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365 results for “Spatial modeling”
Spatial dataset for ecological response models and spatial distribution of Ataeniobius toweri (Cyprinodontiformes: Goodeidae) in the Media Luna spring, Mexico
<p>Dataset for the endangered endemic fish Ataeniobius toweri in the Media Luna spring, Mexico. This information includes field records for the adult and juvenile stage of the species in three sessions corresponding to the summer period, in the years 1999, 2009 and 2019.</p> <p>Our original databases are those concerning the records of the species' presence by life stage and summer period: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_DOMAIN_At_Ad_09.csv?versionId=9566fb68-3948-49ca-b485-2d7aac863445">Occ_records_DOMAIN_At_Ad_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_DOMAIN_At_Ad_19.csv?versionId=6f7a0f47-4990-465c-a02c-191247c395c6">Occ_records_DOMAIN_At_Ad_19.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_DOMAIN_At_Ad_99.csv?versionId=e9158e08-bf91-4097-828f-3ce850e1bee4">Occ_records_DOMAIN_At_Ad_99.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_DOMAIN_At_Ju_09.csv?versionId=5bd51f0e-7215-4657-bfb5-bb49e6cdd57f">Occ_records_DOMAIN_At_Ju_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_DOMAIN_At_Ju_19.csv?versionId=32d0c72a-a548-406d-8694-8d939e73058e">Occ_records_DOMAIN_At_Ju_19.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_DOMAIN_At_Ju_99.csv?versionId=d786131a-f445-44d2-a319-0a75aa666f3f">Occ_records_DOMAIN_At_Ju_99.csv</a>.</p> <p>From the above files, we generate the following dataset:</p> <p>The general basis of the species records by life stage and period: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_by_sector&period_At_Ad_Ju.csv?versionId=13ffb71e-ef52-43f0-9605-e416359d5997">Occ_records_by_sector&period_At_Ad_Ju.csv</a>.</p> <p>The database that includes the 500 background points generated from records of the species' presence by life stage: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pres_back_GLM_At_Ad_09.csv?versionId=7083cda2-a16d-4a25-9404-4c134adc2fa5">Pres_back_GLM_At_Ad_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pres_back_GLM_At_Ad_19.csv?versionId=2cbd76f6-241d-4035-b25b-1ce490362d6a">Pres_back_GLM_At_Ad_19.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pres_back_GLM_At_Ad_99.csv?versionId=c570be15-2b21-4a2e-9377-0e7962a26d1b">Pres_back_GLM_At_Ad_99.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pres_back_GLM_At_Ju_09.csv?versionId=11624daf-2da0-4bb1-95d4-b3c917c94c4f">Pres_back_GLM_At_Ju_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pres_back_GLM_At_Ju_19.csv?versionId=1c06bcd0-4e34-49c4-859e-100f3401e225">Pres_back_GLM_At_Ju_19.csv</a> y <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pres_back_GLM_At_Ju_99.csv?versionId=e10d7c51-f6f8-407c-8141-1b0196d00aed">Pres_back_GLM_At_Ju_99.csv</a>.</p> <p>The databases with the extracted values of the underwater coverage (UC) and water depth (WDp) variables from the presence and background points: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/GBM_rel_contribution_At_Ad_09.csv?versionId=0735fbdb-5f3d-4842-ae3e-a45e849bb12f">GBM_rel_contribution_At_Ad_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/GBM_rel_contribution_At_Ad_19.csv?versionId=3ba6c099-e807-4e07-b1ef-32a63d347a50">GBM_rel_contribution_At_Ad_19.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/GBM_rel_contribution_At_Ad_99.csv?versionId=49e89e06-7740-425a-be2e-db5f38c0aeca">GBM_rel_contribution_At_Ad_99.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/GBM_rel_contribution_At_Ju_09.csv?versionId=fcc857f9-4332-4e15-901e-c4deecda46ab">GBM_rel_contribution_At_Ju_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/GBM_rel_contribution_At_Ju_19.csv?versionId=34dd733a-af86-46ad-a973-4e90e9299117">GBM_rel_contribution_At_Ju_19.csv</a> y <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/GBM_rel_contribution_At_Ju_99.csv?versionId=c7fe7fa9-e85b-45ba-9681-21f6a9cd3924">GBM_rel_contribution_At_Ju_99.csv</a>.</p> <p>The database with the results of the relative contribution for each variable by life stage and summer period: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Summary_GBM_At_Ad_Ju.csv?versionId=f0b0b4d4-62b0-4d95-b6f4-ec5e00b97633">Summary_GBM_At_Ad_Ju.csv</a>.</p> <p>The databases containing the correlation values between the UC and WDp variables, by Pearson's method, for each point of presence and background: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pearson_corr_At_Ad_09.csv?versionId=99e74579-6edf-46e2-8f5d-3dd06a9a325f">Pearson_corr_At_Ad_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pearson_corr_At_Ad_19.csv?versionId=ea4d938e-133e-426d-b11a-85ed877876f8">Pearson_corr_At_Ad_19.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pearson_corr_At_Ad_99.csv?versionId=a499ab48-6be0-447c-9521-8e500769d4de">Pearson_corr_At_Ad_99.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pearson_corr_At_Ju_09.csv?versionId=da583fda-0bc5-4769-bab8-bfb3c365232e">Pearson_corr_At_Ju_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pearson_corr_At_Ju_19.csv?versionId=88e89cc1-2bfa-45cb-b6af-c87737a6fbd7">Pearson_corr_At_Ju_19.csv</a> y <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pearson_corr_At_Ju_99.csv?versionId=fddca19c-fa6b-460d-a571-966229fad955">Pearson_corr_At_Ju_99.csv</a>.