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109 results for “Inspection”
Fig. 3. 2 in Spores of Paenibacillus larvae, Ascosphaera apis, Nosema ceranae and Nosema apis in bee products supervised by the Brazilian Federal Inspection Service
Fig. 3. 2% agarose gel stained with SYBR Safe of the multiplex PCR products from royal jelly samples (1–10) obtained from markets of the state of São Paulo, Brazil. M, ® molecular marker 100 pb (Invitrogen); C+, positive control for N. ceranae (218 pb), N. apis (321 pb), A. apis (485 pb) and P. larvae (700 pb); C−, negative control.
Fig. 4. 2 in Spores of Paenibacillus larvae, Ascosphaera apis, Nosema ceranae and Nosema apis in bee products supervised by the Brazilian Federal Inspection Service
Fig. 4. 2% agarose gel stained with SYBR Safe of the multiplex PCR products from honey samples (1–17) obtained from markets of the state of São Paulo, Brazil. M, ® molecular marker 100 pb (Invitrogen); C+, positive control for N. ceranae (218 pb), N. apis (321 pb), A. apis (485 pb) and P. larvae (700 pb); C−, negative control.
Fig. 5. 2 in Spores of Paenibacillus larvae, Ascosphaera apis, Nosema ceranae and Nosema apis in bee products supervised by the Brazilian Federal Inspection Service
Fig. 5. 2% agarose gel stained with SYBR Safe of the multiplex PCR products from pollen samples (1–10) obtained from markets of the state of São Paulo, Brazil. M, ® molecular marker 100 pb (Invitrogen); C+, positive control for N. ceranae (218 pb), N. apis (321 pb), A. apis (485 pb) and P. larvae (700 pb); C−, negative control.
FireSafetyNet: An Image-Based Dataset with Pretrained Weights for Machine Learning-Driven Fire Safety Inspection
<p>This dataset offers a diverse collection of images curated to support the development of computer vision models for detecting and inspecting Fire Safety Equipment (FSE) and related components. Images were collected from a variety of public buildings in Germany, including university buildings, student dormitories, and shopping malls. The dataset consists of self-captured images using mobile cameras, providing a broad range of real-world scenarios for FSE detection.</p> <p>In the journal paper associated with these image datasets, the open-source dataset FireNet (Boehm et al. 2019) was additionally utilized for training. However, to comply with licensing and distribution regulations, images from <a href="https://www.firenet.xyz/">FireNet</a> have been excluded from this dataset. Interested users can visit the FireNet repository directly to access and download those images if additional data is required. The provided weights (.pt), however, are trained on the provided self-made images and FireNet using YOLOv8.</p> <p>The dataset is organized into six sub-datasets, each corresponding to a specific FSE-related machine learning service:</p> <ol> <li> <p><strong>Service 1: FSE Detection</strong> - This sub-dataset provides the foundation for FSE inspection, focusing on the detection of primary FSE components like fire blankets, fire extinguishers, manual call points, and smoke detectors.</p> </li> <li> <p><strong>Service 2: FSE Marking Detection</strong> - Building on the first service, this sub-dataset includes images and annotations for detecting FSE marking signs.</p> </li> <li> <p><strong>Service 3: Condition Check - Modal</strong> - This sub-dataset addresses the inspection of FSE condition in a modal manner, focusing on instances where fire extinguishers might be blocked or otherwise non-compliant. This dataset includes semantic segmentation annotations of fire extinguishers. For upload reasons, this set is split into <em>3_1_FSE Condition Check_modal_train_data (containing training images and annotations) </em>and <em>3_1_FSE Condition Check_modal_val_data_and_weights (containing validation images, annotations </em>and<em> the best weights).</em></p> </li> <li> <p><strong>Service 4: Condition Check - Amodal</strong> - Extending the modal condition check, this sub-dataset involves amodal detection to identify and infer the state of FSE components even when they are partially obscured. This dataset includes semantic segmentation annotations of fire extinguishers. This dataset includes semantic segmentation annotations of fire extinguishers. For upload reasons, this set is split into <em>4_1_FSE Condition Check_amodal_train_data (containing training images and annotations) </em>and <em>4_1_FSE Condition Check_amodal_val_data_and_weights (containing validation images, annotations </em>and<em> the best weights).</em></p> </li> <li> <p><strong>Service 5: Details Extraction - Inspection Tags</strong> - This sub-dataset provides a detailed examination of the inspection tags on fire extinguishers. It includes annotations for extracting semantic information such as the next maintenance date, contributing to a thorough evaluation of FSE maintenance practices.</p> </li> <li> <p><strong>Service 6: Details Extraction - Fire Classes Symbols</strong> - The final sub-dataset focuses on identifying fire class symbols on fire extinguishers.</p> </li> </ol> <p>This dataset is intended for researchers and practitioners in the field of computer vision, particularly those engaged in building safety and compliance initiatives.</p>
Beyond Coverage Path Planning: Can UAV Swarms Perfect Scattered Regions Inspections? - Data Collected and Presented for the Experiments
