It's no secret that data quality is really important to the Vital Signs community. We all want the data we collect and the observations we share to be useful to and trusted by someone(s) other than ourselves, and to contribute to a significant statewide effort that none of us could possibly accomplish alone.
Learning is also really important. Real messy science learning generally happens in safe places where it's okay to try and make mistakes and try again. Collecting great data takes time, patience, practice, peer support, and community guidance.
We want both. High quality data. A killer learning environment. Vital Signs was carefully designed so we can have both. Here's an overview of the 5 step process that gets us all the quality data and messy learning we can handle.
Step this way to quality data
- Automated Check of select data fields to make sure things like geographic coordinates and water quality measurements are within range.
- Quality Check by a fellow Vital Signs participant prior to publishing.
- Peer Review and critique by a fellow Vital Signs participant prior to publishing.
- Expert Review of each species observation by members of the Vital Signs community who have clearly demonstrated a certain familiarity and savvy with a particular species (no PhD required).
- Community Review of all published data by members of the Vital Signs community through public comments and identification support.
Positioning your data
It's ultimately your choice in how you would like to position your data. The most highly-positioned observations have been Auto-checked, Quality Checked, Peer Reviewed by a fellow Vital Signs participant, Expert Reviewed, and Community Reviewed via comments and ID support. At the other extreme are data that have undergone only the required Auto-check and Quality Check. Middle of the road observations include observations that are Peer Reviewed by you (way better than nothing if you're investigating solo), and those that have been reviewed by an expert but not the community.
We know. Not all of these checks are completely within your control. We will do our very best to ensure a timely Expert Review, and will motivate and encourage the community to take advantage of their mighty review powers.
See and use only the highest quality data
The Advanced Search on the Explore data, View data on map, and Sort & export data pages offers you the choice to view only observations that have been reviewed and confirmed by a Species Expert. You can further inform your search in the Sort & export data table to see the metadata associated with each observation.
A Collective Responsibility
As participants in this community, it is our collective responsibility to insist on and help move our entire community of novices and experts alike towards publishing the highest quality species observations and habitat data possible. You can help by:
Publishing your best work
- Make careful observations in the field
- Have your work quality checked and peer reviewed by someone else in the community
- Learn from your mistakes and make improvements next time
Checking and reviewing a peer’s work
Reviewing published observations
- Leave comments that applaud great data
- Leave comments that offer tips for improving data quality
- Agree with or suggest a different species identification, and fully explain your position
- Flag observations that have suspect coordinates
Share your expertise
- Be an Expert Reviewer for a species you are intimately familiar with and proven at identifying
- Add your identification tips & tricks to the discussion forum
- Contribute a lesson plan that helps improve quality
- Teach the importance and value of data quality and the peer review process to students and novices
- Post a project that explains how you identify a species, support your claims with solid written & photo evidence, or write field notes that make everyone wish they were right there with you
Let’s all feel comfortable making some constructive noise when something seems amiss and we have a great idea to improve it. Community involvement will move us towards great, then greater data.
Quality conversations during Institutes & Trainings
Teacher Institutes and Citizen Scientist Trainings impart the need for and value of high quality data. We cover the importance of it, how to achieve it, and – most importantly – we practice, practice, practice data collection, quality assurance, peer review, and community review ourselves in a safe and encouraging environment.
