Find research datasets worth reusing

opencc-by-4.0Jun 2023View details →

FIG. 21. Trichosurus vulpecula TMM M-849 in Transverse Canal Foramen And Pericarotid Venous Network In Metatheria And Other Mammals

FIG. 21. Trichosurus vulpecula TMM M-849 (Phalangeridae, Diprotodontia), adult specimen, endocast reconstruction showing osteological features associated with pericarotid venous network and related vasculature (data source, table 2; color key, fig. 4). Views: A, ventral; B, same, with transverse basisphenoid sinus superimposed; C, oblique right lateral; D, closeup of mesocranial osteological features in T. vulpecula AMNH 48055; and E, oblique caudoventral surface, intact caudal cranium. In B and C, endocast of caudal branch foramen projecting from carotid groove (cf. Dromiciops, fig. 15A, D). In A and D, in addition to caudal branch foramen, note foramina (single asterisk, box) perforating endocranial floor, linking hypophyseal fossa to transverse canals by means of hypophyseal canaliculi (cf. fig. 22C, D). Separate craniopharyngeal foramen not identifiable. In E, note foramen (double asterisks) medial to position of foramen ovale (see text). Key: atcf, accessory transverse canal foramen; atcl (?rbtc), accessory transverse canal (?rostral branch of transverse canal); cbf, caudal branch foramen; cc, carotid canal; cchc, caudal condylohypoglossal canal; cchf, caudal condylohypoglossal canal; ccs, sulcus leading to carotid canal; ctbs, caudal portion of transverse basisphenoid sinus; encf, endocranial carotid canal; encg, endocranial carotid groove; etbs, eminence of transverse basisphenoid sinus; evpf, extracranial continuation of ventral petrosal sinus; fo, foramen ovale; jf, jugular foramen; le, lateral extension of transverse basisphenoid sinus; mdnc, canal for mandibular nerve; mxns, sulcus for maxillary nerve; onvs, sulcus for

opencc-by-4.0Feb 2011View details →

Fig. 3. Genealogical network for the combined 16S in Introduction Of An Indochinese Freshwater Crab Sayamia Germaini (Crustacea: Brachyura: Gecarcinucidae) To Taiwan: Morphological And Molecular Evidence

Fig. 3. Genealogical network for the combined 16S rRNA and COI haplotypes observed within the clades of Sayamia germaini (collected from Taiwan, Vietnam and Cambodia) and S. sexpunctata (Malaysia). The ancestral haplotype, or root of the network, is indicated by a square. Unlabelled nodes indicate inferred haplotypes not found in the sampled populations

Train and Evaluation Code, Road Classification Models and Test set of the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification"

This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road classification models corresponding to the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification". The scripts make use of the Tensorflow with Keras framework and the additional required dependencies. The training and validation set is based on the binary SROADEX dataset (https://zenodo.org/records/6482346) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 546 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area of 28.5 km * 18.5 km and features binary road labels. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study. The structure of the information shared in this repository is as follows: The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders. The material is distributed under a CC-BY 4.0 license.

Local optima network metrics from the IEEE CEC 2024 paper "Information flow and Laplacian dynamics on local optima networks"

Local optima network metrics from the IEEE CEC 2024 paper "Information flow and Laplacian dynamics on local optima networks".  There are two CSV files: one for each of the two iterated local search confgurations used to construct the networks (low or high). In each file, a row contains information about one QAPLIB instance. Easch row contains all the metrics computed for the associated LON and also algorithm performance data on the instance.

opencc-zeroMar 2024View details →

Biomass production at 2085 horizon for the Maurienne valley (French Alps) estimated using a Bayesian Belief Network

