not approved
Enhancing Data Collection for Impact Measurement through Automation and Academic Collaborations
Current Project Status
Unfunded
Amount
Received
₳0
Amount
Requested
₳119,500
Percentage
Received
0.00%
Solution

Automate impact data gathering, reducing labor and errors. Access reliable impact data, improving decision-making and resource allocation. Cost-effective with academic collaborations for data quality.

Problem

The current data gathering process for impact measurement is labor-intensive, time-consuming, and prone to human error, leading to potential data inaccuracy and high costs plus, not all data is public

Feasibility
Value for money
Impact / Alignment

Team

2 members

Enhancing Data Collection for Impact Measurement through Automation and Academic Collaborations

Please describe your proposed solution.

Problem Statement

In a world struggling with climate change, social inequality, and governance issues, strategic resource allocation is vital. However, a gap exists in measuring the impact of initiatives tackling these challenges, hindering informed decision-making. The current data gathering process for impact measurement is labor-intensive, time-consuming, and prone to human error, leading to potential data inaccuracy and high costs.

Solution

Our solution streamlines the data gathering process by automation, web scrapping tools and APIs, to reduce labor intensity, time consumption, and human errors. We'll create a comprehensive list of APIs, each serving as one of many options for each rating category, ensuring diverse, reliable data. Data processing will be automated using libraries like Pandas in Python.

With our solution, stakeholders can make informed decisions by accessing accurate and reliable impact data. This not only improves resource allocation but also reduces costs associated with manual data collection and analysis. By offering a scalable and technology-driven approach, our solution empowers organizations to drive positive change and create a more sustainable future.

Academic collaborations will tap into university resources and expertise. Students, guided by professors and a project manager, will contribute to data collection and processing tasks. Formalized through MoUs, these collaborations will ensure data quality and reliability. This approach combines automation's efficiency with academic expertise, creating a cost-effective solution for enhancing impact measurement data collection.

How does your proposed solution address the challenge and what benefits will this bring to the Cardano ecosystem?

From a sustainability perspective, the proposed solution is a significant step forward for the Cardano ecosystem. By enhancing the efficiency, cost-effectiveness, and accuracy of the Impact Measurement System (IMS), we can provide a more robust tool for measuring the impact of digital assets on a wide range of sustainability indicators, such as climate change, social inequality, and governance issues.

Currently, the Cardano ecosystem lacks comprehensive impact measurement tools. This gap stops the ability of investors and developers in the ecosystem to make informed decisions about which digital assets are truly contributing to sustainable development. By filling this gap, our solution can enhance the transparency and accountability of digital assets in the Cardano ecosystem, contributing to a more sustainable digital asset industry.

How do you intend to measure the success of your project?

We will measure these results at regular intervals throughout the project and after its completion. This will involve regular reviews of the data collection process, cost analysis, and data validation checks.

We will share the measurements with stakeholders through regular progress reports and updates. These will be shared via email, social media, and on the Metera protocol and Sustainable ADA platforms.

Success for this Project:

Success for this project would be the successful integration of automation technologies and academic collaborations into the data collection process for the IMS. This would result in a significant reduction in the time and cost of data collection and processing, as well as an improvement in the accuracy and reliability of the data. Ultimately, success would mean that more digital assets can be rated more accurately and cost-effectively, contributing to more informed decision-making in the digital asset community.

Main Risks:

<u>Technical Challenges:</u> The integration of automation technologies and APIs could face technical challenges, such as compatibility issues or unexpected bugs.

Collaboration Challenges:

There could be challenges in establishing and managing collaborations with universities, such as differences in expectations or communication issues.

<u>Data Quality</u>: There is a risk that the automated data collection process could result in lower quality data, affecting the accuracy of the ratings.

Risk Mitigation:

<u>Technical Challenges:</u> We will have a dedicated team of experienced developers to handle the technical aspects of the project. They will conduct thorough testing and debugging to ensure the smooth integration of automation technologies and APIs.

<u>Collaboration Challenges:</u> We will establish clear communication channels and regular check-ins with our university partners. We will also formalize the collaborations through Memorandums of Understanding (MoUs) that clearly outline the responsibilities of each party.

