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1,891 results for “Field data”
Infrasound array data recorded in July-August, 2019, at Mt. Etna (Italy) during the VOSSIA field experiment
<p>We present infrasound data recorded by two infrasound arrays installed at Mt. Etna (Italy) within the framework of the VOSSIA (Volcanic emissions analysis through Seismic and Infrasound Advanced monitoring) project. VOSSIA was supported by the Trans-National Access component of the EUROVOLC project (European Network of Observatories and Research Infrastructures for Volcanology, EU Horizon 2020 Research Infrastructure Project grant No 731070).</p> <p>This data repository includes continuous raw waveforms recorded by two 6-element, small-aperture, infrasound arrays during July-August, 2019. The arrays, ENEA and ENCR, were installed on Mt. Etna in proximity of the summit Nord East and South East craters, respectively. ENEA was equipped with Chaparral M60 sensors (<a href="http://chaparralphysics.com/specs/specs_model60UHP.pdf">http://chaparralphysics.com/specs/specs_model60UHP.pdf</a>), while IST2018 microphones (<a href="https://doi.org/10.1016/j.jvolgeores.2019.106668">https://doi.org/10.1016/j.jvolgeores.2019.106668</a>) were installed at ENCR. Data at both arrays were recorded with a sampling frequency of 100 Hz and 24-bit resolution using DiGOS Datacube<sup>3</sup> digitizers (<a href="https://digos.eu/seismology-and-cubes/">https://digos.eu/seismology-and-cubes</a>).</p> <p>Waveform data are provided as day-long files in MSEED format (<a href="https://ds.iris.edu/ds/nodes/dmc/data/formats/">https://ds.iris.edu/ds/nodes/dmc/data/formats/</a>).</p> <p>We also provide metadata including:</p> <p>1) Station coordinates (.csv file station_coords.csv);</p> <p>2) Instrument_response.rar: Instrument response information in different formats (individual RESP files, SEED DATALESS, xlm DATALESS)</p>
Hubble and Spitzer data around a z=7.2 quasar in the GOODS-North Field
<p>This dataset provides 10-arcminute HST and Spitzer mosaics of the GOODS-North survey field, centered on the z=11.1 galaxy GNz11 (<a href="https://ui.adsabs.harvard.edu/abs/2016ApJ...819..129O/abstract">Oesch et al. 2016</a>) and including a z=7.2 quasar (Fujimoto et al., in prep). Complete mosaics are available at https://s3.amazonaws.com/grizli-v1/Mosaics/index.html.</p> <p>The mosaics were produced with the <a href="https://github.com/gbrammer/grizli">grizli</a> (HST) and <a href="https://github.com/gbrammer/golfir">golfir</a> (Spitzer) software modules.</p> <p>Also included are the 2D extracted grism spectra (gnz7qso_16155.beams.fits) that can be analyzed with grizli.</p> <p><strong>Table 1:</strong> HST exposure summary</p> <pre><strong>Filter N t(ks) Proposal ID</strong> f435w 6 7.2 9583 f606w 23 9.3 9583,10339,12443,13779 f775w 35 19.1 9583,9727,9728,10339,11600 f814w 42 23.8 12442,12443,12444,12445,13420 f850lp 82 36.9 9583,9727,9728,10189,10339,12444,13063 f105w 6 3.3 12442,13420 f125w 8 4.4 12443,12444,12445 f140w 6 1.2 11600 f160w 8 5.4 12443,12444,12445 g102 4 5.0 13420 g141 8 8.6 11600 </pre>
Numerical weather simulation using COSMOiso in June 2019 during L-WAIVE field campaign: selected model output and post-processed data.
<p>This dataset consists of extracts from a simulation with the isotope-enabled regional numerical weather prediction model COSMOiso, which covers the timespan of the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) field campaign taking place in June 2019 in the Annecy valley in the French Alps (Chazette et al. 2021).The simulation has a horizontal resolution of 0.1° (~10km) and 40 vertical levels.</p><p>This COSMOiso simulation is used in Thurnherr et al. (submitted) to compare stable water isotope measurements from various platforms. Here, we provide selected model outputs and post-processed data used in this comparison study. The post-processed data contain:</p><ol><li>COSMOiso output files for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Pressure weighted total and subcolumn averages for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Vertical cross section of selected variables at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated time series of subcolumn and total column averages at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated variables along the flight tracks from the L-WAIVE campaign (see Sodemann and Seidl, 2023).</li></ol><p>See also README files for more details on the provided data.</p><p>To access further model output and post-processed data, please contact the dataset authors.</p>
Hydrodynamic field data near Galveston, Texas wetland edges to help assess storm impacts and erosion
<p>Water free surface elevation measurements via submerged pressure transducers along transects near Galveston Bay wetland edges</p>
Wind measurement data from the publication: "Development of a load model validation framework applied to synthetic turbulent wind field evaluation"
<h3>Dataset description:</h3> <p>This datasat represents supplementary material used in the contribution "Development of a load model validation framework applied to<br>synthetic turbulent wind field evaluation" by Meyer, Huhn and Gottschall.</p> <p>Wind measurements from the Testfeld BHV are made available. For installation details, see the mentioned reference.</p> <p> </p> <h3>File description:</h3> <ul> <li>Lidar_HWS.nc - Horizontal wind speed measurements (10 min averages) from a WindCube V2 vertical profiler for one day with a low-level jet occurrence ( <div> <div>2021-04-20)</div> </div> </li> <li>Cups_HWS.nc - Horizontal wind speed measurements (10 min averages) from cup anemometer installed on a met mast for the same day</li> <li>Ensemble_averaged_Spectra.nc - Ensemble averaged spectra for neutral and near neutral situations from a Gill Windmaster at 110m above ground level, used to fit the Mann and KSEC model parameters</li> </ul> <h3> </h3> <h3>Referencing:</h3> <p>When used, please cite like the following:</p> <p>Meyer, Paul J., Matthias L. Huhn, and Julia Gottschall. 2024. "Development of a Load Model Validation Framework Applied to Synthetic Turbulent Wind Field Evaluation" <em>Energies</em> 17, no. 4: 797. https://doi.org/10.3390/en17040797</p> <p> </p> <p> </p>
