Name of Project: Algovera Onshore
Proposal in one sentence:
A Web3 eco-system that rewards contributors to data science projects.
Description of the project and what problem is it solving:
Currently, there is a general lack of data scientists in the Ocean eco-system. At the same time, there is a demand side issue for datasets on the marketplace. We are onboarding data scientists to Ocean who are natural consumers of data. Web3 values ensure that data scientists have ownership over their creations, and also push towards decentralization of AI.
- [ ] Conduct UX research for landing page and propose UX workflow
- [ ] Add notebooks for exporting to code and uploading algorithms to Ocean Marketplace
- [ ] Create Plan for Compute-to-Data Infrastructure
- [ ] Create Revenue Model
- [ ] Create Marketing Plan
Which category best describes your project? Pick one.
- [ ] Build / improve applications or integrations to Ocean
Which Fundamental Metric best describes your project? Pick one.
- [ ] Data Consume Volume
What is the final product?:
A web app that provides a frictionless experience of exploring datasets and publishing algorithms on the Ocean Marketplace. A DAO for data scientists where data and models can owned and governed. A breeding ground for ideas to grow between data providers, data solvers and app developers.
How does this project drive value to the “fundamental metric” (listed above) and the overall Ocean ecosystem?
Firstly, the project will increase adoption of the Ocean protocol platform by data scientists. The data science platform Kaggle has over 5 million registered users (https://www.kaggle.com/general/164795). These users do not have the opportunity to take a stake in the underlying Kaggle platform itself. If we assume that we can capture 0.001% of this market (50 individuals) and that 20% of these early adopters (10 individuals) choose to invest in Ocean tokens (with an average investment of 2000 $OCEAN), this results in a Total Value Locked (TVL) of 5,000,000 * 0.001% * 20% * 2000 $OCEAN = 20,000 $OCEAN demand.
Secondly, the project will increase the number of data consumer for existing datasets on the Ocean marketplace by providing tools and resources for data scientists to perform analyses and train models. However, we assume that independent data scientists will not be willing or able to pay the up-front cost of the dataset. Instead, we envision a business model where data scientists are given free access to data by the data providers, in order to build trust in the dataset and attract stakers. We assume that an average high quality data pool has a TVL of 100.000 $OCEAN over one year. We assume that the data science analyses performed by each of the early adopters increases the number of stakers on a dataset by an average of 1%. This gives 100,000 * 1% * 50 = 50,000 $OCEAN demand.
bang = 20,000 + 50,000 = 70,000 $OCEAN
buck = 32,000 $OCEAN
(% chance of success) = 70%
ROI = 70,000 / 32,000 * 0.7 = 1.5
This is above the expected ROI of 1.0.
The new deliverables will further increase the data consume volume by:
- Teaching data scientists how to publish algorithms to the Ocean marketplace. More useful algorithms should result in more data consume volume.
- C2D will enable computations that take longer than 60 seconds on private datasets, which should increase use cases and data consume volume.
- Marketing to data scientists will increase awareness about Ocean and may result in more investors in the token and eco-system.
Funding Requested: (Amount of OCEAN your team is requesting - Round 8 Max @ $17,600 )
Proposal Wallet Address: (must have minimum 500 OCEAN in wallet to be eligible. This wallet is where you will receive the grant amount if selected).
Have you previously received an OceanDAO Grant (Y/N)?
Twitter Handle (if applicable):
Discord Handle (if applicable):
@richardblythman | VisioTherapy#342
Project lead Contact Email:
Country of Residence:
Core Team ****
For each team member, give their name, role and background such as the following.
