{"signature": "0x73e34ad9825801ed68872b0b3e876a29fda35f4f600a98b7316caf3ddae0c66909409a989440587542132ed966fc950a9f030dba2ad28677188c1c0eafefeec61b", "application": {"round": "0x0c0412ddb846096ea1e37de717921ebf4fef9a39", "answers": [{"type": "email", "hidden": true, "question": "Email Address", "questionId": 0, "encryptedAnswer": {"ciphertext": "DxUigcwsiuPcS7Ul7gNThBWxFroUJu9eZ5wF0tonbyw1Xe3zAY1ch7hzxHSBSVRd", "encryptedSymmetricKey": "0cf7af4c5fcd594eb1f7730265e34ccd253480af8d3324ed8ba53375834cc09261c4b6a8d386854bceb0363c0ecfc0006bd4b7e3fdf8e799038ee3ed8a6e0d97d2a0e17407f626c8d73bf14e46106148867900476d6794c9f08ddb7a17c4f58ee171b559bb2dc731b5830749d4039a3ecf741d68a67065c7b468f03aa12ded0400000000000000202a5cf5f11ac18bf6e8dde03a9d966c131c381d5658b6bc5a53898da541c585fde24ba387ced9a0d6b59d3c477edf7da8"}}, {"type": "short-answer", "answer": "Personal Contribution", "hidden": false, "question": "Funding Sources", "questionId": 1}, {"type": "number", "answer": "10", "hidden": false, "question": "Team Size", "questionId": 2}, {"type": "short-answer", "answer": "rrifat1", "hidden": true, "question": "Telegram username", "questionId": 3}, {"type": "paragraph", "answer": "50000", "hidden": true, "question": "Total prior funding for project in USD", "questionId": 4}, {"type": "paragraph", "answer": "Taosheng Shi - https://x.com/TaoshengShi\nMd Rokonuzzaman Rifat - https://x.com/rokonn12", "hidden": true, "question": "Profiles or socials of other main team members publicly associated with project", "questionId": 5}, {"type": "short-answer", "answer": "6", "hidden": true, "question": "How old is the project? (Months)", "questionId": 6}, {"type": "paragraph", "answer": "#### Problem Statement\n\nThe scientific and AI/ML communities are grappling with significant challenges related to data integrity, reproducibility, and collaboration. The reproducibility crisis in scientific research has undermined confidence in results, with a considerable number of studies failing replication attempts. In AI/ML, frequent updates, experiments, and lack of disciplined versioning exacerbate these issues, leading to unreliable models and results. Current data management systems often fail to maintain immutable traceability links between raw data, processing steps, and final outcomes, making it difficult to reproduce and verify results.\n\n#### Solution\n\nJiaoZiFS is a Data-Centric Version Control File System designed to address these challenges by providing a comprehensive framework for data versioning, provenance, and reproducibility. It leverages insights from software engineering and cryptographic technologies to ensure the end-to-end integrity of scientific data and AI/ML pipelines. \nFor this model, Filecoin provides a needed solution by making storage affordable, preventing vendor lock-in and fostering a culture of openness. By storing research objects in a decentralized repository, it becomes possible to share the entire research object with data and code, provide versioning, and ensure reproducibility.\n\n![DeSci Framework](https://github.com/mrrifat/jiaozifs/blob/main/docs/DeSci/framework.jpeg?raw=true)\n\nA dataset management component creates a good separation between training data collection and consumption by defining strongly typed schemas for both, which allows data development and model algorithm development to iterate in their own loop, thus expediting the project development.\n\n#### Roadmap and Timeline for Implementation\n#### Milestone 1: System Design and Components Implementation\n\n- **Tasks**:\n    - System design and implementation for DeSci participants (researchers, institutions, and developers)\n    - Develop Smart Contracts and Oracles\n    - Integrate JiaoZiFS with Filecoin for decentralized storage of scientific data and models\n    - Implement Proxy Re-encryption for secure data sharing\n- **Timeline**: 2 months\n\n#### Milestone 2: System Interactions for Scientific Data Upload, Auction, and Transfer\n\n- **Tasks**:\n    - Implement data upload and exchange functionalities\n    - Develop assessment protocols for scientific data\n    - Enable trading and payment mechanisms for data and research assets\n- **Timeline**: 2 months\n\n#### Milestone 3: Public Launch and Marketing\n\n- **Tasks**:\n    - Execute a marketing campaign targeting research institutions and scientific communities\n    - Provide customer support and gather user feedback for further improvements\n- **Timeline**: 1 month\n\n#### Milestone 4: Continuous Improvement and Scaling\n\n- **Tasks**:\n    - Continuously improve the system based on user feedback\n    - Expand the scope to other use cases requiring provenance and integrity of digital assets\n    - Enhance customer support and maintenance services\n- **Timeline**: 1 year", "hidden": false, "question": "Describe your project in a few paragraphs (ideally including a problem statement, solution, roadmap, and timeline for implementation)", "questionId": 7}, {"type": "paragraph", "answer": "**JiaoZiFS** aligns with the goals of **DeSci** by enhancing data integrity, enabling **decentralized collaboration**, ensuring **secure data sharing**, **promoting open science**, and supporting the **commercialization of scientific data**. Through these capabilities, **JiaoZiFS** fosters a more reliable, transparent, and collaborative scientific ecosystem.", "hidden": false, "question": "How does your project support the mission or goals of DeSci?", "questionId": 8}, {"type": "paragraph", "answer": "The grant funds will be utilized to design and implement core system components, including smart contracts, secure data-sharing mechanisms, and