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Data Management Plans (DMPs) are vital components of effective research data management (RDM). They serve not only as organisational tools but also as a structured framework dictating the collection, processing, sharing/publishing, and management of data throughout the research data life cycle. This can include existing data curation standards, the establishment of data handling protocols, and the creation, when necessary, of community curation policies. Therefore, DMPs present a unique opportunity to harmonise project management efforts for optimising the formulation and execution of project objectives. To harness the full potential of DMPs as project management tools, the SMART approach (i.e., Specific, Measurable, Achievable, Relevant, and Time-bound) emerges as a compelling methodology. During the initial stage of the project proposal, drafted SMART metrics can offer a systematic approach to map work packages (WPs) and deliverables to the overarching project objectives. Then, the Principal Investigators (PIs) can ensure the consortia that all the project potential intellectual assets (i.e., expected research results) were considered properly, as well as their necessary timelines, resources, and execution. It becomes imperative for data stewards (DSs) and governance policymakers to educate and provide guidelines to researchers on the advantages of developing well-curated DMPs that align results with SMART metrics. This alignment ensures that every intellectual asset intended as a research result (e.g., intellectual properties, publications, datasets, and software) within the project is subject to rigorous drafted planning, execution, and accountability. Consequently, the risk of unforeseen setbacks and/or deviations from the original objectives is minimised, increasing the traceability and transparency of the research data life cycle. In addition, the integration of Technology Readiness Levels (TRLs) into this proposed enhanced DMP provides a systematic method to evaluate the maturity and readiness of technologies across scientific disciplines. Regular TRL assessments will allow PIs: (1) to monitor the WP progress, (2) to adapt research strategies if required, and (3) to ensure the projects remain in line with the drafted SMART metrics in the enhanced DMP before the project started. The TRLs can also help PIs maintain their focus on project milestones and specific tasks aligned with the original objectives, contributing to the overall success of their endeavours, while improving the transparency for the reporting and divulgation of the research results. The paper presents the overall framework for enhancing DMPs as project management tools for any intellectual assets using SMART metrics and TRLs, as well as introducing suggested support services for data stewardship teams to assist PIs when implementing this novel framework effectively.

Achieving the highest levels of compliance with the FAIR (findable, accessible, interoperable, reusable) principles for scientific data management and stewardship requires machine-actionable semantic representations of data and metadata. Human and machine interpretation and reuse of measurement datasets rely on metrological information that is often specified inconsistently or cannot be inferred automatically, while several ontologies to capture the metrological information are available, practical implementation examples are few. This work aims to close this gap by discussing how standardised measurement data and metadata could be presented using semantic web technologies. The examples provided in this paper are machine-actionable descriptions of Earth observation and bathymetry measurement datasets, based on two ontologies of quantities and units of measurement selected for their prominence in the semantic web. The selected ontologies demonstrated a good coverage of the concepts related to quantities, dimensions, and individual units as well as systems of units, but showed variations and gaps in the coverage, completeness and traceability of other metrology concept representations such as standard uncertainty, expanded uncertainty, combined uncertainty, coverage factor, probability distribution, etc. These results highlight the need for both (I) user-friendly tools for semantic representations of measurement datasets and (II) the establishment of good practices within each scientific community. Further work will consequently investigate how to support ontology modelling for measurement uncertainty and associated concepts.

There has been a wide interest in high-resolution air quality monitoring with low-cost gas sensor systems in the last years. Such gas sensors, however, suffer from cross-sensitivities, interferences with environmental factors, unit-to-unit variability, aging, and concept drift. Therefore, reliability and trustworthiness of the measurements in the low parts-per-billion (ppb) range remain a concern, particularly over the course of the lifetime of a sensor network in urban environments. In this simulation study, the possibility to continuously recalibrate a wireless sensor network with mobile references and stochastic gradients, computed from encounters, is explored. By using data collected in field experiments, encounters between static and mobile nodes are modeled as a probabilistic process. Moreover, the influence of a collection of design parameters such as base calibration, initial recalibration, choice of optimization algorithm, as well as encounter frequency are analyzed and discussed. With an optimized protocol, it can be shown that long-term reliable measurements with absolute errors of about 50 ppb for CO, 3 ppb for NO2, and 4 ppb for O3 could be achievable with a few mobile references in urban environments.

The ever-increasing penetration of renewable energy sources and distributed generation needs developing and deploying more sophisticated and precise control techniques. The paradigm shift from high-rotational inertia towards inverter-connected facilities has made modern power systems subject to strongly non-stationary operating conditions. In this context, phasor measurement units (PMUs) represent a promising solution due to their accuracy and time synchronization. The current paper further develops the concepts recently published proposing performance assessment of PMUs relying on metrics specifically defined to quantify the estimation accuracy, reporting latency, and response time. Our focus is on determining the confidence interval associated with these metrics and thus derive a robust approach for their application in measurement-based network controlling efforts. The configuration of the used simulation model is detailed, and the state estimation results as a function of the selected measurement weighing criteria are presented, concluding as a possible application of the reliability metrics in Weighted Least Squares and Discrete Kalman Filter state estimators, highlighting the performance enhancement as well as the theoretical limits of the proposed approach.

