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NIASRA
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Versions

Installed Packages

Current Site Packages Directory: 

/usr/lib/ckan/default/lib/python3.10/site-packages
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
Close

Resource Usage

Resource Value
User CPU time 58.274 msec
System CPU time 6.623 msec
Total CPU time 64.897 msec
Elapsed time 75.820 msec
Context switches 63 voluntary, 1 involuntary
Close

HTTP Headers

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
Close

Request Vars

View information

View Function args kwargs
ckan.views.dataset.groups [] id=global-elus, package_type=dataset

COOKIES Variables

Variable Value
'ckan' 'eyJfY3NyZl90b2tlbiI6IjAzNDk2MzUzYzU2NmY0YzI2YmZiOTBlMmE2NWY3NjI4ZTIxMjkyN2EifQ.aAPZpw.g50CB4wPFvA4iwRm1Bk73J81wzU'

SESSION Variables

Variable Value
'_csrf_token' '03496353c566f4c26bfb90e2a65f7628e212927a'
'_permanent' True
'_fresh' False

GET Variables

Variable Value

POST Variables

Variable Value
Close

Config

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
clear_logo_upload ''
computed_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', '/usr/lib/ckan/default/lib/python3.10/site-packages/ckan/templates']
config.mode 'strict'
DEBUG True
debug True
DEBUG_TB_ENABLED True
DEBUG_TB_HOSTS ()
DEBUG_TB_INTERCEPT_REDIRECTS False
DEBUG_TB_PANELS ('flask_debugtoolbar.panels.versions.VersionDebugPanel', 'flask_debugtoolbar.panels.timer.TimerDebugPanel', 'flask_debugtoolbar.panels.headers.HeaderDebugPanel', 'flask_debugtoolbar.panels.request_vars.RequestVarsDebugPanel', 'flask_debugtoolbar.panels.config_vars.ConfigVarsDebugPanel', 'flask_debugtoolbar.panels.template.TemplateDebugPanel', 'flask_debugtoolbar.panels.sqlalchemy.SQLAlchemyDebugPanel', 'flask_debugtoolbar.panels.logger.LoggingPanel', 'flask_debugtoolbar.panels.route_list.RouteListDebugPanel', 'flask_debugtoolbar.panels.profiler.ProfilerDebugPanel', 'flask_debugtoolbar.panels.g.GDebugPanel')
email_to ''
error_email_from ''
EXPLAIN_TEMPLATE_LOADING False
extra_public_paths ''
extra_template_paths ''
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']
PREFERRED_URL_SCHEME 'http'
PROPAGATE_EXCEPTIONS None
REMEMBER_COOKIE_DOMAIN None
REMEMBER_COOKIE_DURATION 31536000
REMEMBER_COOKIE_HTTPONLY True
REMEMBER_COOKIE_NAME 'remember_token'
REMEMBER_COOKIE_PATH '/'
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
SECRET_KEY 'OuonVd5Md43IixzaojMJc6OV1Pk'
SEND_FILE_MAX_AGE_DEFAULT None
SERVER_NAME None
SESSION_COOKIE_DOMAIN ''
SESSION_COOKIE_HTTPONLY True
SESSION_COOKIE_NAME 'ckan'
SESSION_COOKIE_PATH ''
SESSION_COOKIE_SAMESITE 'Lax'
SESSION_COOKIE_SECURE False
SESSION_KEY_PREFIX 'session:'
SESSION_PERMANENT True
SESSION_REFRESH_EACH_REQUEST False
SESSION_TYPE 'cookie'
SESSION_USE_SIGNER False
smtp.mail_from ''
smtp.password ''
smtp.reply_to ''
smtp.server 'localhost'
smtp.starttls False
smtp.user ''
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'}
WTF_CSRF_SECRET_KEY 'string:OuonVd5Md43IixzaojMJc6OV1Pk'
WTF_CSRF_SSL_STRICT True
WTF_CSRF_TIME_LIMIT 3600
WTF_I18N_ENABLED True
Close

