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3,339 results for “heterogeneities”
Effects of three-dimensional soil heterogeneity and species composition on plant biomass and biomass allocation of grass-mixtures
<p>Soil heterogeneity significantly affects plant dynamics such as plant growth and biomass. Most studies developed soil heterogeneity in two dimensions, i.e. either horizontally or vertically. However, soil heterogeneity in natural ecosystems varies both horizontally and vertically i.e. in three dimensions. Previous studies on plant biomass and biomass allocation rarely considered the joint effects of soil heterogeneity and species composition. Thus, to investigate such joint effects on plant biomass and biomass allocation, a controlled experiment was conducted, where three levels of soil heterogeneity and seven types of species compositions were applied. Such soil heterogeneity was developed by filling nutrient-rich and nutrient-poor substrates in an alternative pattern in pots with different patch sizes (small, medium or large), and species compositions was achieved by applying three plant species (i.e. Festuca elata, Bromus inermis, Elymus breviaristatus) in all possible combinations (growing either in monoculture or in mixtures). Results showed that patch size significantly impacted plant biomass and biomass allocation, which differed among plant species. Specially, at the pot scale, with increasing patch size, shoot biomass decreased, while root biomass and R: S ratio increased, and total biomass tended to show a unimodal pattern, where the medium patch supported higher total biomass. Moreover, at the substrate scale, more shoot biomass and total biomass were found in nutrient-rich substrate. Furthermore, at the community scale, two of the three target plant species growing in monoculture had more shoot biomass than those growing together with other species. Thus, our results indicate soil heterogeneity significantly affected plant biomass and biomass allocation, which differ among plant species, though more research is needed on the generalization on biomass allocation. We propose that soil heterogeneity should be considered more explicitly in studies with more species in long-term experiments.</p>
Data from: Soil carbon response to woody plant encroachment: Importance of spatial heterogeneity and deep soil storage
1. Recent global trends of increasing woody plant abundance in grass-dominated ecosystems may substantially enhance soil organic carbon (SOC) storage and could represent a strong carbon (C) sink in the terrestrial environment. However, few studies have quantitatively addressed the influence of spatial heterogeneity of vegetation and soil properties on SOC storage at the landscape scale. In addition, most studies assessing SOC response to woody encroachment consider only surface soils, and have not explicitly assessed the extent to which deeper portions of the soil profile may be sequestering C. 2. We quantified the direction, magnitude, and pattern of spatial heterogeneity of SOC in the upper 1.2 m of the profile following woody encroachment via spatially-specific intensive soil sampling across a landscape in a subtropical savanna in the Rio Grande Plains, USA, that has undergone woody proliferation during the past century. 3. Increased SOC accumulation following woody encroachment was observed to considerable depth, albeit at reduced magnitudes in deeper portions of the profile. Overall, woody clusters and groves accumulated 12.87 and 18.67 Mg C ha-1 more SOC compared to grasslands to a depth of 1.2 m. 4. Woody encroachment significantly altered the pattern of spatial heterogeneity of SOC to a depth of 5 cm, with marginal effect at 5-15 cm, and no significant impact on soils below 15 cm. Fine root density explained greater variability of SOC in the upper 15 cm, while a combination of fine root density and soil clay content accounted for more of the variation in SOC in soils below 15 cm across this landscape. 5. Synthesis: Substantial SOC sequestration can occur in deeper portions of the soil profile following woody encroachment. Furthermore, vegetation patterns and soil properties influenced the spatial heterogeneity and uncertainty of SOC in this landscape, highlighting the need for spatially specific sampling that can characterize this variability and enable scaling and modeling. Given the geographic extent of woody encroachment on a global scale, this undocumented deep soil C sequestration suggests this vegetation change may play a more significant role in regional and global C sequestration than previously thought.
