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3,339 results for “heterogeneities”
Code: A model of wild bee populations accounting for spatial heterogeneity and climate induced temporal variability of food resources at the landscape level
<p><span>The viability of wild bee populations and the pollination services that they provide are driven by the availability of food resources during their activity period and within the surroundings of their nesting sites. Changes in climate and land use influence the availability of these resources and are major threats to declining bee populations. Because wild bees may be vulnerable to interactions between these threats, spatially explicit models of population dynamics that capture how bee populations jointly respond to land use at a landscape scale and weather are needed. Here, we developed a spatially and temporally explicit theoretical model of wild bee populations aiming for a middle ground between the existing mapping of visitation rates using foraging equations and more refined agent-based modelling. The model is developed for <em>Bombus</em> sp. and captures within-season colony dynamics. The model describes mechanistically foraging at the colony level and temporal population dynamics for an average colony at the landscape level. Stages in population dynamics are temperature-dependent triggered with a theoretical generalized seasonal progression, which can be informed by growing degree days (GDD). The purpose of the LandscapePhenoBee model is to evaluate the impact of systematic changes and within-season variability in resources on bee population sizes and crop visitation rates. In a simulation study, we used the model to evaluate the impact of the shortage of food resources in the landscape arising from extreme drought events in different types of landscapes (ranging from different proportions of semi-natural habitats and early and late flowering crops) on bumblebee populations.</span></p>
Subtyping of common complex diseases and disorders by integrating heterogeneous data. Identifying clusters among women with lower urinary tract symptoms in the LURN study
<p>We present a methodology for subtyping of persons with a common clinical symptom complex by integrating heterogeneous continuous and categorical data. We illustrate it by clustering women with lower urinary tract symptoms (LUTS), who represent a heterogeneous cohort with overlapping symptoms and multifactorial etiology. Data collected in the Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN), a multi-center observational study, included self-reported urinary and non-urinary symptoms, bladder diaries, and physical examination data for 545 women. Heterogeneity in these multidimensional data required thorough and non-trivial preprocessing, including scaling by controls and weighting to mitigate data redundancy, while the various data types (continuous and categorical) required novel methodology using a weighted Tanimoto indices approach. Data domains only available on a subset of the cohort were integrated using a semi-supervised clustering approach. Novel contrast criterion for determination of the optimal number of clusters in consensus clustering was introduced and compared with existing criteria. Distinctiveness of the clusters was confirmed by using multiple criteria for cluster quality, and by testing for significantly different variables in pairwise comparisons of the clusters. Cluster dynamics were explored by analyzing longitudinal data at 3- and 12-month follow-up. Five clusters of women with LUTS were identified using the developed methodology. None of the clusters could be characterized by a single symptom, but rather by a distinct combination of symptoms with various levels of severity. Targeted proteomics of serum samples demonstrated that differentially abundant proteins and affected pathways are different across the clusters. The clinical relevance of the identified clusters is discussed and compared with the current conventional approaches to the evaluation of LUTS patients. The rationale and thought process are described for the selection of procedures for data preprocessing, clustering, and cluster evaluation. Suggestions are provided for minimum reporting requirements in publications utilizing clustering methodology with multiple heterogeneous data domains.</p>
Intratumoural heterogeneity and immune modulation in lung adenocarcinoma of female smokers and never smokers
<p>Count matrix and associated meta data for single nucleus transcriptomics of healthy and tumour lung tissue from lung adenocarcinoma patients. Cohort includes young and elderly, female and male, smokers and never smokers.</p>
The ClaDS rate-heterogeneous birth-death prior for full phylogenetic inference in BEAST2
