Automated imaging and deep learning technology enhance rare plankton detection using extensive datasets.
Data, code, model weights, and validation materials supporting the manuscript “Automated Shadowgraph Imaging and Deep Learning for Sub-millimetre Plankton Imaging and Rare-Target Screening” (Ecological Informatics, ECOINF-D-26-00458, revision 2). This version (REV2) supersedes the pre-REV1 deposit: all pre-REV1 objects are retained unchanged, and a new rev2_validation/ directory adds the detection-stage characterisation and blind multi-rater validation produced for the revision. Download and extract ECOINF-D-26-00458_data_and_code_REV2.zip; a top-level README.md and a file-level metadata.csv (with SHA-256 checksums for every file) document the contents, and rev2_validation/README.md provides a figure/table → file reproduction crosswalk and data dictionary. Contents include: Curated training set: 41,487 Lepeophtheirus salmonis copepodid shadowgraph images (positive class) and 44,601 ambient-plankton / hard-negative images (negative class). Trained ConvNeXt binary classifier (architecture and weights). Full C++/Python acquisition, segmentation, inference, metadata-enrichment and compression pipeline. A random 100,001-ROI Austevoll June 2025 field subset, and all 192 high-probability candidate ROIs (64 June + 128 October). A 36,179-ROI Flødevigen catalogue with the PaCMAP atlas builder and the iterative cosine-similarity annotation tool. REV2 additions: the blind four-class annotation tool and the detector gate-recall harness; per-annotator votes, provenance and derived statistics; the degraded positive-control stimulus sets; the source data, figures and analysis scripts behind Figures S1–S7 and Tables S1–S8; and the annotation decision codebook. Licences: data, ROI archives, images, figures, metadata and model weights are released under CC-BY-4.0; source code under the MIT licence. Annotator identities in the blind validation are replaced throughout by expertise roles; the role-to-identity key is held privately by the corresponding author.
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Mats Huserbräten (2026) studied this question.
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