Open Science Framework (OSF) Overview
- OSF is a flexible research coordination platform that hosts project materials, supports preregistration with Registered Report templates, and publishes comparative research artifacts.
- Studies reveal that while OSF enhances transparency, its public sharing alone does not guarantee computational reproducibility due to gaps in dependency documentation and protocol preservation.
- Automated pipelines like osf-to-binder and the use of systematic registration practices illustrate efforts to overcome reproducibility challenges on OSF.
Open Science Framework (OSF) is a platform used to connect “data, pre-publications, and research project progress,” and, in the studies considered here, it functions as an infrastructure for sharing code supplements, preregistering studies through Registered Report (RR) templates, and publishing open comparative research artifacts (Saju et al., 27 May 2025). The same body of work also presents a more restrictive conclusion: OSF is valuable for transparency and sharing, but public availability on OSF does not by itself ensure computational reproducibility, comprehensive protocol documentation, or long-term preservation of referenced materials (Bett et al., 10 Feb 2026).
1. Conceptual role and scope
Within recent research practice, OSF appears in three closely related roles. First, it is a hosting layer for project materials, including repositories that contain code supplements associated with publications. Second, it is a preregistration environment offering structured RR types intended to support study planning, documentation, and pre-data-collection review. Third, it can serve as a public artifact layer for comparative resources that are too large or too dynamic to fit comfortably into a conventional article format (Saju et al., 27 May 2025).
The software engineering RR study characterizes OSF as a platform that helps researchers connect “data, pre-publications, and research project progress,” and treats its RR templates as a practical infrastructure for transparent study conception and community feedback (Bett et al., 10 Feb 2026). The LLM Terms study uses OSF in a different but complementary way: as the location of a publicly available “Table of Terms” and a larger comparative matrix that acts simultaneously as methodological evidence, a snapshot of collected policy text, and a practical reference resource (Davidson et al., 13 Jan 2026). Taken together, these uses suggest that OSF is best understood not as a single workflow, but as a flexible research coordination and dissemination substrate.
2. OSF as a repository layer for code supplements
A direct empirical characterization of OSF as a repository environment comes from the study of R code supplements. That analysis starts from the StatCodeSearch dataset, which is part of the GenCodeSearchNet benchmark and consists of code-comment pairs extracted from R scripts hosted on OSF, with a focus on social science and psychology projects. The dataset contains 1,070 code-comment pairs drawn from 558 unique R scripts across 296 distinct OSF projects, although the reproducibility analysis is conducted at the project level rather than at the code-comment-pair level (Saju et al., 27 May 2025).
A central result is that retrievability itself is unstable. Of the 558 R code files referenced across the 296 projects, 63 files from 32 distinct projects could not be accessed through their original OSF directories. After excluding those unresolvable references, 264 projects remained analyzable. The same study further notes that OSF registrations can preserve snapshots, but they were used by only 58 of the 296 projects, and only 49 of those registrations preserved the referenced files (Saju et al., 27 May 2025).
These observations constrain how OSF should be interpreted in archival terms. Public hosting and even registration do not imply stable future access to the exact files cited by a publication. A plausible implication is that OSF availability is temporally contingent unless preservation practices are used carefully and consistently.
3. Computational reproducibility of OSF-hosted R projects
The strongest quantitative evidence on OSF-hosted code concerns computational reproducibility. Among the 264 retrievable projects, formal environment documentation was almost entirely absent: only 2 projects (0.8\%) contained a DESCRIPTION file, only 1 project (0.4\%) contained a Dockerfile, and none contained renv.lock, sessionInfo.txt, sessionInfo.RData, .Rprofile, dependencies.R, dependency.R, environment.yml, or install.R. In total, 261 of the 264 projects—98.8\%—had no dependency file at all (Saju et al., 27 May 2025).
