Dataset Discovery · Dataset Search · Custom Datasets · Database Services · AI Agent Customization
The data you need is often distributed across government portals, statistical agencies, international organizations, academic databases and other public sources.
DeepSData helps you discover data, verify whether it can be obtained and whether it is suitable for use, and prepare it as a deliverable. When existing public data cannot fully meet the need, we can continue with custom preparation based on specific requirements and further build a thematic database or a dedicated AI agent.
Enter a data name, research topic or application. If no suitable content is found, Xiaoju can continue to structure the requirement and carry out a professional data search.
Quick access
Five core capabilities
Dataset Discovery
Use the dataset library and data-finding guides to understand data types, sources, content, access methods, conditions of use and suitable scenarios.
See what the data contains, where it comes from, how to access it, whether licensing is clear, what it may suit and what has not yet been verified.
These pages support discovery, comparison and selection. Before research, model training or production use, check the original files against your specific task.
Open the public dataset libraryData-finding guides explain where relevant data is commonly found, which public access routes exist, whether application or authorization is needed, and the common gaps and initial feasibility.
Useful before topic selection, project proposals, technical planning or data procurement to judge whether data can be obtained, whether it can meet the need and what is still missing.
View data-finding guidesDataset Search
You do not need to know platform names or query syntax. Tell Xiaoju what you need to accomplish and what the data cannot be missing; Xiaoju first confirms the understanding, then starts the search.
Combine domain terms, synonyms and multiple search routes to gather relevant data candidates.
Check each candidate against the required conditions: met, close, unconfirmed or clearly unsuitable.
List publishers, source pages and visible access routes, while marking file, licence and use boundaries that still require human verification.
Organize exact matches, close candidates, relevant leads, exclusions, source basis, actual gaps and next-step recommendations.
Basic Search — Free provides main candidates, source leads, preliminary matches and clear gaps; Complete Search Report — RMB 10 per run / 1 credit automatically expands specialist terms, Chinese and English names, abbreviations, aliases, search paths and public sources, with a fuller candidate list and search-stage assessment. It does not include manual downloads, file-content, field or sample checks, or a final licensing opinion. Human Deep Verification — Quoted separately checks actual access routes, file structures, fields, samples, licence conditions and access requirements for priority candidates. Custom Dataset — Quoted after assessment covers collection, cleaning, integration, labelling, quality control and formal delivery when public data cannot directly meet the requirement. Reports can be viewed online, saved in the workspace, revisited by version, exported to Word and sent by email; PDF export is available when supported by the runtime.
Ask Xiaoju View a dataset search resultCustom Datasets
When existing data cannot meet requirements for volume, fields, time, geography, format, labels, relationships or quality, we can continue with collection, cleaning, preparation, labelling and verification against explicit technical requirements.
Projects first assess data sources, feasibility, processing approach, main risks and acceptance criteria. A sample can be used to confirm the direction before formal preparation begins.
Deliverables can include primary data, a field dictionary, source notes, README, processing definitions, quality checks, exception notes, version records and agreed scripts or tools.
The value of custom datasets is not merely obtaining a batch of data, but producing a result with clear sources and fields, explainable processing, assessable use scope, and acceptance against agreed standards.
Database Services
When data needs ongoing updates, shared access, repeated queries or long-term maintenance, it can be further developed into a thematic database or data system.
Database services are delivered by project. Scope, deployment environment, update approach, maintenance period and acceptance criteria are confirmed before the project begins.
AI Agent Customization
Turn the team’s data-processing experience, quality standards and operating rules into a dedicated agent that can run repeatedly. Suitable for data cleaning, field matching, information extraction, classification, quality review, batch processing, recurring reports, knowledge Q&A and search support.
Agents are suitable for stable, repetitive and rule-based work. Research design, exception handling, quality decisions and final acceptance remain professional responsibilities.
Why DeepSData
We do not treat a stack of links as a search result or present unverified information as a certain conclusion.
Show only data candidates and sources that were actually found; do not manufacture matches.
Explain which required conditions are met, close, unsuitable or still unconfirmed.
When licences, fields, quantities or access conditions cannot be verified, state that clearly rather than substituting speculation for fact.
Search reports, data files, field notes and processing definitions are delivered as agreed and can be checked against clear standards.
Project services
Some projects served individuals, project teams or specific entities associated with the organizations listed. Names describe the service context only; they do not imply institutional partnerships, official recognition, recommendations or endorsement.
Real user feedback
These comments come from completed data-service projects and have been anonymized.
The dataset split was handled carefully. The training and validation sets had highly consistent distributions with no obvious shift.
The raw data had many formatting problems. They cleaned everything up and standardized the character encoding to UTF-8, so it was ready to use.
The delivery documented the data-processing rules as well. It gives our team a useful reference for building similar datasets in the future.
Across tens of thousands of records, we could hardly find omissions or obvious labelling errors. The quality control was solid.
The delivered dataset could be used for model training immediately. I did not need to write another preprocessing script.
Formats, encodings and naming conventions were consistent across delivery batches, so the files merged without conflicts.
The de-identification was thorough. Information that could identify a specific person or address was removed or transformed.
For multimodal data, the image-to-text relationships were maintained accurately with no obvious mismatches or missing pairs.
Start a consultation
Tell Xiaoju about the research direction, task objective and data requirements. Xiaoju will first help structure the need, then suggest whether to begin with dataset discovery, dataset search, custom datasets, database services or AI-agent customization.
