MODIS Cloud Data - SG.mat

Dataset description:

Description: The dataset contains the MODIS cloud data that has been analyzed in Sengupta et al (2012) and Sengupta et al (2016). The variable that was modeled are the final Q-values...

Source: MODIS Cloud Data

Additional Information

Field	Value
Data last updated	May 10, 2016
Metadata last updated	May 11, 2016
Created	May 11, 2016
Format	unknown
License	Creative Commons Attribution
Datastore active	False
Has views	False
Id	7616baa2-78db-4dbf-9d82-40abdcb42c46
Package id	ca7acdca-5c1a-4abd-b256-a07fbc89a653
Position	1
State	active
Url type	upload

Field

Value

Data last updated

May 10, 2016

Metadata last updated

May 11, 2016

Created

May 11, 2016

Format

unknown

License

Creative Commons Attribution

Datastore active

False

Has views

False

7616baa2-78db-4dbf-9d82-40abdcb42c46

Package id

ca7acdca-5c1a-4abd-b256-a07fbc89a653

Position

State

active

Url type

upload

Package	Version	Homepage	Summary
alabaster	0.7.16	None	A light, configurable Sphinx theme
alembic	1.13.2	https://alembic.sqlalchemy.org	A database migration tool for SQLAlchemy.
arrow	1.3.0	None	Better dates & times for Python
asttokens	3.0.0	https://github.com/gristlabs/asttokens	Annotate AST trees with source code positions
async-timeout	4.0.3	https://github.com/aio-libs/async-timeout	Timeout context manager for asyncio programs
autocommand	2.2.2	https://github.com/Lucretiel/autocommand	A library to create a command-line program from a function
Babel	2.15.0	https://babel.pocoo.org/	Internationalization utilities
backports.tarfile	1.2.0	None	Backport of CPython tarfile module
beautifulsoup4	4.12.3	None	Screen-scraping library
binaryornot	0.4.4	https://github.com/audreyr/binaryornot	Ultra-lightweight pure Python package to check if a file is binary or text.
bleach	6.1.0	https://github.com/mozilla/bleach	An easy safelist-based HTML-sanitizing tool.
blinker	1.8.2	None	Fast, simple object-to-object and broadcast signaling
build	1.2.2.post1	None	A simple, correct Python build frontend
cachelib	0.13.0	https://github.com/pallets-eco/cachelib/	A collection of cache libraries in the same API interface.
certifi	2024.7.4	https://github.com/certifi/python-certifi	Python package for providing Mozilla's CA Bundle.
chardet	5.2.0	https://github.com/chardet/chardet	Universal encoding detector for Python 3
charset-normalizer	3.3.2	https://github.com/Ousret/charset_normalizer	The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet.
ckan	2.11.2	http://ckan.org/	CKAN Software
click	8.1.7	https://palletsprojects.com/p/click/	Composable command line interface toolkit
cookiecutter	2.6.0	https://github.com/cookiecutter/cookiecutter	A command-line utility that creates projects from project templates, e.g. creating a Python package project from a Python package project template.
coverage	7.6.12	https://github.com/nedbat/coveragepy	Code coverage measurement for Python
coveralls	4.0.1	http://github.com/TheKevJames/coveralls-python	Show coverage stats online via coveralls.io
decorator	5.2.1	None	Decorators for Humans
docopt	0.6.2	http://docopt.org	Pythonic argument parser, that will make you smile
docutils	0.20.1	https://docutils.sourceforge.io/	Docutils -- Python Documentation Utilities
dominate	2.9.1	None	Dominate is a Python library for creating and manipulating HTML documents using an elegant DOM API.
exceptiongroup	1.2.2	None	Backport of PEP 654 (exception groups)
executing	2.2.0	https://github.com/alexmojaki/executing	Get the currently executing AST node of a frame, and other information
factory-boy	3.3.0	https://github.com/FactoryBoy/factory_boy	A versatile test fixtures replacement based on thoughtbot's factory_bot for Ruby.
Faker	26.0.0	https://github.com/joke2k/faker	Faker is a Python package that generates fake data for you.
feedgen	1.0.0	https://lkiesow.github.io/python-feedgen	Feed Generator (ATOM, RSS, Podcasts)
Flask	3.0.3	None	A simple framework for building complex web applications.
flask-babel	4.0.0	https://github.com/python-babel/flask-babel	Adds i18n/l10n support for Flask applications.
Flask-DebugToolbar	0.15.1	https://github.com/pallets-eco/flask-debugtoolbar	A toolbar overlay for debugging Flask applications.
Flask-Login	0.6.3	https://github.com/maxcountryman/flask-login	User authentication and session management for Flask.
Flask-Session	0.8.0	None	Server-side session support for Flask
Flask-WTF	1.2.1	None	Form rendering, validation, and CSRF protection for Flask with WTForms.
freezegun	1.5.1	https://github.com/spulec/freezegun	Let your Python tests travel through time
greenlet	3.0.3	https://greenlet.readthedocs.io/	Lightweight in-process concurrent programming
idna	3.7	None	Internationalized Domain Names in Applications (IDNA)
imagesize	1.4.1	https://github.com/shibukawa/imagesize_py	Getting image size from png/jpeg/jpeg2000/gif file
importlib_metadata	8.0.0	None	Read metadata from Python packages
importlib_metadata	8.0.0	None	Read metadata from Python packages
incremental	24.7.2	None	A small library that versions your Python projects.
inflect	7.3.1	None	Correctly generate plurals, singular nouns, ordinals, indefinite articles
inflection	0.5.1	https://github.com/jpvanhal/inflection	A port of Ruby on Rails inflector to Python
iniconfig	2.0.0	None	brain-dead simple config-ini parsing
ipdb	0.13.13	https://github.com/gotcha/ipdb	IPython-enabled pdb
ipython	8.34.0	None	IPython: Productive Interactive Computing
itsdangerous	2.2.0	None	Safely pass data to untrusted environments and back.
jaraco.collections	5.1.0	None	Collection objects similar to those in stdlib by jaraco
jaraco.context	5.3.0	https://github.com/jaraco/jaraco.context	Useful decorators and context managers
jaraco.functools	4.0.1	None	Functools like those found in stdlib
jaraco.text	3.12.1	None	Module for text manipulation
jedi	0.19.2	https://github.com/davidhalter/jedi	An autocompletion tool for Python that can be used for text editors.
Jinja2	3.1.5	None	A very fast and expressive template engine.
lxml	5.2.2	https://lxml.de/	Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API.
Mako	1.3.5	https://www.makotemplates.org/	A super-fast templating language that borrows the best ideas from the existing templating languages.
Markdown	3.6	None	Python implementation of John Gruber's Markdown.
markdown-it-py	3.0.0	None	Python port of markdown-it. Markdown parsing, done right!
MarkupSafe	2.1.5	https://palletsprojects.com/p/markupsafe/	Safely add untrusted strings to HTML/XML markup.
matplotlib-inline	0.1.7	None	Inline Matplotlib backend for Jupyter
mdurl	0.1.2	None	Markdown URL utilities
more-itertools	10.3.0	None	More routines for operating on iterables, beyond itertools
msgspec	0.18.6	https://jcristharif.com/msgspec/	A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML.
mypy	1.10.1	https://www.mypy-lang.org/	Optional static typing for Python
mypy-extensions	1.0.0	https://github.com/python/mypy_extensions	Type system extensions for programs checked with the mypy type checker.
