Largest Dataset Mapping Human Brain

In collaboration with the Lichtman Laboratory at Harvard University, Google is releasing the “H01” dataset, a 1.4 petabyte rendering of a small sample of human brain tissue, along with a companion paper, “A connectomic study of a petascale fragment of human cerebral cortex.” The H01 sample was imaged at 4nm-resolution by serial section electron microscopy, reconstructed and annotated by automated computational techniques, and analyzed for preliminary insights into the structure of the human cortex. The dataset comprises imaging data that covers roughly one cubic millimeter of brain tissue, and includes tens of thousands of reconstructed neurons, millions of neuron fragments, 130 million annotated synapses, 104 proofread cells, and many additional subcellular annotations and structures — all easily accessible with the Neuroglancer browser interface.

This dataset contains videos of specific networks. It is shared for the first time on Kaggle. It is suitable for Computer Vision and DCGAN structures.

INCLUDE

• Full Connected
• Hemibrain Connection
• EM
• Central Complex Structures
• Connects Regions: ADL02od PCT
• Connects Regions: ADM10t
• Connects Regions: APL
• Connects Regions: AVL01lo PCT
• Connects Regions: ExR3
• Connects Regions: MBON01
• Connects Regions: Olfactory LN
• Connects Regions: Ovil N

https://h01-release.storage.googleapis.com/landing.html

Label Green Track: Atemple. LanguageNo in JAZ Listing1: H01. LanguageName1: Atemple. Label Red Track: Atemple. LanguageNo in JAZ Listing2: H01. LanguageName2:Atemple. Language as given: Atemple

Timeseries data from 'H01 (Imiq: 7847)' (imiq_7847_werc_h01) cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=,,satellite@gina.alaska.edu,,feedback@axiomdatascience.com contributor_name=North Slope Science Initiative (NSSI),US Fish and Wildlife Service (US FWS),Geographic Information Network of Alaska (GINA),Arctic Landscape Conservation Cooperative (Arctic LCC, defunded 2019),Axiom Data Science contributor_role=sponsor,sponsor,contributor,sponsor,processor contributor_role_vocabulary=NERC contributor_url=https://northslopescience.org/,https://www.fws.gov/,https://gina.alaska.edu/,https://lccnetwork.org/lcc/arctic,https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=snow_water_equivalent_qc_agg,z,time,surface_snow_thickness_qc_agg,snow_water_equivalent,surface_snow_thickness&time>=max(time)-3days Easternmost_Easting=-150.4478 featureType=TimeSeries geospatial_lat_max=69.5687 geospatial_lat_min=69.5687 geospatial_lat_units=degrees_north geospatial_lon_max=-150.4478 geospatial_lon_min=-150.4478 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from Imiq - Hydroclimate Database and Data Portal at id=111891 infoUrl=https://sensors.ioos.us/#metadata/111891/station institution=UAF Water and Environmental Research Center (WERC) naming_authority=com.axiomdatascience Northernmost_Northing=69.5687 platform=fixed platform_name=H01 (Imiq: 7847) platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=http://ine.uaf.edu/werc/,, sourceUrl=http://ine.uaf.edu/werc/ Southernmost_Northing=69.5687 standard_name_vocabulary=CF Standard Name Table v72 station_id=111891 time_coverage_end=2010-04-22T00:00:00Z time_coverage_start=2009-04-21T00:00:00Z Westernmost_Easting=-150.4478

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An Open Context "subjects" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Sample" record is part of the "Avkat Archaeological Project" data publication.

In Sub Saharan Africa, agriculture’s contribution to employment and Gross Domestic Product (GDP) is estimated to be higher than other sectors. Policies designed and implemented for the agricultural sector could be an influencing factor to the variations in the contributions of agriculture to the annual national GDP. These policies are believed to have shaped and (some) still shaping the landscape of agriculture and national economy. The study analysed agriculture’s GDP contribution during the implementation of various national agricultural policies, and the potential of the policies to foster agrobusiness development in Nigeria between 2000 and 2021. The study adopted mixed-method approach. Primary data were collected through a structured questionnaire administered on 29 purposively sampled state Agricultural Development Programme (ADP) directors across Nigeria. The questionnaire was face-validated by three experts. Reliability test was carryout using Cronbach Alpha approach, which yielded an index of 0.89. Copies of the questionnaire were administered on the respondents through direct contact. Secondary data were collected from the Nigeria’s Federal Ministry of Agriculture and Rural Development, National Bureau of Statistics, and World Bank. Data was analysed with mean, standard deviation, percentages and ANOVA. Findings of the study revealed that the performance of implemented agricultural policies had influence on agricultural sector’s percentage contribution to national GDP, and changes in agriculture’s GDP contribution had significant impact on national GDP growth. The duration of active life of the policies did not influence their performance, like the Root and Tuber Expansion Programme which lasted longer yet performed less than the National Special Programme on Food Security in terms of improvement in agriculture’s GDP contributions. All the policies implemented had several limitations in their ability to foster agribusinesses in Nigeria. The study recommends that future policies should focus on providing sustainable frameworks for developing the business in agriculture through value chain optimisation and the use of the teeming, young, and affordable labour force like China and India did to become global food producers.