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“Metadata is a love note to the future”
The Helmholtz Metadata Collaboration is a cross-domain initiative across the whole Helmholtz Association, which is the largest funding agency in Germany. It follows the goal to develop and establish novel methods and tools documenting and sharing research data by means of enriched metadata, as well as improved interoperability of data across disciplines. The Hub Health of this initiative is anchored in the Division of Medical Image Computing at the German Cancer Research Center Heidelberg.
The FAIR principles are guidelines to make your data, including software, findable, accessible, interoperable and reusable. They are an important component of Open Science.
NCI Imaging Data Commons is tasked with establishing publicly available repository of cancer imaging data, and in this role is developing workflows to harmonize image and image-derived data representation into DICOM, make metadata searchable, and connect imaging metadata with clinical metadata. Thus, this project might be helpful to the HMC project. We will explore this connection this week!
We will investigate relevant metadata descriptions of medical images, cohorts, and medical image analyis pipelines and results like machine learning models.
An additional aspect to look at will be aspects of generating, reviewing and sharing of metadata of research data which contains personally identifiable information.
Common standards, tools and practices can make interoperability much easier. Within this project we want to investigate which tools are already used in our community, which lessons were already learned, and perform experiments regarding interoperability of data and analysis pipelines as well as analysis results.
Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
Bridge, C.P., Gorman, C., Pieper, S. et al. Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology. J Digit Imaging 35, 1719–1737 (2022). https://doi.org/10.1007
Deepa Krishnaswamy, Dennis Bontempi, David Clunie, Hugo Aerts, & Andrey Fedorov. (2023). AI-derived annotations for the NLST and NSCLC-Radiomics computed tomography imaging collections [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7539035
Zaffino P, Marzullo A, Moccia S, Calimeri F, De Momi E, Bertucci B, Arcuri PP, Spadea MF. An Open-Source COVID-19 CT Dataset with Automatic Lung Tissue Classification for Radiomics. Bioengineering. 2021; 8(2):26. https://doi.org/10.3390/bioengineering8020026