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220 results for “landmarks”

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zenodo36/100

Information Science & Library Science Landmarks

This dataset was used in a study for the Information Science & Library Science Landmarks.

opencc-by-4.0Dec 2021View details →

zenodo36/100

Traits, landmarks and outlines: Three congruent sides of a tale on coral reef fish morphology

Quantifying the morphology of organisms remains fundamental in ecology given the form-function relationship. Morphology is quantifiable in traits, landmarks, and outlines, and the choice of approach may influence ecological conclusions to an unknown extent. Here, we apply these three approaches to 111 individual coral reef fish of 40 species common in Micronesia. We investigate the major dimensions of morphological variability among individuals, families, and predefined feeding functional groups. We find that although the approaches are complementary, they coincide in capturing elongation as the main dimension of variability. Further, the choice of approach led to different interpretations regarding the degree of morphological differentiation among taxonomic and feeding functional groups. We also use each morphology dataset to compute community-scale morphological diversity on Palauan reefs and investigate how the choice of dataset affects the detection of differences among sites and wave exposure levels. The exact ranking of sites from highest to lowest morphological diversity was sensitive to the approach used, but not the broad spatial pattern of morphological diversity. Conclusions regarding the effect of wave exposure on morphological diversity were robust to the approach used. Biodiversity hotspots (e.g. areas of exceptionally high diversity and/or endemism) are considered important conservation targets but their location may depend on the biodiversity metric used. In the same vein, our results caution against labelling particular sites as morphological diversity hotspots when metrics consider only a single aspect of morphology.

opencc-by-4.0Dec 2021View details →

dryad36/100

Specimen list and landmark coordinates for the palate of early salamanders

Ecological preferences and life history strategies have enormous impacts on the evolution and phenotypic diversity of salamanders, but the yet established reliable ecological indicators hinder investigations on the paleobiology of early salamanders. Here we statistically demonstrate using geometric morphometric analysis that both the shape of the palate and many non-shape variables particularly associated with vomerine teeth are ecologically informative in early stem- and basal crown-group salamanders. The morphology of the palate is heavily impacted by convergence constrained by feeding mechanisms and also exhibits clear stepwise evolutionary patterns with alternative phenotypic designs to cope with similar functional demand. Paleoecological disparities in early salamanders had took place before the Middle Jurassic and have achieved all ecological preferences in the Early Cretaceous. Metamorphosis is significant in the expansion of ecomorphospace of the palate in early salamanders. The common ancestor of salamanders is metamorphosed and terrestrial, and share unified lifestyles with other modern amphibians.

opencc-zeroMay 2022View details →

zenodo36/100

Projook Landmark Stone

Landmark in Schaumburg from 1873 This model is created with projook.com https://projook.com/art/60fc34c299526800110f38f7 Here you can see, where the images were made and how many images i used for this model. Source: Objaverse 1.0 / Sketchfab

opencc-by-sa-2.5Jan 2021View details →

zenodo36/100

Raw coordinates of 3D landmarks related to the article 'A new zooarchaeological application for geometric morphometric methods: Distinguishing Ovis aries morphotypes to address connectivity and mobility of prehistoric Central Asian pastoralists' by Haruda et al.

Raw coordinates from 3D landmarks of Ovis aries astragali. These bones originate from Final Bronze Age archaeological contexts from central and southeastern Kazakhstan. These relate to the article 'A new zooarchaeological application for geometric morphometric methods: Distinguishing Ovis aries morphotypes to address connectivity and mobility of prehistoric Central Asian pastoralists' by Haruda et al.

opencc-by-4.0Mar 2018View details →

zenodo36/100

3D cranial landmark coordinates from the Athens Collection. A modern Greek population reference dataset for testing 3D-ID software

The present dataset comprises the landmark coordinates of 158 intact crania (80 males and 78 females) of adult individuals from the Athens Collection. The 3D coordinates of up to 34 landmarks have been extracted from high quality textured 3D models produced with photogrammetry. The dataset aims to evaluate the correct classification performance of 3D-ID software. Hence, the dataset contains the landmark 3D coordinates both in Meshlab's PickedPoints files (.pp) but also in 3D-ID's input text format (.3did). The dataset is accompanied by certain GNU Octave scripts and functions used for data conversion and integrity check. For more details see the Dataset Description pdf.

