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13,117 results for “Maps”
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Theoretical concept map</p>
Supplementary data Quantitative elemental mapping of chondritic meteorites using laser ablation-inductively coupled plasma-time of flight-mass spectrometry (LA-ICP-TOF-MS)
<p>Supplementary data Quantitative elemental mapping of chondritic meteorites using laser ablation-inductively coupled plasma-time of flight-mass spectrometry (LA-ICP-TOF-MS)English</p>
Google Map JSON data file
<p>A data file used by several JSON engine.</p>
Pre-built Symphonypy reference objects that can be downloaded and used to map new query datasets
<p>Pre-built Symphonypy reference objects that can be downloaded and used to map new query datasets. The same data as in <a href="https://zenodo.org/record/5090425#.Y9EmLC8w3tg">https://zenodo.org/record/5090425</a>, but for Python port for Symphony.</p> <p>The Symphony algorithm is used to perform reference mapping to these atlases. </p> <ul> <li>Paper: <a href="https://www.nature.com/articles/s41467-021-25957-x">https://www.nature.com/articles/s41467-021-25957-x</a></li> <li>Usage: <a href="https://github.com/potulabe/symphonypy">https://github.com/potulabe/symphonypy</a></li> </ul> <p><strong>References available for download:</strong></p> <ol> <li>10x PBMCs Atlas (pbmcs_10x_reference.h5ad)</li> <li>Pancreatic Islet Cells Atlas (pancreas_plate-based_reference.h5ad)</li> <li>Fetal Liver Hematopoiesis Atlas (fetal_liver_reference_3p.h5ad)</li> <li>Healthy Fetal Kidney Atlas (kidney_healthy_fetal_reference.h5ad)</li> <li>T cell CITE-seq atlas (tbru_ref.h5ad)</li> <li>Cross-tissue Inflammatory Immune Atlas (zhang_reference.h5ad)</li> <li>Tabula Muris Senis (FACS) Atlas (TMS_facs_reference.h5ad)</li> </ol>
mapping
<p>dasfa ea gaegae gaegaeg</p>
eye-tracking data from a survey on zooming in a pan-scalar map
<p><strong>Recording and processing a survey using an eye tracker </strong></p> <p>The eye-tracker used is a Pupil Core from Pupil Labs. The basic eye tracker configuration, i.e. a fixation time of 80 ms to 200 ms, is kept for this experiment.</p> <p> </p> <p>The aim of the experiment is to understand what a person looks at to find their way around a multi-scale map and to understand the different strategies used. To do this, the user will be free to use the map as he wishes, i.e. he can use pan and zoom at will. Four types of tasks will be asked in order to have a maximum of types of use of multi-scale map.The first task is to simulate that a user is using an application like map or Google map and is looking for a specific address. The map application will then zoom in very strongly on the address. The user has little spatial context and it often takes some time to find his way around. To simulate the application, a point is placed on Paris or its surroundings and the display is very zoomed (Paris was chosen because most people have a more or less detailed mental map of Paris). The user is then asked to interact with the map (zooming and panning) until he feels he is sufficiently located, as he would if he had to search for a place on his mobile phone. When he is located, he just needs to move on to the next stage without asking for validation. This stage is carried out in four locations. The four points are located near Montmartre, at the entrance to the catacombs of Paris, in Vincennes and finally at Porte d'Asnières</p> <p><br> The second task is to find a place from an aerial image. The aerial image of a specific area is displayed and the map is zoomed out to the city where the location is located. The user must then try to find the location in the image. Unlike the first task, the user must request validation before proceeding to the next stage.<br> This task is repeated in two different cities. The two images are the tête d'or park in Lyon and a building block next to a railway in Dijon.</p> <p><br> The third task also consists of finding a precise location using textual indications. The user still has to ask for validation to go to the next stage .