Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
172
datasets available to search
ShareScore release 0.9.0
Dataset results
172 results for “Genetic Network”
Data for "Unfolding the structural stability of nanoalloys via symmetry-constrained genetic algorithm and neural network potential"
<p><strong>PtNi_alloy_eam.db</strong> is the dataset (ase.db object) consisting of 55982 intially sampled Pt-Ni alloy structures with EAM energies and forces.</p> <p><strong>PtNi_alloy_dft.db</strong> is the dataset (ase.db object) consisting of the final 6828 resampled Pt-Ni alloy structures with DFT energies and forces calculated by VASP. This is the training set for the NNP, and could be very useful for fitting other machine learning models.</p> <p><strong>PtNi_nanoalloy_vertices_nnp.db</strong> is the dataset (ase.db object) consisting of all the vertices (stable structures) on the convex hulls obtained from NNP-based SCGA runs on 36 Pt-Ni nanoalloy systems. The energies are given by the NNP. Additional information such as mixing energy, motif and symmetry axis are also saved in the dataset and can be queried by the 'data' keyword. An xyz format trajectory of these stable structures is also uploaded.</p> <p>All the input files and scripts for hybrid MC-MD simulations, QBC resampling, DFT calculations, NNP training, NNP-based SCGA runs and convex hull analysis are provided in <strong>inputs_and_scripts.zip</strong>.</p>
Tree mortality risks under climate change in Europe: assessment of silviculture practices and genetic conservation networks
<p>General context: Climate change can positively or negatively affect abiotic and biotic drivers of tree mortality. Process-based models integrating these climatic effects are only seldom used at species distribution scale.</p> <p>Objective: The main objective of this study was to investigate the multi-causal mortality risk of five major European forest tree species across their distribution range from an ecophysiological perspective, to quantify the impact of forest management practices on this risk and to identify threats on the genetic conservation network.</p> <p><br> Methods: We used the process-based ecophysiological model CASTANEA to simulate the mortality risk of \textit{Fagus sylvatica}, \textit{Quercus petraea}, \textit{Pinus sylvestris}, \textit{Pinus pinaster} and \textit{Picea abies} under current and future climate conditions, while considering local silviculture practices. The mortality risk was assessed by a composite risk index \textit{(CRIM)} integrating the risks of carbon starvation, hydraulic failure and frost damage. We took into account extreme climatic events with the \textit{CRIM$_{max}$}, computed as the maximum annual value of the \textit{CRIM}.</p> <p><br> Results: The physiological processes' contributions to \textit{CRIM} differed among species: it was mainly driven by hydraulic failure for \textit{P. sylvestris} and \textit{Q. petraea}, by frost damage for \textit{P. abies}, by carbon starvation for \textit{P. pinaster}, and by a combination of hydraulic failure and frost damage for \textit{F. sylvatica}. Under future climate, projection showed an increase of \textit{CRIM} for \textit{P. pinaster} but a decrease for \textit{P. abies}, \textit{Q. petraea} and \textit{F. sylvatica}, and little variation for \textit{P. sylvestris}. Under the harshest future climatic scenario, forest management decreased the mean \textit{CRIM} for \textit{P. sylvestris}, increased it for \textit{P. abies} and \textit{P. pinaster} and had no major impact for the two broadleaved species. By the year 2100, 38\% to 90\% of the conservation units are at extinction threat (\textit{CRIM$_{max}$}=1), depending on the species.</p> <p><br> Conclusions: Using a process-based ecophysiological model allowed us to disentangle the multiple drivers of tree mortality under current and future climate. Taking into account the positive effect of increased CO$_2$ on fertilization and water use efficiency, the average risks may increase or decrease in the future depending on species and sites. However, considering extreme climatic events, future projections are as pessimistic than those obtained with bioclimatic niche models.