Find research datasets worth reusing

opencc-by-4.0Dec 2021View details →

A multi-uncertainty-set-based robust transmission expansion planning model using an efficient linear AC network

The file uploaded provides input data for the paper "A multi-uncertainty-set-based robust transmission expansion planning model using an efficient linear AC network".

opencc-zeroDec 2021View details →

Network analyses reveal the role of large snakes in connecting feeding guilds in a species-rich Amazonian snake community

In ecological communities, interactions between consumers and resources lead to the emergence of ecological networks and a fundamental problem to solve is to understand which factors shape network structure. Empirical and theoretical studies on ecological networks suggest predator body size is a key factor structuring patterns of interaction. Because larger predators consume a wider resource range, including the prey consumed by smaller predators, we hypothesized that variation in body size favors the rise of nestedness. In contrast, if resource consumption requires specific adaptations, predators are expected to consume distinct sets of resources, thus favoring modularity. We investigate these predictions by characterising the trophic network of a species-rich Amazonian snake community (62 species). Our results revealed an intricate network pattern resulting from larger species feeding on higher diversity of prey, promoting nestedness, and specific lifestyles feeding on distinct resources, promoting modularity. Species removal simulations indicated that the nested structure is favored mainly by the presence of five species of the family Boidae, which because of their body size and generalist lifestyles connect modules in the network. Our study highlights the particular ways traits affect the structure of interactions among consumers and resources at the community level.

opencc-zeroDec 2021View details →

KerNet, 3D fluorescence and segmentation datasets of keratin networks from MDCK, HaCaT, and RPE cells

Mechanobiology requires precise quantitative spatial information on processes taking place in specific 3D microenvironments. Connecting microscopical, molecular, biochemical and cell mechanical data with defined topologies has turned out to be extremely difficult. Establishing such structural and functional 3D maps is particular challenging for the cytoskeleton, which consists of long and interwoven filamentous polymers coordinating subcellular processes and interactions of cells with their environment. Here we present raw data and processed segmented data of fluorescence-tagged keratin intermediate filaments from MDCK, HaCaT and RPE epithelial cells. The 30 datasets contain unprocessed airyscan superresolution 3D image data and numerical network data of the 3D arrangement of the keratin intermediate filament cytoskeleton. Information is provided on network organization at the subcellular level, including mesh arrangement, density and isotropic configuration as well as details on filament morphology such as bundling, curvature and orientation. The datasets contain data in exchange format for 3d software packages. Comparison of the resulting network parameters helps to identify similarities and differences of keratin network organization in epithelial cell types. The described approach and the presented data are pivotal for generating mechanobiological models that can be experimentally tested.

Stacked cross correlation functions for the MeSO-net network and derived 3D Vs model

This dataset contains two major types of data. 1) The yearly stacked cross-correlation functions between 296 MeSO-net stations covering the Kanto basin, Japan. 2) A 3D radially anisotropic Vs model for the Kanto basin. The grid increment for the longitude and latitude is 0.01 degrees and that for the depth is 0.1 km. The complete station list for the 296 MeSO-net stations can be found on the Github page of https://github.com/chengxinjiang/Jiang_Kanto_anisotropy.

Multi-contrast MRI and histology datasets used to train and validate MRH networks to generate virtual mouse brain histology

H MRI maps brain structure and function non-invasively through versatile contrasts that exploit inhomogeneity in tissue micro-environments. Inferring histopathological information from MRI findings, however, remains challenging due to absence of direct links between MRI signals and cellular structures. Here, we provided deep convolutional neural networks, called MRH-Nets, developed using co-registered multi-contrast MRI and histological data of the mouse brain, can estimate histological staining intensity directly from MRI signals at each voxel. The results provide three-dimensional maps of axons and myelin with tissue contrasts that closely mimics target histology and enhanced sensitivity and specificity compared to conventional MRI markers. The dataset contains multi-contrast MRI and histology used for the training and testing and the acquisition parameters. The datasets have been carefully registered to mouse brain images from the Allen Mouse Brain Atlas (https://mouse.brain-map.org). The source codes for MRH-Nets can be found at <a href="https://github.com/liangzifei/MRH-Net">https://github.com/liangzifei/MRH-Net</a>.

