dataesr/scientific-paragraphs-categorization huggingface dataset:
3D{2} example wind_velocity. csdfe. An example of a 3D{2} dataset, i. e. , with three dimensions, d = 3, and one two-component dependent variable, p = 2, is the wind velocity prediction  [14]  dataset as a function of latitude, longitude and time, shown in Listing 10. The value of the dimensions key is an array with three JSON serialized Dimension objects. The first two LinearDimension objects, labeled as longitude and latitudes respectively, describe two linear dimensions sampled at every 0. 5˚for 49 points starting at -102. 5l ongitudes and 13. 5˚latitudes. Together, these two objects create a two-dimensional grid that spans the region around the Gulf of Mexico as depicted in Fig 10. The third dimension is a LabeledDimension object as indicated by the value of the type key. The corresponding labels array lists six date-time stamps entries.
The value of the dependent_variable key is an array with a single JSON serialized ExternalDependentVariable object (lines 34-43) containing a two-component vector dataset as identified by the quantity_type key-value. This value is vector_2 where the first part, vector, indicates vector data, and the last part, 2, gives the number of vector components. The two vector components are labeled as ugrd10m-eastward_wind and vgrd10m-northward_wind, in the array assigned to the component_labels key. The data values are located in an external file as a binary data whose address, relative to the wind_velocity. csdfe file, is the value of the components_url key. The binary data is interpreted as a 32-bit floating-point numerical array. Note, because the binary data does not support array indexing, unlike JSON serialization, the corresponding numerical array of data values is reshaped into a matrix which includes the number of components.
In this case, the reshaped matrix is 49 × 49 × 6 × 2, where the last number is the number of components, p = 2, and the remaining three is the number of points from the Dimension objects. Table  3  contains a description of the number of components, p, for each quantity_type. Underlaid these plots is a map of the Earth corresponding to the given range of latitudes and longitudes. These plots were generated using the Matplotlib library  [16]  for python in addition to the Matplotlib Basemap toolkit  [15]  for rendering maps. The value of the dimensions key is an array with three JSON serialized Dimension objects describing the three spatial dimensions, labeled as x, y, and z respectively. Here, all objects describe a linear dimension with the sampling resolution of 1 mm, and 148, 190 and 160 points along the respective dimension.
The value of the dependent_variables key is an array with a single JSON serialized InternalDependentVariable object (lines 30-51) describing a symmetric matrix dataset as indicated by the value of the quantity_type key. The value symmetric_ matrix_3 emphasizes a six-component dataset as noted in Table  3   Listing 10. CSD model depiction of a meteorology vector dataset. JSON serialized listing of the predicted wind velocities over and around the Gulf of Mexico. The model contains one two-component DependentVariable object and three Dimension objects. Listing was created by the authors using data from the national centers for environment information/ national oceanic and atmospheric administration  [14]. Listing 11. CSD model depiction of an MRI tensor dataset. JSON serialized listing of the diffusion tensor MRI dataset  [17]  of the brain containing one six-component Dependent-Variable object and three Dimension objects.
2D{1,1,2,1,1} example An example of a 2D{1,1,2,1,1} dataset using data from the US National Centers for Environment Information / National Oceanic and Atmospheric Administration  [14]  is given in Listing 12. In this example, the value of the dimensions key is an array with two LinearDimension objects describing the two spatial dimensions, labeled as longitude and latitude, respectively. The value of the dependent_variables key is an array with five Exter-nalDependentVariable objects describing the surface temperature (p 0 = 1), the air temperature at 2 m above ground level (p 1 = 1), the two-component wind velocity vector at 10 m above surface (p 2 = 2), the relative humidity (p 3 = 1), and the air pressure at the sea level (p 4 = 1). Fig  12  depicts the intensity and quiver plots of four dependent variables.
