dataesr/scientific-paragraphs-categorization huggingface dataset:
When applied to zebrafish larvae, this enables simultaneous recording of the quasi-entirety of the neurons (∼ 100, 000 units) at typically 1 brain/second. The BSD algorithm might prove to be particularly useful for such experiments, as the size of individual datasets precludes supervision. Furthermore, the gain in speed with respect to con-oopsi should also be beneficial as it may allow one to carry out the spike inference on the fly. To illustrate this latter claim, we test con-oopsi and BSD inference algorithms on a typical whole-brain recording, consisting of 1,800 successive volumetric stacks sampled at 1 stack/second, each of them comprising 20 z-sections. The experiment is performed on a 5 dpf larva expressing the GCaMP5 reporter panneurally. Voxels were clustered by a factor X. Hence, 255463 fluorescence traces encompassing the brain volume are processed independently.
E. Data analysis In all experiments, the transmembrane voltage V m was recorded over the 1000-ms pacing pause. The number of simulations in which a PVC was observed, n, was recorded to compute the probability p PVC ¼ n=N during the pacing pause. PVCs that did not activate more than 10% of the ventricles were not computed in p PVC. PVCs activating more than 50% of the ventricles were considered propagated PVCs while PVCs that failed to do so were classified as block. The probability of a reentry due to a PVC was also calculated. A reentry lasting for more than 1000 ms is considered to be sustained. In addition, the waiting times until the onset of a PVC, T PVC , was determined as the time elapsed from the end of pacing until the instant of a triggered AP crossing À10 mV. APD was calculated as the difference between T PVC and the repolarization time (time at which the AP waveform crossed a level corresponding to 90% of repolarization to the resting potential).
ACKNOWLEDGMENTS The present work was supported by the  National Institutes of Health  (Grant No. 1RO1 HL 10119601 ), the  Austrian Science Fund  (Grant No. F3210-N18 ), and the  CNPq  ( Conselho Nacional de Desenvolvimento Cient ıfico e Tecnol ogico) -Brazil. Y. S. acknowledges funding from the  National Heart, Lung, and Blood Institute , Grant No. RO1HL101196.
The membrane potential of neurons was not adjusted by injection of a DC current. The ability to fire action potentials (AP) in response to a depolarizing current (200 pA for 2s) was then checked. Electrically evoked AP generation was also tested after the laser stimulation for all the neurons under pharmacological investigation. A total of 80 functional neurons were tested. Data were analyzed on Clampfit software (Molecular Devices). No filtering was applied for data processing unless specified.
Subjects and Study Site This study took place at the Biomedical Primate Research Centre (BPRC) at Rijswijk, the Netherlands, in July and August 2005. The facility held a total of 54 individuals of which 28 were adult females and 26 adult males. The population was composed of six groups living in enclosures with outside areas facing the same courtyard (see Figure  1 ). Thus, some groups (those who faced other groups) could see each other and all groups could hear each other. Note that this colony was moved to the Safari Park Beekse Bergen in 2006, and that the BPRC no longer houses chimpanzees. The survey of sound production using objects or other enclosure elements as a resonating medium (sound-object use, hereafter referred to as So-U) was carried out in two phases. In Phase 1 (from 13th July to 12th August 2005) data were collected for the four groups that had at least two adult males (Dirk's group, Barney's group, Bob's group and Dennis' group).
Data Collection In Phase 1 the goal was to gather as much information about the factors affecting the duration of So-U as possible. One observer (PG) stood in the central courtyard and focused on one group for 15 min at a time. We conducted a total of 162 focal observations (54 per group). Focal observations were spread equally throughout the day (from 8. 30 to 10 am: 15 observations per group, 10. 30-12. 20: 20 observations per group, and 1. 20-5 p. m. : 19 observations per group) and the groups were observed in random order within each time period. During focal group observations the observer wrote down all occurrences of So-U in the group, noting the identity of the individual producing the sound, the duration of So-U, the type of medium used, the technique used, and any information (individual posture, behavior, or external factors) that could indicate the context of occurrence (Table  1 ).
