Managing Provenance for
Reproducibility and Beyond
Juliana Freire
Web and Databases Lab
Scientific Computing and Imaging Institute
School of Computing
University of Utah
What is Provenance?


Oxford English Dictionary: (i) the fact of coming
from some particular source or quarter; origin,
derivation. (ii) the history or pedigree of a work of
art, manuscript, rare book, etc.; concretely, a
record of the ultimate derivation and passage of an
item through its various owners.
Apple Dictionary: Origin, source, place of origin;
birthplace, fount, roots, pedigree, derivation, root,
etymology; formal radix.
Provenance for Reproducibility and Beyond
Juliana Freire
2
Provenance in Art
Rembrandt van Rijn
Self-Portrait, 1659
Andrew W. Mellon Collection
1937.1.72
Provenance for Reproducibility and Beyond
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Provenance in Business



Corporate and accounting scandals, e.g., Enron,
Tyco, WorldCom
Decline of public trust in accounting and reporting
practices
Sarbanes-Oxley Act of 2002 requires detailed
provenance (auditing), e.g., http://en.wikipedia.org/wiki/Sarbanes-Oxley_Act
–  Understand the flow of transactions
….to identify points at which a
misstatement could arise
Provenance for Reproducibility and Beyond
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Provenance in Health Care



Need to keep track of patients’ provenance
Verify compliance to protocols
Mine/analyze this information to make informed
decisions
–  E.g., assess the effectiveness of procedures and drugs for
large populations
–  "Evidence-based medicine is the conscientious, explicit and
judicious use of current best evidence in making decisions
about the care of individual patients.”
http://en.wikipedia.org/wiki/Evidence-based_medicine
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Provenance in Science
Provenance is as (or
more!) important as
the result
  Lab notebooks have
been used for a long time
  What is new?
When

–  Large volumes of data
–  Complex analyses—
computational processes

Writing notes is no
longer an option…
Annotation
Observed
data
DNA recombination
By Lederberg
Provenance for Reproducibility and Beyond
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The Need for Provenance Management
Emp
John
Susan
Provenance for Reproducibility and Beyond
Dept
D01
D02
Mgr
Mary
Ken
Juliana Freire
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Uses of Computational Provenance
anon4876_zspace_20060331.jpg
anon4877_zspace_20060331.jpg
anon4877_lesion_20060401.jpg
Reproducibility
Data quality
Attribution
Informational
How were these images created?
Was any pre-processing applied to the raw data?
What’s the difference?
Who created them?
Are they really from the
same patient?
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Vision: Provenance-Rich Data
Provenance for Reproducibility and Beyond
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Provenance Management: Desiderata
Sensors
User studies
Simulations
Systematic capture
Integrate/
Obtain
  Provenance model
Organize
Data
  Efficient storage
and querying
  Usable tools

Web
Analyze/
Visualize
Databases
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Outline






Focus: provenance for digital data derived by
computational processes
Background: Scientific workflows and provenance
Change-based provenance model
Querying and re-using provenance
The VisTrails system
Emerging applications
–  Provenance-rich publications
–  Provenance in teaching
–  Science 2.0: enhanced collaboration
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Scientific Workflows
and Provenance
Scientific Workflows and Dataflows

Dataflows are directed graphs describing a
computational task
–  Vertices = modules = processing steps + parameters
–  Edges = connections between output and input ports
–  Execution order determined by flow of data from output to
input ports
Input:
Head.120.iso
Provenance for Reproducibility and Beyond
Isosurface Output:
value=57
Juliana Freire
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Scientific Workflows and Dataflows

Dataflows are directed graphs describing a
computational task
–  Vertices = modules = processing steps + parameters
–  Edges = connections between output and input ports
–  Execution order determined by flow of data from output to
input ports

No state or side effects: Outputs are a function of the
I1
inputs
O3 = vtkDataSetMapper(input=O2)
O2 = vtkContourFilter(value=57,input=O1)
O1 = vtkStructuredReader(input=I1)
[Lee and Parks, IEEE 1995]
O1
O2
O3
Provenance for Reproducibility and Beyond
Juliana Freire
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Scientific Workflows and Dataflows

A directed graph describing a computational task
–  Vertices = modules = processing steps + parameters
–  Edges = connections between output and input ports
–  Execution order determined by flow of data from output to
input ports


No state or side effects: Outputs are a function of the
inputs
I1
Simple programming model
–  Good match for visual programming
interfaces
–  Widely used: adopted by most scientific
workflow and visualization systems
–  Easy to optimize and parallelize
O1
O2
O3
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Workflows and Computer Programs
Provenance for Reproducibility and Beyond
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Workflows and Computer Programs
Program
Workflow
Document
Database
<Book>
<Title>The Advanced Html Companion</Title>
<Author> Keith Schengili-Roberts </Author>
<Author> Kim Silk-Copeland</Author>
<Price> 35.96</Price>…
</Book>
A program is to a workflow what an unstructured
document is to a (structured) database.
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Workflows and Computer Programs
Program
Workflow
The Beauty of Structure
Unstructured
Document
Database
Structured
<Book>
<Title>The Advanced Html Companion</Title>
<Author> Keith Schengili-Roberts </Author>
<Author> Kim Silk-Copeland</Author>
<Price> 35.96</Price>…
</Book>
A program is to a workflow what an unstructured
document is to a (structured) database.
Provenance for Reproducibility and Beyond
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Provenance =
Process and Data Dependencies = Graph
volume_vis.wf
used
lung.120.vtk
What is the provenance of:
derived
anon4877_lesion_20060401.jpg
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Provenance and
Data Exploration
Data Exploration and Workflows


