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1,836 results for “flux”
Hubbard Brook Experimental Forest: Chemistry of Streamwater – Monthly Fluxes, Watershed 7, 1965 - ongoing
These data are monthly fluxes of solutes in stream water measured in watersheds of the Hubbard Brook Experimental Forest and are a part of the Hubbard Brook Watershed Ecosystem Record (HBWatER), which is a long-term record of stream and precipitation chemistry and volume. The solute fluxes in stream water are calculated as the product of the volume of stream water and solute concentrations. There are nine gaged watersheds at the Hubbard Brook Experimental Forest, some of which have been subjected to experimental manipulations. The calculation of fluxes is currently supervised by John Campbell (US Forest Service). The long-term stream water record is collected and maintained by the US Forest Service. The collection and management of the long-term stream and precipitation chemistry record was initiated in 1963 by Gene E. Likens, F. Herbert Bormann, Robert S. Pierce, and Noye M. Johnson. HBWatER is currently sustained by Tammy Wooster (Cary IES) and Jeff Merriam (USFS) and the dataset is curated and maintained by a team of researchers: Chris Solomon (Cary IES), Emma Rosi (Cary IES), Emily Bernhardt (Duke), Lindsey Rustad (USFS), John Campbell (USFS), Bill McDowell (UNH), Charley Driscoll (Syracuse U.), Mark Green (Case Western), and Scott Bailey (USFS). Current Financial Support for HBWatER is provided by NSF LTREB # 1907683 and the USDA Forest Service Northern Research Station. 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 US Forest Service, Northern Research Station.
Fluxes of Carbonyl Sulfide at Harvard Forest EMS Tower 2010-2013
Carbonyl sulfide (OCS), the most abundant sulfur compound in the atmosphere, controls the sulfur budget and aerosol loading of the stratosphere in times of low volcanic activity. OCS is also closely tied to the vegetative carbon cycle and may provide an independent measure of the photosynthetic uptake of carbon. However, the detailed nature of the biogeochemical cycling of OCS throughout the seasons in terrestrial ecosystems has not been thoroughly explored. We measured the seasonal response of the ecosystem flux of OCS above a deciduous temperate forest using an infrared laser absorption spectrometer at Harvard Forest (MA, USA) in 2011. Fluxes were calculated using two complementary approaches: gradient – flux (also known as flux – gradient; January – July, 2011) and eddy covariance flux (August – December, 2011), which agreed within the combined error for a period with both measurements. Strong uptake of OCS by the forest was observed during most of the growing season, but significant uptake also occurred when the deciduous leaves were not present. A strong diel cycle of mid-day OCS uptake was observed in May/June and August/September, consistent with OCS uptake by processes parallel to CO2 photosynthetic uptake, at the ecosystem scale, for these months. The results show that while overall OCS and CO2 both show evidence of vegetative uptake, the OCS flux cannot be explained by this process alone and the relationship between OCS and CO2 changes throughout the year above this mixed deciduous forest. The data imply that terrestrial uptake of OCS, and hence the stratospheric sulfur cycle, are potentially quite sensitive to extremes in temperature and soil moisture.
Atmospheric Gaseous Elemental Mercury Fluxes at Harvard Forest EMS Tower 2019-2020
In terrestrial ecosystems, dry deposition of atmospheric gaseous elemental mercury (GEM) is considered the dominant source of mercury accounting for 54% to 94% of mercury loads observed in soils, yet direct quantification of GEM deposition across forests is largely missing. The goal of this project is to quantify atmosphere-surface exchange of GEM at Harvard Forest for one full year, providing the first such record in a non-polluted forest. GEM exchange is measured using micrometeorological techniques using a large measurement tower, the only available method for direct, non-intrusive and time-extended measurements of net GEM exchange at the ecosystem level encompassing all underlying sinks and sources. A second objective was to partition GEM fluxes into canopy and soil contributions via deployment of two corresponding flux systems: one system was deployed above the forest canopy to measure ecosystem-level GEM exchange; a second system was deployed below the canopy to quantify soil contributions. This dataset contains an 18-month record of gaseous elemental mercury concentrations and fluxes measured at the EMS tower at Harvard Forest from May 2019 to August 2020.
Sap-flux and associated environmental data from ash tree monitoring at four urban parks in St. Paul, Minnesota, USA, from May to November of 2023.
