strain data networks - cria.org.br · pdf filetaxonomy modeling data quality ... • does...
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Strain Data Networks
The speciesLink and SIColNetexperiences
Dora Ann Lange Canhos
Centro de Referência em Informação Ambiental - CRIA
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Centro de Referência em Informação Ambiental
CRIA (Reference Center on Environmental Information)
a not-for-profit, non-government organization.
Its aim is to contribute towards a more sustainable use of
Brazil's biodiversity through the dissemination of high
quality data and information generated by the scientific
community
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Focus for the GBRCN information
system
• Linking existing systems – information
exchange
• Quality – data validation
• Data content
• Data usability
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Strategies for data integration
1) Same software and database used by all providers
centralized search
web server usersdata provider 1
data provider 2
data provider 3
search
Slide: Renato de Giovanni (CRIA)
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Strategies for data integration
1) Same software and database used by all providers
� Interesting solution if all providers agree to use the same
system:
� Improvements benefit all participants.
� Shared costs.
� Good performance (although queries are run in the
production database).
� Lack of freedom to make custom adjustments.
� Very difficult to accomplish if providers are already using
their own management software (sometimes developed
with considerable effort).
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Development
team
Coordinating
group
Sp
eci
ali
sts
(>4
00
)
New Developments
HELPHELP
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Editing interface
Web interface
User control
Global corrections
saída rtf para impressão mapas de distribuição
Statistics interface
taxonomiststaxonomistscoordination JBRJcoordination JBRJ
saída xls planilha
data cleaning interface
logs and controls
Importing data from existing listsImporting data from existing lists
Maintenance, correcting bugsMaintenance, correcting bugs
New implementationsNew implementations
development CRIAdevelopment CRIA
Support to the coordinationSupport to the coordination Support to taxonomists
backups, backups, backups, backups …
External resources
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Strategies for data integration
2) Periodically export data to a central database
centralized search
web server usersdata provider 1
data provider 2
data provider 3
standard format
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Strategies for data integration
Examples:
• Common Access to Biological Resources and
Information.
• Began in 1999.
• 28 catalogues from European institutions
(>100K records).
2) Periodically export data to a central database
• 1st phase of the Brazilian network
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administrative
interface
virtual
catalog
users
updates queries
Data providers
(culture collections)
relational database
PostgreSQL
Perl &
Apache
HTTP
SQL
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Strategies for data integration
�Good performance.
�Easier to implement.
� Queries are performed on potentially non current data.
� Onus on providers to transform data into a common format
and periodically export it.
� Experience with SICol: no updates
2) Data providers periodically export data to a central
database
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Strategies for data integration
3) Real time distributed queries
distributed search
data provider 1
data provider 2
data provider 3
web server users
wrapper software
data standard & protocol
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Strategies for data integration
Examples:
3) Real time distributed queries
REMIBRed Mundial de
Información sobre
Biodiversidad
• 1998 - 2003.
• North America.
• MaNIS, HerpNET,
ORNIS & FishNet.
• Started in 1998.
• Mexico.
• Started in 2000.
• 9 major herbaria.
• 6 million records (80%
databased).
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Strategies for data integration
�Access to current data.
�Providers have more confidence and sense of control.
� Performance and scalability bottlenecks.
� Performance limited by the slowest data provider.
3) Real time distributed queries
� Servers sometimes down, network problems.
� When data providers go offline their data become
unavailable.
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Strategies for data integration
4) Data harvesting
data provider 1
data provider 2
data provider 3
web server users
centralized search
data harvester
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Strategies for data integration
Examples:
4) Data harvesting
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Strategies for data integration
� Good performance.
� Queries are performed on potentially non current data.
� Difficult to implement if there are many protocols and data
standards involved.
4) Data harvesting
• It may be necessary to define a common (minimum) field
set for storing data in the central database.
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Data Model: DarwinCore
• Based on specifications developed by the DublinCoreMetadata Initiative. Can be seen as an extension of it for biodiversity data.
• Its latest version consists of a glossary of terms including definitions, examples, and commentaries, including how terms:
– are managed
– can be used
– can be extended for new purposes
• Designed to minimize the barriers to adoption and to maximize reusability in a variety of contexts.
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On the evolution of Darwin Core
~20 different versions!
DwC
first
draft
DwC2
fullDwC2
taxonomy
DwC
terms
2002
48 elements
XML tied to a protocol
2009
172 elements
Generic
Note: ABCD
has 970
terms
DwC2
geography
DwC2
taxongeog
DwC2
gazetteerDwC
1.0
2003 DwC
OBISDwC
1.21
MaNIS
DwC
1.25
DwC
bnhm
DwC
AKNS
1.32
DwC
2Plants
DwC
paleo
DwC
kbif
DwC
kbif 2
DwC2
jrw030315
DwC
1.4DwC
1.4
curatorial DwC
1.4
geospatial
Standardization required!
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Data exchange protocol - TAPIR
• TDWG Access Protocol for Information Retrieval.
• Integrates functionality from DiGIR and BioCASe.
• Completely independent of the data being exchanged: Works with DarwinCore and ABCD.
