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· 2 min read
Ali Safari

Let's have a look at the snippet below:

// Use code snippets or templates for repetitive tasks
// Automate tasks using build tools like Grunt or Gulp
// Use a version control system like Git to manage and track changes
// Use a debugger to quickly identify and fix issues
// Use efficient coding practices, like reducing redundant code and optimizing runtime

Snippet or template can be easily inserted into ur code, and gets modified as needed. But, how to organize our snippets, in order to find and use them efficiently:

  • Categorize your snippets - group related snippets together into categories, such as "HTML", "CSS", "JavaScript", "Bootstrap", etc.

  • Use clear and concise names for your snippets - this will make it easier to search for them later on.

  • Add descriptions to your snippets - this will help you remember what a snippet does and why you created it.

  • Use tags - adding descriptive tags to your snippets can help you search for them more easily.

  • Use a code snippet manager - there are many tools available (some are free) that can help you organize and manage your code snippets. For example, "CodeBox" for Mac, "SnippetsLab" for Mac, "Visual Studio Code" for web development, and many web-based services such as "GitHub's Gist."

Overall, the key is to find an organizational system that works for you and stick to it.


Remember, there are many ways to improve your efficiency as a developer, so feel free to experiment with different tools and techniques to find what works best for you!

· One min read
Ali Safari

Identifying the most pressing or important challenge in automation and lab automation can depend on various factors, such as the specific industry, organization, or application of automation. However, here are some common challenges that are often considered most pressing or important in automation and lab automation respectively:

Increased efficiency and accuracy of lab processesCost of implementing automation
Improved reproducibility of experimentsComplexity of lab processes
Ability to handle large volumes of dataIntegration with existing lab systems
Improved safety of lab personnelTraining of staff on how to use automated systems
Temporary shut down of lab operations during implementation

Technically, one of the most common concerns in lab automation is ensuring the accuracy and reproducibility of automated processes. Lab automation systems may introduce errors or variations that can impact experimental results. Therefore, it is critical to validate and verify the performance of automated systems to ensure that they are producing accurate and reliable results. Additionally, it is important to establish standard operating procedures (SOPs) and quality control measures to ensure that automated processes are consistent and reproducible over time.

· One min read
Ali Safari

Lab automation is the process of using technology to automate laboratory processes and workflows. Here are some general benefits of lab automation:

  • Increased Efficiency

    Lab automation can help to streamline laboratory processes and workflows, reducing the time and effort required to perform routine tasks. This can lead to increased efficiency, productivity, and throughput.

  • Improved Accuracy

    Automation can help to reduce errors and variability in laboratory processes, leading to more consistent and reliable results.

  • Cost Savings

    Lab automation can help to reduce the cost of labor, materials, and equipment, leading to cost savings over time.

  • Data Management

    Automation can help to manage and analyze large amounts of data generated by laboratory processes, leading to better insights and decision-making.

  • Standardization

    Automation can help to standardize laboratory processes and workflows, ensuring consistency and compliance with regulatory requirements.

  • Scalability

    Automation can help to scale laboratory processes and workflows to meet changing demands and increasing throughput.

Overall, lab automation can help to improve the efficiency, accuracy, and cost-effectiveness of laboratory processes, while also providing better data management and analysis capabilities.

· 2 min read
Ali Safari

The statistical package R for many researchers is their first experience to the programming world. This free and popular statistical language is available in online repositories. R can perform powerful data manipulation as well as data visualization.

However, programming for a reasearch focused branch is not limited to R any more, but highly likely starts often with R.

Sometimes, having knowledge in other operating systems other than the popular Windows is needed. Sometimes, an analytical tool require a Linux Operating System. In such case, maybe you need to set up a virtual machine (VM), in order to install and work with a linux based software.

Fortunately, by having the Internet and googling in you side, you can have access to a lot of open source packages and shared repositories that are developed by both researchers and professional developers.

Nowadays, as a researcher if you want to do an analysis, most probably you can find a software or a tool that is available online, or can be further developed by you to fulfill your requirements.

This is the way that science trajectory is leading to, and to be a successful researcher you need to gain some knowledge and skills in computer programming.

As a developer for scientific applications, I have the passion and motivation to go further across deciplines and share my knowledge in programming with scientific comunities and developers.