dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of primary outcomes Descriptive statistics will be used to ascertain feasibility. Willingness to enrol and attrition components will be summarised for participants, overall and in relation to selected baseline characteristics. Differences between followed up patients and those who were lost to follow-up will be examined in terms of baseline characteristics, by a paired sample t-test (normal distribution) or Wilcoxon signed-rank test (non-normal distribution). Quantitative and qualitative data for acceptability and usability will be examined: Quantitative data: each variable measured in acceptability and usability configured a Likert scale. The composite score (mean) of each variable will be calculated and treated as an interval/ratio scale. Pearson correlations between the constructs will be calculated to explore the following hypotheses: perceived usefulness and perceived ease of use is positively and significantly correlated to attitude; perceived ease of use is positively and significantly correlated to perceived usefulness; perceived quality of content will influence perceived usefulness and ease of use.
Repeated measures of the User Experience Questionnaire will be tested by repeated measures analysis of variance method. Qualitative data: for the analysis of self-reported data collected from User Experience Questionnaire and from Interval Question, qualitative thematic/content analysis will be conducted as proposed by Mayring. 39 This method is a technique of summarisation, whereby themes are created in an inductive procedure by reducing, paraphrasing and generalising relevant text passages. Patterns in the text will be found and coded in order to search for themes in the data. The data will be subjected to thematic content analysis with the help of Atlas-ti software.
Quality control The researcher will ensure the accuracy and integrity of the data and reports required. The data included in the m-RE-SIST derived from source documents will be consistent with such documents; otherwise, the discrepancies will be justified. The researcher will keep the study documents for at least 5 years after the study is completed. Data monitoring will be done by the ethics committee of each site. Clinicians will have all the study-related files available, allowing direct access to data or source documents to perform monitoring, audit, review by the ethics committee or any inspection by the competent authorities. All data collected will only be accessible to m-RESIST partners.
EthICs And dIssEMInAtIon Before entering the study, all participants will be legally competent and will provide written informed consent to the clinical team. All the data collected will be treated confidentially and analysed anonymously. Any protocol amendments will be made through the ethics committee of each site. The results of this study will be published in international peer-reviewed journals. A wide dissemination of the project results is planned to take place at European and international level. Patients, caregivers, health professionals, institutions and stakeholders will be targeted as the main recipients of the m-RESIST outcomes.
and writing of the final version of the manuscript, and gave their approval for the submission. Funding This work has been supported by the  Horizon 2020 Framework Programme  of the  European Union  (grant number  643552 ) and partly funded by  FEDER  funds of the  EU  and  CERCA Programme/Generalitat de Catalunya. Trial Sponsor: Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau-IIB Sant Pau, Spain. Competing interests None declared.
Bioinformatic treatment -DNA sequences of Fagus sylvatica and Coenonympha sp. libraries were used to call SNP genotypes (total: ~100 million reads). We developed a homemade pipeline called ProcessMyRAD (fully available at https://github. com/cumtr/pmr) to automatically perform the different steps leading from the raw reads to genotype data. To call the genotypes, ProcessMyRAD relies on the STACKS pipeline  (Catchen et al. , 2013). To reconstruct loci, the STACKS procedure needs to set three thresholds: the minimum number of reads to consider an allele (m), the maximum number of mismatches allowed between two alleles to reconstruct a locus (M), and the maximum number of mismatches allowed between two individual loci to consider them as homologous (n).
We then sequenced the resulting libraries all together. The sequences resulting from these tests were treated with the STACKS program and the following clustering parameters: m=4, M=6, n=8 (based on the results of the section "Bioinformatics tests" for m and M, and with n=M+2 to increase the number if interindividual matches), keeping only one SNP by ddRADseq fragment. To estimate the impact of the wet lab treatment on alleles frequencies, we did not filter out alleles based on their frequency. On the resulting genetic datasets, we determined the number of polymorphic fragments, the mean fragment coverage, the number of SNPs in the fragments, the individual heterozygosity, and the proportion of private alleles in individuals.
