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.
2,837
datasets available to search
ShareScore release 0.9.0
Dataset results
2,837 results for “Climate Data”
Data and code from: Climate change, tree demography, and thermophilization in western US forests
Open the record for dataset details and reuse information.
Standardized directory for US LTER sites. Data for climate, ecosystem, history, and research focus. 2016
This dataset includes exports from what was known as LTER siteDB and from the ILTER database. Both efforts are network databases that are intended to provide a common cross-site view and simple access to LTER site descriptions, history, research themes, and parameters specific to the respective research locations. These exports contain somewhat duplicated information and have a varying degree of completeness. Both provide a snapshot in time of site profiles containing key information on research, history, institutional affiliations, and location for each LTER site, plus average biotic and abiotic site characteristics, and ecosystem classifications.
Hourly climate data: Meterologic Measurements at Cedar Creek Natural History Area
Meteorological measurements include air temperature, precipitation, wind speed and direction, soil temperature, and relative humidity. These measurements are taken on an hourly basis.
Root biomass data: BAC: Biodiversity and Climate
Climate changes forecast for our region by GCM???s and shifts in biodiversity and composition each have the potential to alter ecosystem functioning; their interactive effects are unknown. The "BAC" experiment is designed to determine the direct and interactive effects of plant species numbers, plant community composition, temperature, and precipitation on 11 productivity, C and N dynamics, stability, and plant, microbe, and insect species abundances in CDR grassland ecosystems.
Monthly data from Climate Station 1 (CS01), Coweeta Hydrologic Laboratory, Otto, NC, 1934-2018
This dataset contains base-line climatic information for the main climate station (CS01) at Coweeta Hydrologic Laboratory in Macon County, North Carolina, USA. The station is operated by the Southern Research Station, Forest Service, USDA. CS01 is also a cooperative station with the National Weather Service. Since May 1963 it has been located in a grassy field NW of the junction of Ball Creek and Shope Fork of Coweeta Creek. Previously CS01 was located about 100m SW of its current location. The current elevation is 685.5m (2249ft) at latitude 35-3-37.21N and longitude 83-25-49.02W. During the 1940s, forest re-growth was tolerated up to 150 feet from the station. In August 1949, the larger opening was reestablished. The high elevation precipitation gage is located in a forest opening on Mooney Gap at elevation 1362.6m (4475ft). Additional USDA Forest Service Data for this climate station can be found at this link: http://www.srs.fs.usda.gov/coweeta/tools-and-data/
The Effects of Climate Downscaling Technique and Observational Dataset on Modeled Ecological Responses: Supporting Data Tables
These data have been prepared as a supplement to Pourmokhtarian et al. (2016; full citation below), where complete details on methods can be found. We evaluated three downscaling methods: the delta method (or the change factor method); monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD); and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs from four atmosphere-ocean general circulation models (AOGCMs) (CCSM3, HadCM3, PCM, and GFDL-CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios on two sets of observations (1/8th degree resolution grid vs. individual weather station) to generate the high-resolution climate input for the forest biogeochemical model PnET-BGC (8 ensembles of 6 runs). This dataset consists of three files - 1) a zip archive file of all raw daily downscaled AOGCMs (csv format; years 1960-2099; delta method 2012-2099 only) which were used as input for PnET-BGC model, 2) a zip archive file of all PnET-BGC output files for each model run (csv format; years 1000-2100), and 3) a pdf document file that describes the content of the input and output files. Data were also used from the following Hubbard Brook longterm datasests: Daily Streamflow Watershed 6: http://dx.doi.org/10.6073/pasta/727ee240e0b1e10c92fa28641bedb0a3 Chemistry of Streamwater at the Hubbard Brook Experimental Forest, Watershed 6: http://dx.doi.org/10.6073/pasta/2ec152b0ab1d4e64aa40f4aa9bc492ac Daily Precipitation Watershed 6: http://dx.doi.org/10.6073/pasta/17c8ff8b160bf7893ef39f75a02652e5 Daily Maximum/Minimum Temperature Data: http://dx.doi.org/10.6073/pasta/2a4ab5522ce15f28196a6035802b09e8 Daily Solar Radiation Data: http://dx.doi.org/10.6073/pasta/2fa098a5aa191c64e622b253c0fee5af These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest
Climate data for Green Lake 4 data loggers (CR10-CR1000), 1997 - 2019.
