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The Research Data Alliance (RDA) COVID-19 Working Group members bring various, global expertise to develop a body of work that comprises how data from multiple disciplines inform response to a pandemic combined with guidelines and recommendations on data sharing under the present COVID-19 cicumstances. This extends to research software sharing, in recognition of the key role in software in analysing data. The work has been divided into four research areas (namely, clinical, omics, epidemiology, social sciences) with four cross cutting themes (namely, community participation, indigenous data, legal and ethical considerations, research software), as a way to focus the conversations, and provide an initial set of guidelines in a tight timeframe. The detailed guidelines are aimed to help stakeholders follow best practices to maximise the efficiency of their work, and to act as a blueprint for future emergencies. The recommendations in the document are aimed at helping policymakers and funders to maximise timely, quality data sharing and appropriate responses in such health emergencies.
This work was executed in an intense period over just over 6 weeks, with five iterations, all of which were opened for public community comment. Draft releases and comments are avaialable here (https://doi.org/10.15497/rda00046).
This activity has been conducted under the RDA guiding principles of Openness, Consensus, Balance, Harmonization, Community-driven, and Non-profit and technology-neutral.
LUMOR: An App for Standardized Control and Monitoring of a Porcine Lung and its Nutrient Cycle
(2014)
LUMOR: An App for Standardized Control and Monitoring of a Porcine Lung and its Nutrient Cycle
(2014)
Simulation models in respiratory research are increasingly used for medical product development and testing, especially because in-vivo models are coupled with a high degree of complexity and ethical concerns. This work introduces a respiratory simulation system, which is bridging the gap between the complex, real anatomical environment and the safe, cost-effective simulation methods. The presented electro-mechanical lung simulator, xPULM, combines in-silico, ex-vivo and mechanical respiratory approaches by realistically replicating an actively breathing human lung. The reproducibility of sinusoidal breathing simulations with xPULM was verified for selected breathing frequencies (10–18 bpm) and tidal volumes (400–600 ml) physiologically occurring during human breathing at rest. Human lung anatomy was modelled using latex bags and primed porcine lungs. High reproducibility of flow and pressure characteristics was shown by evaluating breathing cycles (nTotal = 3273) with highest standard deviation |3σ| for both, simplified lung equivalents (μV˙ = 23.98 ± 1.04 l/min, μP = −0.78 ± 0.63 hPa) and primed porcine lungs (μV˙ = 18.87 ± 2.49 l/min, μP = −21.13 ± 1.47 hPa). The adaptability of the breathing simulation parameters, coupled with the use of porcine lungs salvaged from a slaughterhouse process, represents an advancement towards anatomically and physiologically realistic modelling of human respiration.