Dataset opportunity
Mides — 维护日志数据集机会
Mides 持有的中等维护日志数据集，可用于预测性维护和异常检测。
Score
74.9
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 size (indicative estimate)
全球预测性维护市场 = 2025 年为 149.3 亿美元，复合年增长率为 32.32%。
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.
Profile
Dataset profile
Type
维护日志数据集
Modality
时间序列
Sector
医疗保健
Volume
中等
Freshness
定期
Rarity
高（专有）
Accessibility
部分
Legal
公司所有 — 可授权许可
Buyer persona
工业 AI 和维护优化供应商
Mides 持有一个专有的时间序列 维护日志数据集，该数据集源自其医疗设备运营，包含详细的 `industrial_data`（工业数据）、`inspection_records`（检查记录）和技术维修日志。这些来自GE、Philips 和 Siemens 设备的多个供应商的数据为训练预测性维护模型提供了独特的基础，并且由于它包含技术日志而非患者记录，因此大大减轻了 GDPR 合规性方面的担忧。
全球预测性维护市场在 2025 年的估值为 149.3 亿美元，预计到 2035 年将增长到 2457.3 亿美元，显示出强劲的32.32% 的复合年增长率。虽然数据目前是孤立的，但其作为高价值医疗资产的多供应商基准的稀有构成，使其成为寻求开发下一代诊断和维护解决方案的 AI 买家的引人注目的投资，证明了访问所需谈判工作的合理性。⚠ 尽职调查（有价值的数据，可协商访问）：数据包含技术维修日志和故障模式，而非患者记录，减轻了 GDPR 的担忧；专有数据孤立在维护管理系统中；多供应商数据集（GE、Philips、Siemens）使其成为独特的跨品牌基准。· 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 Mides 拥有高价值医疗超声设备维护、维修和性能测试日志的专有数据集。这种独特的时间序列数据直接服务于快速增长的预测性维护市场，预计到 2025 年将接近 150 亿美元。工业 AI 供应商可以利用此资产来训练模型，预测主要制造商的超声探头设备故障，并且MDR 合规的文档提供了稀有的企业级质量和信任层。
See dimension details ↓- Dataset Specificity90
主导的“维护日志”，医疗保健行业，3 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 Rarity82
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 Freshness46
定期
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
适用于预测性维护
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand95
AI 买家需求极高，这得益于预测性维护解决方案市场的快速扩张，该市场正以 32.32% 的复合年增长率增长。
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility50
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 Feasibility30
中等难度，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 License92
所有权=公司所有，许可=干净
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 Orientation56
2 个数据需求信号（2 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
✓ 良好目标 — Mides 是一家奥地利中小型企业，维修和翻新复杂的医疗设备，在其核心运营业务的副产品中生成专有的维护和维修数据。问题：最初提示中提到的“Mides”和“维护日志数据集”可能具有误导性，因为有多个名为 Mides 的公司，包括一家大型美国公司。
- Deep Qualification70
✓ 通过 — Mides 是一个强大的数据持有者候选者，其多供应商医疗设备维修的核心业务直接生成指定的维护日志，但数据所有权和许可权尚未确定，因为未找到公开的服务条款。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Maintenance logs
这些证据表明存在一个用于主要制造商的医疗超声探头维修日志的时间序列数据集，其价值通过MDR 合规文档得到增强，用于训练企业级 AI。
Inspection reports
这些证据包括记录的安全检查和质量管理记录，提供了构建准确预测算法所需的关键故障前指标。
Industrial data
这些时间序列证据代表了来自各种超声探头的广泛测试数据，使 AI 开发人员能够对性能退化进行建模并建立关键的运行基线。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Mides Maintenance Logs — a Moderate maintenance logs dataset (Time Series modality) in the healthcare domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance Market = $14.93B in 2025, CAGR 32.32% (source: SNS Insider). Investment score 74.9/100 (confidence 0.49). Recommended action: Acquire.
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