This manuscript utilizes RNA-Seq to ascertain and document a gene expression profile dataset from peripheral white blood cells (PWBC) of beef heifers at weaning. To achieve this, blood samples were collected during the weaning period, the PWBC pellet was isolated through a processing procedure, and the samples were stored at -80°C for future handling. For this study, heifers were selected post-breeding protocol (artificial insemination (AI) followed by natural bull service) and pregnancy diagnosis. The group comprised those that were pregnant via AI (n = 8) and those that remained open (n = 7). The Illumina NovaSeq platform was used to sequence total RNA derived from post-weaning bovine mammary samples collected concurrently with weaning. High-quality sequencing data were subjected to bioinformatic analysis, utilizing FastQC and MultiQC for quality control, STAR for read alignment, and DESeq2 for the identification of differentially expressed genes. After adjusting for multiple comparisons using Bonferroni correction (adjusted p-value < 0.05) and an absolute log2 fold change of 0.5, the genes were considered to be differentially expressed. The GEO database (GSE221903) now holds publicly accessible raw and processed RNA-Seq data. To the best of our understanding, this is the inaugural dataset that scrutinizes the alteration in gene expression levels commencing at weaning, with the aim of predicting future reproductive performance in beef heifers. Interpretation of the core findings regarding reproductive potential in beef heifers at weaning, as gleaned from this dataset, is documented in the paper “mRNA Signatures in Peripheral White Blood Cells Predicts Reproductive Potential in Beef Heifers at Weaning” [1].
Rotating machines commonly operate within a range of operating parameters. Still, the attributes of the data change in response to their operating parameters. This article provides a time-series dataset, encompassing vibration, acoustic, temperature, and driving current data points, specifically from rotating machines in diverse operational environments. Using four ceramic shear ICP accelerometers, one microphone, two thermocouples, and three current transformer (CT) sensors compliant with the International Organization for Standardization (ISO) standard, the dataset was gathered. A rotating machine's operational profile included normal functioning, bearing issues (inner and outer rings), shaft misalignment, rotor imbalance, and three distinct torque loads (0 Nm, 2 Nm, and 4 Nm). This article details vibration and driving current data collected from a rolling element bearing, tested across a speed range of 680 RPM to 2460 RPM. The existing dataset facilitates the verification of recently developed state-of-the-art techniques in diagnosing faults within rotating machines. Research data curated and shared by Mendeley. To obtain a copy of DOI1017632/ztmf3m7h5x.6, please return it to the proper channel. This is the identifier you are looking for: DOI1017632/vxkj334rzv.7, please acknowledge receipt. DOI1017632/x3vhp8t6hg.7, the digital object identifier for the article, acts as a permanent link to this piece of scholarly work. Provide the document cited by DOI1017632/j8d8pfkvj27.
Part performance can be severely compromised by hot cracking, a prevalent concern in the manufacturing process of metal alloys, and the risk of catastrophic failure exists. Unfortunately, the current body of research in this field is constrained by the limited availability of relevant hot cracking susceptibility data. Using the DXR technique at the 32-ID-B beamline of the Advanced Photon Source (APS) at Argonne National Laboratory, we analyzed hot cracking in ten distinct commercial alloys during the Laser Powder Bed Fusion (L-PBF) process, including Al7075, Al6061, Al2024, Al5052, Haynes 230, Haynes 160, Haynes X, Haynes 120, Haynes 214, and Haynes 718. The hot cracking susceptibility of the alloys, as determined by the post-solidification hot cracking distribution in the extracted DXR images, could be quantified. Building upon our previous work on predicting hot cracking susceptibility [1], we further developed a dataset dedicated to hot cracking susceptibility, which is now available on Mendeley Data to support future research efforts in this field.
The dataset demonstrates how the color tone evolves in plastic (masterbatch), enamel, and ceramic (glaze) components, which were pigmented by PY53 Nickel-Titanate-Pigment calcined at different NiO ratios using a solid-state reaction. Milled frits and pigments, meticulously combined, were applied to the metal for enamel and to the ceramic substance for ceramic glaze work, respectively. For the plastic application, melted polypropylene (PP) was combined with the pigments and formed into plastic plates. The CIELAB color space methodology was applied to applications created for plastic, ceramic, and enamel trials in order to assess the L*, a*, and b* values. The color evaluation of PY53 Nickel-Titanate pigments, with varying proportions of NiO, is facilitated by these data in diverse applications.
