Null results and failed tries are still results, and the information gathered from the project serves deeper, long-term learning. Publishing results that do not confirm what you expect or did not come out statistically significant are a lesson to the future. While the project itself might have been a financial bust, publishing the results prevents duplication and further financial stress. And money is just the half of it. Time spent and wasted on a project becomes a huge personal investment that could be avoided altogether.
Let the technology for glowing plants be one example. Glowing Plant was not just a technologically neat idea; it was a neat approach to funding. There was a lot of press attention surrounding Glowing Plant because they were one of the pioneers in crowd-funded science. The aim of Glowing Plant was to engineer bioluminescent plants with the vision of one day lighting the world sustainably. The project failed for two primary reasons.
First, engineering this technology was more difficult and complex than expected. In order to make tobacco plants glow, the research team had to insert six genes, but they could never get them in at once. The resulting glowing plants were very dim. Media images depicting the glowing plant were shot with a long exposure, making the plant appear much brighter.
The second reason it failed is that the project ran out of money. Along the way, however, Glowing Plant regularly published project updates on their blog. While this was not formatted like a typical research paper, these updates did have information about results, changes in methods, obstacles and approaches. With combined information from both the Glowing Plant and press publications, a natural archive of information and details about the project remains.
Anyone can pick out fragments and attempt to rework, improve or be inspired, taking into account what aspects worked, and what aspects did not. Looking outside of science, there are numerous tech startups that failed but taught huge lessons and paved a new future. One example is Napster. The digital music pioneer came on scene in , providing Internet users with the ability to share and download music. Not even a year later, copyright issues arose and after multiple lawsuits, the original Napster filed for bankruptcy.
Knowing why and how Napster failed enabled better innovations and technological integrations so prevalent today that we take what they did for granted. One of the most popular gadgets and services to stem from this idea of digital music sharing is Apple Music and the iPod. From there, the vision grew out into incorporating music with phones, and then adapting cars with Bluetooth technology or built-in capabilities for music playing and communication.
Whether a project had null results, was dead before it ever began or had numerous failed tries, there is valuable implicit information knotted in with the outcomes. The program aimed to collectively educate participants and foster community support in order to reduce the infant mortality rate. Prior studies in rural communities had significantly positive results; however, this project did not.
Some of the reasons posed for the null results boiled down to how differently an urban setting is from a rural community. In an urban setting, there are challenges to measuring improvements and differences in the chronic nature of diseases. Though the results were not significant, the amount of information the study revealed can help prepare researchers for further studies of this nature.
In fact, the New York Times article mentioned that More revisited the project in using a different approach because of what was learned. I cannot leave off one of the most famous failed experiments of our recent history: The Michelson-Morley experiment. In the 19 th century, scientists believed that since sound waves required sound to travel through, light waves must also require some type of medium.
Particularly, as the earth traveled around the sun, physicists theorized that the speed of light would change. The device split a single beam of light into two beams. These two beams bounce away and then reflect back into a single, recombined beam. When the two light beams recombined, a change in the speed between the two light beams could be detected based on the interference pattern resulting in the recombined light beam.
A less intense beam meant that a difference occurred, and a more intense beam indicated completely synchronized waves.
Michelson and Morely investigated the resulting light patterns, and found no difference. They tweaked the approach, and still found no difference. This null result disproved the existence of the ether. Some publishing outlets are embracing and encouraging the movement to publish null or inconclusive results. Right now there are a few avenues for publishing. While there are platforms solely dedicated to good science that lacked hopeful results, they are few in number.
An article published on edgeforscholars. One of the things that made her research useful is the level of detail provided. It might possibly have helped that other similar projects had impactful results, allowing a detailed comparison between two distinct settings. Publication bias is a real problem and it can have real consequences. But as much as we preach in the name of science, we must acknowledge the realities and obstacles that come alongside these ideals.
The first issue is the time it takes to write up and submit research in general. Moreover, if a lab runs out of funding or spins into a different direction, the project may never even reach completion. What do you do then? If that happens, consider some of the advice in alternative ways to publish. There is always the option of trying to include past, inconclusive research within the body of something more significant. The other option, especially if a project never got to a publishable point, is to publish it on your own platform such as a personal website, blog or collaborative page.
