dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2010). In addition, a feedback module with configurable permissions allows users (such as radiologists) to assess scan quality, selecting from over 30 intensity, movement, coverage, QC annotations, and diffusion-specific artifacts. Finally, LORIS provides protocol checking, where violations are flagged in a real-time scan protocol violations module. After images have passed automated protocol checking, multiple reviewers can then examine an image for diagnostic quality and pathology within the radiological review module. LORIS also includes a suite of behavioral QC modules, such as (i) the conflict resolver module for double data entry mismatches; (ii) the feedback module, enabling users to input and query QC flags and annotations for every field; (iii) data integrity flagging, which verifies every field for consistency and completeness; (iv) a certification tool for assessment and maintenance of examiner reliability across sites, an essential feature for longitudinal studies with significant staff turnover; and (v) real-time statistics.
Tool) enables download of scalar data (e. g. , clinical, behavioral) that can be simultaneously linked to multi-dimensional data (images) through a simple querying interface. This tool uses simple boolean algebra to customize searches and filter results, either longitudinally or cross-sectionally. Typically, this allows simultaneous querying from multiple researchers within a LORIS-based project but can also be configured for querying across multiple LORIS repositories. For future cross-platform interoperability, the extensible NoSQL architecture (i. e. , CouchDB) facilitates federated sharing with other platforms. (iv) Data processing via CBRAIN CBRAIN  (Sherif et al. , 2014 ) is a web platform that enables distributed execution of software pipelines on computing networks and aggregates multiple distributed file systems into a uniform view (Fig.
4 ). The system was designed to abstract the complexities typically associated with the use of HPC resources for image processing. CBRAIN supports multiple file servers called Data Providers, using various file transfer protocols, such as SFTP, SSH, and Amazon S3. Once files are registered on a data provider, they can be processed by any pipeline available in CBRAIN regardless of their physical location. To meet privacy, security and sharing requirements, access permissions can be defined on files and Data Providers-for example, CBRAIN users may keep their resources completely private, share them with specific groups, or make them public to the entire CBRAIN community. CBRAIN maintains a complete data provenance graph where an extensive log of all actions, parameters, software versions and locations are kept, and output files produced by a pipeline are linked to the inputs.
CBRAIN is linked with LORIS through a RESTful web API (Application Programming Interface) to facilitate data sharing. A specific LORIS data provider automates file transfer and registration operations between the two platforms, such that data can be processed using CBRAIN, and outputs re-inserted into LORIS. Figure  5  exemplifies the interaction of three external platforms with CBRAIN: LONI (integration described in  (Frisoni et al. , 2011) ), Virtual Imaging Platform  (Glatard et al. , 2013) , and LORIS. All three projects use the CBRAIN API and Data Providers to share data or virtual machines (VMs), and to access computing resources. A CBRAIN service is operated by the MNI, providing access to a national network of computing resources, which supports numerous projects. For security and support reasons, access to this service is restricted to MNI collaborators, as large resource allocations are limited to selected Canadian scientific projects.
However, for testing purposes, trial accounts can be requested by emailing cbrain-support. mni@mcgill. ca. The CBRAIN source code is freely available (GitHub: https://github. com/aces/cbrain, NITRC: www. nitrc. org/projects/cbrain). Detailed documentation allows anyone to install and configure a CBRAIN service on their own computing and storage resources (e. g. , single machines, large national clusters, grids or clouds, etc. ). Any scientific software installed on these resources (including licensed software) can technically be available through CBRAIN.
(v) Additional features Beyond the foundation described previously, LORIS has supplementary functionality: 1. Imaging Uploader via a web browser 2. Enhanced Visualization and QC tool-BrainBrowser  (Sherif et al. , 2015)  3. Instrument Builder to create data forms in a web browser 4. Personalized dashboard 5. Candidate Information Module for status, demographics, and consent 6. Data Dictionary Builder for ontological consistency and codebook creation 7. Document repository 8. Mobile-friendly interface 9. Survey Module to email measures to participants 10. Configuration module 11. REST API for automated retrieval or modification of data.