To address this absence of metadata, the authors developed an automated pipeline called osf-to-binder. The pipeline takes one or more OSF project identifiers, automatically downloads and unpacks the full repository contents via the OSFClient API, uses flowR to infer dependencies from source code, generates a DESCRIPTION file, and then uses repo2docker to build a runnable Docker image. The R scripts are executed inside the resulting isolated container environment; execution logs are recorded; the Docker image is published to DockerHub; the code is made available on GitHub; and a MyBinder link is generated so that the environment can be launched remotely in RStudio (Saju et al., 27 May 2025).
Even after automated reconstruction, execution barriers remained pervasive. Containerisation failed for 15 projects comprising 35 R scripts. The remaining 249 projects were successfully containerised, yielding 460 R scripts for execution, of which 119 ran successfully without critical errors, corresponding to 25.87\% of the total. At the project level, 51 successful scripts came from 40 projects where all scripts executed successfully, corresponding to full project-level reproducibility for 16.06\% of the 249 containerised projects (Saju et al., 27 May 2025).
The failure analysis shows that reproducibility problems were not reducible to package installation alone. Missing Package errors accounted for 26.1\% of failed scripts; Invalid File or Directory Path for 19.1\%; Missing Object or Function for 18.2\%; Shared Library Load Error for 8.5\%; Package Installation Failure for 8.2\%; File Read Error for 7.9\%; and the remaining 12\% fell into an “Other Errors” category that included RStudio environment assumptions, compressed-file issues, syntax and argument errors, encoding problems, and data-structure mismatches (Saju et al., 27 May 2025).
A concise view of the environment-documentation and execution results is useful:
| Item | Count / proportion | Notes |
|---|---|---|
| Projects analyzed after inaccessible references removed | 264 | Project-level analysis |
| Projects with no dependency file at all | 261 / 264 (98.8\%) | Formal environment documentation largely absent |
| Projects successfully containerised | 249 | 15 failed at containerisation stage |
| Scripts executed in containerised projects | 460 | Drawn from 249 projects |
| Scripts completed successfully without critical errors | 119 / 460 (25.87\%) | Script-level success |
| Fully reproducible containerised projects | 40 / 249 (16.06\%) | All scripts in project succeeded |
The broader conclusion is explicit: OSF sharing alone does not guarantee computational reproducibility. The study attributes the gap to absent dependency declarations, fragile file-path handling, hidden environment assumptions, and limited support for versioned, immutable project states (Saju et al., 27 May 2025).
4. OSF registrations and Registered Report templates
OSF also appears as an infrastructure for preregistration. In the software engineering study, the platform is used as the source of existing RR types, which are then compared against the controlled-experiment documentation guidelines of Jedlitschka et al. (2008). The paper lists 11 OSF RR types in total, including RR.1 — Preregistration, RR.3 — Qualitative Preregistration, RR.6 — Registered Report Protocol Preregistration, RR.7 — OSF-Standard Pre-Data Collection Registration, RR.10 — Replication Recipe (Brandt et al., 2013): Pre-Registration, and RR.11 — Preregistration in Social Psychology (Bett et al., 10 Feb 2026).
The detailed mapping focuses on RR.1, RR.3, RR.10, and RR.11. A central result is that RR.3 covers 33 out of 37 guidelines, RR.1 covers 31 out of 37, RR.11 covers 29, and RR.10 covers 25. RR.3 is therefore identified as the closest available OSF template for software engineering controlled experiments, even though it is not designed specifically for them (Bett et al., 10 Feb 2026).
| RR type | Name | Guidelines covered |
|---|---|---|
| RR.1 | Preregistration | 31 / 37 |
| RR.3 | Qualitative Preregistration | 33 / 37 |
| RR.10 | Replication Recipe (Brandt et al., 2013): Pre-Registration | 25 / 37 |
| RR.11 | Preregistration in Social Psychology | 29 / 37 |
RR.3 is described as having six main phases: Metadata, Study Information, Design Plan, Data Collection, Analysis Plan, and Miscellaneous. These phases include fields such as title, brief project description, contributors, general objectives, guiding research questions, sampling strategies, data sources and data types, stopping criteria, analysis procedures, tools or software, and optional reflections on researcher position, assumptions, values, and possible conflicts of interest (Bett et al., 10 Feb 2026).
