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750 results for “heterogeneous data”
Data from: Intrinsic growth heterogeneity of mouse leukemia cells underlies differential susceptibility to a growth-inhibiting anticancer drug
<p>Cancer cell populations consist of phenotypically heterogeneous cells. Growing evidence suggests that pre-existing phenotypic differences among cancer cells correlate with differential susceptibility to anticancer drugs and eventually lead to a relapse. Such phenotypic differences can arise not only externally driven by the environmental heterogeneity around individual cells but also internally by the intrinsic fluctuation of cells. However, the quantitative characteristics of intrinsic phenotypic heterogeneity emerging even under constant environments and their relevance to drug susceptibility remain elusive. Here we employed a microfluidic device, mammalian mother machine, for studying the intrinsic heterogeneity of growth dynamics of mouse lymphocytic leukemia cells (L1210) across tens of generations. The generation time of this cancer cell line had a distribution with a long tail and a heritability across generations. We determined that a minority of cell lineages exist in a slow-cycling state for multiple generations. These slow-cycling cell lineages had a higher chance of survival than the fast-cycling lineages under continuous exposure to the anticancer drug Mitomycin C. This result suggests that heritable heterogeneity in cancer cells' growth in a population influences their susceptibility to anticancer drugs.</p>
Data from: Soil heterogeneity increases plant diversity after twenty years of manipulation during grassland restoration
The 'environmental heterogeneity hypothesis' predicts that variability in resources promotes species coexistence, but few experiments support this hypothesis in plant communities. A previous 15-y test of this hypothesis in a prairie restoration experiment demonstrated a weak effect of manipulated soil resource heterogeneity on plant diversity. This response was attributed to a transient increase in richness following a post-restoration supplemental propagule addition, occasionally higher diversity under nutrient enrichment, and reduced cover of a dominant species in a subset of soil treatments. Here, we report community dynamics under continuous propagule addition in the same experiment, corresponding to 16-20 y of restoration, in response to altered availability and heterogeneity of soil resources. We also quantified traits of newly added species to determine if heterogeneity increases the amount and variety of niches available for new species to exploit. The heterogeneous treatment contained a factorial combination of altered nutrient availability and soil depth; control plots had no manipulations. Total diversity and richness were higher in the heterogeneous treatment during this 5 y study due to higher cover, diversity, and richness of previously established forbs, particularly in the N-enriched subplots. All new species added to the experiment exhibited unique trait spaces, but there was no evidence that heterogeneous plots contained a greater variety of new species representing a wider range of trait spaces relative to the control treatment. The richness and cover of new species was higher in N-enriched soil, but the magnitude of this response was small. Communities assembling under long-term N addition were dominated by different species among subplots receiving added N, leading to greater dispersion of communities among the heterogeneous relative to control plots. Contrary to the deterministic mechanism by which heterogeneity was expected to increase diversity (greater variability in resources for new species to exploit), higher diversity in the heterogeneous plots resulted from destabilization of formerly grass-dominated communities in N-enriched subplots. While we do not advocate increasing available soil N at large scales, we conclude that the positive effect of environmental heterogeneity on diversity can take decades to materialize and depend on development of stochastic processes in communities with strong establishment limitation.
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.
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.
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.
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>
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>
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>
Spatial data for Chaplin-Kramer et al. "Spatial heterogeneity in forest carbon storage affects priorities for reforestation"
<p>Spatial data generated for the forest carbon edge effects analysis and optimization performed in Chaplin-Kramer et al. "Spatial heterogeneity in forest carbon storage affects priorities for reforestation". The files included are described below.</p><p>fc_stack_hansen_forest_cover2014_compressed_std_forest_edge_result.tif - Carbon (Mg/ha) predicted by the regression model for Hansen forest cover 2014 (this is the LULC map that produced the model with the best fit and was used in the model validation)</p><p>fig1_edge_effect_hansen_compressed_nr_md5_63366a.tif – Figure 1 in the manuscript</p><p>fig2_ipcc1_regression2_overlap3_md5_43e130.tif – Figure 2 in the manuscript</p><p>figS5_ipcc1_regression2_overlap3_avgov_md5_fe8b72.tif – Alternate styling of manuscript Fig. 2 (resampling with "average" instead of "near"), shown in Supplemental Fig. S5</p><p>ipcc_carbon_restoration_limited_md5_cd012d.tif - Carbon (Mg/ha) in restoration LULC calculated with the IPCC Tier 1 approach</p><p>ipcc_tier1_carbon_data.zip - Data needed to run the IPCC approach are contained within two files in the zip folder: the carbon table (carbon zones are rows and ESA LULC codes are columns) IPCC_carbon_table_md5_a91f7ade46871575861005764d85cfa7.csv; and the carbon zones map: carbon_zones_md5_aa16830f64d1ef66ebdf2552fb8a9c0d.gpkg</p><p>ipcc_carbon_esa_compressed_md5_5b4803.tif - Carbon (Mg/ha) in ESA 2014 LULC calculated with the IPCC Tier 1 approach (shown in Fig. S1)</p><p>PNV_jsmith_060420_md5_8dd464e0e23fefaaabe52e44aa296330.tif – Potential Natural Vegetation (PNV) created by Jeff Smith (unpublished) through a cross-walk of ESA to Dinerstein biomes</p><p>regression_carbon_esa_compressed_md5_c867a0.tif - Carbon (Mg/ha) in ESA 2014 LULC calculated with the regression approach (shown in Fig. S1)</p><p>regression_carbon_restoration_md5_1f5ca8.tif - Carbon (Mg/ha) in restoration LULC calculated with the regression approach</p><p>restoration_limited_md5_372bdfd9ffaf810b5f68ddeb4704f48f.tif – the LULC scenario of reforestation (based on PNV, but excluding agriculture and urban areas) </p>
