dataesr/scientific-paragraphs-categorization huggingface dataset:
a b s t r a c t Here we describe "Brain development of deductive reasoning" a pediatric neuroimaging dataset freely available on OpenNeuro. org. This dataset includes neuroimaging and standardized assessment data from 56 participants aged 8. 47-15 years. Functional Magnetic Resonance Imaging (fMRI) data were collected while participants completed both setinclusion and linear-order deductive reasoning tasks. A subset of participants ( n = 45) returned two years later for follow-up standardized assessment testing allowing for future research to investigate individual change in cognitive and academic skill. Previous research on this dataset has not examined the relation of skill and demographic measures to the neural basis of reasoning. Moreover, these studies have not examined the relation of the neural basis of reasoning to that of arithmetic or differences between children and adults in the neural basis of reasoning.
Subject Developmental Cognitive Neuroscience Specific subject area Neuroimaging of Deductive Reasoning in School-aged Children Type of data Tables Images How data were acquired 3T Siemens Trio-Tim scanner, 16-channel head coil. E-prime software was used to display tasks and collect behavioral data. Data format Raw Parameters for data collection All participants were right-handed, native English speakers, having normal or corrected to normal vision and no history of psychological or neurological disorders, prematurity of less than 36 weeks, head injury causing overnight hospitalization, hearing loss, or contraindications for MRI.
Value of the Data • Extensive skill and demographic measures allow for examination of how these variables are related to the neural basis of reasoning. • Longitudinal assessment scores allow for prediction of individual change from behavioral or neuroimaging data. • Matched participants with an external dataset allows for the comparison of the neural basis of reasoning and arithmetic. • Parallel adult participants in an external dataset allows for examination of developmental differences. • Compliance with Brain Imaging Data Structure (BIDS) specifications supports ease of future use.
Data Description All raw data are freely available in the public neuroimaging dataset entitled "Brain Development of Deductive Reasoning" hosted on OpenNeuro. org [1]. The data are organized in accordance with the Brain Imaging Data Structure Specifications which allow for easy reuse of the data as well as utilization of tools that work with this standard  [7]. The dataset includes raw and standardized scores from a battery of neuropsychological standardized assessments and questionnaires used to quantify cognitive and academic skill, structural Magnetic Resonance Imaging (MRI) images, functional MRI images collected while participants completed two deductive reasoning tasks, and behavioral data from those tasks. Table  1  in this article describes the number of subjects having completed each of the fMRI tasks by sex and Fig.
1  provides an illustration of the task design and timing. This dataset has been used in part in one publication  [2]. In addition, this dataset is accompanied by a larger dataset entitled "Brain Correlates of Math Development" which investigates arithmetic development in the same participants  [3]. The arithmetic dataset is described in Suárez-Pellicioni et al. , is freely available on OpenNeuro. org and includes longitudinal fMRI tasks of rhyming, numerosity, multiplication, and subtraction processing  [4]. This dataset is also accompanied by an adult dataset on the same experimental tasks in the scanner, but with a reduced number of individual difference variables outside of the scanner  [ 5 , 6 ]. The presently described dataset contains a new and unique contribution of deductive reasoning fMRI tasks that overlaps with the arithmetic dataset in standardized assessment scores and structural imaging data.
This dataset will allow for the examination of many additional research questions that have not yet been examined. First, participants completed an extensive standardized testing battery. This will allow others to examine the relation of skill measures to the neural basis of reasoning. Second, demographic data will allow researchers to compare variables such as socio-economic status to the neural basis of reasoning. Third, the overlap in subjects with the arithmetic dataset will allow for the examination of the connection between reasoning and arithmetic. Fourth, comparable adult data has been uploaded onto OpenNeuro, so studies can examine developmental differences.
