Find research datasets worth reusing

opencc-by-4.0Sep 2016View details →

Analysis of Network of Discourse, Nature

Note: This dataset is also available as a Git repository, currently located at <a href="https://codeberg.org/cpence/nature-debate.charlespence.net">https://codeberg.org/cpence/nature-debate.charlespence.net</a>. This is the raw data accompanying a network analysis of biologists participating in the "biometry-Mendelism" debate, as it occurred in the correspondence pages of the journal Nature, between 1890 and 1915. It accompanies the following book chapter: Pence, Charles H. forthcoming (likely 2021). "How Not to Fight About Theory: The Debate Between Biometry and Mendelism in Nature, 1890–1915." In De Block, A. and Ramsey, G., eds., The Dynamics of Science: Computational Frontiers in History and Philosophy of Science. Pittsburgh, PA: Univ. of Pittsburgh Press.

opencc-zeroOct 2019View details →

Data from: Co-prescription network reveals social dynamics of opioid doctor shopping

This paper examines network prominence in a co-prescription network as an indicator of opioid doctor shopping (i.e., fraudulent solicitation of opioids from multiple prescribers). Using longitudinal data from a large commercially insured population, we construct a network where a tie between patients is weighted by the number of shared opioid prescribers. Given prior research suggesting that doctor shopping may be a social process, we hypothesize that active doctor shoppers will occupy central structural positions in this network. We show that network prominence, operationalized using PageRank, is associated with more opioid prescriptions, higher predicted risk for dangerous morphine dosage, opioid overdose, and opioid use disorder, controlling for number of prescribers and other variables. Moreover, as a patient's prominence increases over time, so does their risk for these outcomes, compared to their own average level of risk. Results highlight the importance of co-prescription networks in characterizing high-risk social dynamics.

opencc-zeroJul 2020View details →

Data from: Link prediction in real-world multiplex networks via layer reconstruction method

Networks are invaluable tools to study real biological, social and technological complex systems in which connected elements form a purposeful phenomenon. A higher resolution image of these systems shows that the connection types do not confine to one but to a variety of types. Multiplex networks encode this complexity with a set of nodes which are connected in different layers via different types of links. A large body of research on link prediction problem is devoted to finding missing links in single-layer (simplex) networks. In recent years, the problem of link prediction in multiplex networks has gained the attention of researchers from different scientific communities. Although most of these studies suggest that prediction performance can be enhanced by using the information contained in different layers of the network, the exact source of this enhancement remains obscure. Here, it is shown that similarity w.r.t. structural features (eigenvectors) is a major source of enhancements for link prediction task in multiplex networks using the proposed Layer Reconstruction Method and experiments on real-world multiplex networks from different disciplines. Moreover, we characterize how low values of similarity w.r.t. structural features result in cases where improving prediction performance is substantially hard.

opencc-zeroJun 2020View details →

Large-scale metabolic interaction network of the mouse and human gut microbiota

The role of our gut microbiota in health and disease is largely attributed to the collective metabolic activities of the inhabitant microbes. A system-level framework of the microbial community structure, mediated through metabolite transport, would provide important insights into the complex microbe-microbe and host-microbe chemical interactions. This framework, if adaptable to both mouse and human systems, would be useful for mechanistic interpretations of the vast amounts of experimental data from gut microbiomes in murine animal models, whether humanized or not. Here, we constructed a literature-curated, interspecies network of the mammalian gut microbiota for mouse and human hosts, called NJC19. This network is an extensive data resource, encompassing 838 microbial species (766 bacteria, 53 archaea, and 19 eukaryotes) and 6 host cell types, interacting through 8,224 small-molecule transport and macromolecule degradation events. Moreover, we compiled 912 negative associations between organisms and metabolic compounds that are not transportable or degradable by those organisms. Our network may facilitate experimental and computational endeavors for the mechanistic investigations of host-associated microbial communities.

opencc-zeroNov 2020View details →

Invasion of freshwater ecosystems is promoted by network connectivity to hotspots of human activity