Results LORIS (www. loris. ca) was first implemented at the MNI in 1999 to organize data acquisition and analysis for the multisite MRI Study of Normal Brain Development  (Evans and Brain Development Cooperative, 2006). It is currently active in 130 sites worldwide with over 30,000 data collection time points, more than 500 behavioral instruments, and 75,000 variables hosted on its platform. Over 30 TBs of imaging datasets are stored in LORIS projects, comprising more than 200,000 acquisitions over 20,000 separate sessions. CBRAIN (www. cbrain. ca) has been continuously operating at the MNI since 2009, hosting numerous raw and processed datasets. CBRAIN serves more than 300 users from 60 cities and 20 countries, including diverse projects such as PreventAD  (Tremblay-Mercier et al. , 2014) , K-ADNI  (Kim et al.
, 2014) , the Lothian Birth Cohort  (Karama et al. , 2013) , MAVAN, and NeuroDevNet  (Goldowitz and McArthur, 2011). Users from 130 virtual sites access a data storage grid of more than 600 TBs across 25 storage servers. CPU hours from Compute Canada (www. computecanada. ca) and private servers. The MNI ecosystem serves as repositories for the following publicly available studies: The NIH MRI Study of Normal Brain Development  (Evans and Brain Development Cooperative, 2006) : This was the first LORIS repository, completing the full lifecycle of a longitudinal project in 2007. Data were collected for more than 60 clinical instruments with over 8,000 variables, including raw and processed data from different MNI scanners. Data were shared with the research network through the DQT and were eventually federated to the NIH National Database for Autism Research (NDAR; https://ndar.
nih. gov/) for public access, in accordance with the NIH Data Use Certification policy. Access procedures are detailed within the study's official website (http://pediatricmri. nih. gov/nihpd/info/index. html). The NIH has included additional processed data since 2007 and may consider the addition of supplementary data on a case-by-case basis. Infant Brain Imaging Study (IBIS,  Wolff et al. , 2012) : IBIS is an ongoing multisite U. S. Autism Spectrum Disorder study (www. ibisnetwork. org), collecting data from high-risk siblings of autistic probands. Data are incrementally federated to NDAR for public access (NDAR; https://ndar. nih. gov/access. html). The study has collected close to 10,000 phenotypic and imaging variables, with 4,000 subject time points. Approximately 3 TBs of imaging data are presently housed in IBIS, including 2,300 acquisitions that encompass a wide range of modalities, such as structural MRI, resting-state fMRI, diffusion tensor imaging (DTI), and MR spectroscopy.
Selected genetic data (SNPs, CNVs) are also included. Collaborators of the IBIS project (e. g. , EARLI, http://nationalautismnetwork. com/research/researchprograms/earli-project. html) are able to contribute to this dataset, which can be organized as subprojects within the IBIS database. BigBrain  (Amunts et al. , 2013) : The BigBrain dataset (https:// bigbrain. loris. ca) has been accessed by over 20,000 different visitors. It comprises 7,404 stained sections of a postmortem human brain, digitally reconstructed in 3D. The raw data, totaling approximately 1 TB at full resolution, are made freely available for download in slice format as MINC and NIfTI formats. Lower-resolution versions can also be downloaded in 3D for ease of visualization. Crowd sourcing efforts have recently been initiated enabling external research groups to contribute to processing and parcellation of the BigBrain.
The Autism Brain Imaging Data Exchange (ABIDE) (Di  Martino et al. , 2014) : ABIDE (https://abide. loris. ca) employs LORIS' data-sharing platform in hosting 1,112 datasets, comprised of both resting-state and structural MRI, and federated phenotypic data acquired on 539 individuals with autism across 16 international sites. Public access to the ABIDE dataset is granted in accordance with the 1,000 Functional Connectomes Project data usage policies, as outlined at http://fcon_1000. projects. nitrc. org/indi/abide/. No new data are currently being collected for ABIDE; thus, all available data reflect the complete dataset for this neuroimaging initiative. The Open MEG Archive (OMEGA)  (Niso et al. , 2015) : Initiated in 2014, the Open MEG Archive (https://omega. bic. mni. mcgill. ca/main. php) is among the first diversified MEG open-science projects.
It houses growing amounts of resting-state and task-related data, presently totaling 138 sessions from 79 study subjects. These datasets are publicly available for download via the OMEGA LORIS repository, including demographic and behavioral data, MEG, structural MRI for optimal source imaging, and processed data in Brainstorm format  (Tadel et al. , 2011). Access is freely granted to any individual who requests an account via the login page. The OMEGA repository is expected to house additional datasets, including electrophysiology (e. g. , EEG, single cell and field recordings) and imaging (e. g. , fMRI, DWI, PET) modalities.
