Replicable data is the crux of any scientific research. It is crucial if you plan to publish your research in the future. Data replicability simply means that it is possible for an experiment to be carried out again, either by the same scientist or another. If data is not replicable, it may mean that your blood, sweat and tears could be all for nothing. Alas we want to make sure that doesn’t happen! Read on so you can correctly execute your experiments without having to send them to execution.Importance of data replicability
1. Reliability
Replication lets you see patterns and trends in your results. This is affirmative for your work, making it stronger and better able to support your claims. This helps maintain integrity of data. On the other hand, repeating experiments allows you to identify mistakes, flukes, and falsifications. Mistakes may have been the misreading of a result or incorrectly entering data. These are sometimes inevitable as we are only human. However, replication can identify falsifications which can carry serious implications in the future.
2. Peer review
If someone is to thoroughly peer review your work, then they would carry out the experiments again themselves.. If someone were wanting to replicate an experiment,the first scientist should do everything possible to allow replicability.
3. Publications
If your work is to be published, it is crucial for there to be a section on the methods of your work. Hence this should be replicable in order to enable others to repeat your methodology. Also, if your methods are reliable, the results are more likely to be reliable. Furthermore, it will indicate whether your data was collected in a generally accepted way, which others are able to repeat.
4. Variable checking
Being able to replicate experiments and the resulting data allows you to check the extraneous variables. These are variables that you are not actually testing, but that may be influencing your results. Through replication, you can see how and if any extraneous variables have affected your experiment and if they need to be made note of. Through replication, you are more likely to be able to identify the undesirable variables and then decrease or control their influence where possible.
5. Avoid retractions
Replicating data yourself, as well as others doing it, is advisable before you publish the work, if that is your intention. This is because if the data has been replicated and confirmed before publication, it is again more likely to have integrity. In turn, the chance of your paper being retracted decreases. Making it easier for others to replicate data then makes it easier for them to support your data and claims, so it is definitely in your interest to make data replicable.
1. Record everything you do
While carrying out your experiment, you should record every step you take in the process. This is not only because it is good practice and is often required to track what you are doing, but it provides a log to look back at. This, in turn, gives you something to refer back to and enables you to repeat the experiment. It also makes it easier for others to follow the same steps to see if they obtain the same results, which is the whole aim of replicability.
2. Be totally transparent
Sometimes it can be tempting to ignore mistakes or write results more favorably than they actually came out. This also applies to when you repeat experiments, if one is a bit of an outlier, don’t brush it under the rug. That is the point of repeats, to check your methods, equipment. If you are not truthful with what others will be reading and carrying out experiments from in the future, this could significantly skew their results.
3. Make your raw data available
You should make your raw data available for others, so long as it does not compromise patents or such. This would be accompanied by the step-by-step process that you went through and the description of each step.. Having the raw data to compare when repeating experiments yourself or when others replicate it in the future makes it easier since you have something to refer back to.
4. Store you data in an electronic lab notebook
All of these problems with regards to data reproducibility can be tackled using an electronic lab notebook. ELNs’ clever data management allows you to enter data directly into your lab notebook, with an automatic full audit trail. This includes dates and times of creation, editing, deletion, signing and witnessing. Moreover, with an ELN you can create and share protocols or templates, thus making reproducible instructions for future use. If you would like to find out more as to why an ELN may just change your life (in the lab), click here for a comprehensive guide on ELNs
Data reproducibility is one of three main conditions for data integrity. Research also has to have data reproducibility and research reproducibility. These may sound similar, but they are actually quite different. Follow the links to find out the difference between data and research reproducibility.
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