We are publishing articles that use several different datasets acquired from user (clinicians, physicians and radiologists) analysis through interaction to our healthcare systems [1, 2, 3]. We would like to share them so that other researchers can use the same data for post-process analysis and verification.
Our work is a relation between both Human-Computer Interaction (HCI) and Health Informatics (HI) fields by study human behaviour, as a user of an AI-Assisted system for breast cancer diagnosis. From here, we measure several attributes, with several scales (e.g., NASA-TLX, SUS, Time, Number of Clicks, Number of Errors and Qualitative Analysis). It seems to us really interesting to not only publishing around our work but also to provide the information to other researchers.
Our question is as follows:
What strategies are available to share our dataset?
In a near future, we would like to take advantage of Google Dataset Search. However, our idea will be to publish the dataset on Kaggle and then publish an arXiv document to link the dataset. By doing this, we will not only promote the dataset for the scientific community but also have the chance of being cited by.
 Francisco M. Calisto, Alfredo Ferreira, Jacinto C. Nascimento, and Daniel Gonçalves. 2017. Towards Touch-Based Medical Image Diagnosis Annotation. In Proceedings of the 2017 ACM International Conference on Interactive Surfaces and Spaces (ISS '17). ACM, New York, NY, USA, 390-395. DOI: https://doi.org/10.1145/3132272.3134111
 Maicas, Gabriel, Gustavo Carneiro, Andrew P. Bradley, Jacinto C. Nascimento, and Ian Reid. "Deep reinforcement learning for active breast lesion detection from dce-mri." In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 665-673. Springer, Cham, 2017.
 Tiago Dias, Helder Araujo, and Pedro Miraldo. 2016. 3D Reconstruction with Low Resolution, Small Baseline and High Radial Distortion Stereo Images. In Proceedings of the 10th International Conference on Distributed Smart Camera (ICDSC '16). ACM, New York, NY, USA, 98-103. DOI: https://doi.org/10.1145/2967413.2967435