Fish have proven to be model organisms for the study of animal personalities, and a rich literature documents consistent interindividual behavioral differences in a variety of species. However, relatively few studies have examined the ecological consequences of such consistent interindividual differences in behaviors in fish or other organisms, especially under field conditions. In this review and perspective, we discuss the factors that may lead to the formation and maintenance of behavioral types in fish populations. We then examine what is known about the effects of personality variation on individual growth and survival, breeding behaviors and reproductive success, habitat use, diet, and ontogenetic niche shifts, migration and dispersal, as well as potential consequences for species interactions and ecosystem functioning. We focus as much as possible on studies conducted under natural or seminatural conditions, as such field studies are most relevant to elucidating the ecological consequences of behavioral variation. Finally, we discuss the potential importance of consistent individual differences in behaviors to fisheries management and conservation, specifically examining consequences for recreational and commercial fishing, hatchery rearing, and stock enhancement.

Data are becoming increasingly important in science and society, and thus data literacy is a vital asset to students as they prepare for careers in and outside science, technology, engineering, and mathematics and go on to lead productive lives. In this paper, we discuss why the strongest learning experiences surrounding data literacy may arise when students are given opportunities to work with authentic data from scientific research. First, we explore the overlap between the fields of quantitative reasoning, data science, and data literacy, specifically focusing on how data literacy results from practicing quantitative reasoning and data science in the context of authentic data. Next, we identify and describe features that influence the complexity of authentic data sets (selection, curation, scope, size, and messiness) and implications for data-literacy instruction. Finally, we discuss areas for future research with the aim of identifying the impact that authentic data may have on student learning. These include defining desired learning outcomes surrounding data use in the classroom and identification of teaching best practices when using data in the classroom to develop students’ data-literacy abilities.

Authentic, “messy data” contain variability that comes from many sources, such as natural variation in nature, chance occurrences during research, and human error. It is this messiness that both deters potential users of authentic data and gives data the power to create unique learning opportunities that reveal the nature of science itself. While the value of bringing contemporary research and messy data into the classroom is recognized, implementation can seem overwhelming. We discuss the importance of frequent interactions with messy data throughout K–16 science education as a mechanism for students to engage in the practices of science, such as visualizing, analyzing, and interpreting data. Next, we describe strategies to help facilitate the use of messy data in the classroom while building complexity over time. Finally, we outline one potential sequence of activities, with specific examples, to highlight how various activity types can be used to scaffold students' interactions with messy data.

A b s t r a c tCurrent educational reform calls fo r increased integration between science and mathematics to overcome the shortcomings in students' quantitative skills. Data Nuggets (free online resource, http://datanuggets.org) are worksheets that bring data into the classroom, repeatedly guiding students through the scientific method and making claims based on quantitative evidence. Created around recent and ongoing research, Data Nuggets provide background to a scientist and their study system, along with a real data set from their research. We demonstrate the use of Data Nuggets in the classroom and share a lesson that challenges students to answer a scientific question, use a data set to support their claim, and guides them through the construction of graphs to facilitate data interpretation. Data Nuggets can be used across K -l 6 grades and multiple times throughout the school year as students build their quantitative skills.

With improving technology and monitoring efforts, the availability of scientific data is rapidly expanding. The tools that scientists and engineers use to analyse data are changing in response. At the same time, science education standards have shifted to emphasize the importance of students making sense of data in science classrooms. However, it is not yet known whether these exciting new datasets and tools are used science classrooms, and what it would take to facilitate their use. To identify opportunities, research is needed to capture the data practices currently performed in classrooms, and the roles of technology for student learning. Here, we report findings from a survey conducted in the United States of 330 science teachers on the data sources, practices and technologies common to their classroom. We found that teachers predominantly involve their students in analysing relatively small data sets that they collect. In support of this work, teachers tend to use the technologies that are available to them—namely, calculators and spreadsheets. In addition, we found that a subset of teachers used a wide variety of data sources of varying complexity. We discuss what these findings suggest for practice, research and policy, with an emphasis on supporting teachers based on their needs. Practitioner notes What is already known about this topic Collecting and analysing data are central to the practice of science, and these skills are taught in many science classrooms at the pre‐collegiate (grades K‐12) level. Data are increasingly important in society and STEM, and types and sources of data are rapidly expanding. These changes have implications for science teachers and students. What this paper adds We found that the predominant data source science teachers use is student‐collected, small data sets. Teachers use digital tools familiar and available to them: spreadsheets and calculators. Teachers perceive the cost and time it would take to learn to use digital tools to analyse data with their students as key barriers to adopting new tools. Despite the predominance of small, student‐collected data analysed using spreadsheets or calculators, we also found notable variability in the data sources and digital tools some teachers used with their students. Implications for practice and/or policy Many of the changes called for in science education standards and reform documents, regarding how students should collect and analyse data, have not yet been fully realized in pre‐collegiate classrooms. Science teacher educators and science education researchers should build curricula and develop digital tools based on which kinds of data sources and digital tools teachers presently use, while encouraging more complex data useage in the future.