Skill UI

A comprehensive guide for authors submitting academic papers, detailing how to proactively provide reproducible code, datasets, and experimental scripts. This ensures that research results are verifiable and trustworthy, significantly enhancing the credibility of findings in high-tier journals despite the absence of formal artifact evaluation badges.

This skill guides authors on how to position, structure, and strengthen empirical Natural Language Processing (NLP) manuscripts for submission to the Conference on Empirical Methods in Natural Language Processing (EMNLP). It provides deep insights into conference fit, required evidence (baselines, error analysis, datasets), proper framing, and anticipating reviewer expectations for top-tier AI venues.

This guide details the necessary steps for converting an accepted CoRL conference paper into its final, permanent PMLR record. It covers mandatory format changes, including expanding to a 9-page main text, merging the appendix, and de-anonymizing author identities. Crucially, it outlines how to manage and link required external artifacts (code, video, datasets) that cannot be hosted by the conference proceedings, ensuring the work maintains an archival standard.

A comprehensive guide for drafting academic abstracts, specifically tailored for journals focusing on rural and agricultural economics. It enforces a strict five-sentence structure, requiring the inclusion of quantified findings, naming specific household survey datasets (e.g., CFPS, CHIP), and detailing rigorous identification strategies. This skill helps users eliminate boilerplate language and ensure semantic alignment between Chinese and English summaries.

A comprehensive guide for preparing academic manuscripts in Criminology, focusing on data reproducibility and transparency. It details how to create data-availability statements, build master scripts, and handle restricted criminal-justice datasets (such as juvenile records) to meet modern open-science standards.

A comprehensive guide for authors submitting research to CVPR, detailing best practices for packaging code, models, and datasets. It covers requirements for review-time reproducibility (sealed-box rules) and long-term public release (licensing, provenance, and version pinning), ensuring that the work is runnable by any skeptical third party.

A comprehensive guide for authors preparing the final, camera-ready version of a CVPR accepted paper. It details the multi-channel publication requirements (CVF open access, IEEE Xplore), the necessary steps for de-anonymization, ensuring timely dataset release, optimizing presentation formats (Oral, Poster), and conducting rigorous quality assurance checks before final submission.

A comprehensive guide for designing, auditing, and executing rigorous experimental programs for top-tier computer vision papers (e.g., CVPR). It covers essential scientific components, including establishing fair benchmarks, structured ablation studies, documenting failure modes, quantifying computational efficiency, and demonstrating cross-dataset generalization, ensuring the research is reproducible and robust.

This skill guides authors on submitting original, high-quality, and reusable Earth System datasets (observations, reanalysis, model outputs) as primary publications. It assesses whether the work fits the criteria of a 'data paper,' requiring open deposition in a FAIR repository with a persistent DOI, detailed provenance documentation, and rigorous quality control. It serves as a venue-selection and manuscript re-framing tool.

Comprehensive guidelines for structuring and submitting reproducible AI/ML research papers for top-tier conferences. It details two modes: Mode 1 for theoretical/KR work (requiring full, explicit proofs in the supplement), and Mode 2 for empirical/ML work (requiring fixed seeds, cached outputs, and rigorous provenance pinning for datasets, models, and environments). The guide also covers best practices for maintaining double-blind anonymity and properly dividing content between the main body and the supplementary material.

This guide outlines best practices for achieving high levels of research reproducibility for academic papers, particularly in database and systems research. It details methods for pinning dataset versions, logging system environments, mapping empirical claims to verifiable evidence, and structuring availability statements. Adherence ensures the work is robust, transparent, and fully verifiable by the scientific community.

A comprehensive guide for researchers on packaging and documenting NLP artifacts, such as datasets, prompts, and evaluation code, for academic publication. It details best practices for ensuring reproducibility, managing provenance, adhering to licensing standards, and structuring evidence for top-tier conferences like EMNLP.