Data from: A FAIR and modular image-based workflow for knowledge discovery in the emerging field of imageomics
<p>Data and results from the Imageomics Workflow. These include data files from the Fish-AIR repository (https://fishair.org/) for purposes of reproducibility and outputs from the application-specific imageomics workflow contained in the Minnow_Segmented_Traits repository (https://github.com/hdr-bgnn/Minnow_Segmented_Traits).</p> <p>Fish-AIR:<br> This is the dataset downloaded from Fish-AIR, filtering for Cyprinidae and the Great Lakes Invasive Network (GLIN) from the Illinois Natural History Survey (INHS) dataset. These files contain information about fish images, fish image quality, and path for downloading the images. The data download ARK ID is dtspz368c00q. (2023-04-05). The following files are unaltered from the Fish-AIR download. We use the following files:</p> <p>extendedImageMetadata.csv: A CSV file containing information about each image file. It has the following columns: ARKID, fileNameAsDelivered, format, createDate, metadataDate, size, width, height, license, publisher, ownerInstitutionCode. Column definitions are defined https://fishair.org/vocabulary.html and the persistent column identifiers are in the meta.xml file.</p> <p>imageQualityMetadata.csv: A CSV file containing information about the quality of each image. It has the following columns: ARKID, license, publisher, ownerInstitutionCode, createDate, metadataDate, specimenQuantity, containsScaleBar, containsLabel, accessionNumberValidity, containsBarcode, containsColorBar, nonSpecimenObjects, partsOverlapping, specimenAngle, specimenView, specimenCurved, partsMissing, allPartsVisible, partsFolded, brightness, <br> uniformBackground, onFocus, colorIssue, quality, resourceCreationTechnique. Column definitions are defined https://fishair.org/vocabulary.html and the persistent column identifiers are in the meta.xml file.</p> <p>multimedia.csv: A CSV file containing information about image downloads. It has the following columns: ARKID, parentARKID, accessURI, createDate, modifyDate, fileNameAsDelivered, format, scientificName, genus, family, batchARKID, batchName, license, source, ownerInstitutionCode. Column definitions are defined https://fishair.org/vocabulary.html and the persistent column identifiers are in the meta.xml file.</p> <p>meta.xml: A XML file with the metadata about the column indices and URIs for each file contained in the original downloaded zip file. This file is used in the fish-air.R script to extract the indices for column headers.</p> <p>The outputs from the Minnow_Segmented_Traits workflow are:</p> <p>sampling.df.seg.csv: Table with tallies of the sampling of image data per species during the data cleaning and data analysis. This is used in Table S1 in Balk et al. </p> <p>presence.absence.matrix.csv: The Presence-Absence matrix from segmentation, not cleaned. This is the result of the combined outputs from the presence.json files created by the rule “create_morphological_analysis”. The cleaned version of this matrix is shown as Table S3 in Balk et al.</p> <p>heatmap.avg.blob.png and heatmap.sd.blob.png: Heatmaps of average area of biggest blob per trait (heatmap.avg.blob.png) and standard deviation of area of biggest blob per trait (heatmap.sd.blob.png). These images are also in Figure S3 of Balk et al.</p> <p>minnow.filtered.from.iqm.csv: Filtered fish image data set after filtering (see methods in Balk et al. for filter categories).</p> <p>burress.minnow.sp.filtered.from.iqm.csv: Fish image data set after filtering and selecting species from Burress et al. 2017.</p>
Field data collected from pyroclastic and lahar deposits of the 472 AD (Pollena) and 1631 Vesuvius eruptions
<p><strong><span>Field data collected from pyroclastic and lahar deposits of the 472 AD (Pollena) and 1631 Vesuvius eruptions</span></strong></p> <p><span>Mauro A. Di Vito<sup>1</sup>, Ilaria Rucco<sup>2</sup>, Sandro de Vita<sup>1</sup>, Domenico M. Doronzo<sup>1</sup>, Marina Bisson<sup>3</sup>, Elena Zanella<sup>4</sup></span></p> <p><sup><span>1</span></sup><span> Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Vesuviano, Napoli, Italy</span></p> <p><sup><span>2</span></sup><span> Heriot-Watt University, School of Engineering and Physical Sciences, Edinburgh, United Kingdom</span></p> <p><sup><span>3</span></sup><span> Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, Pisa, Italy</span></p> <p><sup><span>4</span></sup><span> Università di Torino, Dipartimento di Scienze della Terra, Torino, Italy</span></p> <p><span> </span></p> <p><span>This dataset is organized in an Excel file, and it includes all the data collected and reviewed during the last 20 years from drill cores, outcrops, archaeological excavations, stratigraphic trenches, and the existing literature. It focuses on the primary (pyroclastic) and secondary (lahar) deposits of the 472 AD (Pollena) and 1631 eruptions from the Somma-Vesuvius volcano. The aim is to collect stratigraphic, stratimetric, sedimentological, lithological and chronological data to generate distribution maps and to validate the numerical simulations and models for the risk assessment. In particular, this dataset is complementary to – and in support of – the full work by Di Vito et al. (2024), in which the distribution of those deposits all around the Somma-Vesuvius complex and further is presented and discussed. Such dataset was used to inform the shallow-water model of lahars by de’ Michieli Vitturi et al. (2024), which in turns was used by Sandri et al. (2024) to elaborate probabilistic maps of lahar invasion in the Somma-Vesuvius and Apennine areas.