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631 results for “Customer”
Example Microscopy Metadata JSON files produced using Micro-Meta App to document the acquisition of example images using a custom-built TIRF Epifluorescence Structured Illumination Microscope
<p><strong>Example Microscopy Metadata JSON files produced using the <a href="https://wu-bimac.github.io/MicroMetaApp.github.io/">Micro-Meta App</a> documenting an example raw-image file acquired using the custom-built TIRF Epifluorescence Structured Illumination Microscope.</strong></p> <p>For this use case, which is presented in Figure 5 of <a href="http://doi: https://doi.org/10.1101/2021.05.31.446382">Rigano et al., 2021</a>, Micro-Meta App was utilized to document:</p> <p>1) The <strong>Hardware Specifications</strong> of the custom build TIRF Epifluorescence Structured light Microscope (TESM; <a href="https://www.pnas.org/content/109/8/E471.long">Navaroli et al., 2010</a>) developed, built on the basis of the based on Olympus IX71 microscope stand, and owned by the Biomedical Imaging Group (http://big.umassmed.edu/) at the Program in Molecular Medicine of the University of Massachusetts Medical School. Because TESM was custom-built the most appropriate documentation level is <strong>Tier 3</strong> (<em>Manufacturing/Technical Development/Full Documentation</em>) as specified by the <a href="https://doi.org/10.5281/zenodo.4710731">4DN-BINA-OME</a> Microscopy Metadata model (<a href="https://doi.org/10.1101/2021.04.25.441198">Hammer et al., 2021</a>).</p> <p>The TESM Hardware Specifications are stored in: <strong>Rigano et al._Figure 5_UseCase_Biomedical Imaging Group_TESM.JSON</strong></p> <p>2) The <strong>Image Acquisition Settings</strong> that were applied to the TESM microscope for the acquisition of an example image (FSWT-6hVirus-10minFIX-stk_4-EPI.tif.ome.tif) obtained by Nicholas Vecchietti and Caterina Strambio-De-Castillia. For this image, TZM-bl human cells were infected with HIV-1 retroviral three-part vector (FSWT+PAX2+pMD2.G). Six hours post-infection cells were fixed for 10 min with 1% formaldehyde in PBS, and permeabilized. Cells were stained with mouse anti-p24 primary antibody followed by DyLight488-anti-Mouse secondary antibody, to detect HIV-1 viral Capsid. In addition, cells were counterstained using rabbit anti-Lamin B1 primary antibody followed by DyLight649-anti-Rabbit secondary antibody, to visualize the nuclear envelope and with DAPI to visualize the nuclear chromosomal DNA.</p> <p>The Image Acquisition Settings used to acquire the FSWT-6hVirus-10minFIX-stk_4-EPI.tif.ome.tif image are stored in: <strong>Rigano et al._Figure 5_UseCase_AS_fswt-6hvirus-10minfix-stk_4-epi.tif.JSON</strong></p> <p><em><strong>Instructional video tutorials on how to use these example data files:</strong></em><br> Use these videos to get started with using Micro-Meta App after downloading the example data files available here.</p> <ul> <li><a href="https://vimeo.com/562022222">Part 1/2</a></li> <li><a href="https://vimeo.com/562022281">Part 2/2</a></li> </ul>
Data set to Conference Paper "The Effect of Queuing Technology on Customer Experience in Physical Retail Environments"
<p>Following an open data policy as supported by the European Union (https://www.openaire.eu/), this is the data set used for the following conference paper: Obermeier, G., Zimmermann, R., & Auinger, A. (2020, July). The Effect of Queuing Technology on Customer Experience in Physical Retail Environments. In <em>International Conference on Human-Computer Interaction</em> (pp. 141-157). Springer, Cham.</p> <p>The present work was conducted within the Innovative Training Network project PERFORM funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 765395. The EU Research Executive Agency is not responsible for any use that may be made of the information it contains.</p>
UnityMol demo movie showing custom user-added menus
<p>This video provides more detailed supportive information about using Unitymol.</p> <p> </p> <p>1) start up UnityMol</p> <p>2) activate the functionality to remote control Unitymol</p> <p>3) edit the provided example script menu-spike1.py to include the right filepath</p> <p>4) execute menu-spike1.py with python</p> <p>5) first there is only one button, allowing you to load the scene</p> <p>6) once loaded, several customized views are available through dedicated buttons</p>
Customers experiencing a power outage in counties of California, 2019
<p>This dataset includes the long form of the time series from 2019/01/01 to 2019/12/31, showing the number of customers who are experiencing a power outage at the city and county level in the State of California, USA. The time series is divided into 10-min intervals. The original data source is <a href="https://poweroutage.us/">poweroutage.us</a>, a data vendor of power outage data across the United States.</p><p>Power outage long-form time series at the county level are provided in the form of CSV files. Each file contains data for the entire county.</p>
