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 year is 2050 and your once-idyllic beachfront vacation home is now flooded up to the second story. The crab your family has enjoyed every Christmas for as long as you can remember has now become an endangered species. The oceans have changed. In Earth 540, Oceanography for Educators, we explore the mechanisms that lead to sea level rise and ocean acidification. We strive to understand how natural processes such as ocean currents, the gulf-stream, tides, plate tectonics, and the Coriolis Effect, affect our oceans and ocean basins. We then predict how man-made issues such as climate change and overfishing will affect our beloved waters and our livelihoods. Want to see into the future? Then this course is for you!

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.

Searchable database of resources and opportunities for students and educators. See Citizen Science opportunities for assignment and activity inspiration. See All Topics index in addition to the Educational Materials search.

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.

Principles of Microeconomics is an adaptation of the textbook, Microeconomics: Markets, Methods, and Models by D. Curtis and I. Irvine, which provides concise yet complete coverage of introductory microeconomic theory, application and policy in a Canadian and global environment. This adaptation employs methods that use equations sparingly and do not utilize calculus. The key issues in most chapters are analyzed by introducing a numerical example or case study at the outset. Students are introduced immediately to the practice of taking a data set, examining it numerically, plotting it, and again analyzing the material in that form. The end-of-chapter problems involve numerical and graphical analysis, and a small number of problems in each chapter involve solving simple linear equations (intersecting straight lines). However, a sufficient number of questions is provided for the student to test understanding of the material without working through that subset of questions. This textbook is intended for a one-semester course, and can be used in a two-semester sequence with the companion textbook, Principles of Macroeconomics. The three introductory chapters are common to both textbooks.

This version of this teaching module was published in Teaching Issues and Experiments in Ecology:

Jim McNeil and Megan A. Jones. April 2018, posting date. Data Management using National Ecological Observatory Network’s (NEON) Small Mammal Data with Accompanying Lesson on Mark Recapture Analysis. Teaching Issues and Experiments in Ecology, Vol. 13: Practice #9 [online]. http://tiee.esa.org/vol/v13/issues/data_sets/mcneil/abstract.html

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Undergraduate STEM students are graduating into professions that require them to manage and work with data at many points of a data management life cycle. Within ecology, students are presented not only with many opportunities to collect data themselves, but increasingly to access and use public data collected by others. This activity introduces the basic concept of data management from the field through to data analysis. The accompanying presentation materials mention the importance of considering long-term data storage and data analysis using public data.

This data set is a subset of small mammal trapping data from the National Ecological Observatory Network (NEON). The accompanying lesson introduces students to proper data management practices including how data moves from collection to analysis. Students perform basic spreadsheet tasks to complete a Lincoln-Peterson mark-recapture calculation to estimate population size for a species of small mammal. Pairs of students will work on different sections of the datasets allowing for comparison between seasons or, if instructors download additional data, between sites and years. Data from six months at NEON’s Smithsonian Conservation Biology Institute (SCBI) field site are included in the materials download. Data from other years or locations can be downloaded directly from the NEON data portal to tailor the activity to a specific location or ecological topic.

In this activity, students will:

- discuss data management practices with the faculty. Presentation slides are provided to guide this discussion.
- view field collection data sheets to understand how organized data sheets can be constructed.
- design a spreadsheet data table for transcription of field collected data using good data management practices.
- view NEON small mammal trapping data to a) see a standardized spreadsheet data table and b) see what data are collected during NEON small mammal trapping.
- use Microsoft Excel or Google Sheets to conduct a simple Lincoln-Peterson Mark-Recapture analysis to estimate plot level species population abundance.

Please note that this lesson was developed while the NEON project was still in construction. There may be future changes to the format of collected and downloaded data. If using data directly from the NEON Data Portal instead of using the data sets accompanying this lesson, we recommend testing out the data each year prior to implementing this lesson in the classroom.

This module was originally taught starting with a field component where students accompanied NEON technicians during the small mammal trapping. As this is not a possibility for most courses, the initial part of the lesson has been modified to include optional videos that instructors can use to show how small mammal trapping is conducted. Instructors are also encouraged to bring small mammal traps and small mammal specimens into the classroom where available.

The Data Sets

The National Ecological Observatory Network is a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle Memorial Institute. This material is based in part upon work supported by the National Science Foundation through the NEON Program.

The following datasets are posted for educational purposes only. Data for research purposes should be obtained directly from the National Ecological Observatory Network (www.neonscience.org).

Data Citation: National Ecological Observatory Network. 2017. Data Product: NEON.DP1.10072.001. Provisional data downloaded from http://data.neonscience.org. Battelle, Boulder, CO, USA

Notes
Version 2.1: Includes correct Lincoln-Peterson Index formula in PPT, faculty, and student notes.

Version 2.0: This version of the teaching module was published in Teaching Issues and Experiments in Ecology. McNeil and Jones 2018. This version reflects updates based on comments from reviewers.

Version 1.0: This version of the teaching module was prepared as part of the 2017 DIG FMN. It was submitted for publication as part of the DIG Special Issue of TIEE.

Cite this work
Researchers should cite this work as follows:

Jim McNeil, Megan A. Jones (2018). Data Management using National Ecological Observatory Network's (NEON) Small Mammal Data with Accompanying Lesson on Mark Recapture Analysis. NEON - National Ecological Observatory Network, (Version 2.1). QUBES Educational Resources. doi:10.25334/Q4M121

Description
This lesson introduces students to working with metadata, which can be broadly thought of as the data ABOUT existing data. Data isn’t complete without metadata, and this lesson will help students understand both how to work with metadata and how to create their own.

Data used: NEON aquatic macroinvertebrate datasets from multiple stations. It could be adapted to use any data sets or taxonomic groups though.

Activities: The lesson involves three major activities. 1) Querying and downloading datasets and corresponding products from NEON. 2) Reading and answering comprehension questions about metadata files that correspond with data files 3) Combining two datasets based off understanding the metadata in exercise 2 (e.g. understanding which columns indicate sampling dates and in which formats will allow them to appropriately combine multiple data sets).

Programs: No specific programming skills or language is required for this lesson. This lesson is designed to be done entirely in common office/student software programs (e.g. Microsoft Word and Microsoft Office) and could be done using online programs (e.g. my university has student licenses for Google Spreadsheets and Google Docs).

Learning objectives:

1 – Students will be able to define ‘metadata’ and understand how metadata is critical for reproducible research.

2 – Students will be able to correctly answer comprehension questions about a metadata file.

3 – Students will be able to apply their understanding of the metadata file to create a new data file from two data sets.

4 – Students will understand the importance of creating and understanding metadata to go along with datasets.

Timing: This lesson was designed to take place in two – 75 minute class periods that are in a workshop format. This lesson could easily be part of a longer lab, homework, or a remote / online / asynchronous assignment.

Notes
This version is current as of Spring 2019 and was classroom taught. I encourage folks to adapt, modify, and make new versions.

Cite this work
Researchers should cite this work as follows:

Whitney, K. S. (2019). Introduction to Data Management and Metadata using NEON aquatic macroinvertebrate data. NEON Faculty Mentoring Network, QUBES Educational Resources. doi:10.25334/SJX1-F373

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.