2,497 publications from this institution
Prevention is better than cureGiven the above considerations, plagiarism is better dealt with by concentrating on an instructional system of prevention.Many institutions have begun to implement courses on the responsible conduct of research, exploring a range of research integrity issues.Although this is a step in the right direction, such instruction benefits mainly students.Legions of seasoned researchers continue to operate without such instruction.Clearly, research integrity needs to be incorporated into continuing education targeted at this group.Training on responsible conduct of research must go beyond the "high crimes" of fabrication, falsification, and plagiarism, all three of which are thankfully still relatively rare in science.Zigmond and Fischer have argued that misdemeanours make up the bulk of the ethical transgressions. 10Instruction on plagiarism should focus on the principles of ethical writing. 11This approach assumes that each of our written works represents an implicit contract between us and our readers in which the reader assumes that, unless otherwise noted, we are the sole authors of the work, the words and ideas are our own, and the ideas, concepts and theories described are accurately and objectively represented to the best of our ability.Since it is doubtful that we will ever eliminate plagiarism or other forms of intentional misconduct, we need to increase our ability to detect these transgressions and effectively prosecute and punish offenders.Instruction in ethical writing indirectly touches on several other traditional forms of misconduct.Consequently, I believe that when we internalise and apply its basic principles they will significantly reduce plagiarism and generalise to other areas of scientific research and personal conduct.I thank Maryellen Reardon for comments on an earlier draft of this article.
CFDP is a protocol that takes advantage of the broadcast nature of CSMA networks to speed up simultaneous one-to-many file transfers (e.g., when booting diskless workstations). The CFDP server listens and services requests for entire files or portions thereof. CFDP clients first determine whether the file they are interested in is already being transferred, in which case they "eavesdrop" and load as much of it as they can or they initiate a new transfer. The clients timeout when the server stops transmitting and if they are still missing parts of the file they request them with a block-transfer request. CFDP is a back-end protocol a front end is needed to handle naming and security issues. A simple such front end is also presented here.
We developed a tool that scrapes and interprets statistical values (DORIS) to analyze reporting errors, which occur if the eye-catcher depicting the level of statistical significance is inconsistent with the reported statistical values. Using 578,132 tests from the top 50 economics journals, we find that 14.88 % of the articles have at least one strong error in the main tests. Our pre-registered analysis suggests that mandatory data and code availability policies reduce the prevalence of strong errors, while suggestive indication of a reversed effect is found for top 5 journals. Integrating DORIS into the review process can help improving article quality.
I am greatly honored by the very insightful comments of Senn,1 Willett,2 and Kraft3 on my commentary.4 Professor Senn1 nicely exemplifies that there is no need to invoke a sequential design to observe inflated effects. Any pilot (underpowered) study would do. Nevertheless, evidence in any field can be seen as sequential data accumulation. Meta-analysis, the total-evidence paradigm, is primarily a sequential (cumulative) enterprise5 and, for most topics, evidence is still in the pilot phase. As suggested by the Cochrane meta-analyses, even the best-conducted meta-analyses may have inflated effects, even after many trials are assembled. Moreover, I agree that additional problems beyond regression-to-the-mean are not necessary to see inflated effects. Yet, I believe I have mentioned enough empirical examples demonstrating inflationary practices4 (see also other writings6,7 and the Cochrane Methodology register8). We need more empirical evidence to measure the relative contribution of various reasons for inflated effects, but some healthy scepticism is warranted, as Senn suggests. Professor Willett2 offers an extremely illuminating and dense statement that would solve the entire problem: “If the original question was reasonable, the results are of interest whether statistically significant or not.” However, the phrasing that follows, “many of us have published many ‘negative’ studies,” is alarming. Why not: “all of us have published primarily ‘negative’ studies, which is what one expects to get usually, even with careful thinking and meticulous design, while dredging the hell out of data”? We can debate how many “negative” studies are expected, but meanwhile 100% of prognostic studies in the International Journal of Cancer in 2005 reported statistically significant results.9 I report here an inflated estimate, the most spectacular percentage—the percentage across 343 journals (1575 articles) was 95.8%. I definitely don't define epidemiology as collection of data that are sifted mindlessly. Epidemiology's strength is the careful thinking and planning, both in exploration and replication. However, the average project and paper is not conceived, designed, conducted and reported by giants of epidemiologic thinking of Willett's calibre. I simply propose that one fully records the process (how this was done) so that everybody can see, admire, replicate, and possibly critique analyses that allow large vibration of effects. I agree that data go beyond simple 2 × 3 tables. This is only one more reason to present them as explicitly as possible, with full attention to any prior biological hypotheses, potential recognized sources of confounding, misclassification, complexity of temporal relationships, and recall and selection biases.10 Reporting 2 × 9 or even 2 × 9000 tables is trivial; computers can readily handle petabytes of information.11 Consortia can be instrumental in enhancing both standardization and transparency of information,12 and efforts such as the Pooling Project of Prospective Studies of Diet and Cancer should attract more followers.13 Introductory epidemiology needs some reappraisal. Specifically, “consistency with other biological information” sounds august, but we need more empirical evidence on its workings. Sometimes we think we know everything about biology, while we know little or nothing. Using “consistency with other biological information” subjectively as the post hoc guarantor of poor data dredging is problematic. The commentary12 by Kraft (with whom I fully agree) nicely exemplifies the dangers of trusting biological consistency by describing how much one particular field (genetic epidemiology) has changed through the advent of “agnostic” genome-wide association studies14 (of which I am a great enthusiast). Several years ago, when I suggested that <10% to 30% of seemingly/partially replicated candidate genetic epidemiologic associations were true,6,15 many colleagues felt I was stubbornly unwilling to see the clear consistency of these candidate associations with other biological information. With the paradigm shift of the genome-wide association studies, the old ship was abandoned by its old-time enthusiasts to sink with all its cargo. (By the way, contrary to the deserters, I still believe that some candidate associations were true.) Now I hear talks celebrating that we have for the first time 200 (and rising) true associations, while 5 years ago the same lofty speakers were confident we already had 2000 true associations. What would happen if traditional epidemiology went through a similar paradigm shift in measurement capacity? Would many/most effects (including several “classics” of introductory epidemiology) shrink or sink? Interestingly, when we make more progress, apparently the associations that remain credible become fewer, claims for causality are toned down, and effects decrease. Kraft3 also highlights some of the dangers of stretching inflated effect sizes for predictive purposes. Certainly great caution is needed in the presentation and interpretation of this otherwise fascinating knowledge.16 One might argue that seasoned epidemiologists would be immune to tricks, e.g. the presentation of results as odds ratios of extreme centiles based on multiplicative models. However, empirical evidence shows that it is seasoned epidemiologists who use these tricks par excellence.17 Ordinary people and even most physicians don't even recognize what an odds ratio is.18 Educating thinking citizens may be useful, but is this feasible when even we scientists oversell our data?