Climatological data were collected from a Niwot Ridge climate station (GL4) throughout the year using a Campbell Instruments CR10 data logger (1997-2013), a CR1000 data logger (2013-2017), and a CR850 data logger (2017-2019). Maximum and minimum values are recorded instantaneously, with the sampling interval being 10 seconds. Daily averages and totals were calculated from 8,640 individual measurements. This instrument was programmed to generate both hourly and daily output.
Traits-climate range limits data and code (Van Nuland et al. 2019; Ecology and Evolution)
<p>Data and code associated with Van Nuland et al. manuscript “<em>Intraspecific trait variation across elevation predicts a widespread tree species’ climate niche and range limits</em>” in Ecology and Evolution.</p>
Supporting Data for "A partial coupling method to isolate the roles of ocean and atmosphere in coupled climate simulations"
<p>Supporting data for "A partial coupling method to isolate the roles of the atmosphere and ocean in coupled climate simulations", submitted to Journal of Advances in modelling Earth system</p> <p>Monthly averaged variables for the 100 and 150 year fully coupled (b.e11.B1850C5CN.f09_g16.abrupt4xCO2.full.POP.100101_110012.nc) and partially coupled (b.e11.B1850C5CN.f09_g16.abrupt4xCO2.partial.POP.100101_115012.nc) abrupt CO2 quadrupling simulations and 40 yr ocean-driven partially coupled simulation (b.e11.B1850C5CN.f09_g16.prescribed.partial.POP.100101_115012.nc)</p> <p>Control simulation data is available on the NCAR HPSS /CCSM/csm/b.e11.B1850C5CN.f09_g16.005</p>
High-resolution future climate data for species distribution models in Europe
<p><strong>Description</strong></p> <p>This dataset contains a set of 13 climatological variables (<code>Variable</code>, <code>VariableName</code>) at a spatial resolution of 1x1km for Europe (nx = 13147, ny = 6071) for historical (<code>ClimatePeriod</code>) and future climate conditions. These variables are a subset of the so-called bioclimatic variables that are often part of global gridded datasets (e.g. <a href="https://worldclim.org/data/bioclim.html">WorldClim</a>, <a href="http://chelsa-climate.org/bioclim/">CHELSA</a>) that have been specifically developed for species distribution modelling and ecological applications.</p> <p>The climatological data correspond to 35-year (<code>Startyear_Endyear</code> = <code>1971_2005</code>) and 30-year (<code>Startyear_Endyear</code> = <code>2041_2070</code>) mean values representing respectively historical and future climate conditions. To account for the future climate conditions, three possible emission scenarios of greenhouse gases as defined by the <a href="https://www.ipcc.ch/">Intergovernmental Panel on Climate Change (IPCC)</a> are used (<code>ClimatePeriod</code> = <code>rcp26</code>, <code>rcp45</code>, <code>rcp85</code>).</p> <p>The complete set of variables (var[1-13]) for which historical and future climate data layers are produced are given below.</p> <p>The source data for the climate layers were assembled from the <a href="https://cordex.org/data-access/">EURO-CORDEX archive</a> (Kotlarski et al., 2014). More specifically, we have used the regional climate model simulations for Europe at a spatial resolution of 12.5x12.5km on which a three-step statistical downscaling approach has been applied:</p> <ol> <li><strong>Processing</strong> (averaging, totals, …) of all available time series of the EURO-CORDEX model experiments (<code>ClimatePeriod</code> = evaluation, historical, rcp) for the climatological variables.</li> <li><strong>Interpolation</strong> of the data layers from the 12.5x12.5km EURO-CORDEX grid to a 1x1km spatial <a href="http://chelsa-climate.org/">CHELSA</a> (Karger et al., 2017) reference grid (see files <code>lat_1km.csv</code> and <code>lon_1km.csv</code>).</li> <li><strong>Calculate differences</strong> between the 1x1km-interpolated variables (<code>Variable</code> = only for var[1-9]) from the evaluation model experiments (or <code>ClimatePeriod</code>) and the corresponding reference bioclimatic CHELSA variables. In order to account for possible biases present in the EURO-CORDEX climate models, these differences (or biases) are then subtracted from the respective 1x1-km-interpolated variables for the historical and rcp model experiments (<code>ClimatePeriod</code>).</li> </ol> <p>The dimensions of the 1x1km grid (excl. the first row and column):</p> <ul> <li>y-dimension = number of columns = 6071</li> <li>x-dimension = number of rows = 13147</li> </ul> <p>The longitudes and latitudes of respectively the southwest and northeast corner of the grid are:</p> <ul> <li>longitude -44.592; latitude 21.991 (southwest corner)</li> <li>longitude 64.967; latitude 72.583 (northeast corner)</li> </ul> <p>The climatological variables are used as input data for the species distribution modelling of Invasive Alien Species for the <a href="https://osf.io/7dpgr/">Tracking Invasive Alien Species (TrIAS)</a> project.