We measured the sap flux density of eighteen ash trees (Fraxinus spp.) of varying health and canopy conditions across four urban parks in the City of St. Paul, MN, USA in summer 2023 with a low-cost, compact data logger system we designed in-house. Although many ash trees in the city have either been killed or removed to control the spread of Emerald Ash Borer, chemical insecticide treatments are available for trees that are in early stages infestation. The trees selected for the research have all been receiving insecticide treatment for a few years, but their health and canopy conditions vary. We also have collocated temperature, soil moisture, and precipitation measurements at the same site for summer 2023.
Fluxes project at North Temperate Lakes LTER: Random lake survey 2004
The overarching goal of this project is to understand carbon and nutrient cycles for a landscape on which terrestrial and freshwater systems are intimately connected in multiple and reciprocal ways. In the Northern Highlands region of Wisconsin, they are studying a spatially complex landscape in which water features make up almost half of the land area, with wetlands (27% of land surface) and lakes (13%) both prevalent throughout the region, interspersed in upland forests. Weather and limnological data from a set of 170 lakes in the NHLD samples summer 2004. The sampled lakes were from a random stratified subsample (N=300 of 7588 total) of all the lakes in the NHLD.
Fluxes project at North Temperate Lakes LTER: Spatial Metabolism Study 2007
Data from a lake spatial metabolism study by Matthew C. Van de Bogert for his Phd project, "Aquatic ecosystem carbon cycling: From individual lakes to the landscape." The goal of this study was to capture the spatial heterogeneity of within-lake processes in effort to make robust estimates of daily metabolism metrics such as gross primary production (GPP), respiration (R), and net ecosystem production (NEP). In pursuing this goal, multiple sondes were placed at different locations and depths within two stratified Northern Temperate Lakes, Sparkling Lake (n=35 sondes) and Peter Lake (n=27 sondes), located in the Northern Highlands Lake District of Wisconsin and the Upper Peninsula of Michigan, respectively.Dissolved oxygen and temperature measurements were made every 10 minutes over a 10 day period for each lake in July and August of 2007. Dissolved oxygen measurements were corrected for drift. In addition, conductivity, temperature compensated specific conductivity, pH, and oxidation reduction potential were measured by a subset of sondes in each lake. Two data tables list the spatial information regarding sonde placement in each lake, and a single data table lists information about the sondes (manufacturer, model, serial number etc.). Documentation:Van de Bogert, M.C., 2011. Aquatic ecosystem carbon cycling: From individual lakes to the landscape. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 156. Also see Van de Bogert, M.C., Bade, D.L., Carpenter, S.R., Cole, J.J., Pace, M.L., Hanson, P.C., Langman, O.C., 2012. Spatial heterogeneity strongly affects estimates of ecosystem metabolism in two north temperate lakes. Limnology and Oceanography 57, 1689-1700.
Fluxes project at North Temperate Lakes LTER: Hydrology Scenarios Model Output
A spatially-explicit simulation model of hydrologic flow-paths was developed by Matthew C. Van de Bogert and collaborators for his PhD project, " Aquatic ecosystem carbon cycling: From individual lakes to the landscape." The model is coupled with an in-lake carbon model and simulates hydrologic flow paths in groundwater, wetlands, lakes, uplands, and streams. The goal of this modeling effort was to compare aquatic carbon cycling in two climate scenarios for the North Highlands Lake District (NHLD) of northern Wisconsin: one based on the current climate and the other based on a scenario with warmer winters where lakes and uplands do not freeze, hereinafter referred to as the "no freeze" scenario. In modeling this "no freeze" scenario the same precipitation and temperature data as the current climate model was used, however temperature inputs were artificially floored at 0 degrees Celsius. While not discussed in