Find research datasets worth reusing

Supplementary Data for "Prediction of solar wind speed by applying convolutional neural network to potential field source surface (PFSS) magnetograms"

These are supplementary data for the paper "Prediction of solar wind speed by applying convolutional neural network to potential field source surface (PFSS) magnetograms". They are: - Python code to construct a neural network model - Saved optimal models (for 8-fold validation) - Selected y-label data (solar wind speed) and corresponding dates, which we eliminate the data identified as ICME

Reusable 5G Private Campus Network Dataset

In the current literature, the measurements on deployed and operational Fifth-Generation (5G) networks are still lacking. This study offers a dataset of New Radio (NR) signal measurements made using the RPTU Kaiserslautern’s private campus 5G network as well as other public 5G networks to close this gap. The dataset includes measurements of signal strengths such as Synchronization Signal Reference Signal Received Power (SS-RSRP) and Synchronization Signal Signal-to-Interference- plus-Noise ratio (SS-SINR) of serving cells, as well as signal strengths of the neighboring public 5G Standalone (SA) and Non-Standalone (NSA) networks, i.e., within the perimeter of the campus. The measurements described here are taken at various indoor and outdoor locations using the Rohde & Schwarz’s (R&S) TSME6 and TSMA6 scanners. The primary goal of the dataset is to allow researchers to reuse data from the 5G SA live network in understanding the radio access network (RAN) and Physical Layer characteristics of the network and use them for machine learning purposes to optimize the network. In addition to this data, several areas where the data can be used for learning and optimization tasks in the network are provided.

opencc-zeroApr 2023View details →

Plant–hummingbird pollination networks exhibit limited rewiring after experimental removal of a locally abundant plant species

In this study, we simulated the local extinction of a hummingbird-pollinated understory plant, Heliconia tortuosa, from tropical forest fragments using a replicated Before-After-Control-Impact (BACI) experimental design while quantifying plant-hummingbird interactions through two parallel techniques: pollen collected from individual hummingbirds ('pollen networks', created from >300 pollen samples) and observations of hummingbirds visiting focal plants ('camera networks', created from >19,000 observation hours). Each response variable was measured during each experimental period (pre and post) in sites with and without H. tortuosa removal (treatment and control).

Microvascular network remodeling in the ischemic brain defined by light sheet microscopy

This dataset contains the raw image data set used in the study entitled "Microvascular network remodeling in the ischemic brain defined by light sheet microscopy" by Hagemann et al.. Images were analysed using VesselExpress software (see also <a href="https://pubmed.ncbi.nlm.nih.gov/37056368/">https://pubmed.ncbi.nlm.nih.gov/37056368/</a>) using the provided config file with gamma values ranging from 50 to 400.

opencc-zeroDec 2022View details →

Data from: Network structure of avian mixed-species flocks decays with elevation and latitude across the Andes

Birds in mixed-species flocks benefit from greater foraging efficiency and reduced predation but also face costs related to competition and activity matching. Because this cost-benefit trade-off is context-dependent (e.g., abiotic conditions, habitat quality), the structure of flocks is expected to vary along elevational, latitudinal, and disturbance gradients. Specifically, we predicted that the connectivity and cohesion of flocking networks would (1) decline towards tropical latitudes and lower elevations, where competition and activity matching costs are higher, and (2) increase with lower forest cover and greater human disturbance. We analysed the structure of 84 flock networks across the Andes and assessed the effect of elevation, latitude, forest cover and human disturbance on network characteristics. We found that Andean flocks are overall open-membership systems (unstructured), though the extent of network structure varied across gradients. Elevation was the main predictor of structure, with more connected and less modular flocks upslope. As expected, flocks in areas with higher forest cover were less cohesive, with better-defined flock subtypes. Flocks also varied across latitude and disturbance gradients as predicted, but effect sizes were small. Our findings indicate that the unstructured nature of Andean flocks might arise as a strategy to cope with harsh environmental conditions.

