Please see "Description of files" word document for overview of excel files included, which present data output from "Addressing pediatric HIV pretreatment drug resistance and virologic failure in sub-Saharan Africa: A cost-effectiveness analysis of diagnostic-based strategies in children ≥ 3 years old," published in Diagnostics.

The land remote sensing community has a long history of using supervised and unsupervised methods to help interpret and analyze remote sensing data sets. Until relatively recently, most remote sensing studies have used fairly conventional image processing and pattern recognition methodologies. In the past decade, NASA has launched a series of remote sensing missions known as the Earth Observing System (EOS). The data sets acquired by EOS instruments provide an extremely rich source of information related to the properties and dynamics of the Earth’s terrestrial ecosystems. However, these data are also characterized by large volumes and complex spectral, spatial and temporal attributes. Because of the volume and complexity of EOS data sets, efficient and effective analysis of them presents significant challenges that are difficult to address using conventional remote sensing approaches. In this paper we discuss results from applying a variety of different data mining approaches to global remote sensing data sets. Specifically, we describe three main problem domains and sets of analyses: (1) supervised classification of global land cover from using data from NASA’s Moderate Resolution Imaging Spectroradiometer; (2) the use of linear and non-linear cluster and dimensionality reduction methods to examine coupled climate-vegetation dynamics using a twenty year time series of data from the Advanced Very High Resolution Radiometer; and (3) the use of functional models, non-parametric clustering, and mixture models to help interpret and understand the feature space and class structure of high dimensional remote sensing data sets. The paper will not focus on specific details of algorithms. Instead we describe key results, successes, and lessons learned from ten years of research focusing on the use of data mining and machine learning methods for remote sensing and Earth science problems.

The National Incident-Based Reporting System (NIBRS) is a part of the Uniform Crime Reporting Program (UCR), administered by the Federal Bureau of Investigation (FBI). In the late 1970s, the law enforcement community called for a thorough evaluative study of the UCR with the objective of recommending an expanded and enhanced UCR program to meet law enforcement needs into the 21st century. The FBI fully concurred with the need for an updated program to meet contemporary needs and provided its support, formulating a comprehensive redesign effort. Following a multiyear study, a "Blueprint for the Future of the Uniform Crime Reporting Program" was developed. Using the "Blueprint," and in consultation with local and state law enforcement executives, the FBI formulated new guidelines for the Uniform Crime Reports. The National Incident-Based Reporting System (NIBRS) was implemented to meet these guidelines. NIBRS data as formatted by the FBI are stored in a single file. These data are organized by various segment levels (record types). There are six main segment levels: administrative, offense, property, victim, offender, and arrestee. Each segment level has a different length and layout. There are other segment levels which occur with less frequency than the six main levels. Significant computing resources are necessary to work with the data in its single-file format. In addition, the user must be sophisticated in working with data in complex file types. While it is convenient to think of NIBRS as a hierarchical file, its structure is more similar to a relational database in that there are key variables that link the different segment levels together. NIBRS data are archived at ICPSR as 11 separate data files per year, which may be merged by using linkage variables. Prior to 2013 the data were archived and distributed as 13 separate data files, including three separate batch header record files. Starting with the 2013 data, the FBI combined the three batch header files into one file. Consequently, ICPSR instituted new file numbering for the data. NIBRS data focus on a variety of aspects of a crime incident. Part 2 (formerly Part 4), Administrative Segment, offers data on the incident itself (date and time). Each crime incident is delineated by one administrative segment record. Also provided are Part 3 (formerly Part 5), Offense Segment (offense type, location, weapon use, and bias motivation), Part 4 (formerly Part 6), Property Segment (type of property loss, property description, property value, drug type and quantity), Part 5 (formerly Part 7), Victim Segment (age, sex, race, ethnicity, and injuries), Part 6 (formerly Part 8), Offender Segment (age, sex, and race), and Part 7 (formerly Part 9), Arrestee Segment (arrest date, age, sex, race, and weapon use). The Batch Header Segment (Part 1, formerly Parts 1-3) separates and identifies individual police agencies by Originating Agency Identifier (ORI). Batch Header information, which is contained on three records for each ORI, includes agency name, geographic location, and population of the area. Part 8 (formerly Part 10), Group B Arrest Report Segment, includes arrestee data for Group B crimes. Window Segments files (Parts 9-11, formerly Parts 11-13) pertain to incidents for which the complete Group A Incident Report was not submitted to the FBI. In general, a Window Segment record will be generated if the incident occurred prior to January 1 of the previous year or if the incident occurred prior to when the agency started NIBRS reporting. As with the UCR, participation in NIBRS is voluntary on the part of law enforcement agencies. The data are not a representative sample of crime in the United States. Recognizing many differences in computing resources and that many users will be interested in only one or two segment levels, ICPSR has decided to make the data available as multiple files. Each NIBRS segment level in the FBI's single-file format has been made into a separate rectangular ASCII data file. Linkage (key) variables are used to perform analyses that involve two or more segment levels. If the user is interested in variables contained in one segment level, then the data are easy to work with since each segment level file is simply a rectangular ASCII data file. Setup files are available to read each segment level. Also, with only one segment level, the issue of

