Category: data

Data in Action

Jeremy Huggett
TAG 2025 conference logo representing a megaphone
TAG 2025 conference logo

I was recently discussant for a TAG conference session on Data Activism (session 14) organised by Jeremy Foot, Joana Valdez-Tullett, and Stu Eve, all of Wessex Archaeology. Their session abstract expressed some dissatisfaction with the current uses to which archaeological data are put and challenged participants to consider a host of questions surrounding our use of data: what more we could do with it, why we might want to do more, and what stands in our way. Their call attracted an interesting variety of papers, ranging across data reuse cases (and associated problems), the sustainability problems associated with unexpected datasets, issues of comparability between different datasets with differing underlying data strategies, the unexpected application of data to unanticipated research questions, and different ways of perceiving and representing temporal dimensions in imagery, for instance.

Reading the abstracts and listening to the papers, it seemed to me that underlying this broad range of issues and approaches were questions relating to our relationships with data and the consequent implications for knowledge creation. From my notes on the presentations, I identified three key areas:

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The Data Interface

Jeremy Huggett
Painting of woman looking out of window by Friedrich
Detail from Woman at a Window, by Casper David Friedrich (1822)

We understand knowledge construction to be social and combinatorial: we build on the knowledge of others, we create knowledge from data collected by ourselves and others, and so on. Although we pay a lot of attention to the processes behind the collection, recording, and archiving of our data, and are concerned about ensuring its findability, accessibility, interoperability, and reusability into the future, we pay much less attention to the technological mediation between ourselves and those same data. How do the search interfaces which we customarily employ in our archaeological data portals influence our use of them, and consequently affect the knowledge we create through them? How do they both enable and constrain us? And what are the implications for future interface designs?

As if to underline the lack of attention to interfaces, it’s often difficult to trace their history and development. It’s not something that infrastructure providers tend to be particularly interested in, and the Internet Archive’s Wayback Machine doesn’t capture interfaces which use dynamically scripted pages, which writes off the visual history of the first ten years or more of development of the Archaeology Data Service’s ArchSearch interface, for example. The focus is, perhaps inevitably, on maintaining the interfaces we do have and looking forward to developing the next ones, but with relatively little sense of their history. Interfaces are all too often treated as transparent, transient – almost disposable – windows on the data they provide access to.

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Data as Mutable Mobiles

Jeremy Huggett
How Standards Proliferate
(c) Randall Monroe https://xkcd.com/927/ (CC-BY-NC)

As archaeologists, we frequently celebrate the diversity and messiness of archaeological data: its literal fragmentary nature, inevitable incompleteness, variable recovery and capture, multiple temporalities, and so on. However, the tools and technologies that we have developed to use and reuse that data do their best to disguise or remove that messiness. Most of the tools and technologies that we employ in the recording, location, analysis, and reuse of our data generally try to reduce its complexity. Of course, there is nothing new in this – by definition, we always tend to simplify in order to make data analysable. However, those technical structures assume that data are static things, whereas they are in reality highly volatile as they move from their initial creation through to subsequent reuse, and as we select elements from the data to address the particular research question in hand. This data instability is something we often lose sight of.

The illusion of data as a fixed, stable resource is a commonplace – or, if not specifically acknowledged, we often treat data as if this were the case. In that sense, we subscribe to Latour’s perspective of data as “immutable mobiles” (Latour 1986; see also Leonelli 2020, 6). Data travels, but it is not essentially changed by those travels. For instance, books are immutable mobiles, their immutability acquired through the printing of multiple identical copies, their mobility through their portability and the availability of copies etc. (Latour 1986, 11). The immutability of data is seen to give it its evidential status, while its mobility enables it to be taken and reused by others elsewhere. This perspective underlies, implicitly at least, much of our approach to digital archaeological data and underpins the data infrastructures that we have created over the years.