In mountains, grasslands managed for livestock production sustain local economies, culture and identity. However, their future fodder production is highly uncertain under climate change: while an extended growing season may be beneficial, more frequent and intense summer droughts could also reduce fodder quantity and quality. Land use and land cover (LULC) changes are another major driver of regional grassland biomass production, but combined effects of future land use transitions and climate change are rarely quantified. We modelled combined climate and LULC scenarios for grassland production of the Maurienne Valley (French Alps) by 2100. We built a Bayesian Belief Network (BBN) from long-term grassland production monitoring data complemented with expert knowledge. We assessed the potential of two candidate adaptations, intensification as an incremental solution, and silvopastoralism as a transformative solution to compensate combined impacts of two climate scenarios and three land use change scenarios. Total biomass production was far more sensitive to LULC than to climate scenarios. Production losses were largest under the Conservation LULC scenario (-28% on average between 2020 and 2085), followed by the Tourism development scenario (-7%) and the Business-as-Usual scenario (+3%). Climate change under RCP 8.5 altered the seasonality of production by increasing potential production from May to July while decreasing summer regrowth. Intensification somewhat compensated effects of climate and LULC changes on biomass production, whereas silvopastoralism offered only marginal gains. The Bayesian network model explicitly captured a future increase in interannual variability in biomass production. Synthesis and application: Changes in LULC are more decisive for global biomass production than climate change. However, under the most extreme climate change scenario (RCP8.5), the seasonal shift in production and increased interannnual variability threaten the current grass-based Protected Designation of Origin production system. Only the intensification adaptation solution showed significant gains in total biomass production. Still, the silvopastoralism would require less investment compared to the intensification and have a similar efficiency when assessing the gains of biomass by the surface concerned with adaptation solutions.

opencc-zeroMar 2024View details →

Hierarchically embedded scales of movement shape the social networks of vampire bats

Social structure can emerge from hierarchically embedded scales of movement, where movement at one scale is constrained within a larger scale (e.g., among branches, trees, forests). In most studies of animal social networks, some scales of movement are unobserved, and the relative importance of the observed scales of movement is unclear. Here, we asked: how does individual variation in movement, at multiple nested spatial scales, influence each individual's social connectedness? Using existing data from common vampire bats (Desmodus rotundus), we created an agent-based model of how three nested scales of movement—among roosts, clusters, and grooming partners—each influence a bat's grooming network centrality. In each of 10 simulations, virtual bats lacking social and spatial preferences moved at each scale at empirically-derived rates that were either fixed or individually variable and either independent or correlated across scales. We found the number of partners groomed per bat was driven more by within-roost movements than by roost switching, highlighting that co-roosting networks do not fully capture bat social structure. Simulations revealed how individual variation in movement at nested spatial scales can cause false discovery and misidentification of preferred social relationships. Our model provides several insights into how nonsocial factors shape social networks.

Transferable Water Potentials Using Equivariant Neural Networks

Training configurations used in the development of the potential described in the related publication.

opencc-zeroApr 2024View details →

Data from: emergence of structure in plant-pollinator networks: low floral resource constrains network specialisation

Specialisation enhances the efficiency of plant-pollinator networks through the exchange of conspecific pollen transfer for floral resources. Floral resources form the currency of plant-pollinator interactions, but the understanding of how floral resources affect the structure of plant-pollinator networks remains modest. Previous theory predicts that optimally foraging animal species will specialise to improve resource acquisition under high resource availability. Although floral resource availability depends on both the plant production and animal consumption of the resources, previous work has assumed that production and availability to be equivalent. This potentially may have led to erroneous inferences on the effect of resource availability on specialisation. We develop a mutualistic Lotka-Volterra consumer-resource model to investigate the influence of floral resource availability on plant-pollinator network structure. The model incorporates animal adaptive foraging behaviour, floral resource dynamics, and density-dependent dynamics. Specialisation, nestedness and modularity of simulated networks generated from the model under a wide range of parameters were explained using the Generalised Linear Model. We found that the distinction between floral resource dynamics and plant density dynamics was necessary for partial specialisation of plant-pollinator networks. This is because floral resource dynamics constraint animal preference due to its depletion by animal species. Floral resource abundance had a positive effect on network specialisation, but animal density had a negative effect on network specialisation. Floral resource dynamics thus play key roles on the structure of plant-pollinator network, distinctive from plant species density dynamics.

AI4Life-MDC24 Challenge data: Fluorescence Microscopy Datasets for Training Deep Neural Networks

This is a subset of the Supporting data for Guy M Hagen, Justin Bendesky, Rosa Machado, Tram-Anh Nguyen, Tanmay Kumar, Jonathan Ventura, Fluorescence microscopy datasets for training deep neural networks, GigaScience, Volume 10, Issue 5, May 2021, giab032, <a href="https://doi.org/10.1093/gigascience/giab032">https://doi.org/10.1093/gigascience/giab032</a> The selected subset contains 79 images from Data Set 4 in the form of a single tiff file.  The paper describing the original dataset is available here: <a href="https://academic.oup.com/gigascience/article/10/5/giab032/6269106">https://academic.oup.com/gigascience/article/10/5/giab032/6269106</a> The original dataset is available here: <a href="http://gigadb.org/dataset/100888">http://gigadb.org/dataset/100888</a> AI4Life has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

Using neural networks to predict hurricane storm surge and to assess the sensitivity of surge to storm characteristics.