File List Data_for_across_study_analyses.txt (MD5: 83c678a737e4d8ec3344c1da869bc2d6) Data_for_within_study_analyses.txt (MD5: 68d9c6ec4a0f64c99f8e36476daedb23) Description Data_for_across_study_analyes.txt - This file contains all data used for the across study analyses presented in Fig. 1. Data_for_within_study_analyes.txt - This file contains all data used for the across study analyses presented in Fig. 3. Metadata (column names, descriptions of data entries, and column sums) for both files is as follows: -- TABLE: Please see in attached file. --

This is a presentation on the Strain Sensing Array to characterize deformation at the FORGE site project by Clemson University, presented by Lawrence Murdoch. The project's objective was to evaluate the feasibility of measuring and interpreting tensor strain data to improve the performance of EGS. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 23, 2024.

Quarterly Percent Change in 3rd Month Employment Level Data 1990 - Present

  Over-the-year percent change in the third month's employment level of a given quarter (Rounded to the tenths place). County, state, and MSA level, by industry, yearly from 1990 - present. About the BLS Unemployment Data including Current Population Survey Demographic Breakdowns: Links to several different datasets, including Current Population Survey results showing seasonally adjusted unemployment data broken out by ethnicity and age, reason for unemployment, and duration of employment prior to unemployment for years including 2017-2019.  Other datasets show over-the-year percent change in the third month's employment level and taxable wages by industry for a given quarter at the County, State, and MSA level yearly from 1990 - present.
  Geography Level: State, County, MSAItem Vintage: 1990-Present
  Update Frequency: YearlyAgency: BLSAvailable File Type: Website link to CSV/Excel/Legacy Flat files download 

  Return to Other Federal Agency Datasets Page

Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

OVERVIEW

This data file, compiled from multiple online sources, presents 2013–2017 publication counts—articles, articles in high-impact journals, books, and books from high-impact publishers—for 2,132 professors and associate professors in 426 U.S. departments of sociology. It also includes information on institutional characteristics (e.g., institution type, highest sociology degree offered, department size) and individual characteristics (e.g., academic rank, gender, PhD year, PhD institution).

The data may be useful for investigations of scholarly productivity, the correlates of scholarly productivity, and the contributions of particular individuals and institutions. Complete population data are presented for the top 26 doctoral programs, doctoral institutions other than R1 universities, the top liberal arts colleges, and other bachelor's institutions. Sample data are presented for Carnegie R1 universities (other than the top 26) and master's institutions.

USER NOTES

Please see our paper in Scholarly Assessment Reports, freely available at https://doi.org/10.29024/sar.36 , for full information about the data set and the methods used in its compilation. The section numbers used here refer to the Appendix of that paper. See the References, below, for other papers that have made use of these data.

The data file is a single Excel file with five worksheets: Sampling, Articles, Books, Individuals, and Departments. Each worksheet has a simple rectangular format, and the cells include just text and values—no formulas or links. A few general notes apply to all five worksheets.