Participants This dataset includes neuroimaging and standardized assessment data from 56 participants aged 8. 47-15 years (mean age = 11. 20, SD = 1. 64, 32 female) at the first time point, session T1. A subset of participants, aged 10. 91-16. 47 years ( n = 45, mean age = 13. 47, SD = 1. 59, 25 females) returned two years later for session T2 to complete follow up standardized testing. Participants completed additional neuroimaging tasks at both session T1 and T2 which are available in a previously published dataset  [5]. All participants were recruited from the greater Chicago area through flyers, advertisements, and community events. Participants were screened and included only if they were right handed, native English speakers, had no uncorrected visual or hearing loss, and had no parent report of neurological disease, epilepsy, prematurity of less than 36 weeks, birth complications requiring admission to the neonatal intensive care, head injury requiring emergency medical evaluation, taking medication affecting the central nervous system, or contraindications for MRI.
MRI acquisition protocol Magnetic Resonance Images were acquired at Northwestern University Center for Advanced Magnetic Resonance Imaging (CAMRI) using a 16-channel head coil in a 3T Siemens Trio-Tim scanner running Siemens Syngo software version MR B17. Participants were provided a righthanded button box to respond to the tasks during the scans. All tasks were presented on a screen behind the scanner in a counterbalanced order, and were viewed through a mirror attached to the head coil. During MPRAGE data acquisition, participants viewed a movie. T1-weighted MPRAGE images were acquired with the following parameters: TR = 2300 ms, TE = 3. 36 ms, matrix size = 256 × 256, bandwith = 240 Hz/Px, slice thickness = 1 mm, number of slices = 160, voxel size = 1 mm isotropic, flip angle = 9 °. Blood oxygen level dependent signal (BOLD) was acquired using a T2-weighted susceptibility weighted single-shot echo planar imaging (EPI) with the following parameters: TR = 20 0 0 ms, TE = 20 ms, matrix size = 128 × 120, bandwidth = 1302 Hz/Px, slice thickness = 3 mm (0.
Quality control Neuroimaging data followed a predefined series of steps to organize the data in accordance with the Brain Imaging Data structure, assess data quality, and remove identifying information. First, all data were converted from Dicom to nifti format with MRI Convert version 2. 0 and necessary imaging parameters were extracted from the dicom header. These parameters are consistent across all subjects and are stored in a data dictionary file at the root level of the dataset for each task. All nifti images were then reoriented to the anterior commissure and facial features were removed from structural images. Facial features were removed by first running the FreeSurfer tool mri_deface on the images  [17]. If, upon visual inspection, the face was not removed, images were then defaced manually by aligning the raw image to a template image using mri_robust_register and then using the inversion of the resulting transformation matrix to transform a facemask to the raw image space which was then multiplied by the raw image  [18].
All structural images were reviewed to ensure no facial features remained. On occasion, participants completed tasks on separate dates or completed structural scans in a different session. Shifted acquisition dates are included in the participants data table at the root level of the dataset. Dates were shifted from -365 to 0 days within a subject and were then shifted back 200 years to make the date shifting transparent. In addition, due to high movement associated with collecting data from pediatric populations all functional images were reviewed for movement using the ArtRepair toolbox  [19]. Any runs containing greater than 25% of all volumes with movement greater than 1. 5 mm of volume to volume translation were removed from the dataset.
Mathieu, J. R. Booth, J. Prado, Distributed Neural Representations of Logical Arguments in School-Age Children, Hum. Brain. Mapp. 36 (2015), 996-1009. https://doi. org/10. 1002/hbm. 22681. source location Northwestern University Center for Advanced Magnetic Resonance Imaging (CAMRI), Chicago, IL Data accessibility Repository name: OpenNeuro Data identification number: 10. 18112/openneuro. ds002886. v1. 0. 0 Direct URL to data: https://openneuro. org/datasets/ds002886/versions/1. 0. 0 Related research R. articles.
