dataesr/scientific-paragraphs-categorization huggingface dataset:
3 ) record, and occasionally the 120 kHz record. Methods for real-time removal of seabed aliased returns, such as Renfree & Demer  32  were not available at the time of these surveys, and automated post processing methods are not yet readily available (but see Blackwell et al. 33   so aliased seabed returns had to be removed manually before school detection took place. The aliased seabed returns are available as Echoview regions (. EVR) files, but if other acoustic processing software is used, the aliased seabed returns must be removed before schools detection or echo integration. The echosounders were run continuously during the KACTAS and KAOS voyages including during CTD and net sampling, so these data should be removed if the objective of future analysis is based on line transect sampling.
Both KACTAS and KAOS surveys were carried out more than 20 years ago so echosounder data logging settings were in part selected due to data storage constraints and available echosounder file format options. As with many acoustic surveys of this time, the KACTAS acoustic survey data were collected using a Simrad EK500 scientific echosounder with data written in the. EK5 format (or Q-telegram) to reduce file size. Whilst the. EK5 format is not used by the next generation of echosounders, it was the best-available data format at the time of the survey and we are not aware of any conversion tools to change. EK5 data to other formats.
Raw acoustic data. If only a few raw acoustic data files are required, the files can be downloaded using the Australian Antarctic Data Centre (AADC) web browser here for KACTAS  22  : https://data. aad. gov. au/datasets/science/AAD_Hydroacoustics_data/3_Data/2000-12_Aurora-Australis_ KACTAS/Datasets/Echosounder/Simrad-EK500/Aurora-Australis/EK5 and here for KAOS  23  : https://data. aad. gov. au/datasets/science/AAD_Hydroacoustics_data/3_Data/2003-01_Aurora-Australis_ KAOS/Datasets/Echosounder/Simrad-EK60/RAW/38H-120H-200H The entire raw acoustic datasets for the KACTAS and KAOS surveys are 18. 2 GB and 106. 2 GB respectively and are available to download from a Simple Storage Service (S3) server. We provide instructions to download the entire KACTAS and KAOS datasets as a separate file called 'Instructions for downloading the entire KACTAS and KAOS raw active acoustic data sets.
We present and share a large database containing electroencephalographic signals from 87 human participants, collected during a single day of brain-computer interface (BCI) experiments, organized into 3 datasets (A, B, and C) that were all recorded using the same protocol: right and left hand motor imagery (MI). Each session contains 240 trials (120 per class), which represents more than 20,800 trials, or approximately 70 hours of recording time. It includes the performance of the associated BCI users, detailed information about the demographics, personality profile as well as some cognitive traits and the experimental instructions and codes (executed in the open-source platform OpenViBE). Such database could prove useful for various studies, including but not limited to: (1) studying the relationships between BCI users' profiles and their BCI performances, (2) studying how EEG signals properties varies for different users' profiles and MI tasks, (3) using the large number of participants to design cross-user BCI machine learning algorithms or (4) incorporating users' profile information into the design of EEG signal classification algorithms.
The goal of the BrainConquest project (ERC Starting Grant project, 2017-2022) is precisely to conducted research works that aimed at addressing this problem, i. e. , to understand, model and optimize user's training in order to provide more efficient MI-BCIs  [10] [11] [12]. As expected, it generated several datasets available to be shared as open data. First, with the Dataset A generated by the study of Pillette et al. 10  , we investigated the impact of experimenters' and users' gender on MI-BCI user training outcomes, i. e. , users performance and experience. Indeed, experimenters have a fundamental role, for example they keep users motivated. While providing emotional and social feedback, they introduce users to BCI with clear instructions. Secondly, with the Dataset B generated by the study Benaroch et al.
11  , we examined the relationship between users' online performance (i. e. , classification accuracy) and the characteristics of the chosen user-specific Most Discriminant Frequency Band (MDFB), by using computational models. Indeed, understanding the processes underlying user-training by modeling it computationally is a key to improve MI-BCI training protocols and adapt them to each user profile. Finally, Dataset C contains 6 additional participants (see section Participants) who completed one of the two experiments described above. Also, the wide variation observed in BCI performance between users remains a critical topic in the field of BCI that has not yet been fully understood and addressed  13, 14. The inclusion of detailed profile data in a large public database is unfortunately rare in the field.