• The yellow column headings represent institutional (departmental) data. The blue column headings represent data for individual faculty.

• iType is institution type, as described in section A.2—TopR (top research universities), R1 (other R1 universities), OD (other doctoral universities), M (master's institutions), TopLA (top liberal arts colleges), or B (other bachelor's institutions). nType provides the same information, but as a single-digit code that is more useful for sorting the rows; 1=TopR, 2=R1, 3=OD, 4=M, 5=TopLA, and 6=B.

• Inst is a four-digit institution code. The first digit corresponds to nType, and the last three digits allow for alphabetical sorting by institution name. Indiv is a one- or two-digit code that can be used to sort the individuals by name within each department. The Inst, nType, and Indiv codes are consistent across the five worksheets.

• For binary variables such as Full professor and Female, 1 indicates yes (full professor or female) and 0 indicates no (associate professor or male).

The five worksheets represent five distinct stages in the data compilation process. First, the Sampling worksheet lists the 1,530 base-population institutions (see section A.3) and presents the characteristics of the faculty included in the data file. Each row with an entry in the Individual column represents a faculty member at one of the 426 institutions included in the data set. Each row without an entry in the Individual column represents an institution that either (a) did not meet the criteria for inclusion (section A.1) or (b) was not needed to attain the desired sample size for the R1 or M groups (section A.3).

The Articles worksheet includes the data compiled from SocINDEX, as described in section A.6. Each row with an entry in the Journal column represents an article written by one of the 2,132 faculty included in the data. Each row without an entry in the Journal column represents a faculty member without any article listings in SocINDEX for the 2013–2017 period. (Note that SocINDEX items other than peer-reviewed articles—editorials, letters, etc.—may be listed in the Journal column but assigned a value of 1 in the Excluded column and a value of 0 in the Article credit and HI article credit columns. We assigned no credit for items such as editorial and letters, but other researchers may wish to include them.) The N and i columns represent, for each article, the number of authors (N) and the faculty member's place in the byline (i), as described in section A.8. The CiteScore and Highest percentile columns were used to identify high-impact journals, as indicated in the HI journal column. The Article credit and HI article credit columns are article counts, adjusted for co-authorship.

The Books worksheet includes data compiled from Amazon and other sources, as described in section A.7. Each row with an entry in the Book column represents a book written by one of the 2,132 faculty. Each row without an entry in the Book column represents a faculty member without any book listings in Amazon during the 2013–2017 period. The publication counts in the Books worksheet—Book credit and HI book credit—follow the same format as those in the Articles worksheet.

The Individuals worksheet consolidates information from the Articles and Books worksheets so that each of the 2,132 individuals is represented by a single row. The worksheet also includes several categorical variables calculated or otherwise derived from the raw data—Years since PhD, for instance, and the three corresponding binary variables. We suspect that many data users will be most interested in the Individuals worksheet.

The Departments worksheet collapses the individual data so that each of the 426 institutions (departments) is represented by a single row. Individual characteristics such as Female and Years since PhD are presented as percentages or averages—% Female and Avg years since PhD, for instance. Each of the four productivity measures is represented by a departmental total, an average (the total divided by the number of full and associate professors), a departmental standard deviation, and a departmental median.

1. Government consumption data published annually, and classified by government function according to the 2014 edition of the Government Finance Statistics Manual (GFSM) compiled by the International Monetary Fund (IMF).2. Purpose of collection: To present data on government consumption by function for each year.3. Data collection method: Compilation of budget and final account data for government departments and affiliated non-profit institutions at all levels of government.

The minimal data set underlying the results described in the manuscript.

Current General Activity; Percent Reporting Increases for Federal Reserve District 3: Philadelphia was 12.40% in October of 2025, according to the United States Federal Reserve. Historically, Current General Activity; Percent Reporting Increases for Federal Reserve District 3: Philadelphia reached a record high of 60.20 in March of 1973 and a record low of 0.00 in April of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Current General Activity; Percent Reporting Increases for Federal Reserve District 3: Philadelphia - last updated from the United States Federal Reserve on October of 2025.

