---
tags: eds-book, collaboration-cafe
---
*This HackMD is re-used under a CC-BY license from [_The Turing Way_ collaboration cafe template](https://github.com/alan-turing-institute/the-turing-way/blob/master/book/website/community-handbook/templates/template-coworking-collabcafe.md)*

# _Environmental Data Science book_ ⛰ 🌳 🏙️ ❄️ 🔥 🌊 online Collaboration Cafe [PUBLIC ARCHIVE]

## Archive: 22 February 2022 | Prepare compelling and reproducible notebooks for the EnvDS book

### Sign up below

**Name + Which is your essential clothing accessory for frozen days? + an emoji to represent it ([emoji cheatsheet](https://github.com/ikatyang/emoji-cheat-sheet/blob/master/README.md))**

* Alejandro + Gloves + :gloves:
* Pirta + Beanie + :billed_cap:
* Tim + Argyle + :socks:
* Ricardo + Scarf + :scarf:

### Conversation Starters

* None

### Breakout rooms: Topic proposals

* No breakout rooms, all in the main room

### Notes and questions

* Alejandro went through the key steps to submit a notebook to the EnvDS book:
* Step 1: Notebook idea
* Log your notebook idea as a [new issue](https://github.com/alan-turing-institute/environmental-ds-book/issues/new/choose) in the project repo
* Once the idea is clear e.g purpose, data sources, packages, and/or you receive a feedback from a collaborator or EnvDS community, you can move to the next step :arrow_down:
* Step 2: Preparation
* Open a terminal in your local/remote machine.
* Fork the EnvDS repository to your personal github account.
* Clone the forked repository into your local/remote machine.
* Go to the folder of the forked repository in your local/remote machine.
* If the environmental system and/or topic doesn't exist create a folder in the forked repository.
* Copy the template of your topic from the community chapter, (see [here](https://github.com/alan-turing-institute/environmental-ds-book/tree/master/book/community/templates)).
* Step 3: Setup
* Open a terminal in your local/remote machine and change the current directory (path) to the directory of the forked repo.
* Verify if you have `conda` i.e. type `conda`. If you don't get results after the verification, follow this [guide for installing conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html).
* Prepare a conda environment for your notebook. Note the environment should use python version of the EnvDS book (python 3.8) and also install `jupyter` which is the library to edit the notebook. The lines below guide you to launch a `jupyter notebook` session, one of the `jupyter` interfaces to edit notebooks.
* conda create -n <environment_name> python=3.8 jupyter
* follow the instructions to activate your environment
* check the list of packages in the [environment.yml](https://github.com/alan-turing-institute/environmental-ds-book/blob/master/environment.yml) of the EnvDS book.
* install relevant packages from the list using `conda install <package-name>`
* type `jupyter notebook`
* If a package relevant for your notebook isn't in the list, you can add a cell to install it before the import libraries section in the notebook. Packages can be installed using `pip -q install <package-name>` where `-q` means to install in silent mode.
* Step 4: Edit the the noebook
* Once you have the environment ready for your notebook, you can modify the sections of the template.
* (optional) follow the 10 rules of compelling notebooks provided by the EarthCube initiative available in their Notebook Template (section [Data processing and analysis](https://github.com/earthcube/NotebookTemplates/blob/main/EC_05_Template_Notebook_for_EarthCube_Long_Version.ipynb)).
* Once happy with the first editions of the notebook. Save it and push the changes. Not sure how to push changes, follow [Turing Way community chapter in Github](https://the-turing-way.netlify.app/collaboration/github-novice.html).
* Step 5: Open a pull request
* Go to the forked github repository in your Github account.
* Click in contribute and open a pull request
* 
* You'll see a form which you should fill and submit according to the information requested.
* Go to the EnvDS main repo and you'll see the PR as below:
* 
* Step 6: Editions
* Click in your pull request
* To facilitate the interaction with the reviewer, we are using ReviewNB and Netlify previews. You can access to ReviewNB clicking in the purple buttom below:
* 
* Note you can continue implementing changes in the forked repo. They'll automatically will change the notebook in the PR.
* Step 6: Review
* Once you're happy with the first version in the pull request, you can change to Ready to review in the checkbox.
* A reviewer will be assigned to your pull request.
* The reviewer will start a discussion of your notebook through the ReviewNB platform.
* Step 7: Publication
* Once both parties, author(s) and reviewer(s) are ha

