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4,496 results for “cycling”
Carbon Cycle Dynamics in Soil Warming Experiments at Harvard Forest 2019
Microbes are responsible for cycling carbon (C) through soils, and predicted changes in soil C stocks under climate change are highly sensitive to shifts in the mechanisms assumed to control the microbial physiological response to warming. Two mechanisms have been suggested to explain the long-term warming impact on microbial physiology: microbial thermal acclimation and changes in the quantity and quality of substrates available for microbial metabolism. Yet studies disentangling these two mechanisms are lacking. To resolve the drivers of changes in microbial physiology in response to long-term warming, we sampled soils from 13- and 28-year-old soil warming experiments in different seasons. We performed short-term laboratory incubations across a range of temperatures to measure the relationships between temperature sensitivity of physiology (growth, respiration, carbon use efficiency, and extracellular enzyme activity) and the chemical composition of soil organic matter. We observed apparent thermal acclimation of microbial respiration, but only in summer, when warming had exacerbated the seasonally-induced, already small dissolved organic matter pools. Irrespective of warming, greater quantity and quality of soil carbon increased the extracellular enzymatic pool and its temperature sensitivity. We propose that fresh litter input into the system seasonally cancels apparent thermal acclimation of C-cycling processes to decadal warming. Our findings reveal that long-term warming has indirectly affected microbial physiology via reduced C availability in this system, implying that earth system models including these negative feedbacks may be best suited to describe long-term warming effects on these soils.
Nitrogen Cycling Dynamics in Sarracenia Purpurea at Harvard Forest 2004-2005
In nutrient poor systems, plants employ many strategies in order to acquire and recycle scarce nutrients, including nitrogen. Low leaf N content is associated with low photosynthetic rates, but carnivorous plants have unusually low photosynthetic rates given their N content. The northern pitcher plant Sarracenia purpurea readily uses any available nitrogen: NH4 and NO3 dissolved in precipitation; N mineralized from captured prey; the scant N in saturated peat; and N remobilized from storage. However, the dynamics of N cycling within S. purpurea are poorly understood. We conducted two greenhouse experiments to examine N-cycling dynamics of S. purpurea at the whole-plant and individual-leaf levels. In the first experiment we assessed assimilation, translocation, storage, and remobilization of 15N supplied to pitchers and roots. In the second experiment, we examined how 15N assimilated by the first pitcher produced at the start of the growing season contributed to the production and maintenance of subsequent pitchers, roots, and rhizomes. Patterns of N cycling were similar at the individual-leaf and whole-plant level. Pitchers assimilated 55 - 69% of available 15N and served both as the largest sink for newly assimilated N (more than 90% of the 15N assimilated during 2004) and the largest source of N remobilization the following spring. In contrast, N assimilated by roots was low and accounted for less than 2.5% of the overall S. purpurea N budget. S. purpurea uses both stored N and newly-acquired N throughout the growing season. The importance of stored N decreases throughout the growing season as newly assimilated N contributes more to later pitcher production.
Rates of benthic metabolism and nutrient cycling in the Parker and Rowley Rivers of the Plum Island Sound estuary, Massachusetts, PIE LTER.
Rates of benthic metabolism and nutrient cycling in the Parker and Rowley Rivers of the Plum Island Sound estuary. Measurements include those conducted at two sites in the Parker River in Spring (high river discharge) and Fall (low discharge) for long-term monitoring, also at other sites throughout the estuary over a variety of seasons and salinities.
