Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
717
datasets available to search
ShareScore release 0.9.0
Dataset results
717 results for “manufacturer”
Statistical and Dynamic Model of Surface Morphology Evolution during Polishing in Additive Manufacturing
<p>This repository maintains data and code associated with our accepted paper in IISE Transactions titled "Statistical and Dynamical model of Surface Morphology Evolution during Polishing in Additive Manufacturing". To briefly summarize,</p> <p><strong>1. Polishing_stagewise_data.zip</strong> - Contains height values measured at 32 different locations on the 3D printed sample using an optical profilometer prior to polishing (Stage 0) and post every stage of polishing (Stages 1 to 6). Please refer to the following paper for experimentation details and process parameters: "<em>Jin, S., A. Iquebal, S. Bukkapatnam, A. Gaynor, and Y. Ding (2019, 10). A gaussian process model-guided surface polishing process in additive manufacturing. Journal of Manufacturing Science and Engineering 142, 1–17.</em>"</p> <p><strong>2. Initial_surface_generation.m</strong> - Script containing the Initial surface generation algorithm using the random circle packing algorithm. This file generates the surface asperity distribution and their graph connectivity of a 3D printed sample prior to polishing (Figure 4(b) in paper). One such realization is stored and compared with experimental data (Refer #3).</p> <p><strong>3. Stage0_fitted_data.mat</strong> - .mat file containing data pertaining to height measures of the 3D printed sample prior to polishing and generated initial surface (simulation) which is statistically similar to the actual data.</p> <p><strong>4. Parameter_fitting_Polishing.m</strong> - Script containing the model capturing polishing dynamics with network formation, evaluated at each stage of polishing. This file generates the Bearing Area Curves of the initial surface simulated after each stage of polishing and compares with experimental data (Figures 3, 5, 6, 7 and 8 in paper). (The script makes use of other functions defined in #5).</p> <p><strong>5. surface_roughness.m, graph_evolution.m, solve_for_d.m, KLDiv.m</strong> and <strong>Gen_hurst.m</strong> - Matlab scripts containing functions that are called within the main script (Parameter_fitting_Polishing.m)</p> <p><strong>6. Simulated_Annealing.zip</strong> - Zip file containing files related to Simulated Annealing Algorithm. Please read the <strong>README_Simulated_Annealing.txt</strong> for instructions to reproduce the optimized parameter solutions.</p> <p><strong>7. pub_fig.m</strong> - Script containing the formatting options for plots and figures.</p>
NEMARCO project: Dataset for the publication "Development of a new manufacturing route for NiCrSiFeB alloys by Direct Energy Deposition Laser Beam process (LMD)"
<p><strong>LMD dataset</strong></p> <p>This dataset gathers data from different parts of the Laser Metal Deposition metal Additive Manufacturing process (DED-LB). The dataset covers not only the process development data for samples manufacturing and monitored data of the melt pool size during the process, but also the metrics associated to the powder feedstock consumption, energy consumption and process efficiency.</p> <p><strong>Motivation</strong></p> <p>Nickel-based NiCrSiFeB alloy (Ni-Cr-Si-B self-fluxing family) are excellent candidates for replacing Cobalt-based alloys in aeronautical components such as sealing rings, valve seats, sliding bearing seats, etc. In this type of components, commonly manufactured by centrifugal casting and conventional processes, high temperature wear and stiffness under complex thermo-mechanical stresses cause lack of sealing and an increase in the wear rate. Metal additive manufacturing by direct laser metal deposition with powder (p-LMD) is presented as a potential manufacturing route for the complex processing of this type of alloys. This research work deals with the development of a new manufacturing route using p-LMD that ranges from the proper selection of the chemical composition for the starting powders, the development of the LMD process parameters to tackle the challenges associated to the wide solidification range and crack susceptibility of Ni-Cr-Si-B alloys, its monitoring and control, as well as the post- processing required to achieve the manufacture of aeronautical components.</p>
Efficacy and Safety Study of a Recombinant Protein-Free Manufactured Factor VIII (rAHF-PFM) in Previously Untreated Hemophilia A Patients
ClinicalTrials.gov study NCT00157157. IPD Sharing: YES. Countries: 10. Publications: 2.
