Students learn how to analyze a research question by identifying its component subtopics through a concept mapping exercise. They watch a short demonstration of the concept mapping process and apply it to the class topic. In small groups, students identify stakeholders related to one of the subtopics identified. They then locate sample reports or datasets created by stakeholders and record their findings on the concept map, ending with a class discussion of their findings.

Students create a spreadsheet (or use the one provided) to gather planet data and put in categories. They massage spreadsheet to tease out relationship between distance from sun and revolution speed. They use data to predict speed and/or distance for Ceres, the new dwarf planet between Mars and Jupiter.

A focus on novel, confirmatory, and statistically significant results leads to substantial bias in the scientific literature. One type of bias, known as “p-hacking,” occurs when researchers collect or select data or statistical analyses until nonsignificant results become significant. Here, we use text-mining to demonstrate that p-hacking is widespread throughout science. We then illustrate how one can test for p-hacking when performing a meta-analysis and show that, while p-hacking is probably common, its effect seems to be weak relative to the real effect sizes being measured. This result suggests that p-hacking probably does not drastically alter scientific consensuses drawn from meta-analyses.

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.

The FOSTER portal is an e-learning platform that brings together the best training resources addressed to those who need to know more about Open Science, or need to develop strategies and skills for implementing Open Science practices in their daily workflows. Here you will find a growing collection of training materials. Many different users - from early-career researchers, to data managers, librarians, research administrators, and graduate schools - can benefit from the portal. In order to meet their needs, the existing materials will be extended from basic to more advanced-level resources. In addition, discipline-specific resources will be created.

The link takes users to "Research Data Management" topic. Howevere, there are other topics to explore.

Note: Unless otherwise stated, all materials created by the FOSTER consortium are licensed under a CREATIVE COMMONS ATTRIBUTION 4.0 INTERNATIONAL LICENSE.

In this article, we accomplish two things. First, we show that despite empirical psychologists’ nominal endorsement of a low rate of false-positive findings (≤ .05), flexibility in data collection, analysis, and reporting dramatically increases actual false-positive rates. In many cases, a researcher is more likely to falsely find evidence that an effect exists than to correctly find evidence that it does not. We present computer simulations and a pair of actual experiments that demonstrate how unacceptably easy it is to accumulate (and report) statistically significant evidence for a false hypothesis. Second, we suggest a simple, low-cost, and straightforwardly effective disclosure-based solution to this problem. The solution involves six concrete requirements for authors and four guidelines for reviewers, all of which impose a minimal burden on the publication process.

This is the lab manual for the Fay Lab at the University of Massachusetts Dartmouth School for Marine Science and Technology. The focus of our work centers around developing interdisciplinary modeling approaches to extend the scope of applications for fisheries and ecosystem-based management. More about our group’s research activity can be found on our website.

This lab manual resource is intended to provide an overview for lab members and others about how we do our work, and our expectations for our team. It is also a space to document institutional knowledge and for important information about procedures and available resources.

The content for this book was developed as part of our group’s participation in the Openscapes Champions program. We are extremely grateful to and acknowledge Dr. Julia Stewart Lowndes’ role in helping shape how our lab both works and how we articulate our identity. 🙏 Thanks also to Ileana Fenwick and Stefanie Butland for their work moving the lab-manual from bookdown to Quarto.

The First Grade Elementary Framework for Science and Integrated Subjects, Sky Explorers uses observation of the sun and moon in the sky as a phenomena for exploring patterns of objects in the sky.  It is part of Elementary Framework for Science and Integrated Subjects project, a statewide Clime Time collaboration among ESD 123, ESD 105, North Central ESD, and the Office of Superintendent of Public Instruction. Development of the resources is in response to a need for research- based science lessons for elementary teachers that are integrated with English language arts, mathematics and other subjects such as social studies. The template for Elementary Science and Integrated Subjects  can serve as an organized, coherent and research-based roadmap for teachers in the development of their own NGSS aligned science lessons.  Lessons can also be useful for classrooms that have no adopted curriculum as well as to serve as enhancements for  current science curriculum. The EFSIS project brings together grade level teams of teachers to develop lessons or suites of lessons that are 1) pnenomena based, focused on grade level Performance Expectations, and 2) leverage ELA and Mathematics Washington State Learning Standards.