Aim: Hotspots of human activity are focal points for ecosystem disturbance and non-native introduction, from which invading populations disperse and spread. As such, connectivity to locations used by humans may influence the likelihood of invasion. Moreover, connectivity in freshwater ecosystems may follow the hydrological network. Here we tested whether multiple forms of connectivity to human recreational activities promotes biological invasion of freshwater ecosystems. Location: England, UK. Time period: 1990-2018. Major taxa studied: 126 non-native freshwater birds, crustaceans, fish, molluscs and plants. Methods: Machine learning was used to predict spatial gradients in human recreation and two high risk activities for invasion (fishing and water sports). Connectivity indices were developed for each activity, in which human influence decayed from activity hotspots according to Euclidean distance (spatial connectivity) or hydrological network distance (downstream, upstream and along-channel connectivity). Generalised linear mixed models identified the connectivity type most associated to invasive species richness of each group, while controlling for other anthropogenic and environmental drivers. Results: Connectivity to humans generally had stronger positive effects on invasion than all other drivers except recording effort. Recreation had stronger influence than urban land cover, and for most groups high risk activities had stronger effects than general recreation. Downstream human connectivity was most important for invasion by most of the groups, potentially reflecting predominantly hydrological dispersal. An exception was birds, for which spatial connectivity was most important, possibly because of overland dispersal capacity. Main conclusions: These findings support the hypothesis that freshwater invasion is partly determined by an interaction between human activity and species dispersal in the hydrological network. By comparing alternative connectivity types for different human activities, our approach could enable robust inference of specific pathways and spread mechanisms associated with particular taxa. This would provide evidence to support better prioritisation of surveillance and management for invasive non-native species.

opencc-zeroJun 2020View details →

Identification of a genetic network for an ecologically relevant behavioral phenotype in Drosophila melanogaster

Pupation site choice of Drosophila third-instar larvae is critical for the survival of individuals, as pupae are exposed to various biotic and abiotic dangers while immobilized during the 3-4 days of metamorphosis. This singular behavioural choice is sensitive to both environmental and genetic factors. Here we developed a high-throughput phenotyping approach to assay the variation in pupation height in Drosophila melanogaster, while controlling for possibly confounding factors. We find substantial variation of mean pupation height among sampled natural stocks and we show that the Drosophila Genetic Reference Panel (DGRP) captures this variation. Using the DGRP stocks for genome wide association (GWA) mapping, 16 loci involved in determining pupation height could be resolved. The candidate genes in these loci are enriched for high expression in the larval central nervous system. A genetic network could be constructed from the candidate loci, which places scrib at the centre, plus other genes known to be involved in nervous system development, such as Egfr and p53. Using gene disruption lines, we could functionally validate several of the initially identified loci, as well as additional loci predicted from network analysis. Our study shows that the combination of high throughput phenotyping with a genetic analysis of variation captured from the wild can be used to approach the genetic dissection of an environmentally relevant behavioural phenotype.

opencc-zeroJul 2020View details →

Evolutionary and ecological processes influence a plant-bumble bee network

Species interactions, such as those between plants and pollinators, are known to be shaped by both evolutionary history and ecological factors. However, the relative importance of neutral- and niche-based ecological processes is still under debate, and little is known about which ecological factors are most important for explaining phylogenetic patterns in interaction networks. Using a plant-bumble bee network comprising 2428 interactions between 29 plant species and 12 bumble bee species in the Himalaya-Hengduan mountains, we tested for phylogenetic signal and whether phylogenetic pattern was explained by 14 plant and four bumble bee ecological traits. We also tested whether trait matching between bumble bee tongue length and flower tube depth explained interaction patterns. All of the measured traits contributed to explaining the phylogenetic attraction pattern, among them, species abundances, nectar volume, and nectar sugar concentration were the most important predictors. Meanwhile, trait mismatch between bumble bee tongue length and flower tube depth failed to predict the interactions, with short-tongued bees generalized across tube depths. Our findings suggest neutral-based process seems to matter more than niche-based processes in this system and help to predict how interactions will respond when key traits are altered by pollinator declines and global change effects.

opencc-by-4.0Jul 2020View details →

Application specific network traffic with specified activities

This dataset contains network traffic of 12 different containerized, isolated applications. It includes headers of network traffic data in the initial two minutes of execution of the application. We have specified each network traffic with an activity that the application was involved in during traffic capturing.   ------------------------------------ Application name, No. of samples, Engaged activity while capturing ------------------------------------ Curl, 60, downloading Firefox, 47, browsing mpg123, 99, audio streaming mplayer, 82, audio streaming mpv, 48, video streaming Slack, 58, no chatting (just opening the app and menus) streamlink, 57, video streaming Trello, 46, just opening the app (just opening the app and menus) Vlc, 102, audio streaming w3m, 60, downloading wget, 20, file downloading wgetaudio, 20, file downloading(audio) wgetvideo, 20, file downloading(video) ydm (mplayer + youtube-dl),61, video streaming