</p> <p>This task is repeated in two different cities.The first was "to find the town hall which is just south of the town centre and next to the library" and the second was "to find the stadium east of the town centre and north of the river Vilaine with a north/south orientation.</p> <p><br> The last task builds on tasks 2 and 3. The map is again zoomed out, an aerial image appears and textual indications are given. This task is repeated on two different cities.</p> <p>The first image is of a building in beauvais with the indication: "the building is in the north west of sqare next to the SNCF station". The second one is a picture of a stadium in lyon with the indication: "the stadium is west of the confluence of lyon".</p> <p><strong>data format :</strong><br> <strong>Coord_fixation_on_map_x_y</strong>: geolocated fixation point with x the survey type 1 or 2 and y the candidate number (id_fixation,x,y,zoom,etape)</p> <p><strong>Pan</strong>: pan on the map during the survey</p> <p><strong>Pan_fixation_on_map</strong> : fixation during a pan</p> <p><strong>zoom</strong>: zoom on the map during the survey</p> <p><strong>zoom_fixation_on_map</strong>: fixation during a zoom</p> <p><strong>stat</strong>: number of zoom, pan and fixation per step</p> <p><strong>result_map_x </strong>= map status every 100 ms during the survey x</p> <p><strong>00x </strong>: export file of the eye-tracker pupil Lab</p> <p> </p>
V6-V10 - Raw Data - Motion-resolved fat fraction mapping with whole-heart free-running multiecho GRE
<p>(PART 2) whole-heart cardiac MRI datasets from healthy volunteers (1.5T)</p> <p>Each folder labelled V6-V10 contains:</p> <p>1) Free-running multiecho GRE (N<sub>TE</sub>=8) raw data file</p> <p>2) ECG-triggered multiecho GRE (N<sub>TE</sub>=5) raw data file</p> <p>3) Matlab file "PhysioInfo.mat" containing the physiological signal information (Pilot Tone raw data, extracted respiratory signal, extracted cardiac signal, extracted cardiac trigger points, and corresponding time stamps).</p> <p>************************************************************************************************************************************************************************************************************************************************************</p> <p>Matlab code to load the raw data, compute the 3D trajectory and read the raw and extracted Pilot Tone signals is available at:</p> <p><a href="https://github.com/QIS-MRI/ReadDataAndTrajectory_FreeRunningFatFractionHeart">https://github.com/QIS-MRI/ReadDataAndTrajectory_FreeRunningFatFractionHeart</a></p> <p>************************************************************************************************************************************************************************************************************************************************************</p> <p>Please cite the following work when using this dataset:</p> <p>Link to publication: <a href="https://onlinelibrary.wiley.com/doi/10.1002/mrm.29680">https://onlinelibrary.wiley.com/doi/10.1002/mrm.29680</a></p> <p>************************************************************************************************************************************************************************************************************************************************************</p> <p>Data was collected and approved for sharing according to the terms of the Ethics authorization CER-VD 2021-00708 (Lausanne, Switzerland).</p>
BridgeDb Complex Identifier Mapping Database
<p>BridgeDb identifier mapping databases for complexes extracted from Wikidata. Code to generate the database (https://github.com/bridgedb/Wikidata2Bridgedb - ComplexIdentifiers.java). </p>
FIGURE 8. Dudanus spp. distribution maps. A in Study on the genus Dudanus (Auchenorrhyncha, Cicadellidae, Deltocephalinae) in Western and Central Europe with description of a new species
FIGURE 8. Dudanus spp. distribution maps. A: Dudanus javieri Remane & Della Giustina (♦), D. pallidus Dlabola (●), D. karra n. sp. (■); B: Localities of D. karra n. sp. in north-eastern Italy and in Slovenia.
