Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
4,496
datasets available to search
ShareScore release 0.7.1
Dataset results
4,496 results for “cycling”
Experimental Organic Rankine Cycle database - v2016.12
<p>Database on experimental Organic Rankine Cycle units. Dec. 2016 version.</p> <p>Supplementary materiel of the ICAE 2016 conference paper entitle "<em>Performance Evaluation and Comparison of Experimental Organic Rankine Cycle Prototypes from Published Data</em>" and the extended paper entitled "<em>Organic Rankine cycle design and performance comparison based on experimental database</em>" publish in Applied Energy - ICAE2016 Special Issue.</p>
Life Cycle Assessment Dataset For Peritoneal Dialysis in Modena
<p>The database outlines a structured pathway for managing patients with end-stage renal disease (ESRD) undergoing peritoneal dialysis (PD). It provides detailed descriptions of the various stages and protocols involved in the treatment process.</p> <ol> <li> <p><strong>Patient Education and Evaluation</strong>: The initial stages focus on educating patients about renal replacement therapy options and assessing their eligibility and suitability for PD. These assessments consider medical history, anatomical factors, and the suitability of the home environment.</p> </li> <li> <p><strong>Pre-Surgical and Surgical Procedures</strong>: The database includes pre-surgical evaluations, the surgical placement of the peritoneal catheter (performed under local anesthesia or through laparoscopic surgery), and subsequent checks to confirm the catheter's functionality.</p> </li> <li> <p><strong>Training and Initiation of PD</strong>: Training sessions for patients are outlined for both continuous ambulatory peritoneal dialysis (CAPD) and automated peritoneal dialysis (APD). These sessions are initiated following confirmation of catheter functionality. The database also specifies the use of products such as CAPD by Fresenius and APD by Baxter and describes the progressive implementation of the dialytic dose.</p> </li> <li> <p><strong>Routine Maintenance and Monitoring</strong>: Routine care includes monthly clinical evaluations, annual peritoneal equilibrium tests (PET), and periodic changes to the terminal catheter set to maintain treatment safety and effectiveness.</p> </li> <li> <p><strong>Handling Complications</strong>: Protocols for managing potential complications are detailed, including procedures for addressing catheter malfunctions, diagnosing and treating peritonitis, and catheter removal when necessary.</p> </li> <li> <p><strong>Data Collection on Patient Outcomes</strong>: The database suggests tracking patient preferences for different therapies and monitoring outcomes at various stages, providing insights into patient choices and clinical results.</p> </li> </ol> <p>This comprehensive database is designed to standardize and optimize the delivery of PD care. It offers healthcare professionals in nephrology a detailed framework for improving patient outcomes and streamlining clinical workflows.</p>
Belowground nitrogen cycling in a montane grassland exposed to elevated CO2, warming and drought
<p>#### Data description<br> Data from a multi-factorial global change experiment (elevated CO<sub>2</sub>, warming and drought) in a montane grassland experiment in Austria (ClimGrass). Variables presented are soil nitrogen cycling rates measured using isotope pool dilutions.</p> <p>Companion paper will be linked following manuscript publication.