</p> <p> </p> <p>Abbreviation for column:</p> <p>X Longitude<br> Y Latitude<br> LAImax Leaf area index max reach<br> Nha Density per hectar<br> Vha Volume per hectar<br> NEE Net ecosystem exchange<br> NPP net primary production<br> Reco Respiration ecosystem<br> GPP Gross primary production<br> Etveg Evapotranspiration canopy<br> Etsol Evapotranspiration sol<br> TR tree transpiration<br> ETP evapotranspiration potentiel<br> BiomassOfReserves Biomass of reserve<br> rw ring width<br> dbh diameter at breast heast<br> height height<br> BBday Budburst date<br> rFD risk of frost<br> CRIM_max Maximum combined risk index of mortality reach<br> rNSC risk of carbon starvation<br> rPLC risk of embolism<br> rPLC_max Maximum risk of embolism reach<br> CRIM combined risk index of mortality<br> Climate Climatic model<br> rNSC_max maximum risk of carbon starvation reach<br> rFD_max Maximum risk of frost reach<br> Scenario_Sylvicol null means no silvulcture simulated<br> species species<br> Country Country<br> alt_watch altitude of climate simulated<br> grid_watch number of the pixel point of WATCH<br> grid_eurocordex number of the pixel point of Eurocordex<br> Pinus_sylvestris 0 abscence ; 1 presence<br> Fagus_sylvatica 0 abscence ; 1 presence<br> Quercus_petraea 0 abscence ; 1 presence<br> Picea_abies 0 abscence ; 1 presence<br> Pinus_pinaster 0 abscence ; 1 presence</p> <p> </p>
Data from: Discordant patterns of genetic and phenotypic differentiation in five grasshopper species co-distributed across a microreserve network
<p>Conservation plans can be greatly improved when information on the evolutionary and demographic consequences of habitat fragmentation is available for several co-distributed species. Here, we study spatial patterns of phenotypic and genetic variation among five grasshopper species that are co-distributed across a network of microreserves but show remarkable differences in dispersal-related morphology (body size and wing length), degree of habitat specialization and extent of fragmentation of their respective habitats in the study region. In particular, we tested the hypothesis that species with preferences for highly fragmented microhabitats show stronger genetic and phenotypic structure than co-distributed generalist taxa inhabiting a continuous matrix of suitable habitat. We also hypothesized a higher resemblance of spatial patterns of genetic and phenotypic variability among species that have experienced a higher degree of habitat fragmentation due to their more similar responses to the parallel large-scale destruction of their natural habitats. In partial agreement with our first hypothesis, we found that genetic structure, but not phenotypic differentiation, was higher in species linked to highly fragmented habitats. We did not find support for congruent patterns of phenotypic and genetic variability among any studied species, indicating that they show idiosyncratic evolutionary trajectories and distinctive demographic responses to habitat fragmentation across a common landscape. This suggests that conservation practices in networks of protected areas require detailed ecological and evolutionary information on target species in order to focus management efforts on those taxa that are more sensitive to the effects of habitat fragmentation.</p>
Unveiling the genetic networks: Exploring the dynamic interaction of photosynthetic phenotypes in woody plants across varied light gradients
<p><em>Background:</em></p> <p>Understanding the mechanisms by which genes control and regulate complex quantitative traits during periods of fluctuating resources remains a challenging and uncertain task in photosynthesis studies. Most studies have focused on the structure of photosynthesis, the photosynthetic response under stress, or the genetic mechanisms involved in photosynthetic effects and neglected the interactive genetic mechanism that governs various traits through significant quantitative trait loci (QTLs). Results In this study, we have developed a differential dynamic system that enables the identification of QTLs based on the photosynthetic phenotypic and genotypic data under varying levels of light intensity gradients. The framework not only allows for the assessment of the direct effects of QTLs on phenotypes but also captures how they influence interactions among phenotypes as light intensities change. We have analyzed the genetic effects and genetic variance, visualized the genetic network associated with photosynthesis interactions, and validated the effectiveness and stability of the DDS framework. Pivotal QTLs were identified individually to uncover the process and pattern of interaction. Through functional annotation, we made an intriguing discovery that seemingly unimportant QTLs can still have significant genetic effects on phenotypic changes through their regulation with other QTLs. Conclusions This finding emphasizes the significance of considering the interactive genetic architecture when seeking to understand the genetic interaction mechanism of photosynthesis in natural populations of woody plants. Moreover, our research provides a novel framework that can be extended to explore the interactive genetic architecture among organisms, contributing to a deeper understanding of stress resistance mechanisms in woody plants.</p>
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.
