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DataONE NSF Reverse Site Visit Washington DC. April 7-8, 2016
DataONE: Data Observation Network for Earth Amber Budden Director for Community Engagement and Outreach
DIBBS/DataNet Best Practices Workshop Pittsburgh May 17th 2016
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Vin
es, T
. H. e
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://dx
.doi
.org
/10.
1016
/j.cu
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12 21 26 95 95 96 97 266
676
Metadata standards
Science and Data Challenges
2
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Providing universal access to data about life on earth and the environment that sustains it
Building community Developing sustainable
data discovery and interoperability solutions
Supporting researcher tools and services
DataONE Mission
Plan%
Collect%
Assure%
Describe!
Preserve%
Discover%
Integrate%
Analyze%
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Scientists
Academia
Non-profit
Govern-ment
Community
Private Industry
Federal Local
Citizen-scientists
Citizen-activists
Policymakers Administrators
Advisory Committee Decision Makers
Judiciary
Teachers
Informal Educators
Curriculum Builders
Libraries
Librarians
Professional Societies
Foundations
Think Tanks
Libraries
Librarians
Publishers
Administration Researchers
Students
Teaching Faculty
Libraries
Librarians
State
Libraries
Librarians
Institutions
Stakeholder Matrix
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Use other researchers’ datasets if easily accessible
Willing to share data across a broad group of researchers
Appropriate to create new datasets from shared data
84%
81%
76%
Currently share all of their data 6%
Scientists want to share data
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Use other researchers’ datasets if easily accessible
Willing to share data across a broad group of researchers
Appropriate to create new datasets from shared data
84%
81%
76%
Currently share all of their data 6%
Scientists want to share data
12 21 26 95 95 96 97
266
676
DIF DwC DC EML FGDC Open GIS
ISO My Lab none
Metadata standards
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never occasionally monthly weekly daily
Metadata creation
Conversion of data/datasets for ingest
Selection of data/datasets for repository
Selection of data/datasets for ingest
67%
75%
66%
70%
Libraries not yet providing data services
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DataONE Cyberinfrastructure Coordinating Nodes
Components for a flexible, scalable, sustainable network
Coordinating Nodes • retain complete
metadata catalog • indexing for search • network-wide services • ensure content
availability (preservation) • replication services
www.dataone.org/coordinating-nodes
8
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Components for a flexible, scalable, sustainable network
DataONE Cyberinfrastructure Member Nodes
www.dataone.org/member-nodes
Coordinating Nodes • retain complete
metadata catalog • indexing for search • network-wide services • ensure content
availability (preservation) • replication services
Member Nodes • diverse institutions • serve local community • provide resources for
managing their data • retain copies of data
9
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Components for a flexible, scalable, sustainable network
DataONE Cyberinfrastructure Investigator Toolkit
www.dataone.org/investigator-toolkit
Coordinating Nodes • retain complete
metadata catalog • indexing for search • network-wide services • ensure content
availability (preservation) • replication services
Member Nodes • diverse institutions • serve local community • provide resources for
managing their data • retain copies of data
10
Investigator Toolkit
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2012 2 3 4 2013 2 3 4 2014 2 3 4 2015 2 3 4 2016 2 3 4
DataONE Member Nodes Current and Upcoming
Upcoming Member Nodes
11
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Data Holdings
12
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DataONE Search
13
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Download Stats Dataset Views
14
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Member Node Profiles
15
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User Profiles
16
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User Experience Testing
2009 2010 2011 2012 2013 2014 2015
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New features Provenance of figures
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New features Provenance of data
19
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Data Life Cycle Framework for resources
Plan%
Collect%
Assure%
Describe!
Preserve%
Discover%
Integrate%
Analyze%
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Community Engagement Education and Outreach
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DataONE Webinar Series
22
num
ber o
f ind
ivid
uals
webinar event
0 50
100 150 200 250 300 350
1 2 3 4 5 6 7 8 9 10 11
64.7%
1
2
3
4
5
Relevance Speaker Knowledge
Format
98.6%
5 po
int r
espo
nse
scal
e (1
low
)
www.dataone.org/webinars
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Data Management Modules
Lesson 10: Analysis and Workflows
Typical data analyses
Data processing: may include selecting a subset of data for analysis, merging multiple data sets, manipulating data for usability, or data transformationGraphical analysis: makes it easier to see patterns and can aid in the identification of outliersStatistical analysis: conventional statistics are used to analyze experimental data; descriptive statistics are used to analyze observational or descriptive dataScience is iterative: the process that results in the final
product can be complex.