Data for "Lockwood, J. W., Lin, N., Oppenheimer, M., & Lai, C.-Y. (2022). Using neural networks to predict hurricane storm surge and to assess the sensitivity of surge to storm characteristics. Journal of Geophysical Research: Atmospheres, 127, e2022JD037617. https://doi.org/10.1029/2022JD037617"

opencc-by-4.0Apr 2015View details →

Fig. 2. Minimum spanning network for haematozoa mitochondrial DNA cytochrome b in Prevalence and genetic diversity of haematozoa in South American waterfowl and evidence for intercontinental redistribution of parasites by migratory birds

Fig. 2. Minimum spanning network for haematozoa mitochondrial DNA cytochrome b haplotypes detected in South American waterfowl. Shaded circles represent unsampled nodes. All circles are drawn proportional to the frequency at which haplotypes were observed. Lines separating nodes are drawn to scale based on the number of nucleotide mutations, unless otherwise indicated by hash marks. Only haplotypes with a length of 358 bp or greater were included. Haplotype name abbreviations are as follows: Haem = Haemoproteus, Leuc = Leucocytozoon, and Plas = Plasmodium.

opencc-zeroApr 2024View details →

Fecal bacteria contamination of floodwaters and a coastal waterway from tidally-driven stormwater network inundation

Inundation of coastal stormwater networks by tides is widespread due to sea-level rise (SLR). The water quality risks posed by tidal water rising up through stormwater infrastructure (pipes and catch basins), out onto roadways, and back out to receiving water bodies are poorly understood but may be substantial given that stormwater networks are a known source of fecal contamination. In this study, we (1) documented temporal variation in concentrations of Enterococcus spp. (ENT), the fecal indicator bacteria standard for marine waters, in a coastal waterway over a two-month period and more intensively during two perigean spring tide periods, (2) measured ENT concentrations in roadway floodwaters during tidal floods, and (3) explained variation in ENT concentrations as a function of tidal inundation, antecedent rainfall, and stormwater infrastructure using a pipe network inundation model and robust linear mixed effect models. We find that ENT concentrations in the receiving water body vary as a function of tidal stage and antecedent rainfall, but also site-specific characteristics of the stormwater network that drains to the waterbody. Tidal variables significantly explain measured ENT variance in the waterway, however, runoff drove higher ENT concentrations in the receiving waterway. Samples of floodwaters on roadways during both perigean spring tide events were limited, but all samples exceed thresholds for safe public use of recreational water. These results indicate that inundation of stormwater networks by tides could pose public health hazards in receiving water bodies and on roadways, which will likely be exacerbated in the future due to continued SLR.