7.8 Earthquake Damage Mapping in Turkey and Syria
<p>In the early hours of February 6th, 2023, a devastating earthquake of magnitude 7.8 in Gaziantep and magnitude 7.5 in Ekinözü struck Turkey and Syria, causing widespread loss of life and property. In the aftermath of the quake, more than 36,000 fatalities and numerous injuries were reported in one week. The unprecedented magnitude of the earthquake highlights the delicate nature of human life, yet it also underscores the resilience of the human spirit.</p> <p>To assist in the aftermath of this disaster, the present study aims to assess the damage caused by the earthquake through the analysis of Sentinel-1 satellite data. We adopt a combination of coherence and displacement parameters to create a damage map of the affected areas. First, we calculate the difference in coherence between pre- and post-earthquake images and retain only pixels with a coherence value higher than 0.25 and a negative change in coherence. The resulting change in coherence is expressed as a percentage (diff_coherence_percent) after converting it to a percentage by multiplying it by 100.</p> <p>Next, we enhance positive displacement values and take the absolute value of all displacement values, expressing the magnitude of displacement as a percentage (disp_percent) by dividing it by a threshold of 0.9 m and multiplying it by 100. Finally, we compute the damage percentage as the weighted average of both “diff_coherence_percent” and “disp_percent”, with equal weighting.</p> <p>The results of our study can provide valuable insights into the impact of the earthquake and help guide recovery efforts by providing a map of the damage caused by the quake. Advanced radar technology and satellite imagery highlight the importance of utilizing cutting-edge tools in studying natural disasters and their impacts on communities.</p>
MAP 1 in Description of a new species of Uroballus Simon, 1902 (Araneae: Salticidae) from Malaysia, with the longest spinnerets of any known jumping spider
MAP 1. Collecting localities of the described species of Uroballus: U. henicurus Simon, 1902 (diamond), U. koponeni sp. n. (circle), U. octovittatus Simon, 1902 (square), and U. peckhami Żabka, 1985 (asterisk).
S5: Probing the Milky Way and Magellanic Clouds potentials with the 6-D map of the Orphan-Chenab stream
<p>The data associated with the "S5: Probing the Milky Way and Magellanic Clouds potentials with the 6-D map of the Orphan-Chenab stream" paper. </p> <p> </p> <p>We provide</p> <ul> <li>* the Stan models used to extract stellar stream properties</li> <li>The measurement of radial velocity, stream track on the the sky, proper motions and stream densities for Orphan-Chenab.</li> <li>The list of candidate OC stream members from S5 and other surveys. </li> <li>The best fit stream N-body model</li> <li>the posterior chains from the stream/potential model.</li> <li>The enclosed mass profiles for LMC and MW (figure 15 and 17 of the paper)</li> </ul>
Laminated glass mapping data: Laminated glass recycling
<p>Denuo in representation of FERVER (FERVER, the European Federation of Glass Recyclers) is responsible in the leading of the mapping of PVB. By developping an online survey with specific questions on the collection and proccessing of laminated glass from the construction and/or automotive sector, the aim is to give a clear illustration of the PVB waste from laminated glass all over Europe and its potential for recycling. In this context, this dataset shows information about the recycling of the laminated glass</p>
Laminated glass mapping data: Laminated glass collected and processed
<p>Denuo in representation of FERVER (FERVER, the European Federation of Glass Recyclers) is responsible in the leading of the mapping of PVB. By developping an online survey with specific questions on the collection and proccessing of laminated glass from the construction and/or automotive sector, the aim is to give a clear illustration of the PVB waste from laminated glass all over Europe and its potential for recycling. In this context, this dataset shows the quantity of laminated glass collected and processed by the surveyed companies</p>
Laminated glass mapping data: Final destination of PVB
<p>Denuo in representation of FERVER (FERVER, the European Federation of Glass Recyclers) is responsible in the leading of the mapping of PVB. By developping an online survey with specific questions on the collection and proccessing of laminated glass from the construction and/or automotive sector, the aim is to give a clear illustration of the PVB waste from laminated glass all over Europe and its potential for recycling.</p> <p>In this context, this dataset shows the final destination of the PVB of the surveyed companies</p>
Studies Rejected and Selected on the Systematic Mapping of Open Science Practices in Software Engineering
<p>Files in .xlsx format, describing the rejected and selected studies, recovered in a Systematic Mapping about Open Science Practices in Software Engineering.</p>
Semantic Census X3ML Mappings
<p>Semantic Census X3ML Mappings files</p>
The D5 map of the super D5 emitter in eastern China
<p>The D5 map of the super D5 emitter in eastern China, which can be run and viewed via Google Earth or ArcGIS Earth. </p>
Hyper-Local Air Quality Maps
<p>Dataset contains measurements collected under the HAIR - AssitstIoT Project in the city of Valencia</p>
Figure S1. Fusion map of propensity score matching for high-risk and very high-risk patient.
<p>Figure S1. Fusion map of propensity score matching for high-risk and very high-risk patient.</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.