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2,142 results for “by contact”
CoMix social contact data (France)
<p>CoMix social contact data for France.</p> <p>We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Guillaume Béraud at the Centre Hospitalier Universitaire de Poitiers.</p>
Replication Data for: Geometric Transformers for Protein Interface Contact Prediction
<p>This dataset contains replication data for the paper titled "Geometric Transformers for Protein Interface Contact Prediction". The dataset consists of pickled Python dictionaries containing pairs of DGLGraphs that can be used to train and validate protein interface contact prediction models. It also contains our best model checkpoints saved as PyTorch LightningModules. Our GitHub repository, DeepInteract, linked in the "Additional notes" metadata section below provides more details on how we use these files as examples for cross-validation.</p>
Contact Endoscopy – Narrow Band Imaging (CE-NBI) Data Set for Laryngeal Lesion Assessment
<p>The endoscopic examination of subepithelial vascular variations of vocal folds can provide complementary diagnostic information for clinicians regarding the development of benign and malignant laryngeal lesions. As one novel technique, Contact Endoscopy combined with Narrow Band Imaging (CE-NBI) can provide real-time and enhanced visualization of these vascular structures. Several studies have addressed the concern of subjective evaluation of CE-NBI images, resulting in the development of multiple computer-based solutions. </p> <p>We introduce the CE-NBI data set, the first publicly available data set with enhanced and magnified visualization of vocal fold subepithelial blood vessels. It comprises 11144 images of 210 adult patients with benign and malignant lesions in the vocal fold. Image annotations include as following for all images of every patient: </p> <ul> <li> <p>Diagnosed laryngeal histopathology label. </p> </li> </ul> <ul> <li> <p>Lesion type benign-malignant label. </p> </li> <li> <p>Leukoplakia diagnosis label. </p> </li> </ul> <p>The dataset consists of two main categories: benign and malignant images. In each category, the images of every patient are ordered according to the laryngeal histopathology class. Additionally, one Excel file is provided to map the image files of each patient to three image labels and image dimensions. </p> <p>This data has successfully been used to perform clinical evaluations as well as design and develop multiple Machine Learning (ML)-based algorithms for laryngeal cancer assessment. </p>
On the formulation and implementation of extrinsic cohesive zone models with contact - data set
<p>This data set contains data relating to the paper "On the formulation and implementation of extrinsic cohesive zone models with contact", <a href="https://doi.org/10.1016/j.cma.2022.115545">https://doi.org/10.1016/j.cma.2022.115545</a> , specifically:<br> 1. the meshes used to conduct finite element analyses,<br> 2. the results of those finite element analyses (in the form of vtk files and numpy pickles), and<br> 3. some images of the meshes and the total displacement at the end of the analyses.<br> <br> The corresponding code to generate and read the data is available at https://github.com/nickcollins-craft/On-the-formulation-and-implementation-of-extrinsic-cohesive-zone-models-with-contact (which is the preferred method), or alternatively via https://doi.org/10.5281/zenodo.6939391.</p>
Social contact data for IDPs in Somaliland (2019)
<p>Social contact data for internally displaced people (IDP) living in Digaale IDP camp in Somaliland. Participants reported all their direct contacts in the 24 hours preceding the survey. This survey was conducted in 2019. See the corresponding paper for more information: <a href="https://doi.org/10.1016/j.epidem.2022.100625">https://doi.org/10.1016/j.epidem.2022.100625</a></p> <p>Data is formatted to be used in the <em>socialmixr</em> package in <em>R</em>.</p>
Nationally Representative Social Contact Patterns among U.S. adults, August 2020-September 2021
