# Digitizing physical presence
## The process of digitizing presence
I have digitized my physical presence on my bike in regards to spatiotemporal dimensions (Wright et al., 2003, p. 48) of data, in order to probe the epistemic implications for such data. In order to accomplish this, I needed to decide on a data-collecting platform fit for the task; I chose Strava because of its approachability, supposed reliability, and – critically – its promises to uphold my rights to export any acquired data relating to me in a machine-readable format, as a EU citizen. I then strapped my phone to my road-bike via a cheaply acquired silicone phone-holder designed for bikes, and routinely and systematically recorded any and all bike rides conducted on my road-bike – meaning regular day-to-day commutes have been excluded from collection. The data collection was in effect from August 27th until September 24th of 2020.
A part of what I wanted to experience and examine was indeed “going in blind” from that point on; not knowing exactly in what format the data would be delivered to me, and, more importantly, with which taxonomic and curational efforts, in terms of the categorization and structuring of metadata, already applied to it, as described by Katharina Weinstock as:

> … curatorial exploration of personal cosmologies placed a special emphasis on catalogues, collections, and taxonomies - formats that seem to have proven most qualified to suggest idiosyncratic reconnections of “the order of things” (Weinstock, 2020, p. 231).

The dataset has been appended as the ZIP-file named [“export_66439101.zip”](https://curatingdata.broholttrans.dk/resources/export_66439101.zip) in its entirety. I’ve exercised care in the preservation of the original structure and naming-schemes, as a measure of portraying the above-mentioned efforts. I have, though, made sure to anonymize pieces and bits of data which can be considered personally identifiable throughout the dataset. But I haven’t anonymized the start- and end-coordinates from the rides, neither in the “raw” (Gitelman, 2013) activity-data, nor as they have been converted from GPX- into CSV-data via the website “https://www.gpsvisualizer.com”, as I would consider that ‘tampering’ with the data itself. I consider the aggregated dataset consisting of timestamped coordinates as the end result of my inquiry, which has been appended as [20200924080218-22138-data.csv](https://hackmd.io/@martinbtrans/assignment-1-dataset). Removing singular data entries on a subjective basis would endanger those exact epistemic considerations which I’ve sought to investigate. Some ethical considerations here have been weighed between the ability of the data to expose personally identifiable information (relating to me and myself only, though), and the benefits of research (Markham & Buchanan, 2017).

## Data and its representational value
Throughout this process, I’d been reflecting upon the epistemic implications of the data, considering whether the data represents my “being-in-the-world” (Zahavi, 2019), or just arbitrary bits and bytes which happens to afford being read as timestamped coordinates, which turns out to correspond to my bike rides. I found that the epistemic value inscribed with that data is subjective to me, as machines processing the data, or other individuals glancing through it, have no recollection with the physicalness of generating the data – that aspect is mine alone. I take the meaning of this to be, that the data has truthful value to me as I alone can correlate the data to my social knowledge, and consider it as representational value for others.
The data handed over to me, representing my physical being in the world, from Strava consist of mainly quantitatively generated metadata (such as total lengths, durations spent, elevations and derived speeds). But there are a few ‘sprinkles’ of qualitative data, consisting of my perceived exertion, as Strava prompts the user to enter as an activity finishes, and naming of each activity – which is ironically algorithmically determined if not manually filled in, being attributed various quantitative metadata acquired from the activity itself and the chosen privacy settings. The result of this being, that most of my bike-rides were simply named ‘Afternoon Ride’.
Getting the data off of the collection platform (Strava, in this case) was at no time throughout the process considered as difficult, as it is my right, defined in the GDPR (European Parliament and Council of European Union, 2016, article 15). It posed a challenge actually remembering and differentiating my personal memories of the bike rides, and in order to name the individual rides in the final dataset, as a way of distancing myself from the algorithmically derived names. I had no other option than to consult my calendar, and the questionable nature of the nam-ing process is reflected in the names as they appear in the dataset.

