Proposal Submission
Name of Project:
Onshore OCEAN
Which category best describes your project? Pick one.
Build / improve applications or integrations to Ocean
Which Fundamental Metric best describes your project? Pick one.
Market WAU (Weekly market participants in Ocean Market or across all data markets)
Proposal in one sentence:
Kaggle for Data Scientists utilizing Ocean Protocol.
Description of the project and what problem is it solving:
Worked examples and tutorial series are useful tools for onboarding new data scientists, who need guided learning rather than simply reading documentation. With project Manta Ray retired, there is currently no solution for this. We propose a Kaggle-like approach for getting data scientists to use Ocean. The current proposal focuses on writing Medium articles, developing a landing page for the platform and building proof-of-concept Jupyter Notebooks for exploratory data analysis (EDA) and training simple deep learning models using Ocean protocol. In future (as we gather metrics and feedback), this will be expanded to interactive Jupyter Notebook micro-courses, and finally a comprehensive data science platform built on Ocean.
What is the final product?:
Medium articles, an application landing page and proof-of-concept Jupyter Notebooks, as a starting point.
Grant Deliverables: (Provide us with a list of deliverables for the funding provided)
- Medium Articles
- Long Term Roadmap
- Proof-of-Concept Jupyter Notebooks for exploratory data analysis (EDA) and training simple deep learning models using Ocean protocol
Funding Requested: (Amount of OCEAN your team is requesting - Round 7 Max @ 32,000)
32,000
If your proposal is voted to receive a OceanDAO Grant, how would the proposal contribute a value greater than the grant amount back to the Ocean Ecosystem (best expressed as “Expected ROI”)?
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.
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).
0x7af50AE9F5c72e917Fa020F406b23F0b2B685437
Project lead Contact Email:
Twitter Handle (if applicable):
https://twitter.com/CryptoTeuthida?s=09
https://twitter.com/richardblythman?s=09
Discord Handle (if applicable):
@MuddyDonut#3083
@richardblythman | VisioTherapy#3425
Country of Residence:
US, Ireland
Have you previously received an OceanDAO Grant (Y/N)?
N
How does this project drive value to the “fundamental metric” (listed above) and the overall Ocean ecosystem?
By providing a routinely updated and improved tutorial platform for data scientists, we are able to capture members for the new data economy. Improving and maintaining these initial steps is vital for user adoption and maintaining existing practitioners. Ocean Protocol provides immense value to data scientists, but current onboarding is too esoteric for everyone. By building a maintainable tutorial series, we are able to routinely gather user feedback and standardize the initial user interactions with the protocol.
Proposal Details
Project Deliverables - Category:
- Blog posts will be published on medium.com
- Web app will be hosted and linked when the initial sign up page has been completed
- The web app will be built on MongoDB, Express.js, Vue.js, and Node.js
- User insights can be derived from Medium article engagement and monthly reporting to the community.
Project Deliverables - Roadmap
- What is the project roadmap? That is: what are key milestones, and the target date for each milestone.
Q3
- July:
- First round of medium articles published on “How to use Ocean for Data Science”
- 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
- August:
- Second round of medium articles published on “How to use Ocean for Data Science”
- Wire frames for an application landing page
- Landing page launched and hosted
- September:
- Third round of medium articles published on “How to use Ocean for Data Science”
- 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.
Team members
For each team member, give their name, role and background such as the following.
Julian Martinez
- Role: Full stack developer, Data Scientist
- Relevant Credentials (e.g.):
-
Background/Experience:
- BI Engineer at AmerisourceBergen (Fortune #8)
- Data Analyst for cybersecurity startup A-LIGN, and local retailer
Richard Blythman
- Role: Machine Learning Engineer, Data Scientist
- Relevant Credentials (e.g.):
-
Background/Experience:
- Video Intelligence Researcher at Huawei Technologies
- Research Fellow (Computer Science), Trinity College Dublin
- Machine Learning R&D Engineer at FotoNation, Xperi
