This goes together with a research paper also uploaded here describing practical steps to transparency in computational journalism with two case studies.

1. Towards Editorial Transparency in
Computational Journalism
Jennifer A Stark
Nick Diakopoulos
The University of Maryland, College of Journalism, Computational
Journalism Lab

2. What do we
mean by
“Transparency”
?
“the ways in which people both
inside and external to journalism
are given a chance to monitor,
check, criticize and even intervene
in the journalistic process.”
Deuze, M. 2005. What is journalism?: Professional
identity and ideology of journalists reconsidered.
Journalism. 6, 4 (2005), 442–464

3. What do we
mean by
“Transparency”
?
Storytelling:
Make the steps / data used to
create your story visible to the
audience.
Tool making:
Sharing the code with thorough
documentation.

4. Why Share Our Work?
Benefits to yourself, fellow journalists,
audience

5. Accountability
Document Process
Stimulate Alternative
Stories / viewpoints
Double check data, code,
analysis, and conclusions /
interpretation
Facilitate future work / future
you / fellow journalists / field
Novel work, or extensions to
your original work.

6. Case Study 1: Storytelling
(Uber)
How?

7. Transparency promotes Accountability,
Documentation, Further Storytelling
Share raw collected data: GitHub, Google Drive
(consider size)
Open Source code sharing platform: GitHub,
Jupyter

9. Transparency promotes Accountability,
Documentation, Further Storytelling
Share raw collected data: GitHub, Google Drive
(consider size)
Open Source code sharing platform: GitHub,
Jupyter
Project and Code Documentation: README.md
APIs

11. Transparency promotes Accountability,
Documentation, Further Storytelling
Share raw collected data: Google Drive (consider
size)
Open Source code sharing platform: GitHub,
Jupyter
Project and Code Documentation: README.md
Accountability: share data collection / processing
/ wrangling and analysis
Interim processed data: .csv files
Replicability: programmatic steps where possible
APIs

12. How?
Case Study 2: Tool Making
(Twitter Bot)

13. Twitter Bot: Transparancy promotes
accessibilityOpen Source code sharing platform:
GitHub, Jupyter
Project and Code Documentation:
README.md
Language / platform agnostic:
configuration file
• How much to parameterize?
• Case-by-case uniqueness? Instructions
within code and README documentation Comment APIs

14. Documentation!
Takes longer than you think
Consider it an investment
Documentation within code
Documentation in GitHub repository
(README.md)
Reciprocal links between news
article and GitHub repository
Links to reference material (eg
APIs, preceding work)

15. Licences
Nobody should use your Code
or Data if it is not licenced
Code licences
https://opensource.org/licenses
Data licences
http://opendatacommons.org/about/
Multiple licences
http://choosealicense.com/non-software/

16. Why Share Our Work?
Evidence difficult to measure at this time
“IRL”

17. Sunlight Labs
Policy makers (eg Transport, AARP)

19. Hobbyists / Individuals
Kate Rabinowitz –
“Civic data scientist”
http://www.datalensdc.com/index2.html
About:
“DataLensDC has been featured in
The Washingtonian,
The Atlantic's CityLab,
Washington City Paper,
WJLA ABC 7 News, and more”

20. Final Thoughts
Reinventing the wheel | Reuse code
Stack overflow for sharing code /
solutions?
http://area51.stackexchange.com/proposals/1
03335/data-journalism/
Data or file repository?:
https://quiltdata.com (or something
similar?? I have not tried this tool)

21. Thank you!
@_JAStark
starkja@umd.edu