Skip to main content
Powered by ShareScore

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.

5

datasets available to search

ShareScore release 0.9.0

Reset

Dataset results

5 results for “resource concentration”

Learn how ShareScore rates datasets ↗
dryad36/100

Data for: Relatively rare root endophytic bacteria drive plant resource allocation patterns and tissue nutrient concentration in unpredictable ways

<p><span><span><span><span><span><span><span><span><span><span><span><b>Premise of Study</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>Plant endophytic bacterial strains can influence plant traits such as leaf area and root length. Yet, the influence of more complex bacterial communities in regulating overall plant phenotype is less explored. Here, we conducted two complementary experiments to test if we can predict plant phenotype response to changes in microbial community composition. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Methods</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>In the first study, we inoculated a single genotype of <i>Populus deltoides</i>with individual root endophytic bacteria and measured plant phenotype. Next, single inoculation data were used to predict phenotypic traits in mixed three-member community inoculations, which we tested in the second experiment. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Key Results</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>When in isolation, each bacterial endophyte significantly but weakly altered plant phenotype relative to non-inoculated plants. In mixture, bacterial strain <i>Burkholderia</i>BT03, constituted at least 98% of community relative abundance. Yet, plant resource allocation and tissue nutrient concentrationswere disproportionately influenced by <i>Pseudomonas </i>sp.GM17, GM30, and GM41. We found a 10% increase in leaf mass fraction and a 11% decrease in root mass fraction when replacing<i>Pseudomonas </i>GM17 with GM41 in communities containing both <i>Pseudomonas </i>GM30 and <i>Burkholderia</i>BT03. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusions</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>Our results indicate that interactions among endophytic bacteria may drive plant phenotype over the contribution of each strain individually. Additionally, we have shown that low-abundant strains contribute to plant phenotype challenging the assumption that the dominant strains will drive plant function.</span></span></span></span></span></span></span></span></span></span></span></p>

opencc-zeroJul 2020View details →
dryad36/100

US federal resource allocations are inconsistent with concentrations of energy poverty

Open the record for dataset details and reuse information.

publicSep 2024View details →
dryad36/100

Data for: Relatively rare root endophytic bacteria drive plant resource allocation patterns and tissue nutrient concentration in unpredictable ways

Open the record for dataset details and reuse information.

publicJul 2020View details →
dryad28/100

Data from: Fine root tradeoffs between nitrogen concentration and xylem vessel traits preclude unified whole-plant resource strategies in Helianthus

Open the record for dataset details and reuse information.

publicDec 2016View details →
zenodo24/100

Visualization of the Multidimensional Volumetric Data-base by Video - Mapping Technology in field of Operational Oceanography (Algerian basin) (zooplankton expressed as carbon in sea water - mass concentration of chllorophyl a in sea water,Wekeo Data ) During 2022 year : (educational support resource in space oceanography)

<p>The multidimensional view of the Earth and its immediate environment that is provided by space borne sensors, operating at many wavelengths and directed at many different phenomena, has revolutionized man&#39;s understanding of his planet and the surrounding space environment.<strong>(John H. McElroy.,1985)</strong>,</p> <p>Earth observation satellites measuring in the visible and infrared spectral domain provide a global perspective for many&nbsp; required to determine the role of the ocean in the global climate system, as well as the effects on the ocean of a changing climate&nbsp;<strong>(James A. Yoder and all.,2014)</strong>.</p> <p>Data visualization by video graphics technology is a digital modeling technique also a description or analogy used to help visualize something that cannot be observed directly which exploits the bases of scientific knowledge in a data processing system by the use of mathematical and statistical tools and analysis and forecasting methods to visualize what is hidden behind the data. This work is inspired by the general principle of numerical modeling and data processing, which takes into consideration (the observation of natural phenomena, and the statistical processing of scientific data, which are at the base of the functioning of natural variation)</p> <p>&nbsp;</p> <p><strong>Bibliographic reference:</strong><br> <strong>-Monitoring Earth&#39;s Ocean, Land, and Atmosphere from Space-Sensors, Systems, and Applications, edited by Abraham Schnapf, American Institute of Aeronautics and Astronautics, 1985<br> -Optical Radiometry for Ocean Climate Measurements, Elsevier Science &amp; Technology, 2014</strong></p> <p>&nbsp;</p>

restrictedcc-by-4.0Jul 2023View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record