Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,339
datasets available to search
ShareScore release 0.9.0
Dataset results
3,339 results for “heterogeneities”
Data from: Reporting tumor molecular heterogeneity in histopathological diagnosis
Background: Detection of molecular tumor heterogeneity has become of paramount importance with the advent of targeted therapies. Analysis for detection should be comprehensive, timely and based on routinely available tumor samples. Aim: To evaluate the diagnostic potential of targeted multigene next-generation sequencing (TM-NGS) in characterizing gastrointestinal cancer molecular heterogeneity. Methods: 35 gastrointestinal tract tumors, five of each intestinal type gastric carcinomas, pancreatic ductal adenocarcinomas, pancreatic intraductal papillary mucinous neoplasms, ampulla of Vater carcinomas, hepatocellular carcinomas, cholangiocarcinomas, pancreatic solid pseudopapillary tumors were assessed for mutations in 46 cancer-associated genes, using Ion Torrent semiconductor-based TM-NGS. One ampulla of Vater carcinoma cell line and one hepatic carcinosarcoma served to assess assay sensitivity. TP53, PIK3CA, KRAS, and BRAF mutations were validated by conventional Sanger sequencing. Results: TM-NGS yielded overlapping results on matched fresh-frozen and formalin-fixed paraffin-embedded (FFPE) tissues, with a mutation detection limit of 1% for fresh-frozen high molecular weight DNA and 2% for FFPE partially degraded DNA. At least one somatic mutation was observed in all tumors tested; multiple alterations were detected in 20/35 (57%) tumors. Seven cancers displayed significant differences in allelic frequencies for distinct mutations, indicating the presence of intratumor molecular heterogeneity; this was confirmed on selected samples by immunohistochemistry of p53 and Smad4, showing concordance with mutational analysis. Conclusions: TM-NGS is able to detect and quantitate multiple gene alterations from limited amounts of DNA, moving one step closer to a next-generation histopathologic diagnosis that integrates morphologic, immunophenotypic, and multigene mutational analysis on routinely processed tissues, essential for personalized cancer therapy.
MAG for Heterogeneous Graph Learning
<p>We provide an academic graph based on a snapshot of the <strong>Microsoft Academic Graph</strong> from <strong>26.05.2021.</strong> The Microsoft Academic Graph (MAG) is a large-scale dataset containing information about scientific publication records, their citation relations, as well as authors, affiliations, journals, conferences and fields of study. We acknowledge the Microsoft Academic Graph using the URI <a href="https://aka.ms/msracad">https://aka.ms/msracad</a>. For more information regarding schema and the entities present in the original dataset please refer to: <a href="https://docs.microsoft.com/en-us/academic-services/graph/reference-data-schema">MAG schema</a>. </p> <p><strong>MAG for Heterogeneous Graph Learning</strong><br> We use a recent version of MAG from May 2021 and extract all relevant entities to build a graph that can be directly used for heterogeneous graph learning (node classification, link prediction, etc.). The graph contains all English papers, published after 1900, that have been cited at least 5 times per year since the time of publishing. For fairness, we set a constant citation bound of 100 for papers published before 2000. We further include two smaller subgraphs, one containing <em>computer science</em> papers and one containing <em>medicine</em> papers. </p> <p><em><strong>Nodes and features</strong></em><br> We define the following nodes:</p> <ul> <li><strong>paper </strong>with mag_<em>id, graph_id, </em><em>normalized title</em>, <em>year of publication</em>, <em>citations </em>and a 128-dimension <em>title embedding </em>built using word2vec<br> No. of papers:<em> 5,091,690</em> (all), <em>1,014,769 </em>(medicine), <em>367,576 </em>(computer science);</li> <li><strong>author </strong>with mag_<em>id, graph_id, normalized name, citations</em><br> No. of authors: <em>6,363,201 </em>(all), <em>1,797,980 </em>(medicine), <em>557,078 </em>(computer science);</li> <li><strong>field </strong>with mag_<em>id, graph_id, level, citations </em>denoting the hierarchical