Therefore in both these experiments we collected via 6 questionnaires user's demographic, personality profile as well as some cognitive traits (see subsection Questionnaires). This added value, together with the relatively large number of users can, among others, help the community to: (1) study the relationships between BCI users' profiles and their BCI performances, (2) study how EEG signal properties vary for different users' profiles and MI tasks, (3) use the large number of participants to design cross-user BCI machine learning algorithms or (4) incorporate users' profile information into the design of EEG signal classification algorithms. The database preparation described in this paper was part of a Data Management Plan of the European Research Council Starting Grant Project BrainConquest to make the project data Findable, Accessible, Interoperable and Reusable.
There are a few public EEG-BCI databases about motor BCIs, mostly on motor-imagery and/or sensori-motor BCI and several of these databases include a substantial number of subjects, e. g. , 52, 54, 55, 62 and 109 for  [15] [16] [17] [18] [19]. Among these databases, most do not provide information on the users' profile. Databases from  16, 19  contain some information on the users' demographics as well as on their subjective states during BCI use, but no information on their personality or cognitive traits. Only the database from  15  contains such information, for a database with 52 subjects. Therefore, to the best of our knowledge, our extensive database of EEG and user demographic, personality profiles as well as their cognitive traits that we share and describe in this manuscript, would be the largest BCI database (in terms of number of participants) that includes both EEG data and user profile information.
Indeed it contains electroencephalographic (EEG) signals from 87 participants, with more than 20,800 trials in total which represent about 70 hours of recording. It also contains the users' online performances in the two BCI experiments, a large number of responses (more than 6200 items) included in the 6 questionnaires related to demographic information, spatial abilities, pre-and post-experiment user state, learning style, personality profile and cognitive traits. Moreover, the personality traits we measured are more detailed in our database than in  15  (16 personality factors for our database versus 5 big personality traits from that of  15  ), and our database contains different cognitive traits. We also provide information on the study design and instructions, methods, and codes used to conduct the studies (scenarios and scripts used to run the experiments with the free and open-source BCI platform OpenViBE  20  ).
Methods Participants. Eighty-seven (87) participants completed one of the two BCI experiments based on the same protocol. They are organized as follows: • 60 participants (Dataset A; 29 women; age 19-59, M = 29, SD = 9. 32) performed the study on the impact of experimenters' and users' gender  10   Recruitment was limited to volunteer participants aged 18-60 years old, with no history of neurological or psychiatric disorders, normal vision (or corrected) and naive MI-BCI user, i. e. using a MI-BCI system for the first time. Eight different experimenters conducted the experiments. In Dataset A, the design of the research question implied a particular balance in the gender of the participants and experimenters. As a results, 3 women and 3 men experimenters each ran the experiment with 5 women and 5 men participants.
There was no authority relationship between participants and experimenters as they did not know each other. In the Dataset B and C, two other experimenters (2 women) conducted the experiments. The studies were conducted according to the relevant ethical research guidelines set out in the Declaration of Helsinki  21. Before participating in each study, both participants and experimenters gave informed consent. Participants thus signed an informed consent text in which they agreed both to participate in the studies and to share their EEG and experimental data. In the consent form each participant was asked whether they agreed to have their data used anonymously for publication in journals and conferences. Indeed they were informed that the database will be fully anonymized and all precautions would be taken to protect the privacy of all participants before the corresponding data can be publicly released and shared to the whole research community, in conformity with applicable ethical and legal requirements.
We explained that to ensure replicability/reproducibility and to enable others to improve on our work, sharing the collected data on open data platforms was also an objective and outcome of our research projects, which are part of the ERC Open Research Data Pilot program. It was also clarified that they were completely free to end their participation in the experiment at any time without providing any reasons. The studies have been approved and reviewed by Inria's ethics committee, the COERLE (Approval number: 2018-13).
