Find research datasets worth reusing

Data from: A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images

Accurate species identification is a prerequisite to assess the medical relevance of a mosquito specimens. In monitoring or surveillance programs, mosquitoes are typically identified based on morphological characters, which can be supported by molecular biological assays. Both methods require intensive experience of the observers and well-equipped laboratories. The use of convolutional neural networks (CNNs) to identify species based on images may be a cost-effective and reliable alternative. In this proof-of-concept study, we developed a CNN to identify seven Aedes species by wing images, only. While previous studies used images of the whole mosquito body, the nearly two-dimensional wings may facilitate standardized image capture and thereby reduce the complexity of the CNN implementation. Mosquitoes were sampled from different sites in Germany. Their wings were mounted and photographed with a professional stereomicroscope. The data set consisted of 1,155 wing images from seven Aedes species, including the exotic species Aedes albopictus und six native Aedes species, as well as 554 wings from different non-Aedes mosquitoes. The wing images were used to train a CNN to differentiate between Aedes and non-Aedes mosquitoes and to classify the seven Aedes species. The training was conducted separately for grayscale and RGB images. Image processing, data augmentation, training, validation and testing were conducted in python using deep-learning framework PyTorch. For both input images, i.e. grayscale and RGB images, our best-performing CNN configuration achieved an accuracy of 100% to discriminate Aedes from non-Aedes mosquito species. The accuracy to predict the Aedes species reached 93% for grayscale images and 96% for RGB images. Aedes albopictus could be identified with an accuracy of 100%. In conclusion, wing images are sufficient to identify mosquito species by CNN based image classification. Thus, wing images can represent a useful complement for CNN-based image classification, e.g. for damaged mosquito specimens. Larger training data sets with further mosquito species and a greater variety of images are required to improve and test broad applicability.

Receiver function data from Jammu and Kashmir seismological NETwork

This data constitutes Radial component P-wave receiver functions computed at Gaussian width 2.5 for teleseismic earthquakes recorded at 20 broadband stations of the Jammu and Kashmir Seismological NETwork (JAKSNET). These P-RFs are used for modeling the crustal structure of the Jammu and Kashmir Himalaya.

Data from: Heat stress conditions affect the social network structure of free‐ranging sheep

Extreme weather conditions, like heatwave events, are becoming more frequent with climate change. Animals often modify their behaviour to cope with environmental changes and extremes. During heat stress conditions, individuals change their spatial behaviour and increase the use of shaded areas to assist with thermoregulation. Here, we suggest that for social species, these behavioural changes and ambient conditions have the potential to influence an individual's position in its social network, and the social network structure as a whole. We investigated whether heat stress conditions (quantified through the temperature humidity index) and the resulting use of shaded areas, influence the social network structure and an individual's connectivity in it. We studied this in free‐ranging sheep in the arid zone of Australia, GPS‐tracking all 48 individuals in a flock. When heat stress conditions worsened, individuals spent more time in the shade and the network was more connected (higher density) and less structured (lower modularity). Furthermore, we then identified the behavioural change that drove the altered network structure and showed that an individual's shade use behaviour affected its social connectivity. Interestingly, individuals with intermediate shade use were most strongly connected (degree, strength, betweenness), indicating their importance for the connectivity of the social network during heat stress conditions. Heat stress conditions, which are predicted to increase in severity and frequency due to climate change, influence resource use within the ecological environment. Importantly, our study shows that these heat stress conditions also affect the animal's social environment through the changed social network structure. Ultimately, this could have further flow on effects for social foraging and individual health since social structure drives information and disease transmission.

opencc-by-4.0Feb 2024View details →

Dataset of 'Physics-informed neural networks for high-resolution weather reconstruction from sparse weather stations'

Dataset of the article 'Physics-informed neural networks for high-resolution weather reconstruction from sparse weather stations', recently published in Open Research Europe (DOI 10.12688/openreseurope.17388.1). The code which implements the physics-informed neural network can be found at https://github.com/AlvaroMS90/PINNs-for-high-resolution-weather-reconstruction-from-sparse-weather-stations. Funded by the European Union under action HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships, call HORIZON-MSCA-2021-PF-01 (project number 101059984 with acronym PERSEVERE). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. <pre> </pre>

