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1,557 results for “precision”
Analytical Framework for Precise Relative Motion in Low Earth Orbits
<p>The data sets provided here can be used to recreate the plots of the paper “Analytical Framework for Precise Relative Motion in Low Earth Orbits” available at this <a href="https://arc.aiaa.org/doi/10.2514/1.G004716">link</a>.</p> <p>That paper presents a practical and efficient analytical framework for the precise modelling of the relative motion in low Earth orbits.</p>
Data to "Predicting precision grip grasp locations on three-dimensional objects"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Klein, L. K. ^, Maiello, G. ^, Paulun, V. C., & Fleming, R. W. (in press). <br> Predicting precision grip grasp locations on three-dimensional objects. PLOS Computational Biology<br> ^co-first authors </p> <p>A preprint version of the manuscript is currently available at: https://doi.org/10.1101/476176</p>
Dataset for paper entitled "Folding and Bending Planar Coils for Highly Precise Soft Angle Sensing"
<p>This dataset includes all results presented in the paper entitled "Folding and Bending Planar Coils for Highly Precise Soft Angle Sensing", published in Advanced Materials Technologies, vol.5, 2000659, 2020<br> DOI: 10.1002/admt.202000659.<br> URL:<br> https://onlinelibrary.wiley.com/doi/full/10.1002/admt.202000659</p> <p>List of data in this dataset:<br> Fig.1-Theoretical Analysis and Basic characteristics.xlsx<br> Fig.2-Experimental results-Coil design.xlsx<br> Fig.3-Cyclic Bending and Folding.xlsx<br> Fig.4-Folding Angle Sensing Performance Evaluation.xlsx<br> Fig.5-Case studies.xlsx</p> <p>All the data included in this dataset were collected and processed by Dr. Hongbo Wang.</p> <p>Contact person:<br> Dr. Hongbo Wang, ustcwhb@gmail.com</p>
History, Adoption and Key impacts of precision agriculture
<p>Precision agriculture technologies have revolutionized modern farming practices, offering innovative solutions to optimize crop production, minimize resource use, and enhance environmental sustainability. This research paper explores the historical evolution, adoption trends, and importance of precision agriculture technologies in contemporary agriculture.</p>
Proteins required for stereocilia elongation during mammalian hair cell development ensure precise and steady heights during adult life
<p>This dataset contains all source data for Hartig <em>et al </em>2024, PNAS, including:</p> <p>Data files</p> <p>Raw images and TDT ABR/DPOAE files</p> <p>ROIS and raw measurements from quantifications in ImageJ</p> <p>R scripts for data visualization and statistics</p> <p>Reports of statistical analyses including diagnostic qq plots and distributions</p>
Subcellular behavior model enables highly precise temporal super-resolved live-cell imaging
<div> <div>This repository contains the preprocessed dataset for [SuB-VFI](https://github.com/sduzzx857/SuB-VFI), including the real datasets we collected and the simulated testing and training datasets. You can refer to the Github repository for details.</div> <div> </div> <div>The simulated testing datasets can be downloaded from [the 2014 ISBI Particle Tracking Challenge](http://bioimageanalysis.org/track/).</div> <div>The EB1 datasets can be downloaded from the paper [The dynamic behavior of the APC-binding protein EB1 on the distal ends of microtubules](https://www.cell.com/current-biology/fulltext/S0960-9822(00)00600-X?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS096098220000600X%3Fshowall%3Dtrue). We used *Movie2* from the Supplementary data.</div> <br> <div>The CCR5 datasets can be downloaded from the paper [Tracking receptor motions at the plasma membrane reveals distinct effects of ligands on CCR5 dynamics depending on its dimerization status](https://elifesciences.org/articles/76281). We used *Video4* in the Results section. </div> <div> </div> <div>The Lysosome datasets can be downloaded from [Content-Aware Frame Interpolation Microscopy Datasets](https://zenodo.org/records/10076346). We used data from the `Zproject` folder within the compressed file `Source_Data_Lysosomes_z-proj_Fig_5.zip`</div> </div>
High-precision Aftershock Locations and Fault Planes of the 2016-2017 Central Italy Sequence
<p>The earthquake catalog includes high-precision hypocenter relocations for 390,334<br> earthquakes recorded during the 2016-2017 Amatrice (Central Italy) <br> earthquake sequence. The relative locations were computed by double-difference inversion of a <br> combination of INGV phase picks and cross-correlation differential <br> times measured from correlated seismograms with correlation coefficients > 0.7.</p> <p>Planes of normal faults (idx=1-5) are derived from PCA analysis of 2 months of aftershock <br> locations in the CAT4 catalog following large events. Surfaces of detachment faults (idx=7-10) are derived from mapping out the location of correlated earthquakes. </p> <p>Citation: Waldhauser, F., Michele, M., Chiaraluce, L., Di Stefano, R., & Schaff, D. P. (2021). Fault planes, fault zone structure and detachment fragmentation resolved with highprecision aftershock locations of the 2016-2017 central Italy sequence. Geophysical Research Letters, 48, e2021GL092918. https://doi.org/10.1029/2021GL092918</p>
