This resource is a lesson on data cleaning and wrangling in R using the tidyverse package. It introduces R beginners to using R, best practices with R, the R environment, and basic coding with R

How does energy flow in and out of our atmosphere? Explore how solar and infrared radiation enters and exits the atmosphere with an interactive model. Control the amounts of carbon dioxide and clouds present in the model and learn how these factors can influence global temperature. Record results using snapshots of the model in the virtual lab notebook where you can annotate your observations.

This winning entry in the museum's Young Naturalist Awards 1999 by Mary, a 13 year old student from New York, takes a look at how rocks and fossils provide much of Earth's history. Her essay discusses the great variety of sedimentary, metamorphic, and igneous rocks found on the north shore of Long Island, how all three types were formed and the specific rocks she collected.

Collection and SortingCleaning and ShreddingMelting and ExtrusionCooling and SolidificationFinishing and DecorationQuality ControlPackaging and Distribution

This activity uses simulation to help students understand sampling variability and reason about whether a particular samples result is unusual, given a particular hypothesis. By using first candies, then a web applet, and varying sample size, students learn that larger samples give more stable and better estimates of a population parameter and develop an appreciation for factors affecting sampling variability.

An open source crime mapping software originally developed by the US Department of Justice.

Data on growth by region, population decline, population density, center of population, and local government area populations.

Half a century after they were first described, relational database systems remain by far the most popular choice for the storage of large datasets. The book starts by describing the practical and theoretical reasons why this is so.

It then shows how to analyse a data requirement and use it to design and develop a database. Through a series of practical exercises and prepared scripts, it teaches SQL, using a freely downloadable, fully working database system (SAP SQL Anywhere™ for Windows 7 and above, MacOS 10.9 and above, and Linux)

The author works as an independent consultant. He had a career in database systems, and taught the subject for more than two decades, as a course tutor for the UK Open University. He is a Fellow of the Higher Education Academy.

This is a festschrift including nine scientific papers and six abstracts of papers written by Dr. Gerard Rushton or his former graduate students and colleagues to celebrate his retirement from teaching at the University of Iowa, Iowa City, Iowa, USA. The festschrift begins with Rushton’s own review of his research advances in Behavioral Geography, Economic Geography and Health Geography that coincide with three recurring phases of his academic career during 45 years of teaching at the University of Iowa. Following this, each paper by a former student or colleague reviews the special personal and academic contributions of Rushton to him or her in one of those research areas. Each paper then proceeds to review the author’s (or authors’) contributions to scientific theory and empirical analysis that he or she (or they) has(ve) subsequently advanced or evolved from Rushton’s original contributions. These papers are scientific contributions of interest to an academic readership, as opposed to personal or anecdotal recollections.

This webliography is intended for librarians seeking to enhance their own knowledge and assist peers in improving their data management awareness. The webliography is organized by content type, first with more foundational materials such as established data management curricula and then with current awareness and community materials such as social media.

This packet was developed to teach psychological research methods and statistics as a no-cost, open access course. Secondary goals are to teach computational reproducibilty and create teaching materials that can be shared among colleagues teaching similar courses at Haverford College and around the globe. This packet includes links to video lectures, video tutorials, open access textbooks, statistical analysis activities, and a data sharing repository. This packet was developed during the 2020/21 academic year while the course was being taught remotely due to Covid. The syllabus and course materials were developed keeping asynchronous versus synchronous learning in mind.

This is an activity about image resolution. Learners will recreate a solar image taken by the Solar Dynamics Observatory (SDO) using various sizes of building bricks, and discuss how their recreations relate to image resolution. Learners will also compare SDO images to solar images from older spacecraft to see how improved technology helps scientists learn more about the Sun.

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

*** *** ***

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

Size, Scales, and Specialization was developed as part of an effort by the Quantitative Biology at Community Colleges group to provide materials that incorporate mathematical concepts into biology courses. The activity uses published estimates of cell type numbers in the human body along with size, density and weight as a lens to have students calculate ratios, explore exponents, and better understand how the various cell types contribute to an average human's total weight and size. The activity is applicable for majors and non-majors biology courses, and maps to Chapter 4 of the OpenStax Biology 2e textbook. This activity could also be used in a mathematics course as a biologically relevant example.

The activity contains a pre-assessment to gauge student understanding of the material and provides an opportunity for students to predict the number of various cell types, as well as the mass of various cell types, in the human body. This prediction activity is followed with a guided approach to calculating these values. After guiding the students in this activity, students will then have a chance to practice the activity on a new set of cell data provided.

After completing this module students should be able to:

- Compare and contrast the structure and function of different cell types.
-- List the largest and the smallest cells in the body based on number.
-- List the largest and the smallest cells in the body based on mass.
- Describe the advantages of specialization in eukaryotic cells.
-- Give examples of how specialization in cell types affects cell size (volume) and shape.
- Perform measurements and conversions using the metric system.
-- Measure the scale of cell size variation in the human body
-- Calculate the relative proportions of cell types in the human body by mass and frequency

Statistical table showing respondents's perceptions towards the nation's progress in coping with illegal drugs.

Statistical table of attitudes towards the honesty and ethical standards of judges organized by demographic characteristics. Lower income earners and elderly citizens tend to rate judges lower.

This article, part of Biodiversity Counts, examines how to responsibly collect and keep specimens. The article discusses the reasons why collecting specimens is important and the behaviors and attitudes that define responsible collecting for both scientists and students.

This Excel file contains the most comprehensive bibliography of research ever created on Restorative Justice (1972-2018). Specific subtopics on RJ can be filtered via 5 tabs (general, education, policing, law, international) . Useful for practitioners or scholars.

Key figures and trends of turnovers for various industries released on quarterly basis.