opencc-by-nc-nd-4.0Jan 2019View details →

zenodo36/100

Distal Femoral Landmarks from Skeletons with Total Knee Arthroplasty

This dataset contains raw 3D landmark coordinates for the articular surface of the distal femur. The sample consists of 43 modern adult human skeletons from the University of Tennessee Donated Skeletal Collection. 12 of the skeletons received unilateral total knee arthroplasty (TKA) during life due to osteoarthritis; landmarks for these individuals were taken on the non-replaced (healthy) antimere. The remaining 31 individuals serve as controls, having received no knee replacement during life and showing no signs of joint degenerative disease.

opencc-by-4.0Aug 2024View details →

zenodo36/100

Toward Visual Pronunciation Learning: A Speech-to-Articulatory Animation Pipeline Leveraging wav2vec 2.0 and rtMRI Landmarks

Each Video Includes 5 sections: <ul> <li>Top left-most: Phoneme Transcription from Original dataset of USC-TIMIT Dataset.</li> <li>Top middle-left: Generated Articulatory Animation from speech input from this paper.</li> <li>Top middle-right: Ground Truth from refined contour dataset from Refined rtMRI Landmark-Based Vocal Tract Contour Labels.</li> <li>Top right-most: rtMRI data from USC-TIMIT Dataset.</li> <li>Below: Sentences and words</li> </ul>

