trust threads : active curation and publishing in sead
TRANSCRIPT
Cooperative agreement #OCI0940824
Trust Threads: Minimal Provenance for Data Publication and Reuse ���
���Beth Plale���
Data To Insight Center���Science Director, Pervasive Technology Institute���
School of Informatics and Comptuing���Indiana University ���
May 7, 2015
The impact that digital data is having on science is increasing because of
phenomenal data growth.
Na9onal Data Integrity Conference
Data growth results in
• New and bigger research ques9ons : energy, clean and abundant water, sustainable food, healthy popula9on
• Requires data understood across discipline boundaries
Can technology innova9on (tools, data management, knowledge representa9on) accelerate
frequency of reuse and repurpose of scien9fic digital data?
Na9onal Data Integrity Conference
• Data can carry with it thin threads of informa9on “trust threads” that connect data to both its past and its future.
• In carrying this minimal provenance, data becomes more trustworthy.
• Trustworthiness cri9cal to successful sharing, reuse of data in science and technology research
Provenance suitcase
Na9onal Data Integrity Conference
Data provenance
Data Provenance
Lineage of data product. Like piece of art, tells what has happened to data product through its life. Shows rela9onships between products
Provenance for two polar ice sheet images showing problems with one (on right, missing contribu9ng files)
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2012
Month of ice sheet images, linked by template passed from day to day in processing
Story of Publishing
Table of Contents • The Flood • The Publishing Exercise
Terminology as used in this story • Data publishing : act of making a data set that is associated with a peer reviewed scien9fic publica9on publicly available
• Data preserva8on : steps undertaken to ensure a data set is available and useable by subsequent genera9ons of scien9sts
• Data sharing : reuse and repurpose of a data set for by a third party
• Long tail science: science u9lizing data from mul9ple sources; data and models can be manipulated on worksta9on or small cluster
Na9onal Data Integrity Conference
Cast of Characters
• Hydrologist (PI level) • Hydrologist Postdoc • Data scien9st (PI level) • Research programmer • Data curator (CLIR Fellow) • University library (data management services)
Na9onal Data Integrity Conference
Extreme Mississippi Flood 2011
Thanks to
SEAD funded by Na8onal Science Founda8on. B. Plale and P. Kumar, co-‐PIs.
-‐-‐ Praveen Kumar and his team at UIUC
Extreme Mississippi Flood 2011*
• Birds Point New Madrid (BPNM) Floodway is an agricultural region located west of Mississippi River just south of its confluence with the Ohio River, near city of Cairo, Illinois.
• Policy established following disastrous 1927 Flood permits levees surrounding BPNM Floodway to be inten9onally breached during extreme floods.
• During historic flood stages of May 2011, Floodway was ac9vated for first 9me since 1937. U.S. Army Corps of Engineers used blas9ng agents on May 2, 3 and 5, 2011 to create ar9ficial breaches.
* A. E. Goodwell, Z. Zhu, D. Duaa, J. A. Greenberg, P. Kumar, M. H. Garcia, B. L. Rhoads, R. R. Holmes, G. Parker, D. P. Berreaa, R. B. Jacobson (2014). Assessment of Floodplain Vulnerability during Extreme River Flood 2011, Environmental Science and Technology.
Why study breach?
Impact was drama9c. Parts of agricultural floodplain were inundated for over a month.
Na9onal Data Integrity Conference
Sudden eleva9on drop at O’Bryan Ridge(B1, B2) resulted in substan9al erosion there.
Oxbow of old river bed (Ten Mile Pond) should have seen substan9al erosion but didn’t because trees had filled into area since 1937.
Grey area is floodplain, blue is Mississippi River. Green circles are USGS installed sensors. Levee breach at point A.
Credit for images: A. E. Goodwell, et al. (2014). Assessment of Floodplain Vulnerability during Extreme River Flood 2011, Environmental Science and Technology.
Sudden eleva9on drop at O’Bryan Ridge resulted in substan9al erosion -‐ see B1, B2.
Part II: Publishing Exercise
• Fall 2014 Postdoctoral Fellow asked to publish datasets associated with 2014 ES&T paper.
• SEAD services used to ingest into ins9tu9onal repository at her university
• Ques9ons Fellow had to address: – What do I publish? – Why do I publish? – How do I publish?
