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10,013 results for “observational”
Long-term Hydrographic Mooring Data from the Georgia Coastal Ecosystems LTER Salinity Monitoring Program - Primary 30 Minute Observational Data
Conductivity, temperature and sub-surface water pressure were measured continuously at fixed hydrographic moorings distributed across the Georgia Coastal Ecosystems LTER study area to document spatial and temporal variability of salinity and its relationship to water level and river discharge. Mooring locations were chosen to span the salinity gradient as well as to take advantage of existing physical infrastructure (e.g. docks or pilings) for mounting instruments and proximity to marsh study sites. Eight moorings were established between 2001 and 2003 to characterize salinity patterns in the three primary sounds in the GCE domain (Sapelo, Doboy and Altamaha), and a ninth mooring was added near a freshwater tidal forest along the Altamaha River in 2014. Observations were logged at 30 minute intervals by Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately quarterly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms, and short-duration gaps (<6 hours) due to instrument swaps, quality control analysis or brief data interruptions were filled by interpolation. Long-duration gaps due to instrument or mooring loss were filled with null values to produce a monotonic time series. This data set includes cumulative 30 minute observations at all 9 moorings through 31-Dec-2022, and will be updated annually to include observations from the prior year.
Observed phenological indicators and environmental drivers at global change experiments at the Jornada Basin LTER site, 2014-2020
This dataset contains plant phenological data extracted from phenocams installed at a global exchange experiment involving Chihuahuan desert plant communities at the Jornada Basin LTER site in southern New Mexico, U.S.A. Cycles of plant growth, termed phenology, are tightly linked to environmental controls, and our overarching objective in this study is to determine if temperature or precipitation are relatively more important for determining shrub and grass greenup date (start of season) and senescence date (end of season). At these camera locations, we experimentally manipulated incoming precipitation at the Jornada Basin LTER for over a decade and recorded plant leaf phenology at the daily scale for seven years using phenocams. The data here are derived from raw "phenocam" camera data collected at two ongoing studies at the Jornada Basin LTER site, one studying ecosystem responses to long term changes in water and nitrogen availability, and one studying plant productivity and partitioning responses to water availability and herbivory (studies 349 and 456, respectively). Phenocams at the sites have collected images since 2014, and basic color and greenness data extracted from those images are available in a companion dataset on EDI (knb-lter-jrn.210574001). This dataset includes the derived annual and quarterly phenological indices and greenness indices for each plot monitored by phenocams, and temperature and precipitation variables aggregated to the same frequency. The dataset also includes R code and input files used to generate these derived data. See Currier and Sala 2022 for more details. This study is ongoing.
Vegetative Phenology observations at the Andrews Experimental Forest, 2009 - Present
The vegetation phenology study is part of a larger effort to understand the influence of climate variability and change on trophic interactions in mountainous terrain. Phenology Core Sites were selected to capture the variation in elevation and topography across Lookout Creek watershed. Priority was given to sites with long-term air and soil temperature records (Reference stands) and previous phenology observations (Reference stands and stream gauging stations). Sixteen sites were established. At each site five individuals of from each 18 common species (if occurring within the site) from tree, shrub and herb layers were mapped and marked for observation. Weekly observations are conducted each year beginning in March or April depending on winter conditions and snowpack and continuing through June or July. Plant vegetative and reproductive phenophases are scored using a numbered system adapted to each plant species.
The dynamics of marsh-channel slump blocks: an observational study using repeated drone imagery
We analyzed the spatial and temporal dynamics of slump blocks within Dean Creek, a creek located on Sapelo Island (GA) and surrounded by salt marsh. To accomplish this, we utilized 11 images of Dean Creek captured between March 2020 and March 2021, which were acquired by using a DJI Matrice 210 UAV. For each image, we manually digitized the perimeters of slump blocks, which have the distance from intact marsh boundary exceeding 0.3 m. These digitized perimeters were subsequently converted into polygons to determine the size, number and cumulative area of slump blocks in each image. Temporal changes to the slump blocks between subsequent images were also analysed. The coordinate system of these polygon data is WGS 1984 UTM Zone 17N.
