The bid tabulations provided herein are preliminary and are for informational purposes only. The purpose of providing this preliminary information is to improve process transparency. The information contained in the preliminary bid tabulations is subject to change pending math review, analysis of all bids, and review of documentation provided. Bids: All bids estimated at $25,000 or more are publicly opened and read at the time, date, and place specified in the bid document. RFP/RFQ/RFI: Responses to RFP/RFQ/RFI are not publicly opened. Names of respondents will be made available based on the names identified on the sealed package and is, therefore, subject to change. For bid tabulations before June 2016 see https://datacatalog.cookcountyil.gov/d/pn38-yupm

The SLO_BIDDING_DATA dataset includes data regarding tendered bids, accepted bids and price of capacity bids (monthly aggregates). It is intended for market players and system operators to follow the prices on the market and amount of purchased flexibility.

Two data sets in the CIM XML format are available with information about bidding for DSOs. One document is covering the process type "congestion management", the other for "voltage control". Due to CGMES being TSO oriented, some attributes are missing to fully describe DSO processes. Therefore we had to extend the attribute "processType" to cover DSO needs.

Addition information about the Slovenian demo is available in the OneNet 10.4 deliverable (OneNet_D10.4_V1.0.pdf (onenet-project.eu)).

XSD is compliant with ReserveBid_Document defined by ENTSO-E (Reserve bid document UML model and schema (entsoe.eu)).

This is an archive of bid tabulations before June 2016. For current bid tabulations see https://datacatalog.cookcountyil.gov/d/32au-zaqn. The bid tabulations provided herein are preliminary and are for informational purposes only. The purpose of providing this preliminary information is to improve process transparency. The information contained in the preliminary bid tabulations is subject to change pending math review, analysis of all bids, and review of documentation provided. Bids: All bids estimated at $25,000 or more are publicly opened and read at the time, date, and place specified in the bid document. RFP/RFQ/RFI: Responses to RFP/RFQ/RFI are not publicly opened. Names of respondents will be made available based on the names identified on the sealed package and is, therefore, subject to change.

The "Competitions, Bids & Dispatch Data" data table contains information of the competitions facilitated, bids received and dispatch instructions from the DPS (Piclo) platform. The table gives the following information:MWH tendered per licence areaMWH contracted per licence areaMWH dispatched per licence area For additional information on column definitions, please click the Dataset schema link below. 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).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)

Bid Management Software for General Contractors Market size was valued at USD 769.57 Million in 2023 and is projected to reach USD 1515.59 Million by 2030, growing at a CAGR of 11.98% during the forecast period 2024-2030.Global Bid Management Software for General Contractors Market DriversThe market drivers for the Bid Management Software for General Contractors Market can be influenced by various factors. These may include:Efficiency and Time Savings: By streamlining the entire bidding process and eliminating manual labor, paperwork, and administrative duties, bid management software helps general contractors save time and run more efficiently.Increased Precision and Accuracy: By providing tools for accurate cost estimation, bid generation, and project documentation, these software programs reduce mistakes and raise the precision of contractor bids.Improved Cooperation and Communication: By offering centralized platforms for communication, document sharing, and real-time updates, bid management software promotes improved coordination during the bidding process among project teams, suppliers, and subcontractors.Competitive Advantage: By presenting polished and thorough bids, exhibiting their expertise, and increasing their chances of getting contracts in a competitive market, general contractors who use bid management software get an advantage over their rivals.Regulatory Compliance: Software that guarantees adherence to industry-specific norms and standards makes it easier to comply with regulatory standards, documentation requirements, and legal components of the bidding process.Improved Bid Success Rate: The software's capacity to produce thorough, accurate, and expert bids raises the likelihood of obtaining contracts, which improves the success rate of landing projects.

The "Flexibility Bids" data table includes all bids received through the Flexibility Procurement Dynamic Purchasing System (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.This table gives the following information:Summary of bid prices offeredBid approval informationReasons for bid rejections (if rejected)Participating Flexibility ProvidersFor additional information on column definitions, please click the Dataset schema link below. 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).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)

The bid tabulations provided herein are preliminary and are for informational purposes only. The purpose of providing this preliminary information is to improve process transparency. The information contained in the preliminary bid tabulations is subject to change pending math review, analysis of all bids, and review of documentation provided. Bids: All bids estimated at $25,000 or more are publicly opened and read at the time, date, and place specified in the bid document. RFP/RFQ/RFI: Responses to RFP/RFQ/RFI are not publicly opened. Names of respondents will be made available based on the names identified on the sealed package and is, therefore, subject to change.

