Travis Masterson · Figshare · 2019
<b>Data supporting the paper: </b>Association between regional brain volumes and BMI z-score change over one year in children<br><b><br></b><b>Abstract:</b><b>Purpose: </b>Associations between brain region volume and weight status have been observed in children cross-sectionally. However, it is unclear if differences in brain region volume precede weight gain. <b>Methods: </b> Two high-quality structural brain images were obtained approximately one year apart in 65 children aged 9-12 years old. Children’s height and weight were also measured at each scan. Structural images were processed using the FreeSurfer software-package providing volume measures for regions of interest including the entorhinal cortex, nucleus accumbens, and hippocampus. Age- and sex-adjusted BMI z-scores (BMIz) were calculated at both timepoints. The association between brain region volume and BMIz was examined cross-sectionally using linear regression and longitudinally using structural equation modeling. All models were adjusted by estimated cranial volume to account for individual variation in head size and were corrected for multiple comparisons (pFDR<0.05). <b>Results</b>: The sample of children was primarily healthy weight at baseline (79.78%). Cross-sectionally at the one-year follow-up, a positive relationship was observed between right hippocampal volume and BMIz ( ). Longitudinally a negative relationship was observed between left entorhinal volume at baseline and BMIz at the one-year follow-up ( ). <b>Conclusion</b>: These results suggest that measured volumes from certain regions of the brain that have been associated with BMI in adults are associated with both concurrent BMIz and BMIz change over one-year in a primarily healthy weight sample of children. As the entorhinal cortex integrates signals from both reward and control regions, this region may be particularly important to weight management during child development.<b><br></b>
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Masterson, Travis · Figshare · 2019
<b>Data supporting the paper: </b>Association between regional brain volumes and BMI z-score change over one year in children<br><b><br></b><b>Abstract:</b><b>Purpose: </b>Associations between brain region volume and weight status have been observed in children cross-sectionally. However, it is unclear if differences in brain region volume precede weight gain. <b>Methods: </b> Two high-quality structural brain images were obtained approximately one year apart in 65 children aged 9-12 years old. Children’s height and weight were also measured at each scan. Structural images were processed using the FreeSurfer software-package providing volume measures for regions of interest including the entorhinal cortex, nucleus accumbens, and hippocampus. Age- and sex-adjusted BMI z-scores (BMIz) were calculated at both timepoints. The association between brain region volume and BMIz was examined cross-sectionally using linear regression and longitudinally using structural equation modeling. All models were adjusted by estimated cranial volume to account for individual variation in head size and were corrected for multiple comparisons (pFDR<0.05). <b>Results</b>: The sample of children was primarily healthy weight at baseline (79.78%). Cross-sectionally at the one-year follow-up, a positive relationship was observed between right hippocampal volume and BMIz ( ). Longitudinally a negative relationship was observed between left entorhinal volume at baseline and BMIz at the one-year follow-up ( ). <b>Conclusion</b>: These results suggest that measured volumes from certain regions of the brain that have been associated with BMI in adults are associated with both concurrent BMIz and BMIz change over one-year in a primarily healthy weight sample of children. As the entorhinal cortex integrates signals from both reward and control regions, this region may be particularly important to weight management during child development.<b><br></b>
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MacNicol, Eilidh · OSF Preprints (OSF Preprints) · 2021
Volumetric data derived from the RESILIENT longitudinal MRI study
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United States Department of Transportation. Federal Highway Administration · ICPSR Data Holdings · 2025
<b>Description from the original website</b><br>State highway agencies collect traffic volume data through both temporary traffic counting and continuous traffic counting programs and report their continuous counting data to FHWA on a monthly basis. Data listed here are reported by states following data format prescribed in FHWA's 2001 Traffic Monitoring Guide. Starting with the 2020 datasets, the traffic monitoring data released is piped delimited, quality reviewed and used in the Traffic Volume Trends (TVT) process with the same field names as contained in the 2001 TMG (the original data [prior to 2020] with the rigid fixed column format).<br><b>Comments</b><br>The mentioned 2001 Traffic Monitoring Guide is included here. A related data set with traffic volume estimates is here: https://www.datalumos.org/datalumos/project/235201/version/V1/view.<br>
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Jiří Kavalík · Digital Repository (National Repository of Grey Literature) · 2010
In the present work we want to design and implement simple tool for basic editing of volume data like resizing, rotating, tresholding retouching, which could be extended as needed. Currently, there are tools for volume data visualisation and simple editors for modding computer games with voxel-based graphics. There is not an editor with basic set of tools for editing volumes in resolution of medical scanners available to common user. The designed and implemented editor can run on common hardware and oth ers set of simple brushes and filters to the user and means to implement diferent tools as plugins for C# programmer.
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Benetta, A, Busson, Philippe, Hansen, Magnus et al. · CERN Bulletin · 1997
Study of data reduction criteria to be used in the CMS Ecal readout system.