</p> <p><strong>Variables</strong></p> <ul> <li><strong>Variable</strong> (VariableName): Unit</li> <li><strong>var1</strong> (AnnualMeanTemperature): °C</li> <li><strong>var2</strong> (AnnualAmountPrecipitation): mm year<sup>-1</sup></li> <li><strong>var3</strong> (AnnualVariationPrecipitation): coefficient of variation</li> <li><strong>var4</strong> (AnnualVariationTemperature): stdev</li> <li><strong>var5</strong> (MaximumTemperatureWarmestMonth): °C</li> <li><strong>var6</strong> (MinimumTemperatureColdestMonth): °C</li> <li><strong>var7</strong> (TemperatureAnnualRange): °C</li> <li><strong>var8</strong> (PrecipitationWettestMonth): mm</li> <li><strong>var9</strong> (PrecipitationDriestMonth): mm</li> <li><strong>var10</strong> (30yrMeanAnnualCumulatedGDDAbove5degreesC): °C days</li> <li><strong>var11</strong> (AnnualMeanPotentialEvapotranspiration): mm day<sup>-1</sup></li> <li><strong>var12</strong> (AnnualMeanSolarRadiation): W m<sup>-2</sup></li> <li><strong>var13</strong> (AnnualVariationSolarRadiation): stdev</li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>varX_VariableName_ClimatePeriod_Startyear_Endyear.csv</strong>: climatological data layers for the 13 variables listed above</li> <li><strong>lon_1km.csv</strong>: longitudes for the 1x1km grid</li> <li><strong>lat_1km.csv</strong>: latitudes for the 1x1km grid</li> </ul>
Data and model output for figures in "Variable particle size distributions reduce the sensitivity of global export flux to climate change"
<p><strong>Associated publication</strong></p> <p>This dataset was used to generate analyses and figures in the following publication:</p> <p>Leung, S., Weber, T., Cram, J. A., & Deutsch, C. Variable particle size distributions reduce the sensitivity of global export flux to climate change. <em>Submitted to Biogeosciences.</em></p> <p><strong>Associated code</strong></p> <p>After downloading this dataset, run the associated MATLAB code at the following link to generate the figures and analyses in the above publication:</p> <p>https://doi.org/10.5281/zenodo.4117382</p>
Data from: Heritable variation in root secondary metabolites is associated with recent climate
<p>1. Plants can adapt to changing environments by adjusting the production and maintenance of diverse sets of bioactive secondary metabolites. To date, the impact of climatic conditions relative to other factors such as soil abiotic factors and herbivore pressure on the evolution of plant secondary metabolites is poorly understood, especially for plant roots.</p> <p>2. We explored associations between root latex secondary metabolites in 63 Taraxacum officinale populations across Switzerland and climatic conditions, soil abiotic parameters, root herbivore pressure and cytotype distribution. To assess the contribution of environmental effects, root secondary metabolites were measured in F0 plants in nature and F2 plants under controlled greenhouse conditions.</p> <p>3. Concentrations of root latex secondary metabolites were most strongly associated with climatic conditions, while current soil abiotic factors or root herbivore pressure did not show a clear association with root latex chemistry. Results were similar for natural and controlled conditions, suggesting heritable variation rather than environmental plasticity as underlying factor.</p> <p>4. Synthesis. We conclude that climatic conditions likely play a major role in the evolution of root secondary metabolites. These results may hint at a novel role of root latex metabolites in tolerance of abiotic stress.</p>
Data from: Climate and local environment structure asynchrony and the stability of primary production in grasslands
<p><b>Aim</b>: Climate variability threatens to destabilize production in many ecosystems. Asynchronous species dynamics may buffer against such variability when decreased performance by some species is offset by increased performance of others. However, high climatic variability can eliminate species through stochastic extinctions or cause similar stress responses among species, reducing buffering. Local conditions, such as soil nutrients, can further alter production stability directly or by influencing asynchrony. We test these hypotheses using a globally distributed sampling experiment.</p> <p><b>Location</b>: Grasslands in North America, Europe and Australia.</p> <p><b>Time period</b>: Annual surveys over five-year intervals occurring between 2007 and 2014.</p> <p><b>Major taxa studied</b>: Herbaceous plants.</p> <p><b>Methods</b>: We annually sampled per-species cover and aboveground community biomass (net primary productivity; NPP), plus soil chemical properties, in twenty-nine grasslands. We tested how soil conditions, combined with precipitation and temperature variability, affect species richness, asynchrony and temporal stability of primary productivity. We used bivariate relationships and structural equation modeling to examine proximate and ultimate relationships.