Find research datasets worth reusing

opencc-by-4.0Sep 2023View details →

Data set from the 6G-SANDBOX platforms benchmarking and assessment for different documented trial networks - Athens facility

This data set covers core network and end-to-end measurements at the Athens facility, part of the 6G-SANDBOX infrastructure.

Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Model Simulation Data

WRF-Chem simulated daily mean PM2.5 concentrations for: 1) with fires  2) without fires simulations.

Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Purple Air data

Daily mean PM2.5 concentrations collected by Purple Air sensors between 2023-08-16 and 2023-12-01. Concentrations have been RH adjusted using the Nilson et al (2022) adjustment.

Deep learning based on convolutional neural networks to classify nanobiomechanical data

Open the record for dataset details and reuse information.

Data from the article titled Mapping of networks and professional capital in the La Algarabía occupational center.

Project PID2020-117020GB-I00 , funded by : Ministerio de Ciencia e Innovación de España/ AEI/10.13039/501100011033 and by the predoctoral contracts grant for the training of PhD implemented by the [grant number PRE2021-098075]

Network formation as Narrative: Case Study Data

This data is the companion to a forthcoming paper entitled Network Formation As Narrative In Computer Role-playing Games. It consists of several excel sheets and gephi files with some graphs of social network data from a campaign of Mount & Blade: Warband, a campaign of The Exile Princes, and some hand-curated data on networks presented in the narrative of the early part of Baldur's Gate 3.

opengpl-3.0-or-laterOct 2024View details →

Supplemental Dataset Excel files and Source Data Excel file for "START domains generate paralog-specific regulons from a single network architecture"

Supplemental Dataset Excel files and Source Data Excel file for "START domains generate paralog-specific regulons from a single network architecture" in Nat Comms

openapache2.0Jul 2024View details →

Data for "Information sharing within a social network is key to behavioral flexibility – lessons from mice tested under semi-naturalistic conditions"

Data for "Information sharing within a social network is key to behavioral flexibility – lessons from mice tested under semi-naturalistic conditions", currently under review in Science Advances.

opencc-zeroDec 2016View details →

Data from: Linking social and spatial networks to viral community phylogenetics reveals subtype-specific transmission dynamics in African lions

1.Heterogeneity within pathogen species can have important consequences for how pathogens transmit across landscapes; however, discerning different transmission routes is challenging. 2.Here we apply both phylodynamic and phylogenetic community ecology techniques to examine the consequences of pathogen heterogeneity on transmission by assessing subtype specific transmission pathways in a social carnivore. 3.We use comprehensive social and spatial network data to examine transmission pathways for three subtypes of feline immunodeficiency virus (FIVPle) in African lions (Panthera leo) at multiple scales in the Serengeti National Park, Tanzania. We used FIVPle molecular data to examine the role of social organization and lion density in shaping transmission pathways and tested to what extent vertical (i.e., father and/or mother offspring relationships) or horizontal (between unrelated individuals) transmission underpinned these patterns for each subtype. Using the same data, we constructed subtype specific FIVPle co-occurrence networks and assessed what combination of social networks, spatial networks, or co-infection best structured the FIVPle network. 4.While social organization (i.e., pride) was an important component of FIVPle transmission pathways at all scales, we find that FIVPle subtypes exhibited different transmission pathways at within- and between-pride scales. A combination of social and spatial networks, coupled with consideration of subtype co-infection, was likely to be important for FIVPle transmission for the two major subtypes, but the relative contribution of each factor was strongly subtype specific. 5.Our study provides evidence that pathogen heterogeneity is important in understanding pathogen transmission, which could have consequences for how endemic pathogens are managed. Furthermore, we demonstrate that community phylogenetic ecology coupled with phylodynamic techniques can reveal insights into the differential evolutionary pressures acting on virus subtypes, which can manifest into landscape-level effects.

opencc-zeroDec 2017View details →

Data from: Split between two worlds: automated sensing reveals links between above- and belowground social networks in a free-living mammal