Automated Earthquake Catalogues for CHARGE & CHARSME/CSN networks in South America (Pampean Flat Slab)

This repository includes: 1. Gzipped automated catalogues of earthquake locations with P and S arrival times for: <ul> <li>The CHARGE network - charge.data.gz</li> <li>The CHARSME and CSN networks - charsme_csn.data.gz</li> </ul> 2. A README explaining the information in each column

Protein network analysis links the NSL complex to Parkinson's disease via mitochondrial & nuclear biology

This online depository corresponds to manuscript : Protein network analysis links the NSL complex to Parkinson’s disease via mitochondrial & nuclear biology. Authors: Katie Kelly, Patrick A. Lewis, Helene Plun-Favreau, Claudia Manzoni Whilst the majority (~90-95%) of PD cases are sporadic, much of our understanding of the pathophysiological basis of disease can be traced back to the study of rare, monogenic forms of disease. However, in the past decade, the availability of Genome-Wide Association Studies (GWAS) has facilitated a shift in focus, toward identifying common risk variants conferring an increased risk of developing PD across the population.  A recently developed mitophagy screening assay of GWAS candidates, has functionally implicated the non-specific lethal (NSL) complex, a chromatin remodeler, in the regulation of PINK1-mitophagy. Here, a bioinformatics approach has been taken to investigate the interactome of the NSL complex, to unpick its relevance to PD progression. The mitochondrial interactome of the NSL complex has been built, mining 3 separate repositories: PINOT, HIPPIE and MIST, for curated, literature-derived protein-protein interaction (PPI) data. A multi-layered approach has been taken to; i) build the ‘mitochondrial’ NSL interactome, applying PD gene-set enrichment analysis to explore the relevance of the NSL mitochondrial interactome to PD and, ii) build the PD-oriented NSL interactome, using functional enrichment, to uncover biological pathways underpinning the NSL /PD association.

Occupancy history of Seville's bike-sharing network. January-June 2021

Occupancy history of Seville's bike-sharing network and station coordinates. January-June 2021

opencc-zeroMay 2023View details →

Data for: Patterns of species diversity in a network of artificial wetlands

Aim Artificial island habitats such as human-made wetlands are emerging novel ecosystems. Understanding the drivers of diversity in such artificial systems is essential for balancing the goals of biodiversity conservation and human socio-economic needs. Location Telangana State, India. Methods We surveyed water birds in a network of 57 artificial wetlands and assessed four macroecological biodiversity patterns: spatial beta diversity, temporal beta diversity, species-abundance distributions (SADs), and the species–area relationship (SAR). We employed a mix of phenomenological and mechanistic models to examine the four macro-ecological patterns. We hypothesised that the wetland bird communities are primarily structured by immigration–extinction dynamics and thus that spatial and temporal beta diversity would be high, the within-wetland SADs would exhibit a large number of rare species and a monotonically declining overall shape, and that the SAR across wetlands would be strongly increasing. Results Spatial and temporal beta diversity were both high and mostly attributable to turnover rather than nestedness. While the pooled SAD exhibited an interior mode, the SAD for individual wetlands was generally log-series distributed, consistent with a model in which immigration among wetlands is high. The SAR exhibited an increasing trend, with the "small-island effect", which reflects constraints on immigration and is often observed for true island archipelagos, being absent. Main Conclusions We tentatively conclude that bird diversity in this network of artificial wetlands is mainly structured by immigration–extinction dynamics, although we acknowledge that some of the patterns are also consistent with niche dynamics and future research should measure relevant biotic and abiotic variables in these wetlands. We encourage future work in which our rich data set is used to fit dynamic models that permit more detailed quantitative inferences about mechanisms structuring diversity in this novel ecosystem, which can ultimately also inform conservation management.

opencc-zeroMay 2023View details →

Data from: The fat body cortical actin network regulates Drosophila inter-organ nutrient trafficking, signaling, and adipocyte cell size

