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A presentation for the python users group in Berlin.&nbsp

This release adds the first part of the packaging chapter to the guide. It also updates the authorship list to include everyone that has provided feedback. What's Changed add infra to build sphinx book by @lwasser in https://github.com/pyOpenSci/python-package-guide/pull/1 Update contributing and readme and add circle ci by @lwasser in https://github.com/pyOpenSci/python-package-guide/pull/7 CONTENT: documentation text and styles by @lwasser in https://github.com/pyOpenSci/python-package-guide/pull/6 Fix: HTML PROOFER? 😭 by @lwasser in https://github.com/pyOpenSci/python-package-guide/pull/2 ADD: Update package theme to furo and add sitemap and opengraph by @lwasser in https://github.com/pyOpenSci/python-package-guide/pull/13 Review 2 (reviews open): Add: documentation section to package guide by @lwasser in https://github.com/pyOpenSci/python-package-guide/pull/19 Add: precommit hooks and clean up all files by @lwasser in https://github.com/pyOpenSci/python-package-guide/pull/43 Fix: move book to pydata_sphinx_theme by @lwasser in https://github.com/pyOpenSci/python-package-guide/pull/45 Add: matomo to guide by @lwasser in https://github.com/pyOpenSci/python-package-guide/pull/56 (Review 2 - complete) Packaging section - part 2 by @lwasser in https://github.com/pyOpenSci/python-package-guide/pull/55 Fix image as fonts are working with svg by @lwasser in https://github.com/pyOpenSci/python-package-guide/pull/60 Fix: ordered list on complex package page by @lwasser in https://github.com/pyOpenSci/python-package-guide/pull/62 A11y: Use PyData Theme's Sphinx Design Components to fix dark mode contrast by @hugovk in https://github.com/pyOpenSci/python-package-guide/pull/61 fix: incorrect backend key by @henryiii in https://github.com/pyOpenSci/python-package-guide/pull/64 fix: incorrect license specification by @henryiii in https://github.com/pyOpenSci/python-package-guide/pull/65 Add: zenodo file to repo for citation by @lwasser in https://github.com/pyOpenSci/python-package-guide/pull/20 New Contributors Most of the contributors in this PR as comments and edits were new !! @hugovk made their first contribution in https://github.com/pyOpenSci/python-package-guide/pull/61 @henryiii made their first contribution in https://github.com/pyOpenSci/python-package-guide/pull/64 Full Changelog: https://github.com/pyOpenSci/python-package-guide/compare/v0.0...v0.1

A conda environment and docker container to support using earth and environmental data in Python.

Also moved to pydata_sphinx_theme!

This release includes an updated and approved COC. It also includes a new stewardship committee and associated protocol to respond. Finally it moves the guide from sphinx book to pydata_sphinx_theme for more flexibility across our builds.

Presentation from

Presentation about pyOpenSci given to the python berlin users group in june 2023

This also will test zenodo updates / build with new authors given a new .zenodo file

