Many of the challenges with human-environment data are exacerbated by critical methodological shortcomings. Many of the core tasks of data science, such as data manipulation, machine learning, or artificial intelligence, are made more difficult by the complex nature of human-environment data. Data science and cognate fields are the loci of exciting research in addressing these methodological challenges. Information science on data lifecycles is being adapted to the needs of data science, including research in the methods of metadata, ontologies, and data provenance. Computer science and related fields adapt approaches like parallelism and distributed computing to work with big human-environment data. There is also ongoing work in cloud computing and high-performance computing to address the needs of complex spatiotemporal data sets. The private and public sectors invest heavily in smart computing, embedded processing, and the Internet of Things (IoT). An extraordinary amount of effort is dedicated to sharing data and workflows to support reproducibility in science. Finally, data science has advanced how it handles data that capture spatial and temporal patterns and processes.