</p> <p>#### Metadata<br> climgrass_soil_N_cycling.csv data description</p> <p>Year: 2017<br> Harvest: three harvests (May 30, July 25, October 3)<br> Season: numerical column for harvest number<br> Plot: location of plot within the ClimGrass experiment<br> Treatment: eight treatment levels<br> c0t0 (ambient CO<sub>2</sub>, ambient temperature)<br> c0t1 (ambient CO<sub>2</sub>, + 1.5°C)<br> c0t2 (ambient CO<sub>2</sub>, + 3°C)<br> c1t1 (+150 ppm, +1.5°C)<br> c2t0 (+300 ppm, ambient temperature)<br> c2t2 (+300 ppm, +3°C)<br> c0t0-d (ambient CO<sub>2</sub>, ambient temperature, extended drought)<br> c2t2-d (+300 ppm, +3°C, extended drought)<br> CO2_ppm: three values of carbon dioxide enrichment treatment (+0, +150, or +300 ppm)<br> Temp_C: three values of elevated temperature treatment (+0, +1.5, +3°C)<br> Drought: two levels (control, drought)<br> Prot_depoly: gross protein depolymerization rates (micrograms nitrogen per grams dry soil per day = µg N g-1 d-1)<br> FAA_uptake: gross free amino acid uptake rates (µg N g-1 d-1)<br> MRT_FAA_hrs: mean residence time of free amino acids (hours)<br> FAA: free amino acids (µg N g-1)<br> Mineralization: gross mineralization rates (µg N g-1 d-1)<br> Nitrification: gross nitrification rates (µg N g-1 d-1)</p> <p>#### References<br> Additional information on the experimental design can be found in the following paper:<br> Piepho, H.-P., Herndl, M., Pötsch, E.M., Bahn, M., 2017. Designing an experiment with quantitative treatment factors to study the effects of climate change. Journal of Agronomy and Crop Science 203, 584–592. doi:https://doi.org/10.1111/jac.12225</p> <p>More information on the isotope pool dilution method used can be found in the following paper:<br> Wanek, W., Mooshammer, M., Blöchl, A., Hanreich, A., Richter, A., 2010. Determination of gross rates of amino acid production and immobilization in decomposing leaf litter by a novel 15 N isotope pool dilution technique. Soil Biology and Biochemistry 42, 1293–1302. doi:10.1016/j.soilbio.2010.04.001</p>
Multiplexed DNA-FISH imaging dataset, drosophila embryos, nuclear cycles 11-14
<p>Multiplexed DNA-FISH imaging dataset from Drosophila embryos at nuclear cycles 11-14.</p> <p>Examples on how to load and use this dataset can be found at this <a href="https://github.com/NollmannLab/Goetz_etal">GitHub repository</a>.</p> <p><strong>Data processing details</strong></p> <p>Barcodes were segmented using a neural network (<a href="https://github.com/stardist/stardist"><em>stardist</em></a>) specifically trained for the detection of 3D diffraction limited spots produced by our microscope. To extract the position of the barcode with sub-pixel accuracy, a subsequent 3D Gaussian fit of the regions segmented by <em>stardist</em> was performed with Big-FISH (<a href="https://github.com/fish-quant/big-fish">https://github.com/fish-quant/big-fish</a>). Barcode localizations with intensities lower than 1.5 times that of the background were filtered out.</p> <p>Nuclei were segmented from projected DAPI images using <em><a href="https://github.com/stardist/stardist">stardist</a> </em>with a neural network trained for detection of nuclei from <em>Drosophila</em> embryos under our imaging conditions. Barcodes were then attributed to single nuclei by using the XY coordinates of the barcodes and the DAPI masks of the nuclei. Finally, pairwise distance matrices were calculated for each single nucleus.</p> <p><strong>Processed data in Figures</strong></p> <p>This new version of the dataset contains the raw data for each of the figures in the manuscript:</p> <p><strong>Associated publication</strong></p> <p><strong>Multiple parameters shape the 3D chromatin structure of single nuclei at the doc locus in </strong><em>Drosophila</em>.</p> <p>Markus Götz, Olivier Messina, Sergio Espinola, Jean-Bernard Fiche, Marcelo Nollmann</p> <p>Nature Communications (2022).</p>
Multiplexed fluorescence imaging based on cycles, raw and processed data.
