cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4231/ney0-9f04
title: Intelligent Snow Plow: FORCE America 5100ex Controller Calibration

description: &lt;p&gt;[[Video(https://www.youtube.com/watch?v=mBDVzy91g4w)]]&lt;/p&gt;

&lt;p&gt;This video demonstrates how to calibrate the spreader/auger system, offload rate, and speed control to ensure trucks accurately control the delivery of road salt over a range of driving speeds.&lt;/p&gt;

&lt;p&gt;The guidance provided in this resource is based on the intelligent snowplow database for Indiana managed by the Joint Transportation Research Program (JTRP). The database itself is not available for download in PURR because it is both prohibitively large and continually updated, but this resource is part of a series of tools based on those data.&lt;/p&gt;

&lt;p&gt;For fleet management and training, Automatic Vehicle Location devices were installed on all 1,100 Indiana Department of Transportation (INDOT) fleet trucks beginning in October 2020.  The resulting intelligent snowplow database stores real-time vehicle location, salt spreader controller and vehicle data, and dash images every minute, allowing agencies to actively monitor and deploy their assets. INDOT manages and maintains over 28,000 miles of roadways. Maintenance of the roadways includes pavement repair in the summer as well as snow removal and de-icing in the winter. The prioritization of assets during winter storm events is crucial and impacts travel and safety. The objective of this project was to identify and develop tools INDOT could provide its operators to effectively perform winter operation de-icing activities. This project examined application methods and data to provide analytics and make data-driven decisions for state-wide deployment and operations. Discovery of calibration metrics partnered with fleetwide telematics enabled the development of analytic dashboards that allowed real-time evaluations and adjustments to be made during winter operation activities. These tools will allow the agency to better treat and enhance safety for road users.&lt;/p&gt;
description: This video provides detailed instructions and graphics for calibrating a road salt spreader truck with a FORCE America 5100ex Controller.
DOI: 10.4231/xbet-8a88
title: Intelligent Snow Plow: Calibration Workshop Training Time-lapse

description: &lt;p&gt;[[Video(https://youtube.com/watch?v=axT7D303oVA)]]&lt;/p&gt;

&lt;p&gt;This video shows the complete volumetric based method using 1 truck for three consecutive calibration procedures.  Three calibrations for the truck took 22 minutes making this process quicker than legacy methods.&lt;/p&gt;

&lt;p&gt;The information provided in this resource is based on the intelligent snowplow database for Indiana managed by the Joint Transportation Research Program (JTRP). The database itself is not available for download in PURR because it is both prohibitively large and continually updated, but this resource is part of a series of tools based on those data.&lt;/p&gt;

&lt;p&gt;For fleet management and training, Automatic Vehicle Location devices were installed on all 1,100 Indiana Department of Transportation (INDOT) fleet trucks beginning in October 2020.  The resulting intelligent snowplow database stores real-time vehicle location, salt spreader controller and vehicle data, and dash images every minute, allowing agencies to actively monitor and deploy their assets. INDOT manages and maintains over 28,000 miles of roadways. Maintenance of the roadways includes pavement repair in the summer as well as snow removal and de-icing in the winter. The prioritization of assets during winter storm events is crucial and impacts travel and safety. The objective of this project was to identify and develop tools INDOT could provide its operators to effectively perform winter operation de-icing activities. This project examined application methods and data to provide analytics and make data-driven decisions for state-wide deployment and operations. Discovery of calibration metrics partnered with fleetwide telematics enabled the development of analytic dashboards that allowed real-time evaluations and adjustments to be made during winter operation activities. These tools will allow the agency to better treat and enhance safety for road users.&lt;/p&gt;
description: Calibration of agency trucks could take upwards to 2 hours per vehicle if an agency must travel to have trucks weighed twice at offsite scales. The innovative volumetric based method allows calibration to be performed on-site and can be done in...
DOI: 10.4231/r3xj-0875
title: Intelligent Snow Plow: February 15, 2021 Winter Storm Activity

description: &lt;p&gt;[[Video(https://youtube.com/watch?v=XV2_1Sfj_B8)]]&lt;/p&gt;