<u>Data Quality</u>: We will implement rigorous data validation and cleaning processes to ensure the quality of the data. We will also leverage the academic expertise of our university partners to ensure that the data collection process adheres to the highest standards of quality.

Benefits:

<u>Efficiency: </u>Automation will significantly reduce the time it takes to collect data, thereby increasing the efficiency of the impact measurement process. This allows us to deliver results faster and respond more quickly to changes in the digital asset landscape.

<u>Cost-Effective</u>: Collaborations with universities will provide access to academic resources and expertise at a lower cost, significantly reducing the overall budget for data collection. Universities are often eager to provide students with real-world experience, and this collaboration offers a mutually beneficial opportunity. We gain access to fresh perspectives and cutting-edge academic knowledge, while the students gain valuable industry experience.

<u>Decentralization of Knowledge:</u> By collaborating with different universities, we are decentralizing the knowledge base that contributes to the impact measurement process. This ensures a diversity of perspectives and approaches, enhancing the robustness and comprehensiveness of our ratings.

<u>Accuracy:</u> Automation and academic expertise will enhance the accuracy and reliability of the data. Automation eliminates the risk of human error in data collection and processing, while academic expertise ensures that the latest research and methodologies are applied to our data analysis.

Please describe your plans to share the outputs and results of your project?

Measurement and Sharing Plans:

We will measure these results at regular intervals throughout the project and after its completion. This will involve regular reviews of the data collection process, cost analysis, and data validation checks.

We will share the measurements with stakeholders through regular progress reports and updates. These will be shared via email, social media, and on the Metera protocol and Sustainable ADA platforms.

Sharing Outputs and Results:

We plan to share the outputs and results of our project through various channels. This includes publishing reports on the Metera protocol, IMS, and Sustainable ADA platforms, sharing updates on social media, and presenting our findings at relevant conferences and events.

What is your capability to deliver your project with high levels of trust and accountability?

The team are the co-founders of Metera Protocol, an asset managment DeFi protocol with an impact thesis, built on the Cardano blockchain that enables the creation and support of tokenized portfolios known as Metera Tokenized Portfolios (MTKs) which has a solid community of more than 2500 supporters, solid partnerships such as Mlabs, TxPipe, Clarity Dao, Osmium Dao, VyFi, more than 2 years building the product and a solid track record of first delivering and then asking for funds to keep improving.

We have developed with own funds the basis of the IMS tool alongside some third parties experts on the field. Now, we want to keep onboarding the right people to keep guiding us in the correct direction to make this tool fully robust and usefull in the Ecosystem.

The future team for this task will also consist of a project development manager, a sub-manager, data scientists and four students from our partner mexican universities. They will bring a mix of professional experience and academic expertise, ensuring the successful implementation of the project. The project development manager and sub-manager will oversee the project and coordinate with the universities, data scientist will create the streamlines to gather the data needed, the students will contribute to the data collection and processing tasks under the guidance of their professors. This blend of professional and academic expertise will ensure the project's success while also providing valuable learning opportunities for the students.

What are the main goals for the project and how will you validate if your approach is feasible?

Success for this project would be the successful integration of automation technologies and academic collaborations into the data collection process for the IMS. This would result in a significant reduction in the time and cost of data collection and processing, as well as an improvement in the accuracy and reliability of the data. Ultimately, success would mean that more digital assets can be rated more accurately and cost-effectively, contributing to more informed decision-making in the digital asset community.

Please provide a detailed breakdown of your project’s milestones and each of the main tasks or activities to reach the milestone plus the expected timeline for the delivery.

Milestone Stages:

<u>Milestone 1</u>: Project Initiation and Planning (1 month)

Tasks: Detailed project plan, including timelines, resource allocation, and risk management strategies.

<u>Milestone 2</u>: Development of Automation Tools and API Integration (2 months)

Tasks: Automated system for data collection and processing, comprehensive list of APIs. Api integration, testing, error handilng, deployment and security.

<u>Milestone 3</u>: Establishment of University Collaborations and Training (1 month)

Tasks: Formalized collaborations with universities, trained students ready to contribute to the project.

<u>Milestone 4</u>: Implementation and Testing (2 months)

Tasks: Test data streamlines, enhanced IMS with integrated automation technologies and academic collaborations.