Data Re-coding for Phase 1 To ensure a sufficiently large and reliable dataset allowing for So-U analysis we introduced categories for contexts, drumming techniques, and objects used for sound production respectively. As So-U occurred frequently when individuals were tense, aggressive, or displayed we regrouped these contexts into one category that we termed "socio-or emotionally negative contexts. " Socio-or emotionally positive contexts comprised sexual activity, playing, teasing, nest-building, and attention seeking. Any outside-group noise and unidentified contexts (Figure  2 ) were excluded from data analyses. Note that comparing So-U durations between contexts when working with data that were not controlled for durations of the behavioral category itself might be questionable. We took this limitation into account when discussing the results.
We computed estimates, standard errors and 95% confidence intervals (CI) for models whose cumulative model weights reached 90% of the total weights. We ran model selections and averages using the R Package MuMIn  (Barton, 2009). The model was run following a negative binomial error distribution using the R package glmmADMB  (Fournier et al. , 2012). The full model included the following predictor variables: (i) the medium, (ii) the complexity of the technique, (iii) the context, (iv) the sex, and (v) all possible interactions of these main effects except for the interaction "technique-medium. " Indeed, among all the So-U performed with mixed techniques (33 events) 32 occurred with the same medium (plastic container) preventing us to investigate the effect of this interaction. Note that only one female used a small medium (and in only one occasion).
Phase 2: Video Recording of So-U for Further Rhythmical Analysis In Phase 2 we conducted all occurrence sampling of So-U in all six groups to increase our chances of filming a long, decontextualized, and rhythmically complex So-U resembling Barney's original performance. We recorded the identity of the performer and the context of occurrence. For interesting bouts rhythmical analysis was conducted in the same way as in  Dufour et al. (2015) , i. e. , by checking the possibility of rhythmical patterns in a series of impacts (Ljung-box test analysis), and then checking the predictability of the next section using autocorrelation analysis (see Supplementary Methods). For each sequence data were analyzed using R (R Core Team, 2013). P level of significance was set at 0. 05.
The second iteration of the Autism Brain Imaging Data Exchange (ABIDE II) aims to enhance the scope of brain connectomics research in Autism Spectrum Disorder (ASD). Consistent with the initial ABIDE effort (ABIDE I), that released 1112 datasets in 2012, this new multisite open-data resource is an aggregate of resting state functional magnetic resonance imaging (MRI) and corresponding structural MRI and phenotypic datasets. ABIDE II includes datasets from an additional 487 individuals with ASD and 557 controls previously collected across 16 international institutions. The combination of ABIDE I and ABIDE II provides investigators with 2156 unique cross-sectional datasets allowing selection of samples for discovery and/or replication. This sample size can also facilitate the identification of neurobiological subgroups, as well as preliminary examinations of sex differences in ASD.
Background & Summary Multiple sources of evidence have substantiated models of abnormal neural connectivity in autism spectrum disorder (ASD)  [1] [2] [3] [4] [5]. At the macroscale, abnormal connections among brain regions have been revealed by functional and structural neuroimaging in children, adolescents and, adults with ASD  1, [6] [7] [8] [9]. Yet, both the complexity of the brain connectome  10, 11  and the striking heterogeneity of ASD  [12] [13] [14] [15] [16]  have hampered efforts to specify the nature of putative dysconnections. In response, open-data sharing is increasingly being encouraged to rapidly amass the large-scale datasets needed to confront heterogeneity, engage a broader range of scientific disciplines, and facilitate independent replications  [17] [18] [19] [20] [21].
To bring the open data sharing model to autism neuroimaging, the Autism Brain Imaging Data Exchange (ABIDE)  22  was launched in 2012. The initial ABIDE initiative-now termed ABIDE I-was the first open-access brain imaging repository of resting state functional magnetic resonance imaging (R-fMRI) and corresponding structural data of individuals with ASD and typical controls (N = 539 and 573, respectively) aggregated from multiple international institutions. Here, we introduce ABIDE II (Data Citation 1), a new multi-site open data resource containing 1,044 independent datasets (ASD N = 487; Controls N = 557) created to enhance the significance of the questions that can be addressed regarding the neural correlates of ASD and accelerate the pace of discovery. The initial ABIDE I effort established the feasibility of aggregating multisite data without prior harmonization, leading to more than 55 peer-reviewed studies in the 48 months since inception.
