National Database for Autism Research (NDAR) is an extensible, scalable informatics platform for autism spectrum disorder-relevant data at all levels of biological and behavioral organization (molecules, genes, neural tissue, behavioral, social and environmental interactions) and for all data types (text, numeric, image, time series, etc.). NDAR was developed to share data across the entire ASD field and to facilitate collaboration across laboratories, as well as interconnectivity with other informatics platforms. NDAR Homepage: http://ndar.nih.gov/

Input datasets on Ohio Birth and Autism will not be made accessible to the public due to the fact that they include individual-level data with PII. Output data are all available in tabulated form within the published manuscript. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Input data can be obtained from Applications from owners of the data (Children's Hospital and Ohio Department of Health). The tabulated output data is found in the manuscript. Format: Input datasets on Ohio Birth and Autism will not be made accessible to the public due to the fact that they include individual-level data with PII. Output data are all available in tabulated form within the published manuscript (e.g., results of regression models, measures of central tendency, population characteristics, etc.).

This dataset is associated with the following publication: Kaufman, J., M. Wright, G. Rice, N. Connolly, K. Bowers, and J. Anixt. AMBIENT OZONE AND FINE PARTICULATE MATTER EXPOSURES AND AUTISM SPECTRUM DISORDER IN METROPOLITAN CINCINNATI, OHIO. ENVIRONMENTAL RESEARCH. Elsevier B.V., Amsterdam, NETHERLANDS, 171: 218-227, (2019).

Background: Whilst cannabis is known to be toxic to brain function and brain development in many respects it is not known if its increasing availability is associated with the rising US autism rates, whether this contribution is sufficient to effect overall trends and if its effects persist after controlling for other major covariates.

Methods: Longitudinal epidemiological study using national autism census data from the US Department of Education Individuals with Disabilities Act (IDEA) 1991-2011 and nationally representative drug exposure (cigarettes, alcohol, analgesic, and cocaine abuse, and cannabis use monthly, daily and in pregnancy) datasets from National Survey of Drug Use and Health and US Census (income and ethnicity) and CDC Wonder population and birth data. Geotemporospatial and causal inference analysis conducted in R.

Results: 266,950 autistic of a population of 40,119,464 eight year olds 1994-2011. At the national level after adjustment daily cannabis use was significantly related (β-estimate=4.37 (95%C.I. 4.06-4.68), P<2.2x10-16) as was cannabis exposure in the first trimester of pregnancy (β-estimate=0.12 (0.08-0.16), P=1.7x10-12). At the state level following adjustment cannabis use was significant (from β-estimate=8.41 (3.08-13.74), P=0.002); after adjustment for varying cannabis exposure by ethnicity and other covariates (from β-estimate=10.88 (5.97-15.79), P=1.4x10-5). Cannabigerol (from β-estimate=-13.77 (-19.41—8.13), P = 1.8x10-6) and Δ9-tetrahydrocannabinol (from β-estimate=1.96 (0.88-3.04), P=4x10-4) were also significant. Geospatial state-level modelling showed an exponential relationship between ASMR and both Δ9-tetrahydrocannabinol and cannabigerol exposure; effect size calculations reflected this exponentiation. Exponential coefficients for the relationship between modelled ASMR and THC- and cannabigerol- exposure were 7.053 (6.39-7.71) and 185.334 (167.88-202.79; both P<2.0x10-7).

In inverse probability-weighted robust generalized linear models ethnic cannabis exposure (from β-estimate=3.64 (2.94-4.34), P=5.9x10-13) and cannabis independently (β-estimate=1.08 (0.63-1.54), P=2.9x10-5) were significant. High eValues in geospatial models indicated that uncontrolled confounding did not explain these findings. Therefore the demonstrated relationship satified the criteria of causal inference. Dichotomized legal status was geospatiotemporally linked with elevated ASMR.

Conclusions: Data show cannabis use is associated with ASMR, is powerful enough to affect overall trends, and persists after controlling for other major drug, socioeconomic, and ethnic-related covariates. Selected cannabinoids are exponentially associated with ASMR. The cannabis-autism relationship satisfies criteria of causal inference.

