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1,221 results for “Aggregators”
Leaf area predicts conspecific spatial aggregation of woody species
<p><strong>Aim:</strong> Addressing how woody plant species are distributed in space can reveal inconspicuous drivers that structure plant communities. The spatial structure of conspecifics varies not only at local scales across co-existing plant species but also at larger biogeographical scales with climatic parameters and habitat properties. The possibility that biogeographical drivers shape the spatial structure of plants, however, has not received sufficient attention.</p> <p><strong>Location:</strong> Global synthesis.</p> <p><strong>Time period:</strong> 1997 - 2022.</p> <p><strong>Major taxa studied:</strong> Woody angiosperms and conifers.</p> <p><strong>Methods:</strong> We carried out a quantitative synthesis to capture the interplay between local scale and larger scale drivers. We modelled conspecific spatial aggregation as a binary response through logistic models and Ripley's L statistics and the distance at which the point process was least random with mixed effects linear models. Our predictors covered a range of plant traits, climatic predictors and descriptors of the habitat.</p> <p><strong>Results:</strong> We hypothesized that plant traits, when summarized by local scale predictors, exceed in importance biogeographical drivers in determining the spatial structure of conspecifics across woody systems. This was only the case in relation to the frequency with which we observe aggregated distributions. The probability of observing spatial aggregation and the intensity of it was higher for plant species with large leaves but further depended on climatic parameters and mycorrhiza.</p> <p><strong>Main Conclusions:</strong> Compared to climatic variables, plant traits perform poorly in explaining the spatial structure of woody plant species, even though leaf area is a decisive plant trait that is related to whether we observe homogenous spatial aggregation and its intensity. Despite the limited variance explained by our models, we found that the spatial structure of woody plants is subject to consistent biogeographical constraints and that these exceed beyond descriptors of individual species, which we captured here through leaf area.</p>
Data Files for "Subsidies for Close Substitutes: Aggregate Demand for Residential Solar Electricity"
<p>Data files for "Subsidies for Close Substitutes: Aggregate Demand for Residential Solar Electricity" [https://doi.org/10.1016/j.euroecorev.2024.104848]. Findings of the paper can be replicated using these data files, along with code at https://github.com/xabajian/AP_Solar/. Please contact Alexander Abajian <xander.abajian@gmail.com> with any questions regarding the enclosed files.</p>
Fig. 2b in Aggregation and negative interactions in low-diversity and unsaturated monogenean (Platyhelminthes) communities in Astyanax aeneus (Teleostei) populations in a neotropical river of Mexico
Fig. 2b. Resemblance (Jaccard index) between components of community (August).
Fig. 2a in Aggregation and negative interactions in low-diversity and unsaturated monogenean (Platyhelminthes) communities in Astyanax aeneus (Teleostei) populations in a neotropical river of Mexico
Fig. 2a. Resemblance (Jaccard index) between components of community (February).
An audit of some processing effects in aggregated occurrence records
<p>These files include original and processed data from a study of data filtering by the Atlas of Living Australia (ALA) and the Global Biodiversity Information Facility (GBIF). The study has been submitted for publication on 2018-03-04 as the paper "An audit of some filtering effects in aggregated occurrence records" and details of data sources and processing are in that manuscript. ALA and GBIF sourced occurrence records from the Australian Museum (AM) [Malacology], Museums Victoria (MV) [Entomology] and the New Zealand Arthropod Collection (NZAC). The unprocessed ALA and GBIF data are in the zipped files AM-ALA_download.zip, MV-ALA_download.zip, AM-GBIF_verbatim.download.zip, AM-GBIF_occurrence.download.zip, MV-GBIF_verbatim.download.zip, MV-GBIF_occurrence.download.zip, NZAC-GBIF_verbatim.download.zip and NZAC-GBIF_occurrence.download.zip. The processed data are in a single zip file name_change_tables_and_data_notes_ver_.zip. The processed data are my own work and are here made available under a CCA 4.0 licence. Full citations for the ALA and GBIF data are as follows:<br> AM Malacology from ALA<br> Atlas of Living Australia occurrence download at https://biocache.ala.org.au/occurrences/search?&q=collection_uid%3Aco114 accessed on Wed Feb 14 18:44:13 AEDT 2018<br> AM from GBIF<br> Australian Museum (2017). Australian Museum provider for OZCAM. Occurrence Dataset https://doi.org/10.15468/e7susi accessed via GBIF.org on 2018-02-14 [Malacology section records extracted and archived here]<br> MV from ALA<br> Atlas of Living Australia occurrence download at https://biocache.ala.org.au/occurrences/search?&q=data_resource_uid%3Adr342 accessed on Wed Jan 31 06:42:40 AEDT 2018 [Entomology section records extracted and archived here]<br> MV from GBIF<br> Museums Victoria (2017). Museums Victoria provider for OZCAM. Occurrence Dataset https://doi.org/10.15468/lp1ctu accessed via GBIF.org on 2018-01-30 [Entomology section records extracted and archived here]<br> NZAC from GBIF<br> Wilton A (2018). New Zealand Arthropod Collection (NZAC). Version 1.67. Landcare Research. Occurrence Dataset https://doi.org/10.15468/lrgzz9 accessed via GBIF.org on 2018-01-08</p>
SmartRoadSense: aggregated road surface roughness dataset
<p>Dataset of aggregated road surface quality data points, collected through the mobile crowdsensing application SmartRoadSense.</p>
Spatial aggregation and seedling survival of the Borneo Ironwood (Eusideroxylon zwageri)
<b>Description: </b><p>This dataset was collected by the Imperial College London MRes Tropical Forest Ecology Field Course as a group project in Maliau Basin. Fieldwork was carried out on 29 Jan - 1 Feb 2018. A 4-ha plot was set up to assess the spatial aggregation of the Borneo Ironwood (Eusideroxylon zwageri, local name Belian) and seedling survival. All trees with DBH >1cm were mapped with XY coordinate rounded to nearest 1m. DBH class was rounded down to the nearest 10cm, e.g. size 10, 20, 30 etc. Size class 1 refers to trees with DBH between 1 and 10cm. A swamp area on the north and northeast of ~1-ha was not assessible hence data defficient, but as Belian is not known to growth in water logged conditions the species was presumed to be absent in this swamp area. Belian seedlings were surveyed in 540 5x5m subplots in the centre zone of the 4-ha plot. In each seedling plot, healthy and damaged (primarily herbivory damage by mammals, defined as damaged when top shoot, i.e. apical meristem, was missing). Other variables recorded in seedling plots include slope angle and ground vegetation cover.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/152"><b>MRes Tropical Forest Ecology Field Course</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Natural Environment Research Council (Directed grant, NE/P00363X/1, <a href="https://gtr.ukri.org/projects?ref=NE%2FP00363X%2F1">https://gtr.ukri.org/projects?ref=NE%2FP00363X%2F1</a>)</li><li>Imperial College London (MRes Tropical Forest Ecology field course program)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=277">here</a></p><p><b>Files: </b>This consists of 1 file: Belian_data_for_SAFE_upload_LQ_10Jan_2019.xlsx</p><p><b>Belian_data_for_SAFE_upload_LQ_10Jan_2019.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Belian tree coordinates</b> (described in worksheet Belian trees)</p><p>Description: All trees with DBH >1cm were mapped in a 4-ha plot with XY coordinate rounded to nearest 1m. Each row corresponds to a Belian tree. DBH class was rounded down to the nearest 10cm, e.g. size 10, 20, 30 etc. Size class 1 refers to trees with DBH between 1 and 10cm. A swamp area on the north and northeast of ~1-ha was not assessible hence data defficient, but as Belian is not known to growth in water logged conditions the species was presumed to be absent in this swamp area.</p><p>Number of fields: 9</p><p>Number of data rows: 91</p><p>Fields: </p><ul><li><b>X10</b>: X coordinate of the southwest corner of 10x10 m subplot (Field type: Numeric)</li><li><b>Y10</b>: Y coordinate of the southwest corner of 10x10 m subplot (Field type: Numeric)</li><li><b>X</b>: X coordinate within the 10x10m subplot (local X) (Field type: Numeric)</li><li><b>Y</b>: Y coordinate within the 10x10m subplot (local Y) (Field type: Numeric)</li><li><b>class</b>: DBH rounded down to the nearest 10cm, e.g. size class 10, 20, 30 etc. Size class 1 refers to trees with DBH between 1 and 10cm (Field type: Numeric Trait)</li><li><b>remark</b>: remark on whether tree is a resprout from the base of an older tree and other tree condition information (Field type: Comments)</li><li><b>collected_by</b>: members of field teams: 4 groups of 4 students each (Field type: Comments)</li><li><b>X_coord</b>: X coordinate within the 4-ha plot (global X) (Field type: Numeric)</li><li><b>Y_coord</b>: Y coordinate within the 4-ha plot (global Y) (Field type: Numeric)</li></ul></li><li><p><b>Belian seedling plots</b> (described in worksheet Belian seedlings)</p><p>Description: Belian seedlings were surveyed in 540 5x5m subplots in the centre zone of the 4-ha plot. Each row corresponds to a seedling plot. In each seedling plot, healthy and damaged (primarily herbivory damage by mammals, defined as damaged when top shoot, i.e. apical meristem, was missing). Other variables recorded in seedling plots include slope angle and ground vegetation cover.</p><p>Number of fields: 10</p><p>Number of data rows: 540</p><p>Fields: </p><ul><li><b>X10</b>: X coordinate of the southwest corner of 10x10 m subplot (Field type: Numeric)</li><li><b>Y10</b>: Y coordinate of the southwest corner of 10x10 m subplot (Field type: Numeric)</li><li><b>healthy</b>: count of healthy Belian seedlings within 5x5m plot. "healthy" is defined as opposed to "damaged" (see next data column) (Field type: Abundance)</li><li><b>damaged</b>: count of damaged Belian seedlings within 5x5m plot. "damaged" is defined as when a seedling suffered severe herbivory damage (including dead seedlings) with the apical meristem entirely missing (eaten by mammals) (Field type: Abundance)</li><li><b>slope</b>: mean slope angle for each seedling subplot, a measure of habitat topography (Field type: Numeric)</li><li><b>veg_cover</b>: ground vegetation cover for each subplot, a measure of habitat and interspecific competition (Field type: Ordered Categorical)</li><li><b>cache</b>: when seedlings occur in dense clusters especially under fallen tree or in buttress crevices, this may indicate they were deposited there by rodents, i.e. caching (Field type: Comments)</li><li><b>remark</b>: remark on habitat. seedling plots on footpath or water should be excluded from data analysis (Field type: Comments)</li><li><b>collected_by</b>: members of field teams: 4 groups of 4 students each (Field type: Comments)</li><li><b>total</b>: total number of Belian seedlings (Field type: Abundance)</li></ul></li></ol><p><b>Date range: </b>2018-01-29 to 2018-02-01</p><p><b>Latitudinal extent: </b>4.7383 to 4.7383</p><p><b>Longitudinal extent: </b>116.9713 to 116.9713</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Plantae<br> - Tracheophyta<br> -  - Magnoliopsida<br> -  -  - Laurales<br> -  -  -  - Lauraceae<br> -  -  -  -  - <i>Eusideroxylon</i><br> -  -  -  -  -  - <i>Eusideroxylon zwageri</i><br></div><p></p>
Quantitative Characterisation of α-Synuclein Aggregation in Living Cells through Automated Microfluidics Feedback Control
<p>Data, computational software, and supplemental movies generated in the study: "Quantitative Characterisation of α-Synuclein Aggregation in Living Cells through Automated Microfluidics Feedback Control."</p>
Self-Healing Microcapsules as Concrete Aggregates for Corrosion Inhibition in Reinforced Concrete
<p>Corresponding data set for Tran-SET Project No. 17CLSU08. Abstract of the final report is stated below for reference:</p> <p>"Reinforced Concrete (RC) structures are vital to the US’s civil infrastructure for their strength and versatility. Unfortunately, RC elements deteriorate rapidly when exposed to corrosive environments. One possible solution is to extend the life of RC elements and systems using microencapsulated corrosion inhibitors to reduce the rebar corrosion rate. The capsules house an anodic corrosion inhibitor agent including calcium nitrate (CN) and triethanolamine (TEA). The integration of such microencapsulated materials will enhance the durability and extend the useful life by controlling the corrosion precursors and the corrosion process during damage evolution. Therefore, this work aims to develop and characterize the performance of microcapsules containing corrosion inhibitors (CN-C and TEA-C) in comparison to those introduced as admixtures (CN-A and TEA-A) for reinforced concrete applications. For the corrosion tests, all samples were subjected to continuous ponding, wet/dry cycles, and fog chamber exposure to simulate different environments. The results showed that TEA-C is more effective in giving a corrosion protection than TEA-A and the Control. In contrast, the corrosion protection performance of both CN-A and CN-C was alike. The corrosion kinetics was slightly reduced on inhibited rebars compared to unprotected rebars (the Control). When comparing TEA-C and CN-C, in the presence of each stimulus (pH changes for TEA-C, cracks for CN-C), TEA-C protected the rebar better than CN-C. For the admixture samples (TEA-A and CN-A) that do not need stimuli in the concrete, a stable and better corrosion protection was provided by CN-A. The outcome of this proof-of-concept study in the laboratory validates the merit of the proposed technology for corrosion control in RC structures."</p>
Soil-Recycled Aggregate-Geopolymer Road Base/Subbase Mixtures: Steps Towards Sustainability
<p>Corresponding data set for Tran-SET Project No. 18GTLSU10. Abstract of the final report is stated below for reference:</p> <p>"This study deals with the development of Soil-Geopolymer mixtures using flyash, alkali activator and recycled aggregates (RAG) including recycled concrete (RCA) and reclaimed asphalt (RAP) as an alternative to soil-cement for pavement base and subbase layers. Several mix constituents were varied such as flyash type and content, RCA and RAP content and ratio of sodium silicate and sodium hydroxide. Experiment design was established and mechanical and durability characteristics of Soil-RAG-Geopolymer mixtures were evaluated and then compared to the conventional soil-cement mixtures. The results of the testing showed that for the selected Soil-RAG-Geopolymer mixtures the strength, stiffness, permanent deformation, and durability characteristics were either comparable or better than the soil-cement mixtures. However, such mixtures required more curing time at room temperature to achieve needed strength. In order to further optimize the practical applications of this technology in the field, other variables such as molarity of alkali activator, curing conditions, early strength development at room and ambient temperatures, gradation of RAG and shrinkage characteristics need be investigated."</p>
5-bus system and a real 221-bus low voltage network in the UK for analysing phase unbalance impacts on aggregated flexibility
<p>Two case studies for modelling flexibility services in low voltage distribution networks and quantifying the impacts of voltage unbalance limits and phase coordination constraints. The cases are in the OpenDSS format (.dss).<br>More details in the GitHub repository: https://github.com/AndreyChurkin/3FlexAnalyser.jl</p>
ALDIS cloud to ground lightning strike occurrence aggregated to spatiotemporal ERA5 cells (summer months 2010 to 2019)
<p>The dataset contains a binary classification whether at least one cloud-to-ground lightning flash as detected by the ALDIS [1] lightning location system occurred in the previous hour in an ERA5 grid cell. Data with an amplitude between -2 kA and +15 kA are excluded.<br><br></p> <p>The data cover 8.25E to 16.75E longitude and 45.25N to 49.75N latitude.<br><br>Non-commercial use is allowed conditional on proper citation of the data source, ALDIS, and the accompanying manuscript <em>Ehrensperger, G., Simon, T., Mayr, G. J., and Hell, T.: Identifying lightning processes in ERA5 soundings with deep learning, Geosci. Model Dev., 18, 1141–1153, <a href="https://doi.org/10.5194/gmd-18-1141-2025">https://doi.org/10.5194/gmd-18-1141-2025</a>, 2025</em>.</p>
Figure 4 in Discrete aggregate analysis of ovoid egg shapes in various bird species
Figure 4. Ovoid profiles constructed in the boundaries of lateral arcs 0.75–1.75D.
Figure 9 in Discrete aggregate analysis of ovoid egg shapes in various bird species
Figure 9. Cross-ratio of double segments.
Excitatory synaptic structural abnormalities produced by templated aggregation of α-syn in the basolateral amygdala
<p>Tabular raw data for each graph in the manuscript</p>
Global Biotic Interactions: GloBI aggregated DwC-A
Global Biotic Interactions (GloBI) provides an infrastructure and data service that aggregates or combines existing biotic interaction datasets to provide easy access to biotic interaction data.<p></p>Global Biotic Interactions (GloBI) provides an infrastructure and data service that aggregates or combines existing biotic interaction datasets to provide easy access to biotic interaction data. <p></p>https://www.globalbioticinteractions.org/
Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks
<p>Data and Inferred Networks accompanying the manuscript entitled - “Aggregation of recount3 RNA-seq data improves the inference of consensus and context-specific gene co-expression networks” </p> <p>Authors: Prashanthi Ravichandran, Princy Parsana, Rebecca Keener, Kaspar Hansen, Alexis Battle </p> <p>Affiliations: Johns Hopkins University School of Medicine, Johns Hopkins University Department of Computer Science, Johns Hopkins University Bloomberg School of Public Health</p> <p>Description: </p> <p>This folder includes data produced in the analysis contained in the manuscript and inferred consensus and context-specific networks from graphical lasso and WGCNA with varying numbers of edges. Contents include:</p> <ul> <li> <p>all_metadata.rds: File including meta-data columns of study accession ID, sample ID, assigned tissue category, cancer status and disease status obtained through manual curation for the 95,484 RNA-seq samples used in the study. </p> </li> <li> <p>all_counts.rds: log2 transformed RPKM normalized read counts for 5999 genes and 95,484 RNA-seq samples which was utilized for dimensionality reduction and data exploration </p> </li> <li> <p>precision_matrices.zip: Zipped folder including networks inferred by graphical lasso for different experiments presented in the paper using weighted covariance aggregation following PC correction.</p> </li> <ul> <li> <p>The networks can be found as follows. First, select the folder corresponding to the network of interest - for example, Blood, this will then include two or more folders which indicate the data aggregation utilized, select the folder corresponding appropriate level of data aggregation - either all samples/ GTEx for blood-specific networks, this includes precision matrices inferred across a range of penalization parameters. To view the precision matrix inferred for a particular value of the penalization parameter X, select the file labeled lambda_X.rds</p> </li> <li> <p>For select networks, we have included the computed centrality measures which can be accessed at centrality_X.rds for a particular value of the penalization parameter X. </p> </li> <li> <p>We have also included .rds files that list the hub genes from the consensus networks inferred from non-cancerous samples at “normal_hubs.rds”, and the consensus networks inferred from cancerous samples at “cancer_hubs.rds”</p> </li> <li> <p>The file “context_specific_selected_networks.csv” includes the networks that were selected for downstream biological interpretation based on the scale-free criterion which is also summarized in the Supplementary Tables. </p> </li> </ul> <li> <p>WGCNA.zip: A zipped folder containing gene modules inferred from WGCNA for sequentially aggregated GTEx, SRA, and blood studies. Select the data aggregated, and the number of studies based on folder names. For example, blood networks inferred from 20 studies can be accessed at blood/consensus/net_20. The individual networks correspond to distinct cut heights, and include information on the cut height used, the genes that the network was inferred over merged module labels, and merged module colors. </p> </li> </ul>
Post-processed SAM (System for Atmospheric Modeling) simulation output for "Tipping to an Aggregated State by Mesoscale Convective Systems"
<p>Statistics output files for all variables, for a select number of SAM (System for Atmospheric Modeling v. 6.11) simulation runs used in the study "Tipping to an Aggregated State by Mesoscale Convective Systems". The following simulations are included: DIU, OCEAN, DIU2OCEAN branch A1, DIU2OCEAN branch A2.</p>
Spreadsheet for analysis of illness-death model with aggregated data
<p>Spreadsheet for calculation of a recurrence equation and analysis of fixed points in the illness-death model.</p>
Transgenic A53T mice have astrocytic -synuclein aggregates in dopamine and striatal regions
<p>Statistical analysis carried out on astrocyte quantification data derived from 6 month transgenic A53T PD mice. </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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