# LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping

arXiv: https://arxiv.org/abs/2502.20139 · HTML: https://mlsanity.com/paper/2502.20139/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 73 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [TUDataset: A collection of benchmark datasets for learning with graphs](https://mlsanity.com/paper/2007.08663/) (2020)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)](https://mlsanity.com/paper/2003.12206/) (2020)
- [When Do Neural Nets Outperform Boosted Trees on Tabular Data?](https://mlsanity.com/paper/2305.02997/) (2023)
- [Why do tree-based models still outperform deep learning on tabular data?](https://mlsanity.com/paper/2207.08815/) (2022)
- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [Leakage and the Reproducibility Crisis in ML-based Science](https://mlsanity.com/paper/2207.07048/) (2022)
- [Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results](https://mlsanity.com/paper/2106.02447/) (2021)
- [A Comprehensive Benchmark of Machine and Deep Learning Across Diverse Tabular Datasets](https://mlsanity.com/paper/2408.14817/) (2024)
- [An Efficient Model-Agnostic Approach for Uncertainty Estimation in Data-Restricted Pedometric Applications](https://mlsanity.com/paper/2409.11985/) (2024)

## Built on by (most cited first, 3 of 3)

- [Modern Neural Networks for Small Tabular Datasets: The New Default for Field-Scale Digital Soil Mapping?](https://mlsanity.com/paper/2508.09888/) (2025)
- [Kriging prior Regression: A Case for Kriging-Based Spatial Features with TabPFN in Soil Mapping](https://mlsanity.com/paper/2509.09408/) (2025)
- [Rejections Based on Predictive Uncertainty Enable Reliable Routine Soil Spectroscopy](https://mlsanity.com/paper/2606.21179/) (2026)

## Abstract (arXiv)

Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studies usually rely on a single dataset with restricted access, leading to incomplete and potentially misleading conclusions. To address these issues, we introduce an open-access dataset collection called Precision Liming Soil Datasets (LimeSoDa). LimeSoDa consists of 31 field- and farm-scale datasets from various countries. Each dataset has three target soil properties: (1) soil organic matter or soil organic carbon, (2) clay content and (3) pH, alongside a set of features. Features are dataset-specific and were obtained by optical spectroscopy, proximal- and remote soil sensing. All datasets were aligned to a tabular format and are ready-to-use for modeling. We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. This comparison included multiple linear regression (MLR), support vector regression (SVR), categorical boosting (CatBoost) and random forest (RF). The results showed that although no single algorithm was universally superior, certain algorithms performed better in specific contexts. MLR and SVR performed better on high-dimensional spectral datasets, likely due to better compatibility with principal components. In contrast, CatBoost and RF exhibited considerably better performances when applied to datasets with a moderate number (< 20) of features. These benchmarking results illustrate that the performance of a method is highly context-dependent. LimeSoDa therefore provides an important resource for improving the development and evaluation of statistical methods in DSM.