Latest monthly statistics on Learning Disabilities and Autism (LDA) from the Assuring Transformation collection and MHSDS collection. This publication brings together the LDA data from the Assuring Transformation collection and the LDA service specific statistics from the Mental Health Statistics Data Set (MHSDS). There are differences in the inpatient figures between the MHSDS and AT data sets and work is underway to better understand these. NHS Digital plans to publish additional monthly comparator data from this work in future publications. The MHSDS LDA data are currently labelled experimental as they are undergoing evaluation. Further information on the quality of these statistics is available in the Data Quality section of the main report. It is planned that the MHSDS will become the sole source of inpatient LDA data in the future, replacing Assuring Transformation. There is a slight difference in scope between the two data collections. The MHSDS data is from providers based in England and includes care provided in England but may be commissioned outside England. Whereas the Assuring Transformation data are provided by English commissioners and healthcare will typically be provided in England but also includes data on care commissioned in England and provided elsewhere in the UK. The release comprises: Assuring Transformation Publication: This statistical release published by NHS Digital makes available the most recent data relating to patients with learning disabilities and/or autistic spectrum disorder receiving inpatient care commissioned by the NHS in England MHSDS LDA Publication: This publication provides statistics relating to NHS funded secondary mental health, learning disabilities and autism services in England. These statistics are derived from submissions made using version 4.0 of the Mental Health Services Dataset (MHSDS). Prior to May 2018 the LDA service specific statistics were included in the main MHSDS publication. Each publication consists of the following documents: A report which presents England level analysis of key measures. A monthly CSV file which presents key measures at England level. A metadata file to accompany the CSV file, which provides contextual information for each measure. An easy read version of both main reports highlighting key findings in an easy-to-understand way. We hope this information is helpful and would be grateful if you could spare a couple of minutes to complete a short customer satisfaction survey. Please use the link to the form at the bottom of this page to provide us with any feedback or suggestions for improving the report.

BackgroundPrevious studies reported that autistic adolescents and adults tend to exhibit extensive choice switching in repeated experiential tasks. However, a recent meta-analysis showed that this switching effect was non-significant across studies. Furthermore, the relevant psychological mechanisms remain unclear. We examined the robustness of the extreme choice-switching phenomenon, and whether it is driven by a learning impairment, feedback-related aspects (e.g., avoiding losses), or alternatively a different information sampling strategy.MethodsWe recruited an online sample of 114 US participants (57 autistic adults and 57 non-autistic). All participants performed the Iowa Gambling task, a four-option repeated choice task. Standard task blocks were followed by a trial block with no feedback.ResultsThe findings replicate the extreme choice switching phenomenon (Cohen’s d = 0.48). Furthermore, the effect was found with no difference in average choice rates denoting no learning impairment, and was even observed in trial blocks with no feedback (d = 0.52). There was no evidence that the switching strategy of autistic individuals was more perseverative (i.e., that similar switching rates were used in subsequent trial blocks). When adding the current dataset to the meta-analysis, the choice switching phenomenon is significant across studies, d = 0.32.ConclusionsThe findings suggest that the increased choice switching phenomenon in autism may be robust and that it represents a distinct information sampling strategy and not poor implicit learning (or a bias in the sensitivity to losses). Such extended sampling may underlie some of the phenomena previously attributed to poor learning.

Dataset Card for The ASD QA Dataset (validation set)

  Dataset Summary

A dataset for question-answering used for building an informational Russian language chatbot for the inclusion of people with autism spectrum disorder and Asperger syndrome in particular, based on data from the following website: https://aspergers.ru.

  Languages

Russian

  Dataset Structure

The dataset inherits SQuAD 2.0 structure.

  Source Data

https://aspergers.ru… See the full description on the dataset page: https://huggingface.co/datasets/missvector/asd-qa-val.

