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Dataset results
11 results for “IT Ticket”
Cardiac Patient Bed Head Ticket Dataset
<p>This data set comprises the data which are manually extracted from the Bed Head Tickets(BHT) of patients who transferred from the Emergency Treatment Unit (ETU) to the cardiac ward in Teaching Hospital Karapitiya Galle. The data set contains 19 features as follows</p> <ol> <li>Systolic Blood Pressure (SBP) </li> <li>Diastolic Blood Pressure (DBP) </li> <li>Heart Rate (HR) </li> <li>Respiratory Rate (RR) </li> <li>Body temperature (BT) </li> <li>SpO<sub>2</sub> </li> <li>Age </li> <li>Gender </li> <li>Glasgow Coma Scale (GCS) </li> <li>Sodium Level (Na) </li> <li>Potassium Level (K) </li> <li>Chloride Level (Cl) </li> <li>Urea </li> <li>Creatinine </li> <li>Consumption of alcohol </li> <li>Smoking status</li> <li>Family History of Ischemic Hear Diseases (FHIHD) </li> <li>Triage Score </li> <li>Outcome</li> </ol> <p> </p>
Semantic Similarity of IT Support Tickets
<p>Collection of 300 support tickets manually labeled for semantic similarity, obtained from a IT support company in the Florianópolis (Brazil) region. Each ticket is represented by an unstructured text field, which is typed by the user that opened the call. The labeling process was performed in 2022 by three IT support professionals. The corpus contains tickets in many languages, mainly English, German, Portuguese and Spanish.</p> <p>All Personal Identifiable Information (PII) and sensitive information were removed (substituted by a tag indicating the original content, for instance: the sentence "this text was written by Leonardo" is converted to "this text was written by [NAME]"). The removal was performed in three steps: first, the automated machine learning-based tool AWS Comprehend PII Removal was used; then, a sequence of custom regular expressions was applied; last, the entire corpus was manually verified.</p>
Classification of IT Support Tickets
<p>Collection of 2229 support tickets manually classified into 7 categories, obtained from a IT support company in the Florianópolis (Brazil) region. Each ticket is represented by an unstructured text field, which is typed by the user that opened the call. The classification process was performed in 2020 by three IT support professionals. The corpus contains tickets in many languages, mainly English, German, Portuguese and Spanish.</p> <p>All Personal Identifiable Information (PII) and sensitive information were removed (substituted by a tag indicating the original content, for instance: the sentence "this text was written by Leonardo" is converted to "this text was written by [NAME]"). The removal was performed in three steps: first, the automated machine learning-based tool AWS Comprehend PII Removal was used; then, a sequence of custom regular expressions was applied; last, the entire corpus was manually verified.</p> <p> </p>
Relationship of Community Characteristics to Harvest Reporting: Comparative study of household surveys and harvest tickets in Alaska
Accurate harvest reporting is critical for wildlife management. Rural Alaskan communities reported a median of 42% of moose harvested via traditional harvest tickets compared to those reported in household surveys. This harvest-report ratio did not change over time. Twice as many moose were reported harvested during subsistence household surveys (n = 8,039) than on hunter harvest tickets (n = 3,557). Percentage of the community that was indigenous, used and shared moose, and absence of a wildlife biologist or road access were associated with low harvest-report ratios. Analysis revealed that household surveys provide important information about moose harvest rates and their use should be expanded. Reporting rates might be improved by building trust through respectful dialogue between hunters and managers and by placing more emphasis on the benefits of reporting harvests and less emphasis on enforcement.
Code and Data for "Routescore: Punching the Ticket to More Efficient Materials Development"
<p>Code and dataset for the manuscript "Routescore: Punching the Ticket to More Efficient Materials Development" by Seifrid, <em>et al.</em></p>
Ticket_Booth (3)
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FCBarcelona Ticket Prices
<p>This dataset contains the prices for the different sections of FCBarcelona (Men's football, women's football, basket, handball, roller hockey, futsal and youth men's football) for the 2022-23 season (scraped from its website the 22 november 2022)</p>
Anomalies in Microservice Architecture (train-ticket) based on version configurations
<p>The work contains ten datasets containing monitoring data (logs, Jaeger Traces and Prometheus KPI data). The datasets contain monitoring data from train-ticket, a benchmark system for microservices. The dataset includes a short description with explanations of identified anomalies.</p> <p>The structure of the folder is as follows:</p> <p><strong><changed-microservice>_<third-party-library>_<version>_<date-when-data-was-collected></strong></p> <p>Each folder stores the respective data:</p> <ol> <li>Logs: <ul> <li>original log file: LOGS_<change-microservice>_<third-party-library>_<Version>.txt</li> <li>parsed log files required for Loglizer (anomaly detection technique): <ul> <li>LOGS_<change-microservice>_<third-party-library>_<Version>.txt_structured.csv</li> <li>LOGS_<change-microservice>_<third-party-library>_<Version>.txt_templates.csv</li> </ul> </li> </ul> </li> <li>KPI Data: Monitoring_<change-microservice>_<third-party-library>_<Version></li> <li>Traces: Traces_<change-microservice>_<third-party-library>_<Version></li> </ol> <p> </p> <p> </p> <p> </p>
train-ticket dataset for FAMOS
Open the record for dataset details and reuse information.
Effectiveness Assessment of E-Ticketing Technology in Construction of Transportation Projects
<p>The construction of highway infrastructure has devoted significant resources towards e-Construction to reduce the paperwork and automate the tasks in daily operations. Electronic Ticketing (e-Ticketing) is one such component of e-Construction that aids in the digital transfer of material tickets such as asphalt and concrete which accounts for more than fifty per cent of construction costs. Despite the benefits of e-Ticketing, many state departments and agencies are unwilling to transition into this technology. No studies have identified the cause of the delay in the implementation process, developed a framework to comprehend the platform's full potential, quantified savings, and suggested strategies for overcoming limitations. Therefore, this study aims to (1) Indicate inefficiencies in the conventional paper ticketing framework and identify suitable technology to overcome the inefficiencies. (2) Identify benefits, quantify the reduction in inspection staff and time savings incurred by implementing e-Ticketing technology. (3) Develop a multi-criteria decision-making model for implementation of e-Ticketing platform in the perspective of state DOTs and general contractors. (4) Identify and rank the major limitations in the process of implementing e-Ticketing technology and suggest suitable strategies to overcome them. To achieve the study's objective, review section is followed by a semi-structured interview and survey results. Based on the survey responses collected from 20 state DOTs, the study categorized the critical effectiveness indicators into 3 categories and ranked the operational challenges using the Relative Importance Index. The study analyses the critical effectiveness indicators (CEIs) of e-Ticketing technology and presents a fuzzy index-based decision-making model for evaluating the adoption priorities. A comparison was made between the required number of inspectors prior to and after the implementation of e-Ticketing, and it was found that projects requiring multiple inspectors could reduce their workforce by 25% by implementing e-Ticketing. The study's findings will assist practitioners with an assessment tool to gain insights relating to priority levels in implementing the e-Ticketing technology.</p>
Ticket vending machine
This is a vintage self-service ticket machine, like those used in the early 20th century in London. The model is game-ready, low-poly, textured in Substance Painter. Source: Objaverse 1.0 / Sketchfab
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International Brain Laboratory public data
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OpenNeuro
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