Semantic Scholar

Semantic Scholar AI

Semantic Scholar AI

Semantic Scholar AI

Semantic Scholar AI

Semantic Scholar AI

Semantic Scholar AI

Semantic Scholar AI

Semantic Scholar AI

Semantic Scholar AI

Semantic Scholar AI

[P] citracer: a small CLI tool to trace where a concept comes from in a citation graph

A paper cites 50+ references, but how do you trace a specific concept through the entire citation tree back to the papers that introduced it? No existing tool answers this... so I built one! You give it a PDF (or an arXiv/DOI link) and a concept. It parses the bibliography, finds every sentence where the concept appears (regex, optionally through embeddings using sentence-transformers), identifies which references are cited nearby, downloads those papers, and repeats recursively. The output is an interactive graph you can explore in your browser. It also has a reverse mode: "which papers cite this paper while mentioning a given concept?", useful for forward-tracing how an idea spread. I built it during my PhD (self-supervised learning for time series anomaly detection) because I kept doing this manually and it was eating entire afternoons. Now a 5-depth trace runs in a few minutes. Open source, pip-installable, no API key required (though a free Semantic Scholar key speeds things up a lot). GitHub: https://github.com/marcpinet/citracer Happy to hear feedback, especially edge cases that break it. submitted by /u/Roux55 [link] [comments]

I built 9 free Claude Code skills for medical research — from lit search to manuscript revision

I'm a radiology researcher and I've been using Claude Code daily for about a year now. Over time I built a set of skills that cover most of the research workflow — from searching PubMed to preparing manuscripts for submission. I open-sourced them last week and wanted to share. What's included (9 skills): search-lit — Searches PubMed, Semantic Scholar, and bioRxiv. Every citation is verified against the actual API before being included (no hallucinated references). check-reporting — Audits your manuscript against reporting guidelines (STROBE, STARD, TRIPOD+AI, PRISMA, ARRIVE, and more). Gives you item-by-item PRESENT/PARTIAL/MISSING status. analyze-stats — Generates reproducible Python/R code for diagnostic accuracy, inter-rater agreement, survival analysis, meta-analysis, and demographics tables. make-figures — Publication-ready figures at 300 DPI: ROC curves, forest plots, flow diagrams (PRISMA/CONSORT/STARD), Bland-Altman plots, confusion matrices. design-study — Reviews your study design for data leakage, cohort logic issues, and reporting guideline fit before you start writing. write-paper — Full IMRAD manuscript pipeline (8 phases from outline to submission-ready draft). present-paper — Analyzes a paper, finds supporting references, and drafts speaker scripts for journal clubs or grand rounds. grant-builder — Structures grant proposals with significance, innovation, approach, and milestones. publish-skill — Meta-skill that helps you package your own Claude Code skills for open-source distribution (PII audit, license check). Key design decisions: Anti-hallucination citations — search-lit never generates references from memory. Every DOI/PMID is verified via API. Real checklists bundled — STROBE, STARD, TRIPOD+AI, PRISMA, and ARRIVE checklists are included (open-license ones). For copyrighted guidelines like CONSORT, the skill uses its knowledge but tells you to download the official checklist. Skills call each other — check-reporting can invoke make-figures to generate a missing flow diagram, or analyze-stats to fill in statistical gaps. Install: git clone https://github.com/aperivue/medical-research-skills.git cp -r medical-research-skills/skills/* ~/.claude/skills/ Restart Claude Code and you're good to go. Works with CLI, desktop app, and IDE extensions. GitHub: https://github.com/aperivue/medical-research-skills Happy to answer questions about the implementation or take feature requests. If you work in a different research domain, the same skill architecture could be adapted — publish-skill was built specifically for that. submitted by /u/Independent_Face210 [link] [comments]

[R] Lag state in citation graphs: a systematic indexing blind spot with implications for lit review automation

Something kept showing up in our citation graph analysis that didn't have a name: papers actively referenced in recently published work but whose references haven't propagated into the major indices yet. We're calling it the lag state — it's a structural feature of the graph, not just a data quality issue. The practical implication: if you're building automated literature review pipelines on Semantic Scholar or similar, you're working with a surface that has systematic holes — and those holes cluster around recent, rapidly-cited work, which is often exactly the frontier material you most want to surface. For ML applications specifically: this matters if you're using citation graph embeddings, training on graph-derived features, or building retrieval systems that rely on graph proximity as a proxy for semantic relevance. A node in lag state will appear as isolated or low-connectivity even if it's structurally significant, biasing downstream representations. The cold node functional modes (gateway, foundation, protocol) are a related finding — standard centrality metrics systematically undervalue nodes that perform bridging and anchoring functions without accumulating high citation counts. Early-stage work, partially heuristic taxonomy, validation is hard. Live research journal with 16+ entries in EMERGENCE_LOG.md. submitted by /u/ismysoulsister [link] [comments]

