dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Tsunami waves represent undeniably a complex natural process. Moreover, they represent a major risk for exposed coastal areas including also the local populations, infrastructure, etc. The present work is devoted to the modelling tsunami generation and propagation processes. Moreover, this article is designed as a tutorial paper in order to show to the readers how easily these processes can be modelled in the framework of the FreeFem++, which is a free software (under the LGPL license). FreeFem++ offers a large variety of triangular finite elements (linear and quadratic Lagrangian elements, discontinuous P 1 , RAVIART-THOMAS elements, etc. ) to solve Partial Differential Equations (PDEs). It is an integrated product with its own high level programming language and a syntax close to mathematical formulations, making the implementation of numerical algorithms very easy.
Among the features making FreeFem++ an easy-to-use and highly adaptive software we recall the advanced automatic mesh generator, mesh adaptation, problem description by its variational formulation, automatic interpolation of data, color display on line, postscript printouts, etc. The FreeFem++ programming framework offers the advantage to hide all technical issues related to the implementation of the finite element method. Traditionally, tsunami waves are modelled using hydrostatic models  [4] [5] [6] [7]. In the present manuscript we employ a non-hydrostatic BOUSSINESQ-type system to be specified below. This class of models is distinguished by the application of the so-called BOUSSINESQ approximation  [8]. They can be used to study a variety of water wave phenomena in harbors, coastal dynamics and, of course, tsunami generation and propagation problems  [9] [10] [11] [12] [13].
We also mention that we change the depth close to the shoreline to 100 m in order to avoid the run-up problem in this study. Finally, all types of meshes used in our computations are depicted in Figure  9. The bathymetry data downloaded from the NOAA website are in geographical degrees coordinates and we need to convert them back to meters and a CARTESIAN system. So, on the first hand, we must know the degree of Latitude (South and North) and of Longitude (West and East) of our domain where we can deduce the Latitude lat0 =. 5(lat South + lat North ) and the Longitude long0 =. 5(long West + long East ). On the other hand, we must take into account the spherical shape of the EARTH, even if it does not play significant role because of the small spatial scale of the experiments. So, we know that the radius of the EARTH near the equator is R equator = 6378, 137 km, and near to the pole R pole = 6356, 752 km, thus the radius of our domain equals to: R = R 2 equator cos(lat0 • π/180) 2 + R 2 pole sin(lat0 • π/180) 2 R equator cos(lat0 • π/180) 2 + R pole sin(lat0 • π/180) 2.
Conclusion and Outlook In this manuscript we demonstrated how to discretize a simplified version of the BBM-BBM system (1) using the FEM and dedicated open-source software FreeFem++. The use of this numerical technique was demonstrated in view of applications to tsunami wave modelling  [25, 37]. The concrete cases of wave propagation in the MEDITERRANEAN sea and in JAVA island region (INDONESIA) were considered. The digital computing environment that we developed allows the integration of realistic data (bathymetry and geography) in a relatively simple software framework. The codes used in this study are made freely available for all our readers. Moreover, a novel mesh and domain adaptation technique was proposed to speed-up substantially the computations. The gain in terms of the CPU time after applying this technique can be clearly seen in Figure  23.
Funding: This work has been supported by the  French National Research Agency, through Investments for Future Program  (ref. ANR-18-EURE-0016 -Solar Academy ). Acknowledgments: This work would not be possible without a precious help of  Professors Frédéric HECHT ,  Dimitrios MITSOTAKIS , and  Olivier PANTZ. The Authors would like to thank also both Referees for their invaluable comments which allowed us to improve our manuscript.
Several large-scale brain-imaging datasets are suitable for atlasing, wherein dierences can be mitigated across subjects and protocols together with standardized dataprocessing routines. Yet, as they have dierent scopes, not all requirements are met for cognitive mapping. For instance, the Human Connectome Project (HCP)  [10, 11]  and CONNECT/Archi  [12, 13]  datasets provide large subject samples as they are focused in population analysis across dierent modalities; task-fMRI data combine here 24 and 28 conditions, respectively, which is scarce for functional atlasing. Another example is the studyforrest dataset  [14, 15, 16, 17] , that includes a variety of task data on complex auditory and visual information, but restricted to naturalistic stimuli. Additionally, one shall note that within-subject variability reduces task-fMRI replicability; thus, more data per subject can in fact facilitate reliability of group-level results  [18].
