dandiset.yaml

# DO NOT EDIT THIS FILE LOCALLY. ALL LOCAL UPDATES WILL BE LOST.
# It can be edited online at https://dandiarchive.org/dandiset/001051
# and obtained from the dandiarchive.
'@context': https://raw.githubusercontent.com/dandi/schema/master/releases/0.6.7/context.json
about: []
access:
- schemaKey: AccessRequirements
  status: dandi:OpenAccess
assetsSummary:
  approach:
  - name: electrophysiological approach
    schemaKey: ApproachType
  dataStandard:
  - identifier: RRID:SCR_015242
    name: Neurodata Without Borders (NWB)
    schemaKey: StandardsType
  measurementTechnique:
  - name: analytical technique
    schemaKey: MeasurementTechniqueType
  - name: spike sorting technique
    schemaKey: MeasurementTechniqueType
  - name: signal filtering technique
    schemaKey: MeasurementTechniqueType
  - name: multi electrode extracellular electrophysiology recording technique
    schemaKey: MeasurementTechniqueType
  numberOfBytes: 1799705339503
  numberOfFiles: 663
  numberOfSubjects: 27
  schemaKey: AssetsSummary
  species:
  - identifier: http://purl.obolibrary.org/obo/NCBITaxon_10090
    name: Mus musculus - House mouse
    schemaKey: SpeciesType
  variableMeasured:
  - Units
  - ProcessingModule
  - LFP
  - ElectricalSeries
citation: Bennett, Corbett; Sridhar, Arjun (2024) Large-scale Neuropixels recordings
  through SHIELD implant during visual change detection task with dynamic gating of
  engagement (Version draft) [Data set]. DANDI archive. https://dandiarchive.org/dandiset/001051/draft
contributor:
- affiliation: []
  email: corbennett@gmail.com
  includeInCitation: true
  name: Bennett, Corbett
  roleName:
  - dcite:ContactPerson
  schemaKey: Person
- includeInCitation: true
  name: Sridhar, Arjun
  schemaKey: Person
dateCreated: '2024-06-05T21:28:46.759168+00:00'
description: "This dataset was collected at the Allen Institute. It includes data
  from 99 electrophysiology sessions featuring multi-Neuropixel recordings throughout
  the left hemisphere while mice performed a visual change detection task. This dataset
  is closely related to the Allen Institute Visual Behavior Neuropixels (VBN) dataset,
  but differs in two important ways: \nFirst, whereas the VBN recordings were focused
  on visual cortical areas and underlying subcortical regions, this dataset features
  recordings from throughout the left hemisphere, including frontal and medial cortical
  areas and the striatum. \nSecond, we have modified the behavioral task for this
  dataset to experimentally manipulate task engagement. During the VBN recordings,
  an hour of active behavior was followed by a passive behavior block during which
  the lick spout was retracted and mice were presented with the same visual stimuli
  but now with no opportunity to lick for reward. In practice, mice often satiated
  before the passive block. For this dataset, we interposed a 'no-reward' block in
  the middle of active behavior. During this new epoch, the lick spout remained extended
  but licks for visual changes no longer triggered reward. At the end of the no-reward
  block, auto-rewards were given to indicate that rewards were once again available,
  and many mice resumed licking for changes.\n\nTo better understand how to access
  and analyze this dataset, we encourage potential users to refer to the resources
  below. The data in DANDI is structured as follows: each subject has session NWBs
  identified by date of acquisition. Then, there are LFP NWBs for up to 6 probes for
  each session, identified by a probe id. For the LFP, each session NWB has a probes
  table that has the probe ids for the LFP data associated with that session. Use
  this table to get the probe ids and corresponding LFP NWBs. Examples  of opening
  a NWB file and accessing the probes table can be seen at the GitHub below under
  tutorials. In addition, there is a dynamic gating sessions metadata table at the
  GitHub repository below that has an acquisition date column, which can be used to
  map to a session id (the session column in the metadata tables). This will be useful
  for parsing the metadata tables for multi-session analysis.\n\n1) This repository
  includes a quick-start tutorial notebook as well as metadata tables for the sessions,
  probes, channels and units included in this dataset: https://github.com/AllenInstitute/SHIELD_Dynamic_Gating_Analysis\n2)
  To learn more about the basic visual change detection task as well as the general
  structure of the nwb files, consult the documentation available for the Allen Observatory
  Visual Behavior Neuropixels dataset here: https://portal.brain-map.org/circuits-behavior/visual-behavior-neuropixels\n\nThis
  dataset was used in the following preprint: \nSHIELD: Skull-shaped hemispheric implants
  enabling large-scale-electrophysiology datasets in the mouse brain [https://doi.org/10.1101/2023.11.12.566771]"
ethicsApproval: []
id: DANDI:001051/draft
identifier: DANDI:001051
keywords: []
license:
- spdx:CC-BY-4.0
manifestLocation:
- https://api.dandiarchive.org/api/dandisets/001051/versions/draft/assets/
name: Large-scale Neuropixels recordings through SHIELD implant during visual change
  detection task with dynamic gating of engagement
protocol: []
relatedResource: []
repository: https://dandiarchive.org
schemaKey: Dandiset
schemaVersion: 0.6.7
studyTarget: []
url: https://dandiarchive.org/dandiset/001051/draft
version: draft
wasGeneratedBy: []