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This paper describes a data collection campaign and the resulting dataset derived from smartphone sensors characterizing the daily life activities of 3 volunteers in a period of two weeks. The dataset is released as a collection of CSV files containing more than 45K data samples, where each sample is composed by 1332 features related to a heterogeneous set of physical and virtual sensors, including motion sensors, running applications, devices in proximity, and weather conditions. Moreover, each data sample is associated with a ground truth label that describes the user activity and the situation in which she was involved during the sensing experiment (e.g., working, at restaurant, and doing sport activity). To avoid introducing any bias during the data collection, we performed the sensing experiment in-the-wild, that is, by using the volunteers' devices, and without defining any constraint related to the user's behavior. For this reason, the collected dataset represents a useful source of real data to both define and evaluate a broad set of novel context-aware solutions (both algorithms and protocols) that aim to adapt their behavior according to the changes in the user's situation in a mobile environment.

The proposal of new energy systems based on renewable energies requires thorough research in order to derive technically reliable and economically sustainable systems. One of the key inputs of such research is constituted by reliable databases of renewable resources. Despite the great effort of the scientific community in recent years, most current databases are far from optimal. Although some databases are based on real data, they lack adequate spatial resolution and/or temporal coverage. Other databases are obtained by estimating renewable energy potential from meteorological reanalysis; however, these estimates are subject to high uncertainty. One of the main problems when building these renewable resource databases is the lack of actual values of installed capacity. In this study we present the SOlar and Wind Installed Spanish Power (SOWISP) database. SOWISP provides the actual installed capacity of wind and photovoltaic solar energy in each Spanish town, with a monthly resolution, and covering the period of 2015–2020. SOWISP has been developed and validated based on a careful and thorough compilation of different public databases. It covers the need for a publicly available database with sufficient spatial and temporal resolution suitable for the analysis of energy systems. Moreover, SOWISP, along with other freely available datasets, supports many modern applications. In addition, a Python package (available on GitHub) was developed for managing this database, Spanish Government, Junta de Andalucia PID2019-107455RB-C21/AEI/92 10.13039/501100011033, European Commission, UE-Junta de Andalucia PID2019-107455RB-C22/AEI/10.13039/501100011033 TEP-220 PAIDI2020-DOC_01110

This article presents data from three experiments in which we triggered and measured cognitive dissonance in meat-eaters. Cognitive dissonance is a well-established concept in the social psychology literature; however, empirical measures are scarce. In all datasets, we used textual information and/or images related to meat consumption as means to trigger cognitive dissonance. Cognitive dissonance data was collected in Study 1 with a Likert scale, whereas Study 2 and 3 used a Semantic Bipolar scale. Each experiment was programmed with Qualtrics and consisted of four conditions. Data was collected online; Study 1 utilized social media to recruit participants while Study 2 and 3 were hosted on Prolific. All datasets contain items on participants’ socio-demographic background, liking of different food products, cognitive dissonance, and a measure of meat avoidance. The data can be used to analyze the effect of information provision on cognitive dissonance and meat avoidance. Moreover, the relationship between socio-demographic variables and cognitive dissonance, as well as other exploratory purposes regarding meat avoidance can be explored. Furthermore, researchers can use the data to investigate differences between Likert scales and Semantic Bipolar scales. This data is related to the paper Can images and textual information lead to meat avoidance? The mediating role of cognitive dissonance[1].

Two novel mycobacteriophages (Prann and LeoAvram) belonging to the family Siphoviridae were isolated from soil samples of Northern India. Genomic DNA of both the phages was extracted, and further sequenced using Illumina technology. Complete genome annotation of both the isolates was performed using DNA Master. Prann and LeoAvram had linear genomes of 68398bp and 47079bp, respectively, with G+C contents of 60-70%. A total of 99 and 75 ORFs were predicted in Prann and LeoAvram, respectively. Based on sequence similarity to known phage proteins, functions were assigned to 44 and 53 genes, respectively. These proteins could be classified into five major groups, viz., phage structural proteins, proteins for recombination, lytic enzymes, proteins involved in DNA / RNA metabolism, and in regulation. Mycobacterium smegmatis was used in this work as a safe surrogate for Mycobacterium tuberculosis, the causative agent for tuberculosis, a major infectious disease worldwide with developing antibiotic resistance. This is the first report of M. smegmatis phages from Northern India.

Sugarcane (Saccharum officinarum L.) is one of the main annual crops grown in the Central Highlands of Vietnam. By understanding the taxonomic and functional profiles of root endophytic microbiome, we can develop a new cultivation technique for the sustainable production of this plant. In this work, a representative sample was obtained by mixing the roots collected from five different sugarcane fields in Dak Lak Province, the Central Highlands, in 2021. The genomic DNA of the endophytic bacteria was extracted using the DNeasy Powersoil kit, and the V1–V9 regions of the 16S rRNA genes were amplified by PCR. Libraries of 16S rRNA gene amplicons were prepared using the Swift amplicon 16S plus ITS panel kit. Finally, the Illumina MiSeq platform was used to sequence the 16S rRNA gene amplicons library. The raw data of the endophytic microbiome were uploaded to the National Center for Biotechnology Information (NCBI) with Bioproject PRJNA923851 and can be retrieved at https://www.ncbi.nlm.nih.gov/Traces/study/?acc=%20PRJNA923851. The obtained data provide basic information on the root endophytic microbiome of sugarcane cultivated in the Central Highlands of Vietnam. The data can also be useful for further developing a new technique for sustainable sugarcane production based on indigenous microorganisms. This work is the first report on the endophytic microbiome of sugarcane cultivated in this region.

This dataset includes four large field-of-view scanning electron microscopy (SEM) images together with associated Matlab scripts aimed for the analysis used in the joint publication. Each of the four stitched images is generated from a large number (between 15500 and 24500) high-resolution (195nm/pixel) scans, which have been stitched into four images stored as tiff-files. The images show the cross-section of fiber bundles in composite laminate and are well-suited for local fiber volume determination. The image resolution corresponds to between 600 and 2000 pixels covering each fiber. The imaged samples are from composite laminates with an overall fiber volume fraction in the range of 55% to 60%. The local fiber volume fraction is found both for the full cross-section, as an average fiber volume fraction over the individual bundles, and as a local fiber volume fraction found in a moving averaging box with a size corresponding to 5x5 fiber diameter (80x80 µm2) areas.