</p> <p><b>Results</b>: Climate variability strongly predicted asynchrony, whereas NPP stability was more related to soil conditions. Species richness was structured by both climate variability and soils, and in turn increased asynchrony. Temperature and precipitation variability caused a unimodal asynchrony response, with asynchrony lowest at low and high climate variability. Climate impacted stability indirectly through its effect on asynchrony, with stability increasing at higher asynchrony due to lower inter-annual NPP variability. Soil conditions had no detectable effect on asynchrony but increased stability by increasing mean NPP, especially when soil organic matter was high.</p> <p><b>Main Conclusions</b>: We found globally consistent evidence that climate modulates species asynchrony, but that the direct effect on stability is low relative to local soil conditions. Nonetheless, our observed unimodal responses to temperature and precipitation variability suggest asynchrony thresholds, beyond which there are detectable destabilizing impacts of climate on primary productivity.</p>
Data from: Behavioral constraints on local adaptation and counter-gradient variation: implications for climate change
<p>Resource allocation to growth, reproduction, and body maintenance varies within species along latitudinal gradients. Two hypotheses explaining this variation are local adaptation and counter-gradient variation. The local adaptation hypothesis proposes that populations are adapted to local environmental conditions and are therefore less adapted to environmental conditions at other locations. The counter-gradient variation hypothesis proposes that one population out performs others across an environmental gradient because its source location has greater selective pressure than other locations. Our study had two goals. First, we tested the local adaptation and counter-gradient variation hypotheses by measuring effects of environmental temperature on phenotypic expression of reproductive traits in the burying beetle, <i>Nicrophorus orbicollis</i> Say, from three populations along a latitudinal gradient in a common garden experimental design. Second, we compared patterns of variation to evaluate whether traits co-vary or whether local adaptation of traits preclude adaptive responses by others. Across a latitudinal range, <i>N. orbicollis</i> exhibits variation in initiating reproduction and brood sizes. Consistent with local adaptation, (1) beetles were less likely to initiate breeding at extreme temperatures, especially when that temperature represents their source range; (2) once beetles initiate reproduction, source populations produce relatively larger broods at temperatures consistent with their local environment. Consistent with counter-gradient variation, lower latitude populations were more successful at producing offspring at lower temperatures. We found no evidence for adaptive variation in other adult or offspring performance traits. This suite of traits does not appear to coevolve along the latitudinal gradient. Rather, response to selection to breed within a narrow temperature range may preclude selection on other traits. Our study highlights that <i>N. orbicollis</i> uses temperature as an environmental cue to determine whether to initiate reproduction, providing insight into how behavior is modified to avoid costly reproductive attempts. Furthermore, our results suggest a temperature constraint that shapes reproductive behavior.</p>
Data produced for "Crop switching reduces agricultural losses from climate change in the United States by half"
<p>This data archive includes all of the results from the models used to<br> produce the paper "Crop switching reduces agricultural losses from<br> climate change in the United States by half". It contains three main<br> archives:</p> <p>1. inputs: The temperature and water stress indicators for each crop,<br> along with county-level log-yields. Both the Bayesian and OLS<br> models are fit to this data. Static covariates are available in<br> us-bioclims-new.csv.<br> <br> Files:<br> <br> data-inputs-futureedds1.zip - data-inputs-futureedds3.zip:<br> GCM-specific quantifications of the exceedance degree days used as<br> temperature predictors.<br> <br> data-inputs-other.zip: All other input files.</p> <p>2. bayes: Each of the variables fit in the Bayesian model, for each<br> MCMC draw from the posterior distribution. The contained directory<br> includes for each crop versions with constant variance (-variance)<br> and under cross-validation (-cv). The counties are ordered<br> according to fips-usa.csv.</p> <p> Files:</p> <p> data-bayes-checks: Posterior predictive check outputs.</p> <p> data-bayes-full-constvar-*.zip: MCMC draws for the all-years fit<br> assuming constant variance. Divided to make the files more<br> manageable.<br> <br> data-bayes-full-varvar.zip: MCMC draws for the all-years fit<br> assuming county-specific variances.