Building Data Literacy Through Community and Citizen Science

Skills and Competencies

The recipient of this certification has demonstrated all of the following skills associated with applying their knowledge of data literacy to citizen science projects:

• Articulate how citizen science benefits science and society.

• Describe data literacy and how it can help you make better decisions.

• Differentiate the types of data collected and used by scientists and citizen science participants.

• Critically interpret data-rich graphs and charts.

• Explain how data quality impacts the credibility of scientific conclusions.

Earn the Building Data Literacy Through Community and Citizen Science badge

The Building Data Literacy Through Community and Citizen Science program equips learners with the skills to contribute to citizen science projects that require data literacy while developing their data literacy in ways that will support others' participation in citizen science projects.

Visit https://scistarter.org/training-data-literacy to learn more about earning this badge and growing your knowledge of how data literacy can be applied in everyday situations.

What is SciStarter?

SciStarter is a globally acclaimed, online citizen science hub where millions of people connect with thousands of scientific research projects in need of their help.

SciStarter is supported by the National Science Foundation, NASA, the National Library of Medicine, the Institute for Museum and Library Services, the Gordon and Betty Moore Foundation, Verizon, the Girl Scouts of the USA and others seeking to support and sustain engagement in science, catalyze customized experiences and pathways, track progress and measure collective impact among their communities.

SciStarter is a research affiliate of Arizona State University and North Carolina State University.

Visit https://scistarter.org/ to learn more.

Next Generation Science Standards

This badge award aligns with the "Analyzing and Interpreting Data" subsection of the Science and Engineering Practices section of the Next Generation Science Standards.

Analyzing and Interpreting Data

Scientific investigations produce data that must be analyzed in order to derive meaning. Because data patterns and trends are not always obvious, scientists use a range of tools—including tabulation, graphical interpretation, visualization, and statistical analysis—to identify the significant features and patterns in the data. Scientists identify sources of error in the investigations and calculate the degree of certainty in the results. Modern technology makes the collection of large data sets much easier, providing secondary sources for analysis.

(Learn more about these practices, by visiting the NGSS website)

Primary School Practice:

• Use observations (firsthand or from media) to describe patterns and/or relationships in the natural and designed world(s) in order to answer scientific questions and solve problems.

Elementary School Practice:

• Compare and contrast data collected by different groups in order to discuss similarities and differences in their findings.

Middle School Practices:

• Construct, analyze, and/or interpret graphical displays of data and/or large data sets to identify linear and nonlinear relationships.

• Analyze and interpret data to determine similarities and differences in findings.

Research, Resources, and Citations:

• NGSS Lead States. 2013. Next Generation Science Standards: For States, By States. Washington, DC: The National Academies Press.

• This award is supported by funds from the National Library of Medicine, National Institutes of Health under cooperative agreement number 3UG4LM012342-05S1 at the University of Pittsburgh, Health Sciences Library System.

Visit https://scistarter.org/training-data-literacy to learn more about the program.