* Some useful resources mentioned in the meeting:
* [Turing Way community chapter in Github](https://the-turing-way.netlify.app/collaboration/github-novice.html)
* Setting a [conda environment](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)
* [Guide for organising community calls](https://the-turing-way.netlify.app/project-design/pd-overview/pd-overview-repro.html?highlight=collaboration)
* Library for [spectral indices](https://awesome-ee-spectral-indices.readthedocs.io/en/latest/list.html)

* Celebrations :rocket:
* Four great notebooks ideas!
* [name=Alejandro]: COSMOS-UK Sensor Visualisation, see [issue#49](https://github.com/alan-turing-institute/environmental-ds-book/issues/49)
* [name=Ricardo]: Long timeseries phenology using Landsat data, see [issue#52](https://github.com/alan-turing-institute/environmental-ds-book/issues/52)
* [name=Tim]: Concatenating a gridded rainfall dataset into a time series, see [issue#53](https://github.com/alan-turing-institute/environmental-ds-book/issues/53)
* [name=Pirta] Nutrientscape mapping in optically shallow tropical coastal waters, see [issue#54](https://github.com/alan-turing-institute/environmental-ds-book/issues/54)

### Request for reviews!

* None

### Feedback at the end of the call

* None

## Archive: 23 November 2021 | FAIR data in Environmental Sciences

](https://i.imgur.com/f27a9aX.jpg)
*_The Turing Way_ project illustration by Scriberia. Used under a CC-BY 4.0 licence. DOI: [10.5281/zenodo.3332807](https://doi.org/10.5281/zenodo.3332807).*

### Sign up below

**Name + What is your recent favorite resource or tool or app or software? + an emoji to represent it ([emoji cheatsheet](https://github.com/ikatyang/emoji-cheat-sheet/blob/master/README.md))**

* Alejandro + _The Turing Way_ + :milky_way:
* Bea + _scivision_ + :koala:

### Conversation Starters

* None

### Breakout rooms: Topic proposals

* Main room (silent mode)
* Bea: working on the submission of her PhD thesis.
* Alejandro:
* adding helpful resources about FAIR and example of research repositories for Environmental Sciences.
* checking which sample data within the Environmental Data Science book can be curated in [the Environmental Data Science Zenodo community](https://zenodo.org/communities/the-environmental-ds-community/?page=1&size=20).

### Notes and questions

* Alejandro
* Useful resources about FAIR :
* [FAIR Cookbook](https://fairplus.github.io/the-fair-cookbook/content/home.html): an online resource for the Life Sciences with recipes to make and keep data FAIR.
* [The Turing Way: The FAIR Principles](https://the-turing-way.netlify.app/reproducible-research/rdm/rdm-fair.html?highlight=fair): light introduction to FAIR principles, pointing to key resources in the topic.
* [Library Carpentry: FAIR Data and Software](https://librarycarpentry.org/lc-fair-research/aio/index.html): lesson exploring the meaning of FAIR elements.
* Research data platforms:
* General
* [re3data.org](https://www.re3data.org/): initiative indexing research data platforms by content topic and knowledge domain.
* [Stats datacite](https://stats.datacite.org/): dashboard mapping the registration of persistent identifiers (DOIs) for research data and other research outputs.
* Environmental science (list of platforms with the highest number of total DOIs registrations according to Stats datacite):
* [Global Biodiversity Information Facility](https://www.gbif.org/)
* [FAO Global Information System of the International Treaty on Plant Genetic Resources for Food and Agriculture (PGRFA)](https://ssl.fao.org/glis/)
* [PANGAEA](https://www.pangaea.de/): earth system research.
* [Environmental Data Initiative (EDI)](https://portal.edirepository.org/nis/home.jsp): platform suited to curate environmental data, includes code snippets to import the data across multiple programming languages (python, R).

* Other interesting FAIR-driven platforms:
* [ROHub](https://reliance.rohub.org/): research object management platform supporting the preservation and lifecycle management of scientific investigations, research campaigns and operational processes. It implements FAIR digital objects and specific metadata for data-cube in Earth Science.