his dissertation, Van de Bogert considered two other climate scenarios each using the same precipitation and temperature data as the current climate scenario. These scenarios involved running the model after artificially raising and lowering the current temperature data by 10 degrees Celsius. Thus, four scenarios were considered in this modeling effort, the current climate scenario, the "no freeze" scenario, the +10 degrees scenario, and the -10 degrees scenario. These data are the outputs of the model under the different scenarios and include average monthly temperature, average monthly rainfall, average monthly snowfall, total monthly precipitation, daily evapotranspiration, daily surface runoff, daily groundwater recharge, and daily total runoff. Note that the results of how temperature inputs influence aquatic carbon cycling under these different scenarios is not included in this data set, refer to Van de Bogert (2011) for this information.Documentation: Van de Bogert, M.C., 2011. Aquatic ecosystem carbon cycling: From individual lakes to the landscape. Pr
Flux Tower Data for Fowling Point Marsh at the Virginia Coast Reserve 2007-2008
This dataset contains measurements of CO2 and H2O concentrations, temperature and wind speed made every 1/10th of a second. Important note: due to the high frequency of measurements, this dataset is very large (>25 GB) and may take a long time to download. For this reason the main vcrflux_cpole.csv file is provided as well in compressed (.gz and .zip) forms which are only about 6 GB. There are two additional files. Tide_PPT_temp_2007_2008.csv contains 1-per-minute data on tide level, temperature at different heights on the tower and precipitation. Wind_light_temp_2007_2008.csv contains 1-minute summaries of wind, light, and temperature. The research was conducted at the Virginia Coastal Reserve Long Term Ecological Research (VCR LTER) site on the Eastern Shore of Virginia, USA. The flux tower site (37deg 24'39.85"N, 75deg 50'0.53"W) a lagoonal salt marsh which is located near the area of Fowling point. The site is located at about 2.2 kilometers away from the mainland and 10.7 kilometers away from Hog Island, the nearest barrier island. The flux tower is situated at about 80 meters away from the creek edge, on the lagoonal salt marsh.
Fowling Point Marsh Flux Tower Data, 2014-2017
A flux tower was operated on Fowling Point Marsh near Nassawadox, VA from 2014-2017. The flux tower is located about 2 km from the mainland and about 85 m away from a major creek edge. The marsh soil is tidally inundated on a semi-diurnal cycle, with extreme levels of inundation reaching 1.0 m above the mean marsh surface. Continuous meteorological and eddy covariance measurements were made on a 7-m flux tower located in the salt marsh. An eddy covariance unit was mounted at 3.7 m above the sediment surface. Data for BioMet sensors are provided as a table. BioMET data consists of 1min averaged meteorological data. Proprietary LiCOR .ghg files at 20Hz are included in Tape ARchive (TAR) files. Individual GHG files can be renamed as .zip files and uncompressed to reveal an internal metadata file and the data itself. A PDF file identifies the instrumentation and the wiring used to connect them. A summary file contains statistical summaries of the original GHG files.
Data and software: Stress and heat flux via automatic differentiation
<h4><strong>glp-archive</strong></h4><h2><strong>Code and Data for "Stress and heat flux with automatic differentiation"</strong></h2><p>This repository contains data, code, and related artefacts supporting the following publication (<a href="https://arxiv.org/abs/2305.01401">preprint</a>):</p><p>Stress and heat flux via automatic differentiation</p><p>by Marcel F. Langer, J. Thorben Frank, and Florian Knoop</p><p><i>J. Chem. Phys.</i> 159, 174105 (2023) <a href="https://doi.org/10.1063/5.0155760">doi:10.1063/5.0155760</a></p><p>This repository is available at <a href="https://github.com/sirmarcel/glp-archive">https://github.com/sirmarcel/glp-archive</a>. Selected versions are archived on Zenodo, under <a href="https://doi.org/10.5281/zenodo.7852529">doi:10.5281/zenodo.7852529</a>.