Skill UI

This guide provides best practices for preparing code, data, and supporting artifacts for academic submissions, focusing on achieving reproducibility for peer review (especially in double-blind settings). It covers crucial steps including artifact anonymization, establishing clear data availability statements, structuring the repository (README, env, data, src), ensuring licensing compliance, and planning for permanent post-acceptance archiving.

This comprehensive guide outlines the mandatory data and replication policies for submissions to the Journal of Human Resources (JHR). It details best practices for creating robust submission packages, including Data Availability Statements, code packages, and plans for public repository deposit. It ensures maximum reproducibility and adherence to CC0 licensing standards for academic research.

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 provides comprehensive guidelines for authors preparing linguistic manuscripts, focusing on data documentation, reproducibility, and ethical sharing practices. It details how to properly share quantitative data, elicited fieldwork corpora, and phonetic measurements while adhering strictly to community consent, licensing terms, and best academic practices. It emphasizes that transparency must be coupled with ethical responsibility.

A comprehensive, final quality control checklist designed for submitting manuscripts to PNAS Nexus. It serves as a master gatekeeper, ensuring compliance across fit, open access licensing (APC/CC BY), structural integrity, statistical rigor, data deposition (DOI/accession numbers), and required metadata. It helps writers confirm nothing is missing before final submission.

This comprehensive guide details the mandatory production steps required after a research paper is accepted for publication in Springer LNCS. Key tasks include de-anonymizing the manuscript (restoring true names, affiliations, and funding details), applying the specific llncs.cls format, finalizing metadata with ORCIDs, integrating reviewer feedback, permanently linking artifacts using DOIs, and completing the CC-BY open-access licensing forms.

This guide details the methodology for packaging scientific artifacts (code, solvers, data, logs) for machine learning submissions. It focuses on creating double-blind, self-contained ZIP archives that allow reviewers to independently and quickly verify complex probabilistic claims, even under strict time constraints. It covers best practices for anonymity, structured README generation, and handling data/licensing.

Provides comprehensive guidelines for packaging and submitting research artifacts (datasets, models, code) for major conferences. It details the difference between mere 'existence' (badge verification) and true academic 'reproducibility,' covering archival strategies, web data licensing, takedown resilience, and minimal viable package structure for long-term scholarly impact.

Prepares a skill in any state — archive, folder, bare SKILL.md, or raw idea — for public distribution on lawve.ai. Gates for legal use and safety, handles provenance and licensing, applies compliance transforms, validates, and packages a zip with submission-form entries.