Sediment Experimentalist Network (SEN):Sharing and reusing methods and data in

Earth surface process experiments

Leslie Hsu (IEDA, Lamont-Doherty Earth Observatory), Wonsuck Kim (UT Austin), Brandon McElroy (U Wyoming), Raleigh Martin (UCLA)

Charles Nguyen, Danny Im, UMN

August 2014, NCED SIESD at UMNSAFL

Do we need a Sediment Experimentalist Network?

1. Expectations about research data are evolving1. Data deluge: There is more

data from new technologies

2. Funding agencies are asking for data management plans.

3. Journals are asking for links to archived full datasets

4. Metrics for datasets are being developed, allowing better attribution

2. Experimental data is long tail data

Long Tail CharacteristicsMore specialisedLow volumeOn C drivesHard to findHeterogeneousCollected by many peopleCitizen scienceEtcEtc

Long Tail: Environmental and

Earth sciences

The Head: Astronomy, Climate,High Energy Physics, Genomics

L. Wybornhttp://juliegood.wordpress.com/tag/long-tail/

3. Grand challenges in experimental geomorpholgy require data syntheses

• Repeatibility• Scalability• Autogenic vs. Allogenic processes

4. Informatics programs are growing

• NSF ACI Advanced Cyberinfrastructure• AGU ESSI Earth and Space Science Informatics• NSF EarthCube program• ESIP: Earth Science Information Partners• SEAD: data services for managing data• GeoSoft: documenting and sharing software and scripts• CINERGI: providing community access to tools and resources

Yes, we need SEN.SEN’s goal is to integrate the efforts of sediment experimentalists and build a knowledge base for guidance on best practices for data collection and management, and to be the liaison to cyberinfrastructure and geoinformatics communities.

Three components of SEN

SEN-EC Experimental

Collaboratories

• Facilitate collaboration between experimental laboratories

• Develop collaborative infrastructure

• Broadcast experiments • Distributed experiments• Experimental

reproducibility

SEN-KB Knowledge

Base

• Develop online resources for experimental data management

• SEN data catalog and wiki

• SEN-Wiki• Recruit datasets for

inclusion in online repositories

SEN-ED Education &

Data Standards

• Facilitate community discussion of data practices and standards

• Disseminate guidelines• Provide training about data

management and sharing

Today’s objectives

• What is SEN, why do we need it?

• What is the data life cycle?

• How can SEN help you?

• Tell SEN what you need

• SEN data challenge

Normal degradation of data

Michener et al. (1997)

The Data Life Cycle1. Proposal Development and Data Management Plans

2. Project Start‐up

3. Data Collection and File Creation

4. Data Analysis

5. Data Sharing (through publication)

6. Depositing Data

7. …Discovery and back to 1…

http://www.icpsr.umich.edu/files/ICPSR/access/dataprep.pdf

Conception

Gestation

Birth

Maturation

Adult Life

Death, Burial

Planning2012

Jorge Cham

The funding agencywants your plan and the promise that the data will be available forever.

Data Management Plans

Many funding agencies now require a data management plan

Many resources exist for creating data management plans, e.g.• IEDA Data management plan tool• CDL (California Digital Libraries) tool• MIT Data Management Plans page

Proposal Information

http://www.iedadata.org/compliance/plan

Data Acquisition / Processing Summary

Proposed Data Products

Data collection

Preparing data for sharing

Science 11 February 2011: Vol. 331 no. 6018 pp. 692-693

Preparing data for sharing

Data templates and standards exist• Sample descriptions• Analytical geochemical data• Cruise data (dives, sensors)

Standards are discipline specific, and must be developed with community input

Depositing data

In the recent past, the most popular place to deposit datasets with long-term accountability was in journal appendices (Supplemental Material)

Formatting requirements inhibited data archiving (e.g. plain text, page size limits)

Depositing data

Now, there are many options for depositing datasets, e.g.

• Discipline-specific online repositories

• General, institutional repositories• Self-publishing with e.g. FigShare

Data and software publication

• Data papers• Dataset publication and peer review• Persistent identifiers• Software publication and licenses• Altmetrics (e.g. impactstory.org)

SEN and the data lifecycle

The experimental life cycle parallels the data life cycle.

SEN activities are designed to help in each step.

S. Ahn

SEN Activities

• Workshops• Training• Tools• News• Experiments• Discussion list

Future events

● Nov 2014: Utrecht workshop

● Dec 2014: Proposed AGU Town Hall – Sharing and publishing data in Earth Surface Process Science

● Fall 2015: Binghamton Geomorphology SymposiumExperimental Geomorphology: Sharing and Reusing Data

Tell SEN what you need

SEN Data Catalog, http://sedexp.net

Create user

or

sedimentexpsiesd2014

SEN Wiki, http://sedexp.net

SEN Sediment and Instrument Lists

Where have others bought sediment and instruments?goo.gl/NUA5mS (Tabs 1 and 2)

SEN and CINERGI Resource Viewer

What websites, databases, and journals might help me?goo.gl/Yp5Aud

SEN 2014 Challenge

http://goo.gl/YKTsNm

For a trip to an upcoming SEN workshop!(students at a U.S. Institution)

Summary

1. What is SEN, why do we need it?

2. What is the data life cycle?

3. What are some tools that SEN provides to help me discover and manage data?

4. How can I earn a trip to a SEN workshop?

Links and ReferencesSEN homepage: workspace.earthcube.org/sen

EarthCube Program: www.earthcube.org

Michener, William, James Brunt, John Helly, Thomas Kirchner, Susan Stafford. (1997). “Nongeospatial Meta data for the Ecological Sciences Ecological Applications, Vol. 7, No. 1, pp. 330–342.http://www.icpsr.umich.edu/files/ICPSR/access/dataprep.pdf

Nature Articles• http://www.nature.com/ngeo/journal/v4/n9/pdf/ngeo1259.pdf• http://www.nature.com/ngeo/journal/v4/n9/pdf/ngeo1248.pdf

Science Special Data Issue: http://www.sciencemag.org/content/331/6018/692.short

Data Planning• http://www.iedadata.org/compliance/plan• https://dmp.cdlib.org/• http://libraries.mit.edu/guides/subjects/data-management/plans.html

Contact: lhsu@ldeo.columbia.edu