Comparison between the Complete Database and the Compressed Database.

The dataset contains 2

Data Compression Software Market Outlook

In 2023, the global data compression software market size was valued at approximately USD 1.5 billion, and it is projected to reach around USD 3.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.5% during the forecast period. The market growth is driven by the increasing need for efficient data management and storage solutions as data volumes continue to surge globally. This growth in market size is a reflection of the expanding demand across various sectors that rely heavily on data for their operations.

One of the significant growth factors for this market is the exponential increase in data generation. As businesses and individuals produce more data than ever before, the need for effective data compression solutions becomes paramount. This is particularly true in sectors like IT and telecommunications, where data transmission costs can be significantly reduced through compression. Additionally, the rising adoption of cloud services has further amplified the demand for data compression software, as organizations look to optimize their storage and bandwidth usage.

Another critical driver is the advancement in data compression technologies. Innovations in algorithms and the development of more sophisticated compression techniques have made it possible to achieve higher compression ratios without compromising data integrity. This technological progress is enabling businesses to manage large volumes of data more efficiently, thereby reducing storage costs and improving data transfer speeds. Furthermore, the integration of artificial intelligence and machine learning into compression algorithms is expected to enhance the performance and efficacy of data compression solutions.

The growing emphasis on data security and privacy also bolsters the demand for data compression software. Compressed data is often less susceptible to breaches and unauthorized access, providing an additional layer of security. With increasing regulatory requirements and the heightened awareness of data privacy among consumers and businesses, data compression solutions are becoming a critical component of data protection strategies. This trend is particularly evident in sectors like BFSI and healthcare, where data sensitivity is paramount.

In the realm of data management, Data Deduplication Tools are becoming increasingly vital. These tools help in eliminating redundant copies of data, which not only optimizes storage usage but also enhances data retrieval efficiency. As organizations continue to generate vast amounts of data, the ability to deduplicate data effectively can lead to significant cost savings and improved data management strategies. By removing duplicate data, businesses can ensure that their storage systems are utilized more efficiently, which is crucial in today's data-driven environment. Moreover, data deduplication is particularly beneficial in backup and disaster recovery scenarios, where storage space is at a premium and quick data recovery is essential. The integration of data deduplication with data compression software further amplifies its benefits, providing a comprehensive solution for efficient data management.

Regionally, North America is expected to dominate the data compression software market, owing to its advanced IT infrastructure and the presence of major technology companies. However, other regions such as Asia Pacific are projected to exhibit significant growth rates due to the rapid digital transformation and increasing investments in IT infrastructure. The market dynamics across different regions highlight diverse growth opportunities and challenges, influenced by factors such as economic conditions, technological adoption rates, and regulatory environments.

Component Analysis

The data compression software market can be segmented by components into software and services. The software segment, which includes standalone data compression tools and integrated software solutions, forms the backbone of this market. This segment is driven by the constant need for efficient data storage and transmission solutions across various industries. Businesses are increasingly adopting sophisticated software that can compress large data volumes without losing critical information, thereby optimizing their storage and bandwidth usage. Advancements in software development, including the integration of AI and machine learning, are further enhancing the capabilities of data compression tools

The data storage burden resulting from CESM simulations continues to grow, and lossy data compression methods can alleviate this burden, provided that key climate variables are not altered to the point of affecting scientific conclusions. This dataset was generated to evaluate the effects of two leading lossy compression algorithms, sz and zfp, on daily output data from the CESM-LENS dataset. In particular, it contains daily data for variables TS (surface temperature) and PRECT (precipitation rate) from the historical forcing period (1920-2005) for CESM-LENS ensemble member 30. The provided data has been compressed and reconstructed via two popular compressors: sz 1.4.13 and zfp 0.5.3 with a number of different absolute error tolerances. Errors due to compression can be determined by comparing these reconstructed files to the original CESM-LENS timeseries data, and statistical methods can evaluate the errors at different spatiotemporal scales. While both compression algorithms show promising fidelity with the original output, detectable artifacts are introduced even at relatively tight error tolerances.

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WhatsGNU_Kp_Ortholog.zip: Klebsiella pneumoniae Version-04/28/2020 (compressed 46,072,343 proteins in 8752 genomes to 1,466,934 protein variants). Updated File to fix issue with download.

File Compression Tools Market Outlook

The global file compression tools market size is projected to reach USD XX billion by 2032 from USD XX billion in 2023, growing at a CAGR of XX% during the forecast period. The steady growth in the market size is driven by the increasing digitization across various industries, which necessitates efficient data management solutions like file compression tools.

The growing volume of digital data generated daily is one of the primary growth factors for the file compression tools market. As businesses and individuals increasingly rely on digital platforms for communication and data storage, the need for efficient file compression tools becomes paramount. These tools help in saving storage space and reducing the time and bandwidth required for data transfer, thus enhancing overall productivity. Furthermore, the rise of cloud computing and the need for seamless data transfer between cloud and on-premises environments further boost the demand for advanced file compression solutions.