packaging	24.1	None	Core utilities for Python packages
packaging	24.2	None	Core utilities for Python packages
parso	0.8.4	https://github.com/davidhalter/parso	A Python Parser
passlib	1.7.4	https://passlib.readthedocs.io	comprehensive password hashing framework supporting over 30 schemes
pexpect	4.9.0	https://pexpect.readthedocs.io/	Pexpect allows easy control of interactive console applications.
pillow	10.4.0	None	Python Imaging Library (Fork)
pip	25.0.1	None	The PyPA recommended tool for installing Python packages.
pip-tools	7.4.1	None	pip-tools keeps your pinned dependencies fresh.
platformdirs	4.2.2	None	A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`.
pluggy	1.5.0	https://github.com/pytest-dev/pluggy	plugin and hook calling mechanisms for python
polib	1.2.0	https://github.com/izimobil/polib/	A library to manipulate gettext files (po and mo files).
prompt_toolkit	3.0.50	https://github.com/prompt-toolkit/python-prompt-toolkit	Library for building powerful interactive command lines in Python
psycopg2	2.9.9	https://psycopg.org/	psycopg2 - Python-PostgreSQL Database Adapter
ptyprocess	0.7.0	https://github.com/pexpect/ptyprocess	Run a subprocess in a pseudo terminal
pure_eval	0.2.3	http://github.com/alexmojaki/pure_eval	Safely evaluate AST nodes without side effects
Pygments	2.19.1	None	Pygments is a syntax highlighting package written in Python.
PyJWT	2.8.0	https://github.com/jpadilla/pyjwt	JSON Web Token implementation in Python
pyparsing	3.1.2	None	pyparsing module - Classes and methods to define and execute parsing grammars
pyproject_hooks	1.2.0	None	Wrappers to call pyproject.toml-based build backend hooks.
pysolr	3.9.0	https://github.com/django-haystack/pysolr/	Lightweight Python client for Apache Solr
pytest	8.2.2	None	pytest: simple powerful testing with Python
pytest-cov	5.0.0	https://github.com/pytest-dev/pytest-cov	Pytest plugin for measuring coverage.
pytest-factoryboy	2.7.0	https://pytest-factoryboy.readthedocs.io/	Factory Boy support for pytest.
pytest-freezegun	0.4.2	https://github.com/ktosiek/pytest-freezegun	Wrap tests with fixtures in freeze_time
pytest-rerunfailures	14.0	None	pytest plugin to re-run tests to eliminate flaky failures
pytest-split	0.9.0	https://jerry-git.github.io/pytest-split	Pytest plugin which splits the test suite to equally sized sub suites based on test execution time.
python-dateutil	2.9.0.post0	https://github.com/dateutil/dateutil	Extensions to the standard Python datetime module
python-magic	0.4.27	http://github.com/ahupp/python-magic	File type identification using libmagic
python-slugify	8.0.4	https://github.com/un33k/python-slugify	A Python slugify application that also handles Unicode
pytz	2024.1	http://pythonhosted.org/pytz	World timezone definitions, modern and historical
PyYAML	6.0.1	https://pyyaml.org/	YAML parser and emitter for Python
redis	5.0.7	https://github.com/redis/redis-py	Python client for Redis database and key-value store
requests	2.32.3	https://requests.readthedocs.io	Python HTTP for Humans.
responses	0.25.3	https://github.com/getsentry/responses	A utility library for mocking out the `requests` Python library.
rich	13.9.4	https://github.com/Textualize/rich	Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal
rq	1.16.2	None	RQ is a simple, lightweight, library for creating background jobs, and processing them.
setuptools	76.0.0	None	Easily download, build, install, upgrade, and uninstall Python packages
simplejson	3.19.2	https://github.com/simplejson/simplejson	Simple, fast, extensible JSON encoder/decoder for Python
six	1.16.0	https://github.com/benjaminp/six	Python 2 and 3 compatibility utilities
snowballstemmer	2.2.0	https://github.com/snowballstem/snowball	This package provides 29 stemmers for 28 languages generated from Snowball algorithms.
soupsieve	2.6	None	A modern CSS selector implementation for Beautiful Soup.
Sphinx	7.3.7	None	Python documentation generator
sphinx-rtd-theme	2.0.0	https://github.com/readthedocs/sphinx_rtd_theme	Read the Docs theme for Sphinx
sphinxcontrib-applehelp	2.0.0	None	sphinxcontrib-applehelp is a Sphinx extension which outputs Apple help books
sphinxcontrib-devhelp	2.0.0	None	sphinxcontrib-devhelp is a sphinx extension which outputs Devhelp documents
sphinxcontrib-htmlhelp	2.1.0	None	sphinxcontrib-htmlhelp is a sphinx extension which renders HTML help files
sphinxcontrib-jquery	4.1	None	Extension to include jQuery on newer Sphinx releases
sphinxcontrib-jsmath	1.0.1	http://sphinx-doc.org/	A sphinx extension which renders display math in HTML via JavaScript
sphinxcontrib-qthelp	2.0.0	None	sphinxcontrib-qthelp is a sphinx extension which outputs QtHelp documents
sphinxcontrib-serializinghtml	2.0.0	None	sphinxcontrib-serializinghtml is a sphinx extension which outputs "serialized" HTML files (json and pickle)
SQLAlchemy	1.4.52	https://www.sqlalchemy.org	Database Abstraction Library
sqlalchemy2-stubs	0.0.2a38	http://www.sqlalchemy.org	Typing Stubs for SQLAlchemy 1.4
sqlparse	0.5.0	None	A non-validating SQL parser.
stack-data	0.6.3	http://github.com/alexmojaki/stack_data	Extract data from python stack frames and tracebacks for informative displays
text-unidecode	1.3	https://github.com/kmike/text-unidecode/	The most basic Text::Unidecode port
toml	0.10.2	https://github.com/uiri/toml	Python Library for Tom's Obvious, Minimal Language
tomli	2.0.1	None	A lil' TOML parser
tomli	2.0.1	None	A lil' TOML parser
towncrier	23.11.0	None	Building newsfiles for your project.
traitlets	5.14.3	None	Traitlets Python configuration system
typeguard	4.3.0	None	Run-time type checker for Python
types-python-dateutil	2.9.0.20241206	https://github.com/python/typeshed	Typing stubs for python-dateutil
typing_extensions	4.12.2	None	Backported and Experimental Type Hints for Python 3.8+
typing_extensions	4.12.2	None	Backported and Experimental Type Hints for Python 3.8+
tzlocal	5.2	None	tzinfo object for the local timezone
urllib3	2.2.2	None	HTTP library with thread-safe connection pooling, file post, and more.
uWSGI	2.0.28	https://uwsgi-docs.readthedocs.io/en/latest/	The uWSGI server
watchdog	4.0.1	https://github.com/gorakhargosh/watchdog	Filesystem events monitoring
wcwidth	0.2.13	https://github.com/jquast/wcwidth	Measures the displayed width of unicode strings in a terminal
webassets	2.0	http://github.com/miracle2k/webassets/	Media asset management for Python, with glue code for various web frameworks
webencodings	0.5.1	https://github.com/SimonSapin/python-webencodings	Character encoding aliases for legacy web content
Werkzeug	3.0.6	None	The comprehensive WSGI web application library.
wheel	0.45.1	None	A built-package format for Python
wheel	0.43.0	None	A built-package format for Python
WTForms	3.1.2	None	Form validation and rendering for Python web development.
zipp	3.19.2	None	Backport of pathlib-compatible object wrapper for zip files
zipp	3.19.2	None	Backport of pathlib-compatible object wrapper for zip files
zope.interface	6.4.post2	https://github.com/zopefoundation/zope.interface	Interfaces for Python