Future New Orders; Percent Reporting Decreases for Federal Reserve District 3: Philadelphia was 4.60% in November of 2025, according to the United States Federal Reserve. Historically, Future New Orders; Percent Reporting Decreases for Federal Reserve District 3: Philadelphia reached a record high of 56.50 in December of 1973 and a record low of 0.00 in September of 1982. Trading Economics provides the current actual value, an historical data chart and related indicators for Future New Orders; Percent Reporting Decreases for Federal Reserve District 3: Philadelphia - last updated from the United States Federal Reserve on November of 2025.

Herewith we present the extended 1Hz dataset of wind measurements from a Skipheia meteorological station on the island of Frøya on the western coast of Norway, Trondelag.

The data binned in 10 min averages can be find at: https://doi.org/10.5281/zenodo.2557500

The site represents an exposed coastal wind climate with open sea, land and mixed fetch from various directions. UTM-coordinates of the Met-mast: 8.34251 E and 63.66638 N. See the map for details (NorwegianMapping Authority): https://www.norgeskart.no/#!?project=norgeskart&layers=1003&zoom=3&lat=7035885.49&lon=539601.41&markerLat=7077031.483032227&markerLon=170902.83203125&panel=searchOptionsPanel&sok=Titranveien

Presented data were gathered between years 2009-2016.

Data&hardware summary:

Years 2009-2016: Mast2 equipped with 6 pairs of 2D sonic anemometers at 10, 16, 25, 40, 70, 100 m above the ground, independent temperature measurements at the same heights and near the ground; pressure and relative humidity from local meteostation (Sula, 20 km away).

Years 2014-2016: Mast4 equipped with 2 pairs of 2D sonic anemometers at 40 and 100 m above the ground. The distance between the masts is 79 m.

Data is binned in years and months and stored in a ‘*.txt’ tab-separated values file.

Data column order is described in SkipheiaMast2_header.txt and SkipheiaMast4_header.txt, where WSx is the wind speed (m/s), WDx is the wind direction (360 deg), ATx is the air temperature (deg C) and x designates the instrument number. The instruments are numbered starting from the ground.

Example: For Mast2 (6 pairs of anemometers, ground temperature + 6 temperature sensors on the mast) that means that AT0 is the ground temperature. WS1 and WS2 are wind speed records at 10 m level. WS3 and WS4 are wind speed records at 16 m. For Mast4 (2 pairs of anemometers) that means that WS1 and WS2 are wind speed records at 40 m level. WS3 and WS4 are wind speed records at 100 m.

Detailed site description with wind climate description can be found in attached analysis: Site analysys.pdf.

Additional information and analysis can be found in listed below works, using data from Frøya site:

Bardal, L. M., & Sætran, L. R. (2016, September). Spatial correlation of atmospheric wind at scales relevant for large scale wind turbines. In Journal of Physics: Conference Series (Vol. 753, No. 3, p. 032033). IOP Publishing, doi:10.1088/1742-6596/753/3/032033, https://iopscience.iop.org/article/10.1088/1742-6596/753/3/032033/pdf

Bardal, L. M., & Sætran, L. R. (2016). Wind gust factors in a coastal wind climate. Energy Procedia, 94, 417-424, https://doi.org/10.1016/j.egypro.2016.09.207

IEA Wind TCP Task 27 Compendium of IEA Wind TCP Task 27 Case Studies, Technical Report, Prepared by Ignacio Cruz Cruz, CIEMAT, Spain Trudy Forsyth, WAT, United States, October 2018; Chapter 1.8. https://community.ieawind.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=8afc06ec-bb68-0be8-8481-6622e9e95ae7&forceDialog=0

Domagalski, P., Bardal, L. M., & Sætran, L. Vertical Wind Profiles in Non-neutral Conditions-Comparison of Models and Measurements from Froya. Journal of Offshore Mechanics and Arctic Engineering, doi: 10.1115/1.4041816, http://offshoremechanics.asmedigitalcollection.asme.org/article.aspx?articleid=2711333&resultClick=3

Møller, M., Domagalski, P., & Sætran, L. R. (2019, October). Characteristics of abnormal vertical wind profiles at a coastal site. In Journal of Physics: Conference Series (Vol. 1356, No. 1, p. 012030). IOP Publishing. https://iopscience.iop.org/article/10.1088/1742-6596/1356/1/012030

Møller, M., Domagalski, P., and Sætran, L. R.: Comparing Abnormalities in Onshore and Offshore Vertical Wind Profiles, Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-40 , in review, 2019.

