Sediment Experimentalist Network (SEN): 
Sharing and reusing methods and data in 
Earth surface process experiments 
Lesli...
Do we need a Sediment Experimentalist Network?
1. Expectations about research data 
are evolving 
1. Data deluge: There is more 
data from new technologies 
2. Funding a...
2. Experimental data is long tail data 
Long Tail Characteristics 
More specialised 
Low volume 
On C drives 
Hard to find...
3. Grand challenges in experimental 
geomorpholgy require data 
syntheses 
• Repeatibility 
• Scalability 
• Autogenic vs....
4. Informatics programs are growing 
• NSF ACI Advanced Cyberinfrastructure 
• AGU ESSI Earth and Space Science Informatic...
Yes, we need SEN. 
SEN’s goal is to integrate the efforts of sediment experimentalists and build 
a knowledge base for gui...
Three components of SEN 
SEN-EC 
Experimental 
Collaboratories 
• Facilitate collaboration 
between experimental 
laborato...
Today’s objectives 
• What is SEN, why do we need it? 
• What is the data life cycle? 
• How can SEN help you? 
• Tell SEN...
Normal degradation of data 
Michener et al. (1997)
The Data Life Cycle 
Conception 
1. Proposal Development and Data Management Plans 
2. Project Start‐up 
Gestation 
3. Dat...
Planning 
2012 
The funding agency 
wants your plan and the 
promise that the data 
will be available forever. 
Jorge Cham
Data Management Plans 
Many funding agencies now require a data 
management plan 
Many resources exist for creating data 
...
Proposal Information 
http://www.iedadata.org/compliance/plan
Data Acquisition / Processing Summary
Proposed Data Products
Data collection
Preparing data for sharing 
Science 11 February 2011: 
Vol. 331 no. 6018 pp. 692-693
Preparing data for sharing 
Data templates and standards 
exist 
• Sample descriptions 
• Analytical geochemical data 
• C...
Depositing data 
In the recent past, the most 
popular place to deposit datasets 
with long-term accountability 
was in jo...
Depositing data 
Now, there are many options for 
depositing datasets, e.g. 
• Discipline-specific online 
repositories 
•...
Data and software publication 
• Data papers 
• Dataset publication and peer review 
• Persistent identifiers 
• Software ...
SEN and the 
data lifecycle 
The experimental life 
cycle parallels the 
data life cycle. 
SEN activities are 
designed to...
SEN Activities 
• Workshops 
• Training 
• Tools 
• News 
• Experiments 
• Discussion list
Future events 
● Nov 2014: Utrecht workshop 
● Dec 2014: Proposed AGU Town Hall – 
Sharing and publishing data in Earth Su...
Tell SEN what you need
SEN Data Catalog, http://sedexp.net 
Create user 
or 
sedimentexp 
siesd2014
SEN Wiki, http://sedexp.net
SEN Sediment and Instrument Lists 
Where have others bought sediment and instruments? 
goo.gl/NUA5mS (Tabs 1 and 2)
SEN and CINERGI Resource Viewer 
What websites, databases, and journals might help me? 
goo.gl/Yp5Aud
SEN 2014 Challenge 
http://goo.gl/YKTsNm 
For a trip to an upcoming SEN workshop! 
(students at a U.S. Institution)
Summary 
1. What is SEN, why do we need it? 
2. What is the data life cycle? 
3. What are some tools that SEN provides to ...
Links and References 
SEN homepage: workspace.earthcube.org/sen 
EarthCube Program: www.earthcube.org 
Michener, William, ...
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Sediment Experimentalist Network (SEN): Sharing and reusing methods and data in Earth surface process experiments

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Presentation given to the Summer Institute for Earth Surface Dynamics (SIESD) 2014 at St. Anthony Falls Laboratory, University of Minnesota, about the Sediment Experimentalist Network (SEN). SEN is an EarthCube Research Coordination Network, whose goal is to integrate the efforts of sediment experimentalists and build a knowledge base for guidance on best practices for data collection and management.

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Sediment Experimentalist Network (SEN): Sharing and reusing methods and data in Earth surface process experiments

