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256 results for “parcels”
Ecological Survey of Central Arizona: a survey of key ecological indicators in parcels of residential areas in the greater Phoenix metropolitan area, ongoing since 2010
The Ecological Survey of Central Arizona (ESCA) is an extensive field survey and integrated inventory designed to capture key ecological indicators of the CAP LTER study area consisting of the urbanized, suburbanized, and agricultural areas of metropolitan Phoenix and the surrounding Sonoran desert. The survey is conducted every five years at approximately 200 sample plots (30m x 30m) that were located randomly using a tessellation-stratified, dual-density sampling design. Study plots cover habitats throughout the CAP LTER study area ranging from native Sonoran desert sites to residential yards to an airport tarmac. In 2010, the survey was expanded to include an assessment of residential parcels overlapping the survey plot at sites in residential areas. Many of the same variables that are measured in the 30m x 30m survey plot are measured in the parcel, including an inventory of perennial plants, and the biovolume of trees. In addition, a detailed assessment of characteristics of the parcel is performed. Investigators interested in data from the broader Ecological Survey of Central Arizona that includes all survey plots can find those data in the Environmental Data Initiative data repository at [this](https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-cap&identifier=652) link.
Urban Residential Surface and Subsurface Hydrology: Synergistic Effects of Low-Impact Features at the Parcel Scale
Accurately predicting the hydrologic effects of urbanization requires an understanding of how hydrologic processes are affected by low‐impact development practices. In this study, we explored how growing season surface runoff, deep drainage, and evapotranspiration on a residential parcel are affected by several low‐impact interventions, including three "impervious‐centric" interventions (disconnecting downspouts, disconnecting sidewalks, and adding a transverse slope to the driveway and front walk), two "pervious‐centric" interventions (decompacting soil and adding microtopography), and all possible "holistic" combinations. Results were compared to both a highly and moderately compacted baseline parcel under an average and a dry weather scenario for a temperate climate. We find that under reasonable assumptions for highly compacted soil, pervious areas are a major source of runoff and disconnecting impervious surfaces may be relatively less effective without improving soil conditions. Under both highly and moderately compacted soil conditions, combining efforts to decompact soil with impervious disconnection has a synergistic effect on reducing surface runoff and increasing deep drainage and evapotranspiration. All combinations of interventions enhance infiltration, but the partitioning of additional root zone water between deep drainage and evapotranspiration depends on the weather scenario. Importantly, when all low‐impact interventions are applied together, growing season deep drainage is higher than that from a vacant lot with no impervious surfaces. We infer that ecohydrologic interfaces between impervious and pervious areas are strong controls on urban hydrologic fluxes and that high‐resolution, process‐based models can be used to account for these interfaces and thereby improve predictions of the hydrologic effects of low‐impact interventions.
NECCPB-1: The first cropland parcel boundary dataset from meter-level imagery of Northeast China
<p>The Northeast China Plain is one of the world's three largest black soil regions, characterized by high organic matter content, rich nutrients, and strong water retention capabilities. Suitable climate conditions and abundant rainfall promote the growth of crops such as corn, soybeans, and rice, making it one of the main grain production bases in China, accounting for about one-fifth of the country's grain output. The grain production in the Northeast China black soil region is crucial for food security in China and globally. This area's farmland parcels are the basic units of agricultural production and the cornerstone of precision agriculture management, providing detailed information on cultivated land location, boundaries, shape, and area. Utilizing this parcel-scale information, governments and farm managers can devise more precise planting strategies and optimize management methods, thereby enhancing the quality and productivity of crops, ensuring a continuous food supply, and promoting sustainable agricultural development.</p> <p>The first cropland parcel boundary dataset from meter-level imagery of Northeast China (NECCPB-1) was developed based on deep learning models and a custom-designed automatic parcel merging strategy. A total of 10.22 TB of very-high-resolution (VHR) imagery was downloaded and uploaded, covering the entire region of Northeast China and an area of 1,240,000 km². After further removal of non-cropland regions based on phenological differences, 32,395,946 parcels were obtained.</p> <p> Rigorous validation using manually drawn reference parcels demonstrated that this dataset had high accuracy in parcel delineation (Extraction Precision, EP: 0.85) and high consistency with the reference parcels (|Completeness Deviation|, |CompD|: 0.02; Intersection over Union, IoU: 0.90). Further comparison with official Third Survey reports confirmed the high reliability of the NECCPB-1 dataset, which exhibited an average relative difference of -3.6% and an absolute relative difference of 9.8%.</p> <p>A series of cross-validations with seven widely used cropland datasets (ESA_GLC10, ESRI_GLC10, FROM_GLC10, CLD10, GLAD250, GFSAD30, and SinoLC-1). The recall, precision, and F1 scores of the NECCPB-1 were calculated as 0.91, 0.93, and 0.92, respectively, using publicly validated sample points of land cover. Moreover, NECCPB-1 performed best regarding cropland completeness, achieving an intersection ratio (IR) of 0.94, as calculated using the reference parcels.</p> <p>Due to the extensive size of the dataset and potential policy considerations, access to the data will be granted based on specific inquiries. Please contact us at zhengjia@iga.ac.cn or guotianhao@iga.ac.cn for further details. Please indicate your purpose and other details. Thank you!</p>
North Temperate Lakes LTER Vilas County Parcels
Parcel boundaries for selected townships in Vilas County, Wisconsin.