</p> <p> data-bayes-cv-constvar.zip: MCMC draws for the cross-validation fit<br> assuming constant variance.</p> <p> data-bayes-cv-varvar-*.zip: MCMC draws for the cross-validation fit<br> assuming county-specific variances. Divided to make the files more<br> manageable.</p> <p>3. optim: Optimization model results. The results/ directory contains<br> the optimization results for each set of assumptions presented in<br> the appendix, applied to the average yield levels. The results-mc/<br> directory contains profit, yield, and optimization results for each<br> draw of the MCMC independently.</p> <p> Files:<br> <br> data-optim-inputs-mc.zip: Locally optimal cropping files, under<br> MCMC draws of the model.<br> <br> data-optim-results-mc.zip: Constrainted optimization model results,<br> under MCMC draws of the model.<br> <br> data-optim-results.zip: Constrained optimization model results,<br> using the average parameters of the model.<br> <br> data-optim-other.zip: Other outputs of the optimization process.<br> </p>
Data from: No‐till establishment improves the climate benefit of bioenergy crops on marginal grasslands
<p>Expanding biofuel production is expected to accelerate the conversion of unmanaged marginal lands to meet biomass feedstock needs. Greenhouse gas production during conversion jeopardizes ensuing climate benefits, but most research to date has focused only on conversion to annual crops and only following tillage. Here we report the global warming impact of converting USDA Conservation Reserve Program (CRP) grasslands to three types of bioenergy crops using no-till (NT) versus conventional tillage (CT). In three CRP fields planted to continuous corn, switchgrass, or restored prairie we established replicated NT and CT plots. For the two years following an initial soybean year in all fields, we found that, on average, NT conversion reduced nitrous oxide (N2O) emissions by 50% and carbon dioxide (CO2) emissions by 20% compared to CT conversion. Differences were higher in year 1 than in year 2 in the continuous corn field, and in the two perennial systems the differences disappeared after year 1. In all fields net CO2 emissions (as measured by eddy covariance) were positive for the first two years following CT establishment, but following NT establishment net CO2 emissions were close to zero or negative, indicating net C sequestration. Overall, NT improved the global warming impact of biofuel crop establishment following CRP conversion by over 20-fold compared to CT (-6.01 Mg CO2e ha-1 yr-1 for NT vs. -0.25 Mg CO2e ha-1 yr-1 for CT, on average). We also found that IPCC estimates of N2O emissions (as measured by static chambers) greatly underestimated actual emissions for converted fields regardless of tillage. Policies should encourage adoption of NT for converting<br> marginal grasslands to perennial bioenergy crops in order to reduce carbon debt and maximize climate benefits.</p>
Data from: Climate change amplifies plant invasion hotspots in Nepal
Aim Climate change has increased the risk of biological invasions, particularly by increasing the climatically suitable regions for invasive alien species. The distribution of many native and invasive species has been predicted to change under future climate. We performed species distribution modelling of invasive alien plants (IAPs) to identify hotspots under current and future climate scenarios in Nepal, a country ranked among the most vulnerable countries to biological invasions and climate change in the world. Location Nepal Methods We predicted climatically suitable niches of 24 out of the total 26 reported IAPs in Nepal under current and future climate (2050 for RCP 6.0) using an ensemble of species distribution models. We also conducted hotspot analysis to highlight the geographic hotspots for IAPs in different climatic zones, land cover, ecoregions, physiography, and federal states. Results Under future climate, climatically suitable regions for 75% of IAPs will expand in contrast to a contraction of the climatically suitable regions for the remaining 25% of the IAPs. A high proportion of the modelled suitable niches of IAPs occurred on agricultural lands followed by forests. In aggregation, both extent and intensity (invasion hotspots) of the climatically suitable regions for IAPs will increase in Nepal under future climate scenarios. The invasion hotspots will expand towards the high-elevation mountainous regions. In these regions, land use is rapidly transforming due to the development of infrastructure and expansion of tourism and trade. Main conclusions Negative impacts on livelihood, biodiversity, and ecosystem services, as well as economic loss caused by IAPs in the future, may be amplified if preventive and control measures are not immediately initiated. Therefore, the management of IAPs in Nepal should account for the vulnerability of climate change-induced biological invasions into new areas, primarily in the mountains.
