Scientific Computing at NCEAS
Jim Regetz & Rick Reeves
National Center for Ecological Analysis & Synthesis
Winter 2011
NCEAS Scientific Computing team
Who we are. . .
Rick Reeves: Geography, spatial data processing/analysis Jim Regetz: Ecology, statistics, numerical modeling
Where you can find us. . .
Office: Rm 301 (by the elevators)
Email: [email protected], [email protected] Web: http://www.nceas.ucsb.edu/scicomp
NCEAS technology support areas
Analysis
Collaboration
Data & Metadata
Synthesis is an uphill battle
Discovery
Integration
Analysis
Publication
The literature is the final resting place for much of our knowledge.
Data synthesis and re-analysis are hard in a publication-driven world!
Synthesis is an uphill battle
Discovery
Integration
Analysis
Publication
The literature is the final resting place for much of our
knowledge. Data synthesis
and re-analysis are hard in a publication-driven world!
Collaboration tools and services
Tech support available on site
Networking Printing Laptops Lab computers Video conferencing Smartboards Remote collaboration
Password-protected group website
Analytical server, source code repository, and more
NCEAS Help Wiki:
Collaboration tools and services
Tech support available on site Networking Printing Laptops Lab computers Video conferencing Smartboards Remote collaboration
Password-protected group website
Analytical server, source code repository, and more
NCEAS Help Wiki:
Analysis support
Data discovery & acquisition
Consult with us about “base layer” data (e.g., global climate, digital terrain models)
Data management & analysis
Recommend best practices and appropriate software Discuss statistical & analytical approaches
Lend a hand with programming, etc. Documentation & archiving
How/where can you capture your analytical procedures? How/where can you archive data products?
Analysis support
Data discovery & acquisition
Consult with us about “base layer” data (e.g., global climate, digital terrain models)
Data management & analysis
Recommend best practices and appropriate software Discuss statistical & analytical approaches
Lend a hand with programming, etc.
Documentation & archiving
How/where can you capture your analytical procedures? How/where can you archive data products?
Analysis support
Data discovery & acquisition
Consult with us about “base layer” data (e.g., global climate, digital terrain models) Data management & analysis
Recommend best practices and appropriate software Discuss statistical & analytical approaches
Lend a hand with programming, etc.
Documentation & archiving
How/where can you capture your analytical procedures? How/where can you archive data products?
Analytical software comparison
Flexible Scripted Reliable Cross-platfo rm Op en source Free! R x x x x x x Matlab x x x x SAS x x x x Excel GRASS x x x x x x PostGIS x x x x x x ArcGIS x x x
Analytical software comparison
Flexible Scripted Reliable Cross-platfo rm Op en source Free! R x x x x x x Matlab x x x x SAS x x x x Excel GRASS x x x x x x PostGIS x x x x x x ArcGIS x x x
Analytical software comparison
Flexible Scripted Reliable Cross-platfo rm Op en source Free! R x x x x x x Matlab x x x x SAS x x x x Excel GRASS x x x x x x PostGIS x x x x x x ArcGIS x x x
Analytical software comparison
Flexible Scripted Reliable Cross-platfo rm Op en source Free! R x x x x x x Matlab x x x x SAS x x x x Excel GRASS x x x x x x PostGIS x x x x x x ArcGIS x x x
Your responsibilities
1 Identify a Data Point of Contact for your group
2 Establish a timetable for documenting/archiving data 3 Create metadata entries in the NCEAS Data Repository
for your data sets -as soon as possible!
4 Archive completed data sets in the Data Repository NCEAS Data Repository
Your responsibilities
1 Identify a Data Point of Contact for your group
2 Establish a timetable for documenting/archiving data
3 Create metadata entries in the NCEAS Data Repository
for your data sets -as soon as possible!
4 Archive completed data sets in the Data Repository NCEAS Data Repository
Your responsibilities
1 Identify a Data Point of Contact for your group
2 Establish a timetable for documenting/archiving data
3 Create metadata entries in the NCEAS Data Repository
for your data sets -as soon as possible!
4 Archive completed data sets in the Data Repository NCEAS Data Repository
Your responsibilities
1 Identify a Data Point of Contact for your group
2 Establish a timetable for documenting/archiving data
3 Create metadata entries in the NCEAS Data Repository
for your data sets -as soon as possible!
4 Archive completed data sets in the Data Repository
NCEAS Data Repository
NCEAS Ecoinformatics program
Research
Better managing heterogeneous data Capturing reproducible scientific workflows
Interpreting data/analyses using semantic technologies
Tools & services
Metadata standard for scientific data (EML)
Online repository for ecological data/metadata (KNB) Advanced storage system for data/metadata (Metacat) Desktop software for managing metadata (Morpho) Distributed scientific workflow platform (Kepler)
NCEAS Ecoinformatics program
Research
Better managing heterogeneous data
Capturing reproduciblescientific workflows
Interpreting data/analyses using semantic technologies
Tools & services
Metadata standard for scientific data (EML)
Online repository for ecological data/metadata (KNB) Advanced storage system for data/metadata (Metacat) Desktop software for managing metadata (Morpho) Distributed scientific workflow platform (Kepler)
NCEAS Ecoinformatics program
Research
Better managing heterogeneous data Capturing reproducible scientific workflows
Interpreting data/analyses using semantic technologies Tools & services
Metadata standard for scientific data (EML)
Online repository for ecological data/metadata (KNB)
Advanced storage systemfor data/metadata (Metacat)
Desktop software for managing metadata (Morpho)
Real world data vary in format, structure, semantics
Let data vary, but formalize metadata
Metadata makes data useful
Needed to assess/interpret data Provides critical context
Owner, Abstract, Methods Variables, Sites, Units
Ecological Metadata Language (EML)
Metadata standard: rules about what and how to say things
Developed by scientists, for scientists Human-readable, yet machine-interpretable
Let data vary, but formalize metadata
Metadata makes data useful Needed to assess/interpret data Provides critical context
Owner, Abstract, Methods Variables, Sites, Units
Ecological Metadata Language (EML)
Metadata standard: rules about what and how to say things
Developed by scientists, for scientists Human-readable, yet machine-interpretable
Data documentation made easy
Basic option: Web upload
http://data.nceas.ucsb.edu
Simple online form General metadata only Upload data files
Data documentation made easy
Basic option: Web upload
http://data.nceas.ucsb.edu
Simple online form General metadata only Upload data files
Data documentation made easy
Basic option: Web upload http://data.nceas.ucsb.edu
Simple online form General metadata only Upload data files
Advanced option: Install Morpho desktop software (free!)
Create & save detailed metadata using wizards
1 Manage your data on your own computer
2 Share with specific colleagues: set access priveleges 3 Share metadata/data with entire community
