Interagency Data Stewardship/LifeCycle/Preservation Forum/TeleconNotes/2016-10-17meetingnotes

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Meeting Agenda and Notes - DS Committee - 2016-10-17 2PM EDT

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Attendees: David Moroni, Nancy Ritchey, Shelley Stall, Nate, James, Madison Langseth, Nancy Todd, Shannon Leslie, Ruth Duerr, Heather Brown, Denise Hills, Bruce Caron, Martin Hadley, Nancy Hoebelheinrich, Sophie Hou


Notes:

1) Data Management Training Clearinghouse Project: End of Project Report Out (10 minutes + ~5 minutes for Q&A)

  • Nancy provided a brief background regarding the Data Management Training Clearinghouse, including some of the key questions that the Clearinghouse is trying to address.
  • The main funding source for the Clearinghouse project came from USGS Community for Data Integration (CDI) after the project team submitted a proposal for the Clearinghouse.
  • Other key supporting organizations are: ESIP and DataONE; plus additional contributions from NCAR.
  • This phase of the Clearinghouse project is concluding with a soft launch of the Clearinghouse (beta version) that will take place this week.
  • The current link to the Clearinghouse is: dmtclearinghouse.esipfed.org
  • Please note that log in to ESIP is required in order to view the complete beta site.
  • Nancy provided demos for all three of the Clearinghouse’s functions: Search, Browse, and Submit.
  • Nancy would like to invite everyone who has data management training/learning resources that s/he would like to share to create submissions to the Clearinghouse.
  • Participating in the submission process will also help in allowing everyone to help in testing the Clearinghouse’s workflows.
  • Question: Is the Clearinghouse looking for someone to help in submitting additional resources?
  • Answer: Yes; anyone who would like to submit resources can start working with the current release of the Clearinghouse.
  • Going forward, Nancy has worked with another USGS PI and Erin from ESIP to submit a second proposal to USGS CDI.
  • This proposal will focus mainly on usability evaluations, addition of resources, as well as additional enhancement to the Clearinghouse’s features.


2) Data Visualization Presentation (20 minutes + ~10 for Q&A)

  • Presenter: Martin Hadley, Oxford University (@martinjhnhadley; http://blogs.it.ox.ac.uk/acit-rs-team/team/martin-john-hadley/)
  • Presentation Title: “Data Visualisation is Important: Visualisation aren’t Just Furniture for Publications”
  • Martin typically provides hands-on training for data visualization.
  • Data visualization is a very important way for researchers to leverage for communicating data/results of the projects to the greater community.
  • “Changing the Equation on Scientific Data Visualization” and “Do Altmetrics Work? Twitter and Ten Other Social Web Services” are two papers that Martin would encourage us to review.
  • Additional references are also provide in his slides.
  • Visualizations are useful for:
  • Exploratory data analysis.
  • Summarize trends in an easily consumable manner.
  • Physically demonstrate comparisons between groups of data (ex: segment size of data)
  • Present connections otherwise difficult to communicate (ex: demonstrating which departments of an academic institution are collaborating together).
  • There are also several reasons for using visualization:
  • To facilitate data processing by machine and comprehension by people.
  • To stimulate additional/multiple questions to be asked/examined.
  • Interactive visualization:
  • Examples:
  • Benefits:
  • Provides alternative access to data.
  • Allow users to slice through data.
  • Visualization is not just “pretty to look at”; visualization needs to be functional.
  • Graphical Perception Theory:
  • This theory helps in determining the best visual presentation technique for data.
  • Pi charts can be difficult for people to determine the relative sizes presented in the chart. In comparison, bar charts can be understood easier because people can determine differences in line lengths easier.
  • Another example of how to improve visual presentation of data is to use “violin plots” versus “bar charts”.
  • “Creative” presentations of data, such as using interesting background/frame images, need to be used carefully , so that these images do not distract users from the actual data.
  • Also, it can be challenging to indicate to users that a particular visualization is interactive.
  • Visualization can also help with reproducibility of research.
  • Example: Include ORCID for all the visualizations published.
  • Some examples of visualization tools/scripts:
  • Question: What are the preservation techniques that can be used with/applied to the visualizations that have been created?
  • Answer: In terms of publishers, they only provide support for the visualizations as long as the publications are still popular. This is not a long-term preservation strategy, however. In general, static versions of the visualizations should be provided, so that the visualizations could be reused with a different script if needed.
  • Ruth suggested that Data Conservancy is working on initial solutions for this issue, but Data Conservancy is still at the beginning of the process.


3) Discussion of Winter Meeting Sessions and AGU

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