Templates

snippets/csrf_input.html

Variable Value
csrf_token <function generate_csrf at 0x7fe10bdccee0>
current_user <ckan.model.user.AnonymousUser object at 0x7fe106299ba0>
g <flask.g of 'ckan.config.middleware.flask_app'>
request <Request 'http://192.168.122.4/dataset/groups/global-elus' [GET]>
session <SecureCookieSession {'_csrf_token': '03496353c566f4c26bfb90e2a65f7628e212927a', '_permanent': True, '_fresh': False}>

package/group_list.html

Variable Value
csrf_token <function generate_csrf at 0x7fe10bdccee0>
current_user <ckan.model.user.AnonymousUser object at 0x7fe106299ba0>
dataset_type 'dataset'
g <flask.g of 'ckan.config.middleware.flask_app'>
group_dropdown []
pkg_dict {'author': 'Sayre, R., J. Dangermond, C. Frye, R. Vaughan, P. Aniello, S. Breyer, D. Cribbs, D. Hopkins, R. Nauman, W. Derrenbacher, D.Wright, C. Brown, C. Convis, J. Smith, L. Benson, D. Paco VanSistine, H. Warner, J. Cress, J. Danielson, S. Hamann, T. Cecere, A. Reddy, D. Burton, A. Grosse, D. True, M. Metzger, J. Hartmann, N. Moosdorf, H. Dürr, M. Paganini, P. DeFourny, O. Arino, S. Maynard, M. Anderson, and P. Comer. ', 'author_email': '', 'creator_user_id': '0ed6839a-e7cd-4504-9192-fa73b3cc8756', 'id': '03a2ff61-3021-405b-bf0b-614f3fa2ce20', 'isopen': False, 'license_id': 'cc-nc', 'license_title': 'Creative Commons Non-Commercial (Any)', 'license_url': 'http://creativecommons.org/licenses/by-nc/2.0/', 'maintainer': 'Andrew Zammit Mangion', 'maintainer_email': 'azm [at] uow.edu.au', 'metadata_created': '2015-01-25T02:11:46.436349', 'metadata_modified': '2017-02-06T02:35:59.602201', 'name': 'global-elus', 'notes': 'Description:\r\n-------\r\n\r\nIn response to the need and an intergovernmental commission for a high resolution and data-derived global ecosystem map, land surface elements of global ecological pattern were characterised in an ecophysiographic stratification of the planet. The stratification produced 3,923 terrestrial ecological land units (ELUs) at a base resolution of 250 meters. The ELUs were derived from data on land surface features in a three step approach. The first step involved acquiring or developing four global raster datalayers representing the primary components of ecosystem structure: bioclimate, landform, lithology, and land cover. These datasets generally represent the most accurate, current, globally comprehensive, and finest spatial and thematic resolution data available for each of the four inputs. The second step involved a spatial combination of the four inputs into a single, new integrated raster dataset where every cell represents a combination of values from the bioclimate, landforms, lithology, and land cover datalayers. This foundational global raster datalayer, called ecological facets (EFs), contains 47,650 unique combinations of the four inputs. The third step involved an aggregation of the EFs into the 3,923 ELUs.\r\n\r\n![http://esriurl.com/EcoTapestry](http://blogs.esri.com/esri/esri-insider/files/2014/12/EcoTapApp_SS_620_ds.jpg)\r\n\r\nUse constraint:\r\n-------\r\n\r\nAlthough these data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty expressed or implied is made regarding the display or utility of the data on any other system, or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. The U.S. Geological Survey shall not be held liable for improper or incorrect use of the data described and/or contained herein. We make every effort to provide and maintain accurate, complete, usable, and timely information on our Web sites. These data and information are provided with the understanding that they are not guaranteed to be correct or complete. Users are cautioned to consider carefully the provisional nature of these data and information before using them for decisions that concern personal or public safety or the conduct of business that involves substantial monetary or operational consequences. Conclusions drawn from, or actions undertaken on the basis of, such data and information are the sole responsibility of the user. Any use of trade, product or firm names is for descriptive purposes only and does not imply endorsement by the U. S. Government.\r\n\r\nData quality:\r\n-----\r\n\r\nThe primary accuracy assessment approach was to compare EFs at randomly generated points to their corresponding locations on high resolution imagery. This match of EFs to imagery was generally very high, with the following level of confirmation observed: Africa, 91%; California, 98%; Australia, 98%; and elsewhere in North America, 97%. The secondary accuracy assessment approaches included comparing ELUs to their corresponding ecosystem labels on the three GEO continental-scale ecosystem maps for South America, the conterminous United States, and Africa. The results for those comparisons were 88%, 87%, and 94%, for South America, the conterminous United States, and Africa, respectively. The comparison of EFs to other sources of thematic information yielded the following probable matches: Africa (81%), California (88%), Australia (96%) and elsewhere in North America (93%). Finally, for the Degree Confluence project points, a 100% match between the EFs and the VGI (photos and descriptions) was observed for Australia, and a 98% match was observed for elsewhere in North America.\r\n\r\nThe quality of the data used in the global stratification will obviously influence the quality of the derived ecosystem products, and anomalous values were found in each of the input layers. While some of these data quality issues are discussed below, it is important to first note that both the input layers and the output products should be considered as collaborative best efforts and works in progress, rather than definitive, current, and complete representations of their themes. The production of any high resolution, globally comprehensive datalayer that characterises a particular feature of the environment is an ambitious and sometimes very difficult undertaking. These efforts to develop and disseminate best available datasets are appreciated by the scientific community, and making the information broadly available is the best way to ensure it can be improved over time. Identification of anomalous values and other data