Data sets for phylogenomic analyses in: Ant backbone phylogeny resolved by modelling compositional heterogeneity among sites in genomic data
<p>Ants are the most ubiquitous and ecologically dominant arthropods on Earth, and understanding their phylogeny is crucial for deciphering their character evolution, species diversification, and biogeography. Although recent genomic data have shown promise in clarifying intrafamilial relationships across the tree of ants, inconsistencies between molecular datasets have also emerged. Here I re-examine the most comprehensive published Sanger-sequencing and genome-scale datasets of ants using model comparison methods that model among-site compositional heterogeneity to understand the sources of conflict in phylogenetic studies. My results under the best-fitting model, selected on the basis of Bayesian cross-validation and posterior predictive model checking, identify contentious nodes in ant phylogeny whose resolution is <a>modelling-dependent. </a>I show that the Bayesian infinite mixture CAT model outperforms empirical finite mixture models (C20, C40 and C60) and that, under the best-fitting CAT-GTR+G4 model, the enigmatic <a><em>Martialis</em> </a><em>heureka</em> is sister to all ants except Leptanillinae, rejecting the more popular hypothesis supported under worse-fitting models, that place it as sister to Leptanillinae. These analyses resolve a lasting controversy in ant phylogeny and highlight the significance of model comparison and adequate modelling of among-site compositional heterogeneity in reconstructing the deep phylogeny of insects.</p>
Data from: Heterogeneity promotes resilience in restored prairie: implications for the 'environmental heterogeneity hypothesis'
<p>Enhancing resilience in formerly degraded ecosystems is an important goal of restoration ecology. However, evidence for the recovery of resilience and its underlying mechanisms requires long-term experiments and comparison to reference ecosystems. We used data from an experimental prairie restoration that featured long-term soil heterogeneity manipulations and data from comparable remnant (reference) prairie to (1) quantify the recovery of ecosystem functioning (i.e., productivity) relative to remnant prairie, (2) compare resilience of restored and remnant prairies to a natural drought, and (3) test whether soil heterogeneity enhances resilience of restored prairie. We compared sensitivity and legacy effects between prairie types (remnant and restored) and among four prairie sites that included two remnant prairie sites and prairie restored under homogeneous and heterogeneous soil conditions. We measured sensitivity and resilience as the proportional change in aboveground net primary productivity (ANPP) during and following drought (sensitivity and legacy effects, respectively) relative to average ANPP based on four pre-drought years (2014-2017). In non-drought years, total ANPP was similar between remnant and restored prairie, but remnant prairie had higher grass productivity and lower forb productivity compared to restored prairie. These ANPP patterns generally persisted during drought. Sensitivity of total ANPP to drought was similar between restored and remnant prairie, but grasses in restored prairie were more sensitive to drought. Post-drought legacy effects were more positive in restored prairie, and we attributed this to the more positive and less variable legacy response of forb ANPP in restored prairie, especially in the heterogeneous soil treatment. Our results suggest that productivity recovers in restored prairie and exhibits similar sensitivity to drought as remnant prairie. Furthermore, imparting heterogeneity promotes forb productivity and enhances prairie resilience to drought.</p>
Data from: Born in heterogenous landscapes: birth timing, body mass and growth of roe deer (Capreolus capreolus) fawns in contrasting habitats
<p>Although the widespread effects of global change impact almost all ecosystems, we lack a detailed understanding of how wildlife that thrive in human-dominated environments are able to adjust their life history to modifications in land use of their natural habitat. In particular, spatial variation in environmental conditions is predicted to influence development during the crucial early life phase, with marked impacts on individual performance and population dynamics for long-lived species. Large herbivores such as roe deer (<em>Capreolus capreolus</em>), a synanthropic species, have increased substantially in number and distribution over the last half century across Europe. Roe deer have been particularly successful, gradually colonizing agricultural landscapes to cope with a global warming-driven phenological mismatch in their natural forest habitat. However, to date, little is known about how habitat heterogeneity impacts their demographic performance in this heavily human-impacted environment. Specifically, we predicted that fawns born in predominantly cultivated local habitats would achieve faster early development due to the food subsidies obtained by their mothers from agricultural crops. Contrary to our expectations, fawns in semi-natural forest habitats were around 10% heavier at birth than those born in more mixed (by 0.163 ± 0.058 kg) and open (by 0.169 ± 0.006 kg) agricultural habitats. However, all fawns subsequently grew at a similar average rate (0.148 ± 0.058 kg/day), irrespective of their habitat. This habitat-dependent variation in birth mass appeared to be driven by reproductive phenology, as i) early-born fawns were heavier than late-born fawns, and ii) mothers living in the forest gave birth around 10 days earlier than those living in the mixed and open habitats. As natural habitats become increasingly scarce and fragmented due to the activities of humans, the prospects for many wild populations will depend on their ability to subsist in the heavily modified habitats of anthropogenic landscapes.</p>
Data of "Self-consistency Reinforced minimal Gated Recurrent Unit for surrogate modeling of history-dependent non-linear problems: application to history-dependent homogenized response of heterogeneous materials"