Data from: Findings from an exploration of a social network intervention to promote diet quality and health behaviours in older adults with COPD: a feasibility study

Background: Diet quality in older people with Chronic Obstructive Pulmonary Disease (COPD) is associated with better health and lung function. Social factors, such as social support, social networks and participation in activities, have been linked with diet quality in older age. A social network tool – GENIE (Generating Engagement in Network Involvement) – was implemented in a COPD community care context. The study aimed to assess the feasibility of the GENIE intervention to promote diet quality and other health behaviours in COPD. Methods: Twenty-two community-dwelling older adults with COPD were recruited from a local COPD Service. Participants were offered usual care or the GENIE intervention. Process evaluation methods were used to assess intervention implementation, context and mechanisms of impact; <a name="_Hlk11247522">these included observations of patient interactions with the intervention, documented in observational field notes and in films of a patient group discussion.</a> Diet quality was assessed by food frequency questionnaire; 'prudent' diet scores were used to describe diet quality at baseline and at 3-month follow-up. Change in diet quality was expressed per month, from baseline to follow-up. Results: Feasibility data showed that the GENIE intervention could be implemented in this sample of community-living older people. The intervention was acceptable to clinicians and older people with COPD, especially for those with less severe disease, when facilitated appropriately and considering the levels of literacy of participants. There was no significant change in diet quality in the intervention group over the follow-up period (median change in prudent diet score per month, (interquartile range (IQR)): 0.03, (-0.24 – 0.07)); whereas an overall fall in diet quality was observed in the control group (-0.15, (-0.24 – 0.03)). Conclusion: The process evaluation findings suggest that this intervention is feasible and acceptable to both patients and clinicians. Although the sample size achieved in this study was small, findings suggest that the intervention may have a protective effect against declines in diet quality, and other health behaviours, in an older COPD population. Findings from this feasibility study indicate that further evaluation of the GENIE intervention is warranted in a larger study, with a longer follow-up.

opencc-by-4.0Aug 2020View details →

Content Validity of a Questionnaire Exploring the Knowledge Translation Network Among Nursing Professionals

Expert responses to the validation questionnaire of a knowledge transfer questionnaire in nursing networks In addition to the answers, you can see the calculations made and the results obtained.

opencc-by-4.0Aug 2020View details →

Predicting Phenotype from Multi-Scale Genomic and Environment Data using Neural Networks and Knowledge Graphs

Background: To mitigate the effects of climate change on public health and conservation, we need to better understand the dynamic interplay between biological processes and environmental effects. Machine learning (ML) methods in general, and Deep Learning (DL) methods in particular, are a potential way forward because they are able to cope with the nonlinearity of natural systems. However, there are several barriers that exist, including the absence of ML-ready data. We propose to develop a machine learning framework capable of predicting phenotypes based on multi-scale data about genes and environments. A critical part of this framework are data transformation methods that map the heterogeneous input data into formats that are consumable by the ML techniques. The central hypothesis of this research is that deep learning algorithms and biological knowledge graphs will predict phenotypes more accurately across more taxa and more ecosystems than do current numerical and traditional statistical modeling methods. Our long term goal is to develop predictive analytics for organismal response to environmental perturbations using innovative data science approaches. This pilot project on predicting emergent properties of complex systems and multidimensional interactions is funded by the NSF (Award # 1939945, 1940059, 1940062, 1940330).    Results: We have established shared project governance, communication channels, project timeline, and data and computing environment across four universities. We have successfully reached out to three other projects for broader collaboration.