<p>The CovidVu study is a national probability survey that collected data on SARS-CoV-2 infection in the U.S. in three rounds: August-December 2020, March-April 2021, and July-September 2021. The goal of this study was to estimate cumulative incidence of SARS-CoV-2 infection in the United States. Surveys were mailed to randomly sampled households in the U.S., and one adult member of the household filled out the questionnaire and sent back SARS-CoV-2 specimens for testing. The data collected included information on social contacts by age, location, and whether they were physical or nonphysical contacts, as well as key demographic information on participants. Survey weights were used to determine unbiased estimates of key parameters, and were based on the population of noninstitutionalized, housed adults (>=18 years of age) in the U.S. Weights were calculated with respect to gender, age, race/ethnicity, education, income, marital status, and census division. For more in-depth information, please visit https://prismhealth.emory.edu/covidvu/ to view recent publications using this data.</p>
Reliquary of contacts for: A pragmatic approach to complex citations, closing the provenance gap between IPCC AR6 figures and CMIP6 simulations
<p>Photos and metadadata pannels of a "Reliquary of contacts for: A pragmatic approach to complex citations, closing the provenance gap between IPCC AR6 figures and CMIP6 simulations" produced to support the "A pragmatic approach to complex citations, closing the provenance gap between IPCC AR6 figures and CMIP6 simulations" presentation given at EGU 2024.</p> <p>------</p> <p>With ever growing abilities to process greater volumes of data the abiiity to sustain the citability and tracability of the underluing source data within outputs such as publications is becoming increasingly challenging. With a range of use-cases, work on how to handle complex citations from the perspective of those producing outputs, journals and those handling the knowledge graph and associated services, is exmaning a how to handle these situations in a sustainable and manageable fashion.<br><br>At the European Geophysical Union (EGU) General Assembly in Vienna, 2024, a pragmatic solution using Zenodo to store 'reliquary' objects was presented. The poster presentation demonstrated the use of existing strucutres within a Zenodo object to address the complex citation use-case around figure, the related data and the source datasets related to the IPCC's AR5 figure data. I.e. how to utulise the existing constructs of a Zenodo item and the range of available metadata fields to give an off-the-shelf solution to allow tracability to the specific datasets used (via their Handle identifiers) and citability of the higher level, DOI-ed dataset collections within which the specific Handle-ed datasets were selected from. Additionally, the connectivity between these two levels of PID objects was also captured within the stored files around which the rich metata was captured.<br><br>The concept of a complex citation 'reliquary' as a metadtata rich object, acting as a referencable nexus in the knowledge graph has been put forth as a solution to the complex citation challenge. It borrows the concept from its historical use, denoting a container or shrine, often richly embellished, for sacred relics (e.g. saints bones, artefacts etc). In the same way here we have both the rich metadata 'container' around the specific details (the 'bones in the box', with their preserved connectivity).<br><br>However, the term 'reliquary' is often a hard one to convey, being somewhat of an obscure term (likewise the term 'nexus' may also be one lacking wider recogniton). Thus, to aid the discussions around the presentation by Pascoe et al. (2024) at the EGU 2023 General Assembly, a physical representation of a metadata reliquary object was produced.<br><br>The purpose of this object was two fold:<br><br> - The first was to show how the reliquary container itself is metadata rich, detailing through the use of ORCIDS, RORs and a DOI, references to external items, complemented by further metadata concerning the specifics of the reliquary's own metadata (its title and the credit for the artist that created it). Futher more, the relationship between the reliquary and those referenced parties/objects was also captured. The contents were also used to demonstrate the importance of making the contents useful for onward users (in this case contact details on business cards). <br> - The second, and for the funder of this piece, arguably the most important aspect was a degree of outreach this provided, both to engage the audience of Pascoe et al (2024), and directly to the artist to demonstrate the importance of this work to the international research data management community and overall to aid engagemeng with the funder's work.<br><br>This resource is provided here as a repository of images of the reliquary itself and in context at the EGU 2024 event as a potential resource others may use to aid further discussions around the use of reliquaries with regards to complex citations. The slides provided of the reliquary box labels are also provided with some annotation to further expand on the metadata aspects of their content.</p>