A database of 168 904 hauls covering the period from 1965 to 2019, from 46 surveys containing both fisheries-dependent (fishing vessels) and -independent data (scientific surveys) were collated from across the eastern Atlantic (Greater North Sea, Celtic Sea, Bay of Biscay and Iberian coast) and Metropolitan French Mediterranean waters. Data on diadromous fish (the European sturgeon (Acipenser sturio), allis shad (Alosa alosa), twait shad (Alosa fallax), Mediterranean twaite shad (Alosa agone), European eel (Anguilla anguilla), thinlip mullet (Chelon ramada), river lamprey (Lampetra fluviatilis), sea lamprey (Petromyzon marinus), smelt (Osmerus eperlanus), European flounder (Platichthys flesus), Atlantic salmon (Salmo salar) and the sea trout (Salmo trutta)) presence-absence was extracted and cleaned. The gear type and gear category which caught these species, their spatial location, and the date of capture (year and month), were also cleaned and standardised. Very little is known about diadromous fish at-sea and modelling data-poor and poorly detectable species such as diadromous fish is challenging for species conservation. Furthermore, databases which contain both scientific surveys and fisheries-dependent data on data-poor species at the temporal and geographical scale of this database are uncommon. This data could therefore be used to improve knowledge of diadromous fish spatial and temporal trends, and modelling techniques for data-poor species.

This perspective article on using partial least squares structural equation modelling (PLS-SEM) is intended as a guide for authors who wish to publish datasets that can be analysed with this method as stand-alone data articles. Stand-alone data articles are different from supporting data articles in that they are not linked to a full research article published in another journal. Nevertheless, authors of stand-alone data articles will be required to clearly demonstrate and justify the usefulness of their dataset. This perspective article offers actionable recommendations regarding the conceptualisation phase, the types of data suitable for PLS-SEM and quality criteria to report, which are generally applicable to studies using PLS-SEM. We also present adjusted versions of the HTMT metric for discriminant validity testing that broaden its applicability. Further, we highlight the benefit of linking data articles to already published research papers that employ the PLS-SEM method.

Clinical diagnosis of inborn errors of metabolism in the suspected patients is usually guided by the initial general investigations in the laboratory such as the concentration of ammonia, blood gases status, blood glucose and ketones. The establishment of a biochemical diagnosis in patients with inborn errors of metabolism depends on the detection of the specific metabolites in the abnormal metabolic pathway which can appear in any of the body fluids but are most commonly tested in blood and urine samples. Acylcarnitine and/or acylcarnitine ratio in patients with carnitine acylcarnitine translocase and carnitine palmitoyl transferase deficiency showed an abnormal profile regardless of the metabolic status of patients. The acylcarnitine was derived from the analysis of dried blood spot using multiple reaction monitoring (MRM) which was performed using quadrupole mass spectrometry. The dataset presented in this article was generated from analysis of acylcarnitines in the 17,121 dried blood spots from symptomatic Malaysian patients less than fifty years old who exhibited symptoms suggestive of inborn errors of metabolism, but had a normal acylcarnitine profile. A precursor or ion scan of m/z 85 was selected for the analysis. Quantification of each analyte was obtained using the signal intensity ratio of the acylcarnitine to its internal standard. The acylcarnitines analyzed included C0, C2, C3, C3DC, C4, C5, C5:1, C5DC, C5OH, C6, C8, C10, C12, C14, C16, C18, C18:1, C16OH, C18OH and C18:1OH and was analyzed using Neolynx V4.0 software. We decided to choose the 1st and 99th percentiles as the minimum and maximum cut-offs. The filtered part of data in this article was used in the article Novel mutations associated with Carnitine-Acylcarnitine Translocase and Carnitine Palmitoyl Transferase 2 deficiencies in Malaysia. This dataset is intended to enable the scientific communities to get access to the raw dataset for future translational research use in inborn errors of metabolism as very few acylcarnitine data was developed and published for the symptomatic patients suspected of inborn errors of metabolism especially in the Asian population.

The petrophysical and petrothermal characterization of the sedimentary succession of the Oliana anticline in the Southern Pyrenees has been performed on the basis of mineral density, connected porosity, permeability, P-wave velocity and thermal conductivity measurements of rock samples distributed along this anticline. This dataset was used to explain: (I) the variability of petrophysical rock properties along the Oliana anticline, (II) the distribution of thermal conductivity along the sedimentary units of the anticline, (III) the relationships between the fold and petrology concerning the mineral density, connected porosity, permeability, P-wave velocity and thermal conductivity of rocks and (IV) the tectonic and diagenetic controls underlying the observed relationships, as described in the research article: "Petrological, petrophysical and petrothermal study of a folded sedimentary succession: the Oliana anticline (Southern Pyrenees), outcrop analogue of a geothermal reservoir - Global and Planetary Change Journal (2023)”. This contribution presents here the raw and statistical datasets used to discuss the potential of the Oliana anticline as a geothermal reservoir analogue and also includes an extended methodological section that proposes a new procedure to measure the thermal conductivity of highly heterogeneous coarse-grained sedimentary rocks using the Modified Transient Source Plane (MTPS) method on a TCi C-Therm thermal analyzer. These complete datasets can be used to better discuss and understand the principal limitations of outcrop analogue studies applied to unconventional geothermal reservoirs in foreland basins on the basis of the analysis of rock petrophysical and petrothermal properties. Furthermore, the data obtained in the Oliana anticline can be used to understand the structural, diagenetic and petrological factors that can modify the petrophysical and petrothermal properties of rocks and to discuss the potential of foreland basin margins to be used as geothermal reservoirs, comparing the data obtained in Oliana with studies developed in similar geological settings worldwide.