Supplementary material 1 from: Datta A, Schweiger O, Kühn I (2020) Origin of climatic data can determine the transferability of species distribution models. NeoBiota 59: 61-76. https://doi.org/10.3897/neobiota.59.36299
Variable selection using cluster analsys based on Spearman's rank corellation and UPGMA method for agglomeration
Data from: Climate and soil nutrients differentially drive multidimensional fine root traits in ectomycorrhizal‐dominated alpine coniferous forests
<ol> <li><span><span>Fine root traits vary greatly with environmental changes, but the understanding of root-trait variation and its drivers is limited over broad geographical scales, especially for ectomycorrhizal (ECM)-dominated conifers in alpine forests. Herein, the covariation patterns of and environmental controls for fine root traits among ECM-dominated conifers were examined to test whether and how climate and soil nutrients differentially affect fine root trait variations.</span></span></li> <li><span><span>Eight traits of first- and second-order roots were measured, i.e., root diameter (RD), specific root length (SRL), branching intensity (BRI), root tissue density (RTD), mycorrhizal colonization rate (MCR), and concentrations of carbon (C), nitrogen (N) and phosphorus (P), across 76 alpine coniferous populations on the eastern Tibetan Plateau, China.</span></span></li> <li><span><span>Our results showed that variations of the fine root traits fell into two major dimensions: the first dimension (32.39% of the total variance) was mainly represented by RD and SRL, potentially conveying a tradeoff between root lifespan and efficiency of resource foraging; the second dimension (23.70% of the variance) represented coordinated variation for root nutrients (i.e., N and P) and RTD, which depicts the conservation-acquisition tradeoff in resource uptake, i.e., root economic spectrum (RES). Variations in RD and SRL were mainly driven by climatic variables, characterized by a significant increase in RD and a decrease in SRL with increasing mean annual precipitation. In contrast, variations in fine root nutrients (i.e., N and P) and RTD were primarily driven by soil fertility, showing a significant increase in root N and P concentrations but a decrease in RTD with increasing soil resource levels.</span></span></li> <li><span><span><i>Synthesis. </i>Our study clearly shows two distinct dimensions of the variation of fine root traits in ECM-dominated alpine coniferous forests, providing further evidence of the inherent multidimensionality of root traits. Moreover, our findings highlight different roles of climatic and soil variables in driving the variation of fine root traits, potentially leading to the multidimensionality of root traits. This study provides new insights for understanding and predicting shifts in plant belowground strategies in climate-sensitive alpine forests worldwide.</span></span></li> </ol>
Data from: Dynamics of deep soil carbon - insights from 14C time series across a climatic gradient
Quantitative constraints on soil organic matter (SOM) dynamics are essential for comprehensive understanding of the terrestrial carbon cycle. Deep soil carbon is of particular interest, as it represents large stocks and its turnover times remain highly uncertain. In this study, SOM dynamics in both the top and deep soil across a climatic (average temperature ~1-9 °C) gradient are determined using time-series (~20 years) 14C data from bulk soil and water-extractable organic carbon (WEOC). Analytical measurements reveal enrichment of bomb-derived radiocarbon in the deep soil layers on the bulk level during the last two decades. The WEOC pool is strongly enriched in bomb-derived carbon, indicating that it is a dynamic pool. Turnover time estimates of both the bulk and WEOC pool show that the latter cycles up to a magnitude faster than the former. The presence of bomb-derived carbon in the deep soil, as well as the rapidly turning WEOC pool across the climatic gradient implies that there likely is a dynamic component of carbon in the deep soil. Precipitation and bedrock type appear to exert a stronger influence on soil C turnover time and stocks as compared to temperature.
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.