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1,506 results for “objects”
Multi-Scale Computational Design of Metal-Organic Frameworks for Carbon Capture Using Machine Learning and Multi-Objective Optimization
<p>This repository contains CIF files for metal-organic frameworks and Grand canonical Monte Carlo (GCMC) simulation results for the article <em>Multi-Scale Computational Design of Metal-Organic Frameworks for Carbon Capture Using Machine Learning and Multi-Objective Optimization</em> by Zijun Deng and Lev Sarkisov.</p>
Data and STL files supporting article: Encoding Quadrupolar Capillary Information into Saddle- Shaped Objects for Self-Assembly
<ul> <li>Data and STL files supporting the article entitled: Encoding Quadrupolar Capillary Information into Saddle-Shaped Objects for Self-Assembly</li> <li>Text files for Figures 3 and 5 are the raw positions and orientations of the particles extracted frame by frame from experimental videos. </li> <li>Text files for Figure 4 are the liquid elevation around objects.</li> <li>Figure_roughness.pdf shows zoomed-in pictures of our typical object, focusing on edge roughness for the readers to evaluate the edge roughness.</li> </ul>
Object A-3
<p>Object A-3. Average nucleotide identity results for comparisons between Pssc reference strains and cucurbit-associated strains to type strains of <em>P. syringae</em> and <em>P. cerasi</em>. Kiersten Fullem, Dissertation, University of Florida 2024.</p>
Objectives for CSP pilots of WT for ENTICE workbench
<p>Data set for the objectives of ENTICE with WT pilots</p>
Beyond human faces: Adaptation aftereffects in complex natural objects
<p>Raw participant data for the corresponding manuscript.</p>
The Toybox Dataset of Visual Object Transformations-Part 3: Vehicles
<p>We introduce a new video dataset called Toybox for computer vision research. Videos in Toybox come from first-person, wearable camera recordings of common household objects and toys being manually manipulated to undergo structured transformations like rotations and translations. This part three of the three-part dataset. Part three contains videos of toy vehicles: car, truck, airplane and helicopter. Part one and two include animals and households, respectively.</p>
DataCrate: a method of packaging, distributing, displaying and archiving Research Objects
<p>In characterizing the term <i>Research Object</i> the call for proposals for Research Object 2018 uses the phrase "multi-part research outcomes with their context". The <a href="https://github.com/UTS-eResearch/DataCrate/">DataCrate specification</a><a href="https://data.research.uts.edu.au/examples/v1.0/datacrate-RO-2018/data/paper.html#fn1">1</a> is a research data packaging and dissemination specification designed to capture exactly that; outcomes (also inputs) and context.</p><p>DataCrate specifies how to gather together data in such a way that it can (a) be packaged via zip, tar, a disc image, a multi-part package or (b) be hosted on a web server or file share for inspection by potential users and/or used directly on High Performance Computing systems or otherwise accessed and analyzed.</p><p>DataCrates can contain any kind of data, and the contextual information may include, but is not limited to, data about the people, software and equipment used in the research as well as supporting documents such as publications, funding agreements or README files.</p><p> </p>
Dataset for publication: 'Search Space Representation and Reduction Methods to Enhance Multi-Objective Water Supply Monitoring Design'
<p>This is the dataset the publication "Search Space Representation and Reduction Methods to Enhance Multi-Objective Water Supply Monitoring Design" is based on.</p>
Intermediate data objects from running the machine learning code for Shen et al, Nature, 2018
<p>These are the RData objects of the processed cfMeDIP data that were used to run the machine learning analyses in "Sensitive tumour detection and classification using plasma cell-free DNA methylomes", Nature, 2018. This archive also includes the models we generated, and training and validation data matrices that people can use to fit new models and evaluate performance. These objects are to be used with the scripts and markdown at doi: 10.5281/zenodo.1242697 . The ReadMe contains descriptions. </p>
Zemax files for NA=0.29 and NA=0.40 long-working-distance lens objectives assembled by commercial on-catalog singlets
<p>Zemax files of two lens objective with numerical apertures (NA) of 0.29 and 0.4. The objective lenses are made entirely from commercial on-catalog Ø1'' singlets. Both the objectives can correct the spherical aberrations due to the standard flat vacuum glass windows with various thickness. The diffraction-limited fields of view for NA=0.29 and NA=0.4 objectives are 600 μm and 150 μm. The spatial resolutions for the two objectives are 1.8 μm and 1.3 μm at the design wavelength.</p>
data for preprint: MARGO (Massively Automated Real-time GUI for Object-tracking),a platform for high-throughput ethology by Werkhoven et al
