Digital Data Stories updates!
Hello July! We’re about six months into the Digital Data Stories (DDS) project here at the Alexandria Archive Institute (AAI). So, it’s time to update y’all on our program.
This quarter, our efforts centered on three goals. These were: to continue developing the publications we started, to have a possible sustainability partner evaluate the data story prototype, and to select Open Context (OC) projects for future data stories.
We’ve continued working on our publications through the blogs we’ve published since March. Check them out here, here, and here. These are acting as starting points for future academic publications.
Here’s a preview of what the design style will look like for our PDF versions of the data stories.
With the blog acting as a pasture for those ideas, we continued grazing our prototype. When that was moseying along, we reached out to potential sustainability partners with evaluation in mind.
However, we realized that each partner had unique goals for teaching resources or course deliverables and they needed something different than our prototype. So, even with the prototype moooving along, in beta testing with our small herd, it wasn’t ready for those partners. Though, we’ll let y’all know when we open it for wider testing.
But, that was awesome! Why? Because those differences made us explore what we want from data stories and decide what we want future collaborations to look like.
So those partners won’t evaluate the prototype yet, but the discussions inspired us to explore more of the projects curated by OC. Looking through these, we decided on a set of materials, techniques, and sources that we want to write data stories for.
Here’s an example of a couple of the cool projects Open Context hosts.
Although we can’t do a data story for every project, we can ensure that the tutorials are diverse. This diversity can include material, meaning the kinds of artifacts included in a data set. For example, our cattle bones prototype focuses on animal remains.
Another kind of diversity can be in technique. This focuses on the skill we want to teach in the tutorial and that might require a particular kind of data set. For example, we want to do a tutorial that has a spatial component. So, we need a data set that includes detailed spatial information.
The last kind of diversity that we want is ensuring that the data source is diverse. We can think about this in two ways. Diversity in terms of where the data come from and diversity in terms of who helped contribute to the data set.
Both kinds of diversity are important because we are creating data stories for many regions of the world and want to highlight data contributed by people of diverse backgrounds. We want to do this because archaeology is dominated by particular voices and stories from particular places. To change that, we need to be specific about whose work we include and what areas we talk about.
In the meantime, we’re happy with the progress that the DDS project has made and we look forward to pursuing our next set of goals.
