This folder contains data from a fictional participant that you can use to test BIDS Manager (https://github.com/Dynamap/BIDS_Manager).

A free form text ( README ) describing the dataset in more details that SHOULD be provided. For an example, see e.g.: https://github.com/bids-standard/bids-starter-kit/blob/main/templates/README.MD

The raw BIDS data was created using BIDScoin 4.5.1.dev0 All provenance information and settings can be found in ./code/bidscoin For more information see: https://github.com/Donders-Institute/bidscoin

This folder contains data organised in BIDS format to test BIDS Manager-Pipeline (https://github.com/Dynamap/BIDS_Manager/tree/dev).

BIDS Phenotype Segregation Example COPY OF "The NIMH Healthy Research Volunteer Dataset" (ds003982)

Modality-agnostic files were copied over and the CHANGES file was updated. Data was segregated using:

python phenotype.py segregate subject -i ds003982 -o segregated_subject

phenotype.py came from the GitHub repository: https://github.com/ericearl/bids-phenotype

THE ORIGINAL DATASET ds003982 README FOLLOWS

A comprehensive clinical, MRI, and MEG collection characterizing healthy research volunteers collected at the National Institute of Mental Health (NIMH) Intramural Research Program (IRP) in Bethesda, Maryland using medical and mental health assessments, diagnostic and dimensional measures of mental health, cognitive and neuropsychological functioning, structural and functional magnetic resonance imaging (MRI), along with diffusion tensor imaging (DTI), and a comprehensive magnetoencephalography battery (MEG).

In addition, blood samples are currently banked for future genetic analysis. All data collected in this protocol are broadly shared in the OpenNeuro repository, in the Brain Imaging Data Structure (BIDS) format. In addition, blood samples of healthy volunteers are banked for future analyses. All data collected in this protocol are broadly shared here, in the Brain Imaging Data Structure (BIDS) format. In addition, task paradigms and basic pre-processing scripts are shared on GitHub. This dataset is unique in its depth of characterization of a healthy population in terms of brain health and will contribute to a wide array of secondary investigations of non-clinical and clinical research questions.

This dataset is licensed under the Creative Commons Zero (CC0) v1.0 License.

Recruitment

Inclusion criteria for the study require that participants are adults at or over 18 years of age in good health with the ability to read, speak, understand, and provide consent in English. All participants provided electronic informed consent for online screening and written informed consent for all other procedures. Exclusion criteria include:

• A history of significant or unstable medical or mental health condition requiring treatment
• Current self-injury, suicidal thoughts or behavior
• Current illicit drug use by history or urine drug screen
• Abnormal physical exam or laboratory result at the time of in-person assessment
• Less than an 8th grade education or IQ below 70
• Current employees, or first-degree relatives of NIMH employees

Study participants are recruited through direct mailings, bulletin boards and listservs, outreach exhibits, print advertisements, and electronic media.

Clinical Measures

All potential volunteers first visit the study website (https://nimhresearchvolunteer.ctss.nih.gov), check a box indicating consent, and complete preliminary self-report screening questionnaires. The study website is HIPAA compliant and therefore does not collect PII ; instead, participants are instructed to contact the study team to provide their identity and contact information. The questionnaires include demographics, clinical history including medications, disability status (WHODAS 2.0), mental health symptoms (modified DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure), substance use survey (DSM-5 Level 2), alcohol use (AUDIT), handedness (Edinburgh Handedness Inventory), and perceived health ratings. At the conclusion of the questionnaires, participants are again prompted to send an email to the study team. Survey results, supplemented by NIH medical records review (if present), are reviewed by the study team, who determine if the participant is likely eligible for the protocol. These participants are then scheduled for an in-person assessment. Follow-up phone screenings were also used to determine if participants were eligible for in-person screening.

In-person Assessments

At this visit, participants undergo a comprehensive clinical evaluation to determine final eligibility to be included as a healthy research volunteer. The mental health evaluation consists of a psychiatric diagnostic interview (Structured Clinical Interview for DSM-5 Disorders (SCID-5), along with self-report surveys of mood (Beck Depression Inventory-II (BD-II) and anxiety (Beck Anxiety Inventory, BAI) symptoms. An intelligence quotient (IQ) estimation is determined with the Kaufman Brief Intelligence Test, Second Edition (KBIT-2). The KBIT-2 is a brief (20-30 minute) assessment of intellectual functioning administered by a trained examiner. There are three subtests, including verbal knowledge, riddles, and matrices.

