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| license: cc-by-4.0 | |
| pretty_name: ca1-online-decoding task data | |
| tags: | |
| - neuroscience | |
| - calcium-imaging | |
| - two-photon | |
| - hippocampus | |
| - place-cells | |
| - terminal-bench-science | |
| # ca1-online-decoding | |
| Data for the terminal-bench-science task `ca1-online-decoding`: decode a mouse's position in an | |
| open field from a stream of raw two-photon calcium imaging of hippocampal CA1, one frame at a time. | |
| This card is the only place the provenance is written down; the task deliberately gives the agent | |
| no acquisition metadata beyond the frame rate, the pixel scales and the plane alternation stated in | |
| its instruction. | |
| ## Source | |
| Zong, W., Obenhaus, H. A., Skytoen, E. R., et al. (2022). Large-scale two-photon calcium imaging in | |
| freely moving mice. Cell 185(6), 1240-1256. Deposited in the NIRD Research Data Archive, | |
| DOI 10.11582/2022.00008, licence CC BY 4.0. One session of one mouse imaged in CA1 with the MINI2P | |
| miniature two-photon microscope while foraging in a square open field: mouse 97289, session | |
| 2021-03-22, the deposit's `ca1-97289` tarball, ScanImage TIFFs `97289-20210322_00001.tif` to | |
| `_00003.tif` (8700 pages each) and their DeepLabCut tracking files. | |
| ## What was done | |
| The three TIFFs were read in order (26,100 pages, int16, 256 x 256, the focus alternating between | |
| two planes 50 micrometres apart on every page at 14.49 frames per second) and split in time only, | |
| at page 13,050, into `input/` (the agent's training segment) and `verification/` (the held-out | |
| segment the verifier streams to the agent's program and grades on). Pages are written as acquired, | |
| interleaved and unregistered, to a plain BigTIFF without the ScanImage headers. `tracking.csv` in | |
| each folder is the DeepLabCut output for the same pages, one row per page, the file's three header | |
| rows kept, the index column restarted at 0. Nothing else was changed. The task's | |
| `authoring/provenance/build_data.py` is the exact script. | |
| | file | pages / rows | sha256 | | |
| |---|---|---| | |
| | `ca1-online-decoding/input/frames.tif` | 13050 | 43a65fc72f910fbe8d2e1bc498b6b464f7fa241afd26497460c46c92c0082662 | | |
| | `ca1-online-decoding/input/tracking.csv` | 13050 | 8ca8f8753fc9687398e69cface8a8687813247b6f547304bbe0f34b9240bac27 | | |
| | `ca1-online-decoding/verification/frames.tif` | 13050 | 687bf07ec619e3234d2450bdac21d420245fa9b429ccd747e358dd1cbec2b12a | | |
| | `ca1-online-decoding/verification/tracking.csv` | 13050 | da9a5e8c5cf0a74d4b02c682b558b640ef0e91ba1f38b846c6a5372b3c12a122 | | |
| The camera scale, 0.08 cm per pixel, is `TrackingResolution` in the deposit's | |
| `ExperimentInformation.mat`; the imaging scale is 0.96 micrometres per pixel. | |
| The `input/` files are pulled into the agent's environment image and the `verification/` files | |
| into the verifier image only, each at a pinned commit of this repository and checked against the | |
| hashes above at build time. | |