The document discusses the challenges associated with using web data as a source for linked science, highlighting issues such as validity, sampling bias, and reproducibility. It points out the difficulties in semantic annotation and archiving due to large, constantly changing datasets and legal restrictions. The author advocates for awareness of sampling bias and suggests a possible need for stricter data publication standards in research.

1. Using the Web as a Data
Source: Challenges for
Linked Science
Carsten Keßler Hunter College, City University of New York
http://carsten.io @carstenkessler

2. Data collection
Then and now

5. http://www.sysomos.com/inside-twitter/twitter-friendship-data

6. Validity issues
Sampling bias, anyone?

9. „Scottish independence referendum results“ von Brythones. Lizenziert unter CC-BY-SA 4.0 über Wikimedia Commons - https://commons.wikimedia.org/wiki/File:Scottish_independence_referendum_results.svg#/media/File:Scottish_independence_referendum_results.svg

11. Reproducibility issues
The ivory tower, locked up

14. Linked Science
…to the rescue?

15. Does LISC work here?
• Semantic annotation, linking and archiving is problematic:
• large datasets
• in constant flux
• legal restrictions
• terms of service may change at any time
• legislation required to permit archiving

16. Can we fix sampling bias?
• Probably not, but…
• need to be aware of it
• if possible, test against other datasets to check for validity of
conclusions
• can we somehow encode this, maybe as provenance?

17. Should we enforce
data publication?
• Tradeoff between:
• Learning about interesting
new research going on at
those companies, without
any ability to verify or
reproduce results
• Implementing and
enforcing strict rules for
conference and journal
outlets that would force
authors to provide the
data used in their analysis

18. Thank you.
Carsten Keßler Hunter College, City University of New York
http://carsten.io @carstenkessler