For bid tabulations before June 2016 see https://datacatalog.cookcountyil.gov/d/pn38-yupm

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

The bid tabulations listed below reflect grand totals only of the valid bids received. They may not reflect any added or deducted alternatives. Evaluations will be made by the project manager or his representative for correct price extensions, meeting specifications, etc. Once those specifications are completed, the department’s governing board or commission will award the bid to the lowest, responsive, responsible bidder meeting the bid’s specifications. The bids listed may only be the top three responsive bids. Please click on the bid number below to review the results from that particular bid.

Construction Bid Comparison Software Market Outlook

According to our latest research, the global Construction Bid Comparison Software market size reached USD 1.42 billion in 2024, reflecting robust demand across the construction sector. The market is anticipated to grow at a CAGR of 10.7% from 2025 to 2033, with the forecasted market value expected to reach USD 3.57 billion by 2033. This impressive growth trajectory is fueled by increasing digital transformation within the construction industry, rising project complexities, and the urgent need for streamlined bid management processes worldwide.

One of the primary growth factors driving the Construction Bid Comparison Software market is the rapid digitization of the construction sector. As companies face mounting pressures to deliver complex projects on time and within budget, the adoption of advanced software solutions has become crucial. These platforms enable contractors, project owners, and architects to efficiently manage, compare, and evaluate bids from multiple vendors, reducing manual errors and enhancing transparency. The integration of real-time analytics, cloud computing, and artificial intelligence within these solutions further enhances their value proposition, allowing stakeholders to make data-driven decisions that mitigate risks and improve project outcomes. This shift towards automation and digital workflows is expected to continue fueling market expansion, especially as construction firms seek to remain competitive in an increasingly technology-driven environment.

Another significant driver for the Construction Bid Comparison Software market is the growing emphasis on cost optimization and operational efficiency. With construction costs rising globally due to inflation, supply chain disruptions, and labor shortages, organizations are leveraging bid comparison tools to identify the most cost-effective suppliers and subcontractors. These platforms provide comprehensive visibility into pricing structures, scope of work, and vendor qualifications, enabling more accurate and fair bid evaluations. By standardizing the bid analysis process, construction firms can minimize procurement risks, avoid costly change orders, and ensure compliance with regulatory standards. As more organizations recognize the tangible benefits of these solutions, adoption rates are expected to surge, particularly among large enterprises managing multiple, high-value projects.

Furthermore, the increasing complexity of construction projects, especially in sectors such as infrastructure and commercial real estate, is compelling stakeholders to invest in robust bid management tools. The proliferation of multi-phase, multi-vendor projects necessitates a centralized platform to manage bid documentation, track revisions, and facilitate seamless communication among all parties involved. Modern bid comparison software offers customizable templates, automated workflows, and integration capabilities with other project management tools, making it easier for teams to collaborate and maintain audit trails. The demand for such features is particularly pronounced in regions experiencing rapid urbanization and infrastructure development, where the scale and scope of projects are expanding significantly.

In the context of the construction industry, Shift Bidding Software is becoming an increasingly valuable tool for managing labor resources efficiently. As construction projects grow in complexity and scale, the need for flexible and dynamic workforce management solutions becomes critical. Shift Bidding Software allows construction firms to optimize their labor allocation by enabling workers to bid for shifts based on availability and preference. This not only enhances worker satisfaction and productivity but also ensures that projects are staffed with the right skills at the right time. By leveraging this technology, construction companies can reduce labor costs, minimize scheduling conflicts, and improve overall project efficiency, making it a strategic asset in the competitive construction landscape.

From a regional perspective, North America currently dominates the Construction Bid Comparison Software market, accounting for the largest revenue share in 2024. The region's leadership is attributed to the presence of major construction firms, high technology adoption rates, and stringent regulatory

The bid tabulations provided herein are preliminary and are for informational purposes only. The purpose of providing this preliminary information is to improve process transparency. The information contained in the preliminary bid tabulations is subject to change pending math review, analysis of all bids, and review of documentation provided. Bids: All bids estimated at $25,000 or more are publicly opened and read at the time, date, and place specified in the bid document. RFP/RFQ/RFI: Responses to RFP/RFQ/RFI are not publicly opened. Names of respondents will be made available based on the names identified on the sealed package and is, therefore, subject to change.