opencc-by-4.0Sep 2024View details →

zenodo36/100

Craniofacial landmark coordinates of DS mouse models

<h2>DATA Description</h2> We make available the data used for craniofacial analysis of nine Down syndrome mouse models. For each DS model, one Zip file is available and contains -      a minimum of four text files with all individual coordinates by genotype (DS model or wt control) for the skull or the mandibule (Mdb): o   the first line of the text file describes the content: Cranium or mandible coordinate + wt control or DS model genotype + Sex (male or female) o   XYZ coordinates o   39L (how many landmarks) 3 (how many coordinates) 10A (how many samples) o   The list of used landmarks (see figure 1 and Tables S1-S2) o   Before the coordinate, the ID (number) of the individual is indicated  -        Tiff files for all individuals used with the model that are in two folders, one for the DS/Dp model and one for the wt controls. All Tiff files are named with the ID number of the individual.  <h2>Down Syndrome mouse models used</h2> We used the Dp(16)1Yey and Tg(Dyrk1a) (official name Dp(16Lipi-Zbtb21)1Yey and Tg(Dyrk1a)189N3Yah) models (Li et al. 2007; Guedj et al. 2012) which were maintained on the C57BL/6J genetic background. We also used the SD: CRL Dp (11Lipi-Zbtb21)1Yah (short name Dp(Rno11)) rat model generated in the lab (Birling et al. 2017) that carries a duplication of the Lipi-Zbtb21, an interval similar to the mouse Dp(16)1Yey, found on rat chromosome 11. New lines were generated via an in vivo chromosomal recombination technique, which combines a transposon system (Ruf et al. 2011) and a meiotic recombination system Cre-loxP (Hérault et al. 1998). The transposon system consists of the transposase enzyme and its substrate, the transposon. The enzyme recognizes specific repeat sequences (ITR) flanked on either side of a given DNA sequence (in this case, a vector containing a specific loxP site) (Ruf et al. 2011). Once two loxP sites bound a region of interest, successive crosses bring a transgene expressing the Cre recombinase into the same individual. In this animal, the Cre enzyme recombines the sequences of the loxP sites to produce a duplication (or partial trisomy) of the region of interest (Hérault et al. 1998; Hérault et al. 2010). The new mouse models have been developed with the following segmental duplications in the Mmu16 (Figure 1). For Dp(16Samsn1-Cldn17)7Yah (Dp(16)7Yah) we duplicated the segment between Samsn1 and Cldn17. Dp(16Tiam1-Clic6))8Yah (Dp(16)8Yah) presents a duplication between Tiam1 and Clic6. Dp(16Cldn17-Brwd1))9Yah (Dp(16)9Yah) displays a duplication in the interval between Cldn17 and Brwd1. Dp(16Tmprss15-Setd4)10Yah (Dp(16)10Yah) has the segment between Tmprss15 and Setd4 duplicated, similar to Dp(16Tmprss15-Grik1)11Yah (Dp(16)11Yah), but this model presents a region duplicated until Grik1. Dp(16Tmprss15-Zbtb21)12Yah (Dp(16)12Yah) has the duplicated region from Tmprss15 to Zfp295, and Dp(16Cldn17-Vps26c(Dyrk1aKO))13Yah (Dp(16)13Yah) from Cldn17 to Vps26c, up to the sequence of Dyrk1a which is inactivated. All lines were maintained on C57BL/6J genetic background. Figure 1 : Relative position of the duplicated interval in the DS mouse models is indicated by a line. <h2>Mouse samples from the DS mouse models</h2> To generate the data, we made cohorts of mice housed under specific pathogen-free (SPF) conditions, treated in compliance with the animal welfare policies of the French Ministry of Agriculture (law 87 848). As a major genotype effect compared to sex was previously described elsewhere independently (Redhead et al. 2023), we decided to use females. For each mouse line, about ten littermates by each genotype, DS, and wild-type (WT) were collected (n = 180).  We tried to have balanced males and females in the cohorts. For example, For the Dp(16)1Yey line, six females plus five males for the dup carrier and six males plus three females for control were used.   Nevertheless, this was not the case in all the other lines, with sometimes more female individuals collected than males, because males were used to breed the lines. <h2>Micro-computed tomography scan of the skull and mandibule of mutant and control mouse lines</h2> Animals were euthanized with the standard procedure at 14 weeks old.  