This a sample BIDS dataset created for continous integration of the Connectome Mapper 3.

This dataset was acquired at the Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland, using a 3T Siemens Prisma MRI scanner.

It adopts the sub-/ses- structure and contains one T1w anatomical MRI (MPRAGE), one diffusion MRI (DSI) , and one resting-state functional MRI as well as additional Freesurfer derivatives.

It is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. (See https://creativecommons.org/licenses/by/4.0/ for more details)

Changes

Version 2.1

• Fix issues with the resampling of the DWI and rfMRI scans with Slicer. They were regenerated in version 2.1 with `mri_convert` to better handle the 4th dimension.
• For the sake of the size of the dataset, only 100 frames in the fMRI recording has been kept and the sourcedata/ folder has been dropped but can be easily be retrieved in the previous 2.0 version (https://zenodo.org/record/5788803#.Yb2-giYo8bV).

Version 2.0

• For testing purposes, scans found in the root sub-01 directory have been downsampled to 2x2x2 mm3 (MPRAGE), and to 3x3x3 mm3 (DSI and rfMRI) with the ResampleScalarVolume module of Slicer 4.6.2. A copy of the output produced in the terminal by Slicer has been created in the `code/` directory.
• Original data have been placed in sourcedata/ in concordance to BIDS.

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

Modality-agnostic files were copied over and the CHANGES file was updated.

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.

Brainstorm - Auditory Dataset

License

This dataset (MEG and MRI data) was collected by the MEG Unit Lab, McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Canada. The original purpose was to serve as a tutorial data example for the Brainstorm software project (http://neuroimage.usc.edu/brainstorm). It is presently released in the Public Domain, and is not subject to copyright in any jurisdiction.

We would appreciate though that you reference this dataset in your publications: please acknowledge its authors (Elizabeth Bock, Peter Donhauser, Francois Tadel and Sylvain Baillet) and cite the Brainstorm project seminal publication (also in open access): http://www.hindawi.com/journals/cin/2011/879716/

Presentation of the experiment

Experiment

• One subject, two acquisition runs of 6 minutes each
• Subject stimulated binaurally with intra-aural earphones (air tubes+transducers)
• Each run contains:
  • 200 regular beeps (440Hz)
  • 40 easy deviant beeps (554.4Hz, 4 semitones higher)
• Random inter-stimulus interval: between 0.7s and 1.7s seconds, uniformly distributed
• The subject presses a button when detecting a deviant with the right index finger
• Auditory stimuli generated with the Matlab Psychophysics toolbox
• The specifications of this dataset were discussed initially on the FieldTrip bug tracker

MEG acquisition

• Acquisition at 2400Hz, with a CTF 275 system, subject in seating position
• Recorded at the Montreal Neurological Institute in December 2013
• Anti-aliasing low-pass filter at 600Hz, files saved with the 3rd order gradient
• Recorded channels (340):
  • 1 Stim channel indicating the presentation times of the audio stimuli: UPPT001 (#1)
  • 1 Audio signal sent to the subject: UADC001 (#316)
  • 1 Response channel recordings the finger taps in response to the deviants: UDIO001 (#2)
  • 26 MEG reference sensors (#5-#30)
  • 274 MEG axial gradiometers (#31-#304)
  • 2 EEG electrodes: Cz, Pz (#305 and #306)
  • 1 ECG bipolar (#307)
  • 2 EOG bipolar (vertical #308, horizontal #309)
  • 12 Head tracking channels: Nasion XYZ, Left XYZ, Right XYZ, Error N/L/R (#317-#328)
  • 20 Unused channels (#3, #4, #310-#315, #329-340)

• 3 datasets:

  • S01_AEF_20131218_01.ds: Run #1, 360s, 200 standard + 40 deviants

  • S01_AEF_20131218_02.ds: Run #2, 360s, 200 standard + 40 deviants

  • S01_Noise_20131218_01.ds: Empty room recordings, 30s long

  • File name: S01=Subject01, AEF=Auditory evoked field, 20131218=date(Dec 18 2013), 01=run

• Use of the .ds, not the AUX (standard at the MNI) because they are easier to manipulate in FieldTrip

