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  1. '@context': https://raw.githubusercontent.com/dandi/schema/master/releases/0.6.4/context.json
  2. about: []
  3. access:
  4. - schemaKey: AccessRequirements
  5. status: dandi:OpenAccess
  6. acknowledgement: DHK and MN acknowledge support from the United States Army grant
  7. MURI W911NF1910280 and the National Institutes of Health grants U19NS128613 and
  8. R01AT012312.
  9. assetsSummary:
  10. approach:
  11. - name: microscopy approach; cell population imaging
  12. schemaKey: ApproachType
  13. dataStandard:
  14. - identifier: RRID:SCR_015242
  15. name: Neurodata Without Borders (NWB)
  16. schemaKey: StandardsType
  17. measurementTechnique:
  18. - name: analytical technique
  19. schemaKey: MeasurementTechniqueType
  20. - name: surgical technique
  21. schemaKey: MeasurementTechniqueType
  22. - name: two-photon microscopy technique
  23. schemaKey: MeasurementTechniqueType
  24. numberOfBytes: 5324416350
  25. numberOfFiles: 14
  26. numberOfSubjects: 9
  27. schemaKey: AssetsSummary
  28. species:
  29. - identifier: http://purl.obolibrary.org/obo/NCBITaxon_10090
  30. name: Mus musculus - House mouse
  31. schemaKey: SpeciesType
  32. variableMeasured:
  33. - ProcessingModule
  34. - TwoPhotonSeries
  35. - ImagingPlane
  36. - OpticalChannel
  37. - PlaneSegmentation
  38. citation: Zhao, Yue; Boster, Kimberly; Kelley, Douglas; Raicevic, Nikola (2023) BrainFlowZZZ
  39. (Version draft) [Data set]. DANDI archive. https://dandiarchive.org/dandiset/000491/draft
  40. contributor:
  41. - affiliation: []
  42. email: yuezhao@rochester.edu
  43. includeInCitation: true
  44. name: Zhao, Yue
  45. roleName:
  46. - dcite:ContactPerson
  47. - dcite:DataManager
  48. - dcite:Maintainer
  49. schemaKey: Person
  50. - affiliation:
  51. - identifier: https://ror.org/022kthw22
  52. name: University of Rochester
  53. schemaKey: Affiliation
  54. identifier: 0000-0001-5178-128X
  55. includeInCitation: true
  56. name: Boster, Kimberly
  57. roleName:
  58. - dcite:Researcher
  59. schemaKey: Person
  60. - affiliation:
  61. - identifier: https://ror.org/022kthw22
  62. name: University of Rochester
  63. schemaKey: Affiliation
  64. email: d.h.kelley@rochester.edu
  65. identifier: 0000-0001-9658-2954
  66. includeInCitation: true
  67. name: Kelley, Douglas
  68. roleName:
  69. - dcite:ContactPerson
  70. - dcite:ProjectLeader
  71. - dcite:ProjectManager
  72. schemaKey: Person
  73. - awardNumber: MURI W911NF1910280
  74. contactPoint: []
  75. identifier: https://ror.org/035w1gb98
  76. includeInCitation: false
  77. name: United States Department of the Army
  78. roleName:
  79. - dcite:Funder
  80. schemaKey: Organization
  81. - awardNumber: U19NS128613 and R01AT012312
  82. contactPoint: []
  83. identifier: https://ror.org/01cwqze88
  84. includeInCitation: false
  85. name: National Institutes of Health
  86. roleName:
  87. - dcite:Funder
  88. schemaKey: Organization
  89. - affiliation:
  90. - identifier: https://ror.org/022kthw22
  91. name: University of Rochester
  92. schemaKey: Affiliation
  93. email: nraicevi@u.rochester.edu
  94. includeInCitation: true
  95. name: Raicevic, Nikola
  96. roleName:
  97. - dcite:Author
  98. schemaKey: Person
  99. dateCreated: '2023-04-26T03:36:47.214653+00:00'
  100. description: "Dataset from the 2023 manuscript titled **_Sizes and Shapes of Perivascular