Briefly, the mouse heads were dissected apart from the body. A polystyrene section was interposed between the mandible and maxilla to separate the jaws. After dissection, samples were fixed in a 4% paraformaldehyde solution (PFA), washed with water, and stored in 70% ethanol. The mouse heads were scanned using the Quantum FX micro-computed tomography imaging system (Caliper Life Sciences, Hopkinton, MA, USA) to evaluate the morphology of the skull and mandible. The images obtained were delivered in DICOM format. The scan parameters used to carry out the scanning of the samples correspond to 2 scans of every sample, anterior part, and posterior part using the mode Scan Technique Fine of 2 minutes, with a field of view (FOV) of 40 mm, the voltage 90 kV, CT 160 μA, resolution pixel size 10 µm and the capture size for live mode viewing in small, live current 80kV. Imaging Processing For each sample, two scans were obtained, one from the anterior area of the skull and one from the posterior region. FIJI software was used to unite these two scans and create a single file, performing the plugin “Stitching” and saving one file in TIFF format for each individual per model (WTs and Duplication or transgenic genotype). This format can be opened using different image processors. We make the level of raw data available here in the folder TIFF of each model. Then, Stratovan Checkpoint software (Stratovan Corporation, Sacramento, USA, Version 2018.08.07. Aug 07, 2018.) was used to place the landmarks (Table S1 and S2; Figure 2) and extract the 3D coordinates for all the landmarks in all the samples. So, for each DS model, you will find an additional text file with individuals' landmark coordinates. You will find four text files for each model, 2 for the skull and 2 for the mandible, divided into WT and DS models. Morphometrics is the quantification and statistical analysis of form. Form is the combination of size and shape of a geometric object in an arbitrary orientation and location (shape is what remains of the geometry of such an object once it is standardized for size). Various approaches can be employed when conducting morphometric analysis. The method of interest in this study is the landmark-based method, which is a conventional approach that relies on phenotypic measurements such as linear distances, angles, weights, and areas. In this case, we used 61 landmarks, 39 in the skull and 22 in the mandible (Figure 2), to obtain the 3D coordinates of the structure (Hallgrimsson et al. 2015). Figure 2. Landmark positions for the skull and mandibule analysis. Based on 3D coordinates, Euclidean Distance Matrix Analysis (EDMA) is one of the principal tools for analyzing landmark-based morphometric data (Lele et Richtsmeier 2001). This method builds a matrix of linear distances between all possible pairs of landmarks for each specimen (Lele et Richtsmeier 1991). Morphological differences between groups can be pinpointed to specific linear distances on an object through pairwise comparisons of mean form or shape matrices, followed by bootstrapping to estimate the significance of these differences (Lele et Richtsmeier 2001). In this study, two tests were done for each group of samples, first to analyze the form of the skull and mandibles with form difference matrix (FDM) and then the shape with the shape difference matrix (SDM). In addition, to track the landmarks associated with a significant change and understand where they are located in the CF structures, “EDMA FORM or SHAPE Influence landmark analysis” was performed (Cole et Richtsmeier 1998). The purpose of this test is to search which landmarks present a Relative Euclidean distance (RED) > 1.05 or < 0.95 (outside of the confidence interval 97,8%), meaning, which landmarks show a bigger difference in linear distances between every landmark and in what direction. Another way to handle landmark-based data is using a multivariate statistical analysis of form, geometric morphometric. This method relies on the superimposition of landmark coordinate data to place individuals into a common morpho-space. The most used superimposition form is the Generalized Procrustes (GP) method and Principal Component Analysis (PCA). This method places multiple individual specimens into the same shape space by scaling, translating, and rotating the landmark coordinates around the centroid of every sample (Rohlf et Slice 1990). As an alternative, we took advantage of Stratovan Checkpoint (Stratovan Corporation, Sacramento, USA) to create population average models and perform a voxel-based analysis, where we can observe directly in 3D models the changes between populations. Finally, using the 3dMD Vultus® software, we created Procrustes average models created in Checkpoint to perform a landmarking calculation. References Birling, Marie-Christine, Laurence Schaeffer, Philippe André, Loic Lindner, Damien Maréchal, Abdel Ayadi, Tania Sorg, Guillaume Pavlovic, et Yann Hérault. 2017. « Efficient and Rapid Generation of Large Genomic Variants in Rats and Mice Using CRISMERE ». Scientific Reports 7 (1): 43331. https://doi.org/10.1038/srep43331. Cole, T. M., et J. T. Richtsmeier. 