quality issues in underlying data is important for both the understanding of unexpected results, and for the improvement of the input datasets. The bioclimates layer, as mentioned, represents an interpolated data surface from point observations obtained at meteorological stations. Some areas of the planet are not well-covered by weather stations, and the modeled climate regions in those areas (e.g. western Sahara Desert region) were developed from very little data. Moreover, we felt the original bioclimate regions were underrepresentative of aridity, and we modified the data accordingly. The landforms layer was built from a 250 m global DEM, and 250 m was the base resolution of the effort, given the big data nature of the effort and the difficulty of working at finer spatial resolutions. Nevertheless, 90 m and 30 m global DEMs do exist, and a finer spatial resolution global landforms layer could be developed. The global lithology layer, built as a compendium of a variety of best available regional and national scale lithology datasets, lacks complete attribution at all levels of the hierarchy, and does not attempt to reconcile or harmonise classes across maps from adjacent geographies produced by different organizations. The above-mentioned limitations in the data really represent opportunities for collective improvements in the characterization of ecologically important Earth surface features, and we anticipate working with these data providers and others in future collaborations to advance the quality, currency, resolution, and accessibility of Earth science data.\r\n\r\nProcedure:\r\n-----\r\n\r\nThe fundamental approach undertaken was to stratify the Earth into physically distinct areas with their associated land cover. The stratification was executed as a geospatial combination of the four input layers (bioclimate, landform, lithology, and land cover) to produce a single raster datalayer where every cell represented a unique combination of the four inputs. Following the production of the foundational raster datalayer, a data reduction step was undertaken to reduce the large number of combinations produced from the union of the input datalayers. The approach was undertaken in three steps. Step One involved acquiring or developing the four input raster base layers (bioclimates, landforms, lithology, and land cover), and reconciling them to a standard, 250 meter global raster framework. The choice of 250 m as the base resolution for the project was based on the availability of a global 250 m digital elevation model (Danielson and Gesch, 2011) whose raster framework could be used as the geospatial reference standard, as well as the desire to improve over the typical square kilometer resolution associated with many global data products (e.g. Gesch et al., 1999; Hijmans et al., 2005). Step Two involved combining all four raster inputs into a single master 250 m global raster datalayer where each cell was the resulting combination of the values from the four input rasters. This foundational raster dataset was called the ecological facets (EFs) layer. Finally, Step Three involved reducing the many classes of EFs resulting from the spatial combination into a more manageable and cartographically approachable number of ecological land units (ELUs). The aggregation was achieved by generalising the input layer attribute classes. This approach to developing global ELUs can be considered as classification neutral in the sense that no a priori ecosystem classification was used to label the mapped entities.\r\n\r\nOther:\r\n----------\r\n\r\nFor more user-friendly information on this dataset visit http://blogs.esri.com/esri/esri-insider/2014/12/09/the-first-detailed-ecological-land-unitsmap-in-the-world/. An interactive application for viewing the data is available at http://ecoexplorer.arcgis.com/eco/\r\n\r\nReferences:\r\n---------\r\n\r\nSayre, R., J. Dangermond, C. Frye, R. Vaughan, P. Aniello, S. Breyer, D. Cribbs, D. Hopkins, R. Nauman, W. Derrenbacher, D.Wright, C. Brown, C. Convis, J. Smith, L. Benson, D. Paco VanSistine, H. Warner, J. Cress, J. Danielson, S. Hamann, T. Cecere, A. Reddy, D. Burton, A. Grosse, D. True, M. Metzger, J. Hartmann, N. Moosdorf, H. Dürr, M. Paganini, P. DeFourny, O. Arino, S. Maynard, M. Anderson, and P. Comer. 2014. A New Map of Global Ecological Land Units — An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages.', 'num_resources': 1, 'num_tags': 5, '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 Ecological Land Units (ELUs)', 'type': 'dataset', 'url': '', 'version': '', 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2015-01-25T13:12:29.526458', 'datastore_active': False, 'description': '', 'format': 'TIF', 'hash': '', 'id': 'a9361969-8413-4e3b-9423-32262592924c', 'last_modified': None, 'metadata_modified': '2015-01-25T13:12:29.526458', 'mimetype': None, 'mimetype_inner': None, 'name': 'USGS Geosciences and Environmental Change Science Center (GECSC) Outgoing Datasets', 'package_id': '03a2ff61-3021-405b-bf0b-614f3fa2ce20', 'position': 0, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'http://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/', 'url_type': None}], 'tags': [{'display_name': 'bioclimate', 'id': '5b7ad1ae-9d20-4659-b774-c7302c969f4f', 'name': 'bioclimate', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'ecosystems', 'id': '3d088806-ca29-4558-b0a0-6f9b9d685e8e', 'name': 'ecosystems', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'land cover', 'id': 'f8aeb8b0-4202-47ea-a7a6-471bea6a47c0', 'name': 'land cover', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'landforms', 'id': '52c5e391-69d2-48f0-9f3b-cf33c56b4a20', 'name': 'landforms', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'lithology', 'id': '84bd1f5a-8b14-42aa-bc45-73b25c1ff5f4', 'name': 'lithology', 'state': 'active', 'vocabulary_id': None}], 'extras': [], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}
request <Request 'http://192.168.122.4/dataset/groups/global-elus' [GET]>
session <SecureCookieSession {'_csrf_token': '03496353c566f4c26bfb90e2a65f7628e212927a', '_permanent': True, '_fresh': False}>
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Route List