Na9onal Data Integrity Conference
Kinds of Data Used • Map of land eleva9on (Digital Eleva9on Model). LIDAR data from 2005 and 2011. US Army Corps of Engineers. Images
• Max velocity of water. Hydrosed 2D hydrology model. Modeled data
• Woody vegeta9on mapping. AVIRIS – airborne visible/infrared imaging. Images
• Water level sensors. USGS Water level sensors. Sensor data
• Soil/landscape sensi9vity to erosion. K/T func9on. Computed values
Published: single Research Object
• 5 Hydrosed model max velocity files (whole Birds Point New Madrid floodway (BPNM), Case 1 and 2: O'Bryan, Case 1 and 2 Ten Mile Pond) – Whole BPNM: Umax_BPNM.tif 1.47 MB – Case 1 O’Bryan: Umax_OBR_Case1.tif 3.23 MB – Case 2 O’Bryan: Umax_OBR_Case2.tif 3.23 MB – Case 1 Ten Mile Pond: Umax_TMP_Case1.tif 6.59 MB – Case 2 Ten Mile Pond: Umax_TMP_Case2.tif 6.59 MB
• K/T Factor soil map: KT_BPNMShape.tif 7.34 MB • Classified AVIRIS map (woody vegeta8on) AVIRIS_50M_BPNMshape.tif
919.48 KB • Original differen8al DEM (1.5 m resolu9on) Sub_Original_5ft.tif
2.84 GB • Final differen8al DEM (10 m resolu9on) Sub_Corrected_10m.tif
72.75 MB • Final differen8al DEM (3 m resolu9on) Sub_Corrected_3m.tif
798.12 MB
Publishing Exercise
– Why do I publish? • A: publish data necessary to reproduce images in published paper
– What do I publish? • A: publish data necessary to reproduce images in published paper
Na9onal Data Integrity Conference
Implica9ons
• Umax_BPNM.tif 1.47 MB • Umax_OBR_Case1.tif 3.23 MB • Umax_OBR_Case2.tif 3.23 MB • Umax_TMP_Case1.tif 6.59 MB • Umax_TMP_Case2.tif 6.59 MB • KT_BPNMShape.tif 7.34 MB • AVIRIS_50M_BPNMshape.tif 919.48 KB • Sub_Original_5ft.tif 2.84 GB • Sub_Corrected_10m.tif 72.75 MB • Sub_Corrected_3m.tif 798.12 MB
• TIF files (geoTIF) not sufficient for data reuse
• 3.74 GB Research Object too large for typical repositories
• Data to support images is 3.74 GB, data sufficient for new research substan9ally larger
• Libraries need partnerships with large scale storage providers
Technology frame of reference: SEAD
SEAD Leadership
Cooperative agreement #OCI0940824
Clockwise from top: Margaret Hedstrom, UMich; Beth Plale, IU; Praveen Kumar, UIUC; Jim Myers, UMich; Sandy Payette, UMich
SEAD – Researcher’s Perspective
Collec9on and analysis of data
SEAD Sharing and publica9on of data
Time
Cura9on engagement
SEAD Technology
• Ac8ve Project Spaces : Collabora9on / file sharing space for research projects and staging area for data cura9on prior to publishing
• Ac8ve Cura8on and Publishing Services i. Publish workflow : push publishable data (as Research Objects) through publish lifecycle ii. People, Data, Things service iii. Matchmaker : select appropriate preserva9on and discovery environment
Project Spaces ... organize, describe, visualize
geo dashboard
dataset collec9ons with metadata
People, Data, Things Service: linked data of profiles for researchers, repositories, data, and
Trust Threads provenance
Matchmaker : decision support service selects appropriate preserva9on environment based on needs of repository, data, creator
Cloud Storage
IR
Project Space
Project
Project Space Project
Space
Match maker
Cura9on Work Flow Submit
Submit
Computa9onal Environments
Tools Apps
Project Space
Project Space
Project Space
Long Tail of D
ata Prod
ucers
Domain Repository
Scien9
fic W
orkflow
PDT service
• At point in 9me researcher ready to publish data, she makes decision about what data – from amongst all data sources used, consulted, and created, should be include in publishable result.
• Data Curator: At this point (at latest) is when data curator should get engaged
• Curator works in-‐situ and alongside researcher to assemble publishable object – Occurs in SEAD Project Spaces
Intent to publish
Tracking life of research object
Research Object
Unique ID
Agents
StatesRelationships
Content
Research Object Framework
• Data creator• Curator• Data re-use scientist
• Live• Curated• Published
• Aggregates • Related to • Describes • Derived from • Versioned from
• Files• Bitstreams• Pointers• Annotative
information
Bundles of resources that use common standards and services to transfer and consume them
Research Object Publish reuse lifecycle
• Research Object begins as Live Object (LO), this is when all future content is s9ll in state of high flux.
• Live Object state is collabora9ve, mul9-‐person project team working together in single shared project space in SEAD environment
Research Object Publish reuse lifecycle
• At point in 9me when researcher begins assembly for publishing, she will prune and organize material to publish into new directory or set of directories, or tag specific files.
• This pruned content becomes the Cura?on Object (CO)
Research Object Publish reuse lifecycle
• Once researcher and digital curator agree that content and descrip9ons of research product are ready, researcher signals intent to publish whereupon research object moves from its state as a Cura9on Object to a new state as a Published Object (PO).
• PO exists in “control zone” where changes are tracked in a rigorous manner.
Publish reuse lifecycle
Behavior diagram : controlled transi9ons from birth to publish
Objects transi9on from liale control in change tracking to full tracking
“is version of”
rela9onship means
something substan9ally different from “is derived from”.
Cri9cal to be able to tell them apart in future.
Cloud Storage
IR
Project Space
Project Space Project
Space
Match-‐ Maker
Cura9on Work Flow Submit
Submit
Computa9onal Environments
Tools Apps
Project Space
Project Space
Project Space
Long Tail of D
ata Prod
ucers
Domain Repository
Scien9
fic W
orkflow
VA Registry Services
Wild West Boundary Waters Control Zone
Stages implemented in Ac9ve Cura9on and Publishing Services
From point in 9me where data is first conceived as ready for publica9on through to its first reuse is
cri9cal stage in data’s life.
If we are unable to bring technical rigor to handling object cura9on, rela9onships, and deriva9ons in this early stage, we lose cri9cal
opportunity for enhancing trustworthiness of data object:
an opportunity that is irrecoverable.
With special thanks
• Hydrologist postdoc Allison Goodwell, Professor Praveen Kumar, UIUC
• Dr. Inna Kouper, IU • IU SEAD developer team: Scoa McCaulay, Isuru Suriarachchi, Aravindh Varadharaju, Charitha Arachchige, Yuan Luo
• Na9onal Science Founda9on