We also evaluated reproducibility by performing a Principal Component Analysis on genetic data with the R-package adegenet  (Jombart, 2008) , and looking at the distances between replicates in the PCA projection space. In addition, the replicates were used to estimate the proportion of inconsistencies in our final genetic dataset. These inconsistencies can take two different forms: errors of genotypes (due to PCR errors or ADO) or fragment absence (due to a lack of reproducibility of the experiment). We measured the genotyping inconsistency rate by identifying the proportion of loci with inconsistencies among the three replicates. Then, by looking at the fragments in the three replicates, we could estimate the proportion of "true" fragment absences, when the fragment was missing in all three replicates, and the proportion of "false" fragment absences, when the fragment was missing in just one or two of the replicates.
Test on bioinformatic parameters Impact of bioinformatic treatment -We estimated the influence of the m (ustacks) and M (cstacks) values on ddRADseq fragment reconstruction and downstream analyses. For this purpose, we used the standard ddRADseq wet protocol described above on 30 individuals of Fagus sylvatica coming from three different populations (Sainte Beaume, Digne, and Bauges, France; see Table  S1 ) and 30 individuals of Coenonympha butterflies from three different species (C. arcania, C. gardetta and C. macromma; see Table  S1 ). We then repeated the bioinformatic pipeline with different m values ranging from 1 to 15 and different values of M ranging from 1 to 25. When the m value varied, M and n were fixed to 6. When the M value varied, m was fixed to 4 and n was equal to M. For all these tests, the remaining steps of the procedure were exactly the same and the last step of genetic dataset export was performed by keeping only one SNP by RAD tag fragment and without any filtering on allelic frequency.
To estimate the influence of the m and M values on RAD tag fragment recovery, we determined the number of reconstructed fragments, their mean coverage and the proportion of polymorphic fragments for each value of M and m tested. We also evaluated the impact of these parameters on population genetics results by performing, for all m and M values, some of the most commonly used analyses using ddRADseq data  (Capblancq et al. , 2015; Kjeldsen et al. , 2016; Black et al. , 2017; Nunziata et al. , 2017; Settepani et al. , 2017; Elleouet & Aitken, 2018; Sherpa, Rioux, Pougnet-Lagarde, et al. , 2018) , i. e. mean individual heterozygosity, F ST among populations (estimated with the adegenet R package  (Jombart, 2008) ), Principal Component Analysis (PCA, using the adegenet R package  (Jombart, 2008) ), genetic structure with sNMF (using the LEA R package  (Frichot & François, 2015) ) and evolutionary history reconstruction using Approximate Bayesian Computation (performed with the diyABC program  (Cornuet et al.
For example, the individual heterozygosity increased from 0. 09 to 0. 27 for Fagus sylvatica individuals when the number of cycles increased from 10 to 25. In the same way, the number of private alleles doubled when the number of PCR cycles increased from 10 to 25 for both Coenonympha sp. and Fagus sylvatica samples. Finally, 10 cycles of PCR lowered Substantially the number of fragments and SNPs as well as the number of private alleles in the final genetic dataset.
Reproducibility of the experiment and estimation of inconsistencies The library of the 11 Fagus sylvatica triplicates produced a mean of 7,547 fragments with a mean coverage of 20. 3 reads per fragments. The PCA performed on the complete genetic dataset showed very consistent results across the 11 tested individuals (Fig. 3 ). Interindividual genetic variability was higher than inter-replicate genetic variability. All triplicates clustered in the PCA plot and the different individuals could easily be differentiated. Moreover, considering the eigenvalues, the 10 first axes retained most of the genetic variance within the three replicates * 11 sample tests (92%). For example, PC1 strongly discriminates individual VTX_H_83 from the rest of the sampling and PC2 differentiates individuals SB_H_42 and BG_1_1.
No association between the initial DNA concentration after extraction and the consistency of ddRADseq results was observed (data not shown). Regarding the estimation of genotype inconsistencies, the results were congruent across the 11 tested individuals (Fig. 4 ). The maximum inconsistency rate was just above 4% and the minimum is around 1. 6%. Similarly, we obtained a good proportion of fragments recovery among replicates. Between 66% and 90% of the ddRADseq fragments were found in all three replicates (Fig. 4 ). The individuals with low fragment recovery rate were the exact same ones that showed a great variability in the reproducibility experiment (see above). Some fragments were missing for all three replicates, and the proportions of missing fragments were pretty homogeneous across individuals, varying from 5% to 13%.