</span></p> <p><span>All the data are organized in columns: the first four aim to identify the sites, and so there is a numeric identification code (ID), the name of the site (NAME), and the metric coordinates (East-North) in the UTM WGS 84 – Zone 33 reference projection (X, Y). The last two columns are “MUNICIPALITY” and “PROVINCE” and give a spatial location to the points.</span></p> <p><span>For the two eruptions, several columns have been created:</span></p> <p><span><span>·<span> </span></span></span><span>“472_PRIM”, “1631_PRIM” and “472_ASH” and “1631_ASH” indicate, respectively, the fallout primary deposits of the eruptions and the primary ash, particularly the ash related to the last phases of the eruptions (generally phreatomagmatic).</span></p> <p><span><span>·<span> </span></span></span><span>“472_SYN” and “1631_SYN” indicate the syn-eruptive lahars related to the two eruptions, recognized from the similar composition between the primary deposit and the lahar and from the evidence of a short-term exposure between the two.</span></p> <p><span><span>·<span> </span></span></span><span>“472_POST” AND “1631_POST” indicate the post-eruptive lahars related to the two eruptions. They are considered “post” when in the deposit there are pumices belonging to older eruptions, indicating their involvement in the progressive erosion of the slopes and valleys, and when there is evidence of long periods without deposition, such as the presence of</span><span> </span><span>slightly humified surfaces or traces of human artifacts (excavations, ploughing).</span></p> <p><span><span>·<span> </span></span></span><span>“EROSION_fallout_472” refers to the sites where it was possible to find erosional unconformities between the pyroclastic deposit of the 472 AD eruption and the lahar, as well as between the lower and upper lahar flow units. The erosional features are for example the lack of one or more primary eruptive layers (eroded by the overlying deposit), a change in the granulometry, or lateral discontinuity of the deposit. </span></p> <p><span><span>·<span> </span></span></span><span>“Pdyn (kPa)”, “v (m/s)”, “C (%)” and “T” are all the parameters quantified to validate the numerical models and to assess the hazard from lahars. Pdyn is the flow dynamic pressure, which represents the capability of the flow to entrain a clast, and it depends on the velocity (v) and the flow density, which in turn results from a combination of the density of the particles and the water through the “C (%)”, that is the particle volume concentration. To calculate the flow dynamic pressure and the velocity, the parameters taken into account are the dimensions of the biggest clasts and the nature of the clasts (limestone, ceramic, brick, tephra, lava, sandstone, iron) found in the lahar deposits. The concentration is estimated considering some sites in which the flow expands in correspondence with some obstacles (for example a Roman wall). This can be assumed to be the initial height of the flow before the emplacement. Finally, “T” refers to the estimated deposition temperature of the deposit quantified by the magnetic analysis, in particular in some sites where the lahar interacted with anthropogenic structures.</span></p> <p><span><span>·<span> </span></span></span><span>“DEPOSIT (472)” indicates the type of lahar deposit (syn- or post-eruptive) of the 472 AD eruption in which the fragments were found.</span></p> <p><span><span>·<span> </span></span></span><span>“MULTIPLE LAHAR UNITS” indicates the sites in which multiple flow units are vertically identified in the lahar deposits. They are generally a result of rapid and progressive aggradation of multiple flow pulses, each one resulting from single-pulse “en masse” emplacement</span><span>.</span></p>
The Spitzer Spectroscopic Data Fusion - Merged Spectroscopic Redshift Catalogs in Spitzer Fields
<p>The Spitzer Spectroscopic Data Fusion merges miscellaneous spectroscopic information available within "popular" extragalactic survey fields.</p> <p>Last Updated on 20 March 2025 - <a href="https://zenodo.org/record/6368347">https://zenodo.org/record/6368347</a> - <a href="https://www.mattiavaccari.net/df/specz">https://www.mattiavaccari.net/df/specz</a></p> <p>Based on the Spitzer Data Fusion Project - <a href="https://doi.org/10.5281/zenodo.7850783">https://doi.org/10.5281/zenodo.7850783</a> - <a href="https://mattiavaccari.net/df">https://mattiavaccari.net/df</a></p> <p>Merged Spec-Z ("specz-merged") catalogs merge miscellaneous spec-z information available within a given field. Different spec-z catalogs available within a given field are merged (using a search radius of 1.0 arcsec), and for sources with multiple spec-z measurements the most reliable one is chosen following the (largely arbitrarily) assumed order of decreasing reliability indicated below for each field. If CAT1,...,CATN spec-z catalogs are available in a given field, Z_1 from CAT1 (i.e. NED) is adopted as "best" redshift (i.e. ZBEST), if available, otherwise Z_2 from CAT_2 is adopted if available, and so on up to Z_N and CAT_N. In using ZBEST it's thus important to bear in mind that this is not necessarily actually the "best" redshift for science purposes, and in particular that the choice of NED as CAT1 is often not ideal. However, Z_1,...,Z_N are included to allow users to define the "best" redshift based on their science needs when multiple redshift estimates are available for a given source. ZFLAG specifies which catalog is providing the ZBEST value according to the CATN number below. ZCLASS is meant to provide further info about the class/quality of the spectroscopic redshift measurement, but for the time being is not populated and is simply a copy of ZFLAG. ZWHERE is an additional binary/bit flag indicating in which of the N catalogs each source was given a spec-z estimate in. ZWHERE will e.g. be 2^0=1 if a redshift if available <em>only</em> from CAT_1, whereas it will be 2^0+2^1=3 if a redshift is available *only* from CAT1 and CAT2,<br>so that a source with a redshift available from all catalogues will have ZWHERE=2^0+...+2^n.</p> <p>See AAAREADME.SPECZ-MERGED within the ZIP archive for further information.</p>