For bid tabulations before June 2016 see https://datacatalog.cookcountyil.gov/d/pn38-yupm

The submitting agency SHALL be responsible for ensuring the accuracy of the bid document posting(s), closing time, delivery of bid, evaluation, awards and any other procurement documentation. The agency shall not waive any state or federal law or agency’s requirements on purchases.

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 Analytics Platform Market Outlook

According to our latest research, the global Construction Bid Analytics Platform market size reached USD 1.41 billion in 2024, with a robust growth trajectory expected over the coming years. The market is projected to grow at a CAGR of 13.7% from 2025 to 2033, reaching an estimated market value of USD 4.14 billion by 2033. The primary growth driver for this market is the increasing adoption of digital tools and analytics in the construction industry to enhance efficiency, reduce project costs, and streamline the bidding process. As per our most recent findings, the integration of advanced analytics and automation into construction bidding workflows is fundamentally transforming project management and procurement strategies globally.

The rapid digitization of the construction sector is a key catalyst for the expansion of the Construction Bid Analytics Platform market. As construction projects become larger and more complex, stakeholders are under mounting pressure to deliver projects on time and within budget. Bid analytics platforms provide data-driven insights that help contractors and project owners make informed decisions, optimize resource allocation, and mitigate risks. With the growing emphasis on cost efficiency and transparency, the demand for platforms capable of aggregating, analyzing, and visualizing bid data has surged. Additionally, the rise of Building Information Modeling (BIM) and integration with other digital construction tools is further accelerating the adoption of bid analytics solutions across the industry.

Another major growth factor is the increasing complexity of regulatory requirements and compliance standards in the construction industry. Governments and regulatory bodies worldwide are mandating more stringent documentation, reporting, and transparency in public and private sector construction projects. This has created a need for sophisticated analytics platforms that can not only streamline the bidding process but also ensure compliance with all relevant standards. By automating document management, bid tracking, and compliance reporting, these platforms enable stakeholders to reduce the risk of errors, avoid costly penalties, and enhance overall project governance. The ability of bid analytics platforms to provide real-time insights and historical data analysis is proving invaluable for both large enterprises and small and medium-sized enterprises (SMEs) seeking a competitive edge.

The proliferation of cloud computing and mobile technologies is also significantly contributing to the market's growth. Cloud-based construction bid analytics platforms offer scalability, flexibility, and remote access, making them particularly attractive for geographically dispersed project teams. These platforms facilitate real-time collaboration among contractors, subcontractors, architects, and project owners, regardless of their location. The surge in remote work trends and the need for seamless communication have further propelled the adoption of cloud-based solutions. As organizations increasingly prioritize digital transformation, the integration of artificial intelligence (AI), machine learning (ML), and predictive analytics into bid management workflows is becoming a key differentiator for market leaders.

From a regional perspective, North America currently dominates the Construction Bid Analytics Platform market, accounting for the largest share in 2024. This can be attributed to the early adoption of advanced construction technologies, a highly competitive construction sector, and robust investments in infrastructure development. Europe follows closely, driven by stringent regulatory frameworks and a strong focus on sustainability and efficiency in construction projects. The Asia Pacific region is anticipated to witness the fastest growth during the forecast period, fueled by rapid urbanization, increasing construction activities, and government initiatives to modernize infrastructure. Latin America and the Middle East & Africa are also expected to experience steady growth, supported by rising investments in large-scale infrastructure projects and the gradual shift towards digital construction solutions.

The emergence of a Construction eProcurement Platform is revolutionizing the way construction firms manage their proc

This dataset contains recaptured identity documents from the BID dataset, originally published in the paper "BID dataset: a challenge dataset for document processing tasks". The purpose of this dataset is to aid the domain of recaptured identity document detection.

This is version 1 of the dataset and it consists of three zip files - v1-screen-recaptures.zip. This file contains the identity documents recaptured from a computer monitor using two iPhone models (iphone8 and iPhone12) - v1-printed-document-recaptures.zip. This file contains the same identity documents are above, but printed using an inkjet and laser printer, and recaptured using two iPhone models (iphone8 and iPhone12) - v1-plastic-covered-recaptured.zip. Contains the same printed recaptures as above, but with a plastic cover used in an attempt to obscure surface details.