Stimulation delays

• Delay #1: Production of the sound.
  Between the stim markers (channel UDIO001) and the moment where the sound card plays the sound (channel UADC001). This is mostly due to the software running on the computer (stimulation software, operating system, sound card drivers, sound card electronics). The delay can be measured from the recorded files by comparing the triggers in the two channels: Delay between 11.5ms and 12.8ms (std = 0.3ms) This delay is not constant, we will need to correct for it.
• Delay #2: Transmission of the sound.
  Between when the sound card plays the sound and when the subject receives the sound in the ears. This is the time it takes for the transducer to convert the analog audio signal into a sound, plus the time it takes to the sound to travel through the air tubes from the transducer to the subject's ears. This delay cannot be estimated from the recorded signals: before the acquisition, we placed a sound meter at the extremity of the tubes to record when the sound is delivered. Delay between 4.8ms and 5.0ms (std = 0.08ms). At a sampling rate of 2400Hz, this delay can be considered constant, we will not compensate for it.
• Delay #3: Recording of the signals.
  The CTF MEG systems have a constant delay of 4 samples between the MEG/EEG channels and the analog channels (such as the audio signal UADC001), because of an anti-aliasing filtered that is applied to the first and not the second. This translate here to a constant delay of 1.7ms.
• Delay #4: Over-compensation of delay #1.
  When correcting of delay #1, the process we use to detect the beginning of the triggers on the audio signal (UADC001) sets the trigger in the middle of the ramp between silence and the beep. We "over-compensate" the delay #1 by 1.7ms. This can be considered as constant delay of about -1.7ms.
• Uncorrected delays: We will correct for the delay #1, and keep the other delays (#2, #3 and #4). After we compensate for delay #1 our MEG signals will have a constant delay of about 4.9 + 1.7 - 1.7 = 4.9 ms. We decide not to compensate for th3se delays because they do not introduce any jitter in the responses and they are not going to change anything in the interpretation of the data.

Head shape and fiducial points

• 3D digitization using a Polhemus Fastrak device driven by Brainstorm (S01_20131218_*.pos)
• More information: Digitize EEG electrodes and head shape

• The output file is copied to each .ds folder and contains the following entries:

  • The position of the center of CTF coils
  • The position of the anatomical references we use in Brainstorm: Nasion and connections tragus/helix, as illustrated here.

• Around 150 head points distributed on the hard parts of the head (no soft tissues)

Subject anatomy

• Subject with 1.5T MRI
• Marker on the left cheek
• Processed with FreeSurfer 5.3

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

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.

Description: This dataset includes all bids received through the Flexibility Procurement Dynamic Purchasing System (DPS) Piclo Platform. It is crucial for stakeholders seeking insights into the current market liquidity of DSO Flexibility and researching average bid prices. This dataset complies with Ofgem Licence Condition C31E. Contents: Summary of bid prices offered Bid approval information Reasons for bid rejections Participating Flexibility Providers Purpose: The dataset aims to provide visibility and insight into the SPEN Flexibility Market. It enables users to research average accepted bid prices to inform future market bids. This is part of Licence Condition 31E, which requires DSO companies to publish all competitions and their outcomes after contracting a Flexibility Service. Source: Compiled by the DPS (Piclo) Platform. Date Range: Covers all competitions to date and is updated monthly. License Areas: Includes both SPM and SPD license areas. Granularity: Detailed on a monthly basis, specifying all competition data facilitated on the DPS (Piclo) platform.DisclaimerWhilst all reasonable care has been taken in the preparation of this data, SP Energy Networks does not accept any responsibility or liability for the accuracy or completeness of this data, and is not liable for any loss that may be attributed to the use of this data. For the avoidance of doubt, this data should not be used for safety critical purposes without the use of appropriate safety checks and services e.g. LineSearchBeforeUDig etc. Please raise any potential issues with the data which you have received via the feedback form available at the Feedback tab above (must be logged in to see this).Example Use CasesFlexibility Service Providers / Energy SuppliersFlexibility service providers could use the data to:Identify flexibility opportunities in the SPEN area by analyzing historical bid prices, participating providers, and the reasons behind accepted or rejected bids.Plan their service offerings and resource allocation based on specified MW capacity, locations, and past competition outcomes.Analyze market signals and trends to optimize their participation in future flexibility markets.Potential Platform ProvidersAs our third-party platform provider, DPS platofrms could use the data to:Provide detailed insights into upcoming flexibility procurement needsHelp FSPs plan and allocate their resources more effectively based on the latest bidding data.Consultancies / Competitor AnalysisConsultancies and competitors can leverage this data to:Identify flexibility opportunities in the SPEN area by analysing historical bid prices, participating providers, and the reasons behind accepted or rejected bids.Research service offerings and resource allocation for their clients based on information provided, such as specified MW capacity, locations, and past competition outcomes.Analyse market signals and trends to identify opportunities for their clients to optimise their knowledge and make informed decisions on DSO Flexibility markets.Data TriageAs part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process.Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Click here to access the Data Triage documentation for the Flexibility Bids, Competitions and Registered Assets dataset. To access our full suite of Data Triage documentation, visit the SP Energy Networks Data & Information.Download dataset metadata (JSON)

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

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

python phenotype.py aggregate subject -i segregated_subject -o aggregated_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.