Field data obtained from the implementation of the Field Protocols (1, 2, and 3) from WP3 in B-GOOD Project
<p>Dataset of the field data obtained from the implementation of the field protocols 1, 2, and 3, developed under B-GOOD (Giving Beekeeping Guidance by cOmputatiOnal-assisted Decision making, Grant agreement No. 81762) project in WP3.</p> <p>The primary goal of field data collection was to gather data that could be used for the validation of floral resources maps and models developed across the various tasks within WP3, and also to fill specific information gaps during the development of the phenological model. The fieldwork was conducted in three countries (Portugal, Belgium, and the United Kingdom) using three field protocols developed under B-GOOD and described in Milestone MS15.</p> <p>These protocols are part of Tasks 3.1 and 3.3 and serve various purposes. The primary goal of Field Protocol 1: "<em>Assessment of plant species composition on key landscape elements/habitats important for bees</em>" was to determine the species composition in selected key plant communities (<em>i.e.</em> landscape elements or habitats) important for bees. Field Protocol 1 was used to determine the plant species composition of specific ALMaSS landscape elements and to confirm/validate the plant composition of some BIOEUNIS habitat types, developed in Task 3.1. The main goal of Field Protocol 2: "<em>Assessment of Phenology of Floral Resources for Bees</em>" was to determine the flower phenology of targeted plant species to construct flowering phenological curves for targeted plant species. Field Protocol 2 was used to validate the floral resource models developed in Task 3.2 (see Section 2.4). The primary goal of Field Protocol 3: "<em>Floral resources evaluation (detailed method)</em>" was to conduct a detailed evaluation of the floral resources in each landscape window with B-GOOD mini-apiaries to map resource availability. Field protocol 3 was divided into two parts: Part 1 - “<em>Assessment and quantification of floral resources</em>”, aiming to determine the species composition, species cover, and flower abundance; and Part 2 - “<em>Flowering species characterization</em>”, aiming to quantify the number of flowers per individual plant, and the nectar and pollen production of target plant species. Field Protocol 3 was also used to make a detailed assessment of plant species composition and plant resources at the landscape level, as well as to fill the gaps in knowledge about pollen and nectar production of some target plant species. The field data collected by the implementations of the field protocols could be categorized into three main groups: Plant Species Composition (Field Protocol 1 and Field Protocol 3: Part 1), Phenology of Floral Resources (Field Protocol 2), and Flowering Species Characterization (Field Protocol 3: Part 2).</p> <p>Field protocols have been implemented in Portugal, the United Kingdom, and Belgium. Field protocols 2 and 3 were fully implemented in the three countries. However, field protocol 1 was not implemented as a stand-alone field protocol in Portugal and the United Kingdom due to logistical and time constraints primarily caused by the COVID pandemic. However, this does not hamper our ability to obtain landscape-specific plant composition data because the information gathered in this protocol can be derived entirely from the implementation of the first part of Field Protocol 3. As a result, for Portugal and the United Kingdom, Field Protocol 1 data was derived from the first part of Field Protocol 3. The full dataset gathered by the implementation of these three protocols is available here.</p> <p>The files “field-data-protocol-1-be.xlsx”, “field-data-protocol-1-pt.xlsx” and “field-data-protocol-1-uk.xlsx” have the field data obtained from the implementation of Field Protocol 1: "Assessment of plant species composition" in Belgium, Portugal, and the UK, respectively.</p> <p>The files “field-data-protocol-2-be.xlsx”, “field-data-protocol-2-pt.xlsx” and “field-data-protocol-2-uk.xlsx” have the field data obtained from the implementation of Field Protocol 2: "Assessment of Phenology of Floral Resources" in Belgium, Portugal, and the UK, respectively.</p> <p>The files “field-data-protocol-3-part-1-be.xlsx”, “field-data-protocol-3-part-1-pt.xlsx “and “field-data-protocol-3-part-1-uk.xlsx” have the field data obtained from the implementation of Field Protocol 3 - Part 1 “Assessment and quantification of floral resources” in Belgium, Portugal, and the UK, respectively.</p> <p>The file “field-data-protocol-3-part-2-be-pt-uk.xlsx” have the field data obtained from the implementation of Field Protocol 3 - Part 2 - “Flowering species characterization” in Belgium, Portugal, and the UK.</p> <p>For further details, see Alves da Silva et al. 2020. Field protocols for the assessment of Floral Resources. Milestone MS15 EU Horizon 2020 B-GOOD Project. GA No. 817622 and Ziółkowska et al 2022. Floral Resource Models Validation Deliverable D3.4 EU Horizon 2020 B-GOOD Project, GA No. 817622.</p>
Data supporting 'Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers'
