#InvisibleLabor is a massive problem for accurate scientific documentation.
If we don’t know what people did, we can’t evaluate it for reliability.
And contributors aren’t credited for their work.
We address these issues in our #BTSCON2022 seminar. (1/5)
https://drive.google.com/file/d/1gM81mC5vAWXTLfyFr0y2cAr2y8noCqUn/view
In case you missed my talk at #BTSCON2022:
I discussed our work on developing a better understanding of what information needs to be documented to create a true open science.
The work begins with #ScienceReading and specifically with identifying the events that took place as part of the scientific process purportedly described in papers. Once we do that, we immediately see that there is a ton of information about how the science was done that
never got documented anywhere. The repercussions of this paucity were recently described by Nate Breznau and team in this thread
https://twitter.com/BreznauNate/status/1586264454181933058?s=20&t=Ks9c8kKJkAuEXpZtEuULlg…
How this relates to tools like #Tenzing https://alexholcombe.medium.com/announcing-tenzing-ceca6789d88c by https://fediscience.org/@alexh is that the events identified in the scientific process didn’t just happen. 3/5
Up to now, people sit and reason through what work might have been done. Our tools provide a clear framework (aka: data) to understand and list all the labor involved so that it can be properly credited and documented.
Talk here: https://drive.google.com/file/d/1gM81mC5vAWXTLfyFr0y2cAr2y8noCqUn/view
DM me for more details on how to do this with your work. 5/5
Monica Gonzalez-Marquez