Workflows have been traditionally used to automate
repetitive tasks
In exploratory tasks, change is the norm!
–  Data analysis and exploration are iterative processes
Data
Workflow
Data
Product
Specification
Data
Manipulation
Perception &
Cognition
Knowledge
Exploration
User
Figure modified from J. van Wijk, IEEE Vis 2005
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Exploration and Creativity Support
Reflective reasoning is key in the exploratory
processes
“Reflective reasoning requires the ability to store
temporary results, to make inferences from stored
knowledge, and to follow chains of reasoning
backward and forward, sometimes backtracking
when a promising line of thought proves to be
unfruitful. …the process is slow and laborious”
Donald A. Norman
  Need external aids—tools to facilitate this process

–  Creativity support tools

[Shneiderman, CACM 2002]
Need aid from people—collaboration
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Data Exploration and Workflows
raw data:CT scan
workflow
Files (workflow specifications)
anon4877_voxel_scale_1_zspace_20060331.srn
anon4877_textureshading_20060331.srn
anon4877_textureshading_plane0_20060331.srn
anon4877_goodxferfunction_20060331.srn
anon4877_lesion_20060331.srn
Provenance for Reproducibility and Beyond
Notes
Initial
visualization
withAdded
z-scaling
texture
corrected!
andAdded
shading!
plane to
visualize
Found good
internal
transfer
structure!
Identified
function!
lesion tissue!
Juliana Freire
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Data Exploration and Workflows: Issues


Hard to assemble and refine workflows
Data provenance is maintained manually through filenaming conventions and detailed notes
–  A time-consuming process



Hard to understand the exploratory process and
relationships among workflows
Hard to further explore the data, e.g., locate relevant
data products/workflows and modify them
Hard to collaborate, and work is likely to be lost if
creator leaves
The generation and maintenance of workflows is a
major bottleneck in the scientific process
Provenance for Reproducibility and Beyond
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Change-Based Provenance


Treat workflow as a first-class data item
Provenance = changes to computational tasks
–  Add a module, add a connection, change a parameter value
[Freire et al., IPAW 2006]
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Change-Based Provenance


Treat workflow as a first-class
data item
Provenance = changes to
computational tasks
–  Add a module, add a connection,
change a parameter value
addModule
deleteConnection
addConnection
addConnection
setParameter
Provenance for Reproducibility and Beyond
Juliana Freire
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Change-Based Provenance


Treat workflow as a first-class
data item
Provenance = changes to
computational tasks
–  Add a module, add a connection,
change a parameter value


A vistrail node vt corresponds to
the workflow that is constructed
by the sequence of actions from
the root to vt
vt = xn ◦ xn-1 ◦ … ◦ x1 ◦ Ø
Extensible change algebra
vistrail
x1
x2
x3
[Freire et al, IPAW 2006]
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Provenance Beyond Reproducibility


Support for reflective reasoning
Ability to compare data products
[Freire et al., IPAW 2006]
Provenance for Reproducibility and Beyond
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Computing Workflow Differences
No need to compute subgraph
isomorphism!
  A vistrail is a rooted tree: all
nodes have a common
ancestor—diffs are welldefined and simple to
compute
vt1 = xi ◦ xi-1 ◦ … ◦ x1 ◦ Ø
vt2 = xj ◦ xj-1 ◦ … ◦ x1 ◦ Ø
vt1-vt2 = {xi, xi-1, …, x1, Ø} –
{xj, xj-1, …,x1 , Ø}
  Different semantics:

–  Exact, based on ids
–  Approximate, based on module
signatures
Provenance for Reproducibility and Beyond
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Provenance Beyond Reproducibility



Support for reflective reasoning
Ability to compare data products
Explore parameter spaces and compare results
[Freire et al., IPAW 2006]
Provenance for Reproducibility and Beyond
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Exploring the Change Space


Scripting workflows: Parameter explorations are
simple to specify and apply
Exploration of parameter space for a workflow vt
(setParameter(idn,valuen) ◦ … ◦ (setParameter(id1,value1) ◦ vt )

Exploration of multiple workflow specifications
(addModule(idi,…) ◦ (deleteModule(idi) ◦ v1 )
…
(addModule(idi,…) ◦ (deleteModule(idi) ◦ vn )



