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.
8
datasets available to search
ShareScore release 0.7.1
Dataset results
8 results for “Flux partitioning”
Supplemental data and code for "Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff"
<p>This dataset provides all data compiled and generated for the manuscript entitled "Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff" (https://doi.org/10.1016/j.oneear.2023.08.002). This includes the boundaries for 3614 hydrological catchments, the curated data used for analysis and modelling, the developed machine learning model, shapley values and area of applicability results, and data for global extrapolation</p> <p>It also contains a markdown file ('code.html') which shows how to access and use the data, and generic sample codes used to generate these results.</p> <p> </p> <p> </p> <p> </p>
Data from: Partitioning between atmospheric deposition and canopy microbial nitrification into throughfall nitrate fluxes in a Mediterranean forest
1. Microbial activity plays a central role in nitrogen (N) cycling, with effects on forest productivity. Though N bio-transformations, such as nitrification, are known to occur in the soil, here we investigate whether nitrifiers are present in tree canopies and actively process atmospheric N. 2. This study was conducted in a Mediterranean holm oak (Quercus ilex L.) forest in Spain during the transition from hot dry summer to cool wet winter. We quantified NH4+—N and NO3-—N fluxes for rainfall (RF) and throughfall (TF) and used δ15N, δ18O, and Δ17O to elucidate sources of NO3-. Finally, we characterized microbial communities and abundance of nitrifiers on foliage, RF and TF water through metabarcoding and quantitative Polymerase Chain Reaction, respectively. 3. NO3—N fluxes at the site were larger in TF than RF, suggesting a contribution from dry deposition, as also supported by δ15N and δ18O. However, Δ17O indicated that about 20% of NO3- in TF derived from canopies nitrification in August, after a severe drought, with a lower proportion in September (≈ 8%). This seasonal partitioning between biologically and atmospherically derived NO3- coincided with a decreasing trend of the abundance of archaeal nitrifiers. Tree canopies and TF had more diverse microbial communities than RF. Yet, RF showed higher variability in microbial composition, likely associated to the origin of air masses. 4. Synthesis. Atmospheric N deposition is significantly altered after passing through tree canopies. While nitrification has been proposed as one of the mechanisms responsible for these changes, very few studies directly investigate its occurrence. Here, we showed that nitrification by epiphytic leaf microbes contributed to increasing NO3 in TF and that nitrifiers' activity was reduced going from the dry and hot summer to the cool winter. Overall, these results highlight the power of coupling microbial community analysis, functional gene amplification and stable isotope approaches to examine ecosystem-scale processes.
Challenges and limitations of applying the flux variance similarity (FVS) method to partition evapotranspiration in a montane cloud forest
<p>Dataset</p> <table> <tbody> <tr> <td>Name</td> <td>Description</td> </tr> <tr> <td>FVS_ori.zip</td> <td>the output from FVS method</td> </tr> <tr> <td>ModFVS.zip</td> <td>the output from ModFVS method</td> </tr> <tr> <td>CLM.zip</td> <td>the output from CLM </td> </tr> <tr> <td>Chilan_30min_sap_velocity_20200601_20211120_QC.csv</td> <td>the sap flow data in Chi-Lan</td> </tr> <tr> <td>*_clim.csv</td> <td>the observation data in Chi-Lan and Lien-Hua-Chih</td> </tr> </tbody> </table> <p> </p> <p>Codes for Analysis</p> <table> <tbody> <tr> <td>Name</td> <td>Description</td> </tr> <tr> <td>*.ipynb</td> <td>the python code used for analyzing output</td> </tr> <tr> <td>*_FVS_process.py</td> <td>the python code used for process ModFVS method</td> </tr> </tbody> </table> <p> </p> <p>ModFVS method (fluxpart-0.2.10+rhtest-py3-none-any.whl)</p> <ul> <li>use "pip install fluxpart-0.2.10+rhtest-py3-none-any.whl" to install the package</li> <li> <p>To specify a maximum allowable relative humidity when calculating WUE, set a value for "max_rh" in "wue_options". For example, to set the max RH to 95%, you would change your example code to this:</p> <p>wue_options = {"meas_ht": 23.7,"canopy_ht":10, "ppath": "C3","ci_mod":ci_mod, "max_rh":95}</p> </li> <li> <p>Note that this code is a fork of (https://github.com/usda-ars-ussl/fluxpart)</p> </li> </ul> <p> </p> <p> </p>
Climate Impacts of Parameterizing Subgrid Partitioning of Land Surface Heat Fluxes to the Atmosphere with the NCAR CESM1.2
<p>The modified code as well as the CAM5 output for all the simulations in this study (V0 for the CTL run, CON1 for the EXP run, and PCON1R for EXP_COR run).</p> <p>The CESM1.2.1-CAM5.3 source code can be downloaded through the CESM official website https://www.cesm.ucar.edu/models/cesm1.2/cesm/doc/usersguide/x290.html#download_ccsm_code. Its output files are named in V0*.nc.</p> <p>The modified code for the EXP run in the study is in CON1.tar, with its CAM5 output files named in CON1*.nc.</p> <p>The modified code for the EXP_COR run in the study is in PCON1R.tar, with its CAM5 output files named in PCON1R*.nc</p>
Partitioning of water and CO2 fluxes at NEON sites into soil and plant components: a five-year dataset for spatial and temporal analysis
<p>This dataset includes estimates of transpiration, evaporation, soil respiration, and plant net photosynthesis obtained using five partitioning approaches. Flux components are available at 47 NEON sites over a period of five years. Additional meteorological inputs and water-use efficiency data are also included.</p>
Varying partitioning of surface turbulent fluxes regulates temperature-humidity dissimilarity in the convective atmospheric boundary layer
<p>This dataset contains the data used in the submitted manuscript of Liu, Liu, Huang, and Xiao 2021. Please refer to the manuscript for the detailed description of the dataset.</p>
Data from: Partitioning between atmospheric deposition and canopy microbial nitrification into throughfall nitrate fluxes in a Mediterranean forest
Open the record for dataset details and reuse information.
Numerical Investigation of Observational Flux Partitioning Methods for Water Vapor and Carbon Dioxide
Open the record for dataset details and reuse information.
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.