Chemical data accompanying the manuscript "Chromium cycling in redox-stratified basins challenges δ53Cr paleoredox proxy applications" in Geophysical research Letters
<p>Water column and sediment chromium concentration and stable isotope data and ancillary metal data from Lake Cadagno, Switzerland. These data accompany a manuscript by the same authors in Geophysical Research Letters (doi: 10.1029/2022GL099154).</p> <p> </p> <p>The associated CTD data are available in the following Zenodo dataset: Sepúlveda Steiner, O., Carlino, C., Haizmann, E., Roman, S., Wüest, A., & Bouffard, D. (2022). Lake Cadagno 2017 CTD and water quality monitoring [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.7127882">http://doi.org/10.5281/zenodo.7127882</a></p>
Microbial, Plant, and Soil Impacts on Soil Nutrient Cycling in Harvard Forest and Greater Boston 2021-2022
Microbes are the driving force behind nutrient cycling within soils, secreting enzymes necessary to break down organic matter, immobilizing nutrients and C, or transferring nutrients to plant hosts. Even though nutrients would almost never move through ecosystems without microbes, we know little about how their composition and activity is related to ecosystem nutrient cycling, and their importance relative to plant and soil abiotic factors. In this study, we sought to determine which commonly measured soil microbial community characteristics best explain soil N and P cycling, and the relative contributions of microbial, plant, and abiotic factors in explaining these processes.
Dataset and analyses for publication entitled: “Acclimation of the nitrogen cycle to changes in precipitation”
This dataset contains data and analysis code for the paper entitled “Acclimation of the nitrogen cycle to changes in precipitation" by Currier et al. As the frequency of precipitation extremes are expected to increase, especially in arid regions, we asked how prolonged shifts in water availability facilitate acclimation of the N cycle in a semiarid grassland. Using natural abundances of stable nitrogen isotopes for dominant plants and soils and rainfall manipulation experiments, we tested the hypothesis that N cycling will interact with water availability further amplifying the openness of the N cycle through time. For the dominant plant species, we found the relationship for N availability vs. ambient annual precipitation to be significantly positive, contrary to global spatial models. We also considered the temporal dynamics of our experiments, which imposed directional rainfall manipulations in duration ranging from 5 to 14 years. The slopes of these relationships decreased (became less positive) with more time since the onset of the directional precipitation extremes. These data and metadata supplement long-term foliar and soil isotope data from the Jornada LTER (Dataset ID: knb-lter-jrn.210586001) with a large spatial dataset from NEON data package DP1.10026.001 and Craine et al. 2018 (https://doi.org/10.5061/dryad.v2k2607).
Stress-associated brain activation across the hormonal contraceptive cycle
Open the record for dataset details and reuse information.
The Southern Ocean carbon cycle 1985-2018: Mean, seasonal cycle, trends and storage - Data
<p>Postprocessed data set used for RECCAP2 Southern Ocean chapter:</p><p>Hauck, Gregor, et al.: The Southern Ocean carbon cycle 1985-2018: Mean, seasonal cycle, trends and storage</p><p>The raw data is available at: Müller, Jens Daniel. (2023). RECCAP2-ocean data collection [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7990823</p><p>Scripts for plotting are available at https://github.com/RECCAP2-ocean/Southern-Ocean and a frozen version of the scripts is deposited at:</p><p>Judith Hauck, Luke Gregor, Cara Nissen, Lavinia Patara, Mark Hague, & Precious Mongwe. (2023). The Southern Ocean carbon cycle 1985-2018: Mean, seasonal cycle, trends and storage - Scripts. Zenodo. https://doi.org/10.5281/zenodo.10076121</p><p> </p>
Energy Cycle Characteristics for 5G/6G Networks Supported by RES, UAVs, and RISs