0D{1,1} example J_vs_s. csdf. The CSD model also allows the serialization of datasets without a coordinate grid. A 0D{1,1} datasets, for example, has no dimensions, d = 0, and two single-component dependent variable, p 0 = 1 and p 1 = 1. The listing for such a dataset  [18]  is given in Listing 13. In this example, the two "correlated" dependent variables are the 29 Si-29 Si nuclear spin couplings, 2 J, across a Si-O-Si linkage and the s-character product on the O and two Si along the Si-O bond across the Si-O-Si linkage  [18]. The value of the dependent_ variables key is an array with two JSON serialized InternalDependentVariable object (lines 7-16 and 17-27). The first object, named as Gaussian computed J-couplings, describes the 2 J couplings. The data values are stored as a Base64 string in the components key.
The Base64 decoded binary array is interpreted as a 32-bit floating-point numerical array following the value of the numeric_type key. The second object is named as the product of s-characters. Here, the data values are again stored as a Base64 string, which after decoding is interpreted as a 32-bit floating-point numerical array. A scatter plot revealing the correlation between the two dependent variables from the dataset in Listing 13 is presented in Fig  13. Listing 12. CSD model depiction of a meteorology dataset with multiple dependent-variables. JSON serialized listing of multiple dependent variables including scalar and vector on a two-dimensional grid. Listing was created by the authors using data from the US National Centers for Environment Information / National Oceanic and Atmospheric Administration  [14].
Listing 13. CSD model depiction of a computational dataset. JSON serialized listing of quantum chemistry calculation of nuclear spin-spin coupling constant between 29 Si nuclei across a Si-O-Si linkage in small cluster molecule. An example dataset with two Dependent-Variable objects and no Dimension objects. The listing was created by the authors using data from reference  [18]. These plots are overlaid on the coastline map of the Earth corresponding to the latitude and longitudes. These coastline were rendered using the Matplotlib Basemap toolkit  [15]. The plots were generated using the Matplotlib library  [16]  for python. The figure was created by the authors using data from reference  [14]. https://doi. org/10. 1371/journal. pone. 0225953. g012.
Conclusions We have designed the Core Scientific Dataset (CSD) Model as a lightweight, portable, versatile, resourceful, and standalone data model that is capable of handling multi-dimensional and correlated datasets from various spectroscopies, diffraction, microscopy, and imaging techniques. A guiding principle in the design of this model was to encapsulate only the minimal metadata necessary to represent the correlated datasets sampled on a common orthogonal coordinate grid. The model also allows for sparse sampling on this grid. Throughout the model, we make use of the ScalarQuantity class, which is composed of a numerical value and any valid SI unit symbol or any number of accepted non-SI unit symbols. This approach enables tremendous flexibility in allowing the dataset model to be agnostic of the scientific domain.
Historically, this may have been perceived as a potential barrier to software implementation of the CSDM, however, in recent years libraries capable of parsing units have become freely available for various computing environments such as Matlab, Mathematica, and python. The CSD model is independent of the hardware, operating system, application software, and file-serialization method used for data exchange. The model provides a mechanism for the inclusion of additional application-specific metadata without compromising its fundamental role as a data exchange and archiving standard. When serialized using JSON serialization the resulting file format is human readable and integrable with most object-oriented programming languages and software. The serialization of the CSD model has been adopted as an open dataset file format in NMR software development under our control, i.
The six components, labeled as Dxx, Dxy, Dxz, Dyy, Dyz, and Dzz respectively, are stored as Base64 strings as the value of the components key. Each Base64 decoded binary array is interpreted as 32-bit floating-points array and subsequently reshaped to a 148 × 190 × 160 matrix. The symmetric matrix data from the brain_MRI. csdf file was partially processed as a second-rank symmetric diffusion tensor to determine the isotropic diffusion coefficients. The intensity plots in Fig 11 depicts the projection of the isotropic diffusion coefficients on to the three spatial dimensions. Listing 9. CSD model depiction of a RGB image dataset. JSON serialized listing of an RGB image dataset containing two Dimension objects, and one DependentVariable object with three components corresponding to red, green and blue color intensities.
the Coordinated Universal Time (UTC) formatted according to the ISO-8601 standard. The optional geographic_coordinate attribute indicates where the CSDM file was last serialized and holds a GeographicCoordinate object, inside which are three attributes: the required latitude and longitude, and the optional altitude. Positive latitude values indicate latitudes north of the equator, while negative values indicate latitudes south of the equator. Longitude values are relative to the zero meridian, with positive values extending east of the meridian and negative values extending west of the meridian. Positive altitudes indicate above sea level while negative values indicate below sea level. All three are ScalarQuantity types. The optional boolean read_only attribute is set to true for archived datasets-informing applications that the dataset should not be modified or overwritten.
