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.
91
datasets available to search
ShareScore release 0.9.0
Dataset results
91 results for “hydraulic data”
Codes, Catalogues and Data for "Deep Learning Phase Pickers: How Well Can Existing Models Detect Hydraulic-Fracturing Induced Seismicity from a Downhole Array"
<p><strong>Codes, Catalogues and Data available for:</strong> <br>"Deep Learning Phase Pickers: How Well Can Existing Models Detect Hydraulic-Fracturing Induced Seismicity from a Downhole Array"</p> <p><strong>Catalog</strong> folder: Contains the CMM (beam-forming based) event catalogue as well as event and station information for the PNR-1z site.</p> <p><strong>Classification Test</strong> folder: Jupyter notebooks that run the classification tests and mseed input data of isolated phases (P, S, Noise).</p> <p><strong>DL_model_catalogues</strong> folder: Contains full catalogues for each DL phase picker (GPD, U-GPD, EQT and PhaseNet) and the LinMEF-filtered catalogues.</p> <p><strong>Model_run_docs</strong> folder: Util/core files for PhaseNet and EQTransformer to read data with different sampling frequencies (i.e., not 100 Hz)</p> <p><strong>Data</strong> folder: Contains one hour of continuous downhole data (11th December 2018, 9am-10am) from the PNR-1z dataset.</p>
Condition monitoring of hydraulic systems Data Set at ZeMA
<p><strong>Abstract:</strong></p> <p>The data set addresses the condition assessment of a hydraulic test rig based on multi sensor data. Four fault types are superimposed with several severity grades impeding selective quantification.</p> <p> </p> <p><strong>Source:</strong></p> <p>Creator: ZeMA gGmbH, Eschberger Weg 46, 66121 Saarbrücken<br> Contact: t.schneider <strong>'@'</strong> zema.de, s.klein <strong>'@'</strong> zema.de, m.bastuck <strong>'@'</strong> lmt.uni-saarland.de, info <strong>'@'</strong> lmt.uni-saarland.de</p> <p> </p> <p><strong>Data Set Information:</strong></p> <p>The data set was experimentally obtained with a hydraulic test rig. This test rig consists of a primary working and a secondary cooling-filtration circuit which are connected via the oil tank [1], [2]. The system cyclically repeats constant load cycles (duration 60 seconds) and measures process values such as pressures, volume flows and temperatures while the condition of four hydraulic components (cooler, valve, pump and accumulator) is quantitatively varied.</p> <p> </p> <p><strong>Attribute Information:</strong></p> <p>The data set contains raw process sensor data (i.e. without feature extraction) which are structured as matrices (tab-delimited) with the rows representing the cycles and the columns the data points within a cycle. The sensors involved are:<br> Sensor Physical quantity Unit Sampling rate<br> PS1 Pressure bar 100 Hz<br> PS2 Pressure bar 100 Hz<br> PS3 Pressure bar 100 Hz<br> PS4 Pressure bar 100 Hz<br> PS5 Pressure bar 100 Hz<br> PS6 Pressure bar 100 Hz<br> EPS1 Motor power W 100 Hz<br> FS1 Volume flow l/min 10 Hz<br> FS2 Volume flow l/min 10 Hz<br> TS1 Temperature °C 1 Hz<br> TS2 Temperature °C 1 Hz<br> TS3 Temperature °C 1 Hz<br> TS4 Temperature °C 1 Hz<br> VS1 Vibration mm/s 1 Hz<br> CE Cooling efficiency (virtual) % 1 Hz<br> CP Cooling power (virtual) kW 1 Hz<br> SE Efficiency factor % 1 Hz<br> <br> The target condition values are cycle-wise annotated in ‘profile.txt’ (tab-delimited). As before, the row number represents the cycle number. The columns are<br> <br> 1: Cooler condition / %:<br> 3: close to total failure<br> 20: reduced effifiency<br> 100: full efficiency<br> <br> 2: Valve condition / %:<br> 100: optimal switching behavior<br> 90: small lag<br> 80: severe lag<br> 73: close to total failure<br> <br> 3: Internal pump leakage:<br> 0: no leakage<br> 1: weak leakage<br> 2: severe leakage<br> <br> 4: Hydraulic