Real Dataset From Broadband Customers of a Brazilian Telecom Operator
<p>This dataset includes information from broadband users of a telecom operator located in Brazil.</p> <p>It includes modem parameters extracted from the operator's Network Management System (NMS) and customer complaint information extracted from the operators' CRM. The modem is an Optical Network Terminal (ONT), located in customer premises, responsible for providing Wi-Fi for the customer, and is the terminal for the optical fiber in a Gigabit Passive Optical Network (GPON)<br>Collection Date: January/23 to June/23</p> <p>The modem data was collected for six (6) months for a set of specific customers. At the end of the period, data from this set of customers were collected using the Customer Relationship Management (CRM) system. If the customer complained during that period, we filtered the modem parameters on the day of the last complaint. If the customer has not complained within the period, we use the parameters from the last parameter collection from its modem.</p> <p>The file contains raw data in Comma-Separated Values (CSV) format using UTF-8 encoding and has a set of different parameters.</p> <p>"Customer ID" - A unique ID for the customer in the dataset. The customer's personal information was anonymized.<br>"Latency" - Value of the latency the user is experiencing. Latency is the time it takes for data to get from one network point to another.<br>"Jitter" - Value of the jitter (variation in latency) the user is experiencing.<br>"Packet Loss" - The percentage (%) of packets lost during the transmission<br>"Channel2_quality" - The Quality of the customer's 2.4GHz channel, rated on a scale of 1 to 5 by the modem<br>"Channel5_quality" - The Quality of the customer's 5GHz channel, rated on a scale of 1 to 5 by the modem<br>"N distant devices" - The Number of devices far from the modem (more than 10 m)<br>"CRM_Complaint?" - Indicates whether the user complains (value = 1) about its broadband experience or not (value = 0).</p>
MIDAS2 protocol example custom genome collection dataset v1
<p>Example input database of custom genome collection for MIDAS2 protocols.</p> <p>Two genomes from two species (<em>Staphylococcus epidermidis </em>and <em>Streptococcus mutans </em>)</p>
Dataset of 30 energy customers with flexibility data, and distributed generation, considering residential, small commerce, large commerce, and industrial customers
<p>The dataset has 30 customers: ten residential, ten small commerce, five large commerce, and five industrial customers. The combination of several energy customer types allows the creation of a dataset with different types of consumption profiles, generation, and flexibility, and, therefore, different values of participation in demand response events.</p> <p>The residential profiles of the considered customers use the data available in the Working Group on Intelligent Data Mining and Analysis (IDMA): https://site.ieee.org/pes-iss/data-sets/</p> <p>The values represent a week period using 15 minutes reading periods. All the values are expressed in kWh and the matrixes were created as [customer x time_period].</p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Custom Dataset Collected for Energy Manager
<ul> <li>Change directory to inside the dataset</li> <li>Create virtual python environment and install the contents in requirements.txt</li> <li>Run the commands in sample_commands.txt file to get an idea on how the sample_pvm_to_csv.py script works</li> </ul> <p><strong>Data description:</strong><br> Light sensor (APDS9960) - Solar irradiance; Power sensor (INA226) - solar panel open circuit voltage (OCV), solar panel closed circuit current (CCC), solar panel closed circuit shunt voltage (CCSV); DHT22 - temperature and humidity data are collected using a custom setup (check the custom_setup.pdf file), placed indoors, next to a curtain less glass window (the plane of the sensing surfaces were almost 45 degree to the window glass. The spectral distortions made by the glass on the solar irradiance observed is unknown - sorry! The sensors used are INA226, Solar panel (1.5W, 137x81x2.5 mm, 16% efficiency, V & I at peak power :5.5V & 270ma), APDS9960 and DHT22.</p> <p>The custom setup transmits collected data to the server. The server creates a new log file everyday with '.txt' file extension. When the server restarts and begins logging, the first file created has the time information of when the log file was created, suffixed with number '0' before the file extension. This suffix is incremented day by day, until the next interruption to the server, where it create a new log file with the current time and suffix '0', and the whole cycle continues. The log files provided here are raw and unfiltered.<br> <br> A header with 'UTC' time was transmitted by the custom setup to the server whenever it restarts. It contains column information. The sampling period is 0.2s and only the time stamp for the first data is found in the header. The timestamp for the rest is calculated using the index / serial number and the sample period. The index and the data from each sensor are separated by '#'. If a sensor provides multiple data, then those data are separated by ','.</p>