<h1>Development of the Self-Consistency reinforced Minimum Recurrent Unit (SC-MRU)</h1> <p>This directory contains the data and algorithms generated in publication<sup><a href="#fn-1-5292">1</a></sup></p> <h2>Table of Contents</h2> <ol> <li><a href="#dependencies-and-prerequisites">Dependencies and Prerequisites</a></li> <li><a href="#structure-of-repository">Structure of Repository</a></li> <li><a href="#part-1-data-preparation">Part 1: Data preparation</a></li> <li><a href="#part-2-rnn-training">Part 2: RNN training</a></li> <li><a href="#part-3-multiscale-analysis">Part 3: Multiscale analysis</a></li> <li><a href="#part-4-reproduce-paper1-figures">Part 4: Reproduce paper[^1] figures</a></li> </ol> <h2>Dependencies and Prerequisites</h2> <p> </p> <ul> <li> <p>Python, pandas, matplotlib, texttabble and latextable are pre requisites for visualizing and navigating the data.</p> </li> <li> <p>For generating mesh and for vizualization, gmsh (<a href="http://www.gmsh.info" target="_blank" rel="nofollow noreferrer noopener">www.gmsh.info</a>) is required.</p> </li> <li> <p>For running simulations, cm3Libraries (<a href="http://www.ltas-cm3.ulg.ac.be/openSource.htm" target="_blank" rel="nofollow noreferrer noopener">http://www.ltas-cm3.ulg.ac.be/openSource.htm</a>) is required.</p> </li> </ul> <h3>Instructions using apt & pip3 package manager</h3> <p>Instructions for Debian/Ubuntu based workstations are as follows.</p> <h3>python, pandas and dependencies</h3> <div> <pre><code> sudo apt install python3 python3-scipy libpython3-dev python3-numpy python3-pandas</code></pre> </div> <h3>matplotlib, texttabble and latextable</h3> <div> <pre><code> pip3 install matplotlib texttable latextable</code></pre> </div> <h3>Pytorch (only for run with cm3Libraries)</h3> <ul> <li>Without GPU</li> </ul> <div> <pre><code> pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu</code></pre> </div> <ul> <li>With GPU</li> </ul> <div> <pre><code> pip3 install torch torchvision torchaudio</code></pre> </div> <h3>Libtorch (for compiling the cells)</h3> <ul> <li>Without GPU: In a local directory (e.g. <code>~/local</code> with <code>export TORCHDIR=$HOME/local/libtorch</code>)</li> </ul> <div> <pre><code> wget https://download.pytorch.org/libtorch/cpu/libtorch-shared-with-deps-2.1.1%2Bcpu.zip unzip libtorch-shared-with-deps-2.1.1%2Bcpu.zip</code></pre> </div> <ul> <li>With GPU: In a local directory (e.g. <code>~/local</code> with <code>export TORCHDIR=$HOME/local/libtorch</code>)</li> </ul> <div> <pre><code> wget https://download.pytorch.org/libtorch/cu121/libtorch-shared-with-deps-2.1.1%2Bcu121.zip unzip libtorch-shared-with-deps-2.1.1+cu121.zip</code></pre> </div> <h2>Structure of Repository</h2> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/All_Path_Res">All_Path_Res</a>: results of the direct numerical simulations used as training and testing data, see details in <a href="#part-1-data-preparation">Part 1: Data preparation</a>.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/ConstRVE">ConstRVE</a>: script to run direct numerical finite element simulations, see details in <a href="#part-1-data-preparation">Part 1: Data preparation</a>.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/MultiScale">MultiScale</a>: scripts to run and visualise the multiscale analyses, see details in <a href="#part-3-multiscale-analysis">Part 3: Multiscale analysis</a>.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU">SC_MRU</a>: implementation of the RNN and scripts to train them, see details in <a href="#part-2-rnn-training">Part 2: RNN training</a>.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/TrainingData">TrainingData</a>: scripts to collect, normalise and truncate the RVEs direct simulation results as training and testing data, see details in <a href="#part-1-data-preparation">Part 1: Data preparation</a>. The director also contained the storred processed data used in <sup><a href="#fn-1">1</a></sup>.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/TrainingPaths">TrainingPaths</a>: scripts to generate the different loading paths for the direct numerical simulations used as training and testing data, see details in <a href="#part-1-data-preparation">Part 1: Data preparation</a>.</li> </ul> <h2>Part 1: Data preparation</h2> <h3>Generate the loading paths</h3> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingPaths/testGenerationData.py">TrainingPaths/testGenerationData.py</a> is used to generate random walk paths, with the options <ul> <li><code>Rmax = 0.11</code> # bound on the final Green Lagrange strain</li> <li><code>TimeStep = 1.</code> # in second</li> <li><code>EvalStep = [1e-4,5e-3]</code> #Bounds on the Green Lagrange increments</li> <li><code>Nmax = 2500</code> #maximum length of the sequence</li> <li><code>k = 4000</code> # number of path to generate</li> <li>The path are storred by default in <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/ConstRVE/Paths">ConstRVE/Paths/</a>. The path has to be existing before launching the script. You can change the name in line 123 <code>saveDir = '../ConstRVE'+'/Paths/'</code>.</li> <li>Examples of generated paths can be found in <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/ConstRVE/PathsExamples">ConstRVE/PathsExamples/</a></li> <li>The command to be run from the directory <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/TrainingPaths">TrainingPaths</a> is</li> </ul> </li> </ul> <div> <pre><code>(mkdir ../ConstRVE/Paths) #if needed python3 testGenerationData.py</code></pre> </div> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingPaths/generationData_Cyclic.py">TrainingPaths/generationData_Cyclic.py</a> is used to generate random cylic paths, with the options <ul> <li><code>Rmax = [np.random.uniform(0.,0.04),np.random.uniform(0.,0.06),np.random.uniform(0.0,0.09),0.12]</code> # bound on the final Green Lagrange strain is random</li> <li><code>TimeStep = 1.</code> # in second</li> <li><code>EvalStep = [1e-4,5e-3]</code> #Bounds on the Green Lagrange increments</li> <li><code>Nmax = 2500</code> #maximum length of the sequence</li> <li><code>k = 2000</code> # number of path to generate</li> <li>The path are stored by default in <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/ConstRVE/Paths">ConstRVE/Paths/</a>. You can change the name in line 123 <code>saveDir = '../ConstRVE'+'/Paths/'</code>.