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Dataset results
302 results for “registration”
Study of Nivolumab in Patients With Classical Hodgkin's Lymphoma (Registrational)
ClinicalTrials.gov study NCT02181738. IPD Sharing: Not stated. Countries: 10. Publications: 6.
Registrational Study With Omecamtiv Mecarbil (AMG 423) to Treat Chronic Heart Failure With Reduced Ejection Fraction
ClinicalTrials.gov study NCT02929329. IPD Sharing: YES. Countries: 36. Publications: 10.
BIOTRONIK Orsiro Pre-Marketing Registration
ClinicalTrials.gov study NCT02870985. IPD Sharing: NO. Countries: 1. Publications: 2.
Registration Study of Takayasu's Arteritis in China
ClinicalTrials.gov study NCT03199183. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
Registration Study for Rare Type of Pulmonary Hypertension
ClinicalTrials.gov study NCT03169010. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Processed data for: Co-registration of heading to visual cues in retrosplenial cortex
Open the record for dataset details and reuse information.
Data from: Three-dimensional infrared scanning: An enhanced approach for spatial registration of probes for neuroimaging
Open the record for dataset details and reuse information.
Preprocessed Data used by "Machine Learning Enabled Brain Segmentation for Small Animal Image Registration"
<p>Data, preprocessed by the SAMRI package, used to train the models in "Machine Learning Enabled Brain Segmentation for Small Animal Image Registration". The models can be found <a href="https://zenodo.org/record/3759361#.XrKrgBMzZhE">here</a>.</p>
Multimodal Biomedical Dataset for Evaluating Registration Methods (patches from TMA Cores)
<p>The dataset consists of 206 aligned Bright-Field (BF) and Second-Harmonic Generation (SHG) images. All images are of the same size of 834x834 pixels. The training set contains 40 image pairs. A validation set 1 of 25 image pairs for tuning the hyperparameters for the training. A validation set 2 of 7 image pairs for tuning the registration method. The test set for evaluation: 134 image pairs (times two).</p> <p>For each image pair in the test set, a random rotation up to +/-30 degrees and random translations in x and y for up to 100px were applied. For each image pair that was transformed for the test set, we apply the transformation to both modalities and keep their corresponding reference images as well as transformed images, in order to allow for registration using each modality as a reference and the other as a floating image. The metadata.csv file includes the coordinates of the four corners of the reference image as well as the coordinates of the corners of the transformed image after displacement. The origin of this coordinate system is the upper left corner of the reference image, given by (0,0).</p> <p>Example of naming convention in the test set:</p> <ul> <li>R_1B_F8_BF.tif: reference image in Bright-Field</li> <li>R_1B_F8_SHG.tif: reference image in Second-Harmonic Generation</li> <li>T_1B_F8_BF.tif: transformed image in Bright-Field</li> <li>T_1B_F8_SHG.tif: transformed image in Second-Harmonic Generation.</li> </ul> <p>Where the transformations applied to T_1B_F8_BF.tif and T_1B_F8_SHG.tif are identical and hence the coordinates of the corners in metadata.csv are valid for both image pairs.</p> <p>The metadata.csv file contains the following columns:</p> <ul> <li>Filename: identifier for image pair</li> <li>X1_Ref: x-coordinate of upper left corner of reference image</li> <li>Y1_Ref: y-coordinate of upper left corner of reference image</li> <li>X2_Ref: x-coordinate of lower left corner of reference image</li> <li>Y2_Ref: y-coordinate of lower left corner of reference image</li> <li>X3_Ref: x-coordinate of upper right corner of reference image</li> <li>Y3_Ref: y-coordinate of upper right corner of reference image</li> <li>X4_Ref: x-coordinate of lower right corner of reference image</li> <li>Y4_Ref: y-coordinate of lower right corner of reference image</li> <li>X1_Trans: x-coordinate of upper left corner of transformed image</li> <li>Y1_Trans: y-coordinate of upper left corner of transformed image</li> <li>X2_Trans: x-coordinate of lower left corner of transformed image</li> <li>Y2_Trans: y-coordinate of lower left corner of transformed image</li> <li>X3_Trans: x-coordinate of upper right corner of transformed image</li> <li>Y3_Trans: y-coordinate of upper right corner of transformed image</li> <li>X4_Trans: x-coordinate of lower right corner of transformed image</li> <li>Y4_Trans: y-coordinate of lower right corner of transformed image</li> <li>Displacement: Mean Euclidean distance between reference corner points and transformed corner points</li> </ul> <p>The data set was originally produced by the authors of <em>Aligned Collagen Is a Prognostic Signature for Survival in Human Breast Carcinoma</em> (<a href="https://www.sciencedirect.com/science/article/pii/S0002944010002336">https://www.sciencedirect.com/science/article/pii/S0002944010002336</a>). The registered and non-registered sub-image pairs in this data set were created by Johan Öfverstedt and Elisabeth Wetzer.</p>
FUNCERT - Registration dynamics and course certification rates on a Massive Open Online Course (MOOC) platform