Lagrangian Decomposition of the Meridional Heat Transport at 26.5N - Water Parcel Crossings of the RAPID 26.5N Array
<p>This dataset contains the initial and final positions and properties of Lagrangian trajectories evaluated using 5-day mean velocity and tracer fields output from the ORCA0083-N06 ocean sea-ice model hindcast (1958-2015). Numerical water parcels are initialised to sample the full-depth southward transport across the RAPID 26.5N array every month during 2004-2015. Water parcels are advected backwards-in-time using a bespoke version of TRACMASS v7.1 Lagrangian particle tracking tool which enables users to specify a custom domain using a mask netCDF file.</p><p>Particles are initialised on the first-available day of each month (based on the centre of the model 5-day mean field windows) between 2004 and 2015 (inclusive) before being advected backwards-in-time within the North Atlantic Ocean until any one of four termination conditions are met: (1) water parcels reach the RAPID 26.5N array, (2) water parcels reach the OSNAP (West or East) arrays in the subpolar North Atlantic, (3) water parcels reach either the English Channel or Gibraltar Strait, or (4) particles reach the maximum advection time of 25-years. The 25-year maximum advection time ensures that we adequately resolve the subtropical gyre circulation north to the RAPID 26.5N array. The pathway transporting dense North Atlantic Deep Water from the OSNAP arrays to RAPID at 26.5N is not fully resolved in this Lagrangian experiment since these water parcels transit on multi-decadal timescales.</p><p>The number of water parcels initialised in each model-grid cell scales with the total northward transport through that cell, such that the maximum possible transport conveyed by any single particle is 5.0 mSv (mSv == 10-3 Sv), enabling the calculation of robust Lagrangian statistics. In reality, the average. water parcel has an associated volume transport of 3.3 mSv which is conserved throughout its circulation.</p><p>Water parcel locations (converted to geographical coordinates) and properties (conservative temperature, absolute salinity, potential density [TEOS-10]) are output on every model-grid cell crossing. TRACMASS determines particle properties on grid-cell crossings by taking the average of the properties stored at the nearest two T-grid points. Here, we provide the initial and final locations and properties of all water parcels initialised from RAPID 26.5N.</p><p>All Lagrangian experiments were completed using the JASMIN High-Performance Computing facility (<a href="https://jasmin.ac.uk">https://jasmin.ac.uk</a>).</p><p><strong>For a complete description of the ORCA0083-N06 hindcast configuration see:</strong> Moat et al. (2016).</p><p><strong>For a complete description of TRACMASS v7.1 see</strong>: <a href="https://www.tracmass.org">https://www.tracmass.org</a></p>
Ecological Survey of Central Arizona: a survey of key ecological indicators in parcels of residential areas in the greater Phoenix metropolitan area, ongoing since 2010 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-cap/653/2. The abstract below was extracted from the Level 0 data package and is included for context: The Ecological Survey of Central Arizona (ESCA) is an extensive field survey and integrated inventory designed to capture key ecological indicators of the CAP LTER study area consisting of the urbanized, suburbanized, and agricultural areas of metropolitan Phoenix, and the surrounding Sonoran desert. The survey is conducted every five years at approximately 200 sample plots (30m x 30m) that were located randomly using a tessellation-stratified dual-density sampling design. Study plots cover habitats throughout the CAP LTER study area ranging from native Sonoran desert sites to residential yards to an airport tarmac. In 2010, the survey was expanded to include an assessment of residential parcels overlapping the survey plot at sites in residential areas. Many of the same variables that are measured in the 30m x 30m survey plot are measured in the parcel, including an inventory of perennial plants, and the biovolume of trees. In addition, a detailed assessment of characteristics of the parcel is performed. Investigators interested in data from the broader Ecological Survey of Central Arizona that includes all survey plots should should search the data catalog for 'ecological survey of central arizona' or 'survey 200' to locate those and other data related to the CAP LTER's ESCA.