- Role: Full stack developer, Data Scientist
- Relevant Credentials (e.g.):
- BI Engineer at AmerisourceBergen (Fortune #8)
- Data Analyst for cybersecurity startup A-LIGN, and local retailer
- Role: Machine Learning Engineer, Data Scientist
- Relevant Credentials (e.g.):
- Video Intelligence Researcher at Huawei Technologies
- Research Fellow (Computer Science), Trinity College Dublin
- Machine Learning R&D Engineer at FotoNation, Xperi
- Role: Full Stack Developer
- Relevant Credentials (e.g.):
- MSc Computer Science, University of Bath
- CORU Registered Chartered Physiotherapist
- Founder Oak Digital Health
- UG Cert Innovation and Entrepreneurship
- Role: Front-end developer, UX Researcher
- Relevant Credentials (e.g.):
- Data Analyst for Healthcare company Leading Edge Administrators,
- Workforce and Real-time Analyst for Healthcare company U.S. Imaging
Project Deliverables - Category:
IF: Build / improve applications or integration to Ocean, then:
- UX designs on Figma will be linked
- Notebooks will be hosted on JupyterHub
- Plan for C2D will be made public in a Google Doc
- Revenue model and marketing plan will be available on our Notion page
Project Deliverables - Roadmap
- Any prior work completed thus far?
Manta Ray Research: At first, we located the Manta Ray notebooks and got them running locally. We also did some digging to find out why they were retired. After talking to Trent, it turned out that they weren’t a worthwhile pursuit. Firstly, they were designed for use with public datasets. Ocean doesn’t really have a USP here compared with common HTTP of the internet. Also, these notebooks essentially showcased the functionality of ocean.py. There’s already documentation for that. Also, we think that this functionality should be hidden from a data scientist user as much as possible. Overall, we learned that the notebooks should show off the USPs of Ocean Protocol (e.g. developing on private datasets) for data science. We defined several interesting use cases for data scientists on our Notion page
PoC Notebooks: We created a JupyterHub instance here (ADD LINK) to host the notebooks (until our web app is up and running). This reduces the friction for onboarding data scientists by pre-installing packages like ocean.py and setting the config. The notebooks focus on teaching a workflow for data science on private datasets. We have uploaded some standard public datasets to the Rinkeby test net (see here). While these datasets are freely available online, data scientist can practice using the Ocean marketplace to learn about the process. They will then be able to apply this process to work with datasets that are not publicly available. For this round, we will add notebooks to show the workflow for exporting and uploading algorithms to the Ocean Marketplace.
Landing Page: We have just launched our landing page. Check it out here. We recently switched from using Vue.js to React.js for compatibility with the Ocean Marketplace GUI. You can check out wire frames for the landing page design we are working towards here. Also note that we have changed our name to Algovera. Algo for algorithm. Ver for truth. Gov for governance, and the AL looks like AI. AI governance.
Wire Frames for Web App: We are moving towards an integrated web app to replace the hosted notebooks. You can check out the wire frames for the web app here. Have a look at the mood board here for some of our inspirations.
User Acquisition Plan: We have created a preliminary plan for acquiring users. You can check it out here.
Notion: We now have an extensive Notion project set up to help to improve transparency. You can check it out here and keep up with our progress. Of particular interest for OceanDAO is our deliverables tracking and our Kanban board.
Business Plan: We have begun working on a business plan. We started with mapping out the problems faced by Data Solvers and Data Providers. You can check it out here.
Alternate Future Summit: We will be showcasing and presenting at the conference. You can check out our showcase page here.
Kernel Block 4: We have just been accepted to the KB4 accelerator. We want to create a breeding ground for collaborations between data scientists and data providers.
Team Additions: We welcomed Hithesh and Tinesha to the team this month Check out our team directory here.
- What is the project roadmap? That is: what are key milestones, and the target date for each milestone.
First round of medium articles published on “How to use Ocean for Data Science”
- This is not necessary. The team is going to focus on building the application and self hosting any tutorial content
- Reach out to Ocean community to see if anyone has access to old Manta Ray notebooks and inspect how much can be re-used/modified
- Community feedback for a focused USP approach, “How can we help Data Practitioners earn income?”
- Long Term Roadmap
- Wire frames for an application landing page
- Notion Wiki launch
- Landing page launched and hosted
- Landing page metrics report and alpha website
- Complete proof-of-concept Jupyter Notebooks for exploratory data analysis (EDA) and training simple deep learning models using Ocean protocol
Future Plans (Q4-)
- Our future plans involve building a Jupyter notebook platform dedicated to increasing adoption, similar to the Kaggle website for modern data scientists.