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2.4106260679999423, "decode_time": 0.04394897500003481, "encode_mb_per_sec": 10.542255900656363, "decode_mb_per_sec": 578.248682469348, "original_size": 26647920, "compressed_size": 10278858, "array_shape": [ 13323960 ], "array_dtype": "int16", "timestamp": 1741096829.732088, "cache_status": "cached", "dataset": "fmri-ds005880", "algorithm": "blosc2-9-delta", "algorithm_version": "1", "dataset_version": "2", "system_version": "v6" }, { "compression_ratio": 2.2130855303485877, "encode_time": 0.09569188099999337, "decode_time": 0.1987645330000305, "encode_mb_per_sec": 265.5756854612378, "decode_mb_per_sec": 127.85699996912697, "original_size": 26647920, "compressed_size": 12041071, "array_shape": [ 13323960 ], "array_dtype": "int16", "timestamp": 1753375462.017307, "cache_status": "new", "dataset": "fmri-ds005880", "algorithm": "blosc2-1-lpc", "algorithm_version": "1", "dataset_version": "2", "system_version": "v6" }, { "compression_ratio": 2.2130855303485877, "encode_time": 0.09854521300007946, "decode_time": 0.1989031049999994, "encode_mb_per_sec": 257.88606179813064, "decode_mb_per_sec": 127.76792443561156, "original_size": 26647920, "compressed_size": 12041071, "array_shape": [ 13323960 ], "array_dtype": "int16", "timestamp": 1753375465.6561263, "cache_status": "new", "dataset": "fmri-ds005880", "algorithm": "blosc2-5-lpc", "algorithm_version": "1", "dataset_version": "2", "system_version": "v6" }, { "compression_ratio": 2.678791902239484, "encode_time": 2.2454585999998926, "decode_time": 0.20235537799999292, "encode_mb_per_sec": 11.317704494596184, "decode_mb_per_sec": 125.58814665973111, "original_size": 26647920, "compressed_size": 9947738, "array_shape": [ 13323960 ], "array_dtype": "int16", "timestamp": 1753375472.5596147, "cache_status": "new", "dataset": "fmri-ds005880", "algorithm": "blosc2-9-lpc", "algorithm_version": "1", "dataset_version": "2", "system_version": "v6" }, { "compression_ratio": 2.6631648063033486, "encode_time": 0.3012574124999787, "decode_time": 0.2705694974999915, "encode_mb_per_sec": 84.35788078625362, "decode_mb_per_sec": 93.92572749132314, "original_size": 26647920, "compressed_size": 10006110, "array_shape": [ 13323960 ], "array_dtype": "int16", "timestamp": 1741352545.5739927, "cache_status": "cached", "dataset": "fmri-ds005880", "algorithm": "wavpack-1", "algorithm_version": "1", "dataset_version": "2", "system_version": "v6" }, { "compression_ratio": 2.7559061144109873, "encode_time": 0.3708597739999959, "decode_time": 0.3736457689999497, "encode_mb_per_sec": 68.52573040086229, "decode_mb_per_sec": 68.01478565558669, "original_size": 26647920, "compressed_size": 9669386, "array_shape": [ 13323960 ], "array_dtype": "int16", "timestamp": 1741352548.4586654, "cache_status": "cached", "dataset": "fmri-ds005880", "algorithm": "wavpack-2", "algorithm_version": "1", "dataset_version": "2", "system_version": "v6" }, { "compression_ratio": 2.812708396795732, "encode_time": 0.49779199700003574, "decode_time": 0.5580402035000134, "encode_mb_per_sec": 51.052321135742595, "decode_mb_per_sec": 45.54051254776275, "original_size": 26647920, "compressed_size": 9474114, "array_shape": [ 13323960 ], "array_dtype": "int16", "timestamp": 1741352551.6925893, "cache_status": "cached", "dataset": "fmri-ds005880", "algorithm": "wavpack-3", "algorithm_version": "1", "dataset_version": "2", "system_version": "v6" }, { "compression_ratio": 2.8205429455625532, "encode_time": 0.6517902285000332, "decode_time": 0.7451731460000133, "encode_mb_per_sec": 38.99020847264381, "decode_mb_per_sec": 34.104069673020646, "original_size": 26647920, "compressed_size": 9447798, "array_shape": [ 13323960 ], "array_dtype": "int16", "timestamp": 1741352555.1678364, "cache_status": "cached", "dataset": "fmri-ds005880", "algorithm": "wavpack-4", "algorithm_version": "1", "dataset_version": "2", "system_version": "v6" } ], "algorithms": [ { "name": "bzip2-1", "description": "Bzip2 compression at level 1 (fastest).", "long_description": "# Bzip2 Algorithm\n\nBzip2 is a compression algorithm that uses the Burrows-Wheeler transform along with Huffman coding. It typically achieves better compression than traditional LZ77/LZ78-based algorithms but at the cost of speed.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- bzip2-1: Fastest compression\n- bzip2-3: Better compression than level 1\n- bzip2-5: Medium compression\n- bzip2-7: Better compression than level 5\n- bzip2-9: Maximum compression\n\n### Delta Encoding\n- bzip2-9-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "bzip2" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/bzip2/__init__.py" }, { "name": "bzip2-3", "description": "Bzip2 compression at level 3.", "long_description": "# Bzip2 Algorithm\n\nBzip2 is a compression algorithm that uses the Burrows-Wheeler transform along with Huffman coding. It typically achieves better compression than traditional LZ77/LZ78-based algorithms but at the cost of speed.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- bzip2-1: Fastest compression\n- bzip2-3: Better compression than level 1\n- bzip2-5: Medium compression\n- bzip2-7: Better compression than level 5\n- bzip2-9: Maximum compression\n\n### Delta Encoding\n- bzip2-9-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "bzip2" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/bzip2/__init__.py" }, { "name": "bzip2-5", "description": "Bzip2 compression at level 5 (medium).", "long_description": "# Bzip2 Algorithm\n\nBzip2 is a compression algorithm that uses the Burrows-Wheeler transform along with Huffman coding. It typically achieves better compression than traditional LZ77/LZ78-based algorithms but at the cost of speed.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- bzip2-1: Fastest compression\n- bzip2-3: Better compression than level 1\n- bzip2-5: Medium compression\n- bzip2-7: Better compression than level 5\n- bzip2-9: Maximum compression\n\n### Delta Encoding\n- bzip2-9-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "bzip2" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/bzip2/__init__.py" }, { "name": "bzip2-7", "description": "Bzip2 compression at level 7.", "long_description": "# Bzip2 Algorithm\n\nBzip2 is a compression algorithm that uses the Burrows-Wheeler transform along with Huffman coding. It typically achieves better compression than traditional LZ77/LZ78-based algorithms but at the cost of speed.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- bzip2-1: Fastest compression\n- bzip2-3: Better compression than level 1\n- bzip2-5: Medium compression\n- bzip2-7: Better compression than level 5\n- bzip2-9: Maximum compression\n\n### Delta Encoding\n- bzip2-9-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "bzip2" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/bzip2/__init__.py" }, { "name": "bzip2-9", "description": "Bzip2 compression at maximum level 9.", "long_description": "# Bzip2 Algorithm\n\nBzip2 is a compression algorithm that uses the Burrows-Wheeler transform along with Huffman coding. It typically achieves better compression than traditional LZ77/LZ78-based algorithms but at the cost of speed.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- bzip2-1: Fastest compression\n- bzip2-3: Better compression than level 1\n- bzip2-5: Medium compression\n- bzip2-7: Better compression than level 5\n- bzip2-9: Maximum compression\n\n### Delta Encoding\n- bzip2-9-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "bzip2" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/bzip2/__init__.py" }, { "name": "bzip2-9-delta", "description": "Bzip2 compression at level 9 with delta encoding.", "long_description": "# Bzip2 Algorithm\n\nBzip2 is a compression algorithm that uses the Burrows-Wheeler transform along with Huffman coding. It typically achieves better compression than traditional LZ77/LZ78-based algorithms but at the cost of speed.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- bzip2-1: Fastest compression\n- bzip2-3: Better compression than level 1\n- bzip2-5: Medium compression\n- bzip2-7: Better compression than level 5\n- bzip2-9: Maximum compression\n\n### Delta Encoding\n- bzip2-9-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "bzip2", "delta_encoding", "1d" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/bzip2/__init__.py" }, { "name": "zlib-1", "description": "Zlib DEFLATE compression at level 1 (fastest).", "long_description": "# Zlib Algorithm\n\nZlib is a data compression library that uses the DEFLATE algorithm, which combines LZ77 and Huffman coding. It provides a good balance between compression ratio and speed, making it widely used in many applications.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- zlib-1: Fastest compression\n- zlib-3: Better compression than level 1\n- zlib-5: Medium compression\n- zlib-7: Better compression than level 5\n- zlib-9: Maximum compression\n\n### Delta Encoding\n- zlib-9-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "zlib" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/zlib/__init__.py" }, { "name": "zlib-3", "description": "Zlib DEFLATE compression at level 3.", "long_description": "# Zlib Algorithm\n\nZlib is a data compression library that uses the DEFLATE algorithm, which combines LZ77 and Huffman coding. It provides a good balance between compression ratio and speed, making it widely used in many applications.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- zlib-1: Fastest compression\n- zlib-3: Better compression than level 1\n- zlib-5: Medium compression\n- zlib-7: Better compression than level 5\n- zlib-9: Maximum compression\n\n### Delta Encoding\n- zlib-9-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "zlib" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/zlib/__init__.py" }, { "name": "zlib-5", "description": "Zlib DEFLATE compression at level 5 (medium).", "long_description": "# Zlib Algorithm\n\nZlib is a data compression library that uses the DEFLATE algorithm, which combines LZ77 and Huffman coding. It provides a good balance between compression ratio and speed, making it widely used in many applications.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- zlib-1: Fastest compression\n- zlib-3: Better compression than level 1\n- zlib-5: Medium compression\n- zlib-7: Better compression than level 5\n- zlib-9: Maximum compression\n\n### Delta Encoding\n- zlib-9-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "zlib" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/zlib/__init__.py" }, { "name": "zlib-7", "description": "Zlib DEFLATE compression at level 7.", "long_description": "# Zlib Algorithm\n\nZlib is a data compression library that uses the DEFLATE algorithm, which combines LZ77 and Huffman coding. It provides a good balance between compression ratio and speed, making it widely used in many applications.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- zlib-1: Fastest compression\n- zlib-3: Better compression than level 1\n- zlib-5: Medium compression\n- zlib-7: Better compression than level 5\n- zlib-9: Maximum compression\n\n### Delta Encoding\n- zlib-9-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "zlib" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/zlib/__init__.py" }, { "name": "zlib-9", "description": "Zlib DEFLATE compression at maximum level 9.", "long_description": "# Zlib Algorithm\n\nZlib is a data compression library that uses the DEFLATE algorithm, which combines LZ77 and Huffman coding. It provides a good balance between compression ratio and speed, making it widely used in many applications.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- zlib-1: Fastest compression\n- zlib-3: Better compression than level 1\n- zlib-5: Medium compression\n- zlib-7: Better compression than level 5\n- zlib-9: Maximum compression\n\n### Delta Encoding\n- zlib-9-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "zlib" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/zlib/__init__.py" }, { "name": "zlib-9-delta", "description": "Zlib DEFLATE compression at level 9 with delta encoding.", "long_description": "# Zlib Algorithm\n\nZlib is a data compression library that uses the DEFLATE algorithm, which combines LZ77 and Huffman coding. It provides a good balance between compression ratio and speed, making it widely used in many applications.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- zlib-1: Fastest compression\n- zlib-3: Better compression than level 1\n- zlib-5: Medium compression\n- zlib-7: Better compression than level 5\n- zlib-9: Maximum compression\n\n### Delta Encoding\n- zlib-9-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "zlib", "delta_encoding", "1d" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/zlib/__init__.py" }, { "name": "zstd-4", "description": "Zstandard compression at level 4 (fast compression).", "long_description": "# Zstandard Algorithm\n\nZstandard (zstd) is a fast real-time compression algorithm developed by Facebook. It provides a wide range of compression levels and supports custom dictionaries for improved compression.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- zstd-4: Fast compression\n- zstd-7: Balanced speed/compression\n- zstd-10: Better compression\n- zstd-13: High compression\n- zstd-16: Very high compression\n- zstd-19: Ultra high compression\n- zstd-22: Maximum compression\n\n### Advanced Variants\n\n#### Delta Encoding (zstd-22-delta)\nStores differences between consecutive values. Effective for sequences where adjacent values are similar, like time series data.\n\n#### Linear Predictive Coding (zstd-22-lpc)\nUses a Markov model to predict values based on previous samples. The prediction residuals are then compressed using zstd. This can significantly improve compression for data with temporal correlations.\n\n#### Linear Predictive Coding with Zero RLE (zstd-22-lpc-zrle)\nCombines Markov prediction with zero run-length encoding. Particularly effective for sparse data where many values are zero, as it efficiently encodes runs of zeros while using Markov prediction for the non-zero regions.\n", "version": "1", "tags": [ "zstd" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/zstd/__init__.py" }, { "name": "zstd-7", "description": "Zstandard compression at level 7 (balanced speed/compression).", "long_description": "# Zstandard Algorithm\n\nZstandard (zstd) is a fast real-time compression algorithm developed by Facebook. It provides a wide range of compression levels and supports custom dictionaries for improved compression.