Welcome to the central repository for information on waterhackweek projects. This content is distributed on our Learning Resources, as well as the Waterhackweek webpage. Please read more about how to lead tutorials and projects Project Guidelines and Project management on the Waterhackweek Wiki. Learn more about How to Collaborate with Waterhackweek if you are interested in funding your leadership, tutorial development, project, learning community tool development, participation grants, sponsor community efforts, or to propose new ways to advance the ideas sparked at Waterhackweek.
We try to keep it simple, but these are complex projects. Deep dive from here, but please update regularly so that everyone can follow behind you. The Evolving Team Project is a 2020 experiment on ourselves. We are using a weekly meetup Fridays, July-August 2020, to teach ourselves how to organize all-virtual team projects for diverse teams and interests. Project Leads and participants are invited to use the project to onboard to Waterhackweek, or to contribute in any way that sparks imagination and community. All other projects are designed for team work Aug 31-Sep 4, 2020. Note the bottom of the page has archived projects available as foundations for new projects.
| Project Name | Where we work: Slack, Github, Hydroshare, JupyterHub | Cyberinfrastructure & Data Science Tools | Team | Team Lead | Project Lead | Data Science Lead | Geoscience Domain |
|---|---|---|---|---|---|---|---|
| Evolving Team Cooties: Infectiously Open, Wildly Transmissible, Hilariously Accessible | #teamcooties whw2020_flattenthecurve HydroShare Resource TBD jupyterhub.cuahsi.org | Cloud Widgets in Jupyter, Visualization with Bokeh, Data Smashing (COVID-19, National Water Model), Statistics for Public Translation | WHW Organizers & Contributing Friends: Philip, Mary | Christina | Tony | Scott | Open Global GeoHealth |
| The Next Amazing Project | Where we work: #funSlack, hyperlinkGithub, Hydroshareresource | XSEDE, Pangeo, Scikitlearn, landlab, matplotlib, plotly, pandas, xarray, numpy, scipy, cartopy ... Cyberinfrastructure & Data Science Tools | Enthusiast Team | Team Lead | Project Lead | Data Science Lead | Geoscience Domain |
During Waterhackweek we will be facilitating open hacking sessions most of the afternoons. The purpose of these sessions is for you to gain hands-on experience in working together on a well-defined problem related to geospatial sciences.
Hacking is a session of focused, highly collaborative computer programming, in which we create conditions for rapid absorption of new ideas and methods. Visit our hacking central page for more specific information on this approach.
Increasingly, research and software development is being conducted across groups of people with diverse skills and backgrounds. We believe this collaborative work leads to more innovative solutions to complex problems. At waterhack week, our goal is to explore with you some of the skills needed to navigate technical and social challenges of working in these kinds of collaborative settings.
On day 1 we will facilitate the sharing of ideas and formation of people into small teams (2-5 people) each team will identify a:
- Coordinate team in the direction of the research question pitch. Data, code, and tools originally proposed may change. Avoid dispensing and coordinating tasks.
- Everyone should code something or get their hands on the data in their way.
- The project scientist makes sure that their team all meet their individual goals, and that the team evolves.
- Pitch project. Interact with all interested participants during networking. Coordinate team building with other instructors to an optimal size of 4-7 total participants.
- Populate original GIthub project repository and readme following the Project Guidelines.
- Coordinate with 1-2 Data Scientists to support your team, especially Github workflows that work for everyone to be able to contribute code.
- Manage data. Be sure you have permission and consent to use data you share in the project. Follow professional standards while using data for training that may be used in future publications. Here is a good resource on publication of data and consent ethics.
- Any group can ask any data scientist for help.
- Interact with as many people as possible.
- Enable and encourage participants to solve their problems in a way that builds confidence in their skills
- Data scientists will be responsible for supporting at least one project, but expected to float around as needed.
- Support for one project means that on Monday, the team’s data scientist should coordinate a Github workflow that works best for the team by facilitating a team conversation.
- Mentor: Remove personal/interpersonal roadblocks
- Be in tune with progress toward individual goals (discrete daily personal brief checking)
- Facilitate team changes
- Technology Dam breaker: in a kind non-life threatening manner - what library or tool can this team use they may not know about?
- Innovation is unpredictable! Sensitive subject matter and personal physical boundaries may become an issue so find a process with your team to check-in regularly to address any issues to be aware of as a group. If you find that you are not feel comfortable sharing with the Team, please reach out privately to discuss with an organizer or other instructor.
- Be supportive! Learning software and personal growth both require vulnerability and a safe space. If anyone displays negative attitudes, judgments or insensitivity, we want to authentically address it. If there are concerns outside of the scope you are comfortable addressing, speak with another leader about it or please contact Christina.
