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Accept Data: Reality Check

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    WE'RE EXCITED

    THAT...

    BUT IT CAN BE CHALLENGING

    BECAUSE...
    Citizen science partnerships can yield copious amounts of data.

    		Such data are an investment.
    • Must protect data investment through back-ups and/or relationships
    • Finding an appropriate platform or designing a new one
    • Addressing data security and privacy issues, such as encrypting and managing secure data (e.g., for endangered species locations)
    Sharing data between different projects to address complex issues or questions.

    		Most existing projects were not designed with this goal in mind. Challenges include:
    • Communication between projects with different databases.
    • Unifying data to make available to all users
    • Determining shared protocols and data standards
    • Determining database standards and meta-data structure
    See one example in the broader monitoring community (link to NBII).
    Citizen science can empower participants to explore their own questions.

    		Challenges may arise on several fronts:
    • Designing or finding user-friendly tools that allow meaningful access to, and manipulation of, project data (some tech tools).
    • Do participants have access to an internet connection that supports such tools?
    • Can the data be utilized in a way that is personally and/or locally relevant?
    Publicly collected data can be a public resource.

    		There is the risk that data are open to misuse and/or bad analysis which could reflect poorly on a project. Consider balancing access with control, such as data security and documentation of use. Demands consideration of more elaborate models for data sharing.

     

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    WE FIND CHALLENGES...

    BUT SOME IDEAS ARE...
    Recognizing different error types in the data:
    • honest vs. intentional
    • expected values vs. false positives OR false negatives
    • outliers

    		Data visualization tools can help perceive outliers, and early errors can be caught with "smart forms." See some How-to Tips.
    Tagging data by observer and data quality in a manner that respects all contributions. Seeking quality data, and data of known quality.

    		Identify skilled volunteers and leverage their skills to advance the program (e.g., recognize experts who can respond to questions of other volunteers).
    Generating meaningful data (meets research goals, provides complete coverage, tells a story...).


    		Data outcomes that are meaningful in context are more fund-able and self-sustaining.
    Determining how long the data are going to live (hopefully forever) and where/how to archive it.

    		Easily accessible archives facilitate use of past data and mid-stream data in combination with current data sets.
    MIS (management information systems) must have a sense of place.

    		Take the opportunity at the outset to align data infrastructure in a way that specifically targets project goals.
    Science-related public goods (e.g., data) need a publicly supported infrastructure to support them. Data systems, software tools, training resources, etc.


    		Funders should be actively encouraged to understand and support a public platform. Grant recipients should likewise be eager to accept grant conditions that will encourage their programs to advance a shared platform by producing and releasing open source code, using creative commons licenses on content, providing open APIs, etc.
    Having a common language for understanding the story behind the data.



    Fostering a sense of ownership and discovery despite large, absorbing datasets.


     

     

    Know of any opportunities for or challenges to this step?  Soon you will be able to share them through our discussion forum.

     

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