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From July 2019 to January 2021, six models of PM2.5 air sensors were operated at seven air quality monitoring sites across the U.S. in Arizona, Colorado, Delaware, Georgia, North Carolina, Oklahoma, and Wisconsin. Common PM sensor data issues were identified, including repeat zero measurements, false high outliers, baseline shift, varied relationships between the sensor and monitor, and relative humidity (RH) influences. While these issues are often easy to identify during colocation, they are more challenging to identify or correct during deployment since it is hard to differentiate between real pollution events and sensor malfunctions. Air sensors may exhibit wildly different performances even if they have the same or similar internal components. Commonly used RH corrections may still have variable bias by hour of the day and seasonally. Most sensors show promise in achieving the U.S. Environmental Protection Agency (EPA) performance targets, and the findings here can be used to improve their performance and reliability further. This evaluation generated a robust dataset of colocated air sensor and monitor data, and by making it publicly available along with the results presented in this paper, we hope the dataset will be an asset to the air sensor community in understanding sensor performance and validating new methods. [ABSTRACT FROM AUTHOR]

This paper introduces new, interdisciplinary perspectives for understanding and analysing what diversity data is and works to do. Combining critical data studies with research on diversity and inclusion in the UK television industry, we explore the case of sexual orientation data. We discuss the ambiguous relationship between diversity data and its use to remedy structural inequity and exclusion. We show that diversity data brings into view a narrow selection of queer workers’ lives and experiences while further marginalising others, and that it is generative and productive rather than merely representative of the world around us. In so doing we provide novel and transferable insights for scholars and practitioners to improve both our understanding of diversity data and its practical use in workplace interventions. [ABSTRACT FROM AUTHOR]

Digital twins (DT) have emerged as expected powerful tools for optimizing products, processes and services within the industry. In urban areas, DT should foster the development of innovative services to make the city even smarter. They should also respond to several key challenges such as economic development, social inclusion and adaptation to the effects of climate change. Although a significant number of papers have examined the three topics of DT, smart city and sustainability, to date, literature has paid little attention to the intersection between them. The aim of this paper is to explore the potential linkages between these three themes. To do this, a bibliometric analysis is carried out. This paper provides the main characteristics of DT for smart cities (1). It defines the concept of “sustainable smart cities” and focuses on the role and contribution of DT for urban sustainability (2). It analyzes the challenges, in terms of engineering (3) and societal (4) that DT imply for sustainability smart cities. These first insights and results are helping to fuel a debate on the place of technology in our societies and the path to be taken towards greater sustainability. [ABSTRACT FROM AUTHOR]

Data standards are available for spinal cord injury (SCI). The International SCI Data Sets were created in 2002 and there are currently 27 freely available. In 2014 the National Institute of Neurological Disorders and Stroke developed clinical common data elements to promote clinical data sharing in SCI. The objective of this paper is to provide an overview of SCI data standards, describe learnings from the traumatic brain injury (TBI) field using data to enhance research and care, and discuss future opportunities in SCI. Given the complexity of SCI, frameworks such as a systems medicine approach and Big Data perspective have been advanced. Implementation of these frameworks require multi-modal data and a shift towards open science and principles such as requiring data to be FAIR (Findable, Accessible, Interoperable and Reusable). Advanced analytics such as artificial intelligence require data to be interoperable so data can be exchanged among different technology systems and software applications. The TBI field has multiple ongoing initiatives to promote sharing and data reuse for both pre-clinical and clinical studies, which is an opportunity for the SCI field given these injuries can often occur concomitantly. The adoption of interoperable standards, data sharing, open science, and the use of advanced analytics in SCI is needed to facilitate translation in research and care. It is critical that people with lived experience are engaged to ensure data are relevant and enhances quality of life. • Data elements and standards are available for spinal cord injury (SCI). • Given the complexity of SCI, frameworks such as a systems medicine approach and Big Data perspective have been proposed. • Opportunities exist to learn from the traumatic brain injury field which has experience with pre-clinical and clinical data standards, and data sharing. • Engagement of people living with SCI is critical to ensure data collected is meaningful and improves quality of life. • The adoption of interoperable standards, data sharing, open science and the use of advanced analytics will facilitate translation in SCI research and care. [ABSTRACT FROM AUTHOR]

• A method is presented to assess certainty in fault data and interpretations. • It is based on the matrix commonly used in risk assessment. • The method justifiable interpretations and decisions about faults to be made. This paper introduces an approach for expressing certainty in the analysis and interpretation of faults, using a modification of the risk matrix commonly used in risk assessment. The certainty matrix uses qualitative or semi-quantitative analyses of both the data used and an interpreted characteristic of individual faults or fault systems. These characteristics may include the existence of faults, the certainty of trace lengths, the age of faults, or the influence of faults on sub-surface fluid flow. This approach improves the ability to make justifiable interpretations and decisions about faults and fault-affected areas, including about issues relevant to geothermal energy exploration or the underground storage of radioactive waste. The use of this approach is illustrated using three faults from Somerset, UK. [ABSTRACT FROM AUTHOR]