Passive Morphological Adaptation for Obstacle Avoidance in a Self-Growing Robot Produced by Additive Manufacturing
<p>Dataset acquired for the obstacle negotiation experiments. The dataset collects the forces obtained by the growing robot when facing obstacles at different inclinations.</p> <p>You an find the related publication on https://doi.org/10.1089/soro.2019.0025</p>
Data from: Association between night-shift work, sleep quality, and health-related quality of life : a cross-sectional study among manufacturing workers in a middle-income setting
<p>Objectives: Night-shift work may adversely affect health. This study aimed to determine the impact of night-shift work on health-related quality of life (HRQoL), and assess whether sleep quality was a mediating factor.</p> <p>Design: Cross-sectional study.</p> <p>Setting: 11 manufacturing factories in Malaysia.</p> <p>Participants: 177 night-shift workers aged 40 to 65 years old were compared with 317 non-night-shift work.</p> <p>Primary and secondary outcomes: Participants completed a self-administered questionnaire on socio-demography and lifestyle factors, short Form-12v2 Health Survey (SF-12), and the Pittsburgh Sleep Quality Index (PSQI). Baron and Kenny's method, Sobel test and multiple mediation model with bootstrapping were used to determine whether PSQI score or its components mediated the association between night-shift work and HRQoL.</p> <p>Results: Night-shift work was associated with sleep impairment and HRQoL. Night-shift workers had significantly lower mean scores in all the eight SF-12 domains (p<0.001). Compared to non-night shift workers, night-shift workers were significantly more likely to report poorer sleep quality, longer sleep latency, shorter sleep duration, sleep disturbances, and daytime dysfunction (p<0.001). Mediation analyses showed that PSQI global score mediated the association between night-shift work and HRQoL. "Subjective sleep quality" (indirect effect=-0.24, standard error [SE]=0.14, bias corrected 95%Confidence Interval [BC 95%CI]: -0.58 to -0.01) and "sleep disturbances" (indirect effect=-0.79, SE=0.22, BC 95%CI: -1.30 to -0.42) were mediators for the association between night-shift work and physical wellbeing, whereas "sleep latency" (indirect effect=-0.51, SE=0.21, BC 95%CI: -1.02 to -0.16) and "daytime dysfunction" (indirect effect=-1.11, SE=0.32, BC 95%CI: -1.86 to -0.58) were mediators with respect to mental wellbeing.</p> <p>Conclusion: Sleep quality partially explains the association between night-shift work and poorer HRQoL. Organisations should treat the sleep quality of night-shift workers as a top priority area for action in order to improve their employees' overall wellbeing.</p>
Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts Data and Scripts
<p>The repository contains the data corresponding to the Paper "Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts".</p> <p>Random30, Random50, Random100, Similiar10, Similar30 and Similar50.zip contain the data sets (obj Files).</p> <p>R30_physical_images.zip and sim50_physical_images.zip contain the photos made from the physical components which are used for the evaluation.</p>
Optimizing the Geometric Configuration and Manufacturing Process of High Mast Illumination Poles
<p>This work presents the development of a high-fidelity model that accounts for the cumulative effect of welding and hot- dip galvanizing on the determining the resulting residual stresses and deformations induced during the manufacturing process of high mast illumination poles (HMIPs). This model is meant to elucidate the root causes of weld toe cracks in HMIPs. A TxDOT pole-to-base plate connection detail was used as the reference model in the analysis. Welding was modeled using the plug-in Abaqus Welding Interface (AWI), which automatically implements a series of sequential thermal and mechanical analyses. Then, the welding stress results were used as initial input to the galvanizing analysis. The cumulative stress results were compared against simulations that only considered the galvanizing process. A parametric study was then conducted to quantify the variation in the residual stresses and equivalent plastic strain magnitudes induced during the welding and galvanizing of HMIPs due to changes in welding and galvanizing practices. The results revealed that the cumulative effects of the different processes involved in the manufacturing of HMIPs contribute to the formation of galvanizing cracks in HMIPs. Also, increasing the dipping submersion speed during galvanizing and lowering the torch temperature magnitude during welding results in fewer zones prone to cracking. Altering the angle of inclination effect did not have a significant impact on the results. Performing variations in the manufacturing practices used for the fabrication of HMIPs can contribute to reducing the extensive inspection procedures conducted post-galvanizing to identify cracks.</p>
Presnya Museum 04 Room of manufacture worker