Medical Evaluation

Medical evaluation includes medical history elicitation and systematic review of systems. Biological and physiological measures include vital signs (blood pressure, pulse), as well as weight, height, and BMI. Blood and urine samples are taken and a complete blood count, acute care panel, hepatic panel, thyroid stimulating hormone, viral markers (HCV, HBV, HIV), C-reactive protein, creatine kinase, urine drug screen and urine pregnancy tests are performed. In addition, blood samples that can be used for future genomic analysis, development of lymphoblastic cell lines or other biomarker measures are collected and banked with the NIMH Repository and Genomics Resource (Infinity BiologiX). The Family Interview for Genetic Studies (FIGS) was later added to the assessment in order to provide better pedigree information; the Adverse Childhood Events (ACEs) survey was also added to better characterize potential risk factors for psychopathology. The entirety of the in-person assessment not only collects information relevant for eligibility determination, but it also provides a comprehensive set of standardized clinical measures of volunteer health that can be used for secondary research.

MRI Scan

Participants are given the option to consent for a magnetic resonance imaging (MRI) scan, which can serve as a baseline clinical scan to determine normative brain structure, and also as a research scan with the addition of functional sequences (resting state and diffusion tensor imaging). The MR protocol used was initially based on the ADNI-3 basic protocol, but was later modified to include portions of the ABCD protocol in the following manner:

1. The T1 scan from ADNI3 was replaced by the T1 scan from the ABCD protocol.
2. The Axial T2 2D FLAIR acquisition from ADNI2 was added, and fat saturation turned on.
3. Fat saturation was turned on for the pCASL acquisition.
4. The high-resolution in-plane hippocampal 2D T2 scan was removed and replaced with the whole brain 3D T2 scan from the ABCD protocol (which is resolution and bandwidth matched to the T1 scan).
5. The slice-select gradient reversal method was turned on for DTI acquisition, and reconstruction interpolation turned off.
6. Scans for distortion correction were added (reversed-blip scans for DTI and resting state scans).
7. The 3D FLAIR sequence was made optional and replaced by one where the prescription and other acquisition parameters provide resolution and geometric correspondence between the T1 and T2 scans.

At the time of the MRI scan, volunteers are administered a subset of tasks from the NIH Toolbox Cognition Battery. The four tasks include:

1. Flanker inhibitory control and attention task assesses the constructs of attention and executive functioning.
2. Executive functioning is also assessed using a dimensional change card sort test.
3. Episodic memory is evaluated using a picture sequence memory test.
4. Working memory is evaluated using a list sorting test.

MEG

An optional MEG study was added to the protocol approximately one year after the study was initiated, thus there are relatively fewer MEG recordings in comparison to the MRI dataset. MEG studies are performed on a 275 channel CTF MEG system (CTF MEG, Coquiltam BC, Canada). The position of the head was localized at the beginning and end of each recording using three fiducial coils. These coils were placed 1.5 cm above the nasion, and at each ear, 1.5 cm from the tragus on a line between the tragus and the outer canthus of the eye. For 48 participants (as of 2/1/2022), photographs were taken of the three coils and used to mark the points on the T1 weighted structural MRI scan for co-registration. For the remainder of the participants (n=16 as of 2/1/2022), a Brainsight neuronavigation system (Rogue Research, Montréal, Québec, Canada) was used to coregister the MRI and fiducial localizer coils in realtime prior to MEG data acquisition.

Specific Measures within Dataset

Online and In-person behavioral and clinical measures, along with the corresponding phenotype file name, sorted first by measurement location and then by file name.

This archive contains a raw DICOM dataset acquired (with informed consent) using the ReproIn naming convention on a Siemens Skyra 3T MRI scanner. The dataset includes a T1-weighted anatomical image, four functional runs with the “prettymouth” spoken story stimulus, and one functional run with a block design emotional faces task, as well as auxiliary scans (e.g., scout, soundcheck). The “prettymouth” story stimulus created by Yeshurun et al., 2017 and is available as part of the Narratives collection, and the emotional faces task is similar to Chai et al., 2015. These data are intended for use with the Princeton Handbook for Reproducible Neuroimaging. The handbook provides guidelines for BIDS conversion and execution of BIDS apps (e.g., fMRIPrep, MRIQC). The brain data are contributed by author S.A.N. and are authorized for non-anonymized distribution.