Another hurtle is the fear of what these results might do to your career. All of the raw data that underlies any published conclusions should be readily available to fellow researchers and reviewers of the published article.
Depositing the raw data in a publicly available database would reduce the likelihood that researchers would select only those results that support a prevailing attitude or confirms previous work. Such sharing would accelerate scientific discoveries, and enable scientists to interact and collaborate at a meaningful level. Data integrity and assay reproducibility can be greatly improved by using authenticated, low-passage reference materials.
Cell lines and microorganisms verified by a multifaceted approach that confirms phenotypic and genotypic traits, and a lack of contaminants, are essential tools for research. By starting a set of experiments with traceable and authenticated reference materials, and routinely evaluating biomaterials throughout the research workflow, the resulting data will be more reliable, and more likely to be reproducible. Experimental reproducibility could be considerably improved if researchers were trained how to properly structure experiments and perform statistical analyses of results.
By strictly adhering to a set of best practices in statistical methodology and experimental design, researchers could boost the validity and reproducibility of their work. If scientists pre-register proposed scientific studies including the approach prior to initiation of the study, it would allow careful scrutiny of all parts of the research process and would discourage the suppression of negative results.
By publishing negative data, it helps to interpret positive results from related studies and can help researchers adjust their experimental design so that further resources and funding are not wasted It is important that research methodology is thoroughly described to help improve reproducibility.
Researchers should clearly report key experimental parameters, such as whether experiments were blinded, which standards and instruments were used, how many replicates were made, how the results were interpreted, how the statistical analysis was performed, how the randomization was done, and what criteria were used to include or exclude any data.
There is a varied and influential group of organizations that are already working to improve the reproducibility of scientific research. The following is a list of initiatives aimed at supporting one or more aspects of the research reproducibility issue. ASCB continues to identify methods and best practices that would enhance reproducibility in basic research.
From its original analysis, the ASCB task force identified and published several recommendations focused on supporting existing efforts and initiating new activities on better training, reducing competition, sharing data, improving peer reviews, and providing cell authentication guidelines.
Biological resource centers, such as ATCC, provide the research community with standardized, traceable, fully authenticated cell lines and microorganisms to aid in assay reproducibility. At ATCC, microbial strains are authenticated and characterized through genotypic, phenotypic, and functional analyses to confirm identity, purity, virulence, and antibiotic resistance. Komen SPP , which focuses on the best practices for receiving, managing, authenticating, culturing, and preserving cell cultures.
To further support cell authentication and reproducibility in the life sciences, ATCC also provides STR profiling and mycoplasma detection testing as services to researchers. To help improve rigor, reproducibility, and transparency in scientific research, the NIH issued a notice in that informed scientists of revised grant application instructions that focused on improving experimental design, authenticating biological and chemical resources, analyzing and interpreting results, and accurately reporting research findings.
These efforts have led to the adoption of similar guidelines by journals across numerous scientific disciplines and has resulted in cell line authentication becoming a prerequisite for publication. This initiative was designed to provide evidence of reproducibility in cancer research and to identify possible factors that may affect reproducibility. Here, selected results from high-profile articles are independently replicated by unbiased third parties to evaluate if data could be consistently reproduced.
For each evaluated study, a registered report delineating the experimental workflow is reviewed and published before experimentation is initiated; after data collection and analysis, the results are published as a replication study. Many peer-reviewed journals have updated their reporting requirements to help improve the reproducibility of published results. The Nature Research journals, for example, have implemented new editorial policies that help ensure the availability of data, key research materials, computer codes and algorithms, and experimental protocols to other scientists.
Researchers must now complete an editorial policy checklist to ensure compliance with these policies before their manuscript can be considered for review and publication. Most people familiar with the issue of reproducibility agree that these efforts are gaining traction. However, progress will require sustained attention on the issue, as well as cooperation and involvement from stakeholders across various fields.
