Reproducibility in experiments is necessary to verify claims and to reuse prior work in experiments that advance research. However, the traditional model of publication validates research claims through peer-review without taking reproducibility into account. Workflows encapsulate experiment descriptions and components and are suitable for representing reproducibility. Additionally, they can be published alongside traditional patterns as a form of documentation for the experiment which can be combined with linked open data. For reproducibility utilising published datasets, it is necessary to declare the conditions or restrictions for permissible reuse. In this paper, we take a look at the state of workflow reproducibility through a browser based tool and a corresponding study to identify how workflows might be combined with traditional forms of documentation and publication. We also discuss the licensing aspects for data in workflows and how it can be annotated using linked open data ontologies.

This document reviews the stability of the main LHC operational parameters, namely orbit, tune, coupling and chromaticity. The analysis will be based on the LSA settings, measured parameters and real-time trims. The focus will be set on ramp and high energy reproducibility as they are more diflicult to assess and correct on a daily basis for certain parameters like chromaticity and coupling. The reproducibility of the machine in collision will be analysed in detail, in particular the beam offsets at the IPS since the ever decreasing beam sizes at the IPs make beam steering at the IP more and mode delicate.

The paper inhales more than 6,700 individual pieces of research, all meta-analyses that themselves encompass 64,076 estimates of economic outcomes. That’s right: It’s a meta-meta-analysis. And in this case, Doucouliagos never meta-analyzed something he didn’t dislike. Of the fields covered in this corpus, half were statistically underpowered—the studies couldn’t show the effect they said they did. And most of the ones that were powerful enough overestimated the size of the effect they purported to show. Economics has a profound effect on policymaking and understanding human behavior. For a science, this is, frankly, dismal.

Within the Open Science discussions, the current call for “reproducibility” comes from the raising awareness that results as presented in research papers are not as easily reproducible as expected, or even contradicted those original results in some reproduction efforts. In this context, transparency and openness are seen as key components to facilitate good scientific practices, as well as scientific discovery. As a result, many funding agencies now require the deposit of research data sets, institutions improve the training on the application of statistical methods, and journals begin to mandate a high level of detail on the methods and materials used. How can researchers be supported and encouraged to provide that level of transparency? An important component is the underlying research data, which is currently often only partly available within the article. At Elsevier we have therefore been working on journal data guidelines which clearly explain to researchers when and how they are expected to make their research data available. Simultaneously, we have also developed the corresponding infrastructure to make it as easy as possible for researchers to share their data in a way that is appropriate in their field. To ensure researchers get credit for the work they do on managing and sharing data, all our journals support data citation in line with the FORCE11 data citation principles – a key step in the direction of ensuring that we address the lack of credits and incentives which emerged from the Open Data analysis (Open Data - the Researcher Perspective https://www.elsevier.com/about/open-science/research-data/open-data-report ) recently carried out by Elsevier together with CWTS. Finally, the presentation will also touch upon a number of initiatives to ensure the reproducibility of software, protocols and methods. With STAR methods, for instance, methods are submitted in a Structured, Transparent, Accessible Reporting format; this approach promotes rigor and robustness, and makes reporting easier for the author and replication easier for the reader.

This handbook is about translating insights from experts in code and data into practical terms for empirical social scientists. We are not ourselves software engineers, database managers, or computer scientists, and we don’t presume to contribute anything to those disciplines. If this handbook accomplishes something, we hope it will be to help other social scientists realize that there are better ways to work. Much of the time, when you are solving problems with code and data, you are solving problems that have been solved before, better, and on a larger scale. Recognizing that will let you spend less time wrestling with your RA’s messy code, and more time on the research problems that got you interested in the first place.

Growing pressure in Australia to translate pre-clinical and clinical research into improving treatment outcomes (https://www.nhmrc.gov.au/research/research-translation-0) means that concerns about the irreproducibility of published data slowing research translation (Collins and Tabak, 2014) must be addressed.