- We aim to please. Keep your instructors updated as to whether your learning and personal goals for the hackweek are being met. If they aren’t, we will do everything we can to help you meet your goals.
- Everyone at hackweek is primarily participating on a volunteer basis. Please respect that this is a new community growing on donated time. If resolution to any issue is beyond the scope of hackweek, we will direct you to more resources.
once formed, each team will be guided through exercises to help narrow in on a set of tasks that are doable within the 5 days. A brief project outline will be posted to GitHub, following the "Project Guidelines" below. Each morning will start with a daily stand-up meeting to check-in on progress and challenges. On day 3 we will have a mid-week project check-in. On day 5 each team will present their work in a series of lightning talks.
if you have a project idea already brewing, we encourage you to share that with the team on our general Slack channel. We can add additional channels as the project ideas develop. Reel free to explore various projects and initiate conversations. The goal is to gather as much information as you can to inform your decision about which team to join when we meet in person. Contact a waterhack organizer if you would like assistance in assessing whether a project is well-scoped, or if you need help with a particular dataset.
We have some tables where data scientists will congregate and participants can find any kind of support they need.
We have the Instructor Bios and some sticky notes put up at one end of the room. This can be used to identify who is available and what they want to be asked about.
By definition, anything could happen in a hackweek project! If you want it to happen faster, ask us for help. If you want help avoiding a downturn in productivity, teamwork, or creativity, we can help come up with ideas and solutions.
- TBD - someone as big as Jacob Deppen - support data scientists, project scientists, instructors and organizers.
- TBD - someone as deep as Madhavi Srinivasan - support participants, especially new code contributors on Github and Jupyter Notebooks
- Scott Black - support on HydroShare and almost every other question
- Christina Bandaragoda- curious about anything that is not working, celebrating surprises by collecting data; handles anything that is not working (event design, questionable team behavior, awkward personal situations, or code of conduct violations).
Each project requires a brief project summary in the readme.md of each GitHub project folder. Below is a template for the project summary. You can visit the project folder on the geohack GitHub page to see existing examples.
Project Overview: Why is this important? What is the potential here?
Shared Project Goals: [List the top three in terms of computer science and data science learning]
- Create innovative visualizations for new datasets
- Implement common methods on emerging platforms
- Establish a new workflow for a new user or audience
Individual Learning Opportunities[Community Norms currently include Python 3+, HydroShare Data Repository, Github Code Repository]
- Cloud computing with Jupyter Notebooks on CUAHSI HydroShare
- List of Specific Software Libraries, Cyberinfrastructure Platforms, Datasets and Other Technologies
All programming levels welcome. We'll be primarily using Python but other languages can be used too!
On-boarding Steps: All programming levels welcome. Time Committment and Language Expectations. A. We'll be primarily using Python but other languages can be used too! B. It will take 10 min to read the materials for experienced python programmers with Github and HydroShare accounts. It will take 2-5 hours to onboard and get comfortable for those new coding or new users of these platforms.
Organizational and Personal Disclosures, Disclaimers, and Expectations
References and Links to Sources
At the hackweek, the project lead will pitch their project, and work with the Team to fork their repository to the Waterhackweek organization, and build a team plan for governing collaboration for the week, including a detailed description of the Team Project.
Brief title describing the proposed work.
List all participants on the project. Choose one team member to act as project lead, and identify one geohack organizer as the data science lead.
What geospatial problem are you going to explore? Provide a few sentences. If this is a technical exploration of software or data science methods, explain why this work is important in a broader context.
List one specific application of this work.
If you already have some data to explore, briefly describe it here (size, format, how to access).
List the specific tasks you want to accomplish or research questions you want to answer.
How would you or others traditionally try to address this problem?
Building from what you learn at geohackweek, what new approaches would you like to try to implement?
Each project requires a brief project summary in the readme.md of each project folder. Here is a template for the project summary. You can view existing projects to see examples.
Brief title describing the proposed work.
List all participants on the project. Choose one team member to act as project lead, and identify one geohack organizer as the data science lead.
What water data science problem are you going to explore? Provide a few sentences. If this is a technical exploration of software or data science methods, explain why this work is important in a broader context.
HydroShare is CUAHSI's online collaboration environment for sharing data, models, and code.
Learn more about how to get started with HydroShare here : https://help.hydroshare.org/introduction-to-hydroshare/getting-started/
Data storage and limits: While code repositories like Github are optimized for version control and code publication, they have a strict file size limitation of 100 MB and total repository limit of 1GB. HydroShare has a file size limit of 1 GB and each user receives automatically an allocation of 20 GB of space for their resources when they sign up. Users who need more space should contact [email protected] and explain who they are and why they need more space.