Results can be conveniently compared in the
VisTrails spreadsheet
Can create animations too!
Caching to avoid redundant computations [Bavoil et
al., IEEE Vis 2005]
Provenance for Reproducibility and Beyond
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Provenance Beyond Reproducibility




Support for reflective reasoning
Ability to compare data products
Explore parameter spaces and compare results
Support for collaboration
[Ellkvist et al., IPAW 2008]
Provenance for Reproducibility and Beyond
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Collaborative Exploration

Collaboration is key to data exploration
–  Translational, integrative approaches to science


Store provenance information in a database
Synchronize concurrent updates through locking
–  Real-time collaboration [Ellkvist et al., IPAW 2008]

Asynchronous access: similar to version control
systems
–  Check out, work offline, synchronize
–  Users exchange patches

No need for a central repository—support for
distributed collaboration
–  For details see Callahan et al., SCI Institute Technical
Report, No. UUSCI-2006-016 2006
Provenance for Reproducibility and Beyond
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Vistrail Synchronization

Version tree is monotonic
–  Actions are always added, never deleted

Merging two vistrails is simple
+
Provenance for Reproducibility and Beyond
=
Juliana Freire
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Change-Based Provenance: Summary

General: Works with any system that has undo/redo!
Provenance for Reproducibility and Beyond
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Provenance Enabling 3rd-Party Tools
Autodesk Maya
ParaView
VisIt
ImageVis3d
[Callahan et al., IPAW 2008]
Provenance for Reproducibility and Beyond
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Provenance Plugin for ParaView
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Change-Based Provenance: Summary



General: Works with any system that has undo/redo!
Concise representation
Uniformly captures data and workflow provenance
–  Data provenance: where does a specific data product come
from?
–  Workflow evolution: how has workflow structure changed over
time?



Results can be reproduced
Detailed information about the exploration process
Provenance beyond reproducibility:
–  Scientists can return to any point in the exploration space
–  Scalable exploration of the parameter space—results can be
compared side-by-side in the spreadsheet
–  Support for collaboration
–  Understand problem-solving strategies—knowledge re-use
Provenance for Reproducibility and Beyond
Juliana Freire
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Querying and Re-Using Provenance
Querying Provenance
head.120.vtk

Graph traversal
–  Derivation lineage
–  Data dependencies
Find the process that led to
resampled-head-vis.png
Which data sets contributed
to resampled-head-vis.png

resampled-head-vis.png
Provenance for Reproducibility and Beyond
Graph patterns
Find all invocations of
vtkContourFilter with
isosurface value = 57 that
are preceded by resampling
Juliana Freire
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Query Interfaces and Usability

Sample query from Provenance Challenge:
–  Find all invocations of procedure align_warp using parameter
“model” set to “12” that ran on a Monday.

New provenance query language [Scheidegger et al., CCPE 2008]
wf{*}:
x where x.module = AlignWarp and
x.parameter('model') = '12' and
(log{x}: y where y.dayOfWeek = 'Monday')
Workflow Evolution
Workflow
Execution
For details see http://twiki.gridprovenance.org/bin/view/Challenge/VisTrails


Query from REDUX, Microsoft
Much simpler than other approaches which use SQL, SparQL,
Prolog….
But who is going to write those queries?
Provenance for Reproducibility and Beyond
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Querying Provenance by Example


Provenance is represented as graphs: hard to specify
queries using text!
Querying workflows by example [Scheidegger et al., TVCG
2007; Beeri et al., VLDB 2006; Beeri et al. VLDB 2007]
–  WYSIWYQ -- What You See Is What You Query
–  Interface to create workflow is same as to query
Provenance for Reproducibility and Beyond
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Querying and Efficiency

Query containment
–  Given a query graph q, a w in W satisfies q if q and w are
isomorphic


Subgraph isomorphism is NP-complete…
Leverage existing approaches to graph indexing
which attempt to reduce the number of
isomorphism checks [Yan et al., SIGMOD 2004, Zhao et al.,
VLDB 2007]
–  Index construction: Identify frequent features F in W and
link each fi in F to the set of Wi ⊂ W
–  Query evaluation: Given a query q, identify the features in
q; compute the intersection of the graphs associated with
each feature; and for each resulting graph check whether it
is isomorphic to q
Provenance for Reproducibility and Beyond
Juliana Freire
43
Querying and Efficiency

Wildcard queries
–  Given a partial query graph q, a w in W satisfies q if q and
w are isomorphic
)('("*+,#%"*(-,
!"#$%&'('
.$"/'(#+,#%"*(-,
)('("*+,#%"*(-,
)2'("'$&#3
)2'("'$&#3
.$"/'(#+,#%"*(-,
0#1'/2$#,
!"#$%&'()'*#+&,)
-%/*,$2,)'1&
!"#$%&'('
0#1'/2$#,
45"$%6&)
!"#$%&'('
)*+,#%"-(.,
/$"0'(#+,#%"-(.,
45"$%6&)
6:;<=>
0'7%-/!-,#1(
-./0,*'"1
0'9%-/!-,#1(
!"#$%&'('75#,8
;:6
12"$%3&*
)'4%.0!.,#5(
3405-'6,)&
)243.%#$5
45"$%6'21
45"$%6'21
!"#$%
3:)*
0596-%#$1
0576-%#$1
45"$%0#2-,(
45"$%0#2-,(
896:0#2-,(
?>6@0#2-,(
*+,
*-,
12"$%)#6.,(
7839)#6.,(
*.,
&#'"()'
Provenance for Reproducibility and Beyond
Juliana Freire
44
Querying and Efficiency