integration with **Filecoin/IPFS** for decentralized storage. They will also support the development of functionalities for data upload, auction, and transfer, public launch preparations, and continuous system improvements. Additionally, the funds will finance marketing efforts, scientific seminars, workshops, and university outreach activities to **onboard traditional scientists onto DeSci**, fostering collaboration and promoting the adoption of JiaoZiFS within the scientific community.", "hidden": false, "question": "Describe how funds from the grants program will be used for your project. ", "questionId": 9}, {"type": "paragraph", "answer": "Our project didn't participate any past grant rounds.", "hidden": true, "question": "If you participated in past grant rounds (i.e. DeSci GR15/ Beta) using a different project payout wallet address, please share it here.", "questionId": 10}, {"type": "paragraph", "answer": "We didn't participate in any past grant rounds. However we were able to reach different DeSci initiatives to support our project. ", "hidden": false, "question": "If this project participated in past grant rounds, please share any new updates or milestones since the last round.", "questionId": 11}, {"type": "paragraph", "answer": "We are affiliated with several prominent research institutions and DeSci initiatives and have collaborated with industry leaders like **Filecoin/IPFS** to integrate cutting-edge technologies like blockchain and decentralized storage. We have no potential conflicts of interest and are committed to promoting transparency and collaboration within the scientific community.", "hidden": true, "question": "Is there any other information you would like to share about your project, previous work, other project affiliations, or potential conflicts of interest? (or anything not covered elsewhere in this application that you feel is relevant to share)", "questionId": 12}, {"type": "checkbox", "answer": ["Yes"], "hidden": false, "question": "Does 50% of your team reflect the diversity of the community your project serves with a particular emphasis on including women, Black, Indigenous, and People of Color (BIPoC), individuals with disabilities, and members of the LGBTQ+ community?", "questionId": 13}, {"type": "multiple-choice", "answer": "I already read the above exclusion criteria and understand those exclusion criteria are not permitted and will result in rejection or removal from the round.", "hidden": true, "question": "Please read the following exclusion criteria that are not permitted and will result in rejection or removal from the round.", "questionId": 14}], "project": {"id": "0x282208be2dd36ffc9e1f27ba6d569418160657b406d46b6ddd6c9387d63353a1", "title": "JiaoZiFS(JZFS)", "logoImg": "bafkreifbk24ttyukbsotmz7qv7t7ypykswejyvxqvcbiwpu7mf44f2hz2e", "metaPtr": {"pointer": "bafkreib63nkoxldmgguasnqipzpkhaieeqm33w6ghagxd7rid7ojj5bo64", "protocol": "undefined"}, "website": "https://github.com/GitDataAI/jiaozifs", "bannerImg": "bafkreiatbuiv7a46yaxoxyvfgwjjzhiox3qa77hymyc6l47boynchdqxxe", "createdAt": 1721720995468, "userGithub": "", "credentials": {"twitter": {"type": ["VerifiableCredential"], "proof": {"jws": "eyJhbGciOiJFZERTQSIsImNyaXQiOlsiYjY0Il0sImI2NCI6ZmFsc2V9..xLexsDsPe6ZQMfs1cpkxuNAFHx68I7DiFM5zTLhgbQljcZe3naCdA5zY4i1l8oad6zPv28aYuwrpg5ZCzSQqAw", "type": "Ed25519Signature2018", "created": "2024-07-23T07:47:43.584Z", "proofPurpose": "assertionMethod", "verificationMethod": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC#z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC"}, "issuer": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC", "@context": ["https://www.w3.org/2018/credentials/v1"], "issuanceDate": "2024-07-23T07:47:43.584Z", "expirationDate": "2024-10-21T07:47:43.584Z", "credentialSubject": {"id": "did:pkh:eip155:1:0x81A1EA5A462ba21A328d206d4c15A7Af8812ad3c", "hash": "v0.0.0:HGt08kYYzClnzKWCIDJQWVWaAmjloeafJs+c9tJaEQI=", "@context": [{"hash": "https://schema.org/Text", "provider": "https://schema.org/Text"}], "provider": "ClearTextTwitter#GitDataAI"}}}, "description": "## Background\n\nThe scientific community is facing a crisis of reproducibility: confidence in scientific results is damaged by concerns regarding the integrity of experimental data and the analyses applied to that data. Experimental integrity can be compromised inadvertently when researchers overlook some important component of their experimental procedure, or intentionally by researchers or malicious third-parties who are biased towards ensuring a specific outcome of an experiment. The scientific community has pushed for “open science” to add transparency to the experimental process, asking researchers to publicly register their datasets and experimental procedures. We argue that the software engineering community can leverage its expertise in tracking traceability and provenance of source code and its related artifacts to simplify data management for scientists. Moreover, by leveraging smart contract and blockchain technologies, we believe that it is possible for such a system to guarantee end-to-end integrity of scientific data and results while supporting collaborative research.\n\n## Introduction\n\nAs reported in a recent Nature article, the scientific research community faces a “reproducibility crisis”. 70% of the 1,576 scientists surveyed (from various fields, including chemistry, physics, earth and environmental science, biology, and medicine) reported that they had tried and failed to reproduce another scientist’s experiments. A 2012 review of 2,047 biomedical and life-science research articles that had been retracted found that 43% of those retractions were due to fraud (or suspected fraud). To maintain confidence in scientific results, we must ensure the integrity of the scientific workflow, from data collection to article publication.