Civil Rights
Movements, leaders, victories and the continuing fight for equality.
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Explore the people, places, events, achievements, struggles and stories that shaped our journey.
Movements, leaders, victories and the continuing fight for equality.
Innovation, patents, science, technology and world-changing contributions.
Pioneers, champions, Negro Leagues, records, activism and excellence.
Meet the people whose lives, choices and achievements shaped the journey.
Black towns, communities, institutions and places where history happened.
Moments that changed communities, movements, institutions and the nation.
Wilmington, North Carolina once had a thriving Black middle class and an elected interracial government. In 1898 white supremacists used violence to overthrow that government, kill Black residents and drive many others from the city.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
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| ZIM archive format | |
|---|---|
 | |
| Filename extension |
.zim |
| Internet media type | text/plain, application/octet-stream |
| Magic number | 0x44D495A |
| Latest release | 6.3 April 16, 2025 |
| Type of format | Data compression |
| Compression | Zstandard, LZMA2 |
| Container for | Wikimedia and other web content for offline use |
| Open format? | Yes |
| Free format? | Yes |
| Website | https://wiki.openzim.org/wiki/OpenZIM |
The ZIM file format is an open file format that stores website content for offline usage.[1] The format is defined by the openZIM project, which also supports an open-source ZIM reader called Kiwix. The format is primarily used to store the contents of Wikipedia and other Wikimedia projects, including articles, full-text search indices and auxiliary files.[2][3]
ZIM is an abbreviation for 'Zeno improved',[4][5] as it replaced the earlier Zeno file format. Since 2021, the library defaults to Zstandard file compression,[6] and also supports LZMA2 compression, as implemented by the XZ Utils library. Most notably, the compression ratio can be up to 3∶1 with almost all of the space savings taking place within the clusters.[7]
Source: Wikipedia. Article content is retrieved live through the MediaWiki API.
The ZIM file format is an open file format that stores website content for offline usage. The format is defined by the openZIM project, which also supports an open-source ZIM reader called Kiwix. The format is primarily used to store the contents of Wikipedia and other Wikimedia projects, including articles, full-text search indices and auxiliary files. ZIM is an abbreviation for 'Zeno improved', as it replaced the earlier Zeno file format. Since 2021, the library defaults to Zstandard file compression, and also supports LZMA2 compression, as implemented by the XZ Utils library. Most notably, the compression ratio can be up to 3∶1 with almost all of the space savings taking place within the clusters.
gzip is a file format and a file compression program. The program uses the Deflate algorithm to compress and decompress a single file using the gzip file format. gzip was released in 1992 as a free software replacement for the compress program because its compression algorithm, LZW, was covered by patents from Unisys and IBM, which did not expire until 2003 and 2004. Jean-Loup Gailly designed the gzip file format, which was later specified by RFC 1952, and originally wrote the gzip program. Mark Adler wrote the decompression part. gzip is now developed by the GNU project. As the file format can be decompressed via a streaming algorithm, it is commonly used in stream-based technology such as Web protocols, data interchange and ETL (in standard pipes).
bzip2 is a file format and a file compression program. The program uses the Burrows–Wheeler algorithm to compress and decompress a single file using the bzip2 file format. libbzip2 is a lossless data compression software library used by the bzip2 program. bzip2 is not a file archiver and thus relies on separate external utilities such as tar for tasks such as handling multiple files, and other tools for encryption, and archive splitting. bzip2 was initially released in 1996 (originally named bzip) by Julian Seward. It compresses most files more effectively than older LZW and Deflate compression algorithms but is slower. bzip2 is particularly efficient for text data, and decompression is relatively fast. The algorithm uses several layers of compression techniques, such as run-length encoding (RLE), Burrows–Wheeler transform (BWT), move-to-front transform (MTF), and Huffman coding. bzip2 compresses data in blocks between 100 and 900 kB and uses the Burrows–Wheeler transform to convert frequently recurring character sequences into strings of identical letters. The move-to-front transform and Huffman coding are then applied. The compression performance is asymmetric, with decompression being faster than compression. The algorithm has gone through multiple maintainers since its initial release, with Micah Snyder being the maintainer since June 2021. There have been some modifications to the algorithm, such as pbzip2 and lbzip2, which use multi-threading to improve compression speed on multi-CPU and multi-core computers. bzip2 is suitable for use in big data applications with cluster computing frameworks like Hadoop and Apache Spark, as a compressed block can be decompressed without having to process earlier blocks. The bundled bzip2recover utility tries recovering readable parts of damaged bzip2 data. It works by searching for individual blocks and dumping them into separate files.
In information technology, lossy compression, or irreversible compression, is the class of data compression methods that uses inexact approximations and partial data discarding to represent the content. These techniques are used to reduce data size for storing, handling, and transmitting content. Higher degrees of approximation create coarser images as more details are removed. This is opposed to lossless data compression (reversible data compression) which does not degrade the data. The amount of data reduction possible using lossy compression is much higher than using lossless techniques. Well-designed lossy compression technology often reduces file sizes significantly before degradation is noticed by the end-user. Even when noticeable by the user, further data reduction may be desirable (e.g., for real-time communication or to reduce transmission times or storage needs). The most widely used lossy compression algorithm is the discrete cosine transform (DCT), first published by Nasir Ahmed, T. Natarajan and K. R. Rao in 1974. Lossy compression is most commonly used to compress multimedia data (audio, video, and images), especially in applications such as streaming media and internet telephony. By contrast, lossless compression is typically required for text and data files, such as bank records and text articles. It can be advantageous to make a master lossless file which can then be used to produce additional copies from. This allows one to avoid basing new compressed copies on a lossy source file, which would yield additional artifacts and further unnecessary information loss.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Madam C.J. Walker