Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

Public Available Data Set of Process Flows from Internal Physical Inspections in the Failure Analysis Laboratory

This data set was generated in accordance with the semiconductor industry and contains data of certain process flows in Failure Analysis (FA) laboratories focusing on the identification and analysis of anomalies or malfunctions in semiconductor devices. It comprises logistic data about the processing steps for the so-called Internal Physical Inspection (IPI).A so-called IPI job is given as a sequence of tasks that must be performed to complete the job they belong to. It has an assigned unique ID and timestamps indicating the submission, the end, and the deadline to be met. A job also has an IPI classification assigned to it, providing general guidelines on the operations to be performed.Every task within a job has its own type and working time, as well as the assigned resources. There are two main resources involved:            - the equipment; the machine used to perform the task,            - the operator; the person who performed the task.In addition, general information about the type of the device to be analyzed is also available, such as the given (anonymized) package and basictype. Data also include the number of stressed samples within a device and the samples a task is performed on.The dataset includes data from 4 years, specifically from January 2020 to December 2022.Finally, the exact column structure is given as follows (python 3.9.5 datatype):<ul><li>JOB_ID [int64]: the unique ID of the job</li><li>JOB_SUBMISSION_DATE [object]: the date of the job submission</li><li>JOB_REQ_END_DATE [object]: the required end date (deadline)</li><li>JOB_FINISH_DATE [object]: the actual end date</li><li>JOB_BASICTYPE_H [object]: the given basictype denotation</li><li>JOB_PACKAGE_H [object]: the package denotation of the device</li><li>JSH_QTY_STRESSED [float64]: number of stressed samples</li><li>TASK_SUBMISSION_DATE [object]: the date of the task submission</li><li>TASK_WORKING_TIME [float64]: the amount of time (hours) the task needs to be completed</li><li>TASK_SAMPLE_NO [object]: the samples the task was performed on </li><li>TASK_CEQ_ID [float64]: the ID of the machine used to perform the task</li><li>TASK_CTKS_ID [int64]: the ID representing the task type</li><li>TASK_USR_ID [int64]: the ID of the operator performing the task</li><li>CIPI_LEVEL_0 [object]: a series of IPI classifications, indicating what is required to execute for a specific job</li></ul>

opencc-by-4.0Dec 2023View details →

Infection Inspection: Classifications and images of ciprofloxacin-treated Escherichia coli clinical isolates

This dataset includes a .csv file with the image metadata and a folder of RGB images of E. coli grown from clinical isolates with varying concentrations of the antibiotic ciprofloxacin and varying minimum inhibitory concentrations. The E. coli cell membranes are stained with Nile Red and the DNA is stained with DAPI. The details of the image data collection are included in: https://doi.org/10.1038/s42003-023-05524-4. The classification data come from a Zooniverse citizen science project, Infection Inspection. (https://www.zooniverse.org/projects/conor-feehily/infection-inspection) Volunteers learned how to interpret ciprofloxacin response phenotypes as antibiotic-sensitive or antibiotic-resistant, and their classifications are included in the Metadata.csv file.This dataset could be used for further analysis into the volunteer classifications, or the image data could be used for further image feature analysis of the ciprofloxacin response phenotypes.

opencc-by-4.0Oct 2023View details →

SubPipe: A Submarine Pipeline Inspection Dataset for Segmentation and Visual-inertial Localization

