The Corps of Engineers' K715 map series (150000) was digitized, and this led to the acquisition of these items [1]. The database's vector layers, encompassing the island's entirety (9251 km2), include a breakdown of a) land use/land cover, b) road network, c) coastline, and d) settlements. In the original map's legend, six road network classifications and thirty-three land use/land cover classifications are delineated. Furthermore, the 1960 census was integrated into the database to attribute population figures to settlements (towns and villages). Under the same governing body and methodology, this census was the final one to capture the entire population of Cyprus, which had been divided into two sections five years after the map's publication, directly following the Turkish invasion. In summary, the dataset is valuable for both cultural and historical preservation and for evaluating the diverse development trajectories of landscapes that have been governed under different political structures since 1974.

For the evaluation of a nearly zero-energy office building's performance within a temperate oceanic environment, a dataset was meticulously crafted between May 2018 and April 2019. This dataset's source material is the research paper 'Performance evaluation of a nearly zero-energy office building in temperate oceanic climate', employing field data analysis. Evaluation of air temperature, energy use, and greenhouse gas emissions from the Brussels, Belgium reference building is provided by the data. The dataset's significance stems from its novel data collection strategy, offering comprehensive insights into electricity and natural gas consumption, plus detailed indoor and outdoor temperature readings. Clinic Saint-Pierre's Brussels, Belgium energy management system data is compiled and refined, forming the foundation of the methodology. As a result, the data is one of a kind and does not appear on any other publicly available platform. Using an observational approach, this paper's methodology for data generation focused on field-based measurements of air temperature and energy performance metrics. Scientists focusing on thermal comfort and energy efficiency in energy-neutral buildings will find this data paper beneficial, specifically in the context of identified performance gaps.

Catalytic peptides, biomolecules of low cost, are adept at catalyzing chemical reactions, including ester hydrolysis. Current literature documentation furnishes a list of catalytic peptides, compiled in this dataset. Several factors were scrutinized, including the length of the sequence, its composition, net charge, isoelectric point, hydrophobicity, the inclination for self-assembly, and the catalytic process mechanism. To facilitate the training of machine learning models, a readily usable SMILES representation was produced for each sequence alongside the analysis of its physico-chemical properties. This presents a rare chance to construct and validate pilot predictive models. This dataset, a reliable product of manual curation, empowers the benchmark for comparing new models against models trained on automatically assembled peptide-oriented data sets. Besides this, the dataset affords a glimpse into the presently developing catalytic mechanisms, thereby providing a platform for the creation of future-generation peptide-based catalysts.

Thirteen weeks' worth of data from Sweden's area control, part of the flight information region, form the basis of the SCAT dataset. Within the dataset, detailed information from almost 170,000 flights is integrated with airspace data and weather forecasts. Flight data includes updated flight plans, air traffic control clearances, surveillance information, and trajectory prediction data, all generated by the system. Data spanning each week is unbroken, yet the 13 weeks are distributed across a year, introducing fluctuations in weather and seasonal traffic patterns. This dataset exclusively comprises scheduled flights, with none of them having been implicated in any incident reports. macrophage infection The removal of sensitive data encompasses military and private flight information. The SCAT dataset has the potential to support research related to air traffic control, including specific inquiries. Considering transportation trends, environmental concerns, and optimization approaches enabled by automation and artificial intelligence solutions.

Yoga, renowned for its benefits to both physical and mental health, has experienced a surge in global popularity as a preferred exercise and relaxation method. However, the complexity of yoga poses can be daunting, especially for beginners who might encounter difficulties with achieving proper alignment and positioning. To tackle this problem, a collection of various yoga poses is essential for creating computer vision algorithms that can identify and interpret yoga stances. To achieve this, we constructed image and video datasets encompassing a range of yoga asanas, all captured using the Samsung Galaxy M30s mobile device. The dataset contains a comprehensive visual record of 10 Yoga asana, illustrating both correct and incorrect postures through 11344 images and 80 videos. The image dataset's structure comprises ten subfolders, each further divided into Effective (correct) and Ineffective (incorrect) step folders. The video dataset contains a series of four videos dedicated to each posture, including 40 videos demonstrating correct postural alignment and 40 videos demonstrating incorrect postural alignment. App developers, machine learning researchers, yoga instructors, and practitioners alike find this dataset invaluable, enabling them to cultivate apps, refine computer vision algorithms, and hone their practice. We firmly hold that this dataset format will lay the groundwork for the creation of innovative technologies, empowering individuals to refine their yoga practice, such as posture-detection and -correction aids or individualized recommendations corresponding to individual skills and necessities.