\n\nTowards addressing this crisis, there has been a rise of online repositories to share scientific data, protocols, or findings. By publishing the data that led to a scientific result, researchers invite the reproduction of their experiments, even in cases where future researchers might have difficulties re-acquiring that same data (i.e., if specialized and expensive machinery were required). However, researchers have been slow to adopt such repositories: they are often seen as a burden, requiring time and effort to decide what data should be released and what form it should be. Moreover, some researchers may be wary to make large datasets available before they believe that they have achieved the maximum benefit (in terms of additional publications) from that data.\n\nEven if researchers do choose to make their datasets public, this still does not solve the problem of data integrity. Who is to say that the data reported is unmodified? Or, in the event that it is advertised as modified, that it is in fact modified in the exact way described? If a researcher claims that they are releasing an entire dataset, are they? These challenges can arise even if we assume that no researchers would purposely make a false claim, as managing large ecosystems of data repositories is complex, and datasets are vulnerable to accidental corruption.\n\nHistorically, lab science data has been maintained in lab notebooks, where researchers might sign and date each page to attest to the authenticity of each step and collected data. But as data collection and analysis have become increasingly electronic, this record has begun to vanish. At the same time, provenance has itself grown complex, as research collaborations may be geographically distributed involving data from multiple researchers and analyses may make use of historical datasets. Moreover, the increasingly sophisticated scripts and tools for data analysis, which may themselves be shared or not shared, make it important to also maintain traceability links to the exact code used to analyze primary data, particularly if scripts are later improved or found to have defects.\n\nIdeally, a data management system should:\n- Support private collaboration — enabling researchers at the same lab or across institutions to collaborate in generating and analyzing unpublished private data before publication\n- Support data collection and analysis workflows — ensuring that the system is well integrated into all steps of the data generation and analysis process and that its complexities do not become a barrier to adoption\n- Support integration with the diverse data repositories already in use, as well as with the diverse authentication and authorization systems used by academic and research institutions\n- Provide guarantees of the integrity of the data and results — maintaining immutable traceability links between the primary data collected, scripts used to analyze data, and the results of analyses\n- Allow, but not require, full disclosure of datasets\n- Allow the general public to easily discover datasets that have been publicly released\n- Be open and decentralized, having no single point of failure or trust\n\nCurrent scientific data management tends to focus on the dissemination of final results, with many repositories and approaches focusing on where those archives should be stored and indexed. What is lacking is a system that researchers can use in their lab, as they perform their research, to maintain immutable traceability links between the original data as captured, the transformations and analyses applied to it, and the results presented for publication.\n\nWe argue that by leveraging insights from software engineering and cryptography, we can provide the scientific research community with a data management infrastructure that meets all of these goals.\n\n## Proposed Solution\n\nFilecoin provides a needed solution by making storage affordable, preventing vendor lock-in and fostering a culture of openness. By storing research objects in a decentralized repository, it becomes possible to share the entire research object with data and code, provide versioning, and ensure reproducibility.\n\n![DeSci Framework](https://github.com/mrrifat/jiaozifs/blob/main/docs/DeSci/framework.jpeg?raw=true)\n\nA dataset management component creates a good separation between training data collection and consumption by defining strongly typed schemas for both, which allows data development and model algorithm development to iterate in their own loop, thus expediting the project development.\n\n## Conclusion\n\nThe reproducibility crisis in scientific research highlights the urgent need for a robust, transparent, and secure data management system. By leveraging insights from software engineering and cryptographic technologies, we propose a solution that ensures end-to-end integrity of scientific data and results, while supporting collaborative research. \n\nUltimately, this approach not only addresses the current reproducibility crisis but also fosters a culture of transparency and collaboration in the scientific community. By making it easier to manage, share, and verify data, we pave the way for more reliable and trustworthy scientific discoveries, thereby restoring confidence in scientific research.", "lastUpdated": 0, "projectGithub": "", "projectTwitter": "GitDataAI"}, "recipient": "0x8cb64114bE40D2486e8de697f59E0c11C658af75"}}