To obtain as many cognitive signatures as possible and simultaneously achieve a wide brain coverage at a ne scale, extensive functional mapping of individual brains over dierent psychological domains is necessary. Within this context, the Individual Brain Charting (IBC) project pertains to the development of a 1. 5mm-resolution, task-fMRI dataset acquired in a xed environment, on a permanent cohort of 12 participants. Data collection from a broad range of tasks, at high spatial resolution, yields a sharp characterization of the neurocognitive components common to the dierent tasks. This extension corresponds to the second release of the IBC dataset, meant to increase the number of psychological domains of the rst one  [19]. It both aims at a consistent mapping of elementary spatial components, extracted from all tasks, and a ne characterization of the individual architecture underlying this topographic information.
Likewise, components on incentive salience, already tackled in the HCP Gambling task, are now included in a task battery addressing positive-incentive value. Yet also, a battery on mental time travel brings in new modules pertaining to time orientation and cardinal-direction judgment. Data from both releases are organized in 25 tasks most of them reproduced from other studies and they amount for 205 contrasts described on the basis of 110 cognitive atoms, extracted from the Cognitive Atlas  [21]. Here, we give an account focused on the second release of the experimental procedures and the dataset organization and show that raw task-fMRI data and their derivatives represent functional activity in direct response to behavior. Data collection is ongoing and more releases are planned for the next years.
Participants The present release of the IBC dataset consists of brain fMRI data from eleven individuals (one female), acquired between April 2017 and July 2019. The two dierences from the cohort of the rst release are:  (1)  the replacement of participant 2 (sub-02) by participant  15 (sub-15) ; and  (2)  the absence of data from participant 8 (sub-08). Regarding the latter, data will be acquired in the future and included in one of the upcoming releases. Age, sex and handedness of this group of participants is given on Table  1. Handedness was determined with the Edinburgh Handedness Inventory  [23]. All experimental procedures were approved by a regional ethical committee for medical protocols in Île-de-France (Comité de Protection des Personnes -no. [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31]  and a committee to ensure compliance with data-protection rules (Commission Nationale de l'Informatique et des Libertés -DR-2016-033).
MRI Equipment The fMRI data were acquired using an MRI scanner Siemens 3T Magnetom Prisma t along with a Siemens Head/Neck 64-channel coil. Behavioral responses were obtained with two MR-compatible, optic-ber response devices that were interchangeably used according to the type of task employed: (1) a ve-button ergonomic pad (Current Designs, Package 932 with Pyka HHSC-1x5-N4); and (2) a pair of in-house custom-made sticks featuring one-top button. MR-Confon package was used as audio system in the MRI environment. All sessions were conducted at the NeuroSpin platform of the CEA Research Institute, Saclay, France.
Experimental Procedure Upon arrival to the research institute, participants were instructed about the execution and timing of the tasks referring to the upcoming session. All sessions were composed of several runs dedicated to one or a group of tasks as described in Section Experimental Paradigms. The structure of the sessions according to the MRI modality employed at every run is detailed in Table  2. Specications about imaging parameters of the referred modalities as well as procedures undertaken toward recruitment of participant 15 plus handling and training of all participants are described in  [19]. As a side note, data pertaining to tasks of the rst release were also acquired for participant 15.
Ideally and as mentioned in Section Background & Summary, the main purpose of each release is to provide the dataset with a greater variety of cognitive modules from as many new psychological domains as possible, at the same time that a better coverage with the already existing ones is also attained. All material used for stimulus presentation have been made publicly available (see Section Code Availability), together with video annotations of the corresponding protocols. Video annotations refer to video records of complete runs that are meant to be consulted for a better comprehension of the task paradigms. For each subject, the paradigm-descriptors' les describing the occurrence of the events are part of the dataset, following BIDS Specication. 3 : Overview of the tasks featuring the second release of the IBC dataset.