, 2013). The similarity threshold (M) determines the minimum sequence homology to consider that two sequences are variants of the same locus. Choosing a too high M can wrongly impede the clustering of different alleles of the same locus, while a too low value could lead to the merging of paralog regions of the genome  (Catchen et al. , 2013). We could have expected that such variation in loci reconstruction and SNPs identification would influence, to some extent, the population genetic analyses performed with most ddRADseq datasets. However, our results suggest that the bioinformatic treatment has only a marginal influence on population genetic results. Indeed, no change in genetic differentiation, clustering or demographic inferences were detected neither at the inter-species level for the animal model nor at the intraspecific level for the plant model  (Fig 6, 7, S7 and S8).
ACKNOWLEDGEMENTS The authors belong to the DivAdapt and MALBIO research teams at the  Laboratoire d'Écologie Alpine (LECA). We would like to thank  Nadir Alvarez  and  Marianne Gagnon  for their useful comments on the manuscript. TCa acknowledges support from the  ANR  project  APPATS  ( ANR-15-CE02-0004 ) and all authors acknowledge the LECA for funding support. The LECA is part of the  Labex OSUG@2020  ( ANR10 LABX56 ). Most of the computations presented in this paper were performed using the CIMENT infrastructure, which is supported by the  Auvergne-Rhône-Alpes region  ( GRANT CPER0713 CIRA ).
In some cases of pharmaco-resistant and focal epilepsies, intracranial recordings performed epidurally (electrocorticography, ECoG) and/or in depth (stereoelectroencephalography, SEEG) can be required to locate the seizure onset zone and the eloquent cortex before surgical resection. In SEEG, each electrode contact records brain's electrical activity in a spherical volume of 3 mm diameter approximately. The spatial coverage is around 1% of the brain and differs between patients because the implantation of electrodes is tailored for each case. Group studies thus need a large number of patients to reach a large spatial sampling, which can be achieved more easily using a multicentric approach such as implemented in our F-TRACT project (f-tract. eu). To facilitate group studies, we developed a software-IntrAnat Electrodes-that allows to perform virtual electrode implantation in patients' neuroanatomy and to overlay results of epileptic and functional mapping, as well as resection masks from the surgery.
IntrAnat Electrodes is based on a patient database providing multiple search criteria to highlight various group features. For each patient, the anatomical processing is based on a series of software publicly available. Imaging modalities (Positron Emission Tomography (PET), anatomical MRI pre-implantation, post-implantation and post-resection, functional MRI, diffusion MRI, Computed Tomography (CT) with electrodes) are coregistered. The 3D T1 pre-implantation MRI gray/white matter is segmented and spatially normalized to obtain a series of cortical parcels using different neuroanatomical atlases. On post-implantation images, the user can position 3D models of electrodes defined by their geometry. Each electrode contact is then labeled according to its position in the anatomical atlases, to the class of tissue (gray or white matter, cerebro-spinal fluid) and to its presence inside or outside the resection mask.
, 2017). A limited number of electrodes can be implanted, their trajectories being constrained by the blood vessel organization which is patient-specific  (Rodionov et al. , 2013; Zuluaga et al. , 2014)  and adapted to the EZ hypothesis from pre-implantation data  (Narizzano et al. , 2017). The brain is thus sampled differently between patients, and the final surgical resection may differ, even for similar types of epilepsy. During SEEG exploration, recordings of different processes are obtained, e. g. , resting state oscillations, epileptic seizures, responses to cognitive tasks and to cortical stimulations. For group studies on the reproducibility of those signals, a large number of patients is therefore needed, involving multicentric data pooled over long periods. To facilitate these studies, we developed a research software called IntrAnat Electrodes, which is free and open-source under the GNU-GPL license, coded in Python and based on software largely distributed within the neuroimaging community (SPM, ANTs, Freesurfer, BrainVisa).