Data from: Landscape heterogeneity is key to forecasting outcomes of plant reintroduction
Conservation and restoration projects often involve starting new populations by introducing individuals into portions of their native or projected range. Such efforts can help meet many related goals, including habitat creation, ecosystem service provisioning, assisted migration, and the reintroduction of imperiled species following local extirpation. The outcomes of reintroduction efforts, however, are highly variable, with results ranging from local extinction to dramatic population growth; reasons for this variation remain unclear. Here, we ask whether population growth following plant reintroductions is governed by variation at two scales: the scale of individual habitat patches to which individuals are reintroduced, and larger among-landscape scales in which similar patches may be situated in landscapes that differ in matrix type, soil conditions, and other factors. Quantifying demographic variation at these two scales will help prioritize locations for introduction and, once introductions take place, forecast population growth. This work took place within a large-scale habitat fragmentation experiment, where individuals of two perennial forb species were reintroduced into eight replicate ~50 ha landscapes, each containing a set of five ~1 ha patches that varied in their degree of isolation (connected by habitat corridors or unconnected) and edge-to-area ratio. Using data on individual growth, survival, reproductive output, and recruitment collected one to two years after reintroduction, we developed models to forecast population growth, then compared forecasts to observed population sizes, three and six years later. Both the type of patch (connected and unconnected) and identity of the landscape to which individuals were reintroduced had effects on forecasted population growth rates, but only variation associated with landscape identity was an accurate predictor of subsequently observed population growth rates. Models that did not include landscape identity had minimal forecasting ability, revealing the key importance of variation at this scale for accurate prediction. Of the five demographic rates used to model population dynamics, seed production was the most important source of forecast error in population growth rates. Our results point to the importance of accounting for landscape-scale variation in demographic models and demonstrate how such models might assist with prioritizing particular landscapes for species reintroduction projects.
Spatial and temporal heterogeneity in pollinator communities maintains within-species floral odour variation
<p>Flowering plants emit complex bouquets of volatile organic compounds (VOCs) to mediate interactions with their pollinators. These bouquets are undoubtedly influenced by pollinator-mediated selection, particularly in deceptively-pollinated species that rely on chemical mimicry. However, many uncertainties remain regarding how spatially and temporally heterogeneous pollinators affect the diversity and distribution of floral odour variation. Here, we characterized and compared the floral odours of ten populations of deceptively-pollinated <em>Arum maculatum</em> (Araceae), and inter-annual and decadal variation in pollinator attraction within these populations. Additionally, we transplanted individuals from all sampled populations to two common garden sites dominated by different pollinator species (<em>Psychoda phalaenoides</em> or <em>Psycha grisescens</em>), and compared pollinator attraction rates to investigate whether populations maintained odour blends adapted to a specific pollinator. We identified high within- and among-population variation in a common blend of VOCs found across the range of <em>A. maculatum</em>. We also observed shifts in pollinator community composition within several populations over 1-2 years, as well as over the past decade. Common garden experiments further revealed that transplanted inflorescences generally attracted the dominant local pollinator species in both transplant sites. However, one population (Forêt du Gâvre, France) appears to exclusively attract <em>P. grisescens</em>, even when transplanted to a <em>P. phalaenoides</em>-dominated site. Together, our results suggest that maintaining diverse floral odour bouquets within populations may be advantageous when pollinator communities vary over short timescales. We propose that temporally-replicated ecological data are one potential key to understanding variation in complex traits such as floral odour, and in some cases may reveal resiliency to shifting pollinator communities.</p>
Data from: Interacting effects of unobserved heterogeneity and individual stochasticity in the life-history of the Southern fulmar
1.Individuals are heterogeneous in many ways. Some of these differences are incorporated as individual states (e.g., age, size, breeding status) in population models. However, substantial amounts of heterogeneity may remain unaccounted for, due to unmeasurable genetic, maternal, or environmental factors. 2.Such unobserved heterogeneity (UH) affects the behavior of heterogeneous cohorts via intra-cohort selection and contributes to inter-individual variance in demographic outcomes such as longevity and lifetime reproduction. Variance is also produced by individual stochasticity, due to random events in the life cycle of wild organisms, yet no study thus far has attempted to decompose the variance in demographic outcomes into contributions from unobserved heterogeneity and individual stochasticity for an animal population in the wild. 3.We developed a stage-classified matrix population model for the Southern fulmar breeding on Ile des Pétrels, Antarctica. We applied multi-event, multi-state markrecapture methods to estimate a finite mixture model accounting for UH in all vital rates and Markov chain methods to calculate demographic outcomes. Finally, we partitioned the variance in demographic outcomes into contributions from unobserved heterogeneity and individual stochasticity. 4.We identify three UH groups, differing substantially in longevity, lifetime reproductive output, age at first reproduction, and in the proportion of the life spent in each reproductive state. 14% of individuals at fledging have a delayed but high probability of recruitment and extended reproductive lifespan. 67% of individuals are less likely to reach adulthood, recruit late and skip breeding often but have the highest adult survival rate. 19% of individuals recruit early and attempt to breed often. They are likely to raise their offspring successfully, but experience a relatively short lifespan. Unobserved heterogeneity only explains a small fraction of the variances in longevity (5.9%), age at first reproduction (3.7%) and lifetime reproduction (22%). 5.UH can affect the entire life cycle, including survival, development, and reproductive rates, with consequences over the lifetime of individuals and impacts on cohort dynamics. The respective role of unobserved heterogeneity versus individual stochasticity varies greatly among demographic outcomes. We discuss the implication of our finding for the gradient of life-history strategies observed among species and argue that individual differences should always be accounted for in demographic studies of wild populations.