This file contains COVID-19 death counts, death rates, and percent of total deaths by jurisdiction of residence. The data is grouped by different time periods including 3-month period, weekly, and total (cumulative since January 1, 2020). United States death counts and rates include the 50 states, plus the District of Columbia and New York City. New York state estimates exclude New York City. Puerto Rico is included in HHS Region 2 estimates.Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file.Data during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death.Death counts should not be compared across states. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly.The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York, New York City, Puerto Rico; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington.Rates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf).Rates are based on deaths occurring in the specified week/month and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly/monthly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly/monthly) rate prevailed for a full year.Sub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).

Introduction: Very low-calorie diets with hospitalization have demonstrated promise as a viable therapeutic option for severe obesity and its associated comorbidities. However, large studies providing a comprehensive longitudinal observation of patients undergoing this therapy are lacking. We evaluated the effectiveness of treating severe obesity in hospitalized patients, using very low-calorie diets and clinical support to develop lifestyle changes. Methods: This study was a retrospective cohort comparing exposure variables in a secondary data analysis with a pre-post treatment design. Data were obtained from medical records of patients with severe obesity (grade II or III) treated in a Brazilian obesity specialist hospital from 2016 to 2022. The patients underwent a very low-calorie diet (500–800 kCal/day) and immersive changes in lifestyle habits, monitored by a multidisciplinary team. At 3 months, 777 patients presented complete data and 402 presented complete data at 6 months. The study compared changes in bioimpedance and laboratory tests, between men and women and age groups. Results: Three months of hospitalization yielded significant reductions in weight, body mass index (BMI), body fat, skeletal muscle mass, glucose, inflammatory, and lipid parameters. These reductions were more pronounced after 6 months, nearly doubling those observed at 3 months. In women, BMI and fat mass reduced by 10.4% and 15.2% at 3 months and 20.4% and 31.3% at 6 months, respectively. In men, BMI and fat mass decreased by 12.9% and 25.3 at 3 months and 23.6% and 45.3% at 6 months, respectively. Elderly individuals (aged ≥ 60 years) had smaller reductions in BMI and fat mass than non-elderly individuals (aged < 60 years) but still presented significant improvements. Conclusion: This study suggests the viability of treating severe obesity by hospitalization with low-calorie diets and immersive lifestyle changes. This treatment modality significantly improves anthropometric measurements, glucose, lipids, and inflammatory markers, thereby reducing cardiovascular risk.

An Open Context "types" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This record is part of the "Presidio of San Francisco" data publication.

SFARI_EEG multi-paradigm dataset (Raw)

Overview

This dataset provides raw EEG from children recorded across seven complementary paradigms designed to assay neuro-oscillatory function spanning basic sensory through cognitive control and motor systems. This dataset contains data for three groups of children: 44 typically developing (TD), 66 autism spectrum disorder (ASD), and 28 sibling of individuals with ASD (SIB) participants, all between 8 and 13 years of age. Data are organized in BIDS 1.10.1 format (Dataset Type: raw).

The scientific motivation is to enable robust, large-sample mapping of oscillatory dysfunction in autism spectrum disorder (ASD) across the major frequency bands (theta, alpha, beta, gamma) using a common acquisition platform and harmonized annotations. By sampling multiple assays within the same participants, these data support both targeted hypothesis-driven analyses and data-driven discovery (e.g., network/feature selection approaches for biomarker development and predictive modeling of dimensional traits relevant to social cognition and motor function).

