# kb

id: burden-of-knowledge-20260505-0
file: data/en.wikipedia.org/wiki/Burden_of_knowledge-0.md
category: reference
title: Burden of knowledge
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Burden_of_knowledge
date_saved: 2026-05-05T03:14:27.531708+00:00
instance: kb-cron
parent: burden-of-knowledge-20260505-0
summary:
The burden of knowledge describes the difficulty of adding to a scientific field as the amount of previous work that must be understood increases over time. This is seen across scientific disciplines. Evidence for this burden includes a trend that people are older before they receive their first patent or publish in a prestigious journal, and the need for scientific groups to be larger as scientists need to collaborate so that the team has sufficient understanding of prior work. == Scholarship == Explicit scholarship of this idea has entered the mainstream with the works of Benjamin Jones, in particular The Burden of Knowledge and the Death of the Renaissance Man, and with the works of Jan Brendel and Sascha Schweitzer The Burden of Knowledge in Mathematics. == Overview == Theory and empirical studies reflect the case that researchers and innovators are not born with the required expertise and must first undertake education. With accumulating information and discoveries, the time to digest and improve on extant knowledge takes longer. Similarly, frontiers of knowledge advance at an overall increasing rate and are shifting over time. The "burden of knowledge" refers to the difficulty of catching up with this evolving knowledge frontier. A


id: conflicts-of-interest-in-academic-publishing-20260505-0
file: data/en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing-0.md
category: reference
title: Conflicts of interest in academic publishing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing
date_saved: 2026-05-05T03:14:28.683683+00:00
instance: kb-cron
parent: conflicts-of-interest-in-academic-publishing-20260505-0
summary:
Conflicts of interest (COIs) often arise in academic publishing. Such conflicts may cause wrongdoing and make it more likely. Ethical standards in academic publishing exist to avoid and deal with conflicts of interest, and the field continues to develop new standards. Standards vary between journals and are unevenly applied. According to the International Committee of Medical Journal Editors, "[a]uthors have a responsibility to evaluate the integrity, history, practices and reputation of the journals to which they submit manuscripts". Conflicts of interest increase the likelihood of biases arising; they can harm the quality of research and the public good (even if disclosed). Conflicts of interest can involve research sponsors, authors, journals, journal staff, publishers, and peer reviewers. == Avoidance, disclosure, and tracking == The avoidance of conflicts of interest and the changing of the structure of institutions to make them easier to avoid are frequently advocated for. Some institutional ethics policies ban academics from entering into specific types of COIs, for instance by prohibiting them from accepting gifts from companies connected with their work. Education in ethical COI management is also a tool for avoiding COI problems. Disclosure of COIs has been debated since the 1980s; there is a general consensus


id: conflicts-of-interest-in-academic-publishing-20260505-1
file: data/en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing-1.md
category: reference
title: Conflicts of interest in academic publishing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing
date_saved: 2026-05-05T03:14:28.683683+00:00
instance: kb-cron
parent: conflicts-of-interest-in-academic-publishing-20260505-0
summary:
==== Owners and governing bodies ==== The owner of an academic journal has ultimate power over the hiring and firing of editorial staff; editors' interests in pleasing their employers conflict with some of their other editorial interests. Journals are also more likely to accept papers by authors who work for the journals' hosting institutions. Some journals are owned by publishers. When journals print reviews of books published by their own publishers, they rarely (as of 2013) add COI disclosures. The publishers' interest in maximizing profit will often conflict with academic interests or ethical standards. In the case of closed-access publications, publishers' desire for high subscription income may conflict with an editorial desire for broader access and readership. There have been multiple mass resignations of editorial boards over such conflicts, which are often followed by the editorial board founding a new, non-profit journal to compete with their former one. Some journals are owned by academic societies and professional organisations. Leading journals can be very profitable and there is often friction about revenue between the journal and the member society that owns it. Some academic societies and professional organisations are themselves funded by membership fees and/or donations. If the owners benefit financially


id: conflicts-of-interest-in-academic-publishing-20260505-2
file: data/en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing-2.md
category: reference
title: Conflicts of interest in academic publishing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing
date_saved: 2026-05-05T03:14:28.683683+00:00
instance: kb-cron
parent: conflicts-of-interest-in-academic-publishing-20260505-0
summary:
=== COIs of journal staff === Personal conflicts of interest faced by journal staff are individual. If a person leaves the journal—unlike the COIs of journals as institutions—their personal COIs will go with them. As of 2015, COIs of journal staff are less commonly reported than those of authors. For instance, one 2009 World Association of Medical Editors (WAME) policy document states, "Some journals list editors' competing interests on their website but this is not a standard practice". The ICMJE, however, requires that the COIs of editors and journal staff be regularly declared and published. One 2017 Open Payments study of influential US medical journals found half of the editors received payments from industry; another study that used a different sample of editors reported two-thirds. As of 2002, systems for reporting wrongdoing by editors often do not exist. Many journals have policies limiting COIs staff can enter into; for instance, accepting gifts of travel, accommodation, or hospitality may be prohibited. As of 2016, such policies are rarely published. Most journals do not offer COI training; as of 2015, many journals report a desire for better guidance on COI policy. === COIs of peer reviewers === The ICJME recommendations require peer