accumulator / bar:<br> 130: optimal pressure<br> 115: slightly reduced pressure<br> 100: severely reduced pressure<br> 90: close to total failure<br> <br> 5: stable flag:<br> 0: conditions were stable<br> 1: static conditions might not have been reached yet</p> <p> </p> <p><strong>Relevant Papers:</strong></p> <p>[1] Nikolai Helwig, Eliseo Pignanelli, Andreas Schütze, ‘Condition Monitoring of a Complex Hydraulic System Using Multivariate Statistics’, in Proc. I2MTC-2015 - 2015 IEEE International Instrumentation and Measurement Technology Conference, paper PPS1-39, Pisa, Italy, May 11-14, 2015, doi: 10.1109/I2MTC.2015.7151267.<br> [2] N. Helwig, A. Schütze, ‘Detecting and compensating sensor faults in a hydraulic condition monitoring system’, in Proc. SENSOR 2015 - 17th International Conference on Sensors and Measurement Technology, oral presentation D8.1, Nuremberg, Germany, May 19-21, 2015, doi: 10.5162/sensor2015/D8.1.<br> [3] Tizian Schneider, Nikolai Helwig, Andreas Schütze, ‘Automatic feature extraction and selection for classification of cyclical time series data’, tm - Technisches Messen (2017), 84(3), 198 – 206, doi: 10.1515/teme-2016-0072.</p> <p><br> </p> <p><strong>Citation Request:</strong></p> <p>Nikolai Helwig, Eliseo Pignanelli, Andreas Schütze, ‘Condition Monitoring of a Complex Hydraulic System Using Multivariate Statistics’, in Proc. I2MTC-2015 - 2015 IEEE International Instrumentation and Measurement Technology Conference, paper PPS1-39, Pisa, Italy, May 11-14, 2015, doi: 10.1109/I2MTC.2015.7151267.</p>
Experiment and analysis data of Study on the Effect of Pore-Clogging Caused by Fenton's Reagent on the Hydraulic Conductivity Using Time-lapsed Hydraulic Tomography
<p>The Microsoft Office Excel data file ('HT data.xlsx') contains two sheets: the first sheet includes the raw data from the first and second HT experiments, labeled as '1st' and '2nd' respectively; the second sheet contains their processed data.</p> <p>The compressed file ('Models.rar') includes all the models, such as the forward and inverse models. Each model contains its own input files and an executable file.</p>
Observed soil water retention and soil hydraulic conductivity data and fits to those data by RIAfitter and KRIAfitter
<p>Weber soils SWRC.7z: zipped file with the Windows folders that hold all input and output data related to fitting the RIA parameterization (de Rooij, 2022, 2024a) to soil water retention data of 13 soils.</p> <p>Weber soils UHCC.7z: the zipped folder structure with fits of several models (de Rooij, 2024b, c) for the unsaturated hydraulic conductivity for the same 13 soils as the other file. The fitting code (KRIAfitter) used the fitted parameters of the other file plus soil hydraulic conductivity data to produce the fits.</p> <p>Both files contain Excel files that process the input and output of all fits.</p> <p>The soils are taken from Weber et al. (2019). The support of T.K.D. Weber in providing the data is gratefully acknowledged,</p> <p>N.B. The output format may differ somewhat from that reported in the User Manual of de Rooij (2024c) because the fits were used to improve details of the code and the way information is provided in the output files. These variations do not affect the parameter fitting process.</p> <p>References</p> <p>de Rooij, G. H.: Technical note: A sigmoidal soil water retention curve without asymptote that is robust when dry-range data are unreliable, Hydrol. Earth Syst. Sci., 26, 5849–5858, doi: 10.5194/hess-26-5849-2022, 2022.</p> <p>de Rooij, G.: Fitting the parameters of the RIA parameterization of the soil water retention curve (2.0), Zenodo [code], doi: 10.5281/zenodo.6491978, 2024a.</p> <p><br>de Rooij, G. H.: Averaging or adding domain conductivities to calculate the unsaturated soil hydraulic conductivity, Vadose Zone J., e20329, doi: 10.1002/vzj2.20329, 2024b.</p> <p><br>de Rooij, G.: Fitting the junction model and other models for the unsaturated hydraulic conductivity curve: KRIAfitter, Zenodo [code], doi: 10.5281/zenodo.14047942, 2024c.</p> <p><br>Weber, T. K. D., Durner, W., Streck, T., and Diamantopoulos, E.: A modular framework for modeling unsaturated soil hydraulic properties over the full moisture range, Water Resour. Res., 55, 4994-5011, doi: 10.1029/2018WR024584, 2019.</p> <p> </p>