<p>This dataset contains images collected (and processed) for the experiments of Beyond Coverage Path Planning: Can UAV Swarms Perfect Scattered Regions Inspections?" journal article, a work that defines a new path planning problem for UAVs - the Fast Inspection of Scattered Regions (FISR) - and introduces a novel method that deals with this problem - the multi-UAV Disjoint Areas Inspection (mUDAI) method. For the validation of the introduced methodology, two sets of real-world experiments were executed, one small-scale in Galatsi, Athens, were two mUDAI missions were depolyed, with two different optimization objectives for the data collection procedure (Mazimized Coverage Objective - MCO, and Balanced Coverage Objective - BCO), and one large scale in ZEP-Kissos, Thessaloniki, where a Coverage Path Planning (CPP) mission, and 2 mUDAI missions, one with a single and one with two UAVs, using both the MCO criterion for the data collection, were deployed. Regarding the CPP mission, both the collected images, and the processed results (to generate 2D, 3D, elevation, and plant health maps) are included.</p> <p>In this <a title="mUDAI - ChoosePath platform guide" href="https://sites.google.com/view/mudai-platform/" target="_blank" rel="noopener">page</a> you can find a guide for the on-line platform hosting demo instances of the algorithms used for the deployment of all experiments.</p> <p>In case you use this data, please cite the article:<br>(Article under review - more information to be included soon)</p>
Linked collectors and determiners for: Metallesthes specimens inspected and supporting information published in Metallesthes revision.
Natural history specimen data linked to collectors and determiners held within, "Metallesthes specimens inspected and supporting information published in Metallesthes revision". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/4677560c-a8d9-4e3e-b883-d3c6d97ded49">https://bionomia.net/dataset/4677560c-a8d9-4e3e-b883-d3c6d97ded49</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/4677560c-a8d9-4e3e-b883-d3c6d97ded49">https://gbif.org/dataset/4677560c-a8d9-4e3e-b883-d3c6d97ded49</a>. Formatted as a Frictionless Data package.
Images and results from a visual inspection of AIA spikes
<p>This upload contains images and results used in a manuscript submitted by P.R. Young et al. to the Solar Physics journal. The preprint is available at: <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210802624Y/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv210802624Y/abstract</a>.</p> <p>The files are:</p> <p>RESULTS.txt - The results of the spike analysis.<br> make_spike_images.pro - An IDL routine for generating the pdf files<br> (requires software in the Solarsoft IDL<br> distribution to run)<br> spike_images_*_*.pdf - A set of pdf files containing AIA images. For<br> each spike there is a pair of images in a<br> row. Each image is 30" x 30" in size and<br> centered on the spike. The left panel shows<br> the original image, and the right panel shows<br> the despiked image. There are 45 pdf files in<br> all. </p> <p> </p>
Health Record Hiccups - 5526 real-world time series with change points labelled by crowd-sourced visual inspection
<p>5526 real-world time series with labels for the location of all abrupt changes in level, variability, trend, presence/absence of data points, and irregular outliers. The time series were produced from a range of electronic health record data extracts from a large UK hospital group. Values in each data field were aggregated by day/week/month, and numeric summary values calculated for each timepoint from the (often non-numeric) data by applying simple functions (e.g. number of values present, percentage of missing values, number of distinct values, median value). Labels were produced by visual inspection of time series plots from ~2000 volunteers, via the Health Record Hiccups project on the Zooniverse platform (https://www.zooniverse.org/projects/phuongquan/health-record-hiccups). Volunteers drew a vertical line on the image wherever they saw a change point (green line if they were certain, yellow line if they were unsure). Consensus labels per image were calculated using density based clustering with noise (R v3.6.3, dbscan v1.1-5), and converted back to a date.</p>
Drones for Railway Infrastructure Inspection
<p>LMT in collaboration with Latvijas Gaisa Satiksme and Airborne RF performed an operation deployment – Inspection of Railway Infrastructure with Rail Baltica as a use case. With this trial, we enabled 3rd autonomy level of drone flight, the development of a new business case, BVLOS, and remote detection of security threats and C2 only through the cellular network. This is a significant step forwards to increased railway security!</p>
DocumeNDT Uniaxial Compression Strength (UCS) tests – tomographic inspections dataset