EEG data acquisition. Participants sat comfortably in a chair in front of a computer screen see Fig. 1. EEG data was acquired using 27 active scalp electrodes (i. e. , Fz, FCz, Cz, CPz, Pz, C1, C3, C5, C2, C4, C6, F4, FC2, FC4, FC6, CP2, CP4, CP6, P4, F3, FC1, FC3, FC5, CP1, CP3, CP5, P3, 10-20 system), referenced to the left earlobe, the ground electrode is placed in FPz position. Figure  2  represents the 32 active electrodes as well as the reference and the ground electrodes used for the experiments. The electromyographic (EMG) signals of both hands were recorded using two active electrodes located 2. 5 cm below the skinfold on each wrist. The electrooculography (EOG) signals of one eye was recorded using three active electrodes. Two of them were located below and above the eye and one was located on the side.
For Dataset A the EOG signals of the right eye were recorded, and then due to a change in the layout of the experimental room, in Dataset B the EOG signals of the left eye were recorded. Physiological signals were measured using two g. USBAmp amplifiers (g. tec, Austria), sampled at 512 Hz, and processed online using the open-source BCI platform OpenViBE 2. 1. 0 (Dataset A) or OpenViBE 2. 2. 0 (Dataset B)  20. In the Dataset C Participants C83 and C85 were collected with OpenViBE 2. 1. 0 and the remaining 4 participants with OpenViBE 2. 2. 0. This platform enables to design, study and use BCIs in real-time. The recording room was dimly-lit. The raw signals were recorded without any hardware filters (see all information about the amplifier used https://www. gtec. at/product/ gusbamp-research/).
xlsx" file. The questionnaires described in this section were all completed in French by the participants. A translated (English) version of the questionnaires is provided in the "TranslationQuestionnaires" file. All questionnaires used in the two experiments are described below. The identification of the columns in the "Performance. xlsx" file is given at the beginning of each question. Demographic information. Answers to the demographic questionnaire are provided as numbers, using the code below, where each answer is associated with a numerical code. As mentioned above each item corresponds to the name of a column in the "Performance. xlsx" file. Note that a problem in the recovery of raw data from this questionnaire for Dataset B resulted in the loss of the answer to the question "level of knowledge about cognitive sciences/neurosciences" for 13 participants.
Data Records A global compressed file (BCI Database. zip) containing the data of the two experiments is provided on the general-purpose open-access repository Zenodo at: https://doi. org/10. 5281/zenodo. 8089820  32. It contains 4 folders named "Instructions", "OV-scenarios", "Questionnaires" and "Signals" and 1 file "Performances. xlsx". A description of the different folders is provided in the following paragraphs, and Fig. 6  represents the database structure. Instructions. We provide the instructions read by experimenters during the experiments, in both English "EN-instruction. pdf " and French "FR-instructions. pdf ", as well as the checklist "En-Checklist. pdf " used during the experiments so that the experimenter would not forget anything during the experiment.
OV-Scenarios. All the programs required to perform the online (real-time) BCI experiments (e. g. , visual interfaces or EEG classification algorithms) were implemented using the free and open-source BCI OpenViBE platform  20. On OpenViBE, the scenario is the name given to a set of OpenViBE modules used to perform a given step of a BCI protocol, e. g. , acquire training data, calibrate a classifier or run an online BCI. We thus shared them all in the "OV-Scenarios" folder that contains the "OV-ScenariosA" and the "OV-ScenariosB" folders, for the scenarios used for Datasets A and B respectively. These folders contain the following 8 scenarios: • Monitoring: 0-monitoring. xml shows online signals acquired with the electrodes (see Fig. 2 ). Using this scenario, the experimenters checked the quality of the EEG signal and asked the participants to close their eyes to observe activity in the alpha band.
xml" selects the most discriminant frequency band based on the concatenated file, with either the Blankertz's algorithm  29  -script frequencyBandSelection. py or the Benaroch's variant with constraints  11  -script frequencyBandSelection-v2. py. It saves the information obtained in a configuration file with extension ". cfg". • Train-CSP: "5-train-CSP. xml" uses the Common Spatial Pattern algorithm to optimize three pairs of spatial filters, using the concatenated file. It saves the information obtained in a configuration file with extension ". cfg". • Classifier-trainer: "6-classifier-trainer. xml" calibrates the Linear Discriminant Analysis classifier, with the concatenated file. It saves the information obtained in a configuration file with extension ". cfg". • Online: "7-online. xml" was used during runs R3 to R6.
