Scientific workflows are used to improve research outcomes and can be custom designed for specific studies. They can cover different aspects of laboratory operations, computer modeling, and data analysis, and can be automated to speed up the process.
Scientific workflows are used to improve how research is done. The most common types focus on lab workflows, computational workflows, and data analysis workflows. Many institutions develop science workflows to reduce errors, improve outcomes, and accelerate discovery. Workflows are often custom designed to work for specific studies and can vary by industry and the type of data that needs to be collected and analysed.
Scientific workflows for the laboratory can cover different aspects of operations. In many institutions there are specific workflows for preparing a sample for testing. For example, a hospital clinic might have well-defined procedures for acquiring a patient sample, accessing that sample, scanning and barcoding it, storing it, and preparing it for testing. In this case, the scientific workflow might be a combination of written procedures to follow and computer software that tracks the sample from collection to examination. Information about the status of a sample is often made available through a web portal for easy access. Laboratory operations also use workflows to ensure that necessary materials, such as chemical reagents needed to conduct a test, are available and available when an experiment is performed.
Another form of science workflow for the lab involves running the actual experiment. For example, a researcher might want to photograph a certain region of the sky or sequence the DNA of a certain cell. In both cases, a workflow would determine the steps a researcher would follow to complete the observation. For example, astronomers would need to obtain the coordinates of the region they wish to study, input that data into the telescope’s tracking system, and position a digital camera to capture an image. Such workflows could be used by an institution to schedule time on special equipment to ensure optimal use of that equipment as well.
Much research today is done through computer modeling and simulation. Scientific workflows for this type of research might include procedures for program creation, troubleshooting, and submission and execution of these computational jobs. In many labs, a researcher can submit work online to a data center. Jobs are then automatically scheduled based on various factors, including priority level, available resources, and job duration. In most cases, job status information is available online, so those who have submitted a job can quickly check its status to see if it’s scheduled to run, run, or completed.
Once an experiment is done, a lab test is completed, or a computer simulation is run, another type of scientific workflow is often needed to analyze the collected data. Often, the first step in a scientific analysis workflow is to extract data from a specific database, file or website. The data then needs to be put into a suitable format. It could be something as simple as converting a temperature reading in degrees Fahrenheit to degrees Celsius or a distance measurement from feet to miles. The data is then run through an analysis routine and may need to be compared with other data. The final results are then presented to the researcher, usually in the form of a report, file or visual display.
In all of these types of scientific workflows, the advantage is that research has standardized ways to do their work. In some cases, automation can be applied to some stage of a workflow. Automation helps offload the work from the researcher and speed up the process.
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