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256 results for “parcel”
Text-fig. 2. Museum plan from 1903 – author Otto Tille. (City council Lázně Bělohrad, construction board, construction documentation, parcel no. 191). / Plán muzea z roku 1903 – autor Otto Tille. (Městský úřad Lázně Bělohrad, stavební úřad, stavební dokumentace k č. p. 191). in Frič Museum In Lázně Bělohrad
Text-fig. 2. Museum plan from 1903 – author Otto Tille. (City council Lázně Bělohrad, construction board, construction documentation, parcel no. 191). / Plán muzea z roku 1903 – autor Otto Tille. (Městský úřad Lázně Bělohrad, stavební úřad, stavební dokumentace k č. p. 191).
Parcel-guided rTMS for Major Depressive Disorder
ClinicalTrials.gov study NCT07287839. IPD Sharing: NO. Countries: 0. Publications: 1.
Data from: Census parcels cropping system classification from multitemporal remote imagery: a proposed universal methodology
Open the record for dataset details and reuse information.
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: 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.
Conserved genetic signatures parcellate cardinal spinal neuron classes into local and projection subsets [P0NeuronData]
GEO Series GSE171605. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic and anatomic parcellation of 5-HT3AR expressing cortical interneuron subtypes revealed by single-cell RNA sequencing
GEO Series GSE90860. Mus musculus. 223 samples. Type: Expression profiling by high throughput sequencing.
Distinct Layer 6b transcriptomic subtypes parcellate the cortical mantle
GEO Series GSE299810. Mus musculus. 8 samples. Type: Other.
Air parcels extracted from LES simulations of a deep convective cloud observed during DC3 field campaign
<p>The dataset consists of 500 files describing air parcels retrieved from the simulation of the deep convective cloud event observed on May 29, 2012, during the DC3 field campaign. 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>
Data from: An essential role of the intraparietal sulcus in response inhibition predicted by parcellation-based network
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Simulated 12-Month Ice Parcel Tracks from Gridded Sea Ice Motion, Version 1
This data set provides tracer parcels to simulate sea ice advection paths in the Northern Hemisphere combined with sea ice concentration data. Sea ice parcels are identified in 25 km grid cells where sea ice concentrations are greater than 15% and tracked daily using Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors (NSIDC-0116). Locations are updated weekly to determine whether an ice parcel has melted and should be tracked. Columns represent the EASE-grid x, y, and sea ice concentration values for each week over the 12 month tracking period and rows depict distinct simulated ice parcels.
Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation
<p><strong>Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation</strong></p> <p>This repository contains data contributed and used in our MICCAI 2021 paper:<br> Fidon, L. et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation.</p> <p>Our code is publicly available at<br> <a href="https://github.com/LucasFidon/fetal-brain-segmentation-partial-supervision-miccai21">https://github.com/LucasFidon/fetal-brain-segmentation-partial-supervision-miccai21</a></p> <p><strong>Files:</strong></p> <ul> <li>FeTA2021_Release1and2Corrected_v4.zip: contains 90 fetal brain T2w MRIs of the FeTA 2021 dataset with updated manual segmentations</li> <li>MICCAI21_partial_supervision_trained_models.zip: contains the weights of our pre-trained deep neural networks for fetal brain 3D MRI segmentation</li> </ul> <p>The FeTA 3D MRIs and segmentations contained in this repository shall be used only for research and education purposes.</p> <p>Segmentation labels:<br> 1: white matter (excluding corpus callosum; see below)<br> 2: intra-axial cerebrospinal fluid (CSF)<br> 3: cerebellum<br> 4: extra-axial CSF<br> 5: cortical gray matter<br> 6: deep gray matter<br> 7: brainstem<br> 8: corpus callosum</p> <p><strong>Updates:</strong></p> <p><strong>Version 4 (November 2022)</strong><br> 8 cases were forgotten by mistake when uploading version 3 (sub-feta004, sub-feta008, sub-feta017, sub-feta020, sub-feta021, sub-feta022, sub-feta023, sub-feta027). I have added them back in version 4, including the corpus callosum segmentations for those cases.<br> <br> Thank you to those who let me know about this. Please do not hesitate to reach out to me (lucas.fdon@gmail.com) if you think something is wrong.</p> <p><strong>Version 3 (July 2022)</strong></p> <p>Added some corpus callosum segmentations that were missing in version 2.</p> <p><strong>Version 2 (Avril 2022)</strong></p> <p>3D MRI and refined manual segmentation have been added for:<br> - the 10 fetal brain MRIs of the testing set of the FeTA data release 1 (sub-feta081 to sub-feta090).