TAMPAR: Visual Tampering Detection for Parcels Logistics in Postal Supply Chains
<p>TAMPAR is a real-world dataset of parcel photos for tampering detection with annotations in <a href="https://cocodataset.org/#format-data">COCO format</a>. For details see our paper and for visual samples our <a href="https://a-nau.github.io/tampar/">project page</a>. Features are: </p><ul><li>>900 annotated real-world images with >2,700 visible parcel side surfaces</li><li>6 different tampering types</li><li>6 different distortion strengths</li></ul><p>Relevant computer vision tasks:</p><ul><li>bounding box detection</li><li>classification</li><li>instance segmentation</li><li>keypoint estimation</li><li>tampering detection and classification</li></ul><p>If you use this resource for scientific research, please consider citing our WACV 2024 <a href="https://arxiv.org/abs/2311.03124">paper</a> <i>"TAMPAR: Visual Tampering Detection for Parcel Logistics in Postal Supply Chains".</i></p>
Synthetic parcel data for Lyon
<p>This data set contains a synthetic parcel data set for Lyon that has been generated during the Horizon 2020 project LEAD. It represents individual parcels that need to be delivered during an average day in Lyon with individual coordinates, based on a synthetic population for the city.</p>
Dataset (81 forest parcels) supplementing the publication "Owner attitudes and landscape parameters drive stand structure and valuable habitats in small-scale private forests of Lower Saxony (Germany)"
<p>The dataset about 81 small-scale private forest parcels contains the answer variables and predictors used in the publication "Owner attitudes and landscape parameters drive stand structure and valuable habitats in small-scale private forests of Lower Saxony (Germany)".</p>
2021 New York City Daily Residential Parcel Volume and Stops
<p>These datasets give <strong>2021 NYC residential parcel volume and stops </strong>information. The four datasets represent the estimated volume and stops served by Amazon, FedEx, UPS, and USPS respectively.</p> <p>The dataset is generated by using the Pluto 2021, 2020 census, 2020 USPS postal diary, and the market share among the four companies in 2021. In total,1.92 million daily residential parcels are estimated in the whole NYC area. Each row represents a unique stop. It contains the census tract ID the stop belongs to, its coordinates, and the delivery (FTA) and pickup (FTP) volume. We assume that no pickup volume is assigned to Amazon and USPS due to their service characteristics. In the case of USPS, no direct pickup service will be provided. Instead, the parcel will be directly handed to post offices for pickup. The pickup volume proportional to Amazon's market share is evenly distributed to FedEx and UPS pickup services.</p> <p>"myGraph.pickle": pickle file storing the NYC OSM network graph.</p> <p>"Shapfile.zip": Shapefiles containing different geographic features of NYC.</p> <p>"Centroid_OSM.zip": OSM maps storing the centroid of NYC census tracts.</p> <p>"NTA_dist.zip": VKT result based on NTA. </p> <p>"Service Zone": facility locations represented by OSM node ID and the service areas defined by census tracts</p>
Ecological Survey of Central Arizona: a survey of key ecological indicators in parcels of residential areas in the greater Phoenix metropolitan area, ongoing since 2010 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/115/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-cap/653/2. The abstract below was extracted from the Level 0 data package and is included for context: The Ecological Survey of Central Arizona (ESCA) is an extensive field survey and integrated inventory designed to capture key ecological indicators of the CAP LTER study area consisting of the urbanized, suburbanized, and agricultural areas of metropolitan Phoenix, and the surrounding Sonoran desert. The survey is conducted every five years at approximately 200 sample plots (30m x 30m) that were located randomly using a tessellation-stratified dual-density sampling design. Study plots cover habitats throughout the CAP LTER study area ranging from native Sonoran desert sites to residential yards to an airport tarmac. In 2010, the survey was expanded to include an assessment of residential parcels overlapping the survey plot at sites in residential areas. Many of the same variables that are measured in the 30m x 30m survey plot are measured in the parcel, including an inventory of perennial plants, and the biovolume of trees. In addition, a detailed assessment of characteristics of the parcel is performed. Investigators interested in data from the broader Ecological Survey of Central Arizona that includes all survey plots should should search the data catalog for 'ecological survey of central arizona' or 'survey 200' to locate those and other data related to the CAP LTER's ESCA.
