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717 results for “Manufactures”
Non-inferiority Study of ZK-A03 in Treatment of Herpes Zoster Before and After the Alteration of the Active Ingredient Manufacturer
ClinicalTrials.gov study NCT05806918. IPD Sharing: NO. Countries: 0. Publications: 0.
Host-informed molecular engineering improves antigen quality and enables integrated manufacturing of a trivalent subunit vaccine
GEO Series GSE159338. Komagataella phaffii. 27 samples. Type: Expression profiling by high throughput sequencing.
A high-density microfluidic bioreactor for the automated manufacturing of CAR T cells
GEO Series GSE261103. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Identification of smRNA-based cell-type markers for the stem cell manufacture system with a statistical scoring function
GEO Series GSE206925. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Impact of CD4+ T cell help on CD8+ CAR T cell gene expression during ex-vivo manufacturing
GEO Series GSE245427. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
Innovation in small and micro manufacturing enterprises in Johannesburg
<p>A firm-level dataset focused on innovation in small and micro manufacturing enterprises in Johannesburg, collected in 2019 by the DSI/NRF South African Research Chair in Industrial Development (SARChI-ID).</p>
HybridCAD: A Comprehensive Dataset for Hybrid Additive-Subtractive Manufacturing Feature Recognition in B-Rep CAD Models
<p>The <em>HybridCAD</em> dataset is a novel resource tailored for hybrid additive-subtractive feature recognition in Computer-Aided Design (CAD) models, uniquely combining features from both manufacturing processes. Building on the <em>MFCAD</em> and <em>MFCAD++</em> datasets, <em>HybridCAD</em> introduces additive manufacturing features alongside traditional subtractive ones, enabling the exploration and development of machine learning models for more complex, hybrid manufacturing applications. This dataset is especially suited for automatic feature recognition (AFR) research and model training in hybrid manufacturing contexts.</p> <h3>Dataset Composition</h3> <p>The dataset consists of 8,938 Boundary Representation (B-Rep) CAD models distributed into three main directories:</p> <ol> <li> <p><strong>STEP Files</strong>:</p> <ul> <li>Contains CAD models in STEP format, each representing a distinct part with hybrid manufacturing features.</li> <li>Generated using PythonOCC CAD software, these CAD files serve as the foundation for feature recognition tasks.</li> </ul> </li> <li> <p><strong>Feature Labels</strong>:</p> <ul> <li><strong>File</strong>: <code>feature_labels.txt</code></li> <li>Provides unique label IDs for each B-Rep face in every CAD model, denoting the manufacturing feature name it belongs to.</li> <li>Each CAD model face is labelled according to one of 29 hybrid additive-subtractive features, allowing for accurate and detailed feature recognition.</li> </ul> <ul> <li> </li> </ul> </li> <li> <p><strong>Hierarchical B-Rep Graphs</strong>:</p> <ul> <li>Stored in HDF5 format for structured access to B-Rep data.</li> <li>Detailed structural information is available in <code>h5_structure.txt</code>, explaining the hierarchical arrangement of B-Rep graphs.</li> </ul> </li> </ol> <h3>Dataset Splits</h3> <p>The dataset is split into training, validation, and testing sets as follows:</p> <ul> <li><strong>Training Set</strong>: 6,256 samples (70%)</li> <li><strong>Validation Set</strong>: 1,342 samples (15%)</li> <li><strong>Testing Set</strong>: 1,340 samples (15%)</li> </ul> <h3>Hybrid Manufacturing Features</h3> <p><em>HybridCAD</em> includes a diverse range of additive and subtractive manufacturing features, expanding beyond the subtractive-only features of <em>MFCAD++</em>. This inclusion enables the exploration of hybrid manufacturing processes and the recognition of a broader feature set in CAD models. The complete feature list includes:</p> <p>Label Feature<br>0 Chamfer<br>1 Through hole<br>2 Triangular passage<br>3 Rectangular passage<br>4 6-sided passage<br>5 Triangular through slot<br>6 Rectangular through slot<br>7 Circular through slot<br>8 Rectangular through step<br>9 2-sided through step<br>10 Slanted through step<br>11 O-ring<br>12 Blind hole<br>13 Triangular pocket<br>14 Rectangular pocket<br>15 6-sided pocket<br>16 Circular end pocket<br>17 Rectangular blind slot<br>18 Vertical circular end blind slot<br>19 Horizontal circular end blind slot<br>20 Triangular blind step<br>21 Circular blind step<br>22 Rectangular blind step<br>23 Round<br>24 Extrude cylinder<br>25 Extrude rectangle<br>26 Extrude triangle<br>27 Extrude hexagon<br>28 Extrude pentagon<br>29 Stock</p>
Supplementary data repository for publication "Machine hammer peening as a post-processing treatment for powderbed manufactured maraging steel X3NiCoMoTi18-9-5 (1.2709)"
<p>Supplemantary data repository for investigation on powderbed 3D printed tesnile specimens in raw and postprocessed conditions.</p> <p>This readme contains a brief description of the chosen materials, parts and testing devices as well as the perfomed testing methods, including tensile testing, hardness testing and microstructural analysis, with references to the respective supplementary data in the repository. An explanation of the data aquisition and processing methods, together with the used nomenclature is given as well.</p> <p>The dataset is compressed as `Dataset.zip` to preserve the folder structure.</p> <p>See README.MD or README.PDF for detailed description of the dataset.</p> <p> </p>
Expression data from C. elegans exposed to pristine and aged ZnO manufactured nanoparticles
GEO Series GSE114881. Caenorhabditis elegans. 15 samples. Type: Expression profiling by array.
