dragracer is an R package of data sets for all available seasons of RuPaul’s Drag Race, excluding All Stars. It’s updated at the end of each season. This blog post describes these data in some detail while also showcasing some of the things you can do with the provided. Steven Miller offers this R package for two reasons. First, the fandom for this show is large and there is a discernible subset of the fandom that is interested in the R programming language. He offers this package as a collection of accessible data with which they can experiment. He also offers this as a love letter of a kind to RuPaul’s Drag Race and all the contestants that have appeared on it.

This research resource suggests you some best sites of dataset collection which some of you might have known earlier. In the research field of machine learning, it is always tough to find the related dataset and if found it is always hard to filter some. Now that technologies have imporved, this article suggests some well known resources to collect your dataset related to your research. 

The FAIR Cookbook is created by researchers and data managers professionals, and is an online resource for the Life Sciences with recipes that help you to make and keep data Findable, Accessible, Interoperable and Reusable (FAIR).

The FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. However, the FAIR Principles are aspirational and generic. The FAIR Cookbook guides researchers and data stewards of the Life Science domain in their FAIRification journey; and also provides policy makers and trainers with practical examples to recommend in their guidance and use in their educational material.

HomeBank is a resource for shared multi-hour, real-world recordings of children’s everyday experiences (for example, daylong home recordings using the LENA system), plus tools for analyzing those recordings. It is a component of the TalkBank system.

OpenAlex is a fully open catalog of the global research system. Its dataset describes scholarly entities and how those entities are connected to each other. OpenAlex provides documentation and guidance on how to use API to retrieve thier data. Thus, one can this resource to prepare an API workshop or for professional development.

Working as if they are engineers aiming to analyze and then improve data collection devices for precision agriculture, students determine how accurate temperature sensors are by comparing them to each other. Teams record soil temperature data during a class period while making changes to the samples to mimic real-world crop conditions—such as the addition of water and heat and the removal of the heat. Groups analyze their collected data by finding the mean, median, mode, and standard deviation. Then, the class combines all the team data points in order to compare data collected from numerous devices and analyze the accuracy of their recording devices by finding the standard deviation of temperature readings at each minute. By averaging the standard deviations of each minute’s temperature reading, students determine the accuracy of their temperature sensors. Students present their findings and conclusions, including making recommendations for temperature sensor improvements.