Data from: Chromosome-scale inference of hybrid speciation and admixture with convolutional neural networks

Inferring the frequency and mode of hybridization among closely related organisms is an important step for understanding the process of speciation and can help to uncover reticulated patterns of phylogeny more generally. Phylogenomic methods to test for the presence of hybridization come in many varieties and typically operate by leveraging expected patterns of genealogical discordance in the absence of hybridization. An important assumption made by these tests is that the data (genes or SNPs) are independent given the species tree. However, when the data are closely linked, it is especially important to consider their non-independence. Recently, deep learning techniques such as convolutional neural networks (CNNs) have been used to perform population genetic inferences with linked SNPs coded as binary images. Here we use CNNs for selecting among candidate hybridization scenarios using the tree topology (((P1,P2),P3),Out) and a matrix of pairwise nucleotide divergence (dXY) calculated in windows across the genome. Using coalescent simulations to train and independently test a neural network showed that our method, HyDe-CNN, was able to accurately perform model selection for hybridization scenarios across a wide-breath of parameter space. We then used HyDe-CNN to test models of admixture in Heliconius butterflies, as well as comparing it to a random forest classifier trained on introgression-based statistics. Given the flexibility of our approach, the dropping cost of long-read sequencing, and the continued improvement of CNN architectures, we anticipate that inferences of hybridization using deep learning methods like ours will help researchers to better understand patterns of admixture in their study organisms.

Data from: Evolutionary networks from RADseq loci point to hybrid origins of Medicago carstiensis and Medicago cretacea

Premise: Although hybridization has played an important role in the evolution of many plant species, phylogenetic reconstructions that include hybridizing lineages have been historically constrained by the available models and data. RADseq has been a popular sequencing technique for the reconstruction of hybridization in the Next Generation Sequencing era. However, the utility of RADseq for the reconstruction of complex evolutionary networks has not been thoroughly investigated. Conflicting phylogenetic relationships in the genus Medicago have been mainly attributed to hybridization but the specific hybrid origins of taxa have not been yet clarified. Methods: We obtained new molecular data from diploid species of Medicago section Medicago using single-digest RADseq to reconstruct evolutionary networks from gene trees, an approach that is computationally tractable with datasets that include several species and complex hybridization patterns. Results: Our analyses revealed that assembly filters to exclusively select a small set of loci with high phylogenetic information led to the most divergent network topologies. Conversely, alternative clustering thresholds or filters on the number of samples per locus had a lower impact on networks. A strong hybridization signal was detected for M. carstiensis and M. cretacea, while less clear signals were observed for M. rugosa, M. rhodopea, M. suffruticosa, M. marina, M. scutellata and M. sativa. Conclusions: Complex network reconstructions from RADseq gene trees were not robust under variations of the assembly parameters and filters. But when most divergent networks were discarded, all remaining analyses consistently supported a hybrid origin for M. carstiensis and M. cretacea.

opencc-by-4.0Aug 2020View details →

Analysis of factors influencing the network teaching effect of college students in a medical school during the COVID-19 epidemic

Analysis of factors influencing the network teaching effect of college students in a medical school during the COVID-19 epidemic

Data from: Dung beetle-megafauna trophic networks in Singapore's fragmented forests

We investigated trophic networks between dung beetles and megafauna species in five forest fragments in Singapore varying in size and isolation. We found that Singapore's dung beetle communities were attracted to extant and extinct dung types from different dietary groups. All forest fragment networks were similar, and displayed high generalism and high nestedness.

Bitcoin Transaction Network

This Bitcoin transaction network data as extracted with the bitcoind client. Due to the nature of Bitcoin, all transactions are published to a P2P network. These can be downloaded and extracted with any of the available client programs. This dataset includes the data used in our papers: <ol> <li>Kondor, D., Pósfai, M., Csabai, I., & Vattay, G. (2014). Do the rich get richer? An empirical analysis of the BitCoin transaction network. PLoS ONE, 9(2), e86197. https://doi.org/10.1371/journal.pone.0086197</li> <li>Kondor, D., Csabai, I., Szüle, J., Pósfai, M., & Vattay, G. (2014). Inferring the interplay between network structure and market effects in Bitcoin. New Journal of Physics, 16(12), 125003. https://doi.org/10.1088/1367-2630/16/12/125003</li> </ol>

opencc-by-4.0Jun 2020View details →

Global Energy Virtual Water Trade Network and Country Electricity Water Footprints

Global energy trade accounts for between 22 and 32% of total energy consumption, approximately 2.0 x 1011 GJ of energy. Based on concepts of the energy-water nexus and water footprinting, we determine the water intensity of the energy trade for 11 different energy commodities, including primary (i.e., fossil fuels) and secondary (i.e., electricity) energy. In this data description, we present a database on the water footprint of country-to-country energy trade for 2010--2018 and water intensity values of electricity for each country. The database includes the country of origin, trade partner, type of energy commodity, quantity traded, value of the energy trade, a mean water footprint value in m3, and an estimated range of uncertainty water footprint. These data provide the basis for assessing international virtual water trade, water scarcity concerns, and the environmental implications for a changing global energy system.