Ahadzadeh et al. (2017)  collected data from 273 students (62% female) in Malaysia and found that Instagram usage was associated with body dissatisfaction (e. g. , higher levels of internalization of the thin ideal, body surveillance, and drive for thinness) in college students with lower self-esteem. Additionally, they found that for people who care more about their appearance, this affects the magnitude of discrepancy between their actual and ideal features. In a subsequent study,  Ahadzadeh et al. 's (2017)  research stressed that the negative influence of Instagram usage on body satisfaction is mediated by self-schema and self-discrepancy sequentially (i. e. , the negative effect is stronger for users with lower levels of self-esteem). Hendrickse et al. (2017)  conducted a survey with 185 female undergraduate U.
Prichard et al. (2017)  examined the impact of different "fitspiration" images (image with versus without text; body performing exercise versus posed body) on women's body image. The findings demonstrate that irrespective of body appearance or presence of text, exposure to fitspiration images decreases body satisfaction and increases negative mood. Dumas et al. (2017)  investigated different like-seeking behaviors on Instagram and individual differences in the frequency of like-seeking behavior, as well as motives for Instagram use as mediators of these relationships. The results show that normative (e. g. , using filters or hashtags) or deceptive (e. g. , buying likes or changing one's appearance in photos using software) factors affected like-seeking behavior.
When deriving fragility functions for buildings, explicit modelling of the uncertainty in the hazard estimation has also been proposed by  Ioannou et al. (2015)  and  Yazgan (2015) , who show that significantly flatter fragility functions are obtained as a result. More recently,  Ioannou et al. (2020)  have explored the building damage database for the 1980 Irpinia earthquake (Italy) in order to derive empirical fragility functions: they have tested statistical models of increasing complexity (e. g. , uncertainty in the estimation of the IM, spatial correlation of the intra event residual, presence of recorded observations). To this end, a Bayesian updating framework, based on a Markov-chain Monte-Carlo (MCMC) sampling algorithm, has been developed in order to obtain posterior distributions of the fragility parameters.
Then, Sect. 3 details the methodological developments carried out in order to apply a Bayesian updating approach to the damage data and to deliver updated RR functions for the buried pipelines. Finally, in Sect. 4, stochastic loss scenarios from the Mw 7. 0 Kumamoto earthquake are generated, using two distinct sets of assumptions (i. e. , a priori vs. a posteriori models and parameters): in both cases, Sobol' indices  (Sobol' 1990)  are computed in order to identify the contribution of each uncertain component to the total variability. To this end, two loss metrics are introduced for the water supply network, namely one based on aggregated physical damage (total number of repairs needed) and the other based on systemic behaviour (connectivity loss between sinks and sources). 2 Available data and modelling assumptions for the M w 7.
This Bayesian approach uses very similar principles as the updating method proposed by  Worden et al. (2018)  for the version 4. 0 of ShakeMap ®. For the earthquake main-shock, PGV shake-maps are generated by considering a M w 7. 0 shallow strike-slip rupture. Recorded data from 83 K-NET and 22 KiK-net stations are exploited, by selecting the stations that are within 100 km of the epicentre. The exponential spatial correlation model by  Jayaram and Baker (2009)  is used, with a correlation distance d = 25 km for PGV. Two GMPEs are tested in the shake-map procedure, namely the global model by  Chiou and Youngs (2008)  and the specific PGV model that has been developed for Japan by  Si and Midorikawa (2000). Source-to site distance metrics, such a distance to rupture R rup and Joyner-Boore distance R jb , are estimated from three different 3-D models of the fault geometry (see fault projection surfaces in Fig.
Bayesian updating framework for the estimation of new parameters As shown in the previous section, a higher-level statistical model is needed in order to cope with the limited amount of data. Therefore, a Bayesian updating framework is developed with the OpenBUGS software  (Lunn et al. 2009). The global idea consists in generating a prior distribution of RR parameters from available equations in the literature, and in updating the distribution with the reported damage data and the corresponding ground-motion estimates. The Bayesian updating algorithm in based on the one initially developed by  Ioannou et al. (2020)  for the estimation of empirical fragility curves for buildings. The main differences reside in the use of an explicit spatial correlation model for the ground-motion field, and in the selection of a different probabilistic model for the sampling of damage states.