<br> - the 40 fetal brain MRIs new in the FeTA data release 2 (sub-041 to sub080).<br> The same pre-processing as in version 1 below has been performed.<br> The code used for the pre-processing is available at<br> https://github.com/LucasFidon/fetal-seg-preprocessing</p> <p>Some additional refinements of the manual segmentation have been performed for sub-feta001 to sub-feta040.</p> <p>See participant.tsv for more information about the 3D MRI studies.</p> <p>The pathology for each study has been diagnosed by two radiologists in our team and can be found in participant.tsv.<br> Those are not present in the original FeTA dataset release 2.1</p> <p><strong>Version 1</strong><br> Summary of the changes done on the original FeTA dataset (data release 1) [1].</p> <p>The only modifications we did are detailed below (pre-processing and manual segmentation).</p> <p>Pre-processing:<br> -registration to the neurotypical fetal brain atlas of Gholipour et al [2]<br> -resampling to 0.8mm isotropic<br> -automatic mask via label propagation using a neurotypical [2] and a spina bifida [3] fetal brain atlas.</p> <p>The code used for the pre-processing is available at<br> https://github.com/LucasFidon/fetal-seg-preprocessing</p> <p>Segmentation:<br> Manual corrections of all manual segmentations by Michael Aertsen, Lucas Fidon, and Philippe Demaerel for the 38 3D MRIs in the FeTA dataset release 1.<br> For 2 cases, the segmentations were not corrected manually (see /Excluded).</p> <p>[1] 10.5281/zenodo.4541605<br> [2] http://crl.med.harvard.edu/research/fetal_brain_atlas/<br> [3] 10.7303/syn25887675</p> <p><strong>How to cite:</strong></p> <p>If you use the deep neural network weights please cite:</p> <ul> <li>Fidon, L., Aertsen, M., Emam, D., et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. MICCAI (2021).</li> </ul> <p>if you use the FeTA data (release 1 and 2) and the corrected segmentations that we contributed please cite:</p> <ul> <li>Payette, K., de Dumast, P., Kebiri, H. et al. An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset. Sci Data 8, 167 (2021). https://doi.org/10.1038/s41597-021-00946-3</li> <li>Fidon, L., Aertsen, M., Emam, D., et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. MICCAI (2021).</li> </ul> <p><strong>Terms of use - FeTA 3D MRIs and segmentations:</strong></p> <p>This is an agreement (“Agreement”) between you the downloader (“Downloader”) and the owner of the materials (“User”) governing the use of the Fetal Tissue Annotation and Segmentation Dataset to be downloaded.</p> <p>I. Acceptance of this Agreement</p> <p>By downloading or otherwise accessing the Fetal Tissue Annotation and Segmentation Dataset, the Downloader represents his/her acceptance of the terms of this Agreement.</p> <p>II. Data ownership</p> <p>The owner of the Fetal Tissue Annotation and Segmentation Dataset is the University Children’s Hospital Zurich.</p> <p>III. Use of the Materials</p> <p>Fetal Tissue Annotation and Segmentation Dataset is used only for research and education. Any other kind of use you will lead to the recall of all datasets, stop of collaboration, and legal consequences. This Agreement represents the entire agreement between Downloader and User with respect to the downloading and use of the Materials and supersedes all prior or contemporaneous communications and proposals (whether oral, written, or electronic) between Downloader and User with respect to downloading or using the Materials. </p> <p> </p>
Porto area Parcel Delivery Demand dataset
<p>This is an anonymized last-mile parcel delivery demand dataset for several depots at the city center of Porto area, Portugal. The dataset does not include exact orders' delivery points, but includes the latitude and longitude of the centroid point of postal codes at which the delivery is aimed at. The dataset includes 6-months of delivery demand from January 2022 to June 2022.</p>
Non-Inferiority Comparison of Migrane® Versus Parcel ® For Treatment of Tensional Cephaleia
ClinicalTrials.gov study NCT01408069. IPD Sharing: Not stated. Countries: 0. Publications: 0.
ADHD200 preprocessed regional time series processed with NIAK and the ROI1000 brain parcellation
<p>See README.md.</p>
The trajectories of deep convective cloud air parcels over Manaus, Brazil simulated by MIMICA LES / CRM
<p>The dataset contains trajectories of the deep convective clouds simulated with MIMICA code based on soundings retrieved on April 8, 2020, April 23, 2020, and April 27, 2020, over Manaus, Brazil.</p> <p><br> OUTPUT folder contains all 500 air parcel trajectories retrieved from MIMICA.<br> OUTPUT_MEAN folder contains all the bin averaged parcels</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>
ScienceDex guides
Understand access before you commit
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.