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- zstd-4: Fast compression\n- zstd-7: Balanced speed/compression\n- zstd-10: Better compression\n- zstd-13: High compression\n- zstd-16: Very high compression\n- zstd-19: Ultra high compression\n- zstd-22: Maximum compression\n\n### Advanced Variants\n\n#### Delta Encoding (zstd-22-delta)\nStores differences between consecutive values. Effective for sequences where adjacent values are similar, like time series data.\n\n#### Linear Predictive Coding (zstd-22-lpc)\nUses a Markov model to predict values based on previous samples. The prediction residuals are then compressed using zstd. This can significantly improve compression for data with temporal correlations.\n\n#### Linear Predictive Coding with Zero RLE (zstd-22-lpc-zrle)\nCombines Markov prediction with zero run-length encoding. Particularly effective for sparse data where many values are zero, as it efficiently encodes runs of zeros while using Markov prediction for the non-zero regions.\n", "version": "1", "tags": [ "zstd" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/zstd/__init__.py" }, { "name": "zstd-10", "description": "Zstandard compression at level 10 (better compression).", "long_description": "# Zstandard Algorithm\n\nZstandard (zstd) is a fast real-time compression algorithm developed by Facebook. It provides a wide range of compression levels and supports custom dictionaries for improved compression.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- zstd-4: Fast compression\n- zstd-7: Balanced speed/compression\n- zstd-10: Better compression\n- zstd-13: High compression\n- zstd-16: Very high compression\n- zstd-19: Ultra high compression\n- zstd-22: Maximum compression\n\n### Advanced Variants\n\n#### Delta Encoding (zstd-22-delta)\nStores differences between consecutive values. Effective for sequences where adjacent values are similar, like time series data.\n\n#### Linear Predictive Coding (zstd-22-lpc)\nUses a Markov model to predict values based on previous samples. The prediction residuals are then compressed using zstd. This can significantly improve compression for data with temporal correlations.\n\n#### Linear Predictive Coding with Zero RLE (zstd-22-lpc-zrle)\nCombines Markov prediction with zero run-length encoding. Particularly effective for sparse data where many values are zero, as it efficiently encodes runs of zeros while using Markov prediction for the non-zero regions.\n", "version": "1", "tags": [ "zstd" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/zstd/__init__.py" }, { "name": "zstd-13", "description": "Zstandard compression at level 13 (high compression).", "long_description": "# Zstandard Algorithm\n\nZstandard (zstd) is a fast real-time compression algorithm developed by Facebook. It provides a wide range of compression levels and supports custom dictionaries for improved compression.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- zstd-4: Fast compression\n- zstd-7: Balanced speed/compression\n- zstd-10: Better compression\n- zstd-13: High compression\n- zstd-16: Very high compression\n- zstd-19: Ultra high compression\n- zstd-22: Maximum compression\n\n### Advanced Variants\n\n#### Delta Encoding (zstd-22-delta)\nStores differences between consecutive values. Effective for sequences where adjacent values are similar, like time series data.\n\n#### Linear Predictive Coding (zstd-22-lpc)\nUses a Markov model to predict values based on previous samples. The prediction residuals are then compressed using zstd. This can significantly improve compression for data with temporal correlations.\n\n#### Linear Predictive Coding with Zero RLE (zstd-22-lpc-zrle)\nCombines Markov prediction with zero run-length encoding. Particularly effective for sparse data where many values are zero, as it efficiently encodes runs of zeros while using Markov prediction for the non-zero regions.\n", "version": "1", "tags": [ "zstd" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/zstd/__init__.py" }, { "name": "zstd-16", "description": "Zstandard compression at level 16 (very high compression).", "long_description": "# Zstandard Algorithm\n\nZstandard (zstd) is a fast real-time compression algorithm developed by Facebook. It provides a wide range of compression levels and supports custom dictionaries for improved compression.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- zstd-4: Fast compression\n- zstd-7: Balanced speed/compression\n- zstd-10: Better compression\n- zstd-13: High compression\n- zstd-16: Very high compression\n- zstd-19: Ultra high compression\n- zstd-22: Maximum compression\n\n### Advanced Variants\n\n#### Delta Encoding (zstd-22-delta)\nStores differences between consecutive values. Effective for sequences where adjacent values are similar, like time series data.\n\n#### Linear Predictive Coding (zstd-22-lpc)\nUses a Markov model to predict values based on previous samples. The prediction residuals are then compressed using zstd. This can significantly improve compression for data with temporal correlations.\n\n#### Linear Predictive Coding with Zero RLE (zstd-22-lpc-zrle)\nCombines Markov prediction with zero run-length encoding. Particularly effective for sparse data where many values are zero, as it efficiently encodes runs of zeros while using Markov prediction for the non-zero regions.\n", "version": "1", "tags": [ "zstd" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/zstd/__init__.py" }, { "name": "zstd-19", "description": "Zstandard compression at level 19 (ultra high compression).", "long_description": "# Zstandard Algorithm\n\nZstandard (zstd) is a fast real-time compression algorithm developed by Facebook. It provides a wide range of compression levels and supports custom dictionaries for improved compression.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- zstd-4: Fast compression\n- zstd-7: Balanced speed/compression\n- zstd-10: Better compression\n- zstd-13: High compression\n- zstd-16: Very high compression\n- zstd-19: Ultra high compression\n- zstd-22: Maximum compression\n\n### Advanced Variants\n\n#### Delta Encoding (zstd-22-delta)\nStores differences between consecutive values. Effective for sequences where adjacent values are similar, like time series data.\n\n#### Linear Predictive Coding (zstd-22-lpc)\nUses a Markov model to predict values based on previous samples. The prediction residuals are then compressed using zstd. This can significantly improve compression for data with temporal correlations.\n\n#### Linear Predictive Coding with Zero RLE (zstd-22-lpc-zrle)\nCombines Markov prediction with zero run-length encoding. Particularly effective for sparse data where many values are zero, as it efficiently encodes runs of zeros while using Markov prediction for the non-zero regions.\n", "version": "1", "tags": [ "zstd" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/zstd/__init__.py" }, { "name": "zstd-22", "description": "Zstandard compression at maximum level 22 (highest compression).", "long_description": "# Zstandard Algorithm\n\nZstandard (zstd) is a fast real-time compression algorithm developed by Facebook. It provides a wide range of compression levels and supports custom dictionaries for improved compression.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- zstd-4: Fast compression\n- zstd-7: Balanced speed/compression\n- zstd-10: Better compression\n- zstd-13: High compression\n- zstd-16: Very high compression\n- zstd-19: Ultra high compression\n- zstd-22: Maximum compression\n\n### Advanced Variants\n\n#### Delta Encoding (zstd-22-delta)\nStores differences between consecutive values. Effective for sequences where adjacent values are similar, like time series data.\n\n#### Linear Predictive Coding (zstd-22-lpc)\nUses a Markov model to predict values based on previous samples. The prediction residuals are then compressed using zstd. This can significantly improve compression for data with temporal correlations.\n\n#### Linear Predictive Coding with Zero RLE (zstd-22-lpc-zrle)\nCombines Markov prediction with zero run-length encoding. Particularly effective for sparse data where many values are zero, as it efficiently encodes runs of zeros while using Markov prediction for the non-zero regions.\n", "version": "1", "tags": [ "zstd" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/zstd/__init__.py" }, { "name": "zstd-22-delta", "description": "Zstandard compression at level 22 with delta encoding for improved compression of sequential data.", "long_description": "# Zstandard Algorithm\n\nZstandard (zstd) is a fast real-time compression algorithm developed by Facebook. It provides a wide range of compression levels and supports custom dictionaries for improved compression.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- zstd-4: Fast compression\n- zstd-7: Balanced speed/compression\n- zstd-10: Better compression\n- zstd-13: High compression\n- zstd-16: Very high compression\n- zstd-19: Ultra high compression\n- zstd-22: Maximum compression\n\n### Advanced Variants\n\n#### Delta Encoding (zstd-22-delta)\nStores differences between consecutive values. Effective for sequences where adjacent values are similar, like time series data.\n\n#### Linear Predictive Coding (zstd-22-lpc)\nUses a Markov model to predict values based on previous samples. The prediction residuals are then compressed using zstd. This can significantly improve compression for data with temporal correlations.\n\n#### Linear Predictive Coding with Zero RLE (zstd-22-lpc-zrle)\nCombines Markov prediction with zero run-length encoding. Particularly effective for sparse data where many values are zero, as it efficiently encodes runs of zeros while using Markov prediction for the non-zero regions.\n", "version": "1", "tags": [ "zstd", "delta_encoding", "1d" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/zstd/__init__.py" }, { "name": "zstd-22-lpc", "description": "Zstandard compression at level 22 with linear predictive codingn for exploiting temporal correlations in the data.", "long_description": "# Zstandard Algorithm\n\nZstandard (zstd) is a fast real-time compression algorithm developed by Facebook. It provides a wide range of compression levels and supports custom dictionaries for improved compression.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- zstd-4: Fast compression\n- zstd-7: Balanced speed/compression\n- zstd-10: Better compression\n- zstd-13: High compression\n- zstd-16: Very high compression\n- zstd-19: Ultra high compression\n- zstd-22: Maximum compression\n\n### Advanced Variants\n\n#### Delta Encoding (zstd-22-delta)\nStores differences between consecutive values. Effective for sequences where adjacent values are similar, like time series data.\n\n#### Linear Predictive Coding (zstd-22-lpc)\nUses a Markov model to predict values based on previous samples. The prediction residuals are then compressed using zstd. This can significantly improve compression for data with temporal correlations.\n\n#### Linear Predictive Coding with Zero RLE (zstd-22-lpc-zrle)\nCombines Markov prediction with zero run-length encoding. Particularly effective for sparse data where many values are zero, as it efficiently encodes runs of zeros while using Markov prediction for the non-zero regions.\n", "version": "1", "tags": [ "zstd", "lpc_prediction", "1d" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/zstd/__init__.py" }, { "name": "zstd-22-lpc-zrle", "description": "Zstandard compression at level 22 with linear predictive codingn and zero run-length encoding for sparse data.", "long_description": "# Zstandard Algorithm\n\nZstandard (zstd) is a fast real-time compression algorithm developed by Facebook. It provides a wide range of compression levels and supports custom dictionaries for improved compression.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- zstd-4: Fast compression\n- zstd-7: Balanced speed/compression\n- zstd-10: Better compression\n- zstd-13: High compression\n- zstd-16: Very high compression\n- zstd-19: Ultra high compression\n- zstd-22: Maximum compression\n\n### Advanced Variants\n\n#### Delta Encoding (zstd-22-delta)\nStores differences between consecutive values. Effective for sequences where adjacent values are similar, like time series data.\n\n#### Linear Predictive Coding (zstd-22-lpc)\nUses a Markov model to predict values based on previous samples. The prediction residuals are then compressed using zstd. This can significantly improve compression for data with temporal correlations.\n\n#### Linear Predictive Coding with Zero RLE (zstd-22-lpc-zrle)\nCombines Markov prediction with zero run-length encoding. Particularly effective for sparse data where many values are zero, as it efficiently encodes runs of zeros while using Markov prediction for the non-zero regions.\n", "version": "1", "tags": [ "zstd", "lpc_prediction", "zero_rle", "1d" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/zstd/__init__.py" }, { "name": "ANS", "description": "ANS compression via simple_ans for efficient data compression.", "long_description": "# ANS (Asymmetric Numeral Systems) Algorithm\n\nANS is a modern entropy coding method that achieves a compression ratio near arithmetic coding with relatively efficient encoding and decoding. It achieves this by maintaining a state that represents the encoded data in a way that can be efficiently updated. The implementation uses the [simple_ans](https://github.com/flatironinstitute/simple_ans) Python package.\n\n## Variants\n\n### Basic ANS\n- ANS: Standard implementation using the simple_ans library\n- Efficiently encodes integer data by modeling symbol frequencies\n\n### Delta Encoding\n- ANS-delta: ANS compression with delta encoding\n- Stores differences between consecutive values\n- Effective for sequences where adjacent values are similar\n\n### Linear Predictive Coding (LPC)\n- ANS-lpc: ANS with linear predictive codingn\n- Uses a 6th-order Markov model to predict values based on previous samples\n- Compresses the prediction residuals\n- Particularly effective for data with temporal correlations\n\n### Linear Predictive Coding with Zero RLE\n- ANS-lpc-zrle: Combines linear predictive coding with zero run-length encoding\n- Identifies runs of zero values and encodes their lengths\n- Applies Markov prediction to the non-zero regions\n- Ideal for sparse data with many zeros interspersed with correlated non-zero values\n", "version": "11", "tags": [ "ANS", "integer" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/ans/__init__.py" }, { "name": "ANS-delta", "description": "ANS compression via simple_ans with delta encoding for improved compression of sequential data.", "long_description": "# ANS (Asymmetric Numeral Systems) Algorithm\n\nANS is a modern entropy coding method that achieves a compression ratio near arithmetic coding with relatively efficient encoding and decoding. It achieves this by maintaining a state that represents the encoded data in a way that can be efficiently updated. The implementation uses the [simple_ans](https://github.com/flatironinstitute/simple_ans) Python package.