Impacts of environmental heterogeneity on natural selection in a wild bird population
<p>Natural selection has been studied for several decades, resulting in the computation of thousands of selection estimates. Although the importance of environmental conditions on selection has often been suggested, published estimates rarely take into account the effects of environmental heterogeneity on selection patterns. Here, we estimated linear and non‐linear viability selection gradients on morphological traits of 12 days‐old nestlings in a wild population of tree swallows (<i>Tachycineta bicolor</i>) across a large‐scale heterogeneous study system in southern Québec, Canada. We assessed the environmental drivers of nestling survival and evaluated their effects on strength and direction of selection gradients. Separate analyses of environmental variables showed that high temperatures and heavy rainfall caused stronger positive linear selection on morphological traits. Weaker linear selection was also measured in more extensively cultivated areas. Both strength and shape of non‐linear quadratic and correlational components of selection were modified by environmental variables. Considering all environmental variables revealed that precipitation since hatching affected patterns of linear selection on traits, while temperatures since hatching shaped nonlinear selection patterns. Our study underlines the importance of quantifying linear and non‐linear natural selection under various environmental conditions and how the evolutionary response of traits may be affected by ongoing human‐induced environmental changes.</p>
Data from: Spatial heterogeneity of plant-soil feedbacks increases per capita reproductive biomass of species at an establishment disadvantage
Plant–soil feedbacks have been widely implicated as a driver of plant community diversity, and the coexistence prediction generated by a negative plant–soil feedback can be tested using the mutual invasibility criterion: if two populations are able to invade one another, this result is consistent with stable coexistence. We previously showed that two co-occurring Rumex species exhibit negative pairwise plant–soil feedbacks, predicting that plant–soil feedbacks could lead to their coexistence. However, whether plants are able to reproduce when at an establishment disadvantage ("invasibility"), or what drivers in the soil might correlate with this pattern, are unknown. To address these questions, we created experimental plots with heterogeneous and homogeneous soils using field-collected conditioned soils from each of these Rumex species. We then allowed resident plants of each species to establish and added invader seeds of the congener to evaluate invasibility. Rumex congeners were mutually invasible, in that both species were able to establish and reproduce in the other's resident population. Invaders of both species had twice as much reproduction in heterogeneous compared to homogeneous soils; thus the spatial arrangement of plant–soil feedbacks may influence coexistence. Soil mixing had a non-additive effect on the soil bacterial and fungal communities, soil moisture, and phosphorous availability, suggesting that disturbance could dramatically alter soil legacy effects. Because the spatial arrangement of soil patches has coexistence implications, plant–soil feedback studies should move beyond studies of mean effects of single patch types, to consider how the spatial arrangement of patches in the field influences plant communities.
Data from: A trait-based framework for discerning drivers of species co-occurrence across heterogeneous landscapes
Null model analysis of species co-occurrence patterns has long been used to gain insight into community assembly but is often limited to identifying non-random patterns without providing clarity about underlying ecological mechanisms. This challenge is especially apparent when sampling units are spread across a heterogeneous landscape or along an environmental gradient because multiple mechanisms can produce similar co-occurrence patterns. We developed a trait-based approach for discriminating between environmental filtering and biotic interactions as the probable driver of co-occurrence patterns across environmentally heterogeneous sites. We demonstrate our framework by analyzing the co-occurrence of small mammals over elevation in three independent mountain ranges in the Great Basin of the western United States. Our sampling design accounts for landscape scale environmental variability and within-site habitat heterogeneity. We identified 52 non-random species pairs, of which 36 were aggregated and 16 were segregated. For each pair, we determined which mechanism was the likely ecological explanation using a hypothesis-testing framework based on functional trait similarity. Expectations of biotic interactions were based on similarity of diet and body size whereas habitat affinity and geographic range were used for environmental filtering. Only four pairs were consistent with expectations under biotic interactions, including pairs for which competitive exclusion has previously been documented. In addition to analyzing individual pairs, we used binomial tests of observed versus expected totals of intra- and inter-guild pairs to determine assemblage-wide deviations from random community structure. Signatures of environmental filtering were consistent across mountain ranges and scales. Despite differences in species composition and significant pairs among data sets, our approach revealed consistent mechanistic conclusions, emphasizing the value of trait-based methods to co-occurrence and community assembly.