id: conflicts-of-interest-in-academic-publishing-20260505-3
file: data/en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing-3.md
category: reference
title: Conflicts of interest in academic publishing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing
date_saved: 2026-05-05T03:14:28.683683+00:00
instance: kb-cron
parent: conflicts-of-interest-in-academic-publishing-20260505-0
summary:
The ICMJE requires that "All those designated as authors should meet all four criteria for authorship, and all who meet the four criteria should be identified as authors. Those who do not meet all four criteria should be acknowledged." Academics who have had publication ethics training and those who are aware of the ICMJE authorship criteria are more stringent in their concepts of authorship and are more likely to consider breaches of authorship as misconduct, as are more junior researchers. Awareness is low; one study found only about half of researchers had read the ICJME criteria. === COIs of study sponsors === If a study requires outside funding, this can be a major source of conflicting interests; for instance in cases where the manufacturer of a drug is funding a study into its safety and efficacy or where the sponsor hopes to use the research to defend itself in litigation. Sponsors of a study may involve themselves in the design, execution, analysis, and write-up of a study. In extreme cases, they may carry out the research and ghostwrite the article with almost no involvement from the nominal author. Movie-style credits are advocated as a way to avoid this. There are


id: design-of-experiments-20260505-0
file: data/en.wikipedia.org/wiki/Design_of_experiments-0.md
category: reference
title: Design of experiments
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Design_of_experiments
date_saved: 2026-05-05T03:14:30.097260+00:00
instance: kb-cron
parent: design-of-experiments-20260505-0
summary:
The design of experiments (DOE), also known as experiment design or experimental design, is the design of any task that aims to describe and explain the variation of information under conditions that are hypothesized to reflect the variation. The term is generally associated with experiments in which the design introduces conditions that directly affect the variation, but may also refer to the design of quasi-experiments, in which natural conditions that influence the variation are selected for observation. In its simplest form, an experiment aims at predicting the outcome by introducing a change of the preconditions, which is represented by one or more independent variables, also referred to as "input variables" or "predictor variables." The change in one or more independent variables is generally hypothesized to result in a change in one or more dependent variables, also referred to as "output variables" or "response variables." The experimental design may also identify control variables that must be held constant to prevent external factors from affecting the results. Experimental design involves not only the selection of suitable independent, dependent, and control variables, but planning the delivery of the experiment under statistically optimal conditions given the constraints of available resources. There are multiple approaches


id: design-of-experiments-20260505-1
file: data/en.wikipedia.org/wiki/Design_of_experiments-1.md
category: reference
title: Design of experiments
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Design_of_experiments
date_saved: 2026-05-05T03:14:30.097260+00:00
instance: kb-cron
parent: design-of-experiments-20260505-0
summary:
Comparison In some fields of study it is not possible to have independent measurements to a traceable metrology standard. Comparisons between treatments are much more valuable and are usually preferable, and often compared against a scientific control or traditional treatment that acts as baseline. Randomization Random assignment is the process of assigning individuals at random to groups or to different groups in an experiment, so that each individual of the population has the same chance of becoming a participant in the study. The random assignment of individuals to groups (or conditions within a group) distinguishes a rigorous, "true" experiment from an observational study or "quasi-experiment". There is an extensive body of mathematical theory that explores the consequences of making the allocation of units to treatments by means of some random mechanism (such as tables of random numbers, or the use of randomization devices such as playing cards or dice). Assigning units to treatments at random tends to mitigate confounding, which makes effects due to factors other than the treatment to appear to result from the treatment. The risks associated with random allocation (such as having a serious imbalance in a key characteristic between a treatment group and a control group)


