Open, reproducible, inclusive science
Artwork in progress! Share this vision with you
Our shared goal; open, reproducible, inclusive science. But many paths to get there. Not sure which one, they meander. You can choose your own adventure but there are some long pathways to get here, and most of us are bushwacking. What openscapes does is act as park rangers to help researchers find their path through the Openscapes Champions program.
Many paths Training individuals is not enough. Need to help research groups work together around computation We know they’re busy Diff kinds of environments shown in the picture Skunk: They’re close but just haven’t seen it yet. One of the benefits of funding them through CZI is to help show what’s possible
Transition: That’s what the Openscapes Program does
we are the rangers: we are experts in the data analysis landscape and can help guide you and help you choose a path that is tailored to your immediate needs, and we’ll all learn cool things together
we know about the geology, wildlife, and history We are not posted signs or information placards: we are rangers. We are human interaction to help guide Learn together. We guide your teams but also the whole cohort (learn across teams) Intentional choice of animals: we are part of the open data science landscape Additional Details (depending on audience): we can hand out maps so you can go on your own, or we can help guide you more hands-on You can make your own path! But we’ll help you do so safely, how to avoid trampling vegetation or getting hurt, how to find minipaths again we also identify potential pitfalls (current inefficiencies with your team’s workflow) and helping you make sure you have the right gear as you head out on the trails we can also give you a heads up if the weather is going to change or if you might not be wearing the right clothes / equipment, if there have been bear sightings, animal tracks, trail conditions, etc. Also paths underwater and in the air: different domains and interconnection
Many paths: pro and con. Hard to figure out which path to be on. You can take the long way or helping them find that shortcut. Taking that time up-front to be part of the cohort that helps them save time down the line. We all want researchers to be efficiently as possible but that can often feel like a meandering path.
We have this common goal together. Common goals: we know that CZI knows open, reproducible, collab science. We know tht people aren’t taught that. It’s more than training. You need to be mentored, with your team, on your research. Theory of change. We all know: they’re busy, adn training isn’t cutting it alone. This pic. There is so much intrigue here. What is she going to tell us about this picture.