The filenames used here are the same used in the BID dataset.

Version 1 of this dataset is used in the research paper An Investigation into the Application of the Meijering Filter for Document Recapture Detection. Please cite version 1 as follows:

@inproceedings{magee_2023, location = {Manchester, UK}, title = {An Investigation into the Application of the Meijering Filter for Document Recapture Detection. 12th International Conference on Intelligent Information Processing (ICIIP 2023).}, isbn = {978-1-4244-4295-9}, eventtitle = {12th International Conference on Intelligent Information Processing (ICIIP 2023)}, author = {Magee, John, and Sheridan, Stephen and Thorpe, Christina.}, url = {https://arrow.tudublin.ie/scschcomcon/400/}, year={2023}, }

We present simulation results from a study with The Virtual Brain (TVB). Structural, functional and simulated data have been prepared in accordance with Brain Imaging Data Structure (BIDS) standards and annotated according to the openMINDS metadata framework. This simultaneous electroencephalography (EEG) - functional magnetic resonance imaging (fMRI) resting-state data, diffusion-weighted MRI (dwMRI), and structural MRI were acquired for 50 healthy adult subjects (18 - 80 years of age, mean 41.24±18.33; 31 females, 19 males) at the Berlin Center for Advanced Imaging, Charité University Medicine, Berlin, Germany. We constructed personalized models from this multimodal data of 50 healthy individuals with TVB. We calculated the optimal parameters on an individual basis that predict multiple empirical features in fMRI and EEG, e.g. dynamic functional connectivity and bimodality in the alpha band power, and analyzed inter-individual differences with respect to optimized parameters and structural as well as functional connectivity in a previous study (Triebkorn et al. 2024). We present this large comprehensive empirical and simulated data set in an annotated and structured format following the BIDS Extension Proposal for computational modeling data. We describe how we processed and converted the diverse data sources to make it reusable. In its current form, this dataset can be reused for further research and provides ready-to-use data at various levels of processing including the thereof inferred brain simulation results for a large data set of healthy subjects with a wide age range.

Documentation Scans

## Overview

Documentation Scans is a dataset for object detection tasks - it contains Tables annotations for 620 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

We disseminate a dataset comprising paired 3T and 7T MRI scans from 20 healthy volunteers, with manual hippocampal subfield annotations on 7T T2-weighted images. This dataset is designed to support the development and evaluation of both 3T-to-7T MR image synthesis models and automated hippocampal segmentation algorithms on 3T images. We assessed the image quality using MRIQC. The dataset is freely accessible on IEEE DataPort, a data repository created by IEEE and can be found at the following URL: https://ieeexplore.ieee.org/document/10218394/algorithms?tabFilter=dataset. The shared dataset comprises four principal directories.The first directory contains raw MRI data in .ima format within rawdata_DICOM. Additionally, the acquired MRI scans were converted from DICOM to the Neuroimaging Informatics Technology Initiative (NIfTI) format and organized in accordance with the Brain Imaging Data Structure (BIDS) format by employing the BIDScoin Python application (version 4.3.0) and stored in rawdata_BIDS directory.The third directory pertains to hippocampal subfield segmentation and includes two subdirectories: 'hippo_subfield\7T_T2w_0.7_for_subfield_delineation', featuring 7T T2w MRI data downsampled to a 0.7 mm slice thickness through B-spline interpolation, post Gaussian smoothing denoising and N4 bias field correction using Advanced Normalization Tools (ANTs); and 'hippo_subfield\hippo_label', which contains the manual segmentation labels for hippocampal subregions for each subject. The fourth directory, \MRIQC, designated for the results of quality control assessments. For each participant, the \MRIQC directories contain \anat and \func subdirectories, which hold image quality metric reports for T1w, T2w, and resting-state functional scans. These quality metrics, available in both .html and .json formats, aid in evaluating data quality and provide estimates of motion, signal-to-noise ratios, and intensity non-uniformities, supplemented with visual reports.It is noteworthy that, due to detectable head motion during the original scans, the 3T T2w images for two participants were subject to rescanning. Subsequently, only the datasets from these supplementary sessions have been preserved within the rawdata\BIDS directory for further quality evaluation. Additionally, Diffusion Weighted Imaging (DWI) sequences included in the rawdata\DICOM directory for 3T MRI were not matched with 7T MRI sequences and, thus, are excluded from the BIDS-formatted shared dataset.