Research-and-Development Time Series for Roper Technologies, Inc.. Roper Technologies, Inc. designs and develops vertical software and technology enabled products in the United States, Canada, Europe, Asia, and internationally. It operates through three segments: Application Software, Network Software, and Technology Enabled Products. The Application Software segment offers comprehensive management, diagnostic and laboratory information management, enterprise software and information solutions, K-12 school administration, transportation management, financial and compliance management, cloud-based and integrated payment processing, campus technology and payment, and cloud-based financial analytics and performance management software; cloud-based software for the property and casualty insurance industry; and foodservice technologies. The Network Software segment provides cloud-based data, collaboration, and estimating automation software; electronic marketplace; visual effects and 3D content software; cloud-based software for the life insurance and financial services industries; supply chain software; health care services and software; data analytics and information; and pharmacy software solutions. The Technology Enabled Products segment offers r ultrasound procedures accessories; dispensers and metering pumps; wireless sensor network and solutions; automated surgical scrub and linen dispensing equipment; water meters; optical and electromagnetic precision measurement systems; RFID card and credential readers; and medical devices. The company also provides autism and IDD care software, a software and services platform that helps therapists who serve children and adults diagnosed with autism spectrum disorder and related intellectual and developmental disabilities (IDD). The company distributes and sells its products through direct sales offices, manufacturers' representatives, resellers, and distributors. The company was formerly known as Roper Industries, Inc. and changed its name to Roper Technologies, Inc. in April 2015. The company was incorporated in 1981 and is based in Sar

BackgroundPrevious studies reported that autistic adolescents and adults tend to exhibit extensive choice switching in repeated experiential tasks. However, a recent meta-analysis showed that this switching effect was non-significant across studies. Furthermore, the relevant psychological mechanisms remain unclear. We examined the robustness of the extreme choice-switching phenomenon, and whether it is driven by a learning impairment, feedback-related aspects (e.g., avoiding losses), or alternatively a different information sampling strategy.MethodsWe recruited an online sample of 114 US participants (57 autistic adults and 57 non-autistic). All participants performed the Iowa Gambling task, a four-option repeated choice task. Standard task blocks were followed by a trial block with no feedback.ResultsThe findings replicate the extreme choice switching phenomenon (Cohen’s d = 0.48). Furthermore, the effect was found with no difference in average choice rates denoting no learning impairment, and was even observed in trial blocks with no feedback (d = 0.52). There was no evidence that the switching strategy of autistic individuals was more perseverative (i.e., that similar switching rates were used in subsequent trial blocks). When adding the current dataset to the meta-analysis, the choice switching phenomenon is significant across studies, d = 0.32.ConclusionsThe findings suggest that the increased choice switching phenomenon in autism may be robust and that it represents a distinct information sampling strategy and not poor implicit learning (or a bias in the sensitivity to losses). Such extended sampling may underlie some of the phenomena previously attributed to poor learning.

As autonomous vehicle (AV) innovation continues to gain worldwide momentum and with it the potential for expanded mobility, it is vital to consider how this technology can contribute to improved access for persons with disabilities, who represent one in four U.S. adults, or over 60 million persons. To date, there has been limited research focused on investigating strategies for designing and deploying self-driving vehicles so they can best accommodate the often diverse needs of persons living with one or more disabilities. This research discusses findings from a series of four autonomous shuttle rides followed by focus group sessions convened with adults with disabilities to document their direct experience as passengers during Princeton University’s 2019 SmartDrivingCar Summit. A core intent of this research was to gather feedback and recommendations on AV from persons with disabilities who had acquired a level of familiarity with the technology through a direct vehicle encounter, transcending their knowledge of AV solely as an abstract concept. Ultimately, the research team captured invaluable information on participant initial impressions of AV; their in-vehicle trip experience; interest in utilizing AV in the future; and AV-related concerns. Findings demonstrate that the majority of participants had both positive initial and post-trip impressions of AV and were extremely interested in utilizing AV to meet their current trip needs. Participants also offered insightful recommendations that can contribute to continued development of AV technology so that it can more fully accommodate the travel needs of persons with diverse disabilities.

