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1,891 results for “Field data”
Soil and canopy temperature data from the Arctic LTER Moist Acidic Tussock Experimental plots (MAT89) from 2012 to 2018, Toolik Field Station, North Slope, Alaska
Soil and canopy temperature data from the Arctic LTER 1989 Moist Acidic Tussock Experimental plots(MAT89). The station was established in 1990 in block 2 of a 4 block random block design. The plots are located on a hillside near Toolik Lake, Alaska (68 38' N, 149 36'W). Treatments include - control (CT), greenhouse (GH), greenhouse plus nitrogen and phosphorus (GHNP) shade (SH), shade plus nitrogen and phosphorus (SHNP) and nitrogen and phosphorus (NP). Profiles include above and within canopy, 10, 20 and 40 centimeter soil depths. Not all treatments have a complete profile. Meteorological data was also collected but are included in a separate data set.
Dark respiration and photosynthesis data from Dry Heath Nitrogen & Phosphorus addition plots, Arctic LTER, Toolik Field Station, Alaska, summers 2023-2024.
To determine the effects of weather variability on Arctic plant functioning, we conducted this study looking at the response of plant dark respiration and photosynthesis to short-term, high-frequency, temperature and light variability. We measured Betula nana, Chamaenerion angustifolium, and Calamagrostis stricta from the dry heath tundra N&P fertilized plots. We took measurements through two summer seasons. The first summer we obtained data regarding responses to variable temperature and light, and in the second summer we obtained the dark respiration to temperature response and photosynthesis to light response curves.
Data from nitrogen isotopic analyses used to calculate biological nitrogen fixation (BNF) rates and field measurements from lichen, bryophyte, litter, and soil samples in MAT2006 plots, Arctic LTER, Toolik Field Station, Alaska, summers 2022-2023.
This dataset contains nitrogen (N) fixation and isotope data from experimental samples collected at Toolik Lake, Alaska during the 2022 and 2023 growing seasons. Sampling was conducted across multiple block treatments to capture spatial variability and included four substrate types: lichen, moss, litter, and soil. Within each plot, substrates were collected systematically along transects to ensure representative sampling, with lichen samples collected opportunistically due to lower abundance. Samples were incubated in the field under ambient conditions using 15N₂ to measure biological nitrogen fixation (BNF). In 2023, a short-term wetting experiment was conducted to assess the influence of moisture on BNF rates, with subsamples exposed to controlled additions of water. Across both years, data include isotope ratios, incubation conditions, moisture, fresh and dry biomass, and treatment assignments. The dataset provides information on BNF across substrate types, moisture regimes, and fertilization treatments in Arctic tundra. These data support investigation of N cycling processes, the influence of moisture and fertilization on fixation rates, and variability across vegetation types. The dataset is complete for the two field seasons (2022 and 2023) and includes sample- and block-level metadata necessary for reuse in ecological and biogeochemical research.
Meteorological data from El Verde Field Station: NADP Tower
Meteorological sensors are located at the top of a 20 m tower, the NADP Tower, behind the main buildings of El Verde Field Station, 350 masl. No large trees are present near the tower. Sensors are connected to a Campbell 1000X data logger, with a storage module, and downloaded every two weeks using a wireless radio connection from the laboratory to the tower. Data are compiled at the station before it is made available. The station was initiated in 2000. Complementary manual measurements of rainfall and maximum and minimum air temperatures are taken on a daily basis at a separate physical location. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Coastal marine megaripple fields are metabolic hotspots - Data for figures and tables
This dataset contains the data used to create figures 2-5 in the paper: Berg, P. in press. Coastal marine megaripple fields are metabolic hotspots with highly dynamic oxygen exchange. L&O Letters (publication expected in 2023). Details on the nature of the data and how it was collected are included in the paper.
