A trace becomes data not when it is found, but when it is made addressable within an organised field of relations. A photograph in a personal folder can be inspected as an image; once supplied with date, place, author, method, scale and a stable identifier, it can also be retrieved, linked and compared with other materials. That operation is productive, but not neutral. Classification selects which differences become searchable and which details disappear into an unrecorded remainder. The same is true of a video, drawing, field note, map or exhibition document. ScalarArchitecture proposes that the movement from document to metadata, metadata to dataset and dataset to comparison should be described as a series of transformations rather than a technical cleaning process that leaves the source unchanged. LAPIEZA-LAB’s archives, Filmados, COPOS and Socioplastics supply examples of heterogeneous cultural and spatial traces whose relations depend on titles, version histories, identifiers and indexing decisions. A dataset can strengthen comparison only if its objects remain connected to their origins and the limits of their original evidence. Minimal shared metadata may be more useful than forcing fundamentally different records into an artificially uniform table. The practical test is reversible inquiry: can a question posed to the dataset return the reader to an identifiable source, its context and the decisions through which it was transformed? Data do not escape interpretation. Their value lies in making interpretive operations visible enough to be criticised, revised and reused.
References
Bowker, G.C. (2005) Memory Practices in the Sciences. Cambridge, MA: MIT Press.
Bowker, G.C. and Star, S.L. (1999) Sorting Things Out. Cambridge, MA: MIT Press.
boyd, d. and Crawford, K. (2012) ‘Critical Questions for Big Data’, Information, Communication & Society, 15(5), pp. 662–679.
D’Ignazio, C. and Klein, L.F. (2020) Data Feminism. Cambridge, MA: MIT Press.
Drucker, J. (2014) Graphesis. Cambridge, MA: Harvard University Press.
Edwards, P.N. (2010) A Vast Machine. Cambridge, MA: MIT Press.
Gitelman, L. (2014) Paper Knowledge. Durham: Duke University Press.
Gitelman, L. (ed.) (2013) ‘Raw Data’ Is an Oxymoron. Cambridge, MA: MIT Press.
Gray, J., Gerlitz, C. and Bounegru, L. (2018) ‘Data Infrastructure Literacy’, Big Data & Society, 5(2).
Kitchin, R. (2014) The Data Revolution. London: Sage.
Latour, B. (1987) Science in Action.
Latour, B. (2005) Reassembling the Social. Oxford: Oxford University Press.
Leonelli, S. (2016) Data-Centric Biology: A Philosophical Study. Chicago: University of Chicago Press.
Leonelli, S. (2019) ‘What Distinguishes Data from Models?’, European Journal for Philosophy of Science, 9:22. DOI: 10.1007/s13194-018-0246-0.
Leonelli, S. and Trappes, R. (2026) ‘The Nature of Research Environments’, European Journal for Philosophy of Science, 16:45. DOI: 10.1007/s13194-026-00755-8.
Manovich, L. (2001) The Language of New Media.
Ribes, D. and Jackson, S.J. (2013) ‘Data Bite Man’, in Gitelman, L. (ed.) Raw Data Is an Oxymoron.
Star, S.L. (1999) ‘The Ethnography of Infrastructure’, American Behavioral Scientist, 43(3), pp. 377–391.