\n\n## Variants\n\n### Basic ANS\n- ANS: Standard implementation using the simple_ans library\n- Efficiently encodes integer data by modeling symbol frequencies\n\n### Delta Encoding\n- ANS-delta: ANS compression with delta encoding\n- Stores differences between consecutive values\n- Effective for sequences where adjacent values are similar\n\n### Linear Predictive Coding (LPC)\n- ANS-lpc: ANS with linear predictive codingn\n- Uses a 6th-order Markov model to predict values based on previous samples\n- Compresses the prediction residuals\n- Particularly effective for data with temporal correlations\n\n### Linear Predictive Coding with Zero RLE\n- ANS-lpc-zrle: Combines linear predictive coding with zero run-length encoding\n- Identifies runs of zero values and encodes their lengths\n- Applies Markov prediction to the non-zero regions\n- Ideal for sparse data with many zeros interspersed with correlated non-zero values\n", "version": "11", "tags": [ "ANS", "integer", "delta_encoding", "1d" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/ans/__init__.py" }, { "name": "ANS-lpc", "description": "ANS compression via simple_ans with linear predictive codingn for exploiting temporal correlations in the data.", "long_description": "# ANS (Asymmetric Numeral Systems) Algorithm\n\nANS is a modern entropy coding method that achieves a compression ratio near arithmetic coding with relatively efficient encoding and decoding. It achieves this by maintaining a state that represents the encoded data in a way that can be efficiently updated. The implementation uses the [simple_ans](https://github.com/flatironinstitute/simple_ans) Python package.\n\n## Variants\n\n### Basic ANS\n- ANS: Standard implementation using the simple_ans library\n- Efficiently encodes integer data by modeling symbol frequencies\n\n### Delta Encoding\n- ANS-delta: ANS compression with delta encoding\n- Stores differences between consecutive values\n- Effective for sequences where adjacent values are similar\n\n### Linear Predictive Coding (LPC)\n- ANS-lpc: ANS with linear predictive codingn\n- Uses a 6th-order Markov model to predict values based on previous samples\n- Compresses the prediction residuals\n- Particularly effective for data with temporal correlations\n\n### Linear Predictive Coding with Zero RLE\n- ANS-lpc-zrle: Combines linear predictive coding with zero run-length encoding\n- Identifies runs of zero values and encodes their lengths\n- Applies Markov prediction to the non-zero regions\n- Ideal for sparse data with many zeros interspersed with correlated non-zero values\n", "version": "11", "tags": [ "ANS", "integer", "lpc_prediction", "1d" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/ans/__init__.py" }, { "name": "ANS-lpc-zrle", "description": "ANS compression via simple_ans with linear predictive codingn and zero run-length encoding for sparse data.", "long_description": "# ANS (Asymmetric Numeral Systems) Algorithm\n\nANS is a modern entropy coding method that achieves a compression ratio near arithmetic coding with relatively efficient encoding and decoding. It achieves this by maintaining a state that represents the encoded data in a way that can be efficiently updated. The implementation uses the [simple_ans](https://github.com/flatironinstitute/simple_ans) Python package.\n\n## Variants\n\n### Basic ANS\n- ANS: Standard implementation using the simple_ans library\n- Efficiently encodes integer data by modeling symbol frequencies\n\n### Delta Encoding\n- ANS-delta: ANS compression with delta encoding\n- Stores differences between consecutive values\n- Effective for sequences where adjacent values are similar\n\n### Linear Predictive Coding (LPC)\n- ANS-lpc: ANS with linear predictive codingn\n- Uses a 6th-order Markov model to predict values based on previous samples\n- Compresses the prediction residuals\n- Particularly effective for data with temporal correlations\n\n### Linear Predictive Coding with Zero RLE\n- ANS-lpc-zrle: Combines linear predictive coding with zero run-length encoding\n- Identifies runs of zero values and encodes their lengths\n- Applies Markov prediction to the non-zero regions\n- Ideal for sparse data with many zeros interspersed with correlated non-zero values\n", "version": "11", "tags": [ "ANS", "integer", "lpc_prediction", "zero_rle", "1d" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/ans/__init__.py" }, { "name": "lzma-9", "description": "LZMA compression at maximum preset 9 for highest compression ratio.", "long_description": "# LZMA Algorithm\n\nLZMA (Lempel-Ziv-Markov chain Algorithm) is a compression algorithm that provides high compression ratios. It uses a dictionary compression scheme similar to LZ77 but with sophisticated modeling of repeating patterns using Markov chains.\n\n## Variants\n\n### Standard Compression\n- lzma-9: Maximum compression preset (level 9)\n - Focuses on achieving the highest possible compression ratio\n - Uses larger dictionary sizes and more complex modeling\n - Generally slower than other compression algorithms but produces smaller files\n\n### Delta Encoding\n- lzma-9-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "lzma" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/lzma/__init__.py" }, { "name": "lzma-9-delta", "description": "LZMA compression at preset 9 with delta encoding for improved compression of sequential data.", "long_description": "# LZMA Algorithm\n\nLZMA (Lempel-Ziv-Markov chain Algorithm) is a compression algorithm that provides high compression ratios. It uses a dictionary compression scheme similar to LZ77 but with sophisticated modeling of repeating patterns using Markov chains.\n\n## Variants\n\n### Standard Compression\n- lzma-9: Maximum compression preset (level 9)\n - Focuses on achieving the highest possible compression ratio\n - Uses larger dictionary sizes and more complex modeling\n - Generally slower than other compression algorithms but produces smaller files\n\n### Delta Encoding\n- lzma-9-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "lzma", "delta_encoding", "1d" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/lzma/__init__.py" }, { "name": "brotli-4", "description": "Brotli compression at level 4 (faster).", "long_description": "# Brotli Algorithm\n\nBrotli is a general-purpose data compression algorithm developed by Google. It uses a combination of modern compression techniques including LZ77, Huffman coding, and context modeling.\n\n## Variants\n\nWe provide several variants with different compression levels:\n\n### Standard Compression\n- brotli-4: Fast compression with moderate ratio\n- brotli-6: Balanced between speed and compression\n- brotli-8: Better compression at cost of speed\n- brotli-11: Maximum compression level\n\n### Delta-Encoded Compression\n- brotli-11-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, like time series data.\n", "version": "1", "tags": [ "brotli" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/brotli/__init__.py" }, { "name": "brotli-6", "description": "Brotli compression at level 6 (balanced).", "long_description": "# Brotli Algorithm\n\nBrotli is a general-purpose data compression algorithm developed by Google. It uses a combination of modern compression techniques including LZ77, Huffman coding, and context modeling.\n\n## Variants\n\nWe provide several variants with different compression levels:\n\n### Standard Compression\n- brotli-4: Fast compression with moderate ratio\n- brotli-6: Balanced between speed and compression\n- brotli-8: Better compression at cost of speed\n- brotli-11: Maximum compression level\n\n### Delta-Encoded Compression\n- brotli-11-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, like time series data.\n", "version": "1", "tags": [ "brotli" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/brotli/__init__.py" }, { "name": "brotli-8", "description": "Brotli compression at level 8 (better compression).", "long_description": "# Brotli Algorithm\n\nBrotli is a general-purpose data compression algorithm developed by Google. It uses a combination of modern compression techniques including LZ77, Huffman coding, and context modeling.\n\n## Variants\n\nWe provide several variants with different compression levels:\n\n### Standard Compression\n- brotli-4: Fast compression with moderate ratio\n- brotli-6: Balanced between speed and compression\n- brotli-8: Better compression at cost of speed\n- brotli-11: Maximum compression level\n\n### Delta-Encoded Compression\n- brotli-11-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, like time series data.\n", "version": "1", "tags": [ "brotli" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/brotli/__init__.py" }, { "name": "brotli-11", "description": "Brotli compression at maximum level 11.", "long_description": "# Brotli Algorithm\n\nBrotli is a general-purpose data compression algorithm developed by Google. It uses a combination of modern compression techniques including LZ77, Huffman coding, and context modeling.\n\n## Variants\n\nWe provide several variants with different compression levels:\n\n### Standard Compression\n- brotli-4: Fast compression with moderate ratio\n- brotli-6: Balanced between speed and compression\n- brotli-8: Better compression at cost of speed\n- brotli-11: Maximum compression level\n\n### Delta-Encoded Compression\n- brotli-11-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, like time series data.\n", "version": "1", "tags": [ "brotli" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/brotli/__init__.py" }, { "name": "brotli-11-delta", "description": "Brotli compression at level 11 with delta encoding.", "long_description": "# Brotli Algorithm\n\nBrotli is a general-purpose data compression algorithm developed by Google. It uses a combination of modern compression techniques including LZ77, Huffman coding, and context modeling.\n\n## Variants\n\nWe provide several variants with different compression levels:\n\n### Standard Compression\n- brotli-4: Fast compression with moderate ratio\n- brotli-6: Balanced between speed and compression\n- brotli-8: Better compression at cost of speed\n- brotli-11: Maximum compression level\n\n### Delta-Encoded Compression\n- brotli-11-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, like time series data.\n", "version": "1", "tags": [ "brotli", "delta_encoding", "1d" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/brotli/__init__.py" }, { "name": "lz4-0", "description": "LZ4 compression at level 0 (fastest).", "long_description": "# LZ4 Algorithm\n\nLZ4 is a lossless compression algorithm focused on compression and decompression speed. It belongs to the LZ77 family of byte-oriented compression schemes and is particularly well-suited for real-time compression scenarios.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- lz4-0: Fastest compression mode\n- lz4-3: Minimum high compression mode\n- lz4-10: Higher compression\n- lz4-16: Maximum compression\n\n### Delta Encoding\n- lz4-16-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "lz4" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/lz4/__init__.py" }, { "name": "lz4-3", "description": "LZ4 compression at level 3 (minimum high compression).", "long_description": "# LZ4 Algorithm\n\nLZ4 is a lossless compression algorithm focused on compression and decompression speed. It belongs to the LZ77 family of byte-oriented compression schemes and is particularly well-suited for real-time compression scenarios.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- lz4-0: Fastest compression mode\n- lz4-3: Minimum high compression mode\n- lz4-10: Higher compression\n- lz4-16: Maximum compression\n\n### Delta Encoding\n- lz4-16-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "lz4" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/lz4/__init__.py" }, { "name": "lz4-10", "description": "LZ4 compression at level 10.", "long_description": "# LZ4 Algorithm\n\nLZ4 is a lossless compression algorithm focused on compression and decompression speed. It belongs to the LZ77 family of byte-oriented compression schemes and is particularly well-suited for real-time compression scenarios.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- lz4-0: Fastest compression mode\n- lz4-3: Minimum high compression mode\n- lz4-10: Higher compression\n- lz4-16: Maximum compression\n\n### Delta Encoding\n- lz4-16-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "lz4" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/lz4/__init__.py" }, { "name": "lz4-16", "description": "LZ4 compression at level 16 (highest compression).", "long_description": "# LZ4 Algorithm\n\nLZ4 is a lossless compression algorithm focused on compression and decompression speed. It belongs to the LZ77 family of byte-oriented compression schemes and is particularly well-suited for real-time compression scenarios.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- lz4-0: Fastest compression mode\n- lz4-3: Minimum high compression mode\n- lz4-10: Higher compression\n- lz4-16: Maximum compression\n\n### Delta Encoding\n- lz4-16-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "lz4" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/lz4/__init__.py" }, { "name": "lz4-16-delta", "description": "LZ4 compression at level 16 with delta encoding.", "long_description": "# LZ4 Algorithm\n\nLZ4 is a lossless compression algorithm focused on compression and decompression speed. It belongs to the LZ77 family of byte-oriented compression schemes and is particularly well-suited for real-time compression scenarios.\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- lz4-0: Fastest compression mode\n- lz4-3: Minimum high compression mode\n- lz4-10: Higher compression\n- lz4-16: Maximum compression\n\n### Delta Encoding\n- lz4-16-delta: Maximum compression with delta encoding\n\nDelta encoding stores differences between consecutive values rather than the values themselves. This can improve compression for sequences where adjacent values are often similar, such as time series data.