Wildfire severity alters drivers of interaction beta-diversity in plant-bee networks

Spatial variation in species interactions (interaction β-diversity) and its ecological drivers are poorly understood, despite their relevance to community assembly, conservation, and ecosystem functioning. We investigated effects of wildfire severity on patterns and four proximate ecological drivers of interaction β-diversity in plant-bee communities across three localities in the Northern Rocky Mountains (Montana, USA). Wildfires decreased interaction β-diversity but increased interaction frequency (number of visits) and richness (number of links). After controlling for interaction frequency and richness, standardized effect sizes of interaction β-diversity were highest following mixed-severity wildfires, intermediate following high-severity wildfires, and lowest in unburned landscapes, suggesting that wildfire increases spatial aggregation of plant-bee interactions. Moreover, higher effect sizes in burned landscapes were largely determined by turnover in the species composition of both trophic levels rather than by interaction rewiring (spatial turnover in local species interactions not due to species turnover). The underrepresented level of rewiring indicated spatial consistency in post-disturbance patterns of interactions among co-occurring species. Together, our findings suggest that wildfire alters the β-diversity of mutualistic species interactions via linked assembly of plant-bee communities and provide insights into how environmental change alters complex networks of species interactions.

opencc-by-4.0Jun 2021View details →

Connecting Intelligence and Smart Orchestration for B5G/6G Networks

Hexa-X project will pave the way towards future 6G system concepts by explorative research. Hexa-X is working out a set of 6G technology enablers to interconnect human, physical, and digital worlds. An essential tool to empower this 6G vision is the application of Artificial Intelligence (AI)/Machine Learning (ML) technologies to significantly improve efficiency and service experience by the research on "Connecting Intelligence". Wide exploration on the potential of AI/ML is necessary to monetise generated data and optimize network management. This can substantially enhance the cost, energy consumption, trust level and service efficiency of network infrastructure. AI allows networks to faster and more precisely adapt to scenarios and traffic demand by acting on predictive orchestration mechanisms in future 6G. Network orchestration should incorporate a complete end-to-end perspective in its decisions and enforcing actions from far edge/devices to RAN disaggregated functions, edge computing, core and cloud elements. This presentation will give an overview on the research and application of AI/ML algorithms to a holistic network orchestration and smart service management.

Collaboration Spotting X - A Visual Network Exploration Tool

Due to many technological advancements, the amount of connected data drastically increased in the last decade. The analysis of this data and the insights it generates show great potential for supporting decision making processes in various industries and aspects of our lives. Multiple visual analytics solutions have been proposed to gain further insights into such data and gain explainable results. However, the majority of existing solutions are either closed sourced, not available or no longer developed. To mitigate the issues above and based on findings from expert interviews conducted using an existing tool, this paper introduces Collaboration Spotting X, a new network-based interactive visual analytics and information retrieval tool prototype. This prototype enables users to explore connected network datasets such as social network data and bibliometric data using multiple visual cues and interactions. Furthermore, to gain an insight into how this prototype is perceived by users and identify further improvements, a preliminary study with a class of 37 computer science graduate students is described. The study findings show that the students perceive Collaboration Spotting X as a useful tool that helps them complete tasks through visualisation and interaction. Additionally, multiple aspects were identified that might have caused users to experience in addition to positive emotions also some negative emotions during usage. These aspects might have also contributed to a lower usability score. Finally, multiple improvement directions have been identified, which will be implemented in future developments.

Direct observation of a dynamical glass transition in a nanomagnetic artificial Hopfield network

Source files used to produce the publication "Direct observation of a dynamical glass transition in a nanomagnetic artificial Hopfield network."

Little brown myotis social networks

Bats are a group of mammals well known for forming dynamic social groups. Studies of bat social structures are often based upon the frequency at which bats occupy the same roosts because observing bats directly is not always possible. However, it is not always clear how closely bats occupying the same roost associate with each other, obscuring whether associations result from social relationships or factors such as shared preferences for roosts. Our goal was to determine if bats cohabitating buildings were also found together inside roosts by using anti-collision technology for PIT tags, which enables simultaneous detection of multiple tags. We PIT-tagged 293 female little brown myotis (Myotis lucifugus) and installed antennas within two buildings used as maternity roosts in Yellowstone National Park. Antennas were positioned at roost entryways to generate cohabitation networks and along regions of attic ceilings in each building to generate intra-roost networks based on proximity of bats to each other. We found that intra-roost and cohabitation networks of buildings were significantly correlated, with the same bats tending to be linked in both networks, but that bats cohabitating the same building often roosted apart, leading to differing assessments of social structure. Cohabitation rates implied that bats associate with a greater number of their roost-mates than was supported by observations within the roost. This caused social networks built upon roost cohabitation rates to be denser, smaller in diameter, and contain nodes with higher average degree centrality. These results show that roost cohabitation does not reflect preference for roost-mates in little brown myotis, as is often inferred from similar studies, and that social network analyses based on cohabitation may provide misleading results.