Living room of the worker of textile manufacture, 1900-s In the 1900s the Presnya region in Moscow was at the center of Moscow's industrial developmant. A worker of one of the Prokhorov factories, could live in such a room. It is characteristic thet the new towns'people, like all the newly arrived from the village to work. Presnya Museum in Moscow. Diorama. Fisheye photos. Source: Objaverse 1.0 / Sketchfab
Big Data Analytics: Data-driven optimization of manufacturing companies
<p>This dataset contains the result of a survey conducted to identify the transformation of manufacturing companies by big data analytics. It contains 154 participations from employees of manufacturing companies of which 133 have completed all questions. The dataset is UTF8 encoded and available in German (original) as well as English (translated).</p>
CiP Discrete Manufacturing Dataset (CNC Miling Machine (Process Data))
<p>Dataset recorded in the process learning factory CiP at the Institute for Production Management, Technology and Machine Tools, TU Darmstadt for the purpose of demonstrating and testing InterQ developed solutions. It was used to demonstrate the framework's effectiveness in a case study, involving process monitoring of a real-world Computer Numerical Control milling process. The dataset includes accelerometer data and multiple types of realistic concept drifts. The uploaded file includes the link and password for accessing the dataset in PTW's cloud infrastructure.</p>
CiP Discrete Manufacturing Dataset (CNC Miling Machine (Process Data))
<p>Dataset recorded in the process learning factory CiP at the Institute for Production Management, Technology and Machine Tools, TU Darmstadt for the purpose of demonstrating and testing InterQ developed solutions. It was used to demonstrate the framework's effectiveness in a case study, involving process monitoring of a real-world Computer Numerical Control milling process. The dataset includes accelerometer data and multiple types of realistic concept drifts. The uploaded file includes the link and password for accessing the dataset in PTW's cloud infrastructure.</p><p> </p>
Supplemental data to AALE 2024 publication "Adaptive manufacturing: dynamic resource allocation using multi-agent reinforcement learning"
<p>Release as supplementary material for our contribution at AALE 2024: "Adaptive manufacturing: dynamic resource allocation using multi-agent reinforcement learning"<br><br>The evaluation datasets stored in this collection are used to compare the performance of multi-agent reinforcement learning. In addition, the performance of other methods such as (meta-) heuristic algorithms or single agent reinforcement learning algorithms or novel methods of search space reduction can also be compared.</p>
Fault Detection and Inventory Management in Manufacturing with Plastic Bricks: A Dataset
<p><strong>Dataset for Smart Manufacturing</strong></p> <p>The dataset contains images of plastic bricks showcasing various colors, shapes, and minor surface damages, designed to represent the use-cases of quality classification and inventory management in manufacturing. The use cases are separated and include 3 categories for quality classification and 24 categories for inventory classification. The data is analyzed in the publication titled "Demonstrating Computer Vision to Small- and Medium-sized Enterprises in Manufacturing: Towards Overcoming Costs and Implementation Challenges". The associated research explores the development of a simple computer vision demonstrator and its demonstration to small- and medium-sized enterprises. </p> <p><strong>Structure of Files</strong></p> <blockquote> <p>quality_classification (224 images)</p> <p> defect</p> <p> defect_free</p> <p> empty</p> <p>inventory_classification (2732 images)</p> <p> beige_large</p> <p> beige_small</p> <p> blue_bright_large</p> <p> 21 further categories (colour_shape)</p> </blockquote>
Ancient figurine, manufactured by humans
Прадавній зразок портативного мистецтва. Форму скульптурці надано ручною обробкою. Датування та інтерпретація навизначені. Максимальна довжина — 18 см. Ancient portable rock art figurine. Evidentially manufactured by human beings. Chronological attribution and meaning are not clear. Maximum length — 18 cm. Source: Objaverse 1.0 / Sketchfab
Manufacturing of high strength and high conductivity copper with laser powder bed fusion
<p>Additive manufacturing (AM), known as 3D printing, enables rapid fabrication of geometrically complex copper (Cu) components for electrical conduction and heat management applications. However, pure Cu or Cu alloys produced by 3D printing often suffer from either low strength or low conductivity at room and elevated temperatures. Here, we demonstrate a design strategy for 3D printing of high strength, high conductivity Cu by uniformly dispersing a minor portion of lanthanum hexaboride (LaB<sub>6</sub>) nanoparticles in pure Cu through laser powder bed fusion (L-PBF). We show that trace additions of LaB<sub>6</sub> to pure Cu result in an improved L-PBF processability, an enhanced strength, and improved thermal stability, all whilst maintaining a high conductivity. The presented strategy could expand the applicability of 3D-printed Cu components to more demanding conditions where high strength, high conductivity, and thermal stability are required.</p>