Overview

This dataset contains raw and pre-processed EEG data from a mobile EEG study investigating the additive effects of task load, motor demand, and environmental complexity on attention. More details will be provided once the manuscript has passed peer-review.

All preprocessing and analysis code is deposited in the code directory. The entire MATLAB pipeline can be reproduced by executing the run_pipeline.m script. In order to run these scripts, you will need to ensure you have the required MATLAB toolboxes and R packages on your system. You will also need to adapt def_local.m to specify local paths to MATLAB and EEGLAB. Descriptive statistics and mixed-effects models can be reproduced in R by running the stat_analysis.R script.

See below for software details.

Citing this dataset

For more information, see the dataset_description.json file.

Format

Dataset is formatted according to the EEG-BIDS extension (Pernet et al., 2019) and the BIDS extension proposal for common electrophysiological derivatives (BEP021) v0.0.1, which can be found here:

https://docs.google.com/document/d/1PmcVs7vg7Th-cGC-UrX8rAhKUHIzOI-uIOh69_mvdlw/edit#heading=h.mqkmyp254xh6

Note that BEP021 is still a work in progress as of 2021-03-01.

Generally, you can find data in the .tsv files and descriptions in the accompanying .json files.

An important BIDS definition to consider is the "Inheritance Principle" (see 3.5 in the BIDS specification: http://bids.neuroimaging.io/bids_spec.pdf), which states:

Any metadata file (​.json​,​.bvec​,​.tsv​, etc.) may be defined at any directory level. The values from the top level are inherited by all lower levels unless they are overridden by a file at the lower level.

Details about the experiment

Forty-four healthy adults aged 18-40 performed an oddball task involving complex tone (piano and horn) stimuli in three settings: (1) sitting in a quiet room in the lab (LAB); (2) walking around a sports field (FIELD); (3) navigating a route through a university campus (CAMPUS).

Participants performed each environmental condition twice: once while attending to oddball stimuli (i.e. counting the number of presented deviant tones; COUNT), and once while disregarding or ignoring the tone stimuli (IGNORE).

EEG signals were recorded from 32 active electrodes using a Brain Vision LiveAmp 32 amplifier. See manuscript for further details.

MATLAB software details

MATLAB Version: 9.7.0.1319299 (R2019b) Update 5 MATLAB License Number: 678256 Operating System: Microsoft Windows 10 Enterprise Version 10.0 (Build 18363) Java Version: Java 1.8.0_202-b08 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode

• MATLAB (v9.7)
• Simulink (v10.0)
• Curve Fitting Toolbox (v3.5.10)
• DSP System Toolbox (v9.9)
• Image Processing Toolbox (v11.0)
• MATLAB Compiler (v7.1)
• MATLAB Compiler SDK (v6.7)
• Parallel Computing Toolbox (v7.1)
• Signal Processing Toolbox (v8.3)
• Statistics and Machine Learning Toolbox (v11.6)
• Symbolic Math Toolbox (v8.4)
• Wavelet Toolbox (v5.3)

The following toolboxes/helper functions were also used:

• EEGLAB (v2019.1)
• ERPLAB (v8.02)
• ICLabel (v1.3)
• clean_rawdata (v2.3)
• bids-matlab-tools (v5.2)
• dipfit (v3.4)
• firfilt (v2.4)
• export_fig (v3.12)
• ColorBrewer (v3.1.0)

R software details

R version 3.6.2 (2019-12-12)

Platform: x86_64-w64-mingw32/x64 (64-bit)

locale: _LC_COLLATE=English_Australia.1252_, _LC_CTYPE=English_Australia.1252_, _LC_MONETARY=English_Australia.1252_, _LC_NUMERIC=C_ and _LC_TIME=English_Australia.1252_

attached base packages:

• stats
• graphics
• grDevices
• utils
• datasets
• methods
• base

other attached packages:

• sjPlot(v.2.8.7)
• emmeans(v.1.5.1)
• car(v.3.0-10)
• carData(v.3.0-4)
• lme4(v.1.1-23)
• Matrix(v.1.2-18)
• data.table(v.1.13.0)
• forcats(v.0.5.0)
• stringr(v.1.4.0)
• dplyr(v.1.0.2)
• purrr(v.0.3.4)
• readr(v.1.4.0)
• tidyr(v.1.1.2)
• tibble(v.3.0.4)
• ggplot2(v.3.3.2)
• tidyverse(v.1.3.0)

loaded via a namespace (and not attached):

