Replicate Definition Environmental Science

Predictions of an impending crisis in the quality control mechanism of science go back decades. Derek de Solla Price – who is considered the father of scientometrics, the quantitative study of science – predicted that science could achieve “senility” as a result of its own exponential growth. [58] Some of today`s literature seems to confirm this prophecy of “defection” by lamenting the decline in attention and quality. [59] [60] According to the definition adopted by the United States federal government in 2000, research misconduct is the fabrication of data, falsification of data, or plagiarism “by proposing, conducting or reviewing research, or communicating research results” (Office of Science and Technology Policy, 2000, p. 76262). Federal policy requires research institutions to report all allegations of misconduct in federally funded research projects that have moved from the investigation phase to a full investigation and to report on the results of these investigations. Some publication practices also make it difficult to replicate and track the severity of the reproducibility crisis, as papers often do not contain enough descriptions for other scientists to replicate the study. The Reproducibility: Cancer Biology project showed that out of 193 experiments out of 53 leading cancer papers published between 2010 and 2012, only 50 experiments from 23 papers had authors who provided enough information for researchers to repeat the studies, sometimes with modifications. In none of the 193 papers reviewed were experimental protocols fully described, and replication of 70% of experiments required the demand for important reagents.

[46] [47] The above-mentioned study of empirical results in the Strategic Management Journal found that 70% of the 88 articles could not be reproduced due to a lack of sufficient information for data or procedures. [49] [53] In the area of water resources and management, the majority of the 1,987 articles published in 2017 were not replicable due to a lack of information available online. [54] OUTCOME 5-1: There is a mixed awareness of issues related to reproducibility in all fields and even in the fields of science and technology. High-quality surveys of researchers are expensive and present significant challenges, including creating comprehensive sampling frames, obtaining appropriate response rates, and minimizing other non-response biases that may affect respondents differently at different stages of their careers or in different professional settings or fields of study (Corley et al., 2011; Peters et al., 2008; Scheufele et al., 2009). As a result, more scientists have already attempted to collect papers on topics related to reproducibility and reproducibility (Baker, 2016; Boulbes et al., 2018) relied on convenience samples and other methodological decisions that limit the conclusions that can be drawn from the data from these surveys on attitudes in the broader scientific community or even for specific subfields. More methodologically rigorous surveys that follow guidelines for open scientific practice and other issues related to reproducibility are emerging.6 See Appendix E for a discussion of conducting reliable surveys of scientists. Metascience is the use of scientific methodology to study science itself. Metascience aims to improve the quality of scientific research while reducing waste. It is also called “research on research” and “science of science” because it uses research methods to examine how research is done and where improvements can be made.

Metascience covers all areas of research and has been described as “an overview of science”. [109] In the words of Ioannidis: “Science is the best thing that has happened to men. But we can do better. [110] University incentives – such as employment, grants, and status – can cause scientists to compromise on good research practices (Freeman, 2018). Decisions about hiring, promoting, and hiring professors are often based largely on a researcher`s “productivity,” e.g., the number of publications, the number of citations, and the amount of grants received (Edwards & Roy, 2017). Some have suggested that these incentives could lead researchers to ignore standards of scientific behavior, rush to publication, and overemphasize positive outcomes (Edwards & Roy, 2017). Formal models have shown how these incentives can lead to high rates of non-reproducible outcomes (Smaldino and McElreath, 2016). Many of these incentives may be well-intentioned, but they could have the unintended consequence of reducing the quality of the science produced, and a lower quality of science is less reproducible. RESULT 5-2: Efforts to replicate studies to detect the effect of an intervention in a study population may find a similar direction of action, but a different (often smaller) effect size. Several experts who have studied replicability in and between science and technology shared their views with the Committee. Brian Nosek, co-founder and director of the Center for Open Science, said there was “not enough information to provide an estimate with certainty across all domains and even in individual domains.” In a recent paper on scientific progress and problems, Richard Shiffrin, professor of psychology and neuroscience at Indiana University, and colleagues argued that there is “no viable method for creating a quantitative metric, either in science or in the field” to measure scientific progress (Shiffrin et al., 2018, p. 2632).

Skip Lupia, now chief of the Directorate of Social, Behavioral and Economic Sciences at the National Science Foundation, said there isn`t enough information to definitively address the extent of non-reproducibility and non-reproducibility, but there is evidence of P hacking and publication bias (see below) that are problems. Steven Goodman, co-director of Stanford University`s Meta-Research Innovation Center (METRICS), suggested that the focus should not be on the rate of non-replication of individual studies, but on cumulative evidence of all studies and convergence to truth. He suggested the right question: “How effective is the scientific enterprise in producing reliable knowledge, what influences this reliability and how can we improve it?” Reproducibility is a subtle and nuanced topic, especially when widely discussed in scientific and technical research. An attempt by a second researcher to replicate a previous study is an attempt to determine whether applying the same methods to the same scientific question produces similar results. Beginning with a review of methods for assessing reproducibility, in this chapter we discuss the evidence related to the extent of non-reproducibility in scientific and technical research and examine the factors that influence reproducibility. A study published in Nature Human Behaviour in 2018 replicated 21 social and behavioral science papers from Nature and Science and found that only about 62% were able to successfully replicate the original results. [36] [37] RESULT 5-5: Replication studies in natural and clinical sciences (general biology, genetics, oncology, chemistry) and social sciences (including economics and psychology) report replication frequencies ranging from less than one in five studies to more than three in four studies. Online repositories, in which data, protocols and results can be stored and evaluated by the public, aim to improve the integrity and reproducibility of research. Examples of such repositories are the Open Science Framework, the Registry of Research Data Repositories and Psychfiledrawer.org. Websites like the Open Science Framework offer badges for using open science practices to incentivize scientists.