An important part of the current project has been to make the interface user-friendly, allowing its use for research studies by neurologists and neurosurgeons without specialized training. IntrAnat Electrodes is based on BrainVisa 1  and developed as a BrainVisa toolbox. This allows the use of all functionality proposed by the community, mainly MarsAtlas parcellation  (Auzias et al. , 2016) , Morphologist for segmentation and sulci recognition 2  and Freesurfer for alternate segmentation  (Dale et al. , 1999; Fischl et al. , 1999). Existing software for electrodes positioning-Jimbo Dicom Viewer 3  , SEEG Assistant  (Narizzano et al. , 2017) , EpiNav  (Rodionov et al. , 2013; Vakharia et al. , 2017) , BFM  (Wang et al. , 2016) , iELVis  (Groppe et al. , 2017) -allow mainly to precisely position the electrodes, using the coregistered 3D pre and post-implantation images and other coregistered 3D imaging modalities.
As a BrainVisa toolbox, IntrAnat Electrodes uses data organized in a SQLite 4  database making group studies easier, and allows combining and displaying a large amount of data for each subject leading to better insights from the available data. The software source code is available here: https://git hub. com/IntrAnatSEEGSoftware/IntrAnat with a basic documentation here: https://github. com/IntrAnatSEEG Software/IntrAnat/wiki. A docker image for Intranat Electrodes including all required dependencies is available on the public docker repository: https://store. docker. com/community/images/demanp/intranat. Please use the ''release'' tag to download the stable version (docker pull demanp/intranat:release).
Summary IntrAnat software is composed of three different interfaces: ImageImport, LocateElectrodes and GroupDisplay, that are described in respective parts of the methods. Figure  1  summarizes the software and data organization. Briefly, ImageImport allows the user to import all 3D images and run the coregistration between them, compute the deformation field to the MNI, and performs segmentation/parcellation of the 3D image pre-implantation using MarsAtlas or/and Freesurfer. LocateElectrodes allows to position electrodes and to label their contacts according to multiple criteria (anatomical or SEEG analysis results, such The left panel shows the possible classes of imported images, which can be specified as "pre," "post," and "postop" according to the (stereoelectroencephalography, SEEG) protocol.
On the right-hand side, it is possible to specify whether images where obtained with or without injection of Gadolinium, in order to improve the results of subsequent segmentation routines. as presence of a response during a specific cognitive task). GroupDisplay allows to select patients according to criteria from contact and resection labeling done in LocateElectrodes and can display data from those patients in a common template. FIGURE 4 | LocateElectrodes interface allows to select a patient in the database (1), using searching criteria (2). The user can navigate in the different imported images using (3), fusion them easily (4), generate the resection mask (5) if the T1 post-resection was imported. Using (6), the user can add, position and name electrodes after choosing a predefined 3D model.
First Interface: ImageImport It allows the importation of all data into the database: images (native or resulting from processing pipelines such as Destrieux's parcellation, or cortical thickness performed by Freesurfer) in Nifti 5 and mgz 6 formats, and SEEG files (only Micromed TM TRC format for now). There is no anonymization process coded in IntrAnat Electrodes, therefore all data must have been anonymized before being imported. When importing the data, IntrAnat Electrodes incorporates the patient identification code specified in the graphical user interface (GUI) in the patient name data field (Figure  2 ). By default, for each modality 5 https://nifti. nimh. nih. gov/nifti-1/ 6 https://surfer. nmr. mgh. harvard. edu/fswiki/FsTutorial/MghFormat three time points are defined, called pre, post and postOp for pre-implantation, post-implantation with the electrodes within the head of the patient, and post-resection surgery.
The font color of the table file is used to set the colormap: non-stimulated contacts in gray, stimulated contacts with no clinical observation in black, stimulated contacts where the patient had a response but without a link to epilepsy in blue, stimulated contacts where the patient had a response similar to the beginning of a seizure in red (2). Alternatively, one can display the classification of the response (using the background color of the line 1 of the table file): motor in red, sensitive in blue, sensory in green, vegetative in yellow, emotional in purple, experiential in pink and superior function in brown (3). The GUI (4) allows to display all or part of these classifications. Contacts not recorded in the SEEG file are not represented. coregistration routines. All transformation matrices are saved in the BrainVisa database and are applied by the BrainVisa referential management system.