<p><strong>Note: An updated dataset covering the majority of Greenland's marine-terminating glaciers is available as part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) project through the National Snow and Ice Data Center (NSIDC) at <a href="https://doi.org/10.5067/B28FM2QVVYWY">https://doi.org/10.5067/B28FM2QVVYWY</a>. </strong></p> <p>Data supporting the paper:</p> <blockquote> <p>Chudley, T. R., Howat, I. M., Yadav, B. N., & Noh, M. J. (2022). Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers. <em>The Cryosphere. </em>16, 2629–2642, https://doi.org/10.5194/tc-16-2629-2022</p> </blockquote> <p>Dataset consists of four netCDF files containing stacked Sentinel-2 velocity data of four Greenlandic outlet glaciers (Helheim Glacier, Jakobshavn Isbræ, Store Glacier, and Kangerlussuaq) between 2017 and 2021. Velocity data are derived and corrected following the methods outlined in Chudley <em>et al.</em> (2022). </p> <p>NetCDF files are created by, and tested to be readable by, Python's xarray package.</p> <p>The dimensions of the netCDF file are as follows:</p> <ul> <li><strong>X</strong> - <em>x </em>coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li><strong>Y</strong> - <em>y</em> coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li><strong>time</strong> - temporal midpoint of velocity field.</li> </ul> <p>The variables of the netCDF file are as follows:</p> <ul> <li><strong>dmag</strong> - the absolute magnitude of the velocity, in metres per day.</li> <li><strong>dx</strong> - the velocity in the <em>x</em> direction, in metres per day.</li> <li><strong>dy</strong> - the velocity in the <em>y</em> direction, in metres per day.</li> <li><strong>date1</strong> - the date and time of the first scene acquisition.</li> <li><strong>date2</strong> - the date and time of the second scene acquisition.</li> <li><strong>baseline</strong> - the temporal baseline, in days, between scene acquisitions.</li> <li><strong>orbit_pair</strong> - the combination of orbital pathways in the string format 'RXXX_RYYY', where XXX is relative orbit number of the first scene and YYY the relative orbit number of the second scene.</li> <li><strong>mag_rmse</strong> - the root mean square error of the absolute velocity of the off-ice area. </li> <li><strong>dx_mean</strong> - the mean velocity of the off-ice area in the <em>x</em> direction.</li> <li><strong>dx_sd</strong> - the standard deviation of the velocity of the off-ice area in the <em>x</em> direction.</li> <li><strong>dy_mean</strong> - the mean velocity of the off-ice area in the <em>x</em> direction.</li> <li><strong>dy_sd</strong> - the standard deviation of the velocity of the off-ice area in the <em>y</em> direction.</li> </ul>
Succession in abandoned fields: Chronosequence data verified by monitoring of semi-permanent plots - Dataset underlying paper
<p>In task 3.1 of the WILDCARD project (www.wildcard-project.eu), we performed the analyses to identify the similarity between successional patterns in permanent plots and chronosequences on abandoned fields. This data is needed to know the accuracy of a chronosequence approach to reveal general successional vegetation pattens compared to the results obtained from semi-permanent plots. Raw data was provided by external collaborators. Results can be applied to other types of successional studies and restoratin projects, they verified the reliability of chronosequence approach. Rewiew process (3 reviewers plus coordinationg editor)</p>
Data: Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
<p>Network parameters of continuous normalizing flows trained for the <span class="math-tex">\(\varphi^4\)</span> theory.</p> <p>Corresponding article: Learning Lattice Quantum Field Theories with Equivariant Continuous Flows [<a href="https://arxiv.org/abs/2207.00283">2207.00283</a>]</p> <p>Abstract: We propose a novel machine learning method for sampling from the high-dimensional probability distributions of Lattice Field Theories, which is based on a single neural ODE layer and incorporates the full symmetries of the problem. We test our model on the <span class="math-tex">\(\varphi^4\)</span> theory, showing that it systematically outperforms previously proposed flow-based methods in sampling efficiency, and the improvement is especially pronounced for larger lattices. Furthermore, we demonstrate that our model can learn a continuous family of theories at once, and the results of learning can be transferred to larger lattices. Such generalizations further accentuate the advantages of machine learning methods.</p>
Predicted times of bow Shock crossings at Venus from the ESA/Venus Express mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm
<p><strong>CHARACTERISTICS</strong><br> Planet: <strong>Venus</strong><br> Radius: <strong>R<sub>V</sub> = 6051.8 km</strong> (volumetric mean planetary radius)<br> Spacecraft: <strong>ESA/Venus Express</strong><br> Spacecraft coordinates system: <strong>Venus Solar Orbital (VSO)</strong> equivalent to <em>Sun-State </em>coordinate system:</p> <ul> <li>+<em>X<sub>VSO</sub></em> points towards the Sun from the planet’s centre,</li> <li>+<em>Z<sub>VSO</sub></em> towards Venus’ North pole and perpendicular to the orbital plane defined as the <em>X<sub>VSO</sub></em>–<em>Y<sub>VSO</sub></em> plane passing through the centre of Venus,</li> <li><em>Y<sub>VSO</sub></em> completes the orthogonal system.</li> </ul> <p>Time span: <strong>01/04/2006 to 25/11/2014</strong><br> Total number N of candidate bow shock crossings in the database: <strong>N = 4950</strong><br> Number of quasi-parallel bow shock crossings: <strong>N<sub>||</sub> = 844</strong><br> Number of quasi-perpendicular bow shock crossings: <strong>N<sub><span class="math-tex">\(\perp\)</span></sub> = 4106</strong></p> <p><strong>ORIGINAL DATASETS USED</strong><br> The original Venus Express/MAG data repository on which these algorithms were applied is available on ESA's Planetary Science Archive system (PSA) at: https://archives.esac.esa.int/psa/ftp/VENUS-EXPRESS/MAG/. For this study, 1-Hz magnetic field data was used.</p> <p><strong>METHOD</strong><br> To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described for the Mars case in:<br> Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2021), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, <em>Journal of Geophysical Research</em>, <strong>127</strong>, e2021JA029942. <a href="https://doi.org/10.1029/2021JA029942">https://doi.org/10.1029/2021JA029942</a></p> <p>They consist of two consecutive steps: </p> <ol> <li>Predictor geometric algorithm based on 2D or 3D existing fits for prediction of the Venus bow shock position. The original fits were taken from 2D conic fits in the plane <span class="math-tex">\(\left(X_\text{VSO}, \sqrt{Y_\text{VSO}^2+Z_\text{VSO}^2}\right)\)</span>performed on the datasets of <strong>Persson et al. (2023)</strong>, Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, <em>Zenodo</em> (<a href="http://doi.org/10.5281/zenodo.7679677">https://doi.org/10.5281/zenodo.7679677</a>).