| user_dict |
{'id': '0a859259-94bf-4ec3-a97f-dbb66600e927', 'name': 'yi', 'fullname': 'Yi Cao', 'created': '2019-08-06T13:52:14.811089', 'about': None, 'last_active': None, 'activity_streams_email_notifications': False, 'sysadmin': True, 'state': 'active', 'image_url': None, 'display_name': 'Yi Cao', 'email_hash': '4dd369fbe40e8a255b24da647842a417', 'number_created_packages': 7, 'image_display_url': None, 'datasets': [{'author': 'Matthew Sainsbury-Dale ', 'author_email': 'msdale@uow.edu.au', 'creator_user_id': '0a859259-94bf-4ec3-a97f-dbb66600e927', 'id': '9559f1ba-bd8b-41a7-8229-726e7bf88db8', 'isopen': True, 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': 'Matthew Sainsbury-Dale ', 'maintainer_email': 'msdale@uow.edu.au', 'metadata_created': '2021-07-22T01:33:58.346488', 'metadata_modified': '2022-07-18T21:42:36.839390', 'name': 'sydney_sa_regions', 'notes': 'The Australian Statistical Geography Standard (ASGS) defines a series of nested geographical areas in Australia known as Statistical Area (SA) Levels. SA3 regions are aggregations of SA2 regions, and SA2 regions are aggregations of SA1 regions. This data set contains the shapefiles of all SA1, SA2, and SA3 regions across Australia at the time of the 2011 census. \r\n\r\nThis data set also contains income information from the 2011 census, at the SA1 and SA2 level in New South Wales (NSW). Specifically, it contains the number of families of various types within a range of weekly income brackets.\r\n\r\nSainsbury-Dale, Zammit-Mangion, and Cressie (2021) used a subset of this data set in a study on poverty levels in an area of (NSW) surrounding Sydney. \r\n\r\nThe shapefiles were originally downloaded from the [Australian Bureau of Statistics (ABS)](https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/1270.0.55.001July\\%20201.)).\r\n\r\n\r\n\r\n## References\r\n\r\nSainsbury-Dale, M., Zammit-Mangion, A., and Cressie, N. (2021) “Modelling, Fitting, and Prediction with Non-Gaussian Spatial and Spatio-Temporal Data using FRK”, *arXiv:2110.02507*.', 'num_resources': 5, 'num_tags': 3, 'organization': {'id': '4207b363-dfff-475d-a24c-fd97ca7f9e9a', 'name': 'cei', 'title': 'Centre for Environmental Informatics (CEI)', 'type': 'organization', 'description': '', 'image_url': 'http://niasra.uow.edu.au/content/groups/webasset/@web/@inf/@math/documents/mm/uow170808.png', 'created': '2014-11-25T12:33:11.014268', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '4207b363-dfff-475d-a24c-fd97ca7f9e9a', 'private': False, 'state': 'active', 'title': 'Australian Statistical-Area (SA) Level Regions and Census Income Data (2011)', 'type': 'dataset', 'url': '', 'version': '', 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2021-07-27T05:32:22.308186', 'datastore_active': False, 'description': '', 'format': 'ZIP', 'hash': '', 'id': '8300cace-6885-47fd-a021-37595ac8dc84', 'last_modified': '2021-07-27T05:32:22.256252', 'metadata_modified': '2021-07-27T05:32:22.308186', 'mimetype': None, 'mimetype_inner': None, 'name': 'SA1 regions', 'package_id': '9559f1ba-bd8b-41a7-8229-726e7bf88db8', 'position': 0, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'https://hpc.niasra.uow.edu.au/ckan/dataset/9559f1ba-bd8b-41a7-8229-726e7bf88db8/resource/8300cace-6885-47fd-a021-37595ac8dc84/download/sa1.zip', 'url_type': 'upload'}, {'cache_last_updated': None, 'cache_url': None, 'created': '2021-07-27T05:36:10.171772', 'datastore_active': False, 'description': '', 'format': 'ZIP', 'hash': '', 'id': '5390f101-8636-472c-b1dc-f7066e9f9898', 'last_modified': '2021-07-27T05:36:10.121776', 'metadata_modified': '2021-07-27T05:36:10.171772', 'mimetype': None, 'mimetype_inner': None, 'name': 'SA2 regions', 'package_id': '9559f1ba-bd8b-41a7-8229-726e7bf88db8', 'position': 1, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'https://hpc.niasra.uow.edu.au/ckan/dataset/9559f1ba-bd8b-41a7-8229-726e7bf88db8/resource/5390f101-8636-472c-b1dc-f7066e9f9898/download/sa2.zip', 'url_type': 'upload'}, {'cache_last_updated': None, 'cache_url': None, 'created': '2021-07-27T05:36:32.639049', 'datastore_active': False, 'description': '', 'format': 'ZIP', 'hash': '', 'id': '59607912-e088-4429-a096-90638c1159b0', 'last_modified': '2021-07-27T05:36:32.587288', 'metadata_modified': '2021-07-27T05:36:32.639049', 'mimetype': None, 'mimetype_inner': None, 'name': 'SA3 regions', 'package_id': '9559f1ba-bd8b-41a7-8229-726e7bf88db8', 'position': 2, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'https://hpc.niasra.uow.edu.au/ckan/dataset/9559f1ba-bd8b-41a7-8229-726e7bf88db8/resource/59607912-e088-4429-a096-90638c1159b0/download/sa3.zip', 'url_type': 'upload'}, {'cache_last_updated': None, 'cache_url': None, 'created': '2021-07-27T05:37:35.336758', 'datastore_active': False, 'description': '', 'format': 'CSV', 'hash': '', 'id': '6fb106aa-9912-44ea-9ce5-e564beb34105', 'last_modified': '2021-07-27T05:37:35.283436', 'metadata_modified': '2021-07-27T05:37:35.336758', 'mimetype': None, 'mimetype_inner': None, 'name': 'NSW SA1-region data', 'package_id': '9559f1ba-bd8b-41a7-8229-726e7bf88db8', 'position': 3, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'https://hpc.niasra.uow.edu.au/ckan/dataset/9559f1ba-bd8b-41a7-8229-726e7bf88db8/resource/6fb106aa-9912-44ea-9ce5-e564beb34105/download/sydneysa1data.csv', 'url_type': 'upload'}, {'cache_last_updated': None, 'cache_url': None, 'created': '2021-07-27T05:37:54.588168', 'datastore_active': False, 'description': '', 'format': 'CSV', 'hash': '', 'id': 'a8ed615a-64d7-43e3-97e9-183db212e15a', 'last_modified': '2021-07-27T05:37:54.514247', 'metadata_modified': '2021-07-27T05:37:54.588168', 'mimetype': None, 'mimetype_inner': None, 'name': 'NSW SA2-region data', 'package_id': '9559f1ba-bd8b-41a7-8229-726e7bf88db8', 'position': 4, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'https://hpc.niasra.uow.edu.au/ckan/dataset/9559f1ba-bd8b-41a7-8229-726e7bf88db8/resource/a8ed615a-64d7-43e3-97e9-183db212e15a/download/sydneysa2data.csv', 'url_type': 'upload'}], 'tags': [{'display_name': 'ASGS', 'id': 'f516c40e-9c37-4548-b627-2d78997ad3c7', 'name': 'ASGS', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'SA', 'id': '76e4fd9b-a69f-4dff-b01e-fa484fd760f1', 'name': 'SA', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Sydney', 'id': '0c2334f4-ad4d-4013-8c50-3566ff450755', 'name': 'Sydney', 'state': 'active', 'vocabulary_id': None}], 'extras': [], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'author': 'Matthew Sainsbury-Dale', 'author_email': 'msdale@uow.edu.au', 'creator_user_id': '0a859259-94bf-4ec3-a97f-dbb66600e927', 'id': '9aa14ca4-796a-41a6-b1f0-03758ed5ec9a', 'isopen': True, 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': 'Matthew Sainsbury-Dale', 'maintainer_email': 'msdale@uow.edu.au', 'metadata_created': '2021-07-22T00:55:54.065288', 'metadata_modified': '2022-07-18T21:39:07.018829', 'name': 'chicago_crime_dataset', 'notes': 'This data set contains a complete list of crimes committed in Chicago between the years 2001 – 2019 (inclusive), and was studied by Sainsbury-Dale, Zammit-Mangion, and Cressie (2021). The data were provided by the Chicago Police Department and originally downloaded from the now retired open data source website, Plenario.