</li> <li>The command to be run from the directory <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/TrainingPaths">TrainingPaths</a> is</li> </ul> </li> </ul> <div> <pre><code>(mkdir ../ConstRVE/Paths) #if needed python3 generationData_Cyclic.py</code></pre> </div> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingPaths/countPathLength.py">TrainingPaths/countPathLength.py</a> gives average, minimum and maximum lengths of the generated paths and the distribution of the <code>\Delta R</code>. By default the paths are read in <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/ConstRVE/Paths">ConstRVE/Paths/</a> but the directory can be given as an argument. The file can be used to read <ul> <li>either the generated loading paths</li> </ul> </li> </ul> <div> <pre><code> python3 countPathLength.py '../ConstRVE/PathsExamples'</code></pre> </div> <ul> <li> <ul> <li>or the results of the <a href="#generate-the-rves-direct-simulation-results">simulations</a></li> </ul> </li> </ul> <div> <pre><code> python3 countPathLength.py '../All_Path_Res/Path_Res9'</code></pre> </div> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingPaths/graphData.py">TrainingPaths/graphData.py</a> generates illustrations from randomly picked paths in <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/ConstRVE/Paths">ConstRVE/Paths/</a> and generate png figures.</li> </ul> <h3>Generate the RVEs direct simulation results</h3> <ul> <li>Uses the loading paths existing in <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/ConstRVE/Paths">ConstRVE/Paths/</a>.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/ConstRVE/rve.geo">ConstRVE/rve.geo</a> is the RVE geometry file that can be read by gmsh (<a href="http://www.gmsh.info" target="_blank" rel="nofollow noreferrer noopener">www.gmsh.info</a>).</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/ConstRVE/rve.msh">ConstRVE/rve.msh</a> is the RVE mesh file that can be read by gmsh (<a href="http://www.gmsh.info" target="_blank" rel="nofollow noreferrer noopener">www.gmsh.info</a>).</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/ConstRVE/utilsFunc.py">ConstRVE/utilsFunc.py</a> contains python tools to be used.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/ConstRVE/Rve_withoutInternalVars.py">ConstRVE/Rve_withoutInternalVars.py</a> is used to run all the RVE simulations: <ul> <li>This requires cm3Libraries (<a href="http://www.ltas-cm3.ulg.ac.be/openSource.htm" target="_blank" rel="nofollow noreferrer noopener">http://www.ltas-cm3.ulg.ac.be/openSource.htm</a>).</li> <li>All the ouptus are stored in <code>All_Path_Res/Path_Res12</code>, you can change the name in line 71 <code>Path_Res = '../All_Path_Res/Path_Res12/'</code>. The results of RVE simulations are saved as the sequence (one configuration per line) of the Green-Lagrange strains and Second Piola-Kirchhoff stress (in column). One example can be found in <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/All_Path_Res/Path_Res1/data_path1000.csv">All_Path_Res/Path_Res1/data_path1000.csv</a>.</li> <li>The command to be run from <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/ConstRVE">ConstRVE</a> directory is</li> </ul> </li> </ul> <div> <pre><code> python3 Rve_withoutInternalVars.py</code></pre> </div> <h3>Collect, normalised and truncate the RVEs direct simulation results as training and testing data</h3> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingData/CheckNanData.py">TrainingData/CheckNanData.py</a> is used to check the integrity of the direct numerical simulations results <ul> <li>DNS results are read from <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/All_Path_Res/Path_res1">All_Path_Res/Path_res1</a> to <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/All_Path_Res/Path_res11">All_Path_Res/Path_res11</a> subdirectories.</li> <li>The command to be run from <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/TrainingData">TrainingData</a> directory is</li> </ul> </li> </ul> <div> <pre><code> python3 CheckNanData.py</code></pre> </div> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingData/CollectData.py">TrainingData/CollectData.py</a> is used to gather all the direct numerical simulations results <ul> <li>DNS results are read from <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/All_Path_Res/Path_res1">All_Path_Res/Path_res1</a> to <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/All_Path_Res/Path_res11">All_Path_Res/Path_res11</a> subdirectories. It can be changed in line 31 <code>for ll in range(11):</code>.</li> <li>It saves the bounds and raw data in <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingData/Processed_Data/Bounds_GS">TrainingData/Processed_Data/Bounds_GS</a> and <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingData/Processed_Data/Origin_GS">TrainingData/Processed_Data/Origin_GS</a>, respectively.</li> <li>The command to be run from <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/TrainingData">TrainingData</a> directory is</li> </ul> </li> </ul> <div> <pre><code> python3 CollectData.py</code></pre> </div> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingData/Normalization.py">TrainingData/Normalization.py</a> is used to normalise the gathered direct numerical simulations results <ul> <li>Bounds and raw data are read from <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingData/Processed_Data/Bounds_GS">TrainingData/Processed_Data/Bounds_GS</a> and <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingData/Processed_Data/Origin_GS">TrainingData/Processed_Data/Origin_GS</a>, respectively.</li> <li>It saves the normalized training (75%) and testing (25%) data in <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingData/Processed_Data/Normalized_GS_Train">TrainingData/Processed_Data/Normalized_GS_Train</a> and <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingData/Processed_Data/Normalized_GS_Test">TrainingData/Processed_Data/Normalized_GS_Test</a>, respectively.</li> <li>The command to be run from <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/TrainingData">TrainingData</a> directory is</li> </ul> </li> </ul> <div> <pre><code> python3 Normalization.py</code></pre> </div> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingData/GaussCollectData.py">TrainingData/GaussCollectData.py</a> is an alternative using Gaussian normaliation and is used to gather all the direct numerical simulations results.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingData/GaussNormalization.py">TrainingData/GaussNormalization.py</a> is an alternative to normalise following a Gaussian the gathered direct numerical simulations results.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingData/Data_Padding.py">TrainingData/Data_Padding.py</a> is used to pad and trim the normalised data <ul> <li>Normalized training (75%) and testing (25%) data in <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingData/Processed_Data/Normalized_GS_Train">TrainingData/Processed_Data/Normalized_GS_Train</a> and <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingData/Processed_Data/Normalized_GS_Test">TrainingData/Processed_Data/Normalized_GS_Test</a>, respectively.</li> <li>The final length of the sequence (including zero padding and trimming) is given by <code>N = 200</code>.