Field-lab data and analyses results West Kalimantan, Indonesia
<p>Field data included in this compressed folder were collected over an area of approximately 23,500 hectares in West Kalimantan, Indonesia. The folder includes Excel and txt files with the following contents: field measurements of the peat thickness at 63 coring sites, laboratory analyses results of the samples collected in the field, field and lab measurements of electrical conductivity. Moreover, there is a file containing all the measurements of peat thickness and soil elevation extracted from the figures contained in previous studies. Finally, the folder also includes the results obtained from the inversion of the Airborne Electromagntic (AEM) data collected with the SkyTEM instrument over the study site, and specifically the resistivity of the soil layers obtained from the inversion and the peat thickness corresponding to the 45 Ohmm threshold. The folder also includes the Python codes used for the statistical analyses explained in the paper.</p>
Data to "Point-wise correlations between 10-2 Humphrey visual field and OCT data in open angle glaucoma"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Cirafici, P., Maiello, G., Ancona, C., Masala, A., Traverso, C.E., & Iester M. (in press) Point-wise correlations between Humphrey visual field and OCT data in open angle glaucoma. Eye</p>
Data bundle for "Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine learning"
<p>Prepared by Tom McAuliffe (t.mcauliffe17@imperial.ac.uk)</p> <p>This repository is a release of the raw data and analysis results for: 'Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine learning' </p> <p>The raw data is given as 'yprime.h5' - this contains patterns and metadata in the Bruker-exported format.</p> <p>Scripts for dataset decomposition into latent factors are given in 'Scripts'.</p> <p>Our spherical analysis code is included in 'SphericalAngleDF'.</p> <p>Outputs of our analysis code are contained in 'Analysis'.</p> <p>Figures for the paper are included in 'Figures'.</p> <p> </p>
Magnetization versus applied dc magnetic field data for Li0.7[Cr(pyz)2]Cl0.7·(THF)
<p>Magnetization versus applied dc magnetic field data in the –7 to 7 T field range from 1.85 K to 520 K for Li<sub>0.7</sub>[Cr(pyz)<sub>2</sub>]Cl<sub>0.7</sub>·(THF). The data are provided in a tab-delimited text format.</p>
Datasets For "Estimating Maximum Extent of Auroral Equatorward Boundary using Historical and Simulated Surface Magnetic Field Data", Blake et al. (2020), JGR
<p>Datasets and sample Python codes for the 2020 paper <em>"Estimating Maximum Extent of Auroral Equatorward Boundary using Historical and Simulated Surface Magnetic Field Data"</em>, by Blake et al., submitted to the Journal of Gephysical Research, Space Physics. </p> <p>Up-to-date Python codes can be found at <a href="https://github.com/TerminusEst/Auroral_Boundary_Geomag">https://github.com/TerminusEst/Auroral_Boundary_Geomag</a></p> <p>The complete SWMF simulation folders (including parameter and log files etc.) can be requested from <a href="https://ccmc.gsfc.nasa.gov/index.php">NASA's Community Coordinated Modeling Center</a>.</p> <p>#########</p> <p><strong>Data/ </strong>contains the following:</p> <p><strong>Data/HIST_DATA.txt </strong>contains the minimum Dst values and calculated maximum extents of the auroral equatorward boundaries for 25 years of INTERMAGNET data (1991-2016). The fourth column is the standard deviation of the calculated auroral boundary in degrees. </p> <p><strong>Data/Boundary_Fits.csv </strong>contains the calculated minimum Dst values, and calculated auroral boundaries using Method 1 and Method 2 (see main paper's ttext), for each of the 15 SWMF simulations. Also included are the uncertainties for each calculation.</p> <p><strong>Data/SWMF_outputs/ </strong>contains 15<strong> </strong>.txt files,<strong> </strong>each of which correspond to an SWMF simulation of the same name given in Table 1 in the main text. These data are for the magnetic longitude, magnetic latitude and maximum calculated <em>E<sub>H</sub> </em>(V/km) for each simulation.</p> <p>#########</p> <p><strong>Codes/ </strong>contains two python scripts, and some sample data. These scripts correspond to Section 2 in the main text:</p> <p>1) <strong>Boundary_Calc.py</strong> calculates the extent of the auroral boundary using magnetic latitudes and maximum calculated <em>E<sub>H</sub></em> values from multiple INTERMAGNET sites. </p> <p>2) <strong>Efield_Calc.py </strong>calculates the E-field for a single INTERMAGNET site using the Quebec 1-D resistivity model.</p> <p>A more detailed description of these codes can be found here: <a href="https://github.com/TerminusEst/Auroral_Boundary_Geomag">https://github.com/TerminusEst/Auroral_Boundary_Geomag</a></p> <p> </p>