Many animals socialize in two or more major ecological contexts. In nature, these contexts often involve one situation in which space is more constrained (e.g. shared refuges, sleeping cliffs, nests, dens or burrows) and another situation in which animal movements are relatively free (e.g. in open spaces lacking architectural constraints). Although it is widely recognized that an individual's characteristics may shape its social life, the extent to which architecture constrains social decisions within and between habitats remains poorly understood. Here we developed a novel, automated-monitoring system to study the effects of personality, life-history stage and sex on the social network structure of a facultatively social mammal, the California ground squirrel (Otospermophilus beecheyi) in two distinct contexts: aboveground where space is relatively open and belowground where it is relatively constrained by burrow architecture. Aboveground networks reflected affiliative social interactions whereas belowground networks reflected burrow associations. Network structure in one context (belowground), along with preferential juvenile–adult associations, predicted structure in a second context (aboveground). Network positions of individuals were generally consistent across years (within contexts) and between ecological contexts (within years), suggesting that individual personalities and behavioural syndromes, respectively, contribute to the social network structure of these free-living mammals. Direct ties (strength) tended to be stronger in belowground networks whereas more indirect paths (betweenness centrality) flowed through individuals in aboveground networks. Belowground, females fostered significantly more indirect paths than did males. Our findings have important potential implications for disease and information transmission, offering new insights into the multiple factors contributing to social structures across ecological contexts.

opencc-zeroDec 2015View details →

Data from: Forecasting potential emergence of zoonotic diseases in Southeast Asia: network analysis identifies key rodent hosts

1. Within complex ecological systems, identifying animal species likely to play a key role in the emergence of infectious zoonotic diseases remains a major challenge. One approach consists of using information on current ecological and parasitological similarities among host species in order to predict the most likely pathways for future pathogen spillover. 2. Using field data acquired from 15 sympatric rodent species in various habitats in Thailand, Cambodia and Laos, we built networks based on shared parasites (17 helminth and 15 microparasite species) and shared habitats among rodent species and humans. We investigated the architectures of bipartite and unipartite networks using modularity, subgroups partitioning or node centrality, to assess the relative epidemiological importance of particular rodent species. 3. Our results showed that Rattus tanezumi, Bandicota savilei and R. exulans were consistently found to be members of subgroups that included humans in unipartite and bipartite networks on zoonotic agents and shared habitats. High values of centrality in shared zoonotic agents were found for the same three rodent species, whereas high values of shared habitats were observed for two of them. Although phylogenetically related rodent species likely shared both habitats and parasites, a lack of habitat specialisation was associated with increased zoonotic parasite sharing. 4. Our results emphasize the disproportionate importance of these three rodent species, through their high degree of connectivity with humans, which may represent a high risk for direct zoonotic spillover. Moreover, due to its high centrality in habitats, R. tanezumi may also play a key role as a bridge host. 5. The recent discovery of new arenaviruses in rodents in Southeast Asia, with associated disease in humans in Cambodia, provides an opportunity to test this empirically. The three rodent species identified using our network approach are some of the potential maintenance hosts for these new emerging arenaviruses. 6. Synthesis and applications. Our results on rodents and their pathogens in Southeast Asia show that network analysis has a high potential to improve the surveillance of emerging zoonotic pathogens by targeting key host species and potential "emerging' pathogen–rodent interactions in complex and heterogeneous landscapes.

opencc-zeroDec 2017View details →

Data from: Structural and defensive roles of angiosperm leaf venation network reticulation across an Andes-Amazon elevation gradient

1.The network of minor veins of angiosperm leaves may include loops (reticulation). Variation in network architecture has been hypothesized to have hydraulic and also structural and defensive functions. 2.We measured venation network trait space in eight dimensions for 136 biomass-dominant angiosperm tree species along a 3,300 m elevation gradient in southeastern Peru. We then examined the relative importance of multiple ecological, and evolutionary predictors of reticulation. 3.Variation in minor venation network reticulation was constrained to three axes. These axes described branching vs. reconnecting veins, elongated vs. compact areoles, and high vs. low density veins. Variation in the first two axes was predicted by traits related to mechanical strength and secondary compounds, and in the third axis by site temperature. 4.Synthesis. Defensive and structural factors primarily explain variation in multiple axes of reticulation, with a smaller role for climate-linked hydraulic factors. These results suggest that venation network reticulation may be determined more by species interactions than by hydraulic functions.

opencc-by-4.0Jun 2021View details →

Figure data for the manuscript "SI-traceable frequency dissemination at 1572.06 nm in a stabilized fiber network with ring topology"

This file contains the data shown in Fig. 1, Fig 3, Fig. 4,  Fig. 5 and Fig. 6 of the manuscript "SI-traceable frequency dissemination at 1572.06 nm in a stabilized fiber network with ring topology". Additional information on the data and processing procedure are available from the author upon reasonable request.

opencc-zeroDec 2016View details →

Data from: Three-dimensional trajectories and network analyses of group behaviour within chimney swift flocks during approaches to the roost

