Addressing “micro-challenges” of archaeological data creation and reuse

Cross-posted at The Cambridge Core blog and reproduced here with minor changes.

If we want to use archaeological data to address “grand challenge”-type questions, we need to first confront a myriad of “micro challenges” in reusing data.

Digital technologies and services have become a normal part of everyday archaeological practice. But how well does our training and professional development prepare us to use these ubiquitous tools? While many archaeologists routinely use databases and spreadsheets, how many of those archaeologists have formal training in how to organize data?

A recent paper published in Advances in Archaeological Practice (and available for free download through the end of May 2020) centers on this key point: If we accept the ethical and professional responsibilities of archiving our research data, then we had better make sure our data is worth archiving.

The way we create and organize data plays an important role in shaping future analysis and reuse– not just for the original creators of a given dataset but also for wider communities. While our community needs infrastructure and services like Open Context, tDAR, and the Archaeological Data Service to publish and preserve research data, these services cannot reverse problems in data creation practices that take place in the field or in the lab.

Computing power and sophisticated software alone will not make usable or useful data. On February 2, 2020, co-author Eric Kansa joked on Twitter about how the palindromic date 02-02-2020 was one of the very rare instances when joint European-British-American archaeological excavation teams would consistently write down today’s date on their finds tags. This comment seems to have touched a nerve, and saw 44 retweets, 270 “likes”, and more than 6,000 views in 24 hours. Clearly, many in our profession have encountered the frustrations and challenges of ambiguous and inconsistent data, even with something as trivial as expressing today’s date!

If we want to use archaeological data to address “grand challenge”-type questions, we need to first confront a myriad of “micro challenges” in reusing data. This requires understanding what characterizes reusable data. To explore this question, this paper draws on several years of experience in curating and reusing data, especially reusing data to support synthetic research requiring the integration of many, many datasets created by different researchers.

Archaeologists need to appreciate how data creation practices integrally relate to all aspects of professional practice, including our ethical and stewardship responsibilities. Our paper highlights how these responsibilities begin long before we deposit data in a repository and it provides some practical guidance on how archaeologists can better create and describe data that meet a broader set of needs, including the needs of potential reusers.

Data creators need to make improvements in our creation and dissemination practices if we want to see our data live on through reuse. This paper describes a pathway for archaeologists to incrementally adopt some simple steps to create more reusable data. If you want to cut to the chase, check out Table 1, where we suggest a ladder of increasingly robust data creation and documentation practices.

Creating good data doesn’t require technical wizardry. Rather, it’s about taking time to be more intentional about data and more intentional in how you contextualize your data with a wider world of information. Although our paper builds on understanding in data reuse gained from several years of collaborative research with library and information scientists (especially the DIPIR and SLO-data projects), we want to see much wider engagement across the archaeological community in addressing the challenges inherent in communicating with data.

By: Sarah Whitcher Kansa and Eric C. Kansa

Article free to access until 31st May: Archaeological Analysis in the Information Age: Guidelines for Maximizing the Reach, Comprehensiveness, and Longevity of Data

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