A presentation given to the pangeo community october 2022

Data-intensive skills are becoming increasingly critical in earth and environmental science. As data sets become larger, more numerous, and harder to analyze, there is a need for scientists who can develop earth and environmental data science (EDS) workflows that are open, reproducible, and use modern programming languages. To leverage the full potential of EDS, attention should also be focused on broadening participation by members of groups who have been historically underrepresented in data science and STEM. The Earth Data Science Corps (EDSC) program, hosted at Earth Lab (CIRES/CU Boulder) with partners from tribal colleges and minority-serving institutions throughout the Front Range and Northern Great Plains, offers one approach to introducing EDS to underserved communities. EDSC engages participants with fundamental Python programming tools and packages (pandas, numpy, matplotlib), along with resources to work with time- series, and vector and raster spatial data (earthpy, geopandas, rioxarray) in the context of EDS. Beginning in 2020, EDSC has built capacity for our partners to teach EDS at their home institutions and helped students develop confidence and comfort in a variety of different science and data science areas. Through a paid internship experience that includes a series of technical workshops and group projects, EDSC participants are assessed in the following five areas: (i) science identity, (ii) science practices, and (iii) Python, (iv) data science, and (v) science communication skills. Using a retrospective pre-then-post design, Likert-type survey items were analyzed using Rasch modeling to measure growth in EDSC participants’ self-reported comfort and confidence in the five areas outlined above. Analysis of variance (ANOVA) of person ability measures from Rasch modeling of survey responses collected during Year 1 of EDSC revealed significant differences from pre- to post- program, and between the five assessment areas. Post-hoc analysis pointed to significant growth in each of the five assessment areas with the exception of Science Identity. Collection and analysis of Year 2 EDSC survey responses is ongoing and results are forthcoming. Implications for teaching earth and environmental data science to members of historically underrepresented communities will be discussed.

Poster from Earth Lab and ESIIL Education teams for AGU 2022 (Chicago, IL).

the previous release had a mismatch between the license on zenodo and the license file in the repo. this fixes that issue.

This updates the zenodo file with a new license and also updates the paper.md file to meet JOSE requirements

just a small patch fixing the zenodo file

Poster about the pyOpenSci project, presented at the TDWG 2021 Biodiversity Informatics conference (https://www.tdwg.org/conferences/2021/).

There is a mismatch between demand for a data-cable workforce with skills at the intersection of earth and data science (earth data science) and the capacity to teach those skills, particularly at schools serving historically underrepresented communities. The Earth Data Science Corps (EDSC) is a fully-online model that builds capacity for faculty and undergraduate students at institutions serving underrepresented communities to teach and learn earth data science. The EDSC builds sustainable teaching capacity while training students in critical data and career development skills in a virtual space. Access to these skills is further democratized by publishing curricular materials online as open education resources for anyone to use.Here we will present best practices for teaching and learning EDS skills online, derived from a program-wide evaluation that considers underrepresented groups, supports mentorship and community building, and scales to broaden reach. We will discuss tools comprising our learning environment including a cloud-based JupyterHub and autograding platform. We will also review best practices for online content delivery including 1. pairing synchronous video training with open education resources to facilitate active, student-directed learning; 2. using discussion forums and chat to encourage engagement, and 3. managing student mentoring challenges in a virtual space to support project-based learning.Initial findings indicate that online learning can effectively build confidence in using EDS skills but requires careful consideration of teaching delivery. Further, the use of small breakout rooms paired with chat supports class level engagement. Finally, open education resources can support a flipped classroom approach. Mentoring students in a virtual space further requires additional training of mentors to ensure communication lines with students remain open. Collecting real-time student feedback and modifying our delivery will also be discussed.

There is a mismatch between demand for a data-cable workforce with skills at the intersection of earth and data science (earth data science) and the capacity to teach those skills, particularly at schools serving historically underrepresented communities. The Earth Data Science Corps (EDSC) is a fully-online model that builds capacity for faculty and undergraduate students at institutions serving underrepresented communities to teach and learn earth data science. The EDSC builds sustainable teaching capacity while training students in critical data and career development skills in a virtual space. Access to these skills is further democratized by publishing curricular materials online as open education resources for anyone to use.Here we will present best practices for teaching and learning EDS skills online, derived from a program-wide evaluation that considers underrepresented groups, supports mentorship and community building, and scales to broaden reach. We will discuss tools comprising our learning environment including a cloud-based JupyterHub and autograding platform. We will also review best practices for online content delivery including 1. pairing synchronous video training with open education resources to facilitate active, student-directed learning; 2. using discussion forums and chat to encourage engagement, and 3. managing student mentoring challenges in a virtual space to support project-based learning. Initial findings indicate that online learning can effectively build confidence in using EDS skills but requires careful consideration of teaching delivery. Further, the use of small breakout rooms paired with chat supports class level engagement. Finally, open education resources can support a flipped classroom approach. Mentoring students in a virtual space further requires additional training of mentors to ensure communication lines with students remain open. Collecting real-time student feedback and modifying our delivery will also be discussed.

