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220 results for “landmarks”
Five spatial landmark datasets
<p>This release contains different open data, multi-source and heterogeneous landmark datasets. They were used to study their heterogeneity, complementarity, and quality on the one hand, and to define a data warehouse of landmarks for mountain rescue (See related <a href="https://dx.doi.org/10.1080/23729333.2019.1615730">article</a> for further details), on the other hand. This archive is released for transparency and reproducibility purposes.</p> <p>The landmark datasets, except BDTOPO dataset that are coming from a shapefile, are collected from APIs in a JSON format. Then, the datasets are transformed into a tabular format to store the location of a landmark (point geometry), the type related to the classification of landmark in sources, and a name if the landmark has one. The extraction was build at the end of the year 2021.</p> <p>Sources of dataset offer a provenance diversity: authoritative or crowdsourced sources, specialized on a thematic domain or general-interest usage.</p> <p>Here after, a short description of landmark datasets:</p> <ul> <li>« Dataset_POI_BDTopo.csv »: points of interest or activity from the French National Mapping Agency. Extracted from the national topographic data (BDTOPO) that is opendata since January 2021. The licence of this dataset is Etalab 2.0.</li> <li> <p>« Dataset_RandoEcrinsParcnational_RandoParcDuVercors »: protected area led by French public institutions publish touristic data dedicated to a recreational use. The licence of this dataset is also Etalab 2.0.</p> </li> <li> <p>« Dataset_Camptocamp_org.csv »: Camptocamp (C2C) is a website dedicated to more or less experienced mountaineers. Landmark concern topographical guidelines for leisure activities (running, cycling, climbing, etc.). The licence is CC-by-nc-nd.</p> </li> <li> <p>« Dataset_RefugesInfo.csv »: Refuges.info, as the name suggests, provides detailed information concerning shelters and others landmarks such as water points, summits, etc. The licence is CC-By-Sa 2.0.</p> </li> <li> <p>« Dataset_Openstreetmap_org.csv »: OpenStreetMap (OSM) is the very well known collaborative project proposing many types of topographic and thematic spatial data. All data have been downloaded from an API endpoint. The centroid calculation was applied on the polygonal and linear geometries in order to build the point geometries. The licence is Open Data Commons Open Database License (ODbL).</p> </li> </ul>
Matching results between landmark in different sources and landmark in a referenced dataset (BDTOPO)
<p>The four datasets represent the results of a two sequentials processus. The first processus consists on a automatic matching between landmark in different sources and landmark in a referenced dataset (french national topographic data: BDTOPO). Then the links 1:1 are manually validated by experts in the second processus.</p> <p>The four different datasets and the BDTOPO dataset are archived <a href="https://doi.org/10.5281/zenodo.6480986">here</a>.</p> <p>The data matching algorithm is described in this <a href="http://dx.doi.org/10.5311/JOSIS.2015.10.194">paper</a>.</p> <p>Each file represents the result matching for features belonging to a data source with:</p> <p>- the name of file depending on the data source</p> <p>- the column "id_source" corresponds to the identifier of the landmark in data source</p> <p>- the column "types_of_matching_results" describes the type of matching result:</p> <ul> <li>« 1:0 »: means that a landmark from a data source (e.g. Camptocamp) has no homologue landmark in BDTOPO</li> <li>« 1:1 validated »: means that a homologous feature exist in BDTOPO and the link was validated</li> <li>« 1:1 non validated »: means that the matching link was not validated</li> <li>« without candidates »: represents the non-matched landmarks because there are no candidates in BDTOPO or because the landmark in data source is far away from its homologous in BDTOPO</li> <li>« uncertain »: uncertainty cases are complex cases where any decision is taken by the data matching algorithm</li> </ul> <p>- the column "id_candidat" corresponds to the identifier of the landmark in BDTOPO if and only if there is a validated matching link</p> <p>- the column "samal" corresponds to the <a href="https://doi.org/10.1080/13658810410001658076">Samal distance</a></p> <p>The matching results are obtained using an ontology application named <a href="http://choucas.ign.fr/doc/ontologies/index-fr.html">OOR</a>. These specific results are obtained using the version of OOR V1.0.1 which is an improved version and contains new concepts compared to the first release 1.0.0. The new version of OOR (i.e. 1.0.1) will be released by the end of May 31 2022. The new link will be added here.</p> <p>This archive is released for transparency and reproducibility purposes.</p>
