The document outlines a data services needs assessment conducted at the University of Oregon, involving interviews with 25 faculty members across various departments. Key themes include the necessity for improved data management tools, such as electronic lab notebooks and laboratory information management systems, to enhance data organization, workflow management, and preservation practices. Considerations for successful implementation include costs, user-friendliness, and integration with research processes, with an emphasis on collaboration among stakeholders.

1. Brian Westra
University of Oregon
bwestra@uoregon.edu

2. Data services needs assessment: 2009-2010
Interviewed 25 faculty:
Biology
Center for Advanced Materials Characterization at Oregon
Chemistry
Computer & Information Science
Geological Sciences
Human Physiology
Institute for a Sustainable Environment
Museum of Natural and Cultural History
Physics
Psychology

3. o Connecting data sources to data viewing and
usage
o Data organization
o Metadata/annotation of files
o Recording workflow, procedures, provenance
Preservation, archiving and publishing data
were farther down the list

4. Clearly articulated need and opportunity;
also tie-in to data management plan
implementations
Logical extension of the role for libraries
beyond traditional services
Support for e-Science is a goal
Working in the data lifecycle/ecosystem is more
robust than ‗just‘ archiving/preservation

5. Maintaining, preserving and adding value to
digital research data throughout its lifecycle.
http://www.dcc.ac.uk/digital-curation/what-digital-curation

6. File management tools: i.e., Sharepoint
Best practices: naming conventions, version
control software
Are there other solutions or services?

7. Going beyond file management systems to
embedded, more holistic tools/systems:
o Electronic Lab Notebooks
o Content/format-specific data management
software

8. ―…how a laboratory tracks and manages its
information resources, particularly the data
that represents the laboratory‘s product.‖
(Avery, McGee, & Falk, 2000)
―a data and sample management system that is
designed to improve the management of
laboratory workflow‖ (―Clinical LIMS,‖ 2011)
Most basic function: sample handling and
reporting.

9. Data (create, store, share, organize, analyze)
+
information (notes)
May include: sample handling, storeroom inventory,
signatures, collaboration, protocols and SOPs,
embedded workflows, data analysis and
visualization
LIMS and ELN functions and features often overlap

10. Many of them! UWisconsin-Madison RFI responses
included these vendors:
o Accelrys
o Agilent
o Amphora
o Axiope
o Contur
o IDBS
o Kinematik
o Labtrack
o Notebookmaker
o Rescentris
o Waters

11. Continuously changing field of vendors and
products
o Nature article
o Other options: open source, or a mix of basic tools,
often used in open science

12. Some UO considerations:
o Academic audience (vs. FDA compliance)
o Cost – S/W, hardware, sys-admin, training
o Interface and ease of use
o Account management
o Platform
o Research domain integration*
o Metadata support*
o Data file management*
*curation characteristics

13. o Research domain
o Workflow integration with analytical tools, methods
o Data capture from typical hardware/sources
o Ontologies
o Metadata
o Capture/extraction
o Representation, standards
o Export with files
o Data file management
o File format standards, transformations
o Export options
o Metadata
o Provenance, version control
o Archiving raw and derivatives

14. Wisconsin-Madison RFI
o Some highlights from an excellent list of
considerations
o Good process
o Plan to field test with 60 participants

15. What might be your ―make or break‖ issues?
How would you assign weights or ranking to
the metrics?
1. Costs
2. Platform
3. Product lock-in
4. etc.

16. ‗Ground truth‘ the
metrics and
values/comparators
Satellite or high-altitude
(pre-pilot) might not
conform to on the ground
(during the pilot)
http://www.seawead.org/index.php?option=c
om_content&view=article&id=29:ground-
truthing&catid=9&Itemid=9

17. Have realistic team work load and timeline
expectations
It‘s progress! It may be difficult to apply
measures of curation capacity to an ELN
o Archiving and preservation capacity
o Exportable relational (semantic) representation
o Publication of data

18. It may be more realistic to ask:
o Will this help you (the PI) find and understand the
data and notes this week/ next year/after the
student is gone?
o Can this improve your ability to do data
management (and write a better plan for the next
grant proposal)?
o Is it simple enough that it will become part of the
routine?
i.e., folklore: info everyone knows but no one
records

19. Example: publish direct to ChemSpider
Chemspider record
ELN data exchange project: Dial-a-molecule

20. A compelling reason for faculty to participate
Collaboration and coordination with
stakeholders (Office of Research, IT,
Libraries, research faculty, Tech Transfer)
Champion(s) – these are usually not easy or
inexpensive to implement, in the lab or with
limited budgets

21. What is the scope of a ―pilot case‖?
o Duration
o Number of participants
o Hardware capacity
o Level of training and support
o Evaluation criteria and roles
o Exit strategy – and dealing with success
Who‘s going to pay for this (right now)?
Might anticipate who is going pay for this (if it
works well and goes to production)

22. ―Data you enter in the ELN software will be stored in a secure
location, however; at the end of the pilot period, the data will
be removed and we cannot guarantee that it can be recovered
fully from the ELN. Therefore, we very strongly encourage you
to keep an additional copy of all data and notebook entries in
electronic and/or hard copy format during the pilot as a backup
measure and as a means of keeping a complete and continuous
record of your work during the pilot period.‖
https://academictech.doit.wisc.edu/informed-consent-electronic-lab-notebook-pilot

23. Many biology labs produce a lot of still images
and video
Cresko lab - UO

24. Open Microscopy Environment (OME)-developed
system for image file management

25. Embeds/supports curation:
o Uses a metadata standard for description (OME
XML)
o Employs file format standards (import to tiff)
o Can archive raw and derivative files
o Provides intuitive organizational schema
o Annotation and description support on multiple
levels
o Export of files with metadata

26. video

27. It‘s open source – what is the level of
support/installation base? Longevity/stability?
How well does it fit into the workflow of the lab?
Can it support the proprietary formats generated
in the labs?
What are the IT/systems requirements?

28. Finding a host and participants
Establishing realistic expectations
o Host obligations
o Project scope

29. DCXL: Digital Curation for Excel
Discussion: what other options are you
exploring?

31. Avery, G., McGee, C., & Falk, S. (2000). Product Review: Implementing LIMS: A ―how-to‖ guide. Analytical
Chemistry, 72(1), 57 A-62 A. American Chemical Society. doi:10.1021/ac0027082
CIO Office, U. of W.-M. (n.d.). Charter 6.7: eLab Notebooks | CIO Office | UW-Madison. Retrieved February 9, 2012, from
http://www.cio.wisc.edu/plan-docs-Charter6-7.aspx
Clinical LIMS. (2011). Retrieved from http://www.scientificcomputing.com/product-IN-Clinical-LIMS-
072811.aspx?terms=LIMS
Giles, J. (2012). Going paperless: The digital lab. Nature, 481(7382), 430-1. doi:10.1038/481430a
PerkinElmer. (n.d.). PerkinElmer Informatics. Retrieved February 9, 2012, from http://www.cambridgesoft.com/?l=en
Rescentris. (n.d.). Rescentris | CERF Software. Retrieved February 9, 2012, from http://rescentris.com/cerf-software/
University of Dundee & Open Microscopy Environment. (n.d.). About OMERO — OME. Retrieved February 9, 2012, from
http://www.openmicroscopy.org/site/products/omero
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electronic-lab-notebook-pilot
University of Wisconsin-Madison. (n.d.-a). Electronic Lab Notebooks | Technology Solutions for Teaching and Research.
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