Acknowledgements The main part of this research was carried out when one of the authors (P. G. ) was staying at the  Disaster Prevention Research Institute of Kyoto University at Uji , Kyoto, Japan; co-supported by the  French Institute Carnot Mobility Fund at BRGM , and by the Core-to- Core Collaborative research program  of the  Earthquake Research Institute , the  University of Tokyo  and the  Disaster Prevention Research Institute, Kyoto University. The support of  Uki City , who has provided the water pipeline network damage information, is greatly acknowledged.
[36] compared H s parse tree against sub-trees of T s parse tree. They transformed the hypothesis making use of an extensive semantic knowledge from sources like DIRT  [40] , WordNet, Wikipedia and a database of acronyms. Additionally, they took advantage of hand coded complex grammar rules for rephrasing in English. In  [37] , two textual entailment approaches are presented. The first one is based primarily on the concept of lexical overlap, considering a bag-of-words similarity overlap measure to form a mapping of terms in the hypothesis to the source text. The second system is a lexico-semantic matching between the text and the hypothesis that attempts an alignment between chunks in the hypothesis and chunks in the text, and a representation of the text and hypothesis as two dependency graphs.
For that, this system transforms the hypothesis making use of extensive semantic knowledge from sources like DIRT, WordNet [9], VerbOcean, Wikipedia and a database of acronyms. Participants Accuracy  [41]  0,746  [42]  0,721  [44]  0,706  [45]  0,659  [46]  0,608 The approach proposed in  [44]  is based on constructing structural features from the abstract tree descriptions, which are automatically extracted from syntactic dependency trees of T and H. These features are then applied by a subsequence-kernel-based classifier that learns to decide whether the entailment relation holds between two texts. In  [45] , they design different strategies to recognise true entailment and false entailment. The similarity between hypothesis and text is measured to recognise true entailment. They detect the exact entity and relation mismatch to recognise the false entailment.
The output of BLEU is then taken as the confidence score. Finally, they perform an optimization procedure to choose the best threshold according to the percentage of success of correctly recognized entailments. The value obtained was 0. 157. Thus, if the BLEU output is higher than 0. 157, the entailment is marked as true, otherwise as false. This procedure achieves 0. 495 of accuracy in recognizing TE. In  [23] , the entailment data is treated as an aligned translation corpus. In particular, they use the GIZA++ toolkit  [54]  to induce alignment models. However, the alignment scores alone were next to useless on the RTE-1 development data, predicting entailment correctly only slightly above chance. As a consequence, they introduced a combination of metrics intended to measure translation quality.
TEG Corpus Large scale annotation projects such as TreeBank  [68] , PropBank  [69] , TimeBank  [70] , FrameNet  [71] , SemCor  [72] , and others play an important role in NLP research, encouraging the development of new ideas, tasks, and algorithms. The construction of these datasets, however, is costly in both annotator-hours and financial cost. Since the performance of many NLP tasks is limited by the amount and quality of data available to them  [73] , one promising alternative for some tasks is the collection of non-expert annotations. The availability and the increasing popularity of crowdsourcing services have been considered as an interesting opportunity to meet the needs above and design criteria. Crowdsourcing services have been recently used with success for a variety of NLP applications  [74].
Although Amazon Mechanical Turk (MTurk) is directly accessible only to US citizens, the Figure Eight Platform service 3  provides a crowdsourcing interface to MTurk for non-US citizens. The main idea in using crowdsourcing to create NLP resources is that the acquisition and annotation of large datasets, needed to train and evaluate NLP tools and approaches, can be carried out cost-effectively by defining simple Human Intelligence Tasks (HITs) routed to a crowd of non-expert workers, called "Turkers", hired through on-line marketplaces.