It displays the cues and feedback described in Figs. 4, 5  while recording the participants' physiological data. It uses the mi-bci-graz-simulator. lua script. The "OV-Scenarios" folder also contains the list of all electrodes used to record EEG signals, provided in the "Channel-list. xml" document, as well as the "sham. ov" file. It also contains the "scripts" folder containing the 2 Lua and the Python scripts associated to the OpenViBE scenarios described above. Questionnaires. We provide the Mental Rotation test used during the experiments in english "MentalRotation(EN). pdf", as well as a translation of 4 questionnaires in "TranslationQuestionnaires. docx" notably the Demographic and Social information, the Pre and Post-session questionnaires, and the Index of Learning style. All of these questionnaires are also provided in French.
Signals. The "Signals" folder contains 3 folders: Dataset A (60 participants), the Dataset B (21 participants) and the Dataset C (6 participants), and the "Performance. xlsx" file. Each Dataset contains a folder for each participant with 11 files in total. Among them, eight raw EEG recordings in GDF format (General Data Format for Biomedical Signals)  37  with the open and closed eyes (OE/CE) EEG baseline, the first two acquisition runs and the four online training runs. Three files corresponding to the configuration files of the classifier output, the Common Spatial Pattern (CSP) spatial filter  31  and the MDFB frequency bands selected are also provided in. cfg format.
technical Validation In this database, we have decided to share all the recorded trials and channels, including the noisy ones, together with the annotation that describes their quality. Infact, a branch of BCI and EEG research is dedicated to designing signal processing algorithms to detect, reject or clean noise in EEG signals  38  or to designing machine learning algorithms robust to such noise. Noisy trials and channels could thus be used for this purpose, and would reflect typical signals recorded during real BCI use. To analyze our signals quality, we first performed an online signal quality analysis followed by an offline signal analysis. Online analysis. At the beginning of each BCI experiment, the EEG signal quality (e. g. , raw signals, noise, electrode placement, artefacts occurrence) was carefully assessed by the trained experimenters.
7. Results show a relatively large proportion (55%) of participants with statistical chance level (CL) accuracy. This proportion is similar to the illiteracy rate reported in  16  (53,7%). BCI performance varied largely between participants (Fig. 8 ) (mean 63. 35% ± 17,36%) with (R1 = 61,92% ± 16,87%, R2 = 63,62% ± 18,24%, R3 = 63,14% ± 17,34%, R4 = 64,82% ± 17,16%). Performance ranged from 40% to almost perfect control 99,37%, individual chance level being 58,7% with p = 0. 05  40. For most participants, performance was also highly variable between runs. Specifically, the within-subject variability in performance between runs ranged from 1. 25% to 27. 7% (mean 7. 13% ± 3. 81%) see Fig. 8. ERSP analysis. If our database is to be used for neurophysiological analyses, we would recommend rejecting noisy trials and channels.
Usage Notes To analyse the raw EEG, we recommend using a standard open-source toolbox for EEG data, such as EEGLab  41  (https://sccn. ucsd. edu/eeglab/index. php) in Matlab (The MathWorks Inc. Natick, MA, USA) or MNE Python  46. EEG data also contain the triggers (a. k. a events) associated to each cue and phase of the BCI trials and runs. Such triggers are known as "stimulations" in OpenViBE. The OpenViBE stimulations we used in this database and their numeric values for the experiments are: To know more about all OpenViBE stimulations, please refer to the website online (http://openvibe. inria. fr/ stimulation-codes). Note that there is a slight difference between the size of the acquisition and online files contained in the two datasets  To access and download the free and open source OpenViBE BCI platform, go to: http://openvibe.
inria. fr/. The source code for all versions of OpenViBE is available at http://openvibe. inria. fr/pub/src/, for each version you will find a README with build instructions in the archive you have downloaded. You can also find the sources at https://gitlab. inria. fr/openvibe/meta. To install the corresponding versions, which will allow you to reuse the scenarios of the experiments corresponding to this manuscript, you need to download older versions of OpenViBE. OpenViBE 2. 1. 0 (for dataset A) is available at http://openvibe. inria. fr/pub/bin/win32/ and OpenViBE 2. 2. 0 (for dataset B) is available at http://openvibe. inria. fr/pub/bin/win64/. You will then need to open all the scenarios provided in zenodo  32  and you will be able to run them.
