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Background NOAA’s Hazard Mapping System (HMS) smoke product comprises smoke plumes digitised from satellite imagery. Recent studies have used HMS as a proxy for surface smoke presence. Aims We compare HMS with airport observations, air quality station measurements and model estimates of near-surface smoke. Methods We quantify the agreement in numbers of smoke days and trends, regional discrepancies in levels of near-surface smoke fine particulate matter (PM2.5) within HMS polygons, and separation of total PM2.5 on smoke and non-smoke days across the contiguous US and Alaska from 2010 to 2021. Key results We find large overestimates in HMS-derived smoke days and trends if we include light smoke plumes in the HMS smoke day definition. Outside the western US and Alaska, near-surface smoke PM2.5 within areas of HMS smoke plumes is low and almost indistinguishable across density categories, likely indicating frequent smoke aloft. Conclusions Compared with airport, Environmental Protection Agency (EPA) and model-derived estimates, HMS most closely reflects surface smoke in the Pacific and Mountain regions and Alaska when smoke days are defined using only heavy plumes or both medium and heavy plumes. Implications We recommend careful consideration of biases in the HMS smoke product for air quality and public health assessments of fires.

The documentation of performance during functional imaging represents a standard procedure employed to control for compliance, sensorimotor, and cognitive demands. In the case of motor tasks, preciseness, force, and frequency have a significant impact on the magnitude of functional activation. Questionnaires are used in psychological investigations to control for cognitive demand, while psychophysiological documentation is employed to record bodily responses. For longitudinal intervention studies, it is of utmost importance to implement meticulous pre- and post-performance controls and balance to accurately assess changes over time. Any changes in compliance may introduce additional uncontrolled variables, which can hinder the interpretation of functional magnetic resonance imaging (fMRI)-related changes. This narrative review presents strategies for controlling and balancing performance in functional imaging approaches to document neuroplasticity in rehabilitative studies. These strategies include not only motor-related aspects, such as precision, velocity, and force, but also timing aspects, such as the start and stop of movement periods. In addition, it discusses strategies for the modulation and control of movement aspects with visual feedback, as well as for the control of physiological changes during experimental modulation. Although these measures require additional care, which is often more demanding than the neuroimaging part of the study, they are crucial for a relevant interpretation and publication of fMRI studies. [ABSTRACT FROM AUTHOR]

Renewable energy sources are increasing globally, mainly due to efforts to achieve net zero emissions. In Brazil, solar photovoltaic electricity generation has grown substantially in recent years, with the installed capacity rising from 2455 MW in 2018 to 47,033 MW in August 2024. However, the intermittency of solar energy increases the challenges of forecasting solar generation, making it more difficult for decision-makers to plan flexible and efficient distribution systems. In addition, to forecast power generation to support grid expansion, it is essential to have adequate data sources, but measured climate data in Brazil is limited and does not cover the entire country. To address this problem, this study evaluates the global horizontal irradiance (GHI) of four global reanalysis datasets—MERRA-2, ERA5, ERA5-Land, and CFSv2—at 35 locations across Brazil. The GHI time series from reanalysis was compared with ground-based measurements to assess its ability to represent hourly GHI in Brazil. Results indicate that MERRA-2 performed best in 90% of the locations studied, considering the root mean squared error. These findings will help advance solar forecasting by offering an alternative in regions with limited observational time series measurements through the use of reanalysis datasets. [ABSTRACT FROM AUTHOR]

The advancements in artificial intelligence have encouraged the application of deep learning in various fields. However, the accuracy of deep learning algorithms is influenced by the quality of the dataset used. Therefore, a high-quality dataset is critical for deep learning. Data augmentation algorithms can generate large, high-quality datasets. The dataset quality is mainly assessed through qualitative and quantitative evaluations. However, conventional qualitative evaluation methods lack the objective and quantitative parameters necessary for battery synthetic datasets. Therefore, this study proposes the application of the rate of change in linear regression correlation coefficients, Dunn index, and silhouette coefficient as clustering indices for quantitatively evaluating the quality of synthetic time-series datasets of batteries. To verify the reliability of the proposed method, we first applied the TimeGAN algorithm to an open-source battery dataset, generated a synthetic battery dataset, and then compared its similarity to the original dataset using the proposed evaluation method. The silhouette coefficient was confirmed as the most reliable index. Furthermore, the similarity of datasets increased as the silhouette index decreased from 0.1053 to 0.0073 based on the number of learning iterations. The results demonstrate that the insufficient quality of datasets used for deep learning can be overcome and supplemented. Furthermore, data similarity can be efficiently evaluated regardless of the learning environment. In conclusion, we present a new synthetic time-series dataset evaluation method that is more reliable than the conventional representative evaluation method (the training loss rate). [ABSTRACT FROM AUTHOR]

Soil moisture plays a crucial role in surface hydrological processes and land–atmosphere interactions. It can influence vegetation growth directly, serving as a significant indicator for monitoring agricultural drought. However, spatially continuous datasets of root zone soil moisture rely on model simulations, introducing numerous uncertainties associated with model parameters and input data. Currently, multiple soil moisture products derived from model simulations exist, but their representation at spatial scales remains unclear. Moreover, their abilities to express soil–atmosphere and soil–vegetation interactions within land–atmosphere coupling are not understood, leading to divergent inclinations toward drought. This study investigates the performance of five soil moisture products, European Centre for Medium-Range Weather Forecasts Reanalysis v5-Land (ERA5-Land), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and SoMo.ml, under drought conditions. The bias, correlation, and difference of standard deviation (STDD) were calculated between these products and the observations from International Soil Moisture Network stations. The causal probability of soil, meteorological, and agricultural drought was calculated using the causal-effect Peter and Clark (PC) Momentary Conditional Independence (MCI) method to evaluate the data propensity of these products. ERA5-Land and SoMo.ml gave a similar performance with the highest accuracy, which was attributed to the use of the same meteorological forcing data. The biases of soil moisture from these two products at surface, middle and deep depths against station observations are below 0.1 m3/m3, and the STDD is within 0.05 m3/m3. The accuracy of GLDAS is comparatively lower, characterized by lower correlations (below 0.2 for deeper layers) and high bias (above 0.15 and 0.2 for middle and deep layers, respectively). This discrepancy could be attributed to substantial biases in the precipitation forcing data. ERA5-Land shows higher spatial resolution and greater spatial heterogeneity, whereas MERRA-2 underperformed in this area. MERRA-2 had the strongest connection to agricultural drought, with a propensity probability of 0.477. Conversely, SoMo.ml demonstrates the strongest connection to meteorological drought, with a propensity probability of 0.234. Due to the errors in simulated and observational data during the MERRA data assimilation, substantial biases in the soil moisture data, and low accuracy in meteorological forcing of GLDAS, there was no clear causal relationship between soil moisture drought and meteorological drought between these two products. These findings provide recommendations for the use of soil moisture products in drought research.

