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473 results for “Soil temperatures”
Ecosystem-Scale Rainfall Manipulation in a Piñon-Juniper Forest at the Sevilleta National Wildlife Refuge, New Mexico: Soil Temperature Data (2006-2013)
Climate models predict that water limited regions around the world will become drier and warmer in the near future, including southwestern North America. We developed a large-scale experimental system that allows testing of the ecosystem impacts of precipitation changes. Four treatments were applied to 1600 m2 plots (40 m × 40 m), each with three replicates in a piñon pine (Pinus edulis) and juniper (Juniper monosperma) ecosystem. These species have extensive root systems, requiring large-scale manipulation to effectively alter soil water availability.  Treatments consisted of: 1) irrigation plots that receive supplemental water additions, 2) drought plots that receive 55% of ambient rainfall, 3) cover-control plots that receive ambient precipitation, but allow determination of treatment infrastructure artifacts, and 4) ambient control plots. Our drought structures effectively reduced soil water potential and volumetric water content compared to the ambient, cover-control, and water addition plots. Drought and cover control plots experienced an average increase in maximum soil and air temperature at ground level of 1-4° C during the growing season compared to ambient plots, and concurrent short-term diurnal increases in maximum air temperature were also observed directly above and below plastic structures. Our drought and irrigation treatments significantly influenced tree predawn water potential, sap-flow, and net photosynthesis, with drought treatment trees exhibiting significant decreases in physiological function compared to ambient and irrigated trees. Supplemental irrigation resulted in a significant increase in both plant water potential and xylem sap-flow compared to trees in the other treatments. This experimental design effectively allows manipulation of plant water stress at the ecosystem scale, permits a wide range of drought conditions, and provides prolonged drought conditions comparable to historical droughts in the past – drought events for whi
Spectral data presented in Hinrichs J L, Lucey P G. Temperature-dependent near-infrared spectral properties of minerals, meteorites, and lunar soil.
<p>In this dataset, we present the spectral data in paper: Hinrichs, J. L., & Lucey, P. G. (2002). Temperature-dependent near-infrared spectral properties of minerals, meteorites, and lunar soil. <em>Icarus</em>, <em>155</em>(1), 169-180.</p>
Moisture and temperature effects on the radiocarbon signature of respired carbon dioxide to assess stability of soil carbon in the Tibetan Plateau
<p>Radiocarbon data set and code for the prediction of D14C values in bulk soil and respired CO2.</p> <p>Lab results for TOC, TN, TIC, Dap and physico-chemical properties of grassland and peatland soils</p> <p> </p>
Changes in community-weighted trait mean, functional diversity, soil chemical properties and temperature along an elevational gradient in Tenerife, Canary Islands
<p>This dataset comprises community-weighted trait means and functional diversity of leaf traits, chemical soil properties and temperature recorded in roadside (disturbed) and interior (less disturbed) plots, along an elevational gradient of 2,300 m in Tenerife, Canary Islands. The leaf traits measured were specific leaf area (SLA), nitrogen, nitrogen to phosphorus ratio, leaf dry matter content (LDMC) and carbon to phosphorus ratio. The soil chemical properties measured were pH, nitrogen, nitrogen to phosphorus ratio, carbon to phosphorus ratio, calcium, potassium, magnesium and cation exchange capacity. Also the scores of the three first axes derived from a PCA analysis including the soil chemical properties are included. The temperature variables consist of bioclimatic variables Bio10 (mean temperature of the warmest quarter) and Bio11 (mean temperature of the coldest quarter). This dataset has been used for the analysis presented in Ratier Backes et al. (2021).</p>
Soil property, microbial abundance, and plant and invertebrate biomass data across a natural soil temperature gradient in Iceland from August 2018
<p><span>This is a dataset of soil physiochemical properties, bacterial and fungal abundance, and above and belowground plant and invertebrate biomass, sampled at 40 plots in the Hengill geothermal valley, Iceland, from 15<sup>th</sup> to 22<sup>nd</sup> August 2018. The plots span a temperature gradient of 10</span><span>-35 °C over the sampling period, and this temperature gradient is consistent over time. The dataset also includes data on the decomposition rate of soil organic matter, which was sampled at 60 plots in the Hengill valley from May to July 2015.</span></p>
Combined_ST_SM_Changes_Impacts soil temperature observations
