Using Generative Art to Convey Past and Future Climate Transitions
March 31st, 2023
Here, a protocol is presented to visualize climate data as generative art.
It can be challenging to convey climate data, especially when the timescales are far greater than those people usually think about. This protocol allows users to create generative animations with visuals scaled to data. With this procedure, the data can be transformed into animations that convey information without relying on charts or graphs.
It is highly customizable in terms of data used and the visual aspects displayed. It would be interesting for a social experiment to be conducted on public perception of climate data based on different modes of data presentation. This work could be one visual technique alongside more traditional or artistic methods.
Coding in any new language or learning new software can be challenging. Online resources and internet searches for key terms or codes can help. After downloading the coding and visualization software, download the data and begin to code.
Drag the desired MPT file, MPT 1, MPT 2, MPT 3, or MPT 4 into the code editor to visualize it. In the explorer menu, click on the desired MPT folder, MPT 1, MPT 2, MPT 3, or MPT 4 to reveal a dropdown menu. Then click on the script, followed by clicking on index.html.
Left click the window portion with the code for index. html and select Open with live server from the menu. Repeat the steps, starting from selecting the MPT folder in the explorer menu for each subset of time.
To view the visualization based on future projections, open the future folder on the computer and drag either the accumulation or transition folder into the code editor. Next, select the folder name in the explorer window and click on index.html. Left click the portion of the window with code for index.
html and select Open with live server from the menu. To edit the visualizations, select the folder of interest in the explorer window of the code editor and open the main script file by clicking sketch.js. Perform any edits to visualization parameters within this code.
Look for code annotations with detailed descriptions of the code and its function, following double slashes and further identified by green texts. Define the variables that will be linked to data or used to customize visual parameters. Next, save the edits by simultaneously pressing the command and S keys.
View updated visuals by navigating to the index. HTML file. In the explorer window, left clicking and selecting Open with live server from the menu.
Visualizations corresponding to unique geological time periods were generated. Visual aspects such as color, size, and speed were quantitatively scaled to estimates of sea surface temperature, nitrogen, isotopic composition, and the rate of climate change. The data were either measured on deep sea sediments or modeled from the intergovernmental panel on climate change, or IPCCs representative carbon pathway, or RCP scenarios.
The animation generated from MPT 1 code corresponded to the earliest time segment about 1.2 to 1.118 million years ago. Well prior to glacial-interglacial lengthening and glacial cooling. The MPT 2 code corresponded to about 1.112 to 1.06 million years ago, immediately before glacial-interglacial lengthening and glacial cooling.
MPT 3 generated the visuals for the second latest segment of time with longer glacial-interglacial cycles, about 1.06 million to 900, 000 years ago. MPT 4 code generated the visuals for the most recent time segment, about 900, 000 to 600, 000 years ago when longer glacial-interglacial cycles were more established. The accumulation code generated model projection for future anthropogenic warming based on temperature estimates of RCP 8.5 model averages for New York.
The transition code created a simpler visual of the same, showing only the outline of orbs moving across the background. The product format allowed for the customization and presentation of data, creating immersive science communication exhibits. When editing the code, ensure it corresponds to the current data sections.
For future projections, only have the transition or the accumulation folders in the working space. Otherwise, the program will not work. This technique opens doors for including scientific data in graphic design.
Generative and immersive art are rapidly growing fields with many opportunities for art to interact with science and data.
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Overview
This protocol presents a method for visualizing climate data through generative art, transforming complex information into engaging animations. It allows for customization in both data representation and visual aesthetics, offering an alternative to traditional data presentation methods.
Key Study Components
Area of Science
- Data visualization
- Generative art
- Climate science
Background
- Conveying climate data can be difficult due to varying timescales.
- Traditional charts and graphs may not effectively communicate complex information.
- Generative art provides a novel approach to data representation.
- Customization enhances the relevance and impact of visualizations.
Purpose of Study
- To create a protocol for generating animations from climate data.
- To explore public perception of climate data through different visual presentations.
- To provide an alternative to conventional data visualization techniques.
Methods Used
- Development of a customizable protocol for data visualization.
- Creation of generative animations based on climate data.
- Exploration of visual aesthetics in data representation.
- Potential for social experiments on data perception.
Main Results
- Successful transformation of climate data into engaging animations.
- Demonstrated the effectiveness of generative art in data communication.
- Highlighted the importance of customization in visual presentations.
- Suggested further research on public perception of climate data.
Conclusions
- Generative art can serve as a valuable tool for visualizing complex data.
- This method may enhance understanding of climate issues.
- Further exploration of visual techniques is warranted.
Frequently Asked Questions
Executive Industry Relevance
Translating complex, multi-variable climate datasets into immersive generative visualizations demonstrates a scalable framework for scientific data communication. This approach enables R&D teams to explore new modalities for presenting quantitative outputs, supporting hypothesis interrogation and stakeholder engagement across discovery and translational research. The method's adaptability to diverse datasets positions it as a reusable capability for visualizing dynamic biological or environmental systems in biopharma pipelines.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables visual interrogation of multi-dimensional datasets to clarify system-level feedbacks.
- Supports mechanistic de-risking by mapping variable interactions through data-driven animations.
- Facilitates hypothesis testing by linking quantitative variables to visual parameters.
Screening & Assay Development
- Prepares validated visual frameworks for representing assay outputs and system responses.
- Standardizes data presentation, improving reproducibility and cross-team communication.
- Enables scalable visualization of quantitative assay results for rapid screening evaluation.
Translational & Preclinical Research
- Aligns visual outputs with disease-relevant or environmental biomarker data when applicable.
- Supports continuity from discovery through preclinical validation by enabling consistent data visualization strategies.
- Provides a platform for integrating modeled and empirical data in translational studies.
Pipeline & Workflow Integration
This generative visualization method can be positioned from early discovery through translational research, supporting data-driven decision-making and communication across the R&D continuum.
- Discovery Biology: Facilitates hypothesis testing and pathway clarification by visually mapping variable interactions.
- Screening: Provides reproducible, quantitative visual outputs for comparing experimental conditions.
- Analytics: Enables integration of geochemical, biological, or modeled data into unified visual readouts.
- Translational Research: Supports alignment of visualizations with preclinical or biomarker data when relevant.
- Enterprise Reuse: Offers a flexible, scalable framework for visualizing diverse datasets across multiple R&D programs.
Operational & Enterprise Impact
- Scientific Value: Enhances predictive confidence and reduces ambiguity in interpreting complex datasets.
- Operational Value: Standardizes and scales data visualization for reproducibility and cross-functional use.
- Strategic Value: Improves communication of quantitative findings, supporting informed go/no-go decisions.
- Portfolio Impact: Enables risk-adjusted prioritization by clarifying data-driven insights across programs.
Implementation Considerations
- Requires expertise in coding, data visualization, and scientific data interpretation.
- Needs compatible software infrastructure for generative animation and data integration.
- Demands cross-team standardization of data formats and visualization parameters.
- Adaptable to a range of model systems and data types with appropriate customization.
- Dependent on accurate linkage between quantitative variables and visual outputs.