S118 | PFASFCCMIGEX | 68 PFAS in Migrating & Extractable Food Contact Chemicals (FCCmigex)
<p>This is the collection associated with list S118 PFASFCCMIGEX 68 PFAS in Migrating & Extractable Food Contact Chemicals (FCCmigex) on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>List of the 68 PFASs identified in migrating and extractable food contact chemicals (FCCmigex- see <a href="../records/10551195">S112</a>) by the Food Packaging Forum based on 47 studies as published in Phelps et al (2024) DOI:<a href="https://pubs.acs.org/doi/10.1021/acs.est.3c03702">10.1021/acs.est.3c03702</a>.</p>
IODP Expedition 379 Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
IODP Expedition 371 Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
IODP Expedition 360 Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
IODP Expedition 397 Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
FitHiChIP: Identification of significant chromatin contacts from HiChIP data
<p>FitHiChIP is a computational method for identifying chromatin contacts among regulatory regions such as enhancers and promoters from HiChIP/PLAC-seq data.</p> <p><strong>Functionalities</strong> of FitHiChIP include:</p> <p>1) Calling significant interactions / loops / contacts from a HiChIP / PLAC-seq data </p> <p>2) Identifying peaks (enriched segments) from a HiChIP data (i.e. HiChIP peak caller)</p> <p>3) Finding differential loops among non-differential loci between two different categories of HiChIP samples, each with one or more replicates.</p> <p><strong>GitHub page</strong>: <a href="https://github.com/ay-lab/FitHiChIP">github.com/ay-lab/FitHiChIP</a></p> <p><strong>Documentation</strong>: <a href="https://ay-lab.github.io/FitHiChIP/">https://ay-lab.github.io/FitHiChIP/</a></p> <p><strong>Citation</strong>: Please check the above documentation regarding citation of FitHiChIP</p> <p>About this repository: All the data and results provided here correspond to the published manuscript. The file <strong>Data_Summary.xlsx</strong> summarizes for each figure, corresponding tables storing the related datasets.</p>
Reference data for the Perram and Wertheim (1985) contact function of ellipsoids
<p>Reference data for the Perram and Wertheim (1985) contact function of ellipsoids</p> <p>This dataset provides reference values of the contact function of two ellipsoids, as defined by Perram and Wertheim (Perram, J. W., & Wertheim, M. S. (1985). Statistical mechanics of hard ellipsoids. I. Overlap algorithm and the contact function. Journal of Computational Physics, 58(3), 409–416. <a href="https://doi.org/10.1016/0021-9991(85)90171-8">DOI:10.1016/0021-9991(85)90171-8</a>). This paper will be referred to as PW85 in what follows.</p> <p>Reference values of the <code>F</code> function</p> <p>The data is shared as a HDF5 file <code>pw85_ref_data-YYYYMMDD.h5</code>, which contains the following datasets (to be described below)</p> <ul> <li><code>directions</code>: a 12×3 array,</li> <li><code>F</code>: a 108×108×12×9 array,</li> <li><code>lambdas</code>: a length-9 array,</li> <li><code>radii</code>: a length-3 array,</li> <li><code>spheroids</code>: a 108×6 array.</li> </ul> <p>The attached Python script <code>pw85_gen_ref_data.py</code> was used to generate the data; it uses the <a href="http://mpmath.org/">mpmath</a> library.</p> <p>Mathematical definition of the contact function</p> <p>The contact function is defined in PW85 as the maximum over <code>(0, 1)</code> of the <code>F</code> function which is defined as follows [see Eq. (3.7) in PW85, with slightly different notations]</p> <pre><code>F(λ) = λ(1-λ)r₁₂ᵀ⋅Q⁻¹⋅r₁₂,</code></pre> <p>where <code>0 ≤ λ ≤ 1</code> is a scalar, <code>r₁₂</code> is the center-to-center vector. <code>Q</code> is the matrix defined as follows</p> <pre><code>Q = (1-λ)Q₁ + λQ₂,</code></pre> <p>where <code>Qᵢ</code> is the symmetric, positive definite matrix that defines ellipsoid <code>Ωᵢ</code> through</p> <pre><code>m ∈ Ωᵢ iff (m-cᵢ)ᵀ⋅Qᵢ⁻¹⋅(m-cᵢ) ≤ 1,</code></pre> <p>where <code>cᵢ</code> is the center of <code>Ωᵢ</code>. Then, the contact function <code>F₁₂</code> is defined as the maximum of <code>F</code> [see Eq. (3.8) in PW85]</p> <pre><code>F₁₂(r₁₂, Q₁, Q₂) = max{ F(λ), 0 ≤ λ ≤ 1 }.