cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4231/wzr4-hx92
title: Data: Students' Reading and Using of the Negative Sign

description: &lt;p&gt;In this study, we explored second and fifth graders&rsquo; noticing of negative signs and incorporation of them into their strategies when solving integer addition problems. Fifty-one out of 102 second graders and 90 out of 102 fifth graders read or used negative signs at least once across the 11 problems. Among second graders, one of their most common strategies was subtracting numbers using their absolute values, which aligned with students&rsquo; whole number knowledge-pieces and knowledge-structure. They sometimes preserved the order of numbers and changed the placement of the negative sign (e.g., &minus;9 + 2 as 9 &ndash; 2) and sometimes did the opposite (e.g., &minus;1 + 8 as 8 &ndash; 1). Among fifth graders, one of the most common strategies reflected use of integer knowledge-pieces within a whole-number knowledge-structure, as they added numbers&rsquo; absolute values using whole number addition and appended the negative sign to their total. For both grade levels, the order of the numerals, the location of the negative signs, and also the numbers&rsquo; absolute values in the problems played a role in students&rsquo; strategies used. Fifth graders&rsquo; greater strategy variability often reflected strategic use of the meanings of the minus sign. Our findings provide insights into students&rsquo; problem interpretation and solution strategies for integer addition problems and supports a blended theory of conceptual change. Adding to prior findings, we found that entrenchment of previously learned patterns can be useful in unlikely ways, which should be taken up in instruction.  Included here is the data for students reading and using of the negative signs.&lt;/p&gt;
description: This spreadsheet contains data on second and fifth graders' reading and use of the negative sign when solving a series of integer addition problems. This data comes from a pretest.
DOI: 10.4231/kvtf-w862
title: Intelligent Snow Plow: February 5, 2021 Windrow Activities

description: &lt;p&gt;[[Video(https://youtube.com/watch?v=qEbXo4a191Y )]]&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 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/d8xt-js45
title: Intelligent Snow Plow: Integrated Connected Vehicle Data and INDOT Snowplow 63678 Telematics Feed from February 15-16, 2021, Winter Storm

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

&lt;p&gt;Indiana Department of Transportation recently installed telematics on all snow plow trucks to actively monitor storm impact and response measures. This video provides truck dash camera images and location at one-minute intervals synchronized with a time-space heatmap of connected vehicle probe data speeds.&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 explains and illustrates the integration of an INDOT plow truck dashboard camera with floating car data obtained from connected vehicles.
DOI: 10.4231/vxzq-9g72
title: Intelligent Snow Plow: February 8, 2021 Winter Storm Activity

description: &lt;p&gt;[[Video(https://youtube.com/watch?v=bnyK6SYCDhY )]]&lt;/p&gt;

&lt;p&gt;Indiana Department of Transportation 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.&lt;/p&gt;

&lt;p&gt;This visualization shows snow plow deployment in response to winter storm activity. The blue circles are the active trucks overlaid with the statewide Multi-Radar/Multi-Sensor (MRMS) forecast. 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 shows snow plow deployment in response to winter storm activity.
DOI: 10.4231/rr0d-c529
title: Intelligent Snow Plow: February 10, 2021 Winter Storm Activity

description: &lt;p&gt;[[Video(https://youtube.com/watch?v=G2V-UxfwG3U )]]&lt;/p&gt;

&lt;p&gt;Indiana Department of Transportation 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.&lt;/p&gt;

&lt;p&gt;This visualization shows snow plow deployment in response to winter storm activity. The blue circles are the active trucks overlaid with the statewide Multi-Radar/Multi-Sensor (MRMS) forecast. 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 shows snow plow deployment in response to winter storm activity.
DOI: 10.4231/7dsj-1475
title: Intelligent Snow Plow: December 16, 2020 Winter Storm Activity

description: &lt;p&gt;[[Video(https://youtube.com/watch?v=1sTyUUH1FIA)]]&lt;/p&gt;

&lt;p&gt;Indiana Department of Transportation 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.&lt;/p&gt;

&lt;p&gt;This visualization shows snow plow deployment in response to winter storm activity. The blue circles are the active trucks overlaid with the statewide Multi-Radar/Multi-Sensor (MRMS) forecast. 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 shows snow plow deployment in response to winter storm activity.
DOI: 10.5281/zenodo.17981838
title: The Fusion Between Artificial Intelligence and Accounting

description: Artificial Intelligence (AI) has transformed multiple sectors over time, and accounting is noexception. This discipline, traditionally based on manual records and repetitive tasks, nowfinds in AI an opportunity to optimize time and resources, reduce errors, and providereal‑time strategic information to stakeholders. This study analyzes the role of AI in modernaccounting through a qualitative review of academic sources published between 2015 and2024. The results show significant advances in automation, fraud detection, digital auditing,and predictive analysis. Although AI enhances efficiency, accountants must continuouslyadapt to technological changes to avoid professional gaps. AI does not replace theaccountant but redefines the profession toward analytical, supervisory, and ethically guideddecision‑making tasks.
DOI: 10.4231/3pqe-np07
title: Seedling growth and fall armyworm feeding preference influenced by dhurrin production in sorghum

description: &lt;p&gt; &lt;/p&gt;

