For example, a article compared hypothesized effect sizes against non-hypothesized effect sizes and found that effects were significantly larger when the relationships had been hypothesized, a finding consistent with the presence of HARKing Bosco et al. Before conducting an experiment, a researcher must make a number of decisions about study design. These decisions—which vary depending on type of study—could include the research question, the hypotheses, the variables to be studied, avoiding potential sources of bias, and the methods for collecting, classifying, and analyzing data.
Poor study design can include not recognizing or adjusting for known biases, not following best practices in terms of randomization, poorly designing materials and tools ranging from physical equipment to questionnaires to biological reagents , confounding in data manipulation, using poor measures, or failing to characterize and account for known uncertainties. These results were widely publicized and used to support austerity measures around the world Herndon et al.
One error was an incomplete set of countries used in the analysis that established the relationship between debt and economic growth. When data from Australia, Austria, Belgium, Canada,.
The Reinhart and Rogoff error was fairly high profile and a quick Internet search would let any interested reader know that the original paper contained errors. Many errors could go undetected or are only acknowledged through a brief correction in the publishing journal.
A study looked at a sample of more than , p- values reported in eight major psychology journals over a period of 28 years. The study found that many of the p- values reported in papers were inconsistent with a recalculation of the p- value and that in one out of eight papers, this inconsistency was large enough to affect the statistical conclusion Nuijten et al.
Errors can occur at any point in the research process: measurements can be recorded inaccurately, typographical errors can occur when inputting data, and calculations can contain mistakes.
If these errors affect the final results and are not caught prior to publication, the research may be non-replicable. Unfortunately, these types of errors can be difficult to detect. In the case of computational errors, transparency in data and computation may make it more likely that the errors can be caught and corrected. For other errors, such as mistakes in measurement, errors might not be detected until and unless a failed replication that does not make the same mistake indicates that something was amiss in the original study.
Errors may also be made by researchers despite their best intentions see Box During the course of research, researchers make numerous choices about their studies. When a study is published, some of these choices are reported in the methods section.
A methods section often covers what materials were used, how participants or samples were chosen, what data collection procedures were followed, and how data were analyzed. The failure to report some aspect of the study—or to do so in sufficient detail—may make it difficult for another researcher to replicate the result. Similarly, if a researcher does not give adequate information about the biological reagents used in an experiment, a second researcher may have difficulty replicating the experiment.
Even if a researcher reports all of the critical information about the conduct of a study, other seemingly inconsequential details that have an effect on the outcome could remain unreported. Just as reproducibility requires transparent sharing of data, code, and analysis, replicability requires transparent sharing of how an experiment was conducted and the choices that were made.
This allows future researchers, if they wish, to attempt replication as close to the original conditions as possible. At the extreme, sources of non-replicability that do not advance scientific knowledge—and do much to harm science—include misconduct and fraud in scientific research. Instances of fraud are uncommon, but can be sensational. Researchers who knowingly use questionable research practices with the intent to deceive are committing misconduct or fraud.
It can be difficult in practice to differentiate between honest mistakes and deliberate misconduct because the underlying action may be the same while the intent is not. Reproducibility and replicability emerged as general concerns in science around the same time as research misconduct and detrimental research practices were receiving renewed attention.
Interest in both reproducibility and replicability as well as misconduct was spurred by some of the same trends and a small number of widely publicized cases in which discovery of fabricated or falsified data was delayed, and the practices of journals, research institutions, and individual labs were implicated in enabling such delays National Academies of Sciences, Engineering, and Medicine, ; Levelt Committee et al.
In the case of Anil Potti at Duke University, a researcher using genomic analysis on cancer patients was later found to have falsified data. This experience prompted the study and the report, Evolution of Translational Omics: Lessons Learned and the Way Forward Institute of Medicine, , which in turn led to new guidelines for omics research at the National Cancer Institute. Around the same time, in a case that came to light in the Netherlands, social psychologist Diederick Stapel had gone from manipulating to fabricating data over the course of a career with dozens of fraudulent publications.
