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Data from: Context-dependent signaling of coincident auditory and visual events in primary visual cortex
- 负责人：
- DOI：
- doi:10.5061/dryad.82r5q83
- 摘要：
- feature coded by the auditory cortex neurons projecting to primary visual cortex (V1). In V1, a small number of layer 1 interneurons gates this cross-modal
Data from: Response to short-term deprivation of the human adult visual cortex measured with 7T BOLD
- 负责人：
- 关键词：
- DOI：
- doi:10.5061/dryad.tp24j18
- 摘要：
- response to the deprived eye, changing ocular dominance of V1 vertices, consistent with homeostatic plasticity. The boost is strongest in V1, present in V2
Data from: Thalamocortical synapses in the cat visual system in vivo are weak and unreliable
- 负责人：
- DOI：
- doi:10.5061/dryad.57pv818
- 摘要：
- d simultaneous recordings in LGN and V1 in cat in vivo to characterize the dynamic properties of thalamocortical synaptic transmission in monosynaptically
Data from: Faster processing of moving compared to flashed bars in awake macaque V1 provides a neural correlate of the flash lag illusion
- 负责人：
- Subramaniyan, Manivannan
- DOI：
- doi:10.5061/dryad.md2n292
- 摘要：
- known to perceive the illusion. Towards this, we recorded neural responses to flashed and moving bars from primary visual cortex (V1) of awake, fixating
Data from: Surface color and predictability determine contextual modulation of V1 firing and gamma oscillations
- 负责人：
- DOI：
- doi:10.5061/dryad.4809qj4
- 摘要：
- The integration of direct bottom-up inputs with contextual information is a core feature of neocortical circuits. In area V1, neurons may reduce thei
Global Flood Hazard Frequency and Distribution;;Global Flood Hazard Frequency and Distribution, v1 (1985 \u2013 2003)
- 负责人：
- DOI：
- doi:10.7927/h4668b3d
- 摘要：
- The Global Flood Hazard Frequency and Distribution is a 2.5 minute grid derived from a global listing of extreme flood events between 1985 and 2003 (poor or missing data in the early/mid 1990s) compiled by Dartmouth Flood Observatory and georeferenced to the nearest degree. The resultant flood frequency grid was then classified into 10 classes of approximately equal number of grid cells. The greater the grid cell value in the final data set, the higher the relative frequency of flood occurrence. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).
Global Agricultural Lands: Pastures, 2000;;Pastures, v1 (2000)
- 负责人：
- DOI：
- doi:10.7927/h47h1ggr
- 摘要：
- The Global Pastures data set represents the proportion of land areas used as pasture land (land used to support grazing animals) in the year 2000. Satellite data from Modetate Resolution Imaging Spectroradiometer (MODIS) and Satellite Pour l'Observation de la Terre (SPOT) Image Vegetation sensor were combined with agricultural inventory data to create a global data set. The visual presentation of this data demonstrates the extent to which human land use for agriculture has changed the Earth and in which areas this change is most intense. The data was compiled by Navin Ramankutty, et. al. (2008) and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
Data from: Membrane potential dynamics of spontaneous and visually evoked gamma activity in V1 of awake mice
- 负责人：
- DOI：
- doi:10.5061/dryad.4754j
- 摘要：
- unclear. Here, we characterized the intracellular dynamics of PVs and PYRs during spontaneous and visually evoked gamma activity in layers 2/3 of V1
Updated Spiny Mouse Transcriptome Assembly (Now Includes Embryo-Specific Transcripts)
- 负责人：
- DOI：
- doi:10.5281/zenodo.1188363
- 摘要：
- reads was assessed using FastQC v0.11.6 (https://github.com/s-andrews/FastQC; 50f0c26), with MultiQC v1.4 (https://github.com/ewels/MultiQC; baefc2e
Updated Spiny Mouse Transcriptome Assembly (Now Includes Embryo-Specific Transcripts)
- 负责人：
- DOI：
- doi:10.5281/zenodo.1188364
- 摘要：
- reads was assessed using FastQC v0.11.6 (https://github.com/s-andrews/FastQC; 50f0c26), with MultiQC v1.4 (https://github.com/ewels/MultiQC; baefc2e
