By 2025, biotech will produce more data than any other field. Yet, there's a problem. The quality of this data isn't keeping up with the quantity. This makes it tough to use R&D data effectively in large language models (LLMs) and get valuable insights.
Quality of data is not improving as fast as the quantity of data in Biotech
As biotech data grows, sharing information within and between labs becomes crucial. We need to link instruments, experiments, and results, and also connect different labs under one system. That's why when building a biology data system, it's essential to focus on APIs from the start. Many LIMS struggle to effectively manage workflows that involve large-scale data processing, such as Next-Generation Sequencing and High-Throughput Screening.
Most lab management systems (LIMS) and electronic lab notebooks (ELNs) don't prioritize APIs. If they have them, they're often added later and don't work well. To make the most of your data, choose a LIMS designed with APIs in mind from the beginning. Scispot is the API-first alternative LIMS that is built grounds up with scalable data infrastructure.
Take genomics and proteomics as examples. In genomics, lab teams generate lots of genetic data. This needs to be analyzed by scientists using bioinformatics. An API-first LIMS makes sharing this data easy, speeding up discoveries. In proteomics, researchers study proteins using techniques like mass spectrometry. The data then goes to computational scientists for analysis. Again, APIs ensure fast, effective data sharing, helping in drug discovery and development.
APIs also help in projects involving many labs, like developing new drugs or vaccines. Different labs might work on separate parts of a project. A shared API system lets all labs access and contribute data easily, making research faster and more coherent.This is especially important in clinical trials spread across multiple locations. APIs allow for real-time data sharing, which can speed up trials and bring new treatments to patients faster.
In short, as biotech becomes a major data producer, an API-first approach in LIMS is vital. It ensures efficient data sharing, quality, and helps in faster scientific breakthroughs and healthcare improvements.
API-first LIMS helping computational biologists share data seamlessly with their wet lab counterparts
The key features of API-first LIMS are:
Ability to share structured (experiment metadata, post processed instrument files) and un-structured data (entries) programmatically
Ability to build your applications on top of your ELN and LIMS systems
Ability to bulk update your samples, plates, and inventory with human readable IDs
Scispot solves the above features with an API - docs.scispot.com
The API helps bridge the gap between CLI and GUI, enabling faster science.
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