PeriCBD was designed to establish a platform for the investigation of individual differences in brain-mind development associated with perinatal factors among children aged 3–10 years.

Adveritising data and real time bidding data from multiple screens (TV, mobile, and web) and detailed performance metrics that span impressions, clicks, geographic data, view-ability, and demographic targeting. Our dataset ensures high accuracy, derived from a proprietary advertising technology platform trusted by leading brands and agencies to deliver cross-platform campaigns.

This dataset includes key metrics from ad auctions, bids & wins such as: -impressions -geographic data -clicks -viewability -demographic targeting -click-through rates (CTR)

How is the data generally sourced?

This dataset is sourced from auction-level insights, tracking bids, wins, and performance metrics across major ad exchanges and programmatic platforms. Data collection adheres to strict compliance standards, ensuring transparency and reliability.

What are the primary use cases or verticals of this Data Product?

Primary use cases include:

Predictive analytics: Build models to forecast campaign success.

Audience segmentation: Create more personalized and targeted ad experiences.

Campaign optimization: Optimize ad placement, timing, and performance.

Ad personalization: Drive engagement by tailoring ads to demographic and geographic audiences.

Industries served include advertising, media, retail, and e-commerce, with applicability in both programmatic and direct ad placements.

Advertising Data is a key component of our comprehensive data suite, designed to empower companies and marketers with actionable insights. Enables a holistic view of the advertising ecosystem, helping clients achieve higher ROI and better campaign outcomes.

BIDS-formatted Magnetic Resonance Imaging mouse brain data example used in the SAMRI test suite.

Snapshot img

Real Time Bidding Market Size 2024-2028

The real time bidding market size is forecast to increase by USD 15.6 billion at a CAGR of 21.8% between 2023 and 2028.

Real-time bidding (RTB) is a dynamic online bidding process that enables advertisers to purchase ad inventory in real time through an auction. This market is witnessing significant growth due to the digital transformation and increasing participation of overseas buyers in e-commerce. However, the possibility of fraud in RTB is a major challenge. Demand-Side Platforms (DSPs) and Supply-Side Platforms (SSPs) play crucial roles in this process, facilitating programmatic buying through the exchange of cookie data. Mobile games are a significant sector for RTB, as they offer a large and engaged user base. Advertisers leverage DSPs to target specific audiences, while SSPs provide inventory from various sources. RTB's auction-based model ensures efficient ad placement and maximizes returns for both buyers and sellers. This streamlined process is essential for businesses looking to effectively reach their target audience in today's digital marketplace.

What will be the Size of the Market During the Forecast Period?

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Real-Time Bidding (RTB) is a programmatic advertising technology that revolutionizes the way advertisers purchase online ad impressions. This method enables automated, real-time auctions for online ad inventory, allowing advertisers to place bids on individual impressions based on specific targeting criteria. In the context of e-commerce and digital media, RTB is increasingly popular among advertisers due to its efficiency and precision. The DSP represents the advertisers and their advertising campaigns, while the SSP manages the inventory of online content available for auction. The automated auction process is initiated when an ad impression becomes available on a website or mobile application. The SSP sends a bid request to multiple DSPs, which then use computer-based algorithms to evaluate the potential value of the impression based on the advertiser's targeting criteria. Advertisers can employ various strategies, such as open auctions, private auctions, or hybrid auction models, to participate in the bidding process. In an open auction, all DSPs can bid on the impression, while private auctions limit participation to selected DSPs. The hybrid auction model combines elements of both open and private auctions. Once the bidding process concludes, the highest bidder secures the ad impression. This real-time, automated process ensures that advertisers reach their target audience efficiently and effectively, minimizing media wastage and optimizing campaign performance.



