This data was collected by St. Louis County Board of Elections. It is part of a larger collection (Historical St. Louis County Elections), organized by municipality. Faculty in the Department of Political Science at Washington University in St. Louis, Dr. Brian Crisp and Dr. Matt Gabel, digitized the materials at Washington University in St. Louis and agreed with St. Louis County to have the digital copies deposited in the Open Scholarship Digital Research Materials Repository at Washington University to make it more widely accessible.

These 120 MRI datasets are being released to the public along as part of the materials for “Temporal interpolation alters motion in fMRI scans: magnitudes and consequences for artifact detection” by Power et al. in PLOS ONE.

Included for each subject is a T1-weighted anatomical image (MP-RAGE) and one or more T2*-weighted scans (resting state BOLD scans)

All subjects - were “typical” young adults that reported no significant neurological or psychiatric history - were right-handed and reported that English was their first language - were scanned at Washington University in Saint Louis on a Siemens MAGNETOM Tim Trio 3T scanner with a Siemens 12-channel head coil - were scanned using interleaved ascending product sequences for T2* data - were scanned in the eyes-open resting state fixating a white crosshair on a black background

The data have been described in multiple publications from the Petersen/Schlaggar group, - beginning with Power et al., 2013 “Evidence for hubs in human brain networks” in Neuron - and most comprehensively in Power et al., 2014 “Methods to detect, characterize, and remove motion artifact in resting state fMRI” in Neuroimage - as well as several other publications - see these publications for further details on acquisitions and demographics

Becky Coalson of the Petersen/Schlaggar group collated these scans and de-identified them for public release - the accompanying file “WU120_subject_information.txt” contains for each subject - the release subject number (1-120) - the same number used in the present publication - the subject number used in Power et al., 2014 - see the WU120_Supplememtal_Cohort_Illustration.pdf from Power et al., 2014 - the subject number to publicly reference the subject - for question/communication purposes with Becky or the Petersen/Schlaggar group - the owner/contributor of the data - age of subject at scanning - sex - handedness - English only speaker - paradigm of the resting state scans - total scan time in resting state - number of rest runs - number of volumes per run - TR of the runs - scanner used (Siemens Tim Trio was in bay3) - slice order (AFNI convention); sequences were ascending interleaved

If there are questions about the scans, please contact Becky Coalson (becky@npg.wustl.edu) or contact the Petersen/Schlaggar group via their website.

JDP 12/23/15

Comments added by Openfmri Curators

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General Comments

Defacing

Pydeface was used on all anatomical images to ensure deindentification of subjects. The code can be found at https://github.com/poldracklab/pydeface

Quality Control

Mriqc was run on the dataset. Results are located in derivatives/mriqc. Learn more about it here: https://mriqc.readthedocs.io/en/latest/

Where to discuss the dataset

1) www.openfmri.org/dataset/ds000243/ See the comments section at the bottom of the dataset page. 2) www.neurostars.org Please tag any discussion topics with the tags openfmri and ds000243. 3) Send an email to submissions@openfmri.org. Please include the accession number in your email.

Known Issues

• As per one of the published paper explaining the dataset, its is noted that 2 subjects out of all have different TR for functional runs. However, when checked with submitter, they confirmed that all subjects data shared here have same TR.

This binary dataset is based on “WUSTL-IIoT-2021: A New Dataset for Industrial IoT Intrusion Detection Systems” (Zolanvari et al., 2021), originally published on IEEE DataPort (https://doi.org/10.21227/h5c2-dq55) under a CC BY 4.0 license and also at "https://www.cse.wustl.edu/~jain/iiot2/index.html".

This version is fr binary classification of the IIoT traffic flows as attacks or not.

It includes

1. The original Dataset.

2. The corrected dataset In the original release, the IdleTime column recorded the exact end time of the last occurrence of the same flow, rather than indicating the time gap between the current flow's start time and the previous occurrence's end time. The correction ensures that IdleTime now accurately reflects this intended temporal relationship, thereby improving the consistency and reliability of the time-based features for subsequent machine learning analysis.

