Skill UI

This guide teaches authors how to structure scientific manuscripts by correctly partitioning core findings, decisive evidence, and the main narrative into the body text, while moving exhaustive datasets, full protocols, and secondary characterization into the Supporting Information (SI). It ensures the main paper is stand-alone and convincing.

A comprehensive guide for researchers in electronics and signal processing preparing submissions to Acta Electronica Sinica (AES). It outlines best practices for proactively structuring, documenting, and disclosing research artifacts—including code, datasets, and hardware designs—to maximize the paper's credibility and chances of acceptance, even without mandatory journal badges. Covers anonymization, reproducibility standards, and proper disclosure in the manuscript.

This skill guides researchers on the mandatory, rigorous evaluation of complex systems models (e.g., AgSy). It details best practices for model validation, emphasizing the comparison of observed vs. simulated data using independent, held-out datasets. Key components covered include standard fit statistics (RMSE, NSE), sensitivity analysis (identifying core drivers), uncertainty propagation, and multi-objective trade-off/scenario analysis to ensure scientific credibility.

A comprehensive guide for academics submitting AI and statistical research papers. It details how to map every claim (theoretical, algorithmic, or empirical) to verifiable evidence, covering necessary details like datasets, hyperparameters, random seeds, compute resources, and uncertainty estimates. It outlines the standards for achieving high levels of reproducibility, from descriptive to turnkey, ensuring scientific rigor for top-tier conferences.

This guide details best practices for ensuring scientific rigor and reproducibility in complex system research. It outlines the necessity of capturing the full execution state—including hardware steppings, BIOS revisions, kernel configurations, toolchain versions, and dataset provenance—to prevent data decay and validate findings against environmental drift.

This tool guides researchers in transforming initial phenomena or datasets into sharp, high-impact accounting research questions. It assesses the fit of the topic with broad academic scopes (like CAR), determines the most appropriate research tradition (e.g., archival, experimental, field), and helps articulate the core academic tension. It ensures the research advances understanding of accounting itself, rather than just using accounting data for general finance or economics questions.

This guide outlines best practices for packaging research artifacts (prototypes, datasets, instruments, code) for academic review and permanent public release, particularly in HCI venues like CHI. It covers strategies for creating highly reviewable, anonymous submissions (e.g., claims maps, minimal demos) and establishing robust, licensed, permanent archives (e.g., DOIs, OSF), ensuring research reproducibility and auditability.

A comprehensive guide detailing the methodology for packaging, documenting, and staging research artifacts (including code, datasets, knowledge graphs, and demo systems) for CIKM conference submission. It covers best practices for licensing, provenance tracking, versioning, and transitioning artifacts from anonymous review status to citable public releases, ensuring reproducibility and adoptability.

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.