michener workshop montpellier

DataONEData Life Cycle:

Tools and Tips

The DataONE Data Life Cycle

2

Plan

Collect

Assure

Describe

Preserve

Discover

Integrate

Analyze

Field Research

3

Plan

Collect

Assure

Describe

Preserve

Discover

Integrate

Analyze

Monitoring Project

4

Publish

Plan

Collect

Assure

Describe

Preserve

Discover

Integrate

Analyze

Synthesis Project

5

Plan

Collect

Assure

Describe

Preserve

Discover

Integrate

Analyze

Publish

Develop Solutions for Research

6

Plan

Collect

Assure

Describe

Preserve

Discover

Integrate

Analyze


7

Plan

Collect

Assure

Describe

Preserve

Discover

Integrate

Analyze

1. Plan:Create and Follow a Data Management Plan

8

Michener WK (2015) Ten Simple Rules

for Creating a Good Data Management Plan.

PLoS Comput Biol 11(10): e1004525.

doi:10.1371/journal.pcbi.1004525


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Plan

Collect

Assure

Describe

Preserve

Discover

Integrate

Analyze

2. Collect and Organize:Logically Structure the Data to Support Use

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Jones et al. 2007

2. Collect and Organize

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• Columns of data are consistent:

only numbers, dates, or text

• Consistent Names, Codes, Formats (date) used in each column

• Data are all in one table, which is much easier for a statistical program to work with than multiple small tables which each require human intervention

2. Collect and Organize

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• Columns of data are consistent:

only numbers, dates, or text

• Consistent Names, Codes, Formats (date) used in each column

• Data are all in one table, which is much easier for a statistical program to work with than multiple small tables which each require human intervention

Googledocs Forms

Data Entry Tools: Excel

Excel: Data Validation

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Plan

Collect

Assure

Describe

Preserve

Discover

Integrate

Analyze

3. Assure:Incorporate Quality Assurance & Quality

Control

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0

10

20

30

40

50

60

0 10 20 30 40

Quality Engine

MetaDIG DIBBs

3. Assure

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3. Assure

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3. Assure

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3. Assure

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3. Assure

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3. Assure

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3. Assure

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3. Assure

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3. Assure

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3. Assure

• JMP

• R

• MATLAB

• many others

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Plan

Collect

Assure

Describe

Preserve

Discover

Integrate

Analyze

4. Describe:Develop Comprehensive, Standardized

Metadata

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Darwin Core – species and biodiversity

collections

EML – Ecological Metadata Language

ISO 19115 – geospatial data

http://rs.tdwg.org/dwc/

4. Describe

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Tools Specify

Morpho

https://knb.ecoinformatics.org/#tools/morpho

http://specifyx.specifysoftware.org


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Plan

Collect

Assure

Describe

Preserve

Discover

Integrate

Analyze

5. Preserve:Protect and Preserve Data for Long-term

Use

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Catalog of 1,500+ Data Repositories

Exercise• Search for repositories that host particular

types of data (e.g., biodoversity, trait)

• Visit one of the repositories and identify the

services that they offer

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Plan

Collect

Assure

Describe

Preserve

Discover

Integrate

Analyze

6. Discover Search a Domain Portal

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Dryad links to journals

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Provides citation instructions

6. Discover Search a Data Aggregator

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Data Federations (DataONE,

GBIF)

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GBIF)carbon cycling

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GBIF)carbon cycling

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GBIF)carbon cycling plant biomass

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ocean nitrogen avian distribution

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Exercise• Search datadryad.org for plant trait

• Search DataONE.org for plant trait

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6. Discover:Support Discovery of Relevant Data

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Dryad DataONE google

plant trait 2,137 26,300,000

plant trait datadryad 803 1,908 17,400

• Differential content searched

• Automated annotation via ontologies and other

approaches

• Differential filtering

• Different definitions of data sets (e.g., entire

package vs individual data sets)


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Plan

Collect

Assure

Describe

Preserve

Discover

Integrate

Analyze

7. Integrate:Enable Data Integration from Different

Sources

81 Jones et al. 2007

7. Integrate:DataONE Provenance Tracking System

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Plan

Collect

Assure

Describe

Preserve

Discover

Integrate

Analyze

8. Analyze:https://www.vistrails.org

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8. Analyze:http://kepler-project.org

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8. Analyze:http://kepler-project.org

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8. Analyze:https://taverna.incubator.apache.org

8. Analyze:https://www.myexperiment.org/

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Best PracticesWebinar series Lessons and

Exercises

DataONE.orgEducation Resources

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DataONE Vision and Mission

dataone.org

michener workshop montpellier

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