TetraScience
概述
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总部
美国
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成立年份
2014
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公司类型
私营公司
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收入
< $10m
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员工人数
11 - 50
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网站
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公司介绍
TetraScience 开发硬件和软件解决方案来监控和管理研究实验并将数据记录在云中。它提供传感器来监测温度、湿度、加速度等；用于查看实验或工具的相机；电气开关；用于连接包括注射泵和波发生器在内的硬件工具的适配器；和计时器。 TetraScience 为科学家、工程师、管理人员和安全官员提供服务。它与 Dotmatics 建立了战略合作伙伴关系。 Alok Tayi、Salvatore Savo 和 Siping Wang 于 2014 年 10 月 21 日创立了 TetraScience。它的总部位于马萨诸塞州的波士顿。
物联网解决方案
自动化生物过程数据管理标准数据模型 TetraScience 生成包含元数据和结果的标准化 JSON 文件。我们的数据模型有助于自动化分析、流程报告和开发流程优化。元数据标记不同的仪器产生不同的数据，因此，我们提供元数据标记。标记允许用户通过查询和搜索快速分割和查找数据。搜索和消费索引 JSON 可通过 Elasticsearch 和专用 API 获得。使用带有 JSON 返回的特定 API 调用配置报告和其他系统的自动化。
物联网应用简介
TetraScience 是应用基础设施与中间件等工业物联网科技方面的供应商。.
技术栈
TetraScience的技术栈描绘了TetraScience在应用基础设施与中间件等物联网技术方面的实践。
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设备层
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边缘层
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云层
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应用层
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配套技术
技术能力:
无
弱
中等
强
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实例探究.
Case Study
Improving Protein Purification through Data Science: A Case Study on Alexion Pharmaceuticals
Alexion Pharmaceuticals, a biopharmaceutical company, was facing several challenges in its downstream process development operations. The company was struggling with data silos as instrument vendors did not integrate with one another, leading to manual data movement. The proprietary data formats of vendors required their specific tools, which hampered third-party data standardization. Additionally, informatics leads had to manually move, update, and curate files from multiple locations. Scientists were also required to enter peak information into analytical software themselves. Furthermore, existing dashboarding tools had steep learning curves and could not manage metadata. Scientists were constantly needing to test their hypotheses and derive useful insights from heterogeneous data sources.
Case Study
Driving Cost-Effective CRO Collaboration through IoT
Pharmaceutical and biotech organizations frequently collaborate with Contract Research Organizations (CROs) for absorption, distribution, metabolism, and excretion (ADME) testing of pharmacokinetics (PK) properties of drug candidates. However, most CROs use their own data formats for standard assays, which can pose data aggregation challenges to biopharma companies. The ability to harmonize data from different CRO reports is critical to scale up ADME/PK processes. The manual data workflows for pharmacokinetics and pharmacodynamics (PK/PD) studies are laborious. Scientists have to manually check the reports from CROs, which is time-consuming and prone to errors.
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