Resource	Value
User CPU time	22.564 msec
System CPU time	4.444 msec
Total CPU time	27.008 msec
Elapsed time	30.627 msec
Context switches	20 voluntary, 0 involuntary

Key	Value
HTTP_ACCEPT	/
HTTP_ACCEPT_ENCODING	gzip, br, zstd, deflate
HTTP_CONNECTION	close
HTTP_HOST	192.168.122.4
HTTP_USER_AGENT	Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
QUERY_STRING
REMOTE_ADDR	127.0.0.1
REQUEST_METHOD	GET
SCRIPT_NAME
SERVER_NAME	niasra-ckan
SERVER_PORT	8080
SERVER_PROTOCOL	HTTP/1.0

Variable	Value
'_csrf_token'	'9830e367036641261a48a08b8b8b8248b0d56cc3'
'_fresh'	False
'_permanent'	True

Variable	Value
csrf_token	<function generate_csrf at 0x7fe10bdccee0>
current_user	<ckan.model.user.AnonymousUser object at 0x7fe1066a5450>
g	<flask.g of 'ckan.config.middleware.flask_app'>
request	<Request 'http://192.168.122.4/dataset/modis_cloud_data/resource/7616baa2-78db-4dbf-9d82-40abdcb42c46' [GET]>
session	<SecureCookieSession {'_csrf_token': '9830e367036641261a48a08b8b8b8248b0d56cc3', '_fresh': False, '_permanent': True}>

SG.mat

Dataset description:

Additional Information

Versions

Installed Packages

Resource Usage

HTTP Headers

Request Vars

View information

COOKIES Variables

SESSION Variables

GET Variables

POST Variables

Config

Templates

snippets/csrf_input.html

package/resource_read.html

SQLAlchemy queries

Queries Unavailable

Install required libraries:

Configure Flask-SQLAlchemy:

