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367 results for “pass”
Pluvial Flood Emulation with Hydraulics-informed Message Passing
<p>Dataset used in <a href="https://github.com/kanz76/ComGNN">Pluvial Flood Emulation with Hydraulics-informed Message Passing</a></p>
Parameters and predicted probabilities of mode choice and ride pass subscription for microtransit in Arlington, TX
<p>We provide parameters and predicted probabilities of mode choice (at trip level) and ride pass subscription (at individual level) for microtransit in Arlington, TX. The parameters are estimated by <a href="https://arxiv.org/abs/2408.12577">an agent-based, nested behavioral model</a> that is developed in the C2SMARTER project “Multi-modal Tripchain Planner for Disadvantaged Travelers to Incentivize Transit Usage” (Award #69A3551747124). We separate the choice related to microtransit into two parts: travel mode choice and ride pass subscription choice. Synthetic population data from <a href="https://www.replicahq.com/">Replica Inc.</a> and microtransit service data from <a href="https://www.arlingtontx.gov/">City of Arlington</a> are used for estimation. </p> <p>In the lower-branch travel mode choice, individuals decide on mode to use by considering factors such as travel time, cost, trip purpose, tour type, and mode-specific preferences. The mode choice set includes driving, walking, biking, carpool, and microtransit. Different from traditional mode choice models, we allow time and cost parameters to vary across individuals, and we allow mode specific constants to vary across trip OD pairs. This makes sense when we do not have sufficient data for socioeconomic attributes and built environment variables. The assumption we made here is that the impacts of these unobserved variables are included in the nonparametric distribution of individual and OD pair-level parameters.</p> <p>In the upper-branch ride pass subscription choice, individuals decide whether to purchase a weekly ride pass, a monthly ride pass, or no ride pass at all. By subscribing to a ride pass, travelers pay an amount of money in advance and enjoy free microtransit trips until the ride pass expires. The utility of purchasing a ride pass consists of four components: (1) the utility related to the prices of ride passes, (2) the utility related to the change in consumer surplus (or compensating variation) brought by free microtransit trips with a ride pass, (3) the utility specific to microtransit users, and (4) the alternative specific constant of the ride pass. Given the data availability, we consider the ride pass model as a simple MNL model with six parameters to calibrate. These ride pass parameters are calibrated using the Nelder-Mead Simplex Method. The cost function to minimize is the squared distance between the predicted ride pass market share and the observed one.</p> <p>Accordingly, this dataset consists of six .csv files:</p> <ul> <li>Mode_choice_parameters_weekday.csv</li> <li>Mode_choice_parameters_weekend.csv</li> <li>RidePass_subscription_parameters.csv</li> <li>Mode_choice_probability_weekday.csv</li> <li>Mode_choice_probability_weekend.csv</li> <li>Ridepass_subscription_probability.csv</li> </ul> <p> </p> <p><strong>Field definition in "Mode_choice_parameters_weekday.csv" and "Mode_choice_parameters_weekend.csv"</strong></p> <table> <tbody> <tr> <td><strong>Field Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>iid</td> <td>IDs of each synthetic individual</td> </tr> <tr> <td>trip_id</td> <td>IDs of each synthetic trip</td> </tr> <tr> <td>origin_bgrp</td> <td>FIPs code of the block group where the trip starts</td> </tr> <tr> <td>destination_bgrp</td> <td>FIPs code of the block group where the trip ends</td> </tr> <tr> <td>B_AUTO_TT</td> <td>The parameter of auto travel time (vary across individuals)</td> </tr> <tr> <td>B_MICRO_TT</td> <td>The parameter of microtransit waiting time (vary across individuals)</td> </tr> <tr> <td>B_NON_AUTO_TT</td> <td>The parameter of non-auto travel time (vary across individuals)</td> </tr> <tr> <td>B_COST</td> <td>The parameter of travel cost (vary across individuals)</td> </tr> <tr> <td>ASC_MIRCO</td> <td>The alternative specific constant of microtransit (vary across trip OD pairs)</td> </tr> <tr> <td>ASC_DRIVING</td> <td>The