How can you address reproducibility issues in biotech research?

Standardize experimental protocols as much as possible. Clearly define methodologies, reagents, and equipment specifications. Following standardized procedures increases the likelihood of obtaining consistent results across different laboratories.

Mention the smallest detail that was taken care while performing experiments. Crisp and precise documentation along with these details can help increase reproducibility.

Set specific, measurable, achievable, relevant, and time-bound (SMART) objectives for your experiments. Clearly defining what you aim to discover or prove will guide your experimental design. Formulate clear hypotheses based on your research questions. These should be testable statements that your experiments will seek to support or refute. Design your experiments to control variables effectively. Decide on control groups, if necessary, and consider using blind or double-blind study designs to reduce bias. Choose methods that are most suitable for achieving your objectives. Consider the sensitivity, specificity, and limitations of each method.

Plan the experiment well and write down small things which sound trivial. Try to be quantitative concerning results because putting a value on each result is very important to calculate the statistical significance of the results. Biological reagents vary a lot from one lot to another, so writing the lot number used in the experiment is very important, as the activity of specific reagents is not the same. Try to be the best self-critic of your data and see if there is some biased fit in the experiment. Document each data point and analyse them carefully. Discuss the data with your manager and ask if he can ask someone else to validate your results independently. This will provide confidence and reproducibility to your results.

When I first began my career in science, I was under the naive and idealistic perspective that I was in a field that sought to expand knowledge based upon objective truth. I was confused when my boss took umbrage when I honestly questioned the rationale for experiments that he would tell me to perform. He responded in such a demeaning way that I began to question my own intelligence. I soon realized that individual career aspirations and hierarchy were the primary motivators in the deranged capitalist framework. But it was a learning experience for me as I became focused on 'prove you wrong' experiments that would produce the data that would be incontrovertible and crush any dissent from an ignorant manager with a Napoleon complex.

Accurate and detailed documentation is the most impactful action that can be taken to improve reproducibility. However, many people only consider the experiment variable that they control, which can leave out important information. When reporting data or recording your experiment, consider how the following factors are affecting your work: - Temperature and humidity - Reagent batch information - Equipment state and any modifications - Contaminants from previous experiments These factors and more can have key impacts that are not readily apparent.

Treat every experiment as if it was going to be a therapeutic and you can't afford to do harm. Stay up to date with ISO, GxP, USP, etc, standards and write up even the smallest experiment as if it was a big deal. Use good naming conventions for files YYYMMDD_what-happened_initials.extension as an example.

1. Detailed Protocols: Develop comprehensive, detailed protocols outlining each step of the experiment to enhance repeatability. 2. Data Management: Establish robust data management practices, including proper documentation, version control, and storage, to facilitate data reproducibility. 3. Transparent Reporting: Transparently report experimental details, methodologies, and any unexpected observations in publications or documentation. 4. Quality Assurance and Quality Control (QA/QC): Implement QA/QC procedures to monitor and control the quality of experimental processes and data. 5. Documentation Standards: Establish clear documentation standards, including a detailed laboratory notebook, to track experimental steps and outcomes.

Keep detailed records of your experiments. Write down everything you do, observe, and measure in a lab notebook or digital system. Include information like date, time, location, and personnel involved. Record specific details about materials, methods, and procedures. Note any unexpected issues and how you handled them. Document your raw data, intermediate results, and final data using clear formats and labels. Store and back up your data to ensure it's safe and easily accessible.

Begin with data cleaning, which involves removing or correcting erroneous data, handling missing values, and ensuring consistency in data entry. This step is crucial for obtaining accurate results. Use descriptive statistics to summarize the basic features of your data. This includes calculating means, medians, ranges, and standard deviations. It gives a quick insight into the distribution and central tendencies of your data. Employ visualization tools to create graphs and charts. Tools like R, Python's Matplotlib, and Seaborn or commercial software like Tableau can be used. Visualizations such as scatter plots, histograms, and box plots can help identify patterns, trends, and outliers in your data.

