Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
143
datasets available to search
ShareScore release 0.9.0
Dataset results
143 results for “intersectionality”
Analysis of intelligent vehicle technologies to improve vulnerable road users safety at signalized intersections
Open the record for dataset details and reuse information.
Data and code from: At the intersection of soundscapes and roads: Quantifying anthrophony’s influence on wildlife crossing structure use
Open the record for dataset details and reuse information.
Livorno, Urban driving, Automated vehicle approaching intersection
<p><strong>Scenario description</strong>:</p> <p>Test session for AD vehicle approaching an intersection with jaywalking at traffic light</p> <p><strong>Session description</strong>:</p> <p>A "smart" traffic light with a stereocamera sends SPaT and MAP messages describing the topology, actual status of the traffic light, presence of pedestrian, and jaywalking occurrence to other connected vehicles (via DENM) and to the oneM2M platform on the cloud. An AD vehicle consumes the information and autonomously adapts its speed in order to cross the intersection without violating the traffic light phases, or even stop to avoid collision with pedestrian. The influence of other vehicles moving in front is considered too.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Livorno, Urban driving, autonomous speed adaptation approaching intersection
<p><strong>Scenario description</strong>:</p> <p>Test session for AD+connected car and connected cars approaching an intersection regulated by a "smart" traffic light with a stereocamera able to detect jaywalking.</p> <p><strong>Session description</strong>:</p> <p>A "smart" traffic light (with stereocamera) sends SPaT and MAP messages describing the topology and the actual status of the traffic light. If a jaywalking occurrence is detected (pedestrian crossing with the red light) a DENM message is sent to warn vehicles of the presence of the pedestrian to other connected vehicles. The the hazard warning is also sent to the oneM2M platform on the cloud. An AD vehicle consumes the information and autonomously adapts its speed in order to cross the intersection without violating the traffic light phases, or even stop to avoid collision with pedestrian. The influence of other vehicles moving in front is considered too. Goal is to record data for the technical evaluation.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Intersectional Analysis of Visual Generative AI
<p>This data set contains the set of 180 images we created and analysed towards creating an intersectional STS analysis of Stable Diffusion.</p>
Innovative intersection and interchange designs and their use across the Southeast [Dataset]
<p><span>Innovative intersection designs have become more widely implemented in the United States, due to their well-studied operational and safety benefits while minimizing cost and right-of-way acquisition. Although many states have aggressively pursued implementation of these intersections, there is still a demonstrated need to provide additional insight on the design, operation and safety of these innovative intersections. This project helps address this need by both evaluating use of existing innovative intersections and supporting further implementation of these innovative intersection designs through advancing safety analysis, providing guidance on assessing the comfort and safety of pedestrians and bicyclists at innovative intersections, expanding available performance measures, and improving operational analysis through the application of Naturalistic Driving Study (NDS) data.</span></p> <p><span>The data set consists of the Vissim files used to create the simulation model for the emissions analysis.</span></p>
Brick by Brick Bias: Arab Muslim Experience of Intersectionality in Housing
<p>Experimental data on intersectional discrimination against Arab Muslims in the Swedish rental housing market. Definitions for variables are in the Excel data file. The Stata do-file contains the complete analysis in accordance with the paper. The published paper can be accessed here: <a href="https://doi.org/10.1080/1369183X.2024.2366319">https://doi.org/10.1080/1369183X.2024.2366319</a>. </p>
An approximation of inner boundary conditions for wells intersecting highly conductive structures
<p>Inner boundary conditions describe the interaction of groundwater wells with the surrounding aquifer during pumping and are associated with well-skin damage that limits water production and water derived from wellbore storage. Pumping test evaluations of wells during immediate and early-time flow require assignment of inner boundary conditions. Originally, these concepts were developed for vertical well screens, and later transferred to wellbores intersecting highly conductive structures, such as preferential flow zones in fractured and karstic systems. Conceptual models for pumping test analysis in complex bedrock geology are often simplified. Classic analytical solutions generally lump or ignore conditions that limit or enhance well productivity along the well screen at the onset of pumping.</p> <p>Numerical solutions can represent well drawdowns in complex geological settings, such as karst systems, more precisely than many analytical solutions by accounting for additional physical processes and avoiding assumptions and simplifications. Suitable numerical tools for flow simulations in karst are discrete pipe-continuum models that account for various physical processes such as the transient hydraulics of wellbores intersecting highly conductive structures during pumping.</p> <p>https://doi.org/10.1111/gwat.12947</p>
Thwaites Glacier thins and retreats fastest where ice-shelf channels intersect its grounding zone, dataset+code
<p>Thwaites Glacier thins and retreats fastest where ice-shelf channels intersect its grounding zone, updated dataset+code submitted for publication in The Cryosphere. Dataset includes all data produced in this study, including velocity maps derived from speckle tracking of Sentinel 1 images, maps of rates of ice shelf change and the annual mosaic digital surface models from which they were derived, maps of the basal conditions at Thwaites glacier, shapefiles and masks of the annual hydrostatic boundary (grounding line proxy position), and shapefiles of all hydrostatic boundary features, ice shelf basal channels and surface depressions, intermediate polygons used to filter the features, and digital surface model strips with registration data included as attributes. </p>
FIGURE 4 in Is Rosa × archipelagica (Rosaceae, Rosoideae) really a spontaneous intersectional hybrid between R. rugosa and R. maximowicziana? Molecular data confirmation and evidence of paternal leakage
FIGURE 4. Fragments of electropherograms of four sequences (Rosa maximowicziana max7, R. × archipelagica arc4, R. × archipelagica arc3, and R. rugosa rug2) of ndhC–trnV IGS. Blue rectangles indicate substitutions in 117, 173, and 185 positions of the alignment, and an indel T/- in the 228th position of the alignment, differing Rosa maximowicziana and R. rugosa. The sequence arc4 possesses double peaks in corresponding positions, the sequence arc3 is identical to that of max7.
