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compact overview & easy to understand

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nice visualization examples + available code

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Arctic Climate Seasonality and Variability

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Arctic Regional Variability Sea Ice Seasonal Cycle Astronomical Seasons Arctic Large-Scale Atmospheric Circulation Global Climate Seasonality The views presented here only reflect my own. These fig…

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Skip to main content

Manage Your Data

Video Tutorials

Watch tutorials on everything from transferring data to automating tasks

Integrate Your App

Python SDKs

A convenient Pythonic interface to Globus Web APIs

JavaScript SDK

Idiomatic JavaScript API that allows integration of Globus services in web applications and JavaScript runtimes (i.e., Node.js).

Make Your Data Accessible

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History

This book grew out of a course developed at Columbia University called Research Computing in Earth Science. It was written mostly by Ryan Abernathey, with significant contributions from Kerry Key, Tim Crone, and Julius Busecke. By separating the book from the class, we hope to create an open-source community resource for Python education in the Earth and Environmental Sciences.

Motivation and Scope

Computing has become an indispensable tool for nearly all Earth and Environmental Scientists, but it doesn't often appear in our curriculums. This book focuses on data analysis, a subset of computing in which the data already exist, e.g.from observations or from the output of a simulation, but have to be transformed into understanding. There are many different ways to gain understanding, but most workflows often boil down to:

read data files

perform some analysis operations, from very simple (e.g. take the mean) to very complex (e.g. train a deep neural network)

visualize the output in a plot

This book doesn't attempt to teach deep learning; its goal is to teach the basic foundations of Earth and Environmental Data Science which are often overlooked. The material is designed to be accessible for Earth Science graduate students in any discipline, with no prerequisites.

This book is intended to introduce new graduate students to modern computing software, programming tools and best practices that are broadly applicable to the analysis and visualization of Earth and Environmental data. This includes an introduction to Unix, version control, and basic programming in the open-source Python language. The bulk of the content is devoted to in-depth exploration of the numerical analysis and visualization packages which comprise the modern Scientific Python ecosystem, including Numpy, Scipy, Matplotlib, Pandas, Xarray, using real Earth and Environmental datasets.

Learning Goals

After completing all of the material, students should have the ability to:

Use unix commands to work with files and directories

Navigate the JupyterHub Environment effectively

Identify common geoscience data formats and the python packages which can load them

Perform basic exploratory data analysis on Earth and Environmental data, distinguishing between

Use visualization to enhance interpretation of data, including maps and interactive visualizations

Construct complete, well-structured programs in Python

Practice reproducible research through version control and binder

Recommended Course Structure

This course material can be used however you want. However, it was developed for a full-credit, semester-long course that meets twice a week. The idea is to complete one lecture and one homework assignment per week. One of the weekly meetings consists of a lecture in which the instructor presents the week's lecture, with students typing along. The other should be used as "collaborative time," during which the students work on their assignments, ask questions, and interact with their peers. A final project is an important capstone experience for a semester-long course.

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An Introduction to Earth and Environmental Data Science

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History: This book grew out of a course developed at Columbia University called Research Computing in Earth Science. It was written mostly by Ryan Abernathey, with significant contributions from Ke...

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This is a gallery of Jupyter Books built from across the community. If you’d like to add your book to this list, simply add an entry to this gallery.yml file and open a Pull Request to add it. Deep…

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Video tutoria: https://www.youtube.com/watch?v=8JJ101D3knE

1) Our own tutorial

2) comand line? web side version?

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Beginners course PyLadies Vienna: GitHub

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If you start working on a project with someone, you will need to share your git repository with him, so you can both benefit from the collaboration and share the changes.

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EUMETSAT Data Access Client (EUMDAC) guide | EUMETSAT - User Portal

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The EUMETSAT user portal is the one-stop-shop for our satellite data users. By exploring the portal, users can discover, access, and learn how to exploit our data.

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IMO: A relic typical of the scientific community doomed to extinction :P

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yyyyy??????

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https://jupyterbook.org/en/stable/interactive/launchbuttons.html

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How Jupyter Book and Sphinx relate to one another

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Jupyter Book utilizes Sphinx heavily under the hood.
In fact, Jupyter Book can be thought of as an opinionated distribution of Sphinx.
This is a short explanation of how Jupyter Book and Sphinx relate to one another.

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To tutorial with runing the code

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https://github.com/topics/jekyll-theme

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Jekyll * Simple, blog-aware, static sites

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Free hosting with GitHub Pages

Sick of dealing with hosting companies? GitHub Pages are powered by Jekyll, so you can easily deploy your site using GitHub for free- custom domain name and all.

Learn more about GitHub Pages →

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Jekyll * Simple, blog-aware, static sites

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Sick of dealing with hosting companies? GitHub Pages are powered by Jekyll, so you can easily deploy your site using GitHub for free- custom domain name and all. Learn more about GitHub Pages →

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Screen shot from : https://fairprocessframework.org/fda/

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Support to create enviroment and run jupytor notebook

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findability by search engine: Google, Bing, Yandex and Yahoo

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The schemas are a set of 'types', each associated with a set of properties. The types are arranged in a hierarchy.
The vocabulary currently consists of 836 Types, 1361 Properties, and 341 Enumeration values.

Browse the full hierarchy in HTML:
Or you can jump directly to a commonly used type:
See also the releases page for recent updates and project history.

We also have a small set of primitive data types for numbers, text, etc. More details about the data model, etc. are available here.

Developer information / Download Machine Readable files (RDF, JSON-LD, etc):

Extensions

As schema.org has grown, we have explored various mechanisms for community extension as a way of adding more detailed descriptive vocabulary that builds on the schema.org core. Some areas of Schema.org were developed as "named extensions", and have dedicated entry pages. We previously called these "hosted" extensions, but they are best considered simply as views into a single collection of schema definitions.

Hosted Sections

For example, via the auto section there is a property for emissionsCO2, and via the bib section we have a property publisherImprint. However, from the perspective of a publisher, these are simply schema.org properties.

We have a few of these areas:

Note: the 'pending' and 'meta' hosted sections are part of schema.org's schema development process.

We use the 'pending' section as a staging area for new schema.org terms that are under discussion and review. Implementors and publishers are cautioned that terms in the pending section may lack consensus and that terminology and definitions could still change significantly after community and steering group review. Consumers of schema.org data who encourage use of such terms are strongly encouraged to update implementations and documentation to track any evolving changes, and to share early implementation feedback with the wider community.

Attic ( attic.schema.org) is a special area where terms are archived when deprecated from the core and other sections, or removed from pending as not accepted into the full vocabulary. References to terms in the attic area are not normally displayed unless accessed via the term identifier or via the attic.schema.org home page. Implementors and data publishers are cautioned not to use terms in the attic area.

Unlike other core and section terms, these areas may be updated at any time without the need for a full release.

External Extensions

The schema.org steering group does not officially approve external extensions - they are fully independent. We list here some notable extensions that extend schema.org in interesting and useful ways.

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The schemas are a set of 'types', each associated with a set of properties. The types are arranged in a hierarchy. The vocabulary currently consists of 836 Types, 1361 Properties, and 341 Enumeration values. Browse the full hierarchy in HTML: Or you can jump directly to a commonly used type:See also the releases page for recent updates and project history.

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Assessing semantic interoperability in environmental sciences: variety of approaches and semantic artefacts - Scientific Data

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Scientific Data - Assessing semantic interoperability in environmental sciences: variety of approaches and semantic artefacts

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W3C Permanent Identifier Community Group

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Secure, permanent URLs for your Web application that will stand the test of time.

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Screen shot from:

https://fairprocessframework.org/fda/