URL route Endpoint name HTTP methods Is alias Redirect to
/ home.index GET, HEAD, OPTIONS False None
/<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
/api/<int(min=1, max=2):ver>/util/dataset/autocomplete api.dataset_autocomplete GET, HEAD, OPTIONS False None
/api/<int(min=1, max=2):ver>/util/group/autocomplete api.group_autocomplete GET, HEAD, OPTIONS False None
/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
/api/<int(min=1, max=2):ver>/util/tag/autocomplete api.tag_autocomplete GET, HEAD, OPTIONS False None
/api/<int(min=1, max=2):ver>/util/user/autocomplete api.user_autocomplete GET, HEAD, OPTIONS False None
/api/<int(min=1, max=3):ver> api.get_api GET, HEAD, OPTIONS False None
/api/<int(min=3, max=3):ver>/action/<logic_function> api.action GET, HEAD, OPTIONS, POST False None
/api/action/<logic_function> api.action GET, HEAD, OPTIONS, POST False None
/api/i18n/<lang> api.i18n_js_translations GET, HEAD, OPTIONS False None
/api/util/dataset/autocomplete api.dataset_autocomplete GET, HEAD, OPTIONS False None
/api/util/group/autocomplete api.group_autocomplete GET, HEAD, OPTIONS False None
/api/util/organization/autocomplete api.organization_autocomplete GET, HEAD, OPTIONS False None
/api/util/resource/format_autocomplete api.format_autocomplete GET, HEAD, OPTIONS False None
/api/util/snippet/<snippet_path> api.snippet GET, HEAD, OPTIONS False None
/api/util/tag/autocomplete api.tag_autocomplete GET, HEAD, OPTIONS False None
/api/util/user/autocomplete api.user_autocomplete GET, HEAD, OPTIONS False None
/ckan-admin/ admin.index GET, HEAD, OPTIONS False None
/ckan-admin/config admin.config GET, HEAD, OPTIONS, POST False None
/ckan-admin/reset_config admin.reset_config GET, HEAD, OPTIONS, POST False None
/ckan-admin/trash admin.trash GET, HEAD, OPTIONS, POST False None
/ckan/_debug_toolbar/static/<path:filename> _debug_toolbar.static GET, HEAD, OPTIONS False None
/dashboard/ activity.dashboard GET, HEAD, OPTIONS False None
/dashboard/datasets dashboard.datasets GET, HEAD, OPTIONS False None
/dashboard/groups dashboard.groups GET, HEAD, OPTIONS False None
/dashboard/organizations dashboard.organizations GET, HEAD, OPTIONS False None
/dataset/ dataset.search GET, HEAD, OPTIONS False None
/dataset/<id> dataset.read GET, HEAD, OPTIONS False None
/dataset/<id>/dictionary/<resource_id> datastore.dictionary GET, HEAD, OPTIONS, POST False None
/dataset/<id>/history/<activity_id> activity.package_history GET, HEAD, OPTIONS False None
/dataset/<id>/resource/<resource_id> dataset_resource.read GET, HEAD, OPTIONS False None
/dataset/<id>/resource/<resource_id> resource.read GET, HEAD, OPTIONS False None
/dataset/<id>/resource/<resource_id>/delete dataset_resource.delete GET, HEAD, OPTIONS, POST False None
/dataset/<id>/resource/<resource_id>/delete resource.delete GET, HEAD, OPTIONS, POST False None
/dataset/<id>/resource/<resource_id>/download dataset_resource.download GET, HEAD, OPTIONS False None
/dataset/<id>/resource/<resource_id>/download resource.download GET, HEAD, OPTIONS False None
/dataset/<id>/resource/<resource_id>/download/<filename> dataset_resource.download GET, HEAD, OPTIONS False None
/dataset/<id>/resource/<resource_id>/download/<filename> resource.download GET, HEAD, OPTIONS False None
/dataset/<id>/resource/<resource_id>/edit dataset_resource.edit GET, HEAD, OPTIONS, POST False None
/dataset/<id>/resource/<resource_id>/edit resource.edit GET, HEAD, OPTIONS, POST False None
/dataset/<id>/resource/<resource_id>/edit_view/<view_id> dataset_resource.edit_view GET, HEAD, OPTIONS, POST False None
/dataset/<id>/resource/<resource_id>/edit_view/<view_id> resource.edit_view GET, HEAD, OPTIONS, POST False None
/dataset/<id>/resource/<resource_id>/new_view dataset_resource.edit_view GET, HEAD, OPTIONS, POST False None
/dataset/<id>/resource/<resource_id>/new_view resource.edit_view GET, HEAD, OPTIONS, POST False None
/dataset/<id>/resource/<resource_id>/view dataset_resource.view GET, HEAD, OPTIONS False None
/dataset/<id>/resource/<resource_id>/view resource.view GET, HEAD, OPTIONS False None
/dataset/<id>/resource/<resource_id>/view/<view_id> dataset_resource.view GET, HEAD, OPTIONS False None
/dataset/<id>/resource/<resource_id>/view/<view_id> resource.view GET, HEAD, OPTIONS False None
