This resource provides access to videos produced and/or used by the Northern California Training Academy to support training for child welfare practitioners. To learn more about the Academy, please visit humanservices.ucdavis.edu/academy.

This module describes the purpose of using graphs and other data visualization techniques and describes a simple three-step process that can be used to understand and extract information from graphs.

Prepared with pre-algebra or algebra 1 classes in mind, this module leads students through the process of graphing data and finding a line of best fit while exploring the characteristics of linear equations in algebraic and graphic formats. Then, these topics are connected to real-world experiences in which people use linear functions. During the module, students use these scientific concepts to solve the following hypothetical challenge: You are a new researcher in a lab, and your boss has just given you your first task to analyze a set of data. It being your first assignment, you ask an undergraduate student working in your lab to help you figure it out. She responds that you must determine what the data represents and then find an equation that models the data. You believe that you will be able to determine what the data represents on your own, but you ask for further help modeling the data. In response, she says she is not completely sure how to do it, but gives a list of equations that may fit the data. This module is built around the legacy cycle, a format that incorporates educational research feindings on how people best learn.

The Washington State Department of Licensing contracted with the University of Washington to create an educational resource to provide an introduction to data stewardship principles. The course breaks down key concepts to familiarize individuals that are new to data stewardship and for those that wish to learn to think of data as an asset.

Students collect a large set of data (approximately 60 sets) of individual student’s water use and learn how to use spreadsheets to graph the data and find mean, median, mode, and range. They compared their findings to the national average of water use per person per day and use it to evaluate how much water a municipality would need in the event of a recovery from a water shutdown. This analysis activity introduces students to the concept of central tendencies and how to use spreadsheets to find them.

This article assembles free resources from the Weather and Climate issue of the Beyond Penguins and Polar Bears cyberzine into a unit outline based on the 5E learning cycle framework. Outlines are provided for Grades K-2 and 3-5.

Students will learn about and review key geography and census terms, discover how the U.S. Census Bureau organizes space geographically, and understand why census data are collected in this way.

More researchers are preregistering their studies as a way to combat publication bias and improve the credibility of research findings. Preregistration is at its core designed to distinguish between confirmatory and exploratory results. Both are important to the progress of science, but when they are conflated, problems arise. In this webinar, we discuss the What, Why, and How of preregistration and what it means for the future of science. Visit cos.io/prereg for additional resources.

The foundation of health and medical research is data. Data sharing facilitates the progress of research and strengthens science. Data sharing in research is widely discussed in the literature; however, there are seemingly no evidence-based incentives that promote data sharing. Methods A systematic review (registration: doi.org/10.17605/OSF.IO/6PZ5E) of the health and medical research literature was used to uncover any evidence-based incentives, with pre- and post-empirical data that examined data sharing rates. We were also interested in quantifying and classifying the number of opinion pieces on the importance of incentives, the number observational studies that analysed data sharing rates and practices, and strategies aimed at increasing data sharing rates. Results Only one incentive (using open data badges) has been tested in health and medical research that examined data sharing rates. The number of opinion pieces (n = 85) out-weighed the number of article-testing strategies (n = 76), and the number of observational studies exceeded them both (n = 106). Conclusions Given that data is the foundation of evidence-based health and medical research, it is paradoxical that there is only one evidence-based incentive to promote data sharing. More well-designed studies are needed in order to increase the currently low rates of data sharing.

This video is the first in a series of videos related to the basics of power analyses. All materials shown in the video, as well as content from the other videos in the power analysis series can be found here: https://osf.io/a4xhr/

Students will use the U.S. Census Bureau’s QuickFacts data access tool to examine information about three cities, including population, education, and income data. Students will draw conclusions about life in those three cities to determine which city they would like to live in as an adult.

Think you're good at guessing stats? Guess again. Whether we consider ourselves math people or not, our ability to understand and work with numbers is terribly limited, says data visualization expert Alan Smith. In this delightful talk, Smith explores the mismatch between what we know and what we think we know.

