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Curvenote®

@curvenote.com

Curvenote's mission is to free science from static PDF documents to enable researchers to continuously share more interactive, reproducible, and richly-linked #openscience content. Curvenote develops authoring and publishing tools for researchers.

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Curvenote®@curvenote.com · 6d ago

Our CEO @row1.ca joined the Data Engineering Podcast to talk about why science has outgrown the paper and what it will take to fix it. Data, code, narrative - brought together all the way through to publication. It's a hard problem and it's exactly what our team is solving. Worth a listen 👇

Curvenote on the Data Engineering Podcast

Rowan Cockett joined the Data Engineering Podcast to talk about reproducibility, open science, and the future of scientific communication.

curvenote.com

Curvenote®@curvenote.com · 4w ago

Most conference contributions disappear after the closing session. Curvenote powers @scipyconf.bsky.social proceedings, turning talks into citable, discoverable, web-native archives. Every presenter gets a DOI. Every idea stays in the conversation. Learn more 👉 curvenote.com/solutions/co...

Publish Conference Proceedings That Get Discovered | Curvenote

Turn conference outputs into citable, discoverable, interactive proceedings with streamlined workflows and DOIs. Book a demo

curvenote.com

Curvenote®@curvenote.com · last mo.

To go far, go together. @force11.bsky.social #Force2026 lived up to its theme. The conversations around open infrastructure, connected reading, and the future of communication were some of the best we've been part of. Here's what stood out to us 👉 curvenote.com/blog/force11-2026

OpenRxiv & Curvenote’s new Reader launch took to the stage at FORCE11 2026

At FORCE11 2026, Curvenote showcased the newly launched Curvenote Reader deployed over all of openRxiv to enthusiastic feedback from scientific publishing leaders — with callouts on the innovation fro...

curvenote.com

Curvenote®@curvenote.com · last mo.

Stop scrolling to the reference list. Stop squinting at figures. Curvenote Reader puts citations in context as you read. Figures zoom independently. References live where you need them. The research doesn't change. The experience does. Tell us what you think 👉 reader.openrxivlabs.org

Scott C Edmunds@scedmunds.bsky.social · 2mo ago

The future of interactive preprints and papers were on display at the #FORCE2026 conference and here I discuss what was presented and what the future of "living figures" might hold. As well as raving about the @openrxiv.bsky.social labs and @curvenote.com demos scedmunds.substack.com/p/preprints-...

Preprints and Papers Come to Life at FORCE2026

The future of interactive preprints and papers were on display at the FORCE2026 conference in Singapore, and here I discuss what was presented and what the future of "living figures" might hold

scedmunds.substack.com

Curvenote®@curvenote.com · 2mo ago

Reading a paper shouldn't feel like an obstacle course. Curvenote Reader turns existing articles into structured, dynamic reading experiences. Figures, references, and citations connected in the flow of reading. No workflow changes needed. See it in action👇

Jay Shendure@jshendure.bsky.social · 4mo ago

In addition to the bioRxiv this is also pilot for a new interactive preprint developed by @curvenote.com w/ support from @hhmi-science.bsky.social including directly embedded Jupyter notebooks for fig reproduction, data, models, prediction tracks, code, etc shendure.curve.space/articles/evo...

Evolutionary transfer learning enables organism-wide inference of mammalian enhancer landscapes

Understanding and modeling how the human genome encodes gene regulatory programs for thousands of cell types remains a central challenge in genomics and machine learning. However, most human cell types emerge during embryonic, fetal, and pediatric development which are inaccessible to comprehensive molecular profiling. To overcome this, we hypothesized that the mismatch in evolutionary rates between cis-acting enhancers (fast) and the trans-acting regulatory programs that interpret them (slow) creates an opportunity for ‘evolutionary transfer learning’. Specifically, models trained to predict cell type-specific enhancers in one species should generalize to the orthologous cell types and enhancers of related species. To test this, we generated a single-cell atlas of chromatin accessibility spanning mouse embryonic day 10 (E10) to birth (P0). Using combinatorial indexing1, we profiled 3.9 million nuclei from 36 staged embryos, resolving genome-wide accessibility in 36 cell classes and 140 cell types. With the goal of identifying distal enhancers for all cell classes, we trained a series of multi-output deep learning models (CREsted2), each addressing limitations of the preceding approach. An ‘evolution-naive’ model achieves strong performance on heldout peaks, but exhibited two failure modes during genome-wide inference: overprediction at tandem repeats and conflation of promoter and distal enhancer grammars. An ‘evolution-aware’ model resolves these by regrouping accessible regions based on functional coherence across syntenic orthologs, but fails to generalize across species — suggesting insufficient sequence diversity during training. Finally, STEAM (Synteny-aware Transfer learning for Enhancer Activity Modeling), our ‘evolution-augmented’ model, expands the training corpus to include enhancer orthologs from up to 241 mammalian genomes (Zoonomia3) in a synteny-supervised manner. This increases the effective data scale by up to 195-fold, markedly improving generalization across mammals despite greater label noise. We apply STEAM predict enhancers for all major developmental lineages throughout the human, mouse (HumMus) and 239 additional mammalian genomes3 (BabaGanoush), i.e. 32 × 241 = 7,712 genome-wide enhancer tracks. Together, our results unify advances in single-cell profiling, deep learning, and comparative genomics into a framework for the evolutionary transfer learning of noncoding regulatory grammars. More broadly, our work supports the view that model organisms and evolutionarily diverse genomes are indispensable resources for accelerating the AI-enabled exploration of human biology.

shendure.curve.space

Rowan Cockett@row1.ca · 4mo ago

Excited to talk about modular science, Open Exchange Architecture, @curvenote.com and @continuous.foundation tomorrow. Will be a fun day learning all that is going on in this space. 🚀🚀

ATProto Science@atproto.science · 5mo ago

We're very honored to have @row1.ca & @matsulab.com as keynote speakers at #ATScience! Rowan & Matt will share their vision "Towards Modular Open Science" on moving beyond isolated papers toward a world of modular & interoperable research objects, leveraging #atproto as a decentralized backbone 🧪✨

Rowan Cockett@row1.ca · 8mo ago

Very excited to be working with @prereview.bsky.social on Modular Peer Review in 2026! 🚀 Sign up to be part of the working group: continuousfoundation.org/peer-form

Continuous Science Foundation@continuous.foundation · 8mo ago

The Modular Peer Review Working Group with our partners @prereview.bsky.social kicks off in January. Join the cohort shaping how we review modular research outputs like data, code, methods and visuals. continuousfoundation.org/peer-form