Utilize our Twitter dataset for diverse applications to enrich business strategies and market insights. Analyzing this dataset provides a comprehensive understanding of social media trends, empowering organizations to refine their communication and marketing strategies. Access the entire dataset or customize a subset to fit your needs. Popular use cases include market research to identify trending topics and hashtags, AI training by reviewing factors such as tweet content, retweets, and user interactions for predictive analytics, and trend forecasting by examining correlations between specific themes and user engagement to uncover emerging social media preferences.

Context

Although numerous studies have shown the capability of CNNs in effective identification of COVID-19 from CXR images, none of these studies investigated local phase CXR image features as multi-feature input to a CNN architecture for improved diagnosis of COVID-19 disease.

• Study-1: We incorporated datasets [1]-[6] for evaluate our proposed multi-feature CNNs (Paper link) and Github repo is here for reproducing our study. CXR _ ijcar _ mix and Enh _ ijcar _ mix are original CXR images and the corresponding enhanced images were used in our study. covid _ metadata _ ijcar, normal _ metadata _ ijcar, and pneumonia _ metadata _ ijcar are the corresponding metadata files.

• Study-2: In our second study, we incorporated all listing datasets [1]-[8] for proposed multi-feature semi-supervised learning. The used datasets and metadata files are CXR, Enh, covid _ metadata, normal _ metadata, and pneumonia _ metadata.

• Additional COVID-19 dataset: A new COVID-19 dataset [9] was added. It includes 243 scans from 71 subjects.

Thus, the COVID-Ti Dataset has 4038 COVID-19 scans from 2006 subjects in total after merging with the additional COVID-19 dataset. | | Normal |Pneumonia|COVID-19 | --- | --- | | No. imgs | 8851 | 6045 | 4038 | No. subs | 8851 | 6031 | 2006

Please cite our study if you are using this dataset: Qi, X., Brown, L.G., Foran, D.J. et al. Chest X-ray image phase features for improved diagnosis of COVID-19 using convolutional neural network. Int J CARS 16, 197–206 (2021). https://doi.org/10.1007/s11548-020-02305-w (Paper link)

Content

• Data Distribution_ Study-1 (IJCAR): Image size is 299 by 299. Five fold validation was used in our study. In each fold, dataset were split into train: val: test = 60%: 20%: 20% based on the number of subjects | | Normal |Pneumonia|COVID-19 | --- | --- | | No. imgs | 2567 | 2567 | 2567 | No. subs | 2567 | 2567 | 1484

• Data Distribution_ Study-2 To the best of our knowledge, COVID-Ti is the largest COVID-19 CXR dataset, including 3795 scans from 1935 patients. Image size is 299 by 299. | | Normal |Pneumonia|COVID-19 | --- | --- | | No. imgs | 8851 | 6045 | 3795 | No. subs | 8851 | 6031 | 1935

• Data Distribution_ Additional COVID-19 dataset [9] Image size is 299 by 299 | | Normal |Pneumonia|COVID-19 | --- | --- | | No. imgs | 0 | 0 | 243 | No. subs | 0 | 0 | 71

• Uploaded datasets Two types of images are uploaded: the original CXR and the corresponding enhanced CXR.

  • CXR and Enh are datasets for Study-2. Metadatas are covid_ metadata, normal_ metadata, and pneumonia_ metadata.
  • CXR_ ijcar_ mix and Enh_ ijcar_ mix are datasets for Study-1 (IJCAR). Metadatas are covid_ metadata_ ijcar, normal_ metadata_ ijcar, and pneumonia_ metadata_ ijcar.
  • COVID-19_ CXR and COVID-19_ Enh are datesets for the additional COVID-19 dataset. Metadata is additional_ covid_ metadata.

• metadata Each metadata file has four columns: sub_name, img_name, class, dataset. Enhanced images have same images with original CXR image. In the dataset column, rsna corresponding to [1], cohen corresponding to [2], sirm corresponding to [3], fig1 corresponding to [4], actmed corresponding to [5], BIMCV corresponding to [6], TCIA-1 _Rual corresponding to [7], TCIA-4 _rsna corresponding to [8], and Germany corresponding to [9]

Acknowledgements

Thanks to the following every organization and individual's effort for providing the valuable COVID-19 CXR images: [1] RSNA Pneumonia Detection Challenge dataset (https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data) [2] COVID-19 image data collection(covid-chestxray-dataset) collected by J.P. Cohen (https://github.com/ieee8023/covid-chestxray-dataset) [3] The Italian Society of Medical and Interventional Radiology (SIRM) (https://sirm.org/category/senza-categoria/covid-19/) [4] Figure 1 COVID-19 Chest X-ray Dataset Initiative (https://github.com/agchung/Figure1-COVID-chestxray-dataset) [5] ActualMed COVID-19 Chest X-ray Dataset Initiative (https://github.com/agchung/Actualmed-COVID-chestxray-dataset) [6] BIMCV-COVID19 (https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/#1590858128006-9e640421-6711) [7] COVID-19-AR (https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70226443) [8] MIDRC-RICORD-1c (https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70230281) [9] COVID-19 Image Repository (https://github.com/ml-workgroup/covid-19-image-repository)

Inspiration

We hope our dataset and image enhancement technique could be used as many as possible in a varsity of studies to facilitate the development of the more effective COVID-19 diagnosis method. Hope the pandemic end soon.