Daily runoff and nutrient loads for the North Sea and the Baltic Sea based on modelling and observations for the period 1961 to 2019 and adapted to NEMO-SCOBI
<p>This dataset consists of daily values of runoff and reconstructed nutrient loads for the period 1961 to 2019 for the North Sea-Baltic Sea system. Both runoff and nutrient loads were obtained from a model simulation performed with the European application of the Hydrological Predictions for the Environment model v.3.1.8 (E-HYPE). This dataset includes a more realistic number of river outlets than those from observational-based datasets, as not all rivers are monitored, and captures well the interannual variability of all parameters. However, the E-HYPE v.3.1.8 was calibrated to represent 2010 and thus cannot simulate all historical changes related to land management (i.e., the increase of fertilizers in the 1960s). Consequently, the observed rise of nutrients from land due to increased fertilizers and the consequent reduction due to nutrient regulation policy in the 1980s is not captured in the outputs from E-HYPE directly. In the North Sea and the Baltic Sea, this is of primary importance for management policy in eutrophication and deoxygenation. Therefore, we have adapted the E-HYPE nutrient loads based on yearly estimates of historical loads that use riverine concentrations, so that the high tempo-spacial resolution is kept, but with a decadal variability that is closer to reality. This dataset is mainly intended as river forcing for biogeochemical-ocean models (i.e. NEMO-SCOBI), but can also provide information on rivers that are not included in monitoring programs. Information on the dataset and the methods used to create it is given as a downloadable PDF file (E-HYPE DecVar documentation.pdf) together with two datasets and the mesh grid file (area_NEMO-Nordic.nc). The datasets are yearly netCDF files one containing daily runoff and nutrient loads for phosphate, nitrate, ammonium, organic nitrogen and organic phosphorus (zip_ehypeDecVar.zip) and the other one provides monthly silica loads (zip_silica.zip). </p>
Damage Localisation in Fresh Cement Mortar Observed via In Situ (Timelapse) X-ray uCT imaging.
<p>This is dataset to paper: Damage Localisation in Fresh Cement Mortar Observed via In Situ (Timelapse) X-ray uCT imaging.</p>
Relative predation rates on juvenile Chinook Salmon in the lower Stanislaus River, California, 2012-2024 by habitat suitability informed by juvenile Chinook Salmon and black bass observations in the lower Stanislaus and Merced rivers, California, 2012-2017
Overview The purpose of this work was to estimate relative predation on juvenile Chinook Salmon rearing in tributaries of the San Joaquin River, California in relation to meso- and microhabitat factors. Predation rates were estimated using predation bioassays. Ranges of depth and velocity targeted by the bioassays were informed by habitat suitability indices developed prior to field efforts. Juvenile Chinook and Bass Habitat Suitability Indices The purpose of this dataset is to develop habitat suitability indices for juvenile Chinook Salmon (<120mm) on the lower Stanislaus River and nonnative black bass ( Micropterus spp.). on the lower Merced River, both tributaries of the San Joaquin. This data was used to identify target ranges of depth and velocity during predation fieldwork. Occupancy data was collected via snorkel surveys on the lower Stanislaus River in 2018 and 2019 and on the Merced River in 2012 and 2014-2017. Predation Tethering Bioassay Study The purpose of this field study was to estimate relative rates of predation of juvenile Chinook Salmon. Predation rates were estimated using assays of tethered hatchery Chinook Salmon deployed across a range of mesohabitats on the lower Stanislaus River. Habitat suitability was expected to vary across mesohabitats and across depths and velocities sampled within habitats. Cameras were deployed with tethers to identify predators for a subset of predation events happening within the first 1-2 hours of deployment. Assays were deployed monthly March-May in 2022 and 2024. A supplemental set of assays were deployed in May 2023 under wet water year conditions that varied strongly from conditions sampled in 2022 and 2024.