<p>This dataset was created from a larger acquisition in order to provide an example of reasonnable size, as a companion data set to the F1000Research paper preprint DOIXXX.</p> <ul> <li>The original raw data including metadata files are included in <strong>Microscope_Output.zip.</strong></li> <li><strong>Experiment.json</strong> and<strong> channelnames.txt </strong>are the ones generated by the acquisition software. They are the only files needed when starting from one of the processed data set below.</li> <li>The deconvolution obtained with the commercial software Microvolution is also provided in <strong>bu_deconvolution.zip.</strong> To start from Step 1(Extended Depth of Field) instead of Step 0 (deconvolution), unzip this file in your output directory and rename the folder bu_deconvolution to out.</li> <li>The extended field of view 2D images created from step 0 to step 2, provided for convenince in <strong>edfonly.zip</strong></li> <li>The final files generated by trhe Multiplex processor, including the segmentation mask , are provided in<strong> finaloutput.zip</strong>. These files can be used in a specific analysis software.</li> </ul> <p> </p>
Heat balance of selected Brayton cycle
<p>The dataset provides the heat balance of the selected Brayton cycle among the 10 cycles considered. Simulations of several cases defined by a different supercritical CO2 cycle type were performed with Ebsilon software in order to assess the net power block efficiency of the cycle and the Levelized Cost of Electricity (LCOE) of the plant. Due to its highest efficiency among the 10 envisaged Brayton cycle options, it is the Partial Cooling with Intercooling and Reheating cycle that is selected.</p> <p>The datasets could help other people design a sCO2 Brayton cycle.</p> <p>For detailed analysis, please refer to Deliverable 1.1 (Process Parameters of Solar sCO2 Brayton Cycle) to be downloaded at: <a href="https://www.compassco2.eu/wp-content/uploads/2021/02/D1.1_Process-parameters-of-solar-sCO2-Brayton-cycle.pdf">https://www.compassco2.eu/wp-content/uploads/2021/02/D1.1_Process-parameters-of-solar-sCO2-Brayton-cycle.pdf</a></p>
Life cycle inventories for the article: Circular Battery Production in the EU: Insights from integrating Life Cycle Assessment into System Dynamics Modeling on Recycled Content and Environmental Impacts
<p>This repository provides the unregionalized life cycle inventories to the paper "<span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a>".</p> <h2>Contents</h2> <p>The repository is split into 2 parts and comprises the following files:</p> <p><strong>01_production: </strong>contains the necessary life cycle inventories for battery production.</p> <ul> <li><strong>01_primary</strong>: contains the life cycle inventories for battery production from primary materials.</li> <li><strong>02_secondary</strong>: contains the life cycle inventories for battery production from secondary materials.</li> <li><strong>03_active_material</strong>: contains the life cycle inventories for the active battery materials from primary materials.</li> <li><strong>04_active_material</strong>: contains the life cycle inventories for the active battery materials from secondary materials.</li> </ul> <p> </p> <p><strong>02_recycling: </strong>contains the necessary inventories for battery recycling.</p> <ul> <li><strong>01_process</strong>: contains the life cycle inventories for battery recycling.</li> <li><strong>02_intermediate</strong>: contains the life cycle inventories for the intermediate system for battery recycling.</li> <li><strong>03_output</strong>: contains the life cycle inventories for the resulting substances from battery recycling.</li> </ul> <h2>Summary</h2> <p>These files allow to reproduce the results of our study. Each file contains the life cycle inventory of one distinct battery capacity (20, 45, 68, 85, 95, 100 kWh) with a specific cell chemistry (LFP, NCA, NMC333, NMC532, NMC622, NMC811, NMC955) for battery production (based on Knehr et al. 2022) or for battery recycling (based on Blömeke et al. 2023).</p> <h2>Related publication</h2> <p>More details on the scientific context is provided in the publication itself:</p> <p><span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a></p> <h2>Funding</h2> <p>This publication (Raphael Ginster and Steffen Blömeke) was created within the Research Training Group CircularLIB, supported by the Ministry of Science and Culture of Lower Saxony with funds from the program zukunft.niedersachsen of the Volkswagen Foundation (MWK | ZN3678).</p> <p>The publication on which this dataset is based were funded by the German Federal Ministry of Education and Research within the Competence Cluster Recycling & Green Battery (greenBatt) under the grant numbers 03XP0302A (Christian Scheller) and 03XP0331A (Jan-Linus Popien). The authors are responsible for the contents of this publication.</p>