Moreover, RTB is not limited to desktop websites; it is also applicable to mobile applications and mobile games. This versatility makes RTB an essential tool for advertisers seeking to engage with consumers across various digital platforms. In summary, Real-Time Bidding (RTB) is a game-changing technology in digital advertising. It enables automated, real-time auctions for online ad inventory, allowing advertisers to target their desired audience with precision and efficiency. The use of computer-based algorithms, open and private auctions, and hybrid models ensures that advertisers optimize their digital ad spend and minimize media wastage. RTB is applicable to various digital platforms, including websites, mobile applications, and mobile games. Advertisers can leverage cookie data and demographics to target their desired audience, reducing media wastage and optimizing digital ad spend. The RTB process begins with a demand-side platform (DSP) and a supply-side platform (SSP).

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Open auction
  Invitation-only auction


Geography

  North America

    US


  APAC

    China
    Japan


  Europe

    Germany
    UK


  South America



  Middle East and Africa

By Type Insights

The open auction segment is estimated to witness significant growth during the forecast period.

Real-time Bidding (RTB) refers to the automated process of buying online advertising inventory in real time through an auction held on a Demand-Side Platform (DSP) or Supply-Side Platform (SSP). In this marketplace, advertisers bid on impressions for specific audiences based on cookie data and other demographic information. Open auctions, where companies allow multiple bidders to participate, accounted for the largest share of the global RTB market in 2023. However, other categories, such as private marketplaces and programmatic direct, are anticipated to gain more traction in the future, pot

OMEGA - Resting State Sample Dataset

License

• This dataset was obtained from The Open MEG Archive (OMEGA, https://omega.bic.mni.mcgill.ca).

• You are free to use all data in OMEGA for research purposes; please acknowledge its authors and cite the following reference in your publications if you have used data from OMEGA:

• Niso G., Rogers C., Moreau J.T., Chen L.Y., Madjar C., Das S., Bock E., Tadel F., Evans A.C., Jolicoeur P., Baillet S. (2016). OMEGA: The Open MEG Archive. NeuroImage 124, 1182-1187. doi: https://doi.org/10.1016/j.neuroimage.2015.04.028. OMEGA is available at: https://omega.bic.mni.mcgill.ca

Description

Experiment

• 5 subjects x 5 minute resting sessions, eyes open

MEG acquisition

• Recorded at the Montreal Neurological Institute in 2012-2016
• Acquisition with CTF 275 MEG system at 2400Hz sampling rate
• Anti-aliasing low-pass filter at 600Hz, files may be saved with or without the CTF 3rd order gradient compensation
• Recorded channels (at least 297), include:
  • 26 MEG reference sensors (#2-#27)
  • 270 MEG axial gradiometers (#28-#297)
  • 1 ECG bipolar (EEG057/#298) - Not available in the empty room recordings
  • 1 vertical EOG bipolar (EEG058/#299) - Not available in the empty room recordings
  • 1 horizontal EOG bipolar (EEG059/#300) - Not available in the empty room recordings

Head shape and fiducial points

• 3D digitization using a Polhemus Fastrak device driven by Brainstorm. The .pos files contain:
  • The center of the CTF coils
  • The anatomical references we use in Brainstorm: nasion and ears as illustrated here
  • Around 100 head points distributed on the hard parts of the head (no soft tissues).

Subject anatomy

• Structural T1 image (defaced for anonymization purposes)
• Processed with FreeSurfer 5.3
• The anatomical fiducials (NAS, LPA, RPA) have already been marked and saved in the files fiducials.m

BIDS

• The data in this dataset has been organized according to the MEG-BIDS specification (Brain Imaging Data Structure, http://bids.neuroimaging.io) (Niso et al. 2018)

• Niso G., Gorgolewski K.J., Bock E., Brooks T.L., Flandin G., Gramfort A., Henson R.N., Jas M., Litvak V., Moreau J., Oostenveld R., Schoffelen J.M., Tadel F., Wexler J., Baillet S. (2018). MEG-BIDS: an extension to the Brain Imaging Data Structure for magnetoencephalography. Scientific Data; 5, 180110. https://doi.org/10.1038/sdata.2018.110

Release history:

• 2016-12-01: initial release
• 2018-07-18: release OpenNeuro ds000247 (00001 and 00002)