</li> <li>Corrector algorithm based on magnetic field measurements.</li> </ol> <p>We also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted <span class="math-tex"><em>θ</em><sub><em>B</em><em>n</em></sub></span> (ThetaBn). Assuming a locally smooth shock surface, this gives a first indication of the geometry of the shock, so that:</p> <ul> <li><span class="math-tex">45<sup>∘</sup><<em>θ</em><sub><em>B</em><em>n</em></sub><135<sup>∘</sup></span>: quasi-perpendicular shock condition</li> <li><span class="math-tex"><em>θ</em><sub><em>B</em><em>n</em></sub>≤45<sup>∘</sup> and <em>θ</em><sub><em>B</em><em>n</em></sub><span class="math-tex">\(\geq\)</span>135<sup>∘</sup></span>: quasi-parallel shock condition</li> </ul> <p>Uncertainty on these angles is estimated to be ± 5º. </p> <p>For details, see <strong>Simon Wedlund et al. (2022)</strong> above, §2.3 pp. 10-12.</p> <p><strong>VARIABLES DESCRIPTION</strong></p> <p>This database contains the following ASCII variables:</p> <ul> <li>Bow shock times in Venus Express' database (1-s resolution): <em>T</em><sub>bs</sub></li> <li>Venus Solar Orbital coordinates of the shock, in units of Venus radius <em>R</em><sub>V </sub>(<em>R</em><sub>V</sub> = 6051.8 km):<br> <em>X<sub>VSO</sub></em>,<sub> </sub><em>Y<sub>VSO</sub></em>, <em>Z<sub>VSO</sub></em> and Euclidean distance <span class="math-tex">\(R_{VSO} = \sqrt{X_{VSO}^2 + Y_{VSO}^2 + Z_{VSO}^2}\)</span> (in <em>R<sub>V</sub></em>)</li> <li>Solar Zenith angle in degrees: <em>SZA</em> = <span class="math-tex">\(\tan^{-1}{Y_{VSO}^2+Z_{VSO}^2 \over X_{VSO}^2}\)</span> (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface <span class="math-tex">\(\theta_{Bn}\)</span> (ThetaBn, in º, calculated with atan2(norm(cross(<strong>B</strong>,<strong>ñ</strong>),dot(<strong>B</strong>,<strong>ñ</strong>)), with <strong>B</strong> the magnetic field vector and <strong>ñ</strong> the vector normal to the shock surface): <ul> <li>45 < ThetaBn < 135 deg: quasi-<span class="math-tex">\(\perp\)</span> shock</li> <li>ThetaBn <span class="math-tex">\(\leq\)</span> 45 deg & ThetaBn <span class="math-tex">\(\geq\)</span> 135 deg: quasi-|| shock</li> </ul> </li> <li>Interplanetary Magnetic Field (IMF) upstream average vector in VSO coordinates, <em>B<sub>x</sub></em>, <em>B<sub>y</sub></em>, <em>B<sub>z</sub></em> (in nT).</li> <li>Flag for direction of crossing: <ul> <li>flag = 0: magnetosheath <span class="math-tex">\(\longrightarrow\)</span> solar wind (2447 events)</li> <li>flag = 1: solar wind <span class="math-tex">\(\longrightarrow\)</span> magnetosheath (2503 events)</li> </ul> </li> </ul> <p><strong>WARNING</strong></p> <ol> <li>This version of the database is currently in a preliminary stage of application and, as such, is not fully tested. Solar wind upstream magnetic field values (IMF) are given only as a first approximation for each orbit segment. See point 2 for caveats. For carefully manually picked shock crossings, the user is referred to the database of:<br> <strong>Persson et al. (2023)</strong>, Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, <em>Zenodo</em> (<a href="http://doi.org/10.5281/zenodo.7679677">https://doi.org/10.5281/zenodo.7679677</a>)</li> <li>This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic fields. This is aimed at giving a first approximation of the shock area times in the Venus Express data. It is particularly suited to statistical studies and region identification in the Venus Express datasets. As such, this database should be used as a <em>first indicator</em> of the shock location, and <em>with</em> <em>caution</em>: it <strong>CANNOT</strong>, and <strong>WILL NOT </strong>substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.040 R<sub>V</sub> (with R<sub>V</sub> = 6051.8 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind.</li> </ol> <p>Current formatting optimised for MATLAB.</p> <p><strong>ACKNOWLEDGEMENTS</strong><br> C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36. </p> <p><strong>LICENSE AND RIGHTS</strong><br> This database is shared under a Creative Commons CC-BY-4.0 license.</p> <p>Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF), <br> Austrian Academy of Sciences, 2022-10-05<br> Contact email: cyril.simon.wedlund@gmail.com</p>
Performance Data of an Ice-Melting Probe from Field Tests in two Different Ice Environments
<p>This dataset was acquired at field tests of the steerable ice-melting probe "EnEx-IceMole" (Dachwald et al., 2014). A field test in summer 2014 was used to test the melting probe's system, before the probe was shipped to Antarctica, where, in international cooperation with the MIDGE project, the objective of a sampling mission in the southern hemisphere summer 2014/2015 was to return a clean englacial sample from the subglacial brine reservoir supplying the Blood Falls at Taylor Glacier (Badgeley et al., 2017, German et al., 2021).</p> <p>The standardized log-files generated by the IceMole during melting operation include more than 100 operational parameters, housekeeping information, and error states, which are reported to the base station in intervals of 4 s. Occasional packet loss in data transmission resulted in a sparse number of increased sampling intervals, which where compensated for by linear interpolation during post processing. The presented dataset is based on a subset of this data: The penetration distance is calculated based on the ice screw drive encoder signal, providing the rate of rotation, and the screw's thread pitch. The melting speed is calculated from the same data, assuming the rate of rotation to be constant over one sampling interval. The contact force is calculated from the longitudinal screw force, which es measured by strain gauges. The used heating power is calculated from binary states of all heating elements, which can only be either switched on or off. Temperatures are measured at each heating element and averaged for three zones (melting head, side-wall heaters and back-plate heaters).</p>