Figure 5 in Hybrid neural network with genetic algorithms for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumbers field of Ramhormoz, Iran
Figure 5. Tetranychus urticae distribution maps in actual (b, d and f) and classified conditions by MLPNN (c, e and a). The maps of a, c, e and b, d, f have been drawn according to economic threshold of 4, 8 and 12, respectively.
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 12. Genetic Algorithm flowchart
<p>Then next generation are produced by combination of the elites (15%), crossover (55%) and mutation (30%) of the initial population. Elites are the best chromosomes which are directly transferred to the next generation. Because of long chromosome length, for crossover, five points are randomly chosen in each parent as cut points. Figure 11 shows a typical crossover with two cutpoints and Figure 12 illustrates a flowchart for the proposed evolutionary model. Selections are based on Roulette Wheel selection, more detailed information can be found in.</p>
Fig. 2. Minimum spanning network for haemosporidian mtDNA cytochrome b in Haemosporidian parasite infections in grouse and ptarmigan: Prevalence and genetic diversity of blood parasites in resident Alaskan birds
Fig. 2. Minimum spanning network for haemosporidian mtDNA cytochrome b haplotypes isolated from Alaskan grouse and ptarmigan species. Dark circles represent un-sampled nodes. All circles are proportional to the frequency at which the haplotypes were detected. Lines between nodes are drawn to scale based on the number of nucleotide mutations unless otherwise indicated by hash marks.
BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 8 . The Comparision of Run Times
<p>That is distinct that dynamic mutation rate or reduction idea for mutation operator is more<br> better of fixed rate. In fact obtain to high accuracy is result of our idea for mutation operator.<br> The number of hidden layer neurone is important problem for NN. The natural selection by<br> GA help finding the number of hidden layer neurone and it progress on duration generations.<br> The structured model of GANN finds better answer than NN but with much run time in<br> simulation. The learning of GA is much better than NN with back propagation because BP is a<br> method based on gradient descend and local optimum is a serious risk for that.<br> We hope that the number of training samples is more accurate without error, the new<br> algorithm is better. Tests show that the combination of genetic algorithms and neural networks to an<br> acceptable level solves the problem of overfitting.</p>
BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 7 . Test Accuracy with prograess generation
<p>In the training phase, the neural network weights errors are minimized and network design<br> problem which the objective function to an acceptable level. In test step we have better results<br> because weights of neural network are adjusted by genetic algorithm and back propagation method.<br> Of course achievement to accuracy with 83.5% is reason using of good feature with minimum error.</p>
BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 6. Training Accuracy with prograess generation
<p>There are many features will reduce the efficiency of the algorithm and its complexity.<br> Among the methods for selecting the appropriate features, the algorithm is a GA.<br> One of the important parameters for testing methods is accuracy rate on progress generation.<br> In fact accuracy is reverse error in algorithm results. As reader can compare the results of our paper<br> with another works. Figure 4 show that accuracy present for Training step. We achieve to best<br> answers of 800 generation to after generation.</p>
BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 4. Structural Crossover
<p>Guided crossover operator is based on the two point separation from parents are selected<br> Left and right parts of them are related to each other by the condition to be meaningful With this<br> new child of his parents is that. But a new generation of the random choice to have reached this<br> stage. The crossover rate is fixed for our algorithm.</p>
BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 5. Insertion and Deletion Hidden Layer in NN
<p>Change in NN structure is other method that we used to optimization of solution[18].<br> Insertion a hidden layer caused to mutation operator is much natural. As connection with father and<br> mother nodes is easily[20],[21]. Weights of node and errors automatically calculated.<br> For each stage of the implementation of the mutation operator in genetic algorithms, neural<br> networks, only one of the nodes in the hidden layer is selected and inserted. These layers are<br> inserted on condition that the definition does not harm the network structure and the action is<br> meaningful. As an added layer can adjust the weights and the connection to the parent node of a network<br> layer to be removed.</p>
BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 3. The Structure of Neural Network
<p>A neural network (NN), in the case of artificial neurons called artificial neural<br> network (ANN) or simulated neural network (SNN), is an interconnected group of natural<br> or artificial neurons that uses a mathematical or computational model for information<br> processing based on a connectionist approach to computation. In most cases an ANN is an adaptive<br> system that changes its structure based on external or internal information that flows through the<br> network[9].<br> In more practical terms neural networks are nonlinear statistical data modelling or decision<br> making tools. They can be used to model complex relationships between inputs and outputs or<br> to find patterns in data.<br> Two neurons neural network active in memory (ON or 1) or disable (Off or 0), and each<br> edge (synapses or connections between nodes) is a weight. Edges with positive weight, stimulate or<br> activate next active node, and edges with negative weight, disable or inhibit the next connected<br> node (if it is active) ones.</p>
Fig. 2. Statistical parsimony cladogram network representing relationships among the 45 haplotypes for a 615 in Genetic diversity of Halyomorpha halys (Hemiptera, Pentatomidae) in Korea and comparison with COI sequence datasets from East Asia, Europe, and North America
Fig. 2. Statistical parsimony cladogram network representing relationships among the 45 haplotypes for a 615 bp fragment of the COI gene of Halyomorpha halys. Each circle is labeled with haplotype number, and the size of each circle is proportional to the frequency of each haplotype [H3 (n = 353); H1 (n = 285); H22 (n = 43); H8 (n = 34); H33 (n = 23); H2 (n = 16); H32 (n = 8); H7, H9–H13, and H43 (n = 3); H6, H14, H34, H39, and H40 (n = 2); H4–H5, H12, H15–H21, H23, H30–H31, H35–H38, H41, H42, and H44–H51 (n = 1)]. Differing colors indicate countries in which samples were collected.
Figure 4. Haplotype network for Rattus rattus Complex II in Expanding Population Edge Craniometrics and Genetics Provide Insights into Dispersal of Commensal Rats through Nusa Tenggara, Indonesia
Figure 4. Haplotype network for Rattus rattus Complex II. The Nusa Tenggara samples are illustrated on the right of the network.
Figure 3. Haplotype networks for Rattus exulans, R in Expanding Population Edge Craniometrics and Genetics Provide Insights into Dispersal of Commensal Rats through Nusa Tenggara, Indonesia
Figure 3. Haplotype networks for Rattus exulans, R. argentiventer, and Rattus rattus Complex LIV. Sunda refers to the islands of Borneo, Java, and Sumatra; the Indonesian sample (brown) lacks further collection information.
Population genomic evidence that stream networks structure genetic diversity in the narrowly endemic patch-nosed salamander (Urspelerpes brucei)
<p>Described in 2009, the Patch-nosed Salamander (<em>Urspelerpes brucei</em>) is a miniature species of lungless salamander with a geographic range of only ~45 km<sup>2</sup>. This species is endemic to the foothills of the Appalachian Mountains in extreme northeastern Georgia and northwestern South Carolina. The Tugaloo River—a waterway of some 50 m in width that forms the political boundary between the two states—bisects the tiny range of <em>U. brucei</em> and likely acts as a barrier to gene flow. Using RADcap data and a suite of complementary population genomic analyses, we evaluated the role that this river and its tributaries may play in enabling and/or interrupting gene flow among populations of <em>U. brucei</em>, and we investigated patterns of within-population and between-population genetic variation. Our results revealed a general pattern of isolation-by-stream distance and indicated that a population separated by the Tugaloo River is moderately more differentiated than what is explainable by stream distance alone. Unique in both its physiography and geologic history, this region in which <em>U. brucei</em> lives also harbors more than a dozen other species of lungless salamanders. Therefore, the genetic patterns that we have elucidated may have larger implications for differentiation among populations of other species with similar dispersal abilities.</p>
Unveiling the genetic networks: Exploring the dynamic interaction of photosynthetic phenotypes in woody plants across varied light gradients
Open the record for dataset details and reuse information.
Data from: Discordant patterns of genetic and phenotypic differentiation in five grasshopper species co-distributed across a microreserve network
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.