Log Messages

Route List

Profiler not active

flask.g content

Key	Value
__file__	`'/etc/ckan/default/ckan.ini'`
api_token.jwt.algorithm	`'HS256'`
api_token.jwt.decode.secret	`'string:OuonVd5Md43IixzaojMJc6OV1Pk'`
api_token.jwt.encode.secret	`'string:OuonVd5Md43IixzaojMJc6OV1Pk'`
api_token.nbytes	`32`
apitoken_header_name	`'Authorization'`
APPLICATION_ROOT	`'/'`
BABEL_DEFAULT_TIMEZONE	`'UTC'`
BABEL_DOMAIN	`'ckan'`
BABEL_TRANSLATION_DIRECTORIES	`'/usr/lib/ckan/default/lib/python3.10/site-packages/ckan/i18n'`
ckan.activity_list_limit	`31`
ckan.activity_list_limit_max	`100`
ckan.activity_streams_email_notifications	`False`
ckan.activity_streams_enabled	`True`
ckan.auth.allow_admin_collaborators	`True`
ckan.auth.allow_collaborators_to_change_owner_org	`True`
ckan.auth.allow_dataset_collaborators	`True`
ckan.auth.anon_create_dataset	`True`
ckan.auth.create_dataset_if_not_in_organization	`True`
ckan.auth.create_default_api_keys	`False`
ckan.auth.create_unowned_dataset	`True`
ckan.auth.create_user_via_api	`True`
ckan.auth.create_user_via_web	`True`
ckan.auth.enable_cookie_auth_in_api	`True`
ckan.auth.login_view	`'user.login'`
ckan.auth.public_activity_stream_detail	`True`
ckan.auth.public_user_details	`True`
ckan.auth.reveal_private_datasets	`True`
ckan.auth.roles_that_cascade_to_sub_groups	`['admin']`
ckan.auth.route_after_login	`'dashboard.datasets'`
ckan.auth.user_create_groups	`True`
ckan.auth.user_create_organizations	`True`
ckan.auth.user_delete_groups	`True`
ckan.auth.user_delete_organizations	`True`
ckan.base_public_folder	`'public'`
ckan.base_templates_folder	`'templates'`
ckan.cache_enabled	`False`
ckan.cache_expires	`0`
ckan.cors.origin_allow_all	`False`
ckan.cors.origin_whitelist	`[]`
ckan.csrf_protection.ignore_extensions	`True`
ckan.dataset.create_on_ui_requires_resources	`True`
ckan.datasets_per_page	`20`
ckan.datastore.default_fts_index_field_types	`['text', 'tsvector']`
ckan.datastore.default_fts_index_method	`'gist'`
ckan.datastore.default_fts_lang	`'english'`
ckan.datastore.read_url	`'postgresql://datastore_default:ckanniasra123!@localhost/datastore_default'`
ckan.datastore.search.rows_default	`100`
ckan.datastore.search.rows_max	`32000`
ckan.datastore.sqlalchemy.pool_pre_ping	`True`
ckan.datastore.sqlsearch.allowed_functions_file	`'/usr/lib/ckan/default/lib/python3.10/site-packages/ckanext/datastore/allowed_functions.txt'`
ckan.datastore.sqlsearch.enabled	`False`
ckan.datastore.write_url	`'postgresql://ckan_default:ckanniasra123!@localhost/datastore_default'`
ckan.debug_supress_header	`False`
ckan.default.group_type	`'group'`
ckan.default.organization_type	`'organization'`
ckan.default.package_type	`'dataset'`
ckan.default_group_sort	`'title'`
ckan.devserver.host	`'localhost'`
ckan.devserver.multiprocess	`1`
ckan.devserver.port	`5000`
ckan.devserver.ssl_cert	`''`
ckan.devserver.ssl_key	`''`
ckan.devserver.threaded	`True`
ckan.devserver.watch_patterns	`[]`
ckan.display_timezone	`'UTC'`
ckan.download_proxy	`''`
ckan.email_notifications_since	`'2 days'`
ckan.extra_resource_fields	`[]`
ckan.favicon	`'/base/images/ckan.ico'`
ckan.featured_groups	`[]`
ckan.featured_orgs	`[]`
ckan.feeds.author_link	`''`
ckan.feeds.author_name	`''`
ckan.feeds.authority_name	`''`
ckan.feeds.date	`''`
ckan.feeds.limit	`20`
ckan.gravatar_default	`'identicon'`
ckan.group_and_organization_list_all_fields_max	`25`
ckan.group_and_organization_list_max	`1000`
ckan.hide_activity_from_users	`['niasra-ckan']`
ckan.hide_version	`False`
ckan.i18n.extra_directory	`''`
ckan.i18n.extra_gettext_domain	`''`
ckan.i18n.extra_locales	`[]`
ckan.i18n.rtl_languages	`['he', 'ar', 'fa_IR']`
ckan.i18n.rtl_theme	`'css/main-rtl'`
ckan.i18n_directory	`''`
ckan.jobs.timeout	`180`
ckan.legacy_route_mappings	`'{}'`
ckan.locale_default	`'en'`
ckan.locale_order	`[]`
ckan.locales_filtered_out	`[]`
ckan.locales_offered	`[]`
ckan.max_image_size	`50`
ckan.max_resource_size	`10240`
ckan.mimetype_guess	`'file_ext'`
ckan.plugins	`['activity', 'datastore']`
ckan.recaptcha.privatekey	`''`
ckan.recaptcha.publickey	`''`
ckan.redis.url	`'redis://localhost:6379/0'`
ckan.requests.timeout	`5`
ckan.resource_formats	`'/usr/lib/ckan/default/lib/python3.10/site-packages/ckan/config/resource_formats.json'`
ckan.root_path	`'/ckan/{{LANG}}'`
ckan.search.default_include_private	`True`
ckan.search.default_package_sort	`'score desc, metadata_modified desc'`
ckan.search.remove_deleted_packages	`True`
ckan.search.rows_max	`1000`
ckan.search.show_all_types	`'dataset'`
ckan.search.solr_allowed_query_parsers	`[]`
ckan.search.solr_commit	`True`
ckan.site_about	'INTRODUCTION BY DIRECTOR: PROFESSOR Marijka Batterham \r\n----------------------\r\n\r\nThe National Institute for Applied Statistics Research Australia (NIASRA) at the University of Wollongong aims to identify Australia as a place where the discipline of applied statistics collaborates with academics and professionals in science, government, business and industry to tackle strategic problems. Methods for obtaining relevant and reliable data and methods that can be used to extract the information and evidence that the data can provide are fundamental in solving important and complex problems facing society. The huge increase in data available in all fields and relating to every issue challenging a modern society means that there are large gaps between the methods needed to fully exploit these data and the methods available to do so. New statistical methods are needed to handle uncertainty and risk in large and complex data structures and to generate knowledge that can be acted