Similarly, highly publicized concerns about misconduct by Cornell University professor Brian Wansink highlight how consistent failure to adhere to best practices for collecting, analyzing, and reporting data—intentional or not—can blur the line between helpful and unhelpful sources of non-replicability. A subsequent report, Fostering Integrity in Research National Academies of Sciences, Engineering, and Medicine, , emerged in this context, and several of its central themes are relevant to questions posed in this report.
According to the definition adopted by the U. The federal policy requires that research institutions report all. Other detrimental research practices see National Academies of Sciences, Engineering, and Medicine, include failing to follow sponsor requirements or disciplinary standards for retaining data, authorship misrepresentation other than plagiarism, refusing to share data or methods, and misleading statistical analysis that falls short of falsification.
In addition to the behaviors of individual researchers, detrimental research practices also include actions taken by organizations, such as failure on the part of research institutions to maintain adequate policies, procedures, or capacity to foster research integrity and assess research misconduct allegations, and abusive or irresponsible publication practices by journal editors and peer review. Just as information on rates of non-reproducibility and non-replicability in research is limited, knowledge about research misconduct and detrimental research practices is scarce.
As discussed above, new analyses of retraction trends have shed some light on the frequency of occurrence of fraud and misconduct. Allegations and findings of misconduct increased from the mids to the mids but may have leveled off in the past few years.
Analysis of retractions of scientific articles in journals may also shed some light on the problem Steen et al. One analysis of biomedical articles found that misconduct was responsible for more than two-thirds of retractions Fang et al. As mentioned earlier, a wider analysis of all retractions of scientific papers found about one-half attributable to misconduct or fraud Brainard, Others have found some differences according to discipline Grieneisen and Zhang, One theme of Fostering Integrity in Research is that research misconduct and detrimental research practices are a continuum of behaviors National Academies of Sciences, Engineering, and Medicine, While current policies and institutions aimed at preventing and dealing with research misconduct are certainly necessary, detrimental research practices likely arise from some of the same causes and may cost the research enterprise more than misconduct does in terms of resources wasted on the fabricated or falsified work, resources wasted on following up this work, harm to public health due to treatments based on acceptance of incorrect clinical results, reputational harm to collaborators and institutions, and others.
No branch of science is immune to research misconduct, and the committee did not find any basis to differentiate the relative level of occurrence. Some but not all researcher misconduct has been uncovered through reproducibility and replication attempts, which are the self-correcting mechanisms of science.
From the available evidence, documented cases of researcher misconduct are relatively rare, as suggested by a rate of retractions in scientific papers of approximately 4 in 10, Brainard, The overall extent of non-replicability is an inadequate indicator of the health of science. One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it.
When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media.
As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research.
Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science.
Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website. Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.
Switch between the Original Pages , where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text. To search the entire text of this book, type in your search term here and press Enter.
Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available. Do you enjoy reading reports from the Academies online for free? Sign up for email notifications and we'll let you know about new publications in your areas of interest when they're released.
Reproducibility and Replicability in Science Chapter: 5 Replicability. Get This Book. Visit NAP. Looking for other ways to read this? No thanks.
Suggested Citation: "5 Replicability. Reproducibility and Replicability in Science. Page 72 Share Cite. Page 73 Share Cite. Acknowledging the different approaches to assessing replicability across scientific disciplines, however, we emphasize eight core characteristics and principles: Attempts at replication of previous results are conducted following the methods and using similar equipment and analyses as described in the original study or under sufficiently similar conditions Cova et al.
The concept of replication between two results is inseparable from uncertainty, as is also the case for reproducibility as discussed in Chapter 4. Any determination of replication between two results needs to take account of both proximity i.