\r\n\r\nBefore pre-processing, the data set contained 7,138,725 observations. However, 68,904 observations did not have a location recorded and were removed. A further 163 observations were removed as they were recorded at coordinates (36.619446395, -91.686565684), which is on the border of Missouri and Arkansas (certainly not in Chicago, Illinois). This left 7,069,658 valid observations. \r\n\r\n## References \r\n\r\n- Sainsbury-Dale, M., Zammit-Mangion, A., and Cressie, N. (2021) “Modelling, Fitting, and Prediction with Non-Gaussian Spatial and Spatio-Temporal Data using FRK”, *arXiv:2110.02507*\r\n', 'num_resources': 1, 'num_tags': 2, 'organization': {'id': '4207b363-dfff-475d-a24c-fd97ca7f9e9a', 'name': 'cei', 'title': 'Centre for Environmental Informatics (CEI)', 'type': 'organization', 'description': '', 'image_url': 'http://niasra.uow.edu.au/content/groups/webasset/@web/@inf/@math/documents/mm/uow170808.png', 'created': '2014-11-25T12:33:11.014268', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '4207b363-dfff-475d-a24c-fd97ca7f9e9a', 'private': False, 'state': 'active', 'title': 'Crime in Chicago between 2001 and 2019', 'type': 'dataset', 'url': '', 'version': '', 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2021-07-22T01:00:54.159782', 'datastore_active': False, 'description': 'The data contains the following fields: \r\n\r\n- id : An identifier unique to each crime. \r\n- case_number : The case number of each crime. \r\n- date : The date and time at which each crime took place. \r\n- block : The neighbourhood block at which each crime occurred. \r\n- primary_type : A factor indicating the type of each crime (e.g., burgulary, theft, etc.).\r\n- description : A brief description of each crime. \r\n- location_description : A brief description of the location at which each crime occurred.\r\n- arrest : Logical indicating whether or not an arrest was made for each associated crime.\r\n- district : The district at which each crime occurred. \r\n- community_area : The community_area at which each crime occurred. \r\n- fbi_code : Federal Bureau of Investigation (FBI) code of each crime. \r\n- year : The year in which the crime occurred. \r\n- longitude : Latitude location of each. \r\n- latitute : Longitude location of each crime. \r\n- location : Latitude and Longitude (in that order) of each crime. ', 'format': 'Rda', 'hash': '', 'id': 'f7d9ea8f-1506-4046-bf98-a2424935a3bc', 'last_modified': '2021-07-22T01:00:54.087606', 'metadata_modified': '2021-07-22T01:00:54.159782', 'mimetype': None, 'mimetype_inner': None, 'name': 'Crime in Chicago between 2001 and 2019', 'package_id': '9aa14ca4-796a-41a6-b1f0-03758ed5ec9a', 'position': 0, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'https://hpc.niasra.uow.edu.au/ckan/dataset/9aa14ca4-796a-41a6-b1f0-03758ed5ec9a/resource/f7d9ea8f-1506-4046-bf98-a2424935a3bc/download/chicagocrimedf.rda', 'url_type': 'upload'}], 'tags': [{'display_name': 'Chicago', 'id': 'e0b19962-95f7-4966-86cc-480f8252b456', 'name': 'Chicago', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'crime', 'id': 'cee52b6d-d843-43ba-946c-4b9a0a6cc747', 'name': 'crime', 'state': 'active', 'vocabulary_id': None}], 'extras': [], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'author': '', 'author_email': '', 'creator_user_id': '0a859259-94bf-4ec3-a97f-dbb66600e927', 'id': 'a6c7dc9f-517d-4d3a-a8bb-a367e4b38d63', 'isopen': True, 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '', 'maintainer_email': '', 'metadata_created': '2021-05-27T04:49:55.961665', 'metadata_modified': '2021-05-27T06:01:03.382653', 'name': 'oco2_l2_lite_fp_v7r', 'notes': 'This dataset was downloaded from [GES DISC Dataset](https://disc.gsfc.nasa.gov/datasets).\r\n\r\nThe OCO-2 Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.