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10 results for “semantic mapping”
Supplementary Material for Embodied Emotions in Ancient Neo-Assyrian Texts Revealed by Bodily Mapping of Emotional Semantics
<p>This dataset accompanies the article "Embodied Emotions in Ancient Neo-Assyrian Texts Revealed by Bodily Mapping of Emotional Semantics" (Lahnakoski & Bennett et al., submitted). </p> <p>It includes the Neo-Assyrian text corpus that is the basis for the word embeddings, a list of the Akkadian emotion and body words of interest for this study, and the scripts, toolboxes, and data used to generate the heat maps of the body.</p> <p>There is an additional folder containing the high resolution figures included in the article.</p> <p>A detailed ReadMe (README.txt) provides an overview of the folders.</p>
Data for Paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning"
<p><strong>Example Data for DeepReefMap</strong></p> <p>This dataset contains input videos in MP4 format taken with GoPro Hero 10 Cameras in Reefs in the Red Sea to demonstrate the DeepReefMap tool, which is described in the paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning" by Sauder et al.</p> <p>It contains a directory for model checkpoints for semantic segmentation, and for the 3D SLAM component:</p> <p>```<br>checkpoints/<br> segmentation_net.pth<br> sfm_net.pth<br>```</p> <p>It also contains videos to run the reconstruction with. See the detailed instructions for running reconstructions in https://github.com/josauder/mee-deepreefmap</p> <p>```<br>input_videos/<br> GX_SINGLE_VIDEO.MP4<br> GX_VIDEO_1_OF_2.MP4<br> GX_VIDEO_2_OF_2.MP4<br>```</p>
Historical City Maps Semantic Segmentation Dataset
<p>This dataset includes a total of 635 annotated image patches from historical city maps. It is designed for the semantic segmentation of the maps into 5 semantic classes (building blocks, non-built, water, road network, background frame). 330 patches are taken from maps of the city of Paris, while the 305 others are taken from a balanced corpus of city maps from 90 countries all around the world.</p> <p>Please read the detailed informations about data collection methodology, associated metadata and annotation ontology in README.md hereunder :</p>
SeMRA Raw Semantic Mappings Database
<p>An automatically assembled dataset of raw semantic mappings produced by <code>python -m semra.database</code>. This incorporates mappings from the following places:</p> <ol> <li>Ontologies indexed in the Bioregistry (primary)</li> <li>Databases integrated in PyOBO (primary)</li> <li>Biomappings (secondary)</li> <li>Wikidata (primary/secondary)</li> <li>Custom resources integrated in SeMRA (primary)</li> </ol> <p>This is a database of raw mapping without further processing. For processed mapping datasets, we suggest smaller domain-specific processing rules (see <a href="https://github.com/biopragmatics/semra/tree/main/notebooks/landscape">https://github.com/biopragmatics/semra/tree/main/notebooks/landscape</a> for examples). It can be accessed directly via:</p> <ul> <li><code>mappings.sssom.tsv.gz</code> - loadable through any tools supporting SSSOM</li> <li><code>mappings.jsonl.gz</code> - loadable through SeMRA using <a href="https://semra.readthedocs.io/en/latest/api/semra.io.from_jsonl.html" target="_blank" rel="noopener"><code>semra.from_jsonl</code></a></li> </ul> <h2>How to Run the Web App</h2> <ol> <li>Download all artifacts from this Record</li> <li>Make sure that you have Docker running locally</li> <li>Run <code>sh run_on_docker.sh</code> from the command line</li> <li>Navigate to http://localhost:8773 to see the SeMRA dashboard or to http://localhost:7474 for direct access to the Neo4j graph database</li> </ol> <h2>Licensing</h2> <p>Mappings are licensed according to their primary resources. These are explicitly annotated in the SSSOM file on each row (when available) and on the mapping set level in the Neo4j graph database artifacts.</p>
Semantic Enrichment of the Laboratory Data Dictionary of the Study of Health in Pomerania (SHIP-START-4) with LOINC; Detailed Mapping Results
<p>Unlike West Germany, high morbidity and mortality have been observed in East Germany over the last century. The regional population-based Study of Health in Pomerania (SHIP) therefore investigates the long-term progression of sub-clinical findings, their determinants and prognostic values, to acquire knowledge that facilitates early diagnosis and thus helps prevent the progression of disease. The SHIP covers various areas of patient health. Each SHIP data set is accompanied by a data dictionary (DD) which provides descriptions of variables and definitions.</p> <p>This work shows the detailed mapping results of the semantic enrichment of the SHIP-START-4 medical laboratory data dictionary with LOINC codes. This work also provides detailed descriptions of the concepts applied in the semnatic enrichment. The results of this work serve as a critical step towards improving its interoperability and hence FAIRness for the SHIP laboratory-related measurements. </p>
Figure 1: The ECG model-MAPPING BETWEEN SEMANTIC GRAPHS AND SENTENCES IN GRAMMAR INDUCTION SYSTEM
<p>The following Figure 1 shows a sample semantic graph that describes a<br> simple test world.<br> During the processing of the ECG, the base units of the graph are the ECG<br> atoms. An ECG atom corresponds to a primitive statements related to one<br> predicate. It has a structure of one-level deep tree, where the root of the tree<br> is the predicate and the concepts linked to it are the leaves. The child concept<br> of the root predicate may be not only a single concept but it can be another<br> ECG atom.</p>
Towards Green Cartography & Visualization: An automated, semantically-enriched method of generating energy-aware color schemes for digital maps and visualizations
<p>Towards Green Cartography & Visualization: An automated, semantically-enriched method of generating energy-aware color schemes for digital maps and visualizations</p>
Mapping data files to semantic data models using the CaosDB crawler
<p>Data from data acquisition can lead to a high variety of data files on file systems. The figure illustrates that these files can be mapped to semantic data models in the research data management system CaosDB using a customizable crawler.</p>
Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps - data
<p>Data from the article<strong><em> Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps</em></strong> published in Journal of Vision.</p> <p>code: https://zenodo.org/record/5999215<br> data: https://zenodo.org/record/5999046</p> <p><br> Marek A. Pedziwiatr<br> marek.pedziwi@gmail.com<br> February 2022</p>
Semantic Census X3ML Mappings
<p>Semantic Census X3ML Mappings files</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.