Dataset of imaged commercial and custom-made printing filament materials for Computed Tomography imaging of organ body phantoms
<p>The dataset includes a total of 29 filament materials 7 custom-made materials and the selection of 22 commercially available materials.</p> <p>All the materials were printed with a Longer LK4 Pro printer into cubes with dimensions 20 mm x 20 mm x 10 mm.</p> <p>A part of each filament was grinded into pellets, placed into metallic cylinder container and then were heated up to their melting points to receive a homogeneous cylindrical sample of this material.</p> <p>The cubes and the cylindrical samples were scanned at a clinical CT scanner at three anode voltages (kV) and a slice thickness of 0.6 mm.</p>
Amazon Customer Review Data
<p><strong>Dataset</strong>: Amazon Customer Review Data for sentiment analysis</p> <p><strong>Size</strong>: 60889 appox.</p> <p><strong>Format</strong>: .CSV</p> <p><strong>Period</strong>: 2013 to 2019</p> <p><strong>Categories</strong>: 5…… (Mobiles, Smart TV, Books, Mobile Accessories, Refrigerator)</p> <p><strong>Unique_ID</strong>: Customized (Primary Key)</p> <p><strong>Review_Header</strong>: user’s comment in few words</p> <p><strong>Review_Text</strong>: User’s comment in details (3-4 lines)</p> <p><strong>Rating</strong>: (1- Very Low, 2 🡪 Low, 3🡪 Avg, 4 🡪 Good, 5 - Excellent)</p> <p><strong>Posting Period</strong>: 2013 to 2019</p> <p><strong>Own_Rating</strong>: for 1-2 🡪 Negative, 3🡪 Neutral, 4-5 🡪 Positive</p>
Eye image data with gaze labels recorded using custom video-oculography hardware at 120Hz
<p>The repository of eye image data with corresponding gaze labels collected from 40 subjects. The preview contains a collage of random image samples, one per subject. </p> <p>All recorded subjects gave informed consent under an experimental protocol approved by the Institutional Research Board of Texas State University (approval code 2018044) and their data were anonymized prior to public release.</p> <p>The data were recorded using the custom video-oculography (VOG) desktop hardware setup at 120Hz. The full description of this eye-tracking system's capabilities is provided at https://doi.org/10.48550/arXiv.1904.07361.</p> <p>This VOG set contains recordings of the random oblique saccades task. It is comprised of 174 on-screen fixation targets that densely cover the range of ±20.51° horizontally and ±16.7° vertically (in degrees of visual angle). More detail on the presented stimuli can be found at https://doi.org/10.1145/3379156.3391370.</p> <p>The data were also used in Dmytro Katrychuk's Ph.D. thesis "Generating Realistic Eye Images to Evaluate Photosensor Oculography Eye-Tracking for Portable Headsets" (https://hdl.handle.net/10877/19437); with the release for public use in the upcoming publication "An appearance-based gaze estimation as a benchmark for eye image data generation methods" accepted to MDPI Journal of Applied Sciences. </p> <p>Each .zip archive represents a recording from one subject, which includes:</p> <ul> <li>Video of the close eye capture in ".avi" format</li> <li>Calibration data in ".xml" format</li> <li>Gaze data in ".tsv" format</li> <li>On-screen target stimulus position in ".tsv" format</li> </ul> <p>The "src.zip" provides a Python script to unpack each ".avi" video recording to a set of ".png" images. The direct playback of ".avi"s may require special codecs and is not supported. </p> <p>Any additional code will be uploaded to https://github.com/dkatrychuk/psog-eval-diss2023</p> <p>The authors can be contacted at their corresponding emails: Dmytro Katrychuk - d_k139@txstate.edu; Oleg Komogortsev - ok@txstate.edu.</p>
Customer experience dimension in service provider commenters (bahasa)
<p>A dataset containing customer commenters that obtain from user-generated content on Twitter and Instagram @byu_id. The dataset is used Bahasa, and it includes 32.684 raws. The following is an explanation of the variables in each column:</p> <p>- <strong>comment</strong>: comments using Indonesian obtained from January 1 to June 30, 2021. This comment has been through a preprocessing process.</p> <p>- <strong>sentiment</strong>: consists of neutral, positive, and negative sentiments of customers.</p> <p>- <strong>dimension</strong>: there are six dimensions using customer experience proposed by Malviya and Varma (2012).</p>
Precios motos custom de ocasion
<p>Dataset obtenido a partir de la información de compraventa que aparece en la página moto-ocasion.com para motos de tipo custom en todo el territorio nacional y de todos los precios y cilindradas.</p>
FIG. 19 in Customs, rites, and sacrifices relating to a mortuary complex in Late Bronze Age Mongolia (Tsatsyn Ereg, Arkhangai)
FIG. 19. — Orientation of the horse's heads in B10 at Tsatsyn Ereg (n = 30). Data in Appendix 1. Computer aided design: S. Lepetz.
