--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: dataset dtype: string - name: original_label dtype: string - name: original_path dtype: string - name: Kingdom dtype: string - name: Phylum dtype: string - name: Class dtype: string - name: Order dtype: string - name: Family dtype: string - name: Genus dtype: string - name: Species dtype: string - name: proposed_label dtype: string - name: plankton dtype: bool - name: root_class dtype: string - name: qualifier dtype: string - name: Latitude dtype: float32 - name: Humidity dtype: float32 - name: Temperature dtype: float32 - name: Longitude dtype: float32 - name: ObjID dtype: string - name: Depth_max dtype: float32 - name: Depth_min dtype: float32 - name: wikidata_ID dtype: string - name: ecotaxa_ID dtype: string - name: aphia_ID dtype: string - name: NCBI_ID dtype: string - name: BOLD_ID dtype: string - name: timestamp dtype: string splits: - name: train num_bytes: 94132231389 num_examples: 17404047 download_size: 94132231389 dataset_size: 94132231389 license: cc-by-4.0 task_categories: - image-to-text - image-classification - image-text-to-text - image-text-to-image language: - en tags: - plankton - ocean - plankton classification - climate pretty_name: plantonzilla-17m size_categories: - 10M300 μm), collected from 2008-2025 across global oceans, coastal regions, and freshwater environments. ### Notes * Part of [Inria Challenge OcéanIA 🌊](https://oceania.inria.cl). * Models trained on `planktonzilla-17M`: . * Code for dataset and models: . * Contribute: ## Citation Please cite as: > A. G. Contreras Montanares, L. Valenzuela, L. Martí, and N. Sanchez‑Pi, **Planktonzilla: Multimodal dataset and models for understanding plankton ecosystems,** Inria Chile Research Center, Tech. Rep., May 2026, doi: [10.48550/arXiv.2606.00080](https://doi.org/10.48550/arXiv.2606.00080), arXiv: 2606.00080 [cs.CV]. url: ```bibtex @techreport{contrerasmontanares:hal-05621003, title = {Planktonzilla: {M}ultimodal dataset and models for understanding plankton ecosystems}, author = {Contreras Montanares, Alan Gerson and Valenzuela, Luis and Mart{\'i}, Luis and Sanchez-Pi, Nayat}, year = 2026, month = {May}, keywords = {Explainable AI; XAI ; Plankton Classification ; CLIPS ; Multimodal Classification}, eprinttype = {arxiv}, hal_id = {hal-05621003}, hal_version = {v1}, eprint = {2606.00080}, archivePrefix = {arXiv}, primaryClass = {cs.CV}, url = {https://arxiv.org/abs/2606.00080}, doi = {10.48550/arXiv.2606.00080}, institution = {Inria Chile Research Center}, } ``` ## Dataset Structure `planktonzilla-17M` aggregates data from the following imaging systems and oceanographic programs: | Dataset | Imaging System | Images | Classes | License | Source |---------|---|---|---|---|---| | **Global UVP5Net** [1]| UVP5 | 7.41M | 254 | CC-BY-NC-4.0 | [SEANOE](https://www.seanoe.org/data/00964/107583/) | | **WHOI-Plankton** [2]| Microscopy | 3.56M | 103 | MIT | [GitHub](https://github.com/hsosik/WHOI-Plankton) | | **JEDI System/OCEANS_CPICS** [3]| Observatory System | 1.92M | 95 | CC-BY-SA-4.0 | [JAMSTEC](https://dbarchive.biosciencedbc.jp/data/jedisystem-oceansdb/LATEST/README_e.html#Sec3) | | **ZooScanNet** [4] | ZooScan | 1.45M | 120 | CC-BY-NC-4.0 | [SEANOE](https://www.seanoe.org/data/00446/55741/) | | **ZooCAMNet** [5]| ZooCAM | 1.29M | 93 | CC-BY-NC-4.0 | [SEANOE](https://www.seanoe.org/data/00907/101928/) | | **UVP6Net** [6]| UVP6 | 634K | 54 | CC-BY-NC-4.0 | [SEANOE](https://www.seanoe.org/data/00908/101948/) | | **ISIISNet** [7]| ISIIS | 408K | 32 | CC-BY-NC-4.0 | [SEANOE](https://www.seanoe.org/data/00908/101950/) | | **PlanktoScope** [8] | PlanktoScope | 180K | 263 | CC-BY-NC-4.0 | [SEANOE](https://www.seanoe.org/data/00989/110078/) | | **FlowCAMNet** [9]| FlowCAM | 141K | 38 | CC-BY-NC-4.0 | [SEANOE](https://www.seanoe.org/data/00908/101961/) | | **MedPlanktonSet** [10]| IFCB | 77.3K | 139 | CC-BY-NC-4.0 | [Zenodo](https://zenodo.org/records/15471023) | | **SYKE-IFCB 2022** [11]| IFCB | 63.1K | 50 | CC-BY-NC-4.0 | [B2SHARE](https://b2share.eudat.eu/records/abf913e5a6ad47e6baa273ae0ed6617a) | | **PlanktonSet 1.0** [12]| ISIIS-2 | 60.7K | 121 | CC-BY-NC-4.0 | [NOAA NCEI](https://www.ncei.noaa.gov/archive/archive-management-system/OAS/bin/prd/jquery/accession/download/127422) | | **SYKE-ZooScan 2024** [13]| ZooScan | 22.8K | 20 | CC-BY-NC-4.0 | [FairData](https://etsin.fairdata.fi/dataset/6fa42787-9772-41a5-a6fc-0dde489ed908) | | **ZooLake** [14]| Dual Scripps Camera | 17.9K | 35 | CC-BY-4.0 | [EAWAG OpenData](https://opendata.eawag.ch/dataset/52b6ba86-5ecb-448c-8c01-eec7cb209dc7) | | **Lensless** [15]| Lensless Microscope | 6.4K | 10 | CC-BY-4.0 | [IBM Box](https://ibm.ent.box.com/v/PlanktonData) |
pie chart of planktonzilla-17m source datasets
Figure 1. Sample contribution by dataset.
### Dataset Features Each sample (row) in the dataset contains the following features: | Feature | Type | Description | |---------|------|-------------| | **image** | image | PIL Image object in RGB format with variable resolution | | **dataset** | string | Source dataset identifier (e.g., "flowcamnet", "isiisnet", "whoi-plankton") | | **original_label** | string | Class label from the original dataset | | **original_path** | string | Original file path in source dataset | | **Kingdom** | string | Taxonomic Kingdom classification | | **Phylum** | string | Taxonomic Phylum classification | | **Class** | string | Taxonomic Class classification | | **Order** | string | Taxonomic Order classification | | **Family** | string | Taxonomic Family classification | | **Genus** | string | Taxonomic Genus classification | | **Species** | string | Taxonomic Species classification | | **proposed_label** | string | Harmonized label across datasets | | **plankton** | bool | Boolean indicating if the object is plankton | | **root_class** | string | High-level image category (e.g., living, detritus, inert, artifact) | | **qualifier** | string | Additional qualifier of the object (e.g., full_body, part, part_head, part_leg, part_skin, larvae, egg, etc. ) | | **Latitude** | float32 | Geographic latitude of the observation (degrees) | | **Longitude** | float32 | Geographic longitude of the observation (degrees) | | **Depth_min** | float32 | Minimum sampling depth in meters | | **Depth_max** | float32 | Maximum sampling depth in meters | | **Temperature** | float32 | Water temperature at sampling location | | **Humidity** | float32 | Humidity of the sample | | **ObjID** | string | Unique object identifier from the source dataset (Ecotaxa or IFCB)| | **wikidata_ID** | string | Wikidata entity ID for the taxon (e.g., "Q3386609") | | **ecotaxa_ID** | string | EcoTaxa taxon description(s); may contain several separated by ";" | | **aphia_ID** | string | WoRMS AphiaID for the taxon | | **NCBI_ID** | string | NCBI Taxonomy ID for the taxon | | **BOLD_ID** | string | BOLD (Barcode of Life) ID for the taxon | | **timestamp** | string | Acquisition date (YYYY-MM-DD) from WHOI/EcoTaxa | ### Taxonomic Standardization (WoRMS) To ensure consistency across the diverse source datasets, we implemented a rigorous manual standardization process using the World Register of Marine Species (WoRMS). Instead of redefining labels, we relied on the original class names provided by annotators in each source dataset and manually matched each of these labels to their corresponding taxonomic entry in WoRMS. Before matching, the original labels were lightly curated to correct typographical errors and normalize naming variations (e.g., synonyms or inconsistent formatting). Then, for each processed label, the closest valid WoRMS record was identified, and its taxonomic lineage was used to populate the dataset fields (Kingdom to Species). ### Geographic Coverage We have added the sampling location when it is made available in the source dataset.