The paper, however, is explicit that OSF’s current RR ecosystem is incomplete for this use case. No currently available RR type fully satisfies the controlled-experiment guidelines, and combining RR.1 and RR.3 would be necessary to get closer to comprehensive coverage; OSF does not allow this customization. The study also states that Registered Reports do not guarantee reproducibility or replicability, even though they can reduce p-hacking, publication bias, selective reporting, and post hoc hypothesis formulation (Bett et al., 10 Feb 2026).
5. OSF as a host for comparative open research artifacts
A further role of OSF is the publication of open comparative artifacts that extend a paper’s evidentiary base. The LLM Terms study contributes a publicly available OSF resource containing a “Table of Terms” and a larger version of the paper’s comparative table. The resource is described as cross-referenced and categorized, covering Anthropic, OpenAI, DeepSeek, Google, and xAI (Davidson et al., 13 Jan 2026).
The construction procedure is methodologically important. The authors started from Anthropic’s Terms as the most comprehensive baseline, extracted each clause into the table, cross-referenced the other four providers against it, added missing clauses into appropriate thematic categories, and noted exceptions and ambiguities. The OSF resource therefore functions not merely as a file dump but as the structured output of an abductive document-analysis process (Davidson et al., 13 Jan 2026).
The paper assigns the OSF artifact two distinct purposes. It is both an analytical support layer for the paper and an “easily understandable summary of terms” intended as a practical guide for researchers and users navigating “regulatory gray areas.” The same study cautions that the resource is a snapshot of policy text collected in November 2025, that it is comparative rather than comprehensive, and that it should be treated as a comparative policy map rather than as legal advice (Davidson et al., 13 Jan 2026).
This use case is significant because it expands the notion of what an OSF artifact can be. Rather than serving only as storage for datasets or code, OSF here supports a traceable, reusable comparative matrix whose value depends on explicit categorization, cross-referencing, and time-stamping.
6. Limitations, preservation problems, and best-practice implications
Across these studies, the principal misconception about OSF is that public sharing is equivalent to reproducibility. The reproducibility analysis directly contradicts that view: referenced materials may no longer be retrievable, environment specifications are usually absent, and even automated dependency inference plus containerisation yields successful execution for only a minority of scripts (Saju et al., 27 May 2025).
A second misconception is that OSF registrations, by themselves, solve preservation. The evidence is narrower: registrations can preserve snapshots, but they were used in only 58 of 296 projects, and only 49 of those registrations preserved the referenced files. This suggests that long-term preservation on OSF is uneven rather than automatic (Saju et al., 27 May 2025).
A third misconception is that OSF’s preregistration templates already provide a comprehensive domain-specific protocol language. In software engineering controlled experiments, no OSF RR type fully satisfies the reference guidelines, and the platform’s template customization limits prevent the hybridization that the study judged necessary (Bett et al., 10 Feb 2026).
The best-practice implications given in the reproducibility work are correspondingly concrete: stronger practice on OSF should include explicit dependency declarations, persistent links to data, use of relative paths, non-interactive scripts, and better versioning or registration practices (Saju et al., 27 May 2025). The software engineering RR study adds a parallel requirement at the protocol layer: RR-specific guidelines for software engineering are deemed essential, along with better support for SE artifacts such as code repositories, UML diagrams, models, scripts, and domain-specific languages (Bett et al., 10 Feb 2026).
Taken together, these results portray OSF as a valuable but insufficient platform for reproducible computational research unless authors provide much more complete environment documentation and build their code in ways that support later reuse and verification. They also show that OSF’s importance lies not only in hosting files, but in structuring evidence, exposing protocol assumptions, and making the limits of current research workflows empirically visible (Saju et al., 27 May 2025).