Mendeley Data Showcase

This dataset contains measurements of crack densities and shear wave splitting delays in fault zones, used to analyze the relationship between micro- and macrocracking and seismic magnitude. It includes information on local geological features, stress fields, and fault properties, supporting the study of fracture scaling as potential earthquake precursors and their correlation with seismic parameters in different tectonic settings. These data support the study “On the scaling of crack density prior to earthquakes: preliminary observations and implication".

Data set for applications in the paper entitled Testing Structural Exogeneity in Spatial Models: A Nonparametric Approach

This dataset supports the bibliometric analysis presented in the article "Transformative Innovation Policy, Social Innovation, Territorial Development: A Bibliometric Analysis of Emerging Constellation of Research Traditions". It includes the validated corpus of 57 academic articles retrieved from Scopus and Web of Science, covering the intersection of transformative innovation policy, social innovation, and territorial development (2000-2025). The dataset contains the raw exports from both databases, the final cleaned corpus, and the R scripts used to conduct the bibliometric analysis, including keyword co-occurrence networks, thematic mapping, author co-citation networks, and co-authorship networks.

The following tables contain all the raw and processed data for the manuscript "Direct sensing of dietary ω-6 linoleic acid through FABP5-mTORC1 signaling"; DOI: 10.1126/science.adm9805. File names and figures to which the data correspond to are listed below: adm9805_data_s1 - Raw and processed data for fig. S1 adm9805_data_s2 - Raw and processed data for Fig. 1 adm9805_data_s3 - Raw and processed data for fig. S2 adm9805_data_s4 - Raw and processed data for Fig. 2 adm9805_data_s5 - Raw and processed data for fig. S3 adm9805_data_s6 - Raw and processed data for fig. S4 adm9805_data_s7 - Raw and processed data for fig. S5 adm9805_data_s8 - Raw and processed data for fig. S6 adm9805_data_s9 - Raw and processed data for fig. S7 adm9805_data_s10 - Raw and processed data for Fig. 3 adm9805_data_s11 - Raw and processed data for fig. S8 adm9805_data_s12 - Raw and processed data for Fig. 4 adm9805_data_s13 - Nutrient breakdown of diets used in this study adm9805_data_s14 - Raw and processed data for fig. S9 adm9805_data_s15 - Uncropped western blot scans

This dataset contains the primary studies used in a systematic mapping study (SMS) on deep learning-based Distributed Denial of Service (DDoS) detection in Software-Defined Networking (SDN), 5G, and beyond-5G (B5G) network environments. The dataset includes 208 peer-reviewed publications published between 2018 and 2024, retrieved from Scopus and IEEE Xplore using a structured search query. Each entry corresponds to a single study and includes bibliographic information (title, authors, year, publication venue etc.), as well as manually curated and derived attributes. In addition to bibliographic metadata, the dataset provides structured annotations extracted from the title, abstract, and manual tags, including: - learning_type (e.g., deep learning, reinforcement learning) - dl_architecture (e.g., CNN, LSTM, hybrid models) - network_context (e.g., SDN, 5G, IoT, edge) - dataset_used (e.g., CICDDoS2019, CICIDS2017, custom datasets) - evaluation_setting (e.g., offline, simulation/testbed, real network) The annotations were generated through a semi-automated process combining script-based extraction and manual validation based on titles, abstracts, and manually assigned tags, without full-text analysis. In cases where specific information was not explicitly stated, the corresponding fields were marked as "Not specified". This dataset is intended to support reproducibility and transparency of the associated systematic mapping study, as well as to facilitate further research on deep learning-based DDoS detection in programmable networks.

Original WB images related to Supplemental figure 4. From the bottom: (1) H3, (2) H3K9me3, (3) additional staining not shown in the figure, (4) GAPDH, (5) EGFP

ABSTRACT: This paper seeks to investigate the rendition of socio-cultural and rhetorical subtleties of language between Bangla and English, taking as its focal point the translation of Jibanananda Das’s 1973 fiction Malloban by Rebecca Whittington in 2022. The endeavour of rendering such a profound and culturally layered text presents considerable difficulties, as it necessitates the precarious equilibrium of retaining the spirit of the source text while simultaneously making it comprehensible to a new linguistic and cultural milieu. In the course of this undertaking, the interpreter, whose primary language is American English, is compelled to negotiate between domestication and exoticism, thereby generating lexical, semiotic, and syntactic divergences in her version of the source language text. This paper endeavours to evaluate the fidelity of the rendition to the original composition while scrutinising the interpreter’s decisions in capturing the intricate wordplay, idiomatic expressions, metaphors, and narrative nuances that are distinguishing features of Jibanananda Das’s literary craft. This paper further examines the instances where the rendition may inadvertently modify or diminish the original text’s resonance, along with the ramifications of such alterations for readers of the translated composition. Moreover, this study ventures into the affective and cognitive dimensions of bilingual reading.

Supplementary material enclosed feed composition and original western blot in triplicates used for quantification

Data sets of pseudo-hypercalcemia, true hypercalcemia, and control group collected at Yiyang Central Hospital.