Survey of Assistance Dogs International (ADI) and International Guide Dog Federation (IGDF) accredited facilities, candidate ADI accredited facilities, and non-accredited facilities in the U.S. and Canada. Responding facilities provided information on roles of dogs and numbers of dogs placed in homes of individuals with disabilities in 2013 and 2014.

Total-Current-Liabilities Time Series for Roper Technologies, Inc.. Roper Technologies, Inc. designs and develops vertical software and technology enabled products in the United States, Canada, Europe, Asia, and internationally. It operates through three segments: Application Software, Network Software, and Technology Enabled Products. The Application Software segment offers comprehensive management, diagnostic and laboratory information management, enterprise software and information solutions, K-12 school administration, transportation management, financial and compliance management, cloud-based and integrated payment processing, campus technology and payment, and cloud-based financial analytics and performance management software; cloud-based software for the property and casualty insurance industry; and foodservice technologies. The Network Software segment provides cloud-based data, collaboration, and estimating automation software; electronic marketplace; visual effects and 3D content software; cloud-based software for the life insurance and financial services industries; supply chain software; health care services and software; data analytics and information; and pharmacy software solutions. The Technology Enabled Products segment offers r ultrasound procedures accessories; dispensers and metering pumps; wireless sensor network and solutions; automated surgical scrub and linen dispensing equipment; water meters; optical and electromagnetic precision measurement systems; RFID card and credential readers; and medical devices. The company also provides autism and IDD care software, a software and services platform that helps therapists who serve children and adults diagnosed with autism spectrum disorder and related intellectual and developmental disabilities (IDD). The company distributes and sells its products through direct sales offices, manufacturers' representatives, resellers, and distributors. The company was formerly known as Roper Industries, Inc. and changed its name to Roper Technologies, Inc. in April 2015. The company was incorporated in 1981 and is based in Sar

Participants’ characteristics in the two study groups.

Several previous studies have reported atypicality in resting-state functional connectivity (FC) in autism spectrum disorder (ASD), yet the relatively small effect sizes prevent us from using these characteristics for diagnostic purposes. Here, canonical correlation analysis (CCA) and hierarchical clustering were used to partition the high-functioning ASD group (i.e., the ASD discovery group) into subgroups. A support vector machine (SVM) model was trained through the 10-fold strategy to predict Autism Diagnostic Observation Schedule (ADOS) scores within the ASD discovery group (r = 0.30, P < 0.001, n = 260), which was further validated in an independent sample (i.e., the ASD validation group) (r = 0.35, P = 0.031, n = 29). The neuroimage-based partition derived two subgroups representing severe versus mild autistic patients. We identified FCs that show graded changes in strength from ASD-severe, through ASD-mild, to controls, while the same pattern cannot be observed in partitions based on ADOS score. We also identified FCs that are specific for ASD-mild, similar to a partition based on ADOS score. The current study provided multiple pieces of evidence with replication to show that resting-state functional magnetic resonance imaging (rsfMRI) FCs could serve as neural biomarkers in partitioning high-functioning autistic individuals based on their symptom severity and showing advantages over traditional partition based on ADOS score. Our results also indicate a compensatory role for a frontocortical network in patients with mild ASD, indicating potential targets for future clinical treatments.