Whereas many independent methods are used to estimate snow water equivalent (SWE) and its spatial distribution and seasonal variability, a need exists for a systematic characterization of inter-model differences at annual, seasonal, and regional scales necessary to quantify the associated uncertainty in these datasets. This study conducts a multi-scale validation and comparison, based on Airborne Snow Observatory data, of five state-of-the-art SWE datasets in the Sierra Nevada, California, including three SWE datasets from retrospective models: an INiTial REConstruction model (REC-INT), an improved REConstruction model based on the ParBal energy balance model (REC-ParBal), and a Sierra Nevada SWE REConstruction with Data Assimilation (REC-DA), and two operational SWE datasets from the U.S. National Weather Service, including the Snow Data Assimilation System (SNODAS) and the National Water Model (NWM-SWE). The results show that REC-DA and REC-ParBal provide the two most accurate estimates of SWE in the snowmelt season, both with small positive biases. REC-DA provides the most accurate spatial distribution of SWE (R2 = 0.87, MAE = 66 mm, PBIAS = 8.3%) at the pixel scale, while REC-ParBal has the least basin-wide PBIAS (R2 = 0.79, MAE = 73 mm, PBIAS = 4.1%) in the snowmelt season. Moreover, REC-DA underestimates peak SWE by −5.8%, while REC-ParBal overestimates it by 7.5%, when compared with the measured peak SWE at snow pillow stations across the Sierra Nevada. The two operational SWE products—SNODAS and NWM-SWE—are less accurate. Furthermore, the inter-model comparison reveals a certain amount of disagreement in snow water storage across time and space between SWE datasets. This study advances our understanding of regional SWE uncertainties and provides critical insights to support future applications of these SWE data products and therefore has broad implications for water resources management and hydrological process studies.

A new equation of state for argon was developed with the view of extending the range of validity of the equation of state previously proposed by Tegeler et al. and obtaining a better physical description of the experimental thermodynamic data for the whole fluid region (single-phase, metastable, and saturation states). As proposed by Tegeler et al., this equation is also based on a functional form of the residual part of the reduced Helmholtz free energy. However, in this work, the fundamental equation for Helmholtz free energy was derived from the measured quantities CV(ρ, T) and P(ρ, T). The empirical description of the isochoric heat capacity CV(ρ, T) was based on an original empirical description explicitly containing the metastable states. The thermodynamic properties (internal energy, entropy, and free energy) were then obtained by combining the integration of CV(ρ, T). The arbitrary functions introduced by the integration process were deduced from a comparison between calculated and experimental pressure P(ρ, T) data. The new formulation is valid for the whole fluid region from the melting line to 2300 K and for pressures up to 50 GPa. It also predicts the existence of a maximum of the isochoric heat capacity CV along isochors, as experimentally observed in several other fluids. For many applications, an approximate form of the equation of state for the liquid phase may be sufficient. A Tait–Tammann equation is therefore proposed between the triple-point temperature and 148 K. [ABSTRACT FROM AUTHOR]

Renewable energy sources are increasing globally, mainly due to efforts to achieve net zero emissions. In Brazil, solar photovoltaic electricity generation has grown substantially in recent years, with the installed capacity rising from 2455 MW in 2018 to 47,033 MW in August 2024. However, the intermittency of solar energy increases the challenges of forecasting solar generation, making it more difficult for decision-makers to plan flexible and efficient distribution systems. In addition, to forecast power generation to support grid expansion, it is essential to have adequate data sources, but measured climate data in Brazil is limited and does not cover the entire country. To address this problem, this study evaluates the global horizontal irradiance (GHI) of four global reanalysis datasets—MERRA-2, ERA5, ERA5-Land, and CFSv2—at 35 locations across Brazil. The GHI time series from reanalysis was compared with ground-based measurements to assess its ability to represent hourly GHI in Brazil. Results indicate that MERRA-2 performed best in 90% of the locations studied, considering the root mean squared error. These findings will help advance solar forecasting by offering an alternative in regions with limited observational time series measurements through the use of reanalysis datasets.

This paper aims to provide discernment toward establishing a general framework, dedicated to data analysis and forecasting in smart buildings. It constitutes an industrial return of experience from an industrialist specializing in IoT supported by the academic world. With the necessary improvement of energy efficiency, discernment is paramount for facility managers to optimize daily operations and prioritize renovation work in the building sector. With the scale of buildings and the complexity of Heating, Ventilation, and Air Conditioning (HVAC) systems, the use of artificial intelligence is deemed the cheapest tool, holding the highest potential, even if it requires IoT sensors and a deluge of data to establish genuine models. However, the wide variety of buildings, users, and data hinders the development of industrial solutions, as specific studies often lack relevance to analyze other buildings, possibly with different types of data monitored. The relevance of the modeling can also disappear over time, as buildings are dynamic systems evolving with their use. In this paper, we propose to study the forecasting ability of the widely used Long Short-Term Memory (LSTM) network algorithm, which is well-designed for time series modeling, across an instrumented building. In this way, we considered the consistency of the performances for several issues as we compared to the cases with no prediction, which is lacking in the literature. The insight provided let us examine the quality of AI models and the quality of data needed in forecasting tasks. Finally, we deduced that efficient models and smart choices about data allow meaningful insight into developing time series modeling frameworks for smart buildings. For reproducibility concerns, we also provide our raw data, which came from one "real" smart building, as well as significant information regarding this building. In summary, our research aims to develop a methodology for exploring, analyzing, and modeling data from the smart buildings sector. Based on our experiment on forecasting temperature sensor measurements, we found that a bigger AI model (1) does not always imply a longer time in training and (2) can have little impact on accuracy and (3) using more features is tied to data processing order. We also observed that providing more data is irrelevant without a deep understanding of the problem physics. [ABSTRACT FROM AUTHOR]