Accuracy and reproducibility are essential for fostering robust and credible research and for promoting scientific advancement. There are predominant factors that have contributed to the lack of reproducibility in life science research. This issue has come to light in recent years and a number of guidelines and recommendations on achieving reproducibility in the life sciences have emerged, but the practical implementation of these practices may be challenging.
It is essential that the scientific community are objective when designing experiments, take responsibility for depicting their results accurately, and thoroughly and precisely describe all methodologies used. Further, funders, publishers, and policy-makers should continue to raise awareness about the lack of reproducibility and use their position to promote better research practices throughout the life sciences.
By taking action and seeking opportunities for improvement, researchers and key stakeholders can help improve research practices and the credibility of scientific data.
For more information on how you can improve the reproducibility of your research, visit ATCC online. Ioannidis JP. PLoS Medicine 11 : e, PubMed Article Google Scholar.
Feilden T. Baker M. Nature News Feature , May 25 , PLoS Biology 13 : e, In considering the sample size, one must ensure that the experimental units are independently allocated to the experimental condition, the application of the condition is applied independently to the unit, and the experimental units do not influence one another Lazic et al.
A significant concern in cell biology is determining whether cells or sections, for example, can be considered an experimental unit. In cases where an animal is treated and subsequent testing occurs postmortem e.
If data are not independent, one strategy is to analyze clustered data e. Alternatively, there are also procedures to accurately model the true variability in data sets using modern statistical techniques e.
As Stanley Lazic so eloquently concluded in his recent paper Lazic, ,. In the interest of minimizing animal usage and reducing waste in biomedical research Ioannidis et al. An appropriately written section describing the experimental subjects must include a statement of ethical approval Institutional Review Board approval for human research or Institutional Animal Care and Use Committee approval for animals , followed by the total number of participants involved in each experiment.
The authors must also include a clear description of the inclusion and exclusion criteria, which should be prespecified prior to the start of the experiments. Reporting the number of experimental units i. When designing an experiment, one must also account for sex as a biological variable see below. One should carefully review the extant literature to determine whether sex differences might be observed in the study, and if so, design and power the study to test for sex differences.
Omitting this step could compromise the rigor of the study Clayton, , Choices made by investigators during the design and execution of experiments can introduce bias, which may result in the authors reporting false positives Kilkenny et al.
Implementing and reporting randomization and blinding procedures are simple and can be followed using a basic guide Karanicolas et al. Moreover, investigators should report whether experimenters are blind to the allocation sequence and also, in animal studies, report whether controls are true littermates of the test group Galbraith et al. Similarly, once the investigator is blind to the conditions, they should remain unaware of the group in which the subject is allocated and the assessment outcome Landis et al.
Blinding is not always possible. In these cases, procedures to standardize the interventions and outcomes should be implemented and reported so groups are treated as equally as possible. In addition, researchers should consider duplicate assessment outcomes to ensure objectivity Karanicolas et al. Attention to reporting these details will reduce bias, will avoid mistaking batch effects for treatment effects, and will improve the transparency of how the research was conducted.
Many life science disciplines use animal models to test their hypotheses. Few studies provide detailed information regarding housing and husbandry, and those reports that contain the information typically do not provide any level of detail that could allow for others to follow similar housing procedures. At a minimum, the authors should introduce in the abstract the race, sex, species, cell lines, etc.
However, in the methods section, the authors should carefully describe all animal housing and husbandry procedures. Requiring a full description of housing and husbandry procedures will be essential to the rigor and transparency of the published studies and could help determine why some studies are not reproducible. A common practice within research is that findings in one sex usually males are generalized to the other sex usually females. Yet, research consistently demonstrates that sex differences are present across disciplines.
For example, as evidence reveals in a recent issue of JNR see Sex Influences on Nervous System Function , sex not only matters at the macroscopic level, where male and female brains have been found to differ in connectivity Ingalhalikar et al. The National Institutes of Health as well as a number of funding agencies mandates the inclusion of sex as a biological variable, yet this mandate is not enforced by most journals. Starting at the study design, the authors must review whether the extant literature suggests that sex differences might be observed in the study, and if so, then design and power the study to test for sex differences.