</p><h2><strong>Overview</strong></h2><p>Each subfolder in this repository contains a README.md with additional information. The subfolders are:</p><ul><li>results/: Data and code that produced the figures in the manuscript</li><li>work/: Computational workflows, models, etc.</li><li>infra/: Project-specific infrastructure code</li><li>meta/: Scripts for assembling this archive; can be ignored but is retained for transparency.</li></ul><h2><strong>Related external code</strong></h2><p>The work in this repository relies on a few tools that the authors maintain separately:</p><ul><li><a href="https://github.com/sirmarcel/glp">glp</a> implements the quantities discussed in the manuscript</li><li><a href="http://github.com/thorben-frank/mlff">mlff</a> implements the so3krates model</li><li><a href="https://github.com/flokno/tools.mlff">tools.mlff</a> provides tools for the equation of state experiments</li></ul><p>These tools were developed during the work in the manuscript. The following versions/tags reflect what was used to obtain results:</p><ul><li>glp @ v0.1.0 (tag)</li><li>mlff @ v1.0 (branch)</li><li>mlff.tools @ v0.0.1</li></ul><p>We additionally note that the GK-MD functionality has been factored out into <a href="https://github.com/sirmarcel/gkx">gkx</a>.</p><h2><strong>Versions</strong></h2><ul><li>v1.1: published version, archived at <a href="https://doi.org/10.5281/zenodo.8406532">doi:10.5281/zenodo.8406532</a></li><li>v1.0: arXiv submission v1, archived at <a href="https://doi.org/10.5281/zenodo.7852530">doi:10.5281/zenodo.7852530</a></li></ul>
Data for Table S10 of the article "Source-to-sink aeolian fluxes from arid landscape dynamics in the Lut Desert"
<p>Exhaustive list of the 227 individual denudation rates in arid areas compiled to estimate median denudation rate and sediment discharge for the internal river system of the Lut watershed.</p>
Quality-checked horizontal particle flux data collected using a snow particle counter on board the R/V Akademik Tryoshnikov in the Southern Ocean during the austral summer of 2016/17 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Flux of particles (snow, rain and other particles including sea spray) were recorded passing through a photo-electric snow particle counter installed on board the R/V Akademik Tryoshnikov as part of the Antarctic Circumnavigation Expedition (ACE). Data were recorded from January to March 2017 in the Southern Ocean. Here we present the finalised, quality-checked, horizontal particle flux data where counts have been averaged over a one-minute period.</p> <p><strong>Dataset contents</strong></p> <ul> <li>SPC_HPF_windtrue_1min.csv, data file, comma-separated values</li> <li>SPC_HPF_windtrue_1min.png, metadata, portable network graphics</li> <li>SPC_HPF_windtrue_saveplot.py, script, Python code</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This quality-checked horizontal particle flux dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Intermediate processing stage of horizontal particle flux data collected using a snow particle counter on board the R/V Akademik Tryoshnikov in the Southern Ocean during the austral summer of 2016/17 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Flux of particles (snow, rain and other particles including sea spray) were recorded passing through a photo-electric snow particle counter installed on board the R/V Akademik Tryoshnikov as part of the Antarctic Circumnavigation Expedition (ACE). Data were recorded from January to March 2017 in the Southern Ocean. Here we present an intermediate step in data processing, with relative horizontal particle flux of particles with a size between 36 – 2000 μm averaged over one-minute periods. Data are presented in daily files.</p> <p><strong>Dataset contents</strong></p> <ul> <li>SPC_HPF_1min_YYYY_MM_DD.csv, data files, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This one-minute averaged horizontal particle flux dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Quantifying the Sensitivity of Sea Level Change in Coastal Localities to the Geometry of Polar Ice Mass Flux -- Supplemental Data Set: Sea Level Sensitivity Kernels
