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Volume 7
Issue 3
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Data, Volume 7, Issue 3 (March 2022) – 12 articles

Cover Story (view full-size image): The thermodynamic properties of a substance are key to predicting its behavior in physical and chemical systems. We developed a database of 2869 experimental values of enthalpy of formation and 1403 values for entropy for substances composed of stable small molecules, derived from the literature. We used this to build a model to predict the entropy and enthalpy of any small molecule in the gas phase and applied that model to a comprehensive database of 16,417 small molecules. The database will be useful for predicting the outcome of any process that might involve the generation or destruction of volatile products, such as atmospheric chemistry, volcanism, or waste pyrolysis. View this paper
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7 pages, 817 KiB  
Data Descriptor
Towards a National-Scale Dataset of Geotechnical and Hydrological Soil Parameters for Shallow Landslide Modeling
by Pietro Vannocci, Samuele Segoni, Elena Benedetta Masi, Francesco Cardi, Nicola Nocentini, Ascanio Rosi, Gabriele Bicocchi, Michele D’Ambrosio, Massimiliano Nocentini, Luca Lombardi, Veronica Tofani, Nicola Casagli and Filippo Catani
Data 2022, 7(3), 37; https://doi.org/10.3390/data7030037 - 21 Mar 2022
Cited by 4 | Viewed by 3276
Abstract
One of the main constraints in assessing shallow landslide hazards through physically based models is the need to characterize the geotechnical parameters of the involved materials. Indeed, the quantity and quality of input data are closely related to the reliability of the results [...] Read more.
One of the main constraints in assessing shallow landslide hazards through physically based models is the need to characterize the geotechnical parameters of the involved materials. Indeed, the quantity and quality of input data are closely related to the reliability of the results of every model used, therefore data acquisition is a critical and time-consuming step in every research activity. In this perspective, we reviewed all official certificates of tests performed through 30 years at the Geotechnics Laboratory of the Earth Science Department (University of Firenze, Firenze, Italy), compiling a dataset in which 380 points are accurately geolocated and provide information about one or more geotechnical parameters used in slope stability modeling. All tests performed in the past (in the framework of previous research programs, agreements of cooperation, or to support didactic activities) were gathered, homogenized, digitalized, and geotagged. The dataset is based on both on-site tests and laboratory tests, it accounts for 40 attributes, among which 13 are descriptive (e.g., lithology or location) and 27 may be of direct interest in slope stability modeling as input parameters. The dataset is made openly available and can be useful for scientists or practitioners committed to landslide modeling. Full article
(This article belongs to the Section Spatial Data Science and Digital Earth)
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12 pages, 6644 KiB  
Data Descriptor
Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data
by Temesgen Alemayehu Abera, Ilja Vuorinne, Martha Munyao, Petri K. E. Pellikka and Janne Heiskanen
Data 2022, 7(3), 36; https://doi.org/10.3390/data7030036 - 17 Mar 2022
Cited by 8 | Viewed by 4669
Abstract
Taita Taveta County (TTC) is one of the world’s biodiversity hotspots in the highlands with some of the world’s megafaunas in the lowlands. Detailed mapping of the terrestrial ecosystem of the whole county is of global significance for biodiversity conservation. Here, we present [...] Read more.
Taita Taveta County (TTC) is one of the world’s biodiversity hotspots in the highlands with some of the world’s megafaunas in the lowlands. Detailed mapping of the terrestrial ecosystem of the whole county is of global significance for biodiversity conservation. Here, we present a land cover map for 2020 based on satellite observations, a machine learning algorithm, and a reference database for accuracy assessment. For the land cover map production processing chain, temporal metrics from Sentinel-1 and Sentinel-2 (such as median, quantiles, and interquartile range), vegetation indices from Sentinel-2 (normalized difference vegetation index, tasseled cap greenness, and tasseled cap wetness), topographic metrics (elevation, slope, and aspect), and mean annual rainfall were used as predictors in the gradient tree boost classification model. Reference sample points which were collected in the field were used to guide the collection of additional reference sample points based on high spatial resolution imagery for training and validation of the model. The accuracy of the land cover map and uncertainty of area estimates at 95% confidence interval were assessed using sample-based statistical inference. The land cover map has an overall accuracy of 81 ± 2.3% and it is freely accessible for land use planners, conservation managers, and researchers. Full article
(This article belongs to the Special Issue 2nd Edition of Data in Astrophysics & Geophysics: Research and Applications)
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7 pages, 1846 KiB  
Data Descriptor
Transcriptomic Response of Human Nosocomial Pathogen Pseudomonas aeruginosa Biofilms Following Continuous Exposure to Antibiotic-Impregnated Catheters
by Kidon Sung, Dan Li, Jungwhan Chon, Ohgew Kweon, Minjae Kim, Joshua Xu, Miseon Park and Saeed A. Khan
Data 2022, 7(3), 35; https://doi.org/10.3390/data7030035 - 17 Mar 2022
Viewed by 2206
Abstract
Biofilms are complex surface-attached bacterial communities that serve as a protective survival strategy to adapt to an environment. Bacterial contamination and biofilm formation on implantable medical devices pose a serious threat to human health, and these biofilms have become the most important source [...] Read more.