Wildcard queries
–  Given a partial query graph q, a w in W satisfies q if q and w
are isomorphic
–  Useful for exploratory searches and to suggest completions

Existing graph indexing approaches do not efficiently
support these queries
–  Queries are disconnected graphs
–  Result sets are potentially large

Our approach
[Koop et al., submitted 2010]
–  Support vague queries: Index wildcard patterns--2-component frequent subgraphs
–  Summary graphs reduce the number of results that need to
be checked
Provenance for Reproducibility and Beyond
Juliana Freire
45
#!!!
#"!!
941&:0
;<$2
;
=*>?@
2:AB/@
!
"!!
"!!
#!!
$!!
23)45/
%!!
&!!
'!!
974):3
;<#5
;
=->?@
5:AB2@
!
56,782
$!!!
Querying and Efficiency
!
&!
"!!
"&!
#!!
#&!
$!!
$&!
()*+,-./0.1,2)342
%!!
%&!
&!!
!
$!!
6!!
7!!
8!!
#!!! #$!! #6!! #7!! #8!! $!!!
%&'()*+,-+.)/&01/
• For queries with many results, the summary graphs help
dramatically reduce the number of verifications
Provenance for Reproducibility and Beyond
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Creating Workflows

Complex workflows are hard to assemble
–  Programming expertise
–  Domain knowledge
–  Familiarity with different tools
Provenance for Reproducibility and Beyond
Steep learning curve
Juliana Freire
47
Refining Analyses by Analogy

Leverage the wisdom of the crowds in shared
provenance
–  Some refinements are common, e.g., change the
rendering technique, publish image on the Web

Apply refinements by analogy, automatically
Provenance for Reproducibility and Beyond
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Generating Visualizations by Analogy
[Scheidegger et al, IEEE TVCG 2007]
Provenance for Reproducibility and Beyond
http://www.cs.utah.edu/~juliana/videos/Analogies.m4v
Juliana Freire
49
Creating Workflows by Analogy
A
as
1.
Compute difference: ∆(A,B)
–  Just like a patch!
–  But…
D = ∆(A,B) ◦ C may not be a valid
workflow
2.
B
is to
C
is to
A
D
C
Find correspondences between A
and C: map(A,C)
–  Diffuse similarity scores across the
product graph AxC using Eigenvalue
decompositions
3.
4.
Compute mapped difference
∆AC(A,B) =map(A,C) ∆(A,B)
D = ∆AC(A,B) ◦ C
[Scheidegger et al, IEEE TVCG 2007]
Provenance for Reproducibility and Beyond
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The Need for Guidance in
Workflow Design
Provenance for Reproducibility and Beyond
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VisComplete: A Workflow
Recommendation System



Mine provenance collection: Identify graph
fragments that co-occur in a collection of workflows
Predict sets of likely workflow additions to a given
partial workflow
Similar to a Web browser suggesting URL
completions
[Koop et al., IEEE Vis 2008]
Provenance for Reproducibility and Beyond
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VisComplete: A Workflow
Recommendation System



Mine provenance collection: Identify graph
fragments that co-occur in a collection of workflows
Predict sets of likely workflow additions to a given
partial workflow
Similar to a Web browser suggesting URL
completions
Provenance for Reproducibility and Beyond
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VisComplete: Demo
http://www.cs.utah.edu/~juliana/videos/viscomplete_h_264.mov
Provenance for Reproducibility and Beyond
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The VisTrails System



Comprehensive provenance infrastructure for
computational tasks
Focus on exploratory tasks such as simulation,
visualization, and data analysis
Transparently tracks provenance of the discovery
process---from data acquisition to visualization
–  The trail followed as users generate and test hypotheses



Leverage provenance to streamline exploration
Focus on usability—build tools for scientists
Featured as an NSF discovery
Provenance for Reproducibility and Beyond
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The VisTrails System


VisTrails is open source: http://www.vistrails.org
Multi-platform: Linux, Mac, Windows
–  Written in Python + Qt

