Add USRSE 2026 STAMPED poster abstract - #14
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Co-Authored-By: codex-cli 0.143.0 / gpt 5.6-luna <codex@openai.com>
asmacdo
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Handful of nitpicks, take what you like.
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| - **Self-contained:** Everything needed to understand and run the study was made identifiable from one research-object boundary, reducing reliance on undocumented local context (supported by DataLad and git-annex). | ||
| - **Tracked:** Results were linked to their inputs, transformations, execution context, and scientific meaning so their provenance could be inspected (recorded with Git, DataLad run records, `con-duct`, and NIDM/PROV). | ||
| - **Actionable:** Procedures and expected outputs were encoded as executable, testable operations instead of instructions requiring interpretation (implemented through BIDS, tested BIDS Apps, Pixi tasks, manifests, and validation commands). |
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datalad rerun might be useful here too, though the list is already pretty long.
| - **Self-contained:** Everything needed to understand and run the study was made identifiable from one research-object boundary, reducing reliance on undocumented local context (supported by DataLad and git-annex). | ||
| - **Tracked:** Results were linked to their inputs, transformations, execution context, and scientific meaning so their provenance could be inspected (recorded with Git, DataLad run records, `con-duct`, and NIDM/PROV). | ||
| - **Actionable:** Procedures and expected outputs were encoded as executable, testable operations instead of instructions requiring interpretation (implemented through BIDS, tested BIDS Apps, Pixi tasks, manifests, and validation commands). | ||
| - **Modular:** Data, environments, operations, and results retained distinct identities so components could be reviewed, updated, and reused independently (organized as versioned DataLad components). |
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| - **Modular:** Data, environments, operations, and results retained distinct identities so components could be reviewed, updated, and reused independently (organized as versioned DataLad components). | |
| - **Modular:** Data, environments, operations, and results retained distinct identities so components could be reviewed, updated, and reused independently (organized as versioned DataLad subdatasets). |
I read this as a datalad component being a "thing".
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| The scientific value of a computational finding depends on whether others can understand how it was produced, inspect its evidence, and repeat or extend the analysis. Code alone does not provide this: data, parameters, software environments, execution context, and the provenance connecting inputs to outputs must also be identifiable and recoverable. STAMPED defines seven properties for organizing these materials as a durable research object. | ||
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| We applied STAMPED to reproduce an analysis presented at OHBM 2025 on age-dependent bias in cortical morphometry tools using Adolescent Brain Cognitive Development (ABCD) Study data. This case study shows how the principles translate into concrete research-design choices and how those choices make a computational result easier to inspect, execute, verify, and reuse. |
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It might be good to mention AI in the first couple paragraphs, the final paragraph makes the connection to AI feel bolted on. I suppose the strongest place to mention would be to discuss how AI played a role in your application of STAMPED here.
Co-Authored-By: codex-cli 0.143.0 / gpt 5.6-sol <codex@openai.com>
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Summary
Add poster abstract. Addresses con/cierge#34
Validation
git diff --checkThe generated PDF remains a local submission artifact because
2026-usrse/.gitignoreexcludes PDFs, matching the repository’s existing USRSE poster-draft convention.