Replication Package: Product-Line Engineering for Smart Manufacturing: A Systematic Mapping Study on Security Concepts
<p><strong>Welcome to the public repository for the additional content of the paper "Product-Line Engineering for Smart Manufacturing: A Systematic Mapping Study on Security Concepts", accepted at the ICSOFT 2024.</strong></p> <p>This repository provides additional information to the conducted mapping study, including the following file:</p> <ul> <li>analysis_sheet_ICSOFT2024.csv: sheet containing information regarding the analysis results of 43 included papers based on the extraction criteria.</li> </ul>
"WLRI-HRC" - A Dataset of Infrared Images for Human-Robot Collaboration in Manufacturing Environment
<p>This repository contains all needed data sets for the contribution in Journal of Sensors and Sensor Systems "Enhancing human–robot collaboration with thermal images and deep neural networks: the unique thermal industrial dataset WLRI-HRC and evaluation of convolutional neural networks". You may use this data for scientific, non-commercial purposes, provided that you give credit to the owners when publishing any work based on this data.</p> <p><strong>DOI: 10.5194/jsss-14-37-2025</strong></p> <p> </p> <p><strong>or as BibTex:</strong></p> <div> <div>@article{sume_enhancing_2025,</div> <div> title = {Enhancing human–robot collaboration with thermal images and deep neural networks: the unique thermal industrial dataset {WLRI}-{HRC} and evaluation of convolutional neural networks},</div> <div> volume = {14},</div> <div> issn = {2194-8771},</div> <div> shorttitle = {Enhancing human–robot collaboration with thermal images and deep neural networks},</div> <div> url = {https://jsss.copernicus.org/articles/14/37/2025/},</div> <div> doi = {10.5194/jsss-14-37-2025},</div> <div> abstract = {This contribution introduces the use of convolutional neural networks to detect humans and collaborative robots (cobots) in human–robot collaboration (HRC) workspaces based on their thermal radiation fingerprint. The unique data acquisition includes an infrared camera, two cobots, and up to two persons walking and interacting with the cobots in real industrial settings. The dataset also includes different thermal distortions from other heat sources. In contrast to data from the public environment, this data collection addresses the challenges of indoor manufacturing, such as heat distortions from the environment, and allows for it to be applicable in indoor manufacturing. The Work-Life Robotics Institute HRC (WLRI-HRC) dataset contains 6485 images with over 20 000 instances to detect. In this research, the dataset is evaluated for implementation by different convolutional neural networks: first, one-stage methods, i.e., You Only Look Once (YOLO v5, v8, v9 and v10) in different model sizes and, secondly, two-stage methods with Faster R-CNN with three variants of backbone structures (ResNet18, ResNet50 and VGG16). The results indicate promising results with the best mean average precision at an intersection over union (IoU) of 50 (mAP50) value achieved by YOLOv9s (99.4 \%), the best mAP50-95 value achieved by YOLOv9s and YOLOv8m (90.2 \%), and the fastest prediction time of 2.2 ms achieved by the YOLOv10n model. Further differences in detection precision and time between the one-stage and multi-stage methods are discussed. Finally, this paper examines the possibility of the Clever Hans phenomenon to verify the validity of the training data and the models’ prediction capabilities.},</div> <div> language = {English},</div> <div> number = {1},</div> <div> journal = {Journal of Sensors and Sensor Systems},</div> <div> author = {Süme, Sinan and Ponomarjova, Katrin-Misel and Wendt, Thomas M. and Rupitsch, Stefan J.},</div> <div> month = feb,</div> <div> year = {2025},</div> <div> note = {Publisher: Copernicus GmbH},</div> <div> pages = {37--46},</div> <div>}</div> </div>
Dataset: The effect of a keyhole defect on strain localisation in an additive manufactured titanium alloy
