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750 results for “heterogeneous data”
Supplementary data from: Inherent single-cell heterogeneity of the transcriptional response to hypoxia in cancer cells
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Using single-worm data to quantify heterogeneity in Caenorhabditis elegans-bacterial interactions
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Data and code from: Coordinated distributed experiments in ecology do not consistently reduce heterogeneity in effect size
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Data from: Crop and landscape heterogeneity increase biodiversity in agricultural landscapes: A global review and meta-analysis
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Data from: Long-term impacts of changed grazing regimes on the vegetation of heterogeneous upland grasslands
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Subtyping of common complex diseases and disorders by integrating heterogeneous data. Identifying clusters among women with lower urinary tract symptoms in the LURN study
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Data from: Tall, heterogenous forests improve prey capture, delivery to nestlings, and reproductive success for Spotted Owls in southern California
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Data from: Interactions of wood accumulations, channel dynamics, and geomorphic heterogeneity within a river corridor
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Data from: Counteracting cascades challenge the heterogeneity – stability relationship
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Data from: Influence of environmental heterogeneity on genetic diversity and structure in an endemic southern Californian oak
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Data from: Determining the optimal movement strategies in environments with heterogeneously distributed resources and toxicants
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Data from: Using genetic relatedness to understand heterogeneous distributions of urban rat-associated pathogens
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Data from: Evaluating the importance of individual heterogeneity in reproduction to Weddell seal population dynamics using integral projection models
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Data and code from: Cooperation and coordination in heterogeneous populations
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Eight Mile Lake Research Watershed, Thaw Gradient, Ecosystem carbon balance: Eddy covariance CO2 flux data of a heterogenous landscape undergoing permafrost thaw.
In this larger study, we are asking the question: Is old carbon that comprises the bulk of the soil organic matter pool released in response to thawing of permafrost? We are answering this question by using a combination of field and laboratory experiments to measure radiocarbon isotope ratios in soil organic matter, soil respiration, and dissolved organic carbon, in tundra ecosystems. The objective of these proposed measurements is to develop a mechanistic understanding of the SOM sources contributing to C losses following permafrost thawing. We are making these measurements at an established tundra field site near Healy, Alaska in the foothills of the Alaska Range. Field measurements center on a natural experiment where permafrost has been observed to warm and thaw over the past several decades. This area represents a gradient of sites each with a different degree of change due to permafrost thawing. As such, this area is unique for addressing questions at the time and spatial scales relevant for change in arctic ecosystems. Understanding how landscape level physical and biological changes effect carbon cycling is important for estimating the carbon balance of an ecosystem undergoing permafrost thaw.
Data, plotting scripts, and figures for "Applying the swept rule for explicit partial differential equation solutions on heterogeneous computing systems"
<p>This dataset contains the figures, as well as the necessary plotting scripts and data to reproduce them, for the article "Applying the swept rule for explicit partial differential equation solutions on heterogeneous computing systems" by Daniel J. Magee, Anthony S. Walker, and Kyle E. Niemeyer (2020).</p> <p>The plotting scripts were all run using Python 3.7.6, and should be compatible with >3.6, but this has not been tested.</p> <p>The code included in this dataset is released under the BSD 3-Clause License. The figures are shared under the Creative Commons Attribution 4.0 International License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).</p>
Data from: The evolution of parasite host range in heterogeneous host populations
<p>Theory on the evolution of niche width argues that resource heterogeneity selects for niche breadth. For parasites, this theory predicts that parasite populations will evolve, or maintain, broader host ranges when selected in genetically diverse host populations relative to homogeneous host populations. To test this prediction, we selected the bacterial parasite Serratia marcescens to kill Caenorhabditis elegans in populations that were genetically heterogeneous (50% mix of two experimental genotypes) or homogeneous (100% of either genotype). After 20 rounds of selection, we compared the host range of selected parasites by measuring parasite fitness (i.e. virulence, the selected fitness trait) on the two focal host genotypes and on a novel host genotype. As predicted, heterogeneous host populations selected for parasites with a broader host range: these parasite populations gained or maintained virulence on all host genotypes. This result contrasted with selection in homogeneous populations of one host genotype. Here, host range contracted, with parasite populations gaining virulence on the focal host genotype and losing virulence on the novel host genotype. This pattern was not, however, repeated with selection in homogeneous populations of the second host genotype: these parasite populations did not gain virulence on the focal host genotype, nor did they lose virulence on the novel host genotype. Our results indicate that host heterogeneity can maintain broader host ranges in parasite populations. Individual host genotypes, however, vary in the degree to which they select for specialization in parasite populations.</p>
Thermophysical properties and surface heterogeneity of landing sites on Mars from overlapping THEMIS observations-Data
