TL;DR

Annotation protocols and tooling for AI-augmented sensemaking in decentralized science

Project Overview

While Decentralized Science (DeSci) is a promising approach towards effectively broadening access to scientific research, the problem of making sense of the volumes of new information being published in DeSci and traditional science remains unsolved.

Traditional curation methods like peer-reviewed journals are failing to keep pace, resulting in unprecedented information overload and knowledge fragmentation. We contend that making sense of DeSci research requires open access to diverse sources of scholarly sensemaking data. Sensemaking data are the digital traces of sensemaking processes, including explicit annotations (tags, votes, ratings) and commentary made by researchers, as well as implicit behavioral data generated through app usage (reference managers, etc). The problem is that these sensemaking traces are currently scattered and siloed across a multitude of apps and formats and increasingly enclosed by platforms for profit (e.g. Twitter). We propose Open Source Sensemaking, an interoperable and decentralized annotation network, enabling researchers to record, own and share their sensemaking data, thus contributing to the network while remaining resilient to platform capture. The shared sensemaking data will greatly benefit individual and collective sensemaking by enabling the development of diverse discovery services, from simple aggregation of reviews and ratings (e.g., “Goodreads” for scientific research) to more advanced AI-augmented scientific intelligence systems.

Why is this problem important?

Open and accessible sensemaking data and tools are instrumental for making meaningful progress on multiple key challenges for DeSci, including:

• Accessibility and onboarding of researchers and their professional social graphs
• Information overload (helping researchers decide what papers to read next and where to focus their research attention)
• Alternative evaluation mechanisms to traditional peer review, altmetrics (assessment of broader public impacts of scientific research)
• Supporting large-scale distributed collaborations (centralizing discussions, helping to keep everyone on the same page).

These challenges are typically viewed separately, and we believe that illuminating a key root cause underlying all of these issues (fragmented sensemaking data) can help direct attention and efforts toward developing more effective solutions.

Research and development roadmap (what we’ve already done and what we plan to use the funding for)

Completed

• Initial research, survey and proposal for the problem of sensemaking in science. To be presented at the Metascience 2023 conference (https://metascience.info/)
  • Resarch and proposal abstract (https://ronentk.github.io/publication/metasci-sensemaking-23/)
  • An Active Inference Ontology for Decentralized Science: from Situated Sensemaking to the Epistemic Commons (https://zenodo.org/record/6320575#.ZEF6HNLMJhF)

Next up

• Design of an initial prototype focused on social bookmarking, a simple yet important kind of collective sensemaking; organizing, sharing and curating science-related reading lists along with ratings and reviews.
• Incorporation of social graph information using interoperable protocols like Portable Communities (https://www.noo.network/markdown/cp)
• Integration with existing DeSci publishing platforms. For example, building an integration for DeSci Nodes (https://nodes.desci.com/) to allow researchers to share their reading lists publicly and discover content shared by others.