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Productive Friction

Jeremy Huggett
Mastodon vs Twitter meme
Mastodon vs Twitter meme (via https://mastodon.nz/@TheAtheistAlien/109331847144353101)

Right now, the great #TwitterMigration to Mastodon is in full flood. The initial trickle of migrants when Elon Musk first indicated he was going to acquire Twitter surged when he finally followed through, sacked a large proportion of staff and contract workers, turned off various microservices including SMS two-factor authentication (accidentally or otherwise), and announced that Twitter might go bankrupt. Growing numbers of archaeologists opened accounts on Mastodon, and even a specific archaeology-focussed instance (server) was created at archaeo.social by Joe Roe.

Something most Twitter migrants experienced on first encounter with Mastodon was that it worked in a manner that was just different enough from Twitter to be somewhat disconcerting. This was nothing to do with tweets being called ‘toots’ (recently changed to posts following the influx of new users), or retweets being called ‘boosts’, or the absence of a direct equivalent to quote tweets. It had a lot to do with the federated model with its host of different instances serving different communities which meant that the first decision for any new user was which server to sign up with, and many struggled with this after the centralised models of Twitter (and Facebook, Instagram etc.) though older hands welcomed it as a reminder of how the internet used to be. It also had a lot to do with the feeds (be they Home, Local, or Federated) no longer being determined by algorithms that automatically promoted tweets but simply presenting posts in reverse chronological order. And it had to do with anti-harassment features that meant you could only find people on Mastodon if you knew their username and server, and the inability to search text other than hashtags. These were deliberately built into Mastodon, together with other, perhaps more obviously, useful features like Content Warnings on text and Sensitive Content on images, and simple alt-text handling for images.

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Data Detachment

Jeremy Huggett
‘Data detachment’ via Craiyon

A couple of interesting but unrelated articles around the subject of humanities digital data recently appeared: a guest post in The Scholarly Kitchen by Chris Houghton on data and digital humanities, and an Aeon essay by Claire Lemercier and Clair Zalc on historical data analysis.

Houghton’s article emphasises the benefits of mass digitisation and large-scale analysis in the context of the increasing availability of digital data resources provided through digital archives and others. According to Houghton, “The more databases and sources available to the scholar, the more power they will have to ask new questions, discover previously unknown trends, or simply strengthen an argument by adding more proof.” (Houghton 2022). The challenge he highlights is that although digital archives increasingly provide access to large bodies of data, the work entailed in exploring, refining, checking, and cleaning the data for subsequent analysis can be considerable.

An academic who runs a large digital humanities research group explained to me recently, “You can spend 80 percent of your time curating and cleaning the data, and another 80 percent of your time creating exploratory tools to understand it.” … the more data sources and data formats there are, the more complex this process becomes. (Houghton 2022).

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HARKing to Big Data?

Jeremy Huggett
Aircraft Detection Before Radar
A 1920s aircraft detector

Big Data has been described as revolutionary new scientific paradigm, one in which data-intensive approaches supersede more traditional scientific hypothesis testing. Conventional scientific practice entails the development of a research design with one or more falsifiable theories, followed by the collection of data which allows those theories to be tested and confirmed or rejected. In a Big Data world, the relationship between theory and data is reversed: data are collected first, and hypotheses arise from the subsequent analysis of that data (e.g., Smith and Cordes 2020, 102-3). Lohr described this as “listening to the data” to find correlations that appear to be linked to real world behaviours (2015, 104). Classically this is associated with Anderson’s (in)famous declaration of the “end of theory”:

With enough data, the numbers speak for themselves … Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all. (Anderson 2008).

Such an approach to investigation has traditionally been seen as questionable scientific practice, since patterns will always be found in even the most random data, if there’s enough data and powerful enough computers to process it.