Biofilms are complex surface-attached bacterial communities that serve as a protective survival strategy to adapt to an environment. Bacterial contamination and biofilm formation on implantable medical devices pose a serious threat to human health, and these biofilms have become the most important source of nosocomial infections. Although antimicrobial-impregnated catheters have been employed to prevent bacterial infection, there have been concerns about the potential emergence of antibiotic resistance. To investigate the risk of developing resistance, we performed RNA-sequencing gene expression profiling of P. aeruginosa biofilms in response to chronic exposure to clindamycin and rifampicin eluted from antibiotic-coated catheters in a CDC biofilm bioreactor. There were 877 and 178 differentially expressed genes identified in planktonic and biofilm cells after growth for 144 h with control (without antibiotic-impregnation) and clindamycin/rifampicin-impregnated catheters, respectively. The differentially expressed genes were further analyzed by Clusters of Orthologous Groups (COGs) functional classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The data are publicly available through the GEO database with accession number GSE153546. Full article
(This article belongs to the Section Computational Biology, Bioinformatics, and Biomedical Data Science)
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10 pages, 13286 KiB  
Data Descriptor
Large-Scale Dataset for the Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
by Usman Ali, Giuseppe Caso, Luca De Nardis, Konstantinos Kousias, Mohammad Rajiullah, Özgü Alay, Marco Neri, Anna Brunstrom and Maria-Gabriella Di Benedetto
Data 2022, 7(3), 34; https://doi.org/10.3390/data7030034 - 15 Mar 2022
Cited by 13 | Viewed by 4157
Abstract
Understanding radio propagation characteristics and developing channel models is fundamental to building and operating wireless communication systems. Among others uses, channel characterization and modeling can be used for coverage and performance analysis and prediction. Within this context, this paper describes a comprehensive dataset [...] Read more.
Understanding radio propagation characteristics and developing channel models is fundamental to building and operating wireless communication systems. Among others uses, channel characterization and modeling can be used for coverage and performance analysis and prediction. Within this context, this paper describes a comprehensive dataset of channel measurements performed to analyze outdoor-to-indoor propagation characteristics in the mid-band spectrum identified for the operation of 5th Generation (5G) cellular systems. Previous efforts to analyze outdoor-to-indoor propagation characteristics in this band were made by using measurements collected on dedicated, mostly single-link setups. Hence, measurements performed on deployed and operational 5G networks still lack in the literature. To fill this gap, this paper presents a dataset of measurements performed over commercial 5G networks. In particular, the dataset includes measurements of channel power delay profiles from two 5G networks in Band n78, i.e., 3.3–3.8 GHz. Such measurements were collected at multiple locations in a large office building in the city of Rome, Italy by using the Rohde & Schwarz (R&S) TSMA6 network scanner during several weeks in 2020 and 2021. A primary goal of the dataset is to provide an opportunity for researchers to investigate a large set of 5G channel measurements, aiming at analyzing the corresponding propagation characteristics toward the definition and refinement of empirical channel propagation models. Full article
(This article belongs to the Special Issue Measurements of User and Sensor Data from the Internet of Things (IoT) Devices)
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19 pages, 1823 KiB  
Data Descriptor
A Data Resource for Prediction of Gas-Phase Thermodynamic Properties of Small Molecules
by William Bains, Janusz Jurand Petkowski, Zhuchang Zhan and Sara Seager
Data 2022, 7(3), 33; https://doi.org/10.3390/data7030033 - 11 Mar 2022
Cited by 2 | Viewed by 2781
Abstract
The thermodynamic properties of a substance are key to predicting its behavior in physical and chemical systems. Specifically, the enthalpy of formation and entropy of a substance can be used to predict whether reactions involving that substance will proceed spontaneously under conditions of [...] Read more.