<h1>Abstract</h1> This paper presents SubPipe, an underwater dataset for SLAM, object detection, and image segmentation.  SubPipe has been recorded using a lightweight autonomous underwater vehicle (LAUV), operated by OceanScan MST, and carrying a sensor suite including two cameras, a side-scan sonar, and an inertial navigation system, among other sensors. The AUV has been deployed in a pipeline inspection environment with a submarine pipe partially covered by sand. The AUV's pose ground truth is estimated from the navigation sensors. The side-scan sonar and RGB images include object detection and segmentation annotations, respectively. State-of-the-art segmentation, object detection, and SLAM methods are benchmarked on SubPipe to demonstrate the dataset's challenges and opportunities for leveraging computer vision algorithms. To the authors' knowledge, this is the first annotated underwater dataset providing a real pipeline inspection scenario. The dataset and experiments are publicly available <a href="https://github.com/remaro-network/SubPipe-dataset">online.</a> On Zenodo we provide three versions for SubPipe. One is the full version (SubPipe.zip, ~80GB unzipped) and two subsamples: SubPipeMini.zip, ~12GB unzipped and SubPipeMini2.zip, ~16GB unzipped. Both subsamples are only parts of the entire dataset (SubPipe.zip). SubPipeMini is a subset, containing semantic segmentation data, and it has interesting camera data of the underwater pipeline. On the other hand, SubPipeMini2 is mainly focused on underwater side-scan sonar images of the seabed including ground truth object detection bounding boxes of the pipeline. For (re-)using/publishing SubPipe, please include the following copyright text: SubPipe is a public dataset of a submarine outfall pipeline, property of Oceanscan-MST. This dataset was acquired with a Light Autonomous Underwater Vehicle by Oceanscan-MST, within the scope of Challenge Camp 1 of the H2020 <a href="https://remaro.eu/">REMARO</a> project. More information about OceanScan-MST can be found at <a href="https://www.oceanscan-mst.com/">this link</a>. <h1>Cam0 — GoPro Hero 10</h1> <h4>Camera parameters:</h4> <ul> <li>Resolution: 1520×2704</li> <li>fx = 1612.36</li> <li>fy = 1622.56</li> <li>cx = 1365.43</li> <li>cy = 741.27</li> <li>k1,k2, p1, p2 = [−0.247, 0.0869, −0.006, 0.001]</li> </ul> <h1>Side-scan Sonars</h1> Each sonar image was created after 20 “ping” (after every 20 new lines) which corresponds to approx. ~1 image / second. Regarding the object detection annotations, we provide both COCO and YOLO formats for each annotation. A single COCO annotation file is provided per each chunk and per each frequency (low frequency vs. high frequency), whereas the YOLO annotations are provided for each SSS image file. Metadata about the side-scan sonar images contained in this dataset: <table> <tbody> <tr> <td>Images for object detection</td> <td> </td> </tr> <tr> <td># Low Frequency (LF):</td> <td>  5000</td> </tr> <tr> <td>LF image size:</td> <td>2500 × 500</td> </tr> <tr> <td># High Frequency (HF):</td> <td>  5030</td> </tr> <tr> <td>HF Image size</td> <td>5000 × 500</td> </tr> <tr> <td>Total number of images:</td> <td>10030</td> </tr> <tr> <td>Annotations </td> <td> </td> </tr> <tr> <td># Low Frequency:</td> <td>  3163</td> </tr> <tr> <td># High Frequency:</td> <td>  3172</td> </tr> <tr> <td>Total number of annotations:</td> <td>  6335</td> </tr> </tbody> </table>

opencc-by-4.0Sep 2024View details →

Extensive Checklist to cGMP Inspections in Pharmaceutical Manufacturing Plants

cGMP inspections are an essential part of ensuring that pharmaceutical companies produce high-quality, safe, and effective drugs. These inspections help maintain the integrity of the pharmaceutical supply chain and protect public health. Pharmaceutical companies must prioritize continuous cGMP compliance, prepare thoroughly for inspections, and respond promptly to any observations made by inspectors to remain in good standing with regulatory authorities. Here's a comprehensive cGMP inspection checklist for a pharmaceutical company, incorporating requirements from USFDA, EMA, WHO, UKMHRA, TGA, and ANVISA. This checklist includes a rating system to evaluate compliance with each requirement.

opencc-by-nc-sa-4.0Feb 2024View details →

The Automotive Visual Inspection Dataset (AutoVI): A Genuine Industrial Production Dataset for Unsupervised Anomaly Detection