<p><strong>This is the dataset used in the following publication: </strong></p> <div> <div> <div> <p>S. Cao, R. Thomas, A.D. Smith, P. Zhang, L. Meng, H. Liu, J. Guo, J. Donoghue, D. Lunt, The effect of a keyhole defect on strain localisation in an additive manufactured titanium alloy, Journal of Materials Research and Technology, https://doi.org/10.1016/j.jmrt.2024.11.237</p> </div> </div> </div> <p><strong>Contained in this dataset are:</strong></p> <p>A Jupyter notebook which uses the open-source DefDAP Python package (https://github.com/MechMicroMan/DefDAP) to open enclosed HRDIC and EBSD data for two regions in an SLM Ti64 sample, one around a keyhole defect and one ~1mm away in the bulk.</p> <p>Please use the 'master' version of DefDAP: <a href="https://github.com/MechMicroMan/DefDAP/tree/51074e158b0131c69358ddf7eee319e41cf582ca">https://github.com/MechMicroMan/DefDAP/</a></p> <p><strong>Publication abstract:</strong></p> <p>The influence of a keyhole defect on local deformation behaviour in additive manufactured Ti-6Al-4V was investigated by comparing it to a representative bulk region without a defect. High resolution digital image correlation (HRDIC) was used to measure the differences in strain localisation at the microstructural length-scale. A nanoscale speckle pattern was used to allow small changes in strain to be detected and resolved within a single individual lamella and at pre-existing crack locations around the defect. Strain localisation was observed around the defect and formed well below the macroscopic yield stress. In contrast, minimal deformation was found in the bulk at this stress level. Following further deformation into the plastic regime, the strain localisation around the keyhole became more heterogenous with a distinct strain field. A large amount of strain localisation and <c+a> slip was observed either side of the defect normal to the loading direction compared to relatively little in the regions close to the defect in line with the loading direction. This HRDIC observation was consistent with finite element analysis of the expected strain fields around the defect both below and above the yield point. Furthermore, micro-cracks were observed in αp/αp and αp/βt interfaces in both regions with the more pronounced strain fields around the defect leading to an increased number of long micro-cracks than in the bulk. The formation mechanisms of micro-cracks have been discussed, emphasising the role of localised strain caused by the defect.</p> <p> </p>
Dataset for paper entitled, 'Tailoring equiaxed β-grain structures in Ti-6Al-4V coaxial electron beam wire additive manufacturing'
<p>Dataset for paper entitled, 'Tailoring equiaxed β-grain structures in Ti-6Al-4V coaxial electron beam wire additive manufacturing'. Abstract: High-deposition-rate, directed-energy-deposition additive manufacturing (DED-AM) processes typically produce Ti-6Al-4V (Ti64) components with coarse columnar β-grain structures that lead to undesirable mechanical anisotropy, as well as a fine heterogeneous lamellar transformation microstructure, which is very different to that seen standard wrought products. This arises because of the intrinsic lack of constitutional undercooling at the solidification front, and the subsequent high cooling rates and rapid thermal cycling experienced by the deposited material. In this work, the more refined primary β-grain solidification structures and textures seen in components built with the novel coaxial electron beam wire DED AM (CEWAM) process have been characterised in detail, for the first time, with the aim of investigating the potential for this technology to directly replicate the β-annealed damage-tolerant microstructure used in large Ti64 aerospace forgings. Due to its different lower energy density solidification conditions, it has been confirmed, by electron backscatter diffraction (EBSD) analysis and β-grain reconstruction in three orthogonal cross-sections, that the CEWAM process changes the melt conditions to promote β-grain nucleation ahead of the solidification front, which can result in a highly refined, equiaxed, β-grain structure. However, the conditions for refinement were marginal and a mixed grain structure was commonly observed in thicker sections. Additionally, the subsequent grain-growth stability during β-annealing was investigated. It is shown that an equivalent microstructure can be achieved to that seen in a standard β-forged component, by grain structure homogenisation and slow cooling through the β transus, to promote α colony nucleation, allowing direct part substitution. This was made possible by the refined primary β-grain structure achieved during deposition with the CEWAM solidification conditions which, importantly, are also shown to lead to a weaker texture than in a typical forging.</p> <p>Paper doi: https://doi.org/10.1016/j.mtla.2021.101202</p>
Dataset for paper entitled, 'Isomorphic grain inoculation in Ti-6Al-4V during additive manufacturing'
<p>Dataset for paper entitled, 'Isomorphic grain inoculation in Ti-6Al-4V during additive manufacturing'. Abstract: The potential for using isomorphic inoculation (ISI) to grain refine titanium alloys in additive manufacturing was investigated by adding TiAlNb particles to Ti-64 during building test samples. A surviving particle was identified and its crystallographic relationship with the matrix studied by transmission Kikuchi diffraction. The particle and bulk matrix grain were shown to have the same crystallographic orientation, demonstrating that the ISI mechanism of solidification bypasses the nucleation step in favour of direct epitaxial growth.</p> <p>Paper doi: https://doi.org/10.1016/j.mlblux.2020.100057</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.