Mediodorsal thalamus and ventral pallidum contribute to subcortical regulation of the default mode network

Humans and other animals readily transition from externally to internally focused attention, and these transitions are accompanied by coactivation of a group of brain regions collectively known as the default mode network (DMN). While the DMN was considered a cortical network, recent evidence suggests subcortical structures are part of the DMN. Here we investigated the role of ventral pallidum (VP) and mediodorsal thalamus (MD) in DMN regulation in the tree shrew, a close relative of primates. We combine electrophysiology and deep learning-based motion tracking to perform unsupervised classification of behavioral states. We found gamma oscillations in VP and MD coordinated with gamma in the anterior cingulate (AC) cortex specifically during DMN states. Similar enhancements were found for high gamma, but only at subcortical sites. Cross-frequency coupling between gamma and delta oscillations were higher during DMN than other behaviors, underscoring the engagement of MD, VP, and AC circuits. Our findings highlight the importance of VP in DMN regulation in the tree shrew, consistent with rodent studies, and demonstrate a role for MD thalamus in DMN regulation. Our results extend homologies in DMN regulation among mammals, and underline the importance of thalamus and basal forebrain to the regulation of DMN brain states.

Using dung densities to assess the ecological effectiveness of a protected area network

Given recent global endeavors to increase protected area coverage, it is crucial to comprehensively evaluate the efficacy of various area-based conservation strategies in effectively reducing biodiversity loss. Here, we investigated responses of wildlife populations to different protection levels and environmental variables at the landscape scale in the Katavi-Rukwa Ecosystem, western Tanzania. To this end, we conducted line distance sampling surveys and counted dung of six target mammal species (elephant, giraffe, buffalo, zebra, topi, hartebeest) along foot transects within areas differing in protection levels (from strict to less-strictly protected: national park, game reserve, forest reserve, game-controlled area, and unprotected areas). Based on these dung counts, we modelled the spatial distribution of these six mammal species using a species-specific density surface modelling framework. We, found consistent effects of protection level and land-use variables on the spatial distribution of the target mammal species: dung densities were highest in the national park and game reserves, intermediate in less-strictly protected areas and lowest in un-protected areas. Beyond species-specific environmental predictors for dung densities, our results highlight consistent negative associations between dung densities of the target species and distance to cropland and avoidance of areas in proximity to houses. Our findings underpin differences in ecological effectiveness of protected areas within one ecosystem. Protection level and land use play crucial roles in moderating the spatial distribution of all considered mammal species. Our findings suggest that a landscape approach needs to guide effective conservation across the entire protection gradient of the Katavi-Rukwa Ecosystem.

opencc-by-4.0Dec 2023View details →

Mesosphere/lower thermosphere 3-dimensional spatially resolved winds observed by Chinese multistatic meteor radar network using the newly developed VVP method

This dataset supports the article "Mesosphere/lower thermosphere 3-dimensional spatially resolved winds observed by Chinese multistatic meteor radar network using the newly developed VVP method" . The data are provided in MatLab format.

opencc-by-4.0Jan 2024View details →

Replication Package for: International Comovement in the Global Production Network

This package contains the data and code necessary to reproduce the figures and tables in Huo, Levchenko and Pandalai-Nayar (forthcoming). "International Comovement in the Global Production Network," Review of Economic Studies. Detailed instructions are given about accessing the raw data, and running the code to generate output.

Prediction model of the temporal dynamics of severe pest cashew Anacampsis phytomiella using artificial neural networks

Open the record for dataset details and reuse information.

Mechanistic interactions as the origin of modularity in biological networks

Biological networks are often modular. Explanations for this peculiarity either assume an adaptive advantage of a modular design such as higher robustness, or attribute it to neutral factors such as constraints underlying network assembly. Interestingly, most insights on the origin of modularity stem from models in which interactions are either determined by highly simplistic mechanisms or have no mechanistic basis at all. Yet, empirical knowledge suggests that biological interactions are often mediated by complex structural or behavioural traits. Here, we investigate the origins of modularity using a model in which interactions are determined by potentially complex traits. Specifically, we model system elements - such as the species in an ecosystem - as finite-state machines (FSMs) and determine their interactions by means of communication between the corresponding FSMs. Using this model, we show that modularity likely emerges for free. We further find that the more modular an interaction network is, the less complex are the traits that mediate the interactions. Altogether, our results suggest that the conditions for modularity to evolve may be much broader than previously thought.