Fine-scale meteorological observations from walking traverses in two Phoenix Area Social Survey (PASS) 2017 neighborhoods (2019)
This dataset includes human-biometeorological observations from 2.5 km walking traverses with a mobile weather station. The traverses occurred in two 2017 Phoenix Area Social Survey neighborhoods (U18: South Phoenix/Salt River (Audubon) and W15: Camelback Mountain) on one day in June and October, at 12pm and 4pm on each day. Specifically, air temperature, humidity, wind speed, and radiant energy (infrared and solar radiation) in 3-dimensions were measured at 2-second intervals. Additionally, mean radiant temperature was calculated from the radiation measurements. The meteorological observations are spatially referenced with latitude and longitude coordinates. The paths through the neighborhoods were chosen to maximize proximity to PASS 2017 participants’ homes.
LAGOS - Predicted and observed maximum depth values for lakes in a 17-state region of the U.S.
This dataset includes predicted and observed values of maximum depth for lakes in the upper Midwest and northeast United States. All observed values came from LAGOS ver 1.040.0 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS contains a complete census of lakes great than or equal to 4 ha with corresponding geospatial information for a 17-state region of the U.S., and a subset of the lakes has observational data on morphometry and chemistry. Approximately 40 different sources of data were compiled for this dataset and were mostly generated by government agencies (state, federal, tribal) and universities. Here, observed maximum depth values (n = 8164) were used to train and validate a predictive mixed effects model for lake depth using terrestrial and lake morphology as predictors (Oliver et al., submitted). Predicted values (n = 50 607) generated by the model had a root mean squared error of 7.1 m. This research was supported by the NSF Macrosystem Biology awards 1065786, 1065818, and 1065649.
Lake Water Level observations for 1036 lakes in Wisconsin, 1900 - 2015
This dataset contains the daily lake level observations and other lake attributes in Wisconsin. It covers 1036 lakes including 461 seepage lakes and 575 drainage lakes. It has 342,319 observations. The time span of this dataset is between January 1st, 1900 and December 31st, 2015. The data sources include USGS, Wisconsin Department of Natural Resources, North Temperate Lakes-Long Term Ecological Research (NTL-LTER), North Lakeland Discovery Center, Waushara County, and City of Shell Lake. Wisconsin Department of Natural Resources has two data sources: historical lake levels recorded in paper files and a recently-initiated citizen monitoring program. The latter are stored in Wisconsin DNR’s Surface Water Integrated Monitoring System (SWIMS). The data compilation consists of four major steps. First, data were retrieved from different data sources. Then data from different sources but for the same lakes were tied together using the datum information if possible. The WISCID is used to denote unique data sets by lake and data source. If two data sources could be tied to the same datum, they share a WISCID. Third, three rounds of quality assurance and quality control (QAQC) were conducted. Finally, more attributes such as lake area, lake depth, and lake type were added to the lakes. This data compilation was funded by the Wisconsin Groundwater Joint Solicitation.
Plant phenology observations in the black sand extended growing season experiment for East Knoll, Audubon, Lefty, and Trough sites, 2018 - 2020.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Black Sand Extended Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how climate exposure may affect plant communities, NWT LTER researchers established 5 experimental sites, each containing a pair 10 x 40m rectangular plots. These sites include north and south facing aspects, subalpine and alpine tundra meadows and a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot at each site by adding chemically inert black sand, while keeping the second plot as an unmanipulated control; black sand was added to control plots after snow had naturally melted. We used open top warming chambers (OTCs) to increase summer temperature in three subplots within each of the 10 x 40 m plots. This dataset includes measurements of plant species phenology within all snow and warming treatments at all sites except Soddie.
