Add USRSE 2026 STAMPED poster abstract

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+repos:
+- repo: https://github.com/TurtleTech-ehf/snapper
+  rev: v0.9.1
+  hooks:
+  - id: snapper
+    files: ^2026-usrse/stamped-abcd-poster-draft\.md$

2026-usrse/stamped-abcd-poster-draft.md

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+# STAMPED in Practice: Reproducing a Scientific Result on ABCD Neuroimaging Study
+
+## Authors
+
+- John A. Lee <John.A.Lee@dartmouth.edu>, Dartmouth College, ORCID 0000-0001-5884-4247
+- Austin Macdonald <Austin.S.Macdonald@dartmouth.edu>, Dartmouth College, ORCID 0000-0002-8124-807X
+- Yaroslav O. Halchenko <Yaroslav.O.Halchenko@dartmouth.edu>, Dartmouth College, ORCID 0000-0003-3456-2493
+
+## Keywords
+
+STAMPED, reproducibility, research objects, neuroimaging, provenance, AI-assisted science
+
+## Abstract
+
+The scientific value of a computational finding depends on whether others can understand how it was produced, inspect its evidence, and re-execute or extend the analysis.
+Code alone is insufficient: data, parameters, software environments, execution context, and provenance must also be identifiable and recoverable.
+For RSEs, the practical payoff is less time reconstructing context and more efficient review, maintenance, collaboration, and extension of an analysis.
+AI-assisted tools can reduce software-development effort, but their high-volume, nondeterministic output makes explicit context, provenance, and reviewable intermediate states increasingly important.
+STAMPED (<https://stamped-principles.org>) defines seven properties for organizing these materials as a durable research object.
+
+This poster presents our application of STAMPED to an existing analysis presented at OHBM 2025 on age-dependent bias in cortical morphometry tools using Adolescent Brain Cognitive Development (ABCD) Study data (https://abcdstudy.org).
+We use the principles to guide improvements to the research object surrounding the analysis.
+We use AI extensively in development, testing, and documentation, making the reconstruction a practical test of the STAMPED vision for AI-assisted research.
+In this setting, the principles provide a structure for bounding context, preserving inspectable states, connecting automated outputs to evidence, and establishing checkpoints for human review.
+
+To improve the research object across the seven properties, we combine DataLad and git-annex for composition and state; Git, DataLad run records, `con-duct`, and NIDM/PROV for provenance; tested BIDS Apps and Pixi tasks for executable interfaces; Apptainer, BABS, and Slurm for portable, fresh execution; and persistent Git/annex siblings with separate access boundaries for distribution.
+Used together, these tools make data, environments, operations, and results more identifiable, executable, modular, portable, reviewable, and retrievable while exposing interactions among the principles.
+
+Collectively, the decisions on how to implement the principles provide a worked example of how to use STAMPED to guide choices about research-object boundaries, provenance, execution, validation, and distribution.
+The poster reports the practical details of this process—the effort and judgment required, problems encountered, tradeoffs made, evidence produced, and interactions among principles and tools—and invites RSEs to consider which parts apply to their own shared or domain-specific challenges.
+Although we demonstrate the approach through a scientific reproduction, such an effort is more convenient if integrated from the start of the analysis.
+
+
+## Acknowledgments
+
+We thank the authors of the STAMPED principles and the developers and communities behind DataLad, BIDS, BABS, ReproNim, NIDM, Pixi, Apptainer, and `con-duct`.
+
+AI-assisted content disclosure: This submission was prepared with assistance from OpenAI Codex (GPT-5, accessed August 2026).
+The system helped synthesize repository evidence and draft the Abstract and Connection to Mission sections.
+The authors reviewed the source evidence, edited the text, and remain responsible for all claims.
+
+## References
+
+1. Macdonald A, Baker CC, To I, Halchenko YO.
+*STAMPED principles for reproducible research objects*.
+May 2026.
+<https://github.com/stamped-principles/stamped-paper>
+2. Lee JA.
+*STAMPED-dl_morphometrics_biases: an ideal-oriented reconstruction of a neuroimaging analysis*.
+2026.
+<https://github.com/STAMPED-dl-morphometrics-biases/STAMPED-dl_morphometrics_biases>
+3. Nielson DM, Lee JA, Earl E, Moraczewski D, Pereira F. *Age dependent volume estimation biases in recon-all clinical and recon-any*.
+OHBM 2025 poster.
+<https://doi.org/10.17605/OSF.IO/P3KNS>
+4. Halchenko YO, et al. DataLad: distributed system for joint management of code, data, and their relationship.
+*Journal of Open Source Software*.
+2021;6(63):3262.
+<https://doi.org/10.21105/joss.03262>
+5. Zhao C, et al. A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps.
+*Imaging Neuroscience*. 2024;2:imag-2-00074.
+<https://doi.org/10.1162/imag_a_00074>
+6. Center for Open Neuroscience.
+*con-duct: a lightweight wrapper for monitoring command execution*.
+RRID:SCR_025436.
+<https://github.com/con/duct>
+
+## Connection to Mission, Goals, & Interests of US-RSE Community
+
+Research software engineers help determine whether a computational result is merely produced once or becomes a durable scientific contribution.
+STAMPED gives RSEs and researchers a shared vocabulary for the properties that make this difference: coherent research-object boundaries, exact identities, executable procedures, independent components, explicit environments, clean execution, and persistent distribution.
+This case study translates those properties into a concrete implementation and shows how they can be designed into an analysis from the beginning rather than added after publication.
+
+The project also addresses the conference theme, "Advancing Science in the Age of AI," by shifting attention from what AI can do to what evidence AI-assisted work leaves behind.
+Using STAMPED as a shared point of reference, the poster invites RSEs to compare how their teams record decisions, divide responsibility, and evaluate AI-assisted work, and to consider how those expectations could become community review criteria and institutional practice across research domains.