Water Cycle Atlas of India (WCAI) v1.0 [1980-2022, Daily, 0.1°]
<p>WCAI ( Water Cycle Atlas of India) is a long term land surface reanalysis of the Indian subcontinent from Jan 1980 to Dec 2022. It is produced using the Indian Land Data Assimillation System (ILDAS) at the Indian Institute of Technology Delhi, New Delhi India. It provides daily estimates of 16 variables at 0.1 degree resolution. The hydrologic and hydrodynamic model combination used is NoahMP3.6 and HYMAP2 forced with Indian Meteorological Department (IMD) gridded precipitation and MERRA2 reanalysis data. This dataset will be valuable for water balance assessments at continental scale for multiple applications such as water resources planning, soil conservation, urban planning, and natural disaster risk mitigation.</p> <p>Changelog</p> <p>------------------------------------</p> <p>v1.0 - Uncalibrated Model outputs</p> <p> </p> <p> </p>
Value chains under the framework of life cycle assessment indicators
<p>Tables included in the article "Monitoring the bioeconomy: value chains under the framework of life cycle assessment indicators"</p>
Projected fire cycle (yrs) for Canada at a 0.25 degree resolution
<p>These rasters represent the projection of future fire cycles for Canada at a 0.25 degree of resolution. The data was produced in three steps:</p> <ol> <li>Future fire cycles were obtain by projecting annual area burned as in Boulanger et al. (2014) (https://cdnsciencepub.com/doi/full/10.1139/cjfr-2013-0372) at the homogeneous fire regime zone scale. Models used here were improved from those used in Boulanger et al. (2014). Projections were conducted for specific time periods (baseline, 2011-2040, 2041-2070 and 2071-2100) under specific anthropogenic climate forcing scenarios (RCP 4.5 and RCP 8.5). Three Earth System models were used i.e., CanESM2, HadGEM2-ES and MIROC-ESM-CHEM.</li> <li>Values obtained at the homogeneous fire regime zone scale were further "downscaled" at a 250m resolution according to vegetation type (cover x age class) following Bernier et al. (2016) (https://www.mdpi.com/1999-4907/7/8/157) using forest attributes of 2011 as assessed in Beaudoin et al. (2014) (https://cdnsciencepub.com/doi/10.1139/cjfr-2013-0401). </li> <li>Values obtained at a 250m resolution were averaged in 0.25x0.25 degree cells.</li> </ol>
Dataset: Co-composting to close the cycle of resources during rose cultivation in Kenya: An agronomic and pesticide residue assessment
<p>This dataset and these scripts supports the article 'Co-composting to close the cycle of resources during rose cultivation in Kenya: An agronomic and pesticide residue assessment' as published in Cleaner Waste Systems. https://doi.org/10.1016/j.clwas.2024.100154</p> <p>Roses are an important crop for the floricultural sector of Kenya and roses are a perennial crop and under continuous production for six to ten years. The cultivation produces large quantities of green waste, up to 50 kg per hectare per day. In this experiment we focused on exploring the potential of large-scale composting of rose waste in Kenyan rose cultivation. The objective of this study was to examine the potential of composting rose waste in this large-scale commercial setting with low operational costs, exploring its benefits and challenges.</p> <p>In piles of 4000 kg green waste the evolution of three mixtures was closely monitored in terms of their physico-chemical parameters. Furthermore, the pesticide residue levels of mature rose waste were assessed. </p>
Life Cycle Impact Assessment method for ozone depletion based on WMO 2022
