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.

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

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.

Event-related potential dataset (oddball) in BIDS-EEG format.

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.

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.

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?

Request Free Sample

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, potenti

Question 1.1.3a: Is the government required to set pre-defined criteria by which companies become qualified to participate in a licensing process?, 1.1.4b: From 2015 onwards, and prior to each licensing process, did the licensing authority actually disclose a list of biddable or negotiable terms?, 1.1.4a: From 2015 onwards, did the licensing authority publicly disclose minimum pre-defined criteria by which companies become qualified to participate in licensing processes?

Boutiques execution data-set

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

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

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.

This dataset contains a subset of the data that was collected looking at the inhibition of young amateur soccer players. All participants were male, with an average age of 16.4. Participants performed a stop signal task. The dataset contains anatomical and functional MRI images, and information about reaction times.

The bid–ask spread (also bid–offer or bid/ask and buy/sell in the case of a market maker) is the difference between the prices quoted (either by a single market maker or in a limit order book) for an immediate sale (ask) and an immediate purchase (bid) for stocks, futures contracts, options, or currency pairs in some auction scenario. The size of the bid–ask spread in a security is one measure of the liquidity of the market and of the size of the transaction cost.[1] If the spread is 0 then it is a frictionless asset.

eMaryland Marketplace Bids for Fiscal Year 2017 (July 1, 2016 through June 30, 2017)

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.

Construction Bid Management Software Market Outlook

According to our latest research, the global Construction Bid Management Software market size reached USD 1.72 billion in 2024, reflecting robust adoption across the construction sector. The market is experiencing a healthy growth trajectory, with a CAGR of 9.4% projected from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 3.96 billion, driven by increasing digitization, the need for streamlined bidding processes, and the growing complexity of construction projects worldwide. The surge in demand for integrated platforms that offer enhanced collaboration, real-time analytics, and improved risk management is a key growth factor for the market, as per our latest research findings.

One of the primary growth drivers for the Construction Bid Management Software market is the escalating complexity and scale of modern construction projects. As projects become larger and involve multiple stakeholders, the need for efficient bid management becomes indispensable. Companies are increasingly looking for solutions that can automate and centralize the bidding process, reduce manual errors, and ensure compliance with regulatory standards. The software enables project managers and contractors to handle a multitude of bids simultaneously, compare proposals efficiently, and select the best offers based on comprehensive data-driven insights. This not only accelerates project timelines but also significantly reduces administrative overhead, making it a compelling investment for construction firms globally.

Another significant factor contributing to market growth is the rising adoption of cloud-based solutions. Cloud technology has revolutionized bid management by providing remote access, scalability, and seamless collaboration among geographically dispersed teams. With cloud-based Construction Bid Management Software, stakeholders can access real-time updates, share documents securely, and track project progress from any location. The pandemic-induced shift towards remote working further accelerated cloud adoption, as organizations sought to maintain business continuity and collaboration without physical presence. Moreover, cloud deployment models offer cost-effectiveness and flexibility, making advanced bid management tools accessible to small and medium enterprises (SMEs) that previously relied on manual or semi-automated processes.

The growing emphasis on regulatory compliance and risk mitigation is also fueling the adoption of Construction Bid Management Software. Construction projects are increasingly subject to stringent regulatory requirements concerning documentation, transparency, and auditability. Bid management software solutions are equipped with features that ensure all bid submissions adhere to legal standards and project specifications, thereby minimizing the risk of disputes and litigation. Automated tracking and reporting capabilities help organizations maintain comprehensive records, facilitate audits, and demonstrate due diligence. As a result, both large enterprises and SMEs are prioritizing the deployment of these solutions to safeguard their operations and enhance stakeholder trust.

From a regional perspective, North America continues to dominate the Construction Bid Management Software market, accounting for the largest share in 2024. The region's leadership is attributed to the early adoption of digital technologies, a highly competitive construction industry, and the presence of major software providers. Europe and Asia Pacific are also witnessing rapid growth, with Asia Pacific projected to register the highest CAGR during the forecast period. The expansion of infrastructure projects, urbanization, and government initiatives to modernize construction practices are propelling the demand for bid management solutions in these regions. Latin America and the Middle East & Africa, while still emerging markets, are showing increasing interest as construction activities intensify and digital transformation gains momentum.

Component Analysis&l

A listing of open bid opportunities provided by the City of Los Angeles and available on the Regional Alliance Marketplace for Procurement, RAMP at https://www.rampla.org

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.

Business improvement district (BID) program/service output and expense data from FY18.

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.

Tick (Bids | Asks | Trades | Settle) sample data for NZ Bills(Pit) NBBP timestamped in Chicago time