This dataset's scope includes 2476-2479 Polish municipalities and cities (subject to annual fluctuation) for the period from 2004, when Poland joined the EU, up until 2019, prior to the COVID-19 pandemic. Within the newly compiled 113 yearly panel variables, details about budgetary allocations, electoral competitiveness, and investments funded by the European Union are included. Although the dataset originates from publicly accessible sources, extracting, categorizing, consolidating, and refining budgetary data, a task that involved a year's worth of extensive work, required a high level of specialized knowledge. The fiscal variables were constructed using the raw data sets of more than 25 million subcentral governments. The source for the Ministry of Finance data consists of Rb27s (revenue), Rb28s (expenditure), RbNDS (balance), and RbZtd (debt) forms, reported quarterly by all subcentral governments. These data were aggregated according to the governmental budgetary classification keys to form ready-to-use variables. Furthermore, the dataset was instrumental in generating novel EU-financed local investment proxy variables, directly referencing significant investments across various sectors and, in particular, those in sporting venues. Sub-central electoral data for the years 2002, 2006, 2010, 2014, and 2018, which were drawn from the National Electoral Commission, underwent a rigorous process of mapping, cleaning, merging, and then employed to produce new variables indicative of electoral competitiveness. This dataset enables the modeling of fiscal decentralization, political budget cycles, and EU-funded investment within a large representative sample of local government units.

Project Harvest (PH), a collaborative community science project, along with National Atmospheric Deposition Program (NADP) National Trends Network wet-deposition AZ samples, are used by Palawat et al. [1] to measure arsenic (As) and lead (Pb) levels in rooftop rainwater. 4PBA 577 field samples were acquired in the PH region, in addition to the 78 field samples procured by the NADP group. The Arizona Laboratory for Emerging Contaminants, after filtration through a 0.45 µm filter and acidification, used inductively coupled plasma mass spectrometry (ICP-MS) to analyze all samples for dissolved metal(loid)s, which included arsenic (As) and lead (Pb). Evaluating method limits of detection (MLOD) was crucial, and samples exceeding these limits were marked as detectable. Variables of interest, specifically community and sampling time frame, were analyzed using generated summary statistics and box-and-whisker plots. Concludingly, arsenic and lead data is available for potential future use; the information can be helpful in evaluating contamination levels in harvested rainwater collected in Arizona and in guiding community usage of natural resources.

Within the context of diffusion MRI (dMRI), the absence of a clear link between microstructural characteristics and the observed variability in diffusion tensor imaging (DTI) parameters of meningioma tumors constitutes a considerable obstacle. organismal biology One widely accepted view holds that mean diffusivity (MD) from diffusion tensor imaging (DTI) is inversely related to cell density, and fractional anisotropy (FA) is directly related to tissue anisotropy. These associations, though established in a diverse range of tumors, have been challenged regarding their use in understanding intra-tumor variation; several further microstructural characteristics have been proposed as contributing factors to MD and FA. Our study used ex vivo DTI at a 200 mm isotropic resolution, on sixteen excised meningioma tumor samples, to examine the biological factors influencing DTI parameters. The dataset's representation of meningiomas across six different types and two varying grades accounts for the variety of microstructural features exhibited by the samples. DWI signal maps, averaged DWI signals at a given b-value, signal intensities without diffusion encoding (S0), and diffusion tensor imaging (DTI) metrics (MD, FA, FAIP, AD, RD) were aligned to Hematoxylin and Eosin (H&E) and Elastica van Gieson (EVG) stained tissue sections by employing a non-linear landmark-based technique.