Turbulence activates platelet biogenesis to enable clinical scale manufacturing from human iPS cells
GEO Series GSE112512. Homo sapiens. 2 samples. Type: Expression profiling by array.
Influence of manufacturing processes on cell surface properties of probiotic strain Lactobacillus rhamnosus Lcr35®
GEO Series GSE58820. Lacticaseibacillus rhamnosus; Lactobacillus. 9 samples. Type: Expression profiling by array.
Data set documentation of two surveys to identify and validate the critical success factors (CSFs) in the implementation of Lean Six Sigma (LSS) in Colombian manufacturing SMEs
<p>Documentation describing the data sets of two surveys conducted with the aim of identifying and validating critical success factors (CSFs) in the implementation of Lean Six Sigma (LSS) in manufacturing SMEs in Colombia is included.</p> <p>The first, at a general level and named <strong><em>E1</em></strong>, had the objective of identifying manufacturing SMEs in Colombia that have implemented Lean, Six Sigma or LSS. The second, of a specific level and named <strong><em>E2</em></strong>, was applied in the SMEs found in <strong><em>E1</em></strong> and had the objective of identifying and prioritizing the CSFs in the implementation of LSS and to assess their importance and practice using Likert-type scales. It also made it possible to identify the best-known and most used LSS tools and investigate the specific results of LSS implementation in SMEs.</p> <p>The questionnaire proposed by J. Antony et al. (2008) and adapted by Timans et al. (2012) for LSS was used. The consent to use is included in the documentation. The technical concept of four LSS experts in Colombia was requested to adapt expressions to the context.</p> <p>In the statistical validation phase of the identified CSFs, the estimation of a set of statistical criteria commonly used in psychometric research was made using statistical software SPSS® Version 26 from IBM®. The validation of the factorial structure of the complete model made up of the identified factors and their observable variables was done through a Confirmatory Factorial Analysis using the graphic software SPSS Amos® Version 26 also from IBM®.</p> <p>To build the graphic model the observable variables (statements in the questionnaire E2 of Section III) were represented by rectangles and identified with the letter P (question in Spanish) and with the number according to the order of appearance from 1 to 51 (Example, P1). The critical success factors (CSFs), which are the variables that are not directly measurable or latent, were represented with ovals and identified with the letter F for factor and with the number according to the order of appearance in the questionnaire E2 Section III from 1 through 13 (Example, F1)</p> <p>The following documentary support files are included:</p> <ul> <li><em>Dataset documentation.pdf</em> file</li> <li><em>Consent for the use of the E2 questionnaire.pdf</em> file</li> <li>Anonymized data from <strong><em>E1</em></strong> survey with 352 complete responses in the <em>E1_survey_dataset.exc</em> file.</li> <li>Anonymized data from <strong><em>E2</em></strong> survey with 44 complete responses in the <em>E2_survey_dataset.exc</em> file.</li> <li><strong><em>E1</em></strong> survey instrument with all survey items in the <em>E1_survey_instrument.p</em>df file.</li> <li><strong><em>E2</em></strong> survey instrument with all survey items in the <em>E2_survey_instrument.pdf</em> file.</li> <li>Factorial structure of the CSFs of the initial model in the <em>CFA_CSF </em>Amos file.</li> <li><em>CSFs</em> SPSS file.</li> </ul>
Asthma acute care use and semiconductor manufacturing exposure census tract data and covariates
Open the record for dataset details and reuse information.
Earning management and Tax avoidance: Evidence from Vietnamese manufacturing firms
Open the record for dataset details and reuse information.
EDS analysis of the scaffolds manufactured from the marine sponge species Dragmacidon reticulatum and Amphimedon viridis.
<p>EDS analysis of the scaffolds manufactured from the marine sponge species Dragmacidon reticulatum and Amphimedon viridis.</p>
Machine Learning Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview
<p>The data files include bibliometric file. data extracted from bibliometric data.</p>
Wearable Network for Multi-Level Physical Fatigue Prediction in Manufacturing Workers
<p>This repository contains the datasets from the 'Wearble Network for Multi-Level Physical Fatigue Prediction in Manufacturing Workers' study. The repository has been divided into 2 tasks folders Task Harnessing and Task Composite with 43 participant subfolders. </p>
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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.