&lt;p&gt;Indiana Department of Transportation recently installed Parsons telematics on all snow plow trucks to actively monitor truck deployment and response measures. Over 1,100 trucks were equipped with telematic devices to provide feedback on winter storm impact and deployment measures.  Development of a real-time dashboard allowed the agency to monitor deployment measures and response to winter storm precipitation.  The blue circles are the active trucks overlaid with the statewide MRMS forecast.&lt;/p&gt;

&lt;p&gt;The information provided in this resource is based on the intelligent snowplow database for Indiana managed by the Joint Transportation Research Program (JTRP). The database itself is not available for download in PURR because it is both prohibitively large and continually updated, but this resource is part of a series of tools based on those data.&lt;/p&gt;

&lt;p&gt;For fleet management and training, Automatic Vehicle Location devices were installed on all 1,100 Indiana Department of Transportation (INDOT) fleet trucks beginning in October 2020.  The resulting intelligent snowplow database stores real-time vehicle location, salt spreader controller and vehicle data, and dash images every minute, allowing agencies to actively monitor and deploy their assets. INDOT manages and maintains over 28,000 miles of roadways. Maintenance of the roadways includes pavement repair in the summer as well as snow removal and de-icing in the winter. The prioritization of assets during winter storm events is crucial and impacts travel and safety. The objective of this project was to identify and develop tools INDOT could provide its operators to effectively perform winter operation de-icing activities. This project examined application methods and data to provide analytics and make data-driven decisions for state-wide deployment and operations. Discovery of calibration metrics partnered with fleetwide telematics enabled the development of analytic dashboards that allowed real-time evaluations and adjustments to be made during winter operation activities. These tools will allow the agency to better treat and enhance safety for road users.&lt;/p&gt;
description: This visualization of storm activity is overlaid with telematics data from snow plow trucks.
DOI: 10.4231/a0ba-p609
title: Intelligent Snow Plow: February 8, 2021 Tandem Plow Activities

description: &lt;p&gt;[[Video(https://youtube.com/watch?v=Jz2YrCKYVfA)]]&lt;/p&gt;

&lt;p&gt;The dash camera images and locations of the trucks were monitored for the 2020-2021 winter season and used to provide training to other operators.  This video shows the use of snow plow tandem activities.  Operators can observe the accurate spacing and efficiency that can be achieved using tandem snow plowing.&lt;/p&gt;

&lt;p&gt;The information provided in this resource is based on the intelligent snowplow database for Indiana managed by the Joint Transportation Research Program (JTRP). The database itself is not available for download in PURR because it is both prohibitively large and continually updated, but this resource is part of a series of tools based on those data.&lt;/p&gt;

&lt;p&gt;For fleet management and training, Automatic Vehicle Location devices were installed on all 1,100 Indiana Department of Transportation (INDOT) fleet trucks beginning in October 2020.  The resulting intelligent snowplow database stores real-time vehicle location, salt spreader controller and vehicle data, and dash images every minute, allowing agencies to actively monitor and deploy their assets. INDOT manages and maintains over 28,000 miles of roadways. Maintenance of the roadways includes pavement repair in the summer as well as snow removal and de-icing in the winter. The prioritization of assets during winter storm events is crucial and impacts travel and safety. The objective of this project was to identify and develop tools INDOT could provide its operators to effectively perform winter operation de-icing activities. This project examined application methods and data to provide analytics and make data-driven decisions for state-wide deployment and operations. Discovery of calibration metrics partnered with fleetwide telematics enabled the development of analytic dashboards that allowed real-time evaluations and adjustments to be made during winter operation activities. These tools will allow the agency to better treat and enhance safety for road users.&lt;/p&gt;
description: Indiana Department of Transportation recently installed Parsons telematics on all snow plow trucks to actively monitor storm impact and response measures. This data provides dash camera images and location of trucks at one-minute intervals.
DOI: 10.4231/8ker-f373
title: Intelligent Snow Plow: February 6, 2021 Tow Plow Activities

description: &lt;p&gt;[[Video(https://youtube.com/watch?v=qmzKnPLuRUU)]]&lt;/p&gt;

