This dataset provides detailed information on the performance and efficiency of air filters installed in various locations such as shopping malls and hospital ventilation systems. It captures critical parameters like filter type, age, load, pressure drop, and efficiency over time. The dataset also includes measurements of particulate matter (PM2.5 and PM10) concentrations at both the inlet and outlet of the filters, offering insights into how effectively each filter is removing harmful particles from the air. Additionally, it tracks whether the filter requires replacement and flags any anomalies in its performance. This data is valuable for monitoring air quality, optimizing filter maintenance schedules, and ensuring optimal air filtration across different environments.

Esri's ArcGIS Online tools provide three methods of filtering larger datasets using attribute or geospatial information that are a part of each individual dataset. These instructions provide a basic overview of the step a GeoHub end user can take to filter out unnecessary data or to specifically hone in a particular location to find data related to this location and download the specific information filtered through the search bar, as seen on the map or using the attribute filters in the Data tab.

Filter is a configurable app template that displays a map with an interactive filtered view of one or more feature layers. The application displays prompts and hints for attribute filter values which are used to locate specific features.Use CasesFilter displays an interactive dialog box for exploring the distribution of a single attribute or the relationship between different attributes. This is a good choice when you want to understand the distribution of different types of features within a layer, or create an experience where you can gain deeper insight into how the interaction of different variables affect the resulting map content.Configurable OptionsFilter can present a web map and be configured with the following options:Choose the web map used in the application.Provide a title and color theme. The default title is the web map name.Configure the ability for feature and location search.Define the filter experince and provide text to encourage user exploration of data by displaying additional values to choose as the filter text.Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsRequires at least one layer with an interactive filter. See Apply Filters help topic for more details.Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

Water Quality Metrics and Filter Performance Dataset

Description

This dataset provides simulated data on various water quality parameters and their impact on the performance of water filtration systems. The dataset includes 19K+ samples, with attributes such as Total Dissolved Solids (TDS), turbidity, pH, water depth, and flow discharge. These parameters are used to estimate the filter life span (in hours) and filter efficiency (in percentage) under different conditions.

All the conditions for each feature is based on the data found on the Internet.

The dataset is ideal for exploring relationships between water quality metrics and filter performance, building predictive models, or conducting data analysis for environmental and engineering studies.

Features

• TDS (mg/l): Total Dissolved Solids in milligrams per liter (values < 500 mg/l).
• Turbidity (NTU): Measurement of water clarity in Nephelometric Turbidity Units (values < 10 NTU).
• pH: Measure of acidity/alkalinity (range: 6.0 to 8.5).
• Depth (m): Water depth in meters (range: 0.5 to 5 m).
• Flow Discharge (L/min): Water flow rate in liters per minute (range: 1 to 100 L/min).
• Filter Life Span (hours): Estimated lifespan of the filter based on input parameters (minimum value capped at 500 hours).
• Filter Efficiency (%): Estimated filtration efficiency (minimum value capped at 75%).

Applications

• Predicting filter performance based on water quality parameters.
• Analyzing the impact of water quality on filter lifespan and efficiency.
• Training machine learning models for environmental monitoring.

Note: This dataset is entirely synthetic and created for educational and research purposes. It does not represent real-world measurements but can be used to simulate scenarios for water filtration system analysis.

Data Requirement Date Filter describes a required data item for evaluation in terms of the type of data, and date-based filters for that data item. It refers to a constraint of the Data Requirement Structure.

Russia Avg Consumer Price: Tobacco: Cigarettes: Foreign Brands: with Filter data was reported at 131.420 RUB/Pack in Jan 2019. This records an increase from the previous number of 130.140 RUB/Pack for Dec 2018. Russia Avg Consumer Price: Tobacco: Cigarettes: Foreign Brands: with Filter data is updated monthly, averaging 27.510 RUB/Pack from Jan 1995 (Median) to Jan 2019, with 289 observations. The data reached an all-time high of 131.420 RUB/Pack in Jan 2019 and a record low of 1.390 RUB/Pack in Jan 1995. Russia Avg Consumer Price: Tobacco: Cigarettes: Foreign Brands: with Filter data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PA007: Average Consumer Price: Tobacco.

A data science project's primary objective is to analyze and train the data in preparation for the relevant machine learning project. Gathering the necessary data from the beauty domain is a crucial step to provide accurate results for the machine learning project. To ensure that the data gathered is sufficient and relevant, it is vital to identify the appropriate data sources and analyze them. Homemade remedy recipes are becoming increasingly popular around the world. There are numerous remedy recipe videos available on YouTube and Google. The information provided above is required to recommend a remedy based on the conditions. The data set contains 18 different types of skin conditions that were identified by the user through surveys.

The Eight Color Asteroid Survey provides reflectance spectra for minor planets in eight filter passbands. This dataset contains the response curves of the eight filters used in the survey, as well as the response curves of the dichroic beam splitter. The wavelength range covered is from .29 to 1.1 micrometers, with a .005 wavelength interval.

Check out our data lens page for additional data filtering and sorting options: https://data.cityofnewyork.us/view/i4p3-pe6a

This dataset contains Open Parking and Camera Violations issued by the City of New York. Updates will be applied to this data set on the following schedule:

New or open tickets will be updated weekly (Sunday). Tickets satisfied will be updated daily (Tuesday through Sunday). NOTE: Summonses that have been written-off are indicated by blank financials.

Summons images will not be available during scheduled downtime on Sunday - Monday from 1:00 am to 2:30 am and on Sundays from 5:00 am to 10:00 am.

• Initial dataset loaded 05/14/2016.

