Dataset opportunity
Ilivestock — 医疗影像数据集机会
Ilivestock 持有的中等规模医疗影像数据集，可用于诊断人工智能和计算机视觉。
Score
64.3
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
Data Sharing Agreement
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)
Global AI in Agriculture market = $2.71 billion in 2025, CAGR 25.0%.
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.
- 🤝Data partnership
Partnering with Canada’s farming innovators for tech integration
source ↗
Profile
Dataset profile
Type
Medical Imaging Dataset
Modality
Image
Sector
other
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Largely customer-owned — licensing rights to clarify · PII/regulated
Buyer persona
Medical-AI & diagnostic-imaging companies
Ilivestock 提供专门的医疗影像数据集，用于牲畜，结合了视觉数据与来自称重硬件的相应iot_data以及相关的medical_records。该聚合数据集来自英国和加拿大的农场，提供了一个丰富、多模态的信息来源，非常适合开发和训练Diagnostic AI模型，以检测健康问题、监测生长和分析动物行为。
2025 年，全球AI in Agriculture市场价值27.1 亿美元，预计将以25.0% 的复合年增长率增长，显示出巨大的商业潜力。[2] 尽管存在访问复杂性——例如数据由个体农民拥有以及多司法管辖区的隐私要求——但该数据集的稀有性和深度使其成为一项非常有价值的资产。[2] 匿名聚合的需求是寻求在此快速扩张市场中获得竞争优势的买家可解决的挑战。⚠ 尽职调查（有价值的数据，可协商的访问权限）：数据主要由个体农民/客户拥有；需要对来自称重硬件的物联网传感器数据进行匿名聚合；多司法管辖区数据（英国和加拿大业务）；公司充当牲畜记录的数据处理者 · 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 Ilivestock 拥有一个专有的、多模态的数据集，将农场livestock health events与个体performance metrics和基于硬件的IoT data联系起来。对于医疗人工智能开发者来说，这是一个稀有的标记数据源，用于训练和验证用于兽医和农业用途的复杂diagnostic AI模型。这些数据能够创建强大的工具，以改善animal welfare和农场compliance，直接进入每年以超过 25% 的速度扩张的全球人工智能在农业市场。
See dimension details ↓- Dataset Specificity74
dominant 'medical_records', sector other, 3 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity82
proprietary domain data
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 evidence hits
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
real-time/streaming
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
fit for Diagnostic AI
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand90
AI buyer demand is high, driven by the AI in Agriculture market's exponential expansion, which is projected to grow at a CAGR of 25.0%. [2]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility0
PII/regulated
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility0
medium difficulty, independent
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3 evidence types, 3 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License8
ownership=customer_owned, licensing=rights_unclear
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
independent
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation39
1 data-appetite signals (1 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
surplus=high — proprietary data beyond what's already monetised
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
✓ good target — Excellent target: iLivestock is a UK-based SME providing a hardware and software platform for livestock management, whose core business is not selling data but enabling farmers to collect it as a by-product of their operations. Issues: The initial lead description 'Medical Imaging Dataset' is misleading; the company's focus is on operational farm data (weight, breeding, medicine), not imaging.; Their privacy policy mentions they may transfer anonymous aggregated data to third parties, which needs clarification on whether this is a product or for operat
- Deep Qualification90
⚠ needs review — The company is a tooling vendor selling an integrated hardware and software platform for livestock management; the data is explicitly owned by the farmer customers, making direct acquisition complex and dependent on negotiating access with each farmer. [data is owned by the company's customers]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
该时间序列数据源自用于绵羊和牛的软件和硬件集成系统，为预测性farm management模型提供连续的输入流。
Medical records / imaging
该数据集包含在应用程序中捕获的结构化medical records，为训练和验证diagnostic imaging算法提供了必要的地面真实标签和临床背景。
Industrial data
这些证据证实了纵向industrial farm data的存在，使人工智能模型能够将健康诊断与多代pedigree和经济成果（如活重增益）联系起来。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Ilivestock Medical Imaging — a Moderate medical imaging dataset (Image modality) in the other domain. Primary AI use-case: Diagnostic AI. Market signal: Global AI in Agriculture market = $2.71 billion in 2025, CAGR 25.0% (source: The Business Research Company). [2]. Investment score 64.3/100 (confidence 0.49). Recommended action: Data Sharing Agreement.
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