metadata.yaml

about: []
access:
- schemaKey: AccessRequirements
  status: dandi:OpenAccess
acknowledgement: We would like to acknowledge Ramon Nogueira, B Christina Pil, Esther
  A Greeman, Jung M Park, Y Kate Hong, Stefano Fusi, and Randy M Bruno, who co-authored
  with CR the original paper based on this dataset. We would also like to acknowledge
  the members of the Neurodata Without Borders Slack and the DANDI Slack, especially
  Ben Dichter, Satrajit Ghosh, and Yaroslav Halchenko, for their advice on using these
  data standards. The present work was supported in part by a Kavli Neurodata Without
  Borders Seed Grant (S-2021-GR-040) to CR. The original data collection was supported
  in part by a Kavli Institute Postdoctoral Fellowship, a NINDS/NIH NRSA fellowship
  (F32NS096819), and a NARSAD Young Investigator Award (28667) from the Brain & Behavior
  Research Foundation to CR.
assetsSummary:
  approach:
  - name: behavioral approach
    schemaKey: ApproachType
  - name: electrophysiological approach
    schemaKey: ApproachType
  dataStandard:
  - identifier: RRID:SCR_015242
    name: Neurodata Without Borders (NWB)
    schemaKey: StandardsType
  measurementTechnique:
  - name: analytical technique
    schemaKey: MeasurementTechniqueType
  - name: behavioral technique
    schemaKey: MeasurementTechniqueType
  - name: multi electrode extracellular electrophysiology recording technique
    schemaKey: MeasurementTechniqueType
  - name: surgical technique
    schemaKey: MeasurementTechniqueType
  - name: spike sorting technique
    schemaKey: MeasurementTechniqueType
  numberOfBytes: 1996516623953
  numberOfFiles: 4228
  numberOfSubjects: 15
  schemaKey: AssetsSummary
  species:
  - identifier: http://purl.obolibrary.org/obo/NCBITaxon_10090
    name: Mus musculus - House mouse
    schemaKey: SpeciesType
  variableMeasured:
  - BehavioralTimeSeries
  - BehavioralEvents
  - ProcessingModule
  - Units
  - ElectrodeGroup
  - ElectricalSeries
citation: Rodgers, Chris (2022) A detailed behavioral, videographic, and neural dataset
  on object recognition in mice (Version draft) [Data set]. DANDI archive. https://dandiarchive.org/dandiset/000231/draft
contributor:
- affiliation:
  - identifier: https://ror.org/03czfpz43
    name: Emory University
    schemaKey: Affiliation
  email: xrodgers@gmail.com
  identifier: 0000-0003-1762-3450
  includeInCitation: true
  name: Rodgers, Chris
  roleName:
  - dcite:Author
  - dcite:ContactPerson
  - dcite:DataCollector
  - dcite:DataCurator
  - dcite:DataManager
  - dcite:FundingAcquisition
  - dcite:Researcher
  schemaKey: Person
  url: https://chris-rodgers.com
- awardNumber: S-2021-GR-040
  contactPoint: []
  identifier: https://ror.org/00kztt736
  includeInCitation: false
  name: Kavli Foundation
  roleName:
  - dcite:Funder
  schemaKey: Organization
- awardNumber: '28667'
  contactPoint: []
  identifier: https://ror.org/03a63f080
  includeInCitation: false
  name: Brain & Behavior Research Foundation
  roleName:
  - dcite:Funder
  schemaKey: Organization
- awardNumber: 'F32NS096819 '
  contactPoint: []
  identifier: https://ror.org/01s5ya894
  includeInCitation: false
  name: NIH/NINDS
  roleName:
  - dcite:Funder
  schemaKey: Organization
dateCreated: '2022-04-06T14:03:13.712605+00:00'
description: Mice adeptly use their whiskers to touch, recognize, and learn about
  objects in their environment. This behavior is enabled by computations performed
  by populations of neurons in the somatosensory cortex. To understand these computations,
  we trained mice to use their whiskers to recognize different shapes while we recorded
  activity in the barrel cortex, which processes whisker input. Here, we present a
  large dataset of high-speed video of the whiskers, along with rigorous tracking
  of the entire extent of multiple whiskers and every contact they made on the shape.
  We used spike sorting to identify individual neurons, which responded with precise
  timing to whisker contacts and motion. These data will be useful for understanding
  the behavioral strategies mice use to explore objects, as well as the neuronal dynamics
  that mediate those strategies. In addition, our carefully curated labeled data could
  be used to develop new computer vision algorithms for tracking body posture, or
  for extracting responses of individual neurons from large-scale neural recordings.
  For further description, see https://www.biorxiv.org/content/10.1101/2022.05.10.491259v1.
ethicsApproval: []
id: DANDI:000231/draft
identifier: DANDI:000231
keywords:
- mouse behavior
- whisker system
- somatosensory cortex
- barrel cortex
- object recognition
- shape discrimination
- electrophysiology
- pose tracking
- population recordings
- single unit recordings
license:
- spdx:CC0-1.0
manifestLocation:
- https://api.dandiarchive.org/api/dandisets/000231/versions/draft/assets/
name: A detailed behavioral, videographic, and neural dataset on object recognition
  in mice
protocol: []
relatedResource:
- identifier: 10.1101/2022.05.10.491259
  name: A detailed behavioral, videographic, and neural dataset on object recognition
    in mice
  relation: dcite:Describes
  repository: bioRxiv
  schemaKey: Resource
  url: https://www.biorxiv.org/content/10.1101/2022.05.10.491259v1
- name: Companion code
  relation: dcite:Describes
  repository: github
  schemaKey: Resource
  url: https://github.com/cxrodgers/NwbDandiData2022
repository: https://dandiarchive.org
schemaKey: Dandiset
schemaVersion: 0.6.2
studyTarget: []
url: https://dandiarchive.org/dandiset/000231/draft
version: draft
wasGeneratedBy: []