The thermodynamic properties of a substance are key to predicting its behavior in physical and chemical systems. Specifically, the enthalpy of formation and entropy of a substance can be used to predict whether reactions involving that substance will proceed spontaneously under conditions of constant temperature and pressure, and if they do, what the heat and work yield of those reactions would be. Prediction of enthalpy and entropy of substances is therefore of value for substances for which those parameters have not been experimentally measured. We developed a database of 2869 experimental values of enthalpy of formation and 1403 values for entropy for substances composed of stable small molecules, derived from the literature. We developed a model for predicting enthalpy of formation and entropy from semiempirical quantum mechanical calculations of energy and atom counts, and applied the model to a comprehensive database of 16,417 small molecules. The database of small-molecule thermodynamic properties will be useful for predicting the outcome of any process that might involve the generation or destruction of volatile products, such as atmospheric chemistry, volcanism, or waste pyrolysis. Additionally, the collected experimental thermodynamic values will be of value to others developing models to predict enthalpy and entropy. Full article
(This article belongs to the Section Chemoinformatics)
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9 pages, 796 KiB  
Data Descriptor
Dataset Documenting the Interactions of Biochar with Manure, Soil, and Plants: Towards Improved Sustainability of Animal and Crop Agriculture
by Darcy Bonds, Jacek A. Koziel, Mriganka De, Baitong Chen, Asheesh K. Singh and Mark A. Licht
Data 2022, 7(3), 32; https://doi.org/10.3390/data7030032 - 02 Mar 2022
Cited by 2 | Viewed by 2989
Abstract
Plant and animal agriculture is a part of a larger system where the environment, soil, water, and nutrient management interact. Biochar (a pyrolyzed biomass) has been shown to affect the single components of this complex system positively. Biochar is a soil amendment, which [...] Read more.
Plant and animal agriculture is a part of a larger system where the environment, soil, water, and nutrient management interact. Biochar (a pyrolyzed biomass) has been shown to affect the single components of this complex system positively. Biochar is a soil amendment, which has been documented for its benefits as a soil enhancer particularly to increase soil carbon, improve soil fertility, and better nutrient retention. These effects have been documented in the literature. Still, there is a need for a broader examination of these single components and effects that aims at the complementarity and synergy attainable with biochar and the animal and crop-production system. Thus, we report a comprehensive dataset documenting the interactions of biochar with manure, soil, and plants. We evaluated three biochars mixed with manure alongside both manure and soil controls for improvement in soil quality, reduction in nutrient movement, and increase in plant nutrient availability. We explain the experiments and the dataset that contains the physicochemical properties of each biochar–manure mixture, the physicochemical properties of soil amended with each biochar–manure mixture, and the biomass and nutrient information of plants grown in biochar–manure mixture-amended soil. This dataset is useful for continued research examining both the short- and long-term effects of biochar–manure mixtures on both plant and soil systems. In addition, these data will be beneficial to extend the findings to field settings for practical and realized gains. Full article
(This article belongs to the Section Spatial Data Science and Digital Earth)
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14 pages, 2370 KiB  
Data Descriptor
Monitoring a Bolted Vibrating Structure Using Multiple Acoustic Emission Sensors: A Benchmark
by Emmanuel Ramasso, Benoît Verdin and Gaël Chevallier
Data 2022, 7(3), 31; https://doi.org/10.3390/data7030031 - 02 Mar 2022
Cited by 4 | Viewed by 2628
Abstract
The dataset presented in this work, called ORION-AE, is made of raw AE data streams collected by three different AE sensors and a laser vibrometer during five campaigns of measurements by varying the tightening conditions of two bolted plates submitted to harmonic vibration [...] Read more.