</li> <li>It saves the trimmed and padded normalised training and testing data in <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingData/Processed_Data">TrainingData/Processed_Data/</a> as <code>'GS_Train'N</code> and <code>'GS_Test'N</code>, respectively.</li> <li>The command to be run from <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/TrainingData">TrainingData</a> directory is</li> </ul> </li> </ul> <div> <pre><code> python3 Data_Padding.py</code></pre> </div> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingData/Tool.py">TrainingData/Tool.py</a> is a list of function used to normalise dat.</li> </ul> <h2>Part 2: RNN training</h2> <h3>Available rnn</h3> <ul> <li>The different cells are in the following directories <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/MGRU/NNW_SMRU">SC_MRU/MGRU/NNW_SMRU</a>: Neural network with SMRU recurrent cell.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/MGRU/NNW_SCMRU_T">SC_MRU/MGRU/NNW_SCMRU_T</a>: Neural network with SC-MRU-T recurrent cell.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/MGRU/NNW_MGRU">SC_MRU/MGRU/NNW_MGRU</a>: Neural network with orginal MGRU recurrent cell.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/MGRU/NNW_MGRU_M">SC_MRU/MGRU/NNW_MGRU_M</a>: Neural network with modified MGRU recurrent cell.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/FNN_LeakyReLU">SC_MRU/FNN_LeakyReLU/NNW_<code>X</code>Fw</a>: Neural network with SC-MRU-I recurrent cell using <code>X</code> Feed Forward non-linear transition layers.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/Quadratic_NLT">SC_MRU/Quandratic_NLT/NNW_<code>X</code>Q, NNW_Q_Fw, NNW_Fw_Q</a>: Neural network with SC-MRU-I recurrent cell using <code>X</code> quadratic or mixed feed-forward-quadratic non-linear transition layers.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/ReferenceRNN">SC_MRU/ReferenceRNN/NNW_<code>X</code>Layers</a>: Neural network with SC-LMSC recurrent cell using <code>X</code> non-linear transition layers.</li> </ul> </li> </ul> <h3>Compile the neural network models</h3> <ul> <li>In the adequate directory, e.g. <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/MGRU/NNW_SMRU">SC_MRU/MGRU/NNW_SMRU</a> for the Neural network with SMRU recurrent cell.</li> </ul> <div> <pre><code> cd build rm -rf * cmake -DCMAKE_PREFIX_PATH=$TORCHDIR .. make</code></pre> </div> <ul> <li>This create the RNN_<code>CELL</code> model in the build directory</li> </ul> <h3>Train the neural network models</h3> <ul> <li>In the adequate directory, e.g. <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/MGRU/NNW_SMRU">SC_MRU/MGRU/NNW_SMRU</a> for the Neural network with SMRU recurrent cell.</li> <li>Requires to have <a href="#compile-the-neural-network-models">compiled</a> the RNN model.</li> <li>The file <code>Train.py</code> <ul> <li>Uses the RNN_<code>CELL</code> model compiled in the <code>build</code> directory.</li> <li>Uses the trimmed and padded normalised training and testing data in <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/TrainingData/Processed_Data">TrainingData/Processed_Data/</a> as <code>'GS_Train'N</code> and <code>'GS_Test'N</code>, respectively.</li> <li>Can be modified to use the requested ratios of training and testing sequences of different lengths to prepate the mini-baches, e.g.: <ul> <li><code>ratio = [0.2,0.02,0.6,0.06,0.01,0.9]</code></li> <li><code>PathIn1 = ['../../../TrainingData/Processed_data/GS_Train200','../../../TrainingData/Processed_data/GS_Train2500','../../../TrainingData/Processed_data/GS_Test200','../../../TrainingData/Processed_data/GS_Test2500']</code></li> <li><code>PathIn2 = ['../../../TrainingData/Processed_data/GS_Test200','../../../TrainingData/Processed_data/GS_Test400','../../../TrainingData/Processed_data/GS_Test2500','../../../TrainingData/Processed_data/GS_Test400']</code></li> </ul> </li> <li>Saves the mini-batches in <ul> <li><code>PathOut = "TrainingData.pt"</code></li> </ul> </li> <li>Saves the model <code>module-checkpoint.pt</code> and <code>module-checkpoint-optimizer.pt</code>, and loss evolution <code>Loss.txt</code> in <ul> <li>Module/H<code>n</code> with <code>n</code> the number of hiden variables.</li> <li>Output directory can be changes in NNW_<code>CELL</code>.cpp of the cell name <code>CELL</code> (and recompiling).</li> <li>Warm start can be disabled in by commenting <code>torch::load(net, "./Module/H120/module-checkpoint_CM0.pt");</code> and <code>torch::load(optimizer, "./Module/H120/module-optimizer-checkpoint_CM0.pt");</code> in NNW_<code>CELL</code>.cpp of the cell name <code>CELL</code> (and recompiling).</li> <li>Is executed with</li> </ul> </li> </ul> </li> </ul> <div> <pre><code> python3 Train.py</code></pre> </div> <h3>Convert trained c++ models for pyTorch (in view of multiscale simulations)</h3> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/SC_MRU/CheckLoss/Save_RU.py">SC_MRU/CheckLoss/RU.py</a>: functions used to read and use the rnn models.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/SC_MRU/CheckLoss/Save_RNN_script.py">SC_MRU/CheckLoss/Save_RNN_script.py</a>: <ul> <li>Converts c++ trained models to pyTorch models</li> <li>Reads the trained models in SC_MRU/CELL_<code>Kind</code>/NNW_<code>CELL</code>/Module/H<code>N</code>/module.pt</li> <li>Save the converted models to <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/CheckLoss">SC_MRU/CheckLoss/</a> and to <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/MultiScale/Model">MultiScale/Model/</a></li> <li><code>InpType = cell[0]</code> #choose the recurrent cell typeAmong the different available cells <code>cell=["SC_MRU_T","SC_MRU_I","SMRU"]</code>.</li> <li>The command to be run from the directory <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/CheckLoss">SC_MRU/CheckLoss/</a> is</li> </ul> </li> </ul> <div> <pre><code>python3 Save_RNN_script.py</code></pre> </div> <h3>Vizualize loss evolution and testing results</h3> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/SC_MRU/CheckLoss/Data_prepare.py">SC_MRU/CheckLoss/Data_prepare.py</a> contains fucntion used for testing.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/SC_MRU/CheckLoss/LossVS_insertN.py">SC_MRU/CheckLoss/LossVS_insertN.py</a> tests the effect of testing data augmentation: <ul> <li>Requires to have <a href="#compile-the-neural-network-models">compiled</a> the RNN models.