One of the most important and most easily measurable physical characteristics of plant seeds is their weight, which influences and indicates crucial ecological processes. Seed weight affects spatial and temporal dispersibility, and can also influence seed predation and the germination, growth and survival of seedlings. Providing trait data for species missing from international databases is key to promote studies that advance our understanding of the functioning of plant communities and ecosystems, which is an essential issue in the face of the global climate change and biodiversity loss. Compared to species from Western and Northwestern Europe, those with an Eastern or Central European centre of distribution are underrepresented in most international trait databases. Therefore, the creation of specific trait databases is key to help regional studies. In this respect, it is important not only to collect fresh seeds for weight measurements, but also to measure and process data of seeds preserved in collections and make them available to the broader scientific community. In this data paper we provide seed weight data to fill in missing trait data of plant species of Central and Eastern Europe. Our dataset includes weight measurement for 281 taxa of the Central European flora including also some cultivated and exotic species. The seeds were collected between 1971 and 2021 mostly in Central Europe. One part of the measured seeds was collected in the last decade, the other part is from an older seed collection, but all seeds were measured recently. For each species, we collected a minimum of 3 × 100 intact seeds, if possible. The seeds were air-dried at room temperature (approximately 21 °C and 50% relative humidity) for at least two weeks and measured with an accuracy of 0.001 g using an analytical balance. The thousand-seed weights reported here were calculated based on the measured values. Our goal for the future is to incorporate the seed weight data reported here in a regional database (Pannonian Database of Plant Traits – PADAPT) that gathers plant traits and other plant characteristics for the Pannonian flora. The data presented here will facilitate trait-based analyses of the flora and vegetation of Central Europe.

The integration of electric vehicles (EV) into the global car fleet has seen a major shift boosted by the new environmental regulations. The adoption of this low-carbon vehicle is hampered by several constraints in emerging countries and particularly in Morocco, e.g. constraints related to the infrastructure (land purchasing for charging stations, integration with existing power infrastructures, funding and optimized deployment) [1], and constraints related to the lack of standards and regulatory frameworks [2]. For this purpose, our objective is to share with the community a dataset about EV exploitation in the Moroccan context. This dataset [3] could be used to improve the energy management system characterized by a limited driving range and restrictive charging infrastructures. Subsequently, several driving cycles have been done in three main trajectories using data collection in the region of Rabat-Salé-Kénitra (RSK). The collected data contains mainly the date, time, Battery State of Charge (SoC), speed, vehicle's position, weather information, traffic conditions and road speed limits. The dataset collection is done using an onboard developed electronic card that collects the vehicle's internal and external data. Collected data are pre-processes and then stored in a Comma Separated Values (CSV) file. The collected dataset could be used in applications that are related to EV management and planning, such as speed prediction, speed control strategies, rerouting and EV charging scheduling, vehicle-to-grid and grid-to-vehicle, and energy demand forecasting.

Heterosigma akashiwo is a eukaryotic, cosmopolitan, and uni-cellular alga (class: Raphidophyceae), and produces fish -killing blooms. There is a substantial scientific and practical interest in its ecophysiological characteristics that determine bloom dynamics and its adaptation to broad climate zones. A well-annotated genomic/genetic sequence information en-ables researchers to characterize organisms using modern molecular technology. In the present study, we conducted H. akashiwo RNA sequencing, a de novo transcriptome assem-bly of 84,693,530 high-quality deduplicated short-read se-quences. Obtained RNA reads were assembled by Trinity assembler and 144,777 contigs were identified with N 50 values of 1085. Total 60,877 open reading frames with the length of 150 bp or greater were predicted. For further analy-ses, top Gene Ontology terms, pfam hits, and blast hits were annotated for all the predicted genes. The raw data were deposited in the NCBI SRA database (BioProject PR - JDB6241 and PRJDB15108), and the assemblies are available in NCBI TSA database (ICRV01). The annotation information can be obtained in Dryad and can be accessed via doi: 10.5061/dryad.m0cfxpp56.

The collected data included socio-demographic information and measurements of health literacy, e-health literacy, mental well-being, and sleep hygiene behaviors. The study aimed to provide insights regarding health literacy status and related factors in the general population residing in Qazvin province, Iran. The findings of the study will assist health authorities and policy makers in developing and implementing effective interventions, which in turn, improving health literacy in the community. Additionally, the findings can also aid health sector workers, including non-communicable disease researchers, public health experts, health education specialists, epidemiologists, and sociologists, in their work to improve health literacy and overall health outcomes among general population. Therefore, the present study used paper-pencil method with multi-stage cluster sampling to collect data. Trained research associates (n=25) collected data from 9775 people living in Qazvin province between January and April 2022. All the participants completed the study questionnaires using self-reported paper-pencil mode.

Toxoplasmosis chorioretinitis is commonly diagnosed by an ophthalmologist through the evaluation of the fundus images of a patient. Early detection of these lesions may help to prevent blindness. In this article we present a data set of fundus images labeled into three categories: healthy eye, inactive and active chorioretinitis. The dataset was developed by three ophthalmologists with expertise in toxoplasmosis detection using fundus images. The dataset will be of great use to researchers working on ophthalmic image analysis using artificial intelligence techniques for the automatic detection of toxoplasmosis chorioretinitis.

In order to investigate employees’ needs of the Medical Faculty of the University of Freiburg regarding research data management, the BE-KONFORM study was performed in a two-step approach. First, guideline-based qualitative video interviews with four researchers were performed to identify key constructs of relevance. Second, a standardized online survey was conducted from 1(st) to 15(th) of November 2020 based on e-mail invitation by the dean and a faculty newsletter. The questionnaire was provided bilingual (English and German) using a backward-forward translation method, no reminders and incentives were used to increase the response rate. The online survey was programmed in REDCap and was accessible via online link. The target population were members of the Medical Faculty (listed in the newsletter mailing list) regardless of the type of working contract signed. The final dataset contains 236 complete cases (90% German and 10% English). The study includes a randomised module asking for data publication (group A) or not (group B). 113 cases were randomized into group A and 99% of them consented to the publication of the collected research data in anonymized form (n=112). The dataset comprised questions about work-related characteristics (professional status, working experience, scientific field of work), data management-related items (definition of research data management, type of data used, type of storage used for saving data, use of electronic laboratory notebooks), experience and attitudes towards data publication in data repositories, as well as needs and preferences regarding research data management support. The produced data offers the possibility to connect with other data collected in this field in other contexts (faculties or universities).

Raw datasets were sourced from a U.S. based provider of digital gambling payments systems, who has demanded to remain anonymous. The raw datasets cover a time period of 6-years (2015-2021), representing over 300,000 customers and approximately 90 million transaction records. One of these raw datasets is a transaction log file representing customer payment transaction data across a variety of gambling merchants (e.g., online casinos, sportsbooks, and lottery providers). With this article we describe the transaction log file and provide two filtered subsets of the data. The subsets contain 1-year of customer payments transaction records for two gambling merchants: (1) a casino-focused brand and (2) a sports-focused brand. These data will be particularly helpful to researchers in the fields of gambling studies and behavioral sciences, and more generally for data and computer scientists. With digital payments becoming increasingly prevalent across the gambling industry, these data can be used to explore how individuals’ payment behavior can inform us about their gambling behavior. The granularity and timespan of the data provide an opportunity to apply a variety of data science and machine learning techniques.

