Cataract is the leading cause of blindness worldwide, most affecting life in low- and middle-income countries (LMICs), and the mainly used, most appropriate, and most cost-effective cataract surgical technique for LMICs is small incision cataract surgery (SICS). While algorithms have been developed for automated video analysis of surgical performance parameters for the cataract surgical technique predominantly used in high-income settings, so far there were no datasets nor algorithms for SICS available [1, 2, 3]. This MICCAI challenge introduces the first SICS video dataset and offers teams the opportunity to evaluates effectiveness of their phase recognition algorithms. The dataset of 155 patients was recruited at Sankara Eye Hospital in India. Analysis of surgical phases is important because it allows for quantitative comparison between different surgeons, feedback on identified critical steps, and detection of discrepancies from surgical protocols and because it is the first step for automatic assessment of surgical quality (Sim-OSSCAR) [4]. Our contribution is the first public dataset for SICS holding surgical videos and phase annotations of 155 surgeries with 18 distinct phases. Currently, there are other public cataract surgery phase datasets like Cataract-101 (n=101 videos) or the IEEE Cataracts (n=50 videos) but they only show phacoemulsification surgery which is distinct from SICS in the following ways: SICS involves a larger 6-8 mm incision (2-3 mm for phaco), allowing for easier maneuvering of tools. The surgical phases in SICS are distinct, with steps such as Nucleus Delivery, Nucleus Prolapse, and peritomy not performed in phacoemulsification, which instead includes Trenching, Nucleus Emulsification, and Irrigation/Aspiration [5]. MSICS also utilizes specialized tools such as the Vectis, Dialer, Conjunctival Scissors, Simcoe Cannula, Cautery, and Crescent Blade, which are not used in phaco; conversely, phacoemulsification surgery uses tools like Phaco Probe, Irrigation/Aspiration Probe, and Lens Injector [6]. Still there is some overlap between SICS and phacoemulsification and teams could consider using the mentioned datasets for transfer learning strategies. Despite SICS widespread adoption in countries of the global south, no publicly available dataset exists for for this surgery, leaving a critical gap in cataract surgery research.Competitors are expected to submit a algorithm for predicting surgical phases based on the video data we supply and a short paper describing their approach. Additional details can be found below. 1. Müller S, Jain M, Sachdeva B, Shah PN, Holz FG, Finger RP, et al. Artificial Intelligence in Cataract Surgery: A Systematic Review [Internet]. Vol. 13, Translational Vision Science & Technology. Association for Research in Vision and Ophthalmology (ARVO); 2024. p. 20. Available from: http://dx.doi.org/10.1167/tvst.13.4.202. Tabin G, Chen M, Espandar L. Cataract surgery for the developing world. Curr Opin Ophthalmol. 2008;19(1):55-9.3. Sommer, A., Taylor, H. R., Ravilla, T. D., West, S., Lietman, T. M., Keenan, J. D., Chiang, M. F., Robin, A. L., Mills, R. P., Society, f. t. C. o. t. A. O. Challenges of Ophthalmic Care in the Developing World. JAMA ophthalmology 132, 640-644, doi:10.1001/jamaophthalmol.2014.84 (2014).4. Dean, W. H., Murray, N. L., Buchan, J. C., Golnik, K., Kim, M. J., Burton, M. J. Ophthalmic Simulated Surgical Competency Assessment Rubric for manual small-incision cataract surgery. J Cataract Refract Surg 45, 1252-1257, doi:10.1016/j.jcrs.2019.04.010 (2019).5. Martin Spencer. Phaco vs. small-incision. Ophthalmology, 113(2):353, 2006. 2, 36. Maria Grammatikopoulou, Evangello Flouty, Abdolrahim Kadkhodamohammadi, Gwenole Quellec, Andre Chow, Jean Nehme, Imanol Luengo, and Danail Stoyanov. Cadis: Cataract dataset for surgical rgb-image segmentation. Medical Image Analysis, 71:102053, 2021