Background: The trend toward open science increases the pressure on authors to provide access to the source code and data they used to compute the results reported in their scientific papers. Since sharing materials reproducibly is challenging, several projects have developed solutions to support the release of executable analyses alongside articles. Methods: We reviewed 11 applications that can assist researchers in adhering to reproducibility principles. The applications were found through a literature search and interactions with the reproducible research community. An application was included in our analysis if it (i) was actively maintained at the time the data for this paper was collected, (ii) supports the publication of executable code and data, (iii) is connected to the scholarly publication process. By investigating the software documentation and published articles, we compared the applications across 19 criteria, such as deployment options and features that support authors in creating and readers in studying executable papers. Results: From the 11 applications, eight allow publishers to self-host the system for free, whereas three provide paid services. Authors can submit an executable analysis using Jupyter Notebooks or R Markdown documents (10 applications support these formats). All approaches provide features to assist readers in studying the materials, e.g., one-click reproducible results or tools for manipulating the analysis parameters. Six applications allow for modifying materials after publication. Conclusions: The applications support authors to publish reproducible research predominantly with literate programming. Concerning readers, most applications provide user interfaces to inspect and manipulate the computational analysis. The next step is to investigate the gaps identified in this review, such as the costs publishers have to expect when hosting an application, the consideration of sensitive data, and impacts on the review process.
Since operating urban air quality stations is not only time consuming but also costly, and because air pollutants can cause serious health problems, this paper presents the hourly prediction of ten air pollutant concentrations (CO 2 , NH 3 , NO,NO 2 , NO x , O 3 , PM 1 , PM 2.5 , PM 10 and PN 10 ) in a street canyon in Münster using an artificial neural network (ANN) approach. Special attention was paid to comparing three predictor options representing the traffic volume: we included acoustic sound measurements (sound), the total number of vehicles (traffic), and the hour of the day and the day of the week (time) as input variables and then compared their prediction powers. The models were trained, validated and tested to evaluate their performance. Results showed that the predictions of the gaseous air pollutants NO, NO 2 , NO x , and O 3 reveal very good agreement with observations, whereas predictions for particle concentrations and NH 3 were less successful, indicating that these models can be improved. All three input variable options (sound, traffic and time) proved to be suitable and showed distinct strengths for modelling various air pollutant concentrations.health [12][13][14]. Especially in street canyons, both the noise levels and the NO 2 and PM 10 concentrations are high [15]. As an increased traffic density correlates with a high noise level [16], several studies have shown that urban air pollutant concentrations can be well described by metrics of sound [17,18]. Alternatively, another proxy for the traffic density and, in turn, air pollutant concentrations, can be achieved by combining the time of the day with the day of the week [19].While sources of air pollutants beyond traffic, e.g., industry, are also important for certain cities, the main drivers of local pollutant concentrations in the urban atmospheric boundary layer are traffic emissions, background concentrations and meteorological conditions that control the transport of pollutants [20]. Thus, these drivers should be considered as predictors in air pollution models. Since environmental relationships are nonlinear and reasonably complex, this context is well suited to artificial neural networks (ANNs) [18,[21][22][23]. In particular, the modelling of urban NO 2 /NO x concentrations [24] and PM/PN concentrations [18] using a Multilayer Perceptron (MLP) has shown good prediction results. The basic idea of ANNs is to mimic processes that occur in the human brain. They receive and process information and output results, whereby the network can restructure itself during processing [25]. By recognizing certain patterns within input datasets, they can learn the best way to predict the output [26].This study presents the development and evaluation of ten ANN models for predicting urban air pollutant concentrations (CO 2 , NH 3 , NO, NO 2 , NO x , O 3 , PM 1 , PM 2.5 , PM 10 and PN 10 ) using meteorological data, background concentrations and certain predictors of traffic volume, namely sound, traffic and time, as new input variables. We selected ...
Funding agencies increasingly ask applicants to include data and software management plans into proposals. In addition, the author guidelines of scientific journals and conferences more often include a statement on data availability, and some reviewers reject unreproducible submissions. This trend towards open science increases the pressure on authors to provide access to the source code and data underlying the computational results in their scientific papers. Still, publishing reproducible articles is a demanding task and not achieved simply by providing access to code scripts and data files. Consequently, several projects develop solutions to support the publication of executable analyses alongside articles considering the needs of the aforementioned stakeholders. The key contribution of this paper is a review of applications addressing the issue of publishing executable computational research results. We compare the approaches across properties relevant for the involved stakeholders, e.g., provided features and deployment options, and also critically discuss trends and limitations. The review can support publishers to decide which system to integrate into their submission process, editors to recommend tools for researchers, and authors of scientific papers to adhere to reproducibility principles.
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