level of the field where 0 is the highest-level (e.g. <em>computer science</em>)<br> No. of fields: <em>199,457 </em>(all), <em>83,970 </em>(medicine), <em>45,454 </em>(computer science);</li> <li><strong>affiliation </strong>with mag_<em>id, graph_id, citations</em><br> No. of affiliations: <em>19,421 </em>(all), <em>12,103 </em>(medicine), <em>10,139 </em>(computer science); </li> <li><strong>venue </strong>with mag_<em>id, graph_id, citations, type</em> denoting whether conference or journal<br> No. of venues: <em>24,608 </em>(all), <em>8,514 </em>(medicine), <em>9,893 </em>(computer science). </li> </ul> <p><em><strong>Edges</strong></em><br> We define the following edges:</p> <ul> <li><strong>author </strong><em>is_affiliated_with</em><strong> affiliation</strong><br> No. of author-affiliation edges: <em>8,292,253 </em>(all), <em>2,265,728 </em>(medicine), <em>665,931 </em>(computer science);</li> <li><strong>author </strong><em>is_first/last/other </em><strong>paper</strong><br> No. of author-paper edges: <em>24,907,473 </em>(all), <em>5,081,752 </em>(medicine), <em>1,269,485 </em>(computer science);</li> <li><strong>paper </strong><em>has_citation_to</em><strong> paper</strong><br> No. of author-affiliation edges: <em>142,684,074 </em>(all), <em>16,808,837 </em>(medicine), <em>4,152,804 </em>(computer science);</li> <li><strong>paper </strong><em>conference/journal_published_at</em><strong> venue</strong><br> No. of author-affiliation edges: <em>5,091,690 </em>(all), <em>1,014,769 </em>(medicine), <em>367,576 </em>(computer science);</li> <li><strong>paper </strong><em>has_field_L0/L1/L2/L3/L4</em><strong> field</strong><br> No. of author-affiliation edges: <em>47,531,366 </em>(all), <em>9,403,708 </em>(medicine), <em>3,341,395 </em>(computer science);</li> <li><strong>field</strong><em> is_in </em><strong>field</strong><br> No. of author-affiliation edges: <em>339,036 </em>(all), <em>138,304 </em>(medicine), <em>83,245 </em>(computer science);</li> </ul> <p>We further include a reverse edge for each edge type defined above that is denoted with the prefix <em>rev_ </em>and can be removed based on the downstream task. </p> <p><strong>Data structure</strong><br> The nodes and their respective features are provided as separate <em>.tsv</em> files where each feature represents a column. The edges are provided as a pickled python dictionary with schema:</p> <pre><code class="language-python">{target_type: {source_type: {edge_type: {target_id: {source_id: {time } } } } } }</code></pre> <p>We provide three compressed ZIP archives, one for each subgraph (all, medicine, computer science), however we split the file for the complete graph into 500mb chunks. Each archive contains the separate node features and edge dictionary.</p>
Data from: The genetics of adaptation to discrete heterogeneous environments: frequent mutation or large-effect alleles can allow range expansion
Range expansions are complex evolutionary and ecological processes. From an evolutionary standpoint, a populations' adaptive capacity can determine the success or failure of expansion. Using individual-based simulations, we model range expansion over a two-dimensional, approximately continuous landscape. We investigate the ability of populations to adapt across patchy environmental gradients and examine how the effect sizes of mutations influence the ability to adapt to novel environments during range expansion. We find that genetic architecture and landscape patchiness both have the ability to change the outcome of adaptation and expansion over the landscape. Adaptation to new environments succeeds via many mutations of small effect or few of large effect, but not via the intermediate between these cases. Higher genetic variance contributes to increased ability to adapt, but an alternative route of successful adaptation can proceed from low genetic variance scenarios with alleles of sufficiently large effect. Steeper environmental gradients can prevent adaptation and range expansion on both linear and patchy landscapes. When the landscape is partitioned into local patches with sharp changes in phenotypic optimum, the local magnitude of change between subsequent patches in the environment determines the success of adaptation to new patches during expansion.