<p>This dataset provides the most recent <a>characterization factors</a> for ozone depletion based on the latest ozone depletion potentials from the 2022 World Meteorological Organization (WMO) scientific assessment. The dataset is formatted for easy import into life cycle assessment (LCA) software such as Brightway, the Activity Browser, and SimaPro. The characterization factors are available for both 100-year and infinite time horizons.</p> <p>When using the dataset, please cite the folllowing publication:</p> <p>van den Oever, A. E.M., Puricelli, S., Costa, D., Thonemann, N., Lavigne Philippot, M., Messagie, M., Dataset with updated ozone depletion characterization factors for life cycle impact assessment, Data in Brief (in press), 2024, <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.111103" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.dib.2024.111103</a></p>
Amplitude of seasonal cycles of vegetation at global scale AVHRR
<p>Using the AVHRR product provided by NOAA, the NDVI time series has been calculated, a periodogram has been made to study different factors that affect the periodicity of the vegetative cycles. In this case, the amplitude of the cycles.</p>
Number of seasonal cycles of vegetation at global scale AVHRR
<p>Using the AVHRR product provided by NOAA, the NDVI time series has been calculated, a periodogram has been made to study different factors that affect the periodicity of the vegetative cycles. In this case, the number of seasonal cycles of vegetation.</p> <p>Value 1: one cycle</p> <p>Value 2: two cycles</p> <p>Value 3: three cycles</p>
Stability of seasonal cycles of vegetation at global scale
<p>Using the AVHRR product provided by NOAA, the NDVI time series has been calculated, a periodogram has been made to study different factors that affect the periodicity of the vegetative cycles. In this case, the Stability of seasonal cycles of vegetation.</p>
[Luminescence Dataset] The loess sequence of Dolní Věstonice, Czech Republic: A new OSL‐based chronology of the last climatic cycle
<p><strong>Dolní Věstonice - Luminescence Dataset</strong></p> <table> <tbody> <tr> <td> <p><strong>Applicable Licence</strong></p> </td> <td> <p>CC-BY-NC</p> </td> </tr> <tr> <td> <p><strong>Data curators:</strong></p> </td> <td> <p>Markus Fuchs, Sebastian Kreutzer</p> </td> </tr> <tr> <td> <p><strong>Reference original work</strong></p> </td> <td> <p>Fuchs, M., Kreutzer, S., Rousseau, D. D., Antoine, P., Hatté, C., Lagroix, F., Moine, O., Gauthier, C., Svoboda, J., and Lisá, L.: The loess sequence of Dolní Věstonice, Czech Republic: A new OSL‐based chronology of the last climatic cycle, Boreas, 42, 664–677, https://doi.org/10.1111/j.1502-3885.2012.00299.x, 2013.</p> </td> </tr> <tr> <td> <p><strong>How to refer/cite this dataset</strong></p> </td> <td> <p>Please the information auto-generated by Zendo</p> </td> </tr> </tbody> </table> <p><strong>Scope</strong></p> <p>This dataset contains the original luminescence data used to create the chronostratigraphy of the Dolní Věstonice loess profile. By publishing this dataset, we aim to support studies on the general characteristics of luminescence behaviours of natural minerals and support the FAIR guidelines for data sharing. This data is primary <strong>unmodified measurement data with all the possible errors and typos!</strong></p> <p><em>Please note: The dataset was compiled with the greatest care. However, the dataset comes without any guarantee. Human errors are always possible. If you feel that, after reading the original study and this document, the metadata describing the dataset is insufficient, please contact the data curators so that this document can be updated accordingly. Contrary, the primary cannot be updated/modified!</em></p> <p><strong>Dataset structure</strong></p> <p>The dataset consists of sequence files (<code>SEQ</code>) and measurement data (<code>BIN</code>). To each <code>.bin</code> file, there should be one corresponding sequence file. Measurement data are tagged with sample names in the <code>.bin</code> file, e.g., unless, in case of mistakes, samples can be distinguished in the file. </p> <ul> <li> <p><code>...BIN/</code></p> <ul> <li> <p><code>BIN/A-VALUE/:</code>measurement data with <em>a</em>-value measurements; the alpha irradiation was partly done on an external source</p> </li> <li> <p><code>BIN/MAIN/</code> measurement data with data used to estimate the equivalent dose of each