  101. Spaces Surrounding Murine Pial Arteries_** by Raicevic et al. DOI: 10.21203/rs.3.rs-2587250/v1.
  102. \ \n\n## Overview\nThe **14 datasets** from **9 subjects** include the original
  103. 3D two photon microscopy data from three channels which show tracer in the vessel,
  104. PVSs, and microspheres. Additionally, each dataset also includes the final binary
  105. segmentation of the PVS and vessel used to generate the model and statistics in
  106. the manuscript. Additional details regarding the subjects, tracer injection, image
  107. acquisition, and segmentation can be found in the manuscript at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9949243/.\n\n##
  108. Content\nFor easier navigation, below is a mapping between the NWB file names and
  109. the datasets referenced in the manuscript.\n1. sub-21-07-19-b-act (Mouse 6, dataset
  110. K)\n2. sub-21-09-01-b-act (Mouse 8, dataset M)\n3. sub-21-09-20-b-act (Mouse 9,
  111. dataset N)\n4. sub-21-10-08-b-act (Mouse 7, dataset L)\n5. sub-BPN-M4 (Mouse 3,
  112. dataset E and F)\n6. sub-BPN-M6 (Mouse 4, dataset G and H)\n7. sub-BPN-M7 (Mouse
  113. 5, dataset I and J)\n8. sub-BPN-OLD-M2 (Mouse 1, dataset A and B)\n9. sub-BPN-OLD-M3
  114. (Mouse 2, dataset C and D)\n\n## Data Reading Instructions\nThe .nwb files can be
  115. viewed using PyNWB or MatNWB. To install and set up, please visit <https://www.nwb.org/how-to-use/>.
  116. Below, we show how to open and view an .nwb file using MatNWB. \n### Loading the
  117. image data\n```matlab\nnwb = nwbRead(PATH_TO_NWB_FILE);\n\n% first check what color
  118. channels are present\n>> nwb.acquisition\nans = \n 3×1 Set array with properties:\n
  119. \ TwoPhotonSeriesChanA: [types.core.TwoPhotonSeries]\n TwoPhotonSeriesChanB:
  120. [types.core.TwoPhotonSeries]\n TwoPhotonSeriesChanC: [types.core.TwoPhotonSeries]\n```\nThe
  121. above code load the .nwb file and the output tells us that there are three channels
  122. present in the nwb file, which are ChanA, ChanB, and ChanC. Then, to load the actual
  123. data from a channel,\n```matlab\n% load the image data from ChanA\n>> chanAdata
  124. = nwb.acquisition.get('TwoPhotonSeriesChanA').data.load();\n\n% check its shape\n>>
  125. size(chanAdata)\nans =\n 1 512 512 181\n```\n### Loading the segmentation
  126. masks\nFor an overview of the mask for ChanA, for example,\n```matlab\n>> nwb.processing.get('ophys').nwbdatainterface.get('ImageSegmentation').planesegmentation.get('PlaneSegmentationChanA').image_mask.data\nans
  127. = \n DataStub with properties:\n filename: '.\\sub-BPN-M4_ses-20210524-m1_obj-1c8nyxo_ophys.nwb'\n
  128. \ path: '/processing/ophys/ImageSegmentation/PlaneSegmentationChanA/image_mask'\n
  129. \ dims: [512 512 181]\n ndims: 3\n dataType: 'logical'\n```\nTo load
  130. the actual mask data into array (may take several seconds to load),\n```matlab\n%
  131. load mask from ChanA\n>> mask = nwb.processing.get('ophys').nwbdatainterface.get('ImageSegmentation').planesegmentation.get('PlaneSegmentationChanA').image_mask.data.load();\n\n%
  132. check its shape\n>> size(mask)\nans =\n 512 512 181\n```"
  133. ethicsApproval: []
  134. id: DANDI:000491/draft
  135. identifier: DANDI:000491
  136. keywords: []
  137. license:
  138. - spdx:CC-BY-4.0
  139. manifestLocation:
  140. - https://api.dandiarchive.org/api/dandisets/000491/versions/draft/assets/
  141. name: BrainFlowZZZ
  142. protocol: []
  143. relatedResource: []
  144. repository: https://dandiarchive.org
  145. schemaKey: Dandiset
  146. schemaVersion: 0.6.4
  147. studyTarget: []
  148. url: https://dandiarchive.org/dandiset/000491/draft
  149. version: draft
  150. wasGeneratedBy: []
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