References

This page contains the principal academic, software and data sources referenced throughout the Octopoda Biodiversity Analysis project.

Data Source

• IOC-UNESCO. (n.d.). Ocean Biodiversity Information System (OBIS).
  Global open-access marine biodiversity infrastructure used as the primary occurrence-data source for this project.

Data Science and Software

• McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference, 56–61. DOI: 10.25080/MAJORA-92BF1922-00A.

• Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.

• Seabold, S., & Perktold, J. (2010). Statsmodels: Econometric and Statistical Modeling with Python. Proceedings of the 9th Python in Science Conference.

• Microsoft. (2026). Power BI Documentation. Microsoft Learn.

Clustering and Validation

• MacQueen, J. B. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.

• Lloyd, S. P. (1982). Least Squares Quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI: 10.1109/TIT.1982.1056489.

• Rousseeuw, P. J. (1987). Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis. Journal of Computational and Applied Mathematics, 20, 53–65. DOI: 10.1016/0377-0427(87)90125-7.

Potential Future Data Sources

• National Oceanic and Atmospheric Administration (NOAA). Sea Surface Temperature climate datasets.

• Copernicus Marine Service. Marine environmental and Sea Surface Temperature datasets.