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United States Department of Transportation. Federal Highway Administration · ICPSR Data Holdings · 2025
<b>Description from the original website</b><br>State highway agencies collect traffic volume data through both temporary traffic counting and continuous traffic counting programs and report their continuous counting data to FHWA on a monthly basis. Data listed here are reported by states following data format prescribed in FHWA's 2001 Traffic Monitoring Guide. Starting with the 2020 datasets, the traffic monitoring data released is piped delimited, quality reviewed and used in the Traffic Volume Trends (TVT) process with the same field names as contained in the 2001 TMG (the original data [prior to 2020] with the rigid fixed column format).<br><b>Comments</b><br>The mentioned 2001 Traffic Monitoring Guide is included here. A related data set with traffic volume estimates is here: https://www.datalumos.org/datalumos/project/235201/version/V1/view.<br>
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James H. Cole (757805), Maria Laura Filippetti (757806), Matthew P. G. Allin (342006) et al. · Figshare · 2015
<p>This contains all the raw data used in the analysis. This includes demographic, group status, brain volume, hippocampal volume and behavioural measures. Also included are a number of derived metrics included in the study, such as normalised volumes, longitudinal measure error and change in volume or behavioural measure over time. Not included are the values comprising the 3D hippocampal shape maps as these high dimensional data require specific software for visualisation and analysis. The authors will make these available on request, alongside the details of the necessary additional software packages.</p> <p>(XLSX)</p>
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Jiří Kavalík · Digital Repository (National Repository of Grey Literature) · 2010
In the present work we want to design and implement simple tool for basic editing of volume data like resizing, rotating, tresholding retouching, which could be extended as needed. Currently, there are tools for volume data visualisation and simple editors for modding computer games with voxel-based graphics. There is not an editor with basic set of tools for editing volumes in resolution of medical scanners available to common user. The designed and implemented editor can run on common hardware and oth ers set of simple brushes and filters to the user and means to implement diferent tools as plugins for C# programmer.
Cited by 0
Szabolcs Gróf · Digital Repository (National Repository of Grey Literature) · 2009
In the present work we study the methods of volumetric data fusion. The thesis starts by explaining the basic physical principles of selected medical image acquisition methods - Computed Tomography, Magnetic Resonance Imaging and Positron Emission Tomography. This is followed by a basic overview of the organization and format of the processed data. In the next part, the thesis deals with the theoretical and mathematical background of image registration. This includes the explanation of different types of transformations, the different types of interpolation used by the transformation, the mutual information criterion for the alignment of images and the process of optimization. The discussion of fusion visualization methods comes next where 13 methods are explained and example image outputs are provided. The next chapter describes the implementation details of the practical part of the thesis by explaining the two main components of the program: the registration filter component and the fusion visualization viewer component. UML diagrams are shown and the purpose and function of the classes of these components are described in detail. The thesis is closed by the user manual of the demonstrating program and a conclusion of the described methods, which also discusses possible future improvements.
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2006
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Wei Zheng (25452), Chunxian Wu (709171) · Figshare · 2015
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Wu-Tung Cheng, Yue Tian, S.M. Reddy · 2017
With decreasing feature sizes and increasing complexity of fabrication processes for manufacturing VLSI semiconductor devices, more systematic defects occur at the advanced technology nodes. Product yield ramp up is mostly determined by how fast systematic defects are identified and fixed. Given the long times and expense of physical failure analysis (PFA), use PFA on a large number of failing devices to find systematic defects is becoming infeasible. For this reason, volume diagnosis data mining for root cause identification based on statistical methods is used to reduce turnaround time and cost to speed up the process of systematic defect identification. The identified root cause information not only can be used to improve yield analysis but also can reduce PFA cost by focusing on failing devices with systematic defects.
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Javier Sanz‐Valero · Figshare · 2023
Relative search volume data
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Sanz-Valero, Javier · Figshare · 2023
Relative search volume data
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Thomas W. Kelsey (254095), Eleanor Ginbey (2833034), Moti M. Chowdhury (2833031) et al. · Figshare · 2016
<p>Pubmed ID, first author and year of publication are given for sources of extracted data, together with number of measurements and age ranges.</p>
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Sergei S. Orlov · Communications of the ACM · 2000
article Free Access Share on Volume holographic data storage Author: Sergei S. Orlov Stanford Univ., Stanford, CA Stanford Univ., Stanford, CAView Profile Authors Info & Claims Communications of the ACMVolume 43Issue 11Nov. 2000 pp 46–54https://doi.org/10.1145/353360.353363Published:01 November 2000Publication History 13citation2,072DownloadsMetricsTotal Citations13Total Downloads2,072Last 12 Months93Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
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Madison Shoraka (22171205), Herby Jean-Baptiste (22171208), Bettina Buttaro (2391736) et al. · Figshare · 2025
This file contains the volume data plotted in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013425#pcbi.1013425.g002" target="_blank">Fig 2</a>. (TXT)
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Harold P. Alspaugh · Journal of Marketing · 1962
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Madison Shoraka (22171205), Herby Jean-Baptiste (22171208), Bettina Buttaro (2391736) et al. · Figshare · 2025
This file contains the volume data plotted in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013425#pcbi.1013425.g006" target="_blank">Fig 6</a>. (TXT)
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