Scientific Data最新文献

The sharing of multimodal magnetic resonance imaging (MRI) data is of utmost importance in the field, as it enables a deeper understanding of facial nerve-related pathologies. However, there is a significant lack of multi-modal neuroimaging databases specifically focused on these conditions, which hampers our comprehensive knowledge of the neural foundations of facial paralysis. To address this critical gap and propel advancements in this area, we have released the Multimodal Neuroimaging Dataset of Meige Syndrome, Facial Paralysis, and Healthy Controls (MND-MFHC). This dataset includes detailed clinical assessments of 53 individuals with facial paralysis (FP), 31 patients with Meige syndrome (MS), and 102 healthy controls (HC). To promote open access, the BIDS-formatted data and associated quality control reports can be accessed through the Science Data Bank (SciDB). By sharing this comprehensive dataset, our aim is to facilitate further research and exploration into the intricate neural mechanisms underlying facial nerve-related pathologies.

Citizen Science initiatives have a worldwide impact on environmental research by providing data at a global scale and high resolution. Mapping marine biodiversity remains a key challenge to which citizen initiatives can contribute. Here we describe a dataset made of both underwater and aerial imagery collected in shallow tropical coastal areas by using various low cost platforms operated either by citizens or researchers. This dataset is regularly updated and contains >1.6 M images from the Southwest Indian Ocean. Most of images are geolocated, and some are annotated with 51 distinct classes (e.g. fauna, and habitats) to train AI models. The quality of these photos taken by action cameras along the trajectories of different platforms, is highly heterogeneous (due to varying speed, depth, turbidity, and perspectives) and well reflects the challenges of underwater image recognition. Data discovery and access rely on DOI assignment while data interoperability and reuse is ensured by complying with widely used community standards. The open-source data workflow is provided to ease contributions from anyone collecting pictures.

Assessing the dynamics of offshore wind potential and costs is essential for low-carbon energy policy decision-making and energy modeling, but no open-source, spatial explicit and technologically detailed dataset is available. This study addresses this gap by employing a consistent assessment framework that integrates GIS analysis, a wind reanalysis model, a component-based cost model and scenario analysis. It identifies suitable space for offshore wind deployment considering 12 technical and policy constraints, estimates hourly output curves, capacity factors, and technology cost dynamics by components across 5058 grid points with a 10 km resolution from 2020 to 2035 under three technical change scenarios. The dataset has been validated through comparisons with existing offshore wind projects and datasets, and is stored in two formats (GeoTIFF and NetCDF4). This dataset offers extensive potential for use as an input in climate policy and energy system research.

This study highlights the vital role of high-resolution (HR), open-source land cover maps for food security, land use planning, and environmental protection. The scarcity of freely available HR datasets underscores the importance of multi-spectral HR aerial images. We used unmanned aerial vehicle (UAV) to capture images for a centimeter-level orthomosaics, facilitating advanced remote sensing and spatial analysis. Our method compares the efficacy and accuracy of object-based image analysis (OBIA) combined with random forest and convolutional neural networks (CNN) for land cover classification. We produced detailed land cover maps for 27 varied landscapes across Serbia, identifying nine unique land cover classes and assessing human impact on natural habitats. This resulted in a valuable dataset of HR multi-spectral orthomosaics across ecological zones, alongside land cover classification with extensive metrics and training data for each site. This dataset is a valuable resource for researchers working on habitats mapping and assessment for biodiversity monitoring studies on one side and researchers working on novel machine learning methods for land cover classification.

This paper presents an open-source dataset intended to enhance the analysis and optimization of photovoltaic (PV) power generation in urban environments, serving as a valuable resource for various applications in solar energy research and development. The dataset comprises measured PV power generation data and corresponding on-site weather data gathered from 60 grid-connected rooftop PV stations in Hong Kong over a three-year period (2021-2023). The PV power generation data was collected at 5-minute intervals at the inverter-level. The meteorological data was collected at 1-minute intervals from an on-site weather station. The metadata was represented using the Brick schema, which simplifies data comprehension and the development of smart analytics applications. This paper provides a detailed description on the site specifications, data collection method, data records, and data validation. This dataset can be used in various applications - PV generation benchmarking, PV degradation analysis, PV fault detection, solar radiation and PV power generation forecasting, and the simulation and design of PV systems.

