Skill UI

This skill guides authors on positioning and framing manuscripts for the Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING). It provides detailed advice on core fit, required evidence (e.g., documentation, licensing, annotation reliability), and rhetorical structure needed to ensure a paper resonates with the specialized audience of language resources and computational linguistics.

This guide details the rigorous requirements for publishing in top-tier quantitative management journals. It specifically targets research outputs that utilize mathematical models, theoretical proofs, and computational algorithms (e.g., operations research, decision theory). It strongly advises against purely empirical surveys or case studies, emphasizing methodological contribution over policy implication.

This comprehensive guide outlines the mandatory replication and data policy for mathematical finance manuscripts. It provides authors with templates for Data Availability Statements (covering no data, code only, or public data) and details rigorous best practices for ensuring numerical exhibits are fully reproducible. It covers creating exhibit provenance ledgers and managing computational dependencies to maintain academic rigor.

This guide provides expert advice on positioning, framing, and validating medical imaging AI manuscripts for the MICCAI conference. It helps authors tailor their research to meet the venue's specific requirements, focusing on clinical-task grounding, robust validation, and alignment with core scopes like computational anatomy and image-guided intervention. Essential for optimizing conference submission strategy.

A comprehensive guide for revising technical submissions, particularly in computational linguistics and NLP. It details how to enforce linguistic scope discipline, present examples as verifiable arguments, structure the first page for maximum impact, anticipate reviewer limitations, and maintain strict terminology consistency for high-stakes academic publishing.

This guide details the scope, structure, and standards for submitting a 'Primer' to Nature Reviews Methods Primers. It is designed for authors seeking to author an authoritative, cross-disciplinary 'how-to' account of a method (experimental, computational, or analytical). It covers the expected evidence bar, full method lifecycle coverage, reproducibility requirements, and house style, helping authors determine if their topic fits the journal's mandate.

This guide provides methodological standards for interpreting and reporting findings in New Media & Society research. It emphasizes analytical transparency, robustness, and credibility across qualitative, discourse, and computational methods, ensuring all inferences are rigorously defensive and fully traceable to the evidence.

A comprehensive guide for structuring and defending academic research designs, particularly for New Media and Society studies. It provides rigorous criteria for qualitative ethnography, content/discourse analysis, computational data validation, and mixed methods integration. The focus is on strengthening the theoretical argument by justifying choices, validating measures, and defeating leading alternative readings.

Provides comprehensive guidelines for ethical handling, data transparency, and documentation in New Media & Society research. It covers crucial issues like informed consent, anonymization best practices, scraping legal constraints (ToS), and methods for making qualitative or computational claims credible, ensuring responsible data sharing and academic rigor.

This tool acts as an expert orchestrator and router for academic manuscripts in New Media and Society (NM&S). It guides researchers through the complex, interdisciplinary writing lifecycle, ensuring the work maintains both conceptual focus and methodological rigor. Users are routed to specific sub-skills based on their current stage (e.g., identifying a topic, building theory, conducting data analysis, or revising after reviewer feedback) and their chosen method (e.g., qualitative interviews, computational analysis, critical discourse analysis).

A comprehensive guide for authors targeting the NAACL conference. It helps strategize the fit, framing, and rigor required for NLP and Computational Linguistics manuscripts. Use this to structure your paper, understand the evidence bar, and position your work for a top-tier conference audience.

This skill provides comprehensive guidance for submitting research to npj Computational Materials. It defines the journal's scope, requiring rigorous computational methods (DFT, MD, ML, high-throughput screening) that yield generalizable materials insights or predictive capabilities. It educates authors on the required standards, including methodological rigor, reproducibility, and adherence to FAIR data principles, helping authors re-frame their work for a high-impact venue.