<p>DocumeNDT Uniaxial Compression Strength (UCS) Tests - tomographic inspections dataset</p> <p>This repository contains data from the sonic tomography inspections carried out during the cyclic Uniaxial Compression Strength (UCS) tests performed on six stone masonry walls at the laboratory of the Eduardo Torroja Institute for Construction Sciences (IETCC), from the Spanish National Research Council (CSIC). </p> <p>A first sonic tomography inspection was carried out before starting the test (C0) with no loading. Then, cycles of increasing loading were imposed to the wall. Two cycles are performed for each loading level. After reaching the maximum load in each cycle, the load is sustained and a sonic tomography inspection is carried out. </p> <p>The data set is structured in 3 levels of folders:<br> - At first level, the 6 folders correspond to the 6 tested stone masonry walls (Wall 1-6).<br> - At second level, for each wall, there are two folders and a readme file:<br> - The readme file contains specific details about the inspection, e.g., number of emission and reception points or maximum load corresponding to each cycle<br> - The folder 'Coordinates' contains two diagrams of the emission and reception locations in elevation<br> - The folder 'Loading Cycles' contains the sonic raw data recorded in each cycle<br> - At third level, for each loading cycle carried out during the UCS test, there is one folder. Each folder contains:<br> - The folder 'Coordinates' contains the exact coordinates of the emission and reception points in *.txt files<br> - The folder 'Emission raw signal' contains the recorded emission signal for each emission location<br> - The folder 'Reception raw signal' contains the recorded reception signal for each emission location</p> <p>Sonic data are presented in *.csv files, structured in columns. Each column correspond to a reception location. The values correspond to the voltage recorded. Specific details about the csv can be found in the readme file of each wall.</p> <p>The frequency of acquisition is 256000 samples/s.</p> <p>The detailed geometry of all walls (including the inner position of each stone) is publicly available and can be found in https://doi.org/10.5281/zenodo.7713700</p> <p>The experimental results of the UCS test are also publicly available and can be found in https://doi.org/10.5281/zenodo.8341725. </p> <p>Please cite the following related publication:</p> <p>Ortega J, Meersman MFL, Aparicio S, Liébana JC, Anaya JJ, Gonzalez M. Capabilities of sonic tomography to assess historic masonry deformability properties, in situ stress level and damage evolution (2023)</p>
Analyzing App Store Comments and Quality Attributes for Defining an Inspection Checklist for Mobile Educational Games
<p>To evaluate educational games, several techniques have been proposed considering different quality attributes. However, there are still several educational games for the mobile context that have low scores in the app stores. These stores allow users to make comments to evaluate the applications, as this data can be useful for the development team that aims to meet users' expectations. The analysis of comments made by users can help identify which attributes impact the use of mobile educational games. In this paper, an inspection checklist is proposed to evaluate mobile educational games. To complement the attributes identified in the analysis of comments, attributes from existing techniques for evaluating mobile educational games were also considered. The final evaluation form contains a total of 82 attributes distributed in evaluation categories, such as: user interface, mobility, pedagogy, gameplay, among others. To evaluate the proposed technique, an evaluation was carried out with the checklist in two mobile educational games available in the Google Play Store, different from those used to define the technique. The initial results indicate that the proposed checklist allows the identification of problems pointed out by users in the comments left in the app store.</p> <p> </p> <p><a href="https://zenodo.org/api/files/8ca1af4d-6f7c-440f-a272-9366de2ad3ef/SBES%202020%20-%20Ideias%20Inovadoras%20e%20Resultados%20Emergentes%20-%20Analisando%20atributos%20de%20qualidade%20e%20coment%C3%A1rios%20de%20lojas%20de%20aplicativos%20para%20a%20defini%C3%A7%C3%A3o%20de%20uma%20t%C3%A9cnica%20de%20inspe%C3%A7%C3%A3o%20de%20jogos%20educacionais%20m%C3%B3veis.mp4">SBES 2020 - Ideias Inovadoras e Resultados Emergentes</a></p>
dataset of SMartyPerspective: a perspective-based inspection technique for software product lines
<p>dataset of SMartyPerspective: a perspective-based inspection technique for software product lines</p>
Little well for inspection of Roman aqueduct
This is a simple Little well for waters inspection of The imperial aqueduct of S. Lorenzo that was a marvellous work done by engineers and architects of the Roman empire. San Lorenzo was the name of the country that, since May 1872, is called Amaseno. Amaseno, just like the noble river of virgilian memory, at whose source is the small urban center. The river collects the waters of a dozen springs and some of them attracted the attention of the Roman hydraulics to supply water to Terracina (Italy) in the second century. d. C. Source: Objaverse 1.0 / Sketchfab
Local Ultrasonic Resonance Spectroscopy: A Demonstration on Plate Inspection - Dataset
<p><em><strong>The peer-reviewed publication using this dataset has been published in the Journal of Nondestructive Evaluation, and can be accessed via <a href="https://doi.org/10.3390/epidemiologia2030024">https://doi.org/10.1007/s10921-020-00674-5</a>. Please cite this article when using the dataset.</strong></em></p>
A shrewd inspection of vertebral regionalization in large shrews (Soricidae: Crocidurinae)