Multi-environmental trials (MET) with temporal and spatial variance are crucial for understanding genotype-environment-management (GxExM) interactions in crops. Here, we present a MET dataset for winter wheat in Germany. The dataset encompasses MET spanning six years (2015-2020), six locations and nine crop management scenarios (consisting of combinations for three treatments, unbalanced in each location and year) comparing 228 cultivars released between 1963 and 2016, amounting to a total of 526,751 data points covering 24 traits. Beside grain yield, ten agronomic traits, four baking quality traits, plant height, heading date, maturity date and six fungal disease infection indices are included. Additionally, we provide management records, including fertilizer use, plant protection measures, irrigation, and weather data. We demonstrate how this dataset can address four agronomic questions related to GxExM interactions. Further potential applications of the dataset include empirical analyses, genomic and enviromic analyses for breeding targets, or development of decision-supporting models for agricultural management and policy decisions.

Soil erosion in North Africa modulates agricultural and urban developments as well as the impacts of flash floods. Existing investigations and associated datasets are mainly performed in localized urban areas, often representing a limited part of a watershed. The above compromises the implementation of mitigation measures for this vast area under accentuating extremes and continuous hydroclimatic fluctuations. To address this deficiency, we use the Revised Universal Soil Loss Equation to map surface erosion, providing the first insight into the decadal impacts of land degradation, which are largely unconstrained on North Africa's continental scale. We generate soil erosion maps for the major hydrological basins of North Africa using Google Earth Engine and multiple hydroclimatic and land use datasets, covering 5.8 million square kilometers. The generated geospatial dataset integrates land use, soil erodibility, slope, vegetation cover, and land practices. The resulting product is an expansive and publicly available Soil erosion susceptibility maps and rasters dataset (SESMAR). This dataset is a crucial step toward understanding the drivers of soil erosion in this vast, poorly characterized area as well as its potential to be used for future soil conservation campaigns for both agricultural and urban planning. We validate SESMAR using the Global Rainfall Erosivity Database (GloREDa) and the European Soil Data Centre (ESDAC) datasets as well as published peer-reviewed reports across 20 watersheds, demonstrating a robust agreement in assessing the average annual soil loss values and soil erosion classes in local areas covered by independent study teams. Our continental maps show commendable accuracy, supporting scientists, practitioners, and policymakers in their efforts for more resilient land management practices across North Africa to mitigate rising hydroclimatic extremes.

Anisarchus medius (Reinhardt, 1837) is a widely distributed Arctic fish, serving as an indicator of climate change impacts on coastal Arctic ecosystems. This study presents a chromosome-level genome assembly for A. medius using PacBio sequencing and Hi-C technology. The PacBio assembly totaled 739.07 Mb across 697 contigs, with a Contig N50 of 10.004 Mb. Hi-C mapping yielded 23 chromosomes, with a successful mapping rate of 90.53% and a Scaffold N50 of 30.20 Mb. Genome BUSCO integrity was 97.05%. Repetitive sequences accounted for 240.83 Mb (32.58%) of the genome. Non-coding RNA annotations included 4,928 rRNAs, 9,663 tRNAs, 347 snRNAs, and 21 snoRNAs. A total of 30,345 protein-coding genes were identified, encoding 46,603 proteins, with a BUSCO completeness of 94.98%. Molecular pathway related to the endocrine system, carbohydrate metabolism, folding, sorting, and degradation, signal transduction, and transport and catabolism contribute to A. medius adaptation to extreme Arctic environments. This high-quality genome provides valuable genetic resources for understanding Arctic adaptations and supporting polar ecological conservation and management.

We present a new database, EutherianCoP, of fossil mammals which lived globally from the Late Pleistocene to the Holocene. The database includes 13,972 fossil occurrences of 786 extant or recently extinct placental mammal species, plus 155,198 current occurrences for those of them which survived to the present. The occurrences are correlated with radiometric age information. For all species, we provide 32 different traits, inclusive of taxonomic, phenotypic, life history, biogeographic and phylogenetic information. Differently from any other compilation, the occurrences are complemented with estimates of past climatic conditions, including site-interpolated monthly and annual precipitation and temperature, leaf area index, megabiome type and net primary productivity, which are derived from transient paleo model simulations conducted with the Community Earth System Model 1.2 and the BIOME4 vegetation model. All data are further downloadable for further investigation.