<p>Observations of soil temperature used to evaluate ERA5-Land soil temperature</p>
Forest soil temperature data
<b>Description: </b><p>Microclimate datalogger recordings of belowground temperature</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/111"><b>Microclimate stratification in modified forests</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=89">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>Forest soil temperature data</b> (Worksheet Data)</p><p>Dimensions: 411690 rows by 6 columns</p><p>Description: Microclimate datalogger recordings of belowground temperature</p><p>Fields: </p><ul><li><b>Plot</b>: Location of record (Field type: Location)</li><li><b>time</b>: Date and time of record (Field type: Datetime)</li><li><b>Temp</b>: Air temperature 1 m above ground (Field type: Numeric)</li><li><b>LoggerType</b>: Make of datalogger that was used (Lascar or iButton) (Field type: ID)</li><li><b>LoggerID</b>: Unique reference number for the datalogger (Field type: ID)</li></ul><br></li></ol><p><b>Date range: </b>2011-04-20 to 2012-07-10</p><p><b>Latitudinal extent: </b>4.6353 to 4.7714</p><p><b>Longitudinal extent: </b>116.9477 to 117.7028</p>
Dataset from two meteorological stations with water and soil temperature measurements in the Alqueva reservoir (Portugal)
<p>In the multidisciplinary <strong>AL</strong>entejo <strong>O</strong>bservation and <strong>P</strong>rediction systems<strong> </strong>project (ALT20-03-0145-FEDER-000004), which aims to strengthen research and innovation in the Alentejo region (southern Portugal), one of the main objectives was to study and model the meteorological conditions in the Alqueva reservoir, in particular their spatial variations within a few hundred meters.</p> <p>The shared hourly dataset, using Coordinated Universal Time (UTC), covers the period from 2018 to 2023 and includes measurements from two meteorological stations located in the Alqueva reservoir, the largest artificial lake in Europe. One station, Montante, is located on a floating platform with a water depth of approximately 70 meters (38.2235 N, 7.4595 W), to the west of the second station, CidAlmeida (38.21539 N, 7.45454 W), which is about 1 km away on land, very close to the water.</p> <p>According to the World Meteorological Organisation (WMO) standards, the data were sampled every second, and hourly data were calculated in post-processing. The dataset includes hourly accumulated precipitation (<em>mm</em>) and hourly average measurements of surface water temperature (at a depth of 0.25 m), soil temperature (at a depth of 0.15 m) and various meteorological parameters: wind speed (<em>m/s</em>) and direction (<em>degrees</em>), relative humidity (%), upward/downward solar radiation (<em>W/m</em><sup><em>2 </em></sup>) and air temperature (<em>°C </em>). All parameters are measured at both stations, except the hourly average water temperature at a depth of 0.25 m, which is only available at the Montante station, and the hourly accumulated precipitation, hourly average soil temperature and wind direction, which are only available at the CidAlmeida station.</p> <p>Hourly data were not subjected to rejection criteria based on the percentage of errors; instead, a column with this percentage is provided, allowing potential data users to apply their own rejection criteria. Daily extremes (daily maximums and minimums for air temperature, relative humidity, and daily maximum gust) are only provided for days where the percentage of errors does not exceed 25% of the 1440 minutes of each day. The daily error percentage for each of the measured parameters at the two stations for the period 2018-2023 is available.</p> <p>Finally, the repository also includes two codes (one for each weather station) written in Visual Basic, which enable data transmission and real-time statistical processing.</p> <p><strong>Fundings:</strong></p> <p>Gonçalo Rodrigues was supported by the Portuguese Foundation for Science and Technology, I.P (Grant 2020.05752.BD). The work is co-funded by national funds through FCT – Fundação para a Ciência e Tecnologia, I.P., in the framework of the ICT project (references UIDB/04683/2020 and UIDP/04683/2020) and by the ALOP project (ALT20-03-0145-FEDER-000004). </p>
Responses of soil temperature, moisture, and respiration to five-year warming and nitrogen addition in a semi-arid grassland
