there is a twofold problem when it comes to gender relation and, more generally, diversity in Maker Spaces: (a) managers of these spaces may have incomplete information so that they can’t really specify whether there is a problem at all or if so, they cannot estimate the magnitude of the issue or any implications that come with it. (b) Maker communities struggle to find answers when confronted with cultural diversity or vocal opponents of specific approaches to running a maker space.

2. The Make-IT Project
» Collaboration
» Governance
» Value Creation
Make-IT - Christian Voigt (ZSI)8/18/2017 2
http://make-it.io/

3. Session Outline
1. Experimenting With Open Data – Example of
“Exploring The Gender Gap, Age and Membership
Developments”
2. Future Use of Open Data for Fablabs
3. Discussion
Make-IT - Christian Voigt (ZSI)8/18/2017 3

4. Experimenting With Open Data –
Exploring The Gender Gap, Age and
Membership Developments
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Christian Voigt1, Elisabeth Unterfrauner2, Roland Stelzer3
1,2 Zentrum für Soziale Innovation, Vienna, Austria voigt@zsi.at, unterfrauner@zsi.at
3 Happylab GmbH, Vienna, Austria roland.stelzer@happylab.at

5. Gendered Challenges
» Lower participation rates (e.g. 27% at Happylab)
» Male dominated maker cultures
» Gendered Objects (e.g. cake topper vs. robot)
» Gendered tasks (e.g. programming vs. PR)
» Lack of role models (e.g. fab manager, teachers ..)
» Historical stereotypes starting with
» STEM in schools and
» continuing into carrier choices later on as well as
» daily live appliances (classic microwave example)
» Gendered challenges are part of a bigger inequality situation
concerning jobs (Have you ever heard about a gender gap in
hospitals?), pay and promotions …
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6. Approach
» The General Case
» 10 maker spaces interviewed (39 Interviews)
» The Specific Case
» Machine logs and anonymized membership data from Happylab
» Total of 2,723 Viennese Makers
» Related to ≈ 133.000 data points (3d printing, CNC milling, laser
cutter, cutting plotter, PCB etching and transfer press), analyzed
since 2012
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Today’sFocus

7. Outlier detection
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One per cent outliers for laser cutting (blue crosses) and PCB etching (red dots)
For example, power users could well amount up to 30,000 min in total for
laser cutting (i.e. 2.4 hrs every week for 4 years)
Laser Cutter: Median: 437 min, Interquartile Range: 144 - 1,124 min
whereas a value of 53,000 min for the etching printed circuit boards (PCB)
seemed highly unlikely
PCB: Median: 210 min, Interquartile Range: 105 - 540 min

8. Happylab
Membership
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9. Membership developments
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Development of membership types (including all three locations)

10. Demographics
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11. No clear relationship between machine usage,
membership duration or membership type … so far
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12. Age & Flexibility
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13. Machine preferences
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14. Machine use time
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15. Conclusion
» Single most dominant difference is membership (27:73)
» Similar in terms of ‘choice of membership types’
» Age difference ≈ 3 yrs (sig.)
» Membership Duration △ 0.7 yrs (sig.)
» Preferences at machine level:
» Male: CNC milling & 3d printing
» Female: Cutting plotter
» No significant differences re machine usage time
⇢Numbers to be seen as starting points to design actions
addressing these differences (space design, workshop offers,
outreach activities …)
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16. What if we could
- analyze past patterns,
- show current availability and
- anticipate future usage?
Part B
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17. EXAMPLE: Bike Hiring - Open Data for Infrastructures
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18. Global Open Data
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19. Make-IT - Christian Voigt (ZSI)8/18/2017 19
Open Data for Fablabs:
https://api.fablabs.io/

20. Open Data for Fablabs: https://fablabs.ch/#/machine
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21. • Which Data Could You
Share?
What Benefits Would You
See?
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22. BACKUP
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23. Open Data for Makers: Citizen Sensor Boards
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https://www.fablabs.io/projects/smart-citizen-pulling-data