Data from: Axial conduit widening, tree height and height growth rate set the hydraulic transition of sapwood into heartwood
<p><span>The size-related xylem adjustments required to maintain</span><span> a constant leaf-specific sapwood conductance (<em>K<sub>LEAF</sub></em>) with increasing height (<em>H</em>) are still under discussion. Alternative hypotheses are that: (i) the conduit hydraulic diameter (<em>Dh</em>) at any position in the stem and/or (ii) the number of sapwood rings at stem base (<em>NSWr</em>) increase with <em>H.</em> In addition, (iii) lower stem elongation (</span><em>Δ<span>H</span></em><span>) increases the tip-to-base conductance through inner xylem rings, thus possibly the <em>NSWr</em> contributing to <em>K<sub>LEAF</sub></em>.</span></p> <p><span>A detailed stem analysis showed that </span><em><span>Dh</span></em><span><em> </em>increased with the distance from the apex (<em>DCA</em>) in all rings of a <em>P. abies</em> and a <em>F. sylvatica</em> tree. Net of <em>DCA</em> effect, <em>Dh</em> did not increase with <em>H</em>. Using sapwood traits from a global dataset, <em>NSWr</em> increased with <em>H</em> and decreased with </span><em>Δ<span>H</span></em><span>, and the mean sapwood ring width (<em>SWrw</em>) increased with </span><em>Δ<span>H</span></em><span>. A numerical model based on anatomical patterns predicted the effects of <em>H</em> and </span><em>Δ<span>H</span></em><span> on the conductance of inner xylem rings.</span></p> <p><span>Results suggested the sapwood/heartwood transition depends on both <em>H</em> and </span><em>Δ<span>H</span></em><span>, and is set when the C allocation to maintenance respiration of living cells in inner sapwood rings produces a lower gain in total conductance than investing the same C in new vascular conduits.</span></p>
Data from: Hydraulic vulnerability of tropical forests is largely independent of water availability
<p>Tropical rainforest woody plants have been thought to have uniformly low resistance to hydraulic failure and to function near the edge of their hydraulic safety margin, making these ecosystems vulnerable to drought; however, this may not be the case. Using data collected at 30 tropical forest sites for three key traits associated with drought tolerance, we show that site-level hydraulic diversity of leaf turgor loss point, resistance to embolism (P<sub>50</sub>), and hydraulic safety margins (HSMs) is high across tropical forests and largely independent of water availability. Species with high HSMs (>1 MPa) and low P<sub>50</sub> values (<-2 MPa) are common across the wet and dry tropics. This high site-level hydraulic diversity, largely decoupled from water stress, could influence which species are favored and become dominant under a drying climate. High hydraulic diversity could also make these ecosystems more resilient to variable rainfall regimes.</p>
Scripts and Data for "Disentangling the Hydrological and Hydraulic Controls on Streamflow Variability in E3SM V2 – A Case Study in the Pantanal Region"