/dataset/<id>/resource/<resource_id>/views dataset_resource.views GET, HEAD, OPTIONS False None
/dataset/<id>/resource/<resource_id>/views resource.views GET, HEAD, OPTIONS False None
/dataset/<id>/resource/new dataset_resource.new GET, HEAD, OPTIONS, POST False None
/dataset/<id>/resource/new resource.new GET, HEAD, OPTIONS, POST False None
/dataset/<id>/resources/<resource_id>/history/<activity_id> activity.resource_history GET, HEAD, OPTIONS False None
/dataset/activity/<id> activity.package_activity GET, HEAD, OPTIONS False None
/dataset/changes/<id> activity.package_changes GET, HEAD, OPTIONS False None
/dataset/changes_multiple activity.package_changes_multiple GET, HEAD, OPTIONS False None
/dataset/collaborators/<id> dataset.collaborators_read GET, HEAD, OPTIONS False None
/dataset/collaborators/<id>/delete/<user_id> dataset.collaborator_delete GET, HEAD, OPTIONS, POST False None
/dataset/collaborators/<id>/new dataset.new_collaborator GET, HEAD, OPTIONS, POST False None
/dataset/delete/<id> dataset.delete GET, HEAD, OPTIONS, POST False None
/dataset/edit/<id> dataset.edit GET, HEAD, OPTIONS, POST False None
/dataset/follow/<id> dataset.follow OPTIONS, POST False None
/dataset/followers/<id> dataset.followers GET, HEAD, OPTIONS False None
/dataset/groups/<id> dataset.groups GET, HEAD, OPTIONS, POST False None
/dataset/new dataset.new GET, HEAD, OPTIONS, POST False None
/dataset/resources/<id> dataset.resources GET, HEAD, OPTIONS False None
/dataset/unfollow/<id> dataset.unfollow OPTIONS, POST False None
/datastore/dump/<resource_id> datastore.dump GET, HEAD, OPTIONS False None
/feeds/custom.atom feeds.custom GET, HEAD, OPTIONS False None
/feeds/dataset.atom feeds.general GET, HEAD, OPTIONS False None
/feeds/group/<string:id>.atom feeds.group GET, HEAD, OPTIONS False None
/feeds/organization/<string:id>.atom feeds.organization GET, HEAD, OPTIONS False None
/feeds/tag/<string:id>.atom feeds.tag GET, HEAD, OPTIONS False None
/group/ group.index GET, HEAD, OPTIONS False None
/group/<id> group.read GET, HEAD, OPTIONS False None
/group/about/<id> group.about GET, HEAD, OPTIONS False None
/group/activity/<id> activity.group_activity GET, HEAD, OPTIONS False None
/group/admins/<id> group.admins GET, HEAD, OPTIONS, POST False None
/group/bulk_process/<id> group.bulk_process GET, HEAD, OPTIONS, POST False None
/group/changes/<id> activity.group_changes GET, HEAD, OPTIONS False None
/group/changes_multiple activity.group_changes_multiple GET, HEAD, OPTIONS False None
/group/delete/<id> group.delete GET, HEAD, OPTIONS, POST False None
/group/edit/<id> group.edit GET, HEAD, OPTIONS, POST False None
/group/follow/<id> group.follow OPTIONS, POST False None
/group/followers/<id> group.followers GET, HEAD, OPTIONS, POST False None
/group/manage_members/<id> group.manage_members GET, HEAD, OPTIONS, POST False None
/group/member_delete/<id> group.member_delete GET, HEAD, OPTIONS, POST False None
/group/member_dump/<id> group.member_dump GET, HEAD, OPTIONS False None
/group/member_new/<id> group.member_new GET, HEAD, OPTIONS, POST False None
/group/members/<id> group.members GET, HEAD, OPTIONS False None
/group/new group.new GET, HEAD, OPTIONS, POST False None
/group/unfollow/<id> group.unfollow OPTIONS, POST False None
/no/ home.redirect_locale GET, HEAD, OPTIONS False None
/no/<path:path> home.redirect_locale GET, HEAD, OPTIONS False None
/organization/ organization.index GET, HEAD, OPTIONS False None
/organization/<id> organization.read GET, HEAD, OPTIONS False None
/organization/about/<id> organization.about GET, HEAD, OPTIONS False None
/organization/activity/<id> activity.organization_activity GET, HEAD, OPTIONS False None
/organization/admins/<id> organization.admins GET, HEAD, OPTIONS, POST False None
/organization/bulk_process/<id> organization.bulk_process GET, HEAD, OPTIONS, POST False None
/organization/changes/<id> activity.organization_changes GET, HEAD, OPTIONS False None
/organization/changes_multiple activity.organization_changes_multiple GET, HEAD, OPTIONS False None
/organization/delete/<id> organization.delete GET, HEAD, OPTIONS, POST False None
/organization/edit/<id> organization.edit GET, HEAD, OPTIONS, POST False None