Parcel Manager data set
<p>Data contained in this repository can be used for replicating the two use cases of Parcel Manager software application presented in the paper (Colomb et al., 2021, submitted).</p> <p>The first use case enables the simulation of a scenario called <em>test scenario</em> designed specifically to test different parcel division processes and workflows with Parcel Manager. The area under study is a small community (Gennes, 681 inhabitants) located in the east of France. Data used are from the French IGN BD Topo 2018.</p> <p>The second use case enables the comparison of the shape of parcels created by simulation with the shape of parcels in real cases. The comparison concerns the parcel plans in 2003 and 2018 of 11 communities of the Seine-et-Marne department, near Paris capital city (France). Data for 2018 are from the French IGN BD Topo 2018. They have been cleaned manually as follows:</p> <ul> <li>removing parcels that represent driveways or roads which could be used by residential parcels, – removing the tiny parcels resulting from a former division process and that could prevent the access to roads of neighbouring parcels,</li> <li>removing parcels located on water surfaces or railways,</li> <li>removing roads of type ’trails’ and ’stairs’.</li> </ul> <p>Parcel data for 2003 are from the IGN BD-Parcellaire 2003. Building and road data come from the IGN BD-Topo 2005. The cleaning results of the 2018 parcel plan have been copied into the 2003 parcel plan.</p> <p>The road network encompasses every types of paths that are accessible with a car.</p> <ul> <li>Driveways are small paths for cars that connect a house to the road network. Driveways are included in parcels and are not considered as a proper road.</li> <li>Gravel roads are non-asphalted trails. They can occasionally be used by cars. Their level of attraction is low (level 2).</li> <li>Lanes are small roads dedicated to house access. Their level of attraction is maximal (level 4). Note that peripheral roads created with the straight skeleton algorithm are classified as lanes.</li> <li>Streets are roads that connect lanes. Their level of attraction is high (level 3).</li> <li>Arterial roads are high-speed roads connecting communities. Their level of attraction for parcel contact is minimal (level 1).</li> </ul> <p>In the data set created for the <em>t</em><em>est Scenario</em>, the road layer has been manually enriched: missing trails and lanes have been added; type and level of attraction of some road segments have been changed.</p>
Data Instances for: Who moves the locker? A benchmark study of alternative mobile parcel locker concepts
<p>|C|_h.txt</p> <p>|C|: number of customers<br> h: instance</p> <p>|C|;|P|;</p> <p>|C|: number of customers<br> |P|: number of parking spaces</p> <p>Customer (c;size;max_dist;min_time;L;x_1;y_1;...;x_L;y_L;s_1;e_1;...;s_L;e_L)</p> <p>c: customer index <br> size: parcel size<br> max_dist: maximum walking distance<br> min_time: minimum overlap time<br> L: number of whereabouts<br> (x_i,y_i): position of whereabouts i<br> [s_i,e_i]: time window of whereabouts i</p> <p>Parking space (p;x;y)<br> p: parking space index <br> (x,y): position</p>
FLEXPART–ERA-Interim simulations with 3 million parcels globally (1979–2019)
<p><strong>Description</strong><br> This data set contains FLEXPART simulations driven with the reanalysis ERA-Interim from 1 February 1979 to 30 August 2019. The simulations cover the entire globe (90°S to 90°N and 180°E to 179°W) with 3 million homogeneously distributed air parcels representing the entire mass in the atmosphere. </p> <p><strong>Software</strong><br> The following software versions were used: </p> <ul> <li><strong>flex_extract v.7.1.2-dev</strong> (<a href="https://www.flexpart.eu/browser/flex_extract.git/?rev=3ca4c3ef0fa2a11ccf5a1c69e336b0893e2cb6a3">this version</a>); see Tipka et al. (2020) for details. </li> <li><strong>FLEXPART v10.4</strong> (<a href="https://www.flexpart.eu/browser/flexpart.git/?rev=3d7eebf7c4909f59db5ec32c524f88fb846e9fe5">this version</a>); see