Questions AI research labs will receive before purchasing 12,480 data records @datafdn @SuiNetwork [Three stages of data purchase connecting DATA’s Trace with Sui and Walrus] Suppose an AI research lab is seeking data for voice training. If a seller presents a dataset of 12,480 records, the researcher cannot determine the actual volume of desired materials simply by counting total files or reviewing contracts. In fact, the audit example shown by DATA Foundation’s Trace—1.42TB in size—contains 6,140 videos, 3,770 images, 1,710 documents, and 860 audio files. For a lab seeking audio data, it would begin by examining the 860 audio files, representing approximately 6.9% of the total. These figures are not actual transaction results but demo data from Trace—useful for illustrating what information buyers should review. After filtering by file type, researchers must verify the provider’s identity, consent and terms records, and the integrity of delivered files. On the same demo screen, a KYC verification rate of 96.4% refers to contributors, while a latest terms adoption rate of 91.8% reflects the scope of consent version application. Since these metrics use different criteria, buyers must separately assess the provider’s authentication status and terms records per file. The collaboration between DATA Network and Sui, announced on October 12, 2026, aims to link this review process directly to original files and usage rights. At the time of announcement, Trace had registered over 250 million AI data contribution records and processed over 3 million receipts daily. The Sui Foundation invested $30 million in DATA Network, and DATA plans to gradually migrate from its independent blockchain to a Sui-based protocol. [Five steps preserved in a 19.1KB receipt] A single Kled receipt published on Trace contains the SHA-256 hash of a 19.1KB HEIC image, resolution 196 × 348, provider KYC status: Verified, and upload/signature/registration history linked to an on-chain transaction. The upload time was October 6, 2026; provider signature and Trace registration occurred on October 7. This page displays registration history and verification items—not the original image. This receipt’s lifecycle consists of five stages. The “Captured” stage—referring to image capture—is marked “Not recorded yet.” While detailed data includes fields for file occurrence time (occurred_at) and upload time (uploaded_at), these alone cannot confirm the actual camera capture moment. Terms of use are marked “Not recorded” under Origin and “Current” under Identity & Consent versions. These distinctions help buyers break down their review criteria precisely: The hash serves as an identifier to verify what file information was registered; signatures and on-chain records provide evidence of who registered the data and when. Provider authentication status and terms version are separate criteria. The researcher’s right to use data for training must be determined based on actual consent content and licensing agreements. [Transforming one receipt into ten thousand reviews] On Trace’s Verify interface, users can paste up to 10,000 data IDs at once or submit larger lists via CSV. Trace aggregates receipts from a dataset to summarize provider identity verification status, terms version, media type, and modification history, allowing users to drill down into individual records. In the earlier demo of 12,480 records, combining videos and images yields 9,910 entries—roughly 79.4% of the total. A lab seeking audio data and another seeking video data would examine entirely different subsets from the same sales listing. Displaying provider verification rates alongside terms adoption rates enables buyers to select files matching their purpose and then locate corresponding verification records. Trace also includes records from Numo (handling audio and video), oto (handling conversational audio), and other providers. Receipts registered by each app are bundled into Merkle roots and anchored on-chain. Thus, daily processing of over 3 million receipts does not equate to 3 million individual on-chain transactions. This architecture enables registration of massive contribution records while preserving audit trails for individual datasets. [How review changes when records connect to original files] Currently, Trace stores receipts and associated metadata but does not host original files. In announcing its collaboration with Sui, DATA Foundation proposed integrating Walrus as the default storage layer for its application, linking encrypted references to original files with Trace records.For example, if the laboratory receives a file purchased from a provider, it can design a procedure to obtain the reference file from Walrus and compare its hash. This provides evidence to determine whether the content was altered during transmission and how the subsequently processed file differs from the registered reference. Since the reference itself is initially submitted by the provider, this comparison serves as proof of file identity but does not automatically guarantee the authenticity of the original filming conditions or the original consent. Data stored in Walrus is publicly accessible by default, so private originals must be encrypted prior to storage. Sui ecosystem’s Seal combines client-side encryption, Move policies, and a key server to implement conditions under which decryption keys may be granted. A service can be designed to provide keys exclusively to license holders, but it does not include functionality to retroactively revoke already issued keys. Legal usage scope must be separately documented in license and consent records. Trace serves as the layer for searching registration and consent histories; Walrus stores encrypted reference originals; Sui and Seal enable datasets to be treated as programmable assets and enforce access conditions. Trace’s registration and auditing functions are already operational; integrating Walrus-based original storage and Seal-based purchase access control into this service is planned for future implementation. [Decision to Migrate from a Custom Chain to Sui] DATA Network has explained that it is migrating to Sui to reduce the burden of maintaining an independent L1 and enhance scalability. The existing network will continue operating until the transition is complete; $DATA balances will be transferred 1:1, with locking and vesting conditions preserved. The adoption of Walrus and the network migration will proceed incrementally; detailed timelines will be announced separately. DATA will migrate existing registration records from Trace and data provider applications already in operation into the Sui ecosystem. Sui can represent datasets and licenses as programmable objects, while Walrus can store large-scale originals. This shift also frees up development resources previously dedicated to chain operations, allowing greater focus on products for data registration, licensing, and purchase verification. The success of this combination cannot be measured solely by how quickly registration receipts accumulate. More direct product outcomes include whether actual purchasers can select desired files, verify provider authenticity and consent records, compare received files against reference originals, and use data under contractually defined access permissions. The 12,480 demo audit screens demonstrate how purchasers interact with datasets. The 19.1KB receipt shows how thoroughly one can trace the registration history of a single file. The collaboration between DATA and Sui aims to connect these two audit pathways with original storage and access control. If data suppliers for AI training can present records and purchasers can jointly review those records alongside the actual files, then hundreds of millions of registration receipts can become infrastructure usable within individual purchase contracts. #DATAonSuiQuest