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Dataset results
44 results for “2016-2018”
Northern Bobwhite juvenile survival southwest Missouri 2016-2018
<p>These data and code are associated with the publication in Ornithological Applications entitled "Northern Bobwhite juvenile survival is greater on native grasslands managed with fire and grazing, and lower in non-native field borders and strip crop fields." We evaluated the influence of brood age, vegetation cover type, woody vegetation structure, habitat management, and landscape cover on juvenile survival in southwest Missouri 2016-2018.</p>
Dataset_Bencze, Sz. et al Sustainability yield 2016-2018
<p>Yield and grain quality raw data of different emmer and einkorn cultivars relate dto the results published in Bencze, Szilvia, Makádi, Marianna, Aranyos, Tibor J., Földi, Mihály, Hertelendy, Péter, Mikó, Péter, Bosi, Sara, Negri, Lorenzo, & Drexler, Dóra. (2020). Re-Introduction of Ancient Wheat Cultivars into Organic Agriculture—Emmer and Einkorn Cultivation Experiences under Marginal Conditions. Sustainability, 12(4).</p>
Northern Bobwhite juvenile survival southwest Missouri 2016-2018
Open the record for dataset details and reuse information.
Neosho Smallmouth Bass nest habitat, Ozark streams, 2016-2018
These data were collected for a study of Neosho Smallmouth Bass nest habitat. We considered variables at the stream-reach and catchment scales, and measured microhabitat condition of individual nests and nest clusters. Data are from 120 stream reaches sampled from 2016-2018 in the southwestern Ozark Highlands ecoregion (OK, MO, and AR).
Clinical Study Reports published by the European Medicines Agency 2016-2018
<p>Clinical Study Reports published by the European Medicines Agency 2016-2018</p>
Bird records and habitat covariates databases (11 species; Oaxaca 2016-2018)
Open the record for dataset details and reuse information.
Southampton Sailing Robot test logs 2016-2018
<p>These files contain recorded data from the <a href="https://www.sotonsailrobot.org/">Southampton Sailing Robot Team</a>'s boat, the <em>Black Python</em>.<br> The logs cover various test runs, and three <a href="https://www.roboticsailing.org/">WRSC</a> competitions: 2016 (viana), 2017 (horten) and 2018 (calshot).<br> <br> Each test run generated several files:<br> </p> <ul> <li><code>.bag</code> and <code>.bag.metadata</code>: <a href="https://wiki.ros.org/Bags">Rosbag</a> files, recordings of messages sent among parts of the control system, which was built in the ROS 1.x framework. ROS includes tools to inspect and replay these files.</li> <li><code>params-dump-*.jsn</code>: JSON dumps of the parameters used to launch that run, including waypoint GPS coordinates and sensor calibration parameters, among other values.</li> <li><code>gps-trace*.csv</code>: GPS coordinates of the boat during the run, in the format required by WRSC. The three columns are timestamp (`hhmmssdd`), latitude and longitude both converted to integers by multiplying decimal degrees by 1e7, i.e. shifting the decimal point 7 places to the right. All GPS coordinates use the WGS-84 reference.</li> <li><code>gps-raw-nmea*</code>: Raw NMEA-0183 protocol data sent by the GPS receiver. This should be redundant with the CSV data so long as it was parsed correctly.</li> <li><code>map-gps-trace*.html</code>: GPS traces plotted in interactive maps using <a href="https://python-visualization.github.io/folium/">Folium</a>, overlaid on OpenStreetMap tiles.</li> </ul> <p><code>index.html</code> contains a generated overview of the test runs performed, and the files associated with each.<br> <br> The source code used to create these files, including both the control system for the boat and scripts for processing the data is available in a git repository at:<br> <a href="https://github.com/Maritime-Robotics-Student-Society/sailing-robot">https://github.com/Maritime-Robotics-Student-Society/sailing-robot</a><br> <br> Exactly what data is in the different runs varies as the hardware and software were evolving at the same time. We did not record which commit generated each file.</p>
ATom: CO2, CH4, and CO Measurements from Picarro, 2016-2018
This dataset contains atmospheric measurements of CO2, CH4, and CO mixing ratios made with a Picarro G2401 spectrometer during the four ATom campaigns. Picarro G2401 uses Wavelength-Scanned Cavity Ring Down Spectroscopy (WS-CRDS), a time-based measurement utilizing a near-infrared laser to measure a spectral signature of the molecule. For the ATom mission, the Picarro instrument was modified in the laboratory to operate across the full pressure altitude range of flight campaigns. The instrument was also modified to have a shorter measurement interval.