&lt;p&gt;The dash camera images and locations of the trucks were monitored for the 2020-2021 winter season and used to provide training to other operators. This video shows the use of tow plow activities, including the spacing and efficiency that can be achieved in their use.&lt;/p&gt;

&lt;p&gt;The information provided in this resource is based on the intelligent snowplow database for Indiana managed by the Joint Transportation Research Program (JTRP). The database itself is not available for download in PURR because it is both prohibitively large and continually updated, but this resource is part of a series of tools based on those data.&lt;/p&gt;

&lt;p&gt;For fleet management and training, Automatic Vehicle Location devices were installed on all 1,100 Indiana Department of Transportation (INDOT) fleet trucks beginning in October 2020.  The resulting intelligent snowplow database stores real-time vehicle location, salt spreader controller and vehicle data, and dash images every minute, allowing agencies to actively monitor and deploy their assets. INDOT manages and maintains over 28,000 miles of roadways. Maintenance of the roadways includes pavement repair in the summer as well as snow removal and de-icing in the winter. The prioritization of assets during winter storm events is crucial and impacts travel and safety. The objective of this project was to identify and develop tools INDOT could provide its operators to effectively perform winter operation de-icing activities. This project examined application methods and data to provide analytics and make data-driven decisions for state-wide deployment and operations. Discovery of calibration metrics partnered with fleetwide telematics enabled the development of analytic dashboards that allowed real-time evaluations and adjustments to be made during winter operation activities. These tools will allow the agency to better treat and enhance safety for road users.&lt;/p&gt;
description: Indiana Department of Transportation recently installed Parsons telematics on all snow plow trucks to actively monitor storm impact and response measures. This data provides dash images and location of trucks at one-minute intervals.
DOI: 10.4231/wc1p-n150
title: Intelligent Snow Plow: How to Use Parsons Tablet

description: &lt;p&gt;[[Video(https://youtube.com/watch?v=xJXe_IZbE2Q)]]&lt;/p&gt;

&lt;p&gt;This video demonstrates how to login to the Parsons Tablet, perform pre/post trip inspection, and the overall functionality of the tablet.  This video has been uploaded to all tablets to provide the operators with a guide for use of the tablet.&lt;/p&gt;

&lt;p&gt;The information provided in this resource is based on the intelligent snowplow database for Indiana managed by the Joint Transportation Research Program (JTRP). The database itself is not available for download in PURR because it is both prohibitively large and continually updated, but this resource is part of a series of tools based on those data.&lt;/p&gt;

&lt;p&gt;For fleet management and training, Automatic Vehicle Location devices were installed on all 1,100 Indiana Department of Transportation (INDOT) fleet trucks beginning in October 2020.  The resulting intelligent snowplow database stores real-time vehicle location, salt spreader controller and vehicle data, and dash images every minute, allowing agencies to actively monitor and deploy their assets. INDOT manages and maintains over 28,000 miles of roadways. Maintenance of the roadways includes pavement repair in the summer as well as snow removal and de-icing in the winter. The prioritization of assets during winter storm events is crucial and impacts travel and safety. The objective of this project was to identify and develop tools INDOT could provide its operators to effectively perform winter operation de-icing activities. This project examined application methods and data to provide analytics and make data-driven decisions for state-wide deployment and operations. Discovery of calibration metrics partnered with fleetwide telematics enabled the development of analytic dashboards that allowed real-time evaluations and adjustments to be made during winter operation activities. These tools will allow the agency to better treat and enhance safety for road users.&lt;/p&gt;
description: Indiana Department of Transportation recently installed Parsons telematics on all snow plow trucks to actively monitor storm impact and response measures. Use of these devices is crucial in identifying performance metrics for winter storm impact.
DOI: 10.4231/x34h-9z21
title: Intelligent Snow Plow: Automated Precision Brine Application

description: &lt;p&gt;[[Video(https://youtube.com/watch?v=tWk1Wph9ux4)]]&lt;/p&gt;

