2017 SWCS Annual Conference July 30-August 2, 2017 Monona Terrace Convention Center

1. Critical Infrastructure
to Promote Data
Synthesis into
Evidence-Based
Nutrient Management
Sylvie M. Brouder
Jeff. Volenec
Agronomy Department, Purdue University, West Lafayette, IN
72nd SWCS International Annual Conference, Madison, WI
July 31, 2017

2. Motivation…

3. 1997: In the beginning…
Purdue’s Water Quality Field Station: A
Core Facility and bleak landscape
without a DMP safety net…

4. WQFS Video

5. Data in the stacks: Libraries and
the research data afterlife

6. Why Libraries? The skill sets, the
thought process, professional value
system (“public good”), & public
expectation of infallibility /
persistence are right
for the problem…

7. I like
Perez
He has
an ugly
girlfriend
An ugly
girlfriend
means he has
no confidence
Inexplicable human
behavior = buy lottery
tickets but also
insurance
Vanity Fair, 12/2011: Michael
Lewis asks “Why do professional
baseball executives make such
colossal mistakes?”
Best metric: on-base %
Moneyball=myscience/
knowledgetranslationepiphany…

8. Inspiration/Lessons from
Moneyball, Conservation
Agriculture, Ext./Rec Development
and TamiFlu?
Michael Lewis’
Moneyball: The Art of
Winning an Unfair
Game
R. Navarrete, 2013

9. Agriculture and the non-big data problem: Short
data life cycles, long-tail data, and data lost to the
dark side… enlightenment from medicine
Number of data sets
Data
Size
Organ-
ized
big
data
Long-tail data
Dark data
Schematic adapted from Ferguson et al., 2014, “Big data from small
data: data sharing in the long-tail of neuroscience”
Literature limit

10. Examples of valuable but dark data in Agricultural Research ~
Recommendations must come from the “preponderance” of all
evidence (not just the novel result that makes it to a journal…)
Dark research data?
• Orphaned data ~ data collected
but not used in experimental
analysis (increasingly prevalant)
• Null or failed studies (reproving
the null hypothesis) ~ no impact
studies need to contribute to a
“preponderance” of evidence
• Confirmatory studies ~ not novel
so may not be publishable but still
needed for preponderance of
evidence
Dark non-research data
• Data from on-farm
collaboratives and farmer-driven
research efforts
• Data collected by farmers, CCAs,
etc. in current management
protocols (e.g. farm records)
• Monitoring data off equipment,
etc.
• Other??

11. The long and winding road,
That leads to your door… J. Lennon, P. McCartney
Non-compliant
“Digital Natives”
Persistent Players: M.S. Bracke, J.J. Volenec, R. Turco, S. Brandt, T.S. Murrell
Assoc. Dean Plaut, Dean Mullins, M. Witt, P. Fixen, J. Carlson, …
2004 proposal rejection
Natural
hazards
Encouraging
directional
indicators

12. My current vision for evidence-based nutrient recommendations: 10 steps
to real-time data uptake, analysis & customized recommendations (working
backwards)
10. Customized,
credible, nutrient
management
recommendation …
 Self-improving
 References the
users’ data
 Can be modified for
non agronomic
priorities (risk
consideration, time
horizons, etc.)

13. Steps 6 – 9: The cool stuff via the Analytical Framework
6. Automatic reanalysis w/ accruing data
7. Machine learning / artificial intelligence
strategies to minimize human resources
8. Combination analytical strategies that
are directed by scientist using proven
theories & data mining (“unsupervised”)
strategies to surface overlooked linkages,
drivers & proxy measures
9. Tools for “unpacking” the analytical
result to explore new/unexpected
results & discoveries

14. The Foundation: A bit less cool but essential…
1. User enters data via web
portal
2. Portal has imbedded
workflows for ease of use &
auto quality assurance/quality
control (QA/QC)
3. Data anonymized at entry
according to mutually
acceptable terms & conditions
4. User data combined with
existing research data
5. Data archived and preserved
in a “trusted” repository
The Data Repository….

15. Impediments/Challenges Confronting Data
Generators and Downstream Data Users
Meta-data standards
Data standards
Minimum data sets
Provenance
Repositories
Data publishing
Dataset versioning
 Data discovery and retrieval
 Data granularity
 Scholarship of data publishing
 Data ownership
 Business models for data
 Education about data
management, including re-
education

16. Our Focus: Pressing technological challenges to informatics
for all agronomic efforts concern data workflow…
• Data dispersion
– Take advantage of small
datasets collected by many
researchers (not everything
is “BIG”)
• Data heterogeneity
– Varied protocols reflecting
local culture & variation in
1o purpose
• Data provenance
– Need to track data through
multi-step process of
aggregation, modeling,
analysis
Storage is not enough!!!!!

