Posts Tagged ‘AGU’

Survival and thriving at AGU Fall Meeting 2012

December 11th, 2012

AGU Fall Meeting, if I am correct, is the biggest conference in the field of geosciences. For the year 2012 there were over 22000 people participated in the event. Yet, a conference is more than the number of attendees. AGU is not a single combination of a number of academic meeting sessions. There are various workshops, seminars, town halls, exhibitions and social activities together with it.
I once read an article written by the president of UNISCO (two years ago?), in which it is mentioned that the number of earth scientists across the world is about 440000. This is a tiny number comparing with the global population. While approaching San Francisco and Moscone Center, the city and venue of AGU, I could feel the number of earth scientists around me is increasing sharply. Especially along the 4th street to Moscone Center, what one can see during the AGU week should be called a deluge of earth scientists. Personally, I had an interesting feeling – am I driven by the deluge, or I am a part of it?
Back to the conference itself, it is a big conference so I (1) focused on sessions in the division of Earth and Space Science Informatics (ESSI), and prepared my personal schedule for posters and presentations I was interested. I also set (2) in-person meetings with people with whom we want to discuss some issues related to the research projects DCO-DS and GCIS-IMSAP at TW. There were (3) a number of other workshops and activities together with AGU, such as the workshop of Data Management 101 for Early Career Scientists, the workshop about NSF system, the ESSI reception, the Ignite Talk, etc. Some of them cannot be easily found on the AGU web site, but are informed through different channels. Many thanks to people in those email lists (e.g., AGU-ESSI, ESIP-SW) I joined for sending me the messages.
I gave two presentations on Friday: a poster for the modeling works in the GCIS-IMSAP project (Jin is first author), and an oral presentation on the exploratory visualization of earth science data with semantic web technologies. For the first one, David Arctur suggested that we may bring some geospatial components into the model framework. Stephan discussed that if we use GCMD keywords for GCIS, then in the GCMD keywords there is a part of it is for geospatial descriptions. While I was introducing the searching function in our plan for the GCIS-IMSAP project, Deana Pennington suggested we may also consider the user tag functions, that is, a reader can create tags in the NCA report for further use, while this may also be supported by some Semantic Web technologies. I also discussed the GCMD keywords with Tyler Stevens, a researcher in the GCMD keywords, on how to make GCMD keywords more open for use. He likes our feedback and already provided some information.
My oral presentation was based on some work originated from my PhD study. This work used datasets on the server of the British Geological Survey. I got some updates from Timothy McCormick, the Information Sector Manager (Geology) at BGS, on their Linked Data works of lithology. He suggested me to do some further work using their datasets and services. Luis Bermudez and David Arctur from Open Geospatial Consortium (OGC) suggested me to do more work on semantic web and Web Feature Service (WFS) and Sensor Web, and they suggested TW to obtain a membership at OGC to get fresh first-hand progress of OGC works.
AGU is a big event, a schedule is necessary, as those described above. And, there are also many other interesting side-events. Almost every day I crossed by some old friends, for some of them I had lost contact for more than seven years! The exhibit is great and I collected a bag of earth and space science cards, posters and toys for my son – is he going to be an earth scientist?
There is more to say about a seven-day conference with over 22000 participants. I have to stop here. For those issues related to specific research topics and projects at TW we will have further discussion in the separate groups soon.

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Characterizing quality for science data products

December 30th, 2011

Characterizing quality for a science data product is hard. We have been working on this issue in our Multi-Sensor Data Synergy Advisor (MDSA) project with Greg Leptoukh and Chris Lynnes from the NASA Goddard Space Flight Center (GSFC). The following is my opinion on what product quality means and how it can be characterized. This work was presented as a poster at the AGU FM 2011 meeting.

Science product quality is hard to define, characterize, and act upon. Product quality reflects a comparison against standard products of a similiar kind, but it is also reflective of the fitness-for-use of the product for the end-user. Users weigh quality characteristics (e.g. accuracy, completeness, coverage, consistency, representativeness) based on their intended use for the data, and therefore quality of a product can be different based on different users’ needs and interests.  Despite the subjective nature of quality assertions, and their sensitivity to users fitness-for-use, most quality information is provided by the product producer and the subjective criteria used to determine quality is opaque, if available at all.

If users are given product quality information at all, this information usually comes in one of two forms:

  • tech reports where extensive statistical analysis is reported on very specific characteristics of the product
  • in the form of subjective and unexplained statements such as ‘good’, ‘marginal’, ‘bad’.

This is either information overload that is not easy for the user to quickly assess or a near lack of the type of information that a user needs to make their own subjective quality assessment.

Is there a smilar scenario in common-day life where users are presented with quality information that they can readily understand and act upon?

There is, and you see it every day in the supermarket.

a common application of information used to make subjective quality assessments

Nutrition Facts labels provide nutrition per serving information (e.g. amount of Total Fat, Total Carbohydrates, Protein) and how the the listed amounts per serving compare to a perspective daily diet.

The comparison to a standard 2,000 calorie diet provides the user with a simple assessment tool for the usefulness of food item in their unique diet. Quality assertions, such as whether this food is ‘good’, or ‘bad’ for the consumer’s diet are left to the consumer – but are relatively easy to make with the available information.

A ‘quality facts’ label for a scientific data product, showing computed values for community-recognized quality indicators, would go a long way towards enabling a nutrition label-like presentation of quality that is easy for science users to consume and act upon.

an early mockup of a presentation of quality information for a science data product

We have begun working on mockups of what such a presentation of quality could look like, and have constructed a basic quality model that would allow us to express in RDF the information that would be used to construct a quality facts label.

Our quality model primer presents our high-level quality model and its application to an aerosol satellite data product in detail.

Our poster presentation was a hit at AGU, where we received a great deal of positive feedback on it.  This nutrition label-like presentation is immediately familiar, and supports the metaphor of science users ‘shopping’ for the best data product to fit their needs.

We still have a long way to go on developing our presentation, but the feedback from discussions at AGU tells me that our message resonated with our intended audience.

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