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Details of Grant 

EPSRC Reference: EP/R033064/1
Title: Qualified Selves: Co-Creating Meaning Post-Big Data
Principal Investigator: Speed, Professor C
Other Investigators:
Cruickshank, Professor L Richards, Dr D Knowles, Dr B
Researcher Co-Investigators:
Project Partners:
Microsoft
Department: Edinburgh College of Art
Organisation: University of Edinburgh
Scheme: Standard Research
Starts: 01 June 2018 Ends: 30 November 2019 Value (£): 429,479
EPSRC Research Topic Classifications:
Human-Computer Interactions Information & Knowledge Mgmt
Mobile Computing
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
06 Feb 2018 ICT Cross-Disciplinarity and Co-Creation Announced
Summary on Grant Application Form
Individuals are increasingly reliant on digital applications and services to store photos, documents, notes and other valued personal data. They are also accustomed to - and tacitly accept as a hidden cost of using otherwise 'free' services - these applications amassing activity data and metadata from which companies derive significant business value. For example, Facebook makes much of its £22bn yearly revenue by being able to precisely target advertisements to users by deciphering their unique preferences from their likes, tags, contacts, updates, photos, travel patterns etc (some accessed through permissions to Facebook via other apps) [BBC]. There is a highly lucrative, if shadowy, trafficking in users' data: data brokering companies such as Acxiom and Epsilon compile thorough dossiers on people's physical and mental health conditions, sexual orientation, personal vices, and vulnerabilities to aid companies in identifying likely consumers [CBS,SCH]. Meanwhile, there are no corresponding tools accessible for individuals to learn about themselves through their personal data.

Within recent years, there is a growing literacy around data as a medium for generating information and key insights. This is represented in the Quantified Self movement (see, e.g.: http://feltron.com), with individuals self-tracking their patterns of behaviour, physiological responses, productivity, correspondences etc with a view toward enabling personal reflection and gaining greater self-knowledge [LI]. Wearable activity trackers have been appropriated by some for self-diagnostic purposes: e.g. finding correlations between activities and symptoms to make informed changes to improve personal wellbeing [ROO]. There is untapped potential in applying this sensibility toward broader and deeper personal sense-making by drawing connections between the full diversity of one's personal data currently siloed in various services and applications - from the wide array of web services, to mobile appications, wearable and home IoT devices.

Personal Information Management (PIM) is a growing ICT sector with an estimated market worth of £16.5bn [NES]. Focusing on four major activities - keeping, finding, organizing and maintaining - PIM offers valuable insights into how to develop and sustain practices for effectively managing one's own data [KLI].

A particular challenge in developing PIM solutions is the individuality of lay data management techniques and strategies, which map onto people's individual strengths and familiar, established practices; in short, individuals thrive when they are able to develop strategies that work for them and for the particular goals they have defined. Given that many services ostensibly offer information management to users (albeit with pre-set UX constraints), an especially interesting frontier for extending PIM research lies in lifting data out from the applications that are currently managing them to support individualised, goal oriented collection and management of personal data - and further, offering techniques for managing between diverse data types (e.g. the minutia of metadata, narrative/textual data, photographic data, activity data, etc).

This project will fill several important gaps in understandings of personal sense-making, including: 1) in contrast to commercial ends for extracting, collecting and analysing people's personal data, understanding what kinds of self-knowledge would offer significant value to individuals, and how bridging personal data between applications and services might uniquely afford these personal insights; and 2) understanding how people can derive meaning from mixed data types and across applications, unbounded by the goal orientations of the individual applications or services they use to capture their personal data.

Key Findings
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Potential use in non-academic contexts
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