sample</p> </li> <li> <p><code>BIN/PREHEAT_PLATEAU</code> files with combined preheat and dose recovery test results</p> </li> <li> <p><code>BIN/MISC</code> various additional measurements as indicated by the name</p> </li> </ul> </li> <li> <p><code>...SEQ/</code></p> <ul> <li> <p><code>SEQ/A-VALUE/</code></p> </li> <li> <p><code>SEQ/MAIN</code></p> </li> <li> <p><code>SEQ/PREHEAT_PLATEAU</code></p> </li> <li> <p><code>SEQ/MISC</code></p> </li> </ul> </li> <li> <p><code>...DE_CSV_EXPORT</code> The extracted equivalent dose values from the measurements with uncertainties in s. </p> </li> </ul> <p><em>Please note that samples were partly measured on different machines. All files are selections; in the course of the study, we carried out various additional measurements, in particular, preliminary tests; those data are not included in the dataset to keep the dataset comprehensible. </em></p> <p><strong>Used abbreviations</strong></p> <p>Abbreviations as used in the measurement and sequence file names. For technical details, we refer to the original study and the reference therein.</p> <table> <tbody> <tr> <td> <p>ABBREVIATION</p> </td> <td> <p>TERM</p> </td> <td> <p>DESCRIPTION</p> </td> </tr> <tr> <td> <p><code>CG</code></p> </td> <td> <p>coarse grain</p> </td> <td> <p>refers to the used grain size fraction, here: 90-200 µm</p> </td> </tr> <tr> <td> <p><code>DRT</code></p> </td> <td> <p>dose-recovery test</p> </td> <td> <p>measurement data with dose recovery test results; in this study, such measurements were carried out in combination with different preheat settings</p> </td> </tr> <tr> <td> <p><code>FG</code></p> </td> <td> <p>fine grain</p> </td> <td> <p>refers to the used grain size fraction, here: 4-11 µm</p> </td> </tr> <tr> <td> <p><code>IRSLT</code></p> </td> <td> <p>Infrared stimulated test</p> </td> <td> <p>test for feldspar contamination</p> </td> </tr> <tr> <td> <p><code>LM</code></p> </td> <td> <p>linear modulation</p> </td> <td> <p>linearly modulated optically stimulated luminescence measurements</p> </td> </tr> <tr> <td> <p><code>MAIN</code></p> </td> <td> <p>main measurement</p> </td> <td> <p>usually, the measurement used to estimate the equivalent dose</p> </td> </tr> <tr> <td> <p><code>MG</code></p> </td> <td> <p>medium grain</p> </td> <td> <p>refers to the used grain size fraction, here: 38-63 µm</p> </td> </tr> <tr> <td> <p><code>mz</code></p> </td> <td> <p>Moritz</p> </td> <td> <p>Name of the used Risø OSL/TL DA-15 reader</p> </td> </tr> <tr> <td> <p><code>mx</code></p> </td> <td> <p>Max</p> </td> <td> <p>Name of the used Risø OSL/TL DA-15 reader</p> </td> </tr> <tr> <td> <p><code>NEU</code></p> </td> <td> <p>neu</p> </td> <td> <p>a German word translating to 'new'</p> </td> </tr> <tr> <td> <p><code>oDRT</code></p> </td> <td> <p>oDRT</p> </td> <td> <p>a typo for just <code>DRT</code></p> </td> </tr> <tr> <td> <p><code>PreaHeat</code></p> </td> <td> <p>preheat test</p> </td> <td> <p>test against different preheat temperatures</p> </td> </tr> <tr> <td> <p><code>Q</code></p> </td> <td> <p>quartz</p> </td> <td> <p>the measured mineral composition, often in combination with <code>CG</code> , <code>MG</code> , or F<code>G</code> . Example: <code>FGQ</code> : fine grain quartz</p> </td> </tr> </tbody> </table>
Data for Lake Mendota Phosphorus Cycling Model
There is an opportunity to advance both prediction accuracy and scientific discovery for phosphorus cycling in Lake Mendota (Wisconsin, USA). Twenty years of phosphorus measurements show patterns at seasonal to decadal scales, suggesting a variety of drivers control lake phosphorus dynamics. Our objectives are to produce a phosphorus budget for Lake Mendota and to accurately predict summertime epilimnetic phosphorus using a simple and adaptable modeling approach. We combined ecological knowledge with machine learning in the emerging paradigm, theory-guided data science (TGDS). A mass balance model (PROCESS) accounted for most of the observed pattern in lake phosphorus. However, inclusion of machine learning (RNN) and an ecological principle (PGRNN) to constrain its output improved summertime phosphorus predictions and accounted for long term changes missed by the mass balance model. TGDS indicated additional processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve our mass balance modeling approach.