upon. NIASRA is undertaking a program of research developing new statistical methods for obtaining and analysing data relevant to agriculture, energy, engineering, environmental sciences, finance, health, medicine, mining, natural resources, and social sciences. The Institute aims to provide leading-edge research and consulting capacity in applied statistics for Australia and our region through the skills and activities of its staff and research students. \r\n \r\n\r\nNIASRA is committed to developing and applying innovative statistical methods to important problems. It conducts: \r\n\r\n- fundamental methodological research; \r\n- industry-focused and contract research; \r\n- major consulting projects;\r\n- professional education and training. \r\n \r\nNIASRA’s research is focused in Biometry and Bioinformatics, Environmental Informatics, Survey Methodology, and also research in Statistical Education and Statistical and Mathematical Methodology in Finance. It is led by Director Prof David Steel and includes: \r\n\r\n- Centre for Bioinformatics and Biometrics Research – Director Prof Brian Cullis;\r\n- Centre for Environmental Informatics – Director Distinguished Prof Noel Cressie;\r\n- Centre for Sample Survey Methodology – Director Prof Ray Chambers;\r\n- Statistical Consulting Centre - Director A/Professor Marijka Batterham, focussing on support for research within the University in design and analysis.\r\n \r\nThe institute is a Research Strength of the University of Wollongong and has over 20 academic staff members and 25 research students. It has been successful in developing long-term collaborative partnerships. It has two externally funded research professors: Professor Chambers is funded by the Australian Bureau of Statistics and Professor Brian Cullis is funded by CSIRO and the Grains Research Development Corporation. It has formal partnerships with the NZ Ministry of Health and the Australian Market and Social Research Society. \r\n \r\n\r\nThe themes that underpin the research at NIASRA are:\r\n\r\n- statistical design - developing efficient methods of obtaining relevant and reliable data in surveys, experiments, and other data collections;\r\n- complexity of data arising from observations in large scale real populations – often involving hierarchical or auto-correlated data - and computational methods to handle such data; and\r\n- working with major organisations on significant problems. \r\n \r\nInternationally competitive areas of methodological research include: statistical design - survey and experimental design; analysis of complex data from complex populations - survey, biometric, environmental, longitudinal, spatial, aggregated data, and also combining data; statistical modelling – methods for spatial, temporal and spatial-temporal data, semi-parametric and nonparametric methods, time series, quasi-likelihood, multi-level modelling, Bayesian analysis, and estimating equations;\r\ndata quality and survey methods - particularly telephone, household, internet and environmental surveys;\r\nstatistical confidentiality.\r\n \r\nNIASRA offers postgraduate and professional education courses and programs that build on our world-class staff, extensive experience and industry collaborations.'
ckan.site_custom_css	`'a[href]:after {\r\n content: none;\r\n}\r\n.hero {\r\n min-height:0px;\r\n}'`
ckan.site_description	`'National Institute for Applied Statistics Research Australia'`
ckan.site_id	`'niasra-ckan'`
ckan.site_intro_text	'This data portal hosts data of interest to, or used in projects by, researchers at the National Institute of Applied Research Australia (NIASRA), based at the University of Wollongong. All data available to the public can be considered open source. The open data portal software employed here is `CKAN`; more information can be found at http://ckan.org/.'
ckan.site_logo	`'niasra-logo.png'`
ckan.site_title	`'NIASRA'`
ckan.site_url	`'https://hpc.niasra.uow.edu.au'`
ckan.static_max_age	`3600`
ckan.storage_path	`'/var/lib/ckan/default'`
ckan.template_title_delimiter	`'-'`
ckan.theme	`'css/main'`
ckan.upload.group.mimetypes	`['image/png', 'image/gif', 'image/jpeg']`
ckan.upload.group.types	`['image']`
ckan.upload.user.mimetypes	`['image/png', 'image/gif', 'image/jpeg']`
ckan.upload.user.types	`['image']`
ckan.user.last_active_interval	`600`
ckan.user_list_limit	`20`
ckan.user_reset_landing_page	`'home.index'`
ckan.valid_url_schemes	`['http', 'https', 'ftp']`
ckan.views.default_views	`['image_view', 'datatables_view']`
ckan.webassets.path	`''`
ckan.webassets.url	`'/webassets'`
ckan.webassets.use_x_sendfile	`False`
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extra_public_paths	`''`
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here	`'/etc/ckan/default'`
licenses_group_url	`''`
logo_upload	`''`
MAX_CONTENT_LENGTH	`None`
MAX_COOKIE_SIZE	`4093`
package_edit_return_url	`''`
package_hide_extras	`[]`
package_new_return_url	`''`
PERMANENT_SESSION_LIFETIME	`31536000`
plugin_public_paths	`['/usr/lib/ckan/default/lib/python3.10/site-packages/ckanext/activity/public']`
plugin_template_paths	`['/usr/lib/ckan/default/lib/python3.10/site-packages/ckanext/activity/templates', '/usr/lib/ckan/default/lib/python3.10/site-packages/ckanext/datastore/templates']`
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REMEMBER_COOKIE_DURATION	`31536000`
REMEMBER_COOKIE_HTTPONLY	`True`
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REMEMBER_COOKIE_REFRESH_EACH_REQUEST	`False`
REMEMBER_COOKIE_SAMESITE	`'None'`
REMEMBER_COOKIE_SECURE	`False`
