1. “Classifying Scientific Data Objects with Bibliographic Relationship
Taxonomies”
~~~~~~
ASIS&T 2007 Annual Meeting
Sunday, October 21, 2007
Jane Greenberg, Frances McColl Professor; Director, SILS/Metadata Research Center
School of Information and Library Science
Univ. of North Carolina at Chapel Hill

2. Overview
DRIADE – (Digital Repository of Information and Data for Evolution)
Accomplishments: basic metadata challenges
Functional requirements, underlying metadata
Instantiation
Motivation
Research design
Preliminary results
Conclusions
-adaptation natural selection

3. Small science, open access
Small science repositories (SSR)
Knowledge Network for Biocomplexity (KNB), Marine Metadata Initiative (MMI)
Evolutionary biology
Publication process
Supplementary data (Molecular Biology and Evolution, American Naturalists)
“Author,” “deposition date,” not “subject” “species,” ”geo. locator”
Data deposition (Genbank, TreeBase, Morphbank)
NESCent and SILS/Metadata Research Center
ecology, paleontology, population genetics, physiology, systematics + genomics

4. DRIADE’s Goals DRIADE Team
NESCent
PI: Todd Vision, Director of Informatics and Associate Professor, Biology, UNC
Hilmar Lapp, Assistant Director of Informatics
Ryan Scherle, Data Repository Architect
UNC/SILS/MRC
PI: Jane Greenberg, Associate Professor, SILS and MRC
Sarah Carrier, Research Assistant
Abbey Thompson, DRIADE R.A./SILS Masters Student
Hollie White, Doctoral Fellow
Amy Bouck, Biology, Ph.d.student
DRIADE’s Goals
One-stop deposition and shopping for data objects
Support the acquisition, preservation, resource discovery, and reuse of heterogeneous digital datasets
Balance a need for low barriers, with higher-level … data synthesis

5. One stop shopping— an option
DRIADE (Dryad)
Depositor/s
One stop deposition
Specialized
Repositories
Genbank
TreeBase
Morphbank
PaleoDB
DRIADE
-Data objects
supporting
published
research
Journals & journal
repositories
One stop shopping— an option
Researcher/s

6. Accomplishments: basic metadata challenges

7. Accomplishments: addressing basic metadata challenges
Functional requirements
Consensus building (Dec. 06) + SSR workshops (May ‘07)
First class object, **SHARING & REUSE**
Analysis metadata functions in existing repositories (JCDL, 2007)
Functional model
OAIS (Open Archival Information System), DSpace
Level one application profile
Namespace schemas:
Dublin Core
Data Documentation Initiative (DDI)
Ecological Metadata Language (EML)
PREMIS
Darwin Core
Modular scheme:
Journal citation
Data objects
(Carrier, et al., 2007)

8. Functional requirements
Project
Goals/priorities
GBIF
KNB
NSDL
ICPSR
MMI
Heterogeneous digital datasets ▪
Long-term data stewardship
Tools and incentives to researchers
Minimize technical expertise and time required
Intellectual property rights
Datasets coupled w/published research

9. <DRIADE application profile>
Bibliographic Citation Module
dcterms:bibliographicCitation/Citation information
DOI
Data Object Module
dc:creator/Name*
dc:title/Data Set #
dc:identifier/Data Set Identifier
PREMIS:fixity/(hidden)
dc:relation/DOI of Published Article*
DDI:<depositr>/Depositor
DDI:<contact>/Contact Information
dc:rights/Rights Statement
dc:description/Description #
dc:subject/Keywords
dc:coverage / Locality Required*
dc:coverage/Date Range Required*
dc:software/Software*
dc:format/File Format
dc:format/File Size
dc:date/(Hidden) Required
dc:date/Date Modified*
Darwin Core: species/ Species, or Scientific*
Key
* = semi-automatic
# = manual
Everything else is automatic

10. DRIADE metadata application profile….organic…
Level 1 – initial repository implementation
Application profile: Preservation, access/resource discovery, (limited use of CVs)
Level 2 – full repository implementation
Level 1++ expanded usage, interoperability, preservation; administration; greater use of CV and authority control; data sharing and reuse
Level 3 – “next generation” implementation
Considering Web 2.0 functionalities, Semantic Web

11. Instantiation
(Classifying Scientific Data Objects with Bibliographic Relationships Taxonomies)

12. Motivation DRIADE goals: Data objects = first class objects
Data preservation, sharing, use/re-use, validation, repeatability
Is it important to know history of a data object?