* Challenges of FAIR data repositories for Environmental sciences (ES):
* ES is structured as tabular data collected in the field or laboratory (see further discussion in [BEXIS2](https://bdj.pensoft.net/article/72901/)).
* FAIR-enabled data available could be daunting for many ES researchers and organisations due to the lack of awareness, efficient data management tools, infrastructure and skills [see further discussion in BEXIS2](https://bdj.pensoft.net/article/72901/)).
* Spatio-temporal (data cubes) > this seems to be adressed by novel research object management platforms such as [ROHub](https://reliance.rohub.org/).

### Request for reviews!

* None

### Feedback at the end of the call

* Alejandro: Few participants in this particular collaboration cafe. We should restructure the promotion strategy, proposing new topics and/or changing the format for coming collaborations cafes in 2022 :face_with_monocle:.

## Archive: 26 October 2021 - Reproducibility in Environmental Science

### Sign up below

**Name + Share a song that expresses your personality + an emoji to represent it ([emoji cheatsheet](https://github.com/ikatyang/emoji-cheat-sheet/blob/master/README.md))**

* Alejandro + Should stay or Should I go (The Clash) + 🧳
* Sam + Wish you were here (Pink Floyd)
* Matt - BBC Grandstand Theme - :horse_racing:

### Conversation Starters

* EGU22 session was accepted, [Bridging the spatial scales, from surface sensors to satellite sensors: Innovative approaches towards the construction of Earth’s digital twin](https://meetingorganizer.copernicus.org/EGU22/session/43565). Deadlines:
* Abstract submission deadline: 12 January 2022, 13:00 CET
* Travel Support application deadline: 1 December 2021

### Breakout rooms: Topic proposals

* Matt, making a reproducible GitHub code for his MRes dissertation
* Alejandro, preparing contributions guidelines for the Environmentel AI book
* Sam J, exploration of resources for reproducibility and feedback on Matt and Alejandro's topics

### Notes and questions

* Sam J:
* [The Turing Way](https://the-turing-way.netlify.app/welcome), a great resource to guide Environmental scientist in reproducible research.
* [Cornell Dataset Description](https://cornell.app.box.com/v/ReadmeTemplate) a good starting template for dataset documentation!
* Standards in data catalogues, e.g. [STAC](https://stacspec.org) (but it isn't mature)
* Alejandro:
* Zenodo:
* It is great to keep your sample data (up to 50 GB).
* notebooksharing.space
* A nice resource to share notebooks with interactive plotting (up to 10Mb). However, it doesn't allow track changes as [ReviewNB](https://www.reviewnb.com) does.
* Contributors guidelines for the EnvAI book
* Sam suggests example environmental python packages with links to notebooks (e.g. hvplot, geopandas etc.)
* Minimal publishable version guidelines e.g. Binder
* Use external links for general versioning principles e.g. how to pull request in Github
* Provide examples how to create lock environments
* Section of tools for sharing notebooks e.g. ReviewNB, notebooksharing.space
* Matt
* Publishing reproducible code for environmental science
* It can be more important that the process can be reproduced rather than accuracies to the nearest 0.01%
* Use a subset of data to demonstrate the tool where the owners aren't happy to share the whole thing - training & inference
* In env science a visual demonstration of the results can be more useful than a commandline readout of accuracy
* Suggest sensible ranges for hyperparameters in the documentation

### Request for reviews!

* **Sam J**: reviewers need for SEVIRI wildfire data notebook of the EnvAI book, see [PR#12](https://github.com/acocac/environmental-ai-book/pull/12)

### Feedback at the end of the call

* None

## Archive: 28 September - Data preprocessing

**Name + What’s the hardest part about working virtually for you? and the easiest? + an emoji to represent it ([emoji cheatsheet](https://github.com/ikatyang/emoji-cheat-sheet/blob/master/README.md))**

* Alejandro + social interaction, more sleep time + :busts_in_silhouette: :sleeping:
* Sam A. + I still have just as many meetings if not more and it is soooo tiring! :sleeping: :pleading_face:
* Evangeline + Feeling self-conscious on camera, flexibility + :movie_camera: :clock1:

### Conversation Starters

* Met Office / Joint centre for excellence in environmental intelligence conference 16/17 Dec 2021!
* We have a fresh interactive notebook in the Environmental Data S Book :earth_asia::books: The notebook focuses on detecting tree crowns using the *DeepForest* model :deciduous_tree:. Have a look at the rendered version [here](https://acocac.github.io/environmental-ai-book/forest/modelling/forest-modelling-treecrown_deepforest.html). Other recent community contributions are the exploration of sensor data, [Met Office UKV high-resolution atmosphere model data for urban settings](https://acocac.github.io/environmental-ai-book/urban/sensors/urban-sensors-ukv.html) and [MODIS satellite imagery and wildfire data](https://acocac.github.io/environmental-ai-book/wildfires/sensors/wildfires-sensors-modis.html).