Deep learning's recent innovations have fundamentally changed the methods and approaches used to address various challenges and problems. Such innovations will prove highly advantageous in urban planning, automating the process of landscape object detection within a specific urban area. Nevertheless, it is crucial to acknowledge that these data-centric approaches demand substantial volumes of training data to achieve the anticipated outcomes. By leveraging transfer learning techniques, this challenge is addressed by reducing the data requirement and enabling model customization via fine-tuning. Urban environments benefit from the street-level imagery presented in this study, which can be used to fine-tune and deploy custom object detectors. A dataset of 763 images features, for each image, bounding box annotations covering five kinds of outdoor objects: trees, garbage bins, recycling bins, shop fronts, and streetlights. The dataset includes, in addition, sequential footage captured by a camera mounted on a vehicle. This footage documents three hours of driving throughout different regions within the city center of Thessaloniki.
In terms of global oil production, the oil palm, Elaeis guineensis Jacq., holds a prominent position. Even so, the future is expected to show a greater appetite for oil generated by this plant. To discern the crucial factors influencing oil production in oil palm leaves, a comparative evaluation of gene expression profiles was essential. UNC1999 An RNA-sequencing dataset, encompassing three oil yield levels and three genetically disparate oil palm populations, is reported here. Sequencing reads, originating from the Illumina NextSeq 500 platform, were all raw. Our RNA sequencing analysis produced a list of genes, each accompanied by its expression level, which we also present. Oil yield enhancement will be facilitated by the utilization of this transcriptomic data set as a valuable resource.
This paper presents data on the climate-related financial policy index (CRFPI), encompassing globally adopted climate-related financial policies and their binding nature, for 74 countries spanning the period from 2000 to 2020. The data incorporate the index values yielded by four statistical models, as elucidated in reference [3], which contribute to the composite index. UNC1999 Four alternative statistical approaches were built to investigate varying weighting presumptions and highlight how vulnerable the index is to modifications in the steps used for its design. Analysis of the index data unveils the participation of nations in climate-related financial planning and the consequential shortcomings within relevant policy frameworks. Using the data from this paper, researchers can explore further green financial policies by comparing various countries' approaches to specific climate-related financial initiatives or the broader framework of such policies. The data may also be employed to analyze the link between the adoption of green financial policies and modifications to credit markets and to measure their efficacy in regulating credit and financial cycles amidst climate change.
To quantify how reflectance varies with angle, this article presents spectral measurements of various materials within the near-infrared spectrum. In opposition to existing reflectance libraries, including NASA ECOSTRESS and Aster, which are limited to perpendicular reflectance, the new dataset also contains the angular resolution of material reflectance. Using a 945 nm time-of-flight camera instrument, a new method for measuring angle-dependent spectral reflectance of materials was developed. Calibration standards consisted of Lambertian targets with reflectance values set at 10%, 50%, and 95%. Measurements of spectral reflectance materials are taken for angles ranging from 0 to 80 degrees in 10-degree increments, and the data is recorded in tabular form. UNC1999 A novel material classification categorizes the developed dataset, structuring it into four distinct levels of detail. These levels consider material properties, and primarily differentiate between mutually exclusive material classes (level 1) and material types (level 2). The dataset's open access publication is found on Zenodo, version 10.1, with record number 7467552 [1]. Currently, the dataset, encompassing 283 measurements, is consistently extended within the new versions of Zenodo.
The northern California Current, encompassing the highly productive waters of the Oregon continental shelf, is a prime example of an eastern boundary region, characterized by summertime upwelling from equatorward winds and wintertime downwelling driven by poleward winds. In the period from 1960 to 1990, analyses and monitoring programs undertaken off the central Oregon coast enriched our comprehension of oceanographic processes, specifically coastal trapped waves, seasonal upwelling and downwelling within eastern boundary upwelling systems, and seasonal changes in coastal currents. The U.S. Global Ocean Ecosystems Dynamics – Long Term Observational Program (GLOBEC-LTOP), commencing in 1997, maintained its monitoring and process research through scheduled CTD (Conductivity, Temperature, and Depth) and biological sample surveys along the Newport Hydrographic Line (NHL; 44652N, 1241 – 12465W) off the coast of Newport, Oregon.