## Considering my effort as part of mass digitization
Following the same train of thought going forward as Nanna Thylstrup, with “[t]hinking about mass digitization as an ‘assemblage’ [which] allows us to abandon the image of a circumscribed entity in favor of approaching it as an aggregate of many highly varied components and their contingent connections” (Thylstrup, 2018, p. 23), seems to me quite prolific in terms of approaching a such complex phenomenon as mass digitization. The way of perceiving and approaching a process of mass digitization, as Thylstrup describes with a quote from Bruno Latour; “Groups are not silent things, but rather the provisional product of a constant uproar made by the millions of contradictory voices about what is a group and who pertains to what.” (Latour, 2005, p. 35; as quoted in Thylstrup, 2018, pp. 23–24), referring to the manyfold meanings attributed to this phenomenon, by different communities and practices which makes something as complicated as this appear as an coherent assemblage (Thylstrup, 2018, p. 24).
With a departure of the ‘mundane’ data (Pink et al., 2017) being termed as social, the considerations of Langlois, Redden & Elmer (2015) appears plausible, in that

> social data is not only a product of computer science; it is also linked to processes of archiving, of remembering and forgetting. From this perspective, social data becomes thick and multidimensional. It does not simply exist to be classified, measured, and correlated, but as a trace of practices that establish cultural and social continuities and discontinuities (Langlois et al., 2015, p. 11).

I, as an individual, has physically been where the data is pointing and experienced that which has been digitized – that’s the only reason the data’s there in the first place. This digitized external storing of my physical presence is being called into existence purely for archival and statistical reasons, as it – on its own – does not enhance what I knew, or contribute anything inherently new, referring to what Richard Rogers (2017) describes as ‘Natively Digital’ (Rogers, 2017).
But looking beyond the phenomenon of ‘mass digitization’, something is happening with the data; the aggregation of data as a whole – a seemingly mundane transformation of data, from a bunch of timestamped coordinates into larger ‘sets’ of data, upon which ever more metadata can be extracted, and mathematical equations can be performed. I recognize this as efforts of ‘datafication’ (Mejias & Couldry, 2019; Van Dijck, 2014).

## Bibliography
* European Parliament and Council of European Union. (2016, April 27). Regulation (EU) 2016/679 [Official Journal of the European Union]. Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32016R0679&rid=3
* Gitelman, L. (2013). “Raw data” is an oxymoron. The MIT Press.
* Langlois, G., Redden, J., & Elmer, G. (Eds.). (2015). Compromised data: From social media to big data. Bloomsbury Academic, an imprint of Bloomsbury Publishing, Inc.
* Latour, Bruno. (2005). Reassembling the social an introduction to actor-network-theory. OUP.
* Markham, A., & Buchanan, E. (2017). Research Ethics in Context. In M. T. Schäfer & K. van Es (Eds.), The Datafied Society (pp. 201–210). Amsterdam University Press; JSTOR. https://doi.org/10.2307/j.ctt1v2xsqn.19
* Mejias, U. A., & Couldry, N. (2019). Datafication. Internet Policy Review, 8(4). https://doi.org/10.14763/2019.4.1428
* Pink, S., Sumartojo, S., Lupton, D., & Heyes La Bond, C. (2017). Mundane data: The routines, contingencies and accomplishments of digital living. Big Data & Society, 4(1), 12. https://doi.org/10.1177/2053951717700924
* Rogers, R. (2017). Foundations of Digital Methods. In M. T. Schäfer & K. van Es (Eds.), The Datafied Society (pp. 75–94). Amsterdam University Press. https://doi.org/10.1515/9789048531011-008
* Thylstrup, N. B. (2018). The politics of mass digitization. The MIT Press.
* Van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197–208. https://doi.org/10.24908/ss.v12i2.4776
* Weinstock, K. (2020). Rearranging the World: Found Objects and the Collection, Pre- and Post-Internet. In B. von Bismarck & B. Meyer-Krahmer (Eds.), Curatorial Things: Cultures of the Curatorial 4 (pp. 229–253). STERNBER PR.
* Wright, P., McCarthy, J., & Meekison, L. (2003). Making Sense of Experience. In M. A. Blythe, K. Overbeeke, A. F. Monk, & P. C. Wright (Eds.), Funology (Vol. 3, pp. 43–53). Springer Netherlands. https://doi.org/10.1007/1-4020-2967-5_5
* Zahavi, D. (2019). Phenomenology: The basics (Original edition). Routledge, Taylor & Francis Group.

## Appendix
* Appendix "[20200924080218-22138-data.csv](https://hackmd.io/@martinbtrans/assignment-1-dataset)"
* Appendix "[export_66439101.zip](https://curatingdata.broholttrans.dk/resources/export_66439101.zip)"