\r\n\r\nDataset Release Date: 2015-09-08 \r\nSpatial Coverage: -180.0,-90.0,180.0,90.0 \r\nTemporal Coverage: 2014-09-06 to 2017-05-31 \r\nSpatial Resolution:2.25 km x 1.29 km \r\nTemporal Resolution:16 days \r\n\r\nThis dataset was used in the paper ["FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets"](https://arxiv.org/abs/1705.08105v3) to perform global prediction of column-averaged Carbon Dioxide from OCO-2.', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '4207b363-dfff-475d-a24c-fd97ca7f9e9a', 'name': 'cei', 'title': 'Centre for Environmental Informatics (CEI)', 'type': 'organization', 'description': '', 'image_url': 'http://niasra.uow.edu.au/content/groups/webasset/@web/@inf/@math/documents/mm/uow170808.png', 'created': '2014-11-25T12:33:11.014268', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '4207b363-dfff-475d-a24c-fd97ca7f9e9a', 'private': False, 'state': 'active', 'title': 'OCO-2 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files, Retrospective processing V7r.', 'type': 'dataset', 'url': '', 'version': '', 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2021-05-27T04:51:13.544633', 'datastore_active': False, 'description': 'The tarball is about 42GB. Please follow the URL above to download the tarball. ', 'format': 'tar.gz', 'hash': '', 'id': 'eccf696d-71b2-46f1-8c2c-c3db14cf5637', 'last_modified': None, 'metadata_modified': '2021-05-27T04:51:13.544633', 'mimetype': None, 'mimetype_inner': None, 'name': 'OCO2_L2_Lite_FP_V7r', 'package_id': 'a6c7dc9f-517d-4d3a-a8bb-a367e4b38d63', 'position': 0, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'http://hpc.niasra.uow.edu.au/OCO2_L2_LITE_FP.7r.tar.gz', 'url_type': None}], 'tags': [{'display_name': 'FRK', 'id': 'd1ccc110-3504-492f-bad6-328d546bd3f0', 'name': 'FRK', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'OCO2', 'id': 'f4199e45-e179-4519-8ccb-4d55907458ec', 'name': 'OCO2', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'XCO2', 'id': 'c29945ce-a971-4fb0-9275-7d1f83c723e5', 'name': 'XCO2', 'state': 'active', 'vocabulary_id': None}], 'extras': [], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'author': 'Matt Moores', 'author_email': 'mmoores@uow.edu.au', 'creator_user_id': '0a859259-94bf-4ec3-a97f-dbb66600e927', 'id': 'b5db211b-7929-4df1-9c69-150eab792a65', 'isopen': True, 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': 'Yi Cao', 'maintainer_email': 'ycao@uow.edu.au', 'metadata_created': '2021-04-25T23:05:47.225224', 'metadata_modified': '2021-04-26T01:33:48.875370', 'name': 'ndvip089r079_20150503', 'notes': 'The file contains a 1000 x 1000 matrix of surface-reflectance-derived NDVI values for Brisbane, Queensland, Australia, at a resolution of 30m per pixel. These observations were collected by the Landsat-8 Operational Land Imager (OLI) on May 3, 2015 and post-processed by the United States Geographical Survey (USGS) to calculate surface reflectance and NDVI. For more detail, see [https://www.usgs.gov/core-science-systems/nli/landsat/landsat-normalized-difference-vegetation-index](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-normalized-difference-vegetation-index)\r\n\r\n**Format**: \r\nThis 2.5mb binary R data file (.rda) contains a matrix with 1000 rows and 1000 columns, stored in a variable named **ndvi**. \r\n\r\n**Usage**: \r\nload(“ndvip089r07920150503.rda") \r\nimage(t(ndvi[1000:1,]), asp=1) \r\nhist(ndvi, breaks=30, freq=FALSE) \r\n\r\n**References**: \r\nMoores, Matt, Geoff Nicholls, Anthony N. Pettitt, and Kerrie Mengersen (2020). “Scalable Bayesian inference for the inverse temperature of a hidden Potts model.” *Bayesian Analysis* **15**(1), 1-27.\r\n', 'num_resources': 1, 'num_tags': 4, 'organization': {'id': '4207b363-dfff-475d-a24c-fd97ca7f9e9a', 'name': 'cei', 'title': 'Centre for Environmental Informatics (CEI)', 'type': 'organization', 'description': '', 'image_url': 'http://niasra.uow.edu.au/content/groups/webasset/@web/@inf/@math/documents/mm/uow170808.png', 'created': '2014-11-25T12:33:11.014268', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '4207b363-dfff-475d-a24c-fd97ca7f9e9a', 'private': False, 'state': 'active', 'title': 'Normalized difference vegetation index (NDVI) data from the Landsat-8 satellite', 'type': 'dataset', 'url': '', 'version': '', 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2021-04-25T23:07:18.566457', 'datastore_active': False, 'description': '', 