It is a Bash script that implements the algorithm we used to generate the simulated microbiology datasets. The script is discussed in Additional file 1. (SH 9Â kb)

The global market for Document Duplication Detection Software is experiencing robust growth, driven by the increasing need for efficient data management and enhanced security across various industries. The rising volume of digital documents, coupled with stricter regulatory compliance requirements (like GDPR and CCPA), is fueling the demand for solutions that can quickly and accurately identify duplicate files. This reduces storage costs, improves data quality, and minimizes the risk of data breaches. The market's expansion is further propelled by advancements in artificial intelligence (AI) and machine learning (ML) technologies, which enable more sophisticated and accurate duplicate detection. We estimate the current market size to be around $800 million in 2025, with a Compound Annual Growth Rate (CAGR) of 15% projected through 2033. This growth is expected across various segments, including cloud-based and on-premise solutions, catering to diverse industry verticals such as legal, finance, healthcare, and government. Major players like Microsoft, IBM, and Oracle are contributing to market growth through their established enterprise solutions. However, the market also features several specialized players, like Hyper Labs and Auslogics, offering niche solutions catering to specific needs. While the increasing adoption of cloud-based solutions is a key trend, potential restraints include the initial investment costs for software implementation and the need for ongoing training and support. The integration challenges with existing systems and the potential for false positives can also impede wider adoption. The market's regional distribution is expected to see a significant contribution from North America and Europe, while the Asia-Pacific region is projected to exhibit substantial growth potential driven by increasing digitalization. The forecast period (2025-2033) presents significant opportunities for market expansion, driven by technological innovation and the growing awareness of data management best practices.

The Purpose Built Backup Appliance is projected to be valued at $5 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 12%, reaching approximately $15 billion by 2034.

Chromatin immunoprecipitation and sequencing (ChIP-seq) has been widely used to map DNA-binding proteins, histone proteins and their modifications. ChIP-seq data contains redundant reads termed duplicates, referring to those mapping to the same genomic location and strand. There are two main sources of duplicates: polymerase chain reaction (PCR) duplicates and natural duplicates. Unlike natural duplicates that represent true signals from sequencing of independent DNA templates, PCR duplicates are artifacts originating from sequencing of identical copies amplified from the same DNA template. In analysis, duplicates are removed from peak calling and signal quantification. Nevertheless, a significant portion of the duplicates is believed to represent true signals. Obviously, removing all duplicates will underestimate the signal level in peaks and impact the identification of signal changes across samples. Therefore, an in-depth evaluation of the impact from duplicate removal is needed. Using eight public ChIP-seq datasets from three narrow-peak and two broad-peak marks, we tried to understand the distribution of duplicates in the genome, the extent by which duplicate removal impacts peak calling and signal estimation, and the factors associated with duplicate level in peaks. The three PCR-free histone H3 lysine 4 trimethylation (H3K4me3) ChIP-seq data had about 40% duplicates and 97% of them were within peaks. For the other datasets generated with PCR amplification of ChIP DNA, as expected, the narrow-peak marks have a much higher proportion of duplicates than the broad-peak marks. We found that duplicates are enriched in peaks and largely represent true signals, more conspicuous in those with high confidence. Furthermore, duplicate level in peaks is strongly correlated with the target enrichment level estimated using nonredundant reads, which provides the basis to properly allocate duplicates between noise and signal. Our analysis supports the feasibility of retaining the portion of signal duplicates into downstream analysis, thus alleviating the limitation of complete deduplication.

The dataset consists of 59166 jsonl files and is ~895GB compressed. It is a cleaned and deduplicated version of Together's RedPajama. Check out our blog post explaining our methods, our code on GitHub, and join the discussion on the Cerebras Discord.

  Getting Started

You can download the dataset using Hugging Face datasets: from datasets import load_dataset ds = load_dataset("cerebras/SlimPajama-627B")

  Background

Today we are releasing SlimPajama – the largest… See the full description on the dataset page: https://huggingface.co/datasets/cerebras/SlimPajama-627B.