Mendeley Data Showcase

☐ Dataset: ● The LSS MRI AISSLab Dataset is a comprehensive sagittal lumbar spine MRI collection containing 500 patients with fully curated imaging data, expert clinical annotations, and detailed anatomical segmentation masks for the scientifc purpose (noncommercial). ● This dataset was approved by the Institutional Review Board (IRB) and clinically validated by neurosurgeons from the Fırat University Non-Interventional Research Ethics Committee (session number: 2023/12-20; session date: 14.09.2023). The dataset consists of 8,500 sagittal lumbar spine MRI slices and 2,979 expert-verified bounding-box annotations describing foraminal stenosis across the five lumbar levels (L1–L2 through L5–S1). ● A total of 1,396 right foraminal stenosis (RFS) and 1,583 left foraminal stenosis (LFS) regions were annotated. Each annotation specifies the lumbar level, anatomical side, and clinically assigned stenosis grade (Normal, Mild, Moderate, Severe). ● The stenosis severity distribution demonstrates a predominance of early-stage findings, consisting of Normal (67.45%), Mild (17.06%), Moderate (8.53%), and Severe (6.99%) cases. ● The dataset provides also the expert-refined segmentation masks on the middle sagittal slice for key anatomical structures, including vertebrae, intervertebral discs (IVDs), sacrum, posterior A, posterior B, and the anterior background region. These masks were generated through a combined automated-and-manual refinement workflow and reviewed by neurologists to ensure anatomical accuracy. ● The dataset is organized into three components: (1) full sagittal DICOM series for each patient; (2) middle-slice images accompanied by pixel-level masks in PNG/XML format; and (3) full-slice PNG images with corresponding stenosis annotations when visible. ☐ ACKNOWLEDGMENT: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. RS-2023-00256517) and by the TUBITAK (The Scientific and Technological research Council of Turkey) under Grant Number: 123N325. This work was supported by the IITP(Institute of Information & Communications Technology Planning & Evaluation)-ITRC(Information Technology Research Center) grant funded by the Korea government (Ministry of Science and ICT) (IITP-2025-RS-2024-00437191). ☐ Please cite these articles: [1] Salem, Saied, Afnan Habib, Mukhlis Raza, Zaid Al-Huda, Omar Al-maqtari, Bilal Ertuğrul, Özal Yıldırım, Yeong Hyeon Gu, and Mugahed A. Al-antari. "AutoSpineAI: Lightweight Multimodal CAD Framework for Lumbar Spine MRI Assessments." In IEEE-EMBS International Conference on Biomedical and Health Informatics 2025. [2] Al-Antari, Mugahed A., Saied Salem, Mukhlis Raza, Ahmed S. Elbadawy, Ertan Bütün, Ahmet Arif Aydin, Murat Aydoğan, Bilal Ertuğrul, Muhammed Talo, and Yeong Hyeon Gu. "Evaluating AI-powered predictive solutions for MRI in lumbar spinal stenosis: a systematic review." Artificial Intelligence Review 58, no. 8 (2025): 221.

This dataset supports the research article titled "Spatial distribution of organic carbon stocks in intertidal bare-flat sediments based on a cascading remote sensing inversion approach." It provides a comprehensive collection of field-measured sediment parameters and vertical profile data from the intertidal bare flats of the Central Jiangsu coast (specifically the Radial Sand Ridge system). The dataset includes: 1.Field Survey Data: Geographic coordinates (latitude and longitude) for sampling sites across 14 representative transects. 2.Sediment Physical Properties: Measured values for mean grain size (MZ), moisture content (MC, %), bulk density (BD), and sediment organic carbon content (OC).Vertical Profile Information: Layered sediment data (0–10, 10–20, 20–30, 30–40, and 40–50 cm) used to characterize the vertical distribution of organic carbon density (IOCD). 3.Validation Metadata: Observed values used for the calibration and validation of the Sediment Organic Carbon Stock Cascading Remote Sensing Inversion Framework (SOCS-CRIF).These data were used to estimate a total organic carbon stock of 7.76*10^5 t for the 0–50 cm sediment layer in the study area. This dataset is essential for researchers focused on blue carbon assessment, coastal geomorphology, and remote sensing applications in muddy tidal environments.

For decades, the 16S rRNA gene has been a cornerstone of bacterial taxonomy and community analysis. Modern long-read sequencing is accurate enough to detect subtle single-nucleotide differences among multiple 16S gene copies within the same genome (though small insertion/deletion variants remain challenging to resolve). These findings highlight that microbiome analyses must account for intragenomic 16S variation. By leveraging full-length 16S intragenomic sequence variants, it is possible to resolve bacterial community composition at the species and even strain level. In this study, the nearly full-length 16S rRNA gene sequences (~1.2-1.5 kb) of four bacterial isolates (ASW5, SED11, A13, and S14) were analyzed to determine their taxonomic identities and evolutionary relationships. BLAST analysis via the EzBioCloud database identified isolates SED11, A13, and S14 as members of the genus Bacillus, with 16S sequence similarities of 99.54%, 100%, 100%, and 99.8% to the type strains of Bacillus thuringiensis, Bacillus paranthracis, Bacillus halotolerans, and Bacillus velezensis, respectively. These high identity values indicate that each isolate’s 16S sequence is nearly identical to a known species sequence, facilitating confident species-level assignment (typically, ≥98.7% 16S similarity is considered indicative of the same species). Quality filtering and chimera checking using VSEARCH v2.21.1 (with the GOLD/UCHIME database) revealed no chimeric sequences (0% chimera detected) in any of the four 16S rRNA genes, confirming that each sequence was suitable for downstream analysis. Nucleotide composition analysis showed that the sequences ranged from 1,235 to 1,500 bp in length and had very similar base frequency profiles. Each 16S rRNA Gene comprised approximately 20–21% Adenine (A), 24-26% Thymine (T), 22-24% Guanine (G), and 30-32% Cytosine (C). Accordingly, all four isolates exhibited a relatively high GC content in the ~53-55% range. Notably, Isolate ASW5 had a 16S GC content (~53%) comparable to those of the Bacillus isolates (~53-55%), indicating only minor variation in 16S GC content despite the organisms’ distinct genera. In silico secondary structure prediction of each 16S rRNA (using the RNAfold tool) yielded the typical bacterial 16S rRNA stem-loop structure for all isolates. Phylogenetic analysis was performed using MEGA X to construct 16S rRNA gene trees. The resulting neighbor-joining phylogeny clustered the three Bacillus isolates (ASW5, SED11, A13, S14) together within the Bacillus clade. This clustering aligns with the taxonomic identifications and the high 16S sequence similarities to the respective type strains. In summary, all four isolates were unambiguously identified by full-length 16S rRNA sequencing. They showed high GC contents and conserved 16S secondary structures, and each isolate grouped phylogenetically with the expected genus-underscoring the consistency between their molecular composition and phylogenetic affiliations.

Static and Dynamic Stiffness and Damping Characteristics of Ship Limiters

File containing code to reproduce images for acoustic attenuation on Venus, Earth, Mars, Titan, Uranus, and Neptune

Qualitative data research

The dataset comes from a household survey conducted with 205 adults from Dankanda and Pitawala areas in the Knuckles Region of the Matale District, Sri Lanka. Data were collected through face-to-face interviews. The survey includes Likert-scale questions measuring shared identity, networks and norms of reciprocity, two dimensions of trust: general trust and trust on livelihood transactions, and group efficacy, which was used for calculating predictive validity. The questions were designed to reflect local social and livelihood conditions.