</p> <p>And, the databases that contain the probability values for the generation of the ecological response curves in function of the UC variable: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/At_Ad_09_Prob_UC.csv?versionId=240d8a0c-c0f6-4a1f-8983-74320b0863f8">At_Ad_09_Prob_UC.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/At_Ad_19_Prob_UC.csv?versionId=2bff87d1-b910-4cfd-b521-881e56034b19">At_Ad_19_Prob_UC.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/At_Ad_99_Prob_UC.csv?versionId=02050376-e34d-46ac-bdf9-7bc7b1f8570f">At_Ad_99_Prob_UC.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/At_Ju_09_Prob_UC.csv?versionId=a3182956-bbf4-4853-a3a2-4296e7acf79c">At_Ju_09_Prob_UC.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/At_Ju_19_Prob_UC.csv?versionId=21a89330-89d3-442a-a801-19842a3bed70">At_Ju_19_Prob_UC.csv</a> y <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/At_Ju_99_Prob_UC.csv?versionId=ff7d3966-6248-49d2-9000-07e374f0ba76">At_Ju_99_Prob_UC.csv</a>.</p> <p>For more information about the codes where the previous dataset was generated, visit the following repository URL: <a href="https://doi.org/10.5281/zenodo.7603557">https://doi.org/10.5281/zenodo.7603557</a>.</p> <p>Also, to download the UC and WDp variables to run the modeling processes, visit the following repository URL: <a href="https://doi.org/10.5281/zenodo.7603890">https://doi.org/10.5281/zenodo.7603890</a>.</p>
Data Repository for "Integrating Water Quality Data with a Bayesian Network Model to Improve Spatial and Temporal Phosphorus Attribution: Application to the Maumee River Basin"
<p>Data for "Integrating Water Quality Data with a Bayesian Network Model to Improve Spatial and Temporal Phosphorus Attribution: Application to the Maumee River Basin". This repository contains all the processed data used in the simulation (in "processed" folder), part of the raw data (in "raw" folder), and the SWAT simulation results (in "SWAT" folder). The code for processing the raw data, which are either provided here or publicly available online, is provided in the <a href="https://doi.org/10.5281/zenodo.8132662">code repository</a>. The links to the publicly available raw data are also provided in the code repository.</p>
Spatial Transcriptomic Experiment of Triple-Negative Breast Cancer PDX Model PIM001-P model treatment naive sample
<p>Spatial Transcriptomic Experiment of Triple-Negative Breast Cancer PDX Model PIM001-P model treatment naive sample</p> <p>10X Genomics Visium platform. </p> <p><strong>Library Preparation and Sequencing of PIM001P</strong></p> <p>Tissue sections of 10µm thickness were mounted onto the capture areas of the Visium Spatial Gene Expression slide and stained using hematoxylin and eosin. Tissue sections were permeabilized on a thermocycler for 24 minutes, as determined by the Tissue Optimization step. Poly-adenlyated mRNA is released and captured by surface-bound primers within each capture area. Reverse transcription, template switching, extension, and second strand synthesis are performed on the slide. Full-length, spatially barcoded cDNA transcripts are then denatured from the slide and amplified via PCR prior to library construction. Approximately 110 to 375 ng of amplified cDNA was carried forward into library construction. During library construction, cDNA is enzymatically fragmented to target amplicon size then undergoes end repair, A-tailing, adapter ligation, and then amplified using between 14 and 16 PCR cycles. The resulting libraries were quantitated using the Invitrogen Qubit 2.0 quantitation assay and fragment size assessed with the Agilent Bioanalyzer. A qPCR quantitation was performed on the libraries to determine the concentration of adapter ligated fragments using Applied Biosystems ViiA7 Real-Time PCR System and a KAPA Library Quant Kit (p/n KK4824). All samples were pooled equimolarly and re-quantitated by qPCR, and also re-assessed on the Bioanalyzer.</p> <p><strong>Sequencing: </strong></p> <p>150 pM of equimolarly pooled library was loaded onto the NovaSeq 6000 S4 flowcell and sequenced at the recommended 28-10-10-50 read configuration. PhiX Control v3 adapter-ligated library (Illumina p/n FC-110-3001) was spiked-in at 2% by weight to ensure balanced diversity and to monitor clustering and sequencing performance. A minimum of 300 million read pairs per sample was sequenced. FastQ file generation was executed using 10X Genomics’ Space Ranger mkfastq software.</p> <p> </p>
Data from: A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans
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Data from: Surrogate modelling for the prediction of spatial fields based on simultaneous dimensionality reduction of high-dimensional input/output spaces
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Data from: Spatially explicit models of divergence and genome hitchhiking
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Data from: On the sampling design of spatially explicit integrated population models
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Geographic range estimates and environmental requirements for the harpy eagle derived from spatial models of current and past distribution
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Spatial sampling bias and model complexity in stream-based species distribution models: a case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, U.S.A.
