Scientific Data最新文献

In sub-Saharan Africa (SSA), onchocerciasis control has been implemented for many decades, beginning in 1974 under the Onchocerciasis Control Programme in West Africa (OCP) and in 1995 in Central and East Africa (plus Liberia) under the African Programme for Onchocerciasis Control (APOC). Since the establishment of the Expanded Special Project for Elimination of Neglected Tropical Diseases (ESPEN) in 2016, data on mass drug administration (MDA) with ivermectin has been centrally compiled for all endemic countries at implementation unit (IU) level, beginning in 2013. This paper presents HISTONCHO, a dataset collating detailed information on interventions, including vector control, from 1975 through to 2022, using the ESPEN portal (2013-2022), regional and country reports, implementation partners' records, and published literature. Reconstructing such intervention histories is crucial for an understanding of their evolution, modelling their impact, and tailoring future interventions. We discuss strengths and limitations associated with the ESPEN database, and how HISTONCHO can be improved to support modelling of intervention strategies as well as onchocerciasis control and elimination efforts by endemic country programmes.

Frailty is a common condition in older adults, characterized, among other things, by impairments in gait and movement patterns. The proposed FRAILPOL repository addresses the critical gap in geriatric research by offering a comprehensive, open-access, five body-worn inertial sensors (ankles, wrists, and back of sacrum) signals recorded during the Time Up and Go test of 668 participants, community-dwelling older adults. The gait data, as well as the stride-based spatio-temporal parameters along with demographic and health-related information, including cognitive health data, have been grouped according to established clinical criteria into three classes (robust, pre-frailty, and frailty). The technical verification includes classification by reporting results for both binary (robust, frailty) and multi-class (robust, pre-frailty, frailty) classification using classical machine learning models with acceptable accuracy.

The Music Ensemble dataset is a large-scale, cross-national database that provides detailed information about the musical, cognitive, personality, and demographic profiles of young adult musicians and nonmusicians. Data were collected from 1438 participants (aged 18-30) across thirty-five research sites in Europe, North America, South America, and Australia. Participants completed an in-person, in-lab battery of objective tests, including measures of verbal, visuospatial and musical short-term memory, executive functions (updating component), nonverbal reasoning, verbal comprehension, and music perception skills. The battery also included standardized and custom self-report questionnaires assessing music sophistication, music reward, personality traits, socioeconomic status, and demographic characteristics. Music Ensemble was preregistered, and the research protocol followed a standardized procedure across sites. The dataset also includes a large subsample of musicians and nonmusicians that are pair-matched for age, gender, and education (678 pairs). It enables well-powered investigations into the relationship between musical expertise and individual differences in cognition, personality, and demographic variables. It is also suitable for training in statistical and psychometric methods.

Soybean growth is determined by the interaction of genetic, environmental, and management factors. In the context of future climate and climate extremes, understanding genotype by environment interaction (GxE) will be crucial for selecting resilient breeding lines and optimizing management practices to minimize stress. This requires an in depth elucidation of stressful weather conditions and differing temporal responses of genotypes to those conditions. In field studies, however, the environment is often treated as a static factor, and the specific effects of weather variability on crop growth remain poorly understood. Here, we present a longitudinal dataset comprising 17,247 high-resolution RGB images of soybean breeding lines collected throughout eight years in Eschikon, Switzerland. Top-of-canopy images were acquired throughout the entire growing seasons and complemented by hourly weather data, enabling a comprehensive analysis of soybean growth dynamics under varying field conditions. High spatio-temporal image resolution allows detailed analysis of growth dynamics and GxE, supporting identification of stress-tolerant genotypes to improve yield prediction and yield stability.

Eutrophication driven by excessive nutrient inputs poses a growing threat to marine ecosystems worldwide. Macroalgal aquaculture has been recognized as a promising nature-based solution to mitigate nutrient enrichment; however, a systematic global synthesis of nutrient removal capacity across macroalgae species under varying environmental conditions remains lacking. Here, this study presents the first comprehensive open-access global dataset on nutrient removal capacity by marine macroalgae. The dataset comprises 2,011 records from 149 theses or peer-reviewed articles published between 1995 and 2024, covering 113 macroalgae species from 234 sampling sites across 23 countries on six continents. Each record includes publication details, geographic and taxonomic information, environmental parameters, and macroalgal nutrient removal performance (removal rates, efficiencies, and amounts). The dataset is organized into multiple Date tables, including subsets for nutrient-specific removal metrics and in situ experiments, thereby enabling tailored analyses. This resource provides the most extensive synthesis to date of macroalgal nutrient uptake capacity, and supports evidence-based functional macroalgal aquaculture planning, targeted eutrophication management, and marine ecosystem restoration.