See the official website: <a href="https://autovi.utc.fr">https://autovi.utc.fr</a> Modern industrial production lines must be set up with robust defect inspection modules that are able to withstand high product variability. This means that in a context of industrial production, new defects that are not yet known may appear, and must therefore be identified. On industrial production lines, the typology of potential defects is vast (texture, part failure, logical defects, etc.). Inspection systems must therefore be able to detect non-listed defects, i.e. not-yet-observed defects upon the development of the inspection system. To solve this problem, research and development of unsupervised AI algorithms on real-world data is required. Renault Group and the Université de technologie de Compiègne (Roberval and Heudiasyc Laboratories) have jointly developed the Automotive Visual Inspection Dataset (AutoVI), the purpose of which is to be used as a scientific benchmark to compare and develop advanced unsupervised anomaly detection algorithms under real production conditions. The images were acquired on Renault Group's automotive production lines, in a genuine industrial production line environment, with variations in brightness and lighting on constantly moving components. This dataset is representative of actual data acquisition conditions on automotive production lines. The dataset contains 3950 images, split into 1530 training images and 2420 testing images. The evaluation code can be found at <a href="https://github.com/phcarval/autovi_evaluation_code">https://github.com/phcarval/autovi_evaluation_code</a>. Disclaimer All defects shown were intentionally created on Renault Group's production lines for the purpose of producing this dataset. The images were examined and labeled by Renault Group experts, and all defects were corrected after shooting. License Copyright © 2023-2024 Renault Group This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of the license, visit <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. For using the data in a way that falls under the commercial use clause of the license, please contact us. Attribution Please use the following for citing the dataset in scientific work: Carvalho, P., Lafou, M., Durupt, A., Leblanc, A., & Grandvalet, Y. (2024). The Automotive Visual Inspection Dataset (AutoVI): A Genuine Industrial Production Dataset for Unsupervised Anomaly Detection [Dataset]. <a href="https://doi.org/10.5281/zenodo.10459003">https://doi.org/10.5281/zenodo.10459003</a> Contact If you have any questions or remarks about this dataset, please contact us at philippe.carvalho@utc.fr, meriem.lafou@renault.com, alexandre.durupt@utc.fr, antoine.leblanc@renault.com, yves.grandvalet@utc.fr. Changelog <ul> <li>v1.0.0 <ul> <li>Cropped engine_wiring, pipe_clip and pipe_staple images</li> <li>Reduced tank_screw, underbody_pipes and underbody_screw image sizes</li> </ul> </li> <li>v0.1.1 <ul> <li>Added ground truth segmentation maps</li> <li>Fixed categorization of some images</li> <li>Added new defect categories</li> <li>Removed tube_fastening and kitting_cart</li> <li>Removed duplicates in pipe_clip</li> </ul> </li> </ul>

opencc-by-4.0Dec 2024View details →

Data for "Identification of 4876 Bent-Tail Radio Galaxies in the FIRST Survey using Deep Learning Combined with Visual Inspection"

The data are the full versions of tables that will be published in the manuscript titled "Identification of 4876 Bent-Tail Radio Galaxies in the FIRST Survey using Deep Learning Combined with Visual Inspection" by The Astrophysical Journal Supplement Series. The table file named "FIRST_bt_table1.csv" is the full table for "A catalog of 4876 BTRGs identified from VLA FIRST survey".   The table file named "FIRST_bt_table2.csv" is the full table for "Cluster details for BTRGs".

opencc-by-4.0Mar 2017View details →

V4RL Aerial Inspection Dataset

This dataset contains visual and inertial sequences recorded from the ground and the air (using a small rotorcraft) while moving around a building. This data is captured with a hardware-synchronised sensor and ground-truth of the scene has been captured using a laser scanner. Users of this dataset are asked to cite the following paper, where this dataset was introduced: Lucas Teixeira and Margarita Chli, "Real-Time Mesh-based Scene Estimation for Aerial Inspection", in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016

