DataONE Ecology Data Production Study

Please view the email invitation to participate in the study here. (needs to be updated)

Please view the draft list of interview questions here. (needs to be updated)

Purpose: The purpose of the survey is to assess data production in the field of ecology. Specifically the survey will estimate the amount of data that academic researchers produce and the proportion of that data that are “at risk” and end up unused and invisible to the ecological community. Also, the survey will examine the extent of data management practices performed by ecology researchers. Overall, the study aims to motivate researchers in the field of ecology to better share, reuse, and manage their data.

Method: Subjects will be selected through the National Science Foundation’s list of awards which is a publicly accessible database that contains information on research projects and primary investigators who have won funding. The survey will focus on subjects who have previously won awards, now expired, within the Division of Environmental Biology. A list of potential subjects will be divided by program category and random selection of subjects will occur within each program category. Contact information obtained via the NSF awards database and verified by online search, will be used to contact the primary investigator of the project that won funding. A request for participation of the survey will be made by email. However the actual interview will be made via telephone. Subjects will be asked to answer questions about the nature of their research project (eg. how long did the project last, how much ecological data was generated for this project) and general questions regarding their data management practices (eg. how much data has not been used for publication, how much data has been submitted to data repositories).

Respondents: Subjects will be scientific researchers in the field of ecology. Generally this will include ecology researchers who have attained their PhD and have served as primary investigators on a project.

Surveyor: The survey will be conducted in English. The survey will be in the form of a telephone interview with subjects. There will be one interviewer interviewing every subject.

Responses: Questions will be open-ended, allowing subjects to answer in their own words. There may also be a small set of close-ended questions.

Timing: 30-45 minutes. Time will be allocated for asking questions and allowing the subjects to think and respond at their own pace.


  1. Record researcher demographics (when they obtained their PhD, what is their status, who is their employer etc)
  2. Find out about project details and specifics (project length, project status, number of publications produced, degree of collaboration, etc)
  3. Record the amount/types of data generated (identify data types, estimates of total data, rate of data production, amount of data not used, amount of data archived, amount of data stored in databases, etc)
  4. Find out about the data management practices of ecologists (number of projects they run per year, their data management policies, actual data management practices, etc)

2 Replies to “DataONE Ecology Data Production Study”

  1. Also worth thinking about and/or discussing: what strategy will you use to be sure you get enough responses? How many responses are enough? Academics are sometimes quite hard to locate and pin down for questioning…

  2. Looks great, Michelle. A few comments:

    Under “Purpose”: Specifically the survey will estimate the amount of data [that academic] researchers produce and the [proportion of that data that are “at risk” and end] up unused and invisible

    Under “Method”: I would also suggest we verify their contact info via a google search- academics are pretty good at keeping a good online presence.

    Under “Objectives”: Think about WHY you have each of the 4 objectives- how do they relate to the goal of the study? (I am not implying the objectives aren’t good, just that it’s worth carefully considering why you are asking the questions that you are- how will they translate into statistical analyses later on?

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