Amazon Best Seller Dataset

Data from Amazon US on the top 50 selling products for all 40 listed categories. Includes: - Product Name - Product Categories - Raking of product in its category - Product Review Count (Number of Reviews) - Product Review (First page of reviews) - Includes Meta + Full Review + Rating given by review - Cost of item at the time of scraping. - Product URL

Data scraped using Python Selenium and cleaned manually Scraping Notebook

Data Scraping History

PS Dataset will be updated every second week of the month for the time period of 2022 August -> 2023 June

I obtained this data from the Michigan Department of Natural Resource (MDoNR) website which is freely available and posted to an https server. This state agency is in charge of the management of state forests in Michigan. The MDoNR provides very detailed information concerning the timber, such as the various species in the lot, the volumes of each species, the percentage of saw timber and the minimum acceptable bid (the public reserve price).

• Bids: Amount of the bids placed
• Winbid: Amount of the winning bid
• Reserve: Minimum acceptable bid or the public reserve price
• Acre: Bidding lot size in acres
• Actual: Total number of bidders that submit a bid for a particular timber sale auction
• Potential: Total number of bidders that submit an actual bid for any timber sale auction held by the same office the same month
• Payment: Maximum number of payments the winner is allowed to pay off the bid
• Years: Number of harvest years the winner is allowed from the date of sale

For information on the datasets and analysis, see the corresponding github repository. https://github.com/ericschulman/michigan_timber

Contains monthly estimates of the effective bid-ask spread for crypto pairs listed in Binance. Additional code and data are available at https://github.com/eguidotti/bidask

A free form text ( README ) describing the dataset in more details that SHOULD be provided. For an example, see e.g.: https://github.com/bids-standard/bids-starter-kit/blob/main/templates/README.MD

The raw BIDS data was created using BIDScoin 4.5.1.dev0 All provenance information and settings can be found in ./code/bidscoin For more information see: https://github.com/Donders-Institute/bidscoin

Data from the U.S. justice department investigation involving school milk cartons that took place during the early 1990s.

• Bids: Bid level in cents as the dependent variable. We score the bids as a weighted average of each type of milk based on the quantity ordered.
• Raw milk prices: Class 1 minimum raw milk price. This is a price floor for milk producers on the price of raw milk set by Federal Marketing Order 126, Zone 1. Zone 1 corresponds to Dallas. The raw milk prices involves an adjustment for the different zones within Texas. Class 1 refers to the type of milk product, liquid dairy.
• Gasoline prices: U.S. crude oil first purchase price measured in cents. We believe this is the most general oil price possible.
• Population: The number of people in the school district according to the 1990 census.
• Quantity: The quantity of milk to be supplied. This is measured in half-pints. A half-pint corresponds to roughly a carton of milk.
• Meals: The number of meals served at the school over the course of year (for which milk is to be supplied).
• No. Bidders: The number of bidders who entered the auction.
• Escalated: Where bids adjusted for fluctuations in the raw milk.
• Cooler: Coolers are for keeping the milk cold and storing it. This variable indicates whether a cooler should be provided as part of the contract.
• Zone: A binary variables that indicates the zone the school districts are in within the Federal Marketing Order. The zones are different distances from the milk processors plants and have different prices for raw milk based on a fixed differential compared to the Dallas baseline. We label the zones based on distinguishable urban areas. The areas are Dallas (Zone 1), Waco (Zone 3), St. Angelo (Zone 6), Austin (Zone 7), San Antonio (zone 9).
• Escalation: Whether bids are adjusted for changes in the cost of raw milk through out the school year.
• Incumbency: This indicates whether a firm served the school district more than 50 percent of years.

