In this activity, students explore the importance of adequate sampling strategies when conducting a scientific investigation. They are tasked with determining the average temperature of the Earth, using data sets easily found on the Internet, and determine the kind and size of sample necessary to calculate a representative average. The resource includes a student data sheet and an authentic assessment for the module, where students discuss the establishment of a habitation site on Mars. This is Activity C in module 2, titled "Modeling Hot and Cold Planets," of the resource, Earth Climate Course: What Determines a Planet's Climate? The course aims to help students to develop an understanding of our environment as a system of human and natural processes that result in changes that occur over various space and time scales.

In this activity students build a hydrologic model of Mono Lake in MATLAB and then use the model to evaluate the California State Water Board's 1994 decision regulating diversions from the watershed and design their own water management plan for the lake.
In 1941 the natural water balance of Mono Lake was altered when the Los Angeles Department of Water and Power (LADWP) began diverting water from the watershed. The lake level dropped 45 feet by 1982 threatening the local environment. After a long legal battle the California State Water Board issued an order (D. 1631) limiting water diversions by LADWP in order to return the lake to a desirable level. As of June 2019 the lake surface has yet to reach the target elevation.
In developing a water-balance model of Mono Lake students learn to idealize a hydrologic system as stocks and flows, translate their stock and flow diagram into a water balance equation and solve this equation over time using the forward Euler method. Once students have a working lake model they use MATLAB built-in functions to explore the variance and co-variance of hydrologic data and use these to constrain a probabilistic model of the Lake.

Students will learn why families are important social institutions and how family structures, household sizes, and living arrangements have changed substantially since the 1970s. In part 1, students will work in groups of three to four to analyze census data so that they may understand these changes. In part 2, students will watch a clip from the show “Modern Family” and compare their observations with census data.

In this group task students collect data and analyze from the class to answer the question "is there an association between whether a student plays a sport and whether he or she plays a musical instrument? "

Students will analyze and interpret American Community Survey (ACS) data on housing characteristics in the United States, comparing these data with those they collect from their classmates. Students also will determine what their dream home would look like and will use flat, two-dimensional shapes to construct it.

Students in grade two explore the lives of actual people who make a difference in their everyday lives. They differentiate between events that happened long ago and events that happened yesterday by studying their family histories. A number of projects are completed that preserve the past, capture the present, or impact the future, including analyzing information and drawing conclusions about how and why the world has changed. The unit concludes with students creating family history time capsules that preserve the past and present for the future.

This unit plan was originally developed by the Intel® Teach program as an exemplary unit plan demonstrating some of the best attributes of teaching with technology.

This activity serves as an introduction to a narrative writing assignment. To provide context for this activity, teachers will give students an overview of the Census Bureau. Then, students will complete a Quickwrite about their name and its history. After that, students will examine and answer questions about census data on popular last names, listen to a story about names, and complete a Quickwrite about that story. To further prepare for their narrative writing assignment about names (which is not part of this activity), students will jot down their thoughts in a graphic organizer.

Students will explore distributions of various census data sets to determine whether it can be reasonably assumed that those data follow a normal distribution, based on students’ analysis of either a histogram or a normal probability plot for each data set. They will then discuss their findings with a partner who analyzed the other type of graph for each data set.

This activity is a physics lab in which students generate distance vs. time graphs for skateboarders of different masses being pulled with various values of constant force. Students develop procedures, record and graph data, and analyze the data in light of Newton's 2nd law.

Using State Facts for Students, a data access tool from the U.S. Census Bureau, students will explore data about their state and voice their opinions on how the population has changed over time. Students will work in small groups to share their opinions, practicing oral communication and small-group discussion skills.

This assembles material for the LPHY2131 data analysis lab at UCLouvain. The documentation covers the three sessions of the laboratory and provides some additional information. The results that are obtained in this lab can be compared to the published cross-section measurement for the Z and W at the LHC, at 7TeV, by the CMS collaboration: Measurement of the Inclusive W and Z Production Cross Sections in pp Collisions at sqrt(s) = 7 TeV

Observing the 1980s brings together voices from the Mass Observation Project and British Library Sound Archive as an undergraduate level teaching module and raw data.

This module teaches students about ozone’s role in the atmosphere and explores authentic data to compare and contrast conditions that affect ground-level ozone values.

How likely are published findings in the functional neuroimaging literature to be false? According to a recent mathematical model, the potential for false positives increases with the flexibility of analysis methods. Functional MRI (fMRI) experiments can be analyzed using a large number of commonly used tools, with little consensus on how, when, or whether to apply each one. This situation may lead to substantial variability in analysis outcomes. Thus, the present study sought to estimate the flexibility of neuroimaging analysis by submitting a single event-related fMRI experiment to a large number of unique analysis procedures. Ten analysis steps for which multiple strategies appear in the literature were identified, and two to four strategies were enumerated for each step. Considering all possible combinations of these strategies yielded 6,912 unique analysis pipelines. Activation maps from each pipeline were corrected for multiple comparisons using five thresholding approaches, yielding 34,560 significance maps. While some outcomes were relatively consistent across pipelines, others showed substantial methods-related variability in activation strength, location, and extent. Some analysis decisions contributed to this variability more than others, and different decisions were associated with distinct patterns of variability across the brain. Qualitative outcomes also varied with analysis parameters: many contrasts yielded significant activation under some pipelines but not others. Altogether, these results reveal considerable flexibility in the analysis of fMRI experiments. This observation, when combined with mathematical simulations linking analytic flexibility with elevated false positive rates, suggests that false positive results may be more prevalent than expected in the literature. This risk of inflated false positive rates may be mitigated by constraining the flexibility of analytic choices or by abstaining from selective analysis reporting.

Students apply concepts of disease transmission to analyze infection data, either provided or created using Bluetooth-enabled Android devices. This data collection may include several cases, such as small static groups (representing historically rural areas), several roaming students (representing world-travelers), or one large, tightly knit group (representing urban populations). To explore the algorithms to a deeper degree, students may also design their own diseases using the App Inventor framework.

This activity will help students understand that people’s perceptions of the world—places, regions, and environments—are constantly changing with new experiences and information. Students will examine Census Bureau data about Los Angeles, and about the rest of California and the United States, to challenge or confirm these perceptions.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Microbiome sequencing data are very complex. In order to simplify analyses, researchers often perform unsupervised clustering to identify naturally occurring clusters and then investigate the clusters’ associations with various characteristics of interest. However, clustering performance and related conclusions can vary depending on the algorithm or beta diversity metric used. To improve microbiome analysis methods, a new study tested the performance of several metrics on four datasets with well-separated groups and a clinical dataset with less-clear group separation. None of the metrics was universally superior, but certain metrics underperformed under certain conditions. For example, the Bray-Curtis metric performed poorly in a dataset with rare high-abundance OTUs (groups of related bacteria), while the unweighted UniFrac metric performed poorly in a dataset with prevalent low-abundance OTUs..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

Physics I is a first-year physics course which introduces students to classical mechanics. This course has a hands-on focus, and approaches mechanics through take-home experiments. Topics include: kinematics, Newton’s laws of motion, universal gravitation, statics, conservation laws, energy, work, momentum, and special relativity.

Students will use a U.S. Census Bureau data tool called State Facts for Students to analyze the population data of their state. They will write the data in several forms, round the numbers, and then compare their state’s population with that of a nearby state.

In this activity, learners work in groups to determine the mass and volume of four samples: glass marbles, steel washers or nuts, pieces of pine wood, and pieces of PVC pipe. Learners then plot the data points on a large class graph of mass vs. volume to discover that data points for a particular material form a straight line, the slope of which gives the density of the material.