ImmPort, toward repurposing of open access immunological assay data for translational and clinical research

Immunology researchers are beginning to explore the possibilities of reproducibility, reuse and secondary analyses of immunology data. Open-access datasets are being applied in the validation of the methods used in the original studies, leveraging studies for meta-analysis, or generating new hypotheses. To promote these goals, the ImmPort data repository was created for the broader research community to explore the wide spectrum of clinical and basic research data and associated findings. The ImmPort ecosystem consists of four components–Private Data, Shared Data, Data Analysis, and Resources—for data archiving, dissemination, analyses, and reuse. To date, more than 300 studies have been made freely available through the ImmPort Shared Data portal , which allows research data to be repurposed to accelerate the translation of new insights into discoveries

Ten Simple Rules for Reproducible Research in Jupyter Notebooks

Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and thereby drive scientific progress. Since many experimental studies rely on computational analyses, biologists need guidance on how to set up and document reproducible data analyses or simulations. In this paper, we address several questions about reproducibility. For example, what are the technical and non-technical barriers to reproducible computational studies? What opportunities and challenges do computational notebooks offer to overcome some of these barriers? What tools are available and how can they be used effectively? We have developed a set of rules to serve as a guide to scientists with a specific focus on computational notebook systems, such as Jupyter Notebooks, which have become a tool of choice for many applications. Notebooks combine detailed workflows with narrative text and visualization of results. Combined with software repositories and open source licensing, notebooks are powerful tools for transparent, collaborative, reproducible, and reusable data analyses

Reflections on the future of research curation and research reproducibility

In the years since the launch of the World Wide Web in 1993, there have been profoundly transformative changes to the entire concept of publishing—exceeding all the previous combined technical advances of the centuries following the introduction of movable type in medieval Asia around the year 10001 and the subsequent large-scale commercialization of printing several centuries later by J. Gutenberg (circa 1440). Periodicals in print—from daily newspapers to scholarly journals—are now quickly disappearing, never to return, and while no publishing sector has been unaffected, many scholarly journals are almost unrecognizable in comparison with their counterparts of two decades ago. To say that digital delivery of the written word is fundamentally different is a huge understatement. Online publishing permits inclusion of multimedia and interactive content that add new dimensions to what had been available in print-only renderings. As of this writing, the IEEE portfolio of journal titles comprises 59 online only2 (31%) and 132 that are published in both print and online. The migration from print to online is more stark than these numbers indicate because of the 132 periodicals that are both print and online, the print runs are now quite small and continue to decline. In short, most readers prefer to have their subscriptions fulfilled by digital renderings only

Persistent identification and citation of software

This work has been funded by Jisc in the Research@Risk scheme.Software underpins the academic research process across disciplines. To be able to understand, use/reuse and preserve data, the software code that generated, analysed or presented the data will need to be retained and executed. An important part of this process is being able to persistently identify the software concerned. This paper discusses the reasons for doing so and introduces a model of software entities to enable better identification of what is being identified. The DataCite metadata schema provides a persistent identification scheme and we consider how this scheme can be applied to software. We then explore examples of persistent identification and reuse. The examples show the differences and similarities of software used in academic research, which has been written and reused at different scales. The key concepts of being able to identify what precisely is being used and provide a mechanism for appropriate credit are important to both of them.Publisher PDFPeer reviewe

FAIR data management: what does it mean for drug discovery?

The drug discovery community faces high costs in bringing safe and effective medicines to market, in part due to the rising volume and complexity of data which must be generated during the research and development process. Fully utilising these expensively created experimental and computational data resources has become a key aim of scientists due to the clear imperative to leverage the power of artificial intelligence (AI) and machine learning-based analyses to solve the complex problems inherent in drug discovery. In turn, AI methods heavily rely on the quantity, quality, consistency, and scope of underlying training data. While pre-existing preclinical and clinical data cannot fully replace the need for de novo data generation in a project, having access to relevant historical data represents a valuable asset, as its reuse can reduce the need to perform similar experiments, therefore avoiding a “reinventing the wheel” scenario. Unfortunately, most suitable data resources are often archived within institutes, companies, or individual research groups and hence unavailable to the wider community. Hence, enabling the data to be Findable, Accessible, Interoperable, and Reusable (FAIR) is crucial for the wider community of drug discovery and development scientists to learn from the work performed and utilise the findings to enhance comprehension of their own research outcomes. In this mini-review, we elucidate the utility of FAIR data management across the drug discovery pipeline and assess the impact such FAIR data has made on the drug development process

Expanding the circularity of plastic and biochar materials by developing alternative low environmental footprint sensors

Flexible screen-printing technology combined with the use of a nano/material coating for improving electrode functionalities boosted the manufacturing of highly sensitive electrochemical sensors addressing the need for fast and easy-to-handle tests in different application fields. However, due to the large-scale production and disposable and single-use nature of these devices, their environmental footprint should be taken into careful consideration. Herein, the innovative reuse of post-consumer polyethene terephthalate (PET) plastics as an alternative substrate coupled with biochar as an environmentally friendly and cost-effective modifier is described as a sustainable alternative for the production of robust electrochemical sensors. The good printability of reused plastics with graphite inks despite the chemical heterogeneity, different crystallinity, and surface roughness was demonstrated using atomic force microscopy and attenuated total reflection Fourier transform infrared spectroscopy. Functionalization with brewers' spent grain biochar enabled the fabrication of highly performing electrochemical sensors for nitrite detection in water having a limit of detection and a limit of quantification of 3.3 nM and 10.3 nM, respectively, with a linear range spanning from 0.01 to 500 & mu;M, and good reproducibility (RSD% 8%). The innovative intervention of the biochar-multilayer system markedly enhanced the electron transfer process at the electrode interface while simultaneously serving as an absorptive material for the investigated analyte. This work lays a foundation for repurposing end-of-life plastics for the electronics industry and presents a customizable reuse strategy aimed to keep the value of plastics in the economy and reduce waste and leakage into the natural environment

Workflow reuse in practice: a study of neuroimaging pipeline users

Workflow reuse is a major benefit of workflow systems and shared workflow repositories, but there are barely any studies that quantify the degree of reuse of workflows or the practical barriers that may stand in the way of successful reuse. In our own work, we hypothesize that defining workflow fragments improves reuse, since end-to-end workflows may be very specific and only partially reusable by others. This paper reports on a study of the current use of workflows and workflow fragments in labs that use the LONI Pipeline, a popular workflow system used mainly for neuroimaging research that enables users to define and reuse workflow fragments. We present an overview of the benefits of workflows and workflow fragments reported by users in informal discussions. We also report on a survey of researchers in a lab that has the LONI Pipeline installed, asking them about their experiences with reuse of workflow fragments and the actual benefits they perceive. This leads to quantifiable indicators of the reuse of workflows and workflow fragments in practice. Finally, we discuss barriers to further adoption of workflow fragments and workflow reuse that motivate further work