Data and simulation files for "Constraints on the intergalactic magnetic field using Fermi-LAT and H.E.S.S. blazar observations"
<p>In this repository, we provide data files in connection to our paper “Constraints on the intergalactic magnetic field using Fermi-LAT and H.E.S.S. blazar observations” accepted for publication in the Astrophysical Journals and soon available on Arxiv.</p> <p>In the publication, we perform a joint analysis of observations of five blazars with the Fermi Large Area Telescope (LAT) and the High Energy Stereoscopic System (H.E.S.S.) in order to search for signatures of a gamma-ray halo around these sources. The non-detection of such extended emission allows us to place lower limits on the intergalactic magnetic field (IGMF).</p> <p>In this repository, we provide our data analysis products of both H.E.S.S. and LAT data for the case when a template for the halo flux is <em>not</em> included in the data. Furthermore, we provide files that contain the log likelihood profiles as functions of the IGMF in case the halo emission <em>is</em> included. Lastly, we also provide our template files for the halo, generated with <a href="https://crpropa.github.io/CRPropa3/">CRPropa 3</a>.</p> <p>Below, we provide minimal code examples to demonstrate how to read in the specific files.</p> <p><strong>H.E.S.S. observational results</strong></p> <p>We provide the best-fit spectral parameters as well as the flux points (spectral energy distribution; SED) for the H.E.S.S. observations of the five blazars under consideration. The corresponding files are:</p> <ul> <li>hess_fit_result_*.fits which contain the best-fit parameters,</li> <li>hess_sed_file_*.fits which contain the flux points.</li> </ul> <p>In the file names above, the '*' should be replaced with a the corresponding source name, e.g. 1ES0229+200. The files can be read in using astropy:</p> <pre><code class="language-python">from astropy.table import Table src = "1ES0229+200" best_fit_pars = Table.read("hess_fit_result_1ES0229+200.fits") sed = Table.read("hess_sed_file_1ES0229+200.fits")</code></pre> <p><strong>Fermi observational results</strong></p> <p>For Fermi-LAT, we provide the SED files as well as the best-fit models for the region of interests. These files are called:</p> <ul> <li>fermi_avg_file_*.npy provides the best-fit ROI model</li> <li>fermi_sed_file_*.npy provides the SED.</li> </ul> <p>Both of these files are generated with <a href="https://fermipy.readthedocs.io/en/latest/">fermipy</a> and can be read-in the following way:</p> <pre><code class="language-python">import numpy as np # first a little helper function since the # fermipy analysis was run under python 2.7 def convert(data): if isinstance(data, bytes): return data.decode('ascii') if isinstance(data, dict): return dict(map(convert, data.items())) if isinstance(data, tuple): return map(convert, data) return data # Load the ROI fit roi_fit_file = "fermi_avg_file_1ES0229+200.npy" roi_fit = np.load(avg_file, allow_pickle=True, encoding="latin1").flat[0] # if you want to inspect the dictionaries in python 3, you need to run the convert function. # For example, to inspect the central source of the ROI # you would first get the source name src_fgl_name = roi_fit['config']['selection']['target'] # and then you can get the dictionary for the central source src_dict = convert(roi_fit['sources'])[src_fgl_name] # Load the SED sed_file = "fermi_sed_file_1ES0229+200.npy" sed = np.load(sed_file, allow_pickle=True, encoding='latin1').flat[0] # to plot the SED, you can use the SEDPlotter class from fermipy from fermipy.plotting import SEDPlotter SEDPlotter.plot_sed(sed)</code></pre> <p><strong>Likelihood profiles</strong></p> <p>The likelihood profiles as function of the IGMF strengths are provided in the files logl_profile_*_*yr.npz. Their are provided for all five sources and all tested blazar activity times of 10, 10<sup>4</sup>, and 10<sup>7</sup> years. They can be read in with the following code snippet:</p> <pre><code class="language-python">import numpy as np logl = dict(np.load("logl_profile_1ES0229+200_1.0e+07yr.npz")) b_fields = np.array([1.00000e-16, 3.16228e-16, 1.00000e-15, 3.16228e-15, 1.00000e-14, 3.16228e-14, 1.00000e-13]) for k, v in logl.items(): print(k,v)</code></pre> <p>As the print command shows, the python dictionary contains 3 entries: "fermi_only" are the likelihood values for the Fermi data as a function of magnetic field, "combined" are the likelihood values from Fermi and H.E.S.S. combined, and "ps" is the likelihood value of the Fit without halo to the H.E.S.S. data only.</p> <p><strong>Halo simulations</strong></p> <p>Lastly, we also provide the output simulations files from CRPropa. For details how the simulations were run, please consult the accompanying paper, in particular Section 3.1 and Appendix C. For each source redshift, a tar file is provided, which in itself contains 7 hdf5 files with the simulation outputs for each tested magnetic field strength. The name of the files is casc_file_z*.tar.gz. After unpacking the files, they can be read in with your favorite hdf5 library; in python you would need to install h5py. We recommend that you check out <a href="https://github.com/me-manu/simCRpropa">this github repository</a> which provides an advanced python wrapper for CRPropa and functions to read in the files. In particular, you can use <a href="https://github.com/me-manu/simCRpropa/blob/b3f39b5c77c6b97d19f7db387427d857690444d2/simCRpropa/cascmaps.py#L28">this function</a> to read in the files. It also writes a new hdf5 file with parallel transport applied. The written data is also returned together with the configuration dictionary.</p> <pre><code class="language-python">from simCRpropa.cascmaps import stack_results_lso data, config = stack_results_lso("casc_file_z0.140_B1.00e-16.hdf5", "casc_file_z0.140_B1.00e-16_theta_obs0.0.hdf5" )</code></pre> <p>You can provide arbitrary angles between the observer and the jet angles using the theta_obs keyword. Note, however, that the simulations used a jet opening angle of 3 degrees and going beyond that value will return zero halo photons.</p>
