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.
6
datasets available to search
ShareScore release 0.9.0
Dataset results
6 results for “Human Landmarks”
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>
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>
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>
17 Landmarks from modern human skulls
<p>Coordinates of 17 landmarks of modern human skulls. They have not been subjected to Procrustes superimposition. See paper from Bucchi and Fonseca for details: Shape variation among skull regions using geometric morphometrics.</p> <p>Open in R: readRDS(file = "data.rds")</p> <p>The skulls are held at the Subactual Collection of Santiago (Facultad de Ciencias Sociales, Universidad de Chile)</p> <p>Landmark configuration:</p> <table align="left"> <tbody> <tr> <td> <p>Number</p> </td> <td> <p>Landmark</p> </td> <td> <p>Location</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>Nasion</p> </td> <td> <p>The point of the intersection between the frontonasal suture and the midsagittal plane</p> </td> </tr> <tr> <td> <p>2, 8</p> </td> <td> <p>Fontomalar (paired: right and left)</p> </td> <td> <p>Most anterior point of the zygomaticofrontal suture</p> </td> </tr> <tr> <td> <p>3, 9</p> </td> <td> <p>Superior zigotemporale (paired)</p> </td> <td> <p>Most superior point in the temporozygomatic suture</p> </td> </tr> <tr> <td> <p>4, 10</p> </td> <td> <p>Porion (paired)</p> </td> <td> <p>Lateral most part of the superior margin of the external auditory meatus</p> </td> </tr> <tr> <td> <p>5, 11</p> </td> <td> <p>Mastoidale (paired)</p> </td> <td> <p>point at the lowest point of the mastoid process</p> </td> </tr> <tr> <td> <p>6, 12</p> </td> <td> <p>Zygomaxillare (paired)</p> </td> <td> <p>Most inferior point in the zygomaticomaxillary suture</p> </td> </tr> <tr> <td> <p>7, 13</p> </td> <td> <p>Ectomalare (paired)</p> </td> <td> <p>The most posterior point on the alveolar bone</p> </td> </tr> <tr> <td> <p>14, 16</p> </td> <td> <p>Pterion (paired)</p> </td> <td> <p> Intersection of the frontal, sphenoid, parietal and the squamous part of temporal bone</p> </td> </tr> <tr> <td> <p>15, 17</p> </td> <td> <p>Asterion (paired)</p> </td> <td> <p>Point where the temporal, parietal and occipital bones meet</p> </td> </tr> </tbody> </table> <p> </p>
Modern Human Articular Landmarks
<p></p> <p>Skeletal elements of interest were segmented from CT image stacks using Avizo 2021.1 or an automated custom routine in Matlab 2021b. Landmarks were placed on the resulting surface models of these bones using Stratovan Checkpoint. Landmarks follow published definitions (Arias-Martorell, Tallman, Potau, Bello-Hellegouarch, & Perez-Perez, 2015; San Millán, Kaliontzopoulou, Rissech, & Turbón, 2015; Tallman, 2013, 2016); novel landmarks were defined by the author. This dataset contains the raw landmarks exported into excel spreadsheets; landmark figures and definitions are included in Supplemental Information.</p>
Medial HOXA gene expression is a landmark for the definitive haematopoietic fate and a prerequisite for human HSC function
GEO Series GSE76685. Homo sapiens. 42 samples. Type: Expression profiling by array; Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
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.