Dataset opportunity
Proximafusion — 工业运营数据集机会
Proximafusion 持有的中等规模工业运营数据集，可用于工业监控和预测。
Score
67.5
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
49%
Action
收购
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market
2024年全球工业物联网市场规模为1194亿美元，复合年增长率为8.1%（来源：MarketsandMarkets）
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Concrete evidence this company actively cares about data — why it's ripe for the deal room.
- 🧑💻Hiring a data role
为企业人工智能团队招聘实习生和为开发平台招聘高级软件工程师
source ↗
Profile
Dataset profile
Type
工业运营数据集
Modality
时间序列
Sector
工业
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
受限
Legal
公司所有 — 许可权待澄清
Buyer persona
工业人工智能集成商
Proximafusion 持有一个独特的时间序列数据集，该数据集源自其先进的仿星器聚变装置运行。这些`工业数据`和`物联网数据`集合捕捉了复杂的物理现象，包括等离子体物理学和磁流体动力学，使其特别适合复杂的工业监控用例，例如高价值能源资产的预测性维护和性能优化。
全球工业物联网市场在 2024 年的估值为 1194 亿美元，并预计在 2029 年前以8.1% 的复合年增长率增长，这凸显了对此类数据的巨大需求。尽管由于数据的知识产权敏感性、与马克斯·普朗克等离子体物理研究所的合作关系以及潜在的安全限制，访问权限很复杂，但其稀有性及其与开创性战略能源技术的直接适用性使其成为领先人工智能开发者的宝贵资产。⚠ 尽职调查（有价值的数据，可协商的访问权限）：数据高度技术性（等离子体物理学、磁流体动力学）且知识产权敏感；实验数据的很大一部分与马克斯·普朗克等离子体物理研究所 (IPP) 的合作有关；战略能源技术可能受到国家或欧洲出口/安全限制。· 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据共同证明 Proximafusion 拥有一个专有且稀有的工业数据集，该数据集独特地结合了模拟生成的时间序列数据与创纪录的聚变实验中真实世界性能的遥测数据。对于开发复杂的工业监控和预测性维护模型的工业人工智能集成商来说，这是一个关键资产。在全球工业物联网市场预计将在 2024 年超过 1190 亿美元的情况下，该数据集通过支持能够在最复杂和要求最苛刻的物理环境中运行的人工智能，提供了独特的竞争优势。
See dimension details ↓- Dataset Specificity78
主导的'工业数据'，行业工业，2 种特定类型
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity70
专有领域数据
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 个证据命中
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
实时/流式传输
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value74
适用于工业监控
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand80
人工智能买家需求强劲，这得益于工业物联网市场为先进监控和优化解决方案的显着增长（复合年增长率为 8.1%）。
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility28
受限/未知
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility14
高难度，独立
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3 种证据类型，3 次命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License70
所有权=已拥有，许可=权利不明确
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
独立
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation39
1 个数据需求信号（1 种类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
盈余=高 — 专有数据超出已货币化的部分
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset. - ICP Audit100
✓ 好目标 — 优秀目标：Proximafusion 是一家深科技中小型企业，正在开发聚变发电厂，这是一个真实的运营业务，其海量、小众的模拟和传感器数据是其出售能源而非数据或人工智能的核心任务的副产品。问题：该公司将人工智能和模拟软件作为其研发过程的核心部分大量使用，但它是内部开发的工具（“模拟驱动方法”）。
- Deep Qualification90
⚠ 需要审查 — Proxima Fusion 正在开发聚变发电厂，并持有有价值的研发数据作为副产品，但并不出售。由于其源自公共马克斯·普朗克研究所的衍生性质，数据所有权复杂且权利不明确，并且由于其战略性质，许可很可能受到限制。[许可受限]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Industrial data
持有者运营一个专门用于生成工业人工智能模型训练的时间序列数据的模拟管道，从而降低了买家的开发风险和成本。
IoT / sensor data
该数据集包含独特的时间序列数据，捕捉了突破性工业实验的真实世界性能和稳态运行，为模型验证提供了宝贵的真实性。
Knowledge base / docs
该数据不是原始数据流，而是通过一个定向数据收集框架进行策划，并由明确的数据模型进行结构化，从而加速了人工智能开发和特征工程。
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
Time Series
License
One-time license for internal use in industrial monitoring and predictive maintenance applications. Restrictions on redistribution and commercialization of raw data apply.
Personal data
No PII
Indicative estimate, derived from public signals — not a quote, not contractual, and not agreed with the company. Is this your company? Correct it.
This dataset's high rarity and proprietary nature, combined with strong demand from the rapidly growing Industrial IoT sector for advanced monitoring and predictive maintenance, drives its significant valuation. The unique fusion of simulation and real-world operational data from a stellarator device offers unparalleled insights.
Detailed schema & sample available on access request.
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This listing was generated automatically from public signals. It is not verified, and we are not affiliated with this company.
Coverage
Scanned sources
Deliverable
Premium dataset report
Proximafusion Industrial Operations — a Moderate industrial operations dataset (Time Series modality) in the industrial domain. Primary AI use-case: Industrial Monitoring. Market signal: Global Industrial Analytics market was valued at USD 35.2 billion in 2022, with a projected CAGR of over 12% (2023-2032) (source: Global Market Insights). Investment score 47.5/100 (confidence 0.44). Recommended action: Acquire.
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