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5 results for “hyper-aridity”

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zenodo48/100

Indicative distribution map for Ecosystem Functional Group T5.5 Hyper-arid deserts

<p>This archive contains indicative distribution maps and profiles for <strong>T5.5 Hyper-arid deserts</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>

opencc-by-4.0Jul 2021View details →
zenodo40/100

Data for: "High-resolution Soil Moisture Evolution in Hyper-arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the southern Arabian Peninsula"

<p>Data accompanying the publication:&nbsp;High-resolution Soil Moisture Evolution in Hyper-arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the southern Arabian Peninsula. For filenames starting with T: Exponential fit parameters time0 and mag0 for InSAR coherence data. they are binary files,&nbsp;where&nbsp;fit&nbsp;&nbsp;= a*exp(-b*x); a =&nbsp;-log(mag0); b = 1/time0. timeerr contains the uncertainty of the time0 parameter, and maghigh/maglow contain the high and low uncertainty for the mag0 parameter, respectively.&nbsp; For for each frame or overlap region (T101, T28, T130, T28_T101, T130_T28), there is a vrt file (T..._20180524.time0.vrt), which is the metadata file applicable to all files of the same frame. Files starting with mags_times: Exponential fit parameters for ASCAT/SMAP/GLDAS data. the same parameters (time0, timeerr, mag0, maghigh, maglow) can be found in these matlab structure files. In addition, the .mat files&nbsp;contain&nbsp;the offset parameter and related uncertainty, as well as lat/lon information.&nbsp;&nbsp;</p>

opencc-by-4.0Oct 2021View details →
dryad36/100

Depicting the phenotypic space of the annual plant Diplotaxis acris in hyper-arid deserts

<p class="CxSpFirst">The phenotypic space encompasses the assemblage of trait combinations yielding well-suited integrated phenotypes. At the population level, understanding phenotypic space structure requires the quantification of among- and within-population variation in traits and the correlation pattern among them. Here, we studied the phenotypic space of the annual plant <i>Diplotaxis acris</i> occurring in hyper-arid deserts. Given the advance of warming and aridity in vast regions occupied by drylands, <i>D. acris</i> can indicate the successful evolutionary trajectory that many other annual plant species may follow in expanding drylands. To this end, we conducted a greenhouse experiment with 176 <i>D. acris</i> individuals from five Saudi populations to quantify the genetic component of variation in architectural and life-history traits. We found low among-population divergence but high among-individual variation in all traits. In addition, all traits showed a high degree of genetic determination in our study experimental conditions. We did not find significant effects of recruitment and fecundity on fitness. Finally, all architectural traits exhibited a strong correlation pattern among them, whereas for life-history traits, only higher seed germination implied earlier flowering. Seed weight appeared to be an important trait in <i>D. acris</i>, as individuals with heavier seeds tended to advance flowering and have a more vigorous branching pattern, which led to higher fecundity. Population divergence in <i>D. acris</i> might be constrained by the severity of the hyper-arid environment, but populations maintain high among-individual genetic variation in all traits. Furthermore, <i>D. acris</i> showed phenotypic integration for architectural traits and, to a lesser extent, for life-history traits. Overall, we hypothesize that <i>D. acris</i> may be fine-tuned to its demanding extreme environments. Evolutionary speaking, annual plants facing increasing warming, aridity and environmental seasonality might modify their phenotypic spaces towards new phenotypic configurations strongly dominated by correlated architectural traits enhancing fecundity and seed-related traits advancing flowering time.</p>

opencc-zeroOct 2022View details →
dryad36/100

Data and code from: Inverse effects of soil moisture and litter quality on litter decomposition along a gradient from hyper-arid to temperate climate

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publicDec 2025View details →
dryad36/100

Depicting the phenotypic space of the annual plant Diplotaxis acris in hyper-arid deserts

Open the record for dataset details and reuse information.

publicAug 2022View details →

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Allen Brain Atlas

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allen-brain-atlas
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DANDI Archive for NWB datasets

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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.

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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