1. BURIED DEEP: How data about subseafloor life becomes dark and why
PETER DARCH
REBEKAH CUMMINGS
DEPARTMENT OF INFORMATION STUDIES
UNIVERSITY OF CALIFORNIA, LOS ANGELES
American geophysical union fall meeting 2013, san francisco

2. “Dark data” and the “long-tail” of science
What is “dark data” (Heidorn 2008)?
And “dark methods”
Why is this important?
Data
Provenance
Darch and Cummings, AGU Fall Meeting 2013

3. “Dark data” and the “long-tail” of science
What is “dark data” (Heidorn 2008)?
And “dark methods”
Why is this important?
Data
Provenance
Darch and Cummings, AGU Fall Meeting 2013

4. “Dark data” and the “long-tail” of science
What is “dark data” (Heidorn 2008)?
And “dark methods”
Why is this important?
Data
Provenance
Darch and Cummings, AGU Fall Meeting 2013

5. Darch and Cummings, AGU Fall Meeting 2013
Introducing C-DEBI
National Science Foundation Science and Technology Center
Interactions between subseafloor microbial life and geochemistry
Individual and small group support for research
(Accessed 8 October 2013)
Darch and Cummings, AGU Fall Meeting 2013 4

6. Darch and Cummings, AGU Fall Meeting 2013
Introducing C-DEBI
National Science Foundation Science and Technology Center
Interactions between subseafloor microbial life and geochemistry
Individual and small group support for research
(Accessed 8 October 2013)
Darch and Cummings, AGU Fall Meeting 2013 4

7. Darch and Cummings, AGU Fall Meeting 2013
Introducing C-DEBI
National Science Foundation Science and Technology Center
Interactions between subseafloor microbial life and geochemistry
Individual and small group support for research
(Accessed 8 October 2013)
Darch and Cummings, AGU Fall Meeting 2013 4

8. Darch and Cummings, AGU Fall Meeting 2013
CRUISES
International Ocean Drilling Program (IODP)
University-National Oceanographic Laboratory System (UNOLS)
Samples
Personal photo
Samples
BIOLOGICAL ANALYSES IN THE LABORATORY
Quantification, Community structure,
Community composition, Function
Iodp.tamu.org
PHYSICAL ANALYSES IN THE LABORATORY
Crystallographic
Mineralogical
Chemical
Geological
Extraction of DNA/RNA
Low biomass
PCR, amplification
Choice of sequencing facilities (commercial, university)
Choice of sequencing methods
Sequencing
Cleaning sequences, classifying sequences,
Phylogenies, community comparisons etc.
Online databases
Choice of bioinformatics tools (commercial, free,
created by researcher)
LAB-GENERATED
PHYSICAL DATA
ON-BOARD
GEOCHEMICAL DATA
BIOINFORMATICS
DATA
Darch and Cummings, AGU Fall Meeting 2013

9. Darch and Cummings, AGU Fall Meeting 2013
Biological data generated in laboratory (e.g. DNA sequences, proteomic data)
Social
scale
IODP
Disciplinary
Institutional
Journal
C-DEBI
Leaves
laboratory
Leaves field/
academia
Data/laboratory
notebook
retention ends
Mid-range
UNOLS
Publishes
paper
Lab
Local
Months
One to a few years
Indefinite
Time
horizon
LOST DATA
Darch and Cummings, AGU Fall Meeting 2013

10. Darch and Cummings, AGU Fall Meeting 2013
Physical data generated in laboratory (e.g. mineralogical, geochemical)
Social
scale
IODP
Disciplinary
Institutional
Journal
C-DEBI
Leaves
laboratory
Leaves field/
academia
Data/laboratory
notebook
retention ends
Mid-range
UNOLS
Publishes
paper
Lab
Local
Weeks/
months
One to a few years
Indefinite
Time
horizon
LOST DATA
Darch and Cummings, AGU Fall Meeting 2013

11. What happens to laboratory data?
Scientists immediate needs
Darch and Cummings, AGU Fall Meeting 2013

12. What happens to laboratory data?
Scientists immediate needs
Some in disciplinary databases
Darch and Cummings, AGU Fall Meeting 2013

13. What happens to laboratory data?
Scientists immediate needs
Some in disciplinary databases
Other data discovery
Darch and Cummings, AGU Fall Meeting 2013

14. What makes data and methods “dark”?:
Data use in other contexts
Darch and Cummings, AGU Fall Meeting 2013

15. What makes data and methods “dark”?:
Data use in other contexts
Networking and serendipitous encounters
Darch and Cummings, AGU Fall Meeting 2013

16. What makes data and methods “dark”?:
Data use in other contexts
Networking and serendipitous encounters
Short-term research grants
Darch and Cummings, AGU Fall Meeting 2013

17. What makes data and methods “dark”?:
Data use in other contexts
Networking and serendipitous encounters
Short-term research grants
Standardization of data practices
Darch and Cummings, AGU Fall Meeting 2013

18. Darch and Cummings, AGU Fall Meeting 2013
Conclusions
“Dark data”
And “dark methods”
Heterogeneity
Important that methods do not become “dark”
Makes methods more likely to become “dark”
Darch and Cummings, AGU Fall Meeting 2013

19. Darch and Cummings, AGU Fall Meeting 2013
Conclusions
“Dark data”
And “dark methods”
Heterogeneity
Important that methods do not become “dark”
Makes methods more likely to become “dark”
Darch and Cummings, AGU Fall Meeting 2013

20. Darch and Cummings, AGU Fall Meeting 2013
Conclusions
“Dark data”
And “dark methods”
Heterogeneity
Important that methods do not become “dark”
Makes methods more likely to become “dark”
Darch and Cummings, AGU Fall Meeting 2013

21. And thank you for listening
Acknowledgements
Those scientists who have participated in our research
Sloan Foundation (Award # )
National Science Foundation (Award # )
Other members of the Knowledge Infrastructures team at UCLA (PI Prof Christine Borgman, Co-PI Prof Sharon Traweek)
And thank you for listening
Darch and Cummings, AGU Fall Meeting 2013