Acquisition

• System: BioSemi ActiveTwo, 64 channels (BioSemi64 montage)
• Sampling rate: 512 Hz
• Power line frequency: 60 Hz
• Trigger channel: Status (BioSemi)

Participants

• To be included in the ASD group, participants had to meet diagnostic criteria for ASD on the basis of the following measures: 1) autism diagnostic observation schedule 2 (ADOS-2) (Lord et al., 1994); 2) diagnostic criteria for autistic disorder from the Diagnostic and Statistical Manual of Mental Disorders (DSM-5); 3) clinical impression of a licensed clinician with extensive experience in diagnosis and evaluation of children with ASD. Due to precautions during the COVID-19 pandemic, a subset of ASD participants (n=9) was not able to complete the ADOS-2 evaluation, as masking requirements impacted administration. These participants instead underwent the Childhood Autism Rating Scale 2 (CARS-2) and Autism Diagnostic Interview-Revised (ADI-R) for diagnostic assessment. Participants in the TD group met the following inclusion criteria: no history of neurological, developmental, or psychiatric disorders, no first-degree relatives diagnosed with ASD, and enrollment in an age-appropriate grade in school. The SIB group participants met the same criteria as the TD group, except that they had a sibling diagnosed with ASD. Exclusion criteria for all groups included: (1) a known genetic syndrome associated with an IDD (including syndromic forms of ASD), (2) a history of or current use of medication for seizures in the past 2 years, (3) significant physical limitations (e.g., vision or hearing impairments, as screened over the phone and on the day of testing), (4) premature birth (<35 weeks) or having experienced significant prenatal/perinatal complications, or (5) a Full Scale IQ (FS-IQ) of less than 80.

• See participants.tsv and participants.json in each specific paradigm for more details.

Data quality & preprocessing notes

Raw EEG is provided without preprocessing.

Notes

• This work was supported by a grant from the Simons Foundation Autism Research Initiative (SFARI Award # 874845, SM). Support for recruitment and phenotyping of participants was provided by the Human Clinical Phenotyping Core of the NICHD funded Rose. F. Kennedy Intellectual and Developmental Disabilities Research Center (P50 HD105352, SM)

Description of the tasks

Auditory Steady-State Response (ASSR_run)

EEG recorded during an Auditory Steady-State Response (ASSR) in children.

• Participants were seated in a chair in an electrically shielded room (International Acoustics Company, Bronx, New York), 70 cm away from the visual display (Dell UltraSharp 1704FPT). Auditory stimuli were 500-ms binaural click trains at either 27- or 40-Hz, presented through HD 650 Sennheiser headphones at 60 dB SPL. Inter-stimulus interval was randomly jittered between 488-788 ms. On 15% of trials, an oddball stimulus presented at a different frequency (27-Hz for 40-Hz trials, 40-Hz for 27-Hz trials) was randomly intermixed among the standards. Participants were instructed to respond via button-press when they identified an oddball stimulus, to promote attention to the auditory stimuli. Stimuli were presented in four randomly presented blocks of 100 trials—blocked by stimulus type (40-Hz standard, 27-Hz standard), consisting of 170 trials per standard frequency and 30 trials per oddball stimulus.

  Events:

• Codes: '27_Hz_Standard': 21, '40_Hz_Oddball': 12, '40_Hz_Standard': 11, '27_Hz_Oddball': 22, 'Block_27_Hz_Standard': 27, 'Block_40_Hz_Standard': 40, 'Half_Block_Pause': 199, 'Response_button':1}

• Onsets are stimulus onsets derived from the Status channel.

• See each *_events.tsv for per-run details.

Notes: - Please cite:

Face Processing (FAST_run)

EEG recorded during a social attentional task (FAST) in children.

