Published March 19, 2019 | Version v1
Dataset Open

BrainIAK Tutorials: Condensed Datasets

Contributors

Data manager:

  • 1. Princeton University

Description

This is a collection of datasets used by BrainIAK tutorials. These datasets are pre-processed and ready to use. They have been condensed, by reducing the number of subjects from the original studies, to keep the file size small. Each tutorial is paired with a dataset as listed below. The file brainiak_datasets.zip contains the data for all the tutorials, and in unzipped form uses 18GB of space. If you wish to download data for specific tutorials, use the list below to find the correct dataset to download and use.

Tutorial 2:  VDC (Kim et al., 2017) and 02-data-handling (this is a simulated dataset)

Tutorials 3-5: VDC  (Kim et al., 2017)

Tutorial 6: Ninety Six (Kriegeskorte et al., 2008)

Tutorials 7: Face-scene (Turk-Browne et al., 2012). The script for within subject searchlight uses the VDC dataset.

Tutorial 9: Face-scene (Turk-Browne et al., 2012)

Tutorial 8: Latatt (Hutchinson et al., 2016)

Tutorial 10: Pieman2 (Simony et al., 2016)

Tutorial 11: Raider (Haxby et al., 2011) and Pieman2 (Simony et al., 2016)

Tutorial 12: Sherlock_processed (Chen et al., 2017)

Files

02-data-handling-simulated-dataset.zip

Files (19.7 GB)

Name Size Download all
md5:695434febd6a7aae4766c1eb43d6d48f
7.6 kB Preview Download
md5:8b7daeaa81af564ecf66bbc8cd4471d2
9.9 GB Preview Download
md5:ad9aa83fe95f6dad5e3d15b622b4ecdb
268.4 MB Preview Download
md5:6ddf4e6a575fa41bd52d7b9e05c6c799
612.9 MB Preview Download
md5:d32211d5e745674ada5dfc3e85c10109
153.6 MB Preview Download
md5:b77b3b75b6f99631c6c7beeb16042ea1
2.8 GB Preview Download
md5:7ff112140ca0fa74d07da5284814db9b
32.7 MB Preview Download
md5:1a0a8c9f62625c914edfedad9f095a69
268.3 MB Preview Download
md5:f32c5e5271e04ec019c0ae34188c2e00
5.7 GB Preview Download

Additional details

References

  • Kim, G., Norman, K. A., & Turk-Browne, N. B. (2017). Neural Differentiation of Incorrectly Predicted Memories. The Journal of Neuroscience, 37(8), 2022-2031. doi:10.1523/jneurosci.3272-16.2017
  • Kriegeskorte, N., Mur, M., Ruff, D. A., Kiani, R., Bodurka, J., Esteky, H., … Bandettini, P. A. (2008). Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey. Neuron, 60(6), 1126–1141. https://doi.org/10.1016/j.neuron.2008.10.043
  • Turk-Browne, N. B., Simon, M. G., & Sederberg, P. B. (2012). Scene Representations in Parahippocampal Cortex Depend on Temporal Context. Journal of Neuroscience, 32(21), 7202–7207. https://doi.org/10.1523/JNEUROSCI.0942-12.2012
  • Hutchinson, J. B., Pak, S. S., & Turk-Browne, N. B. (2016). Biased Competition during Long-term Memory Formation. Journal of Cognitive Neuroscience, 28(1), 187–197. https://doi.org/10.1162/jocn_a_00889
  • Haxby, J. V., Guntupalli, J. S., Connolly, A. C., Halchenko, Y. O., Conroy, B. R., Gobbini, M. I., … Ramadge, P. J. (2011). A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron, 72(2), 404–416. https://doi.org/10.1016/j.neuron.2011.08.026
  • Chen, J., Leong, Y. C., Honey, C. J., Yong, C. H., Norman, K. A., & Hasson, U. (2017). Shared memories reveal shared structure in neural activity across individuals. Nature Neuroscience, 20(1), 115–125. https://doi.org/10.1038/nn.4450
  • Simony, E., Honey, C. J., Chen, J., Lositsky, O., Yeshurun, Y., Wiesel, A., & Hasson, U. (2016). Dynamic reconfiguration of the default mode network during narrative comprehension. Nature Communications, 7, 12141. https://doi.org/10.1038/ncomms12141