search.facets	`['organization', 'groups', 'tags', 'res_format', 'license_id']`
search.facets.default	`10`
search.facets.limit	`50`
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SEND_FILE_MAX_AGE_DEFAULT	`None`
SERVER_NAME	`None`
SESSION_COOKIE_DOMAIN	`''`
SESSION_COOKIE_HTTPONLY	`True`
SESSION_COOKIE_NAME	`'ckan'`
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SESSION_KEY_PREFIX	`'session:'`
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smtp.mail_from	`''`
smtp.password	`''`
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smtp.server	`'localhost'`
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solr_password	`''`
solr_timeout	`60`
solr_url	`'http://192.168.122.4:8983/solr/ckan'`
solr_user	`''`
sqlalchemy.pool_pre_ping	`True`
sqlalchemy.url	`'postgresql://ckan_default:ckanniasra123!@localhost/ckan_default'`
SQLALCHEMY_RECORD_QUERIES	`True`
TEMPLATES_AUTO_RELOAD	`None`
TESTING	`False`
testing	`False`
TRAP_BAD_REQUEST_ERRORS	`None`
TRAP_HTTP_EXCEPTIONS	`False`
use	`'egg:ckan'`
USE_X_SENDFILE	`False`
WTF_CSRF_CHECK_DEFAULT	`True`
WTF_CSRF_ENABLED	`True`
WTF_CSRF_FIELD_NAME	`'_csrf_token'`
WTF_CSRF_HEADERS	`['X-CSRFToken', 'X-CSRF-Token']`
WTF_CSRF_METHODS	`{'PUT', 'DELETE', 'POST', 'PATCH'}`
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WTF_CSRF_SSL_STRICT	`True`
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csrf_token	<function generate_csrf at 0x7fe10bdccee0>
current_resource_view	None
current_user	<ckan.model.user.AnonymousUser object at 0x7fe1066a5450>
dataset_type	'dataset'
g	<flask.g of 'ckan.config.middleware.flask_app'>
package	{'author': 'Aritra Sengupta', 'author_email': 'atrsrv@gmail.com', 'creator_user_id': '9f494829-8e6c-4a1a-a3cc-748ff3880160', 'id': 'ca7acdca-5c1a-4abd-b256-a07fbc89a653', 'isopen': True, 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': 'Andrew Zammit-Mangion', 'maintainer_email': 'azm@uow.edu.au', 'metadata_created': '2016-05-09T23:05:23.852266', 'metadata_modified': '2017-02-06T02:05:35.117013', 'name': 'modis_cloud_data', 'notes': '## Description:\r\n\r\nThe dataset contains the MODIS cloud data that has been analyzed in Sengupta et al (2012) and Sengupta et al (2016). The variable that was modeled are the final Q-values which reflect the confidence of clear sky for a pixel located at the specified latitude and longitude values. For more details about the MODIS cloud data please see Sengupta et al (2016).\r\n\r\n## Format:\r\n\r\nThe file _mod35_2006180_1245_test_output.out_ contains the following columns, in this order, for each pixel:\r\n\r\n\r\n* scan line number \r\n* pixel number\r\n* surface type\r\n* latitude\r\n* longitude\r\n* initial Q values\r\n* final Q values\r\n\r\nThe Surface types are coded as follows:\r\n\r\n* 0 = water\r\n* 1 = coast\r\n* 2 = desert\r\n* 3 = land\r\n\r\n\r\nSengupta et al (2016) also made use of a sun-glint indicator variable. This is provided in the file _SG.mat_.\r\n\r\n## References:\r\n\r\n* Sengupta, A., Cressie, N., Frey, R., and Kahn, B. H. (2012). "Statistical modeling of MODIS cloud data using the Spatial Random Effects model." In _Proceedings of the 2012 Joint Statistical Meetings_, Alexandria, VA: American Statistical Association, 3111-3123\r\n\r\n* Sengupta, A., Cressie, N., Kahn, B. H., and Frey, R. (2016). "Predictive inference for big, spatial non-Gaussian data: MODIS cloud data and its change-of-support." _Australian & New Zealand Journal of Statistics_. 58, 15–45.\r\n', 'num_resources': 2, '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': 'MODIS Cloud Data', 'type': 'dataset', 'url': '', 'version': '', 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2016-05-10T12:27:57.031023', 'datastore_active': False, 'description': '', 'format': '', 'hash': '', 'id': '5efb3a86-6cd1-4101-b472-141f0ea5c65e', 'last_modified': '2016-05-10T04:44:08.006611', 'metadata_modified': '2016-05-10T12:27:57.031023', 'mimetype': None, 'mimetype_inner': None, 'name': 'mod35_2006180_1245_test_output', 'package_id': 'ca7acdca-5c1a-4abd-b256-a07fbc89a653', 'position': 0, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'https://hpc.niasra.uow.edu.au/ckan/dataset/ca7acdca-5c1a-4abd-b256-a07fbc89a653/resource/5efb3a86-6cd1-4101-b472-141f0ea5c65e/download/mod3520061801245testoutput.zip', 'url_type': 'upload'}, {'cache_last_updated': None, 'cache_url': None, 'created': '2016-05-11T09:15:40.280305', 'datastore_active': False, 'description': '', 'format': '', 'hash': '', 'id': '7616baa2-78db-4dbf-9d82-40abdcb42c46', 'last_modified': '2016-05-10T23:15:40.231919', 'metadata_modified': '2016-05-11T09:15:40.280305', 'mimetype': None, 'mimetype_inner': None, 'name': 'SG.mat', 'package_id': 'ca7acdca-5c1a-4abd-b256-a07fbc89a653', 'position': 1, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'https://hpc.niasra.uow.edu.au/ckan/dataset/ca7acdca-5c1a-4abd-b256-a07fbc89a653/resource/7616baa2-78db-4dbf-9d82-40abdcb42c46/download/sg.mat', 'url_type': 'upload', 'has_views': False}], 'tags': [{'display_name': 'MODIS', 'id': '57e202c4-de6f-4a8a-aa46-23bbfdecfab1', 'name': 'MODIS', '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': []}