<p><strong>Quantifying the Sensitivity of Sea Level Change in Coastal Localities to the Geometry of Polar Ice Mass Flux<br> SUPPLEMENTAL DATA SET: SEA LEVEL SENSITIVITY KERNELS</strong></p> <p>To accompany</p> <p> Jerry X. Mitrovica, Carling C. Hay, Robert E. Kopp, Christopher Harig, and<br> Konstantin Laytchev (2018). Quantifying the Sensitivity of Sea Level Change<br> in Coastal Localities to the Geometry of Polar Ice Mass Flux. Journal of<br> Climate. doi: 10.1175/JCLI-D-17-0465.1.</p> <p>We provide sea level kernels for ~740 tide gauge sites in the Permanent Service for Mean Sea Level (PSMSL) database (Holgate et al., 2013). Kernels associated with sensitivities to Greenland and Alaskan glacier melt are given on a spatial grid covering the globe, with 512 latitude rows (i=1,512) and 1024 longitude (j=1,1024) columns.</p> <p>Longitude values are evenly spaced moving eastward from Greenwich (the jth grid point has an east longitude value of (j-1)×360°/1024). Latitude values are Gauss-Legendre points beginning close to the North Pole and ending near the South Pole. Kernels associated with sensitivities to Antarctic melt are given on a spatial grid covering the globe, with 256 (Gauss-Legendre) latitude rows (i=1,256) and 512 longitude (j=1,512) columns. Longitude values are evenly spaced moving eastward from Greenwich.</p> <p>The format of the files is: </p> <p> grid_sitenumber_region.txt</p> <p>where “region” is either “green” (Greenland), “ant” (Antarctic) or “Alaska” (Alaska). The list of sites (and site numbers) is provided in the sites.txt file. The first 8 sites in this list were test sites and can be ignored.</p>
Majadas de Tietar: Ecosystem level and understorey carbon, water, and energy fluxes in a Mediterranean tree-grass ecosystem
<p>This dataset contains a subset of measurements collected at the experimental site Majadas de Tietar. We collected ecosystem level and understorey carbon, water, and energy fluxes in a Mediterranean Savanna using the eddy covariance technique and a series of meteorological sensors for the time period December 2015 - February 2018. The dataset is used for the development of a series of R packages including 'bigleaf' (Knauer et al., 2018).</p> <p>The experimental site is collected in Majadas de Tietar (Casals et al., 2009) located in western Spain (39°56′25″N 5°46′29″W). The ecosystem is a typical “Iberic Dehesa”, which is characterized by an herbaceous stratum of native pasture and sparse trees, for the majority (~98%) Quercus ilex. The tree density is about 20–25 trees/ha, the fractional cover of trees is about 20%, mean DBH of 46 cm, and a canopy height of about 8 m. (El-Madany et al., 2018). The herbaceous layer is composed of native annual species of the three main functional plant forms (grasses, forbs and legumes), whose fractional cover varies seasonally and is characterized by important inter-annual variations in the seasonal dynamics related to the onset of the dry period.</p> <p>Fluxes were measured with the eddy covariance technique with two different systems, one at ecosystem scale to characterize the fluxes of the whole ecosystem (15.5 m above ground), and one at 1.65 m above ground in an open space to measure the fluxes of the well-established understory grass layer.</p> <p>The description of the set-up, equipment and processing used to calculate ecosystem scale fluxes are described in El-Madany et al., (2018), while for the understory tower can be found in Perez-Priego et al., (2017).</p> <p>The dataset is composed of two files: 'ESLMa_MainTower', which is the ecosystem eddy covariance system, and 'ESLMa_SubCanopy', which is the understory eddy covariance system. The dataset contains half-hourly, processed eddy covariance of the ecosystem and understory tower, as well as the main biometeorological data used in the big-leaf package (net radiation, soil heat fluxes, horizontal wind velocity, atmospheric pressure, precipitation, air temperature). All the processing was conducted with EddyPro software (version 5.2.0, LI-COR Biosciences Inc., Lincoln, NE, USA) and the ustar filtering, gap-filling and partitioning with the R package REddyProc (Wutzler et al., 2018). The variables and the units are described in the Readme.txt file released with the dataset.