Defective nutrient storage and adipocyte enlargement (hypertrophy) are emerging features of metabolic syndrome and type 2 diabetes. How the cytoskeletal network contributes to nutrient uptake, fat storage, and adipocyte size remains poorly understood. Utilizing the Drosophila larval fat body (FB) as a model adipose tissue, we show that a specific actin isoform—Act5C—forms the cortical actin network necessary for inter-organ lipid trafficking. Act5C also promotes FB tissue expansion during larval development so larvae can store sufficient biomass for metamorphosis. We find FB-specific loss of Act5C, but not other Drosophila actins, perturbs FB triglyceride (TG) storage in lipid droplets (LDs), resulting in developmentally delayed larvae that fail to develop into flies. Act5C localizes to the FB cell surface where it intimately contacts peripheral LDs (pLDs), forming a cortical actin network together with spectrins for cell architectural support. While both the cortical actin and spectrin cytoskeletons maintain FB cell surface architecture, we find that only the actin network is required for fat storage. Mechanistically, we show that FBs lacking the Act5C cortical cytoskeleton exhibit a block in lipoprotein (Lpp) secretion from FB cells, and a subsequent disruption of gut:FB inter-organ lipid transport, resulting in mid-gut fat accumulation. Utilizing temporal RNAi-depletion approaches, we also reveal that Act5C is indispensable post-embryogenesis during larval feeding to promote FB cell expansion. Act5C-deficient FBs fail to expand cell sizes, leading to lipodystrophic larvae unable to accrue sufficient biomass for metamorphosis. Collectively, we propose that the Act5C-mediated cortical actin network of Drosophila adipose tissue plays an essential role in post-embryonic inter-organ nutrient transport and FB cell size determination for organismal energy homeostasis and development.

Neural network potential for H/Ru(0001).

Neural network potential for H atoms on static Ru(0001) surface in PROPhet (https://github.com/biklooost/PROPhet) LAMMPS format.

SlumberNet: Deep learning classification of sleep stages using residual neural networks

Sleep research is fundamental to understanding health and well-being, as proper sleep is essential for maintaining optimal physiological function. Here we present SlumberNet, a novel deep learning model based on residual network (ResNet) architecture, designed to classify sleep states in mice using electroencephalogram (EEG) and electromyogram (EMG) signals. Our model was trained and tested on data from mice undergoing baseline sleep, sleep deprivation, and recovery sleep, enabling it to handle a wide range of sleep conditions. Employing k-fold cross-validation and data augmentation techniques, SlumberNet achieved high levels of accuracy (~98%) in predicting sleep stages and showed robust performance even with a small and diverse training dataset. Comparison of SlumberNet's performance to manual sleep stage classification revealed a significant reduction in analysis time (~50x faster), without sacrificing accuracy. Our study showcases the potential of deep learning to facilitate sleep research by providing a more efficient, accurate, and scalable method for sleep stage classification. Our work with SlumberNet demonstrates the power of deep learning in sleep research, and looking forward, SlumberNet could be adapted to human EEG analysis and sleep stage classification. Thus, SlumberNet could be a valuable tool in understanding both sleep physiology and disorders in mammals.

opencc-by-4.0Feb 2023View details →

Topological characterization of the retinal microvascular network visualized by portable fundus camera- effects of chronic disease (TREND2) database