I built a Claude Code skill that catches hallucinated citations — checks .bib files against CrossRef, Semantic Scholar, OpenAlex

After seeing too many AI-assisted papers slip through with fabricated references, I put together a Claude Code skill to automate citation verification before submission. It checks every .bib entry against three databases simultaneously and flags: - Papers that don't exist at all - "Chimeric" citations (real authors + fabricated titles merged) - DOIs that resolve but to completely different papers The problem is worse than most people admit — NeurIPS 2025 analysis found 100+ fake refs in accepted papers. Existing tools just check if a DOI resolves, which misses the actual hallucination. No API keys required. Just point it at your .bib file. GitHub: https://github.com/PHY041/claude-skill-citation-checker Curious what percentage of AI-assisted papers others think have at least one fabricated ref. submitted by /u/BP041 [link] [comments]

Built a website for easily searching and discussing arXiv papers [P]

Hi all! I've been working on this side project to help users easily search, read and discuss papers: https://discuria.org It's heavily focused on AI/ML papers from arXiv, but also covers biology, physics, economics and more through Semantic Scholar and other databases. You can search any topic or category, open up a paper, and leave annotations directly on the paper or comments to discuss with others, or use the AI assistant for questions without having to go to other websites. It also has a read aloud function so you can follow along as it reads. Feel free to try it out and give me any suggestions on improvements! All features are free. submitted by /u/foxy2sexy4u [link] [comments]

We believe the future of science depends on AI systems you can understand, verify, and trust. That’s what Asta is built for. Join us on this journey to advance agents for science. 🌍 ➡️ https://t.co

We believe the future of science depends on AI systems you can understand, verify, and trust. That’s what Asta is built for. Join us on this journey to advance agents for science. 🌍 ➡️ https://t.co/TbxJRIkib4

🔹 AstaBench - a benchmark suite + leaderboards for evaluating agents on scientific tasks. With 2,400+ problems across 11 categories, it shows clearly where agents excel, and where they fall short.

🔹 AstaBench - a benchmark suite + leaderboards for evaluating agents on scientific tasks. With 2,400+ problems across 11 categories, it shows clearly where agents excel, and where they fall short.

🔹 Asta resources - software components for creating & extending agents. This includes full-text search via the Semantic Scholar API and Model Context Protocol (MCP) support, making it easier than

🔹 Asta resources - software components for creating & extending agents. This includes full-text search via the Semantic Scholar API and Model Context Protocol (MCP) support, making it easier than ever for developers to build on Asta.

Here’s what Asta brings together: 🔹 Asta agents - tools to assist researchers with scientific tasks. They can find manuscripts even Semantic Scholar can’t, and summarize insights into a mini literatu

Here’s what Asta brings together: 🔹 Asta agents - tools to assist researchers with scientific tasks. They can find manuscripts even Semantic Scholar can’t, and summarize insights into a mini literature review with links to sources.

You’ve told us that you need AI systems you can understand, verify, and trust. Extending the Semantic Scholar foundation, Asta is built for that—open and transparent by design & grounded in real s

You’ve told us that you need AI systems you can understand, verify, and trust. Extending the Semantic Scholar foundation, Asta is built for that—open and transparent by design & grounded in real scientific workflows.

To our loyal Semantic Scholar community — After 7+ years building intelligent tools for researchers, it’s been exhilarating to watch usage grow to nearly 100M active users. Today, we’re excited to s

To our loyal Semantic Scholar community — After 7+ years building intelligent tools for researchers, it’s been exhilarating to watch usage grow to nearly 100M active users. Today, we’re excited to share our next big step forward: Asta. 🚀 https://t.co/70uoz7zVQS

Let's find some papers 🔍📚

Let's find some papers 🔍📚

The deadline is *today*! ⏰

The deadline is *today*! ⏰

@yawnxyz @allen_ai Hi Jan, we're sorry that the denial has caused inconvenience. Please email us at feedback@semanticscholar.org with your project details, and we'll get back to you. Thank you!

@yawnxyz @allen_ai Hi Jan, we're sorry that the denial has caused inconvenience. Please email us at feedback@semanticscholar.org with your project details, and we'll get back to you. Thank you!

📢 We're seeking an Applied Research Scientist to join our research team! Apply by this Friday!

📢 We're seeking an Applied Research Scientist to join our research team! Apply by this Friday!