> For the complete documentation index, see [llms.txt](https://wiki.medicaiddatalearningnetwork.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://wiki.medicaiddatalearningnetwork.org/getting-started/key-resources.md). # Key Resources This page highlights several key resources for researchers and analysts who are new to the T-MSIS Analytic Files, or TAF. The resources below are a good starting point for understanding what the TAF contain, how to request access, how to assess data quality, and how to report TAF-based analyses transparently. ## ResDAC The [Research Data Assistance Center](https://resdac.org/), or ResDAC, is a central resource for researchers requesting and using CMS data. For TAF users, ResDAC provides information on requesting data, data use agreements, file availability, file-specific documentation, and variable-level data dictionaries. ResDAC also offers an online [introductory workshop](https://resdac.org/workshops/intro-medicaid-chip-taf) on the TAF data. New TAF users should use ResDAC to identify which files are needed for a project, understand the structure of each file, look up variable definitions, and navigate the data request process. ResDAC file pages are especially useful when determining whether a variable exists in a given file and how it is defined. ## CCW The [Chronic Conditions Data Warehouse](https://www2.ccwdata.org/web/guest/home) provides access to the CMS Virtual Research Data Center and is a repository of documentation on TAF, as well as other federal claims datasets. It hosts a [TAF User Guide](https://www2.ccwdata.org/documents/10280/19002246/ccw-taf-rif-user-guide.pdf). ## DQ Atlas The CMS [Data Quality Atlas](https://www.medicaid.gov/dq-atlas/welcome), or DQ Atlas, is an interactive resource for assessing the quality and usability of TAF data. It provides information on TAF data quality by state, year, and TAF release version across a large number of data elements. New TAF users should consult the DQ Atlas before finalizing study states, years, outcomes, or key variables. It is especially useful for assessing whether specific data elements are reliable enough for a planned analysis and for understanding variation in data quality across states and over time. The DQ Atlas website also hosts key TAF written guidance, including [technical documentation](https://www.medicaid.gov/dq-atlas/landing/resources/downloads) and [analytic briefs](https://www.medicaid.gov/dq-atlas/landing/briefs). ## Medicaid Data Learning Network The [Medicaid Data Learning Network](https://www.medicaiddatalearningnetwork.org/), or MDLN, is a community of non-profit and government researchers focused on developing best practices for using TAF data. The MDLN holds monthly virtual Learning Sessions for TAF users to share challenges and solutions associated with working with data. The MDLN also maintains an active listserv for TAF users to pose questions to other members. New TAF users can use MDLN resources to learn about common analytic challenges, understand how other researchers approach TAF-based projects, and find community-developed tools, examples, and guidance. ## TAF Analysis Reporting Checklist The [TAF Analysis Reporting Checklist](http://tafchecklist.org/) is a consensus-based checklist designed to improve transparency and reproducibility in research using the TAF. It identifies key items researchers should report when describing TAF-based analyses and walks through how to transparently address and report on data quality challenges. The checklist can help teams plan their analysis, identify decisions that need to be documented, and anticipate issues that reviewers, editors, or readers may ask about later. ## TAF Open Source Functions The [`taf.functions` GitHub repository](https://github.com/chse-ohsu/taf.functions), developed by Conor Hennessy at OHSU, provides open-source R functions to support common TAF research tasks, including data quality assessment, measure construction, and identifying beneficiaries with specific conditions. The package is intended to help TAF users avoid recreating common data preparation steps and promote more standardized analytic definitions, while still allowing users to adapt functions for their own projects. The repository also maintains [validated crosswalks](https://github.com/chse-ohsu/taf.functions/tree/main/Managed_Care_Crosswalks) to aid in identification of managed care plans in the TAF data. --- # Agent Instructions This documentation is published with GitBook. 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