Pisso et al. (2019) for details. </li> </ul> <p><strong>Forcing data</strong><br> FLEXPART was driven with global reanalysis from ERA-Interim (Dee et al., 2011). The ERA-Interim reanalysis was downloaded and pre-processed using flex_extract v.7.1.2 (Tipka et al., 2020) using 6-hourly reanalyses at 00, 06, 12, and 18 UTC supplemented with 3- and 9-hourly forecasts (03 UTC and 09 UTC are taken from the forecasts initialised at 00UTC; and 15 UTC and 21 UTC are taken from the forecasts at 12 UTC). The settings are detailed in <em>CONTROL_EI_1deg.global</em>. The output of flex_extract were <em>EIgYYYYMMDDHH </em>files for each time step from 1 February 1979 to 30 August 2019 on a global 1° grid and contain the following variables: u, v, etadot, t, q, qc, sp, sshf, ewss, nsss, ssr, lsp, cp, sd, msl, tcc, 10u, 10v, 2t, 2d, z, lsm, sdor, cvl, cvh, sr; where the first 6 variables are available on 60 model layers. <br> <br> <strong>Simulations</strong><br> Simulations with FLEXPART v10.4 were performed in forward mode and subdivided into annual chunks; i.e. simulations for each year were started on December 1st 00 UTC of the previous year and run through to January 5th 18 UTC of the following year in order to run all years in parallel. For example, simulations for the year 2000 were started on December 1st 1999 at 00 UTC and run through to January 5th 2001 18 UTC. The overlap in December guarantees a coherent evaluation of each year with trajectories expanding up to 30 days into the past. <br> <br> Table 1 shows the main settings of the FLEXPART simulations as set in options/COMMAND and options/RELEASES.</p> <table align="left"> <caption><strong>Table 1: Main simulation settings</strong> (set in options/COMMAND; flags not listed here are set to 'off' [0] and in options/RELEASES; note that dates have been omitted for comprehension).</caption> <thead> <tr> <th scope="col">options/COMMAND</th> </tr> </thead> <tbody> <tr> <th scope="row">LDIRECT</th> <td>1</td> <td> <p>Simulation direction in time; 1 (forward)</p> </td> </tr> <tr> <th scope="row"> <p>LOUTSTEP</p> </th> <td>10800</td> <td> <p> Interval of model output (s)</p> </td> </tr> <tr> <th scope="row"> <p>LOUTSAMPLE</p> </th> <td>900</td> <td> <p>Interval of output sampling (s), higher stat. accuracy with shorter intervals</p> </td> </tr> <tr> <th scope="row"> <p>LSYNCTIME</p> </th> <td>900</td> <td> <p> All processes are synchronized to this time interval (s)</p> </td> </tr> <tr> <th scope="row">CTL</th> <td>2</td> <td> <p>CTL>1, ABL time step = (Lagrangian timescale (TL))/CTL, uses LSYNCTIME if CTL<0</p> </td> </tr> <tr> <th scope="row"> <p>IFINE</p> </th> <td>4</td> <td> <p>Reduction for time step in vertical transport, used only if CTL>1</p> </td> </tr> <tr> <th scope="row">IOUT</th> <td>1</td> <td> <p>Output type: [1]mass 2]pptv 3]1&2 4]plume 5]1&4, +8 for NetCDF output</p> </td> </tr> <tr> <th scope="row"> <p>IPOUT</p> </th> <td>1</td> <td> <p>Particle position output: 0] no 1] every output 2] only at end 3] time averaged</p> </td> </tr> <tr> <th scope="row"> <p>LSUBGRID</p> </th> <td>1</td> <td> <p>Increase of ABL heights due to sub-grid scale orographic variations; 1] on</p> </td> </tr> <tr> <th scope="row"> <p>LCONVECTION</p> </th> <td>1</td> <td> <p>Switch for convection parameterization; 1] on</p> </td> </tr> <tr> <th scope="row"> <p>MDOMAINFILL</p> </th> <td>1</td> <td> <p>Switch for domain-filling, if limited-area particles generated at boundary</p> </td> </tr> <tr> <th scope="row">options/RELEASES</th> </tr> <tr> <th scope="row"> <p>NSPEC</p> </th> <td>1</td> <td> <p>Total number of species</p> </td> </tr> <tr> <th scope="row"> <p>SPECNUM_REL</p> </th> <td>1</td> <td> <p>Species numbers in directory SPECIES</p> </td> </tr> <tr> <th scope="row"> <p>LON1</p> </th> <td>-179.00</td> <td> <p>Left longitude of release box -180 < LON1 <180</p> </td> </tr> <tr> <th scope="row"> <p>LON2</p> </th> <td>180.00</td> <td> <p>Right longitude of release box, same as LON1</p> </td> </tr> <tr> <th scope="row">LAT1</th> <td>-90.00</td> <td> <p>Lower latitude of release