I’m here at The Informationist: Collaboration between scientists and librarians to support informatics research at the Embassy Suites in DC. It’s sponsored by Elsevier as part of their Research Connect series.
(stream of consciousness)
Dr John L Schnase, NASA – Science and technology challenges of eco-informatics
Workshop 10 years ago about research directions in biodiversity and ecosystem informatics. Summary and paper in Information Systems (maybe this: doi:10.1007/s10844-006-0027-7). Stream of logic: striking feature of planet is its life, striking feature of life is its biodiversity. More than any individual living thing, it’s the density, biological richness, and ecosystems. Complexity is the most important factor for the field (Mandelbrotty thing ? he said )
Complexity – developed over 3billion years through evolution, etc., sociologically or culturally developed, importance of location at every scale. Change and flux. Characterizing change.
Example/case study: invasive species mapping department of interior/usgs and nasa. Two funding streams cs and ecological sciences. NASA brought cluster computing to reduce usgs compute times by order of magnitude. Reduced cost, robust over different platforms, redistribute software over non-gov’t systems (like iTunes for the app).
audience q: do your tools also work in urban areas? i notice a lot of invasive species
a: not yet, but it’s possible. this is a great area where citizen science can make contributions
audience q: how do you deal with things that move fast – like carp in the x basin
a: they do move fast, but they move in set ways so they can be studied using similar methods. we haven’t done this much with aquatic ecosystems because the nasa earth observing tools don’t go underwater much, but that is interesting.
audience q: temporal
a: we looked at spatial more than temporal, although others are
Steve Kempler, NASA – Tools and services to access NASA earth science data from the GSFC Earth Sciences Data and Information Services Center (GES DISC)
His role is to provide data to groups like John’s (last speaker).
He reviewed the earth observing systems, but there are other unmanned experiments/instruments in space like Hubble. There are several data centers around the country. They process, archive, and distribute data. “data has no significance beyond its existence” Information centers – “data has been given meaning by way of relational connection”.. Knowledge centers…
Their mission is to maximize NASA’s investment in earth observation by making the products available for research and education. They do software engineering, data management, mission support, and operations. NASA Goddard is heavy on atmosphere and hydrology others do more with oceans, etc.
http://mirador.gsfc.nasa.gov – data search, can put in a location (has a gazetteer), can support portals, also available as a web service. Can also browse by science discipline or project. Can type in “hurricane katrina” and the events database knows when that is and will return. provides data in kml, opendap, subsetting, netcdf format conversion (yay kml – i’m going to try this at home)
giovanni: http://giovanni.gsfc.nasa.gov – browsing and analysis tool. Has data lineage (provenance?). export to google earth, data fusion (prototype)
my q: so where does the informationist come in?
a: teach the students (hm). give feedback. point this out to scientists ( but our scientists already know about all of this)
other audience q: do you track citations to this data in the literature and the news media
a: (my answer – the different projects do for sure – they are required to as far as in the literature, maybe not news media, but that would be useful).
Some ideas from the ending panel:
- informationist is part of the team, not “supporting” – there should be no false separation. Informationists are part of the informatics research effort and should be part of every part of research.
- informatics is need/problem driven. informationists can be the ones who can identify what the next research questions are, what is needed in the way of tools and techniques.
- evaluating – at NIH if they give you office space! if they’re willing to pay when you charge back your time and materials, evaluation studies are hard because they don’t have typical patient-related metrics