<p>Data for preprint: MARGO (Massively Automated Real-time GUI for Object-tracking),a platform for high-throughput ethology by Werkhoven et al.<br> <br> index.html provides a read-me for the organization of this repository. For its links to work the two .zip files should be extracted.</p>
Perceptual thresholds of audio-visual spatial coherence for a variety of audio-visual objects
<p>Dataset accompanying the paper "Perceptual thresholds of audio-visual spatial coherence for a variety of audio-visual objects", published at the AES International Conference on Audio for Virtual and Augmented Reality, Remond, WA, USA</p>
Göhringer, F., Löhr-Limpens, M., Hesse, C., & Schenk, T. (2019) 'Grasping discriminates between object sizes less not more accurately than the perceptual system.' Vision. DOI 10.3390/vision3030036
<p>This repository contains 6 data files and 1 readme file.</p>
Quantitative measures of corneal transparency, derived from objective analysis of depth-resolved corneal images, demonstrated with full-field optical coherence tomographic microscopy
<p>Supporting data for: <a href="https://zenodo.org/record/2579947">Quantitative measures of corneal transparency, derived from objective analysis of depth-resolved corneal images, demonstrated with full-field optical coherence tomographic microscopy</a></p>
Kemmelberg (Belgium) condition images metal objects (2019-08-20)
<p>ENGLISH<br> =======</p> <p>The digital recordings, 13 metal objects from the Kemmelberg (K) excavations (Belgium), present visually their physical condition in August 2019:</p> <p>1. captures with the Phase One IQ3 100MP camera, with a Schneider-Kreuznach Macro LS 120 mm f/4.0 lens<br> 2. meets the FADGI 4 stars en Metamorfoze (Full) standards (i.e. Color Encoding (∆E1976), Total Noise, Illuminance Uniformity)<br> 3. processed with Capture One software (version 11.1.1.11)<br> 4. all are 1500 dpi; verifiable on the color-scale target included on each image<br> 5. written in JPEG sRGB IEC61966-2.1 color space (consultation copy) and in TIFF eciRGB v2 color space (preservation copy)</p> <p>6. recordings by Hendrik Hameeuw<br> 7. recorded at "Imaging Lab, KU Leuven Libraries"</p> <p>Recordings made for V.O.B.o.W. vzw.</p> <p>When used in publications (offline and/or online and/or projection), please refer to the lab (7) and photographer (8), and when possible also the technical info of (1) and (2).</p> <p><br> NEDERLANDS<br> ==========</p> <p>De digitale opnames, 13 metalen objecten van de opgravingen Kemmelberk (K) (België), geven visueel hun fisieke conditie in augustus 2019:</p> <p>1. gemaakt met de camera Phase One IQ3 100MP, en met de Schneider-Kreuznach Macro LS 120 mm f/4.0 lens<br> 2. voldoen aan de FADGI 4 sterren en Metamorfoze (Full) standaarden (i.e. Color Encoding (∆E1976), Total Noise, Illuminance Uniformity)<br> 3. verwerkt door Capture One software (versie 11.1.1.11)<br> 4. allen aan 1500 dpi; steeds verifieerbaar met het bijgefotografeerde kleur-schaal target<br> 5. in JPEG sRGB IEC61966-2.1 kleurenruimte en TIFF eciRGB v2 kleurenruimte geschreven </p> <p>6. Opname door Hendrik Hameeuw<br> 7. Opname te "Digitaal Labo (Imaging Lab), KU Leuven Bibliotheken"</p> <p>Opnames voor V.O.B.o.W. vzw.</p> <p>Bij gebruik in publicaties (offline en/of online en/of projectie), gelieve naam van labo (7) en fotograaf (8) te vermelden, en zo ver als mogelijk de technische info onder (1) en (2) te vermelden.<br> </p>
Woodcarving media objects v2 (glbs)
<p>Woodcarving media objects v2 (glbs)</p>
Woodcarving media objects
<p>Woodcarving media objects</p>
Datasets and models for constrained retrosynthesis with multi-objective search and disconnection-aware Chemformer
Open the record for dataset details and reuse information.
Replication package for the paper: "Code Clone Configuration as a Multi-Objective Search Problem"
<p><strong>Replication Package Context</strong></p> <p>This is the replication package for the paper "Code Clone Configuration as a Multi-Objective Search Problem". The paper was originally published in the <em>International Symposium on Empirical Software Engineering and Measurement</em> (ESEM).</p> <div> <div><strong>Search Algorithms and Datasets for MC3 Problem</strong></div> <div>This repository contains implementations of search algorithms for solving the MC3 (Multi-objective Code Clone Configuration) problem. </div> <div>Additionally, it includes various files related to Elasticsearch configurations, I/O operations, and other supporting resources. For more information about the project files and folders, you can read the README.md for general information.</div> </div>
Hydrograph and Recession Flows Predictions Simulations using Deep Learning: Watershed Uniqueness and Objective Functions
<p>This resource contains the results presented in the paper titled 'Hydrograph and Recession Flows Simulations using Deep Learning: Watershed Uniqueness and Objective Functions' by Abhinav Gupta and Sean McKenna.<span><span><br></span></span></p> <p>'</p>
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.