<p>The regionalization of the mammalian spinal column is an important evolutionary, developmental, and functional hallmark of the clade. Vertebral column regions are usually defined using transitions in external bone morphology, such as the presence of transverse foraminae or rib facets, or measurements of vertebral shape. Yet the internal structure of vertebrae, specifically the trabecular (spongy) bone, plays an important role in vertebral function, and is subject to the same variety of selective, functional, and developmental influences as external bone morphology. Here we investigated regionalization of external and trabecular bone morphology in the vertebral column of a group of shrews (family Soricidae). The primary goals of this study were to: 1) determine if vertebral trabecular bone morphology is regionalized in large shrews, and if so, in what configuration relative to external morphology; 2) assess correlations between trabecular bone regionalization and functional or developmental influences; and 3) determine if external and trabecular bone regionalization patterns provide clues about the function of the highly modified spinal column of the hero shrew Scutisorex. Trabecular bone is regionalized along the soricid vertebral column, but the configuration of trabecular bone regions does not match that of the external vertebral morphology, and is less consistent across individuals and species. The cervical region has the most distinct and consistent trabecular bone morphology, with dense trabeculae indicative of the ability to withstand forces in a variety of directions. Scutisorex exhibits an additional external morphology region compared to unmodified shrews, but this region does not correspond to a change in trabecular architecture. Although trabecular bone architecture is regionalized along the soricid vertebral column, and this regionalization is potentially related to bone functional adaptation, there are likely aspects of vertebral functional regionalization that are not detectable using trabecular bone morphology. For example, the external morphology of the Scutisorex lumbar spine shows signs of an extra functional region that is not apparent in trabecular bone analyses. It is possible that body size and locomotor mode affect the degree to which function is manifest in trabecular bone, and broader study across mammalian size and ecology is warranted to understand the relationship between trabecular bone morphology and other measures of vertebral function such as intervertebral range of motion.</p>
An automated system for inspecting rock faces and detecting potential rock falls using machine learning
<p>Rockfall is a hazard in mountainous areas threatening infrastructure and human lives. Rockfall hazards are often mitigated by manual inspections using pry bars. The inspector must access the rock face, hit the rock surface, detect, and remove the loose rocks. This method is very labor demanding, unsafe, and challenging. This research presents a method that automatize the inspection of rock blocks that are prone to rockfall events. A robot is developed to replace the manual hammer tap process and collect the sound data remotely; subsequently, the sound signal is used to identify different types of the discontinuity in rocks in controlled laboratory environment. Machine learning is used to train the method to discriminate between intact rock and rock that may be prone to fall. This methodology was successfully applied to laboratory tests on rock. Finally, the research involves the implementation of this system in field to understand the potential and limitations of the proposing system in automatizing the rock inspections. This research enables the inspectors to collect data remotely, detect loose rocks, and save data for future references.</p>
Current foveal inspection and previous peripheral preview influence subsequent eye movement decisions
<p>Data from:</p> <p>Wolf, C., Belopolsky, A.V., & Lappe, M. (2022). Current foveal inspection and previous peripheral preview influence subsequent eye movement decisions. iScience.</p> <p>The zip folder "Data" contains one .dat file with the data of every individual. "Data" contains all trials of all individuals. Each file has three columns.<br> 1st column: condition index, range: 1-4,<br> 1: no preview / noise inspection;<br> 2: no preview / face inspection;<br> 3: preview / noise inspection;<br> 4: preview / face inspection<br> 2nd column: decision outcome, range 0-1<br> 0: noise image selected<br> 1: face image selected<br> 3rd column: fixation duration in seconds</p> <p>The zip folder "FixData" contains 3 .dat files. Every row in SacIndex.dat corresponds to one trial, every column to one millisecond after primary saccade offset (1000 columns in total) and thus depicts the fixation duration on the inspection target.<br> 0: participant was fixating inspection target<br> 1: participant was making a smaller saccade that started and landed on the inspection target<br> NaN: Fixation period for that trial has ended.<br> The two additional files "ParticipantIndex.dat" and "ConditionIndex.dat" are column vectors that contain one entry for every trial (the participant number or the condition index respectively). The condition index is the same as in the other zip folder (see above). Please note that the trial order in FixData does not reflect the actual trial order of the experiment.</p> <p>For questions please contact chr.wolf[at]wwu.de</p>
AI-BASED INSPECTION OF RECYCLED CARBON FIBRE FABRIC