pkg	<Package id=ca7acdca-5c1a-4abd-b256-a07fbc89a653 name=modis_cloud_data title=MODIS Cloud Data version= url= author=Aritra Sengupta author_email=atrsrv@gmail.com maintainer=Andrew Zammit-Mangion maintainer_email=azm@uow.edu.au notes=## Description: The dataset contains the MODIS cloud data that has been analyzed in Sengupta et al (2012) and Sengupta et al (2016). The variable that was modeled are the final Q-values which reflect the confidence of clear sky for a pixel located at the specified latitude and longitude values. For more details about the MODIS cloud data please see Sengupta et al (2016). ## Format: The file _mod35_2006180_1245_test_output.out_ contains the following columns, in this order, for each pixel: * scan line number * pixel number * surface type * latitude * longitude * initial Q values * final Q values The Surface types are coded as follows: * 0 = water * 1 = coast * 2 = desert * 3 = land Sengupta et al (2016) also made use of a sun-glint indicator variable. This is provided in the file _SG.mat_. ## References: * Sengupta, A., Cressie, N., Frey, R., and Kahn, B. H. (2012). "Statistical modeling of MODIS cloud data using the Spatial Random Effects model." In _Proceedings of the 2012 Joint Statistical Meetings_, Alexandria, VA: American Statistical Association, 3111-3123 * Sengupta, A., Cressie, N., Kahn, B. H., and Frey, R. (2016). "Predictive inference for big, spatial non-Gaussian data: MODIS cloud data and its change-of-support." _Australian & New Zealand Journal of Statistics_. 58, 15–45. license_id=cc-by type=dataset owner_org=4207b363-dfff-475d-a24c-fd97ca7f9e9a creator_user_id=9f494829-8e6c-4a1a-a3cc-748ff3880160 metadata_created=2016-05-09 23:05:23.852266 metadata_modified=2017-02-06 02:05:35.117013 private=False state=active plugin_data=None>
pkg_dict	{'author': 'Aritra Sengupta', 'author_email': 'atrsrv@gmail.com', 'creator_user_id': '9f494829-8e6c-4a1a-a3cc-748ff3880160', 'id': 'ca7acdca-5c1a-4abd-b256-a07fbc89a653', 'isopen': True, 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': 'Andrew Zammit-Mangion', 'maintainer_email': 'azm@uow.edu.au', 'metadata_created': '2016-05-09T23:05:23.852266', 'metadata_modified': '2017-02-06T02:05:35.117013', 'name': 'modis_cloud_data', 'notes': '## Description:\r\n\r\nThe dataset contains the MODIS cloud data that has been analyzed in Sengupta et al (2012) and Sengupta et al (2016). The variable that was modeled are the final Q-values which reflect the confidence of clear sky for a pixel located at the specified latitude and longitude values. For more details about the MODIS cloud data please see Sengupta et al (2016).\r\n\r\n## Format:\r\n\r\nThe file _mod35_2006180_1245_test_output.out_ contains the following columns, in this order, for each pixel:\r\n\r\n\r\n* scan line number \r\n* pixel number\r\n* surface type\r\n* latitude\r\n* longitude\r\n* initial Q values\r\n* final Q values\r\n\r\nThe Surface types are coded as follows:\r\n\r\n* 0 = water\r\n* 1 = coast\r\n* 2 = desert\r\n* 3 = land\r\n\r\n\r\nSengupta et al (2016) also made use of a sun-glint indicator variable. This is provided in the file _SG.mat_.\r\n\r\n## References:\r\n\r\n* Sengupta, A., Cressie, N., Frey, R., and Kahn, B. H. (2012). "Statistical modeling of MODIS cloud data using the Spatial Random Effects model." In _Proceedings of the 2012 Joint Statistical Meetings_, Alexandria, VA: American Statistical Association, 3111-3123\r\n\r\n* Sengupta, A., Cressie, N., Kahn, B. H., and Frey, R. (2016). "Predictive inference for big, spatial non-Gaussian data: MODIS cloud data and its change-of-support." _Australian & New Zealand Journal of Statistics_. 58, 15–45.\r\n', 'num_resources': 2, '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': 'MODIS Cloud Data', 'type': 'dataset', 'url': '', 'version': '', 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2016-05-10T12:27:57.031023', 'datastore_active': False, 'description': '', 'format': '', 'hash': '', 'id': '5efb3a86-6cd1-4101-b472-141f0ea5c65e', 'last_modified': '2016-05-10T04:44:08.006611', 'metadata_modified': '2016-05-10T12:27:57.031023', 'mimetype': None, 'mimetype_inner': None, 'name': 'mod35_2006180_1245_test_output', 'package_id': 'ca7acdca-5c1a-4abd-b256-a07fbc89a653', 'position': 0, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'https://hpc.niasra.uow.edu.au/ckan/dataset/ca7acdca-5c1a-4abd-b256-a07fbc89a653/resource/5efb3a86-6cd1-4101-b472-141f0ea5c65e/download/mod3520061801245testoutput.zip', 'url_type': 'upload'}, {'cache_last_updated': None, 'cache_url': None, 'created': '2016-05-11T09:15:40.280305', 'datastore_active': False, 'description': '', 'format': '', 'hash': '', 'id': '7616baa2-78db-4dbf-9d82-40abdcb42c46', 'last_modified': '2016-05-10T23:15:40.231919', 'metadata_modified': '2016-05-11T09:15:40.280305', 'mimetype': None, 'mimetype_inner': None, 'name': 'SG.mat', 'package_id': 'ca7acdca-5c1a-4abd-b256-a07fbc89a653', 'position': 1, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'https://hpc.niasra.uow.edu.au/ckan/dataset/ca7acdca-5c1a-4abd-b256-a07fbc89a653/resource/7616baa2-78db-4dbf-9d82-40abdcb42c46/download/sg.mat', 'url_type': 'upload', 'has_views': False}], 'tags': [{'display_name': 'MODIS', 'id': '57e202c4-de6f-4a8a-aa46-23bbfdecfab1', 'name': 'MODIS', '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': []}
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/<path:filename>	static	GET, HEAD, OPTIONS	False	None
/about	home.about	GET, HEAD, OPTIONS	False	None
/api/	api.get_api	GET, HEAD, OPTIONS	False	None
/api/<int(min=1, max=2):ver>/i18n/<lang>	api.i18n_js_translations	GET, HEAD, OPTIONS	False	None
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/api/<int(min=1, max=2):ver>/util/organization/autocomplete	api.organization_autocomplete	GET, HEAD, OPTIONS	False	None
/api/<int(min=1, max=2):ver>/util/resource/format_autocomplete	api.format_autocomplete	GET, HEAD, OPTIONS	False	None
/api/<int(min=1, max=2):ver>/util/snippet/<snippet_path>	api.snippet	GET, HEAD, OPTIONS	False	None