Discussion There is clearly an increased global effort to make data available across projects, borders, and technological platforms  (Keator et al. , 2009). As a result, data sharing has become a popular term of late. However, the disarmingly simple term obscures a multitude of challenges that go far beyond utilizing a database system to host data through a public website. The questions are almost endless: What type of data…? Sharing with whom…? For how long…? What transfer formats…? Who "owns" the data…? What privacy safeguards exist…? Data need to be managed and processed in a clear and auditable way, with unique consideration devoted to the curation and sharing of clinical data. For example, subject information cannot be released without consent  (Brakewood and Poldrack, 2013; Choudhury et al.
, 2014; Mennes et al. , 2013). LORIS has several mechanisms to address de-identification, both through removal of identifying data fields and more esoteric anonymization techniques, such as defacing of anatomical images, masking of DICOM headers, and addressing privacy issues, such as HIPAA compliance  (Matlock et al. , 2012). Often lacking in data dissemination initiatives, documentation is a multi-faceted task that must include detailed provenance tracking of data processing  (Keator et al. , 2009). Comprehensive guidelines on how to navigate the database are a sine qua non for a repository to be effective. The careful development of a logical ontology for study variables is critical for the seamless integration, organization, and analysis of the study. This need for common data elements increases exponentially with the introduction of multi-institutional, federated or publicly shared repositories.
Properly defining and documenting a common lexicon allows for precise querying and analysis to be performed on "Big Data"  (Temal et al. , 2008; Turner et al. , 2010; Larson, 2013) , as a small and nuanced change in semantics between sites and researchers can critically undermine the dissemination process. Thus, LORIS has integrated several tools such as a Data Dictionary builder to enable granular definition of data fields, and facilitate project-specific modifications for sharing purposes. Terms such as "interoperability" and "data harmonization" have become popular buzzwords surrounding data-sharing across platforms. Despite this trend, many existing platforms do not fully address the range of dissemination issues, which critically impact the integrity of publicly shared datasets  (Mendelson et al.
, 2014). For instance, the type of imaging format researchers use to share and process data can vary (e. g. , DICOM, MINC, NIfTI, etc. ), either requiring converters and essential libraries (that can produce lossy results) or employing other platforms such as ITK (www. itk. org) or LONI Babel  (Van Horn et al. , 2014). As sharing initiatives proliferate, the "order of complexity" of data organization increases disproportionately  (Mennes et al. , 2013). As a result, an API becomes essential for automated retrieval and dissemination of data, particularly when dealing with database federation. The importance of providing collaborators with easy access to all relevant information cannot be overstated  (Keator et al. , 2013). When dealing with large multisite studies, reproducibility across scanners, sites, and time is imperative  (Liu et al.
, 2010; Jovicich et al. , 2009 Jovicich et al. , , 2013 Jovicich et al. , , 2014)). To identify and track scanner variability and system errors over time and across sites, both living phantom and geometric phantom data are used to assess scanner consistency, and to develop compensatory transformations for distortion corrections. Geometric phantoms include the ADNI phantom  (Gunter et al. , 2009) , the ACR phantom used by NIH MRI Study of Normal Brain Development  (Fu et al. , 2006) , and the LEGO phantom used by the IBIS study  (Caramanos et al. , 2010; Fonov et al. , 2010) , which are used to assess temporal and geometric corrections in gradient field nonlinearity. Additionally, "Living phantom" acquisitions, performed on project staff who are regularly scanned at all sites, provide reproducibility information in a more realistic biological setting than can be obtained from a relatively simple geometric phantom  (Jovicich et al.
, 2009 (Jovicich et al. , , 2013)). Finally, the capacity to share information beyond a project's funding cycle is paramount for the long-term utility of data-sharing initiatives. A PubMed literature review conducted by  Poline et al. (2012)  estimated that of 12,000 datasets with more than 12 subjects, "only a small percentage" of data is available in public repositories, reflecting the notably ephemeral state of most repositories. Concrete planning for long-term stewardship and sustainability is an important element in public datasharing, one that has also begun to garner consideration from funding agencies  (Berman & Cerf, 2013; Toga & Dinov, 2015). To this effect, LORIS and CBRAIN are both scalable initiatives, with stable long-term funding and an open-source community that is actively contributing to their codebases.