A patch-dynamic metacommunity perspective on the persistence of mutualistic and antagonistic bipartite networks

The structure of interactions between species within a community plays a key role in maintaining biodiversity. Previous studies have found that the effects of these structures might substantially differ depending on interaction type, for example, a highly connected and nested architecture stabilizes mutualistic communities, while the stability of antagonistic communities is enhanced in modular and weakly connected structures. Here we show that, when network dynamics are modelled using a patch-dynamic metacommunity framework, the qualitative differences between antagonistic and mutualistic systems disappear, with nestedness and modularity interacting to promote metacommunity persistence. However, the interactive effects are significantly weaker in antagonistic metacommunities. Our model also predicts an increase in connectance, nestedness and modularity over time in both types of interaction, except in antagonistic networks where nestedness declines. At steady state, we find a strong negative correlation between nestedness and modularity in both mutualistic and antagonistic metacommunities. These predictions are consistent with the structural trends found in a large dataset of real-world antagonistic and mutualistic communities.

A Survival Analysis based Volatility and Sparsity Modeling Network for Student Dropout Prediction

KDDCup15.rar and XuetangX.rar are the metadata used in the paper "A Survival Analysis based Volatility and Sparsity ModelingNetwork for Student Dropout Prediction". Both of them have be drawn from the largest MOOC platform in China, XuetangX (see https://www.xuetangx.com/). If any interested parties want to fetch the original dataset, they may follow the URLs bellow: KDDCup 2015 dataset is available at <a href="https://www.biendata.xyz/competition/kddcup2015/data/">https://www.biendata.xyz/competition/kddcup2015/data/</a>. XuetangX dataset is available at <a href="http://moocdata.cn/data/user-activity">http://moocdata.cn/data/user-activity</a>.

Daily NOAA Global Ensemble Forecasting System forecasts for six National Ecological Observatory Network lakes (2021--05-18 to 2021-10-24)

NOAA Global Ensemble Forecasting System output generated at 00 UTC that has been subsetted and temporally downscaled from 6-hr to 1-hr for six lakes in the National Ecological Observatory Network.  The files include all ensembles and the set of variables required to run the General Lake Model. The NEON siteID for the lakes are BARC, SUGG, CRAM, LIRO, PRLA, PRPO.  See https://www.neonscience.org for more information about each lake.

GECCO2022 Automated Algorithm Selection for Radar Network Configuration

This upload is a dataset companion to a paper in the Real World Application track of the GECCO 2022 conference. The dataset includes a binary container for the objective function used in the paper as well as the corresponding experimental logs.

opencc-zeroFeb 2022View details →

Changes in the structure of seed dispersal networks when including interaction outcomes from both plant and animal perspectives

Interaction frequency is the most common currency in quantitative ecological networks, although interaction quality can also affect benefits provided by mutualisms. Here, we evaluate if interaction quality can modify network topology, species' role and whether such changes affect community vulnerability to species loss. We use a well-examined study system (bird-lizard and fleshy-fruited plants in the 'thermophilous' woodland of the Canary Islands) to compare network and species-level metrics from a network based on fruit consumption rates (Interaction Frequency, IF), against networks reflecting functional outcomes: a Seed Dispersal Effectiveness network (SDE) quantifying recruitment, and a Fruit Resource Provisioning network (FRP), accounting for the nutrient supply of fruits. Nestedness decreased in the FRP and the SDE networks, due to the lack of association between fruit consumption rates and (1) nutrient content, and (2) recruitment at the seed deposition sites, respectively. The FRP network showed lower niche overlap due to resource use complementarity among frugivores. Interaction evenness was lower in the SDE network, in response to a higher dominance of lizards in the recruitment of heliophilous species. Such changes, however, did not result in enhanced vulnerability against extinctions. At the plant species level, strength changed in the FRP network in frequently consumed or highly nutritious species. The number of effective partners decreased for species whose seeds were deposited in unsuitable places for recruitment. In frugivores, strength was consistent across networks (SDE vs IF), showing that consumption rates outweighed differences in dispersal quality. In the case of lizards, the increased importance of nutrient-rich species resulted in a higher number of effective partners. Our work shows that although frequency strongly impacts interaction effects, accounting for quality improves our inferences about interaction assembly and species role. Thus, future studies including interaction outcomes from both partners' perspectives will provide valuable insights about the net effects of mutualistic interactions.

