Skill UI

This skill guides authors on how to strategically position their research for the INTERSPEECH conference. It covers core topics such as speech recognition, synthesis, paralinguistics, and audio-language modeling. Use it to re-frame manuscripts from journal or arXiv styles into a selective CS conference narrative, ensuring the work meets the rigorous standards of audio-specific evaluation, datasets, and error analysis expected by the field.

Provides a comprehensive guide for packaging complex research artifacts, integrating hardware, firmware, and datasets. It outlines best practices for achieving academic badges (e.g., Available, Functional, Reusable) and winning the Best Research Artifact Award. Learn how to structure analysis paths, manage raw traces, and ensure reproducibility across different evaluation environments.

A comprehensive guide for authors preparing accepted academic papers for final publication. It details the requirements for a dual-publisher format (ACM and IEEE), covering mandatory steps like de-anonymization, completing dual metadata (ORCID, CCS, DOI), integrating reviewer feedback without scope creep, and permanentizing dataset/firmware links using public DOI archives. This ensures the submission meets the highest standards for academic rigor and publishing platforms.

This comprehensive guide serves as a critical pre-submission audit checklist for high-stakes academic conferences, such as IPSN/SenSys. It covers ensuring double-blind anonymity, selecting the correct technical track (IP or SPOTS), adhering to strict formatting rules (ACM template, page limits), and best practices for submitting anonymized datasets and artifacts. Use this checklist before uploading to maximize acceptance chances.

This guide assists researchers in transforming vague datasets or observations into robust, positive, and economics-driven research questions suitable for prestigious accounting journals (like JAE). It teaches key concepts, such as identifying economic friction, distinguishing between normative and positive questions, establishing plausible identification strategies, and mapping the topic to specific academic domains.

This comprehensive guide outlines the best practices for preparing a complete and reproducible research package for submission to the Journal of Banking & Finance (JBF) following Elsevier Option C guidelines. It details requirements for data availability statements, proper structuring of code and data access materials, and best methods for managing proprietary or restricted datasets (such as CRSP or Compustat), ensuring research transparency and verifiability.

This guide provides a comprehensive framework for building verifiable reproducibility evidence for academic manuscripts. It instructs authors on critical engineering disciplines, including freezing environment dependencies, controlling random seeds, versioning datasets and models, and creating anonymous, self-contained running materials that stand up to rigorous external peer review.

Provides a comprehensive guide on structuring scientific manuscripts, addressing the proper placement of core evidence, detailed proofs, extended experiments, and large datasets in appendices and supplementary materials for academic journals. Ensures crucial findings are in the main text while maintaining clarity and adhering to double-blind review standards.

A comprehensive guide for planning and stress-testing empirical analysis in financial intermediation, specifically for high-impact journals. It covers the construction of bank/loan-level panel datasets, defining variables (growth vs. levels), implementing advanced econometric specifications (e.g., fixed effects), and executing substantial robustness checks (placebo periods, alternative clustering). It also guides the planning of reproducible numerical illustrations for theoretical models.

A mandatory guide for authors submitting to the Journal of Financial and Quantitative Analysis (JFQA). This policy dictates the required archive structure, ensuring that all reported findings can be fully reproduced by depositing source code, raw or pseudo datasets, and a detailed execution roadmap in the JFQA Dataverse. It guarantees the highest standards of academic transparency and reproducibility in financial research.

This specialized tool guides researchers in critically evaluating if an academic idea or dataset qualifies as a genuine, theory-contributing international-business phenomenon. It implements a rigorous multi-gate test, analyzing the phenomenon, level of analysis, international theory contribution, and cross-national variation needed for high-impact publishing in journals like JIBS.

A comprehensive guide to constructing rigorous empirical datasets for academic papers in finance and banking. It covers best practices for handling complex data sources (e.g., bank micro-data, central bank reports, macro series), addressing measurement biases, and ensuring full replicability, adhering to the standards of top-tier economic journals. Focus areas include merger tracking, regime transitions, and variable definition consistency.