High-resolution mass spectrometry (HRMS) has become an important tool for monitoring chemical residues in food, but the time and effort required to evaluate the large amount of data generated by HRMS can be a limiting factor in the widespread application of this tool. Suspect screening, i.e., searching HRMS data against large compound databases or mass lists, represents a practical compromise between using HRMS data to only look for target compounds and performing full non-target analysis. Several different approaches for suspect screening using HRMS data were tested using data from shrimp and eel spiked with veterinary drugs and pesticides as well as from imported aquaculture samples. Most of the analytes (>70%) in the spiked samples were detected and identified by searching against compound databases. To query larger databases and on-line resources such as mzCloud, it was necessary to use software capable of differential analysis and selective filtering, such as Compound Discoverer. Using selective filtering, the number of compounds detected in fish sample extracts could be reduced from tens of thousands to a few hundred by subtracting method blanks and comparing to matrix blank extracts. This smaller list of potential compounds could be further evaluated and compared to available databases and libraries. Analysis of imported aquaculture samples resulted in detection of unexpected contaminants including the dewormer levamisole, the insecticide buprofezin, and potentially the plant alkaloid ricinine. [ABSTRACT FROM AUTHOR]

Design plays a major role in online advertising. While specific aspects such as color or animation have been extensively studied, there is a surprising lack of comprehensive research on the overall impact of visual design and aesthetics. This study delved into the effects of varying levels of visual complexity in banner ads using the eye-tracking method. Out of 108 participants browsing test webpages with specially designed banners, data from 90 adequately measured data sets were used in the study. Notably, ads with a low level of visual complexity outperformed ads with a high level of visual complexity. While users noticed complex ads slightly faster (by 0.84%), they fixated on them significantly less (by 9.09%) and looked at them less frequently (by 4.79%). An implemented survey questionnaire examining the user perception of the banners reinforced the superiority of simple ads, as they were perceived as 4.40% more appealing in comparison. The study further delved into the correlation between objectively and subjectively evaluated data, exploring the credibility of subjective methods in the process. Considering our results and findings from other studies, it was evident that visually complex ads demanded more cognitive effort, could be more distracting, negatively impacted attention, could contribute to banner blindness and were perceived as less appealing. [ABSTRACT FROM AUTHOR]

The advancements in artificial intelligence have encouraged the application of deep learning in various fields. However, the accuracy of deep learning algorithms is influenced by the quality of the dataset used. Therefore, a high-quality dataset is critical for deep learning. Data augmentation algorithms can generate large, high-quality datasets. The dataset quality is mainly assessed through qualitative and quantitative evaluations. However, conventional qualitative evaluation methods lack the objective and quantitative parameters necessary for battery synthetic datasets. Therefore, this study proposes the application of the rate of change in linear regression correlation coefficients, Dunn index, and silhouette coefficient as clustering indices for quantitatively evaluating the quality of synthetic time-series datasets of batteries. To verify the reliability of the proposed method, we first applied the TimeGAN algorithm to an open-source battery dataset, generated a synthetic battery dataset, and then compared its similarity to the original dataset using the proposed evaluation method. The silhouette coefficient was confirmed as the most reliable index. Furthermore, the similarity of datasets increased as the silhouette index decreased from 0.1053 to 0.0073 based on the number of learning iterations. The results demonstrate that the insufficient quality of datasets used for deep learning can be overcome and supplemented. Furthermore, data similarity can be efficiently evaluated regardless of the learning environment. In conclusion, we present a new synthetic time-series dataset evaluation method that is more reliable than the conventional representative evaluation method (the training loss rate).

In-depth analysis of the non-metallic inclusion population in solidified metals is essential considering the high cleanliness requirements for metallic components. For this purpose, automated feature analysis (AFA) is a powerful inclusion analysis tool, providing statistical supported information about quantity, elemental composition, morphology and size of inclusions. However, thorough data evaluation is imperative to ensure accurate and reliable results. The paper introduces the key principles of evaluating AFA data, emphasizing its capabilities and constraints by utilizing four large datasets obtained from solidified steel blocks, each containing a minimum of 4000 features. Additionally, methodologies for identifying characteristic inclusion species within a dataset, constructing suitable rulefiles for inclusion classification through statistical analyzes, and interpreting the resulting outcomes are presented. The size distribution of inclusions is analyzed using population density functions, focusing on the absolute inclusion count within specific size ranges. Cluster analysis is presented considering the inclusions‘ positional parameters.

The big data sampling method for real-time and high-speed streaming data is prone to lose the value and information of a large amount of discrete data, and it is not easy to make an efficient and accurate evaluation of the value characteristics of streaming data. The SDSLA sampling method based on mineral drilling exploration can evaluate the valuable information of streaming data containing many discrete data in real-time, but when the range of discrete data is irregular, it has low sampling accuracy for discrete data. Based on the SDSLA algorithm, we propose a dynamic drilling sampling method SDDS, which takes well as the analysis unit, dynamically changes the size and position of the well, and accurately locates the position and range of discrete data. A new model SDVEM is further proposed for data valuation, which evaluates the sample set from discrete, centralized, and overall dimensions. Experiments show that compared with the SDSLA algorithm, the sample sampled by the SDDS algorithm has higher evaluation accuracy, and the probability distribution of the sample is closer to the original streaming data, with the AOCV indicator being nearly 10% higher. In addition, the SDDS algorithm can achieve over 90% accuracy, recall, and F1 score for training and testing neural networks with small sampling rates, all of which are higher than the SDSLA algorithm. In summary, the SDDS algorithm not only accurately evaluates the value characteristics of streaming data but also facilitates the training of neural network models, which has important research significance in big data estimation. [ABSTRACT FROM AUTHOR]

A new equation of state for argon was developed with the view of extending the range of validity of the equation of state previously proposed by Tegeler et al. and obtaining a better physical description of the experimental thermodynamic data for the whole fluid region (single-phase, metastable, and saturation states). As proposed by Tegeler et al., this equation is also based on a functional form of the residual part of the reduced Helmholtz free energy. However, in this work, the fundamental equation for Helmholtz free energy was derived from the measured quantities CV(ρ, T) and P(ρ, T). The empirical description of the isochoric heat capacity CV(ρ, T) was based on an original empirical description explicitly containing the metastable states. The thermodynamic properties (internal energy, entropy, and free energy) were then obtained by combining the integration of CV(ρ, T). The arbitrary functions introduced by the integration process were deduced from a comparison between calculated and experimental pressure P(ρ, T) data. The new formulation is valid for the whole fluid region from the melting line to 2300 K and for pressures up to 50 GPa. It also predicts the existence of a maximum of the isochoric heat capacity CV along isochors, as experimentally observed in several other fluids. For many applications, an approximate form of the equation of state for the liquid phase may be sufficient. A Tait–Tammann equation is therefore proposed between the triple-point temperature and 148 K.