• Participants were seated in a chair in an electrically shielded room (International Acoustics Company, Bronx, New York), 70 cm away from the visual display (Dell UltraSharp 1704FPT). The stimuli, controlled by Presentation software (Neurobehavioral Systems), were faces ('Social') or objects (‘Non-Social’), each shown as upright and inverted images, along with shadow versions. Participants were instructed to press a button as quickly as possible upon detecting a shadow stimulus (presented at 20% probability). A jittered interstimulus interval (900–1100ms) reduced onset predictability. The task comprised 720 trials across 12 blocks (60 trials/block, ~3 minutes and 40 seconds each). Blocks were organized by stimulus category; each block contained only social stimuli (i.e., upright and inverted faces with their shadow versions) or non-social stimuli. There were six blocks of social and six blocks of non-social in total). Face and object images (upright/inverted) were randomly chosen from a pool of 28 stimuli. All faces depicted a positive emotion, (i.e., smiling faces; see the github folder for stimuli). Shadow faces and objects were chosen across a reduced pool of 5 stimuli. Responses were recorded using a response pad (Logitech Wingman Precision Gamepad), and stimulus and response triggers were sent from the PC acquisition computer via Presentation software.

Events: - Codes: Face_upright=21, Face_inverted=22, Face_upright_shadow=121, Face_inverted_shadow=122, Object_upright=31, Object_inverted=32, Object_upright_shadow=131, Object_inverted_shadow=132 - Onsets are stimulus onsets derived from the Status channel. - See each *_events.tsv for per-run details.

Note: - Please cite: in preparation

Intersensory Attention (Beepflash_run) Cued S1→S2 design indicating whether to attend visual or auditory targets. Primary measures: posterior alpha increases indexing suppression of task-irrelevant sensory input and intersensory attentional gating.

Audiovisual simple reaction time task (AVSRT_run)

EEG recorded during an audiovisual simple reaction-time task (AVSRT) in children.

• Participants were seated in a chair in an electrically shielded room (International Acoustics Company, Bronx, New York), 70 cm away from the visual display (Dell UltraSharp 1704FPT). The stimuli, controlled by Presentation software (Neurobehavioral Systems), included three types: a red disc ('Visual'), a 1000Hz tone ('Audio'), and their simultaneous presentation ('Audiovisual'). Participants were instructed to press a button as quickly as possible upon detecting any stimulus. The auditory stimulus was 1000Hz, 60ms tone presented binaurally (75 dB SPL). The visual stimulus was a red disc subtending to 1.5 degrees, displayed above a fixation cross. The audiovisual stimulus was a simultaneous presentation of both. Each trial presented a pseudo randomly chosen stimulus (A, V, or AV; represented equiprobably), with stimuli delivered through headphones (XX) and displayed on a flat-panel LCD (60Hz). A jittered randomly sampled interstimulus interval (1000-3000ms) reduced onset predictability. The task consisted of 400 trials across 4 blocks (100 trials per block), each block lasting approximately 3 minutes and 40 seconds. Button presses were recorded using a response pad (Logitech Wingman Precision Gamepad). Triggers indicating stimulus latency were sent from the PC acquisition computer via Presentation software.

Events: - Codes: AV=3, A=4, V=5 - Onsets are stimulus onsets derived from the Status channel. - See each *_events.tsv for per-run details.

Note: - Please cite: in preparation

Motor Processing (Motor_run)

EEG recorded during a mobile EEG paradigm in children. This paradigm is recorded using Lab Streaming Layer to synchronize the camera system (for gait measures) with the EEG recordings.

• Participants are either standing on a treadmill (30 sec recordings) or walking on a treadmill (5 min recordings) with either no flow (static background with white dots) or a flow (dots are moving toward the participant). The task is to ignore the white dots and to respond when the fixation cross at the center of the projected image rotate (45 degrees rotation).

Events: - Codes: - Onsets are stimulus onsets derived from the Status channel. - See each *_events.tsv for per-run details.

Cross-sensory attentional switching task (Beep-Flash_run)

EEG recorded during a cross-sensory attentional task (Beep-Flash) in children.

• Participants were seated in a chair in an electrically shielded room (International Acoustics Company, Bronx, New York), 70 cm away from the visual display (Dell UltraSharp 1704FPT). A cued intersensory attention task was employed in which each trial consisted of an instructional cue (S1), an