Presence observations for six tree species prioritized for forest landscape restoration in Ethiopia
<p><strong>Description:</strong></p><p>Geolocations of presence occurrences for a selection of six species (<i>Cordia africana</i>, <i>Croton macrostachyus</i>, <i>Eucalyptus globulus</i>, <i>Faidherbia albida</i>, <i>Grevillea robusta</i>, <i>Juniperus procera</i>) sourced from databases (GBIF, RAINBIO) and from the scientific literature.</p><p>Each record is associated with a DOI, link, or citation to the original source of the data. Observations were filtered using the R package <i>CoordinatesCleaner</i> (Zizka <i>et al</i>. 2019) with the <i>clean_coordinates </i>function to filter for errors that are common to biological collections.</p><p>The breakdown of the number of observations by species is: <i>Cordia africana</i> (84); <i>Croton macrostachyus</i> (129); <i>Eucalyptus globulus (</i>20); <i>Faidherbia albida </i>(31); <i>Grevillea robusta </i>(350); <i>Juniperus procera </i>(115).</p>
Historical Animal Observation Records by Bavarian Forestry Offices (1845)
<p>In 1845, under the scientific direction of Andreas Wagner, the Bavarian government recorded the occurrence of 44 selected vertebrate species across the entire country. To this end, Wagner had a survey questionnaire sent to all 119 forestry offices in the state. The foresters' responses were now systematically recorded and analyzed for the first time. This data set represents the result of this survey. Among other things, it contains 5,467 geo-coded animal observation data.</p> <p>The data is the result of an interdisciplinary collaboration between scientists from the Chair of Computational Humanities at the University of Passau, the Directorate General of the Bavarian State Archives Munich, the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, the Center for Biodiversity Informatics and Collection Data Integration at the Botanical Garden Berlin, and the NFDI4Biodiversity consortium.</p>
Gravity Spy Machine Learning Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b
<p>This data set contains all classifications that the Gravity Spy Machine Learning model for LIGO glitches from the first three observing runs (<a href="https://doi.org/10.7935/K57P8W9D">O1</a>, <a href="https://doi.org/10.7935/CA75-FM95">O2</a> and O3, where O3 is split into <a href="https://doi.org/10.7935/nfnt-hm34">O3a</a> and <a href="https://doi.org/10.7935/pr1e-j706">O3b</a>). Gravity Spy classified all noise events identified by the <a href="https://doi.org/10.1016/j.softx.2020.100620">Omicron trigger pipeline</a> in which Omicron identified that the signal-to-noise ratio was above 7.5 and the peak frequency of the noise event was between 10 Hz and 2048 Hz. To classify noise events, Gravity Spy made <a href="https://en.wikipedia.org/wiki/Constant-Q_transform">Omega scans</a> of every glitch consisting of 4 different durations, which helps capture the morphology of noise events that are both short and long in duration.</p> <p>There are <a href="https://doi.org/10.1088/1361-6382/aa5cea">22 classes</a> used for O1 and O2 data (including No_Glitch and None_of_the_Above), while there are <a href="https://doi.org/10.1088/1361-6382/ac1ccb">two additional classes</a> used to classify O3 data (while None_of_the_Above was removed).