alternative specific constant of driving (vary across trip OD pairs)</td> </tr> <tr> <td>ASC_BIKING</td> <td>The alternative specific constant of biking (vary across trip OD pairs)</td> </tr> <tr> <td>ASC_WALKING</td> <td>The alternative specific constant of walking (vary across trip OD pairs)</td> </tr> <tr> <td>MICRO_P_SHOPPING</td> <td>The interaction effect between microtransit and shopping trip purpose (generic)</td> </tr> <tr> <td>MICRO_P_SCHOOL</td> <td>The interaction effect between microtransit and school trip purpose (generic)</td> </tr> <tr> <td>MICRO_P_OTHER</td> <td>The interaction effect between microtransit and other trip purpose (generic)</td> </tr> <tr> <td>MICRO_T_COMMUTE</td> <td>The interaction effect between microtransit and commute tour type (generic)</td> </tr> <tr> <td>MICRO_T_HOME_BASED</td> <td>The interaction effect between microtransit and home-based tour type (generic)</td> </tr> </tbody> </table> <p> </p> <p><strong>Field definition in "RidePass_subscription_parameters.csv"</strong></p> <table> <tbody> <tr> <td><strong>Field Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>B_COST_RP</td> <td>A transfer factor from trip fare to ride pass price</td> </tr> <tr> <td>B_CS_WEEKDAY</td> <td>The parameter of increased consumer surplus (due to the ride pass) on weekday</td> </tr> <tr> <td>B_CS_WEEKEND</td> <td>The parameter of increased consumer surplus (due to the ride pass) on weekend</td> </tr> <tr> <td>B_MICRO_USER</td> <td>The parameter of a binary variable indicating former microtransit users</td> </tr> <tr> <td>ASC_WRP</td> <td>The alternative specific constant of subscribing weekly ride pass</td> </tr> <tr> <td>ASC_MRP</td> <td>The alternative specific constant of subscribing monthly ride pass</td> </tr> </tbody> </table> <p> </p> <p><strong>Field definition in "Mode_choice_probability_weekday.csv" and "Mode_choice_probability_weekend.csv"</strong></p> <table> <tbody> <tr> <td><strong>Field Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>iid</td> <td>IDs of each synthetic individual</td> </tr> <tr> <td>trip_id</td> <td>IDs of each synthetic trip</td> </tr> <tr> <td>origin_bgrp</td> <td>FIPs code of the block group where the trip starts</td> </tr> <tr> <td>destination_bgrp</td> <td>FIPs code of the block group where the trip ends</td> </tr> <tr> <td>P_biking</td> <td>The predicted probability of choosing biking</td> </tr> <tr> <td>P_carpool</td> <td>The predicted probability of choosing carpool</td> </tr> <tr> <td>P_microtransit</td> <td>The predicted probability of choosing microtransit</td> </tr> <tr> <td>P_driving</td> <td>The predicted probability of choosing driving</td> </tr> <tr> <td>P_walking</td> <td>The predicted probability of choosing walking</td> </tr> </tbody> </table> <p> </p> <p><strong>Field definition in "Ridepass_subscription_probability.csv"</strong></p> <table> <tbody> <tr> <td><strong>Field Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>iid</td> <td>IDs of each synthetic individual</td> </tr> <tr> <td>Micro_user</td> <td>A binary variable indicating whether the individual used microtransit before</td> </tr> <tr> <td>Population_seg</td> <td>Segment ID of the synthetic individual</td> </tr> <tr> <td>BLOCKGROUP</td> <td>FIPs code of the home block group</td> </tr> <tr> <td>P_weekly_pass</td> <td>The predicted probability of subscribing weekly ride pass</td> </tr> <tr> <td>P_monthly_pass</td> <td>The predicted probability of subscribing monthly ride pass</td> </tr> <tr> <td>P_None</td> <td>The predicted probability of subscribing no ride pass</td> </tr> </tbody> </table>
On the road from Switzerland to Italy over mountain pass with hydrogen fuel cell car Hyundai ix35
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Data for Stool Studies Don't Pass the Sniff Test
<p>Data for manuscript "Stool Studies Don't Pass the Sniff Test".</p>
PASS molecular dataset
<p>A dataset containing 475K PASs molecules</p>
Examine National Characteristics & Outcome Measures of GERD Patients Using the PPI Acid Suppression Symptom (PASS) Test for Response
ClinicalTrials.gov study NCT00392002. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ADVATE 2 mL Post-Authorization Safety Surveillance (PASS)
ClinicalTrials.gov study NCT02093741. IPD Sharing: YES. Countries: 4. Publications: 0.