<h2><strong>Overview</strong></h2> <p>The following dataset presents the energy cycle characteristics for 5G/6G mobile systems supported by Renewable Energy Sources (RES) and/or Unmanned Aerial Vehicles (UAVs) and Reconfigurable Intelligent Surfaces (RISs). In addition, within the dataset, the energy gain related to the engagement of RES within the Radio Access Network (RAN) has also been distinguished.</p> <h2><strong>Scenario</strong></h2> <p>The considered network scenario includes 8 three- (<em>_results_gcas.csv</em>) or one-cell (<em>_results_scas.csv</em> & <em>_results_kras.csv</em>) base stations (BSs) placed within the Poznan city (surroundings of the old market) and supported by Renewable Energy Sources — photovoltaic panels (PVs) and/or wind turbines (WTs). The aforementioned base stations can be treated as stationary towers or mobile access points (e.g., drones/UAVs). Those latter have been additionally equipped with RIS devices, which are able to reflect and manipulate a radio signal to influence occurrences such as interferences, coverage, or human exposure. However, the use of RISs has been taken into account only to evaluate the impact of the engagement of such devices on the energy side of the mobile system, omitting the changes in radio characteristics. The network traffic has been assumed to be fixed (64 mobile users (UEs) with 100 Mbps downlink — DL, and 25 Mbps uplink — UL, per each), however, its density in specific parts of the city is modeled randomly for each simulation run. The simulation runs have been performed for 4 dates (vernal equinox, summer solstice, autumn equinox, winter solstice), each one from a different season of the year. The aim of such an approach was to highlight the impact of the time of the day and the year on the energy gain obtained thanks to enabling RES generators. The weather conditions assumed within the simulation are typical for the climate in Poland. </p> <h2><strong>Methodology</strong></h2> <p>The energy-cycle calculations (system's power consumption, renewable energy production, and excessive energy storage) have been based on the mathematical formulas from the scientific literature and performed within the digital simulation runs by using the Green Radio Access Network Design (GRAND) tool (developed by teams from the Ghent University & Poznan University of Technology). The UE-BS association process within the mobile system has been done by doing multi-objective optimization using the Gurobi software, which has taken into account parameters like path loss, predicted power consumption of BSs, and guaranteed DL & UL bit rates for UEs.</p> <h2><strong>Simulation setup</strong></h2> <p>The setup of the input parameters for used mathematical models (power consumption, energy generation, energy storage) has been done in accordance with the values attached within the delivered literature positions (cited within the publications included in the <em>Related works</em> section of the following dataset) and adjusted to the considered study. Furthermore, the data used to model the network environment (building distribution, coverage area, base stations' locations) as well as to predict weather conditions are the real data (for the year 2022) collected by the city hall of Poznan, one of the Polish mobile operators, and weather stations placed in Poznan, respectively. The number of simulation runs performed has been equal to 10 (each run has included energy-cycle calculations for 4 seasons of the year), with the time step of a single run set to 1 hour of the day.</p> <h2><strong>Results</strong></h2> <p>The results of the aforementioned investigations have been included in the attached files, which can be described as follows:</p> <h3><strong>File <em>_results_gcas.csv</em></strong></h3> <p>The first column denotes the date (season of the year), for which the values have been obtained. The columns from second to fifth present observed values of the State of Charge (SoC) of a battery system (in %) for a single network cell on average in a time step. Those columns are the obtained values for the RAN, in which no RES, only PVs, only WTs, and both types of RES generators have been enabled, respectively. </p> <h3><strong>Files <em>_results_scas.csv</em> & <em>_results_kras.csv</em></strong></h3> <p>The first column denotes the date (season of the year), for which the values have been obtained. The second and third columns denote the number of drone base station (DBS) exchanges within the wireless system on average in a particular time step, where no RES and only PVs are enabled, respectively. The fourth and fifth columns present the conventional (fossil-fuels-based) energy consumption (in kWh) for the whole system in a specific time step, in which no RES and only PVs are engaged for all the access nodes. The sixth column is the energy savings (in kWh) related to the use of RES generators within the mobile network. Furthermore, the seventh and eighth columns represent the amount of renewable energy harvested from the solar radiation in total and the peak value of this amount observed during the entire day, respectively.