To assess replicability, one must first specify exactly what attribute of a previous result is of interest. For example, is only the direction of a possible effect of interest? Is the magnitude of effect of interest? Is surpassing a specified threshold of magnitude of interest? Depending on the selected criteria e. Page 74 Share Cite. For example, one study may yield a p -value of 0. However, if the second study had yielded a p -value of 0. Rather than focus on an arbitrary threshold such as statistical significance, it would be more revealing to consider the distributions of observations and to examine how similar these distributions are.
This examination would include summary measures, such as proportions, means, standard deviations or uncertainties , and additional metrics tailored to the subject matter. Page 75 Share Cite. NOTES: The figure shows the issue with using statistical significance as an attribute of comparison Point 8 on 74 of the main text ; the two results would be considered to have replicated if using a proximity-uncertainty attribute Points 3 and 4 on 73 of the main text.
Page 76 Share Cite. Approaches to assessing non-replicability rates include direct and indirect assessments of replicability; perspectives of researchers who have studied replicability; surveys of researchers; and retraction trends. This section discusses each of these lines of evidence.
Assessments of Replicability The most direct method to assess replicability is to perform a study following the original methods of a previous study and to compare the new results to the original ones. Page 77 Share Cite. Based on the content of the collected studies in Table , one can observe that the majority of the studies are in the social and behavioral sciences including economics or in biomedical fields, and methods of assessing replicability are inconsistent and the replicability percentages depend strongly on the methods used.
Offline instantiation is often used to decrease server loads during peak usage periods and to reduce remote connection times.
To instantiate a template offline, you package the template and required data on some type of storage media, such as tape, CD-ROM, and so on. Then, instead of pulling the data from the master site, users pull the data from the storage media containing the template and data.
Multimaster replication and snapshots can be combined in hybrid or "mixed" configurations to meet different application requirements. Mixed configurations can have any number of master sites and multiple snapshot sites for each master.
For example, as shown in Figure , multimaster or n -way replication between two masters can support full-table replication between the databases that support two geographic regions. Snapshots can be defined on the masters to replicate full tables or table subsets to sites within each region. Multimaster replication allows you to replicate changes for each transaction as the changes occur.
Snapshot refreshes are set oriented, propagating changes from multiple transactions in a more efficient, batch-oriented operation, but at less frequent intervals. Master sites detect and resolve the conflicts that occur from changes made to multiple copies of the same data. Administration Tools for a Replication Environment Several tools are available for administering and monitoring your replication environment. Oracle's Replication Manager provides a powerful GUI interface to help you manage your environment, while the replication management API provides you with the familiar application programming interface API to build customized scripts for replication administration.
Additionally, the replication catalog keeps you informed about your replicated environment. Replication environments supporting both multimaster and snapshot replication can be challenging to configure and manage. To help administer these replication environments, Oracle provides a sophisticated management tool called Oracle Replication Manager. Other sections in this book include information and examples for using Replication Manager. However, the Replication Manager online help is the primary documentation source for Replication Manager.
See Also: Chapter 8, "Introduction to Replication Manager" for an introduction to Replication Manager, and see the Replication Manager online help for complete instructions on using Replication Manager. In fact, Replication Manager uses the procedures and functions of the replication management API to perform its work. The replication management API makes it easy for you to create custom scripts to manage your replication environment.
Replication Catalog Every master and snapshot site in a replication environment has a replication catalog. A replication catalog for a site is a distinct set of data dictionary tables and views that maintain administrative information about replication objects and replication groups at the site.
Every server participating in a replication environment can automate the replication of objects in replication groups using the information in its replication catalog. When you use either of these interfaces, all DDL statements are replicated to all of the sites participating in the replication environment. Replication Conflicts Asynchronous multimaster and updateable snapshot replication environments must address the possibility of replication conflicts that may occur when, for example, two transactions originating from different sites update the same row at nearly the same time.
When data conflicts occur, you need a mechanism to ensure that the conflict is resolved in accordance with your business rules and to ensure that the data converges correctly at all sites.