Broadband Radio Service (BRS) and EBS Transmitters

BRS and EBS Transmitter Locations for Broadband Communication

By Homeland Infrastructure Foundation [source]

About this dataset

This dataset provides detailed spatial data on the location of Broadband Radio Service (BRS) and Educational Broadband Service (EBS) transmitters, which are essential for the provision of broadband communication services. The Broadband Radio Service (BRS) is a commercial service that initially focused on transmitting data and video programming to subscribers through high-powered wireless cable systems. However, it has since expanded to include digital two-way systems capable of delivering high-speed, high-capacity broadband services, including two-way Internet access via cellularized communication networks. On the other hand, the Educational Broadband Service (EBS), formerly known as the Instructional Television Fixed Service (ITFS), was primarily used for transmitting instructional material to accredited educational institutions and non-educational establishments like hospitals or training centers.

The dataset includes various important variables such as coordinates represented by latitude (X) and longitude (Y). Additionally, it provides information about license holders (LICENSEE), unique identifiers for each transmitter's call sign (CALLSIGN), location numbers assigned to transmitters at specific sites (LOCNUM), directions of latitude and longitude in terms of North/South or East/West orientation respectively. Other relevant details consist of addresses where transmitters are installed at their respective locations(LOCADD), associated cities(LOCCITY), counties(LOCCOUNTY), states(LOCSTATE), National Environmental Policy Act status with regard to environmental regulations compliance regarding transmitter installations throughout different areas in geographical zones(QZONE).

Furthermore, this dataset facilitates understanding about supporting structures(SUPSTRUC) at each transmission site along with a mention of all types of structures present(ALLSTRUC). Moreover, structural characteristics like structure type(STURCTYPE) are included alongside tower registration numbers(TOWREG).

By combing these comprehensive spatial details related to BRS and EBS transmitters' locations across different zones in a structured manner,this dataset inherently helps analyze their distribution patterns, assess network coverage, and support planning for improved broadband service deployment

How to use the dataset

The Broadband Radio Service (BRS) and Educational Broadband Service (EBS) Transmitters dataset provides valuable spatial data on the location of BRS and EBS transmitters for broadband communication services. This guide will walk you through how to effectively use this dataset to gain insights or perform analysis.

1. Understanding the Dataset

The first step is to familiarize yourself with the dataset columns and their meanings. Here are some important columns in the dataset:

• X, Y: The X-coordinate and Y-coordinate of the transmitter's location in numeric format.
• LICENSEE: The entity or organization that holds the license for the transmitter.
• CALLSIGN: The unique identifier for each transmitter.
• LOCNUM: The location number of each transmitter, represented as a numeric value.
• LAT_DMS, LON_DMS, LATDIR, LONDIR: Latitude and longitude details of each transmitter in degrees, minutes, and seconds format along with their corresponding directions.
• LOCADD: The address of each transmitter's location.
• LOCCITY: The city where each transmitter is located.
• LOCCOUNTY: The county where each transmitter is located
• LOCSTATE: State where each transistor is located
• NEPA: National Environmental Policy Act status of each transistor along with other useful columns such as SUPSTRUC, ALLSTRUC, STRUCTYPE which provide details about supporting structures at the transmission site.

2. Analyzing Transmitter Locations

You can use this dataset to analyze various aspects related to BRS and EBS transmitters' physical locations. This may include:

a) Geospatial Analysis: Utilize X-coordinate (longitude) and Y-coordinate (latitude) data along with corresponding locality information like city names or counties to map the transmitters' distribution geographically. This can help identify areas with high transmitter density or regions lacking proper coverage.

b) Locational Characteristics: Explore additional columns like LOCADD, LOCCITY, LOCCOUNTY, and LOCSTATE to gain insights into the specific geography where transmitters are located. This can be useful for understanding network...

Abstract copyright UK Data Service and data collection copyright owner. This project developed a test for asymmetric information in the UK private health insurance (PHI) market. In contrast to earlier research that considered either a purely private system or one where private insurance is complementary to public insurance, PHI is substitutive of the public system in the UK. Using a theoretical model of competition among insurers incorporating this characteristic, the project linked the type of selection (adverse or propitious) with the existence of risk-related information asymmetries. Using the British Household Panel Survey (BHPS) (held at the UK Data Archive under SN 5151), evidence was found that adverse selection is present in the PHI market, which led to the conclusion that such information asymmetries exist. Further information may be found in the journal article that uses these data: Olivella, P. and Vera-Hernandez, M. (2012) 'Testing for asymmetric information in private health insurance', Economic Journal, retrieved September 3, 2012 from http://onlinelibrary.wiley.com/doi/10.1111/j.1468-0297.2012.02520.x/full (may require subscription). Main Topics: The file includes a subset of data from Waves 6-18 of the BHPS (residents in England, Wales, and Scotland only), covering male employees aged 23-59. See documentation for further details. Multi-stage stratified random sample Compilation or synthesis of existing material The BHPS data were originally gathered by face-to-face interview.

The global number of Twitter users in was forecast to continuously increase between 2024 and 2028 by in total 74.3 million users (+17.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 503.42 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like South America and the Americas.

The number of Twitter users in the United States was forecast to continuously increase between 2024 and 2028 by in total 4.3 million users (+5.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 85.08 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like Canada and Mexico.