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package	{'author': 'Aritra Sengupta', 'author_email': 'atrsrv@gmail.com', 'creator_user_id': '9f494829-8e6c-4a1a-a3cc-748ff3880160', 'id': 'ca7acdca-5c1a-4abd-b256-a07fbc89a653', 'isopen': True, 'license_id': 'cc-by', 'license_title': 'Creative Commons Attribution', 'license_url': 'http://www.opendefinition.org/licenses/cc-by', 'maintainer': 'Andrew Zammit-Mangion', 'maintainer_email': 'azm@uow.edu.au', 'metadata_created': '2016-05-09T23:05:23.852266', 'metadata_modified': '2017-02-06T02:05:35.117013', 'name': 'modis_cloud_data', 'notes': '## Description:\r\n\r\nThe dataset contains the MODIS cloud data that has been analyzed in Sengupta et al (2012) and Sengupta et al (2016). The variable that was modeled are the final Q-values which reflect the confidence of clear sky for a pixel located at the specified latitude and longitude values. For more details about the MODIS cloud data please see Sengupta et al (2016).\r\n\r\n## Format:\r\n\r\nThe file _mod35_2006180_1245_test_output.out_ contains the following columns, in this order, for each pixel:\r\n\r\n\r\n* scan line number \r\n* pixel number\r\n* surface type\r\n* latitude\r\n* longitude\r\n* initial Q values\r\n* final Q values\r\n\r\nThe Surface types are coded as follows:\r\n\r\n* 0 = water\r\n* 1 = coast\r\n* 2 = desert\r\n* 3 = land\r\n\r\n\r\nSengupta et al (2016) also made use of a sun-glint indicator variable. This is provided in the file _SG.mat_.\r\n\r\n## References:\r\n\r\n* Sengupta, A., Cressie, N., Frey, R., and Kahn, B. H. (2012). "Statistical modeling of MODIS cloud data using the Spatial Random Effects model." In _Proceedings of the 2012 Joint Statistical Meetings_, Alexandria, VA: American Statistical Association, 3111-3123\r\n\r\n* Sengupta, A., Cressie, N., Kahn, B. H., and Frey, R. (2016). "Predictive inference for big, spatial non-Gaussian data: MODIS cloud data and its change-of-support." _Australian & New Zealand Journal of Statistics_. 58, 15–45.\r\n', 'num_resources': 2, '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': 'MODIS Cloud Data', 'type': 'dataset', 'url': '', 'version': '', 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2016-05-10T12:27:57.031023', 'datastore_active': False, 'description': '', 'format': '', 'hash': '', 'id': '5efb3a86-6cd1-4101-b472-141f0ea5c65e', 'last_modified': '2016-05-10T04:44:08.006611', 'metadata_modified': '2016-05-10T12:27:57.031023', 'mimetype': None, 'mimetype_inner': None, 'name': 'mod35_2006180_1245_test_output', 'package_id': 'ca7acdca-5c1a-4abd-b256-a07fbc89a653', 'position': 0, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'https://hpc.niasra.uow.edu.au/ckan/dataset/ca7acdca-5c1a-4abd-b256-a07fbc89a653/resource/5efb3a86-6cd1-4101-b472-141f0ea5c65e/download/mod3520061801245testoutput.zip', 'url_type': 'upload'}, {'cache_last_updated': None, 'cache_url': None, 'created': '2016-05-11T09:15:40.280305', 'datastore_active': False, 'description': '', 'format': '', 'hash': '', 'id': '7616baa2-78db-4dbf-9d82-40abdcb42c46', 'last_modified': '2016-05-10T23:15:40.231919', 'metadata_modified': '2016-05-11T09:15:40.280305', 'mimetype': None, 'mimetype_inner': None, 'name': 'SG.mat', 'package_id': 'ca7acdca-5c1a-4abd-b256-a07fbc89a653', 'position': 1, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'https://hpc.niasra.uow.edu.au/ckan/dataset/ca7acdca-5c1a-4abd-b256-a07fbc89a653/resource/7616baa2-78db-4dbf-9d82-40abdcb42c46/download/sg.mat', 'url_type': 'upload', 'has_views': False}], 'tags': [{'display_name': 'MODIS', 'id': '57e202c4-de6f-4a8a-aa46-23bbfdecfab1', 'name': 'MODIS', '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': []}
pkg	<Package id=ca7acdca-5c1a-4abd-b256-a07fbc89a653 name=modis_cloud_data title=MODIS Cloud Data version= url= author=Aritra Sengupta author_email=atrsrv@gmail.com maintainer=Andrew Zammit-Mangion maintainer_email=azm@uow.edu.au notes=## Description: The dataset contains the MODIS cloud data that has been analyzed in Sengupta et al (2012) and Sengupta et al (2016). The variable that was modeled are the final Q-values which reflect the confidence of clear sky for a pixel located at the specified latitude and longitude values. For more details about the MODIS cloud data please see Sengupta et al (2016). ## Format: The file _mod35_2006180_1245_test_output.out_ contains the following columns, in this order, for each pixel: * scan line number * pixel number * surface type * latitude * longitude * initial Q values * final Q values The Surface types are coded as follows: * 0 = water * 1 = coast * 2 = desert * 3 = land Sengupta et al (2016) also made use of a sun-glint indicator variable. This is provided in the file _SG.mat_. ## References: * Sengupta, A., Cressie, N., Frey, R., and Kahn, B. H. (2012). "Statistical modeling of MODIS cloud data using the Spatial Random Effects model." In _Proceedings of the 2012 Joint Statistical Meetings_, Alexandria, VA: American Statistical Association, 3111-3123 * Sengupta, A., Cressie, N., Kahn, B. H., and Frey, R. (2016). "Predictive inference for big, spatial non-Gaussian data: MODIS cloud data and its change-of-support." _Australian & New Zealand Journal of Statistics_. 58, 15–45. license_id=cc-by type=dataset owner_org=4207b363-dfff-475d-a24c-fd97ca7f9e9a creator_user_id=9f494829-8e6c-4a1a-a3cc-748ff3880160 metadata_created=2016-05-09 23:05:23.852266 metadata_modified=2017-02-06 02:05:35.117013 private=False state=active plugin_data=None>
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