Network structural origin of instabilities in large complex systems

Raw data used to generate figures in the following publication: Title: "Network structural origin of instabilities in large complex systems" Authors: Chao Duan, Takashi Nishikawa, Deniz Eroglu, Adilson E. Motter Journal: <a href="https://doi.org/10.1126/sciadv.abm8310">Science Advances 8, eabm8310 (2022)</a> The CSV files are named by the corresponding figure numbers and the quantities (e.g., "Fig1A_data.csv" for data for Fig. 1A and "FigS1A_data_adj_mat.csv" for the adjacency matrix data for Fig. S1A).

opencc-by-4.0Aug 2021View details →

The Global Navigation Satellite System (GNSS) data came from the Crustal Movement Observation Network of China

The dataset reports the estimated vertical Total Electron Content (TEC) from 52 GPS receivers came from the Crustal Movement Observation Network of China on 4 March 2014. Every receiver's data is saved in a TXT file, whose time resolution is thirty seconds.

Training data for benchtop NMR and UV/vis spectroscopy for Artificial Neural Networks

Data set of low-field NMR spectra and UV/vis spectra for the synthesis of mesalazine intermediates, which were used as training or validation data for data processing with artificial neural networks development Low-field NMR spectra for the nitration step: The pure component spectrum of 2ClBA, 3N-2ClBA, and 5N-2ClBA are marked as NMR_pure_spectrum. The concentration levels for 2ClBA, 3N-2ClBA and 5N-2ClBA are in row 1, 2, and 3, respectively. The data sets marked as NMR_ represents low-field NMR-spectra recorded. The reference values for 2ClBA, 3N-2ClBA and 5N-2ClBA are in column 1, 2, and 3, respectively. Datafusion data sets for the hydrolysis and nitration step The NMR data are either recorded or simulated from the pure NMR spectrum of each individual component. The reference values for 2ClBA, 3N-2ClBA, 5N-2ClBA, 3-NSA and 5-NSA are either assigned with UHPLC measurements or calculated from the prepared solutions. The NMR spectra are depicted in datafusion_NMR_training. The reference values for 2ClBA, 3N-2ClBA and 5N-2ClBA are in column 1, 2, and 3, respectively. The UV/vis spectra are depicted in datafusion_UVvis_training. The reference values for 2ClBA, 3N-2ClBA, 5N-2ClBA, 3-NSA and 5-NSA are in column 1, 2, 3, 4, and 5, respectively. Process data The NMR spectra for the stability run and the run with dynamic changes are depicted in process_NMR_. The first column is the time stamp. The UV/vis spectra for the stability run and the run with dynamic changes are depicted in process_UV_. The first column is the time stamp.

Convolutional neural network and data used for applied soundscape classification with Soundscapes 2 Landscapes (S2L)

This repository documents the ABGQI-CNN manuscript (DOI: <a href="https://doi.org/10.1016/j.ecolind.2022.108831">https://doi.org/10.1016/j.ecolind.2022.108831</a>). It contains supplementary materials, data used to train a soundscape classification convolutional neural network (CNN), and data to generate manuscript results. The accompanying code can be found at <a href="https://doi.org/10.5281/zenodo.6038460">https://doi.org/10.5281/zenodo.6038459</a>. Files include: <ul> <li>ABGQI-CNN.tar: saved CNN model weights for the 5-class soundscape classifier using a MobileNetV2 architecture pre-trained with bird vocalization data.</li> <li>ABGQI_mel_spectrograms.tar: spectrograms used for fine-tuning the pre-trained CNN, above, with training, validation, and testing data splits.</li> <li>freesound_licensing.csv: file names and license information related to Freesound auxiliary files.</li> <li>RavenLite_Training_Data_Collection.pdf: a manual for RavenLite ROI annotation.</li> <li>S2L_site_geog-env_data.csv: environmental and geographic data (sans GPS) related to site locations in S2L project 2017-2020.</li> <li>site_avg_ABGQIU_fscore_075_daytime.csv: the average site rate of soundscape components for 5 a.m. to 8 p.m.</li> <li>site_by_hour_ABGQIU_fscore_075.csv: the average hourly site rate of soundscape components</li> <li>site_classifications_beta075.tar: a directory containing a CSV for every site with threshold optimized classifications for each 2-s Mel spectrogram</li> <li>site_prediction_probabilies.tar: a directory containing a CSV for every site with ABGQI-CNN probabilities for each 2-s Mel spectrogram</li> <li>Supplementary_Materials.pdf: includes additional material and analyses related to the accompanying manuscript. </li> </ul> Contact Colin Quinn at cq73@nau.edu for questions related to this repository or if you have an interest in the original wav recordings. Please be aware that underlying software, specifically for the CNN implementation, may not continue stability as python libraries are updated.

Habitat loss shapes the structure and species roles in mutualistic seed dispersal networks

This dataset has the variables used in the paper "Habitat loss shapes the structure and species roles in tropical seed dispersal networks" as well as the script to perform the analysis.