The optional tags attribute holds a set of UTF-8 allowed string values describing keywords associated with the dataset. The description attribute appears in nearly every CSD model object and holds a UTF-8 allowed string describing the instance of the model object. The application attribute also appears in nearly every CSD model object and is a generic object that can be used for storing application-specific metadata within the CSD model. Further details on the expected behavior of application attributes are given in section 2. 5.
Acknowledgments This material is based upon work supported in part by the  US National Science Foundation  under Grant No. DIBBS OAC 1640899. T. V. acknowledges financial support from the  European Commission (H2020 Future and Emerging Technologies  Grant  731475  and  Research Infrastructures  Grant  731005 ). Early support for this work came from the  Le Studium Loire Valley Institute for Advanced Studies, Orleans, France.
This work was supported by  US National Science Foundation  under Grant No. DIBBS OAC 1640899 , https://www. nsf. gov(PJS), and  European Commission  ( H2020   Future and Emerging Technologies Grant   731475  and  Research Infrastructures Grant   731005 ), https://ec. europa. eu/info/index_en(TV). The funders had no role in study design, data collection and analysis,.
All three are ScalarQuantity types. The optional boolean read_only attribute is set to true for archived datasets-informing applications that the dataset should not be modified or overwritten. The optional tags attribute holds a set of UTF-8 allowed string values describing keywords associated with the dataset. The description attribute appears in nearly every CSD model object and holds a UTF-8 allowed string describing the instance of the model object. The application attribute also appears in nearly every CSD model object and is a generic object that can be used for storing application-specific metadata within the CSD model. Further details on the expected behavior of application attributes are given in section 2. 5. DimObjectSubtype The relation between the CSD model attribute value types and the corresponding JSON serialized value type.
Combining EEG and fMRI allows for integration of fine spatial and accurate temporal resolution yet presents numerous challenges, noticeably if performed in real-time to implement a Neurofeedback (NF) loop. Here we describe a multimodal dataset of EEG and fMRI acquired simultaneously during a motor imagery NF task, supplemented with MRI structural data. The study involved 30 healthy volunteers undergoing five training sessions. We showed the potential and merit of simultaneous EEG-fMRI NF in previous work. Here we illustrate the type of information that can be extracted from this dataset and show its potential use. This represents one of the first simultaneous recording of EEG and fMRI for NF and here we present the first open access bi-modal NF dataset integrating EEG and fMRI. We believe that it will be a valuable tool to (1) advance and test methodologies for multi-modal data integration, (2) improve the quality of NF provided, (3) improve methodologies for de-noising EEG acquired under MRI and (4) investigate the neuromarkers of motor-imagery using multi-modal information.
In the first, information extracted from one methodology are integrated or drive the analysis of the second while in symmetrical approaches (data fusion  13  ) a joint generative model is used. These approaches have been poorly explored, complexity of neurovascular coupling being their main limitation. Collecting EEG in the 'harsh' environment of MR scanners entails a series of technical challenges, primarily devoted to the reduction of the strong EEG artefacts generated by MR gradient currents. The degradation of EEG probably represents the main issue of simultaneous EEG-fMRI recordings and several de-noising solutions have been developed  14, 15. However, so far, the corresponding datasets have rarely been released for use by other groups. Moreover, for the implementation of NF, simultaneous EEG-fMRI acquisition and processing need to be executed in real-time, adding complexity and challenge to hardware and software solutions.