Reversal Error (RE) is a common error in algebra problem solving. This error occurs when students recognize the information in the statement but make mistakes when translating some sentences from natural language to algebraic language, reversing the relationship between two variables in comparison word problems. Structural Magnetic Resonance Image (sMRI) data were collected with the purpose of identifying brain anatomical regions related to the RE phenomenon. The aim of the research was to investigate the brain anatomy differences between participants who failed more than 50% of the answers on the task (N=15) and those who responded correctly 100% of the time (N=18). sMRI analysis revealed differences between the two groups, and details about these data can be found in Ventura-Campos et al. (2022) [1]. This data set contains the sMRI (raw data, pre-processed images), and an excel file with personal information such as age and gender, the scanner with which their sMRI were collected, and the group to which each of the 33 subjects belonged.

Until relatively recently, stable sulphur isotope analysis of bone collagen was seldom undertaken in bioarchaeological research. With increasing frequency, its application has proven useful in reconstructing palaeodiets and palaeoecologies, as well as identifying potential migration and mobility patterns. Here, sulphur (δ(34)S) isotope analysis, together with carbon (δ(13)C) and nitrogen (δ(15)N), was performed on six fish and 34 mammal bone collagen samples from 14 prehistoric sites in Lithuania dating from the Late Mesolithic (ca. 7000–5000 cal BC) to the Late Bronze Age (ca. 1100–500 cal BC). We present the first δ(34)S data from Lithuania, including coupled δ(13)C and δ(15)N data, offering a crucial dataset for future research to explore spatial and temporal variability in the region and beyond.

The relevance of affective information triggers cognitive prioritisation, dictated by both the attentional load of the relevant task, and socio-emotional abilities. This dataset provides electroencephalographic (EEG) signals related to implicit emotional speech perception under low, intermediate, and high attentional demands. Demographic and behavioural data are also provided. Specific social-emotional reciprocity and verbal communication characterise Autism Spectrum Disorder (ASD) and may influence the processing of affective prosodies. Therefore, 62 children and their parents or legal guardians participated in data collection, including 31 children with high autistic traits (x̄(age)=9.6-year-old, σ(age)=1.5) who previously received a diagnosis of ASD by a medical specialist, and 31 typically developed children (x̄(age)=10.2-year-old, σ(age)=1.2). Assessments of the scope of autistic behaviours using the Autism Spectrum Rating Scales (ASRS, parent report) are provided for every child. During the experiment, children listened to task-irrelevant affective prosodies (anger, disgust, fear, happiness, neutral and sadness) while answering three visual tasks: neutral image viewing (low attentional load), one-target 4-disc Multiple Object Tracking (MOT; intermediate), one-target 8-disc MOT (high). The EEG data recorded during all three tasks and the tracking capacity (behavioural data) from MOT conditions are included in the dataset. Particularly, the tracking capacity was computed as a standardised index of attentional abilities during MOT, corrected for guessing. Beforehand, children answered the Edinburgh Handedness Inventory, and resting-state EEG activity of children was recorded for 2 minutes with eyes open. Those data are also provided. The present dataset can be used to investigate the electrophysiological correlates of implicit emotion and speech perceptions and their interaction with attentional load and autistic traits. Besides, resting-state EEG data may be used to characterise inter-individual heterogeneity at rest and, in turn, associate it with attentional capacities during MOT and with autistic behavioural patterns. Finally, tracking capacity may be useful to explore dynamic and selective attentional mechanisms under emotional constraints.

Temperature is a key factor influencing microbial growth rates and yields. In literature, the influence of temperature on growth is studied either on yields or rates but not both at the same time. Moreover, studies often report the influence of a specific set of temperatures using rich culture media containing complex ingredients (such as yeast extract) which chemical composition cannot be precisely specified. Here, we present a complete dataset for the growth of Escherichia coli K12 NCM3722 strain in a minimal medium containing glucose as the sole energy and carbon source for the computation of growth yields and rates at each temperature from 27 to 45°C. For this purpose, we monitored the growth of E. coli by automated optical density (OD) measurements in a thermostated microplate reader. At each temperature full OD curves were reported for 28 to 40 microbial cultures growing in parallel wells. Additionally, a correlation was established between OD values and the dry mass of E. coli cultures. For that, 21 dilutions were prepared from triplicate cultures and optical density was measured in parallel with the microplate reader (OD(microplate)) and a UV-Vis spectrophotometer (OD(UV-vis)) and correlated to duplicate dry biomass measurements. The correlation was used to compute growth yields in terms of dry biomass.

Lung cancer, one of the most common causes of cancer-related death worldwide, has been associated with high treatment cost and imposed great burdens. The 5-year postoperative survival rate of lung cancer (13%) is lower than many other leading cancers indicating the urgent needs to dissect its pathogenic mechanisms and discover specific biomarkers. Although several proteins have been proposed to be potential candidates for the diagnosis of lung cancer, they present low accuracy in clinical settings. Metabolomics has thus emerged as a very promising tool for biomarker discovery. To date, many lung cancer-related metabolites have been highlighted in the literature but no database is available for scientists to retrieve this information. Herein, we construct and introduce the first Lung Cancer Metabolome Database (LCMD), a freely available online database depositing 2013 lung cancer-related metabolites identified from 65 mass spectrometry-based lung cancer metabolomics studies. Researchers are able to explore LCMD via two ways. Firstly, by applying various filters in the “Browse Metabolites” mode, users can access a list of lung cancer-related metabolites that satisfy the filter specifications. For each metabolite, users can acquire the value of the fold change (cancer/normal), statistical significance (p-value) of the fold change, and the comparative research designs of all the mass spectrometry-based lung cancer metabolomics studies that identify this metabolite. Secondly, by applying various filters in the “Browse Studies” mode, users can obtain a list of mass spectrometry-based lung cancer metabolomics studies that satisfy the filter specifications. For each study, users can view the type of studied specimen, mass spectrometry (MS) method, MS data processing software, and differential analysis method, as well as all the identified lung cancer-related metabolites. Furthermore, the overview of each study is clearly illustrated by a graphical summary. The LCMD (http://cosbi7.ee.ncku.edu.tw/LCMD/) is the first database that brings together the meaningful information of lung cancer-related metabolites. The development of the LCMD is envisioned to promote the biomarker discovery of lung cancer.