Alignment between type of landmark in different sources and the concept in the spatial reference objects ontology
<p>The five datasets represent a manually alignment between the landmark type of five different datasets archived <a href="https://doi.org/10.5281/zenodo.6480986">here</a> and a common vocabulary extracted from an application ontology defined for mountain rescue purposes, named <a href="https://hamac.ign.fr/owa/redir.aspx?C=cjlWje9SCaYsVOTLbxbOoIBLZUCS56nVb248cRSMTEDSENDFzybaCA..&URL=http%3a%2f%2fchoucas.ign.fr%2fdoc%2fontologies%2foor.owl%2f">Ontology of landmarks</a> (OOR).</p> <p>Each file represents the alignment for features belonging to a data source with the same OOR ontology.</p> <p>For example, the type «bivouac» from camptocamp.org source is aligned with the uri <a href="http://purl.org/choucas.ign.fr/oor#abri">http://purl.org/choucas.ign.fr/oor#abri</a> of the corresponding class «Shelter » in the ontology of landmark. The alignments models can be considered as a ground truth data.</p> <p>The alignments results are obtained using an ontology application named <a href="http://choucas.ign.fr/doc/ontologies/index-fr.html">OOR</a>. These specific results are obtained using the version of OOR V1.0.1 which is an improved version and contains new concepts compared to the first release 1.0.0. The new version of OOR (i.e. 1.0.1) will be released by the end of May 31 2022. The new link will be added here.</p> <p>This archive is released for transparency and reproducibility purposes.</p>
Human Bony Labyrinth: Co-Registered CT and micro-CT Images, Surface Models and Anatomical Landmarks
<p>This data set consists of 23 specimens of the human bony labyrinth. For each specimen clinical CT (0.15×0.15×0.2 mm3, voxel size) and co-registered microCT (0.06 mm isotropic voxel size) images are available. Image labels for the bony labyrinth are provided for the same image coordinates. From the image labels, 3D surface models were generated. In addition, each specimen has a descriptor file containing the coordinates of anatomical landmarks as well as a cochlear coordinate system. The data set can be used to study the morphology of the inner ear or to evaluate (semi-)automated segmentation algorithms (e.g., for the preoperative planning of surgical procedures such as cochlear implantation).</p>
Beak Shape in Birds and Squid: Principal Components Analysis of 2D Landmarks
<p>R code to analyze observations of beak traces from specimens of birds and squid.</p> <p>Notes are in the code. Watch for updates.</p> <p>Where the csv files include data published by different authors, the doi references to the original publications are included in the R code. I took care to correctly download/process/transcribe where applicable, but please do notify me if there are errors.</p>
English Vernaculars for Landmark Taxa
<p>English common names for EOL landmark taxa and families. Updated for dynamic hierarchy version 2.5. </p>
Description and quality metadata files for four spatial landmark datasets
<p>The files define the description and quality metadata of the spatial landmark datasets from camptocamp.org defined <a href="https://doi.org/10.5281/zenodo.6480985">here</a>.</p> <p>Two ISO standards ISO 19115-1:2014 and ISO 19157:2013, recommanded by the INSPIRE Directive for the dissemination of spatial data and the reporting of data quality are used.</p>
Identification of Thalweg and Ridge Networks as Landmarks for Terrain Partitioning
<p>Grid digital elevation models having resolution of 1 m or less are increasingly available to scientists and engineers interested in describing current state and evolution of Earth and space topography. Significant information loss is, however, clearly observed when existing terrain analysis methods are used in geophysical modeling, especially when coarse meshes are needed for computational efficiency. The present study shows how thalweg and ridge networks can be extracted automatically from any high-resolution grid digital elevation model without the need to alter the observed topographic data, and how these networks can be used as landmarks for terrain partitioning. The slopeline network extracted in grid digital elevation models is used to determine ridge points, related average rejunction lengths of slopelines extending from ridge points on opposite slopes, exorheic and endorheic basins. Exorheic and endorheic basins are connected through the spilling saddles from endorheic basins to form the thalweg network, and the related ridge network is identified. The obtained thalweg and ridge networks are characterized by using the known concept of drainage area and the new concept of spread area to provide physically meaningful landmarks for terrain partitioning at the desired level of detail. Although the developed methods are inspired by the observation of gravity-driven processes, they support any investigation in Earth and space science where thalweg and ridge networks are relevant topographic features. Potential impacts are exemplified by quantifications of preserved depressions over a mountain area and benefits from physically meaningful unstructured terrain partitioning in surface flow propagation over a complex floodplain.</p>