TBPos: Dataset for Large-Scale Precision Visual Localization (database files)
<p>Large-scale dataset for visual localization, provided in the format of the well-known InLoc dataset (Taira et al, 2018). Contains co-registered RGB point clouds and a script for generating the rest of the 'database' files for visual localization by the InLoc algorithm. Note: query images are provided in a separate repository.</p>
Dataset for Overscan Detection in Digitized Analog Films by Precise Sprocket Hole Segmentation
<p>This repo includes the self-generated dataset as well as the pre-trained models .</p> <p>ISVC 2020 - 15th International Symposium on Visual Computing</p> <p>Paper: Overscan Detection in Digitized Analog Filmsby Precise Sprocket Hole Segmentation</p> <p> </p> <p>Acknowledgement:</p> <p>Visual History of the Holocaust: Rethinking Curation in the Digital Age. This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Grant Agreement 822670.</p> <p>https://www.vhh-project.eu</p> <p> </p>
More precise tracking of horizontal than vertical target motion with both the eyes and hand
<p>Those files contain individual data from a large cohort of participant (N=62). </p> <p>In the excel file (DATAmain), each sheet presents one set of variables (with individual value for each trial).</p> <p>This file contains information regarding eye and hand tracking performance (distance+lags), as well as smooth pursuit gains. </p> <p>The other files contain data that we used for the detailed analysis of saccades and lags, as well as the scripts that can be run with Perl. One script is for analysing the lag (Danion.pl) and the other one for analysing the saccades (saccades.pl). The other files (.txt and .dat) that were used for these analyses. Note that some library is needed (common_subroutines, draw_figure, and for the anova’s routines_that_use_R), meaning that you need to have R installed. </p> <pre>Regarding data acquisition we employed a program called Docometre that can be uploaded at the following address: http://139.124.68.1/buloup/index.php?selectedMenu=DOCoMETRe&lang=_fr When this program is installed, it needs to be run with BaselineTracking.dcm We also provide .BAS and .T91 files that correspond to the compiled version of each pattern Regarding visual stimuli, another program called ICE needs to be installed on a separate computer that receives information (target+cursor) from docometer, it can be uploaded at : https://trello.com/b/EtNCNrZH/icehttps://trello.com/b/EtNCNrZH/ice ICE needs to be run with Visuomotor.ice Visuomotor.icepro Visuomotor.txt and Visuomotor.icemat in the respective folder (icepro in Protocol folder, icemat and ice in Scenario Folder, and txt in Serie folder) Note that both Docometre and ICE need to be run with similar equipement as our (including Adwin Gold systems, Megatron joystick, video screen, graphic cards, and desktop eyelink providing analog signals to docometre). Adequate numbering of analogic channels needs also to be ensured. </pre>
Uncovering the Triplet Ground State of Triangular Graphene Nanoflakes Engineered with Atomic Precision on a Metal Surface
<p>OPEN DATA related to the research publication:</p> <p>J. Li, S. Sanz, J. Castro-Esteban, M. Vilas-Varela, N. Friedrich, T. Frederiksen, D. Peña, and J. I. Pascual, <em>Uncovering the triplet ground state of triangular graphene nanoflakes engineered with atomic precision on a metal surface</em>, Phys. Rev. Lett. <strong>124</strong>, 177201 (2020) [arXiv:1912.08298]</p> <p>Abstract: Graphene can develop large magnetic moments in custom-crafted open-shell nanostructures such as triangulene, a triangular piece of graphene with zigzag edges. Current methods of engineering graphene nanosystems on surfaces succeeded in producing atomically precise open-shell structures, but demonstration of their net spin remains elusive to date. Here, we fabricate triangulenelike graphene systems and demonstrate that they possess a spin S=1 ground state. Scanning tunneling spectroscopy identifies the fingerprint of an underscreened S=1 Kondo state on these flakes at low temperatures, signaling the dominant ferromagnetic interactions between two spins. Combined with simulations based on the meanfield Hubbard model, we show that this S=1 π paramagnetism is robust and can be turned into an S=1/2 state by additional H atoms attached to the radical sites. Our results demonstrate that π paramagnetism of high-spin graphene flakes can survive on surfaces, opening the door to study the quantum behavior of interacting π spins in graphene systems.</p>
Paulina Polder precise Elevation (Netherlands)
The dataset corresponds to a precise AHN-2 5m resolution Digital Elevation Model (DEM) of the Paulina Polder area in the Netherlands.