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Mining the Grey

Jeremy Huggett
Text mining icon
Text mining icon by Julie McMurray (via Pixabay)

Archaeological grey literature reports were primarily a response to the explosion of archaeological work from the 1970s (e.g. Thomas 1991) which generated a backlog which quickly outstripped the capacity of archaeologists, funders, and publishers to create traditional outputs, and it became accepted that the vast majority of fieldwork undertaken would never be published in any form other than as a client report or summary format. This in turn (and especially in academic circles) frequently raised concerns over the quality of the reports, as well as their accessibility: indeed, Cunliffe suggested that some reports were barely worth the paper they were printed on (cited in Ford 2010, 827). Elsewhere, it was argued that the schematisation of reports could make it easier to hide shortcomings and lead to lower standards (e.g. Andersson et al. 2010, 23). On the other hand, it was increasingly recognised that such reports had become the essential building blocks for archaeological knowledge to the extent that labelling them ‘grey’ was something of a misnomer (e.g. Evans 2015, sec 5), and the majority of archaeological interventions across Europe were being carried out within the framework of development-led archaeology rather than through the much smaller number of more traditional research excavations (e.g. Beck 2022, 3).

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Grey Data

Jeremy Huggett

Row of books with Informed on the spinesIn recent years, digital access to unpublished archaeological reports (so-called ‘grey literature’) has become increasingly transformational in archaeological practice. Besides being important as a reference source for new archaeological investigations including pre-development assessments (the origin of many of the grey literature reports themselves), they also provide a resource for regional and national synthetic studies, and for automated data mining to extract information about periods of sites, locations of sites, types of evidence, and so on. Despite this, archaeological grey literature itself has not yet been closely evaluated as a resource for the creation of new archaeological knowledge. Can the data embedded within the reports (‘grey data’) be re-used in full knowledge of their origination, their strategies of recovery, the procedures applied, and the constraints experienced? Can grey data be securely repurposed, and if not, what measures need to be taken to ensure that it can be reliably reused?

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Data in a Crisis

Jeremy Huggett

One of the features of the world-wide COVID-19 pandemic over the past eighteen months has been the significance of the role of data and associated predictive data modelling which have governed public policy. At the same time, we have inevitably seen the spread of misinformation (as in false or inaccurate information that is believed to be true) and disinformation (information that is known to be false but is nevertheless spread deliberately), stimulating an infodemic alongside the pandemic. The ability to distinguish between information that can be trusted and information which can’t is key to managing the pandemic, and failure to do so lies behind many of the surges and waves that we have witnessed and experienced. Distinguishing between information and mis/disinformation can be difficult to do. The problem is all too often fuelled by algorithmic amplification across social media and compounded by the frequent shortage of solid, reliable, comprehensive, and unambiguous data, and leads to expert opinions being couched in cautious terms, dependent on probabilities and degrees of freedom, and frustratingly short on firm, absolute outcomes. Archaeological data is clearly not in the same league as pandemic health data, but it still suffers from conclusions drawn on often weak, always incomplete data and is consequently open to challenge, misinformation, and disinformation.

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Data as Flux

Jeremy Huggett
The Earthboot device, by Martin Howse (2013)

Data is tricky stuff. It can appear to be self-evident but equally may be elusive. It can be material yet immaterial, tangible but ephemeral, objective yet biased, precise but inaccurate, detailed but broad-brush. It may be big or small, fast or slow, quantitative or qualitative. It may be easily misrepresented, misconceived, misunderstood, misread, misconstrued, misinterpreted. It can be distorted, altered, mangled, wrangled, and reshaped into something it never originally was according to different purposes and agendas. Data is slippery and perilous, but we often overlook its characteristics and peculiarities in the pursuit of interpretation and – hopefully! – knowledge. Looking over this blog, I’ve written a lot about data over the years. In the process, I’ve undoubtedly repeated myself, quite probably contradicted myself, sometimes confused myself, and that’s before considering any of my more formal publications on the subject! For instance, there’s the question of missing and unknown data, data associations, data metaphors, data reuse, data proxies, big data, and quite a lot more besides on data archiving etc.. Not only does this highlight how fundamental data are, but it perhaps underlines the value of a range of different perspectives about the character and nature of data.

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