1998. « A Simple Method for Visualization of Influential Landmarks When Using Euclidean Distance Matrix Analysis ». American Journal of Physical Anthropology 107 (3): 273‑83. https://doi.org/10.1002/(SICI)1096-8644(199811)107:3<273::AID-AJPA4>3.0.CO;2-1. Guedj, Fayçal, Patricia Lopes Pereira, Sonia Najas, Maria-Jose Barallobre, Caroline Chabert, Benoit Souchet, Catherine Sebrie, et al. 2012. « DYRK1A: A master regulatory protein controlling brain growth ». Neurobiology of Disease 46 (1): 190‑203. https://doi.org/10.1016/j.nbd.2012.01.007. Hallgrimsson, Benedikt, Christopher J. Percival, Rebecca Green, Nathan M. Young, Washington Mio, et Ralph Marcucio. 2015. « Chapter Twenty - Morphometrics, 3D Imaging, and Craniofacial Development ». In Current Topics in Developmental Biology, édité par Yang Chai, 115:561‑97. Craniofacial Development. Academic Press. https://doi.org/10.1016/bs.ctdb.2015.09.003. Hérault, Y., M. Rassoulzadegan, F. Cuzin, et D. Duboule. 1998. « Engineering Chromosomes in Mice through Targeted Meiotic Recombination (TAMERE) ». Nature Genetics 20 (4): 381‑84. https://doi.org/10.1038/3861. Hérault, Yann, Arnaud Duchon, Damien Maréchal, Matthieu Raveau, Patricia L. Pereira, Emilie Dalloneau, et Véronique Brault. 2010. « Controlled Somatic and Germline Copy Number Variation in the Mouse Model ». Current Genomics 11 (6): 470‑80. https://doi.org/10.2174/138920210793176038. Lele, Subhash R., et Joan T. Richtsmeier. 2001. An Invariant Approach to Statistical Analysis of Shapes. CRC Press. Li, Zhongyou, Tao Yu, Masae Morishima, Annie Pao, Jeffrey LaDuca, Jeffrey Conroy, Norma Nowak, Sei-Ichi Matsui, Isao Shiraishi, et Y. Eugene Yu. 2007. « Duplication of the Entire 22.9 Mb Human Chromosome 21 Syntenic Region on Mouse Chromosome 16 Causes Cardiovascular and Gastrointestinal Abnormalities ». Human Molecular Genetics 16 (11): 1359‑66. https://doi.org/10.1093/hmg/ddm086. Redhead, Yushi, Dorota Gibbins, Eva Lana-Elola, Sheona Watson-Scales, Lisa Dobson, Matthias Krause, Karen J. Liu, Elizabeth M. C. Fisher, Jeremy B. A. Green, et Victor L. J. Tybulewicz. 2023. « Craniofacial dysmorphology in Down syndrome is caused by increased dosage of Dyrk1a and at least three other genes ». Development 150 (8): dev201077. https://doi.org/10.1242/dev.201077. Rohlf, F. James, et Dennis Slice. 1990. « Extensions of the Procrustes Method for the Optimal Superimposition of Landmarks ». Systematic Biology 39 (1): 40‑59. https://doi.org/10.2307/2992207. Ruf, Sandra, Orsolya Symmons, Veli Vural Uslu, Dirk Dolle, Chloé Hot, Laurence Ettwiller, et François Spitz. 2011. « Large-Scale Analysis of the Regulatory Architecture of the Mouse Genome with a Transposon-Associated Sensor ». Nature Genetics 43 (4): 379‑86. https://doi.org/10.1038/ng.790.   Tables <table> <tbody> <tr> <td> Landmarks Cranium </td> </tr> <tr> <td> 1 </td> <td> Nasale: Intersection of nasal bones, rostral point </td> </tr> <tr> <td> 2 </td> <td> Nasion: Intersection of nasal bones, caudal point    </td> </tr> <tr> <td> 3 </td> <td> Bregma: intersection of frontal bones and parietal bones at midline </td> </tr> <tr> <td> 4 </td> <td> Intersection of parietal bones with anterior aspect of interparietal bone at midline </td> </tr> <tr> <td> 5 </td> <td> Intersection of interparietal bones with squamous portion of occipital bone at midline </td> </tr> <tr> <td> 6 </td> <td> Opisthion, midsagittal point on the posterior margin of the foramen magnum </td> </tr> <tr> <td> 7 </td> <td> Center of alveolar ridge over maxillary incisor, right side </td> </tr> <tr> <td> 8 </td> <td> Anterior Intersection of frontal process of maxilla with frontal bone, right side. </td> </tr> <tr> <td> 9 </td> <td> Anterior notch on frontal process lateral to infraorbital fissure, right side </td> </tr> <tr> <td> 10 </td> <td> Intersection of frontal process of maxilla with frontal and lacrimal bones, right side </td> </tr> <tr> <td> 11 </td> <td> Frontal-squasmosal intersection at temporal crest, right side </td> </tr> <tr> <td> 12 </td> <td> Intersection of zygoma (jugal) with zygomatic process of temporal, superior aspect, left side </td> </tr> <tr> <td> 13 </td> <td> Intersection of zygoma (jugal) with zygomatic process of temporal, inferior aspect, left side </td> </tr> <tr> <td> 14 </td> <td> Most posteroinferior point on the superior portion of the tympanic ring, right side </td> </tr> <tr> <td> 15 </td> <td> Center of alveolar ridge over maxillary incisor, left side </td> </tr> <tr> <td> 16 </td> <td> Anterior Intersection of frontal process of maxilla with frontal bone, left side. </td> </tr> <tr> <td> 17 </td> <td> Anterior notch on frontal process lateral to infraorbital fissure, left side </td> </tr> <tr> <td> 18 </td> <td> Intersection of frontal process of maxilla with frontal and lacrimal bones, left side </td> </tr> <tr> <td> 19 </td> <td> Frontal-squasmosal intersection at temporal crest, left side </td> </tr> <tr> <td> 20 </td> <td> Intersection of zygoma (jugal) with zygomatic process of temporal, superior aspect, right side </td> </tr> <tr> <td> 21 </td> <td> Intersection of zygoma (jugal) with zygomatic process of temporal, inferior aspect, right side </td> </tr> <tr> <td> 22 </td> <td> Most poteroinferior point on the superior portion of the tympanic ring, left side </td> </tr> <tr> <td> 23 </td> <td> Most anterior point of the anterior palatine foramen, right side </td> </tr> <tr> <td> 24 </td> <td> Most posterior point of the anterior palatine foramen, right side </td> </tr> <tr> <td> 25 </td> <td> Most infero lateral point on premaxilla-maxilla suture, right side </td> </tr> <tr> <td> 26 </td> <td> The anterior most point on the central ant/post axis of the right molar alveolus </td> </tr> <tr> <td> 27 </td> <td> Intersection of zygomatic process of maxilla with zygoma (jugal), inferior surface, right side </td> </tr> <tr> <td> 28 </td> <td> Lateral intersection of maxilla and palatine bone posterior to the third molar, right side </td> </tr> <tr> <td> 29 </td> <td> Joining of squasmosal body to zygomatic process of squasmosal, right side </td> </tr> <tr> <td> 30 </td> <td> Most inferior aspect of posterior tip of medial pterygoid process, right side </td> </tr> <tr> <td> 31 </td> <td> Most anterior point of the anterior palatine foramen, left side </td> </tr> <tr> <td> 32 </td> <td> Most posterior point of the anterior palatine foramen, left side </td> </tr> <tr> <td> 33 </td> <td> Most infero lateral point on premaxilla-maxilla suture, left side </td> </tr> <tr> <td> 34 </td> <td> The anterio most point on the central ant/post axis of the left molar alveolus </td> </tr> <tr> <td> 35 </td> <td> Intersection of zygomatic process of maxilla with zygoma (jugal), inferior surface, left side </td> </tr> <tr> <td> 36 </td> <td> Lateral intersection of maxilla and palatine bone posterior to the third molar, left side </td> </tr> <tr> <td> 37 </td> <td> Joining of squasmosal body to zygomatic process of squasmosal, left side </td> </tr> <tr> <td> 38 </td> <td> Most inferior aspect of posterior tip of medial pterygoid process, left side </td> </tr> <tr> <td> 39 </td> <td> Basion, midsagittal point on the anterior margin of the foramen magnum </td> </tr> </tbody> </table>  Table S1: 39 Skull Landmarks.   <table> <tbody> <tr> <td> Landmarks Mandible </td> </tr> <tr> <td> 1 </td> <td> Apex of coronoid process, Right side </td> </tr> <tr> <td> 2 </td> <td> Intersection of molar alveolar rim and base of coronoid process, Right side </td> </tr> <tr> <td> 3 </td> <td> Anterior edge of alveolar process where first molar hits alveolus at the midline, Right side </td> </tr> <tr> <td> 4 </td> <td> Superior-most point on incisor alveolar rim at midline (at bone-tooth junctions), Right side </td> </tr> <tr> <td> 5 </td> <td> Inferior-most point on incisor alveolar rim at midline (at bone-tooth junction), Right side </td> </tr> <tr> <td> 6 </td> <td> Inferior point on mandibular symphysis, Right side </td> </tr> <tr> <td> 7 </td> <td> Anterior edge of the coalescence of curve of masseteric ridge with post-symphyseal rugged area, Right side </td> </tr> <tr> <td> 8 </td> <td> Tip of mandibular angle, Right side </td> </tr> <tr> <td> 9 </td> <td> Posterior midline point on condyle, Right side </td> </tr> <tr> <td> 10 </td> <td> Anterior midline point on condyle, Right side </td> </tr> <tr> <td> 11 </td> <td> Anterior edge of the mental foramen, Right side </td> </tr> <tr> <td> 12 </td> <td> Apex of the coronoid process, left side </td> </tr> <tr> <td> 13 </td> <td> Intersection of molar alveolar rim and base of coronoid process, left side </td> </tr> <tr> <td> 14 </td> <td> Anterior edge of alveolar process where first molar hits alveolus at the midline, left side </td> </tr> <tr> <td> 15 </td> <td> Superior-most point on incisor alveolar rim at midline (at bone-tooth junctions), left side </td> </tr> <tr> <td> 16 </td> <td> Inferior-most point on incisor alveolar rim at midline (at bone-tooth junction), left side </td> </tr> <tr> <td> 17 </td> <td> Inferior point on mandibular symphysis, left side </td> </tr> <tr> <td> 18 </td> <td> Anterior edge of the coalescence of curve of masseteric ridge with post-symphyseal rugged area, left side </td> </tr> <tr> <td> 19 </td> <td> Tip of mandibular angle, left side </td> </tr> <tr> <td> 20 </td> <td> Posterior midline point on condyle, left side </td> </tr> <tr> <td> 21 </td> <td> Anterior midline point on condyle, left side </td> </tr> <tr> <td> 22 </td> <td> Anterior edge of the mental foramen, left side </td> </tr> </tbody> </table>  Table S2: 22 mandible Landmarks.