A machine learning-based high-precision density functional method for drug-like molecules
<h2><strong>Models</strong></h2><p>The repo contains the models and test datasets for our aticles. The energy unit is in <strong>Hartree,</strong> The coordinate unit is in<strong> Bohr.</strong></p><p><strong>## DeePHF</strong></p><p>you need first prepare the `dm_eig.npy` in data_test and do predict `l_e_delta.npy`, you can use</p><p>```</p><p>deepks test -m model.pth -o test/test -d data_test/* -D dm_eig -G</p><p>```</p><p><strong>## DeePKS</strong></p><p>first you should prepare the `atom.npy`, and `energy.npy` in data_test. you can test the datasets by command. </p><p>```</p><p>deepks scf scf_input.yaml -m model.pth -s data_test -d test_out</p><p>```</p><p><strong># Datasets</strong></p><p>All datasets only have `atom.npy` and `energy.npy`. The coordinate unit is `bohr`, and energy unit is `Hartree`.</p><p><strong>## small molecules torsion</strong></p><p>Contains 62 small molecules with 36 conformation for each under CCSD(T)/def2-TZVP.</p><p><br> </p><p>[1] B. D. Sellers, N. C. James, A. Gobbi, A comparison of quantum and molecular mechanical methods to estimate strain energy in druglike fragments, Journal of chemical information and modeling 57 (6) (2017) 1265–127</p><p><br> </p><p><strong>## MPCONF91</strong></p><p>Contains 6 molecules with 91 conformations under LNO-CCSD(T)/def2-TZVP.</p><p><br> </p><p>[1] J. Rezac, D. Bím, O. Gutten, L. Rulisek, Toward accurate conformational energies of smaller peptides and medium-sized macrocycles: Mpconf196 benchmark energy data set, Journal of chemical theory and computation 14 (3) (2018) 1254–1</p><p><br> </p><p><strong>## torsionNet206</strong></p><p>Contains 206 molecules with 4494 conformations under CCSD(T)/def2-TZVP.</p><p><br> </p><p>[1] B. K. Rai, V. Sresht, Q. Yang, R. Unwalla, M. Tu, A. M. Mathiowetz,G. A. Bakken, Torsionnet: A deep neural network to rapidly predict small-molecule torsional energy profiles with the accuracy of quantum mechanics, Journal of Chemical Information and Modeling 62 (4) (2022) 785–80</p><p><br> </p><p><strong>## Out-of-plane bending</strong></p><p>Contains 242 molecules with 3315 conformations under CCSD(T)/def2-TZVP.</p><p><br> </p><p>[1] X. Yang, C. Liu, P. Ren, High order ab initio valence force field with chemical pattern based parameter assignment., Journal of Computational Biophysics and Chemistry 21 (4) (2021) 43</p><p><br><br> </p><p><strong>## DrugBank-T</strong></p><p>Contains 165 molecules with 1155 conformations under CCSD(T)/def2-TZVP.</p><p><br> </p><p>[1] V. Law, C. Knox, Y. Djoumbou, T. Jewison, A. C. Guo, Y. Liu, A. Maciejewski, D. Arndt, M. Wilson, V. Neveu, et al., Drugbank</p><p>4.0: shedding new light on drug metabolism, Nucleic acids research 42 (D1) (2014) D1091–D1097 </p><p>[2] Z. Qiao, M. Welborn, A. Anandkumar, F. R. Manby, T. F. Miller III, Orbnet: Deep learning for quantum chemistry using symmetry adapted atomic-orbital features, The Journal of chemical physics 153 (12) (2020) 124111</p><p><strong>## Notice</strong></p><p>if you use above datasets, please cite the original articals too</p>
Precision viticulture dataset for detailed vineyard mapping composed of geotagged smartphone ground images, phytosanitary status, UAV orthomosaics, 3D point clouds, and RTK GNSS data - Northern Spain, July 2022
<p>This dataset offers a rich multimodal collection of data from vineyards, designed to enhance agricultural research with a focus on vineyard management and disease monitoring. It includes geotagged smartphone ground images in ".7z" format for detailed plant-level analysis, a ".csv" file detailing plants' phytosanitary status for health assessment, UAV-derived 3D Point Clouds and orthomosaics in ".las" and ".tiff" formats for aerial landscape views, and RTK GNSS data in ".shp" format for precise plant geolocations.</p> <p>This dataset can be combined with other datasets to enable a comprehensive view of the vineyards and improve its value:</p> <div> <ul> <li>Ariza-Sentís, Mar, Sergio Vélez, and João Valente. ‘Dataset on UAV RGB Videos Acquired over a Vineyard Including Bunch Labels for Object Detection and Tracking’. <em>Data in Brief</em> 46 (February 2023): 108848. <a href="https://doi.org/10.1016/j.dib.2022.108848">https://doi.org/10.1016/j.dib.2022.108848</a>.</li> <li>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘VineLiDAR: High-Resolution UAV-LiDAR Vineyard Dataset Acquired over Two Years in Northern Spain.’ <em>Data in Brief</em>, October 2023, 109686. <a href="https://doi.org/10.1016/j.dib.2023.109686">https://doi.org/10.1016/j.dib.2023.109686</a>.</li> <li> <div> <div>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘Dataset on Unmanned Aerial Vehicle Multispectral Images Acquired over a Vineyard Affected by Botrytis Cinerea in Northern Spain’. <em>Data in Brief</em> 46 (February 2023): 108876. <a href="https://doi.org/10.1016/j.dib.2022.108876">https://doi.org/10.1016/j.dib.2022.108876</a>.</div> <div> </div> </div> </li> </ul> </div>
Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer
<p>This repository includes source data used to genrtate all tables and figures for published stduy "Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer". Besides, a small set of digital images for different class of cervical smear samples are included.</p>
Breast Micro-Calcifications Dataset with Precisely Annotated Sequential Mammograms
<p><strong>Dataset Version 3 Update</strong></p> <p><strong>The ground truth images (.jpg) match the dimensions of the corresponding original images (.dcm), ensuring consistency across the dataset.</strong><br><br></p> <p><strong>Breast Micro-Calcifications Dataset with Precisely Annotated Sequential Mammograms</strong></p> <p><strong>Citing the Dataset</strong></p> <p>The dataset is released under a Creative Commons Attribution license, so please cite the dataset if it is used in your work in any form. Published academic papers should use the academic paper citation for our paper. Personal works, such as projects or blog posts, should provide a URL to this Zenodo page, though a reference to our paper would also be appreciated.</p> <p><em>Academic paper citation</em></p> <p>Loizidou, K., Skouroumouni, G., Pitris, C. <em>et al.</em> Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications. <em>Eur Radiol Exp</em> <strong>5, </strong>40 (2021). https://doi.org/10.1186/s41747-021-00238-w</p> <p><em>Personal use citation</em></p> <p>Include a link to this Zenodo page - 10.5281/zenodo.14859694</p> <p><strong>ACKNOWLEDGMENT</strong></p> <p>This research is funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 739551 (KIOS CoE) and from the Republic of Cyprus through the Directorate General for European Programs, Coordination and Development.</p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the dataset, or if you experience any issues downloading files, please contact us at cloizi01@ucy.ac.cy.</p> <p><strong>General Information</strong></p> <p>This dataset consists of 100 pairs of mammograms, from two temporally sequential rounds. Specifically, this dataset includes the prior and recent mammograms of CC and MLO view of each patient. This is a complete dataset for the detection and BI-RADS classification of breast micro-calcifications, using digital mammograms. It contains normal (BI-RADS 1), benign (BI-RADS 2), and suspicious (BI-RADS 4-5) cases, and for each mammogram, an image with precise annotation of each individual micro-calcification, by two expert radiologists, is provided. In 32 suspicious cases, the biopsy results are also available.</p> <p><strong>More details are available in the README.txt</strong></p>
A Single-Cell Tumor Immune Atlas for Precision Oncology
<p><strong>Publication version of the Single-Cell Tumor Immune Atlas</strong></p> <p>This upload contains:</p> <ul> <li><strong>TICAtlas.rds:</strong> an rds file containing a Seurat object with the whole Atlas</li> <li><strong>TICAtlas.h5ad:</strong> an h5ad file with the whole Atlas</li> <li><strong>TICAtlas_downsampled.rds:</strong> an rds file containing a downsampled version of the Seurat object of the whole Atlas</li> <li><strong>TICAtlas_downsampled.h5ad:</strong> an rds file containing a downsampled version of the Seurat object of the whole Atlas</li> <li><strong>TICAtlas_metadata.csv: </strong>a comma-separated text file with the metadata for each of the cells</li> </ul> <p>All the files contain the following patient/sample metadata variables:</p> <ul> <li>patient: assigned patient identifiers</li> <li>nCountRNA and nFeatureRNA: number of UMIs and genes per cell</li> <li>percent.mt: percentage of mitochondrial genes</li> <li>gender: the patient's gender (male/female/unknown)</li> <li>source: dataset of origin</li> <li>subtype: cancer type (abbreviations as indicated in the preprint)</li> <li>kmeans_cluster: patients clusters, NA if filtered out before clustering</li> <li>lv1 and lv2: annotated cell type for each of the cells, two level annotation (lv2 has more cell types)</li> </ul> <pre> </pre> <p>If you have any issues with the metadata (i.e. unexpected factors, NA values...) you can use the <strong>TICAtlas_metadata.csv </strong>file.