2009). This ten-week intensive course ran in 2005 and formed part of a master's programme in Distance Education in France. The course's aims were for students to be able to think critically about using the web for learning and to practise their oral and written English skills online. Each week, the students participated in online tutored discussions in the online platform Lyceum.
Data organization In this section, we present one way to transform raw data into research data, how to organize themand how to document them in an exhaustive yet informative manner. Besides folders of data coming from the above-mentioned learning design and the research protocol, we detail those gathering participants' productions, ethics and rights information, and the overall organization of the corpus (entitled a global corpus). Later, another corpus type is presented (distinguished corpora), which can be derived from the global corpus after research and analyses have been performed.
Ethics and rights for OpenData Releasing a corpus as OpenData means allowing other people the possibility of free use, reuse and distribution. In other words, the user may extract part of the researcher's data, mix this part with data from other sources, add her/his own work to build upon the whole data set and distribute the entire result. Therefore, OpenData relies on two sorts of rights-those related to the data collection and those related to the data release. In other words, data collected need to be free of rights, and, secondly, the corpus creator should give the right to use the corpus to the end-user, thanks to a license that imposes minimal constraints. Indeed, internationally, it is even recommended to avoid putting a licence that forbids commercial use and to waive intellectual property rights (IPR) (Open Knowledge, 2013).
Waiving IPRs does not imply that the creators will not be citedor acknowledged. The full bibliographic reference of their work will become prominent in the corpora repository and will guarantee, in the academic world, that end-user researchers can clearly refer to the original creators when submitting their new analysis to a peer-review process. Collecting data that are free of rights implies that the compiler him/herself has the right to use the resources included in the corpus and that participants waive their rights on what they have produced. Their agreements are obtained once they have individually signed a consent form, distributed after an "enlightenment" procedure (see  Mackey &Gass, 2005). During this procedure, researchers have an open discussion with participants, where they explain drawbacks and benefits that may be expected from the course and the research process.
Post research data and component Post research often concerns transcriptions of multimodal interactions, in ways which will be presented below. These transcriptions produce a new set of data which will be assembled into a new LETEC, of a distinct type called a distinguished corpus  (Reffay et al. , 2012). Its size is much reduced, and corresponds to data assembled and produced by a researcher when s/he focuses on a specific research question and aims to publish an article on the specific topic. A distinguished corpus includes a particular transformation of a selected part of the global corpus-for example, the transformation of a video file into an XML/text file of the transcribed interaction data and its associated metadata. Following transcription, data analyses can be performed. Data from the global corpus are not copied, but instead referred to, and the newly distinguished corpus only adds the transformed data for the specific analysis.
Corpus publication Once the content packaging of the corpus is finished, the compiler deposits the corpus in a repository that adheres to the requirements discussed in section 2. This server will provide to the user open access to the information about each corpus stored in the repository with search facilities. It will be connected to harvesters so that its bank of metadata is searchable through each different harvester. It may also offer services such as permalinks to each corpus and data subset, which will identify them in a unique and permanent way. The Mulce repository (2013) gives access to fifty LETEC corpora coming from more than ten different online learning situations that took place between 2001 and 2013. In May 2012, its size was the following: more than one million tokens, coming from 12000 audio turns, 17000 text chat turns, 3000 blogs, 2000 emails, 2700 discussion forum messages, plus more than 9000 non-verbal acts.
LETEC contributions to CALL research The purpose of this section is to present how research on LCI may benefit from the existence of open access corpora. Research is a circular process. For example, LETEC corpora in the Mulce repository have been built out of online learning situations, starting more than thirteen years ago. Data have been reused several times and will be mixed into projects, independent of Mulce, as discussed previously. Let us start with one of the very first steps in examining online multimodal interactions (see also Chapter 9, this volume), i. e. , transcriptions of components of the instantiation part of a LETEC.