\n", "version": "1", "tags": [ "lz4", "delta_encoding", "1d" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/lz4/__init__.py" }, { "name": "blosc2-1", "description": "Blosc2 compression at level 1 (fastest compression).", "long_description": "# Blosc2 Algorithm\n\nBlosc2 is a modern, fast data compression library that builds upon the original Blosc library. It is designed for efficient compression of binary data, particularly optimized for in-memory compression of numerical arrays. Blosc2 uses block-oriented compression with support for multithreading and SIMD instructions.\n\n## Features\n\n- Fast compression and decompression speeds\n- Block-oriented compression for better cache usage\n- Support for various shuffling filters to improve compression ratios\n- Built-in support for delta filtering\n- Uses ZSTD compression codec\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- blosc2-1: Fastest compression with level 1\n- blosc2-5: Balanced speed/compression with level 5\n- blosc2-9: Maximum compression with level 9\n\n### Advanced Variants\n\n#### Delta Encoding (blosc2-9-delta)\nUses Blosc2's delta filter along with maximum compression. The delta filter stores differences between consecutive values, which is particularly effective for time series data where adjacent values are similar. This variant combines:\n- Delta filtering for temporal correlation\n- Bit shuffling for improved compression\n- Level 9 compression for maximum compression ratio\n", "version": "1b", "tags": [ "blosc2" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/blosc2/__init__.py" }, { "name": "blosc2-5", "description": "Blosc2 compression at level 5 (balanced speed/compression).", "long_description": "# Blosc2 Algorithm\n\nBlosc2 is a modern, fast data compression library that builds upon the original Blosc library. It is designed for efficient compression of binary data, particularly optimized for in-memory compression of numerical arrays. Blosc2 uses block-oriented compression with support for multithreading and SIMD instructions.\n\n## Features\n\n- Fast compression and decompression speeds\n- Block-oriented compression for better cache usage\n- Support for various shuffling filters to improve compression ratios\n- Built-in support for delta filtering\n- Uses ZSTD compression codec\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- blosc2-1: Fastest compression with level 1\n- blosc2-5: Balanced speed/compression with level 5\n- blosc2-9: Maximum compression with level 9\n\n### Advanced Variants\n\n#### Delta Encoding (blosc2-9-delta)\nUses Blosc2's delta filter along with maximum compression. The delta filter stores differences between consecutive values, which is particularly effective for time series data where adjacent values are similar. This variant combines:\n- Delta filtering for temporal correlation\n- Bit shuffling for improved compression\n- Level 9 compression for maximum compression ratio\n", "version": "1", "tags": [ "blosc2" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/blosc2/__init__.py" }, { "name": "blosc2-9", "description": "Blosc2 compression at level 9 (maximum compression).", "long_description": "# Blosc2 Algorithm\n\nBlosc2 is a modern, fast data compression library that builds upon the original Blosc library. It is designed for efficient compression of binary data, particularly optimized for in-memory compression of numerical arrays. Blosc2 uses block-oriented compression with support for multithreading and SIMD instructions.\n\n## Features\n\n- Fast compression and decompression speeds\n- Block-oriented compression for better cache usage\n- Support for various shuffling filters to improve compression ratios\n- Built-in support for delta filtering\n- Uses ZSTD compression codec\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- blosc2-1: Fastest compression with level 1\n- blosc2-5: Balanced speed/compression with level 5\n- blosc2-9: Maximum compression with level 9\n\n### Advanced Variants\n\n#### Delta Encoding (blosc2-9-delta)\nUses Blosc2's delta filter along with maximum compression. The delta filter stores differences between consecutive values, which is particularly effective for time series data where adjacent values are similar. This variant combines:\n- Delta filtering for temporal correlation\n- Bit shuffling for improved compression\n- Level 9 compression for maximum compression ratio\n", "version": "1", "tags": [ "blosc2" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/blosc2/__init__.py" }, { "name": "blosc2-9-delta", "description": "Blosc2 compression at level 9 with delta encoding for improved compression of sequential data.", "long_description": "# Blosc2 Algorithm\n\nBlosc2 is a modern, fast data compression library that builds upon the original Blosc library. It is designed for efficient compression of binary data, particularly optimized for in-memory compression of numerical arrays. Blosc2 uses block-oriented compression with support for multithreading and SIMD instructions.\n\n## Features\n\n- Fast compression and decompression speeds\n- Block-oriented compression for better cache usage\n- Support for various shuffling filters to improve compression ratios\n- Built-in support for delta filtering\n- Uses ZSTD compression codec\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- blosc2-1: Fastest compression with level 1\n- blosc2-5: Balanced speed/compression with level 5\n- blosc2-9: Maximum compression with level 9\n\n### Advanced Variants\n\n#### Delta Encoding (blosc2-9-delta)\nUses Blosc2's delta filter along with maximum compression. The delta filter stores differences between consecutive values, which is particularly effective for time series data where adjacent values are similar. This variant combines:\n- Delta filtering for temporal correlation\n- Bit shuffling for improved compression\n- Level 9 compression for maximum compression ratio\n", "version": "1", "tags": [ "blosc2", "delta_encoding", "1d" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/blosc2/__init__.py" }, { "name": "blosc2-1-lpc", "description": "Blosc2 compression at level 1 with linear predictive codingn for exploiting temporal correlations in the data.", "long_description": "# Blosc2 Algorithm\n\nBlosc2 is a modern, fast data compression library that builds upon the original Blosc library. It is designed for efficient compression of binary data, particularly optimized for in-memory compression of numerical arrays. Blosc2 uses block-oriented compression with support for multithreading and SIMD instructions.\n\n## Features\n\n- Fast compression and decompression speeds\n- Block-oriented compression for better cache usage\n- Support for various shuffling filters to improve compression ratios\n- Built-in support for delta filtering\n- Uses ZSTD compression codec\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- blosc2-1: Fastest compression with level 1\n- blosc2-5: Balanced speed/compression with level 5\n- blosc2-9: Maximum compression with level 9\n\n### Advanced Variants\n\n#### Delta Encoding (blosc2-9-delta)\nUses Blosc2's delta filter along with maximum compression. The delta filter stores differences between consecutive values, which is particularly effective for time series data where adjacent values are similar. This variant combines:\n- Delta filtering for temporal correlation\n- Bit shuffling for improved compression\n- Level 9 compression for maximum compression ratio\n", "version": "1", "tags": [ "blosc2", "lpc_prediction", "1d" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/blosc2/__init__.py" }, { "name": "blosc2-5-lpc", "description": "Blosc2 compression at level 5 with linear predictive codingn for exploiting temporal correlations in the data.", "long_description": "# Blosc2 Algorithm\n\nBlosc2 is a modern, fast data compression library that builds upon the original Blosc library. It is designed for efficient compression of binary data, particularly optimized for in-memory compression of numerical arrays. Blosc2 uses block-oriented compression with support for multithreading and SIMD instructions.\n\n## Features\n\n- Fast compression and decompression speeds\n- Block-oriented compression for better cache usage\n- Support for various shuffling filters to improve compression ratios\n- Built-in support for delta filtering\n- Uses ZSTD compression codec\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- blosc2-1: Fastest compression with level 1\n- blosc2-5: Balanced speed/compression with level 5\n- blosc2-9: Maximum compression with level 9\n\n### Advanced Variants\n\n#### Delta Encoding (blosc2-9-delta)\nUses Blosc2's delta filter along with maximum compression. The delta filter stores differences between consecutive values, which is particularly effective for time series data where adjacent values are similar. This variant combines:\n- Delta filtering for temporal correlation\n- Bit shuffling for improved compression\n- Level 9 compression for maximum compression ratio\n", "version": "1", "tags": [ "blosc2", "lpc_prediction", "1d" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/blosc2/__init__.py" }, { "name": "blosc2-9-lpc", "description": "Blosc2 compression at level 9 with linear predictive codingn for exploiting temporal correlations in the data.", "long_description": "# Blosc2 Algorithm\n\nBlosc2 is a modern, fast data compression library that builds upon the original Blosc library. It is designed for efficient compression of binary data, particularly optimized for in-memory compression of numerical arrays. Blosc2 uses block-oriented compression with support for multithreading and SIMD instructions.\n\n## Features\n\n- Fast compression and decompression speeds\n- Block-oriented compression for better cache usage\n- Support for various shuffling filters to improve compression ratios\n- Built-in support for delta filtering\n- Uses ZSTD compression codec\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- blosc2-1: Fastest compression with level 1\n- blosc2-5: Balanced speed/compression with level 5\n- blosc2-9: Maximum compression with level 9\n\n### Advanced Variants\n\n#### Delta Encoding (blosc2-9-delta)\nUses Blosc2's delta filter along with maximum compression. The delta filter stores differences between consecutive values, which is particularly effective for time series data where adjacent values are similar. This variant combines:\n- Delta filtering for temporal correlation\n- Bit shuffling for improved compression\n- Level 9 compression for maximum compression ratio\n", "version": "1", "tags": [ "blosc2", "lpc_prediction", "1d" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/blosc2/__init__.py" }, { "name": "blosc2-9-lpc-zrle", "description": "Blosc2 compression at level 9 with linear predictive codingn and zero run-length encoding for sparse data.", "long_description": "# Blosc2 Algorithm\n\nBlosc2 is a modern, fast data compression library that builds upon the original Blosc library. It is designed for efficient compression of binary data, particularly optimized for in-memory compression of numerical arrays. Blosc2 uses block-oriented compression with support for multithreading and SIMD instructions.\n\n## Features\n\n- Fast compression and decompression speeds\n- Block-oriented compression for better cache usage\n- Support for various shuffling filters to improve compression ratios\n- Built-in support for delta filtering\n- Uses ZSTD compression codec\n\n## Variants\n\n### Standard Compression\nDifferent compression levels trading off speed vs compression ratio:\n- blosc2-1: Fastest compression with level 1\n- blosc2-5: Balanced speed/compression with level 5\n- blosc2-9: Maximum compression with level 9\n\n### Advanced Variants\n\n#### Delta Encoding (blosc2-9-delta)\nUses Blosc2's delta filter along with maximum compression. The delta filter stores differences between consecutive values, which is particularly effective for time series data where adjacent values are similar. This variant combines:\n- Delta filtering for temporal correlation\n- Bit shuffling for improved compression\n- Level 9 compression for maximum compression ratio\n", "version": "1", "tags": [ "blosc2", "lpc_prediction", "zero_rle", "1d" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/blosc2/__init__.py" }, { "name": "wavpack-1", "description": "WavPack compression at level 1, minimum compression level.", "long_description": "# WavPack\n\n[WavPack](https://www.wavpack.com/) is a completely open audio compression format providing lossless, high-quality lossy, and a unique hybrid compression mode. It is designed to offer a versatile and efficient solution for audio compression.\n\n## Variants\n\nWavPack supports different levels of lossless compression, which can be adjusted to balance between compression ratio and encoding/decoding speed. The following variants are available:\n\n- **Fast**: Prioritizes speed over compression ratio.\n- **Normal**: Balanced between speed and compression ratio.\n- **High**: Prioritizes compression ratio over speed.\n- **Very High**: Maximum compression ratio with slower speed.\n", "version": "1", "tags": [ "wavpack", "no_bernoulli" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/wavpack/__init__.py" }, { "name": "wavpack-2", "description": "WavPack compression at level 2, medium compression level.", "long_description": "# WavPack\n\n[WavPack](https://www.wavpack.com/) is a completely open audio compression format providing lossless, high-quality lossy, and a unique hybrid compression mode. It is designed to offer a versatile and efficient solution for audio compression.\n\n## Variants\n\nWavPack supports different levels of lossless compression, which can be adjusted to balance between compression ratio and encoding/decoding speed. The following variants are available:\n\n- **Fast**: Prioritizes speed over compression ratio.\n- **Normal**: Balanced between speed and compression ratio.\n- **High**: Prioritizes compression ratio over speed.\n- **Very High**: Maximum compression ratio with slower speed.