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Part 1: Document Description
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Citation |
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Title: |
Low-Cost Approaches in Neuroscience to Teach Machine Learning Using a Cockroach Model |
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Identification Number: |
doi:10.48349/ASU/W2E1YE |
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Distributor: |
ASU Library Research Data Repository |
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Date of Distribution: |
2024-06-13 |
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Version: |
1 |
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Bibliographic Citation: |
Verpeut, Jessica; Truong, Vincent; Moore, John E.; Ricoy, Ulises M., 2024, "Low-Cost Approaches in Neuroscience to Teach Machine Learning Using a Cockroach Model", https://doi.org/10.48349/ASU/W2E1YE, ASU Library Research Data Repository, V1 |
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Citation |
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Title: |
Low-Cost Approaches in Neuroscience to Teach Machine Learning Using a Cockroach Model |
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Alternative Title: |
SLEAP: Low-cost neuroscience approaches |
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Identification Number: |
doi:10.48349/ASU/W2E1YE |
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Authoring Entity: |
Verpeut, Jessica (Arizona State University) |
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Truong, Vincent (Arizona State University) |
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Moore, John E. (University of Arizona) |
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Ricoy, Ulises M. (University of Arizona) |
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Other identifications and acknowledgements: |
The O'Dell Lab at University of Texas El Paso |
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Other identifications and acknowledgements: |
O'Dell, Laura |
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Other identifications and acknowledgements: |
Esparza, David |
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Producer: |
SOCIAL Neurobiology Lab |
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Software used in Production: |
Python |
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Distributor: |
ASU Library Research Data Repository |
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Access Authority: |
Verpeut, Jessica |
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Access Authority: |
Truong, Vincent |
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Depositor: |
Verpeut, Jessica |
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Date of Deposit: |
2024-05-04 |
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Holdings Information: |
https://doi.org/10.48349/ASU/W2E1YE |
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Study Scope |
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Keywords: |
Medicine, Health and Life Sciences, Machine learning, Neurosciences, Teaching |
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Abstract: |
Dataset of manuscript, SLEAPing with cockroaches: low-cost approaches to teaching machine learning in neuroscience. |
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Date of Collection: |
2022-07-01-2022-07-20 |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Studies |
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SLEAP program used to create prediction files <a href="https://sleap.ai/">https://sleap.ai/</a> <br> T.D. Pereira, N. Tabris, A. Matsliah, D. M. Turner, J. Li, S. Ravindranath, E. S. Papadoyannis, E. Normand, D. S. Deutsch, Z. Y. Wang, G. C. McKenzie-Smith, C. C. Mitelut, M. D. Castro, J. D’Uva, M. Kislin, D. H. Sanes, S. D. Kocher, S. S-H, A. L. Falkner, J. W. Shaevitz, and M. Murthy. Sleap: A deep learning system for multi-animal pose tracking. Nature Methods, 19(4), 2022 |
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Label: |
centered_instance_best_model.h5 |
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Notes: |
application/x-h5 |
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Label: |
centered_instance_training_config.json |
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Notes: |
application/json |
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Label: |
centroid_best_model.h5 |
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Notes: |
application/x-h5 |
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Label: |
centroid_training_config.json |
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Notes: |
application/json |
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Label: |
Cockroach D2.slp |
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Text: |
SLEAP slp file used for training. |
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Notes: |
application/octet-stream |
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Label: |
Cockroach_skeleton.json |
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Text: |
SLEAP skeleton used for machine learning training. |
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Notes: |
application/json |
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Label: |
D2_0_2.mp4 |
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Text: |
Video file used for training. |
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Notes: |
video/mp4 |
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Label: |
README.txt |
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Text: |
README text file. |
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Notes: |
text/plain |
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Label: |
Roach Data using SLEAP.xls |
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Text: |
Raw data with all SLEAP X- Y- coordinates. |
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Notes: |
application/vnd.ms-excel |