Smart data selection can quickly sieve valuable information from initial data. Doing so improves the efficiency of analyzing situations to aid in better decision-making. Past methods have mostly been based on expert experience, which may be subjective and inefficient when dealing with large, complex datasets. Recently, the system analysis method has been exploited to find the key data. However, few studies address the indirect effects and heterogeneity of time series data. In this study, a data selection method, the modified Decision-Making Trial and Evaluation Laboratory (DEMATEL) method based on the objective data grey relational analysis (GRA), is used to enhance the ability to analyze time-series data. GRA was first applied to assess the direct impact in the raw data indicators. Then, a modified DEMATEL was adopted to find the overall impact by including the indirect impact and data heterogeneity. We applied the method to analyze the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and perform the remaining useful life (RUL) prediction of aircraft engines. The results suggest that our method predicts well. Our work offers a nuanced approach of identifying key information in time series data and has potential applications. [ABSTRACT FROM AUTHOR]

The analysis of samples from unsequenced and/or understudied species as well as samples where the proteome is derived from multiple organisms poses two key questions. The first is whether the proteomic data obtained from an unusual sample type even contains peptide tandem mass spectra. The second question is whether an appropriate protein sequence database is available for proteomic searches. We describe the use of automated de novo sequencing for evaluating both the quality of a collection of tandem mass spectra and the suitability of a given protein sequence database for searching that data. Applications of this method include the proteome analysis of closely related species, metaproteomics, and proteomics of extinct organisms.

Amide hydrogen-deuterium exchange mass spectrometry (HDX-MS) has become widely popular for mapping protein-ligand interfaces, for understanding protein-protein interactions, and for discovering dynamic allostery. Several platforms are now available which provide large data sets of amide hydrogen/deuterium exchange mass spectrometry (HDX-MS) data. Although many of these platforms provide some down-stream processing, a comprehensive software that provides the most commonly used down-stream processing tools such as automatic back-exchange correction options, analysis of overlapping peptides, calculations of relative deuterium uptake into regions of the protein after such corrections, rigorous statistical analysis of the significance of uptake differences, and generation of high quality figures for data presentation is not yet available. Here we describe the Deuterium Exchange Correction and Analysis (DECA) software package, which provides all these downstream processing options for data from the most popular mass spectrometry platforms. The major functions of the software are demonstrated on sample data.

The importance of data assets as intangible corporate assets is being emphasized as more business activities based on digital technology are being carried out. This study proposes the development of a data valuation model that can enable companies to use data assets as collateral for loans in financial transactions. To this end, a model was designed with a focus on the cost approach, which is less likely to involve arbitrariness and error among other valuation model approaches. Furthermore, a model simulation was conducted after securing transaction data of a Korean secondhand marketplace provider. Among the total costs of this marketplace provider, the cost of using data reflecting the ratio of data activities was derived, focusing on financial statements and tangible and intangible assets for the last five years. The data asset acquisition costs were derived, and the data replacement costs were calculated by reflecting the past price and wage growth rates. The results revealed that simulation companies could use a total of KRW 26.8 billion worth of data as collateral for a loan. Accordingly, the data valuation model developed in this study will contribute to reinforcing the value of corporate data assets and proposing a new means of corporate financing. [ABSTRACT FROM AUTHOR]

Whereas many independent methods are used to estimate snow water equivalent (SWE) and its spatial distribution and seasonal variability, a need exists for a systematic characterization of inter-model differences at annual, seasonal, and regional scales necessary to quantify the associated uncertainty in these datasets. This study conducts a multi-scale validation and comparison, based on Airborne Snow Observatory data, of five state-of-the-art SWE datasets in the Sierra Nevada, California, including three SWE datasets from retrospective models: an INiTial REConstruction model (REC-INT), an improved REConstruction model based on the ParBal energy balance model (REC-ParBal), and a Sierra Nevada SWE REConstruction with Data Assimilation (REC-DA), and two operational SWE datasets from the U.S. National Weather Service, including the Snow Data Assimilation System (SNODAS) and the National Water Model (NWM-SWE). The results show that REC-DA and REC-ParBal provide the two most accurate estimates of SWE in the snowmelt season, both with small positive biases. REC-DA provides the most accurate spatial distribution of SWE (R2 = 0.87, MAE = 66 mm, PBIAS = 8.3%) at the pixel scale, while REC-ParBal has the least basin-wide PBIAS (R2 = 0.79, MAE = 73 mm, PBIAS = 4.1%) in the snowmelt season. Moreover, REC-DA underestimates peak SWE by −5.8%, while REC-ParBal overestimates it by 7.5%, when compared with the measured peak SWE at snow pillow stations across the Sierra Nevada. The two operational SWE products—SNODAS and NWM-SWE—are less accurate. Furthermore, the inter-model comparison reveals a certain amount of disagreement in snow water storage across time and space between SWE datasets. This study advances our understanding of regional SWE uncertainties and provides critical insights to support future applications of these SWE data products and therefore has broad implications for water resources management and hydrological process studies.

This paper aims to provide discernment toward establishing a general framework, dedicated to data analysis and forecasting in smart buildings. It constitutes an industrial return of experience from an industrialist specializing in IoT supported by the academic world. With the necessary improvement of energy efficiency, discernment is paramount for facility managers to optimize daily operations and prioritize renovation work in the building sector. With the scale of buildings and the complexity of Heating, Ventilation, and Air Conditioning (HVAC) systems, the use of artificial intelligence is deemed the cheapest tool, holding the highest potential, even if it requires IoT sensors and a deluge of data to establish genuine models. However, the wide variety of buildings, users, and data hinders the development of industrial solutions, as specific studies often lack relevance to analyze other buildings, possibly with different types of data monitored. The relevance of the modeling can also disappear over time, as buildings are dynamic systems evolving with their use. In this paper, we propose to study the forecasting ability of the widely used Long Short-Term Memory (LSTM) network algorithm, which is well-designed for time series modeling, across an instrumented building. In this way, we considered the consistency of the performances for several issues as we compared to the cases with no prediction, which is lacking in the literature. The insight provided let us examine the quality of AI models and the quality of data needed in forecasting tasks. Finally, we deduced that efficient models and smart choices about data allow meaningful insight into developing time series modeling frameworks for smart buildings. For reproducibility concerns, we also provide our raw data, which came from one “real” smart building, as well as significant information regarding this building. In summary, our research aims to develop a methodology for exploring, analyzing, and modeling data from the smart buildings sector. Based on our experiment on forecasting temperature sensor measurements, we found that a bigger AI model (1) does not always imply a longer time in training and (2) can have little impact on accuracy and (3) using more features is tied to data processing order. We also observed that providing more data is irrelevant without a deep understanding of the problem physics.