'format': 'binary R data file (.rda)', 'hash': '', 'id': '79f3aed7-8b17-4015-855e-1a2b2964fbed', 'last_modified': '2021-04-25T23:07:18.541208', 'metadata_modified': '2021-04-25T23:07:18.566457', 'mimetype': None, 'mimetype_inner': None, 'name': 'Normalized difference vegetation index (NDVI) data from the Landsat-8 satellite', 'package_id': 'b5db211b-7929-4df1-9c69-150eab792a65', 'position': 0, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'https://hpc.niasra.uow.edu.au/ckan/dataset/b5db211b-7929-4df1-9c69-150eab792a65/resource/79f3aed7-8b17-4015-855e-1a2b2964fbed/download/ndvip089r079_20150503.rda', 'url_type': 'upload'}], 'tags': [{'display_name': 'Landsat', 'id': 'c0fa772b-22db-440f-a49b-9184fa9f1f20', 'name': 'Landsat', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'environment', 'id': 'e11a3571-4c5d-4a12-94f2-2de78265bccf', 'name': 'environment', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'remote-sensing', 'id': '9f338302-6f14-41c2-b733-8db6fca1b86f', 'name': 'remote-sensing', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'spatial dataset', 'id': '1163b337-bc2f-4785-b197-161ed708d694', 'name': 'spatial dataset', 'state': 'active', 'vocabulary_id': None}], 'extras': [], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'author': 'Copernicus Marine Environment Monitoring Service', 'author_email': '', 'creator_user_id': '0a859259-94bf-4ec3-a97f-dbb66600e927', 'id': '550d2a5a-b66c-4318-aac2-c0fcf64370c0', 'isopen': True, 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': 'Yi Cao', 'maintainer_email': 'ycao@uow.edu.au', 'metadata_created': '2021-02-04T22:42:33.155339', 'metadata_modified': '2021-02-04T23:31:35.521717', 'name': 'global-analysis-forecast-phy-001-024', 'notes': 'This dataset contains data from the product GLOBAL ANALYSIS FORECAST PHY 001 024 provided by the [Copernicus Marine Environment Monitoring Service](https://marine.copernicus.eu/). This product contains daily means of several ocean-related variables such as temperature and salinity on a 1/12 degree lon–lat grid. This dataset is posted here for reproducibility of the results in a paper which applies a convolution-neural-network-integro-difference-equation model for modelling sea-surface temperature; it should not be used for any scientific analyses. More details are available [here](https://arxiv.org/pdf/1910.13524.pdf) in Section 4.1. The reference for the published article is:\r\n\r\nZammit-Mangion, A., & Wikle, C. K. (2020). Deep integro-difference equation models for spatio-temporal forecasting. Spatial Statistics, 37, 100408.', 'num_resources': 1, 'num_tags': 6, 'organization': {'id': '4207b363-dfff-475d-a24c-fd97ca7f9e9a', 'name': 'cei', 'title': 'Centre for Environmental Informatics (CEI)', 'type': 'organization', 'description': '', 'image_url': 'http://niasra.uow.edu.au/content/groups/webasset/@web/@inf/@math/documents/mm/uow170808.png', 'created': '2014-11-25T12:33:11.014268', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '4207b363-dfff-475d-a24c-fd97ca7f9e9a', 'private': False, 'state': 'active', 'title': 'Global Analysis Forecast PHY 001 024', 'type': 'dataset', 'url': 'https://arxiv.org/pdf/1910.13524.pdf', 'version': '', 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2021-02-04T22:51:25.248018', 'datastore_active': False, 'description': '', 'format': 'application/x-netcdf', 'hash': '', 'id': '2d66a089-fe71-47ea-8245-6e1f1d469f59', 'last_modified': '2021-02-04T22:51:24.766285', 'metadata_modified': '2021-02-04T22:51:25.248018', 'mimetype': None, 'mimetype_inner': None, 'name': 'GLOBAL ANALYSIS FORECAST PHY 001 024', 'package_id': '550d2a5a-b66c-4318-aac2-c0fcf64370c0', 'position': 0, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'https://hpc.niasra.uow.edu.au/ckan/dataset/550d2a5a-b66c-4318-aac2-c0fcf64370c0/resource/2d66a089-fe71-47ea-8245-6e1f1d469f59/download/global-analysis-forecast-phy-001-0241551608429013.nc', 