See the corresponding github repo for more information: https://github.com/ericschulman/tx_milk_2yp

HED (Hierarchical Event Descriptors) is an evolving framework for the description and formal annotation of events and other information in data. The HED ecosystem includes a structured vocabulary (specified by a HED schema) together with tools for validation and for using HED annotations in data search, extraction, and analysis. This resource is the HED standard schema vocabulary. While HED can be used to annotate any type of data, the current HED community focuses on annotation of events in human neuroimaging and behavioral data such as EEG, MEG, iEEG, fMRI, eye-tracking, motion-capture, EKG, and audiovisual recording. A viewer for all of the HED vocabularies can be found at https://www.hedtags.org/display_hed.html. The HED Standard Organization GitHub repository is https://github.com/hed-standard and the HED project homepage is https://www.hedtags.org Additional HED resources can be found at https://www.hed-resources.org. This release (8.4.0) introduces the annotation attribute that allows HED schemas to be linked to other vocabularies and ontologies. The three formats (XML, MEDIAWIKI, and TSV) now all contain equivalent information.

A dataset providing the number of bids per letting, by year, area, property type. Also contained is information about the winning applicant's housing priority and the year in which they were awarded that priority.

High level priorities

• A refers to an applicant who has an urgent need to move, e.g. domestic violence, homelessness, serious medical issue, leaving armed forces.

• A+ refers to when an applicant has 2 or more Priority A requirements to move, e.g. serious medical issue AND domestic violence.

• B refers to an applicant who has a non-urgent need to move, e.g. overcrowded, minor medical issue, homeless but has accommodation.

• C refers to an applicant who has no priority status to move.

Please note

This dataset was produced as a one off for the city intelligence innovation lab. To find out about the lab click here: https://datamillnorth.org/dataset/city-intelligence-innovation-lab

PMJ-CSA

This MRI dataset for the PMJ-based CSA project. The analysis pipeline is available within the GitHub repository: https://github.com/sct-pipeline/pmj-based-csa

Dataset structure

The MRI dataset contains MRIs from 10 healthy participants with 3 different neck positions: felxion, neutral and extension. The following images are included: - T2w sagittal isotropic 0.6 mm

Three sessions:

• ses-headDown --> flexion
• ses-headNormal --> neutral
• ses-headUp --> extension Note: sub-002 has different acqusisition parameters: 0.7 mm isotropic for ses-headDown and ses-headUp

Derivatives

The derivatives will be organized according to the following: https://intranet.neuro.polymtl.ca/data/dataset-curation.html#derivatives-structure

Convention for JSON metadata:

{ "Author": "Firstname Lastname", "Date": "YEAR-MM-DD HH:MM:SS" }

Derivatives includes:

• Intervertebral discs labels
• Spinal cord segmentation
• Spinal nerve rootet labels
• PMJ label

The UCLH Stroke EIT Dataset - Radiology Reports

Each folder contains the anonymised radiology data, and clinical reports for all patients in the study. The latest version follows the BIDS structure

Full details on the use of these files are given the repository https://github.com/EIT-team/Stroke_EIT_Dataset

Version 4 - Latest BIDS version. Use this version for NIFTI files

Version 3 - Initial BIDS version

Version 2 - Updated DICOM. Use this version if you wish to use the original DICOM files

Version 1 - Initial upload

Consumers-BID

Introduction

This dataset consists of two partitions: Consumers and BID (body image dataset) introduced with our article entitled "*A New Benchmark for Consumer Visual Tracking and Apparent Demographic Estimation from RGB and Thermal Images*" https://doi.org/10.3390/s23239510.

Overview

Consumers includes images of retail consumers captured from RGB cameras and their ground truth bounding-box locations.

BID comprises cropped full-body images of annotated consumers with their ground truth age-range and gender information.

Both datasets are anonymized following the privacy-by-design principle, and their use is intended for various computer vision tasks. However, it is not limited to these tasks. Please refer to the sections below for important information regarding the use of this dataset.

This version only includes the evaluation (test) set partitions. We will soon upload the training partitions as well.

Structure

• Consumers includes 145 video sequences split into 120 training and 25 testing partitions featuring 8700 images/frames in total. The structure of the dataset is as follows:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F17922636%2F064ee2e51239f2c5b0a5e38685689be4%2FConsumersSmall.png?generation=1701373217319160&alt=media" alt="">

where gt.txt includes the holistic annotation information [frame_id, consumer_id, bounding-box] for the entire sequence, readily available for evaluating the tracking algorithm (i.e., compatible with MOT challenge format). Each frameID.txt contains annotation information [consumer_ID bounding_box_coords(center point, box width, and height) age_group gender] per annotated instance in the frame.