opencc-by-4.0Sep 2007View details →

Weakly Supervised Learning for Industrial Optical Inspection

Abstract In the following, we present a synthetic benchmark corpus for detect detection on statistically textured surfaces.We hope that it facilitates to further develop and benchmark classification algorithms for applications of industrial optical inspection. All data is publicly available and can be downloaded from this page. Competition at DAGM 2007 symposium The <a href="https://www.dagm.de/">DAGM (Deutsche Arbeitsgemeinschaft für Mustererkennung e.V., German chapter of the IAPR (International Association for Pattern Recognition))</a> and the <a href="http://www.gnns.de/">GNSS (German Chapter of the European Neural Network Society)</a> offered an open competition on Weakly Supervised Learning for Industrial Optical Inspection held as part of the DAGM symposium in 2007. The competition was inspired by the fact that automated optical inspection allows to reduce the cost of industrial quality control significantly. The competitors had to design a classification algorithm which: <ul> <li>detects miscellaneous defects on various statistically textured backgrounds.</li> <li>learns to discern defects automatically from a weakly labelled training data.</li> <li>works on data whose exact characteristics are unknown at development time.</li> <li>adapts all parameters automatically and does not require any human intervention.</li> <li>has a moderate running time (in this competition 24 hours for training and 12 hours for the test phase).</li> <li>takes into account asymmetric costs for false positive and false negative decisions (1:20 was used for the competition).</li> </ul> Data description Preview Image: <a href="../api/iiif/record:12750201:examples_small.jpg/full/!800,800/0/default.jpg" target="_blank" rel="noopener">https://zenodo.org/api/iiif/record:12750201:examples_small.jpg/full/!800,800/0/default.jpg</a> The data is artificially generated, but similar to real world problems. The first six out of ten datasets, denoted as development datasets, are supposed to be used for algorithm development. The remaining four datasets, which are referred to as competition datasets, can be used to evaluate the performance. Researchers should consider not using or analyzing the competition datasets before the development is completed as a code of honour. In the following we provide some details about the datasets: <ul> <li>Each development (competition) dataset consists of 1000 (2000) 'non-defective' and of 150 (300) 'defective' images saved in grayscale 8-bit PNG format.</li> <li>Each dataset is generated by a different texture model and defect model.</li> <li>'Non-defective' images show the background texture without defects, 'defective' images have exactly one labelled defect on the background texture.</li> <li>All datasets has been randomly split into a training and testing sub-dataset of equal size.</li> <li>Weak labels are provided as ellipses roughly indicating the defective area. Technically, defective images are augmented with a separate grayscale 8-bit image in the PNG format located in a folder 'Label'. The values 0 and 255 denote background and defective area, respectively.</li> </ul> All meta-data is subsumed in a separate ASCII textfile called 'Labels.txt' which is located in the 'Label' folder. The structure is as follows: 1 \n [id of item no. 1] \t [0 if non-defective, 1 if defective] \t [filename of raw image no. 1] \t 0 \t [filename of label image no. 1 if defective, 0 otherwise] \n ... [id of item no. N] \t [0 if non-defective, 1 if defective] \t [filename of raw image no. N] \t 0 \t [filename of label image no. N if defective, 0 otherwise] \n

opencc-by-sa-4.0Sep 2019View details →

MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection

This dataset is a sound dataset for malfunctioning industrial machine investigation and inspection (MIMII dataset). It contains the sounds generated from four types of industrial machines, i.e. valves, pumps, fans, and slide rails. Each type of machine includes seven individual product models*1, and the data for each model contains normal sounds (from 5000 seconds to 10000 seconds) and anomalous sounds (about 1000 seconds). To resemble a real-life scenario, various anomalous sounds were recorded (e.g., contamination, leakage, rotating unbalance, and rail damage). Also, the background noise recorded in multiple real factories was mixed with the machine sounds. The sounds were recorded by eight-channel microphone array with 16 kHz sampling rate and 16 bit per sample. The MIMII dataset assists benchmark for sound-based machine fault diagnosis. Users can test the performance for specific functions e.g., unsupervised anomaly detection, transfer learning, noise robustness, etc. The detail of the dataset is described in [1][2]. This dataset is made available by Hitachi, Ltd. under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license. A baseline sample code for anomaly detection is available on GitHub: <a href="https://github.com/MIMII-hitachi/mimii_baseline/">https://github.com/MIMII-hitachi/mimii_baseline/</a> *1: This version "public 1.0" contains four models (model ID 00, 02, 04, and 06). The rest three models will be released in a future edition. [1] Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” arXiv preprint arXiv:1909.09347, 2019. [2] Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” in Proc. 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019.

opencc-by-4.0Oct 2024View details →

Kingdom of Saudi Arabia. al-Ḥasāʾ, Jabal al-Qāra (جَبَل ٱلْقَارَة). Fired ware being inspected.