To the best of our knowledge, simultaneous EEG-fMRI in a NF loop was implemented only by another research group to train emotional self-regulation  2  : the dataset we share and describe here therefore corresponds to the first implementation of bimodal NF for a motor imagery task. It consists of 64-channels EEG (extended 10-20 system) and fMRI datasets simultaneously acquired during a motor imagery NF task, complemented by structural MRI scans. Recordings were performed in two studies. The first (Fig. 1 ) involved 10 healthy subjects and aimed at assessing the feasibility and the potential of bimodal EEG-fMRI NF over unimodal (EEG or fMRI only) NF  9. In the second study  16  that included 20 volunteers, an original 2D metaphor allowing to separately regulate EEG and fMRI activity was proposed and its impact on NF performance compared to a more "classical" 1D gauge (Fig.
2 ). An analysis of NF scores and BOLD activations was performed and results suggested that 1D feedback is easier to control, while the fMRI activation is more specific when the 2D representation is used. Going beyond these previous works, we have BIDs standardized and comprehensively documented the two datasets: in this manuscript we describe in detail the procedures employed to generate data and the architecture of the dataset, we perform additional validation analysis, we give examples of the type of information that can be extracted and show various prospective uses. This dataset has potential to shed light on the coupling model underlying the EEG and fMRI signals, to advance methodologies for multimodal data integration and fusion techniques and test EEG MRI de-noising algorithms. Since data were acquired during the execution of a simple task for whom neural pathways and activations are well known (motor imagery), this dataset constitutes a simple model to develop and validate methods of data integration at various scales (activation maps, connectome).
This dataset is also of great interest for the NF research field, as it gives new perspectives to improve the estimation of NF scores, or the extraction of features of interest. As dataset derivatives, we computed EEG and fMRI NF scores for each training session. These can be used in a model to learn fMRI informed EEG in order to improve the quality of EEG only NF  17. The development of such approaches has potential to increase the number and quality of NF training sessions and therefore advance clinical applications.
Participants. All experiments were performed according to the Helsinki declaration and under approval by the Institutional Review Board. Thirty right-handed NF-naïve healthy subjects were involved, of which 12 were female (mean age 33. 7 ± 9. 9 years old). All participants signed an informed consent after having been informed about the experimental procedure. Only 27 of them signed the consent to publish their anonymized data, and were therefore included in the public datasets  18, 19.
1. 2 (Brain Products GmbH, Gilching, Germany). An average artefact subtraction approach  14  was used for gradient artefact correction, with four artefact templates. EEG data were then down-sampled to 200 Hz and low-pass filtered at 50 Hz (48 dB slope) for further processing. BCG artefact correction was then performed  15  using a moving template matching method with the following parameters: pulse period 800 ms, correlation threshold 0. 7 and amplitude ratio between the period examined and the pulse model ranging from 0. 6 to 1. 2. The moving pulse template was built averaging the 10 previous detected pulses. The corrected EEG data were subsequently sent to the NF control unit for feature extraction via a fieldtrip (https://github. com/fieldtrip/fieldtrip/) buffer solution. Real-time fMRI pre-processing (slice-time and motion correction) was performed directly by the NF control unit using a custom Matlab script based on SPM 8 (FIL, Wellcome Trust Centre for Neuroimaging, UCL, London, UK).
dat), the header file (*. vhdr) and the marker file (*. vmrk), containing information similar to those described for raw data. In the header file of preprocessed data, channels locations are also specified. In the marker file the location in data points of the identified heart pulse (R marker) are specified as well. EEG data were pre-processed using BrainVision Analyzer II Software, with the following steps: • Automatic gradient artefact correction using the artefact template subtraction method (sliding average calculation with 21 intervals for sliding average and all channels enabled for correction). MRI Data. All DICOM files were converted to Nifti-1 and then in BIDS format (version 2. 1. 4) using dcm2niix (https://github. com/rordenlab/dcm2niix, version v1. 0. 20190720 GVV7. 4. 0) Raw functional MRI.
