1. Capturing and Organizing Scientific Annotations
Greg Riccardi
Florida State University
Riccardi: Workshop on Data Management
March 17, 2004

2. What is an Annotation? An assertion of a relationship among objects
Someone claims that several objects are connected by a relationship and gives evidence of the connection
Includes record of author and date of assertion
Objects are often datasets with provenance
Annotations often assert quality characteristics of data objects
Crucial social components
Attribution, confidence, and validity
Ontologies and compliance with standards
Establishment of object naming strategy
Security policies
Riccardi: Workshop on Data Management
March 17, 2004

3. Example from SkyServer
These object are the same
Telescope and catalog info
SkyQuery
dataset
SkyQuery
dataset
Analysis
Query string
Query string
Riccardi: Workshop on Data Management
March 17, 2004

4. Types and Importance of Annotations
Three types of annotations
Systematic
Semi-structured
Ad Hoc
Annotations are of primary importance in data semantics and analysis
Record of semantics of data
Record of peoples opinions about data
We need tools to make annotations easy to create, organize, understand, and search
Riccardi: Workshop on Data Management
March 17, 2004

5. Systematic Annotations
Collected automatically
Anticipated and organized
Factual
Experimental metadata
See example of Jefferson Lab run log
A run log entry asserts a relationship between the metadata and the raw data
The run number identifies each object
Rows in runBegin table, runEnd table, runFiles table, runComment table
Object identification is much more difficult in most cases
As noted in earlier talks
Experimental metadata is not always collected or curated properly
Riccardi: Workshop on Data Management
March 17, 2004

6. Systematic Provenance Annotations
Derivation provenance
Record of computational creation of data
Must be collected by computations directly
Query provenance
In SkyQuery, user submits query and results dataset is retained in MyDB
The query must be retained to record semantics of dataset
GGF Database Access and Integration Working Group (DAIS)
Deveoping standards for representing queries on databases and other data stores
Provides a data access recipe that can be used to fetch a particular dataset
Morphbank images of scanning electron micrographs
Riccardi: Workshop on Data Management
March 17, 2004

7. Semi-Structured Annotations
Anticipated and organized
Collected mostly by hand
Experimental logbook from Jefferson Lab
Riccardi: Workshop on Data Management
March 17, 2004

8. Jefferson Lab Logbook Run and log daily summaries
Standard logbook entry
Many standard (expected) fields
Comment field filled with ad hoc annotations
“ADB crate” “voltage”
Complaint about logbook usage
Suggested strategy for creating logbook entries
Automatically generated logbook entry
Post processing software creates database entries directly
Image tags point to files on some computer
Riccardi: Workshop on Data Management
March 17, 2004

9. Semi-Structured Annotations
Anticipated and organized
Collected mostly by hand
Experimental logbook from Jefferson Lab
Logbook entry has specific fields
Run id, subject, author, entry_type, system
Entry has an ad hoc field
Searching comment field requires interpretation of words
[Ontologies?]
Search page for log book
Based on predefined structure
Created and used by experts
Riccardi: Workshop on Data Management
March 17, 2004

10. Ad Hoc Annotations Asserts connection between arbitrary objects
Example from morphology
Riccardi: Workshop on Data Management
March 17, 2004

11. Morphology Publication Example
Riccardi: Workshop on Data Management
March 17, 2004

12. Ad Hoc Annotations Asserts connection between arbitrary objects
Example from morphology
Searching is difficult
Ambiguous and inefficient
Google is a search engine for ad hoc annotations
Not based on organized ontology
Not based on document structure
Riccardi: Workshop on Data Management
March 17, 2004

13. Annotating data quality
Suppose that someone finds error in a SkyQuery dataset
Create an ad-hoc annotation
“Objects X, Y, Z in data catalog D are incorrectly identified”
Include annotation in any query?
We don’t know how to carry quality annotations into the query results
Riccardi: Workshop on Data Management
March 17, 2004

14. Organizing Annotations
Need to find ways to structure ad hoc annotations
When structure emerges, capture it
Create specific schemas
Create specific interfaces for collection, display and search
Main goal is to make it easy enough for scientists
They must see advantages to the extra work of structuring their thoughts and conforming to ontologies
Riccardi: Workshop on Data Management
March 17, 2004

15. Querying the Annotation Activity
Publish/Subscribe database strategies
Publish the history of updates
Subscribe to queries on the history
Suppose you are the curator of a SkyQuery database
Someone claims that the object catalog is wrong
You should be informed
Riccardi: Workshop on Data Management
March 17, 2004

16. Example of Annotation Query
These object are the same
Telescope and catalog info
SkyQuery
dataset
SkyQuery
dataset
Analysis
Curator
Query string
Query string
Riccardi: Workshop on Data Management
March 17, 2004

17. Challenges of Ad Hoc Annotations
Establishing globally unique, persistent data object names
Optimizing searches
Result semantics
Ontologies
Capturing structure of frequent annotation styles
Providing user interfaces to define semi-structured annotations
Riccardi: Workshop on Data Management
March 17, 2004

18. Annotations Technology: SAM
Scientific Annotation Middleware
Jim Myers and Al Geist
EMSL Electronic Notebook
Riccardi: Workshop on Data Management
March 17, 2004

19. Annotations Technology: Amaya
Annotations of HTML and XML documents
Project includes browser and document editor
Text annotations attached to XHtml, XML, MathML and SVG
Annotea collaborative annotation technology
Riccardi: Workshop on Data Management
March 17, 2004

20. References SkyQuery and SkyServer Jefferson Lab Logbooks
Jefferson Lab Logbooks
Home page
Today’s runs
Today’s Logbook entries
Run detail page
Logbook entry
Morphbank: Johan Liljeblad & Fredrik Ronquist
Scientific Annotation Middleware
W3C Amaya XML Annotation project
Riccardi: Workshop on Data Management
March 17, 2004