<p>The FUNCERT data set describes 1 million course registration events collected over a 2-year period from the MOOC platform FUN (France Université Numérique, fun-mooc.fr). Each registration event is associated with a timestamp and an indication of whether a completion certificate was ultimately awarded. It was used in the referenced manuscript to model the impact of multiple course registrations on certificate rates.</p> <p>The data set is provided as a single file (FUNCERT.csv) with the following fields.</p> <p><strong>Time (min)</strong> The time of the registration event reported in minutes elapsed since the beginning of data collection. The time stamp is reported as a relative time only, not an exact calendar date, to enhance anonymization.</p> <p><strong>User ID</strong> A unique ID number for each user of the platform during this period. The number has been generated specifically for this data set and does not correspond to user ID numbers used within the FUN platform.</p> <p><strong>Course ID</strong> Each of the 140 offered courses is assigned a unique and anonymous ID code. In some cases courses were offered multiple times within the data collection period. The same Course ID is used across multiple instances of the same course.</p> <p><strong>Certificate</strong> A boolean variable set to 1 if the registration event is associated with a course completion certificate. Of 140 offered courses, 91 awarded certificates during the study period.</p> <p><strong>Age</strong> User age in years at the time of registration. Field may be left blank if data was not provided.</p> <p><strong>Gender</strong> User gender as selected from the options m and f. Field may be left blank if data was not provided.</p>
Augmentation Via Registration: AutoImplant 2020 Augmented Data Set
<p>Augmented data derived from the AutoImplant 2020 Challenge. Data has been augmented via non-linear SyN registrations using Advanced Normalization Tools (ANTs). This data was used to obtain first place in the AutoImplant 2020 challenge. The AutoImplant 2020 Challenge data was derived from the QC500 dataset from qure.ai. The original dataset is licensed under the CC BY-NC-SA 4.0 license (Attribution-NonCommercial-ShareAlike) and complies with the End User License Agreement (EULA) which are both detailed in the "LICENSE" file in this folder. Please refer to the "LICENSE" file for terms of use.</p> <p>If you use this data, please cite the following paper:</p> <p>Ellis D.G., Aizenberg M.R. (2020) Deep Learning Using Augmentation via Registration: 1st Place Solution to the AutoImplant 2020 Challenge. In: Li J., Egger J. (eds) Towards the Automatization of Cranial Implant Design in Cranioplasty. AutoImplant 2020. Lecture Notes in Computer Science, vol 12439. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-64327-0_6">https://doi.org/10.1007/978-3-030-64327-0_6</a></p>
Serial two-photon tomography (STPT) of the brain through bi-channel image registration and deep learning segmentation (BIRDS)
<p>We have developed an open-source software called BIRDS (bi-channel image registration and deep learning segmentation) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image pre-processing, bi-channel registration, automatic annotation, creation of a 3D digital frame, high-resolution visualization, and expandable quantitative analysis. This new bi-channel registration algorithm is adaptive to various types of whole-brain data from different microscopy platforms and shows dramatically improved registration accuracy. Additionally, as this platform combines registration with neural networks, its improved function relative to other platforms lies in the fact that the registration procedure can readily provide training data for network construction, while the trained neural network can efficiently segment incomplete/defective brain data that is otherwise difficult to register. Our software is thus optimized to enable either minute-timescale registration-based segmentation of cross-modality, whole-brain datasets or real-time inference-based image segmentation of various brain regions of interest. Jobs can be easily submitted and implemented via a Fiji plugin that can be adapted to most computing environments.</p>
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Coarse-grained Simulation data (part 2 of 2) for "Long-chain GM1 gangliosides alter transmembrane domain registration through interdigitation"
<p>Coarse-grained simulation data for the paper "Long-chain GM1 gangliosides alter transmembrane domain registration through interdigitation", Biochimica et Biophysica Acta (BBA) - Biomembrane Volume 1859, Issue 5, May 2017, Pages 870–87, DOI: 10.1016/j.bbamem.2017.01.033</p> <p>The simulations were performed using GROMACS 5.0.x [1]. The Martini force field [2,3] was employed.</p> <p>This part (2/2) of the upload contains data for the 10 replica simulations of the systems with GM1 with a normal tail and of the systems with a smaller concentration (1.5%) of GM1 with an extended tail. Trajectories, each 10 microseconds long and stored every 1ns, are given in xtc format. A common run input file for each type of system is given in the tpr format for analysis, although the initial structures of the replicas are different. These files are compatible with GROMACS 5. A common index file in ndx format and a common topology in top format are given for each system type. Simulation parameters, common for all simulated systems, are given in the mdp file. The itp files can be obtained from the Martini homepage http://cgmartini.nl/ </p> <p>The file names denote what kind of tail was used (normal vs. extended), the concentration of GM1 (1.5% (1) vs. 6% (6)), and the replica simulations are numbered from 1 to 10. For the systems without GM1 (NoGM1), the concentration is obviously not given, and there are only 8 replicas (see part 1).</p> <p>Part 1 of this upload is available at https://doi.org/10.5281/zenodo.831672.</p> <p>[1] M.J. Abraham et al., GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 2015, 1–2, pp. 19–25, DOI: 10.1016/j.softx.2015.06.001 </p> <p>[2] S.J. Marrink et al., The MARTINI Force Field:  Coarse Grained Model for Biomolecular Simulations. J. Phys. Chem. B, 2007, 111, pp 7812–7824, DOI:<strong> </strong>10.1021/jp071097f</p> <p>[3] C.A. López et al., Martini Force Field Parameters for Glycolipids. J. Chem. Theory Comput., 2013, 9, pp 1694–1708, DOI: 10.1021/ct3009655 </p>