Theory-of-Mind and Pain Matrices task battery This battery of tasks was adapted from the original task-fMRI localizers of Saxe Lab, intended to identify functional regions-of-interest in the Theory-of-Mind network and Pain Matrix regions. These localizers rely on a set of protocols along with verbal and non-verbal stimuli, whose material was obtained from https://saxelab. mit. edu/localizers. Minor changes were employed in the present versions of the tasks herein described. Because the cohort of this dataset is composed solely of native French speakers, the verbal stimuli were thus translated to French. Therefore, the durations of the reading period and the response period within conditions were slightly increased. Theory-of-Mind Localizer (TOM localizer) The Theory-of-Mind Localizer (TOM localizer) was intended to identify brain regions involved in theoryof-mind and social cognition, by contrasting activation during two distinct story conditions: (1) belief judgments, reading a false-belief story that portrayed characters with false beliefs about their own reality; and (2) fact judgments, reading a story about a false photograph, map or sign  [28].
Spin-Echo (SE) EPI-2D image volumes were acquired in order to compensate for spatial distortions. Similarly to the GE-EPI sequences, two dierent phaseencoding directions, i. e. PA and AP, were employed in dierent runs pertaining to this sequence. There were four runs per session: one pair of PA and AP SE EPI-2D before the start of the GE-EPI sequences and another pair at the end. The parameters for all types of sequences employed are provided in  [19]  as well as in the documentation available on the IBC website: https://project. inria. fr/IBC/data/ Data Analysis.
Preprocessing Source data were preprocessed using the same pipeline employed for the rst release of the IBC dataset. Thus, refer to  [19]  for more complete information about procedures undertaken during this stage. In summary, raw data were preprocessed using PyPreprocess (https://github. com/neurospin/pypreprocess), dedicated to launch in the python ecosystem pre-compiled functions of SPM12 software package v6685 and FSL library v5. 0. Firstly, susceptibility-induced o-resonance eld was estimated from four SE EPI-2D volumes, each half acquired in opposite phase-encoding directions (see Section Imaging Data for details). The images were corrected based on the estimated deformation model, using the topup tool  [41]  implemented in FSL  [42]. GE-EPI volumes of each participant were then aligned to each other, using a rigid body transformation, in which the average volume of all images across runs (per session) was used as reference  [43].
The mean EPI volume was also co-registered onto the T1-weighted MPRAGE (anatomical) volume of the corresponding participant  [44] , acquired during the Screening session (consult  [19]  for details). The individual anatomical volumes were then segmented into tissue types in order to allow for the normalization of both anatomical and functional data into the standard MNI152 space, which was performed using the Unied Segmentation probabilistic framework  [45]. Concretely, the segmented volumes were used to compute the deformation eld for normalization to the standard MNI152 space.
Summary Statistics Because data were collected per task and subject in at least two acquisitions with opposite phase-encoding directions (see Section Imaging Data for details), statistics of their joint eects were calculated under a Fixed-Eects (FFX) model. t -tests were then computed at every voxel for each individual contrast, in order to assess for statistical signicance in dierences among evoked responses. To assure standardized results that are independent from the number of observations, t -values were directly converted into z -values. Data derivatives are thus delivered as individual contrast maps containing standard scores in all voxels that are conned to a grey-matter-mask with an average threshold > 25% across subjects. We note that these postprocessed individual maps were obtained from the GLM t of preprocessed EPI maps and, thus, they are represented in the standard MNI152 space.
