Ease of access to data, tools and models expedites scientific research. In structural biology there are now numerous open repositories of experimental and simulated datasets. Being able to easily access and utilise these is crucial for allowing researchers to make optimal use of their research effort. The tools presented here are useful for collating existing public cryoEM datasets and/or creating new synthetic cryoEM datasets to aid the development of novel data processing and interpretation algorithms. In recent years, structural biology has seen the development of a multitude of machine-learning based algorithms for aiding numerous steps in the processing and reconstruction of experimental datasets and the use of these approaches has become widespread. Developing such techniques in structural biology requires access to large datasets which can be cumbersome to curate and unwieldy to make use of. In this paper we present a suite of Python software packages which we collectively refer to as PERC (profet, EMPIARreader and CAKED). These are designed to reduce the burden which data curation places upon structural biology research. The protein structure fetcher (profet) package allows users to conveniently download and cleave sequences or structures from the Protein Data Bank or Alphafold databases. EMPIARreader allows lazy loading of Electron Microscopy Public Image Archive datasets in a machine-learning compatible structure. The Class Aggregator for Key Electron-microscopy Data (CAKED) package is designed to seamlessly facilitate the training of machine learning models on electron microscopy data, including electron-cryo-microscopy-specific data augmentation and labelling. These packages may be utilised independently or as building blocks in workflows. All are available in open source repositories and designed to be easily extensible to facilitate more advanced workflows if required.

The advancement of artificial intelligence (AI) hinges on the quality and accessibility of data, yet the current fragmentation and variability of data sources hinder efficient data utilization. The dispersion of data sources and diversity of data formats often lead to inefficiencies in data retrieval and processing, significantly impeding the progress of AI research and applications. To address these challenges, this paper introduces OpenDataLab, a platform designed to bridge the gap between diverse data sources and the need for unified data processing. OpenDataLab integrates a wide range of open-source AI datasets and enhances data acquisition efficiency through intelligent querying and high-speed downloading services. The platform employs a next-generation AI Data Set Description Language (DSDL), which standardizes the representation of multimodal and multi-format data, improving interoperability and reusability. Additionally, OpenDataLab optimizes data processing through tools that complement DSDL. By integrating data with unified data descriptions and smart data toolchains, OpenDataLab can improve data preparation efficiency by 30%. We anticipate that OpenDataLab will significantly boost artificial general intelligence (AGI) research and facilitate advancements in related AI fields. For more detailed information, please visit the platform's official website: https://opendatalab.com/.

I bet that you have already seen in movies the IT guy hacking a system by writing commands inside a black window and thought "How cool is that!". Well, in reality, things are not that easy to hack but we do have some basic commands that can help interact with the computer, which is called command-line interface (CLI). The command-line interface is a program on your computer that allows you to create and delete files, run programs, and navigate through folders and files. On a Mac and Linux Systems, it's called Terminal, and on Windows, it's Command Prompt. CLI is not just a fancy method to interact with your computer.

But it's not just a skill for software devs -- learning bash can be valuable for anyone who works with data. In short, Bash is the Unix command-line interface (CLI). You'll also see it called the terminal, the command line, or the shell. It's a command language that allows us to work with files on our computers in a way that's far more efficient and powerful than using a GUI (graphical user interface). Making the switch from graphical user interfaces (GUIs) to a command-line interface can feel overwhelming.

ML-Pack is a small footprint C++ machine learning library that can be easily integrated into other programs. Learn how to use it for spam classification.

A small Python 3 script to download public Tweets from a given Twitter account into a format suitable for AI text generation tools (such as gpt-2-simple for finetuning GPT-2). You can view examples of AI-generated tweets from datasets retrieved with this tool in the /examples folder. Inspired by popular demand due to the success of @dril_gpt2. The script is interacted via a command line interface. You can use this Colaboratory notebook (optimized from the original notebook for this use case) to train the model on your downloaded tweets, and generate massive amounts of Tweets from it.

In the current age of cloud computing, there is now a multitude of mature services available -- offering security, scalability, and reliability for many business computing needs. What was once a colossal undertaking to build a data center, install server racks, and design storage arrays has given way to an entire marketplace of services that are always just a click away. One leader in that marketplace is Amazon Web Services, which consists of 175 products and services in a vast catalog that provides cloud storage, compute power, app deployment, user account management, data warehousing, tools for managing and controlling Internet of Things devices, and just about anything you can think of that a business needs. AWS really grew in popularity and capability over the last decade. One reason is that AWS is so reliable and secure.

PyKEEN (Python KnowlEdge EmbeddiNgs) is a package for training and evaluating knowledge graph embeddings. Currently, it provides implementations of 10 knowledge graph emebddings models, and can be run in training mode in which users provide their own set of hyper-parameter values, or in hyper-parameter optimization mode to find suitable hyper-parameter values from set of user defined values. PyKEEN can also be run without having experience in programing by using its interactive command line interface that can be started with the command pykeen from a terminal. We are currently working on PyKEEN 1.0 which will provide additional features such as several negative sampling approaches and further evaluation metrics. Furthermore, we are integrating additional KGE models.