Reproducibility..
...is at the core of the scientific process. If results are not reproducible, they lose credibility.
Good documentation of the data and the analysis are essential!
Formal Workflow
Analytical pipeline where each step can be implemented in different software systems.
Parameters and requirements for each step are formally recorded.
• Single access point for multiple analyses across software packages
• Keeps track of analysis and provenance to better enable reproducibility
• Workflow can be stored• Allows sharing and reuse of individual steps
or overall workflow
Local contact information
View all Education Modules at https://www.dataone.org/education-modules
Workflows
Definition: Precise description of the procedures used in a project. Can be formal or informal.
Informal workflow
No special software is needed to create workflow diagrams. Workflow diagrams include: • Inputs and outputs• Transformation rules or analytical processes• Decision points• Arrows indicating direction of process flow
Informal Workflow Example
Formal workflow example: Kepler software
Best practices for data analysis
Formally or informally document the workflows used to create results. Include: • Data provenance• Analyses and parameters used• Connections betweeen analyses via inputs and
outputsDocument the code you write for analyses.• Well-documented code is easier to review and
share and enables repeated analyses• Include project level information; script de-
pendencies, inputs, and outputs; parameters; and what happens in individual sections
Construct end-to-end scripts that run the entire process from start to finish without intervention.
Hands-on Exercises for Data Management http://www.dataone.org/education-modules
1"
Hands!on#Activity#1:#Accessing(Data(in(the$Literature"Associated*DataONE*Lecture:*Lesson"1:"Why$Data$Management$
Objectives:"Students"recognize"the"value"of"accessibly"archived"data,"by"experiencing"the"challenges"of"accessing"data"from"published"papers."
Outcomes:*(1)"Students"can"explain"why"accessible"data"archiving"is"valuable."(2)"Students"can"provide"strategies"for"getting"data"from"published"papers,"and"anticipate"challenges"to"accessing"the"data."
Time*Needed:*One"hour"out!of!class,"15"–"30"minutes"in!class"discussion.""
URLs:"Any"resource"for"searching"scientific"literature"(e.g."Web$of$Science,"Google$Scholar,"JSTOR,"BioOne).""
Additional*Files*Needed:"None"
Key*Reading:*Carly"A"Strasser"and"Stephanie"E"Hampton."2012."The"fractured"lab"notebook:"undergraduates"and"ecological"data"management"training"in"the"United"States."Ecosphere"3:art116."doi:"10.1890/ES12!00139.1"
Notes*and*Instructions*for*Instructors:**"An"intended"take!home"lesson"of"this"activity"is"that"access"to"valuable"original"data"can"become"difficult"or"impossible"in"a"short"period"of"time"after"a"paper"is"published,"but"this"loss"of"accessibility"is"avoidable."How"easy"it"is"to"access"original"data"depends"on"the"field;"some"fields"have"developed"a"culture"of"data"sharing"and"data"accessibility,"including"genetics,"climate"studies,"and"geography."Others"do"not"have"this"tradition."Because"of"these"field!specific"cultures,"students’"success"at"accessing"data"will"depend"on"the"topic"and"question"they"chose.""
It"may"be"worth"reviewing"with"the"students"the"different"ways"by"which"scientists"access"others’"data:"data"tables"or"published"data"appendices"within"a"paper,"extracting"(estimating)"data"from"published"graphs,"online"data"archives"or"data"streams"(either"restricted"to"journal"subscribers"or"public),"writing"the"author"and"requesting"the"data"etc.""