2026-usrse/stamped-princples.md

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+# Pragmatic practices for reproducible and open science through case stories and principles
+
+
+## Presenters
+
+Cody C.Baker <cody.c.baker.phd@gmail.com>, Center for Open Neuroscience, Dartmouth College, 0000-0002-0829-4790
+
+
+## Keywords
+
+Reproducible research - Open science - Provenance
+
+## Abstract
+
+Neuroscience increasingly depends on the interplay of code, data, and computational environments, yet the record of how they were used together is often incomplete, scattered across repositories, wikis, and notebooks, or lost entirely.
+This fragmentation undermines rigor, reproducibility, reusability, and efficiency in BRAIN Initiative pipelines that routinely span multiple institutions, archives, and compute platforms.
+Existing frameworks such as FAIR and FAIR4RS govern discovery and interoperability of digital objects, but do not specify how research objects should be structured and managed so they can be re-executed, extended, and audited.
+The community lacks a shared vocabulary for this operational layer.
+
+Building on the YODA and VAMP traditions from neuroimaging, and on patterns that have independently converged across geophysics, genomics, statistics, and neuroimaging over three decades, we formalize seven principles a research object should satisfy: Self-containment, Tracking, Actionability, Modularity, Portability, Ephemerality, and Distributability, collectively STAMPED.
+Each spans a spectrum from practical minimum to aspirational ideal, so adoption is non-prescriptive and incremental. 
+Formal LinkML schemas, an interactive compliance checklist, and the curated collection of examples are provided as enabling tools to this end.
+
+We demonstrate STAMPED through two major neuroscience pipelines.
+OpenNeuroDerivatives reorganized derivative neuroimaging datasets so they exist as independent Ephemeral units that reference raw inputs as subdatasets rather than nesting under them, removing an upward dependency that previously violated Self-containment, Modularity, and Portability.
+DANDI Compute, utilizing the Allen Institute for Neural Dynamics electrophysiology pipeline, packages spike-sorting outputs into nested BIDS-derivative units in which each leaf contains the exact code, runtime logs, outputs, and provenance metadata needed to re-execute the analysis, satisfying STAMPED end-to-end.
+
+These adoptions show that STAMPED provides a tool-agnostic, incrementally adoptable vocabulary that lets researchers, reviewers, collaborators, and emerging AI agents evaluate and improve the operational maturity of computational neuroscience.
+By making research objects re-executable and inspectable by construction, STAMPED converts reproducibility from an aspiration into a measurable property of everyday neuroscience practice.
+
+## References
+
+Austin Macdonald, Cody Baker, Isaac To, Yaroslav O. Halchenko, “STAMPED principles for reproducible research objects”, 26-May-2026. [Online]. Available: osf.io/preprints/metaarxiv/f3h82_v1. 
+Michelle Barker, et al. Introducing the FAIR Principles for research software. Scientific Data, 9(1):622, October 2022. ISSN 2052-4463.
+Michael Hanke, et al. YODA: YODA’s organigram on data analysis, 2018. Slides.
+Alessio P Buccino, Arjun Sridhar, David Feng, Karel Svoboda, Joshua H Siegle (2026). Efficient and reproducible pipelines for spike sorting large-scale electrophysiology data. eLife, 15:RP110170.
+Michael Hanke. What is DataLad and what can it do for you? 2023. https://files.inm7.de/mih/pres/talks/whatisdatalad_2023.html#/