The data in this article includes a variety of analysis, including swelling, viscosity and FT-IR to better understand the thermal-mechanical, viscoelastic and swelling properties of sacran, CNF, and Ag nanoparticles individually and collectively. The fabrication method of Sacran, CNF, and Sac/CNF-Ag composite films is also demonstrated in this data item and are related to the research article “Facile design of antibacterial sheets of sacran and nanocellulose”. This data article summarizes all the information so that it is evident how silver nanoparticle-polysaccharide hydrogels might be employed as an on-demand dressings due to their proven ability to reduce bacterial viability.

The paper presents the collection of physicochemical parameters of bisphenol A (BPA) and its sulfate (BPAS) and glucuronide (BPAG) conjugates, accompanied by data characterizing their absorption, distribution, metabolism and excretion (ADME) behavior following oral administration of BPA. The data were collected from open literature sources and publicly available databases. Additionally, data calculated by using the MarvinSketch 18.30.0 software or predicted by relevant QSAR models built in Simcyp® Simulator were also used. All data were analysed and are fit for purpose if necessary to ensure a reliable prediction of pharmacokinetics of BPA and its conjugates. The data selection process and reasoning for fitting is provided to allow critical assessment and to ensure data transparency. Finally, the sensitivity analysis was performed to assess the influence of the selected parameters on the PBPK model predictions.

An extensive dataset with mixed-mode fracture resistance experimental data (R-curves) and fracture process parameters is presented. The fracture resistance values are extracted from double cantilever beam specimens subject to un-even bending moments. The tested specimens are made of a unidirectional composite which undergoes large-scale fibre bridging during fracture. The data set contains both raw data (forces from two load-cells, time, acoustic emission signals, and opening displacement measurements) and processed data (J-integral, end-opening displacements, fracture process parameters) of each test. In the repository, MATLAB scripts are also provided to facilitate recreating the processed data from the raw data.

The Japanese Catheter Ablation (J‐AB) registry, started in August 2017, is a voluntary, nationwide, multicenter, prospective, observational registry, performed by the Japanese Heart Rhythm Society (JHRS) in collaboration with the National Cerebral and Cardiovascular Center using a Research Electronic Data Capture system. The purpose of this registry is to collect the details of target arrhythmias, the ablation procedures, including the type of target arrhythmias, outcomes, and acute complications in the real‐world settings. During the year of 2019, we have collected a total of 80 795 procedures (mean age of 65.2 years and 66.4% male) from 425 participant hospitals. Detailed data are shown in Figures and Tables., J‐AB is a prospective nationwide multicenter registry designed to collect clinical variables, successful ablation rate and sort‐term outcome, aiming to register all catheter ablation cases performed in Japan. We successfully collected 80 795 procedures during the year of 2019.

Twelve files have been obtained after the extraction of data from raster PDF images of sonic log graphs. These files data regard exploration wells placed in the Adriatic Sea pertaining the Apulia (Branzino-1, Chiara-1, Cristina-1, Famoso-1, Giove-1, Giove-2, Grazia-1, Grifone-1, Medusa-1, Sabrina-1, Simona-1, and Sparviero-1bis), and the related raster sonic log graphs are free accessible at the ViDEPI Project (www.videpi.com) of the Ministry for the Economic Development of the Italian Government. Two columns A and B of data, i.e., interval transit time Δt [µs/ft] and depth [m], respectively, characterise each file. Hence, 18,396 pairs of Δt-depth values have been obtained. The picking of the data occurred by the use of WebPlotDigitizer© free software. These data are relevant for the interpretation of reflection seismic lines throughout the Adriatic Sea, mainly in the offshore the Apulia. Moreover, the interpretation of those reflection seismic lines located in the adjacent offshore zones, such as the Ionian Sea, can benefit from these data; the values can be important for seismological goals around the Apulia, as well. From these data, Δt-depth diagrams can be originated by the use of software capable of building 2D graphs from values in CSV files (e.g. Matlab©).

This article presents data on the mental health and wellbeing of 2,192 Kenyan adolescents collected using self-reported questionnaires administered at four secondary schools in Nairobi and Kiambu counties. The data was collected using a range of validated scales to access for depression (PHQ-8), anxiety (GAD-7), gratitude (GQ-6), social support (MSPSS), perceived control (PCS), happiness and optimism (EPOCH). Data were analyzed using Microsoft Excel and R, and results are reported in terms of frequencies and percentages of responses. Linear mixed models were used to determine factors significantly associated with depression and anxiety symptoms. The study provides a platform for future research into factors associated with depression and anxiety in Kenyan adolescents, and policymakers and mental health actors can use the results to design interventions that promote mental health in this population.

The use of copper-containing fungicides in plant protection has a long history and is today widespread in both organic and conventional agriculture in Germany. The treatments are primarily used to control plant diseases such as downy mildew in vines and hops, collar rot and canker, and apple scab in fruit trees. The regularly re-approval of copper as a pesticide in the European Union is always accompanied by critical discussions about the toxic effects of copper on soil organisms, especially earthworms. This results in the need for independent advice on ecotoxicological risks with field data for the authorities involved in the regulation of copper-containing pesticides. Based on the EU's regulation 1107/2009/EC, the current approval of the active ingredient copper is limited until 2025 and has been included on the list of substitution candidates (article 24) due to its persistency. This category requires a comparative assessment under article 50 with alternatives, mainly less toxic fungicides or resistant cultivars. This data set reflects the spectrum of copper load situation in German vineyards and can be used for risk assessment of artificially applied copper on earthworm and microbial communities in vineyard soils and even other agricultural soils based on field data. It contains 78 sampling areas in total on 10 German quality wine-growing regions. At each sampling area soils of four diagonal sampling points (4 × 0.25 m(2)) were excavated. In sum, soils from 312 sampling points were collected from the top 20 cm soil depth. The samples were collected in April and October between 2010 and 2014. This survey in German wine-growing regions includes following parameters: Soil data: soil texture (sand, clay, silt), pH-value, organic matter content, element concentrations of nutrient and heavy metals (As, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, V, Zn), mobile (“bioavailable”) Cu contents extracted with 1 M NH(4)NO(3) and 0.001 M CaCl(2). Earthworm data: ecological group, taxa, development stage (adult, juvenile), abundance (number of individuals per 0.25 m(2)), biomass (g per 0.25 m(2) FM), element concentration of earthworm tissue of environmentally relevant elements (As, Cd, Co, Cr, Cu, Ni, Pb, V, Zn). Functional microbial community data: long term soil respiration, microbial biomass, metabolic quotient, dehydrogenase activity.