id: design-of-experiments-20260505-2
file: data/en.wikipedia.org/wiki/Design_of_experiments-2.md
category: reference
title: Design of experiments
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Design_of_experiments
date_saved: 2026-05-05T03:14:30.097260+00:00
instance: kb-cron
parent: design-of-experiments-20260505-0
summary:
Let yi be the measured difference for i = 1, ..., 8. The relationship between the true weights and experimental measurements may be represented with a general linear model, with the design matrix W {\displaystyle W} having entries from { − 1 , 0 , 1 } {\displaystyle \{-1,0,1\}} : y = W θ + ϵ {\displaystyle y=W\theta +\epsilon } The first design is represented by an identity matrix while the second design is represented by an 8x8 Hadamard matrix, H {\displaystyle H} , both examples of weighing matrices. The weights are typically estimated using the method of least squares. Using a weighing matrix, this is equivalent to inverting on the measurements: θ ^ A = I − 1 y = y {\displaystyle {\hat {\theta }}_{A}=I^{-1}y=y} θ ^ B = H − 1 y {\displaystyle {\hat {\theta }}_{B}=H^{-1}y} The question of design of experiments is: which experiment is better? Investigating estimate A vs B for the first weight: Var ⁡ ( θ ^ A , 1 ) = Var ⁡ ( y 1 ) = σ 2 {\displaystyle \operatorname {Var} ({\hat {\theta }}_{A,1})=\operatorname {Var} (y_{1})=\sigma ^{2}} Var ⁡ ( θ ^ B , 1 ) = Var ⁡ ( y


id: design-of-experiments-20260505-3
file: data/en.wikipedia.org/wiki/Design_of_experiments-3.md
category: reference
title: Design of experiments
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Design_of_experiments
date_saved: 2026-05-05T03:14:30.097260+00:00
instance: kb-cron
parent: design-of-experiments-20260505-0
summary:
== Causal attributions == In the pure experimental design, the independent (predictor) variable is manipulated by the researcher – that is – every participant of the research is chosen randomly from the population, and each participant chosen is assigned randomly to conditions of the independent variable. Only when this is done is it possible to certify with high probability that the reason for the differences in the outcome variables are caused by the different conditions. Therefore, researchers should choose the experimental design over other design types whenever possible. However, the nature of the independent variable does not always allow for manipulation. In those cases, researchers must be aware of not certifying about causal attribution when their design doesn't allow for it. For example, in observational designs, participants are not assigned randomly to conditions, and so if there are differences found in outcome variables between conditions, it is likely that there is something other than the differences between the conditions that causes the differences in outcomes, that is – a third variable. The same goes for studies with correlational design. == Statistical control == It is best that a process be in reasonable statistical control prior to conducting designed experiments. When


id: design-of-experiments-20260505-4
file: data/en.wikipedia.org/wiki/Design_of_experiments-4.md
category: reference
title: Design of experiments
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Design_of_experiments
date_saved: 2026-05-05T03:14:30.097260+00:00
instance: kb-cron
parent: design-of-experiments-20260505-0
summary:
== See also == Adversarial collaboration – Method of research Bayesian experimental design – Experimental design framework Block design – Structure in combinatorial mathematics Box–Behnken design – Experimental designs for response surface methodology Central composite design – Experimental design in statistical mathematics Clinical study design – Plan for research in clinical medicine Computer experiment – Experiment used to study computer simulation Controlling for a variable – Binning data according to measured values of the variable Experimetrics (econometrics-related experiments) Factor analysis – Statistical method Fractional factorial design – Statistical experimental design approach Glossary of experimental design Grey box model – Mathematical data production model with limited structure Industrial engineering – Branch of engineering which deals with the optimization of complex processes or systems Instrument effect Law of large numbers – Averages of repeated trials converge to the expected value Manipulation checks Multifactor design of experiments software One-factor-at-a-time method – Method of designing experiments Optimal design – Experimental design that is optimal with respect to some statistical criterionPages displaying short descriptions of redirect targets Plackett–Burman design – Type of experimental design Probabilistic design – Discipline within engineering design Protocol (natural sciences) – Procedural method for the design and implementation of an experimentPages


id: further-research-is-needed-20260505-0
file: data/en.wikipedia.org/wiki/Further_research_is_needed-0.md
category: reference
title: Further research is needed
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Further_research_is_needed
date_saved: 2026-05-05T03:14:31.281573+00:00
instance: kb-cron
parent: further-research-is-needed-20260505-0
summary:
"Further research is needed" (FRIN), "more research is needed" and other variants of similar phrases are commonly used in research papers. The cliché is so common that it has attracted research, regulation and cultural commentary. == Meaning == Some research journals have banned the phrase "more research is needed" on the grounds that it is redundant; it is almost always true and fits almost any article, and so can be taken as understood. A 2004 metareview by the Cochrane collaboration of their own systematic medical reviews found that 93% of the reviews studied made indiscriminate FRIN-like statements, reducing their ability to guide future research. The presence of FRIN had no correlation with the strength of the evidence against the medical intervention. Authors who thought a treatment was useless were just as likely to recommend researching it further. Indeed, authors may recommend "further research" when, given the existing evidence, further research would be extremely unlikely to be approved by an ethics committee. Studies finding that a treatment has no noticeable effects are sometimes greeted with statements that "more research is needed" by those convinced that the treatment is effective, but the effect has not yet been found. Since even the largest


