Omkar2118/datasets huggingface dataset:
Roman Republican Coin Dataset

Based on Crawford’s work, we collect the most diverse and extensive image dataset of the reverse sides. For most of the Roman Republic coin classes, the obverse side depicts more discriminative information than the observe side. Our dataset has 228 motif classes, including 100 classes that are the main classes for training and testing, which we call the main dataset RRCD-Main. The images of the additional 128 classes constitute the disjoint test set, RRCD-Disjoint, which we allocate to assess the generalization ability of our models. Therefore, the training and testing can be evaluated on completely disjoint datasets. To the best of our knowledge, RRCD is the most diverse dataset proposed while it is
the largest dataset of the Roman Republican coins
Malnutrition data
Full name: malnutrition data from UN

The malnutrition data, from the United Nations Children's Fund  data warehouse, include two variables, stunted growth and the prevalence of low birth weight, collected in 77 countries from 1985 to 2019. Stunted growth is defined as the proportion of newborns aging from 0 to 59 months with a low height-for-age measurement (below two standard deviations). The stunted growth data represent a point sparseness case with 4-23 recordings per nation. The low birth weight data are a partial sparseness case, with recordings during 2000-2015 only.

It can be used in the functional data analysis and the sparse functional data fitting.

Introducing paper: Sparse Functional Boxplots for Multivariate Curves