• nlme(v.3.1-149)
• pbkrtest(v.0.4-8.6)
• fs(v.1.5.0)
• lubridate(v.1.7.9)
• insight(v.0.12.0)
• httr(v.1.4.2)
• numDeriv(v.2016.8-1.1)
• tools(v.3.6.2)
• backports(v.1.1.10)
• utf8(v.1.1.4)
• R6(v.2.4.1)
• sjlabelled(v.1.1.7)
• DBI(v.1.1.0)
• colorspace(v.1.4-1)
• withr(v.2.3.0)
• tidyselect(v.1.1.0)
• curl(v.4.3)
• compiler(v.3.6.2)
• performance(v.0.5.0)
• cli(v.2.1.0)
• rvest(v.0.3.6)
• xml2(v.1.3.2)
• sandwich(v.3.0-0)
• labeling(v.0.3)
• bayestestR(v.0.7.2)
• scales(v.1.1.1)
• mvtnorm(v.1.1-1)
• digest(v.0.6.25)
• foreign(v.0.8-76)
• minqa(v.1.2.4)
• rio(v.0.5.16)
• pkgconfig(v.2.0.3)
• dbplyr(v.1.4.4)
• rlang(v.0.4.8)
• readxl(v.1.3.1)
• rstudioapi(v.0.11)
• farver(v.2.0.3)
• generics(v.0.0.2)
• zoo(v.1.8-8)
• jsonlite(v.1.7.1)
• zip(v.2.1.1)
• magrittr(v.1.5)
• parameters(v.0.8.6)
• Rcpp(v.1.0.5)
• munsell(v.0.5.0)
• fansi(v.0.4.1)
• abind(v.1.4-5)
• lifecycle(v.0.2.0)
• stringi(v.1.4.6)
• multcomp(v.1.4-14)
• MASS(v.7.3-53)
• plyr(v.1.8.6)
• grid(v.3.6.2)
• blob(v.1.2.1)
• parallel(v.3.6.2)
• sjmisc(v.2.8.6)
• crayon(v.1.3.4)
• lattice(v.0.20-41)
• ggeffects(v.0.16.0)
• haven(v.2.3.1)
• splines(v.3.6.2)
• pander(v.0.6.3)
• sjstats(v.0.18.1)
• hms(v.0.5.3)
• knitr(v.1.30)
• pillar(v.1.4.6)
• boot(v.1.3-25)
• estimability(v.1.3)
• effectsize(v.0.3.3)
• codetools(v.0.2-16)
• reprex(v.0.3.0)
• glue(v.1.4.2)
• modelr(v.0.1.8)
• vctrs(v.0.3.4)
• nloptr(v.1.2.2.2)
• cellranger(v.1.1.0)
• gtable(v.0.3.0)
• assertthat(v.0.2.1)
• xfun(v.0.18)
• openxlsx(v.4.2.2)
• xtable(v.1.8-4)
• broom(v.0.7.1)
• coda(v.0.19-4)
• survival(v.3.2-7)
• lmerTest(v.3.1-3)
• statmod(v.1.4.34)
• TH.data(v.1.0-10)
• ellipsis(v.0.3.1)

We present processed multimodal empirical data from a study with The Virtual Brain (TVB) based on this data. Structural and functional data have been prepared in accordance with Brain Imaging Data Structure (BIDS) standards and annotated according to the openMINDS metadata framework. This simultaneous electroencephalography (EEG) - functional magnetic resonance imaging (fMRI) resting-state data, diffusion-weighted MRI (dwMRI), and structural MRI were acquired for 50 healthy adult subjects (18 - 80 years of age, mean 41.24±18.33; 31 females, 19 males) at the Berlin Center for Advanced Imaging, Charité University Medicine, Berlin, Germany. We constructed personalized models from this multimodal data of 50 healthy individuals with TVB in a previous study (Triebkorn et al. 2024). We present this large comprehensive processed data set in an annotated and structured format following BIDS standards for derivatives of MRI and BIDS Extension Proposal for computational modeling data. We describe how we processed and converted the diverse data sources to make it reusable. In its current form, this dataset can be reused for further research and provides ready-to-use data at various levels of processing for a large data set of healthy subjects with a wide age range.