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This dataset corresponds to the research article “Effective fracture toughness for heterogeneous materials accounting for the worst-case scenario” by Sen Liu and Yongxing Shen*. It comprises multiple .mat files, each filename includes the relevant section number from the paper along with key parameters of the associated numerical example. Each file includes data on the fracture energy, reaction forces and elastic energy throughout the crack nucleation and compelet fracture. Additionally, the phase field solution at complete fracture is also included.

WB blots

replicates the results in the paper "Inflation expectations and time variations in the oil price pass-through", for more details see readme file

MRL Substantial enhancement of strength and corrosion resistance in magnesium alloy via multi-component gradient heterostructure

Blood cancer poses a growing public health concern in Bangladesh, marked by increasing prevalence and considerable challenges in timely diagnosis and treatment. The early symptoms often mimic those of other common diseases, making clinical identification complex—especially in settings with limited access to specialized diagnostic tools. Furthermore, the scarcity of structured clinical data hinders both epidemiological analysis and the development of targeted treatment strategies. To address this gap, the present dataset compiles a diverse and comprehensive range of hematological and clinical indicators from confirmed blood cancer cases across various medical institutions in Bangladesh. This multivariate dataset is designed to support epidemiological research, risk factor assessment, and the development of predictive models for improved diagnostic accuracy. By offering a detailed view of the blood cancer landscape in a resource-limited context, this dataset aims to empower data-driven clinical decision-making and contribute to future advances in personalized oncology care. Clinical and Hematological Parameters Included: Age : The age of the patient in years. Gender : The biological sex of the patient (Male or Female). Cancer_Type : The diagnosed type of blood cancer (AML, ALL, or CLL). Treatment_Type : The primary treatment given to the patient (Chemotherapy or Radiation). Bone Marrow Aspiration : Result of the bone marrow test (Positive, Negative, or Not Done). Total WBC count (/cumm) : The patient's white blood cell count per cubic millimeter. Serum Protein Electrophoresis (SPEP) – Result of protein level test (Normal or Abnormal). Lymph Node Biopsy : Result of biopsy of lymph nodes (Positive, Negative, or Not Done). Lumbar Puncture (Spinal Tap) : Status/result of spinal fluid test. Platelet Count (/cumm) : Platelet count in the patient's blood per cubic millimeter. Treatment_Outcome : Current outcome of the treatment (e.g., Cured, Ongoing, Deceased). Genetic_Data : Genetic mutation data related to blood cancer (BCR-ABL or FLT3). Side_Effects : Severity of side effects experienced (e.g., Mild, Moderate, Severe, None). Comments : Additional notes or observations from the clinician. Diagnosis_Result : Final diagnostic result laboratory confirmation (Confirmed, Suspected, or Ruled Out). Structure of the Dataset: Format: CSV Rows: 2,296 (individual patient records) Columns: 15 (clinical and hematological parameters, including diagnostic result)

The original image for the article "Kindlin-1 promotes mitophagy by inhibiting PINK1 degradation to enhance hepatocellular carcinoma progression and modulates sensitivity to donafenib".

Navigating Legal Labyrinths: A Bibliometric Review of Cross-Border Entrepreneurship and Jurisdictional Conflicts in Global Markets Data

Supplementary file for the Late Cenozoic Hainan OIB-like basalts in SE Asia record the interaction between deep mantle plume and stagnant slab

The correlation between vegetation coverage, soil moisture content and sediment production, and the relationship between finite wind speed data and sediment production

The aircraft image dataset used in this study consists of 8 classes with a total of 4,520 images. The classes in the dataset include: Airbus, ATR, Boeing, C130, F16, Grob, KAI, and Sukhoi. These classes represent various types of aircraft from various manufacturers, both commercial, military, and training aircraft. The selection of this class aims to reflect the diversity of shapes and sizes of each aircraft, so that it can improve the model's ability to distinguish the visual characteristics of each type of aircraft. Some of this dataset, 70%, was collected from my personal work. I collected the work as a dataset by collecting the results of my aircraft photography hobby that I have been doing consistently for a long time. The other 30% of the dataset comes from the photographic work of members of the Indonesian Aviation Photographers Community (KFAI). As part of the KFAI Public Relations Division, I have obtained official permission from the KFAI Supervisory Board to be allowed to use it as a dataset in my research. More details of KFAI : https://www.instagram.com/kfa_indonesia