<p><span>How climate warming interacts with atmospheric nitrogen (N) deposition to affect carbon (C) release from soils remains largely elusive, posing a major challenge in projecting climate change‒terrestrial C feedback. As part of a five-year (2006–2010) field manipulative experiment, this study was designed to examine the effects of 24-hour continuous warming and N addition on soil respiration and explore the underlying mechanisms in a semi-arid grassland on the Mongolian Plateau, China. Across the five years and all plots, soil respiration was not changed under the continuous warming, but was decreased by 3.7% under the N addition. The suppression of soil respiration by N addition in the third year and later could be mainly due to the reductions in the forb-to-grass biomass ratios. Moreover, there were interactive effects between continuous warming and N addition on soil respiration. Continuous warming increased soil respiration by 5.8% in the ambient N plots, but reduced it by 6.3% in the enriched N plots. Soil respiration was unaffected by N addition in the ambient temperature plots yet decreased by 9.4% in the elevated temperature plots. Changes of soil moisture and the proportion of legume biomass in the community might be primarily responsible for the non-additive effects of continuous warming and N addition on soil respiration.</span> This study provides empirical evidence for the positive climate warming‒soil C feedback in the ambient N condition. However, N deposition reverses the positive warming‒soil C feedback into a negative feedback, leading to decreased C loss from soils under a warming climate. Incorporating our findings into C-cycling models could reduce the uncertainties of model projections for land C sink and global C cycling under multifactorial global change scenarios.</p>
Effect of soil temperature on nitrogen fixation in Alnus
<p><span><span><span><span><span><span><span><span><span><span><span><i>Purpose:</i> The short growing season and cold climate of the boreal forest can restrict soil nitrogen availability, limiting plant growth and ecosystem productivity. Vascular nitrogen-fixing plants should have an advantage in low nitrogen environments. Yet, their abundance in the boreal forest is low. How nitrogen fixation is affected when temperature differences occur between the soil and air, especially in the spring when soil temperatures remain cool, has not been documented in actinorhizal shrubs.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><i>Methods:</i> A lab study was performed on <i>Alnus alnobetula</i> subsp. <i>crispa</i> (Aiton) Raus. For 13 weeks, soil was kept at either 10˚C, 14˚C or 16˚C, independently of shoot temperature, at 20˚C.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><i>Results:</i> Soils at 14˚C and 10˚C inhibited whole-plant nitrogen fixation (by 53% and 68%) and photosynthesis (by 43% and 39%), respectively, compared to soils at 16˚C. Reductions in photosynthetic rate were mainly attributed to a reduction in the fixed nitrogen supply and subsequent reduction in chlorophyll formation. Photosynthesis was not reduced immediately, suggesting some utilization of a nitrogen source not supplied from current fixation. Reduced amounts of fixed nitrogen and photosynthates resulted in diminished biomass production and relative growth rate.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><i>Conclusion:</i> The assumed advantages of being a nitrogen-fixing plant in a low nitrogen environment may be constrained by soil temperature to a larger extent than previously considered. This may restrict the abundance of nitrogen-fixing species in the boreal forest. </span></span></span></span></span></span></span></span></span></span></span></p>
Synthetic soil temperature time-series
<p>The synthetic experiments are implemented to investigate the impact of different environmental conditions on the uncertainty of thermal diffusivity estimates. We generate synthetic temperature fields that represent various types of temperature gradients and fluctuations. This is achieved through forward modeling (i.e., heat-conduction process in a heterogeneous medium using an explicit finite difference method) with initial, top, and bottom boundary conditions set equal to the temperature time series observed at a monitoring site in Alaska during summer (synthetic_data_summer.csv ) and autumn (synthetic_data_autumn.csv ), and by assuming a soil column composed of three layers (i.e., top layer at 0.05–0.1 m, middle layer at 0.1–0.42 m, and bottom layer at 0.42–1.05 m). The thermal diffusivity in the three layers is assumed to be constant over time and equal to 0.16, 0.27 and 0.43 mm<sup>2</sup>s<sup>−1</sup> for the case of summer temperatures and 0.25, 0.75 and 0.6 mm<sup>2</sup>s<sup>−1</sup> for autumn.</p> <p>Each .csv file has 14 columns: first column containes information on the date and time on which temperature was recorded, the remaining 13 columns are soil temperature at 0.05, 0.10, 0.15, 0.20 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95 and 1.05 m below the ground surface.</p> <p>Impact of soil temperature trend and fluctuations on thermal diffusivity estimates is evaluated for various synthetic temperature fields including (a) summer trend and fluctuations (synthetic_data_summer.csv ), (b) detrended fluctuations (synthetic_data_summer_detrended.csv ), (c) smoothed out daily and smaller fluctuations (synthetic_data_summer_noDiurnalFluct.csv), and (d) without fluctuations (synthetic_data_summer_noFluct.csv ).</p>