The ability to predict the maintenance needs of machines is generating increasing interest in a wide range of industries as it contributes to diminishing machine downtime and costs while increasing efficiency when compared to traditional maintenance approaches. Predictive maintenance (PdM) methods, based on state-of-the-art Internet of Things (IoT) systems and Artificial Intelligence (AI) techniques, are heavily dependent on data to create analytical models capable of identifying certain patterns which can represent a malfunction or deterioration in the monitored machines. Therefore, a realistic and representative dataset is paramount for creating, training, and validating PdM techniques. This paper introduces a new dataset, which integrates real-world data from home appliances, such as refrigerators and washing machines, suitable for the development and testing of PdM algorithms. The data was collected on various home appliances at a repair center and included readings of electrical current and vibration at low (1 Hz) and high (2048 Hz) sampling frequencies. The dataset samples are filtered and tagged with both normal and malfunction types. An extracted features dataset, corresponding to the collected working cycles is also made available. This dataset could benefit research and development of AI systems for home appliances' predictive maintenance tasks and outlier detection analysis. The dataset can also be repurposed for smart-grid or smart-home applications, predicting the consumption patterns of such home appliances.

In most of the countries, grapes are considered as a cash crop. Currently huge research is going on in development of automated grape harvesting systems. Speedy and reliable grape bunch detection is prime need for various deep learning based automated systems which deals with object detection and object segmentation tasks. But currently very few datasets are available on grape bunches in vineyard, because of which there is restriction to the research in this area. In comparison to the vineyard in outside countries, Indian vineyard structure is more complex, so it becomes hard to work in real-time. To overcome these problems and to make vineyard dataset for suitable for Indian vineyard scenarios, this paper proposed four different datasets on grape bunches in vineyard. For creating all datasets in GrapesNet, natural environmental conditions have been considered. GrapesNet includes total 11000+ images of grape bunches. Necessary data for weight prediction of grape cluster is also provided with dataset like height, width and real weight of cluster present in image. Proposed datasets can be used for prime tasks like grape bunch detection, grape bunch segmentation, and grape bunch weight estimation etc. of future generation automated vineyard harvesting technologies.

A bioinformatic approach was applied to evaluate the effect of treatment with Bevacizumab on the gene expression profile of colorectal adenocarcinoma cells. The transcriptomic profile of Bevacizumab-adapted HCT-116 (Bev/A) colorectal adenocarcinoma cells was determined and compared with that of the corresponding control cell line by Agilent microarray analysis. Raw data were preprocessed, normalized, filtered, and subjected to a differential expression analysis using standard R/Bioconductor packages (i.e., limma, RankProd). As consequence of Bevacizumab adaptation, 166 differentially expressed genes (DEGs) emerged, most of them (123) resulted downregulated and 43 overexpressed. The list of statistically significant dysregulated genes was used as an input for functional overrepresentation analysis using ToppFun web tool. Such analysis pointed at cell adhesion, cell migration, extracellular matrix organization and angiogenesis as the main dysregulated biological process involved in Bevacizumab-adaptation of HCT116 cells. In addition, gene set enrichment analysis was performed using GSEA, searching for enriched terms within the Hallmarks (H), Canonical Pathways (CP), and Gene Ontology (GO) gene sets. GO terms that showed significant enrichment included: transportome, vascularization, cell adhesion and cytoskeleton, extra cellular matrix (ECM), differentiation and epithelial–mesenchymal transition (EMT), inflammation and immune response. Raw and normalized microarray data were deposited in the Gene Expression Omnibus (GEO) public repository with accession number GSE221948.

Mimosa diplotricha (Fabaceae) and Mimosa diplotricha var. inermis are invasive taxa introduced in the Chinese mainland in the 19th century. M. diplotricha has been listed in the list of highly invasive species in China, which has seriously endangered the growth and reproduction of local species. As a poisonous plant, M. diplotricha var. inermis, a variant of M. diplotricha, will also endanger the safety of animals. We report the complete chloroplast genome sequence of M. diplotricha and M. diplotricha var. inermis. The chloroplast genome of M. diplotricha is 164,450 bp long and the chloroplast genome of M. diplotricha var. inermis is 164,445 bp long. Both M. diplotricha and M. diplotricha var. inermis contain a large single-copy region (LSC) of 89,807 bp and a small single-copy (SSC) region of 18,728 bp. The overall GC content of the two species is both 37.45%. A total of 84 genes were annotated in the two species, namely 54 protein-coding genes, 29 tRNA genes, and one rRNA gene. The phylogenetic tree based on the chloroplast genome of 22 related species showed that Mimosa diplotricha var. inermis is most closely related to M. diplotricha, while the latter clade is sister to Mimosa pudica, Parkia javanica, Faidherbia albida, and Acacia puncticulata. Our data provide a theoretical basis for the molecular identification, genetic relationships, and invasion risk monitoring of M. diplotricha and M. diplotricha var. inermis.

The dataset presented contains microtopographies of various materials and processing methods. These microtopographies were measured using a Confocal Laser Scanning Microscope, which provides RGB-D data. This means the dataset comprises accurate height maps for each measurement and microscopic RGB images. The height maps can be used to quantify and characterize small-scale surface features such as pits and grooves, surface roughness, texture direction, and surface anisotropy. These features can significantly impact a material's properties and behavior, making them essential in many fields, such as biomaterials and tribology. Additionally, the dataset contains metadata about the specimens and the measurement conditions, such as material, surface processing method, roughness, and optical magnification. Therefore, this dataset provides an opportunity to develop and test surface classification and characterization algorithms.