  1. 1. Sediment Experimentalist Network (SEN): Sharing and reusing methods and data in Earth surface process experiments Leslie Hsu (IEDA, Lamont-Doherty Earth Observatory), Wonsuck Kim (UT Austin), Brandon McElroy (U Wyoming), Raleigh Martin (UCLA) Charles Nguyen, Danny Im, UMN August 2014, NCED SIESD at UMNSAFL
  2. 2. Do we need a Sediment Experimentalist Network?
  3. 3. 1. Expectations about research data are evolving 1. Data deluge: There is more data from new technologies 2. Funding agencies are asking for data management plans. 3. Journals are asking for links to archived full datasets 4. Metrics for datasets are being developed, allowing better attribution
  4. 4. 2. Experimental data is long tail data Long Tail Characteristics More specialised Low volume On C drives Hard to find Heterogeneous Collected by many people Citizen science Etc Etc Long Tail: Environmental and Earth sciences The Head: Astronomy, Climate, High Energy Physics, Genomics L. Wyborn http://juliegood.wordpress.com/tag/long-tail/
  5. 5. 3. Grand challenges in experimental geomorpholgy require data syntheses • Repeatibility • Scalability • Autogenic vs. Allogenic processes
  6. 6. 4. Informatics programs are growing • NSF ACI Advanced Cyberinfrastructure • AGU ESSI Earth and Space Science Informatics • NSF EarthCube program • ESIP: Earth Science Information Partners • SEAD: data services for managing data • GeoSoft: documenting and sharing software and scripts • CINERGI: providing community access to tools and resources
  7. 7. Yes, we need SEN. SEN’s goal is to integrate the efforts of sediment experimentalists and build a knowledge base for guidance on best practices for data collection and management, and to be the liaison to cyberinfrastructure and geoinformatics communities.
  8. 8. Three components of SEN SEN-EC Experimental Collaboratories • Facilitate collaboration between experimental laboratories • Develop collaborative infrastructure • Broadcast experiments • Distributed experiments • Experimental reproducibility SEN-KB Knowledge Base • Develop online resources for experimental data management • SEN data catalog and wiki • SEN-Wiki • Recruit datasets for inclusion in online repositories SEN-ED Education & Data Standards • Facilitate community discussion of data practices and standards • Disseminate guidelines • Provide training about data management and sharing
  9. 9. Today’s objectives • What is SEN, why do we need it? • What is the data life cycle? • How can SEN help you? • Tell SEN what you need • SEN data challenge
  10. 10. Normal degradation of data Michener et al. (1997)
  11. 11. The Data Life Cycle Conception 1. Proposal Development and Data Management Plans 2. Project Start‐up Gestation 3. Data Collection and File Creation 4. Data Analysis Maturation 5. Data Sharing (through publication) 6. Depositing Data Death, 7. …Discovery and back to 1… Birth Adult Life Burial http://www.icpsr.umich.edu/files/ICPSR/access/dataprep.pdf
  12. 12. Planning 2012 The funding agency wants your plan and the promise that the data will be available forever. Jorge Cham
  13. 13. Data Management Plans Many funding agencies now require a data management plan Many resources exist for creating data management plans, e.g. • IEDA Data management plan tool • CDL (California Digital Libraries) tool • MIT Data Management Plans page
  14. 14. Proposal Information http://www.iedadata.org/compliance/plan
  15. 15. Data Acquisition / Processing Summary
  16. 16. Proposed Data Products
  17. 17. Data collection
  18. 18. Preparing data for sharing Science 11 February 2011: Vol. 331 no. 6018 pp. 692-693
  19. 19. Preparing data for sharing Data templates and standards exist • Sample descriptions • Analytical geochemical data • Cruise data (dives, sensors) Standards are discipline specific, and must be developed with community input
  20. 20. Depositing data In the recent past, the most popular place to deposit datasets with long-term accountability was in journal appendices (Supplemental Material) Formatting requirements inhibited data archiving (e.g. plain text, page size limits)
  21. 21. Depositing data Now, there are many options for depositing datasets, e.g. • Discipline-specific online repositories • General, institutional repositories • Self-publishing with e.g. FigShare
  22. 22. Data and software publication • Data papers • Dataset publication and peer review • Persistent identifiers • Software publication and licenses • Altmetrics (e.g. impactstory.org)
  23. 23. SEN and the data lifecycle The experimental life cycle parallels the data life cycle. SEN activities are designed to help in each step. S. Ahn
  24. 24. SEN Activities • Workshops • Training • Tools • News • Experiments • Discussion list
  25. 25. Future events ● Nov 2014: Utrecht workshop ● Dec 2014: Proposed AGU Town Hall – Sharing and publishing data in Earth Surface Process Science ● Fall 2015: Binghamton Geomorphology Symposium Experimental Geomorphology: Sharing and Reusing Data
  26. 26. Tell SEN what you need
  27. 27. SEN Data Catalog, http://sedexp.net Create user or sedimentexp siesd2014
  28. 28. SEN Wiki, http://sedexp.net
  29. 29. SEN Sediment and Instrument Lists Where have others bought sediment and instruments? goo.gl/NUA5mS (Tabs 1 and 2)
  30. 30. SEN and CINERGI Resource Viewer What websites, databases, and journals might help me? goo.gl/Yp5Aud
  31. 31. SEN 2014 Challenge http://goo.gl/YKTsNm For a trip to an upcoming SEN workshop! (students at a U.S. Institution)
  32. 32. Summary 1. What is SEN, why do we need it? 2. What is the data life cycle? 3. What are some tools that SEN provides to help me discover and manage data? 4. How can I earn a trip to a SEN workshop?
  33. 33. Links and References SEN homepage: workspace.earthcube.org/sen EarthCube Program: www.earthcube.org Michener, William, James Brunt, John Helly, Thomas Kirchner, Susan Stafford. (1997). “Nongeospatial Metadata for the Ecological Sciences Ecological Applications, Vol. 7, No. 1, pp. 330–342. http://www.icpsr.umich.edu/files/ICPSR/access/dataprep.pdf Nature Articles • http://www.nature.com/ngeo/journal/v4/n9/pdf/ngeo1259.pdf • http://www.nature.com/ngeo/journal/v4/n9/pdf/ngeo1248.pdf Science Special Data Issue: http://www.sciencemag.org/content/331/6018/692.short Data Planning • http://www.iedadata.org/compliance/plan • https://dmp.cdlib.org/ • http://libraries.mit.edu/guides/subjects/data-management/plans.html Contact: lhsu@ldeo.columbia.edu

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