• BID (body image dataset) comrpises 6641 cropped images from each Consumers sequence, split into 5507 training images and 1134 images for testing. The structure of the dataset is as follows:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F17922636%2Fadb4074045b5abc61903eacb8ecbbe1f%2FBIDStructureSmall.png?generation=1701373230351014&alt=media" alt="">

• We consider four age groups: (1) below 12–20 years, (2) 21–36, (3) 37–60, and (4) over 61, and each cropped body image corresponds to a single instance of a target consumer, which is also reflected by the image name (frameID_consumerID)
• Annotations are provided within index.txt in the format: [GENDER AGE_GROUP_1 AGE_GROUP_2 AGE_GROUP_3 AGE_GROUP_4]. For example, the 1 hot representation [1 0 1 0 0] denotes a consumer instance of a female in the 21 to 36 age group.

License

This dataset is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0).

Summary of License Terms

-**Attribution (BY):** You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

-**NonCommercial (NC):** You may not use the material for commercial purposes.

-**NoDerivs (ND):** If you remix, transform, or build upon the material, you may not distribute the modified material.

Copyright

Copyright (c) 2023 Angelos P. Giotis, Iason-Ioannis Panagos, Christophoros Nikou

Permission is hereby granted, free of charge, to any person obtaining a copy of Consumers-BID and associated documentation files (the "Dataset"), to use the Dataset under the terms of the CC BY-NC-ND 4.0 license.

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Dataset.

Usage Guidelines

Please adhere to the following guidelines when using this dataset:

-**Attribution:** Provide appropriate attribution as specified in the license.

-**NonCommercial:** Do not use the dataset for commercial purposes without obtaining explicit permission.

-**NoDerivs:** Do not create derivative works or modify the dataset.

GDPR Compliance and Privacy

To ensure compliance with privacy regulations, particularly the General Data Protection Regulation (GDPR), this dataset adheres to the privacy-by-design principle. The dataset consists of two partitions: (1) Consumers, which includes images of retail consumers captured from RGB cameras along with their ground truth bounding-box locations, and (2) BID, comprising cropped full-body images of annotated consumers with their ground truth age-range and gender information. Both datasets are carefully anonymized to protect individuals' privacy.

Anonymization Process

All frames containing facial information are anonymized using deface software (https://github.com/ORB-HD/deface) for blurring. In cases where a person's face is not detected by automated anonymization tools, m...

This dataset contains the EEG recordings of 30 participants in a study conducted by the IT University of Copenhagen brAIn lab, designed to investigate the origins of the Uncanny Valley phenomenon. The study is a follow-up to our pilot study on the Uncanny Valley, also available on Zenodo at https://zenodo.org/records/7948158.

The dataset contains the images that have been shown to the participants, the events, and all the details about the timing and the EEG data. The structure of the dataset follows the Brain Imaging Data Structure specification.

The dataset can be analysed using the scripts available at https://github.com/itubrainlab/uncanny-valley-eeg-study-full-analysis.

A free form text ( README ) describing the dataset in more details that SHOULD be provided

The raw BIDS data was created using BIDScoin 3.0.8 All provenance information and settings can be found in ./code/bidscoin For more information see: https://github.com/Donders-Institute/bidscoin

README

We present a multimodal dataset of intracranial recordings, fMRI, and eye tracking in 20 human participants as they watched the same movie stimulus. Intracranial recordings consist of single neurons, local field potential, and intracranial EEG activity recorded concurrently from depth electrodes targeting the amygdala, hippocampus, and medial frontal cortex while participants underwent intracranial monitoring for localization of epileptic seizures.

Participants watched an 8-min long excerpt from the video “Bang! You’re Dead” and performed a recognition memory test for movie content. 3T fMRI activity was recorded prior to surgery in 11 of these participants while performing the same task. This NWB- and BIDS-formatted dataset includes the spike times of all neurons, field potential activity, behavior, eye tracking, electrode locations, demographics, and functional and structural MRI scans. For technical validation, we provide signal quality metrics, assess eye tracking quality, behavior, the tuning of cells and high-frequency broadband power field potentials to familiarity and event boundaries, and show brain-wide inter-subject correlations for fMRI.

This dataset will facilitate the investigation of brain activity during movie watching, recognition memory, and the neural basis of the fMRI-BOLD signal.