RIP is a method for perference data filtering. The core idea is that low-quality input prompts lead to high variance and low-quality responses. By measuring the quality of rejected responses and the reward gap between chosen and rejected preference pairs, RIP effectively filters prompts to enhance dataset quality. We release 4k data that filtered from 20k Wildchat prompts. For each prompt, we provide 64 responses from Llama-3.1-8B-Instruct and their corresponding rewards obtained from ArmoRM.… See the full description on the dataset page: https://huggingface.co/datasets/facebook/Wildchat-RIP-Filtered-by-8b-Llama.

Many diagnostic datasets suffer from the adverse effects of spikes that are embedded in data and noise. For example, this is true for electrical power system data where the switches, relays, and inverters are major contributors to these effects. Spikes are mostly harmful to the analysis of data in that they throw off real-time detection of abnormal conditions, and classification of faults. Since noise and spikes are mixed together and embedded within the data, removal of the unwanted signals from the data is not always easy and may result in losing the integrity of the information carried by the data. Additionally, in some applications noise and spikes need to be filtered independently. The proposed algorithm is a multi-resolution filtering approach based on Haar wavelets that is capable of removing spikes while incurring insignificant damage to other data. In particular, noise in the data, which is a useful indicator that a sensor is healthy and not stuck, can be preserved using our approach. Presented here is the theoretical background with some examples from a realistic testbed.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Many diagnostic datasets suffer from the adverse effects of spikes that are embedded in data and noise. For example, this is true for electrical power system data where the switches, relays, and inverters are major contributors to these effects. Spikes are mostly harmful to the analysis of data in that they throw off real-time detection of abnormal conditions, and classification of faults. Since noise and spikes are mixed together and embedded within the data, removal of the unwanted signals from the data is not always easy and may result in losing the integrity of the information carried by the data. Additionally, in some applications noise and spikes need to be filtered independently. The proposed algorithm is a multi-resolution filtering approach based on Haar wavelets that is capable of removing spikes while incurring insignificant damage to other data. In particular, noise in the data, which is a useful indicator that a sensor is healthy and not stuck, can be preserved using our approach. Presented here is the theoretical background with some examples from a realistic testbed.

anthracite-org/Stheno-Data-Filtered dataset hosted on Hugging Face and contributed by the HF Datasets community

The spam filter dataset is a collection of data used for training a spam filter algorithm. It typically contains a large number of emails, some of which are labeled as spam and others that are labeled as legitimate. The dataset is used to teach the algorithm to recognize the characteristics of spam emails so that it can accurately classify new emails as spam or not.

One common method for analyzing the spam filter dataset is the Naive Bayes algorithm. This algorithm uses probabilities to determine the likelihood of an email being spam based on its characteristics, such as the presence of certain keywords or the length of the email.

The Naive Bayes algorithm assumes that the presence or absence of each characteristic is independent of the others, and calculates the probability of an email being spam or not based on the joint probability of all the characteristics. This makes it a fast and efficient method for analyzing large datasets, such as the spam filter dataset.

Overall, the spam filter dataset and the Naive Bayes algorithm are powerful tools for combating the proliferation of spam emails in modern communication.

This polygon shapefile represents filters used with I-Site Studio software to filter ground observations collected by terrestrial laser scanner (TLS) survey in Grapevine Canyon near Scotty's Castle, Death Valley National Park, from July 12-14, 2016. Filters were used to remove extraneous data from features such as vegetation, fences, power lines, and atmospheric interference. The resulting points were used to produce a digital terrain model of the area (GrapevineCanyon_TIN.zip in this data release).

The water bottle detection dataset and measurement model dataset for the paper titled "The Semantic PHD Filter for Multi-class Target Tracking: From Theory to Practice" by Jun Chen, Zhanteng Xie and Philip Dames, and the paper titled "Experimental Datasets and Processing Codes for the Semantic PHD Filter" by Zhanteng Xie, Jun Chen and Philip Dames

1. Detection dataset:

Size: Total: 4870 images Training: 4000 images Validation: 870 images

Bottle Classes: Aquafina, Deer, Kirkland, Nestle

Format: PASCAL VOC, Darknet

Folder Structure: - Annotations: containing the xml label files in PASCAL VOC format - ImageSets: containing the training index files - JPEGImages: containing the image data in jpg format - Labels: containing the txt label files in Darknet format

1. Measurement model dataset:

Format: ROSBAG

Duration: 19:59s (1199s)

Topics: /darknet_ros/detection_image 3543 msgs : sensor_msgs/Image /map 1 msg : nav_msgs/OccupancyGrid /sphd_measurements 3585 msgs : sphd_msgs/SPHDMeasurements /tf 142727 msgs : tf2_msgs/TFMessage /tf_static 1 msg : tf2_msgs/TFMessage

Message Types: nav_msgs/OccupancyGrid sensor_msgs/Image sphd_msgs/SPHDMeasurements tf2_msgs/TFMessage

1. Processing codes:

Detection processing: Zenodo: https://doi.org/10.5281/zenodo.7066045 GitHub: https://github.com/TempleRAIL/yolov3_bottle_detector

Measurement model processing: Zenodo: https://doi.org/10.5281/zenodo.7066050 GitHub: https://github.com/TempleRAIL/sphd_sensor_models

AmirMohseni/arena-preference-data-filtered dataset hosted on Hugging Face and contributed by the HF Datasets community

34919 Global export shipment records of Filter Fabric with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This data set contains filter set parameters for all IR filters referenced in any of the data sets archived by the International Halley Watch (IHW) Infrared Studies Network (IRSN).