<p>Matlab scripts for processing and showing the coupled ELM-MOSART coupled simulations for Pantanal region.</p> <p>domain_lnd_Pantanal_default.nc, MOSART_Pantanal_default_c211116.nc, and surfdata_Pantanal_default_c220520.nc are the domain file, MOSART input file, and ELM surface dataset, respectively. </p> <p><a href="https://zenodo.org/api/files/6be468b6-cbfa-4ef4-bb10-0f15812be9bd/Pantanal_half_calibration_CLMCRUNCEPv7.sh">Pantanal_half_calibration_CLMCRUNCEPv7.sh</a> is the bash script to run E3SM with coupled ELM-MOSART configuration. ANd detailed instruction of running E3SMV2 can be found at: https://e3sm.org/model/running-e3sm/e3sm-quick-start/ (last access: Aug 2023).</p> <p>Pantanal_GSIM.zip contains the observed streamflow that used in this study, which is downloaded from <a href="https://doi.pangaea.de/10.1594/PANGAEA.887470">https://doi.pangaea.de/10.1594/PANGAEA.887470</a> (last access: Aug 2023). The reference is Gudmundsson, Lukas; Do, Hong Xuan; Leonard, Michael; Westra, Seth (2018): The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment. Earth System Science Data, 10(2), 787-804, https://doi.org/10.5194/essd-10-787-2018.</p> <p>BFI3.mat is the baseflow index from GSCD and processed to the study domain. The GSCD dataset was download from <a href="http://www.gloh2o.org/gscd/">http://www.gloh2o.org/gscd/</a> (last access: Aug 2023). The reference is Beck, H. E., van Dijk, A. I. J. M., Miralles, D. G., de Jeu, R. A. M., Bruijnzeel, L. A., McVicar, T. R., and Schellekens, J.: Global patterns in base flow index and recession based on streamflow observations from 3394 catchments, Water Resour Res, 49, 7843-7863, <a href="https://doi.org/10.1002/2013WR013918">https://doi.org/10.1002/2013WR013918</a>, 2013.</p> <p>runoff_uncertianty.mat contains the annual runoff time series from GRUN, LORA, and GFRF that processed to the study domain. The GRUN runoff dataset was downloaded from <a href="https://doi.org/10.6084/m9.figshare.9228176">https://doi.org/10.6084/m9.figshare.9228176</a> (last access: Aug 2023). The LORA runoff dataset was downloaded from <a href="https://dap.nci.org.au/thredds/remoteCatalogService?catalog=http://dapds00.nci.org.au/thredds/catalog/ks32/ARCCSS_Data/LORA/v1-0/catalog.xml">https://dap.nci.org.au/thredds/remoteCatalogService?catalog=http://dapds00.nci.org.au/thredds/catalog/ks32/ARCCSS_Data/LORA/v1-0/catalog.xml</a> (last access: Aug 2023). The reference is Hobeichi, S., Abramowitz, G., Evans, J., and Beck, H. E.: Linear Optimal Runoff Aggregate (LORA): a global gridded synthesis runoff product, Hydrol. Earth Syst. Sci., 23, 851-870, 10.5194/hess-23-851-2019, 2019. The GRFR runoff was downloaded from <a href="http://hydrology.princeton.edu/data/mpan/GRFR/runoff/monthly_1deg/">http://hydrology.princeton.edu/data/mpan/GRFR/runoff/monthly_1deg/</a> (last access: Aug 2023). The reference is Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. d. H., Lu, H., Yang, K., Hong, Y., and Wood, E. F.: Global Reach-level 3-hourly River Flood Reanalysis (1980-2019), B Am Meteorol Soc, 1-49, 10.1175/BAMS-D-20-0057.1, 2021.</p> <p>GLAD_Pantanal_half.mat, GLAD_Pantanal_8th.mat are the processed surface water fraction from GLAD at half and 8th spatial resoution. Specifically, The GLAD surface water dynamics was downloaded from <a href="https://console.cloud.google.com/storage/browser/earthenginepartners-hansen/water;tab=objects">https://console.cloud.google.com/storage/browser/earthenginepartners-hansen/water;tab=objects</a> (last access: Aug 2023). The reference is Pickens, A. H., Hansen, M. C., Hancher, M., Stehman, S. V., Tyukavina, A., Potapov, P., Marroquin, B., and Sherani, Z.: Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series, Remote Sens Environ, 243, 111792, <a href="https://doi.org/10.1016/j.rse.2020.111792">https://doi.org/10.1016/j.rse.2020.111792</a>, 2020.</p>
Data from: Interactions between beech and oak seedlings can modify the effects of hotter droughts and the onset of hydraulic failure