Acknowledgements This work was supported by the  European Research Council  with the project  BrainConquest  (grant  ERC-2016-STG-714567 ). We would like to thank all the experimenters that ran the experiment for Dataset A,  R. A ,  P. L ,  L. F ,  Benaroch Camille ,  Caselli Damien ,  Appriou Aurélien. As well as those who ran the experiment for Dataset B and  C ,  Benaroch Camille ,  D. P ,  Maria sayu Yamamoto. Benaroch Camille  created the algorithm tested in the frequency band experience Dataset B and analyzed the result with  Maria sayu Yamamoto. Also ,  Monseigne Thibaut  worked on all the OnpenViBE scenarios and script.
A and B. It is due to the different versions of OpenViBE used during the two experiments. Dataset A was collected with OpenViBE 2. 1. 0 and Dataset B with OpenViBE 2. 2. 0. For Dataset C Participants C83 and C85 were collected with OpenViBE 2. 1. 0 and the remaining 4 participants with OpenViBE 2. 2. 0.
• 21 participants (Dataset B; 8 women; age 19-37, M = 29, SD = 9. 318) performed the study on the MDFB study 11. • 6 additional participants (Dataset C; 4 women; age 20-26, M = 22; SD = 2. 34) performed one of the two experiments after the corresponding studies were published (these participants were thus not included in the previously published analyses). Participants C83 and C85 performed the study of Pillette et al. 10 later and the 4 remaining participants performed the study of Benaroch et al. 11 later on.
Method validation The method validation was performed over an open dataset of emotion elicitation  [29]. The dataset comprises healthy human volunteers undergoing audiovisual stimulations with affective content. The dataset consists of 32 subjects (age range, 19-27 years; median, 27 years; 16 females) visualizing 40 one-minute video clips. Videos were presented after an initial resting period of two-minute and separated by a pad of 5-second. Subjective ratings of arousal and valence were reported by participants, from 1 to 9. Physiological data includes 32-channel EEG and ECG, sampled at 512 Hz. A total of 20 trials were selected for the method validation procedure. 10 trials with high arousal and high valence were labeled as pleasant trials, and 10 trials with high arousal and low valence were labeled as unpleasant trials.
As an additional method of validation, synthetic EEG and HRV data were generated to depict the parallel fluctuations in time of the brain-heart interplay. HRV data was initialized from a white noise signal, from uniformly distributed random numbers between 0 and 1, and then standardized to 100 ms and mean 700 ms. EEG data was generated at 256 Hz sampling frequency as a sum of five oscillators at 2, 6, 10, 20, and 40 Hz. The time-varying oscillator amplitudes were initially generated as white noise and then imposed as an autoregressive process of order 1. White noise was added to the simulated EEG data at 0. 5, 1, 2, and 4 standard deviations. PSV-SDG coupling coefficients were computed from the generated data. Figure  5  shows the simulation results for the interplay between the vagal index and theta oscillations.
WGS long-read Nanopore did not reveal any structural variant at the remaining trans FOXI3 locus. Wholegenome optical mapping also validated the large 2. 06 Mb deletion (Supplementary Fig. 3 ) and did not reveal any abnormality in the remaining copy of the FOXI3 locus. We were able to determine the haplotype of the polymorphic sites of the remaining FOXI3 allele. This haplotype contains two linkage disequilibrium (LD) blocks, and is common in Europeans (12. 9%) and East Asians (19. 7%) (data from LDlink 5. 1 https://ldlink. nci. nih. gov/) (Supplementary Fig. 4 ). No quantitative trait loci (QTLs) were identified in GTEx due to undetectable expression of FOXI3 in adult tissues  [20] [21] [22]. This haplotype is discussed further below.