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Mandatory Data and Code Availability Policy

brycewang-stanford/Awesome-Journal-Skills

This guide outlines the mandatory standards for open science publication, detailing how researchers must deposit all data, code, and raw materials (images, sequences) in public, archived repositories (like Zenodo, GEO, PDB). It emphasizes the need for DOIs/accession numbers and prohibits relying solely on "available on request" statements for primary findings.

Ensuring Data and Research Transparency

popdevr-transparency-and-data

brycewang-stanford/Awesome-Journal-Skills

This guide details the strict requirements for data transparency and reproducibility when submitting manuscripts to the Population and Development Review (PDR). It covers creating data availability statements, depositing materials in FAIR repositories, documenting code with versions, and managing restricted data access, ensuring adherence to modern research integrity standards.

Structuring Supplementary Material For Research Papers

recsys-supplementary

brycewang-stanford/Awesome-Journal-Skills

This guide details the strategic placement of supplementary evidence (e.g., tuning grids, ablation studies, code) for academic submissions, particularly in conference formats like RecSys. It clarifies the difference between material belonging in the main body, the page-counted appendix, and the anonymous repository, ensuring that critical findings are visible and adhere to strict page budget constraints.

Systematic Review Transparency And Reproducibility

revedres-transparency-and-reproducibility

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide detailing how to make systematic reviews and meta-analyses fully auditable and reproducible for academic publishing. It emphasizes adhering to standards like PRISMA and MARS, quantifying inter-coder reliability (e.g., Kappa/ICC), and sharing all open materials, including search strings, coding datasets, and analysis code (via OSF/DOI).

Empirical Analysis Execution Bridge

rt-execution-bridge

brycewang-stanford/Awesome-Journal-Skills

This skill bridges theoretical econometric designs (DiD, IV, RDD, SCM, DML) to concrete, audited statistical estimates. It orchestrates the necessary tool calls to fit models, generate comprehensive diagnostics, and report reliable findings, ensuring that every academic claim is backed by executable, traceable code. Essential for advanced causal inference research.

Data Code And Materials Availability Check

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide to ensure scientific manuscripts meet the highest standards of reproducibility. It mandates documenting the deposition of all necessary data (e.g., GenBank, PDB, GEO), archiving custom analysis code with DOIs (e.g., Zenodo), and detailing sharing plans for unique reagents and materials. Crucial for primary data underlying research conclusions.

Structuring Supplementary Material for SIGGRAPH

siggraph-supplementary

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide detailing how to manage the division of content between the core 7-page paper body and the supplemental materials package for SIGGRAPH submissions. It emphasizes treating the results video as primary evidence, providing best practices for video creation, comparison galleries, and organizing additional technical evidence (code, data, proofs).

Guide to Conference Supplementary Submission

sigmod-supplementary

brycewang-stanford/Awesome-Journal-Skills

This comprehensive guide outlines best practices for creating and submitting supplementary materials for top-tier academic conferences. It addresses the critical challenges of maintaining strict anonymity, adhering to page limits, and structuring extended proofs, code links, and datasets across multi-round, double-anonymous review cycles.

Data and Transparency Posture Guide

spq-data-and-transparency

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide for preparing the data, code, and materials transparency posture when submitting a manuscript to Social Psychology Quarterly (SPQ). It clarifies the journal's guidelines (encouraged, not required) regarding data sharing, covering both quantitative and qualitative research methods while strictly adhering to ethical considerations and ASA norms.

Ensuring Reproducibility in Scientific Publishing

ssi-artifact-evaluation

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide for authors submitting to SCI journals, detailing best practices for making research code and data available. It addresses how to proactively provide reproducible materials, structured documentation, and honest availability statements, even when formal 'Artifact Evaluation' badges are not mandatory. It helps authors improve the robustness and impact of their scientific findings.

Structuring Multi-Tier Academic Conference Papers

uai-supplementary

brycewang-stanford/Awesome-Journal-Skills

This guide details the strategic placement of content for academic submissions, dividing material into Main Part, Appendix, and Supplementary ZIP. It provides best practices for authors to manage proofs, protocols, and code while adhering to strict page limits and double-blind review constraints, ensuring maximum review integrity and clarity.

UIST Supplementary Video Production Guide

uist-supplementary

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide for producing the mandatory supplementary video figure for UIST submissions. This material treats the 3-minute video as core evidence, detailing requirements for resolution (1080p/4K), codec (H.264), and anonymity. It guides authors on scripting the video around key claims, ensuring uncut interaction loops, and maintaining academic rigor to convince reviewers of the system's reality.

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