ArticleCBE life sciences education2019
Getting Messy with Authentic Data: Exploring the Potential of Using Data from Scientific Research to Support Student Data Literacy.
Article in CBE life sciences education, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Lessons in implementing open farm data for diverse stakeholders at Harper Adams Future Farm.Animal frontiers : the review magazine of animal agriculture · 2026Article
- DataVersify: A Framework for Data Literacy Instruction Featuring Scientist Stories.Integrative organismal biology (Oxford, England) · 2026Article
- Revealing undergraduate biology students' conception of variability and error bars within graphing.PloS one · 2026Article
- Fostering Multidisciplinary Collaboration in Artificial Intelligence and Machine Learning Education: Tutorial Based on the AI-READI Bootcamp.JMIR medical education · 2025Article
- Multilevel Selection in the Margins: A Review of Its Representation in Undergraduate Biology Textbooks.Ecology and evolution · 2025Article
- The graph construction competency model for biology (GCCM-Bio): A framework for instruction and assessment of graph construction.Bioscience · 2025Article
- Using anti-DEI policies to dismantle education: A new front in a really old battle.PLoS biology · 2025Article
- Exploring Student Sensemaking When Engaging with Anomalous Data.CBE life sciences education · 2024Article
- Supporting Student Competencies in Graph Reading, Interpretation, Construction, and Evaluation.CBE life sciences education · 2024Article
- Comparing study features is easy but identifying next steps is hard: Evaluating critical thinking through the Biology Lab Inventory of Critical Thinking in Ecology.Ecology and evolution · 2023Article
- R Markdown as a dynamic interface for teaching: Modules from math and biology classrooms.Mathematical biosciences · 2022Article
- Article
- Data-rich textbook figures promote core competencies: Comparison of two textbooks.Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology · 2021Article
- Developing a flexible learning activity on biodiversity and spatial scale concepts using open-access vegetation datasets from the National Ecological Observatory Network.Ecology and evolution · 2021Article
- Online but not remote: Adapting field-based ecology laboratories for online learning.Ecology and evolution · 2021Article
- Integrating Service Learning into a Neuropsychopharmacology Course.Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience · 2021Article
- BioSkills Guide: Development and National Validation of a Tool for Interpreting theCBE life sciences education · 2020Article
- Learning How to Code While Analyzing an Open Access Electrophysiology Dataset.Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience · 2020Article
- The why, when, and how of computing in biology classrooms.F1000Research · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.