Skill UI

This comprehensive guide outlines best practices for packaging and releasing scientific artifacts—including code, training configurations, demonstration data, and policy checkpoints—for robotics research papers. It details how to prepare materials for anonymous reviewer assessment and for durable public release, ensuring the work is fully reproducible by the academic community.

Provides comprehensive guidelines for researchers packaging and releasing artifacts (data, code, instruments) derived from real communities in CSCW research. It emphasizes ethical constraints, de-identification techniques (such as quote search and join attacks), and best practices for protecting studied communities' privacy, ensuring responsible sharing.

A comprehensive guide for designing and auditing empirical research in CSCW. It emphasizes methodological pluralism, defining distinct standards of rigor for various techniques like ethnography, trace analysis, surveys, and mixed methods. The guide mandates pre-design planning, explicitly addressing ethical concerns, unit of analysis, and aggregation logic before data collection.

A comprehensive guide for researchers to determine if their human-centered research belongs in CSCW. It provides a critical unit-of-analysis test (group, community, organization) and a routing decision process to ensure the paper's focus is on cooperative practice, not just individual interaction. Use this before drafting to optimize submission to the correct academic venue.

This comprehensive skill guides researchers through the critical ethical framework for anthropological and social science manuscripts. It emphasizes 'do no harm,' continuous informed consent, community accountability, and proper handling of sensitive data, cultural heritage, and human remains. It ensures that all data sharing and knowledge reuse plans prioritize the rights, privacy, and well-being of interlocutors above academic publication needs.

Provides guidelines for creating robust, verifiable, and reusable Electronic Design Automation (EDA) artifacts for submission to academic conferences like DAC. The focus shifts from chasing conference badges to ensuring community trust and scientific reproducibility. Key elements include pinning the toolchain, using standard benchmarks, providing clear setup instructions, and archiving the release in a DOI-issuing repository.

A comprehensive guide for establishing research reproducibility in Electronic Design Automation (EDA). It details best practices for pinning benchmark suites, disclosing specific tool versions (open/commercial), documenting technology nodes (PDKs), and handling stochastic data flows (seeds, variance). Essential for ensuring that semiconductor design claims and ML-for-EDA results are credible and verifiable by the academic community.

This tool maps the 'dark funnel,' identifying brand awareness and consideration signals from untracked sources like Reddit, AI chatbots, and word-of-mouth. It aggregates diverse data—including branded search trends, community mentions, and podcast data—to provide a comprehensive view of the true buyer journey, allowing marketers to measure influence beyond traditional analytics platforms.

This guide teaches how to restructure complex econometric tables and figures for manuscripts intended for a dual audience of professional economists and non-technical policymakers. The focus is on clarity, interpretability, and impact, ensuring that the main text presents policy-relevant findings (e.g., event studies, welfare decomposition) while relegating dense technical details (full regression dumps, diagnostics) to the appendix. It emphasizes translating standard coefficients into meaningful units like jobs or % of GDP.

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.

This guide instructs authors on how to craft a robust, cross-disciplinary gap statement for high-level earth science journals. It teaches that a paper's contribution must be framed by triangulating evidence across multiple sub-fields (geochemistry, geophysics, geodynamics, etc.) and competing hypotheses, rather than focusing solely on a single methodological community.

This guide provides best practices for ensuring scientific reproducibility in Earth and Planetary Science Letters (EPSL) manuscripts. It details how to structure supplementary materials, data tables, and analytical code to comply with modern research data policies (FAIR principles). It directs authors on depositing data into recognized community repositories (e.g., PANGAEA, Zenodo, EarthChem), ensuring that the paper's findings can be independently validated by any reader.