Analyze your data carefully. Use appropriate statistical methods to examine your results. Check for data quality and accuracy, identifying and correcting any issues. Test your hypotheses and compare your findings to expectations and previous research. Evaluate the significance and implications of your results. Conduct additional analyses to assess how your results depend on assumptions, parameters, and methods, and how they vary in different situations.

Reporting results in a clear and detailed way is key to reproducibility. Scientists get excited about science, and writing down the exact steps becomes an afterthought. At the end of an experiment or project, it can become unclear or ambiguous how the final result was obtained. Ensuring that each simple step is described in appropriate detail is critical. However, since is no consensus on "appropriate detail," it's important to get a second opinion from colleagues that understand your project when publishing final documentation versions. This can be slow and frustrating, but it is worth it since it reduced ambiguity considerably.

Even reporting a negative result could be beneficial. One might have projected to make a specific product, but finally she fails. By publishing the exact procedure and its results, we could at least give insights that this method is not productive.

One of the best examples of writing protocols: Nature protocols. Take a look. They list all the details, but it is not exhausting; it looks fresh. And importantly, they discuss what is important in the procedure (don't touch it or it will break), and what is optional. Imagine that you have to explain it to yourself before you've started the project. How would you break it down to your past self to save time and tears?

Distinguish exploratory studies that you will use to develop hypotheses from larger scale experiments you use to test those hypotheses. Too often those can blur together, slowing exploration and at the same time not providing a really solid conclusion in the end. Also, it's critical to ensure involvement of subject-matter experts from across disciplines. Validation by people who have run a certain type of experiment before is important, but without someone who understands the underlying mechanisms of action you can waste a lot of time on meaningless experiments - and even risk getting the wrong answer.

True repeatability and reproducibility is hard to achieve. I like to use public cloud provider's Marketplaces for that. Here you can spin up the exact environment along with the data and the analysis workflows, and it runs on the same hardware as the original analysis. All of this can be done at the press of a button and it is in a scalable environment so you can point it to your data and do real experiments. While you can achieve the same thing with Infrastructure-as-code templates, I like Marketplaces because here you can leave comments to improve the approach together.

I have attempted to reproduce many experimental steps during my days in the lab. And though I'm pleased to share that most of them did work, I also must say that they didn't work as well as reported. Having this experience repeatedly taught me to take most reports with a grain of salt, and to realize that many key variables often go unreported. That said, most reports provide contact info to the original experimenters, who can be contacted in case any problems arise. This is typically only helpful if no alternatives, since the turnaround time can be days or weeks.

Reproducibility of results is often affected by different practices followed in other labs and also can vary from region to region. I have observed that often a simple experiment results would be difficult to achieve especially in case of cancerous cell lines because of the handling of these lines. Several labs have many people handling the cell culture that can greatly affect the inside happenings of these lines and then they might behave weirdly. The best practice would be to follow the exact culturing conditions and keep consistency in people handling the lines. Same is true for other types of experiments involving DNA, RNA and proteins etc.

Depending on what you want to reproduce, you may find that some experiments reproduce much better than others. Multi parameter experiments that use difficult cell lines or animal models are perhaps the most difficult to reproduce. Highly sensitive assay readouts or fabrication is another hard field to reproduce. You need good control over batches of reagents, instrument qualifications, and sometimes even the environment (humidity, for example). The best approach would be to document what the variables are in great detail so the experiments can be replicated by another person or site or group. When setting up complex, high-throughout research, special attention should be given to reproducibility and statistics to measure deviations.

Familiarity with the learning from Begley's "Six red flags for suspect work" is important in the context of reproducibility. The author explains how to recognize the preclinical papers in which the data won't stand up. He suggests six questions that every author, editor, reviewer and reader should ask themselves: 1) Were experiments performed blinded?; 2) Were basic experiments repeated?; 3) Were all the results presented?; 4) Were there positive and negative controls?; 5) Were reagents validated?; 6) Were statistical tests appropriate?

From a technical perspective the issue of reproducibility in cellular and molecular biology often comes down to six major factors: assays, reagents, cells, equipment, human and data.

As long as the repeats point to the same direction. You can normalise the values by z score or deviation from the mean. And you should have consistency