Mendeley Data Showcase

This dataset comprises 4,200 images, evenly categorized into 5 distinct classes. It was developed for a computer vision project undertaken by undergraduate students in the Department of Computer Science and Engineering. Netrokona University, Netrokona, Bangladesh. The collection features both original and augmented images, all uniformly resized to 512x512 pixels using high-quality LANCZOS interpolation to maintain visual clarity.

Supplementary Materials

The spreadsheet includes the isomeric distribution of 3-hydroxy fatty acids (3-OH FAs) and branched glycerol dialkyl glycerol tetraethers (brGDGTs) at both regional and global scales. It also contains data on total organic carbon (TOC%), total nitrogen (TN%), and various environmental parameters, such as temperature, precipitation, pH, soil moisture content, and grain size.

This dataset is the original data of the paper “Predicting the spatial distribution of invasive species richness using the combination of machine learning and geostatistical algorithms” .The dataset includes forest survey data, eco-climatic data, and topographic and geomorphological data. Among them, the forest survey data comes from the US Forest Inventory and Analysis (FIA) program, which collects information on the occurrence and distribution of invasive plants in all public and private forests in the United States. The ecological and climatic data includes 31 climatic variables, extracted from the WorldClim Global Climate Data (Version 2.1, https://www.worldclim.org/data/worldclim21.html). The topographic and geomorphological data includes three variables: elevation, soil carbon content, and aridity index. Among these, the elevation data comes from WorldClim Global Climate Data, soil carbon content data is extracted from the International Soil Reference and Information Centre (ISRIC-World Soil Information, https://www.isric.org/), and the aridity index is extracted from the Global Aridity Index (http://www.cgiar-csi.org/data). The definitions of each variable are as follows: prov_ID: Eco-region code; LAT/LON: Decimal latitude/longitude; Seasonability: SD of mean annual temperature; Alt: Altitude (m); PLT_TPA/Tpha: Trees per acre/hectare; RelDen: Successional development proportion (0–1); prpfor: Forested plot proportion; plt_drybio_adj/ha: Native tree biomass (English tons/acre/hectare); native_spp: Native tree species richness; PD_all: Phylogenetic diversity of tree species; PSV_all/var: Phylogenetic variability and variance; PSR_all/var: Phylogenetic richness and variance; PSE_all/PSC_all: Phylogenetic evenness/clustering; InvSpRichness: Invasive species richness; soilcarbon: 0–20 cm soil carbon content; aridity: Precipitation/evapotranspiration ratio; BIO1–BIO19: Standard climatic metrics (e.g., temperature, precipitation); vaprmin/max/range/avg: Water vapor pressure metrics (kPa); sradmin/max/range/avg: Solar radiation metrics (KJ/m²/day); windmin/max/range/avg: Wind speed metrics (m/s).

All produced in this study and literature data used in the figures in the manuscript are provided.

This data set was collected through a study was conducted jointly by the University of Reading, UK and the International Crop Research Institute for Semi-Arid Tropics (ICRISAT), Hyderabad, India under a grant from the Global Challenges Research Fund (GCRF) for a project entitled “Through the Looking Glass: Applying a Gender Lens to Agricultural Transformation, Labour Intensification and Nutrition Outcomes in LMICs''. The data collection for this project was undertaken by Dr Padmaja Ravula, Dr Kavitha Kasala and their team at ICRISAT, India.

This dataset supports the retrospective case-control study titled "Is fractal analysis a reliable indicator for predicting early implant loss? A retrospective study.". It contains anonymized patient-level and group-level data, including fractal dimension and lacunarity values derived from panoramic radiographs and cone-beam computed tomography (CBCT) images. Fractal analysis was performed using ImageJ, BoneJ and the FracLac plugin, applying both White & Rudolph's and Kato et al.'s methods. Statistical analyses were conducted in SPSS. The dataset includes raw individual values, descriptive summaries, and group-wise comparative statistics. All data are fully anonymized and formatted for reuse.

Source data including the Masspec data, unprocessed western blot and gels, and statistical source data for "A screen to identify targeted protein degraders discovers a degrader of the undruggable oncoprotein SKP2 " published in Nature Biotechnology.

With the increasing reliance on smartphone, college students are at a higher risk of developing posture-related disorders such as text neck syndrome. This condition is characterized by forward head posture, neck pai and associated musculoskeletal imbalance. The present study aimed to determine the prevalence of text neck syndrome and it’s associated with musculoskeletal risk factor among college students. A study was conducted among 400 college students aged 18-25 years. Participants who used smartphone for more than 4 hours daily were included. The Neck Disability Index (NDI), Nomophobia questionnaire (NMP-Q) were used to assess neck related disability and mobile phone dependence, respectively. Postural parameter such as Craniovertebral angle (CVA), Sagittal Head Angle (SHA) were assessed using lateral digital photographs and analyzed using kinovea software. Ethical clearance and informed consent were obtained prior to data collection. A significant prevalence of text neck syndrome was observed among participants, with higher in NDI and NMP-Q score were found. Lower CVA and SHA values indicating forward head posture found to correlate with reduced postural angle, suggesting a strong association between psychological dependency on smartphone and musculoskeletal dysfunction. The findings reveal a high prevalence of text neck syndrome and it does not majorly affect the neck angle. Early identification and intervention through ergonomics education and digital wellness strategies are essential to reduce the burden of musculoskeletal problems in young adults.

Este manual, por tanto, proporciona una orientación estructurada sobre la producción de contenido jurídico en formato podcast como herramienta didáctica de aprendizaje para estudiantes de derecho, y es por esto que cada episodio busca contar con un diseño pedagógico que facilite la comprensión, reflexión y aplicación del Derecho de las Obligaciones en diversos contextos y afrontando retos contemporáneos en la materia.