3. The unbalanced train data and the test dataset are derived from the corrected dataset.

4. The balanced train dataset using SMOTE, ENN, & LOF.

This data was collected by St. Louis County Board of Elections. It is part of a larger collection (Historical St. Louis County Elections), organized by municipality. Faculty in the Department of Political Science at Washington University in St. Louis, Dr. Brian Crisp and Dr. Matt Gabel, digitized the materials at Washington University in St. Louis and agreed with St. Louis County to have the digital copies deposited in the Open Scholarship Digital Research Materials Repository at Washington University to make it more widely accessible.

Explore the historical Whois records related to wustl.edu (Domain). Get insights into ownership history and changes over time.

to perform real-world IIoT operations

This data was collected by St. Louis County Board of Elections. It is part of a larger collection (Historical St. Louis County Elections), organized by municipality. Faculty in the Department of Political Science at Washington University in St. Louis, Dr. Brian Crisp and Dr. Matt Gabel, digitized the materials at Washington University in St. Louis and agreed with St. Louis County to have the digital copies deposited in the Open Scholarship Digital Research Materials Repository at Washington University to make it more widely accessible.

SustainabilityLabIITGN/WUSTL dataset hosted on Hugging Face and contributed by the HF Datasets community

Explore historical ownership and registration records by performing a reverse Whois lookup for the email address panyiming@wustl.edu..

This data is comprised of U.S. Census tracts for the year 2019 with data from the American Community Survey, CDC social vulnerability index, CDC Places EPA toxic release inventory sites, PM2.5 annual average from the Atmospheric Composition Analysis Group (https://sites.wustl.edu/acag/). This dataset was created as part of the CAFE Introduction to QGIS 101!!! Session on 6/27/2024 and is for training purposes only.

Replication materials for Butler and Pereira "How does Partisanship Influence Policy Diffusion?", Political Research Quarterly. ReadMe.txt provides a description of the datasets and the variables used in the analyses. For any questions, contact me at m.pereira@wustl.edu. Thank you for your interest!

Preprocessed databases for use with the Hecatomb pipeline for viral and phage sequence annotation.

Replication materials for Pereira and Waterbury "Do Voters Discount Political Scandals Over Time?". ReadMe.txt provides a description of the datasets and the variables used in the analyses. For any questions, please contact me at m.pereira@wustl.edu.

This dataverse contains the data and supporting documents for the CES 2020 Team Module of Washington University in St. Louis. This project was supported by the National Science Foundation, Grant Number SES-1948863.

This dataset is the result of studies conducted during phase one (NSF-funded) of the Realities of Academic Data Sharing (RADS) Initiative, based out of the Association of Research Libraries. Studies were conducted with federally-funded researchers and institutional administrators who support data sharing practices within their department or unit at the following institutions: Cornell University, Duke University, University of Michigan, University of Minnesota, Virginia Tech, and Washington University in St. Louis. The 2022 RADS studies were retrospective, investigating data sharing and management activities and support services from 2013 to 2022. Two surveys were utilized to collect data, the Institutional Infrastructure Survey for administrators and the Researcher Survey for federally-funded researchers. This dataset presents data from both of these surveys. Project website: https://www.arl.org/realities-of-academic-data-sharing-rads-initiative/

The WUSTL-EHMS-2020 dataset was created using a real-time Enhanced Healthcare Monitoring System (EHMS) testbed [1]. This testbed collects both the network flow metrics and patients' biometrics due to the scarcity of a dataset that combines these biometrics.

Introduction

This dataset contains a small subset of the task fMRI runs collected in the ongoing Dual Mechanisms of Cognitive Control project (DMCC, http://sites.wustl.edu/dualmechanisms/), intended for use as a benchmark dataset for comparing preprocessing pipelines or other analytic procedures. We have carried out comparisons of preprocessing with fMRIPrep and the HCP Pipelines using this dataset, described in a series of posts beginning with http://mvpa.blogspot.com/2019/01/comparing-fmriprep-and-hcp-pipelines.html,poster Th580 at OHBM 2019 (https://ww5.aievolution.com/hbm1901/index.cfm?do=abs.viewAbs&abs=3941), and OHBM 2020 posters 1930 (https://github.com/datalad-datasets/ohbm2020-posters/files/4827285/OHBM2020_Poster1930_Etzel_b.pdf) and 644 (https://cdn-akamai.6connex.com/645/1827//BraverDMCC_OHBM2020_poster_15922356197908355.pdf). Preprocessed versions of the images will be included under derivatives, for both fMRIprep (version 1.3.2) and HCP (version 3.17.0; this version used Freesurfer alignment, not MSMSulc).