Data from: Beta diversity response to stress severity and heterogeneity in sensitive versus tolerant stream diatoms
Aim: Severity and heterogeneity of stress are major constraints of beta diversity, but their relative influence is poorly understood. Here, we addressed this question by examining the patterns of beta diversity in stress-sensitive versus stress-tolerant stream diatoms and their response to local versus regional factors along gradients of stress severity and heterogeneity. Location: The Adirondack region of New York. Methods: Beta diversity was measured as multivariate dispersion of communities across high stress, low stress, and high + low stress (heterogeneous) environments, encompassing 200 stream samples. Null models were implemented to assess community similarity relative to randomly assembled communities and the importance of local assembly processes vs. the regional species pool. Results: The overall beta diversity was influenced by a combination of severity and heterogeneity of stress, while beta diversity of sensitive species increased with heterogeneity. Beta diversity of tolerant species did not vary with either severity or heterogeneity of stress. Heterogeneity decreased community similarity relative to the null expectation in all groups of species. Stress reduced the importance of local assembly mechanisms for the overall beta diversity and sensitive species beta diversity. In contrast, the importance of local assembly mechanisms increased with stress regarding beta diversity of tolerant species. Main Conclusions: Beta diversity responded to both severity and heterogeneity of stress, but turnover along these gradients was mostly driven by sensitive species. The overall beta diversity and beta diversity of sensitive species became more constrained by the depauperate regional species pool, as opposed to local assembly mechanisms. While heterogeneous stress contributed to beta diversity, severe stress suppressed beta diversity through elimination of sensitive species. Therefore, an increase in beta diversity in an environmentally-stressed region may serve as a forewarning for future loss of sensitive species, should the stress continue to intensify.
Fine‐scale habitat heterogeneity and vole runways influence seed dispersal in Plagiobothrys nothofulvus
<p class="Normal1"><b>PREMISE:</b><i> </i>Seed dispersal allows plants to colonize new sites and contributes to gene flow among populations. Despite its fundamental importance to ecological and evolutionary processes, our understanding of seed dispersal is limited due to the difficulty of directly observing dispersal events. This is particularly true for the majority of plant species that are considered to have gravity as their primary dispersal mechanism. The potential for long-distance movement of gravity-dispersed seeds by secondary dispersal vectors is rarely evaluated. </p> <p class="Normal1"><b>METHODS: </b>We employ whole-genome assays of maternally inherited cpDNA in <i>Plagiobothrys nothofulvus</i> to resolve patterns of genetic variation due to effective (realized) seed dispersal within a 16 hectare prairie that is characterized by a mosaic of habitat types. We evaluate the effects of microgeographic landscape features extracted from micro-UAV aerial surveys on patterns of seed dispersal using landscape genetics methods.</p> <p class="Normal1"><b>RESULTS: </b>We found evidence of high resistance to seed-mediated gene flow (effective dispersal) within patches of <i>P. nothofulvus</i>, and strong genetic structure over distances of less than 20 meters. Geographic distance was a poor predictor of dispersal distance, while landscape features had stronger influences on patterns of dispersal<a name="_Hlk22028649"> (distance and direction of seed movement)</a>. Patterns of dispersal were best predicted by the combined distribution of flower patches, habitat type, and the network of vole runways, with the latter explaining the largest proportion of variation in the model.</p> <p class="Normal1"><b>CONCLUSIONS: </b>Our results suggest that primary dispersal occurs mostly within microhabitats and infrequent secondary dispersal may occur over longer distances due to the activity of small mammals and other vertebrates.</p>
Data and software associated with PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources
<p>Data and software associated with the paper:</p> <p>PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources</p>
Lapedo: Hybrid Skeletons for Programming Heterogeneous Multicore Machines in Erlang
<p>We describe Lapedo, a novel library of hybrid parallel skeletons for programming heterogeneous multi-core/many-core CPU/GPU sys- tems in Erlang. Lapedo’s hybrid skeletons comprise a mixture of CPU and GPU components, allowing skeletons to be flexibly and dynamically mapped to available resources. We also describe a model for deriving near-optimal division of work between CPUs and GPUs, ensuring load balancing between resources. Finally, we evaluate the effectiveness of Lapedo on three realistic use cases from different domains, demonstrat- ing significant speedups compared to executing the same application on only CPU cores or a GPU </p>