Human Inner Ear Anatomy: Labeled Volume CT Data of Inner Ear Fluid Space and Anatomical Landmarks
<p>The provided dataset comprises 43 instances of temporal bone volume CT scans. The scans were performed on human cadaveric specimen with a resulting isotropic voxel size of <span class="math-tex">\(99 \times 99 \times 99 \, \, \mathrm{\mu m}^3\)</span>. Voxel-wise image labels of the fluid space of the bony labyrinth, subdivided in the three semantic classes cochlear volume, vestibular volume and semicircular canal volume are provided. In addition, each dataset contains JSON-like descriptor data defining the voxel coordinates of the anatomical landmarks: (1) apex of the cochlea, (2) oval window and (3) round window. The dataset can be used to train and evaluate algorithmic machine learning models for automated innear ear analysis in the context of the supervised learning paradigm.</p> <p> </p> <p><strong>Usage Notes</strong></p> <p>The datasets are formatted in the HDF5 format developed by the <a href="https://www.hdfgroup.org/solutions/hdf5/">HDF5 Group</a>. We utilized and thus recommend the usage of Python bindings <a href="https://www.h5py.org/">pyHDF</a> to handle the datasets.</p> <p>The flat-panel volume CT raw data, labels and landmarks are saved in the HDF5-internal file structure using the respective group and datasets:</p> <pre><code>raw/raw-0 label/label-0 landmark/landmark-0 landmark/landmark-1 landmark/landmark-2</code></pre> <p>Array raw and label data can be read from the file by indexing into an opened h5py file handle, for example as numpy.ndarray. Further metadata is contained in the attribute dictionaries of the raw and label datasets.</p> <p>Landmark coordinate data is available as an attribute dict and contains the coordinate system (LPS or RAS), IJK voxel coordinates and label information. The helicotrema or cochlea top is globally saved in landmark 0, the oval window in landmark 1 and the round window in landmark 2. Read as a Python dictionary, exemplary landmark information for a dataset may reads as follows:</p> <pre><code class="language-python">{'coordsys': 'LPS', 'id': 1, 'ijk_position': array([181, 188, 100]), 'label': 'CochleaTop', 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]), 'xyz_position': array([ 44.21109689, -139.38058589, -183.48249736])}</code></pre> <p> </p> <pre><code class="language-python">{'coordsys': 'LPS', 'id': 2, 'ijk_position': array([222, 182, 145]), 'label': 'OvalWindow', 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]), 'xyz_position': array([ 48.27890112, -139.95991131, -179.04103763])}</code></pre> <p> </p> <pre><code class="language-python">{'coordsys': 'LPS', 'id': 3, 'ijk_position': array([223, 209, 147]), 'label': 'RoundWindow', 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]), 'xyz_position': array([ 48.33120126, -137.27135678, -178.8665465 ])}</code></pre> <p> </p>
Landmarks, Niwot Ridge LTER Project Area, Colorado
Point shapefile of commonly referenced locations at Niwot Ridge, Green Lakes Valley, and surrounding area.
FIGURE 2. Landmarks used for obtain the linear measurements, 1 in Two new species of the genus Xenotoca Hubbs and Turner, 1939 (Teleostei, Goodeidae) from central-western Mexico
FIGURE 2. Landmarks used for obtain the linear measurements, 1 to 9 standard length (SL); 1 to 5 head length (HL); 4 to 17 head high (HH); 1 to 2 preorbital length (PrOL); 3 to 5 postorbital length (POL); 2 to 3 eye diameter (ED); 8 to 11 body least depth (BLD); 13 to 14 pelvic-anal fin distance (PAD); 14 to 6 pelvic-dorsal fin distance (PDD); 14 to 15 pelvic-pectoral fin distance (PPD); 6 to 13 dorsal-anal fin distance (DAD); 6 to 12 dorsal fin origin to anal fin posterior extent distance (DOAE); 7 to 13 dorsal fin posterior extent to anal fin origin distance (DEAO); 7 to 9 end of dorsal fin-hypural plate distance (EDHP); 9 to 12 end of the anal fin-hypural plate distance (EAHP); 6 to 7 dorsal fin base length (DFL); 12 to 13 anal fin base length (AFL); 15 to 16 pectoral fin base length (PFL); 10 to 12 caudal peduncle length (CPL).
