Skill UI

This guide helps researchers determine the most appropriate top-tier academic conference (e.g., CoNEXT, SIGCOMM, NSDI, IMC) and submission cycle for their networking research. It provides detailed criteria, routing tables, and tests (e.g., platform-realism test, model-swap test) to ensure the contribution is evaluated on a real target and fits the target community's scope and rigor.

A comprehensive strategic guide for authors targeting the Conference and Labs of the Evaluation Forum (CLEF). It helps frame IR/NLP research by defining the appropriate contribution type, evidence bar, and rigorous evaluation methods required for top-tier academic submission, ensuring the paper aligns with the venue's specific community expectations.

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.

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 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.

This guide provides a comprehensive strategy for authors submitting NLP and Computational Linguistics papers to EACL. It covers conference positioning, manuscript re-framing from journal or arXiv styles, and establishes rigorous evidence standards. It helps authors ensure their work aligns with the European ACL community focus, optimizing the paper's narrative structure for high acceptance rates.