Understanding long-term changes of fish diversity and community assembly rules is crucial for freshwater conservation. Growing evidence indicates that studying functional and phylogenetic diversity beyond purely taxonomic considerations can provide different but complementary information on community assembly. Here, the taxonomic, functional and phylogenetic β-diversity of fish communities, as well as the community assembly mechanisms were explored in five impounded lakes of the China’s South-to-North Water Diversion Project (SNWDP) from the 1980s to the 2010s. We found that: 1) there was an obvious trend of species homogenization in the five impounded lakes, but the long-term transformations of different dimensional β-diversities were divergent; 2) water quality and land use variables have greater impacts in multi-dimensional β-diversity; and, 3) community assembly process in taxonomic and functional dimensions were dominated by random process in both periods, while shifted from limiti..., , , GENERAL INFORMATION

1. Title of Dataset: Temporal variations of the multifaceted biodiversity and assembly mechanisms in lake fish assemblages

2. Author Information A. Principal Investigator Contact Information Name: Zhice Liang Institution: Institute of Hydrobiology Address: Wuhan, China Email:

   B. Associate or Co-investigator Contact Information Name: Chuanbo Guo Institution: Institute of Hydrobiology Address: Wuhan, China Email:

3. Date of data collection (single date, range, approximate date): 1980-1989, 2010-2019

4. Geographic location of data collection: Five impounded lakes of the China’s South-to-North Water Diversion Project

5. Information about funding sources that supported the collection of the data: National Natural Science Foundation of China, Award: 32172980; the earmarked fund for CARS-45; Chinese Academy of Sciences, Award: E329020401

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1. Licenses/restrictions placed on the data: CC0 1.0 Un...

This data is used for discovering spatio-temporal wind and wave pattern for identifying ocean energy potential. The study has conducted using multidimensional clustering for obtaining space and temporal clusters simultaneously.

Frameworks exclusively considering functional diversity are gaining popularity, as they complement and extend the information provided by taxonomic diversity metrics, particularly in response to disturbance. Taxonomic diversity should be included in functional diversity frameworks to uncover the functional mechanisms causing species loss following disturbance events. We present and test a predictive framework that considers temporal functional and taxonomic diversity responses along disturbance gradients. Our proposed framework allows us to test different multidimensional metrics of taxonomic diversity that can be directly compared to calculated multidimensional functional diversity metrics. It builds on existing functional diversity-disturbance frameworks both by using a gradient approach and by jointly considering taxonomic and functional diversity. We used previously unpublished stream insect community data collected prior to, and for the two years following, an extreme flood event that occurred in 2013. Using 14 northern Colorado mountain streams, we tested our framework and determined that taxonomic diversity metrics calculated using multidimensional methods resulted in concordance between taxonomic and functional diversity responses. By considering functional and taxonomic diversity together and using a gradient approach, we were able to identify some of the mechanisms driving species losses following this extreme disturbance event.

This study constructed a multidimensional indicator system to evaluate spatio-temporal heterogeneity of China’s import and export trade of 31 provinces from 2000 to 2022. This study describes the distribution of China’s import and export trade by using location Gini coefficient and exploratory spatial analysis. Additionally, Multiple linear regression was used to ascertain the extent of contribution by various factors on the spatio-temporal heterogeneity of import and export trade. The simulation results show that inter-provincial import and export trade displayed distinct spatio-temporal differentiation characteristics with a prominent east-to-west disparity from 2000 to 2022. The trade links between various regions of the country have gradually strengthened, with a corresponding high correlation to the level of economic development. GDP, financial expenditure, freight transportation volume, technology market turnover, foreign investment, and disposable income of all residents, significantly influence the per capita export and import volume. In general, it is suggested that China and developing countries should take effective measures to promote balanced trade development, strengthen regional cooperation and coordination, and promote green trade and sustainable development.