FIG. 15 in Customs, rites, and sacrifices relating to a mortuary complex in Late Bronze Age Mongolia (Tsatsyn Ereg, Arkhangai)
FIG. 15. — Relationship between the number of burnt remains (bars) and the average weight of the remains (black diamonds) for each of the structures at Tsatsyn Ereg.
FIG. 17 in Customs, rites, and sacrifices relating to a mortuary complex in Late Bronze Age Mongolia (Tsatsyn Ereg, Arkhangai)
FIG. 17. — Distribution of the burnt remains by anatomical part for all the circles combined at Tsatsyn Ereg. Computer aided design: M. Coutureau & S. Lepetz.
The Contradiction of Agile Measures: Customer as Focus, but Process as Measured?
<p>These Datasets contain the articles used in the research "The Contradiction of Agile Measures: Customer as Focus, but Process as Measured?" and a tabulation of the full set of measures found in articles chosen.</p> <p>This research study adopts a systematic mapping process to understand which agile approach is related to performance measures, what kind of measures are being used, and if the papers are focused on product measures or process measures.</p>
MMGC: custom Kraken2/Bracken database for analysing the mouse gut microbiome
<p>Custom Kraken2/Bracken database built using the representative genomes for 1,021 microbial species from the mouse gut microbiota. Genomes include isolates and MAGs, but all are near-complete (>90% completeness; <5% contamination; maximum genome size ≤ 8 Mb; maximum contig count ≤ 500; N50 ≥ 10 kb; mean contig length ≥ 5 kb). This database achieved a mean read classification rate of 87.7% when benchmarked on 1,785 independent (i.e. non-contributory) mouse gut shotgun metagenome samples. An equivalent human database (UHGG) only attained classification rates of 36.6%.</p> <p>This database is a publicly available resource to facilitate more efficient/deeper analyses of mouse gut shotgun metagenomes.</p> <p>Find out more about the Mouse Microbial Genome Collection at our <a href="https://github.com/BenBeresfordJones/MMGC">GitHub repository</a>.</p>
Prediction of Churning Credit Card Customers
<p>A business manager of a consumer credit card portfolio is facing the problem of customer attrition. They want to analyze the data to find out the reason behind this and leverage the same to predict customers who are likely to drop off.</p> <p>We could construct a model to predict which customer might be churned and the manager could proactively provide them better services and turn customers' decisions in the opposite direction.</p>
Industrial Benchmark Dataset for Customer Escalation Prediction
<p>This is a real-world industrial benchmark dataset from a major medical device manufacturer for the prediction of customer escalations. The dataset contains features derived from IoT (machine log) and enterprise data including labels for escalation from a fleet of thousands of customers of high-end medical devices. </p> <p>The dataset accompanies the publication "System Design for a Data-driven and Explainable Customer Sentiment Monitor" (submitted). We provide an anonymized version of data collected over a period of two years.</p> <p>The dataset should fuel the research and development of new machine learning algorithms to better cope with real-world data challenges including sparse and noisy labels, and concept drifts. Additional challenges is the optimal fusion of enterprise and log based features for the prediction task. Thereby, interpretability of designed prediction models should be ensured in order to have practical relevancy. </p> <p><strong>Supporting software</strong></p> <p>Kindly use the corresponding <a href="https://github.com/annguy/customer-sentiment-monitor">GitHub repository</a> (https://github.com/annguy/customer-sentiment-monitor) to design and benchmark your algorithms. </p> <p> </p> <p><strong>Citation and Contact</strong><br> </p> <p>If you use this dataset please cite the following publication:</p> <p><br> </p> <pre><code>@ARTICLE{9520354, author={Nguyen, An and Foerstel, Stefan and Kittler, Thomas and Kurzyukov, Andrey and Schwinn, Leo and Zanca, Dario and Hipp, Tobias and Jun, Sun Da and Schrapp, Michael and Rothgang, Eva and Eskofier, Bjoern}, journal={IEEE Access}, title={System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data}, year={2021}, volume={9}, number={}, pages={117140-117152}, doi={10.1109/ACCESS.2021.3106791}}</code></pre> <p> </p> <p>If you would like to get in touch, please contact an.nguyen@fau.de.<br> </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.