ATom: Ultrafine Aerosol Characteristics and Formation, Lower Stratosphere, 2016-2018
This dataset consists of (a) selected aerosol and gas-phase observations made on all four deployments of NASA Atmospheric Tomography Mission (ATom), (b) thermodynamic properties related to aerosol formation derived from these measurements, (c) 48-h back trajectories for ATom-4 observations, and (d) output from the Model of Aerosols and Ions in the Atmosphere (MAIA). ATom observations, thermodynamics, and back trajectories were inputs for MAIA model runs. MAIA runs focused on data from ATom-4 deployment, and output includes aerosol formation rates, and ultrafine particle size distributions and number concentrations in the lowermost stratosphere (LMS). ATom 1-4 deployments included all four seasons from 2016 to 2018. This investigation sought to understand how new particle formation (NPF) can occur in the LMS, factors influencing the amount of NPF, and other potential sources of ultrafine aerosols in this region of the atmosphere. The data are provided in comma-separated value (CSV) format.
Snow Depth, Stratigraphy, and Temperature in Wrangell St Elias NP, Alaska, 2016-2018
This dataset includes data from late-March snow surveys and hourly digital camera images from two study areas within the Wrangell St Elias National Park, Alaska. These data comprise snow density, stratigraphy, and temperature profiles obtained by snow pits; and snow depth data obtained from transects between snow pits. Daily snow depths, adjacent to each pit, were derived from hourly camera images of snow stakes placed adjacent to each pit. These data were collected to constrain and validate a physically-based, spatially-distributed snow evolution model used to simulate snow conditions in Dall sheep habitat. The two study areas are both located within the Jacksina Park Unit (JPU). The first study area, surveyed in 2017, included the northern end of Jaeger Mesa and an area near Rambler mine in the North East of the JPU. The second study area, surveyed in 2018, was within the upper watershed of Pass Creek in the North of the JPU. The remote cameras operated from September 2016 to August 2017 on Jaeger Mesa/Rambler Mine and from September 2017 to July 2018 at Pass Creek.
ATom: Data Stream for Modeling the Reactivity of ATom Air Parcels, 2016-2018
This dataset provides Modeling Data Stream (MDS) and Reactivity Data Stream (RDS) products for each of the four ATom campaigns conducted from 2016 to 2018. MDS files contain the atmospheric constituents needed to model the RDS of the air parcels along ATom flight paths. The MDS is a continuous data stream (every 10 seconds) of the atmospheric content of these key chemical species derived from the in-situ measurements collected along ATom flight paths (as reported in the comprehensive related dataset ATom: Merged Atmospheric Chemistry, Trace Gases, and Aerosols). Values for chemical species measured by multiple instruments were selected from the instrument with better coverage and/or greater precision. Missing values were filled using interpolation for short gaps. For long gaps owing to instrument failure, values were estimated using multiple linear regressions from comparable parallel flights from other ATom campaigns. All species were flagged for instrument source and values were flagged for gap-filling status. In combination, MDS and RDS provide, in essence, a photochemical climatology for each air parcel along ATom flight paths containing the reactive species that control the loss of methane and the production and loss of ozone.