</p> <p>For O1 and O2, the glitch classes were: 1080Lines, 1400Ripples, Air_Compressor, Blip, Chirp, Extremely_Loud, Helix, Koi_Fish, Light_Modulation, Low_Frequency_Burst, Low_Frequency_Lines, No_Glitch, None_of_the_Above, Paired_Doves, Power_Line, Repeating_Blips, Scattered_Light, Scratchy, Tomte, Violin_Mode, Wandering_Line, Whistle</p> <p>For O3, the glitch classes were: 1080Lines, 1400Ripples, Air_Compressor, Blip, <strong>Blip_Low_Frequency</strong>, Chirp, Extremely_Loud, <strong>Fast_Scattering</strong>, Helix, Koi_Fish, Light_Modulation, Low_Frequency_Burst, Low_Frequency_Lines, No_Glitch, None_of_the_Above, Paired_Doves, Power_Line, Repeating_Blips, Scattered_Light, Scratchy, Tomte, Violin_Mode, Wandering_Line, Whistle</p> <p>The data set is described in <a href="https://doi.org/10.1088/1361-6382/acb633"><strong>Glanzer </strong><em>et al</em><strong>. (2023)</strong></a>, which we ask to be cited in any publications using this data release. Example code using the data can be found in this <a href="https://colab.research.google.com/drive/19q_lItODPk7qw_sohlHyWPnAbY0FZyt8?usp=sharing"><strong>Colab notebook</strong></a>.</p> <p>If you would like to download the Omega scans associated with each glitch, then you can use the gravitational-wave data-analysis tool <a href="https://gwpy.github.io/docs/stable/">GWpy</a>. If you would like to use this tool, please install anaconda if you have not already and create a virtual environment using the following command</p> <pre><code class="language-bash">conda create --name gravityspy-py38 -c conda-forge python=3.8 gwpy pandas psycopg2 sqlalchemy</code></pre> <p>After downloading one of the CSV files for a specific era and interferometer, please run the following Python script if you would like to download the data associated with the metadata in the CSV file. We recommend not trying to download too many images at one time. For example, the script below will read data on Hanford glitches from O2 that were classified by Gravity Spy and filter for only glitches that were labelled as Blips with 90% confidence or higher, and then download the first 4 rows of the filtered table.</p> <pre><code class="language-python">from gwpy.table import GravitySpyTable H1_O2 = GravitySpyTable.read('H1_O2.csv') H1_O2[(H1_O2["ml_label"] == "Blip") & (H1_O2["ml_confidence"] > 0.9)] H1_O2[0:4].download(nproc=1)</code></pre> <p>Each of the columns in the CSV files are taken from various different inputs: </p> <p>[‘event_time’, ‘ifo’, ‘peak_time’, ‘peak_time_ns’, ‘start_time’, ‘start_time_ns’, ‘duration’, ‘peak_frequency’, ‘central_freq’, ‘bandwidth’, ‘channel’, ‘amplitude’, ‘snr’, ‘q_value’] contain metadata about the signal from the <a href="https://virgo.docs.ligo.org/virgoapp/Omicron/">Omicron pipeline</a>. </p> <p>[‘gravityspy_id’] is the unique identifier for each glitch in the dataset. </p> <p>[‘1400Ripples’, ‘1080Lines’, ‘Air_Compressor’, ‘Blip’, ‘Chirp’, ‘Extremely_Loud’, ‘Helix’, ‘Koi_Fish’, ‘Light_Modulation’, ‘Low_Frequency_Burst’, ‘Low_Frequency_Lines’, ‘No_Glitch’, ‘None_of_the_Above’, ‘Paired_Doves’, ‘Power_Line’, ‘Repeating_Blips’, ‘Scattered_Light’, ‘Scratchy’, ‘Tomte’, ‘Violin_Mode’, ‘Wandering_Line’, ‘Whistle’] contain the machine learning confidence for a glitch being in a particular Gravity Spy class (the confidence in all these columns should sum to unity). These use the original 22 classes in all cases.</p> <p>[‘ml_label’, ‘ml_confidence’] provide the machine-learning predicted label for each glitch, and the machine learning confidence in its classification. </p> <p>[‘url1’, ‘url2’, ‘url3’, ‘url4’] are the links to the publicly-available <a href="https://gwdetchar.readthedocs.io/en/stable/omega/">Omega scans</a> for each glitch. ‘url1’ shows the glitch for a duration of 0.5 seconds, ‘url2’ for 1 seconds, ‘url3’ for 2 seconds, and ‘url4’ for 4 seconds.</p> <p>For the most recently uploaded training set used in Gravity Spy machine learning algorithms, please see <a href="https://zenodo.org/record/1486046#.YZfcar3MJqs">Gravity Spy Training Set</a> on Zenodo. </p> <p><br> For detailed information on the training set used for the original Gravity Spy machine learning paper, please see <a href="https://zenodo.org/record/1476156#.YZfchL3MJqs">Machine learning for Gravity Spy: Glitch classification and dataset</a> on Zenodo.</p>
Processed glider data: 9 months of hydrographic and ADCP observations in the Gulf of Oman.