/organization/follow/<id> organization.follow OPTIONS, POST False None
/organization/followers/<id> organization.followers GET, HEAD, OPTIONS, POST False None
/organization/manage_members/<id> organization.manage_members GET, HEAD, OPTIONS, POST False None
/organization/member_delete/<id> organization.member_delete GET, HEAD, OPTIONS, POST False None
/organization/member_dump/<id> organization.member_dump GET, HEAD, OPTIONS False None
/organization/member_new/<id> organization.member_new GET, HEAD, OPTIONS, POST False None
/organization/members/<id> organization.members GET, HEAD, OPTIONS False None
/organization/new organization.new GET, HEAD, OPTIONS, POST False None
/organization/unfollow/<id> organization.unfollow OPTIONS, POST False None
/robots.txt home.robots_txt GET, HEAD, OPTIONS False None
/testing/primer util.primer GET, HEAD, OPTIONS False None
/user/ user.index GET, HEAD, OPTIONS False None
/user/<id> user.read GET, HEAD, OPTIONS False None
/user/<id>/api-tokens user.api_tokens GET, HEAD, OPTIONS, POST False None
/user/<id>/api-tokens/<jti>/revoke user.api_token_revoke OPTIONS, POST False None
/user/<id>/groups user.read_groups GET, HEAD, OPTIONS False None
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pkg_dict {'author': 'Sayre, R., J. Dangermond, C. Frye, R. Vaughan, P. Aniello, S. Breyer, D. Cribbs, D. Hopkins, R. Nauman, W. Derrenbacher, D.Wright, C. Brown, C. Convis, J. Smith, L. Benson, D. Paco VanSistine, H. Warner, J. Cress, J. Danielson, S. Hamann, T. Cecere, A. Reddy, D. Burton, A. Grosse, D. True, M. Metzger, J. Hartmann, N. Moosdorf, H. Dürr, M. Paganini, P. DeFourny, O. Arino, S. Maynard, M. Anderson, and P. Comer. ', 'author_email': '', 'creator_user_id': '0ed6839a-e7cd-4504-9192-fa73b3cc8756', 'id': '03a2ff61-3021-405b-bf0b-614f3fa2ce20', 'isopen': False, 'license_id': 'cc-nc', 'license_title': 'Creative Commons Non-Commercial (Any)', 'license_url': 'http://creativecommons.org/licenses/by-nc/2.0/', 'maintainer': 'Andrew Zammit Mangion', 'maintainer_email': 'azm [at] uow.edu.au', 'metadata_created': '2015-01-25T02:11:46.436349', 'metadata_modified': '2017-02-06T02:35:59.602201', 'name': 'global-elus', 'notes': 'Description:\r\n-------\r\n\r\nIn response to the need and an intergovernmental commission for a high resolution and data-derived global ecosystem map, land surface elements of global ecological pattern were characterised in an ecophysiographic stratification of the planet. The stratification produced 3,923 terrestrial ecological land units (ELUs) at a base resolution of 250 meters. The ELUs were derived from data on land surface features in a three step approach. The first step involved acquiring or developing four global raster datalayers representing the primary components of ecosystem structure: bioclimate, landform, lithology, and land cover. These datasets generally represent the most accurate, current, globally comprehensive, and finest spatial and thematic resolution data available for each of the four inputs. The second step involved a spatial combination of the four inputs into a single, new integrated raster dataset where every cell represents a combination of values from the bioclimate, landforms, lithology, and land cover datalayers. This foundational global raster datalayer, called ecological facets (EFs), contains 47,650 unique combinations of the four inputs. The third step involved an aggregation of the EFs into the 3,923 ELUs.\r\n\r\n![http://esriurl.com/EcoTapestry](http://blogs.esri.com/esri/esri-insider/files/2014/12/EcoTapApp_SS_620_ds.jpg)\r\n\r\nUse constraint:\r\n-------\r\n\r\nAlthough these data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty expressed or implied is made regarding the display or utility of the data on any other system, or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. The U.S. Geological Survey shall not be held liable for improper or incorrect use of the data described and/or contained herein. We make every effort to provide and maintain accurate, complete, usable, and timely information on our Web sites. These data and information are provided with the understanding that they are not guaranteed to be correct or complete. Users are cautioned to consider carefully the provisional nature of these data and information before using them for decisions that concern personal or public safety or the conduct of business that involves substantial monetary or operational consequences. Conclusions drawn from, or actions undertaken on the basis of, such data and information are the sole responsibility of the user. Any use of trade, product or firm names is for descriptive purposes only and does not imply endorsement by the U. S. Government.