box, -90 < LAT1 < 90</p> </td> </tr> <tr> <th scope="row">LAT2</th> <td>90.00</td> <td> <p>Upper latitude of release box same format as LAT1</p> </td> </tr> <tr> <th scope="row">Z1</th> <td>0.00</td> <td> <p>Lower height of release box meters/hPa above reference level</p> </td> </tr> <tr> <th scope="row">Z2</th> <td>10000.00</td> <td> <p>Upper height of release box meters/hPa above reference level</p> </td> </tr> <tr> <th scope="row">ZKIND</th> <td>1</td> <td> <p>Reference level 1=above ground, 2=above sea level, 3 for pressure in hPa</p> </td> </tr> <tr> <th scope="row"> <p>PARTS</p> </th> <td> <p>3000000</p> </td> <td> <p>Total number of particles to be released</p> </td> </tr> <tr> <th scope="row">COMMENT</th> <td> <p>"globalRELEASE"</p> </td> <td> <p>Comment, written in the outputfile </p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Stored outputs</strong><br> Parcel positions and characteristics were stored at each 3-hourly time step in binary files names as <em>partposit_YYYYMMDDHH0000. </em>These files contain the following variables for each of the 3 million parcels:</p> <ul> <li>parcel id (a unique parcel identity number)</li> <li>longitude</li> <li>latitude</li> <li>height</li> <li>time step</li> <li>orography</li> <li>potential vorticity</li> <li>specific humidity</li> <li>density</li> <li>height of the surrounding boundary layer</li> <li>height of the surrounding troposphere</li> <li>temperature</li> <li>mass.</li> </ul> <p>One <em>partposit_YYYYMMDDHH0000 </em>file is 172 MB. All files for a month are compressed in monthly archives (as <em>fp-eraint-global_v104_rYYYY_partposit_YYYYMM.tar.gz</em>)<em> </em>and sum up to 393 GB for each annual simulation. The output of the entire simulation spanning 1979–2019 sums up to 17 TB. <br> <br> <strong>Metadata </strong><br> The metadata of each annual simulation is stored in a compressed<em><strong> </strong>fp-eraint-global_v104_rYYYY_meta.tar.gz<strong> </strong></em>archive. Metadata archives contain all namelists (all files from the <em>options</em> folder), invariant simulation outputs (such as <em>header</em>), and job outputs. For easy reference, the atmospheric mass at the time of initialisation is stored in a separate text file <em>atm_mass.txt</em>). The average atmospheric mass is 5.09205935e18 kg; each parcel thus represents approximately 1.697353e12 kg of air. <br> <br> <strong>Post-processing</strong><br> The simulations were setup and their outputs were stored in a way so that post-processing with the Heat- And MoiSture Tracking framEwoRk (HAMSTER, Keune et al., 2022) is possible without further changes. HAMSTER constructs multi-day backward trajectories for a region of interest and facilitates the identification of, e.g., precipitation origins. <br> <br> <strong>Contact and data access</strong><br> Only metadata is uploaded to this repository due to space constraints; access to the full data set can be obtained by contacting the corresponding author Jessica Keune via jessica.keune[at]ugent.be. <br> <br> <strong>References</strong><br> Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, et al. (2011). The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system. <em>Quarterly Journal of the Royal Meteorological Society</em>, <em>137</em>(656), 553-597.<br> Keune, J., Schumacher, D. L., & Miralles, D. G. (2022). A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models. <em>Geoscientific Model Development</em>, <em>15</em>(5), 1875-1898.<br> Pisso, I., Sollum, E., Grythe, H., Kristiansen, N. I., Cassiani, M., Eckhardt, S., et al. (2019). The Lagrangian particle dispersion model FLEXPART version 10.4. <em>Geoscientific Model Development</em>, <em>12</em>(12), 4955-4997.<br> Tipka, A., Haimberger, L., & Seibert, P. (2020). Flex_extract v7. 1.2–a software package to retrieve and prepare ECMWF data for use in FLEXPART. <em>Geoscientific Model Development</em>, <em>13</em>(11), 5277-5310.</p>
A Parcellation Scheme of Mouse Isocortex Based on Reversals in Connectivity Gradients
