Skill UI

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 preparing data-availability statements, shareable data, and reproducible code for conservation biology manuscripts. It outlines best practices for using persistent identifiers (like Dryad or Zenodo), masking sensitive species locations, and documenting full research provenance to meet journal standards.

This skill converts Microsoft Word documents (.docx) into Markdown format. It is essential for accurately analyzing, summarizing, or extracting data from proprietary Word files, as direct parsing is unreliable. It supports single files, batch processing of folders, and is designed to coordinate with sibling skills (PDF and Excel) when handling mixed document types to ensure no data is skipped.

This guide provides comprehensive standards for achieving auditability and transparency in Computer-Supported Cooperative Work (CSCW) research. It teaches researchers how to document non-shareable evidence, such as field interviews, log traces, and system deployments, by focusing on detailed analysis trails, decision logs, and rigorous process documentation, thus strengthening academic rigor.

A guide detailing the rigorous standards for data availability and code reproducibility required for academic manuscripts, particularly in demography. It emphasizes depositing data in FAIR-aligned repositories with persistent identifiers (DOI), providing master scripts with full version control, and documenting restricted data access conditions to ensure scientific transparency.

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.

This guide details the requirements for creating a comprehensive replication package for manuscripts submitted to Econometric Society journals. It ensures full reproducibility of all results, including code, data provenance, and parameter settings, adhering to the journal's Data and Code Availability Policy. Key components include a master script, reproducible data, and detailed documentation.

This guide outlines the mandatory structure for creating a reproducible research package, required by academic journals like the European Economic Review (EER). It details how to properly assemble raw data, master analysis scripts, environmental specifications, and comprehensive documentation (README) to ensure that reported results can be independently verified by others. It is essential for any paper involving empirical analysis or simulations.

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.

A comprehensive guide for structuring academic empirical research projects to ensure the full reproducibility of findings. This methodology covers standard folder layouts, master scripting, data provenance documentation, and best practices required for passing rigorous peer review scrutiny, thereby preventing data loss and calculation errors during revisions.

This comprehensive guide provides rigorous standards for formatting tables and figures for high-impact academic journals (e.g., Economic Research). It covers best practices including three-line table styles, variable definition structures, data source documentation, and figure aesthetics (DPI, color). Essential for ensuring submitted manuscripts meet professional publishing quality.

Guides authors on meeting the rigorous transparency standards required by the European Sociological Review (ESR). It details how to prepare a Data Availability Statement (DAS) and a replication package for statistical and computational manuscripts. The guide covers everything from documenting data sources and versions to handling restricted administrative data, ensuring that the research process is fully reproducible and traceable.