Other-Appropriated-Reserves Time Series for Roper Technologies, Inc.. Roper Technologies, Inc. designs and develops vertical software and technology enabled products in the United States, Canada, Europe, Asia, and internationally. It operates through three segments: Application Software, Network Software, and Technology Enabled Products. The Application Software segment offers comprehensive management, diagnostic and laboratory information management, enterprise software and information solutions, K-12 school administration, transportation management, financial and compliance management, cloud-based and integrated payment processing, campus technology and payment, and cloud-based financial analytics and performance management software; cloud-based software for the property and casualty insurance industry; and foodservice technologies. The Network Software segment provides cloud-based data, collaboration, and estimating automation software; electronic marketplace; visual effects and 3D content software; cloud-based software for the life insurance and financial services industries; supply chain software; health care services and software; data analytics and information; and pharmacy software solutions. The Technology Enabled Products segment offers r ultrasound procedures accessories; dispensers and metering pumps; wireless sensor network and solutions; automated surgical scrub and linen dispensing equipment; water meters; optical and electromagnetic precision measurement systems; RFID card and credential readers; and medical devices. The company also provides autism and IDD care software, a software and services platform that helps therapists who serve children and adults diagnosed with autism spectrum disorder and related intellectual and developmental disabilities (IDD). The company distributes and sells its products through direct sales offices, manufacturers' representatives, resellers, and distributors. The company was formerly known as Roper Industries, Inc. and changed its name to Roper Technologies, Inc. in April 2015. The company was incorporated in 1981 and is based in Sar

Latest Monthly statistics on Learning Disabilities and Autism (LDA) from the Assuring Transformation collection. PLEASE NOTE: This page has been updated on 19 September 2019 with the release of the MHSDS LD publication for May 2019 data. This had previously been delayed for operational reasons as a result of implementing a new data collection system. The release comprises: Assuring Transformation Publication: This statistical release published by NHS Digital makes available the most recent data relating to patients with learning disabilities and/or autistic spectrum disorder receiving inpatient care commissioned by the NHS in England MHSDS LDA Publication: This publication provides statistics relating to NHS funded secondary mental health, learning disabilities and autism services in England. These statistics are derived from submissions made using version 4.0 of the Mental Health Services Dataset (MHSDS). Prior to May 2018 the LDA service specific statistics were included in the main MHSDS publication. Each publication consists of the following documents: · A report which presents England level analysis of key measures. · A monthly CSV file which presents key measures at England level. · A metadata file to accompany the CSV file, which provides contextual information for each measure. · An easy read version of both main reports highlighting key findings in an easy-to-understand way. We hope this information is helpful and would be grateful if you could spare a couple of minutes to complete a short customer satisfaction survey. Please use the link to the form at the bottom of this page to provide us with any feedback or suggestions for improving the report.

This publication brings together the Learning Disabilities and Autism (LDA) data from the Assuring Transformation collection and the LDA service specific statistics from the Mental Health Statistics Data Set (MHSDS). A couple of figures on commissioner counts were corrected in the AT CSV file on 20th May 2021. There are differences in the inpatient figures between the MHSDS and AT data sets and work is underway to better understand these. The MHSDS LDA data are currently labelled experimental as they are undergoing evaluation. Further information on the quality of these statistics is available in the Data Quality section of the main report. There is a slight difference in scope between the two data collections. The MHSDS data is from providers based in England and includes care provided in England but may be commissioned outside England. Whereas the Assuring Transformation data are provided by English commissioners and healthcare will typically be provided in England but also includes data on care commissioned in England and provided elsewhere in the UK. The release comprises: Assuring Transformation Publication. MHSDS LDA Publication: These statistics are derived from submissions made using version 4.1 of the Mental Health Services Dataset (MHSDS). Prior to May 2018 the LDA service specific statistics were included in the main MHSDS publication. MHSDS Multiple Submission Window Model (MSWM) The MHSDS v4.1 data model allows providers to retrospectively submit data for any monthly reporting period until the end of year cut-off as part of the Multiple Submission Window Model (MSWM). So, for 2020-21, providers are able to resubmit data for any previous months until the end of March 2021. (This was possible for the first time in MHSDS v4.0 but just for the end of year submission for March 2020 data). This model allows providers to improve the quality of previous submissions. Historical comparison with previous years should therefore be reviewed in that context. Additional information on the MSWM for MHSDS is available via the link at the bottom of this page (related links). We hope this information is helpful and would be grateful if you could spare a couple of minutes to complete a short customer satisfaction survey. Please use the link to the form at the bottom of this page to provide us with any feedback or suggestions for improving the report.

Demographic and beneficiary type by race-ethnicity of adults with autism spectrum disorder.