id: steven-n-goodman-20260505-0
file: data/en.wikipedia.org/wiki/Steven_N._Goodman-0.md
category: reference
title: Steven N. Goodman
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Steven_N._Goodman
date_saved: 2026-05-05T03:14:32.428701+00:00
instance: kb-cron
parent: steven-n-goodman-20260505-0
summary:
Steven N. Goodman (born 1954) is an American epidemiologist and Professor of Epidemiology and Population Health and of Medicine at the Stanford School of Medicine, where he serves as Associate Dean of Clinical and Translational Research. He is co-founder and co-director of the Meta-Research Innovation Center at Stanford (METRICS), a center dedicated to studying and improving the reproducibility and efficiency of biomedical research, alongside John Ioannidis. He is also founder and director of the Stanford Program on Research Rigor and Reproducibility (SPORR). Goodman has made extensive contributions to the foundations of scientific and statistical inference within the biosciences. In 1999, he coined the term "p-value fallacy" in a pair of landmark papers arguing for the adoption of Bayesian methods in medical research. He is a sibling of journalist and Democracy Now! host Amy Goodman and journalist David Goodman. == Education and career == Goodman received his AB in Biochemistry and Applied Mathematics from Harvard College in 1976, his MD from New York University School of Medicine, and his MHS and PhD in Biostatistics from the Johns Hopkins Bloomberg School of Public Health, completing his doctoral dissertation Evidence and Clinical Trials in 1989 under the supervision of Richard Royall. He completed


id: harking-20260505-0
file: data/en.wikipedia.org/wiki/HARKing-0.md
category: reference
title: HARKing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/HARKing
date_saved: 2026-05-05T03:14:33.664349+00:00
instance: kb-cron
parent: harking-20260505-0
summary:
HARKing (hypothesizing after the results are known) is an acronym coined by social psychologist Norbert Kerr that refers to the questionable research practice of "presenting a post hoc hypothesis in the introduction of a research report as if it were an a priori hypothesis". Hence, a key characteristic of HARKing is that post hoc hypothesizing is falsely portrayed as a priori hypothesizing. HARKing may occur when a researcher tests an a priori hypothesis but then omits that hypothesis from their research report after they find out the results of their test. Post hoc analysis or post hoc theorizing then may lead to a post hoc hypothesis. == Types == Several types of HARKing have been distinguished, including: THARKing Transparently hypothesizing after the results are known, rather than the secretive, undisclosed, HARKing that was first proposed by Kerr. In this case, researchers openly declare that they developed their hypotheses after they observed their research results. CHARKing (or Pure HARKing) CHARKing or "pure HARKing" refers to the practice of constructing new hypotheses after the results are known and presenting them as a priori hypotheses. CHARKing is often regarded as the prototypical form of HARKing. RHARKing RHARKing refers to retrieving old hypotheses from


id: icmje-recommendations-20260505-0
file: data/en.wikipedia.org/wiki/ICMJE_recommendations-0.md
category: reference
title: ICMJE recommendations
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/ICMJE_recommendations
date_saved: 2026-05-05T03:14:34.831046+00:00
instance: kb-cron
parent: icmje-recommendations-20260505-0
summary:
The ICMJE recommendations (full title, "Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals") are a set of guidelines produced by the International Committee of Medical Journal Editors for standardising the ethics, preparation and formatting of manuscripts submitted to biomedical journals for publication. Compliance with the ICMJE recommendations is required by most leading biomedical journals. Levels of real compliance are subject to debate. As of 9 January 2020, 5570 journals worldwide claim to follow the ICMJE recommendations. The recommendations were first issued in 1979 under the title "Uniform Requirements for Manuscripts Submitted to Biomedical Journals" (abbreviated URMs and often shortened to "Uniform Requirements"). After a series of revisions, they were given their current name in 2013. == International Committee of Medical Journal Editors == The International Committee of Medical Journal Editors (ICMJE) was originally known as the Vancouver Group, after the location of their first meeting in Vancouver, British Columbia in Canada. As of 2017 members of the ICMJE are: Annals of Internal Medicine BMJ Bulletin of the World Health Organization Deutsches Ärzteblatt Ethiopian Journal of Health Sciences Iranian Journal of Medical Sciences Journal of the American Medical Association (JAMA) New England Journal of Medicine


