A Brief Look at Warm Data in the Contexts of Natural Sciences, Social Sciences, and Education

Warm Data is a new term coined by Nora Bateson (2016, 2017a, 2017b) to address issues with current research in the natural and social sciences. Such data can be contrasted with the all too familiar “cold data” of measurement and quantification, which is then subjected to statistical analysis. Warm data also can be contrasted with the cool or luke warm data that is associated with qualitative or naturalistic research. Since qualitative research began to gain in popularity, it came under attack from the quantitative research camp. Qualitative researchers were immediately put on the defensive. In order to justify their methods, qualitative researchers had to justify their methods and their data. Although they changed the terms “reliability” and “validity” to fit their qualitative paradigms, in essence they molded their qualitative data into the formulations validity and reliability. Qualitative researchers objectified their data, kept at arm’s length from “subjects,” and used the same official language to describe their research methods. What may have started off as a major departure from the predominant paradigm quickly became a second cousin with lower status, but similar feel. It had become cool data or luke warm data. Since then, a few people have been trying to break away and return to more of a warm data approach, but they have little support or status in their various research communities. But, Nora Bateson’s introduction of this term within the context of “transcontextual research” (Bateson, 2016, 2017b) provides a new rationale for understanding the complexity of multi-system dynamics and issues.

Warm Data, to me, is about enlivening the information we gather about the world, which is in contrast to objectifying and deadening such information. Warm Data doesn’t exclude the emotionality of the living things being observed or of the observer. Warm Data doesn’t exclude the values and aesthetics of the observed and observer. The connectedness between the observed and the observer is noted and valued. Anthropomorphism is not dismissed and ridiculed, but is seen as a valuable way of seeing the connections. The following article on the aeon website struggles with this issue, but seems to move towards the notion of warm data in its exploration of morality in non-human animals:

”The kindness of beasts: Dogs rescue their friends and elephants care for injured kin – humans have no monopoly on moral behaviour”

Our current problems – from personal health issues to societal and global health and disease issues, from local ecosystems and resources to global ecological and resources, and so forth – are not simple problems with a single solution. They are not linear systems with linear cause and effects. All of these problems involve multiple interacting systems in multiple contexts. They involve people’s lives – their well-being and survival. We cannot rely on the isolated, quantified, and objectified data to find ways of dealing with such complex issues. Such approaches are “clinical” and “clean.” They are not messy or plagued with uncertainty. But, in order to deal with the big problems we face, we must be willing to get our hands dirty and deal with uncertainty and the inability to predict outcomes. We must deal with the messiness of warm data. The systems we are dealing with (human being, social systems, ecosystems, the economy, education, and so forth) are themselves messy, uncertain, and unpredictable. They are “warm” in and of themselves. To break them down and mechanize, clean, and objectify them is to remove them from any sense of reality.

We need to start addressing warm data and transcontextual approaches to research – and to learning – in all levels of schooling. Our children and young adults need to be prepared to deal with the problems they will face. And, the old modes of disconnected and objectified research are going to be problematic.

However, young children already use warm data in their thinking, but we systematically suppress such ways of thinking in schools. What we need to do now is to start supporting such ways of thinking and help children sharpen and refine their natural abilities to use warm data and transcontextual thinking (Bloom, 1990).


Bateson, N. (2016). Small arcs of larger circles: Framing through other patterns. Axminster, England: Triarchy Press.

Bateson, N. (2017a). Warm data. Hacker Noon, May28. Available at: https://hackernoon.com/warm-data-9f0fcd2a828c

Bateson, N. (2017b). Warm data: Contextual research and new forms of information. norabateson (posted 5/28/17). Available at: https://norabateson.wordpress.com/2017/05/28/warm-data/

Bloom, J. W. (1990). Contexts of meaning: Young children’s understanding of biological phenomena. International Journal of Science Education, 12(5), 549-561.

About Jeff Bloom

I'm a Researcher with and am on the Advisory Board of the International Bateson Institute and am a professor emeritus with the Department of Teaching & Learning, College of Education, Northern Arizona University.
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