<p>Files, scripts/functions, observed temperature data, and modeled results used in the Ahern et al. paper entitled: Thermophysical properties and surface heterogeneity of landing sites on Mars from overlapping THEMIS observations.</p>
Data accompanying "Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites"
<p>This dataset contains the spatial data underlying the statistical analysis in:</p> <p><em>Assmann et al. (in press) - Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites</em></p> <p>Together with the code and tabular data contained in <a href="https://github.com/jakobjassmann/qhi_phen_ts/">https://github.com/jakobjassmann/qhi_phen_ts/</a> the data in this repository are required to reproduce the figures, tables and statistics reported in the manuscript.</p> <p>This dataset consists of two components:</p> <ol> <li> <p>Multispectral drone observations from the growing seasons 2016 and 2017 for 8 study plots on Qikiqtaruk - Herschel Island in Canada collected with Parrot Sequioa sensors (62 sets of multispectral orhomosaics in total).</p> </li> <li> <p>Post-porcessed Sentinel-2 MSI L2A scenes covering the same 8 study plots, including all scenes for which the area of the plots and their immediate surroundings were cloud free between May and September in 2016 and 2017.</p> </li> </ol> <p>-----------------------------------------------------------------------------------------------------------------</p> <p><strong>Citation</strong>: Jakob J. Assmann, Isla H. Myers-Smith, Jeffrey T. Kerby, Andrew M. Cunliffe and Gergana N. Daskalova.<em> <strong>In press</strong>.</em> Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites. <a href="https://doi.org/10.32942/osf.io/tqekn">https://doi.org/10.32942/osf.io/tqekn</a></p> <p><strong>Legal notice:</strong> This dataset contains modified Copernicus Sentinel [2016, 2017] data.</p> <p><strong>Acknowledgements (from the manuscript):</strong></p> <p>We would like to thank the Team Shrub field crews of the 2016 and 2017 field seasons for their hard work and effort invested in collecting the data presented in this research, this includes Will Palmer, Santeri Lehtonen, Callum Tyler, Sandra Angers-Blondin and Haydn Thomas. Furthermore, we would like to thank Tom Wade and Simon Gibson-Poole from the University of Edinburgh Airborne GeoSciences Facility, as well as Chris McLellan and Andrew Gray from the NERC Field Spectroscopy Facility for their support in our drone endeavours. We also want to express our gratitude to Ally Phillimore, Ed Midchard, Toke Høye and two anonymous reviewers for providing feedback on earlier versions of this manuscript. Lastly, JJA would like to thank IMS, Ally Phillimore and Richard Ennos for academic mentorship throughout his PhD.</p> <p>We thank the Herschel Island—Qikiqtaruk Territorial Park Team and Yukon Government for providing logistical support for our field research on Qikiqtaruk including: Richard Gordon, Cameron Eckert and the park rangers Edward McLeod, Sam McLeod, Ricky Joe, Paden Lennie and Shane Goosen. We thank the research group of Hugues Lantuit at the Alfred Wegener Institute and the Aurora Research Institute for logistical support. Research permits include Yukon Researcher and Explorer permits (16-48S&E and 17-42S&E) and Yukon Parks Research permits (RE-Inu-02-16 and 17-RE-HI-02). All airborne activities were licensed under the Transport Canada special flight operations certificates ATS 16-17-00008441 RDIMS 11956834 (2016) and ATS 16-17-00072213 RDIMS 12929481 (2017).</p> <p>Funding for this research was provided by NERC through the ShrubTundra standard grant (NE/M016323/1), a NERC E3 Doctoral Training Partnership PhD studentship for Jakob Assmann (NE/L002558/1), a research grant from the National Geographic Society (CP-061R-17), a Parrot Climate Innovation Grant, the Aarhus University Research Foundation, and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement (754513) for Jeffrey Kerby, a NERC support case for use of the NERC Field Spectroscopy Facility (738.1115), equipment loans from the University of Edinburgh Airborne GeoSciences Facility and the NERC Geophysical Equipment Facility (GEF 1063 and 1069).</p> <p>Finally, we would like to thank the Inuvialuit people for the opportunity to conduct research in the Inuvialuit Settlement Region.</p>
Data from: Integration and harmonization of trait data from plant individuals across heterogeneous sources
<p>Trait data represent the basis for ecological and evolutionary research and have relevance for biodiversity conservation, ecosystem management and earth system modelling. The collection and mobilization of trait data has strongly increased over the last decade, but many trait databases still provide only species-level, aggregated trait values (e.g. ranges, means) and lack the direct observations on which those data are based. Thus, the vast majority of trait data measured directly from individuals remains hidden and highly heterogeneous, impeding their discoverability, semantic interoperability, digital accessibility and (re-)use. Here, we integrate quantitative measurements of verbatim trait information from plant individuals (e.g. lengths, widths, counts and angles of stems, leaves, fruits and inflorescence parts) from multiple sources such as field observations and herbarium collections. We develop a workflow to harmonize heterogeneous trait measurements (e.g. trait names and their values and units) as well as additional information related to taxonomy, measurement or fact and occurrence. This data integration and harmonization builds on vocabularies and terminology from existing metadata standards and ontologies such as the Ecological Trait-data Standard (ETS), the Darwin Core (DwC), the Thesaurus Of Plant characteristics (TOP) and the Plant Trait Ontology (TO). A metadata form filled out by data providers enables the automated integration of trait information from heterogeneous datasets. We illustrate our tools with data from palms (family Arecaceae), a globally distributed (pantropical), diverse plant family that is considered a good model system for understanding the ecology and evolution of tropical rainforests. We mobilize nearly 140,000 individual palm trait measurements in an interoperable format, identify semantic gaps in existing plant trait terminology and provide suggestions for the future development of a thesaurus of plant characteristics. Our work thereby promotes the semantic integration of plant trait data in a machine-readable way and shows how large amounts of small trait data sets and their metadata can be integrated into standardized data products.</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.