Related code: https://github.com/rutishauserlab/bmovie-release-NWB-BIDS Intracranial recording data: https://dandiarchive.org/dandiset/000623

An fMRI Dataset for Appetite Neural Correlates in People Living with Motor Neuron Disease

This is a motor neuron disease (MND) imaging dataset generated by the Steyn/Ngo Lab at the University of Queensland, Australia. Imaging was conducted at the Herston Imaging Research Facility (HIRF) at Brisbane, Australia.

The raw BIDS data was created using BIDScoin 2.3.1 All provenance information and settings can be found in ./code/bidscoin For more information see: https://github.com/Donders-Institute/bidscoin

Anatomical volumes were refaced using mri_reface 0.3.3 The code can be found in ./code/reface_structural.sh

Identifiable volumes under the derivatives dataset derivatives/fmriprep-v23.1.4 were removed.

Protocol change for sub-43

For this patient we used the 20 channel coil instead of the 64 channel coil as the 64 channel coil did not fit the patients head. Due to the coil change there were a few modifications to the imaging protocol as listed below: PA multiband diffusion block changes: TE changed from 84 ms to 89 ms TR changed from 4700 ms to 5200ms Multiband diffusion AP block 1 changes: TE changed from 84 ms to 89 ms TR changed from 4700 ms to 5200ms AP multiband diffusion block 2 changes: TE changed from 84ms to 89 ms TR changed from 4700 ms to 5200 ms

DWI not available for sub-17

DWI files for sub-17 are incomplete. As such, sub-17/ses-02/dwi is not included as part of the published dataset.

Information

This folder contains the ECoG data from 2 subjects performing a visual task used in the publications of Hermes et al., 2015, Cerebral Cortex "Stimulus Dependence of Gamma Oscillations in Human Visual Cortex" and Hermes et al., 2017, PLOS Biology “Neuronal synchrony and the relation between the blood-oxygen-level dependent response and the local field potential”. Contact: Dora Hermes (dorahermes@gmail.com)

Citing this dataset

If you use this data as a part of any publications, please use the following citation: [1] Hermes D, Miller KJ, Wandell BA, Winawer J (2015). Stimulus dependence of gamma oscillations in human visual cortex. Cerebral Cortex 25(9):2951-9. https://doi.org/10.1093/cercor/bhu091 [2] Hermes D, Nguyen M, Winawer J. (2017). Neuronal synchrony and the relation between the BOLD response and the local field potential. PLOS Biology 15(7). https://doi.org/10.1371/journal.pbio.2001461

This dataset was made available with the support of the Netherlands Organization for Scientific Research www.nwo.nl under award number 016.VENI.178.048 to Dora Hermes and the National Institute Of Mental Health of the National Institutes of Health under Award Number R01MH111417 to Natalia Petridou and Jonathan Winawer. The ECoG data collection was facilitated by the Stanford Human Intracranial Cognitive Electrophysiology Program (SHICEP).

License

This dataset is made available under the Public Domain Dedication and License
v1.0, whose full text can be found at
http://www.opendatacommons.org/licenses/pddl/1.0/.

Task Description

Subjects were presented with images presented on a computer screen. The images spanned about 25x25 degrees of visual angle. Subjects fixated on a dot in the center of the screen that alternated between red and green, changing colors at random times. Subject 1 pressed a button when the fixation dot changed color. Subject 2 fixated on the dot but did not make manual responses because these responses were found to interfere with visual fixation.

Dataset and Stimuli

This data is organized according to the Brain Imaging Data Structure specification. A community- driven specification for organizing neurophysiology data along with its metadata. For more information on this data specification, see https://bids-specification.readthedocs.io/en/stable/

Each subject has their own folder (e.g., sub-01) which contains the raw EcoG data for that subject, as well as the metadata needed to understand the raw data and event timing. In addition, the stimuli/ folder contains the .png files of the presented images.

Stimuli

Stimuli including high contrast vertical gratings (0.16, 0.33, 0.65, or 1.3 duty cycles per degree square wave) and noise patterns (spectral power distributions of k/f^4; k/f^2; and k/f^0).

Raw data

Raw data is stored with the Brainvision data format. This can be read in to memory with the following tools:

• Python: The pybv package (https://github.com/bids-standard/pybv)
• Matlab: BrainVision analyzer (https://www.mathworks.com/products/connections/product_detail/brainvision-analyzer.html)