&lt;p&gt;The greatest advantages of the technology are the safety enhancement, reduction of driver workload, and efficient application of brine.  While operating the vehicle, the driver focuses on brine application, surrounding vehicles and operating the application controls.  This system eliminates two of the distractions as the controller informs the driver on brine application and controls the application rate in each area, allowing the driver to focus on maneuvering the vehicle. Additionally, the application files can be programmed to account for areas that experience low-sunlight, higher winds and other weather. Through documentation, it was determined that brine application without the automated technology requires the driver&rsquo;s attention over 600 times on I-465, which can be very distracting/taxing on the driver on a 2-6 lane interstate with high traffic volumes.&lt;/p&gt;

&lt;p&gt;The information provided in this resource is based on the intelligent snowplow database for Indiana managed by the Joint Transportation Research Program (JTRP). The database itself is not available for download in PURR because it is both prohibitively large and continually updated, but this resource is part of a series of tools based on those data.&lt;/p&gt;

&lt;p&gt;For fleet management and training, Automatic Vehicle Location devices were installed on all 1,100 Indiana Department of Transportation (INDOT) fleet trucks beginning in October 2020.  The resulting intelligent snowplow database stores real-time vehicle location, salt spreader controller and vehicle data, and dash images every minute, allowing agencies to actively monitor and deploy their assets. INDOT manages and maintains over 28,000 miles of roadways. Maintenance of the roadways includes pavement repair in the summer as well as snow removal and de-icing in the winter. The prioritization of assets during winter storm events is crucial and impacts travel and safety. The objective of this project was to identify and develop tools INDOT could provide its operators to effectively perform winter operation de-icing activities. This project examined application methods and data to provide analytics and make data-driven decisions for state-wide deployment and operations. Discovery of calibration metrics partnered with fleetwide telematics enabled the development of analytic dashboards that allowed real-time evaluations and adjustments to be made during winter operation activities. These tools will allow the agency to better treat and enhance safety for road users.&lt;/p&gt;
description: This video illustrates automated technology adaption from the agriculture industry to brine application for roadway winter maintenance.
DOI: 10.4231/p0y7-3428
title: Wheat spike blast image classification using deep convolutional neural networks

description: &lt;p&gt;Wheat blast is a threat to global wheat production, and limited blast-resistant cultivars are available. Current estimations of wheat spike blast severity rely on human assessments, but this technique can have limitations.&lt;/p&gt;

&lt;p&gt;Reliable visual disease estimations paired with RGB images of wheat spike blast can be used to train deep convolutional neural networks (CNN) for disease severity classification.&lt;/p&gt;

&lt;p&gt;Inter-rater agreement analysis was used to measure the reliability of who collected and classified data obtained under controlled conditions. We then trained CNN models to classify wheat spike blast severity.&lt;/p&gt;

&lt;p&gt;Inter-rater agreement analysis showed high accuracy and low bias before model training. Results showed that the CNN models trained provide a promising approach to classify images in the three wheat blast severity categories. However, the models trained without maturing and matured spikes images showed the highest precision, recall, and F1 score when classifying the images. The high classification accuracy could serve as a basis to facilitate wheat spike blast phenotyping in the future. &lt;/p&gt;