Supporting datasets PubFig05 for: "Heterogeneous Ensemble Combination Search using Genetic Algorithm for Class Imbalanced Data Classification"
<p><strong>Faces Dataset: PubFig05</strong></p> <p>This is a subset of the ''PubFig83'' dataset [1] which provides 100 images each of 5 most difficult celebrities to recognise (referred as class in the classification problem). For each celebrity persons, we took 100 images and separated them into training and testing sets of 90 and 10 images, respectively:</p> <p><strong>Person: </strong>Jenifer Lopez; Katherine Heigl; Scarlett Johansson; Mariah Carey; Jessica Alba</p> <p> </p> <p><strong>Feature Extraction</strong></p> <p>To extract features from images, we have applied the HT-L3-model as described in [2] and obtained 25600 features.</p> <p><strong>Feature Selection</strong></p> <p>Details about feature selection followed in brief as follows:</p> <ol> <li> <p><strong>Entropy Filtering:</strong> First we apply an implementation of Fayyad and Irani's [3] entropy base heuristic to discretise the dataset and discarded features using the minimum description length (MDL) principle and only 4878 passed this entropy based filtering method.</p> </li> <li> <p><strong>Class-Distribution Balancing:</strong> Next, we have converted the dataset to binary-class problem by separating into 5 binary-class datasets using one-vs-all setup. Hence, these datasets became <em>imbalanced</em> at a ratio of 1:4. Then we converted them into <em>balanced binary-class</em> datasets using random sub-sampled method. Further processing of the dataset has been described in the paper.</p> </li> <li> <p><strong>(alpha,beta)-k Feature selection:</strong> To get a good feature set for training the classifier, we select the features using the approach based on the (alpha,beta)-k feature selection [4] problem. It selects a minimum subset of features that maximise both within class similarity and dissimilarity in different classes. We applied the entropy filtering and (alpha,beta)-k feature subset selection methods in three ways and obtained different numbers of features (in the Table below) after consolidating them into binary class dataset.</p> </li> </ol> <ul> <li> <p><strong>UAB:</strong> We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets and we took the <em>union</em> of selected features for each binary-class datasets. Finally, we applied the (alpha,beta)-k feature set selection method on each of the binary-class datasets and get a set of features.</p> </li> <li> <p><strong>IAB:</strong> We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets and we took the <em>intersection</em> of selected features for each binary-class datasets. Finally, we applied the (alpha,beta)-k feature set selection method on each of the binary-class datasets and get a set of features.</p> </li> <li> <p><strong>UEAB:</strong> We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets. Then, we applied the entropy filtering and (alpha,beta)-k feature set selection method on each of the balanced binary-class datasets. Finally, we took the <em>union</em> of selected features for each <em>balanced binary-class</em> datasets and get a set of features.</p> </li> </ul> <p>All of these datasets are inside the compressed folder. It also contains the document describing the process detail.</p> <p> </p> <p><strong>References</strong></p> <p>[1] Pinto, N., Stone, Z., Zickler, T., & Cox, D. (2011). Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on (pp. 35–42).</p> <p>[2] Cox, D., & Pinto, N. (2011). Beyond simple features: A large-scale feature search approach to unconstrained face recognition. In Automatic Face Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on (pp. 8–15).</p> <p>[3] Fayyad, U. M., & Irani, K. B. (1993). Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In International Joint Conference on Artificial Intelligence (pp. 1022–1029).</p> <p>[4] Berretta, R., Mendes, A., & Moscato, P. (2005). Integer programming models and algorithms for molecular classification of cancer from microarray data. In Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38 (pp. 361–370). 1082201: Australian Computer Society, Inc.</p> <p> </p>
Software system integration of heterogeneous swarms of robots