\n", "version": "1", "tags": [ "wavpack", "no_bernoulli" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/wavpack/__init__.py" }, { "name": "wavpack-3", "description": "WavPack compression at level 3, high compression level.", "long_description": "# WavPack\n\n[WavPack](https://www.wavpack.com/) is a completely open audio compression format providing lossless, high-quality lossy, and a unique hybrid compression mode. It is designed to offer a versatile and efficient solution for audio compression.\n\n## Variants\n\nWavPack supports different levels of lossless compression, which can be adjusted to balance between compression ratio and encoding/decoding speed. The following variants are available:\n\n- **Fast**: Prioritizes speed over compression ratio.\n- **Normal**: Balanced between speed and compression ratio.\n- **High**: Prioritizes compression ratio over speed.\n- **Very High**: Maximum compression ratio with slower speed.\n", "version": "1", "tags": [ "wavpack", "no_bernoulli" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/wavpack/__init__.py" }, { "name": "wavpack-4", "description": "WavPack compression at level 4, highest compression level.", "long_description": "# WavPack\n\n[WavPack](https://www.wavpack.com/) is a completely open audio compression format providing lossless, high-quality lossy, and a unique hybrid compression mode. It is designed to offer a versatile and efficient solution for audio compression.\n\n## Variants\n\nWavPack supports different levels of lossless compression, which can be adjusted to balance between compression ratio and encoding/decoding speed. The following variants are available:\n\n- **Fast**: Prioritizes speed over compression ratio.\n- **Normal**: Balanced between speed and compression ratio.\n- **High**: Prioritizes compression ratio over speed.\n- **Very High**: Maximum compression ratio with slower speed.\n", "version": "1", "tags": [ "wavpack", "no_bernoulli" ], "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/algorithms/wavpack/__init__.py" } ], "datasets": [ { "name": "bernoulli-0.1", "description": "Binary sequence with 10% probability of ones.", "long_description": "# Bernoulli Dataset\n\nA Bernoulli dataset is a sequence of binary values (0s and 1s) where each value is independently generated with a fixed probability `p` of being 1. These datasets are useful for testing compression algorithms because they have well-defined theoretical properties.\n\n## Variants\n\nWe provide five variants with different probabilities of generating a 1:\n- bernoulli-0.1 (10% ones)\n- bernoulli-0.2 (20% ones)\n- bernoulli-0.3 (30% ones)\n- bernoulli-0.4 (40% ones)\n- bernoulli-0.5 (50% ones)\n\n## Theoretical Properties\n\nThe theoretical entropy H(p) provides a lower bound for compression:\n```\nH(p) = -p log\u2082(p) - (1-p) log\u2082(1-p) bits/symbol\n```\n\nFor example, at p = 0.1 the theoretical minimum is 0.469 bits/symbol, while at p = 0.5 it reaches the maximum of 1.000 bits/symbol.\n", "version": "3c", "tags": [ "bernoulli", "timeseries", "1d", "integer", "discrete", "synthetic", "i.i.d." ], "data_url_raw": "https://tempory.net/f/memobin/datasets/bernoulli-0.1/3c/bernoulli-0.1-3c.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/bernoulli-0.1/3c/bernoulli-0.1-3c.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/bernoulli-0.1/3c/bernoulli-0.1-3c.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/bernoulli/__init__.py" }, { "name": "bernoulli-0.2", "description": "Binary sequence with 20% probability of ones.", "long_description": "# Bernoulli Dataset\n\nA Bernoulli dataset is a sequence of binary values (0s and 1s) where each value is independently generated with a fixed probability `p` of being 1. These datasets are useful for testing compression algorithms because they have well-defined theoretical properties.\n\n## Variants\n\nWe provide five variants with different probabilities of generating a 1:\n- bernoulli-0.1 (10% ones)\n- bernoulli-0.2 (20% ones)\n- bernoulli-0.3 (30% ones)\n- bernoulli-0.4 (40% ones)\n- bernoulli-0.5 (50% ones)\n\n## Theoretical Properties\n\nThe theoretical entropy H(p) provides a lower bound for compression:\n```\nH(p) = -p log\u2082(p) - (1-p) log\u2082(1-p) bits/symbol\n```\n\nFor example, at p = 0.1 the theoretical minimum is 0.469 bits/symbol, while at p = 0.5 it reaches the maximum of 1.000 bits/symbol.\n", "version": "3", "tags": [ "bernoulli", "timeseries", "1d", "integer", "discrete", "synthetic", "i.i.d." ], "data_url_raw": "https://tempory.net/f/memobin/datasets/bernoulli-0.2/3/bernoulli-0.2-3.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/bernoulli-0.2/3/bernoulli-0.2-3.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/bernoulli-0.2/3/bernoulli-0.2-3.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/bernoulli/__init__.py" }, { "name": "bernoulli-0.3", "description": "Binary sequence with 30% probability of ones.", "long_description": "# Bernoulli Dataset\n\nA Bernoulli dataset is a sequence of binary values (0s and 1s) where each value is independently generated with a fixed probability `p` of being 1. These datasets are useful for testing compression algorithms because they have well-defined theoretical properties.\n\n## Variants\n\nWe provide five variants with different probabilities of generating a 1:\n- bernoulli-0.1 (10% ones)\n- bernoulli-0.2 (20% ones)\n- bernoulli-0.3 (30% ones)\n- bernoulli-0.4 (40% ones)\n- bernoulli-0.5 (50% ones)\n\n## Theoretical Properties\n\nThe theoretical entropy H(p) provides a lower bound for compression:\n```\nH(p) = -p log\u2082(p) - (1-p) log\u2082(1-p) bits/symbol\n```\n\nFor example, at p = 0.1 the theoretical minimum is 0.469 bits/symbol, while at p = 0.5 it reaches the maximum of 1.000 bits/symbol.\n", "version": "3", "tags": [ "bernoulli", "timeseries", "1d", "integer", "discrete", "synthetic", "i.i.d." ], "data_url_raw": "https://tempory.net/f/memobin/datasets/bernoulli-0.3/3/bernoulli-0.3-3.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/bernoulli-0.3/3/bernoulli-0.3-3.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/bernoulli-0.3/3/bernoulli-0.3-3.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/bernoulli/__init__.py" }, { "name": "bernoulli-0.4", "description": "Binary sequence with 40% probability of ones.", "long_description": "# Bernoulli Dataset\n\nA Bernoulli dataset is a sequence of binary values (0s and 1s) where each value is independently generated with a fixed probability `p` of being 1. These datasets are useful for testing compression algorithms because they have well-defined theoretical properties.\n\n## Variants\n\nWe provide five variants with different probabilities of generating a 1:\n- bernoulli-0.1 (10% ones)\n- bernoulli-0.2 (20% ones)\n- bernoulli-0.3 (30% ones)\n- bernoulli-0.4 (40% ones)\n- bernoulli-0.5 (50% ones)\n\n## Theoretical Properties\n\nThe theoretical entropy H(p) provides a lower bound for compression:\n```\nH(p) = -p log\u2082(p) - (1-p) log\u2082(1-p) bits/symbol\n```\n\nFor example, at p = 0.1 the theoretical minimum is 0.469 bits/symbol, while at p = 0.5 it reaches the maximum of 1.000 bits/symbol.\n", "version": "3", "tags": [ "bernoulli", "timeseries", "1d", "integer", "discrete", "synthetic", "i.i.d." ], "data_url_raw": "https://tempory.net/f/memobin/datasets/bernoulli-0.4/3/bernoulli-0.4-3.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/bernoulli-0.4/3/bernoulli-0.4-3.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/bernoulli-0.4/3/bernoulli-0.4-3.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/bernoulli/__init__.py" }, { "name": "bernoulli-0.5", "description": "Binary sequence with 50% probability of ones and 50% probability of zeros.", "long_description": "# Bernoulli Dataset\n\nA Bernoulli dataset is a sequence of binary values (0s and 1s) where each value is independently generated with a fixed probability `p` of being 1. These datasets are useful for testing compression algorithms because they have well-defined theoretical properties.\n\n## Variants\n\nWe provide five variants with different probabilities of generating a 1:\n- bernoulli-0.1 (10% ones)\n- bernoulli-0.2 (20% ones)\n- bernoulli-0.3 (30% ones)\n- bernoulli-0.4 (40% ones)\n- bernoulli-0.5 (50% ones)\n\n## Theoretical Properties\n\nThe theoretical entropy H(p) provides a lower bound for compression:\n```\nH(p) = -p log\u2082(p) - (1-p) log\u2082(1-p) bits/symbol\n```\n\nFor example, at p = 0.1 the theoretical minimum is 0.469 bits/symbol, while at p = 0.5 it reaches the maximum of 1.000 bits/symbol.\n", "version": "3", "tags": [ "bernoulli", "timeseries", "1d", "integer", "discrete", "synthetic", "i.i.d." ], "data_url_raw": "https://tempory.net/f/memobin/datasets/bernoulli-0.5/3/bernoulli-0.5-3.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/bernoulli-0.5/3/bernoulli-0.5-3.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/bernoulli-0.5/3/bernoulli-0.5-3.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/bernoulli/__init__.py" }, { "name": "gaussian-q1", "description": "Rounded Gaussian integers with \u03c3=1.", "long_description": "# Gaussian Dataset\n\nThese datasets contain sequences of numbers drawn from Gaussian distributions with \u03bc=0 and various values of \u03c3. All values are independently and identically distributed (i.i.d.).\n\n## Variants\n\nWe provide two types of Gaussian datasets:\n\n### Quantized Integer Variants\nNumbers are drawn from a Gaussian distribution then rounded to the nearest integer:\n- gaussian-q1 (\u03c3=1)\n- gaussian-q2 (\u03c3=2)\n- gaussian-q3 (\u03c3=3)\n- gaussian-q5 (\u03c3=5)\n- gaussian-q8 (\u03c3=8)\n\nThe quantization creates discrete integer values, with larger \u03c3 values producing a wider spread of integers.\n\n### Floating Point Variant\n- gaussian-flt1 (\u03c3=1)\n\nThis variant preserves the continuous nature of the Gaussian distribution using 32-bit floating point numbers.\n", "version": "1", "tags": [ "gaussian", "integer", "discrete", "timeseries", "1d", "synthetic", "i.i.d." ], "data_url_raw": "https://tempory.net/f/memobin/datasets/gaussian-q1/1/gaussian-q1-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/gaussian-q1/1/gaussian-q1-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/gaussian-q1/1/gaussian-q1-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/gaussian/__init__.py" }, { "name": "gaussian-q2", "description": "Rounded Gaussian integers with \u03c3=2.", "long_description": "# Gaussian Dataset\n\nThese datasets contain sequences of numbers drawn from Gaussian distributions with \u03bc=0 and various values of \u03c3. All values are independently and identically distributed (i.i.d.).\n\n## Variants\n\nWe provide two types of Gaussian datasets:\n\n### Quantized Integer Variants\nNumbers are drawn from a Gaussian distribution then rounded to the nearest integer:\n- gaussian-q1 (\u03c3=1)\n- gaussian-q2 (\u03c3=2)\n- gaussian-q3 (\u03c3=3)\n- gaussian-q5 (\u03c3=5)\n- gaussian-q8 (\u03c3=8)\n\nThe quantization creates discrete integer values, with larger \u03c3 values producing a wider spread of integers.\n\n### Floating Point Variant\n- gaussian-flt1 (\u03c3=1)\n\nThis variant preserves the continuous nature of the Gaussian distribution using 32-bit floating point numbers.\n", "version": "1", "tags": [ "gaussian", "integer", "discrete", "timeseries", "1d", "synthetic", "i.i.d." ], "data_url_raw": "https://tempory.net/f/memobin/datasets/gaussian-q2/1/gaussian-q2-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/gaussian-q2/1/gaussian-q2-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/gaussian-q2/1/gaussian-q2-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/gaussian/__init__.py" }, { "name": "gaussian-q3", "description": "Rounded Gaussian integers with \u03c3=3.", "long_description": "# Gaussian Dataset\n\nThese datasets contain sequences of numbers drawn from Gaussian distributions with \u03bc=0 and various values of \u03c3. All values are independently and identically distributed (i.i.d.).\n\n## Variants\n\nWe provide two types of Gaussian datasets:\n\n### Quantized Integer Variants\nNumbers are drawn from a Gaussian distribution then rounded to the nearest integer:\n- gaussian-q1 (\u03c3=1)\n- gaussian-q2 (\u03c3=2)\n- gaussian-q3 (\u03c3=3)\n- gaussian-q5 (\u03c3=5)\n- gaussian-q8 (\u03c3=8)\n\nThe quantization creates discrete integer values, with larger \u03c3 values producing a wider spread of integers.\n\n### Floating Point Variant\n- gaussian-flt1 (\u03c3=1)\n\nThis variant preserves the continuous nature of the Gaussian distribution using 32-bit floating point numbers.\n", "version": "1", "tags": [ "gaussian", "integer", "discrete", "timeseries", "1d", "synthetic", "i.i.d." ], "data_url_raw": "https://tempory.net/f/memobin/datasets/gaussian-q3/1/gaussian-q3-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/gaussian-q3/1/gaussian-q3-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/gaussian-q3/1/gaussian-q3-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/gaussian/__init__.py" }, { "name": "gaussian-q5", "description": "Rounded Gaussian integers with \u03c3=5.", "long_description": "# Gaussian Dataset\n\nThese datasets contain sequences of numbers drawn from Gaussian distributions with \u03bc=0 and various values of \u03c3. All values are independently and identically distributed (i.i.d.).\n\n## Variants\n\nWe provide two types of Gaussian datasets:\n\n### Quantized Integer Variants\nNumbers are drawn from a Gaussian distribution then rounded to the nearest integer:\n- gaussian-q1 (\u03c3=1)\n- gaussian-q2 (\u03c3=2)\n- gaussian-q3 (\u03c3=3)\n- gaussian-q5 (\u03c3=5)\n- gaussian-q8 (\u03c3=8)\n\nThe quantization creates discrete integer values, with larger \u03c3 values producing a wider spread of integers.\n\n### Floating Point Variant\n- gaussian-flt1 (\u03c3=1)\n\nThis variant preserves the continuous nature of the Gaussian distribution using 32-bit floating point numbers.\n", "version": "1", "tags": [ "gaussian", "integer", "discrete", "timeseries", "1d", "synthetic", "i.i.d." ], "data_url_raw": "https://tempory.net/f/memobin/datasets/gaussian-q5/1/gaussian-q5-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/gaussian-q5/1/gaussian-q5-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/gaussian-q5/1/gaussian-q5-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/gaussian/__init__.py" }, { "name": "gaussian-q8", "description": "Rounded Gaussian integers with \u03c3=8.", "long_description": "# Gaussian Dataset\n\nThese datasets contain sequences of numbers drawn from Gaussian distributions with \u03bc=0 and various values of \u03c3. All values are independently and identically distributed (i.i.d.).