• Textual Entailment, without Generality (TEnG), • Other, whichever is most appropriate for the T, H pair under consideration. The work involved in the annotation of the entailment cases of RTE-1 through RTE-5 datasets with TEG, TEnG and Other labels. A total of 2000 T, H pairs known to be in entailment relation were upload, from which 1740 were submitted for evaluation, having 260 Gold pairs contained. We can see that 1203 T, H pairs were annotated as being TEG. Each pair was evaluated by three Turkers, and the final average inter-annotator agreement of 0. 8 was verified. This task proved to be hard for the Turkers, as it is difficult for a human annotator to identify entailment relation and entailment by generality in particular. This is evidenced by the time spent to complete the task (Evaluation Time) and the total number of Judgments (Trusted + Untrusted) needed to achieve the final objective.
g. a longo prazo meaning in Portuguese long-term) and institutionalized phrases (e. g. con carne). In our work, for extraction MWU in first five RTE dataset test, used the Software for the Extraction of N-ary Textual Associations (SENTA)  [78] , which is parameter-free and language-independent thus allowing the extraction of MWU from any raw text. SENTA shows many advantages compared to different methodologies presented so far. It is parameter-free, thus avoiding threshold tuning. It can extract relevant sequences of characters, thus allowing its application to character-based languages. In summary, our experiments are based on two approaches to the calculations: using all words, and using Multi-Word Units.
Measures to Evaluate the Performance Classification or categorization is the task of assigning objects from a universe to two or more classes or categories. In the field of Artificial Intelligence, a CM is a visualization tool typically used in supervised and unsupervised learning. Each column of the matrix represents the instances in a predicted class, while each row represents the instances in an actual class. One benefit of a CM is that it is easy to see if the system is confusing two classes (i. e. commonly mislabeling one as another). When the dataset is unbalanced (when the number of samples in different classes vary greatly), the error rate of a classifier is not representative of the actual performance of the classifier. The entries in the confusion matrix have the following meaning in the context of our study: (i)TP is the number of correct predictions that an instance is Entailment, (ii) FP is the number of incorrect predictions that an instance is No Entailment, (iii) FN is the number of incorrect predictions that an instance in Entailment and (iv) TN is the number of correct predictions that an instance is No Entailment.
Numerical Description of the Filaments In the present work, digital chains using truss elements have been implemented in the commercially available finite element software package Abaqus/Explicit. Using this type of elements, the bending stiffness of the chains is neglected. Within this approach, the forces result from the axial deformation of the truss elements and from friction between the chains. A conventional Coulomb friction law is used to calculate the frictional forces. Moreover, it is expected that the deformation behavior of the textile in thickness direction mainly derives from the rearrangement and relative displacement of the filaments. Since DCE exhibit a constant cross-section, the discretization level of the fibrous mat should be sufficiently refined to reproduce the rearrangement and interaction between the chains accurately.
Data export and preparation For each patient, images, labels of the deep brain structures, entry and target points were exported (Fig. 2A ) from the iPlan Cranial (version 2. 0, Brainlab AG, Munich, Germany) software planning station. This was done using the OpenIGTLink network protocol  (Tokuda et al. , 2009)  implemented in the SlicerIGT  (Ungi et al. , 2016)  plug-in for 3DSlicer  (slicer. org, v4. 8. 1, Fedorov et al. , 2012). The labels corresponding to the manual segmentation of deep brain structures by the neurosurgeon were extracted as binary images (masks). The size of each mask corresponds to the smallest box, which encloses the label (bounding box); the voxel dimensions are the same as in the WAIR image. To ensure homogeneous naming of structures across patients and organization of the dataset, the exported labels were validated based on the metadata obtained from the planning station.
2. Top-level workflow description of the group normalization procedure. After preparation of the surgery on the planning station by the neurosurgeon, the data is exported and prepared (A). Data is first aligned on the MNI152 template with linear registration based on the T1 contrast (B). Aligned images are then non-linearly warped with three different tools (C) in order to compare the results achieved in the deep brain region by each tool (D). the bounding box of the T1 linear atlas. This reduced the size of images, and in turn the processing times in the following steps.