Due to the impact of air quality on health, the use of low-cost gas sensor systems in air quality monitoring has increased. The deficiencies of low-cost gas sensors such as cross-sensitivities, interferences with environmental factors, and unit-to-unit variability have led to multidimensional calibration approaches with machine learning applied to field data. However, the trustworthiness of measurement results remains a concern, since sensor aging and concept drift are common problems. While most machine learning algorithms do not disclose the reliability and trustworthiness of measurement results, Bayesian models offer integrated sanity checking of predictions. Motivated by recent advancements in variational inference, this publication evaluates the potential of variational Bayesian linear regression and variational Bayesian neural networks for the calibration of low-cost gas sensors and sensor systems using laboratory as well as published field data. With the performed analysis, it can be shown that raw sensor measurements outside confidence regions, implicitly defined by the calibration input data, are detectable. In such situations, the uncertainty of the posterior predictive distribution increases, suggesting less trustworthy measurement results and necessity for maintenance.

Application of condition monitoring and anomaly detection have recently become more popular in Metrology. We implemented a condition monitoring system in an existing pressure balance setup using historical data containing anomalies. By simulating leaks, we tested the system with the most probable sources of anomalies. We found that these sources could not cause the anomalies in the historical data. Our measurement of anomaly causes allowed building confidence in the calibration results, further improving the characterization of the setup. Using this knowledge together with further condition monitoring, future work will focus on developing an adaptive control system for the setup.

This position paper opens the discussion about the term digital metrology, the underlying digital trends and the activities related to the digitalization of metrology. Firstly, we present a clarification of terms between digitization and digitalization and the current digitalization strategies of two representative national metrology institutes, members of EURAMET. Subsequently, we describe the current METAS strategy towards digital metrology by its three main pillars and ongoing research and development efforts. Finally, we discuss the state of the digitalization of metrology, as well as presenting our suggestions for the next steps towards the digital transformation of metrology.

Modern power systems rely on distributed sensor networks for many condition monitoring applications. In this context, phasor measurements are typically acquired at each sensitive network node and then aggregated at local or central level in order to estimate the system state or to take control actions as the opening of a circuit breaker. From the metrological point of view, the existing standards focus on the performance compliance of a single sensor, but they do not assess their actual interoperability. In this paper, we address the problem by proposing the inclusion in the transmitted data packet of simple performance metrics to be computed on-line. The significance and consistence of the proposed metrics are assessed in numerically simulated scenarios, inspired by real-world operating conditions. Finally, we discuss potential implementations for the described metrics as standard amendments, adding new features towards smarter grids.

The digitalization of metrology poses a challenge to all members of the metrology community. We propose an approach for digital calibration certificates (DCCs) based on a PDF/A-3 solution that could be a stepping-stone towards the digitalization of metrological services. We present multiple applications of this approach by fulfilling discussed minimum requirements and satisfying needs from both customers and laboratories.

Sensors can suffer from aging drift over their lifetime, thus showing changes in sensitivity and baseline, which then leads to unreliable measurement results and affects digital trust. By performing Monte Carlo simulations, this work evaluated the opportunity to use gradient descent, in combination with reliable mobile nodes as references, for the recalibration of wireless sensor networks in the context of smart cities. In the implemented simulations, sensor aging was treated as a deterministic process while encounters between sensors and mobile references occurred in a stochastic manner. Furthermore, by exchange of data during several encounters between sensors and mobile references, a recalibration data set was constructed. In addition, encounters were weighted according to their recency – more important when being more recent. The results presented here are promising, showing that even few recent encounters might be sufficient for recalibration in order to provide once again trustworthy measurement results.

Aims: The aim was to analyse the psychiatric consultations in nine Italian hospital emergency departments, by comparing the lockdown and post-lockdown periods of 2020 with the equivalent periods of 2019.Methods: Characteristics of psychiatric consultations, patients, and drug prescriptions were analyzed. Joinpoint models were used to identify changes in the weekly trend of consultations.Results: A 37.5% decrease in the number of consultations was seen during the lockdown period and 17.9% after the lockdown. The number of individual patients seen decreased by 34.9% during the lockdown and 11.2% after the lockdown. A significant change in the number of consultations from week 11 to week 18 occurred, followed by a gradual increase. There was a higher percentage of patients with previous psychiatric hospitalizations during the lockdown period (61.1 vs. 56.3%) and a lower percentage after the lockdown (59.7 vs. 64.7%). During the lockdown there was a large increase in psychiatric consultations for substance use disorders, whereas more consultations for manic episodes occurred after the lockdown. A 3.4% decrease was observed in consultations for suicidal ideation and planning during the lockdown, followed by an upward rebound after the lockdown, along with an increase in consultations for suicide attempts. During lockdown antipsychotic and benzodiazepine prescriptions increased by 5.2 and 4.1%, respectively. After the lockdown, the number of compulsory hospitalizations was higher than in 2019.Conclusions: We observed a decrease of psychiatric consultations during and after the lockdown. There was an increase in consultations for manic episodes and suicidality after the lockdown. The focus of psychiatric services must remain high particularly in this latter period.