'url_type': 'upload'}], 'tags': [{'display_name': 'Convolution Neural Network', 'id': '1109693e-dd22-4122-a20b-9cade0a7cabf', 'name': 'Convolution Neural Network', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Deep Learning', 'id': '1377305e-3472-4004-936a-a21466414ff8', 'name': 'Deep Learning', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Dynamic Model', 'id': 'f59144aa-a90d-44b3-8e4a-8ead0287614d', 'name': 'Dynamic Model', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Ensemble Kalman Filter', 'id': '6040e7cc-1bb0-49a2-82e4-c569ecffa1f7', 'name': 'Ensemble Kalman Filter', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Prediction', 'id': 'b31b2699-bea7-49ff-8fcb-e7cb3a35ba20', 'name': 'Prediction', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Spatio-Temporal', 'id': '8742ac30-80d0-4f0c-a8ff-766cb57c9903', 'name': 'Spatio-Temporal', 'state': 'active', 'vocabulary_id': None}], 'extras': [], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'author': 'David Gunawan', 'author_email': '', 'creator_user_id': '0a859259-94bf-4ec3-a97f-dbb66600e927', 'id': '46b020c0-bafd-499a-a85d-47188c43e70f', 'isopen': True, 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': '', 'maintainer_email': '', 'metadata_created': '2020-05-18T03:10:56.174905', 'metadata_modified': '2020-05-18T03:20:32.533695', 'name': 'us-industry-stock-returns', 'notes': 'This is a dataset used for comparing different particle Markov chain\r\nMonte Carlo (PMCMC) methods, found in: *Efficiently combining\r\npseudo marginal and particle Gibbs sampling* by D. Gunawan, C. Carter,\r\nand R. Kohn. This dataset contains de-meaned daily returns for 26\r\nUS industry portfolios, from 11 December 2001 to 11 November 2013\r\n(a total of 3001 daily observations of the 26-dimensional vector of\r\nindustry portfolios). The original dataset is obtained from the website\r\nof [Kenneth French](https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html). ', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '4207b363-dfff-475d-a24c-fd97ca7f9e9a', 'name': 'cei', 'title': 'Centre for Environmental Informatics (CEI)', 'type': 'organization', 'description': '', 'image_url': 'http://niasra.uow.edu.au/content/groups/webasset/@web/@inf/@math/documents/mm/uow170808.png', 'created': '2014-11-25T12:33:11.014268', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '4207b363-dfff-475d-a24c-fd97ca7f9e9a', 'private': False, 'state': 'active', 'title': 'US Industry Stock Returns', 'type': 'dataset', 'url': 'https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html', 'version': '', 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2020-05-18T03:17:36.952975', 'datastore_active': False, 'description': '', 'format': 'ZIP', 'hash': '', 'id': 'ba34f1c4-6499-457c-a33a-1cda4f8c7290', 'last_modified': '2020-05-18T03:17:36.898054', 'metadata_modified': '2020-05-18T03:17:36.952975', 'mimetype': None, 'mimetype_inner': None, 'name': 'DATA CEI Stocks', 'package_id': '46b020c0-bafd-499a-a85d-47188c43e70f', 'position': 0, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'https://hpc.niasra.uow.edu.au/ckan/dataset/46b020c0-bafd-499a-a85d-47188c43e70f/resource/ba34f1c4-6499-457c-a33a-1cda4f8c7290/download/dataceistocks.zip', 'url_type': 'upload'}], 'tags': [{'display_name': 'US', 'id': '3d2ff198-81ca-4333-8e53-efcd48efe816', 'name': 'US', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'economy', 'id': 'd80b6b31-ef50-46e5-b65b-2dd89abcbef3', 'name': 'economy', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'stock', 'id': 'f6893f3e-135a-44e3-969a-2f82a574987e', 'name': 'stock', 'state': 'active', 'vocabulary_id': None}], 'extras': [], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'author': 'J. Bradley, N. Cressie, T. Shi', 'author_email': 'bradley@stat.fsu.edu', 