The dataset presented in this work, called ORION-AE, is made of raw AE data streams collected by three different AE sensors and a laser vibrometer during five campaigns of measurements by varying the tightening conditions of two bolted plates submitted to harmonic vibration tests. With seven different operating conditions, this dataset was designed to challenge supervised and unsupervised machine/deep learning as well as signal processing methods which are developed for material characterization or structural health monitoring (SHM). One motivation of this work was to create a common benchmark for comparing data-driven methods dedicated to AE data interpretation. The dataset is made of time series collected during an experiment designed to reproduce the loosening phenomenon observed in aeronautics, automotive, or civil engineering structures where parts are assembled together by means of bolted joints. Monitoring loosening in jointed structures during operation remains challenging because contact and friction in bolted joints induce a nonlinear stochastic behavior. Full article
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12 pages, 8202 KiB  
Data Descriptor
Bihourly Subterranean Temperature and Relative Humidity Data from the Nullarbor Plain, Australia (Nov 2019–Mar 2021)
by Matej Lipar and Mateja Ferk
Data 2022, 7(3), 30; https://doi.org/10.3390/data7030030 - 01 Mar 2022
Cited by 2 | Viewed by 2970
Abstract
This research provides bihourly temperature and relative humidity data from ten measuring locations in eight caves from one of the largest contiguous arid karst areas in the world, the Nullarbor Plain in south Australia. The current data span the period from November 2019 [...] Read more.
This research provides bihourly temperature and relative humidity data from ten measuring locations in eight caves from one of the largest contiguous arid karst areas in the world, the Nullarbor Plain in south Australia. The current data span the period from November 2019 to March 2021, and represent the first continuous published monitoring of the subterranean features in this area. The data were recorded using ten TGP-4500 Tinytag Plus 2 self-contained temperature (resolution ±0.01 °C or better with a reading range from −25 °C to +85 °C) and relative humidity (resolution ±3.0% or better with a reading range from 0% to 100%) data loggers and are available in the form of a spreadsheet. The text also describes reported (but only occasional) visits to the caves, so that the data for those particular days and/or hours can be treated as anthropogenically influenced. The data have great potential to provide insight into underground karst processes, air mass movements, hydrogeology, speleothems and (palaeo)climate, current climatic changes, and biology. Full article
(This article belongs to the Section Spatial Data Science and Digital Earth)
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11 pages, 1594 KiB  
Data Descriptor
H-Prop and H-Prop-News: Computational Propaganda Datasets in Hindi
by Deptii Chaudhari, Ambika Vishal Pawar and Alberto Barrón-Cedeño
Data 2022, 7(3), 29; https://doi.org/10.3390/data7030029 - 28 Feb 2022
Cited by 2 | Viewed by 3328
Abstract
In this digital era, people rely on the internet for their news consumption. As people are free to express their opinions on social media, much information shared on the internet is loaded with propaganda. Propagandist contents are intended to influence public opinion. In [...] Read more.
In this digital era, people rely on the internet for their news consumption. As people are free to express their opinions on social media, much information shared on the internet is loaded with propaganda. Propagandist contents are intended to influence public opinion. In the mainstream media or prominent news agencies, the authors’ and news agencies’ own bias may impact in the news contents. Hence, it is required to detect such propaganda spread through news articles. Detection and classification of propagandist text require standard, high-quality, annotated datasets. A few datasets are available for propaganda classification. However, these datasets are mostly in English. Hindi is the most spoken language in India, and efforts are needed to detect its propagandist contents. This research work introduces two new datasets: H-Prop and H-Prop-News, which consist of news articles in Hindi annotated as propaganda or non-propaganda. The H-Prop dataset is generated by translating 28,630 news articles from the QProp dataset. The H-Prop-News dataset contains 5500 news articles collected from 32 prominent Hindi news websites. We experiment with the proposed datasets using four supervised machine learning models combined with different feature vectors and word embeddings. Our experiments achieve 87% accuracy using Logistic Regression with TF-IDF feature vectors. The datasets provide high-quality labeled news articles in Hindi and open new avenues for researchers to explore techniques for analyzing and classifying propaganda in Hindi text. Full article
(This article belongs to the Section Information Systems and Data Management)
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16 pages, 2438 KiB  
Review
Artificial Intelligence Computing at the Quantum Level
by Olawale Ayoade, Pablo Rivas and Javier Orduz
Data 2022, 7(3), 28; https://doi.org/10.3390/data7030028 - 25 Feb 2022
Cited by 11 | Viewed by 9827
Abstract
The extraordinary advance in quantum computation leads us to believe that, in the not-too-distant future, quantum systems will surpass classical systems. Moreover, the field’s rapid growth has resulted in the development of many critical tools, including programmable machines (quantum computers) that execute quantum [...] Read more.
