The replication attempts performed with summer frogs failed because of seasonal sensitivity of the frog heart to the unrecognized acetylcholine, making the effects of vagal stimulation far more difficult to demonstrate. With subsequent tests providing supporting evidence, the understanding of the claim improved. What had been perceived as replications were not anymore because new evidence demonstrated that they were not studying the same thing.
The theoretical understanding evolved, and subsequent replications supported the revised claims. That is not a problem, that is progress. This is a useful research activity for advancing understanding, but many studies with this label are not replications by our definition.
That is, they are not designed such that a failure to replicate would revise confidence in the original claim. Failures are interpreted, at most, as identifying boundary conditions. A self-assessment of whether one is testing replicability or generalizability is answering—would an outcome inconsistent with prior findings cause me to lose confidence in the theoretical claims? If no, then it is a generalizability test.
Designing a replication with a different methodology requires understanding of the theory and methods so that any outcome is considered diagnostic evidence about the prior claim. In fact, conducting a replication of a prior claim with a different methodology can be considered a milestone for theoretical and methodological maturity. Replication is characterized as the boring, rote, clean-up work of science.
This misperception makes funders reluctant to fund it, journals reluctant to publish it, and institutions reluctant to reward it. The disincentives for replication are a likely contributor to existing challenges of credibility and replicability of published claims [ 14 ].
Single studies, whether they pursue novel ends or confront existing expectations, never definitively confirm or disconfirm theories. Theories make predictions; replications test those predictions.
Outcomes from replications are fodder for refining, altering, or extending theory to generate new predictions. Replication is a central part of the iterative maturing cycle of description, prediction, and explanation. A shift in attitude that includes replication in funding, publication, and career opportunities will accelerate research progress. Abstract Credibility of scientific claims is established with evidence for their replicability using new data.
Provenance: Commissioned; not externally peer reviewed. Introduction Credibility of scientific claims is established with evidence for their replicability using new data [ 1 ]. Replication redux We propose an alternative definition for replication that is more inclusive of all research and more relevant for the role of replication in advancing knowledge.
Replication resolved The purpose of replication is to advance theory by confronting existing understanding with new evidence. Download: PPT. Fig 1. There is a universe of distinct units, treatments, outcomes, and settings and only a subset of those qualify as replications—a study for which any outcome would be considered diagnostic evidence about a prior claim. Fig 2. A discovery provides initial evidence that has a plausible range of generalizability light blue and little theoretical specificity for testing replicability dark blue.
Conclusion Replication is characterized as the boring, rote, clean-up work of science. References 1. Schmidt S.
Shall we really do it again? The powerful concept of replication is neglected in the social sciences. Rev Gen Psychol. Nat Hum Behav. No support for historical candidate gene or candidate gene-by-interaction hypotheses for major depression across multiple large samples.
Am J Psychiatry. A manifesto for reproducible science. Promoting an open research culture. Here is a real-world example of how one leading manufacturing company uses SIOS to create a high availability solution in the cloud using real-time data replication. Bonfiglioli is a leading Italian design, manufacturing, and distribution company, specializing in industrial automation, mobile machinery, and wind energy products and employing over 3, employees in locations around the globe.
Since most of their applications run in a Windows environment, Bonfiglioli used guest-level Windows Server failover clustering in their VMware environment to provide high availability and disaster protection.
In its on-premises environment, the company uses VMware clustering, which allows Windows Server Failover Clustering WSFC to manage failover to a secondary server in the event of an infrastructure failure. However, it was a challenge to provide this type of protection in the cloud because using guest-clustering with shared-bus disks is not a viable cloud solution.
Creating a cluster in VMware using Raw Device Mapping and shared-bus disks RDM is challenging and creates limitations for backing up the virtual machines. In an on-premises environment, synchronized storage appears to WSFC as a single shared storage disk. This allows Bonfiglioli to restore their virtual machines to Microsoft Azure in the event of a disaster.
Connect with Us Menu Blog Data Replication. Why Replicate Data to the Cloud? Drawbacks of the scheme include slower update operations and difficulty in keeping each location consistent, particularly if the data is constantly changing.
Partial replication is where the data in the database is divided into sections, with each stored in different locations based its importance for each location. Partial replication is useful for mobilized workforces such as insurance adjusters, financial planners, and sales people. These workers can carry partial databases on their laptop or other device and periodically synchronize them with a main server.
For analysts, it may be most efficient to store European data in Europe, Australian data in Australia, and so on, keeping the data close to the users, while the headquarters keeps a complete set of data for high-level analysis. Following a process for replication helps ensure consistency.
Data replication is a complex technical process. It provides advantages for decision-making, but the benefits may have a price. Controlling concurrent updates in a distributed environment is more complex than in a centralized environment. Replicating data from a variety of sources at different times can cause some datasets to be out of sync with others.
This may be momentary, last for hours, or data could become completely out of sync. Database administrators should take care to ensure that all replicas are updated consistently. The replication process should be well-thought-through, reviewed, and revised as necessary to optimize the process. Having the same data in more than one place consumes more storage space. While reading data from distributed sites may be faster than reading from a more distant central location, writing to databases is a slower process.
Apply market research to generate audience insights. Measure content performance. Develop and improve products. List of Partners vendors. Replication is a term referring to the repetition of a research study, generally with different situations and different subjects, to determine if the basic findings of the original study can be applied to other participants and circumstances.
Once a study has been conducted, researchers might be interested in determining if the results hold true in other settings or for other populations.
In other cases, scientists may want to replicate the experiment to further demonstrate the results. For example, imagine that health psychologists perform an experiment showing that hypnosis can be effective in helping middle-aged smokers kick their nicotine habit.
Other researchers might want to replicate the same study with younger smokers to see if they reach the same result. When studies are replicated and achieve the same or similar results as the original study, it gives greater validity to the findings. When conducting a study or experiment , it is essential to have clearly defined operational definitions. In other words, what is the study attempting to measure? When replicating earlier researchers, experimenters will follow the same procedures but with a different group of participants.
So what happens if the original results cannot be reproduced? Does that mean that the experimenters conducted bad research or that, even worse, they lied or fabricated their data? In many cases, non-replicated research is caused by differences in the participants or in other extraneous variables that might influence the results of an experiment.
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