Crowdsourcing enables novel forms of research and knowledge production. It uses cyberspace to collect diverse research participants, coordinate projects and keep costs low. Recently social scientists began crowdsourcing their peers to engage in mass research targeting a specific topic. This enables meta-analysis of many analysts’ results obtained from a single crowdsourced research project, leading to exponential gains in credibility and scientific utility. Initial applications demonstrate positive returns for both original and replication research using various research instruments, and secondary or experimental data. It can provide more reliable Bayesian priors for selecting models and is an untapped mode of theory production that greatly benefit social science. Finally, in addition to the credibility and reproducibility gains, crowdsourcing embodies many core values of the Open Science Movement because it promotes community and equality among scientists.
Human similarity and relatedness judgements between concepts underlie most of cognitive capabilities, such as categorisation, memory, decision-making and reasoning. For this reason, the proposal of methods for the estimation of the degree of similarity and relatedness between words and concepts has been a very active line of research in the fields of artificial intelligence, information retrieval and natural language processing among others. Main approaches proposed in the literature can be categorised in two large families as follows: (1) Ontology-based semantic similarity Measures (OM) and (2) distributional measures whose most recent and successful methods are based on Word Embedding (WE) models. However, the lack of a deep analysis of both families of methods slows down the advance of this line of research and its applications. This work introduces the largest, reproducible and detailed experimental survey of OM measures and WE models reported in the literature which is based on the evaluation of both families of methods on a same software platform, with the aim of elucidating what is the state of the problem. We show that WE models which combine distributional and ontology-based information get the best results, and in addition, we show for the first time that a simple average of two best performing WE models with other ontology-based measures or WE models is able to improve the state of the art by a large margin. In addition, we provide a very detailed reproducibility protocol together with a collection of software tools and datasets as supplementary material to allow the exact replication of our results.
To identify families of experiments that used meta-analysis, to investigate their methods for effect size construction and aggregation, and to assess the reproducibility and validity of their results. We performed a systematic review (SR) of papers reporting families of experiments in high quality software engineering journals, that attempted to apply meta-analysis. We attempted to reproduce the reported meta-analysis results using the descriptive statistics and also investigated the validity of the meta-analysis process. Out of 13 identified primary studies, we reproduced only five. Seven studies could not be reproduced. One study which was correctly analyzed could not be reproduced due to rounding errors. When we were unable to reproduce results, we provide revised meta-analysis results. To support reproducibility of analyses presented in our paper, it is complemented by the reproducer R package. Meta-analysis is not well understood by software engineering researchers. To support novice researchers, we present recommendations for reporting and meta-analyzing families of experiments and a detailed example of how to analyze a family of 4-group crossover experiments.
The suggestions proposed by Lee et al. to improve cognitive modeling practices have significant parallels to the current best practices for improving reproducibility in the field of Machine Learning. In the current commentary on `Robust modeling in cognitive science', we highlight the practices that overlap and discuss how similar proposals have produced novel ongoing challenges, including cultural change towards open science, the scalability and interpretability of required practices, and the downstream effects of having robust practices that are fully transparent. Through this, we hope to inform future practices in computational modeling work with a broader scope.
Current concerns about reproducibility in many research communities can be traced back to a high value placed on empirical reproducibility of the physical details of scientific experiments and observations. For example, the detailed descriptions by 17th century scientist Robert Boyle of his vacuum pump experiments are often held to be the ideal of reproducibility as a cornerstone of scientific practice. Victoria Stodden has claimed that the computer is an analog for Boyle's pump -- another kind of scientific instrument that needs detailed descriptions of how it generates results. In the place of Boyle's hand-written notes, we now expect code in open source programming languages to be available to enable others to reproduce and extend computational experiments. In this paper we show that there is another genealogy for reproducibility, starting at least from Euclid, in the production of proofs in mathematics. Proofs have a distinctive quality of being necessarily reproducible, and are the cornerstone of mathematical science. However, the task of the modern mathematical scientist has drifted from that of blackboard rhetorician, where the craft of proof reigned, to a scientific workflow that now more closely resembles that of an experimental scientist. So, what is proof in modern mathematics? And, if proof is unattainable in other fields, what is due scientific diligence in a computational experimental environment? How do we measure truth in the context of uncertainty? Adopting a manner of Lakatosian conversant conjecture between two mathematicians, we examine how proof informs our practice of computational statistical inquiry. We propose that a reorientation of mathematical science is necessary so that its reproducibility can be readily assessed.
In this paper, we describe DiOS, a lightweight model operating system which can be used to execute programs that make use of POSIX APIs. Such executions are fully reproducible: running the same program with the same inputs twice will result in two exactly identical instruction traces, even if the program uses threads for parallelism. DiOS is implemented almost entirely in portable C and C++: although its primary platform is DiVM, a verification-oriented virtua machine, it can be configured to also run in KLEE, a symbolic executor. Finally, it can be compiled into machine code to serve as a user-mode kernel. Additionally, DiOS is modular and extensible. Its various components can be combined to match both the capabilities of the underlying platform and to provide services required by a particular program. New components can be added to cover additional system calls or APIs. The experimental evaluation has two parts. DiOS is first evaluated as a component of a program verification platform based on DiVM. In the second part, we consider its portability and modularity by combining it with the symbolic executor KLEE.