<p>Paper: Software system integration of heterogeneous swarms of robots</p> <p>Robots> E-puck, E-puck+Raspberry Pi 2 B, Khepera III, Khepera III+Raspberry Pi 2 B, Koala+Laptop.</p> <p>Using the ROS drivers a series of tests were devised to prove that the robots in the swarm can communicate at a software level using ROS. The robots have individual goals, in this case to follow simple movement commands. The robots move after receiving command information from their program whether it is local or from another robot. All programs needed for each test will be called individually. A desktop computer together with the programs SSH and X11VNC were used in the tests to connect remotely to the robots, i.e. the laptop and Raspberry PIs mounted on the robots.</p> <p><em>1. Single-Minded Robot Swarm.</em> A main robot Koala is running all the drivers and programs for each robot. The programs for the robots communicate through the roscore and send commands to their respective robots using Bluetooth.</p> <p><em>2. Swarm with Leader Robot and Standalone Basic Robots. </em>This test is done with three Standalone robots. Each robot runs it’s own drivers and they all connect to a single roscore. In this test a Koala robot is used as a Leader running roscore and rtab map program for ROS (http://wiki.ros.org/rtabmap_ros) to create maps with the Kinect sensor. The mapping information is needed for SLAM. To connect to a single roscore all the robots must have the following two ROS commands run in the terminal to tell the system where the roscore is run and the IP of each robot: <em>export ROS_MASTER_URI=http://Leader_IP:11311 </em>and<em> export ROS_IP=Current_Robot_IP .</em></p> <p><em>3. Swarm with Standalone Robots Connecting to a Central Robot. A</em>ll the robots run their drivers and other high-level programs standalone, but they connect and communicate with each other through a single roscore running on a single robot. In this test, the E-puck robot runs along with its driver and uses gmapping ROS nodes (http://wiki.ros.org/gmapping) for SLAM. The K3 is running the driver and a simple movement program. The Koala robot runs rtab map with the Kinect and has a wireless controller connected via USB for movement commands. The controller Linux drivers are linked to a ROS joy node (http://wiki.ros.org/joy) which will publish the controller data. To prove the communication between robots, the controller on the Koala is used to send movement commands to the Koala and E-puck.</p> <p><em>4. Swarm with Standalone Robots. </em>In this test each robot is completely independent, running roscore and all programs. The robots connect with each other using rosmultimaster nodes (http://wiki.ros.org/multimaster_fkie). All the robots in this test subscribe to the joy node running on the Koala, and thus move receiving commands from the controller.</p> <p><em>5. Combined Swarm - Single-Minded Robot Swarm(s) + Standalone Robots. </em>In the final test all Standalone robots run their own roscore and the rosmultimaster node. The Standalone robots have other simple robots connected to them using Bluetooth and run their own drivers, other more advanced programs and the simple robots’ ROS drivers. A tested goal here is to have simple robots such as those from the first test receive commands from another Standalone robots in the swarm through its Leader robot. The Koala controller joy node was used again publishing data on the ROS network. Any robot driver connected to this network can subscribe to the publishing joy node and thus the simple robots not connected to the Koala with the controller, receive commands from the controller through their own respective Leader robot.</p>
Computational modeling of hemoglobin saturation heterogeneity in capillary networks
<p>This repository contains the C++ code based on OpenFOAM used for simulating oxygen transport with moving red blood cells. The OpenFOAM cases used to generate all results in the research article "The heterogeneity of hemoglobin saturation in capillaries and its relation to red blood cell transit time" are included.</p> <p>The archive 'code-axisymmetric.tgz' contains the code for the simulations in axisymmetric domains. This code works with OpenFOAM 2.1.1.</p> <p>The archive 'code-parallel_capillaries.tgz' contains the code for the simulations with parallel capillaries. This code is based on OpenFOAM 2.3.0.</p> <p>The archive 'code-graph.tgz' contains the simulation code for the simulations in reconstructed capillary networks. The postprocessing and plotting script are also in this archive. This code is based on OpenFOAM 2.3.0.</p> <p>The archive 'code-flow_reconstruction.tgz' contains the code for the flow reconstruction algorithm.</p> <p>The remaining archives contain the OpenFOAM cases that were used to run the oxygen transport simulations reported in the research article "The Heterogeneity of Hemoglobin Saturation in Capillary Networks and its Relation to Red Blood Cell Transit Time".</p>
Antibiotics Change the Growth Rate Heterogeneity and Morphology of Bacteria