\n\n## Variants\n\nWe provide two types of Gaussian datasets:\n\n### Quantized Integer Variants\nNumbers are drawn from a Gaussian distribution then rounded to the nearest integer:\n- gaussian-q1 (\u03c3=1)\n- gaussian-q2 (\u03c3=2)\n- gaussian-q3 (\u03c3=3)\n- gaussian-q5 (\u03c3=5)\n- gaussian-q8 (\u03c3=8)\n\nThe quantization creates discrete integer values, with larger \u03c3 values producing a wider spread of integers.\n\n### Floating Point Variant\n- gaussian-flt1 (\u03c3=1)\n\nThis variant preserves the continuous nature of the Gaussian distribution using 32-bit floating point numbers.\n", "version": "1", "tags": [ "gaussian", "integer", "discrete", "timeseries", "1d", "synthetic", "i.i.d." ], "data_url_raw": "https://tempory.net/f/memobin/datasets/gaussian-q8/1/gaussian-q8-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/gaussian-q8/1/gaussian-q8-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/gaussian-q8/1/gaussian-q8-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/gaussian/__init__.py" }, { "name": "gaussian-flt1", "description": "Floating point Gaussian numbers with \u03c3=1.", "long_description": "# Gaussian Dataset\n\nThese datasets contain sequences of numbers drawn from Gaussian distributions with \u03bc=0 and various values of \u03c3. All values are independently and identically distributed (i.i.d.).\n\n## Variants\n\nWe provide two types of Gaussian datasets:\n\n### Quantized Integer Variants\nNumbers are drawn from a Gaussian distribution then rounded to the nearest integer:\n- gaussian-q1 (\u03c3=1)\n- gaussian-q2 (\u03c3=2)\n- gaussian-q3 (\u03c3=3)\n- gaussian-q5 (\u03c3=5)\n- gaussian-q8 (\u03c3=8)\n\nThe quantization creates discrete integer values, with larger \u03c3 values producing a wider spread of integers.\n\n### Floating Point Variant\n- gaussian-flt1 (\u03c3=1)\n\nThis variant preserves the continuous nature of the Gaussian distribution using 32-bit floating point numbers.\n", "version": "1", "tags": [ "gaussian", "float", "timeseries", "1d", "synthetic", "i.i.d." ], "data_url_raw": "https://tempory.net/f/memobin/datasets/gaussian-flt1/1/gaussian-flt1-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/gaussian-flt1/1/gaussian-flt1-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/gaussian-flt1/1/gaussian-flt1-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/gaussian/__init__.py" }, { "name": "gaussian-ar1-02", "description": "Quantized Gaussian AR(1) process with \u03c6=0.2, \u03c3=3.", "long_description": "# Gaussian AR(1) Dataset\n\nThese datasets contain sequences from autoregressive AR(1) processes with Gaussian innovations. The data is generated using the model:\n\n```\nx[t] = \u03c6 * x[t-1] + \u03b5[t]\n```\n\nwhere:\n- \u03c6 is the autoregressive parameter controlling temporal correlation\n- \u03b5[t] follows a Gaussian distribution with \u03bc=0 and \u03c3=3\n- Values are quantized to integers after generation\n\n## Variants\n\nWe provide three AR(1) variants with different temporal correlation strengths:\n\n- gaussian-ar1-02 (\u03c6=0.2): Weak temporal correlation\n- gaussian-ar1-05 (\u03c6=0.5): Moderate temporal correlation\n- gaussian-ar1-08 (\u03c6=0.8): Strong temporal correlation\n\nEach variant has \u03c3=3 for the Gaussian innovations and uses integer quantization after generation to maintain compatibility with integer-based compression algorithms.\n", "version": "1", "tags": [ "gaussian", "integer", "discrete", "timeseries", "1d", "synthetic", "ar1", "correlated" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/gaussian-ar1-02/1/gaussian-ar1-02-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/gaussian-ar1-02/1/gaussian-ar1-02-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/gaussian-ar1-02/1/gaussian-ar1-02-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/gaussian_ar1/__init__.py" }, { "name": "gaussian-ar1-05", "description": "Quantized Gaussian AR(1) process with \u03c6=0.5, \u03c3=3.", "long_description": "# Gaussian AR(1) Dataset\n\nThese datasets contain sequences from autoregressive AR(1) processes with Gaussian innovations. The data is generated using the model:\n\n```\nx[t] = \u03c6 * x[t-1] + \u03b5[t]\n```\n\nwhere:\n- \u03c6 is the autoregressive parameter controlling temporal correlation\n- \u03b5[t] follows a Gaussian distribution with \u03bc=0 and \u03c3=3\n- Values are quantized to integers after generation\n\n## Variants\n\nWe provide three AR(1) variants with different temporal correlation strengths:\n\n- gaussian-ar1-02 (\u03c6=0.2): Weak temporal correlation\n- gaussian-ar1-05 (\u03c6=0.5): Moderate temporal correlation\n- gaussian-ar1-08 (\u03c6=0.8): Strong temporal correlation\n\nEach variant has \u03c3=3 for the Gaussian innovations and uses integer quantization after generation to maintain compatibility with integer-based compression algorithms.\n", "version": "1", "tags": [ "gaussian", "integer", "discrete", "timeseries", "1d", "synthetic", "ar1", "correlated" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/gaussian-ar1-05/1/gaussian-ar1-05-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/gaussian-ar1-05/1/gaussian-ar1-05-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/gaussian-ar1-05/1/gaussian-ar1-05-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/gaussian_ar1/__init__.py" }, { "name": "gaussian-ar1-08", "description": "Quantized Gaussian AR(1) process with \u03c6=0.8, \u03c3=3.", "long_description": "# Gaussian AR(1) Dataset\n\nThese datasets contain sequences from autoregressive AR(1) processes with Gaussian innovations. The data is generated using the model:\n\n```\nx[t] = \u03c6 * x[t-1] + \u03b5[t]\n```\n\nwhere:\n- \u03c6 is the autoregressive parameter controlling temporal correlation\n- \u03b5[t] follows a Gaussian distribution with \u03bc=0 and \u03c3=3\n- Values are quantized to integers after generation\n\n## Variants\n\nWe provide three AR(1) variants with different temporal correlation strengths:\n\n- gaussian-ar1-02 (\u03c6=0.2): Weak temporal correlation\n- gaussian-ar1-05 (\u03c6=0.5): Moderate temporal correlation\n- gaussian-ar1-08 (\u03c6=0.8): Strong temporal correlation\n\nEach variant has \u03c3=3 for the Gaussian innovations and uses integer quantization after generation to maintain compatibility with integer-based compression algorithms.\n", "version": "1", "tags": [ "gaussian", "integer", "discrete", "timeseries", "1d", "synthetic", "ar1", "correlated" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/gaussian-ar1-08/1/gaussian-ar1-08-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/gaussian-ar1-08/1/gaussian-ar1-08-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/gaussian-ar1-08/1/gaussian-ar1-08-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/gaussian_ar1/__init__.py" }, { "name": "gaussian-lp-03", "description": "Quantized Gaussian lowpass filtered with window size 3, \u03c3=5.", "long_description": "# Gaussian Lowpass Dataset\n\nThese datasets contain sequences of quantized Gaussian noise that has been smoothed using a moving average (lowpass) filter. The data is generated by:\n1. Drawing samples from a Gaussian distribution (\u03c3=5)\n2. Applying a moving average filter of varying window sizes\n3. Quantizing the filtered result to integers\n\nThe moving average filter acts as a lowpass filter, attenuating high-frequency components while preserving low-frequency trends. Larger window sizes result in smoother sequences with stronger low-frequency characteristics.\n\n## Variants\n\nWe provide three variants with different window sizes:\n\n- gaussian-lp-03 (window=3): Light smoothing\n- gaussian-lp-07 (window=7): Moderate smoothing\n- gaussian-lp-15 (window=15): Strong smoothing\n\nEach variant uses \u03c3=5 for the initial Gaussian noise and applies integer quantization after filtering to maintain compatibility with integer-based compression algorithms.\n", "version": "1", "tags": [ "gaussian", "integer", "discrete", "timeseries", "1d", "synthetic", "correlated", "lowpass", "filtered" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/gaussian-lp-03/1/gaussian-lp-03-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/gaussian-lp-03/1/gaussian-lp-03-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/gaussian-lp-03/1/gaussian-lp-03-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/gaussian_lowpass/__init__.py" }, { "name": "gaussian-lp-07", "description": "Quantized Gaussian lowpass filtered with window size 7, \u03c3=5.", "long_description": "# Gaussian Lowpass Dataset\n\nThese datasets contain sequences of quantized Gaussian noise that has been smoothed using a moving average (lowpass) filter. The data is generated by:\n1. Drawing samples from a Gaussian distribution (\u03c3=5)\n2. Applying a moving average filter of varying window sizes\n3. Quantizing the filtered result to integers\n\nThe moving average filter acts as a lowpass filter, attenuating high-frequency components while preserving low-frequency trends. Larger window sizes result in smoother sequences with stronger low-frequency characteristics.\n\n## Variants\n\nWe provide three variants with different window sizes:\n\n- gaussian-lp-03 (window=3): Light smoothing\n- gaussian-lp-07 (window=7): Moderate smoothing\n- gaussian-lp-15 (window=15): Strong smoothing\n\nEach variant uses \u03c3=5 for the initial Gaussian noise and applies integer quantization after filtering to maintain compatibility with integer-based compression algorithms.\n", "version": "1", "tags": [ "gaussian", "integer", "discrete", "timeseries", "1d", "synthetic", "correlated", "lowpass", "filtered" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/gaussian-lp-07/1/gaussian-lp-07-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/gaussian-lp-07/1/gaussian-lp-07-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/gaussian-lp-07/1/gaussian-lp-07-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/gaussian_lowpass/__init__.py" }, { "name": "gaussian-lp-15", "description": "Quantized Gaussian lowpass filtered with window size 15, \u03c3=5.", "long_description": "# Gaussian Lowpass Dataset\n\nThese datasets contain sequences of quantized Gaussian noise that has been smoothed using a moving average (lowpass) filter. The data is generated by:\n1. Drawing samples from a Gaussian distribution (\u03c3=5)\n2. Applying a moving average filter of varying window sizes\n3. Quantizing the filtered result to integers\n\nThe moving average filter acts as a lowpass filter, attenuating high-frequency components while preserving low-frequency trends. Larger window sizes result in smoother sequences with stronger low-frequency characteristics.\n\n## Variants\n\nWe provide three variants with different window sizes:\n\n- gaussian-lp-03 (window=3): Light smoothing\n- gaussian-lp-07 (window=7): Moderate smoothing\n- gaussian-lp-15 (window=15): Strong smoothing\n\nEach variant uses \u03c3=5 for the initial Gaussian noise and applies integer quantization after filtering to maintain compatibility with integer-based compression algorithms.\n", "version": "1", "tags": [ "gaussian", "integer", "discrete", "timeseries", "1d", "synthetic", "correlated", "lowpass", "filtered" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/gaussian-lp-15/1/gaussian-lp-15-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/gaussian-lp-15/1/gaussian-lp-15-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/gaussian-lp-15/1/gaussian-lp-15-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/gaussian_lowpass/__init__.py" }, { "name": "ecephys-000876-ch45", "description": "Raw extracellular electrophysiology recording from DANDI:000876.", "long_description": "# Extracellular Electrophysiology Dataset\n\nExtracellular Electrophysiology datasets contain recordings of electrical activity from neurons in the brain. These recordings are made using microelectrodes that detect the small voltage changes produced by neurons when they fire action potentials (spikes).\n\n## Data Sources\n\nThe datasets are samples from single channels within larger electrophysiology recordings available on the DANDI Archive:\n\n- Channel 45 from a session in [Dandiset 000876](https://dandiarchive.org/dandiset/000876)\n- Channel 101 from a session in [Dandiset 000409](https://dandiarchive.org/dandiset/000409)\n- Channel 0 from a session in [Dandiset 001290](https://dandiarchive.org/dandiset/001290)\n- Only channel from a session in [Dandiset 001259](https://dandiarchive.org/dandiset/001259)\n\n## Dataset Variants\n\nWe provide three types of variants for each sampled channel:\n\n### 1. Raw Data\n- Direct recordings from the electrodes\n- Contains both neural signals and background noise\n- Integer-valued samples representing voltage\n- Sampling rate: 30 kHz\n\n### 2. Filtered Data\n- Bandpass filtered between 300-6000 Hz to isolate spike activity\n- Normalized by estimated noise level\n- Quantized with step size 0.25\n- Stored as 16-bit integers\n\n### 3. Sparse Data\n- Applies activity-based suppression to focus on regions with neural firing\n- Uses adaptive thresholding to detect active regions\n- Suppresses low-activity regions while preserving spike waveforms\n- Also quantized and stored as 16-bit integers\n\n## Compression Considerations\n\nThese datasets present different challenges for compression algorithms:\n\n1. Raw data contains broadband noise and slow drifts\n2. Filtered data emphasizes spike waveforms but maintains continuous values\n3. Sparse data has many near-zero regions interspersed with spike events\n\nThe sparse variants are particularly interesting as they represent a common preprocessing step in neuroscience analysis, where only time periods containing neural activity are retained.\n", "version": "1", "tags": [ "real", "ecephys", "timeseries", "1d", "integer", "correlated" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/ecephys-000876-ch45/1/ecephys-000876-ch45-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/ecephys-000876-ch45/1/ecephys-000876-ch45-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/ecephys-000876-ch45/1/ecephys-000876-ch45-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/ecephys/__init__.py" }, { "name": "ecephys-000409-ch101", "description": "Raw extracellular electrophysiology recording from DANDI:000409.", "long_description": "# Extracellular Electrophysiology Dataset\n\nExtracellular Electrophysiology datasets contain recordings of electrical activity from neurons in the brain. These recordings are made using microelectrodes that detect the small voltage changes produced by neurons when they fire action potentials (spikes).