Gene transcription is largely regulated by cis-regulatory elements. Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) is an emerging technology that can accurately map cis-regulatory elements in animals and plants. However, the presence of cell walls and chloroplasts in plants hinders the extraction of high-quality nuclei, thereby affects the quality of ATAC-seq data. Meanwhile, it is tricky to perform ATAC-seq with different tissue types, especially for those with limited size and amount. Moreover, with rapid growth of ATAC-seq datasets from plants, powerful and easy-to-use data analysis pipelines for ATAC-seq, especially for wheat is lacking. Here, we provided an all-in-one solution for mapping open chromatin in wheat including both experimental and data analysis procedure. We efficiently obtained nuclei with less cell debris from various wheat tissues. High-quality ATAC-seq data from young spike and ovary, which are hard to harvest were generated. We determined that the saturation sequencing depth of wheat ATAC-seq is about 16 Gb. Particularly, we developed a powerful and easy-touse online pipeline to analyze the wheat ATAC-seq data and this pipeline can be easily extended to other plant species. The method developed here will facilitate plant regulatory genome study not only for wheat but also for other plant species. [ABSTRACT FROM AUTHOR]

Fengyun-3E, which is equipped with MicroWave Humidity Sounder 2 (MWHS-2) for detecting both temperature and humidity, is China's latest polar orbiting meteorological satellite and China's first satellite in early-morning orbit. The observation bias and observation error characteristics of MWHS-2 are evaluated by using ERA-5 as the background field. The results show that the biases range from −4 to 2 K, and the observation errors are within 1.5 K except for the window channels. A further analysis of the dependence on the scanning angles indicates that observation errors for the window channels and the 118-GHz channels increase with decreasing scanning angles. Observation errors of the window channels and water vapor channels are also latitudinally dependent, and the maximum errors in the high latitudes reach 2.0 K, while those in the tropical regions are approximately 0.8 K. In addition, the observed brightness temperature of FY-3E MWHS-2 is accompanied by striping noises along the track and high-frequency oscillation noises along the scanlines. The noise mitigation results show that both noises are approximately 0.2 K. [ABSTRACT FROM AUTHOR]

Modern machine and deep learning methods require large datasets to achieve reliable and robust results. This requirement is often difficult to meet in the medical field, due to data sharing limitations imposed by privacy regulations or the presence of a small number of patients (e.g., rare diseases). To address this data scarcity and to improve the situation, novel generative models such as Generative Adversarial Networks (GANs) have been widely used to generate synthetic data that mimic real data by representing features that reflect health-related information without reference to real patients. In this paper, we consider several GAN models to generate synthetic data used for training binary (malignant/benign) classifiers, and compare their performances in terms of classification accuracy with cases where only real data are considered. We aim to investigate how synthetic data can improve classification accuracy, especially when a small amount of data is available. To this end, we have developed and implemented an evaluation framework where binary classifiers are trained on extended datasets containing both real and synthetic data. The results show improved accuracy for classifiers trained with generated data from more advanced GAN models, even when limited amounts of original data are available. [ABSTRACT FROM AUTHOR]

The wear of tension members in timing belts results in a change in the pre-tensioning force. Therefore, a sensing timing belt was developed to measure the pre-tensioning force. For this purpose, a sensor system is integrated into a tooth, with which the acceleration is measured and sent to an external receiver via Bluetooth. In this publication, the detectability of the natural frequencies under different operating conditions is investigated. The acquired data is separated into span specific segments, as each trum segment has different natural frequencies. This paper will analyse occurring disturbance factors and evaluate the detectability of the natural frequencies. [ABSTRACT FROM AUTHOR]

Soil moisture plays a crucial role in surface hydrological processes and land–atmosphere interactions. It can influence vegetation growth directly, serving as a significant indicator for monitoring agricultural drought. However, spatially continuous datasets of root zone soil moisture rely on model simulations, introducing numerous uncertainties associated with model parameters and input data. Currently, multiple soil moisture products derived from model simulations exist, but their representation at spatial scales remains unclear. Moreover, their abilities to express soil–atmosphere and soil–vegetation interactions within land–atmosphere coupling are not understood, leading to divergent inclinations toward drought. This study investigates the performance of five soil moisture products, European Centre for Medium-Range Weather Forecasts Reanalysis v5-Land (ERA5-Land), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and SoMo.ml, under drought conditions. The bias, correlation, and difference of standard deviation (STDD) were calculated between these products and the observations from International Soil Moisture Network stations. The causal probability of soil, meteorological, and agricultural drought was calculated using the causal-effect Peter and Clark (PC) Momentary Conditional Independence (MCI) method to evaluate the data propensity of these products. ERA5-Land and SoMo.ml gave a similar performance with the highest accuracy, which was attributed to the use of the same meteorological forcing data. The biases of soil moisture from these two products at surface, middle and deep depths against station observations are below 0.1 m3/m3, and the STDD is within 0.05 m3/m3. The accuracy of GLDAS is comparatively lower, characterized by lower correlations (below 0.2 for deeper layers) and high bias (above 0.15 and 0.2 for middle and deep layers, respectively). This discrepancy could be attributed to substantial biases in the precipitation forcing data. ERA5-Land shows higher spatial resolution and greater spatial heterogeneity, whereas MERRA-2 underperformed in this area. MERRA-2 had the strongest connection to agricultural drought, with a propensity probability of 0.477. Conversely, SoMo.ml demonstrates the strongest connection to meteorological drought, with a propensity probability of 0.234. Due to the errors in simulated and observational data during the MERRA data assimilation, substantial biases in the soil moisture data, and low accuracy in meteorological forcing of GLDAS, there was no clear causal relationship between soil moisture drought and meteorological drought between these two products. These findings provide recommendations for the use of soil moisture products in drought research. • ERA5-Land and SoMo.ml soil moisture demonstrate superior performance in drought. • GLDAS showed less spatial heterogeneity and almost no long-term trend. • MERRA2 and GLDAS do not show a causal relationship to atmospheric drought. • The discrepancy could be attributed to substantial biases in the forcing data. [ABSTRACT FROM AUTHOR]