Most large treatment effects emerge from small studies, and when additional trials are performed, the effect sizes become typically much smaller. Well-validated large effects are uncommon and pertain to nonfatal outcomes.
It is possible that more than 50% of complex disease risk is attributed to differences in an individual’s environment.1 Air pollution, smoking, and diet are documented environmental factors affecting health, yet these factors are but a fraction of the “exposome,” the totality of the exposure load occurring throughout a person’s lifetime.1 Investigating one or a handful of exposures at a time has led to a highly fragmented literature of epidemiologic associations. Much of that literature is not reproducible, and selective reporting may be a major reason for the lack of reproducibility. A new model is required to discover environmental exposures associated with disease while mitigating possibilities of selective reporting.
One or more systematic reviews of the evidence before launching a new trial serve to document that the clinical question is not answered already in previous trials and to inform the optimal design choices for the new trial. It is rarely documented that thorough systematic reviews are undertaken, and one main reason may be that it is very time and resource demanding to conduct a regular systematic review. To overcome this challenge, we propose a new type of review: the pre-trial systematic review. This review type aims to deliver a comprehensive, but not exhaustive, compendium of existing (published, completed, ongoing, or planned) trials, which can form the basis for deciding whether further trials are needed. The pre-trial systematic review methodology leverages any prior available reviews and clinical trial registries to compile a focused overview of existing trials. In contrast to a traditional systematic review, further assessments and analyses in pre-trial systematic reviews focus exclusively on those defining outcomes that are crucial to decide whether a new trial is needed. After deciding whether a new trial is needed, the overview may be used to justify how the trial should be designed, and it may act as a primer for more exhaustive traditional systematic reviews on the topic.
Memory latency is an important bottleneck in system performance that cannot be adequately solved by hardware alone. Several promising software techniques have been shown to address this problem successfully in specific situations. However, the generality of these software approaches has been limited because current architecturtes do not provide a fine-grained, low-overhead mechanism for observing and reacting to memory behavior directly. To fill this need, this article proposes a new class of memory operations called informing memory operations , which essentially consist of a memory operatin combined (either implicitly or explicitly) with a conditional branch-and-ink operation that is taken only if the reference suffers a cache miss. This article describes two different implementations of informing memory operations. One is based on a cache-outcome condition code, and the other is based on low-overhead traps. We find that modern in-order-issue and out-of-order-issue superscalar processors already contain the bulk of the necessary hardware support. We describe how a number of software-based memory optimizations can exploit informing memory operations to enhance performance, and we look at cache coherence with fine-grained access control as a case study. Our performance results demonstrate that the runtime overhead of invoking the informing mechanism on the Alpha 21164 and MIPS R10000 processors is generally small enough to provide considerable flexibility to hardware and software designers, and that the cache coherence application has improved performance compared to other current solutions. We believe that the inclusion of informing memory operations in future processors may spur even more innovative performance optimizations.
A voltage scaling technique for energy-efficient operation requires an adaptive power-supply regulator to significantly reduce dynamic power consumption in synchronous digital circuits. A digitally controlled power converter that dynamically tracks circuit performance with a ring oscillator and regulates the supply voltage to the minimum required to operate at a desired frequency is presented. This paper investigates the issues involved in designing a fully digital power converter and describes a design fabricated in a MOSIS 0.8-/spl mu/m process. A variable-frequency digital controller design takes advantage of the power savings available through adaptive supply-voltage scaling and demonstrates converter efficiency greater than 90% over a dynamic range of regulated voltage levels.
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MIPS-X-MP is a research project whose end goal is to build a small (workstation-sized) multiprocessor with a total throughput of 100-200 mips. The architectural approach uses a small number (tens) of high performance RISC-based microprocessors (10-20 mips each) The multiprocessor architecture uses software-controlled cache coherency to allow cooperation among processors without sacrificing performance of the processors. Software technology for automatically decomposing problems to allow the entire machine to be concentrated on a single problem is a key component of the research. This report surveys the four key components of the project: high performance VLSI processor architecture and design, multiprocessor architectural studies, multiprocessor programming systems, and optimizing compiler technology.
John Ioannidis and Alan Garber discuss how to use incremental cost-effectiveness ratios (ICER) and related metrics so they can be useful for decision-making at the individual level, whether used by clinicians or individual patients.
