Data

High-speed data acquisition systems based on field-programmable gate arrays (FPGAs) often face synchronization challenges when interfacing with commercial analog-to-digital converters (ADCs), particularly under constrained hardware routing conditions and vendor-specific clocking assumptions. This work presents a vendor-independent FPGA–ADC synchronization architecture that enables reliable and repeatable high-speed data acquisition without relying on clock-capable input resources. Clock and frame signals are internally reconstructed and phase-aligned within the FPGA using mixed-mode clock management (MMCM) and input serializer/deserializer (ISERDES) resources, enabling time-sequential phase observation without the need for parallel snapshot or delay-line structures. Rather than targeting absolute metrological limits, the proposed approach emphasizes a reproducible and transparent data acquisition methodology applicable across heterogeneous FPGA–ADC platforms, in which clock synchronization is treated as a system-level design parameter affecting digital interface timing integrity and data reproducibility. Experimental validation using a custom Kintex-7 (XC7K325T) FPGA and an AFE7225 ADC demonstrates stable synchronization at sampling rates of up to 125 MS/s, with frequency-offset tolerance determined by the phase-tracking capability of the internal MMCM-based alignment loop. Consistent signal acquisition is achieved over the 100 kHz–20 MHz frequency range. The measured interface level timing uncertainty remains below 10 ps RMS, confirming robust clock and frame alignment. Meanwhile, the observed signal-to-noise ratio (SNR) performance, exceeding 80 dB, reflects the phase–noise-limited measurement quality of the system. The proposed architecture provides a cost-effective, scalable, and reproducible solution for experimental and research-oriented FPGA-based data acquisition systems operating under practical hardware constraints.

Metallic engineered nanomaterials (ENMs) have enormous technological potential and are increasingly applied across different fields and products. However, substances (including ENMs) can be detrimental to the environment and human health, thus requiring systematic testing to uncover potential hazardous effects (in compliance with REACH). Although hazard testing traditionally involves the use of animal experiments, recent years have seen a shift towards in silico modeling. High-quality data is required for in silico modeling, which is frequently not readily available for ENMs. Vast amounts of data have been published in literature but they are unstructured and scattered across numerous sources. To mitigate the limitations in data availability, we have compiled and created a nanotoxicity dataset based on published literature. The compiled dataset focuses mainly on acute in vivo endpoints conducted in a laboratory setting using metallic nanomaterials. The data extracted from literature include material information, physico-chemical properties, experimental conditions, endpoint information, and literary meta-data. The dataset presented here is useful for meta-analysis or in silico modeling purposes.

Congested platforms in public transportation systems can jeopardize the safety and comfort of passengers. Real-time crowd size estimation using Device-Free Wireless Sensing (DFWS) can offer a privacy-preserving solution for monitoring and preventing overcrowding. However, no public dataset exists on DFWS in public transportation environments. In this work, we introduce a new dataset comprising two different public transportation environments, which contains data on the presence of rail vehicles at the platform, as well as manual people counts at regular intervals. By providing this dataset, we aim to offer a foundation for other DFWS researchers to explore novel algorithms and methods in public transportation environments.