Despite its success, ABIDE I is limited in regard to sample characterization and sample size. Specifically, despite containing more than 1,000 datasets, ABIDE I was not sufficiently large to furnish optimally sized discovery and replication subsamples. By combining the ABIDE I and ABIDE II data resources, investigators can select larger samples for discovery and replication, depending on their investigative endeavors. Replication samples are needed to minimize false positives and avoid settling for 'approximate replications'  19  -a practice that has plagued biological psychiatry  19  and neuroscience more broadly  17. Additionally, as recently demonstrated, the utility of datasets for prediction increases with sample size-even if heterogeneous data sources are used to amass large samples  23.
Along with increased sample size, ABIDE II provides greater phenotypic characterization than was available across the ABIDE I data collections to better address two key sources of heterogeneity. The first is psychopathology co-occurring with ASD, which has been largely overlooked in the imaging literature  15, 16, 24. Accordingly, ABIDE II actively encouraged investigators to provide phenotypic information regarding co-occurring illness, if assessed. The second source of heterogeneity is driven by sex-related differences. These have been generally ignored in the ASD imaging literature due to the markedly higher prevalence of males with ASD and the tendency of single sites to exclude or minimally represent females. The ABIDE II sample has increased the number of available datasets from females with ASD from 65 in ABIDE I to 138 when ABIDE I+II are combined.
We believe these enhancements will allow investigators to more directly investigate pathophysiology specific to ASD, to potentially identify neurobiological subgroups and facilitate the identification of protective and risk factors. Finally, beyond its focus on intrinsic functional connectivity and other indices of intrinsic brain function, ABIDE II now includes a subset of datasets (N = 284) with diffusion-weighted images. In order to facilitate immediate access and use of ABIDE II, the methods utilized to generate this resource, the resulting currently available data and their technical validation are described below.
Criteria for data contributions We solicited investigators willing and able to openly share their previously collected awake R-fMRI data of individuals with ASD and controls, along with corresponding high-resolution anatomical images and phenotypic information. Contributions have been sought from all charter ABIDE I members and invitations are extended to any other investigators involved in ASD neuroimaging. The present work includes information regarding all contributions received prior to June 24, 2016. Contributions will continue to be accepted up to December 2016. Contributors are encouraged to share at least 20 unique datasets per diagnostic group (i. e. , ASD and controls). Data collections of only individuals with ASD are also accepted as they can be utilized for data-driven explorations addressing heterogeneity e.
g. , refs 25,26. Consistent with prior FCP/INDI efforts  27  , investigators are also encouraged to contribute nearly all MRI datasets, without a priori quality criteria (see Technical Validation for quality assessment (QA) measures incorporated into ABIDE II). The availability of minimal phenotypic information essential for data analyses and sample characterization (i. e. , diagnostic classification, age, sex) is required for contribution. To enhance phenotypic characterization, sharing of additional measures commonly used in ASD research, information on psychiatric comorbidity, medication status, cognition and/or language are highly encouraged. Similarly, to enhance the breath of investigations about the ASD connectome, whenever available, contributions of corresponding diffusion images for each individual are welcome for aggregation.
Finally, prior to data contribution, sites are required to confirm that their local Institutional Review Board (IRB) or ethics committee have approved both the initial data collection and the retrospective sharing of a fully de-identified version of the datasets (i. e. , after removal of the 18 protected health information identifiers including facial information from structural images as identified by the Health Insurance Portable and Accountability Act [HIPAA]). Of note, two institutions provided longitudinal MRI scans from subsets of individuals' datasets (n = 23 ASD and n = 15 controls) previously contributed to ABIDE I. Given the relevance of developmental changes  [28] [29] [30] [31] [32]  , these datasets are also included in the ABIDE II. To distinguish them from the cross-sectional aggregates, these datasets are organized into a separate set of collections focused on longitudinal data using the original ABIDE I IDs.