The extraordinary advance in quantum computation leads us to believe that, in the not-too-distant future, quantum systems will surpass classical systems. Moreover, the field’s rapid growth has resulted in the development of many critical tools, including programmable machines (quantum computers) that execute quantum algorithms and the burgeoning field of quantum machine learning, which investigates the possibility of faster computation than traditional machine learning. In this paper, we provide a thorough examination of quantum computing from the perspective of a physicist. The purpose is to give laypeople and scientists a broad but in-depth understanding of the area. We also recommend charts that summarize the field’s diversions to put the whole field into context. Full article
(This article belongs to the Special Issue Data Processing in Quantum Computing)
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8 pages, 1562 KiB  
Data Descriptor
Impact of p53 Knockout on Protein Data Set of HaCaT Cells in Confluent and Subconfluent Conditions
by Alexander L. Rusanov, Daniil D. Romashin, Peter M. Kozhin, Maxim N. Karagyaur, Dmitry S. Loginov, Olga V. Tikhonova, Victor G. Zgoda and Nataliya G. Luzgina
Data 2022, 7(3), 27; https://doi.org/10.3390/data7030027 - 23 Feb 2022
Cited by 6 | Viewed by 2682
Abstract
The immortalized keratinocytes, HaCaT, are a popular model for skin research (toxicity, irritation, allergic reactions, or interaction of cells). They maintain a stable keratinocyte phenotype and respond to keratinocyte differentiation stimuli. However, programs of stratification and expression of differentiation markers in HaCaT keratinocytes [...] Read more.
The immortalized keratinocytes, HaCaT, are a popular model for skin research (toxicity, irritation, allergic reactions, or interaction of cells). They maintain a stable keratinocyte phenotype and respond to keratinocyte differentiation stimuli. However, programs of stratification and expression of differentiation markers in HaCaT keratinocytes are aberrant. In HaCaT cells, there are two mutant p53 alleles (i.e., R282Q and H179Y) that contain gain-of-function (GOF) mutations resulting from spontaneous immortalization (mutp53). At the same time, mutp53 acts as a transcription factor and also affects the interaction of p63 protein with its transcription targets. Proteins of the p53 family are crucial for regulation of proliferation and differentiation processes in human keratinocytes, although the involvement of mutp53 in these processes is not fully clear. We present data sets obtained as a result of high-performance proteomic analysis of immortalized HaCaT keratinocytes with p53 knockout in two different states, subconfluent and confluent, which are characterized by different intensites of cell differentiation processes. To obtain the proteomic profiles of the cells, we applied LC-MS/MS measurements processed with MaxQuant software (version 1.6.3.4). Full article
(This article belongs to the Section Computational Biology, Bioinformatics, and Biomedical Data Science)
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10 pages, 1021 KiB  
Data Descriptor
Deceptive Content Labeling Survey Data from Two U.S. Midwestern Universities
by Ryan Suttle, Scott Hogan, Rachel Aumaugher, Matthew Spradling, Zak Merrigan and Jeremy Straub
Data 2022, 7(3), 26; https://doi.org/10.3390/data7030026 - 22 Feb 2022
Viewed by 2150
Abstract
Intentionally deceptive online content seeks to manipulate individuals in their roles as voters, consumers, and participants in society at large. While this problem is pronounced, techniques to combat it may exist. To analyze the problem and potential solutions, we conducted three surveys relating [...] Read more.
Intentionally deceptive online content seeks to manipulate individuals in their roles as voters, consumers, and participants in society at large. While this problem is pronounced, techniques to combat it may exist. To analyze the problem and potential solutions, we conducted three surveys relating to how news consumption decisions are made and the impact of labels on decision making. This article describes these three surveys and the data that were collected by them. Full article
(This article belongs to the Special Issue Automatic Disinformation Detection on Social Media Platforms)
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