Another significant growth driver is the increasing adoption of high-definition content. With the proliferation of 4K and 8K videos, high-resolution images, and other large digital files, there is a growing need for robust file compression tools that can handle large file sizes without significant loss of quality. This trend is particularly prominent in the media and entertainment industry, which requires efficient compression tools to manage and distribute high-quality content swiftly. Additionally, the rising use of big data analytics across various sectors also contributes to the increased demand for file compression tools, as they help in efficiently managing and processing large datasets.

Technological advancements in compression algorithms are also propelling the growth of the file compression tools market. Modern compression techniques offer superior compression ratios and faster processing speeds, making them more efficient and reliable. The integration of artificial intelligence and machine learning algorithms in compression tools further enhances their performance, enabling more intelligent and adaptive compression strategies. This continuous innovation ensures that file compression tools remain relevant and capable of meeting the evolving needs of users.

In the context of file compression tools, the role of a Compression Driver is increasingly becoming pivotal. A Compression Driver is essentially a software component or a set of algorithms that manage the compression and decompression processes, ensuring optimal performance and efficiency. These drivers are crucial for maintaining the balance between compression speed and the quality of the compressed files. As data volumes continue to grow, the demand for more sophisticated Compression Drivers that can handle large datasets without compromising on speed or quality is on the rise. This is particularly important for industries that require real-time data processing and transmission, such as telecommunications and finance.

Regionally, North America dominates the file compression tools market, driven by the presence of major technology companies and high adoption rates of advanced digital solutions. The Asia Pacific region, however, is expected to witness the highest growth rate during the forecast period. The rapid digitization of economies, increasing internet penetration, and the proliferation of smartphones and other digital devices in countries like China and India are significant contributing factors. Europe also represents a substantial market, with a strong focus on data protection and efficient data management solutions.

Type Analysis

The file compression tools market is segmented into lossless compression and lossy compression. Lossless compression algorithms enable the complete restoration of the original file without any loss of data. This type of compression is particularly crucial for industries where data integrity is paramount, such as healthcare and BFSI. Lossless compression tools are widely used for compressing text files, databases, and other critical data that must remain unaltered. The increasing emphasis on data security and integrity is driving the demand for lossless compression solutions in these sectors.

In contrast, lossy compression algorithms achieve higher compression ratios by discarding some amount of data, which is generally imperceptible to human senses. This type of compression is ideal for media files su

This data set contains the Magellan C-BIDR (Compressed Resolution Basic Image Data Record) archive products. It also contains documentation files which describe the C-BIDRs. Each C-BIDR data directory contains the compressed image swaths obtained from one orbit and the ancillary files necessary to understand the data. The C-BIDR products archived on this volume are the exact products released by the Magellan Project, with additional PDS labels, swath index files, and documentation added for the convenience of the user.

Dataset Card for "sentence-compression"

  Dataset Summary

Dataset with pairs of equivalent sentences. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from using the dataset. Disclaimer: The team releasing sentence-compression did not upload the dataset to the Hub and did not write a dataset card. These steps were done by the Hugging Face team.

  Supported Tasks… See the full description on the dataset page: https://huggingface.co/datasets/embedding-data/sentence-compression.

1. README.md is a mark-up file that contains descriptions of all files in the .zip file
2. .zip file: Contains subfolders - Data, Output, Program, Excel. After unzipping the programs can run based on this folder structure without modification of path in the code. When programs run successfully it overwrites the current files in the Output folder with new results. Excel folder contains "CTMC_Markov_chain_FINAL_ARTIFACT.xlsx" file which is the visualization artifact.

Data for " Application of Knowledge-Driven Ensemble Algorithms to Improve Preventative Measures for Underage Tobacco Consumption in the U.S." We accessed publicly available retrospective data from the National Youth Tobacco Survey (NYTS), administered by the Centers for Disease Control and Prevention (CDC), on March 16, 2024. The Office of Management and Budget, RTI International’s Institutional Review Board (IRB), and CDC’s IRB approved the original survey design, privacy, and implementation by the CDC. We did not have access to information that could identify individual participants during or after data collection. All participants had written parental consent. Raw datasets are 2021_NYTS_raw and 2022_NYTS_raw. 2022-NYTS-Codebook_508 describes the original dataset and is originally from the CDC website. Preprocessed data that is label encoded is x_resampled and y_resampled, and one-hot encoded data begins with ohe. The 2021 one-hot encoded data is labeled likewise.

Compressed Data for the Github repository on the application of the Shallow Recurrent Decoder (SHRED) to Nuclear Reactor systems.

