Spine or vertebral segmentation is a crucial step in all applications regarding automated quantification of spinal morphology and pathology. With the advent of deep learning, for such a task on computed tomography (CT) scans, a big and varied data is a primary sought-after resource. However, a large-scale, public dataset is currently unavailable.

VerSe is a large scale, multi-detector, multi-site, CT spine dataset consisting of 374 scans from 355 patients. The challenge was held in two iterations in conjunction with MICCAI 2019 and 2020. The tasks evaluated for include: vertebral labelling and segmentation.

Verse20 data in the format, as used in the MICCAI2020 challenge

VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images ## What is VerSe? Spine or vertebral segmentation is a crucial step in all applications regarding automated quantification of spinal morphology and pathology. With the advent of deep learning, for such a task on computed tomography (CT) scans, a big and varied data is a primary sought-after resource. However, a large-scale, public dataset is currently unavailable. We believe VerSe can help here. VerSe is a large scale, multi-detector, multi-site, CT spine dataset consisting of 374 scans from 355 patients. The challenge was held in two iterations in conjunction with MICCAI 2019 and 2020. The tasks evaluated for include: vertebral labelling and segmentation. ## Citing VerSe If you use VerSe, we would appreciate references to the following papers. 1. Sekuboyina A et al., VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images, 2021.
In Medical Image Analysis:

Weekly memory verse challenge for the men's Sunday School class at South New Milford Baptist Church Perfect for daily Bible study, scripture memorization, and spiritual meditation. Each verse has been carefully selected to provide meaningful content for personal growth and reflection.

This dataset is about books. It has 1 row and is filtered where the book is The new outlook Scripture. Book 2, The challenge of the Old Testament. It features 5 columns: author, publication date, book publisher, and BNB id.

https://www.hackerearth.com/challenges/hackathon/enter-the-travel-verse/

Identify trends with Airline ticketing data

The past few years represent the best and worst in air travel in decades. 2019 saw the best year for air travel this century while the pandemic brought long periods of extreme swings in demand. ARC’s data is the world’s largest single source of airline ticketing data.

The goal is to identify a trend that leads to a new prediction using ARC’s data to incorporate it into a marketable data product within the B2B or B2B2C space.

Your task is to find creative ways to apply the vast data store from historical trends mapped into predictive analytics to specific recommendations for consumers and suppliers of air travel — the potential has no limit.

The Challenge - Review the provided airline ticketing dataset below Identify a problem in the travel and tourism industry where advanced awareness of current and future trends using airline ticketing data will solve. - Identify the audience in the B2B or B2B2C space that would find value in the solution. - Using Machine learning, data science technologies and/or advanced analytics to develop a solution that solves the problem that you have identified and defined. (For example, recommender systems, predictive analytics, etc.) - Create an application prototype (program, website, API etc.) and/or visual aid (such as a dashboard or video presentation) to demonstrate the business value of the proposed solution.

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