\r\n\r\nData quality:\r\n-----\r\n\r\nThe primary accuracy assessment approach was to compare EFs at randomly generated points to their corresponding locations on high resolution imagery. This match of EFs to imagery was generally very high, with the following level of confirmation observed: Africa, 91%; California, 98%; Australia, 98%; and elsewhere in North America, 97%. The secondary accuracy assessment approaches included comparing ELUs to their corresponding ecosystem labels on the three GEO continental-scale ecosystem maps for South America, the conterminous United States, and Africa. The results for those comparisons were 88%, 87%, and 94%, for South America, the conterminous United States, and Africa, respectively. The comparison of EFs to other sources of thematic information yielded the following probable matches: Africa (81%), California (88%), Australia (96%) and elsewhere in North America (93%). Finally, for the Degree Confluence project points, a 100% match between the EFs and the VGI (photos and descriptions) was observed for Australia, and a 98% match was observed for elsewhere in North America.\r\n\r\nThe quality of the data used in the global stratification will obviously influence the quality of the derived ecosystem products, and anomalous values were found in each of the input layers. While some of these data quality issues are discussed below, it is important to first note that both the input layers and the output products should be considered as collaborative best efforts and works in progress, rather than definitive, current, and complete representations of their themes. The production of any high resolution, globally comprehensive datalayer that characterises a particular feature of the environment is an ambitious and sometimes very difficult undertaking. These efforts to develop and disseminate best available datasets are appreciated by the scientific community, and making the information broadly available is the best way to ensure it can be improved over time. Identification of anomalous values and other data quality issues in underlying data is important for both the understanding of unexpected results, and for the improvement of the input datasets. The bioclimates layer, as mentioned, represents an interpolated data surface from point observations obtained at meteorological stations. Some areas of the planet are not well-covered by weather stations, and the modeled climate regions in those areas (e.g. western Sahara Desert region) were developed from very little data. Moreover, we felt the original bioclimate regions were underrepresentative of aridity, and we modified the data accordingly. The landforms layer was built from a 250 m global DEM, and 250 m was the base resolution of the effort, given the big data nature of the effort and the difficulty of working at finer spatial resolutions. Nevertheless, 90 m and 30 m global DEMs do exist, and a finer spatial resolution global landforms layer could be developed. The global lithology layer, built as a compendium of a variety of best available regional and national scale lithology datasets, lacks complete attribution at all levels of the hierarchy, and does not attempt to reconcile or harmonise classes across maps from adjacent geographies produced by different organizations. The above-mentioned limitations in the data really represent opportunities for collective improvements in the characterization of ecologically important Earth surface features, and we anticipate working with these data providers and others in future collaborations to advance the quality, currency, resolution, and accessibility of Earth science data.