Principal Component Analysis (PCA) is a well-established technique in remote sensing for the visualization of multidimensional data. It reduces redundancy in multiband or multitemporal imagery, increases the signal-to-noise ratio and provides an opportunity to use multitemporal datasets for change detection. PCA transforms the axes of multidimensional data in such way that the new axes (the principal components) account for variances within the data, with the first PC accounting for the largest variance and the last PC accounting for the smallest variance. In our study PCA of TerraSAR-X time stacks of backscatter intensity and interferometric coherence provided a good spatial overview of the essential information contained within the multiple time slices. The PC1 for both stacks showed the most common features of the contributing images and represented the means of the temporal stacks. The PC1 of the coherence stack accounted for 29% of the variance (or unique information) and mapped (i) water bodies (lakes and river), (ii) rocky outcrops, and (iii) the remaining land surfaces. The PC1 of the backscatter stack accounted for 35% of the variance and was contaminated by such effects as the presence or absence of lake ice and shadow/layover in the rocky outcrops region. Anomalies in seasonal patterns were demonstrated by the higher PCs. The PC2 of the backscatter stack accounted for 22% of the variance and delineated water bodies. The PC3 of backscatter stack accounted for only 4% of the variance in the dataset and represented the spatial variance in river ice conditions during spring. The PC2 of coherence, which accounted for 9.5% of the variance in the coherence stack, represented the spatially variable snow conditions in spring (snowmelt to the south and stable snow cover to the north).

Beta-diversity, or the spatio-temporal variation in community composition, can be partitioned into turnover and nestedness components in a multidimensional framework. Forest structure, including comprehensive characteristics of vertical and horizontal complexity, strongly affects species composition and its spatial variation. However, the effects of forest structure on beta-diversity patterns in multidimensional and multiple-scale contexts are poorly understood. Here, we assessed beta-diversity at local (a 20-ha forest dynamics plot) and regional (a plot network composed of 19 1-ha plots) scales in a Chinese subtropical evergreen broad-leaved forest. We then evaluated the relative importance of forest structure, topography, and spatial structure on beta-diversity and its turnover and nestedness components in taxonomic, functional, and phylogenetic dimensions at local and regional scales. We derived forest structural parameters from both unmanned aerial vehicle light detection and ranging (UAV LiDAR) data and plot inventory data. Turnover component dominated total beta-diversity for all dimensions at the two scales. With the exception of some components (taxonomic and functional turnover at the local scale; functional nestedness at the regional scale), environmental factors (i.e., topography and forest structure) contributed more than pure spatial variation. Explanations of forest structure for beta-diversity and its component patterns at the local scale were higher than those at the regional scale. The joint effects of spatial structure and forest structure influenced component patterns in all dimensions (except for functional turnover) to some extent at the local scale, while pure forest structure influenced taxonomic and phylogenetic nestedness patterns to some extent at the regional scale. Our results highlight the importance and scale dependence of forest structure in shaping multidimensional beta-diversity and its component patterns. Clearly, further studies need to link forest structure directly to ecological processes (e.g., asymmetric light competition and disturbance dynamics) and explore its roles in biodiversity maintenance.

The P-Cube data set is a 100 km resolution subset of many important variables from three Polar Pathfinder Projects in common projection (Lambert azimuthal equal-area) and common grid (EASE-Grid). For the first time, data sets from multiple sensors measuring the Arctic region are being distributed in a manner that facilitates study of polar processes and interactions between them. A "data cube" of the Polar Pathfinder Products, hence the name "P-Cube," allows users to access variables derived from three satellite sensors and use them together. The common spatial resolution for P-Cube is dictated by the lowest resolution product (TOVS data). The P-Cube allows browsing of the multi-sensor data and exploring the relationships among the variables. For AVHRR and SSM/I data, the user may turn to the full resolution, single instrument Polar Pathfinder data sets for greater details. A wide range of polar climate research applications is possible using this multi-dimensional suite of variables from AVHRR, TOVS and passive microwave instruments. The prototype version of the P-Cube included on this CD-ROM has a temporal coverage of January 1, 1988 to December 31, 1989, and a spatial coverage poleward of 60 degrees N latitude. Future versions of the P-Cube will have expanded temporal and spatial coverage, as well as additional variables. The current prototype version of the P-Cube includes the following: atmospheric temperature (10 levels), precipitable water (5 layers), boundary layer stratification parameter, geostrophic drag coefficient, turning angle, and microwave surface emissivity (from TOVS); surface albedo, surface (skin) temperature, solar zenith angle, cloud fraction, standard deviation of surface albedo, and standard deviation of surface temperature (from AVHRR); and total ice concentration, surface type and ice velocity (from AVHRR and SSM/I). From the National Centers for Environmental Prediction (NCEP) surface pressure is provided. Please note that the P-Cube data set is a prototype for a potential long term data set consisting of integrated Polar Pathfinder Products. At the time of the production of the Polar Pathfinder Sampler CD-ROM, the algorithms for a number of variables are still undergoing development and validation.