Introduction Topological characterization of the Retinal microvascular nEtwork visualized by portable fuNDus camera (TREND 2) is a database of digital color eye fundus images created as an addition to TREND  database (https://zenodo.org/badge/DOI/10.5281/zenodo.4521044.svg). TREND 2 databse was created by medical professionals of the Faculty of Medicine of the University of Montenegro in 2023.   Purpose 1) to provide a standard that defines normal and abnormal retinal anatomy and microvascular geometry as it appears when visualized by the portable fundus camera 2) to help the development of new methods for stratification of the risk for the development of various eye diseases, as well as systemic diseases that affect microvasculature 3) to aid the development of biomarkers of accelerated aging 4) to provide a standard that can be used to develop software for segmentation of retinal microvasculature, grading the quality of retinal digital images, and computer-aided diagnosis of systemic and chronic diseases. All color digital images were acquired with a hand-held portable, non-mydriatic MiiS HORUS Scope DEC 200 with 45º FOV and 2560 X 1920 pixel resolution.   Data The TREND public database contains 28 color fundus images of old subjects (20 images from subjects with one or more chronic diseases such as type 2 diabetes mellitus, hypertension or Alzheimer's dementia- O_CD group, and 8 images from subjects with no chronic diseases- O_NCD group). Each image is associated with a corresponding binarized image of the manually segmented microvascular network. <table> <caption>Inclusion and Exclusion Criteria</caption> <thead> <tr> <th scope="col">O_NCD group</th> <th scope="col">O_CD group</th> </tr> </thead> <tbody> <tr> <td>Inclusion Criteria</td> <td>Inclusion Criteria</td> </tr> <tr> <td>- at least 56 years old</td> <td>- at least 56 years old</td> </tr> <tr> <td> - no current acute disease - no history of alcohol, or drug abuse, or psychiatric disease </td> <td> - no current acute disease - no history of alcohol, or drug abuse, or psychiatric disease </td> </tr> <tr> <td> - no history of alcohol, or drug abuse, or psychiatric disease </td> <td>- no history of alcohol, or drug abuse, or psychiatric disease</td> </tr> <tr> <td>- negative history of any chronic disease</td> <td> - controlled hypertension (blood pressure<140/90 mmHg), and/or - controlled type 2 diabetes mellitus, and/or - Alzheimer's dementia </td> </tr> <tr> <td>Exclusion Criteria</td> <td>Exclusion Criteria</td> </tr> <tr> <td> - presence of opacities of the transparent media in both eyes affecting - myopia ≥5 diopters </td> <td> - presence of opacities of the transparent media in both eyes affecting - myopia ≥5 diopters </td> </tr> </tbody> </table> Files: 1_OLD WITH CHRONIC DISEASE_RAW (20 images in tif format) 2_OLD WITH CHRONIC DISEASE_SEGMENTED (20 images in png format) 3_OLD WITH NO CHRONIC DISEASE_RAW (8 images in tif format) 4_OLD WITH NO CHRONIC DISEASE SEGMENTED (8 images in png format) 5_ASSOCIATED DATA (xslx format) 6_RETINAL PATHOLOGY (docx format)

Data supporting the publication "Quadrature nonreciprocity in bosonic networks without breaking time-reversal symmetry"

Data supporting the publication "Quadrature nonreciprocity in bosonic networks without breaking time-reversal symmetry"

Observations of shortwave downward radiation from the operational observation network of Météo-France

This dataset contains the hourly surface shortwave downward radiation (SWD) from the pyranometers of the network operated by Météo-France for the year 2020. This dataset was used to evaluate AROME SWD forecasts in the manuscript "Evaluation of surface surface shortwave downward radiation forecasts by the numerical weather prediction model AROME" by Marie-Adèle Magnaldo et al., submitted to Atmospheric Chemistry and Physics.

opencc-zeroDec 2022View details →

The structure and ecological function of the interactions between plants and arbuscular mycorrhizal fungi through multilayer networks

<ol> <li>Arbuscular mycorrhizas are one of the most frequent mutualisms in terrestrial ecosystems. Although studies on plant mutualistic interaction networks suggest that they may leave their imprint on plant community structure and dynamics, this has not been explicitly assessed. Thus, in the context of plant-fungi interactions, studies explicitly linking plant-mycorrhizal fungi interaction networks with key ecological functions of plant communities, such as recruitment, are lacking. </li> <li>In this study, we analyse, in two Mediterranean forest communities of southern Iberian Peninsula, how plant-AMF networks modulate plant-plant recruitment interaction networks. We use a new approach integrating plant-AMF and plant recruitment networks into a single multilayer structure. We also develop a new metric (Interlayer Node Neighbourhood Integration, INNI) to explore the impact of a given node on the structure across layers.</li> <li>Similarity of plant species in their AMF communities is positively related to the observed frequency of recruitment interactions in the field. Results reveal that properties of plant-AMF networks, such as plant degree and centrality, contribute to explaining properties of the plant recruitment network, such as in- and out-degree (i.e. sapling bank and canopy service) and its modular structure. However, these relationships differed between the two forest communities. Finally, we identify particular AMF that contribute to integrating the neighbourhood of recruitment interactions between plants.</li> <li>This multilayer network approach is useful to explore the role of plant-AMF interactions on recruitment, a key ecosystem function enhanced by fungi. Results provide evidence that the complex structure of plant-AMF interactions impacts functional and structurally plant-plant interactions, which in turn may potentially influence plant community dynamics, through their effects on the structure of the recruitment network.</li> </ol>