</p> <p><strong>References</strong></p> <p>Casals, P. et al., 2009. Soil CO2 efflux and extractable organic carbon fractions under simulated precipitation events in a Mediterranean Dehesa. Soil Biol. Biochem. 41, 1915–1922. <a href="https://doi.org/10.1016/j.soilbio.2009.06.015">https://doi.org/10.1016/j.soilbio.2009.06.015</a>.</p> <p>El-Madany, T.S.,et al., 2018. Drivers of spatio-temporal variability of carbon dioxide and energy fluxes in a Mediterranean savanna ecosystem 21. <a href="https://doi.org/10.1016/j.agrformet.2018.07.010">https://doi.org/10.1016/j.agrformet.2018.07.010</a></p> <p>Knauer, J., et al., 2018. bigleaf - An R package for the calculation of physical and physiological ecosystem properties from eddy covariance data. PLOS ONE, doi:10.1371/journal.pone.0201114</p> <p>Perez-Priego O, et al., 2017. Evaluation of eddy covariance latent heat fluxes with independent lysimeter and sapflow estimates in a Mediterranean savannah ecosystem. Agricultural and Forest Meteorology. 236: 87-99. doi: 10.1016/j.agrformet.2017.01.009.</p> <p>Wutzler, T., et al., 2018. Basic and extensible post-processing of eddy covariance flux data with REddyProc. Biogeosciences Discuss., p. 1-39.</p> <p> </p>
Conserved mechanism of Xrn1 regulation by glycolytic flux and protein aggregation
<p>This dataset contains the raw and processed microscopy data that form the basis of our research article titled <em><a href="https://www.cell.com/heliyon/fulltext/S2405-8440(24)14817-7" target="_blank" rel="noopener">Conserved mechanism of Xrn1 regulation by glycolytic flux and protein aggregation</a></em> (DOI: <a href="https://kwnsfk27.r.eu-west-1.awstrack.me/L0/https:%2F%2Fdoi.org%2F10.1016%2Fj.heliyon.2024.e38786/1/010201924b2cde3a-4e9b4379-d805-432d-b94e-11d8b32354dc-000000/-WTssLbV_1JADo7ZwuFgWEmy9i0=394" target="_blank" rel="noopener noreferrer">doi.org/10.1016/j.heliyon.2024.e38786</a>) publlished in the <a href="https://www.cell.com/">CellPress</a> journal <a href="https://www.cell.com/heliyon/home">Heliyon</a> (<a title="Go to table of contents for this volume/issue" href="https://www.sciencedirect.com/journal/heliyon/vol/10/issue/19"><span><span>Volume 10, Issue 19</span></span></a>, 15 October 2024, e38786).<em> </em>The paper describes the mechanism underlying the binding of <a href="https://www.yeastgenome.org/locus/S000003141">yeast Xrn1</a> to the plasma membrane microdomain stabiliser <a href="https://doi.org/10.1016/j.cub.2017.11.073">eisosome</a> in a glucose-dependent manner. The images stored in the dataset were acquired with a <a href="https://www.iem.cas.cz/en/devices/zeiss-lsm-880-airyscan-en/">Zeiss LSM 880 confocal microscope</a> performed at the <a href="https://www.iem.cas.cz/en/department/microscopy-unit/">Microscopy Service Centre</a> of the <a href="https://www.iem.cas.cz/en/home-en/">Institute of Experimental Medicine CAS</a> supported by the MEYS CR (LM2023050 <a href="https://www.czech-bioimaging.cz/">Czech-Bioimaging</a>). Detailed step-by-step instructions for live microscopy sample preparation that we follow can be found at protocols.io: <a href="https://www.protocols.io/view/live-cell-microscopy-sample-preparation-yeast-cult-8epv5r23dg1b">https://www.protocols.io/view/live-cell-microscopy-sample-preparation-yeast-cult-8epv5r23dg1b</a>. The quantification of microscopy data was performed using our custom-developed Fiji and R scripts that can be found at <a href="https://github.com/jakubzahumensky/microscopy_analysis">https://github.com/jakubzahumensky/microscopy_analysis</a>. Their use is described in detail in the <a href="https://doi.org/10.1101/2024.03.28.587214">https://doi.org/10.1101/2024.03.28.587214</a>. For further information, please refer to the README file attached to the dataset.</p> <p>Note: This final version of datasets supplements the previous datasets of version 1 (DOI: <a href="https://doi.org/10.5281/zenodo.12748899">10.5281/zenodo.12748899</a>) and version 2 (DOI: <a href="https://doi.org/10.5281/zenodo.13772845">10.5281/zenodo.13772845</a>).</p>
Dissolved Cr concentration and stable isotope data presented in "Release from biogenic particles, benthic fluxes, and deep water circulation control Cr and δ53Cr distributions in the ocean interior" (Janssen et al., 2021, EPSL).