Over 13,000 downloads in 2 years

Many users in different disciplines and countries
• Visualizing environmental simulations (CMOP STC)
• Simulation for solid, fluid and structural mechanics
(Galileo Network, UFRJ Brazil)
• Quantum physics simulations (ALPS, ETH
Switzerland)
• Climate analysis (CDAT)
• Habitat modeling (USGS)
• Open Wildland Fire Modeling (U. Colorado, NCAR)
• High-energy physics (LEPP, Cornell)
• Cosmology simulations (LANL)
Provenance for Reproducibility and Beyond
• Study on the use of tms for improving memory
(Pyschiatry, U. Utah)
• eBird (Cornell, NSF DataONE)
• Astrophysical Systems (Tohline, LSU)
• NIH NBCR (UCSD)
• Pervasive Technology Labs (Heiland, Indiana
University)
• Linköping University (Sweden)
• University of North Carolina, Chapel Hill
• UTEP
Juliana Freire
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Climate Data Analysis
[CDAT Project, Lawrence Livermore National Lab]
Provenance for Reproducibility and Beyond
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57 57
Quantum Lattice Models
[ALPS Project, ETH-Zurich]
Provenance for Reproducibility and Beyond
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Coastal Margin Observation & Prediction
[NSF Science & Technology Center for Coastal Margin Observation & Prediction]
Provenance for Reproducibility and Beyond
Juliana Freire
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Comparing Cosmological Simulations
[Cosmic Code Comparison Project, Los Alamos National Lab]
Provenance for Reproducibility and Beyond
Juliana Freire
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Wildfire Prediction
[WRF-Fire Project, University of Colorado-Denver]
Provenance for Reproducibility and Beyond
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Studying the Effects of TMS on Memory
[Psychiatry-U of Utah]
Provenance for Reproducibility and Beyond
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Emerging Applications
Emerging Applications
Publishing
Collaboration
Knowledge
Re-use
Teaching
Reasoning
Reproducibility
Provenance
Obtain
Data
Provenance for Reproducibility and Beyond
Integrate/
Organize
Analyze/
Visualize
Juliana Freire
64
is currently the six-time Grand Champion of the Tour de
France. It reports that the physiological factor most relevant to
performance improvement as he matured over the 7-yr period
from ages 21 to 28 yr was an 8% improvement in muscular
efficiency when cycling. This adaptation combined with relatively large reductions in body fat and thus body weight (e.g.,
78 –72 kg) during the months before the Tour de France
J Appl Physiol 98: 2191–2196, 2005.
contributed
to an impressive
18%doi:10.1152/japplphysiol.00216.2005.
improvement in his powerFirst published
March 17, 2005;
to-body weight ratio (i.e., W/kg) when cycling at a given V̇O2
(e.g., 5.0 l/min or !83% V̇O2 max). Remarkably, this individual
was able to display these achievements despite the fact that he
displayeddeveloped
as Tour
de France
matures
advanced
cancer at agechampion
25 yr and required
surgeries
and chemotherapy.
Scientific Publications and Provenance
Improved muscular efficiency
Edward F. Coyle
Human Performance Laboratory, Department of Kinesiology and
Health Education, The University of Texas at Austin, Austin, Texas
Submitted 22 February 2005; accepted in final form 10 March 2005
maximum oxygen uptake; blood lactate concentration
Provenance
MUCH HAS BEEN LEARNED about the physiological factors that
contribute to endurance performance ability by simply describing the characteristics of elite endurance athletes in sports such
as distance running, bicycle racing, and cross-country skiing.
The numerous physiological determinants of endurance have
been organized into a model that integrates such factors as
maximal oxygen uptake (V̇O2 max), the blood lactate threshold,
and Reproducibility
muscular efficiency, as
theseBeyond
have been found to be the
for
and
most important variables (7, 8, 15, 21). A common approach
has been to measure these physiological factors in a given
athlete at one point in time during their competitive career and
ages 21 to 28 y. Description of this person is noteworthy for
two reasons. First, he rose to become a six-time and present
Grand Champion of the Tour de France, and thus adaptations
relevant to this feat were identified. Remarkably, he accomplished this after developing and receiving treatment for advanced cancer. Therefore, this report is also important because
it provides insight, although limited, regarding the recovery of
“performance physiology” after successful treatment for adFig. 1. Mechanical efficiency when bicycling expressed as “gross efficiency”
vanced
cancer. The
of this
study
will be
to World
report
and
“delta efficiency”
overapproach
the 7-yr period
in this
individual.
WC,
results
from
standardized
laboratory
on this individual
Bicycle
Road
Racing
Championships,
1st and 4thtesting
place, respectively.
Tour de
France
1st,time
Grand
Champion
of the Tour de
in 199921.5,
–2004.22.0, 25.9,
at five
points
corresponding
toFrance
ages 21.1,
and 28.2 yr.
J Appl Physiol • VOL
Downloaded from jap.physiology.org on February 15, 2009
Coyle, Edward F. Improved muscular efficiency displayed as Tour
de France champion matures. J Appl Physiol 98: 2191–2196, 2005. First
published March 17, 2005;doi:10.1152/japplphysiol.00216.2005.—
This case describes the physiological maturation from ages 21 to 28 yr
of the bicyclist who has now become the six-time consecutive Grand
Champion of the Tour de France, at ages 27–32 yr. Maximal oxygen
uptake (V̇O2 max) in the trained state remained at !6 l/min, lean body
weight remained at !70 kg, and maximal heart rate declined from 207
to 200 beats/min. Blood lactate threshold was typical of competitive