<p>68 repeat transects and 2 virtual moorings covering a spring/neap cycle collected by a SeaExplorer glider with T, S, O2, Chl, Optical backscatter, PAR and ADCP data in the Gulf of Oman. Dataset collected as part of the ONR Global project "Shelf slope dyanmics in the Sea of Oman: How submesoscale processes control food and water security". The glider was deployed from the north shore of Oman into the Gulf of Oman, sampling down to 1000m in the oxygen minimum zone.</p> <p> </p> <p>File and variable metadata included in the netCDF files.</p> <p> </p> <p>sea057_M##.ad2cp.#####.nc : Raw ADCP data provided in Nortek .nc format. (version 1.0)</p> <p>SEA057_glider.nc : SeaExplorer data timeseries QC'd and processed into a 1Hz timeseries. (version 1.0)</p> <p>SEA057_ADCP_v2.nc : ADCP data fully processed, binned (2 dbar) and referenced, and then reprojected back onto a timeseries. ADCP data processed as per https://github.com/bastienqueste/gliderad2cp . (version v3)</p> <p> </p>
Calculation of parameter values based on observations for the herbaceous biomass plantation PFT representing Miscanthus in JSBACH3.2
<p>This dataset provides the calculation of parameter values and the observational data that was collected from literature used in these calculations for the re-implementation of a herbaceous biomass plantation (HBP) PFT representing Miscanthus in the dynamic global vegetation model (DGVM) JSBACH3.2 (Egerer et al. subm., Nützel et al. in prep.). The parameters included are the maximum rubisco capacity (Vmax) at 25°C, the PEPcase CO2 specificity (k) and specific leaf area (SLA). Some of the observed parameter values were already compiled in a dataset by Li et al. (2018). These observations were therefore re-used in this dataset (which is specified within the dataset sheets) and complemented with additional observed values from literature that has become available since then or was not included in the study by Li et al. (2018). A detailed methodology of the parameter calculations for JSBACH3.2 can be found on the first sheet of the dataset. </p>
Atmospheric Halocarbon Observations at Finokalia, Crete, Greece
<p>Atmospheric halocarbon (HFC, HCFC) observations (mole fractions) from the site Finokalia (FKL, 35.34 °N, 25.67 °E, 250 m a.s.l.) on the island of Crete, Greece, covering the period December 2012 to August 2013). The measurements were conducted using a gas chromatograph<br> (Agilent 6890) and:mass spectrometer (Agilent 5973) (GC-MS), coupled to an adsorption desorption system (ADS) for preconcentration of samples from the air (Simmonds et al., 1995).</p> <p>The measurements are described in detail in: Schoenenberger, F., S. Henne, M. Hill, M. K. Vollmer, G. Kouvarakis, N. Mihalopoulos, S. O'Doherty, M. Maione, L. Emmenegger, T. Peter, and S. Reimann (2017), Abundance and Sources of Atmospheric Halocarbons in the Eastern Mediterranean, Atmos. Chem. Phys. Discuss., 2017, 1-46, doi: 10.5194/acp-2017-451.</p> <p>The data format is plain text character-separated and follows that used in the AGAGE community. Further details are given at the AGAGE data archive: http://agage.eas.gatech.edu/data_archive/agage/</p>
Satellite-observed surface flow speed within Russell sector, West Greenland, bi-weekly average of 2015-2019
<p>An average horizontal surface ice velocity of Russell sector (Greenland) with 2-week temporal and 150m spatial resolution. Derived from satellite images collected between 2015 and 2019 by Landsat-8, Sentinel-1, and Sentinel-2. The details on the data processing can be found in https://doi.org/10.5194/tc-2021-170.</p> <p><br> Dataset contains 24 independent NetCDF files (one per 2-weeks time step) with maps of vx and vy velocity components, maps of associated uncertainties per velocity component (STD of the 2-weeks averaged raw satellite measurements), and map of number of averaged measurements.</p>
Seasonal evolution of basal conditions within Russell sector, West Greenland, inverted from satellite observations of surface flow