<p>This dataset presents all of the dissolved Cr data included and discussed in “Release from biogenic particles, benthic fluxes, and deep water circulation control Cr and δ<sup>53</sup>Cr distributions in the ocean interior” (Janssen et al., 2021, EPSL). Three primary datasets are included:</p> <ol> <li>Dissolved [Cr], [Cr(III)] and d53Cr in samples from shipboard particle regeneration incubations conducted in the subantarctic Southern Ocean.</li> <li>Dissolved [Cr] in porewater samples from a sediment core collected in the Tasman Sea in primarily calcareous sediments, along with [Cr] and δ<sup>53</sup>Cr in overlying bottom waters.</li> <li>3. A compilation of intermediate and deep water dissolved [Cr] and δ<sup>53</sup>Cr from seawater samples from the Southern, Pacific and Atlantic Oceans</li> </ol>
Daily river metabolism using oxygen flux at 75 sites in Mongolia or the United States in steppe ecoregions
We obtained GIS data to indicate local geomorphology and watershed-scale values for land use, climate, slope, and elevation for each sampling site. We selected our sites using the GIS-based program RESonate (Williams et al., 2013) to represent replicates in multiple watersheds of different geomorphic patches or Functional Process Zones (FPZs). The FPZs are reoccurring longitudinal geomorphic patches that are hypothesized to control biocomplexity, including community composition and system productivity (Thorp et al., 2006). A detailed description of the FPZ delineation methodology we employed has been provided previously (Maasri et al., 2019a; Erdenee et al., 2021). We classified each study site hierarchically by country, ecoregion, river basin, upper (streams higher in the watershed) or lower (low slope rivers of lower elevations), and relatively constrained valley or wide valley. This approach allowed us to assess reach-scale properties that could directly influence the physiological controls most often collected alongside metabolism data. This provided a framework to evaluate how we may understand the determinants of metabolism at multiple scales. We studied three large-scale temperate steppe ecoregions (Terminal Basin, TB; Montane Steppe, MS; and Grassland Steppe, GS) as characterized by Olson et al. (2001) and updated by Dinerstein et al. (2017) in two countries (Mongolia and the United States, Fig. 2). We aggregated our large-scale ecoregions for the US as follows: TB = Great Basin shrub steppe and Sierra Nevada forest, MS = South Central Rockies forest and Wyoming Basin shrub steppe, GS = Nebraska Sand Hills mixed grasslands and Northern Shortgrass prairie. We aggregated our large-scale ecoregions for Mongolia as follows: TB = Altai mountains forest and forest steppe, Gobi Lakes Valley desert steppe, Great Lakes Basin desert steppe, and Khangai Mountains alpine meadows, MS = Selenge-Orkhon forest steppe and Syan Mountains conifer forests, GS = Daurian Forest s
New Hampshire Soil Sensor Network: Soil CO2 Fluxes
The goal of the New Hampshire Soil Sensor Network is to examine spatial and temporal changes in soil properties and processes as the climate changes. Data collected can also calibrate and validate models that examine how ecosystems may respond to changing climate and land use. To determine how soil processes are affected by climate change and land management, this soil sensor network measures snow depth, air temperature, soil temperature, soil volumetric water content, and soil electrical conductivity, as well as soil CO2 fluxes. This data package includes air temperature, soil temperature at 5 cm, and soil volumetric water content at 5 cm, and soil CO2 flux at the time of sampling, as well as gap-filled soil CO2 fluxes using non-linear least squares regression. Data were collected at the following sites: BRT = Bartlett Experimental Forest, Bartlett, NH; BDF = Burley-Demmerit Farm, Lee, NH; DCF = Dowst Cate Forest, Deerfield, NH; HUB = Hubbard Brook Experimental Forest, Woodstock, NH; SBM = Saddleback Mountain, Deerfield, NH; THF = Thompson Farm, Durham, NH; and Trout Pond Brook, Strafford, NH.
CO2 Flux and Temperature Data for Estimating Thermal Acclimation in Ecosystem Respiration
We have compiled an extensive dataset of long-term, hourly CO2 flux measurements from 93 global eddy covariance sites to estimate the thermal response strength in nighttime ecosystem respiration. Flux data was sourced from AmeriFlux (https://ameriflux.lbl.gov/), FluxNet (https://fluxnet.org/), and ICOS (https://www.icos-cp.eu/). The processed data product encompasses five categories. (1) Long-term, directly measured, Ustar-filtered, hourly or subhourly nighttime ecosystem respiration, along with corresponding air temperature, soil temperature, and soil water content at the 93 sites. (2) Long-term gap-filled data, including hourly or subhourly net ecosystem exchange, air temperature, and soil temperature at these sites. (3) Annual topsoil (< 0.1 m) temperature and nighttime ecosystem respiration curves during the growing season, designed for calculating thermal response strength. (4) Estimated thermal response strength across these sites, detailed with geographic, climatic, soil, and vegetation conditions for each site. (5) An R script to calculate thermal response strength using the data product (3).
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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.