</li> <li>Cell type and augmentation kind can be modified: <ul> <li><code>repeat = 10</code> #number of tests</li> <li><code>dataType ='random'</code> #'even' or 'random'</li> <li><code>InpType = cell[0]</code> #choose the recurrent cell typeAmong the different available cells ```cell=["SC_MRU_T","SC_MRU_I","SMRU"]``</li> <li><code>reevaluate= False</code> # <code>False</code> to use the saved loss values and <code>True</code> to evaluate the loss values</li> <li><code>fastshifting=False</code> # <code>False</code> to vizualize before fast shifting (Fig. 11) and <code>True</code> after (Fig. 12). When <code>reevaluate== True</code>, this has no effect: the training with fast-shifting or not has to be done manually, see <a href="#train-the-neural-network-models">details</a>.</li> </ul> </li> <li>Generates new Loss_<code>CELL</code>.txt and TrainingData.pt files in case <code>reevaluate== True</code>, read the existing one if <code>reevaluate== False</code>.</li> <li>Generates Figs. 11 and 12 from <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/CheckLoss">SC_MRU/CheckLoss/</a></li> </ul> </li> </ul> <div> <pre><code> python3 LossVS_insertN.py</code></pre> </div> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/SC_MRU/CheckLoss/Plot_GS.py">SC_MRU/CheckLoss/Plot_GS.py</a> test the different NNW on RVE testing paths: <ul> <li>Requires to have <a href="#compile-the-neural-network-models">compiled</a> the RNN models.</li> <li>Cell type and augmentation kind can be modified: <ul> <li><code>InpType = cell[0]</code> #choose the recurrent cell typeAmong the different available cells ```cell=["SC_MRU_T","SC_MRU_I","SMRU"]``</li> </ul> </li> <li>Generate Figs. 13, 14 and 15 from <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/CheckLoss">SC_MRU/CheckLoss/</a></li> <li>Command to be run from <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/CheckLoss">SC_MRU/CheckLoss/</a> is</li> </ul> </li> </ul> <div> <pre><code> python3 Plot_GS.py</code></pre> </div> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/SC_MRU/CheckLoss/Plot_GS_step.py">SC_MRU/CheckLoss/Plot_GS_step.py</a> tests the different pyTorch NNWs on the RVE testing paths: <ul> <li>Requires to have <a href="#convert-trained-c-models-for-pytorch-in-view-of-multiscale-simulations">converted</a> the RNN models.</li> <li>Cell type and augmentation kind can be modified: <ul> <li><code>InpType = cell[0]</code> #choose the recurrent cell typeAmong the different available cells ```cell=["SC_MRU_T","SC_MRU_I","SMRU"]``</li> </ul> </li> <li>Command to be run from <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/CheckLoss">SC_MRU/CheckLoss/</a> is</li> </ul> </li> </ul> <div> <pre><code> python3 Plot_GS_step.py</code></pre> </div> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/SC_MRU/PlotLoss_HiddenV.py">SC_MRU/PlotLoss_HiddenV.py</a> shows the loss evolution for the different number of hidden variables --<a href="#train-the-neural-network-models">after having trained</a> or using saved loss files-- for the SMRU cell: <ul> <li>Generates Fig. 8.</li> <li>Command to be run from <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU">SC_MRU/</a> is</li> </ul> </li> </ul> <div> <pre><code> python3 PlotLoss_HiddenV.py</code></pre> </div> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/SC_MRU/PlotLoss_3MRU.py">SC_MRU/PlotLoss_3MRU.py</a> shows the loss evolution for the different recurrent units --<a href="#train-the-neural-network-models">after having trained</a> or using saved loss files-- for the 120 hidden variables: <ul> <li>Generates Fig. 9.</li> <li>Command to be run from <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU">SC_MRU/</a> is</li> </ul> </li> </ul> <div> <pre><code> python3 PlotLoss_3MRU.py</code></pre> </div> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/SC_MRU/PlotLoss_RefNL_H120.py">SC_MRU/PlotLoss_RefNL_H120.py</a> shows the loss evolution for the different SC-LMSC and SC-MRU-I cells --<a href="#train-the-neural-network-models">after having trained</a> or using saved loss files-- for 120 hidden variables: <ul> <li>Generates Fig. 10.</li> <li>Command to be run from <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU">SC_MRU/</a> is</li> </ul> </li> </ul> <div> <pre><code> python3 PlotLoss_RefNL_H120.py</code></pre> </div> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/SC_MRU/PlotLoss_MGRU.py">SC_MRU/PlotLoss_MGRU.py</a> shows the loss evolution for the original and modified MGRU --<a href="#train-the-neural-network-models">after having trained</a> or using saved loss files-- and for the 120 hidden variables: <ul> <li>Generates Fig. A.18.</li> <li>Command to be run from <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU">SC_MRU/</a> is</li> </ul> </li> </ul> <div> <pre><code> python3 PlotLoss_MGRU.py</code></pre> </div> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/SC_MRU/PlotLoss_NL.py">SC_MRU/PlotLoss_NL.py</a> shows the loss evolution for the different non-liner transition layers (quadratic and Leaky ReLU) of the SC-MRU-I cell --<a href="#train-the-neural-network-models">after having trained</a> or using saved loss files-- and for the 120 hidden variables: <ul> <li>Generates Fig. B.19.</li> <li><code>Case=1</code> # 0 for quadratic transition blocks and 1 for Leaky ReLU transition blocks</li> <li>Command to be run from <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU">SC_MRU/</a> is</li> </ul> </li> </ul> <div> <pre><code> python3 PlotLoss_NL.py</code></pre> </div> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/SC_MRU/PlotLoss_DiffNL_H120.py">SC_MRU/PlotLoss_DiffNL_H120.py</a> shows the loss evolution for the different non-liner transition layers (hybrid) of the SC-MRU-I cell --<a href="#train-the-neural-network-models">after having trained</a> or using saved loss files-- and for the 120 hidden variables: <ul> <li>Generates Fig. B.20.</li> <li>Command to be run from <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU">SC_MRU/</a> is</li> </ul> </li> </ul> <div> <pre><code> python3 PlotLoss_DiffNL_H120.py</code></pre> </div> <h2>Part 3: Multiscale analysis</h2> <h3>Trained surrogate models</h3> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/MultiScale/Model">MultiScale/Model</a>: contains the different rnn <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/MultiScale/Model/Bounds_GS">MultiScale/Model/Bounds_GS</a>: Bounds.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/MultiScale/Model/DInpFullModel.pt">MultiScale/Model/DInpFullModel.pt</a>: trained rnn with <code>SC-MRU-T</code> recurrent cell.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/MultiScale/Model/IncrementModel.pt">MultiScale/Model/IncrementModel.pt</a>: trained rnn with <code>SC-MRU-I</code> recurrent cell.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/MultiScale/Model/FullModel.pt">MultiScale/Model/FullModel.pt</a>: trained rnn with <code>SMRU</code> recurrent cell.