Magneto-telluric data from the Los Humeros geothermal field in Mexico: time series and edi-files
<p>The data are from the Los Humeros geothermal field in Mexico.</p> <p>The dataset is composed of time series of magneto-telluric data and edi-files obtained from the time series. A file containing the location of the soundings and calibration files are also in the dataset.</p> <p>The Metronix equipment was used to acquire the data.</p> <p>The data were gathered under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727550, and by the Mexican Energy Sustainability Fund CONACYT-SENER, Project 2015-04-268074.</p>
Transient Electromagnetic data from the Los Humeros geothermal field in Mexico: raw data
<p>The dataset is composed of raw data from TerraTEM (from Monex GeoScope, single loop) from the Los Humeros geothermal field, Mexico and their locations in a .csv file.</p> <p>The data were gathered under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727550, and by the Mexican Energy Sustainability Fund CONACYT-SENER, Project 2015-04-268074.</p>
Spectral Induced Polarization (SIP) field data sets for comparison between time and frequency domain (Martin et al. 2020)
<p>Data and scripts for the scientific journal article:</p> <p>Martin, T., Günther, T., Flores Orozco, A. & Dahlin, T. (2020): <br> Evaluation of spectral induced polarization field measurements in time and frequency domain, <br> J. Appl. Geophys., 180, 104141, doi:10.1016/j.jappgeo.2020.104141.</p> <p>The paper deals with the comparison of instruments for measuring induced polarization (IP)<br> data, both in frequency domain (FD) as well in time-domain. Four instruments have been used<br> to measure two profiles (IP1: a=1m, IP5: a=5m) over a known blackshale near the town Schleiz<br> in Thuringia, Germany.</p> <p>Preface: Our vision is not only to publish scientific results, but also openly demonstrate<br> how the results have been produced. This includes the raw data, processed data and inversion<br> results, but also all scripts that have been used for it in a reproducible way.<br> The scripts use the pyBERT (Python Boundless Electrical Resistivity Tomography) based on the<br> pyGIMLi (Python Geophysical Inversion and Modelling Library) computational framework.<br> Here, we used the pyBERT version 2.3.1 based on pygimli 1.1.1, but it should be reachable by<br> similar versions as well. All is based on the classes FDIPdata and TDIPdata.<br> For installing, we refer to the webpages www.pygimli.org and gitlab.com/resistivity-net/bert</p> <p>Folders:<br> figures figures used for production process, generated by the scripts in the folder<br> scripts Python scripts for generating the pdf files<br> IP1 data for the shallow profile using an electrode spacing of a=1m<br> IP5 data for the deeper profile using an electrode spacing of a=5m</p> <p>Each of the two data folders contains data and inversion results in separated folder named<br> according to the instrument names: <br> * SIP256C (Radic Research) - FDIP instrument with remote units (intelligent electrodes)<br> * DAS-1 (Multi-Phase Technology) - FDIP and TDIP instrument using multi-core cables<br> * Terrameter LS2 (GuidelineGeo ABEM) - TDIP instrument using multi-core cables<br> * Syscal Pro Switch72 (Iris instruments) - TDIP instrument using multi-core cables</p> <p>Please see readme files in the individual folders for specific information.</p> <p>Notice: In additon to the published results, we also added more data from the field <br> measurements which completes our results, e.g. <br> - TDIP data with 8s aquisition time<br> - all four instruments for the long profile IP5</p> <p>Complete full-waveform data for the Terrameter LS2 are available on request.</p>
Wind field and cloud layer data for Stuttgart, Germany
<p>This dataset contains computed wind field, cloud base and mixing layer height time series for locations in the City Centre of Stuttgart, Germany. The observations were made using a network of Doppler lidar and ceilometer instrumentation. The measurements were conducted within the framework of the Urban Climate under Change [UC]2 program. </p> <p>Up-to-date information about the program can be found at: <a href="http://uc2-program.org">http://uc2-program.org</a></p> <p>Versions:</p> <ul> <li>1.x.y: Data are in compliance with the [UC]2 Data Standard (<a href="http://uc2-program.org/uc2_data_standard.pdf">http://uc2-program.org/uc2_data_standard.pdf</a>). Included are computed variables for days during the two Intensive Observation Periods (IOPs) in winter and summer of 2017.</li> <li>2.x.y: Data include computed variables for most of the available observations during 2017. The data stores conform to an approach developed for ScaleX.</li> </ul>