Otherwise, the rigor of the study could be compromised. When publishing the results, the authors must account for sex as a biological variable, whenever possible. Investigators must also justify excluding either males or females. The assumptions that females are more variable than males or that females must be tested across the estrous cycle are not appropriate as these are not major sources of variability Beery, This policy is not a mandate to specifically investigate sex differences, but requires investigators to consider sex from the design of the research question through reporting the results Clayton, , In some instances, sex might not influence the outcomes e.
A transparent experimental design, meaning how the experiment is planned to meet the specified objectives, describes all the factors that are to be tested in an experiment, including the order of testing and the experimental conditions. As studies become more complex and interconnected, planning the experimental procedures prior to the onset of experiments becomes essential.
Yet even when the experiments are planned prior to their initiation, the experimental designs are often poorly described and rarely account for alterations in procedures that were used in the study under consideration. The experimental design section should consist of two main components: a a list of the experimental procedures that were used to conduct the study, including the sequence and timing of manipulation; and b an open discussion of any deviations made from the original design.
The description should include an explanation of the question s being tested, whether this is a parameter estimation, model comparison, exploratory study, etc. Assuming the authors planned the analysis prior to data collection, the authors should describe the specific a priori consideration of the statistical methods and planned comparisons Weissgerber, Garovic, Winham, et al.
If the statistical approach deviated from how it was originally designed see, e. This open description could help to improve independent research reproducibility efforts and assist reviewers and readers in understanding the rationale for specific approaches. A precise description of how methodological tools and procedures are prepared and used should also be provided in the experimental design section.
Oftentimes, methodological procedures are truncated, forcing the authors to omit critical steps. Alternatively, the authors may report that the methods were previously described but might have modified those procedures without reporting those changes. Due to current publishing constraints, various caveats that go into the methodological descriptions remain unknown.
Two options are available for publishing full protocols. Rigorous descriptions of the experimental protocols not only require a level of detail in the description of the experimental design, but also require a full account of the resources and how they were prepared and used. A contributing factor to irreproducibility is the poor or inaccurate description of materials. Most studies do not include sufficient detail to uniquely identify key research resources, including model organisms, cell lines, and antibodies, to name a few Vasilevsky et al.
While most author guidelines request that the authors provide the company name, city in which the company is located, and the catalog number of the material, a many authors do not include this information; b the particular product may no longer be available; or c the catalog number or lot number is reported incorrectly, thus rendering the materials unattainable.
A new system is laying the foundation to report research resources with a unique identification number that can be deposited in a database for quick access.
The Resource Identification Initiative standardizes the materials necessary to conduct research by assigning research resource identifiers RRIDs; Bandrowski et al.
To make it as simple as possible to obtain RRIDs, a platform was developed www. While SciCrunch is among the founding platforms, these identifiers can also be found on other sites, including antibodyregistry. Similarly, though more involved, PubChem offers identification for various compounds such as agonists and antagonists. RRIDs have been successfully implemented in many titles throughout Wiley and are also in use by Cell Press and a number of other publishers.
The authors should provide RRIDs and CIDs when describing resources such as antibodies, software including statistical software used, as this is rarely reported , and model organisms, or compounds used, allowing for easy verification by peer reviewers and experimenters.
However, users all too often choose inadequate and incorrect statistical methods or approaches or cannot reproduce their analyses since they have only a rudimentary understanding to each test and when to use them Baker et al.
What's more, the authors do not appropriately describe their statistical approaches in text, partially because tests are performed only after the study is executed. In designing and reporting the experiments, the authors should report normalization procedures, tests for assumptions, exclusion criteria, and why statistical approaches might differ from what the authors originally proposed, if they developed these approaches prior to the onset of data collection.
In addition, the authors must also include the statistical software and specific version thereof, descriptive statistics, and a full account of the statistical outputs in the results section. Errors in statistical outputs often arise when the authors a do not conduct and report a power calculation Strasak et al. Moreover, it might be difficult to reproduce statistical output when the authors do not report the statistical software and specific version thereof, fail to include in the manuscript the exclusion criteria or code used to generate analyses, or explain how modifications to the experimental design might lead to changes in how statistical analyses are approached e.
