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Rebecca Sutton Koeser

Lead Developer at Princeton University Center for Digital Humanities, PhD in English Literature.

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Data as a medium, design collaboration with scientists. Notes from a long paper session at AIGA Converge Design Educators Conference on June 2, 2017.


Disease Modeling as an Interdisciplinary Practice

Courtney Marchese, Quinnipiac University [abstract]

Working as a contractor to make disease modeling software accessible to people beyond scientists. Main contact is a writer who works with the scientists and a programmer.

The diagram they created made perfect sense to them, but it’s useless to anybody else.

“Scientists and designers want to work together but they speak different languages.”

How do you establish that working relationship?

Process: emphasized making this something she could understand (not something pretty), and empathized with them to understand their approach - very basic tools; created a “flow chart” that isn’t really a flow chart but more of a brain dump. In helping the designer understand, they start to see the things that will be hard for other people to understand. “Reading” their chart and asking questions - not as a critique but to understand what they meant (directionality of arrow, choice of colors, emphasis).

Created a flow of what she understood as a discussion point; they didn’t know how to convey visually things that could happen at any point in the process. With this understanding, did her own research to figure out which pieces were cyclical or background conditions.

From the writer’s perspective: main concern was worrying about how much effort it will take for the designer to pick up and understand the scientific lingo and concepts. Requires some level of interest from the designer. Goal from the writer: “Decreased jargon and increased invitation for inquiry” In this case the writer was a key player rather than a middle-man that caused an extra layer.

This kind of work requires more investment from both designer and scientist. Important to educate design students how to collaborate with people in other disciplines. Teach them how to ask the right questions at the right point in the process. It’s a matter of practice.

Data as Medium

Nicole Coleman, Stanford University abstract

Humanities+Design Research lab for spatial and textual analysis at Stanford (lots of DH work). Digital Research architect for Stanford University Libraries.

(Not a designer or an architect.)

Number between 1 and 10 commonly collected as a measure of pain in hospitals. Everyone answers that differently. They say it is used as a point of reference from one visit to the next.

How do we collect, interprect, and act on the basis of data?

Making arguments with data requires knowing where it comes from, how it’s been processed before beginning analysis. Methods and controls to handle uncertainty. Statistics (science of uncertainty).

How do we make sense of data that are not well-documented? That has not been consistently or even intentionally collected?

Humanities as the arts of uncertainty. But we adopt statistical tools designed for something else.

Johanna Drucker’s argument to use “capta” rather than data - captured rather than given. 18th century correpondence Letters based on extant records; layers and layers of undocumented influence; many and unpredictable gaps in the record (cf. Lauren Klein on absence in the archive). Data shaped with additional categorization and annotation, expert opinion. How do we trace the provenance, record layers of perspective?

We conflate evidence, fact, and data. Epistemological, ontological, and rhetorical.

Keep the concept of data but use it across domains. Data as material with which we express ideas and stories; validity based on context (i.e., the goal of the analysis).

Meaning is a product of instruments of process and display.

Interest: not so much what we call data but how we analyze and asses what is done with it.

Graphite in the hands of an engineer, an artist, a journalist - medium is the same but the argument is different; but we don’t make that distinction with data. Expressive qualities of data: foreground plasticity rather than accuracy.

Visualization of data and relation to truth and objectivity. Changes in practices of scientific seeing: objectivity vs truth, science, accuracy, precision; it’s the same with data. Useless debates regarding aesthetics vs. accuracy, seeking an “objective” color palette. Seeing things as they are? We see as we are.

Focus on data as a material for expression so we can evaluate critically.

Scientists working with engineers and with designers. We need a shared of understanding of the data.

Methods are embedded in the data. Access and digitization require computation; transformation of methods. Do we adopt methods from statistics?

Immense complexity in a single datum - e.g. a number between zero and one.

That pain number? Advice from a nurse: “In the emergency room, it’s always a nine.”