</li> </ul> </li> </ul> <h3>Run mutiscale simulations using the surrogates</h3> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/MultiScale/2D_MultiScale/model.geo">MultiScale/2D_MultiScale/model.geo</a>: geometry of the macro-scale model that can be read by gmsh (<a href="http://www.gmsh.info" target="_blank" rel="nofollow noreferrer noopener">www.gmsh.info</a>).</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/MultiScale/2D_MultiScale/model.msh">MultiScale/2D_MultiScale/model.msh</a>: mesh of the macro-scale model that can be read by gmsh (<a href="http://www.gmsh.info" target="_blank" rel="nofollow noreferrer noopener">www.gmsh.info</a>).</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/MultiScale/2D_MultiScale/model.py">MultiScale/2D_MultiScale/model.py</a>: is used to run all the multiscale simulation: <ul> <li>This requires cm3Libraries (<a href="http://www.ltas-cm3.ulg.ac.be/openSource.htm" target="_blank" rel="nofollow noreferrer noopener">http://www.ltas-cm3.ulg.ac.be/openSource.htm</a>).</li> <li>Uses the bounds and trained models in <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/MultiScale/Model">MultiScale/Model</a>.</li> <li><code>InpType = cell[0]</code> #choose the recurrent cell typeAmong the different available cells <code>cell=["SC_MRU_T","SC_MRU_I","SMRU"]</code>.</li> <li><code>factorStep= 100</code> # is the number of steps x 20 during the reloading stage between points B and C.</li> <li>The command to be run from the directory <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/MultiScale/2D_MultiScale">MultiScale/2D_MultiScale/</a> is</li> </ul> </li> </ul> <div> <pre><code>python3 model.py</code></pre> </div> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/MultiScale/FE2">MultiScale/FE2</a>: <ul> <li>Contains the reference displacement-force results of the FE2 simulation.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/MultiScale/FE2/Distributions">MultiScale/FE2/Distributions</a>: contains the macro-scale displacement and stress fields. They can be vizualized with gmsh (<a href="http://www.gmsh.info" target="_blank" rel="nofollow noreferrer noopener">www.gmsh.info</a>) using the mesh file <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/MultiScale/FE2/Distributions/model.msh">model.msh</a>.</li> </ul> </li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/MultiScale">MultiScale/S<code>CELL</code>_120_<code>STEPS</code></a>: <ul> <li>Contain the reference displacement-force results of the rnn-based multiscale simulations for the different recurrent cells <code>CELL</code> and steps number <code>STEPS</code> during the reloading stage between points B and C.</li> <li>For the cases <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/MultiScale">MultiScale/S<code>CELL</code>_120_20</a>, the macro-scale displacement and stress fields are also available and can be vizualized with gmsh (<a href="http://www.gmsh.info" target="_blank" rel="nofollow noreferrer noopener">www.gmsh.info</a>) using the mesh file <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/MultiScale/FE2/Distributions/model.msh">model.msh</a>.</li> </ul> </li> </ul> <h3>Vizualize mutiscale simulations results</h3> <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/MultiScale/2D_MultiScale/plot_force.py">MultiScale/2D_MultiScale/plot_force.py</a>: is used to plot the multiscale simulations results: <ul> <li><code>InpType = cell[0]</code> #choose the recurrent cell typeAmong the different available cells <code>cell=["SC_MRU_T","SC_MRU_I","SMRU"]</code>.</li> <li>The multiscale simulations results to be plotted are saved in the directories <code>CELL_120_Step</code>, where <code>Step</code> # is the number of steps during the reloading stage between points B and C.</li> <li>The command to be run from the directory <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/MultiScale/2D_MultiScale">MultiScale/2D_MultiScale/</a> is</li> </ul> </li> </ul> <div> <pre><code>python3 plot_force.py</code></pre> </div> <ul> <li>To vizualize the macro-scale displacement and stress fields distributions: <ul> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/MultiScale/FE2/Distributions">MultiScale/FE2/Distributions</a>: contains the macro-scale displacement and stress fields. They can be vizualized with gmsh (<a href="http://www.gmsh.info" target="_blank" rel="nofollow noreferrer noopener">www.gmsh.info</a>) using the mesh file <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/MultiScale/FE2/Distributions/model.msh">model.msh</a>.</li> <li><a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/MultiScale">MultiScale/S<code>CELL</code>_120_20</a>, the macro-scale displacement and stress fields are also available and can be vizualized with gmsh (<a href="http://www.gmsh.info" target="_blank" rel="nofollow noreferrer noopener">www.gmsh.info</a>) using the mesh file <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/blob/main/MultiScale/FE2/Distributions/model.msh">model.msh</a>.</li> </ul> </li> </ul> <h2>Part 4: Reproduce paper<sup><a href="#fn-1">1</a></sup> figures</h2> <ul> <li>Fig. 7: The command to be run from the diretory <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/TrainingPaths">TrainingPaths</a> is</li> </ul> <div> <pre><code> python3 countPathLength.py '../ConstRVE/PathsExamples'</code></pre> </div> <ul> <li>Fig. 9: The command to be run from the diretory <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU">SC_MRU/</a> is</li> </ul> <div> <pre><code> python3 PlotLoss_HiddenV.py</code></pre> </div> <ul> <li>Fig. 10: The command to be run from the diretory <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU">SC_MRU/</a> is</li> </ul> <div> <pre><code> python3 PlotLoss_3MRU.py</code></pre> </div> <ul> <li>Fig. 11: The command to be run from the diretory <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU">SC_MRU/</a> is</li> </ul> <div> <pre><code> python3 PlotLoss_DiffNL_H120.py</code></pre> </div> <ul> <li>Figs. 12 and 13: The command to be run from the directory <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/CheckLoss">SC_MRU/CheckLoss/</a> is, see <a href="#vizualize-loss-evolution-and-testing-results">details</a></li> </ul> <div> <pre><code> python3 LossVS_insertN.py</code></pre> </div> <ul> <li>Figs. 14, 15 and 16: The command to be run from the directory <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/CheckLoss">SC_MRU/CheckLoss/</a></li> </ul> <div> <pre><code> python3 Plot_GS.py</code></pre> </div> <ul> <li>Figs. 17(b)(c)(d): The command to be run from the directory <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/MultiScale/2D_MultiScale">MultiScale/2D_MultiScale/</a> is, see <a href="#vizualize-mutiscale-simulations-results">details</a>:</li> </ul> <div> <pre><code>python3 plot_force.py</code></pre> </div> <ul> <li>Figs. 18-23: Need gmsh to vizualize the results stored in <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/MultiScale/FE2/Distributions">MultiScale/FE2/Distributions</a> and <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/MultiScale">MultiScale/S<code>CELL</code>_120_20</a>, see <a href="#vizualize-mutiscale-simulations-results">details</a></li> <li>Fig. A.24: The command to be run from the directory <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/CheckLoss">SC_MRU/CheckLoss/</a> is, see <a href="#vizualize-loss-evolution-and-testing-results">details</a></li> </ul> <div> <pre><code> python3 PlotLoss_MGRU.py.py</code></pre> </div> <ul> <li>Fig. B.25: The command to be run from the directory <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/CheckLoss">SC_MRU/CheckLoss/</a> is, see <a href="#vizualize-loss-evolution-and-testing-results">details</a></li> </ul> <div> <pre><code> python3 PlotLoss_NL.py</code></pre> </div> <ul> <li>Fig. B.26: The command to be run from the directory <a href="../didearot/didearotPublic/publicationsData/2024_scmru/-/tree/main/SC_MRU/CheckLoss">SC_MRU/CheckLoss/</a> is, see <a href="#vizualize-loss-evolution-and-testing-results">details</a></li> </ul> <div> <pre><code> python3 PlotLoss_DiffNL_H120.py</code></pre> </div> <h2>Disclaimer</h2> <p>This project has received funding from the European Union’s Horizon Europe Framework Programme under grant agreement No. 101056682 for the project “DIgital DEsign strategies to certify and mAnufacture Robust cOmposite sTructures (DIDEAROT)”. The contents of this publication are the sole responsibility of ULiege and do not necessarily reflect the opinion of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p> <ol> <li> <p>The work is described in:<br>"<em>Wu, L. and Noels, L. (2024).</em> <strong>Self-consistency Reinforced minimal Gated Recurrent Unit for surrogate modeling of history-dependent non-linear problems: application to history-dependent homogenized response of heterogeneous materials</strong> 424: 116881, <a href="https://doi.org/10.1016/j.cma.2024.116881" target="_blank" rel="nofollow noreferrer noopener">doi: 10.1016/j.cma.2024.116881</a>" which can be downloaded. We would be grateful if you could cite this publication in case you use the files. <a href="#fnref-1-5292">↩</a> <a href="#fnref-1-2">↩<sup>2</sup></a> <a href="#fnref-1-3">↩<sup>3</sup></a></p> </li> </ol>
Data from: Landscape heterogeneity drives population structure in four western bumble bee species
<p>Bumble bees are critical pollinators in wild, agricultural, and urban ecosystems—providing the necessary ecological services for food and crop production. In western North America, mountain ranges have high bumble bee species richness. However, as climate change increases temperatures and restricts montane populations to higher elevational spaces, their ability to disperse and maintain genetic diversity decreases. This genetic isolation could lead to the extirpation of local pollinator communities and an overall loss of pollinators. We analyzed the genetic diversity of four broadly sympatric species of bumble bees across the Rocky and Cascade Mountains of western North America to assess habitat isolation's impact on population genetic structure. We expected species restricted to higher elevation habitats to display higher population structure and less genetic diversity across the landscape. We sampled approximately 150 bees per species from seven to eight sites across each species' range. We genotyped bees with an average of 10 loci and used FST and Bayesian Structure analysis to quantify population differentiation. Using isolation by distance and isolation by resistance analyses, species with both narrow and broad habitat suitability requirements showed evidence of habitat suitability restricting gene flow. Although each species showed varying degrees of genetic structure and gene flow, knowing how habitat heterogeneity drives genetic structure and isolation can help guide conservation efforts and determine focal regions for bumble bee conservation in the face of climate change.</p>
Data for: Viral receptor-binding protein evolves new function through mutations that cause trimer instability and functional heterogeneity
<p>When proteins evolve new activity, a concomitant decrease in stability is often observed because the mutations that confer new activity can destabilize the native fold. In the conventional model of protein evolution, reduced stability is considered a purely deleterious cost of molecular innovation because unstable proteins are prone to aggregation and are sensitive to environmental stressors. However, recent work has revealed that non-native, often unstable protein conformations play an important role in mediating evolutionary transitions, raising the question of whether instability can itself potentiate the evolution of new activity. We explored this question in a bacteriophage receptor binding protein (RBP) during host-range evolution. We studied the properties of the RBP of bacteriophage before and after host-range evolution and demonstrated that the evolved protein is relatively unstable and may exist in multiple conformations with unique receptor preferences. Through a combination of structural modeling and in vitro oligomeric state analysis, we found that the instability arises from mutations that interfere with trimer formation. This study raises the intriguing possibility that protein instability might play a previously unrecognized role in mediating host-range expansions in viruses.</p>
Data from the paper "Porphyrin central metal ion driven self-assembling in heterogeneous ZnTPP – CoTPP films grown on Fe(001)-p(1 × 1)O"
Open the record for dataset details and reuse information.
Spike-MD Data for "CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM"
<p>A synthetic cryo-EM dataset with heterogeneity from a molecular dynamics simulation and ground truth atomic models, poses, labels, mask, and consensus volume:</p> <ul> <li>Spike-MD: 100k particle images (256x256, 1.5A/pix) of SARS-CoV-2 spike protein with conformations sampled from a molecular dynamics simulation from Wieczór et al. (2023)</li> <li>sampled_pdbs.xtc: atomic models stitched into a trajectory</li> <li>seed_structure.pdb: initial seed structure for .xtc trajectory</li> </ul>
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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.