\n\n## Data Sources\n\nThe datasets are samples from single channels within larger electrophysiology recordings available on the DANDI Archive:\n\n- Channel 45 from a session in [Dandiset 000876](https://dandiarchive.org/dandiset/000876)\n- Channel 101 from a session in [Dandiset 000409](https://dandiarchive.org/dandiset/000409)\n- Channel 0 from a session in [Dandiset 001290](https://dandiarchive.org/dandiset/001290)\n- Only channel from a session in [Dandiset 001259](https://dandiarchive.org/dandiset/001259)\n\n## Dataset Variants\n\nWe provide three types of variants for each sampled channel:\n\n### 1. Raw Data\n- Direct recordings from the electrodes\n- Contains both neural signals and background noise\n- Integer-valued samples representing voltage\n- Sampling rate: 30 kHz\n\n### 2. Filtered Data\n- Bandpass filtered between 300-6000 Hz to isolate spike activity\n- Normalized by estimated noise level\n- Quantized with step size 0.25\n- Stored as 16-bit integers\n\n### 3. Sparse Data\n- Applies activity-based suppression to focus on regions with neural firing\n- Uses adaptive thresholding to detect active regions\n- Suppresses low-activity regions while preserving spike waveforms\n- Also quantized and stored as 16-bit integers\n\n## Compression Considerations\n\nThese datasets present different challenges for compression algorithms:\n\n1. Raw data contains broadband noise and slow drifts\n2. Filtered data emphasizes spike waveforms but maintains continuous values\n3. Sparse data has many near-zero regions interspersed with spike events\n\nThe sparse variants are particularly interesting as they represent a common preprocessing step in neuroscience analysis, where only time periods containing neural activity are retained.\n", "version": "1", "tags": [ "real", "ecephys", "timeseries", "1d", "integer", "correlated" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/ecephys-000409-ch101/1/ecephys-000409-ch101-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/ecephys-000409-ch101/1/ecephys-000409-ch101-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/ecephys-000409-ch101/1/ecephys-000409-ch101-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/ecephys/__init__.py" }, { "name": "ecephys-001290-ch0", "description": "Raw extracellular electrophysiology recording from DANDI:001290.", "long_description": "# Extracellular Electrophysiology Dataset\n\nExtracellular Electrophysiology datasets contain recordings of electrical activity from neurons in the brain. These recordings are made using microelectrodes that detect the small voltage changes produced by neurons when they fire action potentials (spikes).\n\n## Data Sources\n\nThe datasets are samples from single channels within larger electrophysiology recordings available on the DANDI Archive:\n\n- Channel 45 from a session in [Dandiset 000876](https://dandiarchive.org/dandiset/000876)\n- Channel 101 from a session in [Dandiset 000409](https://dandiarchive.org/dandiset/000409)\n- Channel 0 from a session in [Dandiset 001290](https://dandiarchive.org/dandiset/001290)\n- Only channel from a session in [Dandiset 001259](https://dandiarchive.org/dandiset/001259)\n\n## Dataset Variants\n\nWe provide three types of variants for each sampled channel:\n\n### 1. Raw Data\n- Direct recordings from the electrodes\n- Contains both neural signals and background noise\n- Integer-valued samples representing voltage\n- Sampling rate: 30 kHz\n\n### 2. Filtered Data\n- Bandpass filtered between 300-6000 Hz to isolate spike activity\n- Normalized by estimated noise level\n- Quantized with step size 0.25\n- Stored as 16-bit integers\n\n### 3. Sparse Data\n- Applies activity-based suppression to focus on regions with neural firing\n- Uses adaptive thresholding to detect active regions\n- Suppresses low-activity regions while preserving spike waveforms\n- Also quantized and stored as 16-bit integers\n\n## Compression Considerations\n\nThese datasets present different challenges for compression algorithms:\n\n1. Raw data contains broadband noise and slow drifts\n2. Filtered data emphasizes spike waveforms but maintains continuous values\n3. Sparse data has many near-zero regions interspersed with spike events\n\nThe sparse variants are particularly interesting as they represent a common preprocessing step in neuroscience analysis, where only time periods containing neural activity are retained.\n", "version": "1", "tags": [ "real", "ecephys", "timeseries", "1d", "integer", "correlated" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/ecephys-001290-ch0/1/ecephys-001290-ch0-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/ecephys-001290-ch0/1/ecephys-001290-ch0-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/ecephys-001290-ch0/1/ecephys-001290-ch0-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/ecephys/__init__.py" }, { "name": "ecephys-000876-ch45-filtered", "description": "Preprocessed version of real-000876-ch45. Bandpass filtered (300-6000 Hz).", "long_description": "# Extracellular Electrophysiology Dataset\n\nExtracellular Electrophysiology datasets contain recordings of electrical activity from neurons in the brain. These recordings are made using microelectrodes that detect the small voltage changes produced by neurons when they fire action potentials (spikes).\n\n## Data Sources\n\nThe datasets are samples from single channels within larger electrophysiology recordings available on the DANDI Archive:\n\n- Channel 45 from a session in [Dandiset 000876](https://dandiarchive.org/dandiset/000876)\n- Channel 101 from a session in [Dandiset 000409](https://dandiarchive.org/dandiset/000409)\n- Channel 0 from a session in [Dandiset 001290](https://dandiarchive.org/dandiset/001290)\n- Only channel from a session in [Dandiset 001259](https://dandiarchive.org/dandiset/001259)\n\n## Dataset Variants\n\nWe provide three types of variants for each sampled channel:\n\n### 1. Raw Data\n- Direct recordings from the electrodes\n- Contains both neural signals and background noise\n- Integer-valued samples representing voltage\n- Sampling rate: 30 kHz\n\n### 2. Filtered Data\n- Bandpass filtered between 300-6000 Hz to isolate spike activity\n- Normalized by estimated noise level\n- Quantized with step size 0.25\n- Stored as 16-bit integers\n\n### 3. Sparse Data\n- Applies activity-based suppression to focus on regions with neural firing\n- Uses adaptive thresholding to detect active regions\n- Suppresses low-activity regions while preserving spike waveforms\n- Also quantized and stored as 16-bit integers\n\n## Compression Considerations\n\nThese datasets present different challenges for compression algorithms:\n\n1. Raw data contains broadband noise and slow drifts\n2. Filtered data emphasizes spike waveforms but maintains continuous values\n3. Sparse data has many near-zero regions interspersed with spike events\n\nThe sparse variants are particularly interesting as they represent a common preprocessing step in neuroscience analysis, where only time periods containing neural activity are retained.\n", "version": "1", "tags": [ "real", "ecephys", "timeseries", "1d", "integer", "correlated", "filtered" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/ecephys-000876-ch45-filtered/1/ecephys-000876-ch45-filtered-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/ecephys-000876-ch45-filtered/1/ecephys-000876-ch45-filtered-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/ecephys-000876-ch45-filtered/1/ecephys-000876-ch45-filtered-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/ecephys/__init__.py" }, { "name": "ecephys-000409-ch101-filtered", "description": "Preprocessed version of real-000409-ch101. Bandpass filtered (300-6000 Hz).", "long_description": "# Extracellular Electrophysiology Dataset\n\nExtracellular Electrophysiology datasets contain recordings of electrical activity from neurons in the brain. These recordings are made using microelectrodes that detect the small voltage changes produced by neurons when they fire action potentials (spikes).\n\n## Data Sources\n\nThe datasets are samples from single channels within larger electrophysiology recordings available on the DANDI Archive:\n\n- Channel 45 from a session in [Dandiset 000876](https://dandiarchive.org/dandiset/000876)\n- Channel 101 from a session in [Dandiset 000409](https://dandiarchive.org/dandiset/000409)\n- Channel 0 from a session in [Dandiset 001290](https://dandiarchive.org/dandiset/001290)\n- Only channel from a session in [Dandiset 001259](https://dandiarchive.org/dandiset/001259)\n\n## Dataset Variants\n\nWe provide three types of variants for each sampled channel:\n\n### 1. Raw Data\n- Direct recordings from the electrodes\n- Contains both neural signals and background noise\n- Integer-valued samples representing voltage\n- Sampling rate: 30 kHz\n\n### 2. Filtered Data\n- Bandpass filtered between 300-6000 Hz to isolate spike activity\n- Normalized by estimated noise level\n- Quantized with step size 0.25\n- Stored as 16-bit integers\n\n### 3. Sparse Data\n- Applies activity-based suppression to focus on regions with neural firing\n- Uses adaptive thresholding to detect active regions\n- Suppresses low-activity regions while preserving spike waveforms\n- Also quantized and stored as 16-bit integers\n\n## Compression Considerations\n\nThese datasets present different challenges for compression algorithms:\n\n1. Raw data contains broadband noise and slow drifts\n2. Filtered data emphasizes spike waveforms but maintains continuous values\n3. Sparse data has many near-zero regions interspersed with spike events\n\nThe sparse variants are particularly interesting as they represent a common preprocessing step in neuroscience analysis, where only time periods containing neural activity are retained.\n", "version": "1", "tags": [ "real", "ecephys", "timeseries", "1d", "integer", "correlated", "filtered" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/ecephys-000409-ch101-filtered/1/ecephys-000409-ch101-filtered-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/ecephys-000409-ch101-filtered/1/ecephys-000409-ch101-filtered-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/ecephys-000409-ch101-filtered/1/ecephys-000409-ch101-filtered-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/ecephys/__init__.py" }, { "name": "ecephys-001290-ch0-filtered", "description": "Preprocessed version of real-001290-ch0. Bandpass filtered (300-6000 Hz).", "long_description": "# Extracellular Electrophysiology Dataset\n\nExtracellular Electrophysiology datasets contain recordings of electrical activity from neurons in the brain. These recordings are made using microelectrodes that detect the small voltage changes produced by neurons when they fire action potentials (spikes).\n\n## Data Sources\n\nThe datasets are samples from single channels within larger electrophysiology recordings available on the DANDI Archive:\n\n- Channel 45 from a session in [Dandiset 000876](https://dandiarchive.org/dandiset/000876)\n- Channel 101 from a session in [Dandiset 000409](https://dandiarchive.org/dandiset/000409)\n- Channel 0 from a session in [Dandiset 001290](https://dandiarchive.org/dandiset/001290)\n- Only channel from a session in [Dandiset 001259](https://dandiarchive.org/dandiset/001259)\n\n## Dataset Variants\n\nWe provide three types of variants for each sampled channel:\n\n### 1. Raw Data\n- Direct recordings from the electrodes\n- Contains both neural signals and background noise\n- Integer-valued samples representing voltage\n- Sampling rate: 30 kHz\n\n### 2. Filtered Data\n- Bandpass filtered between 300-6000 Hz to isolate spike activity\n- Normalized by estimated noise level\n- Quantized with step size 0.25\n- Stored as 16-bit integers\n\n### 3. Sparse Data\n- Applies activity-based suppression to focus on regions with neural firing\n- Uses adaptive thresholding to detect active regions\n- Suppresses low-activity regions while preserving spike waveforms\n- Also quantized and stored as 16-bit integers\n\n## Compression Considerations\n\nThese datasets present different challenges for compression algorithms:\n\n1. Raw data contains broadband noise and slow drifts\n2. Filtered data emphasizes spike waveforms but maintains continuous values\n3. Sparse data has many near-zero regions interspersed with spike events\n\nThe sparse variants are particularly interesting as they represent a common preprocessing step in neuroscience analysis, where only time periods containing neural activity are retained.\n", "version": "1", "tags": [ "real", "ecephys", "timeseries", "1d", "integer", "correlated", "filtered" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/ecephys-001290-ch0-filtered/1/ecephys-001290-ch0-filtered-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/ecephys-001290-ch0-filtered/1/ecephys-001290-ch0-filtered-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/ecephys-001290-ch0-filtered/1/ecephys-001290-ch0-filtered-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/ecephys/__init__.py" }, { "name": "ecephys-000876-ch45-sparse", "description": "Sparse version of real-000876-ch45. Activity-based suppression applied.", "long_description": "# Extracellular Electrophysiology Dataset\n\nExtracellular Electrophysiology datasets contain recordings of electrical activity from neurons in the brain. These recordings are made using microelectrodes that detect the small voltage changes produced by neurons when they fire action potentials (spikes).\n\n## Data Sources\n\nThe datasets are samples from single channels within larger electrophysiology recordings available on the DANDI Archive:\n\n- Channel 45 from a session in [Dandiset 000876](https://dandiarchive.org/dandiset/000876)\n- Channel 101 from a session in [Dandiset 000409](https://dandiarchive.org/dandiset/000409)\n- Channel 0 from a session in [Dandiset 001290](https://dandiarchive.org/dandiset/001290)\n- Only channel from a session in [Dandiset 001259](https://dandiarchive.org/dandiset/001259)\n\n## Dataset Variants\n\nWe provide three types of variants for each sampled channel:\n\n### 1. Raw Data\n- Direct recordings from the electrodes\n- Contains both neural signals and background noise\n- Integer-valued samples representing voltage\n- Sampling rate: 30 kHz\n\n### 2. Filtered Data\n- Bandpass filtered between 300-6000 Hz to isolate spike activity\n- Normalized by estimated noise level\n- Quantized with step size 0.25\n- Stored as 16-bit integers\n\n### 3. Sparse Data\n- Applies activity-based suppression to focus on regions with neural firing\n- Uses adaptive thresholding to detect active regions\n- Suppresses low-activity regions while preserving spike waveforms\n- Also quantized and stored as 16-bit integers\n\n## Compression Considerations\n\nThese datasets present different challenges for compression algorithms:\n\n1. Raw data contains broadband noise and slow drifts\n2. Filtered data emphasizes spike waveforms but maintains continuous values\n3. Sparse data has many near-zero regions interspersed with spike events\n\nThe sparse variants are particularly interesting as they represent a common preprocessing step in neuroscience analysis, where only time periods containing neural activity are retained.