Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 221
Number of items: 221
  • Open Access
    Chittar, C. R., Jang, H., Samuni, L., Lewis, J., Honing, H., van Loon, E. E., & Janmaat, K. R. L. (2023). Music production and its role in coalition signaling during foraging contexts in a hunter-gatherer society. Frontiers in Psychology, 14, Article 1218394. https://doi.org/10.3389/fpsyg.2023.1218394
  • Open Access
    van Erp, J., Sage, E., Bouten, W., van Loon, E., Camphuysen, K. C. J., & Shamoun-Baranes, J. (2023). Thermal soaring over the North Sea and implications for wind farm interactions. Marine Ecology Progress Series, 723, 185-200. https://doi.org/10.3354/meps14315
  • Open Access
    Linssen, H., van Loon, E. E., Shamoun-Baranes, J. Z., Nuijten, R. J. M., & Nolet, B. A. (2023). Migratory swans individually adjust their autumn migration and winter range to a warming climate. Global Change Biology, 29(24), 6888-6899. https://doi.org/10.1111/gcb.16953
  • Open Access
    Schaller, C., Ginzler, C., van Loon, E., Moos, C., Seijmonsbergen, A. C., & Dorren, L. (2023). Improving country-wide individual tree detection using local maxima methods based on statistically modeled forest structure information. International Journal of Applied Earth Observation and Geoinformation, 123, Article 103480. https://doi.org/10.1016/j.jag.2023.103480
  • Open Access
    Li, Q., Steenberg Larsen, K., Kopittke, G., van Loon, E., & Tietema, A. (2023). Long-term temporal patterns in ecosystem carbon flux components and overall balance in a heathland ecosystem. Science of the Total Environment, 875, Article 162658. https://doi.org/10.1016/j.scitotenv.2023.162658
  • Kalb, S., Rijsdijk, K., De Groeve, J., Denkers, L. M., van Loon, E. E., & Foufopoulos, J. (2023, September 18). Reproduction Package Kalb et al. 2023 [Data set]. Universiteit van Amsterdam. https://doi.org/10.21942/uva.23284787.v3
  • Lippert, F., Kranstauber, B., Forré, P., & van Loon, E. E. (2022). Data from: Learning to predict spatio-temporal movement dynamics from weather radar networks [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6874789
  • Lippert, F., Kranstauber, B., Forré, P., & van Loon, E. E. (2022). Data from: Learning to predict spatio-temporal movement dynamics from static sensor networks [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6364941
  • Open Access
    Lippert, F., Kranstauber, B., Forré, P. D., & van Loon, E. E. (2022). Learning to predict spatiotemporal movement dynamics from weather radar networks. Methods in Ecology and Evolution, 13(12), 2811-2826. https://doi.org/10.1111/2041-210X.14007
  • Open Access
    Lippert, F., Kranstauber, B., van Loon, E. E., & Forré, P. (2022). Physics-informed inference of aerial animal movements from weather radar data. Paper presented at Workshop AI for Science: Progress and Promises, New Orleans, Louisiana, United States. https://doi.org/10.48550/arXiv.2211.04539
Page 5 of 23