</p> <h2><strong>Acknowledgment</strong></h2> <p>More details about the conducted studies have been described within the attached papers (<em>Related works</em> section). The data has been collected within the COST CA10210 INTERACT. M. Deruyck is a Post-Doctoral Fellow of the FWO-V (Research Foundation – Flanders, ref: 12Z5621N). The work (including the following dataset preparation) by A. Samorzewski and A. Kliks was realized within project no. 2021/43/B/ST7/01365 funded by the National Science Center in Poland.</p>
Product Images for Life Cycle Assessment Dataset For Peritoneal Dialysis and Haemodialysis in Modena
<p>The database contains a collection of images showcasing the individual components of peritoneal dialysis (PD) products, along with their corresponding weights. These images serve as a visual record for life cycle assessment (LCA) purposes, focusing on the material composition and environmental impact of each product.</p> <ol> <li> <p><strong>Patient Education Materials</strong>: Photographs of educational materials provided to patients, with accompanying data on the weight of the paper and packaging.</p> </li> <li> <p><strong>Catheters and Surgical Kits</strong>: Images display the disassembled components of PD catheters and surgical kits, including tubing, connectors, and packaging. Each image is annotated with the precise weight of the individual components.</p> </li> <li> <p><strong>Dialysis Solution Bags</strong>: The database includes images of both CAPD and APD solution bags, separated into their constituent parts (e.g., plastic bag, solution, and protective wrapping), with weights noted for each component.</p> </li> <li> <p><strong>Connection Devices and Consumables</strong>: Detailed images of connection devices, clamps, and other consumable items, with individual component weights clearly labeled.</p> </li> <li> <p><strong>Packaging and Transport Materials</strong>: Photographs of transport packaging, such as cardboard boxes and plastic wraps, alongside recorded weights for each element.</p> </li> <li> <p><strong>Maintenance Items</strong>: Visuals of terminal catheter sets, cleaning agents, and related products, each accompanied by their respective weight data.</p> </li> <li> <p><strong>Disposal Components</strong>: Images of used solution bags, syringes, and other single-use items, separated into recyclable and non-recyclable components, with weights specified for each.</p> </li> </ol> <p>This image-based database provides a clear and comprehensive reference for the material breakdown and weight distribution of PD product components, essential for conducting a thorough LCA and identifying areas for environmental improvement.</p>
Single-cycle, 643-mW average power THz source based on tilted pulse front in lithium niobate
<p>This data set is associated with the aforementioned paper.</p> <p>The data and the Jupyter notebooks (Python) to reproduce the figures in this paper can be downloaded below. To run a Jupyter notebook as a beginner, it is easiest to download and install anaconda, a Python environment that comes with many packages preinstalled and also offers Jupyter lab/notebook. It is available at <a href="https://www.anaconda.com/download" target="_blank" rel="noopener">https://www.anaconda.com/download</a>.</p> <h2>Fig.01</h2> <p><strong>Fig.01_literature_lithium_niobate_sources.csv</strong> contains a summary table of the last decades of published THz power values obtained with lithium niobate in the tilted pulse front geometry. The accompanying jupyter notebook allows to reproduce the figure that was used in the paper.</p> <h2>Fig.03</h2> <p>Each individual data frame (df), which is saved as an HDF file in the .zip file, contains a "power curve" measurement (i.e. measured THz power as a function of the applied pump power). Whenever a parameter is changed, all positions, angles, THz power and cryostat parameters are saved.