The simulation data (compressed) available are:

• [D1] Molten Salt Fast Reactor (MSFR) in the accidental scenario Unprotected Loss Of Fuel Flow (ULOFF) - Single Transient (Reconstruction mode)
• [D2] Molten Salt Fast Reactor (MSFR) in the accidental scenario Unprotected Loss Of Fuel Flow (ULOFF) - Parametric Transients
• [D3] DYNASTY Experimental Facility - Single Transient (Reconstruction & Prediction mode) and Parametric Transients

Nigeria Imports of containers for compressed or liquified gas from Canada was US$21.28 Thousand during 2024, according to the United Nations COMTRADE database on international trade. Nigeria Imports of containers for compressed or liquified gas from Canada - data, historical chart and statistics - was last updated on July of 2025.

Comprehensive dataset of 1 Compressed natural gas stations in State of Amazonas, Brazil as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Japan Exports of containers for compressed or liquified gas to Denmark was US$3.32 Thousand during 2018, according to the United Nations COMTRADE database on international trade. Japan Exports of containers for compressed or liquified gas to Denmark - data, historical chart and statistics - was last updated on June of 2025.

World elevation dataset

High resolution dataset containing the world elevation above the sea level in meters. See python example to get the estimated elevation from a coordinate.

  Info

This dataset comprises global elevation data sourced from ASTER GDEM, which has been compressed and retiled for efficiency. The retiled data adheres to the common web map tile convention used by platforms such as OpenStreetMap, Google Maps, and Bing Maps, providing compatibility with zoom… See the full description on the dataset page: https://huggingface.co/datasets/Upabjojr/elevation-data-ASTER-compressed-retiled.

755 Global import shipment records of Pre Compressed Press Board Insulation with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The Nimbus-4 BUV Level 2 Compressed Ozone Profile Data collection or CPOZ contains total ozone, reflectivities, ozone mixing ratios and layer ozone amounts measured every 32 seconds during the daylit portion of an orbit. Mixing ratios are given at 19 levels: 0.3, 0.4, 0.5, 0.7, 1, 1.5, 2, 3, 4, 5, 7, 10, 15, 20, 30, 40, 50, 70 and 100 mbar. Layer ozone amounts are provided at 12 layers: 0.24, 0.49, 0.99, 1.98, 3.96, 7.92, 15.8, 31.7, 63.3, 127, 253, and 1013 mbar (bottom of layer value). This product is a condensed version of the BUV High-Density Ozone Data Product or (HDBUV). The data were originally created on IBM 360 machines and archived on magnetic tapes. The data have been restored from the tapes and are now archived on disk in their original IBM binary file format. Each file contains about one day of data (14 orbits). The files consist of data records each with seventy-two 4-byte words. The first record is the header record, followed by a series of data records, and ends with several trailer records that pad out the original blocked records. A typical daily file is about 100 kB in size. The BUV instrument was operational from April 10, 1970 until May 6, 1977. In July 1972 the Nimbus-4 solar power array partially failed such that BUV operations were curtailed. Thus data collected in the later years was increasingly sparse, particularly in the equatorial region. This product was previously available from the NSSDC as the Compressed Ozone Profile (CPOZ) Data with the identifier ESAC-00010 (old ID 70-025A-05P).

This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using a variety of interpolation and reconstruction methods. The deposition consists of: An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (reconstruction_goblet_ID_0_of_1.hdf5). The Python script which was used to create the database (reconstruction_goblet.py). Auxillary Python scripts needed to run the simulations (optim_reconstructions.py, it_reconstruction.py, interp_reconstructions.py, gamp_reconstructions.py, and utils.py). MD5 and SHA256 checksums of the database and Python script files (reconstruction_goblet.MD5SUMS, reconstruction_goblet.SHA256SUMS). The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) . The files are provided as-is with no warranty as detailed in the above mentioned licenses. The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" and "Atomic Force Microscopy Images of Various Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573 and http://dx.doi.org/10.5281/zenodo.60434. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset.

This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using weighted iterative thresholding compressed sensing algorithms.

The deposition consists of:

An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (weighted_it_reconstructions.hdf5).

The Python script which was used to create the database (weighted_it_reconstructions.py).

MD5 and SHA256 checksums of the database and Python script files (weighted_it_reconstructions.MD5SUMS / weighted_it_reconstructions.SHA256SUMS).

The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .

The files are provided as-is with no warranty as detailed in the above mentioned licenses.

The database is split into four parts:

weighted_it_reconstructions.hdf5.tar.xz.part-00

weighted_it_reconstructions.hdf5.tar.xz.part-01

weighted_it_reconstructions.hdf5.tar.xz.part-02

weighted_it_reconstructions.hdf5.tar.xz.part-03

These four parts must be concatenated before the database can be extracted from the tar.xz archive. On Unix-like systems this may be done using:

cat weighted_it_reconstructions.hdf5.tar.xz.part-* > weighted_it_reconstructions.hdf5.tar.xz

after which the archive may be extracted, e.g., using:

tar xfJ weighted_it_reconstructions.hdf5.tar.xz

WARNING: The extracted HDF5 database has a size of 70 GiB.

The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset.