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1,392 results for “accumulation”
PBB01 Belowground Plot Experiment: Aboveground net primary productivity of tallgrass prairie based on accumulated plant biomass
To address the potential interactive effects of fire, aboveground biomass removal, and nutrient amendments on above- and belowground responses, a long-term field experiment was initiated in 1986 as part of the Konza Prairie Long-Term Ecological Research (LTER) program. The general goals of this experiment are: 1) to document both short- and long-term responses of plants and soils to fire, aboveground biomass removal (a surrogate for grazing in these small plots), and nutrient amendments (additions of N and/or P); and 2) to provide a better understanding of the mechanisms underlying tallgrass prairie responses to fire, aboveground biomass removal and nutrient enrichment. Peak foliage biomass is measured annually in late fall (September to October) on the 64 belowground plots. Effects of burning, mowing and N + P additions on aboveground NPP are measured. Two 0.1m2 quadrats harvested per plot). 2003 was the last year the mowing treatment was implemented.
PEB01 Aboveground net primary productivity of tallgrass prairie based on accumulated plant biomass in grazing exclsoures on bison-grazed watersheds
Data set contains estimates of end-of-season standing crop biomass (grams per square meter) of live graminoids, forbs, woody plants, and previous year's dead vegetation in grazing exclosures. Date from exclosures is used to determine long-term effects of bison grazing on aboveground net primary productivity.
PAB03 Aboveground primary productivity of tallgrass prairie based on accumulated plant biomass on LTER watersheds burned at different seasons
Data set contains estimates of standing crop biomass (grams per square meter) of live graminoids, forbs, woody plants, and previous year's dead vegetation for 2 soil types (shallow and deep) and seasonal burning treatments (spring, summer, fall, winter).
PAB05 Aboveground net primary productivity of tallgrass prairie based on accumulated plant biomass on the LTER fire reversal experiment watersheds
Data set contains estimates of end-of-season standing crop biomass (grams per square meter) of live graminoids, forbs, woody plants, and previous year's dead vegetation for 2 soil types (shallow and deep) on the four Fire Reversal Experiment watersheds. This experiment is based on reversing fire treatments on four watersheds, two of which had a history of annual spring burning and two of which had a history of long-term fire suppression. The dataset includes both pre- and post-fire treatments.
MCR LTER: Coral Reef Resilience: Short-term Accumulation of Algal Biomass on Unglazed Ceramic Tiles from July 2010-August 2011
Caged tiles were placed at various sites to measure short-term algal accumulation in the absence of grazing by fishes or large invertebrates. These data document biomass of algae that accumulated on unglazed ceramic tiles (2.5 cm X 2.5 cm) placed inside cages (mesh size = 2.5 cm x 2.5 cm) at various sites around the island of Moorea for a period of 3 to 4 weeks. Three separate experiments were conducted (one during July-August 2010 and two in July-August 2011). In the first experiment (2010_Production), we measured the accumulation of algae after 24 days at two different depths on the forereef (10 and 17 m), and within the lagoon at four different distances from the reef crest (approximately 25, 100, 400, and 700 m). In the second experiment (2011_Production_Summary), we measured the accumulation of algae after 24 or 25 days at six forereef sites (LTER 1, Resilience 2, LTER 3, LTER 4, LTER 5, LTER 6). In this experiment, tiles were placed adjacent to the fish transects (see knb-lter-mcr.6) at a depth of 12 m. In the final experiment (Production_Time_Series), we measured the accumulation of algae on tiles at three day intervals over a period of 31 days. This experiment was conducted at Resilience 2. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Perceptual decisions result from the continuous accumulation of memory and sensory evidence
Open the record for dataset details and reuse information.
Spatial structure, chemotaxis and quorum sensing shape bacterial biomass accumulation in complex porous media
<p>Dataset associated to the publication</p><p>"Spatial structure, chemotaxis and quorum sensing shape bacterial biomass accumulation in complex porous media"</p><p>By</p><p>David Scheidweiler, Ankur Deep Bordoloi, Wenqiao Jiao, Vladimir Sentchilo, Monica Bollani, Audam Chhun, Philipp Engel and Pietro de Anna</p><p>Folder named "Figure_X" contains the original raw data, analysed data and source data for each plot within figure "X" on the manuscript and supplementary information.</p><p>We do not provide raw data for each replica as one flow&growth experiment consists in 50 large images for a total of about 12 GB per dataset. Thus, we provide here the original data for the Wild Type experiment and the control D-luxS mutant. The data for the replicas and other control experiment can be available upon request.</p><p>We provide Matlab scripts to read and analyze the original images.</p>
Critical Zone Observatory in Pianosa Island: Accumulation chambers n.1 and n.2 (27/03/2023-03/04/2024)
<p>The installed instrumentation allows CO2 flux measurements from soil (emission and absorption/uptake) by non steady state closed dynamic accumulation Chambers.</p> <p>The aim is to understand if the NEE (Net Ecosystem Exchange) is increasing or decreasing and which parameters most affect this important index. More over, the NEE parameter is extremely useful in climate models.</p> <p>In particular chamber n.1 is a dark chamber and measures the ecosystem respiration while chamber n.2 is a transparent chamber and measures the NEE. The difference between the flux measured by chamber n.2 and that measured by chamber n.1 allows to calculate the GPP (Gross Primary Production).</p> <p>The dataset includes the Net Ecosystem Exchange (NEE), Ecosystem Respiration (ER), air temperature and atmospheric pressure inside the chamber, soil temperature and soil humidity next to the chamber. It is also available the R2 of the regression line of the CO2 concentration versus time, of each chamber.</p> <p>The data was collected thanks to EU - Next Generation EU Mission 4 "Education and Research - Component 2: "From research to business" - Investment 3.1: "Fund for the realisation of an integrated system of research and innovation infrastructures" - Project IR0000032 - ITINERIS - Italian Integrated Environmental Research Infrastructures System - CUP B53C22002150006.</p>
Data repository for "Interface rotation in Cu/Nb accumulative roll bonded (ARB) nanolaminates"
<p>Data repository for "Interface rotation in Cu/Nb accumulative roll bonded (ARB) nanolaminates"</p> <p>This repository contains raw experimental data for "Interface rotation in Cu/Nb accumulative roll bonded (ARB) nanolaminates" manuscript. Please refer to the manuscript for the data interpretation.</p> <p>The repository structure:</p> <ul> <li><code>CuNbARB-sample-photo.jpg</code> shows a photograph of as-received Cu(63nm)/Nb(63nm) accumulative roll bonded (ARB) nanolaminate sample</li> <li><code>ARB_63nm_DRX</code> contains X-ray diffraction measurements</li> <li><code>RD/TDXFIBmilling</code> folders contain focused ion beam images captured during pillar milling <ul> <li>In <code>RDX/TDX</code>, <code>X</code> refers to pillar number (see Supplementary information.org for the full pillar list). The numbers in the file names inside refer to the corresponding pillars.</li> </ul> </li> <li><code>RD/TDXSEMbefore</code> folders contain scanning electron (SEM) images of the as-fabricated pillars</li> <li><code>RD/TDX-compression</code> folders contain in situ pillar compression data, including some of the SEM images captured before/after the compression, raw load-displacement data (in <code>.hys</code> native Hysitron piconindenter format), load-displacement data exported to raw text (see Supplementary information for examples how to plot load-displacement using the raw text files), SEM videos, SEM videos combined with the load-displacement data, and accelerated videos</li> <li><code>RD/TDX-SEMafter</code> folders contain SEM images of the compressed pillars</li> <li>Supplementary-info folder contains supplementary information</li> </ul> <p>Author: I. Radchenko, W. Zhu, L. Qing, E. Navarro, R. Sahay, P.S. Lee, N. Raghavan, O. Thomas, A.S. Budiman, K. Chen</p>
Supplementary data and codes for "Confinement-induced accumulation accumulation and de-mixing of microscopic active-passive mixtures"
<p>This file contains the data and codes used in the paper</p> <p>“Confinement-induced accumulation accumulation and de-mixing of microscopic active-passive mixtures”, S. Williams et al, 2022.</p> <p>It includes the data used in all the figures and supplementary figures, as well as the codes used for simulations and escape rate estimation. The data files are in .mat format. The codes are Matlab codes with the exception of the analytical estimate of the escape rate which is a Mathematica worksheet.</p>
Data repository for "Spatio-temporal trends of Holocene peat carbon accumulation in China: climatic and human drivers"
<p>Dating results collected from peatlands in China are used to calculate the spatiotemporal trends of the Holocene peat accumulation rate (PAR) and net carbon balance (NCB), including all original dating, calculated intermediate results, and final composite results. This file includes a total of 14 tables (Supplementary Tables S1-S14).</p>
ARTICLE DATASET - ANTHROPOGENIC MICROPARTICLES ACCUMULATION IN SMALL-BODIED SEAGRASS MEADOWS: THE CASE OF TROPICAL ESTUARINE SPECIES IN BRAZIL
<p>This dataset with the data analysis script refers to the publication <a href="https://www.sciencedirect.com/science/article/abs/pii/S0025326X24007768?via%3Dihub"><strong>https://doi.org/10.1016/j.marpolbul.2024.116799</strong></a>.</p> <p>The dataset consists of a spreadsheet containing 9 tabs with survey data described below and their respective captions, found in the first line of each tab.</p> <p>The script for statistical analysis in the R language contains descriptive and statistical analyses, in addition to the functions for creating the graphs displayed in the article.</p> <p><strong>Description of the spreadsheet dataset tabs --------------------------------------------------------------------------------</strong></p> <ol> <li>description - Contains general information about the article</li> <li>meadows - Contains properties related to the characteristics of the multispecific grassland.</li> <li>ap_samples - Contains properties related to the abundances of anthropogenic microparticles, as to classifications by shape, size (mm) and color in units, kg and frequency of occurrence.</li> <li>ap_categories - Contains raw data related to the classifications of anthropogenic microparticles, in terms of shape, size (mm) and color.</li> <li>sediment - Contains raw data related to the classifications of sediment particles.</li> <li>granulometry - Contains properties related to sediment particles in µm, and their classifications by predominance, frequency and kg.</li> <li>shape - Contains raw data related to the classification of the shapes of anthropogenic microparticles in units and kg.</li> <li>size - Contains raw data related to the classification of the sizes of anthropogenic microparticles in units and kg.</li> <li>color - Contains raw data related to the classification of the colors of anthropogenic microparticles in units and kg.</li> </ol>
SPHERA High Resolution Reanalysis over Italy - Hourly accumulated total precipitation
<p><strong>Please refer to the latest-released version (v2) of this dataset</strong></p> <p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at a hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly accumulated total precipitation for the period 1995-2020.</p> <p><strong>Update 09/10/2025</strong>: tpH Dataset Version 2 Released:</p> <p>A new version of the dataset (v2) has been published, incorporating the following improvements and corrections:</p> <ul> <li>Precipitation data have been cleaned to remove duplicated fields that were inadvertently included in the initial release. Additionally, the data have been decumulated to represent hourly precipitation values. In the original version, precipitation was reported as accumulations increasing over the day from 00 UTC to 23 UTC.This format has now been replaced by actual hourly precipitation totals, offering a representation that is more relevant and useful for most applications.</li> <li>Grid inconsistencies present in some GRIB messages have been resolved to ensure structural uniformity across the dataset.</li> <li>Data have been rescued for some of the data holes. In the cases when only 1 hour was missing from the original extraction, the field has been produced by averaging the two fields associated with the previous and next hours to ensure the most continuous data series as possible. Particularly this is the case for the following grib messages: <ul> <li>23 UTC of 31 December 1995</li> <li>23 UTC of 31 December 1998</li> <li>23 UTC of 19 February 2020</li> <li>23 UTC of 13-17-22-30 July 2020</li> <li>23 UTC of 4-14 August 2020</li> </ul> </li> </ul> <p>Other fields currently available on Zenodo are the surface relative humidity at 2-meter height (over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="../records/12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="../records/12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>and the hourly surface air temperature at 2-meter height:</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
Light and confocal micrographs on the response of Mesotaenium endlicherianum SAG 12.97 to a bifactorial environmental gradient, the accumulation of lipid droplets, and the heterologous expression and localisation of signature LD protein homologs to tobacco pollen tubes