<p>Bayesian phylogenetic inference requires a tree prior, which models the underlying diversification process which gives rise to the phylogeny. Existing birth-death diversification models include a wide range of features, for instance lineage-specific variations in speciation and extinction rates. While across-lineage variation in speciation and extinction rates is widespread in empirical datasets, few heterogeneous rate models have been implemented as tree priors for Bayesian phylogenetic inference. As a consequence, rate heterogeneity is typically ignored when reconstructing phylogenies, and rate heterogeneity is usually investigated on fixed trees. In this paper, we present a new BEAST2 package implementing the cladogenetic diversification rate shift (ClaDS) model as a tree prior. ClaDS is a birth-death diversification model designed to capture small progressive variations in birth and death rates along a phylogeny. Unlike previous implementations of ClaDS, which were designed to be used with fixed, user-chosen phylogenies, our package is implemented in the BEAST2 framework and thus allows full phylogenetic inference, where the phylogeny and model are co-estimated from a molecular alignment. Our package provides all necessary components of the inference, including a new tree object and operators to propose moves to the MCMC. It also includes a graphical interface through BEAUti. We validate our implementation of the package by comparing the produced distributions to simulated data, and show an empirical example of the full inference, using a cetaceans dataset.</p>
Data and code from: Cooperation and coordination in heterogeneous populations
<p>One landmark application of evolutionary game theory is the study of social dilemmas. This literature explores why people cooperate even when there are strong incentives to defect. Much of this literature, however, assumes that interactions are symmetric. Individuals are assumed to have the same strategic options and the same potential payoffs. Yet many interesting questions arise once individuals are allowed to differ. Here, we study asymmetry in simple coordination games. In our setup, human participants need to decide how much of their endowment to contribute to a public good. If a group's collective contributions reach a pre-defined threshold, all group members receive a reward. To account for possible asymmetries, individuals either differ in their endowments or their productivities. According to our theoretical equilibrium analysis, such games tend to have many possible solutions. In equilibrium, group members may contribute the same amount, different amounts, or nothing at all. According to the behavioral experiment, however, humans favor the equilibrium in which everyone contributes the same proportion of their endowment. We use these experimental results to highlight the nontrivial effects of inequality on cooperation, and we discuss to which extent models of evolutionary game theory can account for these effects.</p>
Dataset for "Imaging of Small-Scale Heterogeneity and Absorption Using Adjoint Envelope Tomography: Results from Laboratory Experiments"
<p>The codes for Monte-Carlo simulation, scripts used to calculate the misfit kernels, and the processed data of the laboratory experiment.</p>
Text-fig. 2. CT slices on Block 2. Details of other skeletal parts (a). The familiar shape of an ammonite (a, c). Holes, cracks and empty cavities in both the limestone matrix and within the vertebrate fossil (b, c). Heterogeneity of the 'tuffeau' limestone, the more porous areas of the matrix clearly distinguishable from the more compact ones (c). Ferric nodules (c). in Hidden Treasures Uncovered: Successful Detection Of Fossils Below The Surface In Large Limestone Blocks Using A Standard Medical X-Ray Ct Scanner
Text-fig. 2. CT slices on Block 2. Details of other skeletal parts (a). The familiar shape of an ammonite (a, c). Holes, cracks and empty cavities in both the limestone matrix and within the vertebrate fossil (b, c). Heterogeneity of the 'tuffeau' limestone, the more porous areas of the matrix clearly distinguishable from the more compact ones (c). Ferric nodules (c).
Fig. 4 in Spatial Heterogeneity Of Steppe Bird Community In The Azov-Black Sea Enclave Of The European Dry-Steppe Zone (Southern Ukraine)
Fig. 4. Distribution of steppe dominants, co-dominants and rare steppe species (Black Book of Ukraine, 2009) by subregions of the dry-steppe enclave: * the largest areas in most count squares are covered by large bodies of water (seas and their bays, limans, the Dnipro floodplain).
Fig. 1 in Spatial Heterogeneity Of Steppe Bird Community In The Azov-Black Sea Enclave Of The European Dry-Steppe Zone (Southern Ukraine)
Fig. 1. Division of the Azov-Black Sea dry-steppe enclave into count squares of 10x10 km and subregions: 1 — RB Prychornomoria, 2 — Lower Dnipro, 3 — LB Prychornomoria, 4 — N Prysyvashshia, 5 — NW Pryazovia, 6 — Syvash, 7 — Western Crimea, 8 — Central Crimea, 9 —Kerch Peninsula, 10 — Foothills.