For pipeline comparison/analysis testing we believe it is best to have well-understood analysis targets; "positive control" tasks in which it is possible to predict the direction and location of task-related BOLD activity differences. Primary motor and visual cortices have most often been used as controls (e.g., a contrast of finger button press events should show focal activation in the motor cortex), and are especially appropriate when visual or motor responses are of high experimental interest. However, primary sensory cortices do not share the expected activity profile (or physical location) with higher-order cognitive tasks (e.g., primary motor cortex often has higher SNR than prefrontal cortices), and so may be less useful for benchmarking other systems; positive control tasks targeting a wider array of brain areas and cognitive processes are needed.

We propose that contrasting task conditions requiring "high" and "low" cognitive control/effort can serve as such a positive control task: frontoparietal areas should have high > low in BOLD signal. Many task paradigms can be seen as containing high and low cognitive control conditions. We use four here, since they make up our DMCC project. It is certainly not the case that these are the only tasks that could be used for benchmarking (e.g., N-back should also work well), merely that these are the data we have available. It is appealing to use multiple tasks, each of which involves somewhat different stimuli, responses, and timing, as we want to avoid tying any conclusions about preprocessing effectiveness to a particular task. We do not expect identical activation patterns from every task, even for this simple high > low contrast, but the brain areas in each should be broadly similar.

Note that additional control analyses can be carried out with this dataset by contrasting every trial against baseline (i.e., all onset times listed in the _events.tsv, even n/a) for a "task" vs. "not-task" contrast, which should show activity in the visual, motor, as well as cognitive control areas. The contrast "button-push" vs. "not-button-push" is also possible for Cuedts and Stern (add the response_time to the onset in _events.tsv for the time of the button push events), but not Stroop (verbal, rather than button-press responses) nor Axcpt (multiple button-press responses required per trial).

Please contact Jo Etzel (jetzel@wustl.edu) or Todd Braver (tbraver@wustl.edu) with questions and comments, including about access to additional data.

Acquisition and Analysis Notes

The 13 participants included here are unrelated. Nine of them were participants in the Young Adult HCP project (https://www.humanconnectome.org/study/hcp-young-adult); the subject key mapping the subject IDs used here to the HCP IDs is in the HCP ConnectomeDB, titled "DMCC (Dual Mechanisms of Cognitive Control) subject key".

All scans were collected on a 3T Siemens Prisma with a 32-channel head coil, without in-plane acceleration (iPat = none). CMRR multiband sequences were used, multiband factor 4, 2.4 mm isotropic voxels, 1.2 s TR. The physio recordings were made with Siemens equipment (finger plethysmograph and respiration belt); the recordings were extracted using https://github.com/CMRR-C2P/MB/blob/master/readCMRRPhysio.m then converted to plain text and reformatted for BIDS; no filtering or other processing was performed. All recordings we have are included here (several are missing due to equipment or software errors), regardless of quality or number of channels; they should be examined carefully (including onset and trigger times), as the signal clarity varies substantially between participants and runs.

Functional runs were collected with both A to P and P to A encoding directions ("AP" and "PA", respectively, included in the acq name fields); run 1 is always the first run of each task each session and is AP; the second (run 2) is always PA. The two runs of each task (except Rest) were performed together (i.e., Stroop run 1 is always followed by Stroop run 2), but the order of the four tasks was randomly assigned to each person (i.e., one participant might complete Stroop first, then Cuedts, then Axcpt, and finally Sternberg; another participant might complete Sternberg first). There are two resting state runs each session, each of which is 5 minutes long (for 10 minutes total per session). Most often the first (AP) resting state run was before any task runs (but after the anatomical), and the second (PA) after two tasks had been completed. This separation of approximately 45 minutes is unlike the task runs (which were always temporally adjacent) and was done in the hope of reducing participant fatigue.

