{"id":26783144,"url":"https://github.com/aclai-lab/multidata.jl","last_synced_at":"2025-06-20T10:35:15.744Z","repository":{"id":220521670,"uuid":"751858730","full_name":"aclai-lab/MultiData.jl","owner":"aclai-lab","description":"Multimodal datasets for Machine-Learning","archived":false,"fork":false,"pushed_at":"2024-02-25T13:41:35.000Z","size":438,"stargazers_count":3,"open_issues_count":3,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2024-04-25T15:04:56.711Z","etag":null,"topics":["dataframes-jl","julia","machine-learning","multimodal-learning","multimodal-machine-learning"],"latest_commit_sha":null,"homepage":"","language":"Julia","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/aclai-lab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-02-02T13:38:20.000Z","updated_at":"2024-06-07T09:31:24.472Z","dependencies_parsed_at":"2024-02-13T12:48:55.302Z","dependency_job_id":"d5b4af64-21a2-47a5-a662-9cd94382d098","html_url":"https://github.com/aclai-lab/MultiData.jl","commit_stats":null,"previous_names":["aclai-lab/multidata.jl"],"tags_count":4,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aclai-lab%2FMultiData.jl","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aclai-lab%2FMultiData.jl/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aclai-lab%2FMultiData.jl/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aclai-lab%2FMultiData.jl/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/aclai-lab","download_url":"https://codeload.github.com/aclai-lab/MultiData.jl/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":249725069,"owners_count":21316123,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["dataframes-jl","julia","machine-learning","multimodal-learning","multimodal-machine-learning"],"created_at":"2025-03-29T09:18:14.332Z","updated_at":"2025-04-19T15:19:42.241Z","avatar_url":"https://github.com/aclai-lab.png","language":"Julia","readme":"\u003cdiv align=\"center\"\u003e\u003ca href=\"https://github.com/aclai-lab/Sole.jl\"\u003e\u003cimg src=\"logo.png\" alt=\"\" title=\"This package is part of Sole.jl\" width=\"200\"\u003e\u003c/a\u003e\u003c/div\u003e\n\n# MultiData.jl – Multimodal datasets\n\n[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://aclai-lab.github.io/MultiData.jl)\n[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://aclai-lab.github.io/MultiData.jl/dev)\n[![Build Status](https://api.cirrus-ci.com/github/aclai-lab/MultiData.jl.svg?branch=main)](https://cirrus-ci.com/github/aclai-lab/MultiData.jl)\n[![Coverage](https://codecov.io/gh/aclai-lab/MultiData.jl/branch/main/graph/badge.svg?token=LT9IYIYNFI)](https://codecov.io/gh/aclai-lab/MultiData.jl)\n\u003c!-- [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/aclai-lab/MultiData.jl/HEAD?labpath=pluto-demo.jl) --\u003e\n\n\u003c!-- [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://aclai-lab.github.io/MultiData.jl/dev) --\u003e\n\n\n## In a nutshell\n\n*MultiData* provides a **machine learning oriented** data layer on top of DataFrames.jl for:\n- Instantiating and manipulating [*multimodal*](https://en.wikipedia.org/wiki/Multimodal_learning) datasets for (un)supervised machine learning;\n- Describing datasets via basic statistical measures;\n- Saving to/loading from *npy/npz* format, as well as a custom CSV-based format (with interesting features such as *lazy loading* of datasets);\n- Performing basic data processing operations (e.g., windowing, moving average, etc.).\n\n\u003c!-- - Dealing with [*(non-)tabular* data](https://en.wikipedia.org/wiki/Unstructured_data) (e.g., graphs, images, time-series, etc.); --\u003e\n\u003c!--\nIf you are used to dealing with unstructured/multimodal data, but cannot find the right\ntools in Julia, you will find\n[*SoleFeatures.jl*](https://github.com/aclai-lab/SoleFeatures.jl/) useful!\n--\u003e\n\n## About\n\nThe package is developed by the [ACLAI Lab](https://aclai.unife.it/en/) @ University of\nFerrara.\n\n*MultiData.jl* was originally built for representing multimodal datasets in\n[*Sole.jl*](https://github.com/aclai-lab/Sole.jl), an open-source framework for\n*symbolic machine learning*.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faclai-lab%2Fmultidata.jl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faclai-lab%2Fmultidata.jl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faclai-lab%2Fmultidata.jl/lists"}