&lt;p&gt;Genetic mutants coupled with nondestructive phenotyping techniques were used to study the impact of dhurrin on fall armyworm feeding and plant growth in sorghum. A genetic mutation in CYP79A1 that disrupts dhurrin biosynthesis was used to develop sets of near-isogenic lines (NILs) with contrasting dhurrin contents in the Tx623 &lt;em&gt;bmr6&lt;/em&gt; genetic background. The NILs were evaluated for differences in plant growth and FAW feeding damage in replicated greenhouse and field trials. Greenhouse studies showed that dhurrin-free Tx623 &lt;em&gt;bmr6&lt;/em&gt; &lt;em&gt;cyp79a1&lt;/em&gt; plants grew more quickly than wild-type plants but were more susceptible to insect feeding based on changes in green plant area (GPA), total leaf area, and total dry weight over time. The NILs exhibited similar patterns of growth in field trials with significant differences in leaf area and dry weight of dhurrin-free plants between the infested and non-infested treatments. Taken together, these studies reveal a significant metabolic tradeoff between CG biosynthesis and plant growth in sorghum seedlings.&lt;/p&gt;

&lt;p&gt;Plant growth and development data from the greenhouse and field trials from this study are available at this site. The files are designated by location and year with locations shown as greenhouse (GH) or field (WL) then the year the study took place. For example, the GH18_insect_data.xlsx includes data from the greenhouse trials in 2018. Within each file there may be multiple sheets that define the type of data collected. Data for green plant area collected with the Aris TopView Phenotyping System, total leaf area, fresh weight, and dry weight are organized by plot, genotype, and insect infestation treatment. Temporal data from the greenhouse trials include time (hours) when measurements was taken, the plant number, and insect infestation treatment. More details of each of the data and R code files are included in the READ_ME file stored on the PURR site.&lt;/p&gt;
description: Dhurrin plays a key role in host-plant defense of sorghum. Studies of genetic mutants coupled with nondestructive phenotyping techniques revealed a significant metabolic tradeoff between dhurrin production and plant growth in sorghum seedlings.
DOI: 10.5281/zenodo.17981837
title: The Fusion Between Artificial Intelligence and Accounting

description: Artificial Intelligence (AI) has transformed multiple sectors over time, and accounting is noexception. This discipline, traditionally based on manual records and repetitive tasks, nowfinds in AI an opportunity to optimize time and resources, reduce errors, and providereal‑time strategic information to stakeholders. This study analyzes the role of AI in modernaccounting through a qualitative review of academic sources published between 2015 and2024. The results show significant advances in automation, fraud detection, digital auditing,and predictive analysis. Although AI enhances efficiency, accountants must continuouslyadapt to technological changes to avoid professional gaps. AI does not replace theaccountant but redefines the profession toward analytical, supervisory, and ethically guideddecision‑making tasks.
DOI: 10.4231/v572-yx70
title: Intelligent Snow Plow: Integrated Connected Vehicle Data and INDOT Snowplow 63550 Telematics Feed from February 15-16, 2021, Winter Storm

description: &lt;p&gt;[[Video(https://youtube.com/watch?v=IK2VTt8pMg0)]]&lt;/p&gt;

&lt;p&gt;Indiana Department of Transportation recently installed telematics on all snow plow trucks to actively monitor storm impact and response measures. This video provides truck dash camera images and location at one-minute intervals synchronized with a time-space heatmap of connected vehicle probe data speeds.&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 explains and illustrates the integration of an INDOT plow truck dashboard camera with floating car data obtained from connected vehicles.
DOI: 10.34740/kaggle/dsv/14217026
title: US counties COVID 19 dataset
DOI: 10.4231/sfmn-r881
title: Intelligent Snow Plow: Integrated Connected Vehicle Data and INDOT Traffic Camera Feed from February 15-16, 2021, Winter Storm

description: &lt;p&gt;[[Video(https://youtube.com/watch?v=94md2owDcAs)]]&lt;/p&gt;

&lt;p&gt;The camera images were monitored for the 2020-2021 winter season and used to provide training in preparation for storm events. This video provides traffic camera images at three-minute intervals synchronized with a time-space heatmap of connected vehicle probe data speeds.&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 explains and illustrates the integration of an INDOT traffic camera with floating car data obtained from connected vehicles.