opencc-by-4.0Sep 2024View details →

dryad36/100

Coordinates activities of retrosplenial ensembles during resting-state encode spatial landmarks. Part 1 of 2

The brain likely uses off-line periods to consolidate recent memories. One hypothesis holds that the hippocampal output provides a unique, global linking or 'index' code for each memory, and that this code is stored in the cortex in association with locally encoded attributes of each memory. Activation of the index code is hypothesized to evoke coordinated memory trace reactivation thus facilitating consolidation. Retrosplenial cortex (RSC) is a major recipient of hippocampal outflow and we have described populations of neurons there with sparse and orthogonal coding characteristics that resemble hippocampal 'place' cells, and whose expression depends on an intact hippocampus. Using two-photon Ca2+ imaging, we recorded ensembles of neurons in the RSC during periods of immobility before and after active running on a familiar linear treadmill track. Synchronous bursting of distinct groups of neurons occurred during rest both prior to and after running. In the second rest epoch, these patterns were associated with the locations of tactile landmarks and reward. Complementing established views on the functions of the RSC, our findings indicate that the structure is involved with processing landmark information during rest.

opencc-zeroApr 2020View details →

dryad36/100

Coordinates activities of retrosplenial ensembles during resting-state encode spatial landmarks. Part 2 of 2

opencc-zeroApr 2020View details →

zenodo36/100

Figure 1. Landmarks and curves selection. A. Fore wing. B. Hind wing. C in Geometric morphometric analysis of Eysarcoris guttiger, E. annamita and E. ventralis (Hemiptera: Pentatomidae)

Figure 1. Landmarks and curves selection. A. Fore wing. B. Hind wing. C. Pygophore.

opencc-by-4.0Dec 2017View details →

dryad36/100

Landmark coordinates from: Dubied et al. Commonalities and evolutionary divergences of mandible shape ontogenies in rodents

In mammals, significant changes take place during postnatal growth, linked to changes in diet (from sucking to gnawing). During this period, mandible development is highly interconnected with muscle growth and the epigenetic interactions between muscle and bone control the spatialization of bone formation and remodeling in response to biomechanical strain. This mechanism contributes to postnatal developmental plasticity, and may have influenced the course of evolutionary divergences between species and clades. We sought to model postnatal changes at a macroevolutionary scale by analyzing ontogenetic trajectories of mandible shape across 16 species belonging mainly to two suborders of Rodents, Myomorpha and Hystricomorpha, which differ in muscle attachments, tooth growth, and life-history traits. Myomorpha species present a much stronger magnitude of changes over a shorter growth period. Among Hystricomorpha, part of the observed adult shape is set up prenatally, and most postnatal trajectories are genus-specific, which agrees with non-linear developmental trajectories over longer gestational periods. Beside divergence at large scale, we find some collinearities between evolutionary and developmental trajectories. A common developmental trend was also observed, leading to enlargement of the masseter fossa during postnatal growth. The tooth growth, especially hypselodonty, seems to be a major driver of divergences of postnatal trajectories. These muscle- and tooth-related effects on postnatal trajectories suggest opportunities for developmental plasticity in the evolution of the mandible shape, opportunities that may have differed across Rodent clades.

opencc-zeroAug 2021View details →

zenodo36/100

Analysis results reported in "Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps"