Images are not resampled at this stage, as image resampling is performed dynamically in Anatomist, BrainVisa's display software. From the T1 pre-implantation, IntrAnat Electrodes then estimates the mapping to the MNI referential using SPM12  (Ashburner, 2009)  and stores the resulting deformation field in its database. It also runs Morphologist's gray/white segmentation and sulci recognition  (Perrot et al. , 2011) , and MarsAtlas parcellation  (Auzias et al. , 2016). If a contrast agent was used to obtain the T1 pre-implantation image, the user has to specify it (Figure  2 ) in order to allow a specific pre-processing step where IntrAnat Electrodes based on SPM12 will remove all voxels which are not gray or white matter, and then run Morphologist and MarsAtlas on this preprocessed image. If for some reason the SPM12 pre-processing is not satisfactory, IntrAnat Electrodes can use an ANTs commands 7 (denoising, segmentation of gray/white matter using other MRI modalities than T1 are in development;  Avants et al.
, 2011a Avants et al. , ,b, 2014)). It can also use Freesurfer gray white segmentation which, according to our experience, often fails when a contrast agent is used on the T1, except if we specify as input a T2 or a FLAIR in addition to the T1 (Figure  3 ). Matching criteria on the left-hand side of the GUI are: center and year of acquisition, contact localization according to the parcels (here MarsAtlas parcels, but other atlases can be chosen), cognitive response from SEEG data (still under development), resected parcels, stimulated parcels, stimulation parameters, Engel's score. In the shown configuration, the software indicates all patients who had an electrode contact in the right isthmus of the cingulate cortex (R_ICC) and for whom the resective surgery included at least partially the right ICC.
Second Interface: LocateElectrodes LocateElectrodes provides the patient specific multi-modal data visualization and positioning of electrodes (Figures  4, 5 ). The software uses 3D rigid models of the electrodes which were created according to manufacturer specifications of electrodes (Dixi Medical, Alcis ADTech, Medtronic, HKHS Healthcare, etc. ). The electrodes are manually positioned by users by choosing a model in the available list and by clicking on the post-implantation images on the end of the electrode and any other point along its length. Because pre-implantation and post-implantation images are co-registered, the electrodes can then be visualized in the pre-operative segmented images which are used to label contacts. The software can label each electrode contact automatically according to its position in the anatomical atlases (MarsAtlas, Destrieux, Brodmann, AAL or MaxProbMap), and to its presence in the resection mask (Figure  6 ).
Once the file is filled, it is converted into Python dictionaries using openpyxl and integrated to the IntrAnat database. A resection mask can be computed on-demand in the software. It only qualitative and not quantitative as it is sensitive to brain deformation following the surgery. To compute it, the brain mask of the T1 post-resection image is resampled in the T1 pre-implantation referential using the rigid transform computed by ImageImport, as automatic non-rigid registration methods were found not robust enough in this context. It is then subtracted to the pre-implantation brain mask, so all voxels that were identified as gray/white matter in the pre-implantation mask but not in the post-resection mask are kept. A morphological opening is performed to smooth the results. The user then has to click within the main resection volume, and using a connected components function only the component corresponding to where the user clicked will be defined as the resection mask.
DISCUSSION The software presented here is, to our knowledge, the first SEEG dedicated software based on a database interface to select patients according to many criteria. It uses well established research neuroimaging toolboxes (BrainVisa, Freesurfer, SPM and ANTs) to recycle optimal and validated image analysis processes. IntrAnat Electrodes requires Matlab for the glass brain group visualization and parcel quality control as, for now, the MNI normalization is performed through SPM12. Moreover, BrainVisa is interfacing with some FMRIB Software Library (FSL 8  ) function as well, so that IntrAnat Electrodes is a versatile tool that uses in an optimal way the neuroimaging open platforms in the context of SEEG. IntrAnat Electrodes is still under development and a certain number of improvements will soon be provided: 1.