Data from: Integrated molecular imaging reveals tissue heterogeneity driving host-pathogen interactions
All diseases are characterized by distinct changes in tissue molecular distribution. Molecular analysis of intact tissues traditionally requires pre-existing knowledge of, and reagents for, the targets of interest. Conversely, label-free discovery of disease-associated tissue analytes requires destructive processing for downstream identification platforms. Tissue-based analyses therefore sacrifice discovery to gain spatial distribution of known targets, or sacrifice tissue architecture for discovery of unknown targets. To overcome these obstacles, we developed a multi-modality imaging platform for discovery-based molecular histology. We apply this platform to a model of disseminated infection triggered by the important pathogen Staphylococcus aureus, leading to the discovery of infection-associated alterations in the distribution and abundance of proteins and elements in tissue. These data provide an unbiased, three-dimensional analysis of how disease impacts the molecular architecture of complex tissues, enable culture-free diagnosis of infection through imaging-based detection of bacterial and host analytes, and reveal molecular heterogeneity at the host-pathogen interface.
Dataset used in "Estimating Dispersion Coefficient in Flow Through Heterogeneous Porous Media by a Deep Convolutional Neural Network" by Kamrava et al. in Geophysical Research Letters.
<p>Morphology of Heterogeneous Porous Media</p>
Mechanical heterogeneity along single cell-cell junctions is driven by lateral clustering of cadherins during vertebrate axis elongation
<p>Morphogenesis is governed by the interplay of molecular signals and mechanical forces across multiple length scales. The last decade has seen tremendous advances in our understanding of the dynamics of protein localization and turnover at sub-cellular length scales, and at the other end of the spectrum, of mechanics at tissue-level length scales. Integrating the two remains a challenge, however, because we lack a detailed understanding of the subcellular patterns of mechanical properties of cells within tissues. Here, in the context of the elongating body axis of <i>Xenopus</i> embryos, we combine tools from cell biology and physics to demonstrate that individual cell-cell junctions display finely-patterned local mechanical heterogeneity along their length. We show that such local mechanical patterning is essential for the cell movements of convergent extension and is imparted by locally patterned clustering of a classical cadherin. Finally, the patterning of cadherins and thus local mechanics along cell-cell junctions are controlled by Planar Cell Polarity signaling, a key genetic module for CE that is mutated in diverse human birth defects.</p>
HeteroFuzz: Fuzz Testing to Detect Platform Dependent Divergence for Heterogeneous Applications
<p>HeteroFuzz supplementary material.</p>
Data from: Grazing and nitrogen addition restructure the spatial heterogeneity of soil microbial community structure and enzymatic activities
<p>1. In grassland ecosystems, large herbivorous animal grazing activity and increasing nitrogen deposition strongly alters microbial community structure and function. Understanding the effects of grazing and nitrogen addition on the spatial heterogeneity in soil microbial community structure, enzymatic activities and the underlying mechanisms are crucial for making better predictions of soil organic matter dynamics and nutrient cycling. </p> <p>2. We examined the spatial heterogeneity of soil microbial community structure and enzymatic activity associated with changes in soil microclimate, soil characteristics, plant biomass and soil nutrient responses to grazing and nitrogen addition using a manipulative experiment with control (CK), grazing (G), nitrogen addition (N) and grazing plus nitrogen addition (NG) treatments in a <i>Leymus chinensis </i>meadow steppe, in northeastern China. </p> <p>3. The results demonstrated that soil microbial community structure and enzymatic activities showed a high level of spatial dependence [C/(C + C0)≥0.9] in the CK plot. G, N and NG treatments not only reduced the spatial variability ofsoil microbial community structure and enzymatic activities, but also reshaped the spatial links between enzymes activities and microbial community structure. Litter biomass, soil temperature and soil nutrients (soil dissolved inorganic nitrogen or soil dissolved organic carbon) explained 21-27% of the spatial variability of soil microbial community structure in the CK treatment and pH was the strongest driver for the spatial variability of soil enzymatic activities. Meanwhile, the homogenization in soil water content induced by the N addition treatment was a determinant of the reduction in spatial heterogeneity of the microbial community structure. The combination of soil physicochemical properties (bulk density, soil pH and soil dissolved inorganic nitrogen), soil temperature