Solar and interplanetary magnetic field data analyzed in "Optimal frequency-domain analysis for spacecraft time series: Introducing the missing-data multitaper power spectrum estimator"
<p>This dataset contains simultaneous measurements of the interplanetary magnetic field magnitude <B> and the sun's radio flux at 10.7 cm <F10.7>. <B> measurements come from a series of spacecraft located at the L1 point, while <F10.7> was measured by the ongoing monitoring program by Canada's Dominion Radio Astrophysical Observatory. Bartels rotation-averaged data were downloaded from NASA's OMNIWeb, https://omniweb.gsfc.nasa.gov/html/ow_data.html. The file contains other solar wind plasma parameters that were not used in the analysis.</p>
NMR data for "Rapid and simple 13C-hyperpolarization by 1H dissolution dynamic nuclear polarization followed by an in-line magnetic field inversion"
<p>Liquid-state and solid-state NMR data for "Rapid and simple 13C-hyperpolarization by 1H dissolution dynamic nuclear polarization followed by an in-line magnetic field inversion".</p> <p>The data enclosed are NMR data generated by the software Topspin by Burker Biospin. The experiments are dDNP runs that come in two parts: a solid-state and a liquid-state part.</p> <ul> <li>Experiments from 1 to 9 are reference experiments used to quantify polarization in other experiments</li> <li>Experiments 11-19, 21-29, 31-39, ... 61-69 correspond to 6 dDNP runs performed a different samples from the same batch. The numbers correspond between solid and liquid-state datasets</li> </ul> <p>The codes used to analyze the data are available at in a next upload.</p> <p>Refer to the main text of the paper and its supplementary material at 10.26434/chemrxiv-2023-6gd0l for more information.</p>
Plot-level field data and model simulation results, archived to accompany Turner et al. manuscript; reports data from summer 2017 sampling of short-interval fires that burned during summer 2016 in Greater Yellowstone.
Subalpine forests in the northern Rocky Mountains have been resilient to stand-replacing fires that historically burned at 100–300-yr intervals. Fire intervals are projected to decline drastically as climate warms, and forests that reburn before recovering from previous fire may lose their ability to rebound. We studied recent fires in Greater Yellowstone (Wyoming, USA) and asked whether short-interval (less than 30 yrs) stand-replacing fires can erode lodgepole pine (Pinus contorta var. latifolia) forest resilience via increased burn severity, reduced early postfire tree regeneration, reduced carbon stocks, and slower carbon recovery. During 2016, fires reburned young lodgepole pine forests that regenerated after wildfires in 1988 and 2000. During 2017, we sampled 0.25-ha plots in stand-replacing reburns (n=18) and nearby young forests that did not reburn (n=9). We also simulated stand development with and without reburns to assess carbon recovery trajectories. Nearly all prefire biomass was combusted ("crown fire plus") in some reburns in which prefire trees were dense and small (≤ 4 cm basal diameter). Postfire tree seedling density was reduced six-fold relative to the previous (long-interval) fire, and high-density stands (greater than 40,000 stems ha-1) were converted to sparse stands (less than 1,000 stems ha-1). In reburns, coarse wood biomass and aboveground carbon stocks were reduced by 65% and 62%, respectively, relative to areas that did not reburn. Increased carbon loss plus sparse tree regeneration delayed simulated carbon recovery by greater than 150 yrs. Forests did not transition to nonforest, but extreme burn severity and reduced tree recovery foreshadow an erosion of forest resilience.
Landscape Flowering Phenology Field Data for Sites in the Vicinity of Crested Butte, CO
This dataset represents field observations of reproductive development (flowering phenology) in 135 species of flowering plants collected at 12 field sites in the vicinity of Crested Butte, Colorado starting in 2019. Sites were visited approximately weekly from early May until early August, and all species in flower were recorded in 25 segments along a 50m transect at each site, and species were recorded if they were within 1m of either side of the transect. Datasets included in this package are 1) cleaned field observations of flowering phenology, 2) taxonomic identity of all recorded species, 3) spatial data representing the location of the center of each transect segment, and 4) spatial data representing the segment polygons.
Hourly meteorological data gapfilled for sensor downtimes collected near Toolik Field Station, Alaska, summers 2012-2016
This data set includes meteorological parameters collected near Toolik Field Station from 2012 to 2016 under National Science Foundation (NSF) Office of Polar Programs ARC 0908444 (to Laura Gough), ARC 0908602 (to Natalie Boelman), and ARC 0909133 (to John Wingfield). It also includes meteorological data collected by two additional entities that are available on public repositories. Toolik data reflect data collected by the Toolik Envronmental Data Center and Imnavait data reflect data collected by the Arctic Observatory Network (AON). These data have been modified such that that sensor downtimes have been gapfilled by pulling data from the next nearest station. These data are associated with publication DOI: 10.1111/jav.01712
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