Source data Source data of the present release (plus rst and third releases) can be accessed via the public repository OpenNeuro  [49]  under the data accession number ds002685  [50]. This collection comprises ∼1. 1TB of MRI data. A former collection only referring to source data from the rst release is still available in the same repository  [51]. The NIfTI les as well as paradigm descriptors and imaging parameters are organized per run for each session according to BIDS Specication: • the data repository is organized in twelve main directories sub-01 to sub-15; we underline that sub-02, sub-03 and sub-10 are not part of the dataset and corresponding data from sub-08 will be made available in further releases(see Table  1 ); • data from each subject are numbered on a per-session basis, following the chronological order of the acquisitions; we also note that this order is not the same for all subjects; the IBC documentation can be consulted for the exact correspondence between session number and session id for every subject on https://project.
inria. fr/IBC/data/ (session id's of the rst and second releases are respectively provided on Table  2  of  [19]  and Table  2  of the present article); • acquisitions are organized within session by modality; • dierent identiers are assigned to dierent types of data as follows: gzipped NIfTI 4D image volumes of BOLD fMRI data are named as sub-XX_ses-YY_task-ZZZ_dir-AA_bold. nii. gz, in which XX and YY refer respectively to the subject and session id, ZZZ refers to the name of the task, and AA can be either `PA' or `AP' depending on the phase-encoding direction; event les are named as sub-XX_ses-YY_task-ZZZ_dir-AA_event. tsv; single-band, reference images are named as sub-XX_ses-YY_task-ZZZ_dir-AA_sbref. nii. gz. Although BIDS v1. 2. 1 does not provide support for data derivatives, a similar directory tree structure was still preserved for this content.
However, a poor coverage was attained for the bottom of the cerebellum (for more details about what sessions contributed specically for this tSNR deviation, consult Online-only Table  2 ). Yet, as shown by Figure  2 , this issue is not driven by subject, condition or phase-encoding direction eects. It refers instead to a transient irregularity that aected the eld-of-view during the acquisition period. Relevance of the IBC dataset for brain mapping Eect of subject identity and task stimuli on activation: Taking into account the output of the GLM analysis for each acquisition, an assessment was performed at every voxel concerning how much variability of the signal could be explained by the eect of:  (1)  subject identity, (2) condition, and  (3)  phase-encoding direction. To assess the impact of these three factors, a oneway Analysis-of-Variance (ANOVA) of all contrast maps 1782 maps from the 11 subjects was computed and results from the rst-level analysis of the data were obtained for the aforementioned factors.
The resulting statistical maps are displayed on the top of the Figure  2. They show that both subject and condition eects are uniformly signicant at p < 0. 05, corrected for multiple comparisons using False Discovery Rate (FDR). Condition eects are overall higher than subject eects, particularly in sensory cortices like visual, auditory and somato-sensory regions. Eects pertaining to the phase-encoding direction are only signicant in smaller areas comprising superior cortical regions, with special emphasis in the occipital lobe. We hypothesize that such results derive from the fact that PA data accounts for a larger amount than AP data in this dataset (see Table  2 ).
On the other hand, a prominent deviation of the results at the bottom of the cerebellum in comparison with its neighboring regions is present due to a lesser coverage of this area, as reported in Section Data quality and Figure  1. As the dataset becomes larger, one may expect a progressively improvement of brain coverage. However, as referred already in  [19] , this coverage may not be fully attained due to MR-related technical restrictions. Some locations are particularly sensitive to e. g. coil sensitivity or intra-voxel dephasing, which can result in a reduced tSNR in certain functional brain regions.
Future releases will also address in greater depth the auditory system through tonotopy as well as tasks on auditory language comprehension, listening of naturalistic sounds and music perception. Other tasks on biological motion, stimulus salience, working memory, motor inhibition, risk-based decision making and spatial navigation will also integrate these future releases. Finally, although IBC stands foremost as a task-fMRI dataset with a strong emphasis in the task dimension, future releases will be also dedicated to resting-state fMRI data as well as to other MRI modalities, concretely high-resolution T1-and T2-weighted, diusion-weighted, T1-and T2-relaxometry and myelin water fraction. The ocial website of the IBC project (https://project. inria. fr/IBC/) can be consulted anytime for a continuous update about the latest and forthcoming releases.