Supporting Data: Indacenodithiophene-benzothiadiazole Organic Field-Effect Transistors with Gravure Printed Semiconductor and Dielectric on Plastic
<p>The file contains the supporting data for the publication:</p> <p>S.G. Higgins, B.V.O. Muir, M. Heeney, A.J. Campbell. Indacenodithiophene-benzothiadiazole Organic Field-Effect Transistors with Gravure Printed Semiconductor and Dielectric on Plastic. doi: 10.1557/mrc.2015.66. <em>MRS Communications</em> (2015)</p> <p>See 'README.txt' for a description of the contents of the compressed file.</p>
Supporting Data: Self-Aligned Organic Field-Effect Transistors on Plastic with Picofarad Overlap Capacitances and Megahertz Operating Frequencies
<p>The file contains the supporting data for the publication:</p> <p>S.G. Higgins, B.V.O. Muir, G. Dell'Erba, A. Perinot, M. Caironi, A.J. Campbell. Self-Aligned Organic Field-Effect Transistors on Plastic with Picofarad Overlap Capacitances and Megahertz Operating Frequencies. doi: 10.1063/1.4939045. <em>Applied Physics Letters</em> (2016)</p> <p>See 'README.txt' for a description of the contents of the compressed file.</p>
Listening test results for sound field synthesis localization experiment -- head movement data
<p>This data set contains recorded head movements listeners did during several localisation tasks in the context of sound field synthesis. This is an add-on to the actual localisation results provided by [1].</p> <p>[1] Wierstorf, H. (2016). Listening test results for sound field synthesis localization experiment [Data set]. Zenodo. http://doi.org/10.5281/zenodo.55439</p>
Daphnia behavioural data and MFC field data
<p>The uploaded files contain several datasets.</p><p>The <i>Daphnia</i> calibration dataset contains the data obtained from <i>Daphnia</i> behavioural experiments with the use of salt. Videos were taken every hour for 30 seconds of 5 individuals.</p><p>dumpMfcVeniceCertosaS3_2019-10.csv contains field data obtained from 5 MFCs. The file contains the timestamps, energy accumulated per MFC and their total energy accumulated—data collected in Venice Lagoon.</p><p>Millstatt_Mussel_Data contains the mussel behavioural data from Lake Millstatt in Austria (2022). It contains the x y coordinates of the top valve of a Zebra Mussel. The data was taken as a calibration of the valve movement in reaction to a sudden disturbance (in this case, a heavy rock).</p>
Data and results of the example used in the SI-Hg D1 protocol for the SI-traceable calibration of elemental mercury (Hg0) gas generators used in the field
<p>During the SI-Hg project a metrological traceable protocol for the calibration of mercury gas generators used in the field was developed and validated. The SI-Hg calibration protocol specifies the procedures for establishing traceability to the SI units for the quantitative output of elemental mercury generators that are employed in regulatory applications for emission monitoring or testing. This protocol provides methods for</p><ul><li>the experimental procedures to compare the output of elemental mercury gas generators</li><li>the data processing for determination of mercury concentration and the expanded uncertainty of the mercury concentration obtained from the elemental mercury gas generator.</li></ul><p>In the protocol examples are given to explain the data processing, determining the mercury concentration and corresponding uncertainty. In this repository the raw data and results used for the example calculated with the data processing script can be found.</p>
Soil grid data for 3 agricultural fields in Italy (Soil Moisture, soil organic carbon)
<p><span>Soil data collected in an agricultural area with annual crops in Italy (west-central Lombardia Po Valley, province of Pavia). The data refers to soil properties of 320 soil samples for Soil Moisture and 120 for SOC, collected in the topsoil (around 5-10 cm), considering a regular sampling grid, within three agricultural field with different crops (spring-summer cycle) and soils type, Rice-Loamy, Sorghum-Sandy and, Maize-Clay, in a period (before the seeding of the crops), when the soil was bare, in the framework of the EJP Steropes project.</span></p> <p><span>The aim of the collected dataset was to be able to analyse the influence of soil moisture in SOC (WP2 of the STEROPES project) prediction models from remote sensing.</span></p> <p><span>Data in the form of shape file (one shapefile for each agricultural field, for oth Soil Moisture and SOC), and pictures of the soil surface in .jpg format. </span></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.