</p> <p>For more information, <a href="https://genome.cshlp.org/content/early/2021/09/21/gr.273300.120.">read our paper</a>, <a href="https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/Tumor-Immune-Cell-Atlas">check our GitHub</a> and our <a href="https://singlecellgenomics-cnag-crg.shinyapps.io/TICA/">ShinyApp</a>.</p> <p>h5ad files can be read with Python using <a href="https://scanpy.readthedocs.io/en/stable/">Scanpy</a>, rds files can be read in R using <a href="https://satijalab.org/seurat/">Seurat</a>. For format conversion between AnnData and Seurat we recommend <a href="https://mojaveazure.github.io/seurat-disk/">SeuratDisk</a>. For other single-cell data formats you can use <a href="https://github.com/cellgeni/sceasy">sceasy</a>.</p>
Combined proton radiography and irradiation for high-precision preclinical studies in small animals
<p>Data used for the publication "Combined proton radiography and irradiation for high-precision preclinical studies in small animals".</p> <p>The repository contains all data that was used to generate the quantitative results and figures in the submitted manuscript.</p> <p>Further documentation for the provided code can be found here: https://github.com/jo-mueller/radiographic_workflow_evaluation</p>
Breast Masses Dataset with Precisely Annotated Sequential Mammograms
<p><strong>BREAST MASSES DATASET WITH PRECISELY ANNOTATED SEQUENTIAL MAMMOGRAMS</strong></p> <p><strong>Citing the Dataset</strong></p> <p>The dataset is released under a Creative Commons Attribution license, so it is mandatory to cite the dataset if you use it in your work in any form. Published academic papers should use the academic paper citation of our paper. Personal works, such as projects or blog posts, should provide a URL to this Zenodo page, though a reference to our paper would also be appreciated.</p> <p><em>Academic paper citation</em></p> <p>TBA</p> <p><em>Personal use citation</em></p> <p>Include a link to this Zenodo page - 10.5281/zenodo.11446259</p> <p><strong>ACKNOWLEDGMENT</strong></p> <p>The publication of this paper is supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 739551 (KIOS CoE) and the Government of the Republic of Cyprus through the Cyprus Deputy Ministry of Research, Innovation and Digital Policy.</p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the dataset, or if you experience any issues downloading files, please contact us at cloizi01@ucy.ac.cy.</p> <p><strong>General Information</strong></p> <p>This dataset consists of 100 pairs of mammograms, from two temporally sequential rounds. Specifically, this dataset includes the prior and recent mammograms with two mammographic views for each patient. This is a complete dataset for the detection and classification of breast masses, using sequential mammograms. It contains normal (BI-RADS 1), benign (BI-RADS 2), and biopsy-confirmed malignant cases (BI-RADS 6). For each mammogram, an image with precise annotation of each individual mass, by two expert radiologists, is provided.</p> <p><strong>More details are available in the README.txt</strong></p>
Kinematically collected reference fingerprint map (RFM) with the high precision tracking system for feature-based indoor positioning
<p>The offline referencing phase, one of the core phases of the fingerprinting-based indoor positioning system (FIPS), is the key stage for deploying the positioning system. The reference fingerprint map (RFM) is acquired for representing the relationship between location-relevant features and the corresponding locations and used for inferring the user’s location at the online stage. The kinematically collecting the RFM using the mobile device with the help of high precision tracking system is contributed to the community for benchmarking comparison of the indoor positioning performance. The detailed description of the data is cooming soon.<br> </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.