GITBOOK-478: change request with no subject merged in GitBook

data-science/README.md

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 # 📊 Data Science
 
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 Ocean Protocol was built to serve the data science space. 
 
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 Ocean Protocol unlocks _composable data science,_ mixing and matching assets to build end-to-end solutions. On-chain assets ensure participants can trustlessly work together while sharing the entire system's upside.
 
-Explore the rest of this guide to learn more about the data value creation loop, and find your opportunity to build value with Ocean Protocol
+Explore the rest of this guide to learn more about the data value creation loop, and find your opportunity to build value with Ocean Protocol!
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data-science/data-engineers.md

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 # Examples of useful data
 
-Here is the game: Go get useful data and publish it on Ocean. Curating useful datasets is the critical early step in the data value creation loop. The first ones to publish datasets will be well-positioned to have others to build on their work and accrue greater value to the system. Those that start this process can kickstart value creation by working with Ocean Protocol to [sponsor a data challenge](data-challenges/hosting-a-data-challenge.md).
+Here's the game: Go get useful data and publish it on Ocean. Curating useful datasets is the critical early step in the data value creation loop. The first ones to publish useful datasets will be well-positioned to have others build on their work and kickstart additional data value creation. Those that start this process can kickstart value creation by working with Ocean Protocol to [sponsor a data challenge](data-challenges/hosting-a-data-challenge.md).
 
 Below is a short list of potential areas to curate useful data.
 

data-science/the-data-value-creation-loop.md

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-For example, an insurance provider may want to offer a parametric insurance product for farmers to protect against drought in a particular region. Those with a strong skillset in data engineering may focus on the beginning of the value creation loop. They can create pipelines to ingest climate and local weather data and aggregate them together. Data scientists can then build their predictive models on top of 
+Let's framework a potential data value creation loop. An insurance provider wants to offer a drought parametric insurance product for farmers in a particular region. Their risk models indicate if they can accurately predict the value of a particular drought index, they will be able to profitably price their product. They launch a data challenge to curate models to accomplish this task.
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+Those with a strong skill set in data engineering may focus on the beginning of the value creation loop. They create pipelines to ingest climate and local weather data and aggregate them together. They may also include private data provided by the insurance provider. The data engineers publish an asset on Ocean for a regularly updating dataset for computation only
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+Data scientists can then conduct feature engineering and build their predictive models on top of the data engineers' assets. They train their models with C2D, and then publish them as compute assets on Ocean Market. The data scientists can leverage Ocean to begin attaching immutable proof of the models' accuracy to their assets and build trust in their models value.
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+Application developers can now work with the insurance provider to incorporate these models into production. The insurance provider can publish their risk model as a compute asset on Ocean. Application developers build different front-end experiences and businesses on top of the models, focusing on customer acquisition and distribution of the product. 
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+With the product in place, farmers can now improve their lives by tapping a valuable product. Revenue is generated by the insurance product and royalties are automatically distributed to the upstream publishers of the data and model assets. Profits can be reinvested to procure greater data to produce better models and more profitable products, kickstarting sustainable value creation for all stakeholders.
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+The data value creation loop described above is just one in a vast ocean of opportunity. It can be applied across verticals, like DeFi, real estate, climate, sports and healthcare(just to name a few). Endless problems can be addressed by structuring the business problem context, curating the data, building the models, and deploying the assets on Ocean.
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+Take a look in the next sections to find a few ideas for how you can kickstart you own data value creation loop. 
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