Chimney swifts (Chaetura pelagica) are highly manoeuvrable birds notable for roosting overnight in chimneys, in groups of hundreds or thousands of birds, before and during their autumn migration. At dusk, birds gather in large numbers from surrounding areas near a roost site. The whole flock then employs an orderly, but dynamic, circling approach pattern before rapidly entering a small aperture en masse. We recorded the three-dimensional trajectories of ≈1 800 individual birds during a 30 min period encompassing flock formation, circling, and landing, and used these trajectories to test several hypotheses relating to flock or group behaviour. Specifically, we investigated whether the swifts use local interaction rules based on topological distance (e.g. the n nearest neighbours, regardless of their distance) rather than physical distance (e.g. neighbours within x m, regardless of number) to guide interactions, whether the chimney entry zone is more or less cooperative than the surrounding flock, and whether the characteristic subgroup size is constant or varies with flock density. We found that the swift flock is structured around local rules based on physical distance, that subgroup size increases with density, and that there exist regions of the flock that are less cooperative than others, in particular the chimney entry zone.

opencc-by-4.0Aug 2021View details →

Observational Data for: Polarized information ecosystems can reorganize social networks via information cascades

General Information This contains the observational data for the publication: <blockquote> Tokita, Guess, and Tarnita (2021). Polarized information ecosystems can reorganize social networks via information cascades. Proceedings of the National Academies of Science of the United States. </blockquote> Please see the above peer-reviewed article that resulted from this data for more details. Please see the observational/ directory in the Github repository <a href="http://%28https//github.com/christokita/information-cascades)">https://github.com/christokita/information-cascades</a> for the code that analyzes the Twitter data and generates derived data. Data directly pertaining to the four news sources of interest are in the data/ directory, while data that is related to the 4,000 monitored news followers are in the data_derived/ directory. The main data file is in a zipped file. We also included a read me with a description of each directory and subdirectory of the data. Methods For each of our four news sources of interest (CBS News, USA Today, Vox, and the Washington Examiner), we used the Twitter API to sample 3,000 random Twitter users that followed that news source's account. We then used the tweetscores R package to estimate the ideology of each of the 12,000 sampled users. From the original pool of approximately 12,000 sampled users who follow a news outlet of interest, we monitored the follower networks of 1,000 liberal followers of CBS News, 1,000 conservative followers of USA Today, 1,000 liberal followers of Vox, and 1,000 conservative followers of the Washington Examiner. When selecting the 1,000 random users to monitor for a given news outlet, we first conducted filters based on self-reported geolocation and other Twitter profile attributes to reasonably filter down to US-based users who primarily tweet in English. We then pulled the complete follower network of each of our 4,000 monitored users at the beginning and end of a 6-week period from August to September 2020, allowing us to assess who unfollowed these users over this period of time. Finally, using the initial follower networks of each monitored user, we estimated the ideology of up to 50 random followers to create a baseline for the ideological composition of each user’s follower network.  Using the initial and final follower networks of each individual, we calculated the rate of unfollows by opposite-ideology users in their follower network. To determine whether an unfollow event was breaking a cross-ideology social tie, users and their unfollowers were classified simply as either liberal (ideology score < 0) or conservative (ideology score > 0). We excluded from analysis unfollowers for whom we could not estimate ideology, either because the account had been suspended, deleted, or made private, or because they did not follow any of the political, news, or cultural accounts needed to do the estimation. We then compared the proportion of unfollowers that were of the opposite ideology (i.e., conservative unfollowers if the focal user is liberal) against the estimated proportion of opposite-ideology followers in the focal user’s initial follower network. This allowed us to account for the fact that many user’s follower networks were not ideologically balanced and set the baseline expectation that if unfollows were random then the proportion of unfollows by opposite-ideology users should match the proportion of followers that were of the opposite ideology.   Abstract (for main paper) The precise mechanisms by which the information ecosystem polarizes society remain elusive. Focusing on political sorting in networks, we develop a computational model that examines how social network structure changes when individuals participate in information cascades, evaluate their behavior, and potentially rewire their connections to others as a result. Individuals follow proattitudinal information sources but are more likely to first hear and react to news shared by their social ties and only later evaluate these reactions by direct reference to the coverage of their preferred source. Reactions to news spread through the network via a complex contagion. Following a cascade, individuals who determine that their participation was driven by a subjectively “unimportant” story adjust their social ties to avoid being misled in the future. In our model, this dynamic leads social networks to politically sort when news outlets differentially report on the same topic, even when individuals do not know others’ political identities. Observational follow network data collected on Twitter support this prediction:We find that individuals in more polarized information ecosystems lose cross-ideology social ties at a rate that is higher than predicted by chance. Importantly, our model reveals that these emergent polarized networks are less efficient at diffusing information: Individuals avoid what they believe to be “unimportant” news at the expense of missing out on subjectively “important” news far more frequently. This suggests that “echo chambers”—to the extent that they exist—may not echo so much as silence.