\n", "version": "1", "tags": [ "real", "ecephys", "timeseries", "1d", "integer", "correlated", "filtered", "sparse" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/ecephys-000876-ch45-sparse/1/ecephys-000876-ch45-sparse-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/ecephys-000876-ch45-sparse/1/ecephys-000876-ch45-sparse-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/ecephys-000876-ch45-sparse/1/ecephys-000876-ch45-sparse-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/ecephys/__init__.py" }, { "name": "ecephys-000409-ch101-sparse", "description": "Sparse version of real-000409-ch101. Activity-based suppression applied.", "long_description": "# Extracellular Electrophysiology Dataset\n\nExtracellular Electrophysiology datasets contain recordings of electrical activity from neurons in the brain. These recordings are made using microelectrodes that detect the small voltage changes produced by neurons when they fire action potentials (spikes).\n\n## Data Sources\n\nThe datasets are samples from single channels within larger electrophysiology recordings available on the DANDI Archive:\n\n- Channel 45 from a session in [Dandiset 000876](https://dandiarchive.org/dandiset/000876)\n- Channel 101 from a session in [Dandiset 000409](https://dandiarchive.org/dandiset/000409)\n- Channel 0 from a session in [Dandiset 001290](https://dandiarchive.org/dandiset/001290)\n- Only channel from a session in [Dandiset 001259](https://dandiarchive.org/dandiset/001259)\n\n## Dataset Variants\n\nWe provide three types of variants for each sampled channel:\n\n### 1. Raw Data\n- Direct recordings from the electrodes\n- Contains both neural signals and background noise\n- Integer-valued samples representing voltage\n- Sampling rate: 30 kHz\n\n### 2. Filtered Data\n- Bandpass filtered between 300-6000 Hz to isolate spike activity\n- Normalized by estimated noise level\n- Quantized with step size 0.25\n- Stored as 16-bit integers\n\n### 3. Sparse Data\n- Applies activity-based suppression to focus on regions with neural firing\n- Uses adaptive thresholding to detect active regions\n- Suppresses low-activity regions while preserving spike waveforms\n- Also quantized and stored as 16-bit integers\n\n## Compression Considerations\n\nThese datasets present different challenges for compression algorithms:\n\n1. Raw data contains broadband noise and slow drifts\n2. Filtered data emphasizes spike waveforms but maintains continuous values\n3. Sparse data has many near-zero regions interspersed with spike events\n\nThe sparse variants are particularly interesting as they represent a common preprocessing step in neuroscience analysis, where only time periods containing neural activity are retained.\n", "version": "1", "tags": [ "real", "ecephys", "timeseries", "1d", "integer", "correlated", "filtered", "sparse" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/ecephys-000409-ch101-sparse/1/ecephys-000409-ch101-sparse-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/ecephys-000409-ch101-sparse/1/ecephys-000409-ch101-sparse-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/ecephys-000409-ch101-sparse/1/ecephys-000409-ch101-sparse-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/ecephys/__init__.py" }, { "name": "ecephys-001290-ch0-sparse", "description": "Sparse version of real-001290-ch0. Activity-based suppression applied.", "long_description": "# Extracellular Electrophysiology Dataset\n\nExtracellular Electrophysiology datasets contain recordings of electrical activity from neurons in the brain. These recordings are made using microelectrodes that detect the small voltage changes produced by neurons when they fire action potentials (spikes).\n\n## Data Sources\n\nThe datasets are samples from single channels within larger electrophysiology recordings available on the DANDI Archive:\n\n- Channel 45 from a session in [Dandiset 000876](https://dandiarchive.org/dandiset/000876)\n- Channel 101 from a session in [Dandiset 000409](https://dandiarchive.org/dandiset/000409)\n- Channel 0 from a session in [Dandiset 001290](https://dandiarchive.org/dandiset/001290)\n- Only channel from a session in [Dandiset 001259](https://dandiarchive.org/dandiset/001259)\n\n## Dataset Variants\n\nWe provide three types of variants for each sampled channel:\n\n### 1. Raw Data\n- Direct recordings from the electrodes\n- Contains both neural signals and background noise\n- Integer-valued samples representing voltage\n- Sampling rate: 30 kHz\n\n### 2. Filtered Data\n- Bandpass filtered between 300-6000 Hz to isolate spike activity\n- Normalized by estimated noise level\n- Quantized with step size 0.25\n- Stored as 16-bit integers\n\n### 3. Sparse Data\n- Applies activity-based suppression to focus on regions with neural firing\n- Uses adaptive thresholding to detect active regions\n- Suppresses low-activity regions while preserving spike waveforms\n- Also quantized and stored as 16-bit integers\n\n## Compression Considerations\n\nThese datasets present different challenges for compression algorithms:\n\n1. Raw data contains broadband noise and slow drifts\n2. Filtered data emphasizes spike waveforms but maintains continuous values\n3. Sparse data has many near-zero regions interspersed with spike events\n\nThe sparse variants are particularly interesting as they represent a common preprocessing step in neuroscience analysis, where only time periods containing neural activity are retained.\n", "version": "1", "tags": [ "real", "ecephys", "timeseries", "1d", "integer", "correlated", "filtered", "sparse" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/ecephys-001290-ch0-sparse/1/ecephys-001290-ch0-sparse-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/ecephys-001290-ch0-sparse/1/ecephys-001290-ch0-sparse-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/ecephys-001290-ch0-sparse/1/ecephys-001290-ch0-sparse-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/ecephys/__init__.py" }, { "name": "seismic-04A-04B", "description": "Seismic data from Roger Revelle voyage RR1508.", "long_description": "# Seismic Dataset\n\nThis dataset contains marine seismic reflection data. Processed seismic reflection data and associated files from Roger Revelle voyage RR1508: 16 May - 18 June 2015. The research voyage aimed to characterise the thermal regime of the gas hydrate systems on the southern Hikurangi margin east of New Zealand. Data were processed using the Globe Claritas processing software.\n\n## Data Source\n\nThe data comes from a SEG-Y file available on Zenodo (https://zenodo.org/records/8152964). SEG-Y is a standard format for storing seismic data that includes both the recorded waveforms and metadata about the survey.\n\n### Revelle RR1508 Data (seismic-04A-04B)\n- Original floating point values from the SEG-Y file\n- Contains the natural amplitude variations of the seismic waves\n- Stored as 32-bit floating point numbers\n\n### Revelle RR1508 Quantized Data (seismic-04A-04B-quantized)\n- Values are scaled and rounded to integers\n- Uses a quantization step of 10000\n- Stored as 32-bit integers\n\n## Source Details\n\n### Roger Revelle RR1508\nThe data comes from a SEG-Y file available on Zenodo (https://zenodo.org/records/8152964). SEG-Y is a standard format for storing seismic data that includes both the recorded waveforms and metadata about the survey.\n", "version": "1", "tags": [ "real", "seismic", "correlated", "timeseries", "1d", "float" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/seismic-04A-04B/1/seismic-04A-04B-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/seismic-04A-04B/1/seismic-04A-04B-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/seismic-04A-04B/1/seismic-04A-04B-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/seismic/__init__.py" }, { "name": "seismic-04A-04B-quantized", "description": "Seismic data from Roger Revelle voyage RR1508, quantized.", "long_description": "# Seismic Dataset\n\nThis dataset contains marine seismic reflection data. Processed seismic reflection data and associated files from Roger Revelle voyage RR1508: 16 May - 18 June 2015. The research voyage aimed to characterise the thermal regime of the gas hydrate systems on the southern Hikurangi margin east of New Zealand. Data were processed using the Globe Claritas processing software.\n\n## Data Source\n\nThe data comes from a SEG-Y file available on Zenodo (https://zenodo.org/records/8152964). SEG-Y is a standard format for storing seismic data that includes both the recorded waveforms and metadata about the survey.\n\n### Revelle RR1508 Data (seismic-04A-04B)\n- Original floating point values from the SEG-Y file\n- Contains the natural amplitude variations of the seismic waves\n- Stored as 32-bit floating point numbers\n\n### Revelle RR1508 Quantized Data (seismic-04A-04B-quantized)\n- Values are scaled and rounded to integers\n- Uses a quantization step of 10000\n- Stored as 32-bit integers\n\n## Source Details\n\n### Roger Revelle RR1508\nThe data comes from a SEG-Y file available on Zenodo (https://zenodo.org/records/8152964). SEG-Y is a standard format for storing seismic data that includes both the recorded waveforms and metadata about the survey.\n", "version": "4", "tags": [ "real", "seismic", "correlated", "timeseries", "1d", "integer" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/seismic-04A-04B-quantized/4/seismic-04A-04B-quantized-4.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/seismic-04A-04B-quantized/4/seismic-04A-04B-quantized-4.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/seismic-04A-04B-quantized/4/seismic-04A-04B-quantized-4.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/seismic/__init__.py" }, { "name": "ieeg-005592", "description": "iEEG recording from OpenNeuro dataset ds005592, first 10 channels concatenated.", "long_description": "# iEEG Dataset\n\nThis dataset contains intracranial EEG (iEEG) recordings from OpenNeuro dataset ds005592. The data consists of the first 10 channels concatenated into a single continuous time series.\n\n## Data Source\n\nOpenNeuro dataset ds005592:\n- Dataset URL: https://openneuro.org/datasets/ds005592\n- Specific file: sub-01/ses-01/ieeg/sub-01_ses-01_task-sws_run-01_ieeg.edf\n\n## Data Properties\n\n- Type: Intracranial EEG recordings\n- Format: Concatenated time series from first 10 channels\n- Values: 32-bit floating point numbers representing voltage measurements\n", "version": "1", "tags": [ "real", "ecephys", "ieeg", "timeseries", "1d", "correlated", "float" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/ieeg-005592/1/ieeg-005592-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/ieeg-005592/1/ieeg-005592-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/ieeg-005592/1/ieeg-005592-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/ieeg/__init__.py" }, { "name": "ieeg-005592-quantized", "description": "iEEG recording from OpenNeuro dataset ds005592, first 10 channels concatenated, quantized.", "long_description": "# iEEG Dataset\n\nThis dataset contains intracranial EEG (iEEG) recordings from OpenNeuro dataset ds005592. The data consists of the first 10 channels concatenated into a single continuous time series.\n\n## Data Source\n\nOpenNeuro dataset ds005592:\n- Dataset URL: https://openneuro.org/datasets/ds005592\n- Specific file: sub-01/ses-01/ieeg/sub-01_ses-01_task-sws_run-01_ieeg.edf\n\n## Data Properties\n\n- Type: Intracranial EEG recordings\n- Format: Concatenated time series from first 10 channels\n- Values: 32-bit floating point numbers representing voltage measurements\n", "version": "1", "tags": [ "real", "ecephys", "ieeg", "timeseries", "1d", "correlated", "integer" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/ieeg-005592-quantized/1/ieeg-005592-quantized-1.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/ieeg-005592-quantized/1/ieeg-005592-quantized-1.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/ieeg-005592-quantized/1/ieeg-005592-quantized-1.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/ieeg/__init__.py" }, { "name": "fmri-ds005880", "description": "Middle 15 slices from BOLD fMRI recording from ds005880 OpenNeuro dataset.", "long_description": "# Functional MRI Dataset\n\nThis dataset contains functional MRI (fMRI) data from a study investigating neural activity elicited by the diminished seventh chord in listeners with refined music listening skills.\n\n## Data Source\n\nThe data comes from OpenNeuro dataset [ds005880](https://openneuro.org/datasets/ds005880/versions/1.0.1) titled \"BIDS Dataset for the Diminished Seventh Chord\". The specific data used is a single functional run from subject 01.\n\nData were acquired at Imaging Center for Integrated Body, Mind, and Culture Research at National Taiwan University using a 3T MR system (MAGNETOM Prisma, Siemens, Erlangen, Germany).\n\n## Dataset Details\n\n- Type: BOLD fMRI timeseries\n- Format: 4D NIfTI file (3D volumes over time)\n- Data type: 16-bit integers\n- Content: Represents changes in blood oxygenation level dependent (BOLD) signal over time\n- Structure: Each timepoint is a 3D brain volume showing neural activity patterns\n", "version": "2", "tags": [ "real", "fmri", "timeseries", "1d", "integer", "bold", "correlated" ], "data_url_raw": "https://tempory.net/f/memobin/datasets/fmri-ds005880/2/fmri-ds005880-2.dat", "data_url_npy": "https://tempory.net/f/memobin/datasets/fmri-ds005880/2/fmri-ds005880-2.npy", "data_url_json": "https://tempory.net/f/memobin/datasets/fmri-ds005880/2/fmri-ds005880-2.json", "source_file": "https://github.com/magland/benchcompress/blob/main/benchcompress/src/benchcompress/datasets/fmri/__init__.py" } ] }