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256 results for “parcel”
Parcels-WAOM model data: Lagrangian particle trajectories
<p>This data set includes the results of Lagrangian particle tracking experiments with Parsels in Weddell and Ross seas. </p> <p>The following information for each particle is stored once a day: longitude (lon in files), latitude (lat), time (time), depth (z), temperature (temp), salinity (sal), ice draft (ice).</p> <p>Each *.zip archive contains one experiment in netcdf file for one of the seas (Weddell or Ross seas) and for one of four seasons during 20 years of simulations. </p>
Distribution. SE Madagascar, known only from the rainforest region of Andohahela National Park, in the Manangotry Parcel between the Mandrare River in the W and the Manampanihy River in the N. Further work needs to be done to confirm the extent of the distribution, particularly N of Manangotry. in Lepilemuridae
Distribution. SE Madagascar, known only from the rainforest region of Andohahela National Park, in the Manangotry Parcel between the Mandrare River in the W and the Manampanihy River in the N. Further work needs to be done to confirm the extent of the distribution, particularly N of Manangotry.
Distribution. NE Madagascar, known only from the Mananara-Nord region, with the only confirmed reports coming from the Ivontaka-Sud and Verezanantsoro (Ambinanibeorana) parcels of the Mananara-Nord Biosphere Reserve. The N and S limits of the range have not been defined, but they are tentatively assumed to be S of the Fahambahy or Mananara rivers and N of the Simianona, Sandratsio, or Maningory rivers, respectively. Additional surveys are required to determine the full extent of the distribution. in Lepilemuridae
Distribution. NE Madagascar, known only from the Mananara-Nord region, with the only confirmed reports coming from the Ivontaka-Sud and Verezanantsoro (Ambinanibeorana) parcels of the Mananara-Nord Biosphere Reserve. The N and S limits of the range have not been defined, but they are tentatively assumed to be S of the Fahambahy or Mananara rivers and N of the Simianona, Sandratsio, or Maningory rivers, respectively. Additional surveys are required to determine the full extent of the distribution.
Daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions (Part 1)
<p>A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions to study the fate of sea ice in the ‘New Arctic’.</p> <p>Files are multi-part zip files containing trajectory and ancillary data on an annual basis over a sea ice year.</p>
Daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions (Part 3)
<p>A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions to study the fate of sea ice in the ‘New Arctic’.</p> <p>Files are multi-part zip files containing trajectory and ancillary data on an annual basis over a sea ice year.</p>
Daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions (Part 2)
<p>A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions to study the fate of sea ice in the ‘New Arctic’.</p> <p>Files are multi-part zip files containing trajectory and ancillary data on an annual basis over a sea ice year.</p>
Daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions (Part 2a)
<p>A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions to study the fate of sea ice in the ‘New Arctic’.</p> <p>Files are multi-part zip files containing trajectory and ancillary data on an annual basis over a sea ice year.</p>
Distribution. SW & S Madagascar, in the W up to the Tsiribihina River, in the SE it is found near to (just N of) Tolagnaro (= Fort-Dauphin) in the Nahampoana Reserve, although it was probably introduced there; the distributional limit in the SE is the transitional and spiny forest patches of the Mangatsiaka Parcel of Andohahela National Park. in Indriidae
Distribution. SW & S Madagascar, in the W up to the Tsiribihina River, in the SE it is found near to (just N of) Tolagnaro (= Fort-Dauphin) in the Nahampoana Reserve, although it was probably introduced there; the distributional limit in the SE is the transitional and spiny forest patches of the Mangatsiaka Parcel of Andohahela National Park.
Deep convective cloud air parcels
<p>The dataset consists of 500 files describing the idealized deep convective cloud air parcels. Values for coordinate (X, Y, Z), velocity components (U, V, W), potential temperature (PTIL), total water content (QT), water vapor (QV), condensate density (QC), precipitation density (QR), net precipitation change (QR_FLUX), relative humidity (RH), buoyancy (BUOY), temperature (T), pressure (P), resolved TKE (KRES), SGS TKE (SGS_TKE), turbulent diffusion (TDIFF) are provided for each parcel at every time step of the simulation.</p>
Air parcel trajectories data generated by MIMICA code and simulation results generated by a trajectory box model
<p>The dataset includes trajectories of air parcels extracted from the large-eddy (cloud-resolving model) simulations of the deep convective clouds from the Amazon based on the soundings retrieved on April 8, 2020, April 23, 2020, and April 27, 2020, over Manaus, Brazil, as well as the results of the chemical box model simulations quantifying the transport of some atmospheric trace gases abundant in the Amazon.</p>
Studying the wide range of relative humidity in cirrus clouds with large-ensemble parcel model simulations
<p>The model codes, data, and plot scripts used in the paper, "Studying the wide range of relative humidity in cirrus clouds with large-ensemble parcel model simulations".</p> <ul> <li>parcel model code.zip contains model code.</li> <li>Results.zip contains output data of each experiment in this study.</li> <li>Figs and scripts.zip are the NCL scripts used for figures.</li> </ul>
Air parcel trajectories dataset based on modeling of 16 deep convective clouds in the Amazon.