This interpretation of transcription confuses two steps in the research process-the actual transcription and the analysis. Researchers involved in national or international consortiums on speech and/or multimodal corpora have special interest groups around interoperability (e. g. ,Huma-Num, 2014). The idea is that if one understands research as a cumulative process, idiosyncratic models need be compared in order to enhance understanding of human interactions. This implies separating the transcription from analysis processes and using a variety of analysis tools with compatible output formats. Figure  10. 2. belowillustrates this point. It displays a window from the transcription software ELAN (Sloetjes&Wittenburg, 2008) that integrates the video-screen capture of a Copéas session (red box).
Transcription is aligned with the video's time, and buttons in (4) provide different ways of selecting parts of the video and of moving between transcriptions layers. Once the transcription is completed, its contents are saved using a text-structured XML format that offers the possibility of later compiling it with transcriptions of other sessions from the same course and/or reusing the file with analysis software. ELAN is a good example of open-access software. This asset, plus the interoperability one, allows any user, once the distinguished LETEC corpus has been downloaded, to rework on the transcription and add another layer, for example. It is largely used in the aforementioned community on multimodal corpora. There is an even subtler methodological question where transcription is concerned: Are online interactions so complex that it is impossible to compare and make adjustments between transcription codes? Let us take an example and consider the code defined when transcribing online learning sessions in 3D environments where participants interact using avatars  (Wigham & Chanier, 2015).
In order to allow this, data from the different approaches need to be accessible in standard formats, with publications clearly relating to data and data analyses, and explicit information given about the format of the transcriptions, their codes and transcription alignments with video. However,  Sindoni's (2013)  data are not available. The inability to contrast data with other examples, available in open-access formats, is still holding back the scientific advancement of the CALL field. Coming back to the topic of analysis tools, a researcher who has collected and structured her/his data now has at her/his disposal a wide, rapidly evolvingrange of tools for lexical processing, morpho-syntactic tagging, statistics, discourse analysis, etc. Should the researcher choose open-access toolswith interoperable formats, s/he not only paves the way for circular, multi-analysis research processesbut also contributes to the development of these tools; the teams of researchers who developed them are keen to improve them when confronted with requests based on actual data and analysis attempts.
Pre-service teachers will not necessarily take the time to question the findings, taking research conclusions as a given. Indeed, the development of an analytic approach to the reading of research literature takes time, and during training courses, educators do not necessarily have enough time for this process to mature. The second approach focuses on action research with pre-service teachers participating in experiments and adopting either the role of learners or tutors. Here there is either the assumption that trainees will naturally understand what they need to do or, if greater guidance is given, reflective feedback sessions are often conducted with the trainees. In the latter case, attempts to use the same methodology for both data collection and training purposes are often difficult to manage; trainers face the issue that student materials are often heterogeneous and quickly extracted from the on-going experiment, and pre-service teachers may only consider his/her individual practice.
An example of pedagogical corpus Each pedagogical corpus includes a selection of materials from a LETEC corpus and a series of structured teacher-training tasks that have been developed from these materials, based on leads that had been identified in research papers for which the analyses utilized the same data. To illustrate this concept, let us look at a pedagogical corpus, entitled reflective teaching journals that was developed from the research Copéas corpus  (Wigham & Chanier, 2013). From the course data and research articles about the project, the need of encouraging trainee-teachers to foster reflective practice through the writing of teaching journals was identified. Journal writing is a prerequisite for developing reflective practice, but it is not a sufficient condition. It only offers a one-sided view of the course situation.
Conclusion When studying LCI in ecological contexts, there are a number of variables that cannot be controlled. These variables make the comparison of scientific results difficult and the replication of a given learning and teaching experience near impossible. This chapter proposed one possible staged methodology to structure raw data from LCI situations into corpora so as to render them comparable, re-analysable and available to the whole research community. The case-study approach adopted allowed us to present the constitution and diffusion of LEarning and TEaching (LETEC) Corpora, using the example of the online Copéas course. In this presentation, we examined the ethical implications of producing corpora as OpenData and suggested ways in which the transcription of LCI and their analysis can become more systematic and comparable.
