2012 Unidata/EarthCube Workshop Data and Software Survey

Please take a few minutes to provide a snapshot of your use of geoscience data and software. We will have the results of this survey available for discussion during the December 2012 Unidata/EarthCube workshop.

See the Survey Results

Please tell us about yourself: (Optional)  (Why are you asking me this?)

I am:  a university employee a student a government employee from a private company other
1. Finding Data

Many sources of data are used in data assimilation and ensemble prediction. What are some challenges you have faced in terms of readily and efficiently finding the data you need to conduct your research in these areas?

2. Acquiring Data

Where do you go to get your data? What online data portals and observational databases you use most to acquire your data? What improvements you think are needed to make it easier and efficient to acquire or access data?

3. Using Data

Once you have acquired the data you need, what are some challenges you have faced in terms of using the data in your research or educational activities?

4. Software Tools

What software and tools do you use to interact with your data? List the key data processing, analysis, visualization, modeling, verification, GIS, or other software you use. What are the challenges and issues in making effective use of such software? What are their limitations and how do you see them overcome?

5. Missing Tools

What databases, software, and cyberinfrastructure capabilities do you wish existed that are not currently available?

6. Data from Other Disciplines (part 1)

Do you use data from outside your discipline? If so, what are those disciplines and what types of data you use? And how do you use such data?

7. Data from Other Disciplines (part 2)

If you could access data from other disciplines, what fields would they be? And what types of data?

8. Time Spent on Data-related Tasks

It is generally believed that a typical researcher spends 80% of his or her time on routine data-related and computational tasks (e.g., acquiring, processing, reformatting, and transforming data, as well as compiling, debugging, and linking to proper libraries, etc.) and only 20% of time on actually doing science. Is this breakdown representative for you? If not, what percentages do you think reflect you own situation?

9. Reducing Data Friction

What suggestions do you have to reduce such data and/or computational “friction” and increase your research productivity?

10. Other Comments

Please provide any other comments you may have on any of these matters.