Reducing multidimensional relative poverty is one of the important issues in the current global poverty governance field. This article takes 12 ethnic regions in China as the research object and constructs a multidimensional relative poverty measurement system. The calculated multidimensional relative poverty index is decomposed according to provinces, cities, dimensions, and indicators. Then, the Dagum Gini coefficient and convergence analysis are used to analyze spatiotemporal heterogeneity and convergence characteristics. The results show that the multi-dimensional relative poverty situation of various provinces in ethnic minority areas has improved from 2012 to 2021, among which Tibet province is the most serious and Shaanxi is the best. According to the analysis of convergence, it was observed that there is no σ-convergence of multidimensional relative poverty in ethnic areas in general, and there is absolute β-convergence in general and in the southwest and northwest regions, and there is no absolute β-convergence in the northeast region. Based on this, policy recommendations for reducing multidimensional relative poverty are proposed at the end of the article. Compared with previous studies, this article focuses on ethnic regions that are easily overlooked. Starting from the dimensions of economy, social development, and ecological environment, the poverty measurement system has been enriched.

The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics, and knowledge describing the human presence in the planet. The Joint Research Centre (JRC) and the Directorate General for Regional Development (DG REGIO) of the European Commission support the GHSL activities. The GHSL contributes to the international partnership “GEO Human Planet Initiative”. The GHSL methods rely on automatic spatial data mining technologies allowing the extraction of analytics and knowledge from large amount of heterogeneous data including global, fine-scale satellite-image data streams, census data, and crowd sources or volunteering geographic information sources. Spatial data reporting objectively and systematically about the presence of population and built-up infrastructures are necessary for any evidence-based modelling or assessing of i) human and physical exposure to threats as environmental contamination and degradation, natural disasters and conflicts, ii) impact of human activities on ecosystems, and iii) access to resources. The GHS Urban Centre Database (GHS- UCDB) describes spatial entities called “urban centres” accordingly to a set of multi-temporal thematic attributes gathered from the GHSL sources integrated with other sources available in the open scientific domain. The Urban Centres are defined by specific cut-off values on resident population and built-up surface share in a 1x1 km global uniform grid. The input data it is generated by the GHSL, and the operating parameters are set in the frame of the “degree of urbanization” (DEGURBA) methodology. The DEGURBA is a methodology for delineation of urban and rural areas made for international statistical comparison purposes that is developed by the European Commission, the Organization for Economic Co-operation and Development (OECD), the Food and Agriculture Organization of the United Nations (FAO), UN-Habitat and the World Bank. The reference GHSL input data used to delineate the Urban Centres are included in the Community pre-Release of GHS Data Package (GHS CR2018) in support to the GEO Human Planet Initiative. The parameter set used to delineate the Urban Centres from the input data are included in the GHSL settlement classification model SMODv9s10E 2018. The reference epoch for the spatial delineation of the Urban Centres is 2015. The attributes of the GHS-UCDB have different time depth for a maximum of 40 years, depending on availability of the input sources.

We present a linked and manually verified dataset on mobility between European higher education institutions (HEIs) as a multiplex, multipartite, multi-labeled, spatial network. The added values of this work are the accurate identification and geocoding of the institutions, the integration of institutional- and regional economic data of the European Tertiary Register (ETER) and the Global Research Identifier Database (GRID), and the manual verification of information sources. The proposed integrated and validated networked data can be used for studying socio-economic factors of mobility, profiling HEIs, and measuring their attractivity based on the number of incoming students and lecturers. The database gives the opportunity to researchers of the development of multi-layer, multi-dimensional, spatial and temporal networks to validate their algorithms and define transparent and easily interpretable benchmark problems.

Neural activity is coordinated across multiple spatial and temporal scales, and these patterns of coordination are implicated in both healthy and impaired cognitive operations. However, empirical cross-scale investigations are relatively infrequent, due to limited data availability and to the difficulty of analyzing rich multivariate datasets. Here we applied frequency-resolved multivariate source-separation analyses to characterize a large-scale dataset comprising spiking and local field potential activity recorded simultaneously in three brain regions (prefrontal cortex, parietal cortex, hippocampus) in freely-moving mice. We identified a constellation of multidimensional, inter-regional networks across a range of frequencies (2-200 Hz). These networks were reproducible within animals across different recording sessions, but varied across different animals, suggesting individual variability in network architecture. The theta band (~4-10 Hz) networks had several prominent features, including roughly equal contribution from all regions and strong inter-network synchronization. Overall, these findings demonstrate a multidimensional landscape of large-scale functional activations of cortical networks operating across multiple spatial, spectral, and temporal scales during open-field exploration.