cyclists in that it occurred at 76 – 85% V̇O2 max, yet maximal blood
lactate concentration was remarkably low in the trained state. It
appears that an 8% improvement in muscular efficiency and thus
power production when cycling at a given oxygen uptake (V̇O2) is the
characteristic that improved most as this athlete matured from ages 21
to 28 yr. It is noteworthy that at age 25 yr, this champion developed
advanced cancer, requiring surgeries and chemotherapy. During the
months leading up to each of his Tour de France victories, he reduced
body weight and body fat by 4 –7 kg (i.e., !7%). Therefore, over the
7-yr period, an improvement in muscular efficiency and reduced body
fat contributed equally to a remarkable 18% improvement in his
steady-state power per kilogram body weight when cycling at a given
V̇O2 (e.g., 5 l/min). It is hypothesized that the improved muscular
efficiency probably reflects changes in muscle myosin type stimulated
from years of training intensely for 3– 6 h on most days.
whom subsequently raced professio
period of 1989 –1995. The five-tim
Tour de France during the years 19
to possess a V̇O2 max of 6.4 l/min an
a body weight of 81 kg (28). La
subject in our study were not ma
France; however, with the cons
V̇O2 max was at least 6.1 l/min an
weight of 72 kg, we estimate his V̇
85 ml!kg"1 !min"1 during the per
Tour de France. Therefore, his V̇O
weight during his victories of 1999
what higher than what was reporte
1991–1995 and to be among the high
class runners and bicyclists (e.g., 80 –
16, 28, 29)
It is generally appreciated that in
success in endurance sports also req
for prolonged periods at a high per
as the ability to efficiently convert
muscular power and velocity (5, 7,
blood LT (e.g., 1 mM increase in bl
in absolute terms or as a percentag
reasonably good predictor of aero
that a given rate of ATP turnover c
21), and prediction is strengthened
ment of muscle capillary density i
Capillary density is thought to be
muscle’s ability to clear fatiguing m
muscle fibers into the circulation,
98 • JUNE 2005 •
METHODS
General testing sequence. On reporting to the laboratory, training,
racing, and medical histories were obtained, body weight was measured ("0.1 kg), and the following tests were performed after informed consent was obtained, with procedures approved by the
Internal Review Board of The University of Texas at Austin. Mechanical efficiency and the blood lactate threshold (LT) were determined as the subject bicycled a stationary ergometer for 25 min, with
work rate increasing progressively every 5 min over a range of 50, 60,
70, 80, and 90% V̇O2 max. After a 10- to 20-min period of active
recovery, V̇O2 max when cycling was measured. Thereafter, body
composition was determined by hydrostatic weighing and/or analysis
of skin-fold thickness (34, 35).
Measurement of V̇O2 max. The same Monark ergometer (model 819)
equipped with a racing seat and drop handlebars and pedals for
cycling shoes was used for all cycle testing, and seat height and saddle
position were held constant. The pedal’s crank length was 170 mm.
V̇O2 max was measured during continuous cycling lasting between 8
and 12 min, with work rate increasing every 2 min. A leveling off of
oxygen uptake (V̇O2) always occurred, and this individual cycled until
exhaustion at a final power output that was 10 –20% higher than the
Juliana
minimal power output needed to elicit V̇O2 max. A venous blood
sample was obtained 3– 4 min after exhaustion for determination of
blood lactate concentration after maximal exercise, as described
below. The subject breathed through a Daniels valve; expired gases
www.jap.org
Freire
65
is currently the six-time Grand Champion of the Tour de
France. It reports that the physiological factor most relevant to
performance improvement as he matured over the 7-yr period
from ages 21 to 28 yr was an 8% improvement in muscular
efficiency when cycling. This adaptation combined with relatively large reductions in body fat and thus body weight (e.g.,
78 –72 kg) during the months before the Tour de France
J Appl Physiol 98: 2191–2196, 2005.
contributed
to an impressive
18%doi:10.1152/japplphysiol.00216.2005.
improvement in his powerFirst published
March 17, 2005;
to-body weight ratio (i.e., W/kg) when cycling at a given V̇O2
(e.g., 5.0 l/min or !83% V̇O2 max). Remarkably, this individual
was able to display these achievements despite the fact that he
displayeddeveloped
as Tour
de France
matures
advanced
cancer at agechampion
25 yr and required
surgeries
and chemotherapy.
Scientific Publications and Provenance
Improved muscular efficiency
Edward F. Coyle
Human Performance Laboratory, Department of Kinesiology and
Health Education, The University of Texas at Austin, Austin, Texas
whom subsequently raced professio
period of 1989 –1995. The five-tim
Tour de France during the years 19
to possess a V̇O2 max of 6.4 l/min an
a body weight of 81 kg (28). La
subject in our study were not ma
France; however, with the cons
V̇O2 max was at least 6.1 l/min an
weight of 72 kg, we estimate his V̇
85 ml!kg"1 !min"1 during the per
Tour de France. Therefore, his V̇O
weight during his victories of 1999
what higher than what was reporte
1991–1995 and to be among the high
class runners and bicyclists (e.g., 80 –
16, 28, 29)
It is generally appreciated that in
success in endurance sports also req
for prolonged periods at a high per
as the ability to efficiently convert
muscular power and velocity (5, 7,
blood LT (e.g., 1 mM increase in bl
in absolute terms or as a percentag
reasonably good predictor of aero
that a given rate of ATP turnover c
21), and prediction is strengthened
ment of muscle capillary density i
Capillary density is thought to be
muscle’s ability to clear fatiguing m