Increasing importance of niche versus neutral processes in the assembly of plant–herbivore networks during succession

Recent studies suggest that the assembly of trophic interaction networks is the result of both niche (deterministic and selective) and neutral (stochastic) processes, but we know little about their relative importance. Succession following disturbance offers a good opportunity to address this question. Studies of single-trophic guilds suggest that, shortly after a disturbance, such as a fire, neutral assembly processes (e.g. colonisation events) dominate whereas niche processes (selection) become more and more important as succession proceeds. Building on these observations, we predict similar changes in interaction networks during succession, with a shift from stochastic toward selective interactions. To test this, we studied succession of plant–herbivorous insect networks in South Africa after a fire. We sampled a total of 385 herbivorous arthropod species and 92 plant species. For different successional stages and spatial grain sizes, we used network descriptors to estimate plant–herbivore specificity and partner fidelity of plant and herbivore species across networks (i.e. localities). We compared the observed network descriptors to neutral models, and then differentiated selective species (associated with similar partner species in different networks) from neutral species (associated at random with their partners). Our results suggest that specialisation, partner fidelity and the proportion of selective species of plants and herbivores increased with succession, which is consistent with the hypothesis that niche-based processes prevail over neutral processes as succession proceeds. However, in all the successional stages, the majority of species were neutral species, which pinpoints the importance of opportunistic interactions in the assembly of trophic networks.

Alzheimer's disease risk gene BIN1 induces Tau-dependent network hyperexcitability — MEA Axion Biosciences Maestro Recordings, Figure 6

Genome-wide association studies identified the BIN1 locus as a leading modulator of genetic risk in Alzheimer's disease (AD). One limitation in understanding BIN1's contribution to AD is its unknown function in the brain. AD-associated BIN1 variants are generally noncoding and likely change expression. Here, we determined the effects of increasing expression of the major neuronal isoform of human BIN1 in cultured rat hippocampal neurons. Higher BIN1 induced network hyperexcitability on multielectrode arrays, increased frequency of synaptic transmission, and elevated calcium transients, indicating that increasing BIN1 drives greater neuronal activity. In exploring the mechanism of these effects on neuronal physiology, we found that BIN1 interacted with L-type voltage-gated calcium channels (LVGCCs) and that BIN1–LVGCC interactions were modulated by Tau in rat hippocampal neurons and mouse brain. Finally, Tau reduction prevented BIN1-induced network hyperexcitability. These data shed light on BIN1's neuronal function and suggest that it may contribute to Tau-dependent hyperexcitability in AD.

Uncovering structural features that underlie coexistence in an invaded woody plant community with interaction networks at multiple life stages

Understanding the patterns of competitive and facilitative interactions within and among species in plant communities is a central goal of plant ecology, because these patterns determine species coexistence and community dynamics. Network theory provides tools that allow these patterns to be quantified, and can provide greater understanding of important community properties, including community stability, than can documenting pairwise species interactions. I characterized the interactions of multiple, co-occurring invasive and native species in an old field woody plant community to build plant interaction networks at two different life stages. With the goal of identifying structural features that may operate to maintain species coexistence, I characterized the architecture of these networks at multiple scales: the entire network, the substructures that compose the network, and species' roles within substructures. I found that species-level pairwise interactions alone did not provide an accurate or sufficiently detailed picture of community structure. Rather, using a network approach, I identified substructures that have the potential to promote and hinder species coexistence in interactions among seedlings. Characterizing the nuances of network substructures was illuminating, as the size of the substructures and the pattern of interaction intensities within substructures influence the expected effects on species coexistence. Including interactions at multiple life stages was also important; the seedling species that benefited most from the nested structure of facilitative interactions with adults occupied subordinate roles in substructures with other seedlings. This role reversal at different life stages is a potential factor promoting coexistence in the community. Last, the network framework was useful for comparing species' roles between native and invasive members of the community, and the three invasive species in this system had different, life-stage dependent strategies in interactions with co-occurring plants. Synthesis. The interplay of network architecture and substructures within plant communities and among plants at different life stages is important for understanding species coexistence. In the plant community characterized in this study, there were several features that may promote coexistence, and these features were not observable in interactions within a single life stage or when considering pairwise interactions independently.