Lung250M-4B: A Combined 3D Dataset for CT- and Point Cloud-Based Intra-Patient Lung Registration
<p>Point Cloud Data from the Lung250M-4B dataset.</p><p>Visit https://github.com/multimodallearning/Lung250M-4B for image data and associated code.</p>
Dataset for: Substituting confidence for competence in health literacy: A review of studies, citations, and trial registrations
<p><span>Patient health literacy is crucial for effective patient–physician communication, and interventions targeting health literacy can use measures based on either actual performance (competence) or self-ratings (confidence). This paper analyzed the development of these measures through three studies. Study 1 reviewed articles describing the development of novel measures; Study 2 examined the citations of these studies, and Study 3 evaluated data from clinical trials registries. The literature search was conducted from April 14–27, 2023. PubMed was used as the main database in which studies on health literacy measures were searched for the systematic review (Study 1). We then used Google Scholar and the OpenCitations database to describe citation patterns of the included health literacy measures (Study 2). Finally, we evaluated confidence- or competence-based health literacy measures by extracting and analyzing trial data from ClinicalTrials.gov (Study 3). Our review included 55 health literacy measures, among which 23 (42%) were competence-based, 28 (51%) confidence-based, and 4 (7%) assessed both. Recent trends show a shift toward developing more confidence-based measures and a decline in creating new competence-based measures. Confidence-based measures were increasingly cited, whereas citations for competence-based measures have plateaued. Lastly, our findings showed a steady increase in the use of confidence-based measures in recent clinical trials and a decrease in the use of competence-based measures when controlling for sample size. This shift may be detrimental to public health given our limited knowledge about patients’ ability to meet demands of shared decision-making, especially regarding new technologies like artificial intelligence in healthcare.</span></p>
Supplementary material 2 from: Penev L, Paton A, Nicolson N, Kirk P, Pyle RL, Whitton R, Georgiev T, Barker C, Hopkins C, Robert V, Biserkov J, Stoev P (2016) A common registration-to-publication automated pipeline for nomenclatural acts for higher plants (International Plant Names Index, IPNI), fungi (Index Fungorum, MycoBank) and animals (ZooBank). In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 233–246. https://doi.org/10.3897/zookeys.550.9551
IPNI response XML : Explanation note: XML response of IPNI to the query in Suppl. material 1. The response is sent back to Pensoft and contains the registration numbers of the new genus name and the new combination [exemplified with the paper of Robinson and Skvarla (2013)].
Supplementary material 4 from: Penev L, Paton A, Nicolson N, Kirk P, Pyle RL, Whitton R, Georgiev T, Barker C, Hopkins C, Robert V, Biserkov J, Stoev P (2016) A common registration-to-publication automated pipeline for nomenclatural acts for higher plants (International Plant Names Index, IPNI), fungi (Index Fungorum, MycoBank) and animals (ZooBank). In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 233–246. https://doi.org/10.3897/zookeys.550.9551
TaxPub response XML : Explanation note: TaxPub XML returned from ZooBank to Pensoft containing UUIDs of the article, authors and new taxon names [exemplified with the paper of Morffe and Rodríguez (2013)].
Supplementary material 1 from: Penev L, Paton A, Nicolson N, Kirk P, Pyle RL, Whitton R, Georgiev T, Barker C, Hopkins C, Robert V, Biserkov J, Stoev P (2016) A common registration-to-publication automated pipeline for nomenclatural acts for higher plants (International Plant Names Index, IPNI), fungi (Index Fungorum, MycoBank) and animals (ZooBank). In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 233–246. https://doi.org/10.3897/zookeys.550.9551
XML response of IPNI : Explanation note: XML query sent from Pensoft to IPNI on the day of acceptance of the manuscript for publication [exemplified with the paper of Robinson and Skvarla (2013)].
Supplementary material 3 from: Penev L, Paton A, Nicolson N, Kirk P, Pyle RL, Whitton R, Georgiev T, Barker C, Hopkins C, Robert V, Biserkov J, Stoev P (2016) A common registration-to-publication automated pipeline for nomenclatural acts for higher plants (International Plant Names Index, IPNI), fungi (Index Fungorum, MycoBank) and animals (ZooBank). In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 233–246. https://doi.org/10.3897/zookeys.550.9551
XML response of TaxPub : Explanation note: TaxPub XML of a ready-to-publish manuscript submitted from Pensoft to ZooBank [exemplified with the paper of Morffe and Rodríguez (2013)].
ScienceDex guides
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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.