Key Performance Measurements:

  • <u>Efficiency of Data Collection</u>: We will track the time it takes to collect and process data for impact measurement. This will help us assess the efficiency gains from integrating automation technologies. The current situation involves manual data collection, which is time-consuming. After the proposal's completion, we expect a significant reduction in data collection time.

  • <u>Cost of Data Collection:</u> We will monitor the cost of data collection and processing. This will allow us to evaluate the cost-effectiveness of our solution. Currently, the cost of manual data collection is high. After implementing the proposal, we expect a substantial decrease in these costs due to automation and academic collaborations.

  • <u>Accuracy of Data</u>: We will measure the accuracy of the data used in the IMS by comparing it with manually collected data and other reliable sources. This will help us assess the reliability of our automated data collection process. Currently, the accuracy of data can be compromised due to human error in manual data collection. After the proposal's completion, we expect an improvement in data accuracy.

Please describe the deliverables, outputs and intended outcomes of each milestone.

Milestone Deliverables, Outputs, and Outcomes:

  • Milestone 1 - Project Initiation and Planning: The deliverable is the detailed project plan, including timelines, resource allocation, and risk management strategies. The output is the detailed project plan. The indented outcome is to organize efficienlty the project progress.

  • <u>Milestone 2 </u>- Integration of Automation Technologies: The deliverable is the automated system for data collection and processing. The output is the data collected and processed using this system. The intended outcome is increased efficiency and accuracy of data collection.

  • <u>Milestone 3</u> - Establishment of Academic Collaborations: The deliverable is the formalized collaborations with universities. The output is the academic resources and expertise available for the project. The intended outcome is reduced cost of data collection and improved quality of data.

  • <u>Milestone 4</u> - Completion of the Project: The deliverable is the data tools with integrated integrated to the IMS for better data quality to rate digital assets. The output is the improved ratings of digital assets. The intended outcome is more informed decision-making in the digital asset community.

Please provide a detailed budget breakdown of the proposed work and resources.

Captura-de-pantalla-de-2023-07-16-13-38-42-7d239a.png

Assuming an average price of 0.30 USD/ADA during the project timeline, the total ADA budget for the project is 119,500 ADA.

Who is in the project team and what are their roles?

Carlos Ernesto: https://www.linkedin.com/in/ernesto-sampson/

COO and CoFounder of Metera Protocol. Business manager and strategist. With experience in the creation, development and administration on projects. Experience in planning, management, and daily operation of Fintech and Crypto business.

Deep interest in new technologies and passionate about making things happen.

Daniel Sampson: https://www.linkedin.com/in/daniel-sampson-26966a225/

CEO & CoFounder of Metera Protocol

Entrepreneur and innovator in the web3 ecosystem with a strong background in traditional asset management. Experience in marketing, project management and investor relations.

Santiago Portela: https://www.linkedin.com/in/santiago-portela-4a789099/

CSO and CoFunder of Metera Protocol, Santiago is a strategic leader at Metera, guiding the protocol's overall objectives, vision and strategy, developing impact assessment systems, and developing Metera's tokenomics engineering for sustainable decentralized asset management

How does the cost of the project represent value for money for the Cardano ecosystem?

This initiative goes beyond individual interests. It's about equipping the community with the tools to harness the power of automation and academic expertise for impact measurement. Let's collectively democratize the process of digital asset rating, fostering a more inclusive, transparent, and empowered ecosystem.

close

Playlist

  • EP2: epoch_length

    Authored by: Darlington Kofa

    3m 24s
    Darlington Kofa
  • EP1: 'd' parameter

    Authored by: Darlington Kofa

    4m 3s
    Darlington Kofa
  • EP3: key_deposit

    Authored by: Darlington Kofa

    3m 48s
    Darlington Kofa
  • EP4: epoch_no

    Authored by: Darlington Kofa

    2m 16s
    Darlington Kofa
  • EP5: max_block_size

    Authored by: Darlington Kofa

    3m 14s
    Darlington Kofa
  • EP6: pool_deposit

    Authored by: Darlington Kofa

    3m 19s
    Darlington Kofa
  • EP7: max_tx_size

    Authored by: Darlington Kofa

    4m 59s
    Darlington Kofa
0:00
/
~0:00