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Dendritic prioritization through spatial stream network modeling informs targeted management of Himalayan riverscapes under brown trout invasion
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Data from: The role of geospatial hotspots in the spatial spread of tuberculosis in rural Ethiopia: a mathematical modelling
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Data from: Positive plant-soil feedbacks trigger tannin evolution by niche construction: a spatial stoichiometric model
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Data from: Spatially structured statistical network models for landscape genetics
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Data from: The effects of archipelago spatial structure on island diversity and endemism: predictions from a spatially-structured neutral model
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Data from: Why we should care about movements: Using spatially explicit integrated population models to assess habitat source-sink dynamics
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Synthetic soil crusts against green-desert transitions: a spatial model
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Hierarchical spatial modeling of multiple soil nutrients and carbon in heterogeneous landuse patches of the central Arizona-Phoenix research area, from 1999 to 2006.
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Metop-B ASCAT Inter-Calibrated ESDR Level 2 Observed and Modeled Spatial Derivatives of Surface Wind and Wind Stress Version 1.0
This dataset contains the curl and divergence of ocean surface equivalent neutral wind and wind stress, derived from satellite-based scatterometer observations (the MetOp-B ASCAT scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaSUREs program. This product from MetOp-B ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, ScatSat-1, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page. These Level 2 data are provided on a non-uniform grid within the satellite swath at ~12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit - the thumbnail preview shows data for all orbits over a day (typically 14 orbits). Estimates for the curls and divergences are computed over several spatial domains with varying radii from the point of interest, and included as separate variables.<br><br>The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST). This V1.0 of the data was derived from V1.1 of the L2 wind and stress product.
SCATSAT-1 Inter-Calibrated ESDR Level 2 Observed and Modeled Spatial Derivatives of Surface Wind and Wind Stress Version 1.0
This dataset contains the curl and divergence of ocean surface equivalent neutral wind and wind stress, derived from satellite-based scatterometer observations aboard SCATSAT-1, representing the first science quality release of these data funded under the MEaSUREs program. This product from SCATSAT-1 has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, MetOp-B, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page. These Level 2 data are provided on a non-uniform grid within the satellite swath at ~12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit. There are typically 14 orbits per day, and the thumbnail preview shows coverage for the first ten orbits in an example day. Estimates for the curls and divergences are computed over several spatial domains with varying radii from the point of interest, and included as separate variables.<br><br>The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST). This V1.0 of the data was derived from V1.1 of the L2 wind and stress product.
Metop-A ASCAT Inter-Calibrated ESDR Level 2 Observed and Modeled Spatial Derivatives of Surface Wind and Wind Stress Version 1.0
This dataset contains the curl and divergence of ocean surface equivalent neutral wind and wind stress, derived from satellite-based scatterometer observations (the MetOp-A ASCAT scatterometer), representing the first science quality release of these data funded under the MEaSUREs program. This product from MetOp-A ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-B, ScatSat-1, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page. These Level 2 data are provided on a non-uniform grid within the satellite swath at ~12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit - the thumbnail preview shows data for all orbits over a day (typically 14 orbits). Estimates for the curls and divergences are computed over several spatial domains with varying radii from the point of interest, and included as separate variables.<br><br>The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST). This V1.0 of the data was derived from V1.1 of the L2 wind and stress product.
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.