They include:  (1)  the task-stimuli protocols;  (2)  demo presentations of the tasks as video annotations; (3) instructions to the participants; and (4) scripts to extract paradigm descriptors from log les for the GLM estimation. Task-stimuli protocols from Preference, TOM and VSTM+Enumeration batteries were adapted from the original studies in order to comply with the IBC experimental settings, without aecting the design of the original paradigms. MTT battery pertains to an original protocol developed in Python under the context of the IBC project. Protocols of Self and Bang tasks were re-written from scratch in Python with no change of the design referring to the original paradigms. The scripts used for data analysis are available on GitHub under the Simplied BSD license: https://github.
Acknowledgements We thank  Rebecca Saxe  and colleagues for making publicly available the TOM and Pain Matrices task battery and for the availability to further clarify their implementation, designs and analyses. In particular, we would like to thank  Hilary Richardson  for the assistance regarding the implementation of the Pain Movie localizer and extraction of its paradigm descriptors for data analysis. We also thank  Karen L. Campbell  and  Lorraine K. Tyler  for having kindly provided the video and descriptors of the Bang task, which were respectively necessary for a successful re-implementation of the protocol and analysis of data. We thank the  Center for Magnetic Resonance Research, University of Minnesota  for having kindly provided the  Multi-Band Accelerated EPI Pulse Sequence and Reconstruction Algorithms.
We are grateful to  Kamalaker Dadi ,  Loubna El Gueddari  and  Darya Chyzhyk  for their assistance in some MRI acquisitions as well as  Isabelle Denghien  for the advisory and technical support in setting the task protocols. At last, we are especially thankful to all volunteers who have accepted to be part of this challenging study, with many repeated MRI scans over a long period of time. This project has received funding from the  European Union 's  Horizon 2020 Framework Program for Research and Innovation  under Grant Agreement No  720270  ( Human Brain  Project  SGA1 ) and  785907  ( Human Brain  Project  SGA2 ). This article was published in the journal Scientic Data. The nal authenticated version is available online at: https://doi. org/10. 1038/s41597-020-00670-4.
[19]with p ≤ 10 -17 )[19]. We conclude that similarity tends to increase as more data is included in the dataset because new cognitive elements are added to the description of all tasks. condence in food preference food cue reactivity condence judgment Preference Painting painting evaluation visual form discrimination visual color discrimination judgment painting preference visual form discrimination visual color discrimination reward valuation incentive salience condence in painting preference visual form discrimination visual color discrimination condence judgment Preference Face face evaluation visual face recognition Task Description of the Condition Cognitive Labels MTT battery* all references in we/sn island auditory perception memory retrieval spatial localization cardinal-direction judgment temporal cognition space cue on all references in we/sn island auditory perception semantic categorization cardinal orientation time cue on all references in we/sn island auditory perception semantic categorization time orientation westside/southside events auditory perception spatial localization west/south cardinal-direction judgment eastside/northside events auditory perception spatial localization east/north cardinal-direction judgment we/sn past events auditory perception temporal cognition past time we/sn future events auditory perception temporal cognition future time responses to events in we/sn island response selection response execution Preference Food food evaluation food cue reactivity judgment food preference food cue reactivity reward valuation incentive salience.
The dura was carefully removed using a metal needle. Glass pipettes used to record LFP had 1-2 MΩ resistance while those used for juxtacellular patch-clamp recordings typically had 5-7MΩ resistance. LFP and patch electrodes were pulled from borosilicate glass capillaries. Signals were amplified with a Multiclamp 700B patch-clamp amplifier (Molecular Devices), sampled at 20 KHz and filtered online at 10 KHz. Signals were digitized with a Digidata 1440A and acquired, using the pClamp 10 software package (Molecular Devices).
Data analysis Electrophysiological and statistical analysis was performed using built-in and custom-written routines made for Igor Pro (WaveMetrics, Lake Oswego, OR, USA), MATLAB R2017b 9. 3. 0. 713579 Natick, Massachusetts: The MathWorks Inc. ; Origin (Pro) 2016 OriginLab Corporation, Northampton, MA, USA; Prism version 7. 00 for Windows, GraphPad Software, La Jolla California USA; and Python Software Foundation. Python Language Reference, version 3. 6, available at http://www. python. org.