Smart data selection can quickly sieve valuable information from initial data. Doing so improves the efficiency of analyzing situations to aid in better decision-making. Past methods have mostly been based on expert experience, which may be subjective and inefficient when dealing with large, complex datasets. Recently, the system analysis method has been exploited to find the key data. However, few studies address the indirect effects and heterogeneity of time series data. In this study, a data selection method, the modified Decision-Making Trial and Evaluation Laboratory (DEMATEL) method based on the objective data grey relational analysis (GRA), is used to enhance the ability to analyze time-series data. GRA was first applied to assess the direct impact in the raw data indicators. Then, a modified DEMATEL was adopted to find the overall impact by including the indirect impact and data heterogeneity. We applied the method to analyze the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and perform the remaining useful life (RUL) prediction of aircraft engines. The results suggest that our method predicts well. Our work offers a nuanced approach of identifying key information in time series data and has potential applications.

The validation of the measurements quality after on-site diagnostic system installation is necessary in order to provide reliable data and output results. This topic is often neglected or not discussed in detail regarding measurement systems. In the paper recently installed system for soft X-ray measurements is described in introduction. The system is based on multichannel GEM detector and the data is collected and sent in special format to PC unit for further postprocessing. The unique feature of the system is the ability to compute final data based on raw data only. The raw data is selected upon algorithms by FPGA units. The FPGAs are connected to the analog frontend of the system and able to register all of the signals and collect the useful data. The interface used for data streaming is PCIe Gen2 x4 for each FPGA, therefore high throughput of the system is ensured. The paper then discusses the properties of the installation environment of the system and basic functionality mode. New features are described, both in theoretical and practical approach. New modes correspond to the data quality monitoring features implemented for the system, that provide extra information to the postprocessing stage and final algorithms. In the article is described also additional mode to perform hardware simulation of signals in a tokamak-like environment using FPGAs. The summary describes the implemented features of the data quality monitoring features and additional modes of the system.

This study interrelates three adjacent topics in data evaluation. The first is the establishment of a relationship between Bregman divergence and probabilistic alpha-divergence. In particular, we demonstrate that square-root-order probability normalization enables the unification of these two divergence families. This yields a new alpha-divergence, which can be used to jointly derive the alpha-EM algorithm (alpha-expectation-maximization algorithm) and the traditional log-EM algorithm. The second topic is the application of the alpha-EM algorithm in the evaluation of graders scoring raw data over a network. We estimate multinomial mixture distributions in this evaluation problem. We note that the convergence speed of the alpha-EM algorithm is significantly higher than that of the log-EM algorithm. Finally, the third topic is the use of this increase in convergence speed to assign the winning evaluator and miner in a blockchain environment. This is achieved by proof-of-review using evaluation scores, which is a class of proof-of-stake. In the second and third topics, we select terminology from wine tasting for brevity in the exposition. However, this formulation can be applied to a broader class of data in a network environment comprising blockchains.

Abstract In this study, a preliminary quantitative evaluation of global precipitation measurement (GPM) mission precipitation products at sub‐daily time scales was performed in a typical and small watershed with complex terrain in southwestern China. The world heritage Jiuzhaigou Valley, which has been affected by many rainfall‐triggered debris‐flow disasters after the Ms 7.0 earthquake on 8 August 2017, was selected as the study area. The “early”, “late” and “final” half‐hourly GPM precipitations and hourly observations by four rainfall stations were used, and various indicators (general statistical indices, classification indicators, and a joint probability distribution function) were adopted as metrics to evaluate the GPM rainfall. Results indicated that the early and late hourly products showed a medium degree of correlation with ground observations, while the final hourly product showed better performances and may be used for research‐level applications in the study area. GPM products could approximately reproduce the diurnal cycles of observed precipitation whereas the relationship between peak rainfall and duration spell was in good agreement with ground observations. The 1‐hour final product was unable to significantly reduce the rate of missing report for rain although it was calibrated by global monthly data. An event‐based evaluation showed that the GPM performed well for the cumulative rainfall. The assessment results support the development of early‐warning models for rainfall‐triggered geological disasters, provide useful implications on the improvement of rainfall retrieval algorithm in complex mountainous areas, and may be used as reference for scientific studies in poorly sampled regions with similar geographical conditions as the Jiuzhaigou Valley.

An actual task is to establish specific requirements for interlaboratory comparisons (ILC) and, on their basis, to select a criterion for assessing consistency for calibration laboratories (CLs). The paper contains a detailed analysis of the results of the two ILCs for CLs. Particular attention is devoted to consistency analysis of the data obtained from the laboratories. Recommendations for improving the technical competence of CLs with their participation in the ILC are presented. To declare measurement uncertainty, a laboratory must take into account all the main components to this uncertainty.

Fengyun-3E, which is equipped with MicroWave Humidity Sounder 2 (MWHS-2) for detecting both temperature and humidity, is China’s latest polar orbiting meteorological satellite and China’s first satellite in early-morning orbit. The observation bias and observation error characteristics of MWHS-2 are evaluated by using ERA-5 as the background field. The results show that the biases range from −4 to 2 K, and the observation errors are within 1.5 K except for the window channels. A further analysis of the dependence on the scanning angles indicates that observation errors for the window channels and the 118-GHz channels increase with decreasing scanning angles. Observation errors of the window channels and water vapor channels are also latitudinally dependent, and the maximum errors in the high latitudes reach 2.0 K, while those in the tropical regions are approximately 0.8 K. In addition, the observed brightness temperature of FY-3E MWHS-2 is accompanied by striping noises along the track and high-frequency oscillation noises along the scanlines. The noise mitigation results show that both noises are approximately 0.2 K.

