{
    "general_2d": {
        "tags": ["cells, nuclei, bacteria, epithelial, yeast, particles"],
        "dimensionality": [2],
        "description": "For tracking fluorescent nuclei, bacteria (PhC), whole cells (BF, PhC, DIC), epithelial cells with fluorescent membrane, budding yeast cells (PhC), fluorescent particles, .",
        "url": "https://github.com/weigertlab/trackastra-models/releases/download/v0.3.0/general_2d.zip",
        "datasets": {
            "Subset of Cell Tracking Challenge 2d datasets": {
                "url": "https://celltrackingchallenge.net/2d-datasets/",
                "reference": "Maška M, Ulman V, Delgado-Rodriguez P, Gómez-de-Mariscal E, Nečasová T, Guerrero Peña FA, Ren TI, Meyerowitz EM, Scherr T, Löffler K, Mikut R. The Cell Tracking Challenge: 10 years of objective benchmarking. Nature Methods. 2023 Jul;20(7):1010-20."
            },
            "Bacteria van Vliet": {
                "url": "https://zenodo.org/records/268921", 
                "reference": "van Vliet S, Winkler AR, Spriewald S, Stecher B, Ackermann M. Spatially correlated gene expression in bacterial groups: the role of lineage history, spatial gradients, and cell-cell interactions. Cell systems. 2018 Apr 25;6(4):496-507."
            },
            "Bacteria ObiWan-Microbi": {
                "url": "https://zenodo.org/records/7260137",
                "reference": "Seiffarth J, Scherr T, Wollenhaupt B, Neumann O, Scharr H, Kohlheyer D, Mikut R, Nöh K. ObiWan-Microbi: OMERO-based integrated workflow for annotating microbes in the cloud. SoftwareX. 2024 May 1;26:101638."
            },
            "Bacteria Persat": {
                "url": "https://www.p-lab.science",
                "reference": "Datasets kindly provided by Persat lab, EPFL."
            },
            "DeepCell": {
                "url": "https://datasets.deepcell.org/data",
                "reference": "Schwartz, M, Moen E, Miller G, Dougherty T, Borba E, Ding R, Graf W, Pao E, Van Valen D. Caliban: Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning. Biorxiv. 2023 Sept 13:803205."
            },
            "Ker phase contrast": {
                "url": "https://osf.io/ysaq2/",
                "reference": "Ker DF, Eom S, Sanami S, Bise R, Pascale C, Yin Z, Huh SI, Osuna-Highley E, Junkers SN, Helfrich CJ, Liang PY. Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations. Scientific data. 2018 Nov 13;5(1):1-2."
            },
            "Epithelia benchmark": {
                "reference": "Funke J, Mais L, Champion A, Dye N, Kainmueller D. A benchmark for epithelial cell tracking. InProceedings of The European Conference on Computer Vision (ECCV) Workshops 2018 (pp. 0-0)."
            },
            "T Cells": {
                "url": "https://zenodo.org/records/5206119"
            },
            "Neisseria meningitidis bacterial growth": {
                "url": "https://zenodo.org/records/5419619"
            },
            "Synthetic nuclei": {
                "reference": "Weigert group live cell simulator."
            },
            "Synthetic particles": {
                "reference": "Weigert group particle simulator."
            },
            "Particle Tracking Challenge": {
                "url": "http://bioimageanalysis.org/track/#data",
                "reference": "Chenouard, N., Smal, I., De Chaumont, F., Maška, M., Sbalzarini, I. F., Gong, Y., ... & Meijering, E. (2014). Objective comparison of particle tracking methods. Nature methods, 11(3), 281-289."
            },
            "Yeast Cell-ACDC": {
                "url": "https://zenodo.org/records/6795124",
                "reference": "Padovani, F., Mairhörmann, B., Falter-Braun, P., Lengefeld, J., & Schmoller, K. M. (2022). Segmentation, tracking and cell cycle analysis of live-cell imaging data with Cell-ACDC. BMC biology, 20(1), 174."
            },
            "DeepSea": {
                "url": "https://deepseas.org/datasets/",
                "reference": "Zargari, A., Lodewijk, G. A., Mashhadi, N., Cook, N., Neudorf, C. W., Araghbidikashani, K., ... & Shariati, S. A. (2023). DeepSea is an efficient deep-learning model for single-cell segmentation and tracking in time-lapse microscopy. Cell Reports Methods, 3(6)."
            },
            "Btrack" : {
                "url": "https://rdr.ucl.ac.uk/articles/dataset/Cell_tracking_reference_dataset/16595978",
                "reference": "Ulicna, K., Vallardi, G., Charras, G., & Lowe, A. R. (2021). Automated deep lineage tree analysis using a Bayesian single cell tracking approach. Frontiers in Computer Science, 3, 734559."
            },
            "E. coli in mother machine": {
                "url": "https://zenodo.org/records/11237127",
                "reference": "O’Connor, O. M., & Dunlop, M. J. (2024). Cell-TRACTR: A transformer-based model for end-to-end segmentation and tracking of cells. bioRxiv, 2024-07."
            }
        }
    },
    "ctc": {
        "tags": ["ctc", "Cell Tracking Challenge", "Cell Linking Benchmark"],
        "dimensionality": [2, 3],
        "description": "For tracking Cell Tracking Challenge datasets. This is the successor of the winning model of the ISBI 2024 CTC generalizable linking challenge.",
        "url": "https://github.com/weigertlab/trackastra-models/releases/download/v0.3.0/ctc.zip",
        "datasets": {
            "All Cell Tracking Challenge 2d+3d datasets with available GT and ERR_SEG": {
                "url": "https://celltrackingchallenge.net/3d-datasets/",
                "reference": "Maška M, Ulman V, Delgado-Rodriguez P, Gómez-de-Mariscal E, Nečasová T, Guerrero Peña FA, Ren TI, Meyerowitz EM, Scherr T, Löffler K, Mikut R. The Cell Tracking Challenge: 10 years of objective benchmarking. Nature Methods. 2023 Jul;20(7):1010-20."
            }
        }
    }
}