<p>Each experiment was conducted on the Multipad Agarose Plate (MAP) and is labeled with BE followed by a number. This repository contains four main elements:</p> <ol> <li>The file <code>Experiment condition map.json</code> describes how the MAP platforms were set up for each experiment. Some datasets also include data that was discarded. </li> <li>The folder <code>Benchmarking</code> contains data for how we validated the performance of our <code>PadAnalyser</code> package. <code>Originals</code> have a set of images of bacteria affected by antibiotics that produce different morphologies. <code>Annotated_raw</code> is the human-labelled version of these, and <code>Annotated</code> are these converted to a format that can be used by code for comparison. <code>Segmentation Benchmarking.ipynb</code><em> </em>is a Jupyter Notebook that segments the cells and compares the output to the annotated data. </li> <li>The folder <code>Dataframes</code><em> </em>contains Pandas data frames from all experiments, classified by bacteria species. </li> <li>The folder <code>Videos</code> contains debug videos visualising how the segmentation algorithm performed on the different datasets. Contact us for access to the raw data. Videos in the format <code>*_ssi.mp4</code> contain single-cell masks, and <code>*_css.mp4</code> contains colony masks. </li> </ol>
Neuroblastoma cell lines display heterogeneity in differentiation responses
<p>Underlying and extended data for the manuscript "Neuroblastoma cell lines display heterogeneity in differentiation responses", Ferguson et al (2024). Includes Underlying data for Figures 2A-C and Figures 3A-D, and Extended data - Table S1: qRT-PCR primer sequences, Table S2 - List of genes in clusters identified in [7] (Table S2 added after version 1).</p>
Data from: Landscape heterogeneity can partially offset negative effects of habitat loss on mammalian biodiversity in agroecosystems
<p>Intensive, large-scale agriculture promotes the conversion of natural habitats and diversified crops into monocultures, decreasing both native vegetation cover and landscape heterogeneity, leading to landscape simplification. Yet, a key knowledge gap persists on the relative impacts of the loss of native vegetation and landscape heterogeneity on biodiversity. Addressing this gap is pressing to support policies that conciliate agricultural production and biodiversity conservation and to move forward some scientific controversies, as the "land sharing versus land sparing" and "habitat loss versus fragmentation" debates.<br>Through a hierarchical sampling design that maximized variation, while minimizing correlation, between landscape heterogeneity and native vegetation cover, we recorded the occurrence of medium and large-bodied mammals in native vegetation and agricultural areas of 55 landscapes in a global conservation hotspot and a key commodity production area – the Brazilian savanna, Cerrado. We compared simple, additive, and interactive models to investigate the effects of landscape heterogeneity and native vegetation cover on richness and composition of native and invasive mammals. <br>Native and invasive mammal communities were affected by both native vegetation cover and landscape heterogeneity, although the effects of the first was stronger than the later. Both aspects had positive effects on native species richness and negative on invasive species richness, indicating that the loss of native vegetation and the reduction in landscape heterogeneity lead to biotic homogenization. Yet, while landscape heterogeneity benefited most native species, the direction of its effect varied among invasive species and depended on native vegetation cover.<br>Synthesis and applications: Besides reducing habitat loss, avoiding landscape homogenization is key for conciliating agricultural production and biodiversity conservation, pointing to the relevance of policies encouraging crop diversification. As increasing landscape heterogeneity can in part compensate the negative effects of losing native habitat on biodiversity in agroecosystems, policies can gain feasibility by adjusting the balance between native vegetation cover and landscape heterogeneity according to what best suits local restraints and demands.</p>
Dataset: Quantifying the effects of heterogeneities on cohesive sediment erosion
<p>This dataset includes the raw data of the erosion tests and the results with the proposed method from each test case.</p>
The role of dynamics in heterogeneous catalysis: surface diffusivity and N2 decomposition on Fe(111)
<p>Data related to the molecular dynamics simulations reported in the manuscript "The role of dynamics in heterogeneous catalysis: surface diffusivity and N2 decomposition on Fe(111)"</p><p>- Inputs of the MD simulations of surface morphology and dynamics (LAMMPS)</p><p>- Inputs of the OPES simulations for N2 adsorption and dissociation (LAMMPS+PLUMED)</p><p>- MD outputs: trajectory files</p><p>- Code for analysis and post-processed data</p><p>- Jupyter notebook to reproduce the pictures reported in the manuscript</p>
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.