Open data is a vital pillar of open science and a key enabler for reproducibility, data reuse, and novel discoveries. Enforcement of open-data policies, however, largely relies on manual efforts, which invariably lag behind the increasingly automated generation of biological data. To address this problem, we developed a general approach to automatically identify datasets overdue for public release by applying text mining to identify dataset references in published articles and parse query results from repositories to determine if the datasets remain private. We demonstrate the effectiveness of this approach on 2 popular National Center for Biotechnology Information (NCBI) repositories: Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA). Our Wide-Open system identified a large number of overdue datasets, which spurred administrators to respond directly by releasing 400 datasets in one week.

Background The widespread reluctance to share published research data is often hypothesized to be due to the authors' fear that reanalysis may expose errors in their work or may produce conclusions that contradict their own. However, these hypotheses have not previously been studied systematically. Methods and Findings We related the reluctance to share research data for reanalysis to 1148 statistically significant results reported in 49 papers published in two major psychology journals. We found the reluctance to share data to be associated with weaker evidence (against the null hypothesis of no effect) and a higher prevalence of apparent errors in the reporting of statistical results. The unwillingness to share data was particularly clear when reporting errors had a bearing on statistical significance. Conclusions Our findings on the basis of psychological papers suggest that statistical results are particularly hard to verify when reanalysis is more likely to lead to contrasting conclusions. This highlights the importance of establishing mandatory data archiving policies.

Badges are a great way to signal that a journal values transparent research practices. Readers see the papers that have underlying data or methods available, colleagues see that norms are changing within a community and have ample opportunities to emulate better practices, and authors get recognition for taking a step into new techniques. In this webinar, Professor Stephen Lindsay of University of Victoria discusses the workflow of a badging program, eligibility for badge issuance, and the pitfalls to avoid in launching a badging program. Visit cos.io/badges to learn more.

This course focuses on a number of qualitative social science methods that can be productively used in media studies research including interviewing, participant observation, focus groups, cultural probes, visual sociology, and ethnography. The emphasis will primarily be on understanding and learning concrete techniques that can be evaluated for their usefulness in any given project and utilized as needed. Data organization and analysis will be addressed. Several advanced critical thematics will also be covered, including ethics, reciprocity, “studying up,” and risk. The course will be taught via a combination of lectures, class discussions, group exercises, and assignments. This course requires a willingness to work hands-on with learning various social science methods and a commitment to the preparation for such (including reading, discussion, and reflection).

A class covering the basics of writing a successful data management plan for federal funding agencies such as the NEH, NSF, NIH, NASA, and others.

Like footprints in the sand, everything you do on the web leaves a trace.

Every time you open up your web browser or app, every search you make, every purchase you make, meal you order, every friend you have, everything you like, everyone you follow, every website you visit, app you download - basically, every time you browse the web - you leave a trace, a footprint. This data is then gathered by actors on the web who then combine it all to set up a profile of you, which is then sold to advertisers who can then target you with very specific ads of things you might want to purchase.

This resource uses the ad-model of the web as a backdrop to explain how the web works. Search results, recommendations, cookies, dark patterns... the web will hold no secrets to your students!

It will help them understand why and how data on their activity is gathered. This will help them make more informed choices in what websites and apps they decide to use.

A final section will focus on digital detox, steps students can take to reduce their digital footprint and screen time.

--

This resource is part of the information science collection.

Openly accessible online training materials which can be shared and repurposed for RDM training. All contributions in any language are welcome.

Curated by: LauraMolloy

Curation policy: We accept submissions of openly available online RDM training materials which can be re-used by others either in a class environment or for self-teaching. We do not accept irrelevant material, material that is not specifically a learning resource, or material that is licensed in such a way that inhibits reuse without fee. Submissions should clearly specify authoring information if CC-BY is used, and should clearly indicate topic areas, language and any other information that will help users to find appropriate learning resources.

Created: August 14, 2015