MDEmic: a metadata annotation tool to facilitate management of FAIR image data in the bioimaging community

Kunis, Susanne; Hänsch, Sebastian; Schmidt, Christian; Wong, Frances; Strambio‐De‐Castillia, Caterina; Weidtkamp‐Peters, Stefanie

doi:10.1038/s41592-021-01288-z

Cited by 19 publications

(22 citation statements)

References 10 publications

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“…Cloud infrastructural resources are able to execute computational workflows that combine data with computational analysis tools at a large scale 42,43 . However, there is still a considerable lack of data containing machine-actionable metadata 1,3,[44][45][46][47] . To document provenance, ensure reproducibility and support reuse any raw and segmented image in this data set has been associated with a rich set of contextual and expressive metadata 48 , documenting the phenotypic characters, and recording any digital image processing (i.e., increasing contrast, brightness, image fusion) (Fig.…”

Section: Background and Summarymentioning

confidence: 99%

Reference bioimaging to assess the phenotypic trait diversity of bryophytes within the family Scapaniaceae

Peters

König‐Ries

2022

Sci Data

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Macro- and microscopic images of organisms are pivotal in biodiversity research. Despite that bioimages have manifold applications such as assessing the diversity of form and function, FAIR bioimaging data in the context of biodiversity are still very scarce, especially for difficult taxonomic groups such as bryophytes. Here, we present a high-quality reference dataset containing macroscopic and bright-field microscopic images documenting various phenotypic characters of the species belonging to the liverwort family of Scapaniaceae occurring in Europe. To encourage data reuse in biodiversity and adjacent research areas, we annotated the imaging data with machine-actionable metadata using community-accepted semantics. Furthermore, raw imaging data are retained and any contextual image processing like multi-focus image fusion and stitching were documented to foster good scientific practices through source tracking and provenance. The information contained in the raw images are also of particular interest for machine learning and image segmentation used in bioinformatics and computational ecology. We expect that this richly annotated reference dataset will encourage future studies to follow our principles.

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Section: Background and Summarymentioning

confidence: 99%

Reference bioimaging to assess the phenotypic trait diversity of bryophytes within the family Scapaniaceae

Peters

König‐Ries

2022

Sci Data

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“…Scientific workflows allow to fully automate the entire process combining images with software tools utilising the machine-actionable information contained in the meta-data 42,48,67 . Meta-data has been validated using procedures described in 20,45,46 and standardised vocabularies were following the FAIR guiding principles 1 . When improved algorithms have been developed, the entire pipeline can be re-run resulting in improved segmented images without any further intervention.…”

Section: Technical Validationmentioning

confidence: 99%

“…Cloud infrastructural resources are able to execute computational workflows that combine data with computational analysis tools at a large scale 41,42 . However, there is still a considerable lack of data containing machine-actionable meta-data 1,3,[43][44][45][46] . To foster provenance, any raw and segmented image in this data set has been associated with a rich set of contextual and expressive meta-data 47 , documenting the phenotypic attributes, and recording any digital image processing (i.e., increasing contrast, brightness, image fusion).…”

mentioning

confidence: 99%

“…To foster provenance, any raw and segmented image in this data set has been associated with a rich set of contextual and expressive meta-data 47 , documenting the phenotypic attributes, and recording any digital image processing (i.e., increasing contrast, brightness, image fusion). The meta-data has been formatted with community-accepted semantics that allow for machine-actionable data-mining and to create scientific workflow modules that produce segmented composite images automatically by reusing the instructions contained in our meta-data 20,42,45,46,48 . In this Data Descriptor, we present the principles to generate reference images from raw microscopic bioimaging data and show how individual images are associated with technical and expressive meta-data.…”

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confidence: 99%

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Reference BioImaging to assess the phenotypic trait diversity of bryophytes within the family Scapaniaceae

Peters

König-Ries

2022

Preprint

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Macro- and microscopic images of organisms are pivotal in biodiversity research. Despite that bioimages have manifold applications such as for assessing the diversity of form and function, FAIR bioimaging data in the context of biodiversity are still very scarce, especially for difficult taxonomic groups such as bryophytes. Here, we present a high-quality reference dataset containing macroscopic and bright-field microscopic images documenting various phenotypic attributes of the species belonging to the family of Scapaniaceae occurring in Europe. To encourage data reuse in biodiversity and adjacent research areas, we annotated the imaging data with machine-actionable meta-data using community-accepted semantics. Furthermore, raw imaging data are retained and any contextual image processing like multi-focus image fusion and stitching were documented to foster good scientific practices through source tracking and provenance. The information contained in the raw images are also of particular interest for machine learning and image segmentation used in bioinformatics and computational ecology. We expect that this richly annotated reference dataset will encourage future studies to follow our principles.

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“…Those efforts include the Stimulating Peripheral Activity to Relieve Conditions (SPARC) Dataset Format ( Bandrowski et al, 2021 ), A perspective on Microscopy Metadata: data provenance and quality control ( Huisman et al, 2021 ), the Essential Metadata for 3D BRAIN Microscopy ( Ropelewski et al, 2021 ) with metadata standards for 3D microscopy datasets for use by the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative 1 and implemented in the Brain Image Library (BIL) ( Benninger et al, 2020 ), the Recommended Metadata for Biological Images (REMBI) ( Sarkans et al, 2021 ) for light and electron microscopy, the open Metadata Initiative for Neuroscience Data Structures (openMINDS) 2 and the Quality Assessment and Reproducibility for Instruments & Images in Light Microscopy (QUAREP-LiMi) ( Boehm et al, 2021 ). Recent developments also include the 4DN-BINA-OME framework ( Hammer et al, 2021 ), a tier-based microscopy metadata specification extending the Open Microscopy Environment (OME) Data Model ( Goldberg et al, 2005 ) 3 by the 4D Nucleome Initiative (4DN) ( Dekker et al, 2017 ) 4 in collaboration with BioImaging North America (BINA), 5 along with metadata collection tools ( Kunis et al, 2021 ; Rigano et al, 2021 ; Ryan et al, 2021 ). The OME community has also been developing the Next Generation File Format (NGFF), which uses a Zarr-based format for dealing more flexibly with the different scales of microscopy data ( Moore et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data

et al. 2022

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The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI.

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MDEmic: a metadata annotation tool to facilitate management of FAIR image data in the bioimaging community

Cited by 19 publications

References 10 publications

Reference bioimaging to assess the phenotypic trait diversity of bryophytes within the family Scapaniaceae

Reference bioimaging to assess the phenotypic trait diversity of bryophytes within the family Scapaniaceae

Reference BioImaging to assess the phenotypic trait diversity of bryophytes within the family Scapaniaceae

Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data

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