The LETEC methodology is one methodological proposition to help the CALL field better meet the principles of scientific validity and reliability that are fundamental cornerstones of the scientific method, yet difficult to achieve in ecological learning situations. More systematic organization of data and its processing is often perceived as time-consuming. However, it requires a mind-set shift whereby individual researchers do not think of producing one-off analyses on individual learning situations but instead look towards long-term team research projects in which corpora, rather than data, are re-used for new analyses, produced from different perspectives, and are reconsidered and cross-referenced from one LCI experiment to another. This would encourage, firstly, a more circular and multi-analysis research approach within the field and, secondly, scientific debate, both within CALL but also more largely within corpus linguistics, which is based on the possibility to reanalyse, verify and extend original findings and to contrast data with other examples from other research teams and different online environments.
The resin was washed with buffer A supplemented with 1 M NaCl to remove contaminating DNA, and the proteins were eluted in buffer A containing 1 M NaCl and 250 mM imidazole. Eluents were analyzed by SDS-PAGE. Fractions containing the recombinant product were concentrated using centrifugal filtration (Centricon Plus-20, 5000 Da molecular cutoff, Millipore). The proteins were then loaded onto a Superdex 200 (N TAIL ) or Superdex 75 (XD) 16/60 column (GE, Healthcare) and eluted in 10 mM Tris/HCl pH 8, 150 mM NaCl. Protein concentrations were calculated using the theoretical absorption coefficients at 280 nm as obtained using the program ProtParam at the EXPASY server.
Isothermal titration calorimetry ITC experiments were carried out on an ITC200 isothermal titration calorimeter (Microcal) at 20˚C. Protein pairs used in the binding analyses were dialyzed against the same buffer (10 mM Tris/HCl pH 8, 150 mM NaCl) to minimize undesirable buffer-related effects. The dialysis buffer was used in all preliminary equilibration and washing steps. The concentrations of purified wt and mutated N TAIL proteins in the microcalorimeter cell (0. 2 mL) ranged from 25 μM to 180 μM. XD was added from a computer-controlled 40-μL microsyringe via a total of 19 injections of 2 μL each at intervals of 180 s. Its concentration in the microsyringe ranged from 300 μM to 960 μM. A theoretical titration curve was fitted to the experimental data using the ORIGIN software (Microcal).
Molecular dynamics simulations All MD simulations were performed in explicit solvent with periodic conditions with CHARMM and NAMD software packages and CHARMM force field version 27 with CMAP corrections. The initial coordinates of the XD/α-MoRE complex were taken from the crystal structure (PDB code 1T6O)  [44]. The two XD/α-MoRE mutated models bearing either the S491L or the R497G N TAIL substitution, were built with VMD plugin 'mutator' starting from the X-ray structure of the wt complex (PDB code 1T6O). In the case of the S491L variant, the three most favourable orientations of the leucine side chain were generated with Sybyl. Nonprotein derivatives were discarded. Orientation of the side chains of Asn, Gln, and His residues were checked using in-house VMD plugin and the WHAT IF web interface (http://swift.
Minigenome assay The assay was performed essentially as described in  [62, 112]  with minor modifications. 2. 10 4 BSRT7 cells that constitutively express the T7 phage DNA-dependent RNA polymerase  [113]  were seeded in 96-well plates and transfected the day after with 66 ng of pEMC-N (either wt or mutated) 44 ng of pEMC-(Flag/L+P) (a home-made T7-driven bicistronic construct)  [58]  and 90 ng of plasmid encoding for the different minigenomes mixed with the transfection reagent as indicated in the manufacturer protocol (jetPRIME Polyplus-transfection). Two days after transfection, Firefly and NanoLuc activity were measured using the Nano-Glo Dual-Luciferase Reporter Assay (Promega). The background luciferase activity from of both luciferases observed in the absence of active L protein was subtracted from the signal measured in the presence of L, and data obtained from three independent experiments were normalized to each other to level the mean signal observed for all combinations tested at the same time.