Choosing the correct statistical analyses first depends on an appropriate experimental design and mode of investigation exploratory vs. One must decide whether experimental conditions are independent, meaning that no subjects or specimens are related to each other Weissgerber et al. In addition, a transparent and rigorous statistical analysis section must include the following:.
Statement of the factors tested, types of analyses, and what post hoc comparisons were made. Statement of the statistical tests used and details as to why those tests were chosen, including how the authors choose between parametric and nonparametric tests assumptions aside 1. Statement of how replicates were analyzed. Many studies are rejected for publication because of criticism that a study is underpowered, though many more studies are published despite this Button et al.
Reporting how a sample size was predetermined based on power analyses conducted during the experimental design stage is a good way to avoid this criticism. Yet, as more parameters come into play e. Alternatively, if it is conventional to use a specific number of subjects for a particular test, then one can report the calculated effect size for that particular sample size and decide whether more samples would be warranted.
Either way, power and sample size calculations provide a single estimate, ignoring variability and uncertainty as such simulations are highly encouraged see Lazic, Thus, utilizing the post hoc power analysis must be done with extreme care and should never be a substitute for the a priori power analysis.
We also advise authors to determine whether a parametric or nonparametric test is the most appropriate for the obtained data. Analogues to ordinary parametric tests e. Importantly, parametric tests also generally have somewhat more statistical power than nonparametric tests and are more likely to detect a significant effect if one exists. Alternatively, when one's data are better represented by the median, nonparametric tests may be more appropriate, especially when data are skewed enough that a mean might be strongly affected by the distribution tail, whereas the median estimates the center of the distribution.
Nonparametric tests may also be more appropriate when the obtained sample size is small, as occurs in many fields where sample sizes average less than eight per group Holman et al. Beware, however, that meaningful nonparametric testing with sample sizes too low e. Bayesian analyses with small sample sizes are also possible, though estimates are highly sensitive to the specification of the prior distribution. Figures illustrate the most important findings from a study by conveying information about the study design in addition to showing the data and statistical outputs Weissgerber et al.
Simplistic representations to visualize the data are commonly used and are often inappropriate. To change standard practices for presenting data, continuous data should be visualized by emphasizing the individual points; dot plots e. Bar graphs should be reserved for categorical data only.
The use of jittering means that when there are fewer unique combinations of data points than total observations, the totality of the data distribution is not obscured. By adopting these practices, readers will be better able to detect gross violations of the statistical assumptions and determine whether results would be different using alternate strategies Weissgerber et al.
When plotting data, it is important to also report the variability of the data. The SEM , on the other hand, describes the SD of the sample mean as an estimate of the accuracy of the population mean. In other words, the SD shows how many points within the sample differ from the sample mean, whereas the SEM shows how close the sample mean is to the population mean Nagele, Yet, deriving confidence intervals around one's data using SD or the mean using SEM is premised on those data being normally distributed.
Traditional data transformations are an attempt to cope with this phenomenon, but for many, such transformations may not actually serve to resolve anything and may add a layer of unnecessary complexity. In determining which estimate of variability to depict graphically, it is important to remember that the SD is used when one wants to know how widely scattered measurements are or the variability within the sample, but if one is interested in the uncertainty around the estimate of the mean measurement or the proximity of the mean to the population mean, SEM is more appropriate Nagele, When plotting data variability, it is important to consider that when SEM bars do not overlap, the viewer cannot be sure that the difference between the two means is statistically significant see Motulsky, The probability that a scientific research article is published traditionally depends on the novelty or inferred impact of the conclusion, the size of the effect measured, and the statistical confidence in that result Matosin et al.
To combat the stigma of reporting negative results, we encourage authors to provide a full account of the experiment, to explicitly state both statistically significant and nonsignificant results, and to publish papers that have been rigorously designed and conducted, irrespective of their statistical outcomes. In addition, some organizations such as the European College of Neuropsychopharmacology are offering prizes in neuroscience research to encourage publication of data where the results do not confirm the expected outcome or original hypothesis see ECNP Preclinical Network Data Prize.
