{"id":2701,"date":"2015-07-25T21:45:06","date_gmt":"2015-07-25T21:45:06","guid":{"rendered":"https:\/\/notebooks.dataone.org\/?p=2701"},"modified":"2015-07-25T21:45:06","modified_gmt":"2015-07-25T21:45:06","slug":"week-9-the-beginning-of-the-end","status":"publish","type":"post","link":"https:\/\/notebooks.dataone.org\/data-access\/week-9-the-beginning-of-the-end\/","title":{"rendered":"Week 9 \u2013 The beginning of the end"},"content":{"rendered":"
\u201cIf we do not press harder for better metrics, we risk making poor funding decisions or sidelining good scientists\u201d (Lane, 2010).<\/p>\n
I met with my mentor this week to go through my work and lay out final products. I\u2019ll be producing a white paper from my research, as well as a poster; and hopefully a publication at some time in the future.<\/p>\n
The final paper contains a literature review of the benefits of data repositories, and a discussion of broader impacts: economic, social, scientific, and internal. It has been interesting to follow the ongoing public discourse concerning trust in the scientific process, and how data repositories support reproducible science.<\/p>\n
The focus of the paper is on an investigation of the data life cycle. There are a number of metrics that can be captured throughout the data life cycle – many of which DataONE already tracks \u2013 including citations, downloads and other altmetrics. There is the potential for surveys to capture information on changing practice, and the types of usage DataONE is currently supporting (to generate ideas; research; educational use; citizen science).<\/p>\n
However there are other places which may show predictive metrics, particularly by looking for integrated datasets. Investigating Data Management Plans and Work Flows in the Plan, Preserve, Discover and Analyze stages might allow the identification of these datasets. The idea is that these integrated datasets suggest \u201cnew\u201d science \u2013 particularly where they can be shown (through different metadata schemas) to come from different disciplines. The work on provenance metadata would be crucial, providing information on derived datasets. It might also be possible to see new interconnections through the use of the combination of instrumentation from different disciplines by examining workflows. Another potential source to see interconnections from different disciplines would be the tracking of data creators, and possibly data users, perhaps through ORCID\u2019s.<\/p>\n
This has been a great learning experience. It is an area of research in which I will do more work, and hopefully the end result will be a meaningful contribution to DataONE.<\/p>\n
Lane, J. (2010). Let’s make science metrics more scientific.\u00a0Nature,\u00a0464(7288), 488-489.<\/p>\n","protected":false},"excerpt":{"rendered":"
\u201cIf we do not press harder for better metrics, we risk making poor funding decisions or sidelining good scientists\u201d (Lane, 2010). I met with my mentor this week to go through my work and lay out final products. I\u2019ll be producing a white paper from my research, as well as Continue reading Week 9 \u2013 The beginning of the end<\/span>