Data Visualization
Data Visualization - Related Videos
10:58
Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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Cited by 12 •
2011
Visual analytics (VA) is a new approach of analyzing data interactively. In this video, we discuss the data overload problem brought on by high-throughput biological experiments, and propose VA as a solution to such problem. The video demonstrates analysis within and between immunological datasets using a VA tool called Tableau.
08:15
Visualizing Oceanographic Data to Depict Long-term Changes in Phytoplankton
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2023
Here, we present a protocol for converting phytoplankton microscopic images into vector graphics and repetitive patterns to enable visualization of shifts in phytoplankton taxa and biomass over 60 years. This protocol represents an approach that can be utilized for other plankton time series and datasets globally.
09:36
Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation
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2014
This paper describes a method for collecting and analyzing electroencephalography (EEG) data during concurrent transcranial magnetic stimulation (TMS) guided by activations revealed with functional magnetic resonance imaging (fMRI). A method for TMS artifact removal and extraction of event related potentials is described as well as considerations in paradigm design and experimental setup.
09:44
Methods to Test Visual Attention Online
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Cited by 26 •
2015
To replicate laboratory settings, online data collection methods for visual tasks require tight control over stimulus presentation. We outline methods for the use of a web application to collect performance data on two tests of visual attention.
01:30
Data Interpretation
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2026
Research papers often present findings in the form of numerical tables, statistical outputs, and graphical representations. However, numerical results alone do not convey meaning unless researchers examine and place them in context. Data interpretation is the systematic process through which researchers transform raw information into clear, meaningful insights. It begins only after researchers have collected data through experiments, surveys, or observational studies. In their unprocessed form,...
FAQs
Visual encodings determine how readily viewers compare measurements. Position and length can represent quantities, while color and shape can distinguish groups, conditions, or categories. The selected encoding must remain consistent with the data and preserve meaningful comparisons. In immunology and infection studies, this supports clearer interpretation of differences among immune-cell populations, cytokine concentrations, pathogen prevalence, or antibody responses.
Charts, scales, and statistical summaries shape how patterns and differences are perceived. An unsuitable chart or scale can obscure relationships, changes, or variation, whereas an appropriate combination keeps comparisons interpretable. Summaries also help organize complex measurements before researchers examine them across time, tissues, or experimental conditions, making visual conclusions more consistent with the underlying information.
A well-constructed display can expose relationships, trends, and changes that are difficult to recognize in unprocessed data. For example, arranging measurements by time, tissue, or experimental condition may make infection-associated or treatment-associated patterns more apparent. These observations do not replace biological interpretation, but they can highlight results that deserve closer examination and help generate hypotheses about mechanisms.
Researchers can first organize measurements by relevant variables, such as immune-cell population, cytokine concentration, pathogen prevalence, antibody response, time, tissue, or experimental condition. They then select charts, scales, visual encodings, and statistical summaries that support the intended comparisons. Reviewing the resulting plots for clarity and data quality helps identify notable trends before communicating or interpreting the findings.
Interactive dashboards are useful when researchers need to examine complex results across several dimensions, including time, tissues, and experimental conditions. By organizing information for exploration, they can support data quality assessment and make treatment- or infection-associated trends easier to inspect. This format is especially relevant when a single static plot cannot communicate the relationships within diverse immune or pathogen measurements.
Visualization supports mechanism-focused research by making coordinated patterns among immune responses, pathogen prevalence, and experimental conditions easier to inspect. A visible change or relationship can suggest a hypothesis about how infection or treatment affects a biological process. Researchers can then use the displayed evidence to evaluate whether the proposed pattern is consistent across the relevant measurements, tissues, or time points.