Regarding phenotypic datasets, each entry is also reviewed to identify and correct missing data, any impossible entry values (e. g. , beyond published maxima and minima), and extreme outliers (relative to each sample). To ensure uniformity across sites, all entries are recorded as needed and organized in a common template along with a legend of code keys. As a final step in preparation for release, both donating and coordinating sites jointly prepare a narrative for each data collection, documenting information on the methods utilized, funding sources, the investigators involved, whether any link with other databases (e. g. , National Database for Autism Research-NDAR  33  ) exists, along with publications related to the contributed datasets. Before open release, each donating site reviews their reorganized phenotypic records, five random images per imaging modality and their collection-specific narrative for final approval.
Data Records Overview The current ABIDE II dataset encompasses 17 collections of unique independent datasets (i. e. , from individuals whose data were not previously shared in ABIDE I) yielding 487 datasets classified as ASD and 557 as controls (Fig. 1a , Table  1 ). These represent previously collected datasets across 16 sites, including nine charter ABIDE I institutions and seven new members (See Supplementary Table  1  for information on each institution). A simple naming convention is used to label each data collection: oABIDEII> -oinstitution acronym name>_ocollection number>(e. g. , ABIDEII-NYU_1). When a collection in ABIDE II is a continuation of one initiated in ABIDE I, we employ the same collection number used in ABIDE I (or 1 if none was used, e. g. , SDSU_1, KKI_1). For new collections, a unique consecutive number is assigned (e.
g. , BNI_1, KUL_3). Accompanying the primary cross-sectional aggregate, two longitudinal collections are also aggregated in ABIDE II. These include MRI datasets collected as follow-ups to the MRI and phenotypic data released in ABIDE I (N total = 38 unique IDs). These pilot longitudinal collections are identified as oABIDEII> -o institution acronym name>_o-Long> (Table  1 ). All ABIDE II datasets can be accessed, after establishing a login and user password, through FCP/INDI at the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC; http://fcon_1000. projects. nitrc. org/indi/abide/). The datasets are organized by data collection and stored in. tar files, each containing imaging and phenotypic data.
Given the retrospective nature of this data aggregate, assessment protocols used to identify ASD and controls varied across institutions. They are documented in each data collection narrative. Briefly, ASD classification was determined by either 1) combining clinical judgment with 'gold standard' diagnostic instruments-Autism Diagnostic Observation Scale  34, 35  and/or Autism Diagnostic Interview-Revised  36  [ADOS, ADI-R]; (n = 12 data collections; 368 ASD datasets) or 2) by using these 'gold standard' diagnostic instruments only (n = 4 collections; 92 ASD datasets), with one exception. Specifically, in EMC_1 (n = 27 datasets), which was selected from the longitudinal Generation R sample  37   meeting screening cutoffs in at least one of two distinct ASD questionnaires or for whom the mother reported a diagnosis of ASD.
Regarding controls (N = 557, available for 15 collections), all datasets are characterized by absence of ASD diagnosis and absence of history of any other major neurodevelopmental disorders for the vast majority of the datasets (N = 546; 98%). This was determined using semi-structured/unstructured in-person interviews (N = 7 data collections; 353 datasets), or parent/self-(if adults) reports/questionnaires (N = 8; 193 datasets). The remaining 11 control datasets (OHSU_1 data collection) are from individuals assigned a 'rule out' psychiatric disorder, but without ASD or Attention-Deficit/Hyperactivity Disorder (ADHD) diagnoses. Other specific inclusion/exclusion criteria used for selecting controls (e. g. , IQ range, first degree relative with ASD) or ASD (e. g. , absence of reported seizure and genetic syndromes) varied across collections.
Each collection narrative on the ABIDE II website provides details regarding these criteria. Demographics. Across collections, age at time of scanning ranges from 5 to 64 years; four of the collections focused specifically on adults-with one of these collections specifically enrolling on older adults (BNI_1)-and eight enrolling only children and/or adolescents. The remaining five data collections include children, teens and young adults, which allows for cross-sectional age-related explorations (Fig. 1c ; Supplementary Table  3 ). All but four collections include data from both sexes (Fig. 1b ). Reflecting the higher prevalence of males in ASD  38  , 15% of the ASD datasets consist of females versus 31% of the control datasets (Supplementary Table  3 ). Intelligence. Full scale intelligence quotient (FIQ) and/or verbal and/or performance IQ standard scores are provided.