<p>This dataset contains a parcellation of mouse isocortex into a hierarchy of subregions. The parcellation is based on the detection of gradient reversals in voxelized connectivity, as described in our latest preprint manuscript.</p> <p>The connectivity dataset used for this purpose was the voxelized mouse connectome published by the Allen Institute for Brain Science, available at <a href="https://connectivity.brain-map.org">connectivity.brain-map.org</a> (data) and <a href="https://github.com/AllenInstitute/mouse_connectivity_models">github.com/AllenInstitute</a> (code)</p> <p>Region id annotations are provided in .nrrd format. Refer to the <a href="http://teem.sourceforge.net/nrrd/format.html">NRRD documentation</a> for details.</p> <p>The hierarchy is provided in the accompanying json file. For each region, an entry "structure_id_path" is provided that lists the ids of regions on the path from the root (i.e. isocortex) to the region in question.</p> <p>For more information, please read the accompanying manuscript. We will provide the link here, once it is online.</p>
Agricultural Parcel_CO2flux_France-Toulouse_0.30_15102018_15102019
<p>Set of agricultural parcels, with their geometry, crop type, value of CO2 flux (due to the crop vegetation cycle during an agricultural campaign year) and a few other attributes (intermediary results, quality information).</p>
Glasser52: A parcellation for MEG-Analysis
<p><strong>Summary</strong></p> <p>A parcellation with 52 parcels that can be linked to anatomical labels of the HCP-MMP atlas and is suitable for analyses of MEG recordings.</p> <p>The parcellation was created based on the Human Connectome Project Multimodal Parcellation (HCP-MMP) atlas (Glasser et al., 2016) following the procedure suggested by Tait et al. (2021).</p> <p>The underlying idea of this procedure is to 1) calculate the influence of each of the 22 regions of the HCP-MMP atlas on MEG recordings obtained from healthy controls and PD volunteers and 2) to merge and divide regions so that 52 parcels with similar influence on the MEG sensor signal are obtained. This approach allows to start from the anatomical HCP-MMP atlas and adapts it according to functional signals obtained from MEG recordings. Thus, it enables a good compromise between solely anatomically or solely functionally derived parcellations. (For more information see the Additional Information.docx).<br><br><strong>Contents:</strong></p> <blockquote> <p><strong>Glasser52_binary_space-MNI152NLin6_res-8x8x8.nii.gz</strong><br>Parcellation for MEG-Analyses (8mm voxel resolution).</p> <p><strong>Glasser52_binary_space-MNI152NLin6_res-2x2x2.nii.gz</strong><br>Parcellation for MEG-Analyses (2mm voxel resolution).</p> <p><strong>Glasser52_binary_space-MNI152NLin6_res-1x1x1.nii.gz</strong><br>Parcellation for MEG-Analyses (1mm voxel resolution).</p> <p><strong>parcels</strong><br>Folder with images of parcels for 1mm, 2mm, and 8mm resolution.</p> <p><strong>Glasser_Merging.xlsx<br></strong>Excel sheet with detailed information on why parcels were merged or divided.</p> <p><strong>Labels_short.p</strong><br>Short Labels of the Glasser52 labels. Best loaded with <code>pickle.load(open("../Labels_short.p","rb"))</code>.</p> <p><strong>Labels.p</strong><br>Labels of the Glasser52 labels. Best loaded with <code>pickle.load(open("../Labels.p","rb"))</code>.</p> <p><strong>Additional Informatio.docx</strong><br>Description of the motivation, methods used, and creation of the Glasser52 parcellation.</p> <p><strong>parcel_names_and_mni_coordinates.docx</strong><br>Table with MNI-coordinates of Glasser52 parcel centres.</p> </blockquote>
Soil scans for parcels in Netherlands (20170327_vermeulen_spieClass_3)
<p>Parcel soil scan with electric conductivity measurement from Netherlands</p>
Soil scans for parcels in Netherlands (Provincie rondeelClass 3)
<p>Parcel soil scan with electric conductivity measurement</p>
Soil scans for parcels in Netherlands (Vermeulen spieClass 3)
<p>Parcel soil scan with electric conductivity measurement</p>
ScienceDex guides
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.