Raw landmarks related to the paper, "Evolution under intensive industrial breeding: skull size and shape comparison between historic and modern pig lineages "
<p>PLEASE NOTE: This dataset has been superseeded by an updated version which has the correct number of specimens as referred to in the below article. It can be accesssed at: https://doi.org/10.5281/zenodo.14262754</p> <p> </p> <p> </p> <p>Raw coordinates (p x k = 82 x 3) of domestic and wild pig skulls that form the dataset for the paper, "­Evolution under intensive industrial breeding: skull size and shape comparison between historic and modern pig lineages "</p>
Data Matrix Landmarks in Cluttered Indoor Environments
<p>We used the <a href="https://labelbox.com/">LabelBox</a> online toolbox to create this data set.</p> <p>It consists of 6 different cluttered environments:</p> <ol> <li>a laboratory </li> <li>3 different industrial-like environments </li> <li>a corridor</li> <li>hall</li> </ol> <p>We proposed to split the data set into three sets - training, validation, and test sets - as follows: (1) the training set has 156 frames equally distributed by the laboratory and 1 workshop; (2) the validation set is also divided into two environments - the corridor (158 frames) and a different workshop (66 frames); (3) the test set consists of 145 frames collected on a neat hall with overshadowed and over-lightened landmarks in different planes; a classroom laboratory with various electronic equipment arranged in an orderly manner; and a very challenging scenario with multiple pieces of machinery spread out all over the place.</p> <p>One should filter out images with no markers.</p>
ARIA - Accessible Rich Internet Applications Landmarks classified dataset
<p>ARIA Landmarks classified dataset. ARIA Landmarks are regions of web applications that can be accessed by specific keyboard shortcuts and are specially useful for blind users. They comprise the banner, complementary, contentinfo, form, main, navigation, region and search classes.</p> <p>In the dataset, samples represent DOM elements of web applications with different attributes extracted when these elements are rendered in the browser.</p>
Landmark set for zygomatic bones
<p>Landmark configuration for left zygomatic bones (humans). The file includes 7 fixed landmarks and 23 semilandmarks. The data has been subject to Procrustes superimposition.</p>
Prosthetic Hand Landmark dataset
<p>This is a dataset of 7164 prosthetic hand images with corresponding landmark labels. The dataset is partially made of synthetic images made using unity. The goal is to address the lack of representation of prosthetic devices in modern machine learning models. Particularly in the field of segmentation and landmark identification. </p>
Figure 1. Landmark definition. A. Dorsoglandularia 1–4. B in Integrative approach of morphology and geometric morphometrics to species delimiation in Torrenticolidae (Acari: Hydrachnidiae)
Figure 1. Landmark definition. A. Dorsoglandularia 1–4. B. Infracapitulum (Torrenticola). C. Infracapitulum (Monatractides).
Total Capture's MediaPipe Human Pose Landmarks
<p>The dataset contains the human pose (world) landmarks that have been extracted with the MediaPipe Pose Estimator from the Total Capture dataset videos. The dataset only contains the landmarks of the first camera (camera 1) videos. MediaPipe’s “Heavy” version (model complexity of ‘two’) has been used during the process. </p> <p>The dataset was funded by the Horizon Europe Programme under Grant agreement N. 101092612 (Social and hUman ceNtered XR - SUN project).</p>
Fig. 4 in Determination Of Sexual Dimorphism And Morphological Variation Of Pool Barb, Puntius Sophore (Cypriniformes, Cyprinidae), Using Landmark Based Geometric Morphometric Analysis
Fig. 4. Change of body shape along principal component axis (PC 1 = 43.827 %, and PC 2 = 20.578 %). Left side is the lollipop plots. Right side is the transformation grids of shape change.
Fig. 3, a in Determination Of Sexual Dimorphism And Morphological Variation Of Pool Barb, Puntius Sophore (Cypriniformes, Cyprinidae), Using Landmark Based Geometric Morphometric Analysis
Fig. 3, a — eigenvalues plot of the proportion of variance described by each PC, b — scatter plot showing scores on the first two PCs for the sample of non-breeding season and breeding season fish population (female in red, male in blue and non-breeding season population in green).
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.