Conclusion The MNI data-sharing ecosystem, based on the LORIS and CBRAIN platforms, represents a powerful combination of digital infrastructure for international data-sharing initiatives. These platforms leverage 20 years of established MNI tools (e. g. , MINC toolkit, CIVET image processing pipeline) and numerous external software pipelines (e. g. , SPM-batch, FSL, Freesurfer). Future work will involve an increased focus on federating and sharing of existing datasets, with particular emphasis in the areas of authentication, security, and pipeline reproducibility. We will also enhance our use of cloud technology and international platform interoperability for greater flexibility in storage, sharing and remote processing of multi-domain data.
Acknowledgments The authors thank the following people for the sustained efforts towards building and operating LORIS and CBRAIN:  Ehsan Afkhami ,  Katrin Amunts ,  Sylvain Baillet ,  Natacha Beck ,  Pierre Bellec ,  Remi Bernard ,  Kelly Botteron ,  Nicolas Brossard ,  David Brownlee ,  Athena Buckthought ,  Tara Campbell ,  Matt Charlet ,  Francois Chouinard ,  Louis Collins ,  Adriana Di Martino ,  Vladimir Fonov ,  John Harlap ,  Nicolas Kassis ,  Justin Kat ,  Budha Khundrakpam ,  Daniel Kroetz ,  Xavier Lecours-Boucher ,  Stella Lee ,  Gabriel Leonard ,  Claude Lepage ,  Ilana Leppert ,  Jason Lerch ,  Lindsay Lewis ,  John Lewis ,  Oliver Lyttelton ,  Karolina Marasinska ,  Angela McCloskey ,  Evan Luc McIlroy ,  Robert McKinstry ,  Michael Milham ,  Mia Petkova ,  Bruce Pike ,  Tarek Sherif ,  Jordan Stirling ,  Ted Strauss ,  Olga Tsibulevskaya ,  Robert Vincent ,  Dario Vins ,  Alden Woodward ,  Alex Zijdenbos , the  IBIS Network , and the  Brain Development Cooperative Group.
M E T H O D O L O G Y We introduce here the main steps of the proposed adaptive strategy for extraction of multiple seismic sources exhibiting wide dynamic ranges and spatial scales (Fig. 1a ). In the first step (Fig. 1b ), the signal recorded at each station is transformed into CF, designed to extract and enhance multiscale non-stationary statistical features, through multiband HOS or envelope-based structural components. This stage is designed to account for a priori unknown transient signals potentially associated with a variety of sources, and to prepare data for a better performance in posterior steps. In the second step, station-pair time-delay estimate (TDE) functions are evaluated by calculating local cross-correlations (LCCs) of the transformed signal. The imaging functions are then constructed through combination of the station-pair TDE functions and their spatial mapping, according to theoretical phase time difference of arrivals (TDOA).
( 6 ) The final formulation of recursive HOS CF for each time step t i can then be defined by the ratio of two quantities in eq. (  6 ) according to the kurtosis definition provided in eq. (  2 ): CF HOS (t i ) ≡ Ŝ4 (t i ) = m4 (t i ) /( m2 (t i )) 2 = C • (u (t i ) -μ (t i-1 )) 4 + (1 -C) • m4 (t i-1 ) (C • (u(t i ) -μ(t i-1 )) 2 + (1 -C) • m2 (t i-1 )) 2. ( 7 ) Following the same idea, we define the recursive envelope CF as a single component recursive RMS envelope of the recorded signal u(t), using the following expression: CF env (t i ) ≡ RMS (t i ) = C • u 2 (t i ) + (1 -C) • (RMS (t i-1 ) ) 2 (8) where C is a decay constant, similarly to eqs (5) and (6). In the above formulations, the decay constant C can be seen as a smoothing factor, defining how fast the memory of older data points will be discarded.