The data for "Search for gravitational-lensing signatures in the full third observing run of the LIGO–Virgo network"

This material is part of several data products associated with the publication "Search for gravitational-lensing signatures in the full third observing run of the LIGO–Virgo network" from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://arxiv.org/abs/2304.08393">arXiv</a>) and the related material linked from this page.Data releaseThis data release contains plotting scripts, jupyter notebooks and datasets for the figures / tables in the aforementioned paper. Each script/notebook have detailed instructions and comments.How to download all files from this pageIf you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:pip install zenodo_get zenodo-get RECORD_ID_OR_DOIwhere the record ID for the most recent version of this page is v5 and IDs for other versions can be found in the Versions section at the side of this page.

Rapid and Accurate Identification of Stem Cell Differentiation Stages via SERS and Convolutional Neural Networks

<a name="OLE_LINK2"></a>Monitoring the transition of cell states during induced pluripotent stem cell (iPSC) differentiation is crucial for clinical medicine and basic research. However, both identification category and prediction accuracy need further improvement. Here, we propose a method combining Surface-Enhanced Raman spectroscopy (SERS) with convolutional neural networks (CNN) to precisely identify and distinguish cell states during stem cell differentiation. First, mitochondria-targeted probes were synthesized by combining AuNRs and mitochondrial localization signal (MLS) peptides to obtain effective and stable SERS spectra signals at various stages of cell differentiation. Then, the SERS spectra served as input datasets, and their distinctive features were learned and distinguished by CNN. As a result, rapid and accurate identification of six different cell states, including the embryoid body (EB) stage, was successfully achieved throughout the stem cell differentiation process with an impressive prediction accuracy of 98.5%. Furthermore, the impact of different spectral feature peaks on the identification results was investigated, which provides a valuable reference for selecting appropriate spectral bands to identify cell states. This is also beneficial for shortening the spectral acquisition region to enhance spectral acquisition speed. These results suggest the potential for SERS-CNN models in quality monitoring of stem cells, advancing the practical applications of stem cells.

Data from: Recreational vessel networks reveal potential hot spots for marine pest introduction and spread

Recreational vessels are an important pathway for spreading marine non-indigenous species (NIS) around coastal environments globally. However, most vessels are not tracked, limiting our ability to map their movements and identify locations at greater risk of NIS introductions. Using New Zealand as a case study, we quantified spread and risk patterns of recreational vessel movements, using a web-based survey allowing the more than 1,800 respondents to map significant trips with over 12,000 visits. These vessel routes were used to build a network model representing nationwide recreational vessel movements. Two proxies were used to quantify the risk of introduction of marine NIS: (i) incoming hull length and (ii) cumulative residency periods at sites. There was significant variation in the distances travelled, the destinations they visited, and the duration of their stays. New Zealand's recreational boating network contained 317 destinations with over 4,000 unique connections, concentrated within two distinct areas of the country. Network-based metrics and risk proxies quantified the relative importance of domestic locations as incursion or spreading hubs for NIS. This approach highlighted several areas that pose high relative biosecurity risk within the national network, but are underrepresented within current surveillance programmes. Synthesis and applications. Our study demonstrates how the movement dynamics of recreational vessels can be quantified at a regional scale to inform proactive management. The identification of spreading hubs and locations at particular risk of NIS introductions, enables managers to design risk-based and effective surveillance and monitoring programmes. Our network-based approach to determine the biosecurity implications posed by recreational vessels is transferable to other parts of the world. It enables managers to understand the distribution of risk within an area of interest (e.g., a jurisdiction) and develop optimised approaches for mitigating impact.