Fig. 2 in Spatial Heterogeneity Of Steppe Bird Community In The Azov-Black Sea Enclave Of The European Dry-Steppe Zone (Southern Ukraine)
Fig. 2. Similarity of subregions of the dry-steppe enclave in the number of all steppe bird species: 1 — RB Prychornomoria, 2 — Lower Dnipro, 3 — LB Prychornomoria, 4 — N Prysyvashshia, 5 — NW Pryazovia, 6 — Syvash, 7 — Western Crimea, 8 — Central Crimea, 9 —Kerch Peninsula, 10 — Foothills.
Fig. 3 in Spatial Heterogeneity Of Steppe Bird Community In The Azov-Black Sea Enclave Of The European Dry-Steppe Zone (Southern Ukraine)
Fig. 3. Similarity of subregions of the dry-steppe enclave in the number of rare steppe bird species: 1 — RB Prychornomoria, 2 — Lower Dnipro, 3 — LB Prychornomoria, 4 — N Prysyvashshia, 5 — NW Pryazovia, 6 — Syvash, 7 — Western Crimea, 8 — Central Crimea, 9 —Kerch Peninsula, 10 — Foothills.
Data from: Interactions of wood accumulations, channel dynamics, and geomorphic heterogeneity within a river corridor
<p>Natural rivers are inherently dynamic. Spatial and temporal variations in water, sediment, and wood fluxes both cause and respond to an increase in geomorphic heterogeneity within the river corridor. We analyze 16 two-kilometer river corridor segments of the Swan River in Montana, USA to examine relationships between wood accumulations (wood accumulation distribution density, count, and persistence), channel dynamism (total sinuosity and average channel migration), and geomorphic heterogeneity (density, aggregation, interspersion, and evenness of patches in the river corridor). We hypothesize that i) more dynamic river segments correlate with a greater presence, persistence, and distribution of wood accumulations; ii) years with higher peak discharge correspond with greater channel dynamism and wood accumulations; and iii) all river corridor variables analyzed play a role in explaining river corridor spatial heterogeneity. Our results suggest that decadal-scale channel dynamism, as reflected in total sinuosity, corresponds to greater numbers of wood accumulations per surface area and greater persistence of these wood accumulations through time. Second, higher peak discharges correspond to greater values of wood distribution density, but not to greater channel dynamism. Third, persistent values of geomorphic heterogeneity, as reflected in the heterogeneity metrics of aggregation, interspersion, patch density, and evenness, are explained by potential predictor variables analyzed here. Our results reflect the complex interactions of water, sediment, and large wood in river corridors; the difficulties of interpreting causal relationships among these variables through time; and the importance of spatial and temporal analyses of past and present river processes to understand future river conditions</p>
CompanyKG Dataset V2.0: A Large-Scale Heterogeneous Graph for Company Similarity Quantification
<p><strong>CompanyKG</strong> is a heterogeneous graph consisting of 1,169,931 nodes and 50,815,503 undirected edges, with each node representing a real-world company and each edge signifying a relationship between the connected pair of companies.</p> <p><strong>Edges</strong>: We model 15 different inter-company relations as undirected edges, each of which corresponds to a unique edge type. These edge types capture various forms of similarity between connected company pairs. Associated with each edge of a certain type, we calculate a real-numbered weight as an approximation of the similarity level of that type. It is important to note that the constructed edges do not represent an exhaustive list of all possible edges due to incomplete information. Consequently, this leads to a sparse and occasionally skewed distribution of edges for individual relation/edge types. Such characteristics pose additional challenges for downstream learning tasks. Please refer to our paper for a detailed definition of edge types and weight calculations.