Each task scanning run followed a mixed, block/event-related format (e.g., Petersen & Dubis, 2011 Neuroimage), in which each of three task blocks was preceded and followed by a 30 second fixation block. Each task block lasted approximately 180 seconds, with the task trials within each separated by varying inter-trial intervals to facilitate event-related estimation. The standard DMCC GLMs (results of which were used for our fMRIPrep and HCP pipeline comparisons) model both the event and block (sustained) effects. Briefly, event-related analyses were carried out with an FIR-type estimation approach using AFNI software (TENTzero function), with a “knot” (beta coefficient) estimated for every 2 TRs. The number of estimated knots varied across the different tasks, following each task’s trial duration. It is of course not necessary to model both the blocks and events (e.g., modeling the events only with a canonical HRF may work well, particularly for short events such as the button presses), but these are the GLMs of most interest for the DMCC project.

The full DMCC protocol contains three scanning sessions (baseline, proactive, reactive), with task details varying somewhat between the sessions. This DMCC13benchmark dataset contains task runs from the first (baseline) session only (“bas” in the ses- part of the BIDS filenames), so the baseline versions of each task are briefly described here. We defined a particular condition and part of each task as requiring "high" and "low" cognitive control demands, as described below. Note that only a subset of trials in each task are labeled as “high” or “low” for control purposes. Given our TENT modeling of the events, we defined the time during each trial at which we expect maximal high > low activation difference, listed as the “target knot”. These timings can be ignored or converted to seconds (1 TR is 1.2 s) as appropriate if other event modeling is used (e.g., canonical HRF or boxcar averaging).

Task Description: AX-CPT (Axcpt)

The baseline version of the DMCC AX-CPT task (http://sites.wustl.edu/dualmechanisms/axcpt-task) uses letter stimuli (press button 2 only if the current letter is X and the previous letter was A, press button 1 in all other cases and stimuli), with numbers as a no-go condition (i.e., withhold a button press to these stimuli). We consider "BX" trials (first letter not A, but second X) as requiring "high" cognitive control and "BY" trials (neither first nor second letter indicate a target response) as "low". Target knot for high > low is 4, 8 TRs after trial onset.

Task Description: Cued Task-Switching (Cuedts)

The DMCC Cued task-switching task (http://sites.wustl.edu/dualmechanisms/task-switching) included trial pre-cues that indicated to either "Attend Number" or "Attend Letter" as the task for the upcoming target. Target stimuli were composed of a letter-digit pair presented side-by-side. If the cue was "Attend Number", the task was to make an odd/even discrimination (press button 1 if even, button 2 if odd). If the cue was "Attend Letter”, the task was to make a vowel/consonant discrimination (press button 1 if vowel, button 2 if consonant). All baseline trials were performed without reward or punishment incentives. Incongruent trials (those in which the stimulus combination requires different responses, depending on whether it is Letter or Number task; e.g., A 1 or B 2) are considered "high"; congruent trials (the stimulus combination would lead to the same response irrespective of the task; e.g., A 2 or B 1) are considered "low". Target knot for high > low is 4, 8 TRs after trial onset.

Task Description: Sternberg (Stern)

The DMCC (http://sites.wustl.edu/dualmechanisms/sternberg-task) uses the "recent probes" variant of the Sternberg task (Jonides & Nee 2006, PMID: 16337090, DOI: 10.1016/j.neuroscience.2005.06.042). On each trial, a memory set consisting of a word list (ranging from 5 to 8 items) is presented, followed by a short delay period (retention interval), and then a

Presented here is a dataset used for our SCADA cybersecurity research. The dataset was built using our SCADA system testbed described in our paper below [*]. The purpose of our testbed was to emulate real-world industrial systems closely. It allowed us to carry out realistic cyber-attacks.

This data was collected by St. Louis County Board of Elections. It is part of a larger collection (Historical St. Louis County Elections), organized by municipality. Faculty in the Department of Political Science at Washington University in St. Louis, Dr. Brian Crisp and Dr. Matt Gabel, digitized the materials at Washington University in St. Louis and agreed with St. Louis County to have the digital copies deposited in the Open Scholarship Digital Research Materials Repository at Washington University to make it more widely accessible.

The Western Washington University Insect Collection houses approximately 75,000 insect specimens. Our geographic area of focus is the northwestern portion of the contiguous United States. Most of our specimens are from the northwestern portion of Washington State, an area that is poorly represented in other collections in the region. The collection is the largest publicly-held insect collection in populous western Washington.