and root biomass explained 32-43% of the spatial variability of the microbial community structure in the G treatment, and N and G treatments had additive effects on the spatial heterogeneity of total PLFAs by homogenizing root biomass. Plant biomass and microbial community structure were the major drivers for the spatial heterogeneity of enzymatic activities under G, N and NG. In NG, the change in spatial variability of enzymatic activities was dominated by N addition. Regardless of grazing, N addition facilitated the spatial correlation between microbial community structure and enzyme activities. </p> <p>4. Overall, our results revealed the drivers of soil microbial community structure and enzymatic activities spatial pattern shift due to grazing and N addition, highlighting the role that spatial variability in soil microbial community structure and enzymatic activities has on the <i>L. chinensis</i> meadow steppe.</p>
Optimization-based upscaling for gravity segregation with 3D capillary heterogeneity effects
<p>Fine-scale and upscaled simulation input files and MATLAB codes for optimization</p>
Relative influence of predators, competitors, and seascape heterogeneity on behaviour and abundance of coral reef mesopredators
<p>Determining influences of predation and competition on community dynamics is particularly challenging in coral reef systems where interspecific interactions between many predator and prey species play out in patchy landscapes. We used ~1000 stereo-Baited Remote Underwater Video deployments (Stereo-BRUVs) to assess the relative abundance and analysed the behaviour of two size classes of mesopredatory teleosts (lutajnids, serranids, lethrinids) in the presence and absence of larger predators (mesopredatory and apex carcharhinids). For mesopredatory teleosts, the presence of sharks did not influence the abundance, time of arrival in vicinity of the stereo-BRUVs, the probability of feeding on bait, or the delay to feeding. Instead, the number of similar-sized competitors and surrounding habitat features were the strongest drivers of these behavioural metrics. We suggest that for most fishes, the predatory threat posed by highly mobile species such as sharks is likely to be sporadic and transitory, whereas competition is ubiquitous and ever present, particularly for schooling taxa. Ultimately, it is likely that both processes interact to determine behavioural phenotypes as individuals that are inferior competitors can be displaced from safe habitats or prohibited from access to resources and will be more susceptible to predation. Future studies should consider the relative effects of both processes and the degree to which each can be shaped by habitat when investigating trophic dynamics that regulate marine communities.</p>
Data from: Elements of metacommunity structure of diatoms and macroinvertebrates within stream networks differing in environmental heterogeneity
<p><strong>Aim:</strong> Idealized metacommunity structures (i.e. checkerboard, random, quasi-structures, nested, Clementsian, Gleasonian, and evenly spaced) have recently gained increasing attention, but their relationships with environmental heterogeneity and how they vary with organism groups remain poorly understood. Here we tested two main hypotheses: (1) gradient-driven patterns (Clementsian and Gleasonian) occur frequently in heterogeneous environments, and (2) small organisms (here, diatoms) are more likely to exhibit gradient-driven patterns than large organisms (here, macroinvertebrates).</p> <p><strong>Location:</strong> Streams in three regions in China.</p> <p><strong>Taxon:</strong> Diatoms and macroinvertebrates.</p> <p><strong>Methods:</strong> The stream diatom and macroinvertebrate data, as well as the environmental data collected from the same set of sites were used to examine the idealized metacommunity structures via the elements of the metacommunity structure (EMS; coherence, turnover, and boundary clumping) analysis in three regions. We extended the traditional EMS approach by ordering sites along known environmental gradients.</p> <p><strong>Results: </strong>We found that Clementsian structure with high degrees of coherence and turnover, and significantly positive clumping was typically observed in the high-heterogeneity regions, whereas randomness was prevalent in the low-heterogeneity region. Macroinvertebrates exhibited clearer Clementsian structures compared with diatoms, while diatoms showed more randomness compared with macroinvertebrates, indicating a stronger role of environmental filtering for macroinvertebrates than diatoms. In most cases, the results of the more novel EMS approach differed from the results of the traditional EMS technique.</p> <p><strong>Main Conclusions:</strong> Our results suggested that the occurrence of different metacommunity structures may be related with the degree of regional environmental heterogeneity. However, diatom metacommunities were more random than those of macroinvertebrate, and such an unexpected result may result from different dispersal abilities between the two organism groups. In addition, we found that the novel EMS approach increased power in discerning metacommunity structure in comparison to the traditional EMS technique.</p>
Data for: Continuous API Evolution in Heterogenous Enterprise Software Systems
<p>The ability to independently deploy parts of a software system is one of the cornerstones of modern software development, and allows for these parts to evolve independently and at different speeds.</p> <p>A major challenge of such independent deployment, however, is to ensure that despite their individual evolution, the interfaces between interacting parts remain compatible. This is especially important for enterprise software systems, which are often highly integrated and based on heterogenous IT infrastructures.</p> <p>Although several approaches for interface evolution have been proposed, many of these rely on the developer to adhere to certain rules, but provide little guidance for doing so. In this paper, we present an approach for interface evolution that is easy to use for developers, and also addresses typical challenges of heterogenous enterprise software, especially legacy system integration.</p> <p>This dataset contains the questions and results from the survey among developers to roughly assess the applicability of the approach in practice.</p>
Inter and Intra-tumour Heterogeneity of Paediatric-type Diffuse High-Grade Glioma Revealed by Single-Cell Mass Cytometry
<p>raw data (fcs file) from mass cytometry experiments </p>
A Pyrrolidine Functionalized Poly[(ethylene glycol) Methacrylate] Resin as a Heterogeneous Catalyst for Aqueous Aldol Reactions
<p>This repository includes experimental data associated with the following publication: Biesemans, B.; Aljammal, N.; Radhakrishnan, S.; Breynaert, E.; Stevens, C.V.; Lauwaert, J.; Thybaut, J.W. A Pyrrolidine Functionalized Poly[(ethylene glycol) Methacrylate] Resin as a Heterogeneous Catalyst for Aqueous Aldol Reactions. Catalysts 2022, 12, 1389. https://doi.org/10.3390/catal12111389<br> In this study, a pyrrolidine group (AMP) was functionalized on a polymer resin (PEGMA). The synthesis procedure consists of suspension polymerization of the PEGMA resin, introduction of Cl groups (PEGMA-Cl), substitution of Cl groups by Boc-protected pyrrolidine groups (PEGMA-AMP-Boc) and deprotection of the pyrrolidine active sites (PEGMA-AMP). Three batches of PEGMA-AMP catalyst were synthesized with different loadings:<br> - PEGMA-AMP-1 with 0.054 mmol amines/g catalyst;<br> - PEGMA-AMP-2 with 0.190 mmol amines/g catalyst;<br> - PEGMA-AMP-3 with 0.270 mmol amines/g catalyst.</p> <p><br> The resulting PEGMA-AMP catalyst was characterized by <sup>1</sup>H- and <sup>13</sup>C-NMR, FTIR, Elemental Analysis, and a swelling assessment. The catalyst was furthermore assessed as aldol condensation catalyst in the aldol condensation of 4-nitrobenzaldehyde with acetone at 55 °C in a 50/50 vol% mixture of either acetone/water or acetone/n-hexane. The resulting data is presented in the article in both figures and tables.</p> <p>The <sup>1</sup>H- and <sup>13</sup>C-NMR data are available via https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YQ4FYJ</p>
Data from "Selective Electrochemical Reductive Amination of Benzaldehyde at Heterogeneous Metal Surfaces"
<p>Data including DFT outputs, NMR peak areas, currents, voltages, etc. as well as some analysis.</p>
Cellular and molecular heterogeneities and signatures, and pathological trajectories of fatal COVID-19 lungs defined by spatial single-cell transcriptome analysis
<p>Spatial in-situ data analysis.</p>
Figures: The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data
<p>Python figures in pickle format</p>
Raw data of "Heterogeneous integration of superconducting thin films and epitaxial semiconductor heterostructures with Lithium Niobate"
<p>Raw data used to produce figures</p>
Stable chromium isotope fractionation during melt percolation: implication for chromium isotope heterogeneity in the mantle
<p>The Dataset includes major, trace elements, and Cr isotopic data of ultramafic rocks from Baldissero and Balmuccia peridotite massifs in the Italian Alps.</p>
Raw data - Digital scans - Sepsis Induces Heterogeneous Transcription of Coagulation- and Inflammation-Associated Genes in Renal Microvasculature
<p>Raw data of the manuscript "<em>Sepsis Induces Heterogeneous Transcription of Coagulation- and Inflammation-Associated Genes in Renal Microvasculature</em><em>"</em> as published in Thrombosis Research (<a href="https://doi.org/10.1016/j.thromres.2024.03.014" target="_blank" rel="noopener">doi.org/10.1016/j.thromres.2024.03.014</a>) is included. Types of included data are:</p> <p> </p> <p>- NDPI files of digital scans showing immunohistochemical (IHC) protein staining in mouse kidney</p> <p>- NDPI files of digital scans showing Martius Scarlet Blue staining (MSB) in mouse kidney</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.