[Esip-preserve] Student Fellow Telecon/Presentation - Presentation Slides and Recording Available Online

[Hou, Chung-Yi]

Hi everyone,

Thank you very much for attending the presentation this morning.

I have posted the presentation slides on ESIP's wiki page (http://wiki.esipfed.org/images/8/8e/SophieHou_Presentation.pdf).  The recording of the presentation can also be streamed using the following link (presentation starts at the 7:25 time mark):

https://esipfed.webex.com/esipfed/ldr.php?RCID=ca9aaf50e158079632a74660d57e3eaf

If you have any questions or suggestions/advice, please feel welcome to let me know at any time.

Thanks again,
Sophie

Abstract:

 As scientific data volumes, format types, and sources increase rapidly with the invention and improvement of scientific capabilities, the resulting datasets are becoming more complex to manage as well.  One of the significant management challenges is pulling apart the individual contributions of specific people and organizations within large, complex projects.  This is important for two aspects: 1) assigning responsibility and accountability for scientific work, and 2) giving professional credit to individuals (e.g. hiring, promotion, and tenure) who work within such large projects.

  This presentation will provide an overview for the concept of data citation, its current practices, and the strengths and weaknesses of the current data citation methods when applied to climate model dataset.  Using the NCAR Global Climate Four-Dimensional Data Assimilation (CFDDA) Hourly 40km Reanalysis as a case study, the presentation will also demonstrate the creation of a detailed attribution framework using a movie-credit style.  Analogous to acknowledging the different roles and responsibilities shown in movie credits, the methodology developed in the study could be used in general to identify and map out the relationships among the organizations and individuals who had contributed to a dataset.  Finally, discussion questions will be presented in order to consider how this framework could be applied to create data attribution for other dataset types beyond climate models datasets.
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