</code></pre> <p>Parametrization</p> <p>The reference data is restricted to spheroids (equatorial radius: <code>aᵢ</code>; polar radius: <code>cᵢ</code>; direction of axis of revolution: <code>nᵢ</code>)</p> <pre><code>Qᵢ = aᵢ²I + (cᵢ²-aᵢ²)nᵢᵀ⋅nᵢ,</code></pre> <p>(<code>I</code>: identity matrix). The radii take the following values</p> <pre><code>aᵢ, cᵢ ∈ {0.01999, 1.999, 9.999}.</code></pre> <p>These values of the radii are stored in the <code>radii</code> dataset of the HDF5 file. The orientations <code>nᵢ</code> coincide with the vertices of an icosahedron</p> <pre><code>nᵢ = [0, ±u, ±v]ᵀ or nᵢ = [±v, 0, ±u]ᵀ or nᵢ = [±u, ±v, 0]ᵀ,</code></pre> <p>where</p> <pre><code> 1 φ 1+√5 u = ───────, v = ─────── and φ = ────. √(1+φ²) √(1+φ²) 2</code></pre> <p>The orientations are stored in the <code>directions</code> dataset as a 12×3 array. The matrices <code>Qᵢ</code> are precomputed and stored in the <code>spheroids</code> dataset as a 108×6 array (note: 108 = 12 orientations × 3 equatorial radii × 3 polar radii). <code>spheroids[i, :]</code> stores the upper triangular part of the corresponding matrix in row-major order</p> <pre><code>⎡ spheroids[i, 0] spheroids[i, 1] spheroids[i, 2] ⎤ ⎢ spheroids[i, 3] spheroids[i, 4] ⎥. ⎣ sym. spheroids[i, 5] ⎦</code></pre> <p>The scalar <code>λ</code> takes tabulated values (see the <code>lambdas</code> dataset)</p> <pre><code>λ ∈ {0.1, 0.2, …, 0.9}.</code></pre> <p>Note that <code>λ = 0.0</code> and <code>λ = 1.0</code> are excluded, since <code>F</code> is uniformly 0 in that case.</p> <p>Reference values of the <code>F</code> function</p> <p>The reference values of the function <code>F</code> are stored in the <code>F</code> dataset, which is a 108×108×12×9, such that <code>F[i, j, h, k]</code> is the value of <code>F</code> for</p> <pre><code>Q₁ = spheroids[i], Q₂ = spheroids[j], r₁₂ = directions[h] and λ = lambdas[k].</code></pre> <p>Note that the <code>r₁₂</code> vector takes values in the <code>directions</code> dataset. In other words, only unit-length center-to-center vectors are considered here. Indeed, <code>F</code> trivially depends on the norm of <code>r₁₂</code>, which is therefore not considered here in order to reduce the size of the dataset.</p> <p>Reference values of the contact function</p> <p>Note: the following is <em>not</em> implemented yet, as reference values of the contact function were not deemed useful. Indeed, once <code>F</code> is validated, it is straightforward to check that the implementation of <code>F₁₂</code> to be tested indeed maximizes <code>F</code>.</p> <blockquote> <p>The reference values of the contact function <code>F₁₂</code> are stored in the <code>contact_function</code> dataset, which is a 108×108×12×3 array, such that <code>contact_function[i, j, h, k]</code> is the value of <code>F₁₂</code> for</p> <pre><code>Q₁ = spheroids[i], Q₂ = spheroids[j] and r₁₂ = radii[h] * directions[k].</code></pre> <p>Note that the <code>r₁₂</code> vector is not normed, here.</p> </blockquote>
IODP Expedition 398 Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
IODP Expedition 355 Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
IODP Expedition 356 Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
Plasmodesmata Act as Unconventional Membrane Contact Sites Regulating Inter-Cellular Molecular Exchange in Plants.
<p>This table contains peaks aera values from LC-MS for lipidomic quantification of PIP and PIP2. These data were used for Pérez-Sancho, Jessica and Smokvarska, Marija and Glavier, Marie and Sritharan, Sujith and Dubois, Gwennogan and Dietrich, Victor and Platre, Matthieu and Li, Ziqiang Patrick and Paterlini, Andrea and Moreau, Hortense and Fouillen, Laetitia and Grison, Magali S. and Cana-Quijada, Pepe and Moraes, Tatiana Sousa and Immel, Françoise and Wattelet, Valerie and Ducros, Mathieu and Brocard, Lysiane and Chambaud, Clément and Zabrady, Matej and Luo, Yongming and Busch, Wolfgang and Tilsner, Jens and Helariutta, Yrjö and Russinova, Jenny and Taly, Antoine and Jaillais, Yvon and Bayer, Emmanuelle, Plasmodesmata Act as Unconventional Membrane Contact Sites Regulating Inter-Cellular Molecular Exchange in Plants. </p>
IODP Expedition 353 Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
IODP Expedition 359 Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
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.