\r\n\r\nProcedure:\r\n-----\r\n\r\nThe fundamental approach undertaken was to stratify the Earth into physically distinct areas with their associated land cover. The stratification was executed as a geospatial combination of the four input layers (bioclimate, landform, lithology, and land cover) to produce a single raster datalayer where every cell represented a unique combination of the four inputs. Following the production of the foundational raster datalayer, a data reduction step was undertaken to reduce the large number of combinations produced from the union of the input datalayers. The approach was undertaken in three steps. Step One involved acquiring or developing the four input raster base layers (bioclimates, landforms, lithology, and land cover), and reconciling them to a standard, 250 meter global raster framework. The choice of 250 m as the base resolution for the project was based on the availability of a global 250 m digital elevation model (Danielson and Gesch, 2011) whose raster framework could be used as the geospatial reference standard, as well as the desire to improve over the typical square kilometer resolution associated with many global data products (e.g. Gesch et al., 1999; Hijmans et al., 2005). Step Two involved combining all four raster inputs into a single master 250 m global raster datalayer where each cell was the resulting combination of the values from the four input rasters. This foundational raster dataset was called the ecological facets (EFs) layer. Finally, Step Three involved reducing the many classes of EFs resulting from the spatial combination into a more manageable and cartographically approachable number of ecological land units (ELUs). The aggregation was achieved by generalising the input layer attribute classes. This approach to developing global ELUs can be considered as classification neutral in the sense that no a priori ecosystem classification was used to label the mapped entities.\r\n\r\nOther:\r\n----------\r\n\r\nFor more user-friendly information on this dataset visit http://blogs.esri.com/esri/esri-insider/2014/12/09/the-first-detailed-ecological-land-unitsmap-in-the-world/. An interactive application for viewing the data is available at http://ecoexplorer.arcgis.com/eco/\r\n\r\nReferences:\r\n---------\r\n\r\nSayre, R., J. Dangermond, C. Frye, R. Vaughan, P. Aniello, S. Breyer, D. Cribbs, D. Hopkins, R. Nauman, W. Derrenbacher, D.Wright, C. Brown, C. Convis, J. Smith, L. Benson, D. Paco VanSistine, H. Warner, J. Cress, J. Danielson, S. Hamann, T. Cecere, A. Reddy, D. Burton, A. Grosse, D. True, M. Metzger, J. Hartmann, N. Moosdorf, H. Dürr, M. Paganini, P. DeFourny, O. Arino, S. Maynard, M. Anderson, and P. Comer. 2014. A New Map of Global Ecological Land Units — An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages.', 'num_resources': 1, 'num_tags': 5, '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 Ecological Land Units (ELUs)', 'type': 'dataset', 'url': '', 'version': '', 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2015-01-25T13:12:29.526458', 'datastore_active': False, 'description': '', 'format': 'TIF', 'hash': '', 'id': 'a9361969-8413-4e3b-9423-32262592924c', 'last_modified': None, 'metadata_modified': '2015-01-25T13:12:29.526458', 'mimetype': None, 'mimetype_inner': None, 'name': 'USGS Geosciences and Environmental Change Science Center (GECSC) Outgoing Datasets', 'package_id': '03a2ff61-3021-405b-bf0b-614f3fa2ce20', 'position': 0, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'http://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/', 'url_type': None}], 'tags': [{'display_name': 'bioclimate', 'id': '5b7ad1ae-9d20-4659-b774-c7302c969f4f', 'name': 'bioclimate', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'ecosystems', 'id': '3d088806-ca29-4558-b0a0-6f9b9d685e8e', 'name': 'ecosystems', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'land cover', 'id': 'f8aeb8b0-4202-47ea-a7a6-471bea6a47c0', 'name': 'land cover', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'landforms', 'id': '52c5e391-69d2-48f0-9f3b-cf33c56b4a20', 'name': 'landforms', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'lithology', 'id': '84bd1f5a-8b14-42aa-bc45-73b25c1ff5f4', 'name': 'lithology', 'state': 'active', 'vocabulary_id': None}], 'extras': [], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}
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