On the other hand, during Neurofeedback blocks (Task-NF) the image presented was a ball moving in a square that the subject could control self-regulating his EEG and/or fMRI brain activity. fMRI NF Scores. For each subject and NF session, a Matlab structure with BOLD-NF features can be found in XP1/derivatives/sub-xp1*/NF_bold/ In view of BOLD-NF scores computation, fMRI data were preprocessed using SPM8 according to the following steps: slice-time correction, spatial realignment and coregistration with the anatomical scan, spatial smoothing with a 6-mm isotropic Gaussian kernel and normalization to the Montreal Neurological Institute (MNI) template. For each session, a first level GLM analysis was then performed. The resulting activation maps (voxel-wise Family-Wise error corrected at p < 0.
05) were used to define two ROIs (9 × 9 × 3 voxels) around the maximum of activation in the left and right motor cortex. The BOLD-NF scores (fMRI laterality index) were calculated as the difference between percentage signal change in the left and right motor ROIs as for the online NF calculation. A smoothed and normalized version of the NF scores over the precedent three volumes was also computed. To allow for comparison and aggregation of the two datasets XP1 and XP2, NF scores were also computed considering the left motor cortex and a background as for online NF calculation in XP2. In the NF_bold folder, the Matlab files sub-xp1*_task-*_NFbold_scores. mat have the following structure: NF_bold →. nf_laterality (calculated as for online NF calculation) →. smoothnf_laterality →. normnf_laterality →.
In task events files for the different tasks, each column represents: • 'onset': onset time (sec) of an event • 'duration': duration (sec) of the event • 'trial_type': trial (block) type: rest or task (Rest, Task-MI, Task-NF) • 'stim_file': image presented in a stimulus block. During Rest or Motor Imagery (Task-MI) instructions were presented to the subject. During Neurofeedback blocks (Task-NF) the image presented was a ball moving in a square for the bidimensional NF (task-2dNF) or a ball moving along a gauge for the unidimensional NF (task-1dNF) that the subject could control self-regulating his EEG and fMRI brain activity. fMRI NF Scores. For each subject and NF session, a Matlab structure with fMRI-NF features can be found in XP2/derivatives/sub-xp2*/NF_bold/ In the original work, in view of fMRI-NF scores computation, fMRI data were preprocessed using AutoMRI, a home-made software based on spm8 automating the preprocessing according to the following steps: slice-time correction, spatial realignment and coregistration with the anatomical scan, spatial smoothing with a 6-mm isotropic Gaussian kernel and normalization to the Montreal Neurological Institute (MNI) template.
Motion parameters were reported and used to perform a post hoc correlation analysis with fMRI and EEG NF scores calculated as in real-time. To this end and for each NF training session the Framewise Displacement (FD) was computed from the six realignment parameters using the spmup tool within the QAP Package (https://github. com/CPernet/spmup, http://preprocessed-connectomes-project. org/ quality-assessment-protocol/index. html) as for Power et al. 26. A correlation analysis between the FD time-series and the NF signals was then performed. A Matlab structure with the results of this analysis can be found in derivatives/sub-xp*/head_motion. It is composed of the the following fields Globally, the module of the Pearson correlation between fMRI NF scores and FD was always lower than 0. 3 (an arbitrary chosen threshold level indicating fairly low correlation) in XP1 and higher than 0.
3 in 1. 65% of the NF training sessions for XP2. Head motions artifacts correlated to a slightly larger extent with EEG NF scores, as correlation was found higher than 0. 3 in 3. 3% of cases for XP1 and 5% for XP2. To illustrate the type of data and results we can extract from the datasets we show in Fig. 5  an example of fMRI and EEG activation and NF scores for one subject (XP2). BOLD activation maps typically show activation of primary and supplementary motor areas and visual areas during the NF task as compared to rest. Similarly, the average ERD (8-30 Hz) scalp distribution for EEG presents a desynchronization over the motor electrodes, in particular in the contralateral region. BOLD NF scores show quite high (and consistent across sessions) activation of the selected ROI during the NF task, while EEG NF scores are lower and exhibit larger variability across sessions (which is to be expected given the higher NF update rate for EEG and the residual artefacts that affect EEG quality).