(  10 ). (h) RMS envelope CFs of filtered signals. envelope to each of the signals U (t, f n ) (Fig. 4h ). A single time dependent broad-band CF (CF TF (t)) can then be constructed using one of the operators discussed above (eqs 9 and 10). For the example in Fig. 4 , a maximum operator (eq. 9) is used for the earthquake dataset, while a RMS summation operator (eq. 10) is used for the tremor signal. In general, spectral and time-domain (MBF) time-frequency decompositions should provide similar characterization of the signal, when the number of filters in the MBF filter-bank is equal to the number of scales in the spectrogram (see e. g. Mill & Brown 2005). A comparison between the CFs of raw record, the broad-band CFs obtained from spectral decomposition, and the broad-band CFs obtained from MBF analysis is shown in Figs 5(a) and (b) for the case of earthquake and tectonic tremor records, respectively.
The generic broad-band envelope CF is further composed by applying the RMS operator (eq. 10) to the time-frequency MBF envelopes (Fig. 4g ). The resulted detection functions for the horizontal EW components and a subset of 22 stations, selected from the overall dataset (Fig. 7f ), are shown in Fig. 12. In this example we only analysed 20 min of recording corresponding to the most energetic part of the tremor. Next, 3-D maximum likelihood locations are obtained by stacking the TDE functions projected on the theoretical TDOA grids following the steps in Fig. 1(a). Here, we work under the assumption of S-wave propagation, and calculate theoretical traveltime grids for a homogeneous velocity model with the constant velocity of 3. 5 km/s  (Maeda & Obara 2009). Only station-pairs with inter-station distance not exceeding 80 km were used.
The signal processing scheme plays a double role of providing a representation of recorded seismic signal and extracting frequency-dependent information. The main goal of this step is to account for a-priori unknown transient signals, potentially associated with different sources, and to prepare data for a better performance in the detection and location part. The detection and location step of the method is divided in two parts. First, station-pair time delay estimate (TDE) functions are evaluated by cross-correlation of the transformed signals. Then, simultaneous detection and location is performed by estimating the array imaging function corresponding to sum of TDE functions mapped to the corresponding theoretical time difference of arrivals (TDOAs), calculated in a common 3-D coordinate system, under the assumption of propagating phases for a given velocity model.
A main difference with standard beamforming is that the proposed method does not require a delay-and-sum process (i. e. performing an explicit grid-search over all the possible source locations), but rather a direct mapping of the TDE function onto the 3-D space (i. e. a change of support). This property, along with a fully recursive implementation, makes the approach computationally efficient. This, as well, implies the suitability of the method for future real-time applications and for a straightforward parallelization. The parallel version of the code (BackTrackBB) was used for the application example presented in Section 3. 3. It required 22 min to produce the final locations shown in Fig. 13 (a), on a machine with 16 cores (2 x Intel Xeon E5-2690 2. 90 GHz) and 64 GB of RAM. The BackTrackBB code is distributed on open-source basis, and can be downloaded from http://backtrackbb.
A4 Synthetic tests The performance of the method is tested on a synthetic dataset setup for the station geometry of Hi-net in southwestern Japan and composed of 21 stations (Fig. 12a ). We consider four test events located within the 3-D local XYZ grid with 1 km grid spacing defined as in Fig. 12a. An isotopic source is associated with each of the four events. The test is designed to demonstrate the efficiency of both time-frequency signal processing and the space-time detection and location schemes. The synthetic signal at each station corresponds to a Berlage wavelet (e. g. Aldridge 1990) given by:  noise and the wavelet in the frequency range 2. 0-8. 0 Hz (around the predominant frequency of the wavelet) is set to a predefined value. We generate synthetic signals with SNR = [3. 0; 2. 0; 1.
This study has been partly funded by the  Centre de Recherches sur le Stockage Géologique du   CO2 (IPGP/TOTAL/SCHLUMBERGER/ADEME ), and  Uni-vEarthS LabEx program of Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ). Part of the data analysis was im-plemented using ObsPy (Megies et al. 2011). Figures were prepared using GMT (Wessel et al. 2013) and Matplotlib (Hunter 2007).
Systems neuroscience has identified a set of canonical large-scale networks in humans. These have predominantly been characterized by resting-state analyses of the task-unconstrained, mind-wandering brain. Their explicit relationship to defined task performance is largely unknown and remains challenging. The present work contributes a multivariate statistical learning approach that can extract the major brain networks and quantify their configuration during various psychological tasks. The method is validated in two extensive datasets (n = 500 and n = 81) by model-based generation of synthetic activity maps from recombination of shared network topographies. To study a use case, we formally revisited the poorly understood difference between neural activity underlying idling versus goal-directed behavior.