DryadLab workshop Dec 2011
Dates: December 1-3 2011 Place: NESCent, Durham NC
- 1 Purpose
- 2 Participants
- 3 Preparation
- 4 Statistics Packages
- 5 Learning Objectives and Template
- 6 Datasets
- 6.1 Towards a worldwide wood economics spectrum
- 6.2 The strength of phenotypic selection in natural populations
- 6.3 Aging in the natural world: comparative data reveal similar mortality patterns across primates
- 6.4 Hunting to extinction: biology and regional economy influence extinction risk and the impact of hunting in artiodactyls
- 6.5 Interspecies competition in Trilobium
- 7 For more information
- 8 Agenda
- 9 Bibliography
- 10 Meeting Notes
The Dryad Digital Repository is an archive for data underlying articles in the bioscience literature. DryadLab is a planned educational face to Dryad, which will provide teacher and student resources for learning activities that are based on Dryad datasets. It is to be primarily targeted at undergraduate and graduate students.
The goals of DryadLab are threefold. One, to provide high-quality data-centric learning activities touching on important concepts and methods in the bioscience curriculum. Two, to expose students to a data archive and give them appreciation for the value of good data management and data accessibility. Three, to highlight particularly useful, interesting and unique datasets within Dryad and give the authors of those datasets both additional credit and an opportunity to enhance broader impacts.
All DryadLab materials will be linked with the original Dryad datasets and made available under a similarily non-restrictive license. The materials will also promoted through multiple online channels. Those of particular evolutionary interest will be promoted especially through the UC Berkeley Understanding Evolution website.
The aim of this workshop is to develop six of the initial DryadLab modules and establish a template for future modules. The datasets have been preselected, and each one will be assigned to a small group that includes (a) one or more biologists familiar with the data, (b) researchers and educators from the Understanding Evolution Teacher Advisory Committee, and (c) staff from Dryad and NESCent's Education and Outreach group.
The workshop consisted of a mix of large group discussions to help decide upon common approaches, and small group breakouts in which participants worked on producing the modules, with the aim of publishing the completed modules shortly after the workshop.
- Susan Alberts
- Christina Caruso
- Jean DeSaix
- Sarah Diamond
- Sam Donovan
- Elena Feinstein
- Kristin Jenkins: Education and Outreach with NESCent
- Hilmar Lapp
- Louise Mead
- Peter Midford
- Sam Price
- Peggy Schaeffer: Dryad Communications Coordinator, librarian
- Ryan Scherle
- Rob Swanson
- Anna Thanukos
- Dan Ward
- Jory Weintraub
- Todd Vision
- Amy Zanne
- Read original paper, plus background paper suggested by researcher.
- Review dataset.
- Review DryadLab background, and guidelines for development of a DryadLab module.
- See finch example
- There will be preparatory phone calls for each the participants.
- Ask us if you have any questions.
Since many of the modules will employ statistics, it might be useful to share information about statistics tools and packages that we might make use of for these modules. Please include a link to the resource as well as a short description.
Learning Objectives and Template
Understanding Science guide to Identifying your learning goals
Note: Learning objectives can include more specifics, like [what does data literacy look like?]
General (high priority) objectives:
- Data Literacy
- Engage students in scientific reasoning
- Student shows inclination and ability to use a dataset.
- Student knows different data formats.
- Student can document or describe data.
- Student can examine a large dataset and select appropriate data for analysis given hypothesis being tested.
- Student can determine how to visualize data.
- Student can run appropriate analysis
- Student can use program/query to extract appropriate data from a database.
- Student understands how to work with large datasets.
- Student can document workflow.
- Student can maintain integrity of data, particularly when interacting with large datasets
- Interpreting messy/real data
- Student can read and interpret a graph.
- Student can evaluate data quality.
- Student can evaluate methods used to collect data.
- Student can independently input and transform data.
- Students can or identify ways to combine data.
- Students share data.
Analyzing analysis of data:
- Student can recognize variability, contradiction, and confounding factors.
- Students can determine if correct analyses were done and why/why not
- Student can perform a meta-analysis.
- Student knows how to cope with/address missing data, outliers.
Here is a blank copy of the template to download and enter information for your module.
- Dryad data: doi:10.5061/dryad.234
- Participants: Amy Zanne, Jean DeSaix, Elena Feinstein
- Dryad data: doi:10.5061/dryad.166
- Participants: Sarah Diamond, Louise Mead, Peter Midford
- Dryad data: doi:10.5061/dryad.8682
- Participants: Susan Alberts, Dan Ward, Hilmar Lapp
Hunting to extinction: biology and regional economy influence extinction risk and the impact of hunting in artiodactyls
- Dryad data: doi:10.5061/dryad.82
- Participants: Samantha Price, Anna Thanukos, Peggy Schaeffer
- Dryad data: not yet!
- Article: Park T (1948) Ecological Monographs 18(2): 265-307, http://www.jstor.org/stable/1948641
- Participants: Mike Wade, Sam Donovan, Kristin Jenkins, Todd Vision, Rob Swanson
For more information
Please contact Kristin Jenkins at 919.260.7369 or kjenkins(at)nescent.org. For logistics and travel information, contact Danielle Wilson at (919) 668 4545 or danielle(at)nescent.org.
- Evening dinner, 7pm at Pop's
- 8:00 coffee & light breakfast
- 8:30 Welcome - logistics Kristin Jenkins/Danielle Wilson
- 8:45 Introductions
- 9:00 Introductory presentations (20 min each):
- DryadLab (Vision)
- Understanding Evolution (Thanukos)
- Engaging students with data (Donovan)
- 10:00 Research presentations (20 min each)
- 12:30 Lunch on 9th St
- 2:00 Group brainstorming
- 3:30 Break
- 3:45 Breakout groups
- 5:00 Adjourn
- 6:30 Dinner at Piedmont
- 8:00 coffee
- 8:30 Breakout reports
- 9:30 Group discussion
- 10:15 Coffee break
- 10:30 Breakout groups
- 12:00 Lunch catered
- 1:30 Breakout groups
- 4:00 Breakout feedback/group discussion
- 5:00 Adjourn
- 8:00 coffee & light breakfast
- 8:30 Small group - planning next steps
- 10:00 Break
- 10:15 Group discussion - next steps
- Adjourn by 11am
- Carlson et al 2011 Determining Data Information Literacy Needs: A Study of Students and Research Faculty DOI:10.1353/pla.2011.0022.