The "Flexibility Bids" dataset includes all bids received through the Flexibility Procurement Dynamic Purchasing System (DPS) Piclo Platform. It is crucial for stakeholders seeking insights into the current market liquidity of DSO Flexibility and researching average bid prices. This dataset complies with Ofgem Licence Condition C31E.Contents:Summary of bid prices offeredBid approval informationReasons for bid rejectionsParticipating Flexibility ProvidersPurpose: The dataset aims to provide visibility and insight into the SPEN Flexibility Market. It enables users to research average accepted bid prices to inform future market bids. This is part of Licence Condition 31E, which requires DSO companies to publish all competitions and their outcomes after contracting a Flexibility Service. DisclaimerWhilst all reasonable care has been taken in the preparation of this data, SP Energy Networks does not accept any responsibility or liability for the accuracy or completeness of this data, and is not liable for any loss that may be attributed to the use of this data. For the avoidance of doubt, this data should not be used for safety critical purposes without the use of appropriate safety checks and services e.g. LineSearchBeforeUDig etc. Please raise any potential issues with the data which you have received via the feedback form available at the Feedback tab above (must be logged in to see this).Example Use CasesFlexibility Service Providers / Energy SuppliersFlexibility service providers could use the data to:Identify flexibility opportunities in the SPEN area by analyzing historical bid prices, participating providers, and the reasons behind accepted or rejected bids.Plan their service offerings and resource allocation based on specified MW capacity, locations, and past competition outcomes.Analyze market signals and trends to optimize their participation in future flexibility markets.Potential Platform ProvidersAs our third-party platform provider, DPS platofrms could use the data to:Provide detailed insights into upcoming flexibility procurement needsHelp FSPs plan and allocate their resources more effectively based on the latest bidding data.Consultancies / Competitor AnalysisConsultancies and competitors can leverage this data to:Identify flexibility opportunities in the SPEN area by analysing historical bid prices, participating providers, and the reasons behind accepted or rejected bids.Research service offerings and resource allocation for their clients based on information provided, such as specified MW capacity, locations, and past competition outcomes.Analyse market signals and trends to identify opportunities for their clients to optimise their knowledge and make informed decisions on DSO Flexibility markets.Data TriageAs part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process.Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Click here to access the Data Triage documentation for the Flexibility Bids, Competitions and Registered Assets dataset. To access our full suite of Data Triage documentation, visit the SP Energy Networks Data & Information.Download dataset metadata (JSON)

This data set is generated for the purpose of representing Turkish Electricity Day Ahead Market bidding data. The sample consists of 25 single-day bidding data for hourly, block and flexible bids. Along with the data set, a description of the data generation procedure is also provided.

Example subject in BIDS for use with babyAFQ. Includes tractography and other derivatives generated by MRtrix.

Includes minimally preprocessed and co-registered dataset for example subject containing both diffusion weighted and T1 weighted MR images, both in BIDS format.

The dataset in the root directory (i.e. starting with /sub-) should be used as input to many end-to-end pipelines.

The dataset in the preprocessed directory (i.e. starting with /derivatives/preproc/) should be used as input to modelling pipelines such as tractometry or connectivity analysis.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

BIDS example with HED-SCORE schema library annotations

The HED schema library for the Standardized Computer-based Organized Reporting of EEG (SCORE) can be used to add annotations for BIDS datasets. The annotations are machine readable and validated with the BIDS and HED validators.

This example is related to the following preprint: Dora Hermes, Tal Pal Attia, Sándor Beniczky, Jorge Bosch-Bayard, Arnaud Delorme, Brian Nils Lundstrom, Christine Rogers, Stefan Rampp, Seyed Yahya Shirazi, Dung Truong, Pedro Valdes-Sosa, Greg Worrell, Scott Makeig, Kay Robbins. Hierarchical Event Descriptor library schema for EEG data annotation. arXiv preprint arXiv:2310.15173. 2024 Oct 27.

General information

This BIDS example dataset includes iEEG data from one subject that were measured during clinical photic stimulation. Intracranial EEG data were collected at Mayo Clinic Rochester, MN under IRB#: 15-006530.

Events

The events are annotated according to the HED-SCORE schema library. Data are annotated by adding a column for annotations in the _events.tsv. The levels and annotations in this column are defined in the _events.json sidecar as HED tags.

More information

HED: https://www.hedtags.org/ HED schema library for SCORE: https://github.com/hed-standard/hed-schema-library

Contact

Dora Hermes: hermes.dora@mayo.edu

Analysis of ‘Example Dataset for A/B Test’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ilkeryildiz/example-dataset-for-ab-test on 14 February 2022.

--- Dataset description provided by original source is as follows ---

A company recently introduced a new bidding type, “average bidding”, as an alternative to its exisiting bidding type, called “maximum bidding”. One of our clients, ....com, has decided to test this new feature and wants to conduct an A/B test to understand if average bidding brings more conversions than maximum bidding.

The A/B test has run for 1 month and ....com now expects you to analyze and present the results of this A/B test.

--- Original source retains full ownership of the source dataset ---