<p>The dataset is part of an EU-funded project and is included in the paper entitled "AI-based Inspection of Recycled Carbon Fibre Fabric."</p> <p>The work presented in this paper is related to the project “MC4” and has received funding from the European Union’s Horizon Europe research and innovation program under grant agreement No 101057394. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. </p> <p> </p> <p><a href="../api/records/11203952/draft/files/sample_images.zip/content" target="_blank" rel="noopener noreferrer">sample_images.zip</a> - It is a zip file containg the .png images. These are the samples images of the output from the in-house developed sensor.</p> <p><a href="../api/records/11203952/draft/files/dataset_samples.pkl/content" target="_blank" rel="noopener noreferrer">dataset_samples.pkl</a> - It is a pickle file. This contains the dataset whose results are reported in the paper .</p> <p><a href="../api/records/11203952/draft/files/load_data.py/content" target="_blank" rel="noopener noreferrer">load_data.py</a> - This is the sample code to load the data. Explanation to the shapes and the structure of the data are present here.</p>
Bridge Inspecting with Unmanned Aerial Vehicles R&D
<p>Corresponding data set for Tran-SET Project No. 17STLSU11. Abstract of the final report is stated below for reference:</p> <p>"The project achieves through research including literature, on site interviews, and experimentation: 1) a recommendation for a UAV-based system to practically assist in routine bridge inspection work in the State of Louisiana, 2) the identification and description of advantages, disadvantages, and limitations in the use of UAVs for routing bridge inspection work in Louisiana, and 3) provided recommendations for future work. The Yuneec H520 aircraft and its E90 camera are recommended, as is the need for a boat to be included as part of the system. The recommended system has advantages in reaching portions of the bridge that are difficult to reach by human inspectors and includes sufficient image resolution to assist the bridge inspection process. A disadvantage though, is that of the overburden of regulations both from the FAA and for getting permission to inspect a bridge using a UAV. These regulations my render negligible, any gains in efficiency perceived in the use of UAVs for bridge inspection. Also, the UAV is described by the project as an assistance tool for the manual bridge inspection process and cannot replace the needed work of bridge inspectors, as it has limitations. For example, the UAV cannot perform inspections beneath the bridge deck since it may lose its GPS navigation reference. Likewise, it cannot see beneath the surface to tell of concrete components have subsurface cracks or timbers might be hollow. These tests are still the domain of manual bridge inspection. The project provided recommendations with respect to changes in how inspections should be done using the UAV, i.e. in the pre-inspection phase, needed field studies using the UAV, needed economics alternative-tradeoffs studies, and recommendations for augmenting the aircraft and its instruments. The Second phase, i.e. the Implementation Phase, will utilize the information and educational fruits of the technical research phase for tutorials, seminars and to facilitate feedback surveys with engineering firms, the LADOTD, engineering societies, and students."</p>
Augmented Reality Enhancing the Inspections of Transportation Infrastructure: Research, Education, and Industry Implementation
<p>Corresponding data set for Tran-SET Project No. 18STUNM03. Abstract of the final report is stated below for reference:</p> <p>"Transportation infrastructure needs continuous monitoring that is conducted by field inspectors regularly in the field. Currently, infrastructure inspectors climb, measure, and photograph structures annually to inform repair needs and prioritize decisions. In order to promote and accelerate early learner's expertise in decision-making capabilities during infrastructure inspections, this research project developed various software applications using augmented reality (AR) as an inspection tool for bridges and bridge management, more specifically. By objectively quantifying infrastructure field inspections, inspectors can make more accurate field assessments and managers can make better-informed decisions. This project collaborated with stakeholders, national laboratories, DOT agencies such as NCHRP and NMDOT, and local owners like the City of Albuquerque, to inform the needs of AR for field inspections. The results of this study summarized the current limitations of visual inspections from the perspective of the various owners, as well as pilot developments of AR applications and their benchmarked accuracy in comparison with visual methods. The education and training aspect of this project included teaching and exposing AR to high school students, community college students, undergraduate students, and graduate students, as well as industry (bridge inspectors). This research project’s outcome includes a webinar free to access in the NCHRP national website on this topic. The conclusion of this research is that AR can be an effective tool and that industry is interested in specific programming of AR software that matches their bridge management needs."</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.