The data presented here consists of library spectra obtained for use with a laser absorption spectroscopy gas sensor. The spectra include absorbance data for SO(2), SO(3), H(2)O and H(2)SO(4) at 300° C and 350° C temperatures in two wavelength bands, 7-8 µm and 8-9 µm. Datasets were collected in a heated multi-pass absorption Herriott cell using two tunable external cavity quantum cascade laser sources, with the resulting transmission signal measured using a thermoelectrically cooled MCT detector. The absorbance was calculated from measurements taken with and without gas samples and scaled for the length of the multi-pass cell. The data will be useful for scientists and engineers building SO(3) and H(2)SO(4) gas sensing equipment for emission monitoring, process control, and other applications.

New measurements shear-wave splitting (SWS) were determined using a teleseismic dataset from 21 broadband seismic stations belonging to the GSN-BX network in Botswana archived at IRIS-DMC. The stations are distributed across major geologic terrains such as the Kaapvaal craton, Limpopo Mobile Belt and Zimbabwe Craton. Earthquakes with magnitude larger than 5.2 mb with epicentral distances in the range of 90° and 180° were analyzed for SKS (S-wave traversing the core as P-wave) and SKKS (S-wave traversing the core as P-wave with one reflection from inner side of the Core Mantle Boundary) phases, while PKS (P-wave bottoming in the core and converting to S-wave at the Core Mantle Boundary) phases were analyzed in the range of 130°–165°. SWS parameters were calculated at each station by minimizing the energy on the transverse component of the PKS, SKKS, and SKS phases (hereafter collectively called XKS). The measurement included the polarization direction ϕ, which gives a reliable indication of the alignment of olivine in the upper mantle, and, the delay time dt between the arrival times of the fast and slow travelling split shear waves. The value of dt is related to the intrinsic anisotropy and thickness of the anisotropic layer. The SWS parameters provide insights into the present and past deformation processes of upper mantle.

Climate Change and its effects on agriculture and human survival remain a global concern requiring continual research and the use of coping strategies. This paper focus on presenting a data article on climate change effect and use of adaptation strategies by exploiting the insight from a microlevel survey carried out among smallholder maize farmers in South Africa. The data present the change in maize output and income farmers have experienced in the last two growing seasons attributed to the effect of climate change, climate change adaptation and mitigation strategies currently utilized, and constraints faced by the maize farmers. The collected data were analyzed using descriptive statistics and t-Test analysis. The findings revealed that climate change effect is very evident in the area by the significant reduction in output and income observed among the maize farmers, and thus, it is still pertinent for farmers to scale-up their use of adaptation and mitigation strategies in the area. However, the farmers can only effectively and sustainably achieve this if extension agencies provide continuous climate change-related training for maize farmers and government harmoniously work with improved seed production agencies to ensure smallholder maize farmers can adequately access seeds when needed and at subsidized rates.

Assessing the status or exploited marine fish populations often relies on fishery dependent catch and effort data reported by licensed commercial fishers in compliance with regulations and by recreational anglers voluntarily. This invariably leads to bias towards the fraction of a fish population or community that can be legally fished i.e., the stock as defined by legal minimum lengths and spatial boundaries. Data are restricted to populations which continue to be exploited at the expense of obtaining data on previously exploited and unexploited populations [1,2], so if a fishery is contracting spatially over time, then successively less of the overall fish community is monitored with bias towards where biomass is highest or most accessible [3]. A viable alternative is to conduct population monitoring surveys independently of a fishery to obtain information that is more broadly representative of the abundance, composition and size structure of fish communities and their supporting habitats [4–6]. Whereas catch and effort data often must be de-identified and aggregated to protect the confidentiality of fishers’ commercial and personal interests, this constraint does not exist for independently acquired monitoring data, collected at public expense and hence publicly available at high levels of spatial and temporal resolution. Time series underpins the utility of fishery independent survey (FIS) datasets in terms of the life histories of exploited fish species and the time frames of their responses to various combinations of fishing mortality and environmental fluctuations and trends [7]. One-off surveys can establish a baseline and spatial distribution pattern, but regular surveys conducted consistently over time are necessary to detect trends from which population status can be inferred. We present several unique datasets focused on the commercially valuable blacklip abalone (Haliotis rubra), spanning three decades of annually collected data from up to 204 locations on subtidal rocky reefs along a coastline of almost 2500 km, the State of Victoria, Australia. It is rare for data to be collected consistently at this intensity over such a long period of monitoring [2], especially with surveys conducted by small teams of highly skilled research divers, some of whom up until recently had participated in every year. The data comprises ∼28,000 records from ∼4500 site surveys conducted during 1992 to 2021 [2]. Although the fixed site design remained unchanged, the number of sites surveyed varied over time, mostly increasing in number periodically, and the survey method was refined on several occasions. We defined three different variants in the survey method due to technological advancement for both enumerating abalone abundance and measuring shell size structure [7]. The relative abundance counts were standardized using a Bayesian generalized linear mixed model (GLMM) to test for interannual trends whilst allowing for inherent differences among sites, research divers, and their interactions [8].

This article presents the comprehensive mechanical testing data archive from a neutron irradiation campaign of nuclear structural alloys fabricated by powder metallurgy with hot isostatic pressing (PM-HIP). The irradiation campaign was designed to facilitate a direct comparison of PM-HIP to conventional casting or forging. Five common nuclear structural alloys were included in the campaign: 316L stainless steel, SA508 pressure vessel steel, Grade 91 ferritic steel, and Ni-base alloys 625 and 690. Irradiations were carried out in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) to target doses of 1 and 3 displacements per atom (dpa) at target temperatures of 300 and 400 °C. This article contains the data collected from post-irradiation uniaxial tensile tests following ASTM E8 specifications, fractography of these tensile bars, and nanoindentation. By making this systematic and valuable neutron irradiated mechanical behavior dataset openly available to the nuclear materials research community, researchers may now use this data to populate material performance databases, validate material performance and hardening models, design follow-on experiments, and enable future nuclear code-qualification of PM-HIP techniques.

