Declare tab bankruptcy (use tabcopy to copy all the URLs to Roam and CLOSE THE TABS), but add SRS so they resurface periodically. Then chip away at it, slowly whittling down the list as you connect them to your idea graph.
Advantages of this over leaving tabs open:
* Contextualized in time (bc of spot in daily notes time stream)
* Can control its draw on my attention
* Can incrementally formalize
* I'm not staring at 100000000000 tabs
Some more details on the workflow: 1/N
Step 1: Select tabs to clear, and copy urls with tabcopy
Step 2: Paste in Roam dailynotes, nested under a "tabbankcruptcy" tag
For tabs that I want to see again someday (don't want to drop in blackhole: CTRL+SHIFT+4); ones I want to pop up sooner (`CTRL+SHIFT+1` --> pops up again tmrw)
Step 4: The tabs will show up in the linked references for daily notes: at which point I have the option of 1) bumping it again (CTRL+SHIFT+3, for ex., if they don't feel super relevant rn, or CTRL+SHIFT+1 to come back to it again sooner), and/or 2) connect it to the idea graph
"connecting to idea graph" = often connecting it to a "conceptual hook" (e.g., page about key idea or question, or a person/project)
Once linked, I take it out of tab list, but leave in daily notes page: lots of rich context in timeline that increases the "surface" area of idea
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I am *very* excited about the potential for augmented collective intelligence as @RoamResearch begins to roll out multiplayer and API. I think balancing context and privacy is going to be one of the most important challenges to solve to realize this potential. A 🧵on why/how 1/N
There is an immense amount of value in personal knowledge graphs, whether they are represented in a latent way as a linear notebook, or (as is increasingly the case) as personal wikis or [[networked notebooks]]
Personal knowledge graphs used to be a niche domain of Hackers, but, with the rise of tools like @RoamResearch and @TiddlyWiki , is slowly but surely making its way through the rest of the knowledge worker userbases. Some details on this here: github.com/sig-cm/JCDL-20…
This whole exchange produced immensely valuable pedagogical exchanges that will probably be useful to others for years/decades to come! Thinking crazy thoughts now about borderless classrooms / MOOCs / learning communities 1/n
Importantly, the q came in an unexpected "package" (in terms of demographic and form), a beautiful thing in terms of diversifying how qs can be asked and by whom:
1: antibodies may fade quickly (within ~few months to a few years), but T-cells might stick around way longer and still provide some meaningful protection. 2/n
2: there may be some cross-reactive (t-cell?) protection from related coronaviruses, possibly the common cold 3/n
This is a wonderful contribution to the scientific community! Effective network/citation-based tools for exploring the literature are absolutely vital, particularly in domains (like interdisciplinary areas) that are still growing and not yet well-structured/paradigmatic.
The system leverages network-based similarity (co-citation, co-reference) instead of raw citations, and uses these metrics to yield a much more maneageble and explorable set of "leads" for each seed paper.
I should add that it builds on the open dataset provided by @SemanticScholar, who also have been adding very useful tools for exploring the citation graph through "meaningful citations" and exploring where the citation was made.
When you hear that a pandemic virus is mutating, you feel ___ about its impact on humanity
i'm curious bc i see lots of discussions of mutations by virologists on twitter, but i don't see any "more afraid" responses. i could be very wrong, but from what i can tell from virology 101, it's not uncommon for viruses mutate towards being less fatal (to optimize for spread).
This is a good opportunity for subject matter experts (virologists, epidemiologists, public health policy experts) to team up with #AI and #NLP researchers to figure out the best (not necessarily coolest/flashiest) ways to accelerate synthesis and research.
Partnership is crucial: too easy for computational researchers to miss crucial context, and super important to get a crisp sense of the most pressing information needs and pain points for scientists doing the research.