NIST Research Data Framework (RDaF)
Bibliographic record
Abstract
The NIST Research Data Framework (RDaF) is a multifaceted and customizable tool that aims to help shape the future of open data access and research data management (RDM). The RDaF will allow organizations and individual researchers to develop their own RDM strategy. Though NIST is leading the RDaF, most of the content in the current version 2.0, which supersedes preliminary V1.0 and interim V1.5, was obtained via engagement with national and international leaders in the research data community. NIST held a series of three plenary and 15 stakeholder workshops from October 2021 to September 2023. Workshop attendees represented many stakeholder sectors: US government agencies, national laboratories, academia, industry, non-profit organizations, publishers, professional societies, trade organizations, and funders (public and private), including international organizations. The audience for the RDaF is the entire research data community in all disciplines—the biological, chemical, medical, social, and physical sciences and the humanities. The RDaF is applicable from the organization to the project level and encompasses a wide array of job roles involving RDM, from executives and Chief Data Officers to publishers, funders, and researchers. The RDaF is a map of the research data space that uses a lifecycle approach with six stages to organize key information concerning RDM and research data dissemination. Through a community-driven and in-depth process, NIST identified and defined specific, high-priority topics and subtopics for each lifecycle stage. The topics and subtopics are programmatic and operational activities, concepts, and other important factors relevant to RDM which form the foundation of the framework. This foundation enables organizations and individual researchers to use the RDaF for self-assessment of their RDM status. Each subtopic has several informative references—resources such as guidelines, standards, and policies—to help a user understand or implement that subtopic. As such, the RDaF may be considered a “best practices” document. Fourteen overarching themes—topic areas identified as pervasive throughout the framework—illustrate the connections among the six lifecycle stages. Finally, the RDaF includes eight sample profiles for common job functions or roles. Each profile contains topics and subtopics an individual in the given role needs to consider in fulfilling their RDM responsibilities. Individual researchers and organizations involved in the research data lifecycle will be able to tailor these sample profiles or generate entirely new profiles for their specific job function. The methodologies used to generate the content of this publication, RDaF V2.0, are described in detail. An interactive web application has been developed and released that provides an interface for all the components of the RDaF mentioned above and replicates this document. The web application is easy and intuitive to navigate and provides new functionality enabled by the interactive environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.144 | 0.268 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.026 | 0.030 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.011 | 0.009 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.062 | 0.066 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".