Example testing images for PFU detection neural network

This is the example testing images used for the paper of "Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning". The data has been compressed into eight zip files from .001 to .008. To access the data, please follow these steps: 1) Download all eight zipped files. 2) Once the download is complete, click on any one of the zipped files to start the extraction process. 3) The extracted files will automatically be organized into one folder. After extraction, there are three subfolders inside it: 1) Network input: it contained 4 holographic phase images (.mat files) at 12h, 13h, 14h and 15h of incubation for an example postive well and an example negative well. 2) Network output: It contains the network output image (PFU probability map) fot the example postive well and the example negative well. 3) Detection result: It contains the final binary detection result images after thresholding fot the example postive well and the example negative well.

opencc-by-4.0Jul 2022View details →

COVID-19 and Traffic Safety: Exploring Exposure, Crash Frequency and Severity, and Roadway and Network Design

Early COVID-19 lockdowns in the first half of 2020 largely kept people at home, thereby reducing motor vehicle traffic levels. Theoretically, reduced traffic exposure should have resulted in reduced motor vehicle crashes. However, a variety of factors may have complicated this relationship. In order to better understand the impact of COVID-19 lockdowns on traffic safety outcomes, we explore fatalities, injuries, and total crashes before and during the lockdowns on both the national and state levels. We provide descriptive statistics and create negative binomial regressions exploring the role of vehicle, user, and built environment factors on traffic safety outcomes. Findings suggest that crash counts in Region 6 were 35%-50% lower in 2020 during the COVID-19 lockdowns. Crashes that occurred during the COVID-19 lockdowns were more likely to be more severe. Fatal pedestrian crashes across the U.S. decreased during COVID-19 (although not as much as overall fatal crashes) and fatal bicyclist crash counts increased. Drunk drivers were less prevalent in nationwide fatalities but more prevalent in overall Region 6 crashes. Overall, crashes were more likely single-vehicle fixed-object or rollover crashes involving unsafe speeds. In Texas, suburban areas saw the most crashes before and during COVID-19, although they also saw the greatest decrease. Rural Texas crashes were most likely to result in a fatality or serious injury, and that likelihood got worse during the COVID-19 lockdowns. While Texas freeways and arterials saw the largest decreases in crash counts, these functional classifications still had the most crashes. Urban interstates and rural local roads in Texas were notable because these two functional classifications actually saw increases in the number of fatal and serious injury crashes during COVID-19 lockdowns.

opencc-zeroMay 2023View details →

Data from: Task-evoked metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks

External tasks evoke characteristic fMRI BOLD signal deactivations in the default mode network (DMN). However, for the corresponding metabolic glucose demands both decreases and increases have been reported. To resolve this discrepancy, functional PET/MRI data from 50 healthy subjects performing Tetris® were combined with previously published data sets of working memory, visual and motor stimulation. We show that the glucose metabolism of the posteromedial DMN is dependent on the metabolic demands of the correspondingly engaged task-positive networks. Specifically, the dorsal attention and frontoparietal network shape the glucose metabolism of the posteromedial DMN in opposing directions. While tasks that mainly require an external focus of attention lead to a consistent downregulation of both metabolism and the BOLD signal in the posteromedial DMN, cognitive control during working memory requires a metabolically expensive BOLD suppression. This indicates that two types of BOLD deactivations with different oxygen-to-glucose index may occur in this region. We further speculate that consistent downregulation of the two signals is mediated by decreased glutamate signaling, while divergence may be subject to active GABAergic inhibition. The results demonstrate that the DMN relates to cognitive processing in a flexible manner and does not always act as a cohesive task-negative network in isolation.