Clinical research
notebook of Sir Alexander
Fleming, recording
experiments on 11
December 1928 on
‘inhibition by moulds’,
British Library, Additional
MS 56162, f. 26
“
the thoughtless person playing with penicillin treatment is responsible for the death of the
man who eventually succumbs to infection with the penicillin-resistant organism. I hope
this evil can be averted (Alexander Fleming, 1945)
The belief that 'we' had discovered penicillin was not just a description of penicillin, it
also helped to define a post-imperial ‘We’ (Robert Bud)
Microbial art by Sir Alexander Fleming.
Fleming was a member of the
Chelsea Arts Club and keen amateur
artist.
These paintings were technically
difficult to make, as Fleming had to
find microbes with different pigments,
then time the inoculation such that all
the different species matured at the
same time.
Fleming’s experiments in microbial art
anticipate more recent bio-art.
Alexander Fleming Laboratory
Museum, Imperial College
Healthcare NHS Trust
Use of data by ‘smart cities’
Use of predictive
analytics to control rat
infestations in Chicago:
map shows trends in
complaints
Use of data by ‘smart cities’
Identification of
buildings posing risk
of lead poisoning:
‘Lead Safe Illinois'
Figure 1A: Location of fires as predicted before and after the use
of MODA’s model
Original model
Updated model
Actual fires
Original model overpredicted fires in
Downtown Brooklyn,
Park Slope and
Bay Ridge
Updated model accurately
reflects risks in West
Bronx, Downtown,
and Far Rockaways
Observed fire frequency,
2011 to present
Source:NYC – Mayor’s Office of Data Analytics,‘Annual Report 2013’,December 2013,p.14
Models predicting fire risk in New York (from Eddie
Copeland, Big Data in the Big Apple (2015))
www.dublindashboard.ie
Citizen sensing projects from China
In 2014, a photograph of people running the Beijing marathon
in heavy–duty facemasks spread around the world. The person
responsible for the air quality monitoring account of the US
embassy in Beijing once described an extremely severe air quality
reading in the city as ‘crazy bad.’87 As a result, apps that tell people
the official air quality readings are some of the most popular in the
city. B eyond this, students and entrepreneurs are creating low–cost
sensing equipment which people can use to measure their own air
quality:
• Air. Air! is a portable air quality monitor which connects to a
smartphone and was succesfully funded on Kickstarter.
• Design students at Tsinghua University developed an air pollution
monitor that gradually changes colour as air quality worsens,
making technical information easy to understand.88
Image by J.C.Barros. Licence CC BY 2.0.
• Art can also be a powerful way to advocate for change. In
Beijing, designers created F LOAT ‘smart’ kites to map air
pollution over the city and raise awareness of the problem.89
from Tom Saunders and
Peter Baeck, Rethinking
Smart Cities from the
Ground Up (Nesta report,
June 2015)
https://publiclab.org/wiki/dustduino
•
Kitchen and Lauriault (2014): Data are situated, contingent, relational, and
framed, and used contextually to try and achieve certain aims and goals
•
Huggett (2014): Information about the past is situated, contingent, and
incomplete; data are theory-laden, and relationships are constantly
changing depending on context.
•
Linkage of data sets may use probabilistic methods, but failures of linkage
often culturally dependent.
•
Studies of health record registry patterns show that it is the socially
excluded who are more likely to be omitted from hospital records (Harron,
Goldstein and Dibban 2016)
•
Algorithms and factors identified in predictive analytics frequently depend
on a variety of cultural and social assumptions
•
If you don’t involve communities in designing the data, you risk asking the
wrong questions - but how do we go out this?
•
First significant impact of Internet of Things likely to be in built
environment
•
We are already struggling to cope with limited amounts of
data from IoT applications. How we will cope with data from
every room, cupboard and device?
•
Fundamentals like account management, calibration, security
will be even more important, but difficult to manage on such a
huge scale
•
Ethical and ownership considerations?
•
How do we build an Internet of Things in which the
community can work together to address problems like AMR?
•
Many other relevant issues
•
Language: shifting meanings, different
terminologies, all affect methods such as text mining
•
Do we take enough account of issues like AMR in
metadata and search standards?
•
Repurposing of research from e.g. performance
(motion tracking)
•
Cognitive issues (how do we cope with data
deluge?)
John Martin, The Deluge (1834)
Mass Cytometry Data on
Biological Systems is Large,
Hyper-Dimensional, and
Complex
1.2B data points:
1 pa/ent blood sample
24 drugs
2M cells per drug
25 phospho@
proteins per cell
reduced and visualized as
113,000 data points:
Cover of Nature Biotech, Sept 2012.
1 pa/ent blood sample
24 drugs (x axis)
12 s/ mula/on condi/ ons (z axis)
12 cell types (y axis)
14 phospho@
proteins per cell
(nested diagram)
2 drug measures (circle size & color)
Nicole Coleman and Erica Savig, Common Design Strategies for Exploring Intellectual
Geographies in History and Cell Motility in Biology, presentation to joint NEH-AHRC
workshop, Shared Horizons: Data, Biomedicine and the Digital Humanities, University of
Maryland, 2013:
http://mith.umd.edu/sharedhorizons/wp-content/uploads/
Savig_Coleman_SharedHorizons_April2013_cnc.pdf
Current Visualizations Do Not Suffice for Data Exploration
For a single pa/ent and single experimental condi/ on…
(assuming 30 cell types and 30 proteins measured)
900
biaxial plots
30
SPADE trees
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Behbehani, Cytometry, 2012
www.cytobank.org
increasing scale of data context
Considerations To Be Addressed:
• Selecting Appropriate Representations Per Protein
• Maintaining Contextual Organization at Various Scales
• Maintaining Data Structure within Architectural Relationships
• Bottom-Up Emergent Representations Across Scales
• Total Synthesis
Example of 5 different “cell types”
with different combinations of
“protein responses” for 3 different
proteins.
Early Modern Historical
Data is Heterogeneous,
Multi-dimensional, and
Incomplete
We are aVemp/ ng to understand
the intellectual communi/ es that
made up the “Republic of LeVers”
through case studies spanning
three centuries, based on
hundreds of thousands of
documents
(mostly leVers)
and biographical data on
tens of thousands of
individuals.
Joseph Priestley’s Chart of Biography, 1765