In addition to logging any conflicts that may occur in your replicated environment, Oracle replication offers a variety of built-in conflict resolution methods that enable you to define a conflict resolution system for your database that resolves conflicts in accordance with your business rules. If you have a unique situation that Oracle's built-in conflict resolution methods cannot resolve, you have the option of building and using your own conflict routines.
The online help for Replication Manager for instructions on using Replication Manager to configure conflict resolution methods.
Other Options for Multimaster Replication Asynchronous replication is the most common way to implement multimaster replication. However, you have two other options: synchronous replication and procedural replication. A multimaster replication environment can use either asynchronous or synchronous replication to copy data. With asynchronous replication, changes made at one master site occur at a later time at all other participating master sites.
With synchronous replication, changes made at one master site occur immediately at all other participating master sites. When you use synchronous replication, an update of a table results in the immediate replication of the update at all participating master sites. In fact, each transaction includes all master sites. Therefore, if one master site cannot process a transaction for any reason, the transaction is rolled back at all master sites. Although you avoid the possibility of conflicts when you use synchronous replication, it requires a very stable environment to operate smoothly.
If communication to one master site is not possible because of a network problem, for example, no transactions can be completed until communication is re-established. Batch processing applications can change large amounts of data within a single transaction. In such cases, typical row-level replication might load a network with many data changes.
To avoid such problems, a batch processing application operating in a replication environment can use Oracle's procedural replication to replicate simple stored procedure calls to converge data replicas.
Procedural replication replicates only the call to a stored procedure that an application uses to update a table. It does not replicate the data modifications themselves.
To use procedural replication, you must replicate the packages that modify data in the system to all sites. Let us assume that a user of an application wishes to write a piece of data to the database.
This data gets split into multiple fragments, with each fragment getting stored on a different node across the distributed system. The database technology is also responsible for gathering and consolidating the different fragments when a user wants to retrieve or read the data. In such an arrangement, a single system failure can inhibit the retrieval of the entire data.
This is where data replication saves the day. Data replication technology can store multiple fragments at each node to streamline read and write operations across the network. Data replication tools ensure that complete data can still be consolidated from other nodes across the distributed system during the event of a system failure.
Depending on data replication tools employed, there are multiple types of replication practiced by businesses today. Some of the popular replication modes are as follows.
Full table replication means that the entire data is replicated. This includes new, updated as well as existing data that is copied from source to the destination.
This method of replication is generally associated with higher costs since the processing power and network bandwidth requirements are high. However, full table replication can be beneficial when it comes to the recovery of hard-deleted data, as well as data that do not possess replication keys - discussed further down this article.
In this method, the data replication software makes full initial copies of data from origin to destination following which the subscriber database receives updates whenever data is modified. This is more efficient mode of replication since fewer rows are copied each time data is changed.
Transactional replication is usually found in server-to-server environments. Full table replication copies everything from the source to the destination, including new, updated, and existing data. However, this method has several drawbacks. Full table replication requires more processing power and generates larger network loads than copying only changed data. Depending on what tools you use to copy full tables, the cost typically increases as the number of rows copied goes up. Key-based incremental replication — also known as key-based incremental data capture or key-based incremental loading — updates only data changed since the previous update.
Since fewer rows of data are copied during each update, key-based replication is more efficient than full table replication. However, one major limitation of key-based replication is its inability to replicate hard-deleted data, since the key value is deleted when the record is deleted.
Log-based incremental replication is a special case of replication that applies only to database sources. This process replicates data based on information from the database log file, which lists changes to the database. This method works best if the source database structure is relatively static. If columns are added or removed or data types change, the configuration of the log-based system must be updated to reflect the changes, and this can be a time- and resource-intensive process.
For this reason, if you anticipate your source structure requiring frequent changes, it may be better to use full table or key-based replication. Organizations can perform data replication by following a specific scheme to move the data. These schemes are different than the aforementioned methods above. Rather than serving as an operational strategy for continuous data movement, a scheme dictates the way in which data can be replicated in order to best meet the needs of a business: moved in full or moved in parts.
0コメント