opencc-by-4.0Aug 2021View details →

data for Newbury et al Short term fitness effects of bipartite interactions shape network structure of mutualistic and antagonistic communities

speciescountdata.csv contains colony couts and plasmid detection from the main experiment. evonet.csv conatins colony counts of bacteria with and without plasmid pkjk5 from commuities where donors were  o/ p  ancestral/evolved.  AOPV.csv contains optical density data for species a,o,p and v. column 1 is time in hours. Then columns alternate between a, o, p, s, v, a+,o+,p+,s+,v+,s+,v+ (where + denotes plasmid carriage) until column 49. After which the same patten continues, but these were grown with tetracycline. s did not grow at all in the 96-well plate this data was taken from. This is likely due to experimental error, so an additional well plate was used just for s (S.scv). S.csv contains optical density data for species s. column 1 is time in hours. then there are  6 colulmns of s and 6 columns of s+ until column 49. After this the same pattern continues but bacteria were grown with tetracycline.

opencc-by-4.0May 2021View details →

Dataset - seismic data from central-western Italy used in the paper on rapid prediction of ground motion using a Convolutional Neural Network

The dataset published here is the central-western Italy dataset used in the paper "Transfer learning: Improving neural network based prediction of earthquake ground shaking for an area with insufficient training data" (<a href="https://arxiv.org/abs/2105.05075">https://arxiv.org/abs/2105.05075</a>). The code for the paper is available at <a href="https://github.com/djozinovi/TLpredIM">https://github.com/djozinovi/TLpredIM</a>. The abstract of the paper: <blockquote> In a recent study (Jozinović et al, 2020) we showed that convolutional neural networks (CNNs) applied to network seismic traces can be used for rapid prediction of earthquake peak ground motion intensity measures (IMs) at distant stations using only recordings from stations near the epicenter. The predictions are made without any previous knowledge concerning the earthquake location and magnitude. This approach differs from the standard procedure adopted by earthquake early warning systems (EEWSs) that rely on location and magnitude information. In the previous study, we used 10 s, raw, multistation waveforms for the 2016 earthquake sequence in central Italy for 915 events (CI dataset). The CI dataset has a large number of spatially concentrated earthquakes and a dense station network. In this work, we applied the CNN model to an area around area near Pisa, Italy. In our initial application of the technique, we used a dataset consisting of 266 earthquakes recorded by 39 stations. We found that the CNN model trained using this smaller dataset performed worse compared to the results presented in the original study by Jozinović et al. (2020). To counter the lack of data, we adopted transfer learning (TL) using two approaches: first, by using a pre-trained model built on the CI dataset and, next, by using a pre-trained model built on a different (seismological) problem that has a larger dataset available for training. We show that the use of TL improves the results in terms of outliers, bias, and variability of the residuals between predicted and true IMs values. We also demonstrate that adding knowledge of station positions as an additional layer in the neural network improves the results. The possible use for EEW is demonstrated by the times for the warnings that would be received at the station PII. </blockquote>

opencc-by-4.0Oct 2021View details →

Data of "Interaction-based material network: a general framework for (porous) microstructured materials"

<pre>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data) title = "Interaction-based material network: a general framework for (porous) microstructured materials", journal = "Computer Methods in Applied Mechanics and Engineering", pages = " ", year = "202?", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.", author = "Nguyen, Van Dung and Noels, Ludovic"</pre>

opencc-by-4.0Mar 2020View details →

The NGI Forward semantic social network data

The <a href="https://research.ngi.eu/">NGI Forward project</a> is part of the European Union's <a href="https://www.ngi.eu">Next Generation Internet Initiative</a>. It is meant to provide European institutions with policy advice for how to shape the future, human-centric Internet. As part of it, a team of ethnographers coded a specially convened online conversation, then arranged its results into a semantic social network. This dataset encodes that conversation, as well as the results of the coding exercise, in raw data form for further exploration and replication purposes. The dataset is pseudonymized. <ul> <li><a href="https://exchange.ngi.eu/">Funnel website</a> of the project.</li> <li><a href="https://journals.sagepub.com/doi/10.1177/1525822X20908236">About semantic social networks</a>.</li> <li><a href="https://edgeryders.eu/t/long-term-ssna-data-storage-documentation-manual/12786">Data export and documentation process</a> (contains links to the code used to export the data)</li> </ul>