Kingdom of Saudi Arabia. al-Ḥasāʾ, Jabal al-Qāra (جَبَل ٱلْقَارَة). Fired ware being inspected, 1969.

opencc-by-4.0Aug 2022View details →

The DESI Survey Validation: Results from Visual Inspection of the Quasar Survey Spectra

Data files to reproduce the published figures from Alexander et al. (2022), AJ, in press (<a href="https://ui.adsabs.harvard.edu/link_gateway/2022arXiv220808517A/arxiv:2208.08517">arXiv:2208.08517</a>), titled "The DESI Survey Validation: Results from Visual Inspection of the Quasar Survey Spectra". The figure number is encoded in the file name to make it easy to relate the published figures to the corresponding data file. Please see the published paper for detailed information on what is plotted for each figure.

opencc-by-4.0Mar 2023View details →

DocumeNDT stone masonry walls tomographic inspection dataset

This repository contains data from the sonic tomography inspections of six stone masonry walls constructed for an experimental campaign carried out at the laboratory of the Institute for Physical and Information Technologies (ITEFI), from the Spanish National Research Council (CSIC). The data set is structured in 2 levels of folders: - At first level, the 6 folders correspond to the 6 tested stone masonry walls (Wall 1-6). - At second level, for each wall, there are three folders:     - The folder 'Coordinates' contains: (1) the coordinates of the emission and reception points; (2) diagrams of the emission and reception locations in elevation; and (3) readme file with specific details about the inspection, e.g., number of emission and reception points     - The folder 'Emission raw signal' contains the recorded emission signal for each emission location     - The folder 'Reception raw signal' contains the recorded reception signal for each emission location Sonic data are presented in .csv files, structured in columns. Each column correspond to a reception location. The values correspond to the voltage recorded.  The frequency of acquisition is 256000 samples/s. Please cite the following related publication: Ortega J, Meersman MFL, Aparicio S, Liébana JC, Martin R, Anaya JJ, Gonzalez M. An automated sonic tomography system for the inspection of historical masonry walls (2023)

opencc-by-4.0Feb 2022View details →

Probability of Detection applied to X-ray inspection using numerical simulations

In this work, we apply and adapt established Probability of Detection (POD) methods on inline inspection of aluminium cylinder heads using X-ray computed tomography. The CT simulation tool SimCT [4] is used to acquire virtual images of the specimens including artificial defects, which avoids the manufacturing of calibrated defects of known type (e.g., pore, inclusion, crack etc.), size and location. One of the exemplary defects is discussed as representative result together with the generated POD curves as well as its characteristics (i.e., the minimum detected defect, the maximum missed defect, POD(a90) =0.90 and a90/95).

opencc-by-4.0Jan 2020View details →

Table S2. List of museum specimen material inspected for each of the 10 new taxa. Over 300 specimens were examined in total for plumage comparisons.

Supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146

opencc-by-4.0Mar 2020View details →

LOFAR dataset for deep learning assisted data Inspection for radio astronomy

This dataset is used for the training of the magnitude and phase-based VAE in the paper entitled "Deep learning assisted data inspection for radio astronomy". For uploading purposes the dataset has been separated into 4 different .zip files. In order to use this dataset each of the zip files should be extracted into a single directory so that the training can be performed on all files at the same time.  More information can be found on <a href="https://github.com/mesarcik/DL4DI">the project github repository</a>.

opencc-by-4.0Dec 2020View details →

Disease and lesion maps - part of the Scientific opinion on the evaluation of public and animal health risks in case of a delayed post-mortem inspection in ungulates

EFSA was requested to assess the impact on effectiveness of post-mortem inspection in terms of any change in the sensitivity of detection of animal diseases of domestic and wild ungulates listed according to Article 5 of Regulation (EC) No 2016/429 and septicaemia, pyaemia, toxaemia or viraemia, when carried out after up to 24 hours or up to 72 hours after slaughter in comparison to when it is carried out immediately after slaughter. In order to identify the main lesions associated with the target diseases, a so called “disease map” was built, with the information about the clinical forms of the diseases  (acute, subacute, chronic, or latent forms) and clinical signs, as well as potential lesions that could be observed on the respective diseased animals. The following information is indicated for each disease/condition in the disease map: <ol> <li>The susceptible animal species</li> <li>Whether there is any surveillance programme in place in the EU</li> <li>The signs associated with the disease that could be detected at ante mortem inspection;</li> <li>The lesions associated with the disease that could be detected at post mortem inspection</li> <li>The probability of detecting the disease during the PMI as normally carried out.</li> <li>Whether carcass swabbing and/or laboratory tests are normally carried out.</li> </ol> From the disease map, the list of organs to be considered at post mortem inspection and the lesions, a “lesion map” was built by connecting animal species with organs, lesions and corresponding disease. This was done to facilitate the construction of a questionnaire where, for each organ and the five types of lesions, the respondents (meat inspectors) had to provide a numerical answer about how many carcasses out of 100 with the given lesion, will be still detected after 24- or 72-h of refrigerated storage.