• Official TDWG standard.
• Tools and documentation available.
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Choosing standards and protocols
• Choose from existing standards whenever possible:
• This can save you considerable time.
• Will likely avoid interoperability issues in the future.
• Seek compatibility with other initiatives.
• You can benefit from existing tools.
• You may get extra functionality/data.
• Data providers are the pillars of every network:
• Help them improve their data.
• Ensure that data remain curated at the source.
• Show them that data sharing promotes citation and usage, giving them credits and visibility.
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The speciesLink network
descriptive
data
nomenclature
taxonomy
modeling
Data quality
maps
primary data
educationresearch
Decision
making
Biological
collection
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Points to consider
• Biological collections in Brazil
– A small number of “large” collections
– A great number of important small research
collections
• Average characteristics
– Human resources: expertise in informatics (normally
insufficient)
– Equipments and installations (normally insufficient)
– Connectivity (normally slow or unstable)
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Challenges
– Integration of primary data from all taxa, from
distributed collections, using different software in
diverse environments
– Integrating data from collections with low and/or
unstable internet connectivity, using basic
hardware and no computer expertise
– Maintain full control over the data served to the
network at the provider’s end
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Development parameters - architecture
– Collection’s routine must not be altered
• Practically any software is accepted (Excel, Access, Specify, Biota, Brahms, PostgreSQL, MySQL, …)
– Data provider must have full control over the data
• What is sensitive data, what is open and free
• Digitization strategy, data cleaning strategy
– Data provider must be fully acknowledged
– Connectivity problem must be overcome
– Network must be interoperable with international initiatives
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Network architecture
Data
PostgreSQL
Provider
PHP
Mirror
SOAP server
SQL
DataspLinker
Java
CollectionManagement
System
SQL
Collection Collection
Data
Repository
SOAPSOAP
Portal
DiGIRDiGIR
Cache nodeCache node
Translator
Collection
database
Map
pin
g d
ata
fie
lds
Darw
inC
ore
da
ta m
odel
On-line
database
Filter for sensitive data
Free and open access to non sensitive data
Restrictedaccess
Sensitive data
flagged
John Wieczorek
Museum of Vertebrate Zoology, UC Berkeley
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spLinker: software to send data to cache node
� Platform Independent (java)
� Connects to practically any
database
� Offers full control over data
� Checks repository and only sends
updates (low traffic)
� It is possible to filter sensitive
data using regular expressions
dataspLinker
(Java)
Management
system Data
repository
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The development of the speciesLink network
Oct. 2005
709,306 recordsLaunched
Oct. 2002
5,280 records
Estimated
Growth
1.7 million
3.5 million
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Data Sharing
• Does not depend only on the will to share
data
– It must be planned: adequate resources,
expertise, infrastructure
– Must be organized: data models, controlled
vocabulary, communication protocols, ...
– Must be easy or at least “doable”
– Must have a compatible data policy: free and
open access to non sensitive data
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TAPIR Portal
Cache node Cache node
Indicators
Network Manager
Query interface
Data cleaning
Web Sitemapcria
webservice
Data analysis
Reports
Maps
PostGIS
Central Repository
Data Harvester
Collections with
a DiGIR provider
Collections with
spLinker
DiGIR/TAPIR
SOAP
TAPIR
WMS
speciesLink architecture
TAPIR Provider
webservice
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� Adoption of internationally agreed standards and protocols is key
� Support unlocking and sharing of data (make it simple and easy !)
� Enable data providers to have full control of their data determining what can be
openly shared and what is sensitive
� Full credit and acknowledgement to the data providers at all levels !
� Data providers must see the benefit to participate in the network
� Data flagging and data cleaning tools are key to support the identification of data
inconsistencies
� Stable and long term funding is necessary to ensure development and the
persistency of open and free data networks (persistent repositories are critical;
funding mechanisms need to be improved !)
Lessons learned
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CLIOC
CBMA
ColTryp
CMT
TAPIRLinkTAPIRLink
CCFF CCGB
CENT CCBH CLIST CCAMP
CCBSCCBS
μSICol
INCQSCFP
Putting the pieces together
Fiocruz - RJ
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global catalogue distribution maps indicators reports
CLIOC
CBMA
ColTryp
CMT
TAPIRLinkTAPIRLink
CCFF CCGB
CENT CCBH CLIST CCAMP
CCBSCCBS
μSICol
INCQSCFP
Fiocruz - RJ
CBAM
CFAMμSICol
Fiocruz - AM
spLinkerspLinker
CBMAI μSICol Unicamp - SP
datacleaning reports
speciesLink (DarwinCore2)speciesLink (DarwinCore2) SIColNet (DarwinCore2 + microbial)SIColNet (DarwinCore2 + microbial)
the pieces put together
Embrapa
Infomicro
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Quality level ???
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Quality Procedures ???
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Making things possible
• Setting big goals, but…
– Step by step approach
– Problem driven approach
• Make it simple or at least doable
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Partners
National PartnersNational Partners
Sponsors & FundersSponsors & Funders
International PartnersInternational Partners