Data, Volume 6, Issue 9 (September 2021) – 6 articles

Analysis of flow-induced pipe vibrations has been applied in a variety of applications, such as flowrate inference and leak detection. These applications are based on a functional relationship between the vibration features estimated in the pipe walls and the dynamics related to the flow of the substance. The dataset described in this document is comprised of signals acquired using an accelerometer attached to a pipe conveying cold water at specific flowrate values. Tests were carried out under numerals of the ISO 4064-1/2: 2016 standard and were performed in two measurement benches designed for flowmeter calibration, and a total of 80 flowrate values, from 25 L/h to 20,000 L/h, were considered. For each flowrate value, 3 to 6 samples were taken, so that the resulting dataset has a total of 382 signals that contain acceleration values in three axes and a timestamp in microseconds. Full article

The SARS-CoV-2 coronavirus expresses two essential proteases: firstly, the 3Chymotrypsin-like protease (3CLpro) or main protease (Mpro), and secondly, the papain-like protease (PLpro), both of which are considered as viable drug targets for the inhibition of viral replication. In order to perform drug discovery assays for SARS-CoV-2, it is imperative that efficient methods are established for the production and purification of 3CLpro and PLpro of SARS-CoV-2, designated as 3CLpro-CoV2 and PLpro-CoV2, respectively. This article expands the data collected in the attempts to express SARS-CoV-2 proteases under different conditions and purify them under single-step chromatography. Data showed that the use of E. coli BL21(DE3) strain was sufficient to express 3CLpro-CoV2 in a fully soluble form. Nevertheless, the single affinity chromatography step was only applicable for 3CLpro-CoV2 expressed at 18 °C, with a yield and purification fold of 92% and 49, respectively. Meanwhile, PLpro-CoV2 was successfully expressed in a fully soluble form in either BL21(DE3) or BL21-CodonPlus(DE3) strains. In contrast, the single affinity chromatography step was only applicable for PLpro-CoV2 expressed using E. coli BL21-CodonPlus(DE3) at 18 or 37 °C, with a yield and purification fold of 86% (18 °C) or 83.36% (37 °C) and 112 (18 °C) or 71 (37 °C), respectively. The findings provide a guide for optimizing the production of SARS-CoV-2 proteases of E. coli host cells. Full article

Here, we describe the dataset of seismic envelopes used to study the S-wave Q-coda attenuation quality factor $Q_c$ of the Gargano Promontory (Southern Italy). With this dataset, we investigated the crustal seismic attenuation by the $Q_c$ parameter. We collected this dataset starting from two different earthquake catalogues: the first regarding the period from April 2013 to July 2014; the second regarding the period from July 2015 to August 2018. Visual inspection of the envelopes was carried out on recordings filtered with a Butterworth two-poles filter with central frequency $f_c$ = 6 Hz. The obtained seismic envelopes of coda decay can be linearly fitted in a bilogarithmic diagram in order to obtain a series of single source-receiver measures of $Q_c^{}$ for each seismogram component at different frequency $f_c$. The analysis of the trend $Q_c(f_c$) gives important insights into the heterogeneity and the anelasticity of the sampled Earth medium. Full article

A gene is a sequence of DNA bases through which genetic information is passed on to the next generation. Most genes encode for proteins that ultimately control cellular function. Understanding the interrelation between genes without the application of statistical methods can be a daunting task. Correlation analysis is a powerful approach to determine the strength of association between two variables (e.g., gene-wise expression). Moreover, it becomes essential to visualize this data to establish patterns and derive insight. The most common method for gene expression visualization is to use correlation heatmaps in which the colors of the plot represent strength of co-expression. In order to address this requirement, we developed a visualization tool called BioCPR: Biological Correlation Plots in R. This tool performs both correlation analysis and subsequent visualization in the form of an interactive heatmap, improving both usability and interpretation of the data. BioCPR is an R Shiny-based application and can be run locally in Rstudio or a web browser. Full article

Trends in the sciences are indicative of data management becoming established as a feature of the mainstream research process. In this context, the European Commission introduced an Open Research Data pilot at the start of the Horizon 2020 research programme. This initiative followed the success of the Open Access pilot implemented in the prior (FP7) research programme, which thereafter became an integral component of Horizon 2020. While the Open Access phenomenon can reasonably be argued to be one of many instances of web technologies disrupting established business models (namely publication practices and workflows established over several centuries in the case of Open Access), initiatives designed to promote research data management have no established foundation on which to build. For Open Data to become a reality and, more importantly, to contribute to the scientific process, data management best practices and workflows are required. Furthermore, with the scientific community having operated to good effect in the absence of data management, there is a need to demonstrate the merits of data management. This circumstance is complicated by the lack of the necessary ICT infrastructures, especially interoperability standards, required to facilitate the seamless transfer, aggregation and analysis of research data. Any activity aiming to promote Open Data thus needs to overcome a number of cultural and technological challenges. It is in this context that this paper examines the data management activities and outcomes of a number of projects participating in the Horizon 2020 Open Research Data pilot. The result has been to identify a number of commonly encountered benefits and issues; to assess the utilisation of data management plans; and through the close examination of specific cases, to gain insights into obstacles to data management and potential solutions. Although primarily anecdotal and difficult to quantify, the experiences reported in this paper tend to favour developing data management best practices rather than doggedly pursue the Open Data mantra. While Open Data may prove valuable in certain circumstances, there is good reason to claim that managed access to scientific data of high inherent intellectual and financial value will prove more effective in driving knowledge discovery and innovation. Full article

Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order to evaluate existing and novel IMU-based gait analysis algorithms for treadmill walking, a reference dataset that includes IMU data as well as reliable ground truth measurements for multiple participants and walking speeds is needed. This article provides a reference dataset consisting of 15 healthy young adults who walked on a treadmill at three different speeds. Data were acquired using seven IMUs placed on the lower body, two different reference systems (Zebris FDMT-HQ and OptoGait), and two RGB cameras. Additionally, in order to validate an existing IMU-based gait analysis algorithm using the dataset, an adaptable modular data analysis pipeline was built. Our results show agreement between the pressure-sensitive Zebris and the photoelectric OptoGait system (r = 0.99), demonstrating the quality of our reference data. As a use case, the performance of an algorithm originally designed for overground walking was tested on treadmill data using the data pipeline. The accuracy of stride length and stride time estimations was comparable to that reported in other studies with overground data, indicating that the algorithm is equally applicable to treadmill data. The Python source code of the data pipeline is publicly available, and the dataset will be provided by the authors upon request, enabling future evaluations of IMU gait analysis algorithms without the need of recording new data. Full article