opencc-by-4.0Dec 2023View details →

Artifacts of the CGO 2024 Paper: EasyTracker: A Python Library for Controlling and Inspecting Program Execution

This is the archive of the artifacts for the CGO 2024 Paper "EasyTracker: A Python Library for Controlling and Inspecting Program Execution" The EasyTracker library is an open source project, refer to the Home page and Gitlab repository for up-to-date versions:  <ul> <li>Home Page: <a href="https://corse.gitlabpages.inria.fr/easytracker">https://corse.gitlabpages.inria.fr/easytracker</a></li> <li>Repository: <a href="https://gitlab.inria.fr/CORSE/easytracker">https://gitlab.inria.fr/CORSE/easytracker</a></li> </ul> The details of the artifacts generation are described in the paper appendix or in the <code>README.md</code> file included in the main artifacts archive <code>easytracker-artifacts-cgo-2024-v1.2.0.tar.gz</code>. Summary of artifacts construction steps (execution in a Docker container): <ul> <li>ensure Docker is installed with <code>docker --version</code>,</li> <li>download the artifacts archive <code>easytracker-artifacts-cgo-2024-v1.2.0.tar.gz</code>,</li> <li>extract with <code>tar xvzf easytracker-artifacts-cgo-2024-v1.2.0.tar.gz</code>,</li> <li>change dir with <code>cd eastracker-artifacts-cgo-2024</code>,</li> <li>download the EasyTracker sources archive  <code>easytracker-archive-dfe8aa888f.tar.gz</code><a href="../api/records/10428215/draft/files/easytracker-archive-dfe8aa888f.tar.gz/content" target="_blank" rel="noopener noreferrer">,</a></li> <li>extract with <code>tar xvzf easytracker-archive-dfe8aa888f.tar.gz</code>,</li> <li>download the docker image <code>docker-image-easytracker-1.2.0.tar</code>,</li> <li>load the Docker image with <code>docker load -i docker-image-easytracker-1.2.0.tar</code>, </li> <li>generate all artifacts with <code>./in-docker.sh ./run-all.sh</code>,</li> <li>all artifacts are generated in <code>figure-*/</code> directories,</li> <li>refer to the artifacts archive <code>README.md</code> file for more details, or refer to the paper artifacts appendix.</li> </ul> Note that this artifacts archive is extracted from the artifacts repository at tag <code>v1.2.0</code>:  <a title="Opens in new tab" href="https://gitlab.inria.fr/CORSE/easytracker-artifacts-cgo-2024/-/tree/v1.2.0" target="_blank" rel="noopener">https://gitlab.inria.fr/CORSE/easytracker-artifacts-cgo-2024/-/tree/v1.2.0 </a>

opencc-by-4.0May 2022View details →

MetaPro: a web-based metabolomics application for MS data batch inspection and library curation

MetaPro is a metabolomics web analysis platform built on the Aird data format with high performance and high compression. This platform includes a series of necessary functions for metabolomics analysis such as quality control, retention time(RT) alignment, target analysis, untarget analysis, manual integration, batch inspection, MS2 library establishment, and report export, providing efficient data analysis, management and visualization capabilities

opencc-by-4.0Feb 2022View details →

An eye tracking dataset for building façade inspection

This dataset contains eye-tracking data of ten participants (students) for building facade inspection of two structures. The participants are in between their mid-twenties to thirties. The sessions were recorded for the preliminary eye tracking study to understand the inspector's reasoning and sense-making for damage assessment. The dataset was collected using Pro Glasses 3 wearable eye tracking system from Tobii Technology and further post-processing was done using Pro Lab software for data analysis purposes.