### Breakout rooms: Topic proposals

* Sam A. Manufacture Urban Data in GIS format
* Evie. Preprocessing satellite data for crop yield prediction
* Alejandro. Preprocess FluxNet data and related gridded products

### Notes and questions

* We showcased the [SEPAL platform](https://docs.sepal.io/en/latest/cookbook/area_estimation.html) for Vegetation Satellite Image analysis.
* Discussed challenges around scoping and extracting satellite data for machine learning models of vegetation (agricultural crops):
* Appropriate satellite platform (Sentinel/LANDSAT?)
* Preprocessing of radar and optical data (i.e. dealing with cloud cover)
* Appropriate time series/critical dates for plant growth
* Sam A. used ArcGIS pro to extract site-specific temperature information from a gridded netCDF dataset using the Spatial Analyst 'Sample' tool. It is very useful in that it works across the time dimension so I could do this for 1 year of data in one go. It is also possible to set a desired output coordinate system. I could save the data out as a csv file and then use standard python tools like pandas and numpy for further processing
* Sam A. suggests using [Iris package](https://scitools-iris.readthedocs.io/en/latest/) for reprojecting gridded netCDF files. The project is
* Data preprocessing is still too time-consuming, and there is lack of communication of the tools available.

### Request for reviews!

* None

### Feedback at the end of the call

* None

## Archive: 29 June - Data Visualization

**Name + Something you watch (video, movie, documentary. etc) recently that was inspiring for you? + an emoji to represent it ([emoji cheatsheet](https://github.com/ikatyang/emoji-cheat-sheet/blob/master/README.md))**

* Alejandro + [Black Holes: The Edge of All We Know](https://www.rottentomatoes.com/m/black_holes_the_edge_of_all_we_know) + :milky_way:
* Scott + [Coded Bias](https://www.imdb.com/title/tt11394170/) + 🧠
* Tom Andersson + [The Dig (Netflix film on Sutton Hoo dig site)](https://www.wikiwand.com/en/Sutton_Hoo) + :spades:
* Emily + actual paint drying on my bedroom wall + :lower_left_paintbrush:
* Sam Jackson + [Calibre](https://en.wikipedia.org/wiki/Calibre_(film)) + :smile:

### Conversation Starters

* Alejandro: EGU Public call-for-session-proposals all other sessions: Deadline: [6 September 2021](https://www.egu22.eu/)
* Scott: Pangeo European Community is growing and there are plans of coffee chats and regular showcase meetings (see [here](https://cnrs.zoom.us/j/95432814658))

### Breakout rooms: Topic proposals

* Sam J: Regridding MODIS data for wildfires detection
* Tom: Produce script to reproduce IceNet paper figures for *Nature Communications*
* Emily: Visualization of LiDAR data
* Scott: Organizing and Admin EnvSensors WPs project timetable
* Alejandro: Deploying a FluxNet use case visualization outputs for the Environmental Data Science book

### Notes and questions

* Emily showed a cool visualization of a laser scan image (100 GB) using the propietary software of the scanner device. After data preprocessing, she will use libraries for visualizing individual trees.
* Emily says there are also some radar sensors that collect soil data.
* Tools for regridding MODIS data. Sam is using [*satpy*](https://satpy.readthedocs.io/en/stable/overview.html). Suggestions of other existing tools are welcome.
* Tom is making his code nicer i.e. modules and efficient i.e using dask.
* Alejandro shows FluxNet demo
* Emily suggest adding woodlands and shrubs to subset FluxNet data.

### Feedback at the end of the call
* Add a disclaimer collaboration cafes' hackMDs are public.
* Names for breakout rooms.
* We should aim to keep to time, once we are used to the format etc.