</p> <p><strong>Nodes</strong>: The graph includes all companies connected by edges defined previously. Each node represents a company and is associated with a descriptive text, such as "<em>Klarna is a fintech company that provides support for direct and post-purchase payments</em> ...". To comply with privacy and confidentiality requirements, we encoded the text into numerical embeddings using four different pre-trained text embedding models: <a href="https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2">mSBERT</a> (multilingual Sentence BERT), <a href="https://platform.openai.com/docs/guides/embeddings/what-are-embeddings">ADA2</a>, <a href="https://github.com/princeton-nlp/SimCSE">SimCSE</a> (fine-tuned on the raw company descriptions) and <a href="https://github.com/EQTPartners/pause">PAUSE</a>.</p> <p><strong>Evaluation Tasks</strong>. The primary goal of CompanyKG is to develop algorithms and models for quantifying the similarity between pairs of companies. In order to evaluate the effectiveness of these methods, we have carefully curated three evaluation tasks:</p> <ul> <li><strong>Similarity Prediction (SP)</strong>. To assess the accuracy of pairwise company similarity, we constructed the SP evaluation set comprising 3,219 pairs of companies that are labeled either as positive (similar, denoted by "1") or negative (dissimilar, denoted by "0"). Of these pairs, 1,522 are positive and 1,697 are negative.</li> <li><strong>Competitor Retrieval (CR)</strong>. Each sample contains one <em>target company</em> and one of its direct competitors. It contains 76 distinct target companies, each of which has 5.3 competitors annotated in average. For a given target company A with <em>N</em> direct competitors in this CR evaluation set, we expect a competent method to retrieve all <em>N</em> competitors when searching for similar companies to A. </li> <li><strong>Similarity Ranking (SR)</strong> is designed to assess the ability of any method to rank <em>candidate companies</em> (numbered 0 and 1) based on their similarity to a <em>query company</em>. Paid human annotators, with backgrounds in engineering, science, and investment, were tasked with determining which candidate company is more similar to the query company. It resulted in an evaluation set comprising 1,856 rigorously labeled ranking questions. We retained 20% (368 samples) of this set as a validation set for model development. </li> <li><strong>Edge Prediction (EP)</strong> evaluates a model's ability to predict future or missing relationships between companies, providing forward-looking insights for investment professionals. The EP dataset, derived (and sampled) from new edges collected between April 6, 2023, and May 25, 2024, includes 40,000 samples, with edges not present in the pre-existing CompanyKG (a snapshot up until April 5, 2023).</li> </ul> <p><strong>Background and Motivation</strong></p> <p>In the investment industry, it is often essential to identify similar companies for a variety of purposes, such as market/competitor mapping and Mergers & Acquisitions (M&A). Identifying comparable companies is a critical task, as it can inform investment decisions, help identify potential synergies, and reveal areas for growth and improvement. The accurate quantification of inter-company similarity, also referred to as <strong>company similarity quantification</strong>, is the cornerstone to successfully executing such tasks. However, company similarity quantification is often a challenging and time-consuming process, given the vast amount of data available on each company, and the complex and diversified relationships among them.</p> <p>While there is no universally agreed definition of company similarity, researchers and practitioners in PE industry have adopted various criteria to measure similarity, typically reflecting the companies' operations and relationships. These criteria can embody one or more dimensions such as industry sectors, employee profiles, keywords/tags, customers' review, financial performance, co-appearance in news, and so on. Investment professionals usually begin with a limited number of companies of interest (a.k.a. seed companies) and require an algorithmic approach to expand their search to a larger list of companies for potential investment. </p> <p>In recent years, transformer-based Language Models (LMs) have become the preferred method for encoding textual company descriptions into vector-space embeddings. Then companies that are similar to the seed companies can be searched in the embedding space using distance metrics like cosine similarity. The rapid advancements in Large LMs (LLMs), such as GPT-3/4 and LLaMA, have significantly enhanced the performance of general-purpose conversational models. These models, such as ChatGPT, can be employed to answer questions related to similar company discovery and quantification in a Q&A format.