<p><span>Mixing species with contrasting resource use strategies could</span><span> reduce forest vulnerability to extreme events. Yet, how species diversity affects seedling hydraulic responses to heat and drought, including mortality risk, is largely unknown.</span></p> <p><span>Using open-top chambers, </span><span>we assessed how, over several years, species interactions (monocultures vs. mixtures) modulate heat and drought impacts on the hydraulic traits </span><span>of juvenile European beech and pubescent oak. Using modelling, we estimated species interaction effects on timing to drought-induced mortality and the underlying mechanisms driving these impacts. </span></p> <p><span>We show that mixtures mitigate adverse heat and drought impacts for oak (less negative </span><span>leaf water potential, higher stomatal conductance, and delayed stomatal closure) but enhance them for beech (lower water potential and stomatal conductance, narrower leaf safety margins, faster tree mortality). Potential underlying mechanisms include oak's larger canopy and higher </span><span>transpiration</span><span>, allowing for quicker exhaustion of soil water in mixtures.</span></p> <p><span>Our findings highlight that diversity has the potential to alter the effects of extreme events, which would ensure that some species persist even if others remain sensitive. Among the many processes driving diversity effects, differences in canopy size and transpiration associated to the stomatal regulation strategy seem the primary mechanisms driving mortality vulnerability in mixed seedling plantations.</span></p>
Data from: Divergence of vessel diameter explains interspecific variation in hydraulic safety to salinity in the Sundarbans mangrove ecosystem
Open the record for dataset details and reuse information.
Data from: Interactions between beech and oak seedlings can modify the effects of hotter droughts and the onset of hydraulic failure
Open the record for dataset details and reuse information.
Data from: Trading water for carbon in the future: effects of elevated CO2 and warming on leaf hydraulic traits in a semiarid grassland
Open the record for dataset details and reuse information.
Data from: From spawn to survival: Decoding the hydraulic conditions for successful silver carp egg incubation
Open the record for dataset details and reuse information.
Data from: Soil hydraulic properties determined by inverse modeling of drip infiltrometer experiments extended with pedotransfer functions
Open the record for dataset details and reuse information.
Data from: Experimental study of shear and hydraulic bonding strength between casing and cement under complex temperature and pressure conditions
Open the record for dataset details and reuse information.
Data from: Six co-occurring conifer species in northern Idaho exhibit a continuum of hydraulic strategies during an extreme drought year
Open the record for dataset details and reuse information.
Data from: Brittle sedimentary strata focus a multimodal depth distribution of seismicity during hydraulic fracturing in the Sichuan basin, southwest China
Open the record for dataset details and reuse information.
Data from: Hydraulic vulnerability of tropical forests is largely independent of water availability
Open the record for dataset details and reuse information.
Data from: Axial conduit widening, tree height and height growth rate set the hydraulic transition of sapwood into heartwood
Open the record for dataset details and reuse information.
Fire effects on tree physiology, growth, and drought vulnerability: Non-structural carbohydrate, hydraulic function, water potential, and tree growth data
Open the record for dataset details and reuse information.
Data from: Plasticity in plant hydraulic traits: An evaluation of a common-taxa experiment across a climatic gradient in the Western U.S.
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.