Climate indicators are essential for monitoring ongoing climate change, supporting climate impact research, conducting spatial hot spot analyses and assessing attribution questions. These efforts rely on high-quality, reliable datasets that adhere to FAIR data principles. We present a curated dataset of 117 climate indicators for Austria, covering the period from 1961 onward at a 1-km spatial resolution. The dataset includes climate indicators related to temperature, precipitation, radiation, snow, runoff and humidity, with spatial (area means) and temporal (climatological reference period means) aggregations to enable rapid climate impact analysis. The workflow used to compute these indices is supported by a careful technical validation procedure and is designed to ingest diverse climate datasets, enabling the creation of climate indices beyond the scope presented here. Both the dataset and the workflow thus offer a robust, flexible and user-friendly resource for advancing climate research and supporting informed decision-making.

Tactile perception modeling has been important research topic for years. However, due to individual differences and complexity of human cognition, the tactile perception modeling is challenging. One of the main challenges in this field is the lack of a rich dataset with sufficient inter-subject, inter-session, and intra-session diversities. Here, for the first time, we present a dataset of human position and intensity perception for 51 different vibration patterns. The dataset includes the experimental results for 40 individuals (20 female and 20 male) in two different sessions. The experimental results include the perceived intensity and position of each vibration pattern as well as selection time and confidence level. After each session the participants were also asked to fill a questionnaire file. We also collected the anthropometric and demographic data, and the participants underwent Bioelectrical Impedance Analysis (BIA) to measure body composition indicators. The detailed results for each participant is located in the dataset. This dataset can be used to develop tactile perception models, study individuals' perception differences, and design tactile sensory feedback.

Macropharyngodon meleagris is a coral reef-dwelling, benthic predatory fish renowned for its striking coloration and distinctive body patterns. It exhibits pronounced sexual dimorphism and is characterized by the presence of prominent canine-like teeth. Here, we employed PacBio HiFi sequencing combined with Hi-C assembly technology to generate a high-quality, chromosome-level genome assembly of M. meleagris. The final assembly spans 666 Mb across 24 chromosomes, with high contiguity (a contig N50 of 20.57 Mb and a scaffold N50 of 29.79 Mb). Approximately 27.63% of the genome is composed of repetitive elements. A total of 21,940 protein-coding genes were predicted, with 98.76% successfully assigned functional annotations. The assembled genome exhibits high completeness (98.7% BUSCO completeness) and accuracy (98.05% for WGS short reads, 99.86% for HiFi long reads and 92.64% for RNAseq reads).

This study addresses the gap in air quality monitoring metadata reporting by building a classifier for air quality station types and area characteristics. It leverages ultra-high-resolution land cover data, complemented by additional demographic and gridded information. We employ advanced machine learning methods, including convolutional neural networks and transformers. Through a custom training approach, we fine-tune pre-trained models on 7000 images and label +8000 additional monitors, resulting in a robust model for classifying air quality stations by area characteristics (urban, rural) and source type (background, non-background). The result is a global harmonized dataset of governmental air quality station metadata for particulate matter, with  ~ 15000 monitors from 106 countries. For each station, the dataset provides an identifier, geographical coordinates, country, area characteristics, source type, and classification status. This dataset enables global feasibility studies and regional analyses of conditions leading to exposure. By providing a consistent classification of monitoring stations, it also allows for meaningful comparisons of sectoral exposure contributions across countries, regions, and station types, supporting comparative studies and health impact assessments.

Image databases are central to empirical aesthetics, enabling tests of how image statistics relate to observers' appreciation. However, many existing databases have two key limitations: (1) they conflate low-level visual features with high-level semantic content, making it difficult to separate visual from cognitive influences on aesthetic judgments; and (2) they are imbalanced, overrepresenting highly appreciated images. To address these issues, we present the Minimum Semantic Content (MSC) database, a large, systematically curated resource for computational aesthetics. It comprises 10,426 natural scenes with reduced, homogenized semantic content, minimizing cognitive and emotional confounds. Each received 100 individual aesthetic ratings from naïve observers, drawn from a pool of approximately 10,000 participants, via crowdsourcing. The database includes both "beautified" and "uglified" versions, generated with a manipulation technique that promotes uniform coverage across the aesthetic spectrum. This broader distribution mitigates bias and overfitting in models. Validation also shows improved robustness in computational models overall. This database enables researchers to study how perceptual features shape aesthetic judgments, using stimuli with very limited semantic and contextual confounds.