Analysis results reported in "Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps" (<a href="https://www.biorxiv.org/content/10.1101/2022.05.07.490900v1">biorxiv</a>) of <a href="https://doi.org/10.5281/zenodo.7299516">4i data</a>. Analysis of this dataset was done with <a href="https://pypi.org/project/campa/">CAMPA</a>, and all scripts to reproduce these results are available in the<a href="https://github.com/theislab/campa_ana"> CAMPA reproducibility repository</a>.

opencc-by-4.0Nov 2022View details →

zenodo36/100

4i dataset for "Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps"

4i dataset described and analysed in "Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps" (<a href="https://www.biorxiv.org/content/10.1101/2022.05.07.490900v1">biorxiv</a>). Analysis of this dataset was done with <a href="https://pypi.org/project/campa/">CAMPA</a>. The data is in CAMPA-readable format; for more information on the data format, see the <a href="https://campa.readthedocs.io/en/develop/overview.html#data-format">CAMPA documentation</a> . The data contains preprocessed wells of 184A1 and HeLa cells with different chemical treatments and 43 channels. For more information on the data, see the <a href="https://github.com/theislab/campa_ana/blob/develop/workflow/00_setup_and_download_data.ipynb">CAMPA reproducibility repository</a>.

opencc-by-4.0Nov 2022View details →

zenodo36/100

Nulliparous women's bony pelvis anatomical landmarks and dimensions

This repository directory contains source data in file landmarks.zip related to anatomical landmarks on bony pelvis identified independently by 2 observers from MRI (folders Observer_1 and Observer_2). After unzipping, a folder structure appears. Each folder contains FCSV datasets for 67 nulliparous women in the structure N, X, Z, Y line by by line, where N is the landmark number and (X, Y, Z) are the spacial coordinates. Folder Figures contains figures displaying correlations between chosen internal and external body pelvis dimensions. MATLAB function readData.m reads the landmark using functions getLandmarks.m (contains also description of all landmarks) and calculating internal and external bony pelvis dimensions using and calculateDimensions.m (contains also description of all dimensions). Switching between 2 observers on line 5, the function also saves thedata into binary files Observer_1.mat and Observer_2.mat to be analyzed: MATLAB function ICC.m calculates the Intercall Correlation Coefficient using function functionICC.m and also saves the data averaged between both observers to file Observers.mat to be processed further: MATLAB function correlations.m calculates the correlations among chosen external and internal bony pelvis dimensions using functions functionCorrelation.m and functionInletType.m. The output is plotted and save to folder Figures.

opencc-by-4.0Apr 2023View details →

zenodo36/100

Modern Human Articular Landmarks

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.

opencc-by-4.0Apr 2023View details →

zenodo36/100

HPE using Stereo Vision with 3D Landmarks

The present video shows the system's capabilities to perform the head pose estimation in yaw, pitch, and roll angles. It can be seen the response time of the system in comparison with the subject movement.

opencc-by-4.0Jun 2023View details →

zenodo36/100

Lowry Pueblo National Historic Landmark

The Lowry Pueblo National Historic Landmark is located within Canyons of Ancients National Monument in the four corners region of the American Southwest. Containing the highest known density of archaeological sites in the United States, the Canyons of the Ancients National Monument holds evidence of cultures and traditions spanning thousands of years. Sites within the monument include cliff dwellings, single family dwellings, great kivas, multi-storey adobe structures, shrines, rock paintings, petroglyphs and towers. Today, twenty-four native tribes claim cultural affiliation with Canyons of the Ancient National Monument. The Lowry Pueblo National Historic Landmark is a Puebloan "Great House" that contains thirty-seven rooms, eight kivas, and one Great Kiva. Archaeologists have dated the construction and occupation of the site to 100 years in the 11th Century though there is evidence that people continued to use the site into the mid-1200s. Source: Objaverse 1.0 / Sketchfab

opencc-byAug 2017View details →

ClinicalTrials.gov36/100

PREDICT-H Study: Prospective Research on Essential Determinants Influencing Complication Trends in Hypospadias - a Landmark Prospective Multicenter Cohort Study Aimed at Improving Outcome Prediction i

ClinicalTrials.gov study NCT07017842. IPD Sharing: YES. Countries: 1. Publications: 5.

controlledIPD-YESFeb 2026View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record