Paleoinformatics

Paleoinformatics is an interdisciplinary field that applies informatics, data science, and computational methods to paleontological and related paleobiological data. It aims to integrate heterogeneous data types—fossil occurrences, stratigraphic information, morphological character matrices, and paleoenvironmental records—so that researchers can store, query, analyze, and share information at scale. The field supports the digitization of collections, the creation of standardized data schemas, and the development of tools for data mining, visualization, and statistical inference.

Key data types include fossil occurrence data (taxon, locality, age), stratigraphic and geochronological data, morphological matrices used in phylogenetics, and high-resolution 3D models from imaging techniques such as CT scanning. Computational methods encompass database design, geographic information systems, phylogenetic inference, morphometrics, machine learning, and Bayesian and frequentist statistical analyses. Reproducibility and open data are guiding principles, with repositories and standards that enable interoperability. Applications range from reconstructing diversification dynamics and biogeographic patterns to estimating body size evolution, functional morphology, and responses to past climate change.

Prominent resources include large-scale databases of fossil occurrences, such as the Paleobiology Database, which aggregate data from numerous institutions and publications. Data standards and formats—Darwin Core, NeXML, and various ontologies—facilitate interoperability. The field emerged from the digitization of paleontological records and the increasing availability of computational tools in the late 20th and early 21st centuries. Ongoing challenges include incomplete sampling, taxonomic and geographic biases, and the need for robust data curation, metadata, and documentation to ensure long-term usability.