</p> <ul> <li><strong>x1 </strong>is the position of the last mirror before the transmission grating (parallel to the pump beam direction before the crystal) in [mm]</li> <li><strong>x2 </strong>is the position of the first imaging lens in direction of the pump beam propagation direction before the crystal in [mm]</li> <li><strong>x3 </strong>is the position of the second imaging lens in the direction of the pump beam propagation direction before the crystal in [mm]</li> <li><strong>x4 </strong>is the position of the cryostat in the direction of the pump beam before reaching the crystal in [mm]</li> <li><strong>y0 </strong>is the position of the cryostat in the perpendicular direction of the pump beam before reaching the crystal in [mm]</li> <li><strong>α0 </strong>is the angle of the lambda/2 waveplate that allows the pump power to be varied at the crystal in [°]</li> <li><strong>α1 </strong>is the angle of the last mirror before the grating in [°]</li> <li><strong>α2 </strong>is the angle of the transmission grating in [°]</li> <li><strong>thz_power_W </strong>is the obtained power obtained from the Ophir 3A-P-THz power meter in [W]</li> <li><strong>temperature_setpoint_K </strong>is the LakeShore cryostat controller setpoint in [K]</li> <li><strong>temperature_K </strong>is the temperature read from the sensor on the cooling finger (above the crystal) in [K]</li> <li><strong>heater_output </strong>is the amount of power in [%] delivered to the resistive heating element inside the cryostat. 100% corresponds to about 50 W. Its value is controlled by an internal PID loop of the cryostat controller, which tries to stabilize <strong>temperature_K </strong>to <strong>temperature_setpoint_K</strong></li> <li><strong>pump_power </strong>is the average laser power reaching the crystal in [W]. It was calibrated before obtaining the data set by characterizing the lambda/2 waveplate angle <strong>α0</strong> to the value of an NIR power meter just before the cryostat.</li> <li><strong>repetition_rate</strong> is the repetition rate of the laser in [Hz]</li> </ul> <p>As an example, below is one line (for one pump power) of such a data frame:</p> <table> <tbody> <tr> <td> </td> <th>x1</th> <th>x2</th> <th>x3</th> <th>x4</th> <th>y0</th> <th>α0</th> <th>α1</th> <th>α2</th> <th>thz_power_W</th> <th>temperature_setpoint_K</th> <th>temperature_K</th> <th>heater_output</th> <th>pump_power</th> <th>repetition_rate</th> </tr> <tr> <td>0</td> <td>-12.000005</td> <td>2.500039</td> <td>9.100015</td> <td>-5.0</td> <td>-2.0</td> <td>35.905660</td> <td>25.68</td> <td>-23.3</td> <td>0.006000</td> <td>80.0</td> <td>79.883</td> <td>4.4</td> <td>20.0</td> <td>40000.0</td> </tr> </tbody> </table> <p>10 of such power curves were obtained at 100 kHz and 40 kHz and can be found in the respective zip-file.</p> <p> </p> <p><strong>Literature_Power_Efficiency.zip</strong> contains digitzed power and efficiency values from the following references:</p> <ol> <li>X. Wu, D. Kong, S. Hao, et al., "Generation of 13.9-mJ Terahertz Radiation from Lithium Niobate Materials," Advanced Materials 35, 2208947 (2023).</li> <li> <p>P. L. Kramer, M. K. R. Windeler, K. Mecseki, et al., "Enabling high repetition rate nonlinear THz science with a kilowatt-class sub-100 fs laser source," Opt. Express 28, 16951 (2020).</p> </li> <li> <p>T. Kroh, T. Rohwer, D. Zhang, et al., "Parameter sensitivities in tilted-pulse-front based terahertz setups and their implications for high-energy terahertz source design and optimization," Opt. Express, OE 30, 24186–24206 (2022).</p> </li> <li> <p>B. Zhang, Z. Ma, J. Ma, et al., "1.4-mJ High Energy Terahertz Radiation from Lithium Niobates," Laser & Photonics Reviews 15, 2000295 (2021).</p> </li> </ol> <p> </p> <h2>Fig.04</h2> <p><strong>EOS_dfs.p</strong> is a pickle file, contain electro-optic sampling traces, which are already averaged for various pump powers at 40 kHz repetition rate.</p>
Dataset for Gate-to-Gate Life Cycle Assessment of Lithium-Ion Battery Recycling Pre-Treatment
<p>Recycling spent lithium-ion batteries (LIBs) is crucial for improving environmental sustainability and conserving resources. Due to the diversity of LIB applications and recycling technologies, the environmental and energy impacts are not well understood. Comprehensive assessments must consider the distinct operations, methodologies, technology efficiency, and final treatment of materials. This study provides a partial gate-to-gate life cycle analysis (LCA) of a small-scale recycling plant in the Czech Republic, focusing on pre-treatment of spent LIBs from electric vehicles (EVs) and consumer electronics cells (CECs). The study highlights the benefits of recycling pre-treatment for CECs, significantly reducing environmental impact categories (EICs) such as climate change, eutrophication, and resource use. A high secondary use rate of obtained materials is crucial for environmental benefits, with metal reuse from packaging, connectors, and current collectors being especially important.</p>