Published reports of both significant and nonsignificant findings will result in better scientific communication among and between colleagues. Though objectivity of a researcher or group is assumed, conflicts of interest may exist and could be a potential source of bias. Conflicts, whether real or perceived, arise when one recognizes an interest as influencing an author's objectivity.
This can occur when an author owns a patent, or has stock ownership, or is a member of a company, for example. All participants in a paper must disclose all relationships that could be viewed as presenting a real or perceived conflict of interest. When considering whether a conflict is present, one should ask whether a reasonable reader could feel misled or deceived.
While beyond the scope of this article, the Committee on Publication Ethics offers a number of resources on conflicts of interest. One possible way to incorporate all the information listed above and to combat the stigma against papers that report nonsignificant findings is through the implementation of Registered Reports or rewarding transparent research practices.
Registered Reports are empirical articles designed to eliminate publication bias and incentivize best scientific practice. Registered Reports are a form of empirical article in which the methods and the proposed analyses are preregistered and reviewed prior to research being conducted.
This format is designed to minimize bias, while also allowing complete flexibility to conduct exploratory unregistered analyses and report serendipitous findings. The cornerstone of the Registered Reports format is that the authors submit as a Stage 1 manuscript an introduction, complete and transparent methods, and the results of any pilot experiments where applicable that motivate the research proposal, written in the future tense.
These proposals will include a description of the key research question and background literature, hypotheses, experimental design and procedures, analysis pipeline, a statistical power analysis, and full description of the planned comparisons. Following data collection, the authors prepare and resubmit a Stage 2 manuscript that includes the introduction and methods from the original submission plus their obtained results and discussion.
At this stage, the authors must also share their data see also Wiley's Data Sharing and Citation Policy and analysis scripts on a public and freely accessible archive such as Figshare and Dryad or at the Open Science Framework. The authors who practice transparent and rigorous science should be recognized for this work.
One way journals can support this is to award badges to the authors in recognition of these open scientific practices. Badges certify that a particular practice was followed, but do not define good practice. As defined by the Open Science Framework, three badges can be earned. The Open Data badge is earned for making publicly available the digitally shareable data necessary to reproduce the reported results.
The Open Materials badge is earned when the components of the research methodology needed to reproduce the reported procedure and analysis are made publicly available. Additional information about the badges, including the necessary information to be awarded a badge, can be found by clicking this link to the Open Science Framework from the Center for Open Science. The process of peer review is designed to evaluate the validity, quality, and originality of the articles for publication.
Yet, peer reviewers are not immune to making mistakes. For example, several studies were conducted where major errors were inserted into papers. While it is beyond the scope of this article to discuss many of the defects of peer review see Smith, , it is important to note that the changes to the peer review process are ongoing Tennant et al.
However, to quickly improve rigor and transparency in scientific research, peer review should emphasize the design and execution of the experiment. We are not saying that reviewers should focus solely on the experimental design; it is important for reviewers to weigh in on the novel insights of a study and how study results may or may not contribute to the field.
However, to help ensure the accuracy and the validity of a study, emphasis should first be on the experimental design. To assist the reviewers, the authors should submit as part of their manuscript a Transparent Science Questionnaire TSQ , or something equivalent, which identifies where in the manuscript specific elements that could aid in reproducibility efforts are found.
The reviewers use this form to verify that the authors have included the relevant information and ensure that the study was designed and executed objectively, ensuring the study's validity and reliability. Using this or similar forms will also help reviewers to find the relevant information necessary to ensure the appropriateness of the design, which can then allow them to focus on the experimental outcomes.
A multistage review where different parties are concerned with different aspects of the review may be optimal. Because many errors in manuscripts are found in the statistical output, one stage of review should be a statistical review, whereby a statistical editor reviews the statistical analyses of the manuscript to ensure accuracy, but also verifies that the most appropriate statistical tests for that design were used.
Upon completion, the editor will then make a decision as to whether the approach and execution are sufficient and are in line with the reported statistical output. By having experts focus on specific aspects of a research report, journal editors will become more confident that the research published is valid and of high quality and integrity. A challenge in science is for scientists to be open and transparent about the procedures used to obtain results. Groups are continuing to develop systems that help researchers cover every aspect of the experimental design e.
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