To address the limitations of existing stock price prediction models in handling real-time data streams—such as poor scalability, declining predictive performance due to dynamic changes in data distribution, and difficulties in accurately forecasting non-stationary stock prices—this paper proposes an incremental learning-based enhanced Transformer framework (IL-ETransformer) for online stock price prediction. This method leverages a multi-head self-attention mechanism to deeply explore the complex temporal dependencies between stock prices and feature factors. Additionally, a continual normalization mechanism is employed to stabilize the data stream, enhancing the model’s adaptability to dynamic changes. To ensure that the model retains prior knowledge while integrating new information, a time series elastic weight consolidation (TSEWC) algorithm is introduced to enable efficient incremental training with incoming data. Experiments conducted on five publicly available datasets demonstrate that the proposed method not only effectively captures the temporal information in the data but also fully exploits the correlations among multi-dimensional features, significantly improving stock price prediction accuracy. Notably, the method shows robust performance in coping with non-stationary and frequently changing financial market data.

Administrative data play an important role in performance monitoring of healthcare providers. Nonetheless, little attention has been given so far to the emergency department (ED) evaluation. In addition, most of existing research focuses on a single core ED function, such as treatment or triage, thus providing a limited picture of performance. The goal of this study is to harness the value of routinely produced records proposing a framework for multidimensional performance evaluation of EDs able to support internal decision stakeholders in managing operations. Starting with the overview of administrative data, and the definition of the desired framework’s characteristics from the perspective of decision stakeholders, a review of the academic literature on ED performance measures and indicators is conducted. A performance measurement framework is designed using 224 ED performance metrics (measures and indicators) satisfying established selection criteria. Real-world feedback on the framework is obtained through expert interviews. Metrics in the proposed ED performance measurement framework are arranged along three dimensions: performance (quality of care, time-efficiency, throughput), analysis unit (physician, disease etc.), and time-period (quarter, year, etc.). The framework has been judged as “clear and intuitive”, “useful for planning”, able to “reveal inefficiencies in care process” and “transform existing data into decision support information” by the key ED decision stakeholders of a teaching hospital. Administrative data can be a new cornerstone for health care operation management. A framework of ED-specific indicators based on administrative data enables multi-dimensional performance assessment in a timely and cost-effective manner, an essential requirement for nowadays resource-constrained hospitals. Moreover, such a framework can support different stakeholders’ decision making as it allows the creation of a customized metrics sets for performance analysis with the desired granularity.

Exploring the intricate dynamics of aquatic ecosystems present study investigates the spatio-temporal variations in the ecological parameters of the fish community within the Vaishav stream, Kashmir Himalayas. Monthly field investigations were conducted at three distinct sites (SI, SII & SIII) throughout the four seasons (winter, spring, summer, autumn) from November 2019 to October 2020. The findings encompass a total of 630 specimens belonging to 11 fish species, three orders Cypriniformes, Siluriforms and Salmoniformes and four families including Cyprinidae, Nemachelidae, Siluridae and Salmonidae were reported from the study sites. Among collected specimens, Cypriniformes were dominant with nine species followed by order Siluriformes and Salmoniformes with one species each. Out of eleven fish species, six fish species belongs to family Cyprinidae, three to Nemachelidae, one to Siluridae and Salmonidae each. The analysis, employing non-metric multidimensional scaling (NMDS), Principal component analysis (PCA), Analysis of similarity (ANOSIM) and Per-mutational multivariate analysis of variance (PERMANOVA) on fish abundance data highlighted significant differences among the various sites but not across seasons. The results unveil a diverse occurrence and distribution pattern of fishes from upstream to downstream. Furthermore, diversity metrics confirm higher diversity index values downstream, indicating a more conducive environment for fish survival. Jaccard’s index reveals greater similarity in fish fauna between site-II and site-III than site-I and site-III in terms of overlap of fish species composition. The study concludes that anthropogenic activities in the stream catchment area have led to a reduction in fish diversity and abundance, with landscape features significantly influencing fish abundance in this unique Himalayan ecosystem.

Organic dimension (in Vm2) based on T-Patterns’ complexity.