</p> <p>However, graph is still the most natural choice for representing and learning diverse company relations due to its ability to model complex relationships between a large number of entities. By representing companies as nodes and their relationships as edges, we can form a <strong>Knowledge Graph (KG)</strong>. Utilizing this KG allows us to efficiently capture and analyze the network structure of the business landscape. Moreover, KG-based approaches allow us to leverage powerful tools from network science, graph theory, and graph-based machine learning, such as Graph Neural Networks (GNNs), to extract insights and patterns to facilitate similar company analysis. While there are various company datasets (mostly commercial/proprietary and non-relational) and graph datasets available (mostly for single link/node/graph-level predictions), there is a scarcity of datasets and benchmarks that combine both to create a large-scale KG dataset expressing rich pairwise company relations.</p> <p><strong>Source Code and Tutorial:<br></strong><a href="https://github.com/llcresearch/CompanyKG2"><strong>https://github.com/llcresearch/CompanyKG2</strong></a></p> <p><strong>Paper: to be published<br></strong></p>
Satellite-derived crop field boundaries in heterogeneous smallholder-dominated regions in the North of Mozambique
<p>Satellite-based field delineation has rapidly evolved due to recent advances in machine learning for computer vision. However, the scarcity of labeled data for complex and dynamic smallholder landscapes remains a major bottleneck for operational field delineation and downstream applications, particularly in Sub-Saharan Africa. We here provide reference field boundaries collected in the scope of a research project funded by the F.R.S.-FNRS. A detailed description of the data can be found in our corresponding <a href="https://doi.org/10.48550/arXiv.2312.08384">pre-print</a>.</p> <p>The dataset contains multiple files:</p> <ul> <li>sites.gpkg: Vector dataset containing the sampling sites covered by the dataset. We covered 513 sites of 600x600m, or 36 ha each. </li> <li>human_fields_train.gpkg: 1,518 field delineations distributed across 200 sites. Individual fields were manually digitized based on very high resolution satellite imagery in Google Earth Pro. Each polygon contains the corresponding image acquisition date. These fields were used for model training in the paper. </li> <li>human_fields_test.gpkg: 2,199 field delineations distributed across 313 sites. Individual fields were manually digitized based on very high resolution satellite imagery in Google Earth Pro. Each polygon contains the corresponding image acquisition date. These fields were used for evaluation of all experiments described in the paper. </li> <li>pseudo_fields_train.gpkg: 766 pseudo labels obtained from predictions using a <a href="../doi/10.5281/zenodo.7315089">pre-trained FracTAL ResUNet model</a>. The pseudo-labels correspond to the selection using P99(SemCN). For other sets of pseudo-labels please contact us.</li> </ul> <p><strong>Brief description of methods</strong></p> <p>For <strong>site selection</strong>, we developed a stratified random sampling scheme to sample from regions with actively used cropland. To identify these regions, we used an existing map of active and fallow cropland for the growing season of September 2020 through August 2021 (<a href="https://doi.org/10.1016/j.jag.2022.102937">Rufin et al., 2022</a>). We aggregated the map to a 1 ha grid and calculated the proportions of active cropland. We sampled 1,000 sites from regions mapped as containing at least 50% of active cropland within a one-hectare grid cell. We defined a site extent of 600 by 600 meters, or 36 ha, in order to assure that a sufficient number of fields can be delineated, even in regions with comparatively large field size. The selected sites were screened for VHR image quality and acceptable visibility of at least five fields, resulting in 513 sample sites. </p> <p>For <strong>human labels</strong>, we tasked human annotators to collect sparse labels (i.e. at least five fields) per site. We tasked the interpreters