Mendeley Data Showcase

Raw and processed physiological data for figures. Supplemental Information on response characteristics of ventricular and subarachnoid recording electrodes.

The performance of SURE-Pipe was evaluated using simulated and real-world comparative genomics datasets. Simulated genomes were generated using a custom in-house simulator based on the Stan framework, modelling coalescent divergence, point mutations, and structural rearrangements between target and neighbouring clades. Each simulation contained five target and five neighbouring genomes with >97% ANI, representing closely related genomes with subtle sequence variation. Defined shared and unique regions (200–5000 bp) were embedded within target genomes to establish benchmarking ground truth. Shared regions exhibited among target genomes and neighbours, while unique regions were present only in the target group. Additional structural variations, including inversions and duplications, were introduced to mimic realistic genomic rearrangements. Simulations were generated across genome sizes ranging from 100 kb to 75 Mb. For accuracy assessment, 150 independent simulations using 4 Mb genomes were performed, and the outputs of SURE-Pipe v1.1 were compared with KEC v1.1 and FUR v4.3 using nucleotide-level sensitivity, specificity, precision, accuracy, and Matthews correlation coefficient (MCC). Computational scalability was further assessed through genome-size and genome-number scaling experiments. Genome-size benchmarking used datasets of five target and five neighbouring genomes ranging from 0.1 Mb to 640 Mb. Genome-number benchmarking fixed genome size at 5 Mb while increasing target:neighbour datasets from 5T:20N up to 160T:640N genomes. Runtime and peak memory usage were recorded using /usr/bin/time on a Pop!_OS 22.04 LTS workstation equipped with an Intel® Xeon® E-2124G CPU (4 cores, 3.40 GHz) and 16 GB RAM. The pairwise genome comparison mode was validated using six genome pairs spanning diverse taxonomic groups, genome sizes (10.7 kb–69.5 Mb), and GC contents (27–76%), including viral, bacterial, and fungal genomes. Additionally, groupwise genome comparison was performed across 24 closely related Bacillus species to identify species-specific genomic regions. Four genomes per species (>97% ANI) were selected, while neighbouring datasets included reference genomes from 42 Bacillus species. This analysis enabled the identification of conserved intraspecies regions, species-specific unique regions, and regions shared among neighbouring taxa.

Computational scalability assessment of SURE-Pipe Computational performance of SURE-Pipe was evaluated using two simulation-based benchmarking strategies with default tool parameters. First, genome-size scalability was assessed using datasets containing five target and five neighbouring genomes, with genome sizes ranging from 0.1 Mb to 640 Mb. Second, genome-number scalability was evaluated using fixed 5 Mb genomes while progressively increasing dataset sizes at a 1:4 target-to-neighbour ratio, from 5T:20N up to 160T:640N genomes. Runtime and peak memory usage were measured using /usr/bin/time. All analyses were performed on a Pop!OS 22.04 LTS workstation equipped with an Intel® Xeon® E-2124G CPU (4 cores, 3.40 GHz) and 16 GB RAM. These experiments demonstrated the scalability and computational efficiency of SURE-Pipe across diverse genome sizes and dataset complexities while maintaining high detection accuracy for unique and shared genomic regions.

The MATLAB scripts and data analyzed in the paper titled: "Influence of visual field offset on collective behavior: An exploration using the two-dimensional Vicsek model" (https://doi.org/10.1103/bbl5-1yjb)

Raw, processed, and fitted IOP data of manuscript figures.

This panel dataset integrates three core sources: the China Labor-force Dynamic Survey (CLDS, 2012/2014/2016), the U.S. NCEI’s Global Summary of the Day (GSOD) meteorological data, and the China City Statistical Yearbook. Meteorological data were spatially interpolated via inverse distance weighting (IDW) into 0.1°×0.1° grids, with missing values imputed using five nearest stations. City-level annual average temperature (primary variable, plus its squared term for non-linearity testing) and annual precipitation were derived. The dependent variable is the natural logarithm of individual annual total income. Controls include individual attributes (age, education years) and urban socioeconomic indicators (fiscal expenditure-to-GDP ratio, economic agglomeration, secondary industry share, GDP growth rate). Standard data cleaning (coding unification, outlier removal, price deflation) was applied. The final sample includes 29,629 valid observations, with a mean log income of 9.773, average annual temperature of 16.425°C, and mean age of 44.117 years. This dataset merges micro individual heterogeneity with macro climatic and economic data, supporting analyses of temperature’s causal effects on labor income and related mechanisms.

Analise_Filogenia_hondialvs

Dataset and analysis output in SPSS

This dataset contains data related to the Hindi translation, cultural adaptation, validation, and test–retest reliability testing of the Telematic Fugl Meyer Assessment Scale–Upper Extremity (TFMA-UE) in stroke patients. The purpose of the study was to develop a standardized Hindi version of the TFMA-UE and evaluate its psychometric properties for use among Hindi-speaking stroke survivors. The TFMA-UE is a widely used clinical outcome measure for assessing upper extremity motor recovery following stroke, particularly in tele-rehabilitation settings. The dataset includes demographic and clinical details of participants such as age, gender, type of stroke, duration since stroke onset, affected side, and severity of upper limb impairment. It also contains translated assessment forms, scoring records, expert review data, and statistical outputs generated during validation and reliability analysis. The Hindi translation was performed using a standardized forward–backward translation process to ensure semantic and cultural equivalence with the original English version. Content validity was evaluated by a panel of rehabilitation experts, including physiotherapists and neurologists, using item-level and scale-level validity indices. Construct validity was assessed through comparison with established upper extremity functional assessment measures. Reliability analysis included internal consistency and test–retest reliability. Cronbach’s alpha was used to determine internal consistency, while Intraclass Correlation Coefficients (ICC) were used to evaluate stability of scores across repeated assessments. Additional statistical measures such as standard error of measurement (SEM) and descriptive statistics are included in the dataset. All participant information was anonymized, and data collection was conducted according to institutional ethical guidelines. The dataset may be useful for researchers, clinicians, and rehabilitation professionals involved in stroke rehabilitation, tele-rehabilitation, psychometric testing, and cross-cultural adaptation of clinical assessment tools. The validated Hindi TFMA-UE can support reliable remote assessment of upper extremity motor function in Hindi-speaking stroke populations and may contribute to improved accessibility of rehabilitation services in diverse clinical settings.