What refractive index does it apply to the datasets?

For uncleared tissue datasets common in HuBMAP projects, it defaults the refractive index to 1.44 to ensure consistency in optical calculations.

How are wavelengths handled differently than standard parsing?

Instead of duplicating the excitation value as emission, this skill uses a specialized lookup table to map excitation values to physically accurate excitation-emission pairs (e.g., mapping 358nm to 358.0/461.0 for DAPI).

Does this skill work with all microscopy formats?

It is specifically optimized for CODEX/Akoya PhenoCycler experiment.json files and the KINTSUGI processing pipeline.

What primary problem does this skill solve?

It addresses the structural mismatch between CODEX/Akoya metadata exports and KINTSUGI's ExperimentConfig requirements, ensuring fields like 'numCycles' and 'zPitch' are correctly mapped and not lost.

CODEX Experiment JSON Parser

Name: CODEX Experiment JSON Parser
Author: smith6jt-cop

bysmith6jt-cop

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데이터 과학 및 ML

Translates CODEX/Akoya experiment.json metadata into the KINTSUGI ExperimentConfig format with precise field and wavelength mapping.

This skill automates the complex task of importing microscopy metadata from CODEX/Akoya PhenoCycler systems into the KINTSUGI framework. It resolves critical data discrepancies by mapping legacy field names and converting flat excitation lists into accurate excitation-emission pairs using a specialized filter set lookup table. By implementing fuzzy matching for wavelengths and ensuring correct refractive index defaults, it prevents silent data loss and physical inaccuracies during large-scale batch project initialization, specifically for HuBMAP and similar microscopy datasets.

주요 기능

01Automated field name translation between Akoya and KINTSUGI schemas

02Fuzzy matching for filter sets with ±10 nm tolerance

03Validation logic to prevent silent field dropping during data loading

04Intelligent wavelength mapping from flat lists to excitation/emission pairs

05Automatic assignment of refractive index (1.44) for uncleared tissue

061 GitHub stars

사용 사례

01Batch processing large-scale HuBMAP datasets with inconsistent metadata formats

02Initializing KINTSUGI projects from raw CODEX/Akoya PhenoCycler datasets

03Correcting incorrect wavelength configurations where excitation was duplicated as emission

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add smith6jt-cop/skills_registry codex-experiment-json-parsing

For use in Claude.ai and ChatGPT

Download Skill