<p>These micrographs accompany the work "Environmental gradients reveal stress hubs predating plant terrestrialization", posted as a pre-print on bioRxiv https://doi.org/10.1101/2022.10.17.512551 </p> <p>The light and confocal micrographs show the response of Mesotaenium endlicherianum SAG 12.97 to a bifactorial environmental gradient, especially their accumulation of lipid droplets (LDs); in confocal micrographs, LDs appeared as distinct structures upon staining with BODIPY.</p> <p>Further confocal micrographs show the heterologous expression and localisation of signature LD protein homologs detected in Mesotaenium endlicherianum SAG 12.97; heterologous expression was carried out in tobacco pollen tubes were also stained with BODIPY and proteins were tagged with mCherry.</p>
Raw data: Specialized metabolites accumulation pattern in buckwheat is strongly influenced by accession choice and co-existing weeds
<p>Screening suitable allelopathic crops and crop genotypes that are competitive with weeds can be a sustainable weed control strategy to reduce the massive use of herbicides. In this study, three accessions of common buckwheat <em>Fagopyrum esculentum</em> Moench. (Gema, Kora, and Eva) and one of Tartary buckwheat <em>Fagopyrum tataricum</em> Gaertn. (PI481671) were screened against the germination and growth of the herbicide-resistant weeds <em>Lolium rigidum </em>Gaud. and <em>Portulaca oleracea</em> L. The chemical profile of the four buckwheat accessions was characterised in their shoots, roots, and root exudates in order to know more about their ability to sustainably manage weeds and the relation of this ability with the polyphenol accumulation and exudation from buckwheat plants. Our results show that different buckwheat genotypes may have different capacities to produce and exude several types of specialized metabolites, which lead to a wide range of allelopathic and defence functions in the agroecosystem to sustainably manage the growing weeds in their vicinity. The ability of the different buckwheat accessions to suppress weeds was accession-dependent without differences between species, as the common (Eva, Gema, and Kora) and Tartary (PI481671) accessions did not show any species-dependent pattern in their ability to control the germination and growth of the target weeds. Finally, Gema appeared to be the most promising accession to be evaluated in organic farming due to its capacity to sustainably control target weeds while stimulating the root growth of buckwheat plants.</p>
Data and analysis scripts for: Recent acceleration in global ocean heat accumulation by mode and intermediate waters
<p>The folder contains the MATLAB code and data to re-create Figures 1-9 and S1-3 within the publication by <em>Li, Z., England, M. H., & Groeskamp, S. Recent acceleration in global ocean heat accumulation by mode and intermediate waters, Nature Communications</em>, 2023.</p>
Flow accumulation grid generated from 10 meter DEM, Andrews Experimental Forest, 1998
Flow accumulation grid generated from 10 meter DEM, Andrews Experimental Forest. This grid is useful for determining the area of land that drains to a point. The user selects a point on the grid, and the value of that point represents the area (in 100 square meters) that drain to the point. This grid can also be used for generating watershed boundaries and stream networks.
Synthesis of Sea level rise and carbon accumulation rates in United States tidal wetlands
Coastal wetlands accumulate soil carbon more efficiently than terrestrial systems, but sea level rise potentially threatens the persistence of this prominent carbon sink. Here, we combine a published dataset of 372 soil carbon accumulation rates from across the United States with new analysis of 131 sites in coastal Louisiana. The combined database featured 503 measurements of carbon accumulation, spanning broad gradients in mean annual temperature, tide range, and dominant vegetation.
Supplementary Online Material to the paper: Modelling and empirical validation of carbon stock accumulation during the forest transition in France 1850-2015
<p><strong>Supplementary Online Material to the paper:</strong></p> <p><strong>Modelling and empirical validation of carbon stock accumulation during the forest transition in France 1850-2015</strong></p>
Source code and simulation datasets for the paper 'Migration and accumulation of bacteria with chemotaxis and chemokinesis'
<p>Source code and simulation data files for the paper 'Migration and accumulation of bacteria with chemotaxis and chemokinesis'</p> <p>The zipped folder 'SimulationCode.zip' contains Matlab source code files which have been used to generate the simulations in the paper: fixed attractant gradient, axisymmetric agar-plate like migration and transient attractant source. The file 'main.m' controls all simulations run with initial conditions specified in the files with suffix '_ic'. The file 'PDEsolver.m' specifies the finite difference solver used so solve the model PDEs, while 'FourthOrderFD.m' creates the matrices that are required<br> for the finite differnce solver. The chosen scheme is of fourth order accuracy.</p> <p>The zipped folder 'SimulationData.zip' contains Matlab data files generated by running the simulation code. The results correspont to the figures in the paper.</p>
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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.