After"students"have"completed"the"exercise"(see"Student$Instructions,"below),"have"students"discuss"the"challenges"that"they"faced"in"figuring"out"how"to"access"data"from"the"published"literature"that"are"relevant"to"their"question,"and"ways"the"students"came"up"with"to"deal"with"the"challenges."This"can"be"done"as"a"15"to"30"minute"whole!class"discussion"or"in"small"groups"with"a"report!out."Things"to"note"include"whether"accessibility"to"data"varied"depending"on"the"question"addressed,"and"whether"accessibility"depended"on"how"long"ago"the"paper"was"published."Perhaps"culminate"the"discussion"with"questions"about"why"data"underlying"
23 www.dataone.org/education-modules
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DataONE Users Group § A self-organizing, non-biased form of feedback to the
DataONE leadership § A peer teaching opportunity on how best to use and
integrate the DataONE products § An opportunity for DataONE to connect with a broad
community through a network of advocates
24 www.dataone.org/dataone-users-group
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§ Annual summer meetings co-located with ESIP § 2 Co-chairs, 13 member Steering Committee § 310 members
DataONE Users Group
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Please save July 17-18, 2016 for the open DataONE Users Group meeting to be co-located with the Summer ESIP Federation Meeting at the Friday Center, Chapel Hill, North Carolina. The DataONE Users Group (DUG) meeting will be a 2-day event featuring plenary presentations, topical breakout sessions, and community-led discussions.
There is no registration fee to attend and participate in the DUG meeting.
Registration and hotel block will open in the spring, a few months before the meeting. Please visit https://www.dataone.org/dataone-users-group for updates and to join the DUG.
Meeting Theme and Objectives The 2016 Meeting theme, “Expanding Data Networks,” will focus on the new challenges and efforts in making data accessible, discoverable, and deliverable while promoting open data policies, standards, and compliance with funders’ emerging data management requirements. A strong emphasis is on data synthesis and technological progress made in data network infrastructure.
The scientific program of the 2016 meeting will invite talks and posters on the following topics: • Leveraging research data level metrics for large data repositories and data networks • Integrating the needs and inputs of data users to advance and improve data
discoverability • Assessing the progress, impact, and success in promoting open data policies
DataONE encourages DataONE Member Nodes, data scientists, researchers, scientists, students and others to submit abstracts for posters and talks.
Abstract Submission for Posters and Talks Please submit an abstract (250 words maximum) to [email protected] and indicate whether you prefer to present a talk or a poster. Talks will be approximately 10-20 minutes in duration, to be confirmed with development of the agenda. The poster session will be held the evening of Sunday July 17th during the reception event.
Submissions will be reviewed by the DataONE Users Group Steering Committee. Accepted abstracts will be published on the DataONE website.
Important dates Abstract Submission Deadline: April 15th 2016 Author Notification: May 15th 2016
DUG Steering Committee: Felimon Gayanilo (co-chair), Plato Smith (co-chair), Steven Aulenbach, Amber Budden, Debora Drucker, Rebecca Koskela, Myrica McCune, Laura Moyers, Shannon Rauch, Robert Sandusky, Stephanie Simms, Heather Soyka
Save the Date:
DataONE Users Group Meeting Expanding Data Networks DUG Summer Meeting July 17-18th 2016 Durham, NC
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Interest in data sharing has increased
Scientists 26
0 10 20 30 40 50 60 70 80 90 100
Use others' datasets if their data were easily accesible
Willing to share data across a broad group of researchers
It is appropriate to create new datasets from shared data
Currently share all of my data 2010 2014
80%
88%
88%
76%
81%
84%
0% 20% 40% 60% 80% 100%
It is appropriate to create new datasets from the shared data
Willing to share data across a broad group of researchers
Use others' datasets if their data were easily accessible
2010 2014
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Challenges Scientists
27
Scientists
0 5 10 15 20 25 30 35 40 45 50
There is insufficient time to make them available
I need to publish first
Lack of funding
Don't have rights to make data public
There is no place to put them 2010 2014
Scientists need effective mechanisms to their preserve data Scientists need tools and data management skills
DataONE cyberinfrastructure, usability testing and education resources are designed to support these needs
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dataone.org
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www.DataONE.org
@DataONEorg
facebook.com/DataONEorg
vimeo.com/DataONEorg
slideshare.net/DataONEorg [email protected]