Modern machine and deep learning methods require large datasets to achieve reliable and robust results. This requirement is often difficult to meet in the medical field, due to data sharing limitations imposed by privacy regulations or the presence of a small number of patients (e.g., rare diseases). To address this data scarcity and to improve the situation, novel generative models such as Generative Adversarial Networks (GANs) have been widely used to generate synthetic data that mimic real data by representing features that reflect health-related information without reference to real patients. In this paper, we consider several GAN models to generate synthetic data used for training binary (malignant/benign) classifiers, and compare their performances in terms of classification accuracy with cases where only real data are considered. We aim to investigate how synthetic data can improve classification accuracy, especially when a small amount of data is available. To this end, we have developed and implemented an evaluation framework where binary classifiers are trained on extended datasets containing both real and synthetic data. The results show improved accuracy for classifiers trained with generated data from more advanced GAN models, even when limited amounts of original data are available.

The validation of the measurements quality after on-site diagnostic system installation is necessary in order to provide reliable data and output results. This topic is often neglected or not discussed in detail regarding measurement systems. In the paper recently installed system for soft X-ray measurements is described in introduction. The system is based on multichannel GEM detector and the data is collected and sent in special format to PC unit for further postprocessing. The unique feature of the system is the ability to compute final data based on raw data only. The raw data is selected upon algorithms by FPGA units. The FPGAs are connected to the analog frontend of the system and able to register all of the signals and collect the useful data. The interface used for data streaming is PCIe Gen2 x4 for each FPGA, therefore high throughput of the system is ensured. The paper then discusses the properties of the installation environment of the system and basic functionality mode. New features are described, both in theoretical and practical approach. New modes correspond to the data quality monitoring features implemented for the system, that provide extra information to the postprocessing stage and final algorithms. In the article is described also additional mode to perform hardware simulation of signals in a tokamak-like environment using FPGAs. The summary describes the implemented features of the data quality monitoring features and additional modes of the system. [ABSTRACT FROM AUTHOR]

Based on the publications regarding new or recent measurement systems for the tokamak plasma experiments, it can be found that the monitoring and quality validation of input signals for the computation stage is done in different, often simple, ways. In the paper is described the unique approach to implement the novel evaluation and data quality monitoring (EDQM) model for use in various measurement systems. The adaptation of the model is made for the GEM-based soft X-ray measurement system FPGA-based. The EDQM elements has been connected to the base firmware using PCI-E DMA real-time data streaming with minimal modification. As additional storage, on-board DDR3 memory has been used. Description of implemented elements is provided, along with designed data processing tools and advanced simulation environment based on Questa software.

In this study, a preliminary quantitative evaluation of global precipitation measurement (GPM) mission precipitation products at sub‐daily time scales was performed in a typical and small watershed with complex terrain in southwestern China. The world heritage Jiuzhaigou Valley, which has been affected by many rainfall‐triggered debris‐flow disasters after the Ms 7.0 earthquake on 8 August 2017, was selected as the study area. The "early", "late" and "final" half‐hourly GPM precipitations and hourly observations by four rainfall stations were used, and various indicators (general statistical indices, classification indicators, and a joint probability distribution function) were adopted as metrics to evaluate the GPM rainfall. Results indicated that the early and late hourly products showed a medium degree of correlation with ground observations, while the final hourly product showed better performances and may be used for research‐level applications in the study area. GPM products could approximately reproduce the diurnal cycles of observed precipitation whereas the relationship between peak rainfall and duration spell was in good agreement with ground observations. The 1‐hour final product was unable to significantly reduce the rate of missing report for rain although it was calibrated by global monthly data. An event‐based evaluation showed that the GPM performed well for the cumulative rainfall. The assessment results support the development of early‐warning models for rainfall‐triggered geological disasters, provide useful implications on the improvement of rainfall retrieval algorithm in complex mountainous areas, and may be used as reference for scientific studies in poorly sampled regions with similar geographical conditions as the Jiuzhaigou Valley. Key Points: A comprehensive evaluation of hourly GPM precipitation over a high‐profile watershed with complex terrain was carried outA joint probability distribution function was used to evaluate the performance of GPM rainfall dataGPM precipitation at sub‐daily time scales showed a good potential for fine‐scale studies in complex terrain [ABSTRACT FROM AUTHOR]

The analysis of temporal series—in particular, analysis of multisensor data—is a complex problem. It depends on the application domain, the way the data have to be used, and sensors available, among other factors. Various models, algorithms, and technologies have been designed for this goal. Temporal logics are used to describe temporal properties of systems. The properties may specify the occurrence and the order of events in time, recurring patterns, complex behaviors, and processes. In this paper, a new interval logic, called duration calculus for functions (DC4F), is proposed for the specification of temporal series corresponding to multisensor data. DC4F is a natural extension of the well-known duration calculus, an interval temporal logic for the specification of process duration. The adequacy of the proposed logic is analyzed in the case of multisensor data concerning volcanic eruption monitoring. It turns out that the relevant behavior concerns time intervals, not only accumulated history as it is described in other kinds of temporal logics. The examples analyzed demonstrate that a description language is required to specify time series of various kind relative to time intervals. The duration calculus cannot be successfully applied for this task. The proposed calculus allows one to specify temporal series and complex interval-dependent behaviors, and to evaluate the corresponding data within a unifying logical framework. It allows to formulate hypotheses concerning volcano eruption phenomena. However, the expressivity of DC4F comes at the cost of its decidability.

The supply chain financial risk management model is established under the Internet financial model, so as to improve the supply chain's ability and resist risks, a financial risk management model of Internet supply chain is proposed based on data science. Firstly, the forms and countermeasures of financial risk in Internet supply chain is analyzed. The data science analysis method is used to analyze the risk management model, and the supply chain financial risk management model is constructed based on the multivariate piecewise regression analysis and the financing decision effectiveness game. The relationship between supply chain financial risk management and enterprise performance is studied under the Internet finance model, the validity of the financing decision of Internet supply chain is analyzed, and it constructs the decision model of Internet supply chain by descriptive statistical analysis method. The fuzzy decision method is used to analyze the risk assessment of Internet supply chain, and the Simunic model is used to analyze the correlation model of risk management in Internet supply chain. In order to improve the risk management ability of Internet supply chain, the statistical analysis and comprehensive decision of risk control are carried out by using piecewise sample regression analysis. The results of empirical data analysis show that the model has good piecewise fit for financial risk management and management of supply chain under Internet financial model, and the accuracy of data evaluation is high. The model is robust for financial risk management and performance evaluation of supply chain. [ABSTRACT FROM AUTHOR]

