Directory
Published directory entries matching your search.
The HHU ResearchData website displays an Anubis bot verification page that uses a proof-of-work challenge to block automated scraping. This placeholder solution, set up by the site administrator to protect server resources from aggressive scraping while more permanent detection methods are developed, can cause temporary downtime that renders the site's resources inaccessible for all users.
Search computer data collections and supporting datasets from EEC.
Ian's Shoelace Site is a website about shoelaces, covering shoe lacing methods, shoelace knots and shoelace construction. It includes step-by-step lacing tutorials, an interactive lacing designer and pages on lengths and tips.
ILC-CNR for CLARIN-IT repository is a platform for linguistic data and NLP tools. It allows free and safe deposit with choice of license, encouraging open licenses. The repository supports discovery and citation, and hosts resources such as lexical databases.
A reference page in the Clojure documentation covering Java interop, including class access, member access, the dot special form, implementing interfaces and extending classes, arrays, vararg methods and type hints.
A curated GitHub list of resources on audio-driven talking face generation. It organizes research papers and projects, including encoder-decoder and StyleGAN-based methods, pose-controllable and lip-sync work, and 3D model-based approaches.
This webpage provides a curated list of large language model examples, alongside insights on business adoption, language model training, and influential models. It includes comparative analysis of leading LLMs such as OpenAI’s GPT-5, Claude 4.6, Gemini, DeepSeek-R1, Qwen, Llama 4, and xAI Grok-4 and Grok-4.1.
Lasagne is a lightweight library for building and training neural networks in Theano. It supports feed-forward networks such as convolutional neural networks, recurrent networks including LSTM, multiple inputs and outputs, and optimization methods including Nesterov momentum, RMSprop and ADAM. Cost functions are freely definable and gradients derive from Theano's symbolic differentiation.
Little Ball of Fur is a graph sampling extension library for Python and a graph sampling extension library for NetworKit and NetworkX. It consists of methods that can sample from graph structured data, covering vertex, edge, and exploration sampling techniques with a unified application public interface.
The MCHF/MCDHF Database is a NIST collection of transition data from relativistic theories computed by different computational methods. The Landé gJ factor is provided for a few collections. Data is accessed by selecting an element from the periodic table, then a spectrum, a reference number, and a property. Atomic structure codes used in the calculations, including HFFortran95, ATSP, ATSP2K and GRASP2K, are available for download.
MIT 6.S191 is MIT's introductory program on deep learning methods, with applications in vision, robotics, medicine, language, game play, art and more. It is described as a high-intensity bootcamp teaching deep learning fundamentals, with lectures, software labs and a final project proposal competition. Prerequisites assume calculus and linear algebra.
ML-From-Scratch is a GitHub repository that provides bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility, aiming to cover methods from linear regression to deep learning.
NMF.jl is a Julia package for non-negative matrix factorization, which factorizes a non-negative matrix into two lower-rank non-negative matrices. The nnmf function runs initialization and optimization in one call. Implemented methods include multiplicative update, projected alternating least squares, projected gradient and coordinate descent, with random and NNDSVD initialization.
The Nucleic Acid Knowledgebase (NAKB) is a portal for 3D structural information about nucleic acids, succeeding the Nucleic Acid Database. It provides annotations, search, reports, statistics, an atlas and visualization for nucleic-acid containing structures held by the NDB and the Protein Data Bank, covering X-ray, electron microscopy and NMR methods. Each structure links to external resources annotating and analyzing nucleic acid structures and their complexes.
OH SHINT! is an OSINT resource for online investigations, research links, search methods, and public data tools.
Lithuanian OSINT article introducing open-source intelligence concepts, tools, and methods.
OSINT Combine is an OSINT resource for online investigations, research links, search methods, and public data tools.
OSINT Tool Explorer is an OSINT resource for online investigations, research links, search methods, and public data tools.
OSINTgeek is an OSINT resource for online investigations, research links, search methods, and public data tools.
The Pacific Northwest National Laboratory DataHub is a searchable repository of scientific data and publications. The site offers filtering by category, content type and tags across topics such as biology, earth system science and computing. Listed content types include publications, datasets, people, projects, data sources, institutions and software.
PRISM Climate Data is a project of the PRISM Group at Oregon State University that gathers weather observations, applies quality control and mapping methods, and develops spatial datasets showing short- and long-term weather patterns. Datasets cover 1895 to the present and are offered free of charge, with bulk downloads and single grid cell time series.
PyStanfordDependencies is a Python interface for converting Penn Treebank trees to Stanford Dependencies and Universal Dependencies. It offers a get_instance() function with subprocess or jpype backends, and convert_tree() or convert_trees() methods returning Token objects. Visualization options include asciitree, Graphviz dotgraphs, and interactive xdot windows. A Stanford CoreNLP or Parser jar file can be supplied, or downloaded automatically.
PyTorch Geometric Temporal is a temporal extension library for PyTorch Geometric. It contains dynamic and temporal geometric deep learning, embedding and spatio-temporal regression methods from published research papers. It includes a dataset loader, train-test splitter and temporal snapshot iterator for dynamic and temporal graphs, with GPU support and benchmark datasets.
PyVarInf is a Python package that facilitates the use of Bayesian deep learning methods with variational inference for PyTorch, providing facilities to train PyTorch neural network models using variational inference. It approximates the posterior distribution over network weights with a parametric distribution.