FIGURE 2. Rosa maximowicziana. A. Flowers. B in Is Rosa × archipelagica (Rosaceae, Rosoideae) really a spontaneous intersectional hybrid between R. rugosa and R. maximowicziana? Molecular data confirmation and evidence of paternal leakage
FIGURE 2. Rosa maximowicziana. A. Flowers. B. Fruits. Rosa × archipelagica. C. Flowering plants. Rosa rugosa. D. Flowers. E. Fruits. Scale bar: A–B, D–E = 5 cm; C = 10 cm. A, B, E: photo by Ivan Schanzer; C, D: photo by Elena Chubar.
FIGURE 1 in Is Rosa × archipelagica (Rosaceae, Rosoideae) really a spontaneous intersectional hybrid between R. rugosa and R. maximowicziana? Molecular data confirmation and evidence of paternal leakage
FIGURE 1. Sample locations: 1–Russkiy Island (max1, max2); 2–Popova Island (max5, rug3); 3–Poima River (max13); 4–Stenina Island (arc1, arc2, arc3, arc4, max10, max11, rug9, rug10); 5–Bolshoy Pelis Island (max8, max9, rug6, rug7, rug8); 6–Cape Astafyeva (rug11, rug12); 7–Posyet (max3, max4, rug2); 8–Krabbe Peninsula (max12); 9–Very Island (max7, rug5); 10–Kievka village, sea shore (rug4); 11–Kievka village, meadow (max6).
Images and lane topology annotations of signalised intersections in Sydney
<p>This repository conatins imagery data for signalised intersections in Sydney, labels of their lane topologies and code for training neural networks to replicate the lane topologies.</p>
Data for "Computational Desire Line Analysis of Cyclists on the Dybbølsbro Intersection in Copenhagen"
<p>Data for the paper "Computational Desire Line Analysis of Cyclists on the Dybbølsbro Intersection in Copenhagen"</p> <p>**Paper (preprint)**: [https://arxiv.org/abs/2211.01301](https://arxiv.org/abs/2211.01301) </p>
The Intersectional Viborg Screening Program: Cost-(Effectiveness) of Screening for Diabetes and Cardiovascular Diseases
ClinicalTrials.gov study NCT03395509. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Promoting Reductions in Intersectional StigMa (PRISM) - GHANA
ClinicalTrials.gov study NCT04108078. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Intersectionality of Class Discourse - Supporting Materials
<p>Supporting materials for the article "The Intersectionality of Class Discourse: How Boundary Work on Reddit Deepens Narratives of Populism and the White Working Class"</p>
Instances for the Intersection Inspection Rural Postman Problem
<p>These are the instances used in the article titled 'Modeling and Solving the Intersection inspection Rural postman Problem' to be published in the <em>INFORMS Journal on Computing</em>. </p>
Data from: A Flippase-mediated GAL80/GAL4 intersectional resource for dissecting appendage development in Drosophila
Drosophila imaginal discs provide an ideal model to study processes important for cell signaling and cell specification, tissue differentiation, and cell competition during development. One challenge to understanding genetic control of cellular processes and cell interactions is the difficulty in effectively targeting a defined subset of cells in developing tissues in gene manipulation experiments. A recently developed Flippase-induced intersectional GAL80/GAL4 repression method incorporates several gene manipulation technologies in Drosophila to enable such fine-scale dissection in neural tissues. In particular, this approach brings together existing GAL4 transgenes, newly developed enhancer-trap flippase transgenes, and GAL80 transgenes flanked by Flippase recognition target sites. The combination of these tools enables gene activation/repression in particular subsets of cells within a GAL4 expression pattern. Here, we expand the utility of a large collection of these enhancer-trap flippase transgenic insertion lines by characterizing their expression patterns in third larval instar imaginal discs. We screened 521 different enhancer-trap flippase lines and identified 28 that are expressed in imaginal tissues, including two transgenes that show sex-specific expression patterns. Using a line that expresses Flippase in the wing imaginal disc, we demonstrate the utility of this intersectional approach for studying development by knocking down gene expression of a key member of the planar cell polarity pathway. The results of our experiments show that these enhancer-trap flippase lines enable fine-scale manipulation in imaginal discs.
EXPERIMENTAL STUDY OF THE INTENSITY AND COMPOSITION OF TRAFFIC FLOW AT INTERSECTIONS IN THE TERRITORY OF THE CITY OF NUKUS
Open the record for dataset details and reuse information.
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.