Data for the effects of temperature variation on the thermal adaptation of soil microbial respiration
<p>Data for the effects of temperature variation on the thermal adaptation of soil microbial respiration</p>
A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data
<p> </p> <p>A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data.</p> <p>Introduction</p> <p>This dataset is the Planet Labs PBC (VanderSat B.V.) contribution to the ESA 4DMED hydrology project (<a href="https://www.4dmed-hydrology.org/">https://www.4dmed-hydrology.org/</a>). It includes Soil Moisture, Land Surface Temperature and Vegetation Optical Depth for the 4DMED spatial domain and time period (2015-2021) at 1km pixel size. If you use the data please include the following reference:</p> <blockquote> <p>Jaap Schellekens, Tessa Kramer, Michel van Klink, Robin van der Schalie, Yoann Malbeteau, Arjan Geers, Richard de Jeu. (2022) <em>A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data</em>. DOI: 10.5281/zenodo.7684993. Planet Labs PBC/VanderSat B.V., ESA Contract No. 4000136272/21/I-EF</p> </blockquote> <p> </p> <p><em>Figure 1: Average L-Band Soil moisture for 2020 over the 4dmed spatial domain</em></p> <p>Variables and files</p> <p>The dataset consists of the following files and products for the 4DMED domain. Detailed information about the products can also be found at <a href="https://docs.vandersat.com/data_products/soil_water_content/specification.html">docs.vandersat.com</a>:</p> <ul> <li><strong><code>planet-teff-4dmed-V4.0.zip</code></strong> - LST (TEFF) ascending (daytime) and descending (nighttime) <ul> <li><code>TEFF-AMSR2-ASC_V4.0_1000</code> <ul> <li>Land surface temperature daytime (13:30 solar time) at 1 km</li> </ul> </li> <li><code>TEFF-AMSR2-DESC_V4.0_1000</code> <ul> <li>Land surface temperature nighttime (01:30 solar time) at 1 km</li> </ul> </li> </ul> </li> <li><strong><code>planet-teff-qf-4dmed-V4.0.zip</code></strong> - LST (TEFF) quality flags <ul> <li><code>QF-TEFF-AMSR2-ASC_V4.0_1000</code> <ul> <li>Land surface temperature daytime quality flag. <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">docs.vandersat.com flags</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">docs.vandersat.com python example</a></li> </ul> </li> <li><code>QF-TEFF-AMSR2-DESC_V4.0_1000</code> <ul> <li>Land surface temperature daytime quality flag. <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">docs.vandersat.com flags</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">docs.vandersat.com python example</a></li> </ul> </li> </ul> </li> <li><strong><code>planet-vod-4dmed-V4.1.zip</code></strong> - vegetation optical depth C and X band (interpolated from C3S passive soil moisture) <ul> <li><code>VOD_AMSR2_C1_DESC_V41_1000</code> <ul> <li>C1 band Vegetation Optical Depth (nighttime, 01:30 solar time) at 1km (interpolated from 25 km)</li> </ul> </li> <li><code>VOD_AMSR2_X_DESC_V41_1000</code> <ul> <li>X band Vegetation Optical Depth (nighttime, 01:30 solar time) at 1km (interpolated from 25 km)</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-4dmed-V4.0.zip</code></strong> - All soil moisture products (C1, X and L-band) <ul> <li><code>SM-AMSR2-C1-DESC_V4.0_1000</code> <ul> <li>C1 band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>SM-AMSR2-X-DESC_V4.0_1000</code> <ul> <li>X band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>SM-SMAP-L-DESC_V4.0_1000</code> <ul> <li>L band soil moisture (06:00 solar time) at 1km</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-qf-4dmed-V4.0.zip</code></strong> - Soil moisture quality maps see <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">https://docs.vandersat.com/data_products/soil_water_content/data_flags.html</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python</a> <ul> <li><code>QF-SM-AMSR2-C1-DESC_V4.0_1000</code> <ul> <li>C1 band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>QF-SM-AMSR2-X-DESC_V4.0_1000</code> <ul> <li>X band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>QF-SM-SMAP-L-DESC_V4.0_1000</code> <ul> <li>L band soil moisture quality flags (06:00 solar time) at 1km</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-cor-4dmed-V4.0.zip</code></strong> - Yearly correlation maps of soil moisture derived from the difference microwave bands. To be used as an extra quality indicator (for example undetected RFI) or for uncertainty estimation <ul> <li><code>SM-CORR-C1-X-DESC_V4.0_1000</code> - yearly C1 vs X band pearson's correlation maps</li> <li><code>SM-CORR-L-C1-DESC_V4.0_1000</code> - yearly L vs C1 band pearson's correlation maps</li> <li><code>SM-CORR-L-X-DESC_V4.0_1000</code> - yearly L vs X band pearson's correlation maps</li> </ul> </li> <li><strong><code>planet-aux-flags-4dmed-V4.0</code></strong> - Extra flags for frozen soil and bare soil. Determined at 0.25 degree and interpolated to the 4dmed grid <ul> <li><code>QF-SNOWFROZEN-AMSR2-ASC_1000::RD</code> - Frozen soil determined from dayttime data</li> <li><code>QF-SNOWFROZEN-AMSR2-DESC_1000::RD</code> - Frozen soil determined from nighttime data</li> <li><code>QF-BARESOIL-AMSR2-DESC_1000::RD</code> - Bare soil determined from nighttime data</li> <li><code>QF-BARESOIL-AMSR2-ASC_1000::RD</code> - Bare soil determined from daytime data</li> </ul> </li> </ul> <p>All files are archived into one zip file per product group. Each individual netcdf file in the zip file consists of one observation for the whole domain. If you need you can combine the files into one file using the cdo software <a href="https://code.mpimet.mpg.de/projects/cdo">https://code.mpimet.mpg.de/projects/cdo</a> (e.g. <code>cdo -f nc4c mergetime *.nc outfile.nc</code>).</p> <p>License</p> <p>The data for 4DMED is released under the Creative Commons license: CC BY-NC-SA 4.0 (<a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>)</p> <ul> <li>Contains modified Copernicus Sentinel data 2015-2021</li> <li>Contains modified JAXA GCOM-W1/AMSR2 data 2015-2021</li> <li>Contains modified SMAP L1B Radiometer data: Piepmeier, J. R., P. Mohammed, J. Peng, E. J. Kim, G. De Amici, J. Chaubell, and C. Ruf. 2020. SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures, Version 4,5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: <a href="https://doi.org/10.5067/ZHHBN1KQLI20">https://doi.org/10.5067/ZHHBN1KQLI20</a></li> </ul> <p>Contact</p> <p>Jaap Schellekens: <a href="mailto:jaap@planet.com">jaap@planet.com</a></p> <p>Versions</p> <ul> <li>1.0 Initial creation</li> <li>1.1 Adjusted 4DMED Mask. Data itself unchanged but more LST (TEFF) measurements added</li> <li>1.2 Removed VOD and replaced by 25km C3S VOD interpolated to 1km (V4.1)</li> </ul> <p>Further information</p> <p>More information on the data and the flags can be found at <a href="https://docs.vandersat.com/">https://docs.vandersat.com</a> and <a href="https://www.4dmed-hydrology.org/">https://www.4dmed-hydrology.org</a></p> <p>Background publications</p> <p>R.A.M. De Jeu, A.H.A. De Nijs, M.H.W. Van Klink (2016) <em>Method and system for improving the resolution of sensor data</em>, US10643098B2,EP3469516B1, WO2017216186A1</p> <p>De Jeu, R. A., Holmes, T. R., Parinussa, R. M., & Owe, M. (2014). <em>A spatially coherent global soil moisture product with improved temporal resolution</em>. Journal of hydrology, 516, 284-296.</p> <p>Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I. and Forkel, M., 2020. <em>The global long-term microwave vegetation optical depth climate archive (VODCA)</em>. Earth System Science Data, 12(1), pp.177-196.</p> <p>Schmidt, L., Forkel, M., Zotta, R.-M., Scherrer, S., Dorigo, W. A., Kuhn-Régnier, A., van der Schalie, R., and Yebra, M.: <em>Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties, Biogeosciences Discuss.</em> [preprint], <a href="https://doi.org/10.5194/bg-2022-85">https://doi.org/10.5194/bg-2022-85</a>, in review, 2022</p> <p>Van der Schalie, R., de Jeu, R.A.M., Kerr, Y.H., Wigneron, J.P., Rodríguez-Fernández, N.J., Al- Yaari, A., Parinussa, R.M., Mecklenburg, S. and Drusch, M. (2017), <em>The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E</em>, Remote Sensing of Environment, 189, pp.180-193.</p> <p>van der Vliet, M., van der Schalie, R., Rodriguez-Fernandez, N., Colliander, A., de Jeu, R., Preimesberger, W., Scanlon, T., Dorigo, W., 2020. Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records. Remote Sensing 12, 3439. <a href="https://doi.org/10.3390/rs12203439">https://doi.org/10.3390/rs12203439</a></p>
Impact of soil temperature-difference on desert carbon-sink
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Divergent terrestrial responses of soil N2O emissions to different levels of elevated CO2 and temperature
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Changes in community-weighted trait mean, functional diversity, soil chemical properties and temperature along an elevational gradient in Tenerife, Canary Islands
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Data from: The impact of elevated temperature and drought on the ecology and evolution of plant-soil microbe interactions
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Mycorrhiza-dependent drivers of the positive rhizosphere effects on the temperature sensitivity of soil microbial respiration in subtropical forests
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Soil property, microbial abundance, and plant and invertebrate biomass data across a natural soil temperature gradient in Iceland from August 2018
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Responses of soil temperature, moisture, and respiration to five-year warming and nitrogen addition in a semi-arid grassland
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ScienceDex guides
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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.