Influence of soil amendment and crop species on nutrient cycling in a St. Paul urban garden, 2017-2023
An experiment was conducted from 2017-2023 at the University of St. Thomas research garden (Saint Paul, MN) to determine rates of nutrient recycling and loss from compost applied to urban gardens. Thirty-two 4 m2 study plots received one of six different soil amendment treatments, with four different crops growing on each plot. Meteorological data includes hourly measurements of rainfall, solar radiation, temperature and relative humidity, and wind speed and direction, from June 2017-October 2023. Hourly soil moisture measurements were recorded at depths of 10 cm, 20 cm, and 30 cm, from June-December 2021, June-October 2022, and June-October 2023. Annual crop harvest totals from each subplot are reported for 2017-2023. Leachate was collected from lysimeters installed in each of the 132 subplots weekly from June-October of each year (2017-2023), recording total volume. Leachate subsamples were analyzed for NO3-N, NH4-N, and PO4-P. Soil samples were collected at the beginning and end of the growing season in 2017, and every two weeks during the growing season from 2018-2023, and analyzed for pH, organic matter, Bray-1 extractable P, available K, nitrate, and ammonium, at the University of Minnesota Analytical Research Laboratory.
Stanislaus River Steelhead Life Cycle Monitoring Program
The Stanislaus River Steelhead Life Cycle Monitoring Program is an ongoing survey starting in 2021 that aims to estimate the amount of steelhead (Onocorhynchus mykiss) present in the Stanislaus River by recording steelhead and redds observed. In addition to estimating the amount of steelhead, the survey aims to collect data relevant to steelhead spawning, documenting environmental conditions such as temperature and flow, and recording other fish activity present.
Local scale carbon and nitrogen cycling in temperate forests, eastern U.S., 2017-2018
Data collected in 2017-2018 from individual mature canopy trees (and their surrounding soil) and from monospecific common garden plots to assess how aboveground and belowground carbon and nitrogen cycling are related. Data include foliar, litter, root, and soil carbon and nitrogen pools and fluxes. Field sites span the eastern United States, including south-central Indiana (Moores Creek), Maryland (Smithsonian Environmental Research Center), Pennsylvania (Pennsylvania State University common garden), and Massachusetts (Harvard Forest).
Nitrogen Cycling and Environmental Data in Riparian Soils across Biomes
This dataset compiles soil nitrogen cycle data from riparian soils, sourced from peer-reviewed studies published between 1980 and 2023. The selection process was based on three inclusion criteria: (1) studies measuring in-situ net nitrification rates in the top soil layer using the incubating bag technique, (2) studies reporting net nitrification rates from laboratory incubations without altering the initial nitrogen pool, and (3) studies providing field data on soil nitrogen concentrations, moisture, and temperature. The final dataset (D1) includes data from 174 riparian sites across four continents, with the majority of sites (86%) located in North America and Europe, while only 13 were located in the Southern hemisphere. For each site, we gathered data on net nitrification rates and key soil physicochemical properties, including bulk density, depth, moisture (expressed as water-filled pore space, WFPS), temperature, and ammonium and nitrate concentrations. The dataset includes 734 observations from 99 field sites and 120 observations from 45 laboratory-incubated sites. All publications from which data were used are list in dataset 2 (D2). This comprehensive dataset offers valuable insights into nitrogen dynamics in riparian soils, supporting further research into soil nitrogen cycling across diverse biomes and environmental conditions.