The Syrian hamster (SH) is an animal model used in virology, toxicology, and carcinogenesis, where a better understanding of epigenetic mechanisms is required. Finding genetic loci regulated by DNA methylation may assist in the development of DNA methylation-based in vitro assays for the identification of carcinogens. This dataset informs on the regulation of gene expression by DNA methylation. Primary cultures of SH male fetal cells (sex determined by differences in kdm5 loci on the X and Y chromosome) were exposed for 7 days to the carcinogen benzo[a]pyrene (20 µM) from which a morphologically transformed colony was collected and reseeded. The colony bypassed senescence and sustained growth. After 210 days of culture, the cells were collected and divided in 16 aliquots to create 4 experimental groups to test the effects of the DNA methylation inhibitor 5-aza-2′-deoxycytidine (5adC). The experiment was initiated 24 h after cell seeding in 10 cm plates. The groups are naïve cells (N), cells exposed for 48 h to either 0.05% DMSO as vehicle (V), or to 5adC at 1 µM and 5 µM. DNA and RNA libraries were sequenced on an Illumina NextSeq 500. Gene expression was analysed by RNAseq and differentially methylated DNA regions (DMRs: clusters of 200 base pairs (bp), read depth >20, q< 0.05, methylation difference >|25%|) were identified by reduce representation bisulfite sequencing (RRBS). Global genome DNA methylation was similar between the N (mean±SD, 47.3%±0.02) and V groups (47.3%±0.01). Although 5adC reduced methylation, the reduction was larger in the 1 µM (39.2%±0.002) than in the 5 µM group (44.3%±0.01). 5adC induced a total of 612 and 190 DMRs by 1 µM and 5 µM, among which 79 and 23 were in the promoter regions (±3,000 bp from the transcription start site), respectively. 5adC induced a total of 1,170 and 1,797 differentially expressed genes (DEGs) by 1 µM and 5 µM, respectively. The 5 µM treatment induced statistically significant toxicity (% cell viability: group N 97%±8, V 98.8%±1.3, 1 µM 97.3%±0.5, 5 µM 93.8%±1.5), which perhaps reduced cell division and daughter cell numbers with inherited changes in methylation, but increased number of DEGs due to both toxicity and methylation changes. As usually observed in the literature, a small portion of DEGs (4% and 4% at 1 µM and 5 µM, respectively) are associated with DMRs in their promoters. These promoter DMRs by themselves are sufficient among other epigenetic marks to induce DEGs. The dataset provides the genomic coordinates of the DMRs and an opportunity to further examine their roles in distal putative promoters or enhancers (yet to be described in the SH) in contributing to gene expression changes, senescence bypass and sustained proliferation as essential carcinogenic events (see companion paper [1]). Finally, this experiment confirms the possibility in future experiments to use 5adC as a positive control for effects on DNA methylation in cells derived from SH.

Ifosfamide is a cancer-fighting chemotherapeutic that has been detected in aquatic ecosystems. Zebrafish larvae were exposed to either 0, 1 or 100 µg/L ifosfamide in the water for 7 days, and fish were subjected to total RNA extraction and RNA-seq analysis with the Illumina NovoSeq 6000 instrument. Raw sequence data were processed through fastp and clean reads obtained by removing adapter and poly-N sequences, as well as low quality reads. Differential gene expression was performed using the abundance of transcripts that mapped to the zebrafish genome. To uncover putative targets regulated by microRNAs, Pathway Studio 12.0 was used to conduct a subnetwork enrichment analysis. Expression data were used to predict which microRNAs were important for the response to ifosfamide exposure. There were 21 common microRNAs identified in both the "IFOS1" and "IFOS100" datasets. These were MIR150, MIR6515, MIR657, MIR216A, m_Mir741, MIRLET7E, miR-let-7, MIR2392, r_Mir3551, MIR181B1, MIR33A, MIR502, MIR193B, MIR146A, MIR431, MIR647, m_Mir1192, MIR297, MIR328, and MIR4717. Data can be re-used to advance adverse outcome pathways in regulatory toxicology and to refine biomarker discovery for antineoplastics in aquatic environments.

This article presents C3I-SynFace: a large-scale synthetic human face dataset with corresponding ground truth annotations of head pose and face depth generated using the iClone 7 Character Creator “Realistic Human 100” toolkit with variations in ethnicity, gender, race, age, and clothing. The data is generated from 15 female and 15 male synthetic 3D human models extracted from iClone software in FBX format. Five facial expressions - neutral, angry, sad, happy, and scared are added to the face models to add further variations. With the help of these models, an open-source data generation pipeline in Python is proposed to import these models into the 3D computer graphics tool Blender and render the facial images along with the ground truth annotations of head pose and face depth in raw format. The datasets contain more than 100k ground truth samples with their annotations. With the help of virtual human models, the proposed framework can generate extensive synthetic facial datasets (e.g., head pose or face depths datasets) with a high degree of control over facial and environmental variations such as pose, illumination, and background. Such large datasets can be used for the improved and targeted training of deep neural networks.

In spite of being the fifth most spoken native language in the world, Bangla has barely received any attention in the domain of audio and speech recognition. This article represents a speech dataset of Bengali Abusive Words with some non-abusive wors which are very close to the abusive ones. In this work, a multipurpose dataset is presented to recognize automatic slang speech for Bangla language, which was prepared by collection, annotation, and refinement of data. It consists of 114 slang words and 43 non-slang words with 6100 audio clips. For the collection of slang words, 60 native speakers and for non-abusive words, 23 native speakers participated who were, speaking in various dialects from over 20 districts of Bangladesh, and 10 university students participated to evaluate this dataset including annotation and refinements. Researchers can use this dataset to develop an automatic Bengali Slang speech recognition system, and also it can be used as a new benchmark for creating speech recognition-based machine learning models. This dataset can be enrich-ed further, and some background noise in the dataset can be used to simulate a more real-world scenario if desired. Otherwise, these noises could also be removed.

The Zambian Election Panel Survey (ZEPS) data allows for analysis of voter perceptions and choices in the August 2021 elections – and how these were affected by the tactics of the competing parties and candidates. The panel design provides an opportunity to study how, when and why former supporters of incumbent president Lungu ‘defected’ to support his rival, Hakainde Hichilema (HH) in 2021. This multistage panel survey, the first-of-its-kind in Africa, was implemented in three rounds; June 5 – July 5 (R1: n = 1665), July 15 – Aug 11 (R2: n = 1508), and Aug 25 – Oct 3 (R3: n = 1272). These time periods correspond to the early campaign period, late in the campaign period and immediate post-election period, respectively. The survey was conducted over the phone. Responses were disproportionately from urban/peri-urban voters in Central and Lusaka provinces and rural voters in Eastern and Muchinga provinces. 1764 unique responses were collected using SurveyToGo software from Dooblo. 1210 responses were collected for all three rounds.