Across collections, although variation exists with respect to the minimum FIQ, 97% of the datasets have FIQ above 80 (Fig. 1d ). For both groups, mean FIQ is above average, albeit significantly higher in controls versus ASD (Mann Whitney U = 86. 5; P o0. 0001; Supplementary Table  3 ). Handedness. Categorical handedness codes for right, left or mixed handedness are available across all collections. Additionally, handedness strength scores are available for eight collections, enabling dimensional characterization of handedness (n = 244 ASD and n = 327 controls). Across collections, right-handedness is more frequent in both diagnostic groups (84 and 90% for ASD and controls, respectively), though a significantly higher prevalence of non-right-handedness (either left or mixed handedness) occurs in ASD relative to controls (χ 1 2 = 10.
6, P = 0. 01; Supplementary Table  3 ). ASD core measures. Scores from the ADOS and ADI-R are available (Supplementary Table  3 ). Only nine collections share ADOS-2 calibrated severity scores  34  (CSS; N = 9 sites; 228 ASD datasets) recently designed to adjust for differences in age, intellectual abilities and language skills across ADOS modules  39, 40. As illustrated in Fig. 1f , CSS distribution are similar across most sites. ADOS-G 41 scaled total scores are available for 15 collections (n = 280 ASD datasets). Additionally, data from parent or self-report questionnaires commonly used in the field to quantify severity on multiple ASD domains collected across both diagnostic groups are also available. The Social Responsiveness Scale  42  is the most common (n = 378 ASD, n = 407 controls; Fig.
1f ) followed by the Repetitive Behavior Scale Revised  43, 44  (n = 217 ASD, n = 208 controls; Supplementary Table  3 ). Comorbid psychopathology in ASD. Information on psychopathology accompanying ASD is provided either as 1) categorical diagnostic labels (or its absence, if assessed) with corresponding diagnostic code based on the International Classification of Diseases-9th edition  45  (N = 9 data collections; 281 ASD datasets) and/or as severity scores in one or multiple psychopathology dimensions across available for 11 collections (see Supplementary Table  3  for a list of measures used) (Fig. 2 ). Categorical comorbid psychiatric diagnoses were determined based on clinicians' assessments in seven data collections, parent-questionnaires in one data collection (UCD_1) and self-report in another (KUL_3).
MRI data For each of the  GP, GRAPPA acceleration in the phase encoding direction; AP, ASSET acceleration in the phase encoding direction. For partial Fourier, the under-sampled dimension is listed with the under sampling factor; P, phase encoding. For slice orientation; S, sagittal; T, transverse (axial); C, coronal. For phase encoding direction; RL, right-to-left; AP, Anterior to posterior. Reconstructed resolution and image dimensions refer to the images after they have been reconstructed from the k-space data, the matrix size and resolution used for the acquisition may differ. For these categories, RO, read out direction; PE, phase encoding direction, and SL, slice direction. *62 datasets of the KKI-1 collection were acquired with an 8-channel head coil, 149 datasets were acquired with a 32 channel coil and different scanning parameters.
A list of the IDs with the corresponding head coil is provided in the ABIDE II website page for this collection as 'Additional scan Information. ' † ONRC_2 has variable phase encoding direction, specific information for each dataset is provided in the release/website (http://fcon_1000. projects. nitrc. org/indi/abide/). ‡ SDSU has used a GE scanner that refers to TR as the amount of time between the RF pulses that are used to read out the lines of k-space (i. e. , echo space). Here to be consistent across all collections we report TR using the Siemens convention that refers to TR as the time between sequential inversion recovery pulses. Based on the simple equation TR = TI+n*ES, where n is the number of actual (corrected for parallel imaging) phase encoding lines, we calculated the TR of SDSU to be 2,683 ms.