<p>The dataset contains trajectories of 16 deep convective cloud air parcels simulated with MIMICA code based on soundings retrieved over Manaus (Latitude: -3.1, Longitude: -60.0) during the wet season from April 1 until April 14, 2020 at 00 and 12 UTC.</p> <p> </p> <p>Parcel data contains values for coordinate (X, Y, Z), velocity components (U, V, W), potential temperature (PTIL), total water content (QT), water vapor (QV), condensate density (QC), precipitation density (QR), net precipitation change (QR_FLUX), relative humidity (RH), buoyancy (BUOY), temperature (T), pressure (P), resolved TKE (KRES), SGS TKE (SGS_TKE), turbulent diffusion (TDIFF) are provided for each parcel at every time step of the simulation.</p>
Parcel Delivery Demand dataset for the center of Budapest, Hungary
<p>This is an anonymized last-mile dleivery demand dataset for the center of Budapest, Hungary. This dataset has been used in LEAD project in order to analyze the last-mile logistics network though digital modelling and twinning.</p>
GIS Shapefile - Analysis of potential stewardship in support of BES research, parcel level
Parcel-level potential stewardship for Baltimore City. Potential stewardship is that land within a parcel not occupied by buildings, that is land that could potentially undergo "greening." This dataset contians polygons that represent potential stewardship land along with the vegetation that falls within the potential stewardship land. Potential stewardship should be estimated using the polygons with a land use (LU) code equal to 0. Parcel land use codes and census block group information is also present. A cursory analysis of the parcel data indicated that parcel data was outdated for the following block groups: 245102503031, 245102503032, and 245102503033. Note: transportation networks are not part of the parcel data, and thus were appropriately not part of this analysis. In addition a single BLOCKLOT may consist of two or more parcels in certain instances.
GIS Shapefile - Urparian (non-parcel) vegetation (forest and grass) in Baltimore City
Urparian (non-parcel) vegetation (forest and grass) in Baltimore City. The urparian area consists of all roads and rights of way along roads. This area was delineated using Baltimore City parcel data by identifying all "non-parcel" areas. A cursory analysis of the "non-parcel" areas indicated that errors of omission were present due to insufficient parcel data. Vegetation data used in this analysis came from the MD DNR Forest Service IKONOS derived Strategic Urban Forest Assessment (SUFA) vegetation layer.
GIS Shapefile - Urparian (non-parcel) vegetation (forest and grass) in Baltimore City, block group
Block group summary of urparian (non-parcel) vegetation (forest and grass). The urparian area consists of all roads and rights of way along roads. This area was delineated using Baltimore City parcel data by identifying all "non-parcel" areas. A cursory analysis of the "non-parcel" areas indicated that errors of omission were present due to insufficient parcel data. Vegetation data used in this analysis came from the MD DNR Forest Service IKONOS derived Strategic Urban Forest Assessment (SUFA) vegetation layer.
Assessor's parcel data, Ipswich Watershed, Parker Watershed, Plum Island Ecosystem, Massachusetts - vector
Assessor's parcel data for the PIE LTER site. Developed from existing source documents and digital data. Note: Complete metadata is available within the downloaded zip file. This metadata can be viewed with ESRI ArcGIS software, and can be exported to FGDC and ISO metadata formats.
Data from: Census parcels cropping system classification from multitemporal remote imagery: a proposed universal methodology
A procedure named CROPCLASS was developed to semi-automate census parcel crop assessment in any agricultural area using multitemporal remote images. For each area, CROPCLASS consists of a) a definition of census parcels through vector files in all of the images; b) the extraction of spectral bands (SB) and key vegetation index (VI) average values for each parcel and image; c) the conformation of a matrix data (MD) of the extracted information; d) the classification of MD decision trees (DT) and Structured Query Language (SQL) crop predictive model definition also based on preliminary land-use ground-truth work in a reduced number of parcels; and e) the implementation of predictive models to classify unidentified parcels land uses. The software named CROPCLASS-2.0 was developed to semi-automatically perform the described procedure in an economically feasible manner. The CROPCLASS methodology was validated using seven GeoEye-1 satellite images that were taken over the LaVentilla area (Southern Spain) from April to October 2010 at 3- to 4-week intervals. The studied region was visited every 3 weeks, identifying 12 crops and others land uses in 311 parcels. The DT training models for each cropping system were assessed at a 95% to 100% overall accuracy (OA) for each crop within its corresponding cropping systems. The DT training models that were used to directly identify the individual crops were assessed with 80.7% OA, with a user accuracy of approximately 80% or higher for most crops. Generally, the DT model accuracy was similar using the seven images that were taken at approximately one-month intervals or a set of three images that were taken during early spring, summer and autumn, or set of two images that were taken at about 2 to 3 months interval. The classification of the unidentified parcels for the individual crops was achieved with an OA of 79.5%.
MUTUAL INCOMPATIBILITIES IN TRANSLATION OF ENGLISH AND UZBEK LANGUAGES AND MAIN PROBLEMS IN TRANSLATION OF PARCELLED COMPOUNDS.
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Benchmark Dataset of Cropland Parcel Boundaries from Multi-Source Remote Sensing Imagery for AI application
<p><span>We provide a standardized dataset of diversified parcel labels based on multi-source remote sensing imagery. The parcels included in this dataset are sourced from four countries: the Netherlands, Denmark, Spain, and China, encompassing over 1</span><span>4</span><span>0,000 </span><span>cropland parcel</span><span>. The average parcel size across different study areas ranges from 0.05 hectares to 10 hectares. The remote sensing imagery is derived from more than 10 data sources, including satellites, UAV (unmanned aerial vehicle), and public mapping platforms, with spatial resolutions varying from 0.05 meters to 10 meters.</span></p>
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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.