Consequences of non-random tree species loss on litter mass loss, nutrient dynamics, carbon cycling, and decomposer communities across a terrestrial-aquatic interface at Coweeta Hydrologic Lab, Otto, NC
Although litter decomposition is a fundamental ecological process, most of our understanding comes from studies of single-species decay. Recently, litter-mixing studies have tested whether monoculture data can be applied to mixed-litter systems. These studies have mainly attempted to detect non-additive effects of litter mixing, which address potential consequences of random species loss. The focus is not on which species are lost, but the decline in diversity per se. Under global change, species loss is likely to be non-random, with some species more vulnerable to extinction than others. Under such scenarios, the effects of individual species (additivity) as well as of species interactions (non-additivity) on decomposition rates are of interest. To examine potential impacts of non-random species loss on ecosystems, we studied additive and non-additive effects of litter mixing on decomposition. A full-factorial litterbag experiment was conducted using four deciduous leaf species, from which mass loss and nitrogen content were measured. Data were analysed using a statistical approach that first looks for additive identity effects based on the presence or absence of species and then significant species interactions occurring beyond those. It partitions non-additive effects into those caused by richness and or composition.
Manure Cycling Interview Data
Exploring the potential for nutrient circularity in the beef production system requires an understanding of current practices. Manure nutrients produced in feedlots are an ample source of fertilizer for phosphorus deficient crop and hay lands. However, it is unclear how far manure nutrients are travelling from feedlots, what crops they’re being applied to, and whether those grains are in turn integrated into the feedlot operations. The purpose of these interviews was to ascertain the above information from feedlot managers. In addition, we sought contextual information (provenance of cattle, cattle weights/ages, manure treatment, regulations/guidelines, processing facility destination, barriers, suggestions for improvements). To answer our question about potential manure nutrient circularity, we focused and report here the elements pertaining to feed/grain provenance, crops manure was applied to, and export distance for manure.
WAT03 Climate legacy effects shape tallgrass prairie nitrogen cycling
Climate change is expected to shift precipitation regimes in the North American Central Plains with likely impacts on ecosystem functioning. In tallgrass prairies, water and nitrogen (N) can co-limit ecosystem processes, so changes in precipitation may have complex effects on carbon (C) and N cycling. Rates of N supply such as N mineralization and nitrification respond differently to short- and long-term patterns in water availability, and previous climate patterns may exert legacy effects on current N cycling that could alter ecosystem sensitivity to current precipitation regimes. We used a long-term precipitation manipulation at Konza Prairie (Kansas, USA) to assess how previous and current precipitation influence tallgrass prairie N cycling. Supplemental irrigation was applied across upland and lowland prairie for ~25 years to reduce water deficits; in 2017, we reversed some of these treatments and added a reduced rainfall treatment across both historic rainfall regimes, allowing us to assess how previous climate and current rainfall patterns interact to shape N cycling. In lowland prairie, previous irrigation doubled N mineralization and nitrification rates the year following cessation of irrigation. Reduced microbial C/N ratio and lower relative investment in N-acquiring enzymes in previously irrigated lowlands suggested that a wetter climate created a legacy of increased N availability for microbes. Internal plant N resorption increased under short-term irrigation but recovered to ambient levels following previous irrigation. Together, these results suggest that a history of wetter conditions prairie can create a legacy of accelerated N cycling and with consequences for both plant and microbial functioning.
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.