Datasets:

eulerlab
/

eyewire2-data

	# `df_eyewire2_field_level.parquet`

	Loaded via `data_loader.load_df_fields()`. One row per recording field. 5 rows x 22 columns.

	- experimenter: Name of the experimenter who conducted the recording.
	- date: Date of the experiment.
	- exp_num: Experiment number for the given date. Typically corresponds to the eye: 1 = left eye, 2 = right eye.
	- raw_id: Internal recording/table index from the acquisition pipeline; not meaningful for analysis on its own.
	- field: Field identifier ("GCL" + a number indicating the sequential order of the field within the recording session).
	- region: Anatomical region of the recording field. Values seen: "RR" (Right Retina).
	- cond1: Experimental condition identifier for the recording (e.g. "control").
	- ch0_name, ch1_name: Names of the two acquisition channels (`wDataCh0` = OGB-1/calcium indicator channel, `wDataCh1` = SR-101/structural astrocyte-marker channel).
	- genline: Genetic line of the animal (typically "Bl6").
	- animgender: Sex of the animal.
	- eye: Eye used for recording ("left" or "right").
	- setupid: Identifier of the physical 2P rig/setup used for acquisition.
	- prepwmorient: Orientation of the eye-cup preparation in degrees. 0 deg indicates dorsal is toward the back of the chamber; -1 indicates unknown orientation.
	- nxpix, nypix: Width/height of the recorded field in pixels.
	- pixel_size_um: Physical size of one pixel in micrometers.
	- field_ventral_dorsal_pos_um: Ventral-dorsal position of the field's center relative to the optic nerve, in micrometers (negative = ventral, positive = dorsal).
	- field_temporal_nasal_pos_um: Temporal-nasal position of the field's center relative to the optic nerve, in micrometers (negative = temporal, positive = nasal).
	- ch0_average, ch1_average: Average-intensity images (2D, `nxpix` x `nypix`) of the two acquisition channels for the field.
	- roi_mask: 2D integer array (`nxpix` x `nypix`) segmenting the field into ROIs. Each unique positive value identifies one ROI (matching `roi_id` in the ROI-level DataFrame); zero/negative values are background (see `plot_morph.plot_roi_mask`).

	# `df_eyewire2_field_level.parquet`

	Loaded via `data_loader.load_df_fields()`. One row per recording field. 5 rows x 22 columns.

	- experimenter: Name of the experimenter who conducted the recording.
	- date: Date of the experiment.
	- exp_num: Experiment number for the given date. Typically corresponds to the eye: 1 = left eye, 2 = right eye.
	- raw_id: Internal recording/table index from the acquisition pipeline; not meaningful for analysis on its own.
	- field: Field identifier ("GCL" + a number indicating the sequential order of the field within the recording session).
	- region: Anatomical region of the recording field. Values seen: "RR" (Right Retina).
	- cond1: Experimental condition identifier for the recording (e.g. "control").
	- ch0_name, ch1_name: Names of the two acquisition channels (`wDataCh0` = OGB-1/calcium indicator channel, `wDataCh1` = SR-101/structural astrocyte-marker channel).
	- genline: Genetic line of the animal (typically "Bl6").
	- animgender: Sex of the animal.
	- eye: Eye used for recording ("left" or "right").
	- setupid: Identifier of the physical 2P rig/setup used for acquisition.
	- prepwmorient: Orientation of the eye-cup preparation in degrees. 0 deg indicates dorsal is toward the back of the chamber; -1 indicates unknown orientation.
	- nxpix, nypix: Width/height of the recorded field in pixels.
	- pixel_size_um: Physical size of one pixel in micrometers.
	- field_ventral_dorsal_pos_um: Ventral-dorsal position of the field's center relative to the optic nerve, in micrometers (negative = ventral, positive = dorsal).
	- field_temporal_nasal_pos_um: Temporal-nasal position of the field's center relative to the optic nerve, in micrometers (negative = temporal, positive = nasal).
	- ch0_average, ch1_average: Average-intensity images (2D, `nxpix` x `nypix`) of the two acquisition channels for the field.
	- roi_mask: 2D integer array (`nxpix` x `nypix`) segmenting the field into ROIs. Each unique positive value identifies one ROI (matching `roi_id` in the ROI-level DataFrame); zero/negative values are background (see `plot_morph.plot_roi_mask`).