Earth analytics is an intermediate, multidisciplinary course that addresses major questions in Earth science and teaches students to use the analytical tools necessary to undertake exploration of heterogeneous ‘big scientific data’.

When working with data, you often spend the most amount of time cleaning your data. Learn how to write more efficient code using the tidyverse in R.

There are a suite of powerful open source python libraries that can be used to work with spatial data. Learn how to use geopandas, rasterio and matplotlib to plot and manipulate spatial data in Python.

Earth analytics is an advanced, multidisciplinary course that addresses major questions in Earth science and teaches students to use the analytical tools necessary to undertake exploration of heterogeneous ‘big scientific data.’

Data analysis skills and earth science expertise are a powerful combination in today's world of big data and rapid technological advancement. In order to forge a successful career in the 21st century, earth scientists must be able to merge both technical skills and domain expertise. The Earth Analytics Education Initiative is a cutting-edge University of Colorado program that is responding to this new reality by offering undergraduate and graduate courses, a professional certificate, online training materials, an internship program and ultimately, a professional master's degree in Earth Data Analytics. In addition to being timely, the Earth Analytics Education Initiative is also innovative - while there are a wealth of programs that focus on earth systems and data science individually, it is one of the first university programs in the U.S. to combine these disciplines.To ensure that the skills taught in our program make individuals who already work in the sciences or who are interested in finding work in the sciences more marketableto employers, our team surveyed industry representatives who require staff with earth analytics skills in the fall of 2016. The goal of this survey was to inform and guide development of the professional graduate certificate and future full professional master's program in Earth Data Analytics. In tandem with this effort, Earth Lab sent the same survey to members of the academic community - anticipating that some of the tools and identified needs may be different in the academic community compared to the industry community.

Introduction to Earth Data Science is an online textbook for anyone new to open reproducible science and the Python programming language. There are no prerequisites for this material, and no prior programming knowledge is assumed.

Learn how to use version control with Git to work collaboratively and back up your work.

The Earth Analytics Bootcamp is a three-week introductory-level course taught by instructors in Earth Lab and is a part of the Professional Certificate in Earth Data Analytics - Foundations at CU Boulder.

This lesson focuses on ways that scientists identify and use data to understand ecological disturbance events using data from five public datasets. The main lesson focused on interpretation of figures, while optional coding extensions teach R skills.

Open and accessible teaching data for the National Ecological Observatory Network - a figshare case study of Megan Jones' work at the National Ecology Observatory Network.

The scale and magnitude of complex and pressing environmental issues lend urgency to the need for integrative and reproducible analysis and synthesis, facilitated by data-intensive research approaches. However, the recent pace of technological change has been such that appropriate skills to accomplish data-intensive research are lacking among environmental scientists, who more than ever need greater access to training and mentorship in computational skills. Here, we provide a roadmap for raising data competencies of current and next-generation environmental researchers by describing the concepts and skills needed for effectively engaging with the heterogeneous, distributed, and rapidly growing volumes of available data. We articulate five key skills: (1) data management and processing, (2) analysis, (3) software skills for science, (4) visualization, and (5) communication methods for collaboration and dissemination. We provide an overview of the current suite of training initiatives available to environmental scientists and models for closing the skill-transfer gap.

Open and accessible teaching data for the National Ecological Observatory Network - a figshare case study of Megan Jones' work at the National Ecology Observatory Network.

This presentation describes the neondataskills.org portal which provides free and open lessons and data subsets that teach import data skills.

This presentation describes the processes underway to derive NEON higher level data products like Canopy Chemistry. Field Measurement chemistry data were compared to the NDNI algorithm at the SJER field site in California.