How can we support accurate and effective tracking of the life-cycle of data object?
Data objects = first class objects
Data structures as works (Coleman, 2002)
Units of analysis, intellectual products of activity
Work = “propositions expressed (ideational content)”, and “expressions of the propositions” (Smiraglia, 2001)
Data objects are content carriers (Greenberg, 2007)

13. Motivation
Research and…to explain a“work”; bibliographic families; and instantiation
Metadata and Bibliographic Control Models
FRBR (Functional Requirements for Bibliographic Records)
DCAM (Dublin Core Abstract Model)
RDF (Resource Description Framework)
Coleman (2002)
Cutter (1904)
Leaser (1999)
Ranganathan: embodied/expressed
Smiraglia (1999,2001, 2002,etc.)
Tillet (1991, 1992)
Vellucci (1995)
Wilson (1983)
Yee (1995)

14. Research design Research questions Method
Objective: Build an open access repository that accurately reflects the “disciplinary knowledge structures” (relationship among data objects”)
Research questions
Do scientists (developing scientists) view data objects as works?
Do they understand different “instantiations”?
Do they think instantiation tracking is important?
Method
Instantiation identification test and survey
Participants: Scientists, research and publication

15. Procedures, etc.
Verify participant suitability, contextual introduction
Read instantiation definitions and view instantiation diagram (next slide)
Example Instantiation scenario
Scenario: Sherry collects data on the survival and growth of the plant Borrichia frutescens (the bushy seaside tansy)…
Question: What is the relationship between Sherry’s paper data sheet and her excel spreadsheet?
Answer: Equivalent | Derivative | Whole-part | Sequential
(circle one)
8 scenarios (randomized); survey

16. Data object relationships
Equivalence
Derivative
Whole-part
Sequential B
(=data
set A annotated) A
(=data set) A
(=data
set in
Excel) A
(=same
data set
in SAS) A
(=same
data set
on paper) C
(=data set
A revised) A1
(=part 1
of a data set) A2
(=part 2
of a data set) A
(=data set) A1
(=a subset
of A)
Smiraglia, Tillet

17. Results 7 participants (Biochemistry, Biology, Botany/Plant Ecology)
3 Faculty/research associates; 21+ yrs. research/pub.
4 Research assistants; 3 (1-3 yrs.), 1 (5 yrs.)
Instant./
rel. A B
Failing A
Failing B
Equivalent
100%
n/a
Derivative
6 of 7 (86%)
1 BS
1 MS
Whole/part
1 PhD
Sequential
3 of 7 (43%)
2 BS,

18. Results Use of data by others? ht=halftime; s=seldom; na=no answer
Instant./
rel.
Created X data before
Apply to
Work
“freq.”
Other app.
Support last publ. y/n
Equivalent 7
6 (86%)
1 (h/t)
6/1
Derivative
5 (71%)
2 (h/t)
Whole/part
2 (s)
4/2 (1 na)
Sequential 5
3 (43%)
1 (h/t),1(s)
2/3 (2 na)
Use of data by others?
1-5, 1 person
6-20, 3 people
21-99, 1 person
100+, 2 people (PhD)
How important is it to track data life-cycle (relationships)?
0, not
3 very
4 extremely (2Ph.D, 1 MS, 1 BS)

19. Results (participant comments)
Validity: “Whenever anything changes, there’s the ability to make mistakes”
Sheer quantity of data: “We have 30 years worth of data, tracking changes is important”
The impact of changes in scientific nomenclature:
“As time goes on, datasets must be maintained to reflect current understanding of taxonomy and nomenclature, to allow connection of old data and attributes of that data to be associated correctly with new data and attributes of that data. This is a giant problem.”

20. Conclusions and next steps
Use of data created by others increases over time
In general, more seasoned scientists better grasp
Sequential data presented the most difficulty (less seasoned sci.)
Unanimous support, “very  extremely important”
Bibliographic control relationship are applicable to describing the relationships between data objects
Next steps:
Continue to collect data (30-50)
Study additional types of instantiations
Develop applications that effectively track data object life-cycle
How do we encode these relationships? Manually/automatically?
Who should create them? Curator, scientists, collaboratively?
Should we maintain a vocabulary?
Consider implications for other disciplines

21. Thank you!!
Jane Greenberg, Associate Professor, and Director, SILS Metadata Research Center