5,406,032 AI resources indexedWorking toward the world’s largest AI resource index.

Browse resources 13 sections
PDF

Datasets

Data for training, evaluation and research.

How to use the library

Resources

Search results
Search stays within datasets.
Dataset

AI & Law Open-Access Corpus: Annotated Bibliometric Dataset for a PRISMA-Based Systematic Literature Review (2023–2026)

Dataset ini berisi 1.238 rekaman metadata bibliografi dari artikel jurnal akses terbuka yang diperoleh dari basis data Scopus melalui kueri Boolean terstruktur yang menggabungkan istilah "Artificial Intelligence" dan "Law" dalam bidang subjek Ilmu Sosial serta Seni & Humaniora, terbit antara 2023–2026. Setiap rekaman memuat 17 atribut bibliografi asli (penulis, judul, sumber, tahun, jumlah sitasi, DOI, abstrak, kata kunci, dan jenis dokumen) yang diperkaya dengan tiga kolom anotasi hasil analisis: status Inclusion/Exclusion beserta alasan keputusannya yang mengikuti kriteria Topik pada protokol PRISMA 2020 (kategori meliputi keterlibatan subs

Suwarno, Shidarta
cc-by-4.0View resource
Dataset

Image-based & machine learning-guided multiplexed serology test for SARS-CoV-2

Single-cell extracted imaging features created in project "Image-based & machine learning-guided multiplexed serology test for SARS-CoV-2". The dataset includes train (with annotations) and test features used in the manuscript. Four SARS-CoV-2 antigens (S, N, R, M) were imaged separately with serum samples presenting IgG, IgA and IgM antibodies.

Pietiäinen, Vilja, Polso, Minttu, Migh, Ede, Guckelsberger, Christian, Harmati, Maria, Diosdi, Akos, Turunen, Laura, Hassinen, Antti, Potdar, Swapnil, Koponen, Annika, Sebestyen, Edina Guykity, Kovacs, Ferenc, Kriston, Andras, Hollandi, Reka, Burian, Katalin, Terhes, Gabriella, Visnyovszki, Adam, Fodor, Eszter, Lacza, Zsombor, Kantele, Anu, Kolehmainen, Pekka, Kakkola, Laura, Strandin, Tomas, Levanov, Lev, Kallioniemi, Olli, Kemeny, Lajos, Julkunen, Ilkka, Vapalahti, Olli, Buzas, Krisztina, Paav
cc-by-4.0View resource
Dataset

Solar wind in situ data suitable for machine learning (python numpy structured arrays): STEREO-A/B, Wind, Parker Solar Probe, Ulysses, Venus Express, MESSENGER

These are solar wind in situ data arrays in python pickle format suitable for machine learning, i.e. the arrays consist only of numbers, no strings and no datetime objects. See AAREADME_insitu_ML.txt for more explanation. If you use these data for peer reviewed scientific publications, please get in touch concerning usage and possible co-authorship by the authors (C. Möstl, A. J. Weiss, R. L. Bailey, R. Winslow, A. Isavnin, D. Stansby): christian.moestl@oeaw.ac.at or twitter @chrisoutofspace Made with https://github.com/cmoestl/heliocats Load in python with e.g. for Parker Solar Probe data: > import pickle> filepsp='psp_2018_2021_sceq_ndarray

Moestl, Christian, Weiss, Andreas, Bailey, Rachel, Isavnin, Alexey, Stansby, David, Winslow, Reka
MIT LicenseView resource
Dataset

Solar wind in situ data suitable for machine learning (python numpy structured arrays): STEREO-A/B, Wind, Parker Solar Probe, Ulysses, Venus Express, MESSENGER

These are solar wind in situ data arrays in python pickle format suitable for machine learning, i.e. the arrays consist only of numbers, no strings and no datetime objects. See AAREADME_insitu_ML.txt for more explanation. If you use these data for peer reviewed scientific publications, please get in touch concerning usage and possible co-authorship by the authors (C. Möstl, A. J. Weiss, R. L. Bailey, R. Winslow, A. Isavnin, D. Stansby): christian.moestl@oeaw.ac.at or twitter @chrisoutofspace Made with https://github.com/cmoestl/heliocats Load in python with e.g. for Parker Solar Probe data: > import pickle> filepsp='psp_2018_2019_sceq_ndarray

Moestl, Christian, Weiss, Andreas, Bailey, Rachel, Isavnin, Alexey, Stansby, David, Reka Winslow
MIT LicenseView resource

Platform labels describe intended use. Read each resource's author credits, requirements and available access methods.