A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation

<h1>Dataset Description for "A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation"</h1> This dataset accompanies the research paper titled "A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation", currently under review for the AGU Journal JAMES. The study introduces a novel Regional Climate Model (RCM) emulator focusing on high-resolution climate downscaling for the New Zealand region. For additional insights and access to the codebase utilized in this research, please refer to our <a href="https://github.com/nram812/A-Robust-Generative-Adversarial-Network-Approach-for-Climate-Downscaling" target="_new">GitHub repository</a>. <h2>Aims</h2> Our study's overarching goal was to assess the effectiveness of Generative Adversarial Networks (GANs) in a climate downscaling context and is structured around two aims. The first aim of our study is to examine whether GANs can overcome several important limitations of regression-based climate downscaling algorithms (i.e. underestimating the magnitude of extreme events). The second and most important aim of our study is to assess the robustness GAN performance to different training hyperparameters. Our robustness assessment thoroughly scrutinizes GANs for their application in climate downscaling contexts, ensuring that they can learn and capture regional climate processes <h2>Geographic Focus</h2> Our research focuses only on the New Zealand Region (165°E-184°W, 33°S-51°S).   <h2>Data Overview</h2> <h3>Training and Evaluation Data</h3> The training data used in this study (for our RCM emulator) only spans the historical period of simulation. It comprises daily accumulated precipitation as the primary target variable, alongside large-scale predictor variables.  <ul> <li> Resolution: The target variable is presented at a 12km resolution, reflecting the highest resolution face of RCM for the New Zealand region. Predictor variables are coarsened to a 1.5-degree resolution from original CCAM outputs using conservative interpolation.  </li> <li> Period Coverage: <ul> <li>Training Data: 1960-2014</li> <li>Validation Data: 1986-2005</li> </ul> </li> <li> Models: <ul> <li>Training on: ACCESS-CM2</li> <li>Validated on: EC-Earth3, NorESM2-MM</li> </ul> </li> </ul> <h3>File Structure</h3> <ul> <li> Training Data: <ul> <li>Target/Ground Truth (Y): <code>predictor_ACCESS-CM2_hist.nc</code></li> <li>Predictor (X): <code>pr_ACCESS-CM2_hist.nc</code></li> </ul> </li> <li> Evaluation Data: <ul> <li>NorESM2-MM: <ul> <li>Target (Y): <code>NorESM2-MM_historical_precip_compressed.nc</code></li> <li>Predictor (X): <code>NorESM2-MM_histupdated_compressed.nc</code></li> </ul> </li> <li>EC-Earth3: <ul> <li>Target: <code>EC-Earth3_historical_precip_compressed.nc</code></li> <li>Predictor: <code>EC-Earth3_histupdated_compressed.nc</code></li> </ul> </li> </ul> </li> </ul> <h2>Methodological Insights</h2> <ul> <li> Regional Climate Model, Our Regional Climate Model training data is from the Conformal Cubic Atmospheric Model (CCAM) which is a global non-hydrostatic atmospheric model renowned for its variable-resolution cubic grid. . For more information about CCAM, please see the following <a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023JD038530">paper</a>. </li> <li> Predictor and Target Variables: Daily-averaged large-scale prognostic variables, including zonal wind, meridional wind, temperature, and specific humidity, are employed as predictors at the 500mb and 850mb pressure levels. These are normalized (see the GitHub repository for the mean and standard deviation fields). Precipitation is taken as is from CCAM and accumulated for each given day. Static predictors are also used in our model, which is stored in a GitHub repository. </li> <li> Training Framework: Our dataset benefits from the "perfect framework" training strategy, which uses CCAM-coarsened predictor variables. For more information about the perfect and imperfect training frameworks, see the following <a title="review" href="https://journals.ametsoc.org/view/journals/aies/3/2/AIES-D-23-0066.1.xml">review</a> </li> </ul>

Resilience of transportation infrastructure networks to road failures

We provide the code used for the analysis that we used in the article Resilience of transportation infrastructure networks to road failures. As the computation for the RoadNetworks that we analysed in this manuscript is quite large, the computational load is quite high. We pre-computed the load values on the cluster and provide the results here. The code for the Figures in the notebooks will therefore not compute the loads, but just load them from files. We provide a <code>My-road-network.ipynb</code> were you can play around with a smaller RoadNetwork, computing everything locally. Published in J. Wassmer, B. Merz, N. Marwan: Resilience of transportation infrastructure networks to road failures, Chaos, 34, 013124 (2024). <a href="https://dx.doi.org/10.1063/5.0165839" target="_blank" rel="noopener">DOI:10.1063/5.0165839</a>

opencc-by-4.0Jan 2022View details →

Figure 5. Unrooted haplotype network. Each circle represents a in Distribution and molecular differentiation of Culex pipiens complex species in the Middle and Eastern Black Sea Regions of Turkey

Figure 5. Unrooted haplotype network. Each circle represents a haplotype, and the lines above each link indicate one mutation. Small black dots indicate intermediate, missing, or unsampled haplotypes.

Neural Network predictions and ERA5 reference of integrated water vapour, and temperature and specific humidity profiles based on simulated microwave radiometer observations