to collect only fields containing non-tree crops by systematically excluding tree crop plantations from our data. While individual trees in the field interior were included in our labels, trees overlapping with the field boundaries were avoided and the tree canopy was considered as the field boundary for completing the labels. All field delineations underwent an iterative quality assessment, where 18% of the initial field delineations and 7% of the field delineations in a second iteration were discarded. </p> <p>The <strong>pseudo labels </strong>provided here were selected using the 1% most confident predictions from the pre-trained model. The confidence scores were computed as the median of all pixel-level field extent probabilities for each field instance. </p> <p>For more details please read the corresponding paper:</p> <p>Rufin, Wang, Lisboa, Hemmerling, Tulbure & Meyfroidt, P. (2023). <em>Taking it further: Leveraging pseudo labels for field delineation across label-scarce smallholder regions.</em> <a href="https://doi.org/10.48550/ARXIV.2312.08384">https://doi.org/10.48550/ARXIV.2312.08384</a></p>
Heterogeneous Biocatalytic Reduction of 5-(Hydroxy)methyl Furfural Using two Co-immobilised Alcohol Dehydrogenases
<p>Biocatalyst heterogenisation may enable robust processes that can be applied in biorefineries to selectively valorise highly functionalised platform chemicals. In this work, we co-immobilise two dehydrogenases and successfully apply them in the selective reduction of 5-hydroxymethylfurfural (HMF) to 2,5-bis(hydroxymethyl) furan (BHMF) with efficient <em>in situ</em> cofactor regeneration. First, we select the best enzyme candidates (an alcohol dehydrogenase from <em>Escherichia coli</em> together with a thermostable glucose dehydrogenase from <em>Bacillus subtilis</em>) and then screen a variety of carriers and chemistries to find the optimal individual immobilisation protocols for each dehydrogenase. As a result, methacrylate carriers (Purolite™) functionalised with either aldehydes or with epoxy and cobalt-chelate groups co-immobilise both enzymes in high yields with a sufficient activity recovery (>20%). These optimal heterogeneous biocatalysts enable the quantitative bio-reduction of HMF to BHMF with >99% selectivity in only fifteen minutes, exhibiting an outstanding reusability of >15 batch cycles with a total volumetric productivity of ∼5 g L<sup>−1</sup> h<sup>−1</sup> of BHMF. Preliminary experiments on a semipreparative scale with HMF loadings of 40 mM also reach high product conversions (86%). Overall, the judicious selection of enzymes, carriers and reaction conditions enables the design of robust biocatalysts that may contribute to paving the way to the valorisation of highly functionalised chemicals in biorefineries.</p>
Dataset related to the publication "Anomalous radiative transfer in heterogeneous media"
<p>This repository contains Monte Carlo results for anomalous light transport in a spherical geometry with homogeneous (<code>Hom</code>), heterogeneous layered (<code>Lay</code>), and homogeneous layered (<code>VHom</code>) configurations.</p> <p>Part of these data is described in the publication:</p> <p>F. Tommasi et al. "Anomalous radiative transfer in heterogeneous media" <em>Advanced Theory and Simulations</em> (2024) <a href="https://doi.org/10.1002/adts.202400182">https://doi.org/10.1002/adts.202400182</a></p> <p><br>Data is organized in two types of csv files:</p> <ul> <li><code>filename.csv</code> containing information on the average path length and total path length distribution</li> <li><code>filename_Fluence.csv</code> containing information on the fluence rate and radiance at each spherical layer boundary</li> </ul> <p>Filenames contain information on the simulation parameters used:</p> <table> <tbody> <tr> <td><code>ISO</code></td> <td>isotropic scattering</td> </tr> <tr> <td><code>K0_X</code></td> <td>value of the k coefficient of the Generalized Pareto Distribution used in the Monte Carlo simulation (<code>X</code> = 0.3 or 0.7)</td> </tr> <tr> <td><code>1e7</code></td> <td>number of total trajectories considered</td> </tr> <tr> <td><code>DS</code>, <code>noDS</code></td> <td>simulation performed used the proposed set of rules for anomalous transport in bounded media (<code>DS</code>) or with the standard set of rules (<code>noDS</code>).