Modeling the effect of explicit vs implicit representation of grazing on ecosystem carbon and nitrogen cycling in response to elevated carbon dioxide and warming in arctic tussock tundra, Alaska - Dataset A
We use a simple model of coupled carbon and nitrogen cycles in terrestrial ecosystems to examine how explicitly representing grazers versus having grazer effects implicitly aggregated in with other biogeochemical processes in the model alters predicted responses to elevated carbon dioxide and warming. The aggregated approach can affect model predictions because grazer-mediated processes can respond differently to changes in climate from the processes with which they are typically aggregated. We use small-mammal grazers in arctic tundra as an example and find that the typical three-to-four-year cycling frequency is too fast for the effects of cycle peaks and troughs to be fully manifested in the ecosystem biogeochemistry. We conclude that implicitly aggregating the effects of small-mammal grazers with other processes results in an underestimation of ecosystem response to climate change relative to estimations in which the grazer effects are explicitly represented. The magnitude of this underestimation increases with grazer density. We therefore recommend that grazing effects be incorporated explicitly when applying models of ecosystem response to global change.
Modeling the effect of explicit vs implicit representation of grazing on ecosystem carbon and nitrogen cycling in response to elevated carbon dioxide and warming in arctic tussock tundra, Alaska - Dataset B
We use a simple model of coupled carbon and nitrogen cycles in terrestrial ecosystems to examine how explicitly representing grazers versus having grazer effects implicitly aggregated in with other biogeochemical processes in the model alters predicted responses to elevated carbon dioxide and warming. The aggregated approach can affect model predictions because grazer-mediated processes can respond differently to changes in climate from the processes with which they are typically aggregated. We use small-mammal grazers in arctic tundra as an example and find that the typical three-to-four-year cycling frequency is too fast for the effects of cycle peaks and troughs to be fully manifested in the ecosystem biogeochemistry. We conclude that implicitly aggregating the effects of small-mammal grazers with other processes results in an underestimation of ecosystem response to climate change relative to estimations in which the grazer effects are explicitly represented. The magnitude of this underestimation increases with grazer density. We therefore recommend that grazing effects be incorporated explicitly when applying models of ecosystem response to global change.
Ecosystem nutrient cycling in northern hardwood and conifer stands at Cone Pond, Hubbard Brook, and Sleepers River
This dataset provides comprehensive measurements of nutrient concentrations and fluxes in foliage, fine roots, wood, litterfall, and throughfall in hardwood and conifer stands across temperate forest stands at three long-term ecological research sites in the northeastern United States: Cone Pond, NH, Hubbard Brook, NH, and Sleepers River, VT. These sites vary in bedrock composition, parent material, and soil chemistry, but share similar climatic characteristics. Tissue nutrient concentrations were determined in leaves, fine roots, wood, and branches using site- and tissue-specific methods, with additional quality control through certified standards and duplicate sampling. Nutrient fluxes via litterfall and throughfall were measured over multiple years. Nutrient fluxes in roots were estimated from minirhizotron-based turnover rates and fine root biomass. Annual nutrient accumulation and uptake were calculated by integrating biomass production and nutrient concentrations. This dataset supports cross-site comparisons of forest biogeochemistry and provides a basis for evaluating nutrient limitations, cycling processes, and ecosystem responses to environmental gradients in northeastern temperate forests.
Derived Data supporting "On the Seasonal Cycles of Tropical Cyclone Potential Intensity" (Gilford et al. 2017, JoC)
<p>Derived monthly mean tropical cyclone potential intensities (and associated variables) using the Bister and Emanuel 2002 PI algorithm, ftp://texmex.mit.edu/pub/emanuel/TCMAX; from MERRA2 (averaged over 1980-2016) and ERA-I data (averaged over 1980-2013), on 2.5x2.5 degree grids and with the ERA-I land-sea mask already applied. This data supported the publication of Gilford et al. (2017, JoC). When using this data, please include the citation:</p> <p>Daniel M. Gilford, Susan Solomon, and Kerry Emanuel, 2017: On the Seasonal Cycles of Tropical Cyclone Potential Intensity. <em>J. Climate, </em><strong>30</strong>, 6085–6096. doi: <a href="http://journals.ametsoc.org/doi/10.1175/JCLI-D-16-0827.1">10.1175/JCLI-D-16-0827.1</a>.</p> <p> </p>
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.