compress/uncompress -> CS contextualize/recontextualize/recover -> CSCW writing vs reading what writer and medium want what the reader wants synthesize restructure level of detail problem length vs detail information amount summary fuzzy capture details always needed to verify claims and/or understand well enough to replicate find / retrieve ideas many ideas at once -> chunking combine ideas compare / contrast restructure personal/public desktop app graph data structure graph editor use control of capture speed/accuracy tradeoff saving mss connections/links scientific sensemaking scientific creativity limits of human attention and memory visualization and screensize static publication contrains scientific communication years of research into a few seconds worth of reading lossy/lossless the level of detail problem tutoring a core breakdown in scientific communication is lossy compression of claims broadcasts recovered by the entire audience
Once a reader has connected all necessary information for understanding a claim, they don’t have a fast, flexible way to capture, combine, extend, recover, or share those connections. When readers become authors, they are forced to compress unconnected information to fit the medium and the cycle continues.
enabling 1) faster capture of connections made while reading publications, 2) flexible management of those connections in a personal knowledge graph, and 3) linking specific parts of user generated content, such as phrases in scientific claims, to relevant parts of the knowledge graph.