muscle fibers into the circulation,
Submitted 22 February 2005; accepted in final form 10 March 2005
O2 max
O2 max
power production when cycling at a given oxygen uptake (V̇O2) is the
characteristic that improved most as this athlete matured from ages 21
to 28 yr. It is noteworthy that at age 25 yr, this champion developed
advanced cancer, requiring surgeries and chemotherapy. During the
months leading up to each of his Tour de France victories, he reduced
body weight and body fat by 4 –7 kg (i.e., !7%). Therefore, over the
7-yr period, an improvement in muscular efficiency and reduced body
fat contributed equally to a remarkable 18% improvement in his
steady-state power per kilogram body weight when cycling at a given
V̇O2 (e.g., 5 l/min). It is hypothesized that the improved muscular
efficiency probably reflects changes in muscle myosin type stimulated
from years of training intensely for 3– 6 h on most days.
Fig. 1. Mechanical efficiency when bicycling expressed as “gross efficiency”
and “delta efficiency” over the 7-yr period in this individual. WC, World
Bicycle Road Racing Championships, 1st and 4th place, respectively. Tour de
France 1st, Grand Champion of the Tour de France in 1999 –2004.
and 28.2 yr.
J Appl Physiol • VOL
Downloaded from jap.physiology.org on February 15, 2009
"raw data from the January 1993 test that revealed several
additional
published
methodology.
Coyle
Coyle, Edward F. deviations
Improved muscular efficiencyfrom
displayed as the
Tour ages
21 to 28 y. Description
of this person is noteworthy for
de France champion matures. J Appl Physiol 98: 2191–2196, 2005. First two reasons. First, he rose to become a six-time and present
published March 17, 2005;doi:10.1152/japplphysiol.00216.2005.— Grand Champion of the Tour de France, and thus adaptations
used
20-min
ergometer
(not
25 min), including 2This case a
describes
the physiological
maturation from ages 21 to protocol
28 yr relevant to this
feat were identified. Remarkably, he accomof the bicyclist who has now become the six-time consecutive Grand
plished this after developing and receiving treatment for adChampion
of the Tour stages
de France, at ages 27–32
yr. Maximalrespiratory
oxygen
and
3-min
where
exchange ratios (RER)
) in the trained state remained at !6 l/min, lean body vanced cancer. Therefore, this report is also important because
uptake (V̇
it
provides
insight,
although limited, regarding the recovery of
weight remained at !70 kg, and maximal heart rate declined from 207
“performance
physiology”
successful
adexceeded
1.00.
AnwasRER
>1.00 invalidatesafteruse
oftreatment
theforLusk
to 200 beats/min. Blood
lactate threshold
typical of competitive
, yet maximal blood vanced cancer. The approach of this study will be to report
cyclists in that it occurred at 76 – 85% V̇
lactate concentration was remarkably low in the trained state. It results from standardized laboratory testing on this individual
equations
(5) toin estimate
expenditure.”
appears that an 8% improvement
muscular efficiency andenergy
thus at five time
points corresponding to ages 21.1, 21.5, 22.0, 25.9,
98 • JUNE 2005 •
METHODS
www.jap.org
General testing sequence.
On reporting are
to the laboratory,
training,
”…all of the published delta efficiency
values
wrong.
…
racing, and medical histories were obtained, body weight was measured ("0.1 kg), and the following tests were performed after inthere exists no credible evidence
to was
support
Coyle's
formed consent
obtained, with procedures
approved by the
Internal Review Board of The University of Texas at Austin. Mechanical efficiencyefficiency
and the blood lactate threshold
(LT) were deterconclusion that Armstrong's muscle
improved."
mined as the subject bicycled a stationary ergometer for 25 min, with
maximum oxygen uptake; blood lactate concentration
work rate increasing progressively every 5 min over a range of 50, 60,
70, 80, and 90% V̇O2 max. After a 10- to 20-min period of active
recovery, V̇O2 max when cycling was measured. Thereafter, body
composition was determined by hydrostatic weighing and/or analysis
of skin-fold thickness (34, 35).
Measurement of V̇O2 max. The same Monark ergometer (model 819)
equipped with a racing seat and drop handlebars and pedals for
cycling shoes was used for all cycle testing, and seat height and saddle
position were held constant. The pedal’s crank length was 170 mm.
V̇O2 max was measured during continuous cycling lasting between 8
and 12 min, with work rate increasing every 2 min. A leveling off of
oxygen uptake (V̇O2) always occurred, and this individual cycled until
exhaustion at a final power output that was 10 –20% higher than the
Juliana
minimal power output needed to elicit V̇O2 max. A venous blood
sample was obtained 3– 4 min after exhaustion for determination of
blood lactate concentration after maximal exercise, as described
below. The subject breathed through a Daniels valve; expired gases
http://jap.physiology.org/cgi/content/full/105/3/1020
Provenance
MUCH HAS BEEN LEARNED about the physiological factors that
contribute to endurance performance ability by simply describing the characteristics of elite endurance athletes in sports such
as distance running, bicycle racing, and cross-country skiing.
The numerous physiological determinants of endurance have
been organized into a model that integrates such factors as
maximal oxygen uptake (V̇O2 max), the blood lactate threshold,
and Reproducibility
muscular efficiency, as
theseBeyond
have been found to be the
for
and
most important variables (7, 8, 15, 21). A common approach
has been to measure these physiological factors in a given
athlete at one point in time during their competitive career and
Freire
66
Provenance-Rich Publications