</td> </tr> <tr> <td><code>Hom</code></td> <td>homogeneous sphere configuration</td> </tr> <tr> <td><code>NLay</code></td> <td>number of layers used in the layered configurations</td> </tr> <tr> <td><code>mism, no_mism</code></td> <td>configuration with (<code>mism</code>) or without (<code>no_mism</code>) refractive index mismatch with the environment or between layers</td> </tr> <tr> <td><code>Vhom_layN</code></td> <td>layered homogeneous sphere configuration with N layers</td> </tr> <tr> <td><code>mus_const</code>, <code>mus_step</code></td> <td>simulations performed using a constant scattering coefficient (<code>mus_const</code>) or different values across different layers (<code>mus_step</code>)</td> </tr> </tbody> </table> <p> </p> <p>The header of each output file contains general information on the simulation settings as detailed below:</p> <ul> <li>The number of layers</li> <li>The radius of each layer</li> <li>The refractive index of each layer and of the external region</li> <li>The critical angle for entering and exiting the sphere</li> <li>Reduced scattering coefficient of each layer</li> <li>Scattering function</li> <li>Type of illumination source</li> <li>Absorption coefficient of the sphere</li> </ul> <p>Simulations for different scattering strengths are classified based on the optical thickness <code>Taud</code> of the sphere, corresponding to the product between the sphere diameter (10 mm) and the reduced scattering coefficient. Since we have results only for isotropic scattering, <code>Taud</code> is also the product of diameter and scattering coefficient.<br>For a layered sphere, <code>Taud</code> is given by the sum of the products between the scattering coefficient and radial thickness of each layer.<br>The symbol <code>Taua</code> is also used to denote the product between the sphere and the absorption coefficient. Since we have considered only non-absorbing media, this value is always zero.<br>Each simulation is carried out for different values of the reduced scattering coefficient in the layers, keeping their reciprocal ratios fixed, resulting in the following values of Taud: 1E-3, 2E-3, 5E-3, 1E-2, 2E-2, 5E-2, 1E-1, 2E-1, 5E-1, 1, 2, 5, 10, 20, 50, 100, which, in the case of the homogeneous sphere, correspond to reduced scattering coefficients comprised between 1E-4 and 10.<br>The standard error (<code>SE</code>) is also provided, based on the results of 100 independent simulations with 1E5 trajectories each, for a total of 1E7 total trajectories considered.</p> <h3>Structure of the pathlength files (<code>filename.csv</code>)</h3> <p>List of content for the calculated quantities shown:</p> <ul> <li><code>Ksc_Max</code>: maximum number of scattering events in the medium</li> <li><code>de_max(mm)</code>: maximum pathlength followed by photons</li> <li>Number of Photons lost (always zero, all trajectories are collected)</li> <li>Total CW Exiting Radiation and Standard Error (SE)</li> <li>Mean Pathlength for Total Exiting Radiation and SE</li> <li>Solutions of the RTE for the non-absorbing sphere with Lambertian illumination given in terms of the mean pathlength in the sphere and partial pathlength in each layer</li> <li>Partial Mean Pathlengths for in each layer for the non-absorbing case</li> <li>Standard Error on Partial Mean Pathlengths in each layer</li> <li>Discrepancy and SE for the Pathlengths for the non-absorbing case</li> <li>Relative SE is shown for the Partial Mean Pathlengths in the non-absorbing case </li> <li>Total Spread Function (mm-1) versus the pathlength of emerging photons l (mm) for each Taud value</li> </ul> <h3>Structure of the fluence files (<code>filename_Fluence.csv</code>)</h3> <p>List of content for the calculated quantities shown:</p> <ul> <li>Mean pathlength and partial pathlength</li> <li>CW fluence at each layer boundary</li> <li><code>SE</code>: standard error for the CW fluence</li> <li>Discrepancy and SE of the CW fluence with respect to the expected value from the invariance properties. This is reported for each layer interface</li> <li>Relative Error of the CW fluence</li> <li><code>CW FLUX_P</code> at each layer boundary</li> <li>Distribution of CW radiance at each layer boundary for all <code>Taud</code> values. The distribution expected from the invariance property is also listed in the columns adjacent to the Monte Carlo results. Radiance data is reported as a function of the angle at each layer boundary</li> </ul> <p>Additional empty fields in the files refer to output quantities that can be optionally calculated during the simulations. These options were not selected for this study.