sample location per source dataset
Figure 2. Location of samples provided by each dataset.
The following datasets did not provide an explicit geolocation of the samples. We have recovered that information manually, but have abstained from incorporating it to the dataset. | Dataset | Inferred site | Lat, Lon | Confidence | | --- | --- | --- | --- | | `zoocamnet` | Bay of Biscay shelf — PELGAS, R/V *Thalassa* | 45.5, −2.5 | high | | `isiisnet` | Ligurian Sea off Nice — VISUFRONT | 43.5, 7.55 | high | | `medplanktonset` | Gulf of Naples — LTER-MareChiara (SZN) | 40.82, 14.25 | high | | `syke_ifcb_2022` | Baltic Sea — Utö station + ferrybox transects | 59.78, 21.37 | high | | `planktonset1.0` | Straits of Florida — R/V *F.G. Walton Smith*, ISIIS-2 | 25.15, −80.55 | high | | `zoolake` | **Lake Greifensee**, Switzerland — Eawag DSPC | 47.35, 8.68 | high | | `sykezooscan2024` | Baltic Sea — sub-basin unspecified (inferred) | ~59.5, 21.4 | low | | `planktoscope` | *diffuse / global* multi-campaign reference set | — | n/a | | `lensless` | *lab/cultured* — purchased culture, no field site | — | n/a | ### Temporal Coverage Data collection spans from 2008 to 2025 across various oceanographic programs. ## How to Use ### Loading with Hugging Face Datasets ```python from datasets import load_dataset # Load the dataset dataset = load_dataset("project-oceania/planktonzilla-17M") # Access a sample sample = dataset["train"][0] image = sample["image"] species = sample["Species"] source = sample["dataset"] ``` --- ## Acknowledgments We gratefully acknowledge all researchers and institutions who contributed the individual datasets that comprise Planktonzilla-17M. This dataset represents the collaborative effort of the global oceanographic research community. We thank all scientists, taxonomists, and data curators who made their datasets publicly available to advance marine science and plankton research. We also encourage continued community contributions to help complete and refine the remaining taxonomic annotations ## References 1. A. C. Nocera, L. Stemmann, M. Babin, T. Biard, J. Coustenoble, F. Carlotti, L. Coppola, L. Courchet, L. Drago, A. Elineau, L. Guidi, H. Hauss, L. Jalabert, L. Karp‑Boss, R. Kiko, M. Laget, F. Lombard, A. McDonnell, C. Merland, S. Motreuil, T. Panaı̈otis, M. Picheral, A. Rogge, A. Waite, and J.‑O. Irisson, “A global consistent database of plankton and detritus from in situ imaging by the underwater vision profiler 5,” Earth System Science Data Discussions, vol. 2025, pp. 1–37, 2025. DOi: 10.5194/essd-2025-522. url: https://essd.copernicus.org/preprints/essd-2025-522/ 2. Orenstein, E. C., Beijbom, O., Peacock, E. 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