&lt;p&gt;The provided datasets and trained models are an important contribution to the scientific community.&lt;/p&gt;
description: The folder includes i) a wheat spike blast image classification CNN model trained to automatically quantify and classify disease severity, ii) the generated datasets that include images of wheat spike blast severity levels under controlled...
DOI: 10.4231/mecj-an70
title: Intelligent Snow Plow: Importance of Brine Application

description: &lt;p&gt;[[Video(https://youtube.com/watch?v=zfSydQsp1oc)]]&lt;/p&gt;

&lt;p&gt;De-icing activities consume a large portion of agency budgets for winter maintenance activities.  The effective use of brine can reduce material usage and environmental impact while safely reducing snow or ice accumulation on the road surface.&lt;/p&gt;

&lt;p&gt;The information provided in this resource is based on the intelligent snowplow database for Indiana managed by the Joint Transportation Research Program (JTRP). The database itself is not available for download in PURR because it is both prohibitively large and continually updated, but this resource is part of a series of tools based on those data.&lt;/p&gt;

&lt;p&gt;For fleet management and training, Automatic Vehicle Location devices were installed on all 1,100 Indiana Department of Transportation (INDOT) fleet trucks beginning in October 2020.  The resulting intelligent snowplow database stores real-time vehicle location, salt spreader controller and vehicle data, and dash images every minute, allowing agencies to actively monitor and deploy their assets. INDOT manages and maintains over 28,000 miles of roadways. Maintenance of the roadways includes pavement repair in the summer as well as snow removal and de-icing in the winter. The prioritization of assets during winter storm events is crucial and impacts travel and safety. The objective of this project was to identify and develop tools INDOT could provide its operators to effectively perform winter operation de-icing activities. This project examined application methods and data to provide analytics and make data-driven decisions for state-wide deployment and operations. Discovery of calibration metrics partnered with fleetwide telematics enabled the development of analytic dashboards that allowed real-time evaluations and adjustments to be made during winter operation activities. These tools will allow the agency to better treat and enhance safety for road users.&lt;/p&gt;
description: Brining solutions when used correctly can reduce salt usage by 70%, reducing de-icing material cost and reducing their environmental impact.
DOI: 10.4231/heag-qx63
title: Intertexts and Influence: A Comprehensive Table of Intertexts in Philip Roth's Fiction, 1952-2010

description: &lt;p&gt;Many scholars have contemplated the question of those literary influences which shaped the fictional creation of Philip Roth (throughout his career, Roth himself was eager to participate in this act of contemplation). The endeavor to identify such influences is a complex task that may be approached in a variety of ways. As one way--but certainly not the only way--of engaging this problematic, we devised a quantitative tool entitled &ldquo;Intertexts and Influence: A Comprehensive Table of Intertexts in Philip Roth&rsquo;s Fiction, 1952-2010.&rdquo; Using Roth&rsquo;s 53 novels, novellas, and short stories as a database, our table measures every literary: 1) author, 2) work, 3) character, and, 4) passage/verse invoked in his &lt;em&gt;oeuvre&lt;/em&gt;. Our findings are geared toward identifying which figures Roth cited most frequently, and in which of his own works he intensified his rate of intertextual invocation. At the very least, our research tool will help scholars quickly identify when and where Roth cited other writers. At best, it will facilitate the inquiries of those studying Rothian intertextuality--a quality of his fiction which we believe must be recognized as a defining component of his aesthetic. &lt;/p&gt;

&lt;p&gt;In order to make the complexity and richness of data in the spreadsheet easier to analyse computationally, the xlsx spreadsheet was transformed into a table that follows the tidy data principles. Minor typos in coding were edited during transformation.  &lt;/p&gt;
&lt;title&gt;&lt;/title&gt;
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&lt;article citation=""&gt;
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&lt;/article&gt;
description: Using Roth’s 53 novels, novellas, and short stories as a database, our table measures every literary: 1) author, 2) work, 3) character, and, 4) passage/verse invoked in his oeuvre.