ATom: Light-Absorbing Metallic Aerosols, Single Particle Soot Photometer, 2016-2018
This dataset provides mass mixing ratios and number density of light-absorbing metallic aerosols (LAM) in the size range 180-1290 nm obtained with the NOAA Single Particle Soot Photometer (SP2) during the four deployments of the NASA Atmospheric Tomography (ATom) airborne mission from 2016-2018. The NOAA SP2 detects light absorbing aerosols, such as black carbon (BC), via laser-induced incandescence to provide real-time in situ quantification of refractory aerosol mass and number density. The percent of LAM aerosols attributed to anthropogenic iron oxides (FeOx) by mass is also provided.
Burned and Unburned Field Site Data, Noatak, Seward, and North Slope, AK, 2016-2018
This dataset includes field measurements from unburned and burned 10 m x 10 m and 1 m x 1 m plots in the Noatak, Seward, and North Slope regions of the Alaskan tundra during July through August in the years 2016-2018. The data include vegetation coverage, soil moisture, soil temperature, soil thickness, thaw depth, and weather measurements. Measurements were recorded using ocular assessments and standard equipment. Plot photographs are included.
Tree Cover Estimates at 30 m Resolution for Mexico, 2016-2018
The data set provides multi-year (2016-2018) percent tree cover (TC) estimates for entire Mexico at 30 m spatial resolution. The TC data (hereafter, NEX-TC) was derived from the 30 m Landsat Collection 1 product and a hierarchical deep learning approach (U-Net) developed in a previous CMS effort for the conterminous United States (CONUS) (Park et al., 2022). The hierarchical U-Net framework first developed a U-Net model for very high-resolution aerial images (NAIP) using training labels derived from previous work based on an interactive image segmentation tool and iterative updates with expert knowledge (Basu et al., 2015). The developed NAIP U-Net model and NAIP data produced 1-m NAIP TC across all lower 48 CONUS states. A Landsat U-Net model was developed for multi-year and large-scale TC mapping based on the very high-resolution NAIP TC made in the earlier stage. The Landsat U-Net model developed was adopted over the CONUS for testing its transferability, validation, and improvement across Mexico. This dataset provides national-scale percent tree cover estimates over Mexico and can be helpful for studies of carbon cycling, land cover and land use change, etc. The team has been working on improving temporal stability of the product and will update the product once the next version is ready to be shared.
ATom: Measurements from Meteorological Measurement System (MMS), 2016-2018
This dataset contains measurements from the Meteorological Measurement System (MMS) instrument from the four ATom campaigns. MMS is a state-of-the-art instrument for measuring accurate, high resolution in situ airborne state parameters (pressure, temperature, turbulence index, and the 3-dimensional wind vector). These key measurements enable our understanding of atmospheric dynamics, chemistry, and microphysical processes. The MMS is used to investigate atmospheric mesoscale (gravity and mountain lee waves) and microscale (turbulence) phenomena. An accurate characterization of the turbulence phenomenon is important for the understanding of dynamic processes in the atmosphere, such as the behavior of buoyant plumes within cirrus clouds, diffusions of chemical species within wake vortices generated by jet aircraft, and microphysical processes in breaking gravity waves. Accurate temperature and pressure data are needed to evaluate chemical reaction rates as well as to determine accurate mixing ratios. Accurate wind field data establish a detailed relationship with the various constituents and the measured wind also verifies numerical models used to evaluate air mass origin.