Aftershock Relocation of the November 21, 2022 Cianjur (West Java - Indonesia) Earthquake using Temporary Seismic Network

Aftershock Relocation of the November 21, 2022 Cianjur (West Java - Indonesia) Earthquake using Temporary Seismic Network

Predicting the pathways of string-like motions in metallic glasses via path featurizing graph neural networks

String-like motions (SLMs) cooperative, "snake"-like movements of particles—are crucial for dynamics in diverse glass formers. Despite their ubiquity, questions persist: do SLMs prefer specific paths? If so, can we predict these paths? Here, in Al-Sm glasses, our iso-configurational ensemble simulations reveal that SLMs indeed follow certain paths. By designing a graph neural network (GNN) to featurize the environment around directional paths, we achieve a high-fidelity prediction of likely SLM pathways solely based on the static structure. GNN gauges a structural measure to assess each path's propensity to engage in SLMs, akin to a "softness" metric, but for paths rather than for atoms. Our GNN interpretation reveals the critical role of the bottleneck zone along paths in steering SLMs. By monitoring "path-softness", we elucidate SLM-favored paths transit from fragmented to interconnected upon glass transition. Our findings reveal that, beyond atoms or clusters, glasses have another dimension of structural heterogeneity: "paths".

opencc-zeroApr 2024View details →

Carpathian tree-ring network for European beech and Norway spruce

Basic ecological theory suggests that a tradeoff between competitiveness and stress tolerance dictates species range limits at regional extents. However, empirical support for this key theory remains deficient because the necessary spatial and temporal coverage and scalability of field observations have rarely been achieved. We harnessed an extensive dendroecological network (>22,000 tree-ring samples from 816 forest inventory plots) to disentangle competition-limited from climate-limited growth in both overstory and understory trees. Growth synchrony among trees thereby served as an integral metric of climate sensitivity, an approach that we justify in supplementary analyses of growth responses to temperature, precipitation, and the standardized precipitation-evapotranspiration index. Sampling plots were arranged along elevational climate and vegetation gradients throughout the Carpathian Mountains, ranging from mixed-species lowland forests to coniferous forests at high elevations. With mixed-effect modelling, we also identified non-climatic factors (stand characteristics, species diversity, and disturbance history) that modulate spatial patterns in the growth rate and synchrony of European beech (Fagus sylvatica L.) and Norway spruce (Picea abies (L.) Karst.). Beech exhibited reduced growth and increased climate sensitivity towards higher elevations but performed better when species diversity was higher. The growth of spruce increased towards its lower range boundary, but understory cohorts grew poorly under interspecific competition. Overall, climate sensitivity was lower in more productive stands with benign climatic conditions and in recently disturbed sites with reduced stand density. These contrasting performances at mid-elevations where the two species overlap (900 – 1300 m a.s.l.) reflect their evolutionary history, which enables them to be competitive (beech) or cold-stress tolerant (spruce). This history will affect interactions between the two species under climate warming and shape macroecological patterns in the Carpathian ecoregion and likely other parts of Europe. Our findings point to a growing advantage of competitively stronger species in montane and subalpine vegetation zones.

MyAnimeList recommendation network

MAL recommendation network extracted in March 2024. <ul> <li>top_animes.jsonl contains the top 2499 animes.</li> <li>recommendations.jsonl contains the recommendations. Note that it is an undirected graph. If (u,v) exists, then (v,u) exits as well.</li> </ul>

opencc-by-4.0Apr 2024View details →

California MPA network ROV data set and code

Dataset of remotely operated vehicle (ROV) surveys conducted across California's MPA network between 2005 and 2021 and code to conduct analyses and produce plots in the manuscript "Diving deep into the network: quantifying protection effects across California's marine protected area network using a remotely operated vehicle".

opencc-by-4.0Apr 2024View details →

Data and code for the GECCO 2024 paper: "Understanding fitness landscapes in morpho-evolution via local optima networks"

The LON and algorithm run data is available in data/  To run the LON extraction: From gymrem2d-lons/ModularER_2D, run python3 setup.py Direct encoding: python3 lons.py --file direct.cfg LSystem: python3 lons.py --file lsystem.cfg  CPPN: python3 lons.py --file cppn.cfg