This data set contains predictions of the Neural Network retrievals described in [1], where simulated microwave radiometer observations (brightness temperatures, TBs) from the evaluation data subset of [2] (years 2001, 2006, 2011, 2015) were used as input to the Neural Network. As described in Section 3.2 of [1], we trained an ensemble of 20 Neural Networks for each retrieved atmospheric quantity and applied them to the ERA5 evaluation data set to estimate the robustness of the retrievals with respect to random perturbations. The following atmospheric quantities were retrieved:  <ul> <li>temperature profile (variable name 'temp_p', filename suffix 'temp_test_417'),</li> <li>boundary layer temperature profile (variable name 'temp_p', filename suffix 'temp_test_424'),</li> <li>specific humidity profile (variable name 'q_p', filename suffix 'q_test_472'),</li> <li>integrated water vapour (variable name 'iwv_p', filename suffix 'iwv_test_126')</li> </ul> The cryptic 3-digit filename suffixes represent different settings of the Neural Network retrieval. More information can be found in [3]. Variables that do not have the "_p" suffix are ERA5 data and used as reference to estimate errors of the retrievals by comparing them with the predictions. The dimension 'n_s' represents the ERA5 data sample number while the dimension 'n_rand' designates the ensemble of Neural Networks. These files can be created when running run_NN_retrieval (contained in NN_retrieval.py, see [3]) with exec_type='20_runs' and eval_mode=True and test_id either "126", "417", "424" or "472". However, as this might take some hours, we provide them here.   [1]: Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024. [2]: Walbröl, A., and Mech, M.: ERA5 based training, validation and evaluation data for retrievals combining 22-58 GHz with 175-340 GHz microwave radiometer measurements during MOSAiC (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10997365, 2024. [3]: Walbröl, A.: Codes for: Combining low and high frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products (1.0.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.11123136" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11123136</a>, 2024.

Measurements of Carbon-14 of Carbon monoxide (14CO) in a global network

This is a data set containing measurements of [14CO] in a new global network led by the University of Rochester. Measurements are from samples collected approximately biweekly during 2021 at Barrow, Mace Head, Mauna Loa, Barbados, American Samoa, Reunion Island and Baring Head atmospheric observatories.

Low-power scalable multilayer optoelectronic neural networks enabled with incoherent light

Data and Code required for reproduction of results in "Low-power scalable multilayer optoelectronic neural networks enabled with incoherent light|

Extended Evaluation Data of TrADS: a Trust-Aware Decentralized Social Network

This dataset includes the evaluation data for the Paper "TrADS: a Trust-Aware Decentralized Social Network". Within the ZIP File the following files are included in the dataset: <ul> <li>survey.pdf: The survey as PDF print. IFrames of TrADS used during the survey are hidden in the PDF.</li> <li>all.xlsx: An Excelfile containing all the following .CSV files as worksheets.</li> <li>raw_data.csv: The raw data exported from the used survey tool of the conducted empircal user study.</li> <li>group1_unfiltered.csv: All participants' data of group 1.</li> <li>group2_unfiltered.csv: All participants' data of group 2.</li> <li>group1.csv: All filtered participants' data of group 1, who correctly answered the control questions.</li> <li>group1_ueq_data.csv: All filtered participants's ueq+ question responses of group 1. Only including the questions 1 - 4 but not the question about dimension importance (q5).</li> <li>group1_ueq_importance.csv: All filtered participants's ueq+ question responses about personal importance of group 1. Only including the question 5 about dimension importance.</li> <li>group1_ueq_kpis: Including the ueq+ KPI values of all participants in group 1.</li> <li>group2.csv: All filtered participants' data of group 2, who correctly answered the control questions.</li> <li>group2_ueq_data.csv: All filtered participants's ueq+ question responses of group 2. Only including the questions 1 - 4 but not the question about dimension importance (q5).</li> <li>group2_ueq_importance.csv: All filtered participants's ueq+ question responses about personal importance of group 2. Only including the question 5 about dimension importance.</li> <li>group2_ueq_kpis: Including the ueq+ KPI values of all participants in group 2.</li> <li>participants.csv: General information about participants grouped by both groups and joint.</li> <li>likert_questions.csv: Mean Values and Standard Deviations (Std) of all statements rated on a 5-point Likert scale. It includes Means and Stds for Group 1, Group 2, Group 1 + Group 2 concatinated, and the values of the first user study published in the previous paper on TrADS <a href="https://zenodo.org/records/10641724" target="_blank" rel="noopener">(also available in previous dataset on Zenodo)</a>.</li> </ul>

Finite strain continuum phenomenological model describing the shape-memory effects in multi-phase semi-crystalline networks

This dataset comes from the following paper: Matteo Arricca, Nicoletta Inverardi, Stefano Pandini, Maurizio Toselli, Massimo Messori, Giulia Scalet, Finite strain continuum phenomenological model describing the shape-memory effects in multi-phase semi-crystalline networks, Journal of the Mechanics and Physics of Solids, 105955, 2024. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jmps.2024.105955" target="_blank" rel="noopener">https://doi.org/10.1016/j.jmps.2024.105955</a> It contains: <ul> <li>"Notes.pdf" describing all the files uploaded</li> <li>. txt experimental data</li> </ul>