Continuous glucose monitoring (CGM) technology represents a valuable tool for diabetic patients to control and regulate their blood glucose (BG) levels and to reduce adverse metabolic states, for example, by defining glucose alarm thresholds that alert users if the glucose value crosses to an undesired range. Improvement of CGM technology is ongoing, but there are barriers which confine the usefulness of CGM systems. The utility is mainly defined by the operability of the specific device and also by the provided benefit of available CGM software solutions. In order to take best advantage of diabetes therapy, users should be adequately educated in how to use their CGM system and how to interpret the collected data. Different CGM software applications provide partially different CGM reports and statistics. The standardization of this information also would be conducive to the best possible diabetes management. [ABSTRACT FROM AUTHOR]

Within due diligence concepts for raw material supply chains, the traceability of a shipment is a major aspect that has to be taken into account. Cassiterite and coltan are two so-called conflict minerals for which traceability systems have been established. To provide additional credibility to document-based traceability systems the German Federal Institute for Geosciences and Natural Resources (BGR) has developed the analytical fingerprint (AFP) for the minerals coltan, cassiterite, and wolframite. AFP is based on the analysis of a sample from a shipment with a declared origin and evaluates whether the declared origin is plausible or not. This is done by comparison to reference samples previously taken at the declared mine site. In addition to the generation of the analytical data, the data evaluation step, with the aim to state whether the declared origin is plausible or not, is of special importance. Two data evaluation approaches named “Kolmogorov–Smirnov distance (KS-D) approach” and “areas ratio approach” are applied to coltan and cassiterite and result in very low rates of false negative results, which is desired for AFP. The areas ratio approach based on hypothesis testing and a more sophisticated evaluation of the multivariate data structure has some advantages in terms of producing lower rates of false positive results compared to the KS-D approach.

High-quality 3D reconstruction results are very important in many application fields. However, current texture generation methods based on point sampling and fusion often produce blur. To solve this problem, we propose a new volumetric fusion strategy which can be embedded in the current online and offline reconstruction framework as a basic module to achieve excellent geometry and texture effects. The improvement comes from two aspects. Firstly, we establish an adaptive weight field to evaluate and adjust the reliability of data from RGB-D images by using a probabilistic and heuristic method. By using this adaptive weight field to guide the voxel fusion process, we can effectively preserve the local texture structure of the mesh, avoid wrong texture problems and suppress the influence of outlier noise on the geometric surface. Secondly, we use a new texture fusion strategy that combines replacement, integration, and fixedness operations to fuse and update voxel texture to reduce blur. Experimental results demonstrate that compared with the classical KinectFusion, our approach can significantly improve the accuracy in geometry and texture clarity, and can achieve equivalent texture reconstruction effects in real-time as the offline reconstruction methods such as intrinsic3d, even better in relief scenes.

In the context of atomic data computations for astrophysical applications, we review four different types of databases we have implemented for data dissemination: a database for nebular modeling; TIPTOPbase; OPserver; and AtomPy. The database for nebular plasmas is briefly discussed as a study case of a successful project. TOPbase and the OPserver were developed during the Opacity Project, an international consortium concerned with the revision of astrophysical opacities, while TIPbase was part of the Iron Project to calculate radiative transition probabilities and electron impact excitation collision strengths for iron-group ions. AtomPy is a prototype for an open, distributed data-assessment environment to engage both producers and users. We discuss design strategies and implementation issues that may help in the undertaking of present and future scientific database projects.

Most of the targeted discoveries in tuberculosis research have covered previously explored chemical structures but neglected physiochemical properties. Until now, no efficient prediction tools have been developed to discriminate the novelty of screened compounds at early stages. To overcome this deficit, a drastic novel approach must include physicochemical properties filters provided by Chemical Global Positioning System-Natural Product analysis (ChemGPS-NP). Three different screening schemes GSK, GVKBio, and NIAID provided 776, 2880, and 3779 compounds respectively and were evaluated based on their physicochemical properties and thereby proposed as deduction examples. Charting the physiochemical property spaces of these sets identified the merits and demerits of each screening scheme by simply observing the distribution over the chemical property space. We found that GSK screening set was confined to a certain space, losing potentially active compounds when compared with an in-house constructed 459 highly active compounds (active set), while the GVKBio and NIAID screening schemes were evenly distributed through space. The latter two sets had the advantage, as they have covered a larger space and presented compounds with additional variety of properties and activities. The in-house active set was cross-validated with MycPermCheck and SmartsFilter to be able to identify priority compounds. The model demonstrated undiscovered spaces when matched with Maybridge drug-like space, providing further potential targets. These undiscovered spaces should be considered in any future investigations. We have included the most active compounds along with permeability and toxicity filters as supplemented material.

Despite the fact that tropospheric temperature inversions are thought to be an important feature of climate as well as a significant factor affecting air quality, low-level cloud formation, and the radiation budget of the Earth, a quantitative assessment of their representation in atmospheric reanalyses is yet missing. Here, we provide new evidence of the occurrence of low-tropospheric temperature inversions and associated uncertainties in their parameters existing among reanalyses produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) and upper-air soundings for Europe covering the period 2001–2010. The reanalyses utilized here include (1) surface-input reanalyses represented by ERA-20C and CERA-20C as well as (2) full-input reanalyses represented by ERA-Interim and ERA5. The upper-air soundings were derived from the Integrated Global Radiosonde Archive (IGRA), version 2. The data consists mainly of air temperature and geopotential height from the model levels (ModLev) and pressure levels (PresLev) of ECMWF reanalyses. The results show that the frequency of surface-based inversions (SBI) and elevated inversions (EI) is largely in agreement among the reanalyses. The quality of their representation depends, however, on the inversion type, season, and region. Over the vast majority of IGRA upper-air stations, SBI frequency is overestimated and EI frequency is underestimated by ECMWF reanalyses. Substantially larger uncertainties arise from the selection between the data of ModLev and PresLev of the reanalyses—the differences in the frequency of the temperature inversions are particularly large for summertime SBI suggesting that PresLev are not capable of resolving the main features of shallow and weak SBI.

In this article, I explore the nature of secondary analysis and provide a brief history of the method. Qualitative secondary analysis is a relatively under-used method in education and the social sciences, often due to the lack of easily accessible, relevant, trustworthy, and complete data. I address some of the potentials and limitations that influence its use and explore criteria for assessing the quality and sufficiency of preexisting qualitative research data. Qualitative secondary analysis has important implications for qualitative researchers, students and practitioners interested in generating new knowledge via unobtrusive, reliable, valid, and time/cost effective research through the wider use of existing qualitative data.