</p> <p>For convenience, we list below which files were used to prepare each Figure (some files are used multiple times for different Figures):</p> <h3>Figure 2</h3> <p><code>Hom/ISO_Hom_mism_k0_3_1e7_DS.csv</code><br><code>Hom/ISO_Hom_mism_k0_3_1e7_noDS.csv</code><br><code>Hom/ISO_Hom_mism_k0_7_1e7_DS.csv</code><br><code>Hom/ISO_Hom_mism_k0_7_1e7_noDS.csv</code><br><code>Lay/ISO_4Lay_mus_const_k0_3_1e7_DS.csv</code><br><code>Lay/ISO_4Lay_mus_const_k0_3_1e7_noDS.csv</code><br><code>Lay/ISO_4Lay_mus_const_k0_7_1e7_DS.csv</code><br><code>Lay/ISO_4Lay_mus_const_k0_7_1e7_noDS.csv</code><br><code>Lay/ISO_4Lay_mus_step_k0_3_1e7_DS.csv</code><br><code>Lay/ISO_4Lay_mus_step_k0_3_1e7_noDS.csv</code><br><code>Lay/ISO_4Lay_mus_step_k0_7_1e7_DS.csv</code><br><code>Lay/ISO_4Lay_mus_step_k0_7_1e7_noDS.csv</code></p> <h3>Figure 3</h3> <p><code>Lay/ISO_10lay_mism_k0_3_1e7_DS_Fluence.csv</code><br><code>Lay/ISO_10lay_mism_k0_3_1e7_noDS_Fluence.csv</code><br><code>Lay/ISO_10lay_mism_k0_7_1e7_DS_Fluence.csv</code><br><code>Lay/ISO_10lay_mism_k0_7_1e7_noDS_Fluence.csv</code></p> <h3>Figure 4</h3> <p><code>Hom/ISO_Hom_no_mism_k0_3_1e7_DS.csv</code><br><code>Hom/ISO_Hom_no_mism_k0_3_1e7_noDS.csv</code><br><code>Hom/ISO_Hom_no_mism_k0_7_1e7_DS.csv</code><br><code>Hom/ISO_Hom_no_mism_k0_7_1e7_noDS.csv</code><br><code>Hom/ISO_Hom_mism_k0_3_1e7_DS.csv</code><br><code>Hom/ISO_Hom_mism_k0_3_1e7_noDS.csv</code><br><code>Hom/ISO_Hom_mism_k0_7_1e7_DS.csv</code><br><code>Hom/ISO_Hom_mism_k0_7_1e7_noDS.csv</code></p> <h3>Figure 5</h3> <p><code>Hom/ISO_Hom_mism_k0_7_1e7_DS.csv</code><br><code>VHom/ISO_Vhom_lay2_mism_k0_7_1e7_DS.csv</code><br><code>VHom/ISO_Vhom_lay4_mism_k0_7_1e7_DS.csv</code><br><code>VHom/ISO_Vhom_lay8_mism_k0_7_1e7_DS.csv</code><br><code>VHom/ISO_Vhom_lay16_mism_k0_7_1e7_DS.csv</code></p>
The influence of rotation on the preservation of heterogeneities in magma oceans
<p>This dataset contains the data, and codes used to produce the figures, from the upcoming article "The influence of rotation on the preservation of heterogeneities in magma oceans", published in XXX. </p>
Comp-het Data for "CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM"
<p>Synthetic cryo-EM datasets with simulated compositional heterogeneity and their ground truth atomic models, density maps, poses, labels, mask, and consensus volume:</p> <ul> <li>Ribosembly: 335,240 particle images (128x128, 6A/pix) containing 16 bacterial ribosome assembly states from "Cryo-em captures early ribosome assembly in action" from Qin et al. (2023)</li> <li>Tomotwin-100: 100k particle images (128x128, 9A/pix) containing a mixture of 100 different biomolecular complexes from "Tomotwin: generalized 3d localization of macromolecules in cryo-electron tomograms with structural data mining" from Rice et al. (2023)</li> </ul>
Conf-het Data for "CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM"
<p>Synthetic cryo-EM datasets with simulated conformational heterogeneity and their ground truth atomic models, density maps, poses, labels, mask, and consensus volume:</p> <ul> <li>IgG-1D: 100k particle images (128x128, 6A/pix) of IgG with conformations uniformly sampled from a simple one-dimensional continuous circular motion</li> <li>IgG-1D-noisier: The IgG-1D dataset with noise increased from SNR 0.01 to 0.005</li> <li>IgG-1D-noisiest: The IgG-1D dataset with noise increased from SNR 0.01 to 0.001</li> <li>IgG-RL: 100k particle images (128x128, 6A/pix) of IgG with conformations of its flexible linker generated by sampling backbone dihedral angles according to the Ramachandran distributions of disordered peptides</li> </ul>
Fig. 1 in Landscape-B Asin Approach To The Study Of Floristic Diversity (Heterogeneous Catchments Of Steppe And Forest-Steppe Zones Of Altai Krai, Russia, As A Case Study)
Fig. 1. Landscape and floristic regionalization of neighboring the Kasmalinsky basin and the Barnaulka river basin.
ScienceDex guides
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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.