Mendeley Data Showcase

This dataset contains the raw data supporting all main and supplementary figures used in the manuscript "An intranasal adenoviral-vectored vaccine protects against highly pathogenic avian influenza H5N1 in naïve and antigen-experienced animals" intended for publishing in Cell Reports Medicine. The dataset includes antibody and T-cell responses in naïve and vaccine-immunized mice as well as antibody responses in hamsters after intranasal ChAd-vectored H5 vaccination, along with viral titers following H5N1 challenge.

The data is original data collected from the field measurement in the forests of Zegie Peninsula and neighbouring islands.

This dataset contains information on household's socioeconomic and demographic information of 476 sample households in northern Tanzania. This data was collected using household survey questionnaires in 2023.

The branched glycerol dialkyl glycerol tetraethers (brGDGTs) and quantitatively reconstructed temperature record from Yazihai Lake in the northern Shanxi Province, China, covering the period from 5500 to 3500 cal yr BP.

Dataset on household food security

We build the first dataset for unified insect image segmentation that includes both salient and camouflaged categories captured in diverse real-world natural scenes. Compared to existing datasets, ours features greater taxonomic diversity, higher-resolution images, and fine-grained manual annotations, providing a challenging benchmark for unified segmentation tasks.

The stalagmite-based EASM rainfall reconstruction

Supplementary material for the article "Association between timing of hoof trimming in primiparous cows and hoof health and survival in second lactation" J. Dairy Sci. TBC:1–18 https://doi.org/10.3168/jds.2025-27156

This dataset contains 704 app reviews related to generative AI tools, collected from app stores, social media, review sites, and existing publicly available datasets. It is designed for aspect-based sentiment analysis and includes columns for full review text, argument (a specific text span corresponding to a single aspect), identified aspects, sentiment labels, source of the data, and the AI tool being reviewed. A single review may appear in multiple rows when it contains multiple aspects; in such cases, each row represents a unique argument–aspect pair derived from the same review. All personal identifiers have been removed to ensure privacy. Basic preprocessing was performed, including the removal of unnecessary columns and rows and minor text edits. The dataset is intended for research in sentiment analysis, natural language processing, and related tasks.

Distributed Denial of Service (DDoS) attacks aim to disrupt networked services by overwhelming server resources, thereby preventing legitimate users from accessing critical applications. Among various attack vectors, TCP-based flooding attacks remain particularly impactful due to the widespread use of the Transmission Control Protocol in reliable Internet services such as web applications, cloud back-end systems, databases, and enterprise platforms. This dataset presents a time-series representation of TCP flooding attack traffic, derived from the widely used benchmark dataset CICDDoS2019. Unlike the original CICDDoS2019 flow-based CSV files, which describe traffic characteristics at the individual TCP flow level, the proposed dataset aggregates network traffic over fixed 5-second time windows, enabling temporal analysis of server load and attack progression. The dataset was generated by replaying selected CICDDoS2019 packet capture (pcap) files using tcpreplay at varying network speeds of up to 20 Gbps. Network traffic was captured using Wireshark, segmented into consecutive 5-second intervals, and processed to extract time-dependent TCP packet statistics for each interval. This processing strategy enables direct observation of how TCP flooding attacks evolve over time and how they affect server-side traffic intensity. The resulting dataset focuses on TCP-based flooding behaviors, including TCP-SYN, TCP-SYN-ACK, TCP-ACK, and TCP-RST packet activity. Each time window is represented by a compact set of aggregated features that quantify TCP control packet counts and overall TCP traffic volume. The structured, labeled, and time-indexed nature of the data makes the dataset particularly suitable for the development and evaluation of machine learning–based intrusion detection systems (IDS), including both attack detection and severity assessment models. The dataset consists of six CSV files. Two primary files represent time-series traffic captured on 12 January and 11 March, corresponding to the training and testing days defined in CICDDoS2019. These files include the following attributes: pcap file identifier, time window index, counts of SYN, SYN-ACK, ACK, and RST packets, and the total number of TCP packets per interval. Additional CSV files are derived from these base datasets and provide labeled samples for TCP-SYN flooding attack detection and attack severity classification. By providing time-window–based TCP traffic characteristics, this dataset supports research on machine learning–driven intrusion detection, server health monitoring, and adaptive resource management in cloud and virtualized environments, where scaling and mitigation decisions depend on the temporal behavior of incoming traffic rather than individual flow statistics.