PREA, PK And Safety PASS Study Of IV Pantoprazole In Pediatric Subjects
ClinicalTrials.gov study NCT02401035. IPD Sharing: YES. Countries: 9. Publications: 0.
PASS Study To Evaluate The Potential Of Zithromax To Cause Ocular Problems In Pediatric Patients
ClinicalTrials.gov study NCT01919996. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Assessing Effects of Heparin Priming and Pass Number on Tissue Quality of Fine Needle Biopsies
ClinicalTrials.gov study NCT04764396. IPD Sharing: YES. Countries: 1. Publications: 0.
MDR EZ Pass and Precision Flexible Reamer
ClinicalTrials.gov study NCT03914703. IPD Sharing: NO. Countries: 1. Publications: 0.
A Study on Combined Low-pass Whole-genome and Methylome Testing of Bloody Nipple Discharge Specimens for Benign-Malignant Differentiation.
ClinicalTrials.gov study NCT07250204. IPD Sharing: NO. Countries: 1. Publications: 0.
Empa PASS on Urinary Tract Malignancies
ClinicalTrials.gov study NCT03464045. IPD Sharing: NO. Countries: 3. Publications: 0.
The Effect of a Plant Sterols-Enriched Low-Fat Milk on Lipid Profile in Healthy Southern Chinese (COME-PASS)
ClinicalTrials.gov study NCT02541201. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Data from: Why are tropical mountain passes ‘low’ for some species? genetic and stable-isotope tests for differentiation, migration, and expansion in elevational generalist songbirds
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Data from: Simplified models of the symmetric single-pass parallel-plate counterflow heat exchanger: a tutorial
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Data from: Network analyses reveal intra- and interspecific differences in behaviour when passing a complex migration obstacle
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Data from: Accurate geometries for “mountain pass” regions of the Ramachandran plot using quantum chemical calculations
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SWOT Level 2 Lake Single-Pass Vector Data Product, Version D
The SWOT Level 2 Lake Single-Pass Vector Product (SWOT_L2_HR_LakeSP_D) provides geolocated surface water measurements for lakes and unclassified water bodies, derived from high-resolution radar observations collected by the Ka-band Radar Interferometer (KaRIn) on the SWOT satellite. This product reports water surface elevation, surface area, and quality indicators for each observed lake or feature, with polygons generated from the PIXCVec pixel cloud. It includes features linked to the Prior Lake Database (PLD), as well as unassigned features not recognized as lakes or rivers in the PLD or PRD.<br><br>Each granule covers a full KaRIn swath for a single continental pass and contains three ESRI shapefiles: one observation-oriented file of observed PLD lakes, one PLD-oriented file with prior lake features (including unobserved ones), and one file for unassigned features. Observed quantities are referenced to the WGS84 ellipsoid and corrected for geoid height, solid Earth, load, and pole tides, as well as tropospheric and ionospheric path delays. Attributes also include uncertainties, flags for partial or dark water coverage, ice cover, and crossover calibration. Storage change estimates are reported in the PLD-oriented shapefile using both direct and incremental methods under different bathymetric assumptions.<br><br>The LakeSP product provides consistent reach-scale and basin-level lake measurements suitable for time series analyses, hydrologic modeling, and integration with prior lake databases. It enables monitoring of water level changes and surface area dynamics across a wide range of inland water bodies and supports hydrological applications in water balance studies, reservoir management, and climate impact assessment.<br>This dataset is the parent collection to the following sub-collections: <br>https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_obs_D <br>https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_prior_D <br>https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_unassigned_D <br>
SWOT Level 2 River Single-Pass Vector Data Product, Version C
The SWOT Level 2 River Single-Pass Vector Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, slope, width, and discharge derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the "calibration" or "fast-sampling" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the "science" phase of the mission, which is expected to continue through 2025. <br>Water surface elevation, slope, width, and discharge are provided for river reaches (approximately 10 km long) and nodes (approximately 200 m spacing) identified in the prior river database, and distributed as feature datasets covering the full swath for each continent-pass. These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format.<br>Please note that this collection contains SWOT Version C science data products. <br>This dataset is the parent collection to the following sub-collections: <br>https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_node_2.0 <br>https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_reach_2.0 <br>
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.