This learning video presents an introduction to the Flaws of Averages using three exciting examples: the ''crossing of the river'' example, the ''cookie'' example, and the ''dance class'' example. Averages are often worthwhile representations of a set of data by a single descriptive number. The objective of this module, however, is to simply point out a few pitfalls that could arise if one is not attentive to details when calculating and interpreting averages. The essential prerequisite knowledge for this video lesson is the ability to calculate an average from a set of numbers. During this video lesson, students will learn about three flaws of averages: (1) The average is not always a good description of the actual situation, (2) The function of the average is not always the same as the average of the function, and (3) The average depends on your perspective. To convey these concepts, the students are presented with the three real world examples mentioned above.

Students learn how to use and graph real-world stream gage data to create event and annual hydrographs and calculate flood frequency statistics. Using an Excel spreadsheet of real-world event, annual and peak streamflow data, they manipulate the data (converting units, sorting, ranking, plotting), solve problems using equations, and calculate return periods and probabilities. Prompted by worksheet questions, they analyze the runoff data as engineers would. Students learn how hydrographs help engineers make decisions and recommendations to community stakeholders concerning water resources and flooding.

Use this activity to explore forces acting on objects, practice graphing experimental data, and introduce the algebra concepts of slope and intercept of a line. A wooden 2 x 4 beam is set on top of two scales. Students learn how to conduct an experiment by applying loads at different locations along the beam, recording the exact position of the applied load and the reaction forces measured by the scales at each end of the beam. In addition, students analyze the experiment data with the use of a chart and a table, and model/graph linear equations to describe relationships between independent and dependent variables.

The FOSTER portal is an e-learning platform that brings together the best training resources addressed to those who need to know more about Open Science, or need to develop strategies and skills for implementing Open Science practices in their daily workflows. Here you will find a growing collection of training materials. Many different users - from early-career researchers, to data managers, librarians, research administrators, and graduate schools - can benefit from the portal. In order to meet their needs, the existing materials will be extended from basic to more advanced-level resources. In addition, discipline-specific resources will be created.

This course lays the foundation for data science education targeting psychological and brain science students. No previous coding experience is required. The students are introduced to basic concepts and tools for data analysis. The focus is on hands-on practice and enjoyable learning. The course uses python as the programming language, and Jupyter Notebooks as the development environment (our “home base”) for the examples, tutorials, and assignments. The course uses Jupyterlab Notebooks because they are both the industry standard and a nice way to load, visualize, and analyze data as well as describe our findings in one environment. The course teaches how to use git and GitHub.com to document changes and backup our work and, eventually, for use as a collaboration tool.

Students work with data that represent the ages of 24 people to learn the difference between categorizing data in fractile intervals and equal intervals. Students discuss dividing bonus points among class members to understand what per capita means. Then students search for state-level data on personal income per person and visualize them in FRED. The goals are for students to customize a map, observe patterns in mapped data, and note differences across geographical areas.

This resource provides access to materials associated with the Fundamentals in Evidence-Based Decision-Making series at the Northern California Training Academy. To learn more about the series, please visit the Academy website at www.humanservices.ucdavis.edu/academy

This report covers funder data-sharing policies/practices, and provides recommendations to funders and others as they consider their own policies. It was commissioned by Robert Wood Johnson Foundation in 2017. If any comments or questions, please contact Stephanie Wykstra (stephanie.wykstra@gmail.com).

Working as engineering teams in this introductory pneumatics lab, students design and build working pneumatic (air-powered) systems. The goal is to create systems that launch balls into the air. They record and analyze data from their launches.

The concept of geocaching is introduced as a way for students to explore using a global positioning system (GPS) device and basic geographic information (GIS) skills. Students familiarize themselves with GPS, GIS, and geocaching as well as the concepts of latitude and longitude. They develop the skills and concepts needed to complete the associated activity while considering how these technologies relate to engineering. Students discuss images associated with GPS, watch a video on how GPS is used, and review a slide show of GIS basics. They estimate their location using latitude and longitude on a world map and watch a video that introduces the geocaching phenomenon. Finally, students practice using a GPS device to gain an understanding of the technology and how location and direction features work while sending and receiving data to a GIS such as Google Earth.

The following lesson is a cross-curricular multi-day lesson plan for 8th grade science, mathematics, and social studies (CA) exploring the "GMO (genetically modified organisms) Debate." That is, GMO vs. organic, and should Genetically Modified food be labeled.