Environmental Data Analysis and Visualization

Because It’s the Right Thing to Do

Data source of the day

Natural Earth

Ethics in research

Researchers operate inside of a society with norms, values, and expectations

To behave ethically is to behave in a way that is considered socially responsible

Ethics in research

Human subjects

  • Operates on principles of non-maleficence, beneficence, autonomy, and justice

  • Requires informed consent of participants

  • Uses anonymity and confidentiality to protect identities

Animal subjects

  • Operates on principles of non-maleficence, beneficence, and justice

  • Where possible, researchers must aim for replacement, reduction, and refinement

  • Both require approval from the Institutional Review Board (IRB)

Ethics in research

University of Utah

Data ethics

Data science makes use data that is available from a variety of sources, often in ways that are not directly connected to the original data collection process


At the same time, there are limited oversights governing the reuse of data by researchers or others.

Data ethics

Tufts Office of the Vice Provost for Research

Data ethics

Tufts Office of the Vice Provost for Research

Data ethics

  • Non-maleficence: Could the use of this data be harmful?

  • Beneficence: How will the use of this data be beneficial?

  • Autonomy: Did stakeholders contribute this data willingly?

  • Justice: Would use of this data propagate inequities?

Ethical data

  • How were the data obtained?

  • For whom, or for what purpose, were the data obtained?

  • Would stakeholders be comfortable if they knew the data were being collected, stored or shared?

Ethical data

The 2014 Facebook Social Contagion Study

Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. "Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks." Proceedings of the National Academy of Sciences 111 (24): 8788–90. https://doi.org/10.1073/pnas.1320040111.

Confronting biases

  • How might the data be biased?

  • How might the data be manipulated to bias results?

  • How might data be used to promote existing biases?

Confronting biases

Towards Data Science

Activity: Let’s get ethical

Take a moment and go over one or more of your datasets from your project to address the following questions:

  • What do you know about where your data comes from? Where would you find out?

  • What are some potential sources of bias in your data?

  • Are there any ways your use of this data cause harm or propagate biases?

With one of your neighbors, discuss your project in terms of ethical principles of non-maleficence, beneficence, autonomy, and justice.

Data ethics

  • Non-maleficence: Could the use of this data be harmful?

  • Beneficence: How will the use of this data be beneficial?

  • Autonomy: Did stakeholders contribute this data willingly?

  • Justice: Would use of this data propagate inequities?

Ethics in Data Storytelling

Where do we draw the line between narrative and agenda?

National Public Radio

Coming up

  • Linear modeling in R

  • Moving forward with the final project

  • Postering 101