De- and Reassembling Data Infrastructures
Transcript of De- and Reassembling Data Infrastructures
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Carolin Gerlitz(based on joint work with Liliana Bounegru & Jonathan Gray)University of Siegen – Digital Methods Initiative Amsterdam
Infrastructuring eResearch Workshop, Dec 7 2016
DE- & REASSEMBLING DATA INFRASTRUCTURES
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DIGITAL METHODS
Widely used term.Repurposing of (1) digital data (2)
technical features (3) analytical capacities.
How can links, likes, shares, comments etc. be used for research? (Rogers 2013)
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DIGITAL METHODS
Structured extraction & analysis of data.
Two main objectives: (1) study sociality online (2) understand medium specificity and socio-technical configurations
Tool development, repurposing & training.
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EXAMPLE: TCAT
Twitter Capture and Analysis Toolkit (TCAT).
Developed by Erik Borra & Bernhard Rieder.
Data collection & analysis of Twitter data based on Streaming API.
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DE-ASSEMBLING
Digital methods rely on the participation of a variety of actors and entities.
Data & tool chaining.What are the data
infrastructures that underpin dm work? What challenges do they pose?
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DATA PRODUCTION
Data production as distributed accomplishment of users, platform activities, capture mechanisms, third party apps & cross-platform syndication.
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(1) COMMENSURATION
Cross-platform syndication, different interpretation of platform features, bots & automation.
How to commensurate data from heterogeneous sources (Espeland & Stevens 1998)?
s
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(2) MULTIVALENT YET BIASED
Data set out to cater to different analytical interests of stakeholders.
At the same time: support some forms of analysis more than others (interestedness).
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DATA EXTRACTION
Scraping, crawling, API retrieval.Reliant on platform data
structures and API politics, tools, plugins and scripts.
Platforms determine the conditions of access to their data.
Instagram Hashtag Explorer
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DATA ANALYSIS
Reliant on further tools for querying data, calculating metrics, stats or combination of data formats.
DMI TCAT
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(3) METHODOLOGICAL
UNCANNY
Open or commercial tools resonate with known methods – but not quite (Marres & Gerlitz 2015).
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DATA VISUALISATION
Visualisation standards and data outputs.
Which data formats are amenable for which visualisation technique? What interestedness does visualisation introduce?
D3, tableau, Gephi
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(4) TOOL CHAINNG
Assembling of different data sources & tools for different tasks into a methodological apparatus.
Cascades of inscriptions (Ruppert et al 2013).
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(5) DISTRIBUTED TOOL MAKING
Many general purpose tools (incl. extensive documentation).
Heterogeneous developers and emergent standards.
Which tools can be chained?How can open source tools be
maintained and scaled up?
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(6) DATA PUBLICS
Data assemble heterogeneous publics with different objectives, interests, skills & needs (Ruppert 2015, Birchall 2015).
Researchers, companies, organisations, activists, journalism.
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ALLINGING DATA INFRATSRUCTURES
Methodological work as de- & reassembly.
Specific to needs of publics.Alignment & mal-
alignment of data sources, tools, visualisations and research objectives: need for repositories and shared dev.
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(RE)IMAGINING DATA INSTRUCTURES
From data literacy to data infrastructure literacy (Gray et al. 2017).
Accounting for inscription, alignment and malalignment.
Enable to re-think, re-assemble and re-align infrastructures.
Methodological infrastuctural imagination (Bowker 2014).