<p>An annual set of model-inferred basal and surface properties of ice flow at Russell Gletcher sector in Western Greenland with half-month temporal resolution. Derived using the Elmer/Ice ice-flow model by inversion of satellite-observed ice surface velocity (10.5281/zenodo.5535532). The details on the data creatoin can be found in 10.5194/tc-15-5675-2021 .</p> <p>Dataset contains 24 independent NetCDF files (one per 2-weeks time step) with:<br> * alpha - inverted be model basal friction coefficient in log10 (log10(MPa m-1 a)<br> * base - basal topography altitude (m)<br> * lithk - ice thickness (m)<br> * orog - surface altitude (m)<br> * strbasemag - magnitude of basal friction tb (MPa)<br> * xvelbase, yvelbase, zvelbase - 3D basal velocity (m/yr)<br> * xvelmean, yvelmean - vertically average mean horizontal velocity (m/yr)<br> * xvelsurf, yvelsurf, zvelsurf - 3D surface velocity (m/yr)<br> * n - effective pressure (MPa)</p> <p>The additional WinterMeanState NetCDF file (inversion from the mean velocity of january, Febriary, Mars) contains the same set of variables (except the effective pressure), and in addition contains the <em>As</em> Weertman sliding coeffitient.</p> <p>The results have been interpolated from the native unstructured model grid to the regular grid used for the observed velocity (10.5281/zenodo.5535624).</p>
Spectral reflectance data of Mercury's surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015 resampled to a [55399 × 396] tabular data format.
<p>MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>This specific product is stored as a gzip compressed json, where each element is a grid cell.<br> We are in the process to publish a complete pipeline to produce this product from RAW data on https://github.com/epn-ml/MESSENGER-Mercury-Surface-Cassification-Unsupervised_DLR/ .</p> <p>Spectral reflectance data of Mercury’s surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015.<br> MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>0. Pre-filtering<br> We used the most recent dataset that had large-scale photometric corrections and thus was almost free from observation geometry effects.<br> However, extreme geometry are still present and are typically associated with high noise and some residual instrumental effects.<br> Based on our empirical tests, we filtered out observations with an emission/incidence angle ≥80∘.<br> We also calculated the median value per wavelength and per cell grid when constructing the global hyperspectral data cube and filtered out observations falling under the 2nd percentile and above 99.9th percentile to clean some residual geometry effects.<br> With this approach we create an effective noise filter while retaining enough observations to be able to analyse the entirety of the surface of the planet.</p> <p>1. Spectral resmpling<br> Unprocessed MASCS spectra could have 512 or 256 channes, depending on binning.<br> We resampled the data in the spectral dimension to a common wavelength range from 260 nm to 1052 nm with a 4 nm resolution (2 nm spectral sampling), resulting in 396 spectral channels.<br> This approach slightly oversamples the original 4.77 nm spectral resolution and removes some points from the original 200-1050 nm range.<br> The resulting data matrix is expressed in tabular form, with each row representing a single grid cell or pixel on the surface.<br> The elements of each row are the spectral reflectance values from the VIS instrument at 396 (resampled) wavelengths.</p> <p>2. Spatial resmpling<br> The whole dataset of ∼ 5 million spectra is resampled to a planet-wide rectangular grid of 1×1deg in the latitudinal band between ± 80.<br> The cell longitudinal size varies between ∼ 40 km at the equator to a minimum of ∼ 10 km at ±80∘.<br> Thus, the area spanned by each grid cell depends on the latitude. However, the same is true for the acquisition process, where higher spatial resolution is reached near the equator and lower resolution at the poles.</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.