Bridge the gap between the scientific process and
publications
Results that can be reproduced and validated
–  Papers with deep captions
–  Encouraged by ACM SIGMOD and a number of journals


Describe more of the discovery process: people only
describe successes, can we learn from mistakes?
Support dynamic (interactive) publications
–  Evolve over time
–  Blog/wiki like=> Science 2.0
–  E.g., http://project.liquidpub.org
Provenance for Reproducibility and Beyond
Juliana Freire
67
Provenance-Rich Documents
Provenance for Reproducibility and Beyond
Juliana Freire
68
Provenance and Teaching (1)

Leverage provenance to improve the way we teach
CS and Science
–  http://www.vistrails.org/index.php/SciVisFall2008
–  Also used at UNC, Linkoping, UTEP
–  Lecture provenance: student can reproduce results
Provenance for Reproducibility and Beyond
Juliana Freire
69
Provenance and Teaching (2)

Homework provenance provides insights regarding
–  Task complexity and nature: number of actions; structural vs.
parameter changes; task duration
–  Student confusion: large branching factor=lots of trial and error
steps

Very detailed (and honest!) feedback: instructors can
leverage this information
[Lins et al., SSDBM 2008]
Provenance for Reproducibility and Beyond
Juliana Freire
70
Provenance and Teaching (3)

Homework provenance helps students and instructors to
collaborate
–  Student is stuck, sends his provenance
–  Instructor understands studentʼs problem, provides hints---student
can see what instructor did!
–  They can also collaborate in real time [Ellkvist et al., IPAW 2008]
Provenance for Reproducibility and Beyond
Juliana Freire
71
Using Provenance to
Teach Electronic Media
[Langefeld and Kessler,
Submitted 2009]
“[...] The students have gotten to the point where
they demand the VisTrails files for every
demonstration just after I complete [it]”
“[...] students used [a vistrail instead of a reference
model] 62% of the time”
Provenance for Reproducibility and Beyond
Juliana Freire
72
Using Provenance to
Teach Electronic Media
http://www.cs.utah.edu/~juliana/videos/maya_playback_slow.mov
Provenance for Reproducibility and Beyond
Juliana Freire
73
Using Provenance to
Teach Electronic Media
[Langefeld and Kessler,2009]
“[...] The students have gotten to the point where
they demand the VisTrails files for every
demonstration just after I complete [it]”
“[...] students used [a vistrail instead of a reference
model] 62% of the time”
“Students who used provenance produced higherquality models”
Provenance for Reproducibility and Beyond
Juliana Freire
74
Science 2.0



Web 2.0 technologies has opened up new opportunities to
improve collaboration and information sharing in science
[Shneiderman, Science 2008; Waldrop, Scientific American
2008]
Need provenance: determine authorship, enforce intellectual
property rights, validate the integrity of artifacts and assess
their quality, and to reproduce the artifact
Social Data Analysis: Share data, processes and provenance
Provenance for Reproducibility and Beyond
Juliana Freire
75
Conclusions and Future Work





Provenance management is crucial in many
applications
Change-based provenance model
New algorithms and usable tools for querying and
re-using provenance information
The open-source VisTrails system: provides unique
capabilities essential for cyberinfrastructure
Sharing provenance creates new opportunities [Freire
and Silva, CHI SDA, 2008]
–  Expose users to different techniques and tools
–  Users can learn by example; expedite their training; and
potentially reduce their time to insight

Many challenges and several open computer science
questions
Provenance for Reproducibility and Beyond
Juliana Freire
76
Acknowledgments


Thanks to VisTrails and Web&DB groups
This work is partially supported by the National
Science Foundation, the Department of Energy, an
IBM Faculty Award, and a University of Utah Seed
Grant.
Provenance for Reproducibility and Beyond
Juliana Freire
77
Thank you