MIRA 2026 - Modular Interoperable Research Attribution

MIRA 2026 - Modular Interoperable Research Attribution

Catalyzing Modular Interoperable Research Attribution - A workshop to design and prototype interoperable frameworks for modular research attribution. June 7-11, 2026 in Ireland.

A protocol for attributable modular research contribution - Matsuthoughts

I wrote this essay in response to Seemay Chou's call for essays on systemic bottlenecks to science. Structural change in scholarly communication practices i…

modular research preprint server demo with meaningful social context

a concept preprint server demonstrating what kinds of features would be possible in the future with more modular research publishing and an open social protocol to layer in the public discourse in meaningful ways.

Towards Modular Open Science

Science is trapped in a paper-shaped box. In this talk at the ATProto community, Rowan Cockett introduces the structural states of scientific components — the floor, the buckets, and the shelf — and makes the case for a second wave of modular science where modularity emerges from packaging rather than being imposed as a precondition. The talk introduces the Open Exchange Architecture (OXA), a new standard for modular and composable scientific content.

Attribution, Provenance, Reference, Citation, and AI for Research Applications – Understanding the Differences - The Scholarly Kitchen

Building robust citation and attribution into generative AI systems are foundational to usage, credit and trust. We need to expect more from AI.

A Human-Centric Framework for Data Attribution in Large Language Models

In the current Large Language Model (LLM) ecosystem, creators have little agency over how their data is used, and LLM users may find themselves unknowingly plagiarizing existing sources. Attribution of LLM-generated text to LLM input data could help with these challenges, but so far we have more questions than answers: what elements of LLM outputs require attribution, what goals should it serve, how should it be implemented? We contribute a human-centric data attribution framework, which situates the attribution problem within the broader data economy. Specific use cases for attribution, such as creative writing assistance or fact-checking, can be specified via a set of parameters (including stakeholder objectives and implementation criteria). These criteria are up for negotiation by the relevant stakeholder groups: creators, LLM users, and their intermediaries (publishers, platforms, AI companies). The outcome of domain-specific negotiations can be implemented and tested for whether the stakeholder goals are achieved. The proposed approach provides a bridge between methodological NLP work on data attribution, governance work on policy interventions, and economic analysis of creator incentives for a sustainable equilibrium in the data economy.

MIRA — the structure layer for a shared research graph

Research ships as a sealed document. MIRAfying it makes a graph of signed, citable records anchored on the actual data artifacts — so analyses compound without flattening the nuance.

Authorea: multi-disciplinary research preprints

Authorea is the leading collaborative platform to read, write, and publish research.

CoARA – Coalition for Advancing Research Assessment

Catalyze

Catalyze research: discover, build, and fund high-impact research portfolios.