Sharing: each file uploaded to Hydroshare receives a hyperlink that can be shared for easy download and data sharing, even with colleagues who are not HydroShare users.
Publication: once your data is ready for formal publication, you can get a digital object identifier (DOI) for each HydroShare resource.
In HydroShare, the content you upload and publish is referred to as a "resource".
Learn more about how to upload and publish a resource here :
https://help.hydroshare.org/creating-and-managing-resources/
When you create a new resource, you will be taken to the landing page for that resource, where you can edit the metadata. You can also edit the metadata elements of your resource at any point after its creation by clicking on the “Edit Resource” button on the landing page.
Learn more about how to describe your resource using different metadata elements : https://help.hydroshare.org/creating-and-managing-resources/best-practices-for-describing-your-resource-with-metadata/
You can describe your resource using additional metadata elements beyond the default elements provided directly on the page. You can click on the plus icon next to “Additional Metadata” and mention the name and value of the new metadata element.
Sometimes, you may wish to add a detailed description of the contents of your resource, and it is inconvenient to type all of this text into the Abstract element or to include it as “Additional Metadata” elements.
One way to give detailed description of resource files is to include a README.txt or README.md file along with your resources. This README file can contain information related to but not restricted to:
- Organization of different resource files into a directory structure
- Description about the different attributes in the data resource(s). Additionally, one can add if the attribute is categorical or quantitative. If they are categorical and have been encoded, mention what each encoding means. For eg: 0 means female, 1 means male.
Citations include any institutes/universities/organizations that are to be credited for creating and collecting the data in this resource.
Guide for Data Authors and Publishers
Read More Frequently Asked HydroShare Questions here
Best Practices for Designating Authorship
| Project Name | Github Repo | slack channel | Hydroshare |
|---|---|---|---|
| Civil digital infrastructure for real-world health impacts made possible by information flow, data sharing, and data security that enables clean water for everyone. | whw2019_watermesh | #watermesh | [TBD] |
| Mapping Co-Occurrence of Geogenic Groundwater Contaminants of Concern | whw2019_Mapping_Groundwater_Contaminants | #map_gw_contaminants | [TBD] |
| Climate change and ecohydrologic response using Landlab | whw19_landlab_ecohydrology | #landlab_ecohydrology | |
| Understanding the diversity of hydrological regimes in the Andean Amazon region | whw2019_AndeanAmazonHydro | #andeanamazonhydro | [TBD] |
| Effects of wildlife on Tundra hydrology and lake change | whw2019_northern-lakes | #northern_lakes | [TBD] |
| Snow modeling with SUMMA and pysumma | whw2019_snow_modeling | #snow_modeling | |
| Tuolumne snowmelt during rain on snow events | whw2019_snowmelt | #snowmelt | |
| Meteorological forcing data for NWM/WRF-Hydro | meteo_forcing_wrfhydro | #meteo_wrfhydro | |
| How will climate change affect hydrologic extreme events in the Pacific Northwest | whw2019_extremeH2O | #extremeh20 |
| Project Name | Data Science Tools | Data Science Lead | Project Lead | Location |
|---|---|---|---|---|
| Civil digital infrastructure for real-world health impacts made possible by information flow, data sharing, and data security that enables clean water for everyone. | Jim Phuong | Taina Rodriguez Curet | Puerto Rico | |
| Mapping Co-Occurrence of Geogenic Groundwater Contaminants of Concern | matplotlib, plotly, pandas, xarray, numpy, scipy, cartopy | Rohit Khattar | Katya Cherukumilli | India, US |
| Climate change and ecohydrologic response using Landlab | Landlab , numpy , matplotlib , openpyxl , OGH | Sai Nudurupati | Kaiwen Wang | Central New Mexico, USA |
| Understanding the diversity of hydrological regimes in the Andean Amazon region | Tethys , Func-Flow | Yesica Leon-Tinoco | Guido A. Herrera-R | Andean Amazon in Ecuador and Brazil |
| Effects of wildlife on Tundra hydrology and lake change | Google Earth Engine, Visualization in Python | Matthew Bonnema | Paul Mann | Yukon Kuskokwim delta, Alaska |
| Snow Modeling with SUMMA and pysumma | pysumma , xarray | Andrew Bennett | California | |
| Tuolumne snowmelt during rain on snow events | gasterio , numpy , geopandas , matplotlib | Steven Pestana | Lisa Katz | Upper Tuolumne Watershed |
| Meteorological forcing data for NWM/WRF-Hydro | Yu-Fen Huang | Yin-Phan Tsang | Hawaii | |
| How will climate change affect hydrologic extreme events in the Pacific Northwest | Xarray,pandas,cartopy,seaborn | Yifan Cheng | Oriana Chegwidden | Pacific Northwest |