17. What is a data repository
• It is: an emerging mechanisms
for extending data lifecycles
• Moving beyond storage to
preservation & curation
• Example: Research Repositories,
Data Publications
• It is not: just storage, nor a
website, a database, a network…

18. Repository Issues – No Perfect Solution (yet) for Data, a Public
Good
Examples considered:
DataOne-NSF, Soft money-renewed for a second 5 yr term; become a node?
DRYAD: http://www.datadryad.org/; requires linkage to a publication; what
happens to unpublished, negative results critical to systematic reviews?
Professional Societies: Association of Crops, Soils, & Environmental Science
Societies (ACSESS) - expand Digital Library of e-pubs into a repository?
Enhance data discovery.
New Ag Data Commons at USDA National Agricultural Libraries
Purdue Univ. Research Repository (PURR) & the 4R-RR: Attached to an
Institution with a long legacy; Storage for at least 10 yrs -then what?

19. Where are we (PURR / 4R-RR) focusing in the
“data value chain” ~ working behind or upstream
of the “interoperability curtain”
Conceive
• Exp. Des.
• Data Mgt.
Planning
Collect
• Clean
• Rectify
Describe
• Data
Dictionaries
• Meta Data
Discover
Aggregate
• Code / APIs
• Derivative
Data
Synthesize
• BD Analytics
• Statistical
Meta Anal..
Create
New
Knowledge
Interoperability
Produce “Transformative” (Headline) Results
→ Advance Science
Prepare (Preprocess) Data
→ Create tools & workflows
Largely out of sight; sparsely
populated w/ expertise &
solutions
High visibility; crowded
w/ expertise & solutions

20. PURR / 4R-RR Goals: Facilitating best practices
for data sharing…
• Discoverable ~ findable with common search engines
• Accessible ~ downloadable and subject to manipulation
• Intelligible ~ human and machine readable, suitably described, access
rights clearly stated
• Assessable ~ provenance clear & quality/reliability should be evident
• Usable ~ data should be in a generically “actionable” format (not a
pdf!)
• No-nos:
• Simply posting to a website (non-persistent)
• Requirements: New curriculum and infrastructure…

21. Purdue University Research Repository (PURR)
most useful agronomic tool since the RCBD
PURR can assign
unique DOI to aid
data discovery and
provenance
PURR is a “Hub”
Cyber-environment;
includes tools,
models, workspace
along with storage
and publication
capabilities.
So much more than “data storage”….

22. Purdue University Research Repository: What libraries were/are
to books, PURR is/will be to data (plus so much more!)

23. You can search for (“google”, web of
science, …) data published via PURR
NAL terms; important unique terms (Grant #)

24. The workflow is predetermined when
publishing ~ you are prompted to be
comprehensive in the info you provide ~
PU Lib. Information Specialists review it
prior to publication…

25. Supporting documents accessible with
datasets…. Alfalfa P/K study
A rudimentary Data Dictionary

26. LOCKSS: PURR relieves the researcher of the
responsibilities of ensuring data security
Per PURR Policy:
You cannot post
sensitive data
unless you have
removed
identifiers…

27. The 4R Fund Research Repository: Foundational infrastructure for collaboration & synthesis
in nutrient management research & recommendation development (a repository w/in a
repository)
Scott Brandt,
Purdue
University
Libraries
And not
by me…
Includes librarians
who possess the
professional skills
to design
workflows that will
help organize &
store things
(data!!) so
something can be
discovered /
accessed / used.
PURR
Process:
Plan,
Collaborate,
Publish,
Archive

28. Key attribute: Linking of project with archival space ~
data are not accessible to others until you “publish”
Write
Data
Mngmt.
Plan
Create
Project
in
PURR
Collab.
w/
Research
Team
Upload
Data,
Working
Files
Finalize
Dataset
(version)
Upload
Support.
Materials
Create
Data
Pub.
Publish
w/ DOI
PURR
Archives
10+ Yr
Private ~ viewable only to your “team” Searchable,
Accessible,
Retrievable,
Reusable
Policy discussion point with the 4R Fund/IPNI: how long of an embargo post
project completion….????

29. Hands-on Help: Ag. Research Librarians will help 4R-funded Researchers with
Workflows, Policies and Procedures for Curation, Preservation and Publication of
Their Data Including:
Persistent data formats
Licensing data (privacy requirements & policy)
Meta-data / other tags for data discovery
Versioning of accruing data sets
Supporting documentation
Data publishing
Assigning DOIs
PURR has a 10yr commitment to data set preservation / options beyond 10yr
Policies / mechanisms for novel public/private partnerships for data stewardship
Business models for open access data

30. Hands-on help from Agronomists: Best practices
have to become easier to do than not to do…
• Tough Lessons: Single biggest mistake we
have & can make is “build it and they will
come” & not providing enough help
• Many datasets need “special treatment”
• 4R-RR “Data Buddy”
Work one-on-one w/ PIs to help transition
their data from their computers to PURR
Assist with standards: data and meta-data
Make certain minimum data sets are
acquired.
• Challenge: “Data buddies” are
hard to find!
• Youth: not enough wisdom about
the culture of the science
• Established Scientists: don’t have
the data skills, time or both
Meadow Creek Students Partner as Data Buddies
www.hebisd.eduhttp://www.hebisd.edu/media/images/articles/2763f.jpg

31. Final thought: Description not prescription
•Second biggest
mistake…?
•Templates!!!!
•Solution:
•Data dictionaries