ATom: Cloud and Coarse Aerosol Measurements from CAPS Instrument, 2016-2018
This dataset contains cloud type and coarse aerosol contents measured by the University of Vienna's second-generation Cloud Aerosol and Precipitation Spectrometer (CAPS) instrument mounted to the NASA DC-8 aircraft during the four ATom campaigns that occurred from 2016 to 2018. CAPS measures particle size distributions in a size range between nominally 0.5 micrometers and 960 micrometers. The sizes range between approximately 0.5 and 50 micrometers is covered by the optical particle counter component of CAPS-the Cloud and Aerosol Spectrometer with Depolarization Detection (CAS-DPOL). The sizes range from 15 to 930 micrometers is measured with the optical array probe called Cloud imaging Probe (CIP). Cloud types are determined using an algorithm developed to detect and classify clouds using measurements of CAPS. Relative humidity and temperature are considered by the algorithm. The cloud indicator provides a classification on a 1 Hz basis and separates data in cloud-free, aerosol-cloud transition regime (ACTR), liquid clouds, clouds in the mixed-phase temperature regime (MPTR), and cirrus clouds. The coarse aerosol product provides cloud and aerosol particle number concentrations at standard pressure (1013.25 hPa) and standard temperature (273.15 K) in selected size ranges. Particle sizes refer to ammonium sulfate optical equivalent diameter (m=1.52 + 0.0i).
ATom: CAM-chem/CESM2 Model Outputs Along Flight Tracks, 2016-2018
This dataset contains CAM-chem (Community Atmosphere Model with Chemistry) model outputs along ATom flight tracks. CAM-chem is a component of the Community Earth System Model Version 2 (CESM2) and is used for simulations of global tropospheric and stratospheric atmospheric composition and for studies of chemistry-climate interactions. In general, CAM-chem uses the MOZART chemical mechanism, with various choices of complexity for tropospheric and stratospheric chemistry. For this dataset, CAM-chem used the MOZART-TS1 chemical mechanism, and the model was nudged to reanalysis meteorology from MERRA2.
ATom: Comprehensive Aerosol Properties, 2016-2018, Version 2
This dataset contains comprehensive measurements of aerosol microphysical, chemical, and optical properties derived for both dry and ambient conditions from in situ measurements made during the four ATom campaigns. The dataset includes composition-resolved size distributions the integrated mass of sulfate, organics, nitrate, sea salt, dust, black carbon, and other compounds in coarse and fine fractions; extinction and absorption coefficients from each species at both dry and ambient conditions; asymmetry parameters; Angstrom exponents; and fitted lognormal functions to describe the size distribution. Optical parameters are calculated for 10 wavelengths from the near UV to the near IR, and size distributions range from 3 nm to 50 um in diameter. One file contains these data at 1-minute time intervals. Another file contains a subset of these data averaged into 1-km vertical bins for each vertical profile the aircraft made, as well as composition-resolved integrated aerosol optical depth derived from each profile. The concentration of cloud condensation nuclei is calculated for 5 supersaturations.
ATom: Actinic Flux and Photolysis Frequencies from CAFS Instrument, 2016-2018, V2
This dataset contains actinic flux and photolysis frequencies for photodissociation reactions for a variety of chemical species during the four ATom campaigns. Spectrally resolved actinic flux was measured by the down- and up-welling Charged-coupled device Actinic Flux Spectroradiometers (CAFS) from approximately 280-650 nm. Photolysis frequencies were calculated from the actinic flux and published cross sections and quantum yield values for atmospherically relevant molecules. Solar radiation drives the chemistry of the atmosphere, including the evolution of ozone, greenhouse gases, biomass burning, and other anthropogenic and natural trace constituents.
ATom: Back Trajectories and Influences of Air Parcels Along Flight Track, 2016-2018
This dataset contains back trajectories, boundary layer influences, and convective influences of air parcels along NASA DC-8 aircraft's flight tracks during the four ATom campaigns that occurred from 2016 to 2018. Back trajectories were interpolated using National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) meteorology. Back trajectory analysis determines the origin of air masses by modeling the path of an air parcel backward in time. It can be used to better understand the sources of atmospheric compounds. Boundary layer Influences were determined based on 30 Day Back Trajectories. The atmospheric boundary layer is the lowest part of the troposphere that is directly influenced by earth's surface. The boundary layer influences wind patterns and thus the dispersal of pollutants and other atmospheric compounds of interest. Convective influences were based on 10 Day Back Trajectories and NASA Langley cloud products. Convective influences model the effects of convection on the movement of water vapor through the atmosphere, which influences cloud behavior.
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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.