'creator_user_id': '0a859259-94bf-4ec3-a97f-dbb66600e927', 'id': '57754d62-d037-4e94-88e9-b581078340e2', 'isopen': True, 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': 'Yi Cao', 'maintainer_email': 'ycao@uow.edu.au', 'metadata_created': '2019-08-08T02:24:35.966603', 'metadata_modified': '2019-08-29T00:30:18.127277', 'name': 'airs-co_2-feb-2010', 'notes': '## Description\r\n\r\nThis is a benchmark dataset for comparing a number of methods of spatial prediction, found in: *A comparison of spatial predictors when datasets could be very large* by Jonathan R. Bradley, Noel Cressie, and Tao Shi, which can be found [here](https://arxiv.org/abs/1410.7748). This dataset reports level-2 mid-tropospheric CO_2 values at a 17.6 km × 17.6 km spatial resolution, which is obtained from Atmospheric Infrared Sounder (AIRS) data retrieved from 1–9 February 2010. AIRS is a remote sensing instrument on board the Aqua satellite administered by the National Aeronautics and Space Administration (NASA). Among other measurements, it collects CO_2 measurements in the form\r\nof spectra (level 1) that are then converted to mid-tropospheric CO_2 values (level 2) given in units of parts per\r\nmillion (ppm). \r\n\r\nThis dataset is in the form given by Bradley et al. (2016) and is freely available under the Creative Commons Attribution 4.0 Australia License.\r\n\r\nThe ZIP file contains three folders, "Small," "Large," and "VeryLarge," the data in these folders are used in a comparison study in Section 4 of Bradley et al. (2016). \r\n\r\nIn each folder, there are two excel csv files, respectively for the training and the validation datasets. In each excel file, the first two columns are the latitude and longitude, respectively. The third column is mid-tropospheric CO_2 in ppm.\r\n\r\n## Reference\r\n\r\nBradley, J.R., Cressie, N., and Shi, T. (2016). A comparison of spatial predictors when datasets could be very large. *Statistics Surveys*, **10**, 100-131. ', 'num_resources': 1, 'num_tags': 3, 'organization': {'id': '4207b363-dfff-475d-a24c-fd97ca7f9e9a', 'name': 'cei', 'title': 'Centre for Environmental Informatics (CEI)', 'type': 'organization', 'description': '', 'image_url': 'http://niasra.uow.edu.au/content/groups/webasset/@web/@inf/@math/documents/mm/uow170808.png', 'created': '2014-11-25T12:33:11.014268', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '4207b363-dfff-475d-a24c-fd97ca7f9e9a', 'private': False, 'state': 'active', 'title': 'AIRS level-2 CO_2 dataset (1–9 February 2010)', 'type': 'dataset', 'url': '', 'version': '', 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2019-08-08T02:30:15.577326', 'datastore_active': False, 'description': 'training_\\*.csv is training dataset.\r\nvalidation_\\*.csv is validation dataset.', 'format': 'zip,csv', 'hash': '', 'id': 'e84a6a7c-c6a7-4e88-9ff0-b68ffbf6e32c', 'last_modified': '2019-08-11T23:55:15.243134', 'metadata_modified': '2019-08-08T02:30:15.577326', 'mimetype': None, 'mimetype_inner': None, 'name': 'mid-tropospheric CO_2 Feb-1-9 2010', 'package_id': '57754d62-d037-4e94-88e9-b581078340e2', 'position': 0, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'https://hpc.niasra.uow.edu.au/ckan/dataset/57754d62-d037-4e94-88e9-b581078340e2/resource/e84a6a7c-c6a7-4e88-9ff0-b68ffbf6e32c/download/mid-tropospheric-co2-feb-1-9-2010-data.zip', 'url_type': 'upload'}], 'tags': [{'display_name': 'CO_2', 'id': '9a472202-7e3e-445f-8d27-d22779a94976', 'name': 'CO_2', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Methodology', 'id': 'a103ee7b-9814-4ec5-a6b6-b1373adb7f5a', 'name': 'Methodology', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'spatial dataset', 'id': '1163b337-bc2f-4785-b197-161ed708d694', 'name': 'spatial dataset', 'state': 'active', 'vocabulary_id': None}], 'extras': [], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}], 'num_followers': 0} |