Social Media Mining for Health (SMM4H) Shared Task is a massive data source for biomedical and public health applications. Source: SMM4H

TuringBench is a benchmark environment that contains : - Benchmark tasks- Turing Test (i.e., human vs. machine) and Authorship Attribution: (i.e., who is the author of this texts?) - Datasets (Binary and Multi-class settings) - Website with leaderboard The dataset has 20 labels (19 AI text-generators and human). We built this dataset by collecting 10K news articles (mostly Politics) from sources like CNN and only keeping articles with 200-400 words. Next, we used the Titles of these human-written articles to prompt the AI text-generators (ex: GPT-2, GROVER, etc.) to generate 10K articles each. This gives us a sum total of 200K articles and 20 labels. However, since there are two benchmark tasks - Turing Test and Authorship Attribution settings, we have all 20 labels in one dataset for the multi-class setting and only human vs. one AI text-generator, making 19 binary-class datasets.

The twitter emoji dataset obtained from CodaLab comprises of 50 thousand tweets along with the associated emoji label. Each tweet in the dataset has a corresponding numerical label which maps to a specific emoji. The emojis are of the 20 most frequent emojis and hence the labels range from 0 to 19

The TII-SSRC-23 dataset offers a comprehensive collection of network traffic patterns, meticulously compiled to support the development and research of Intrusion Detection Systems (IDS). It presents a dual structure: one part provides a tabular representation of extracted features in CSV format, while the other offers raw network traffic data for each type of traffic in PCAP files. This rich dataset captures both benign and malicious network scenarios, serving as an invaluable resource for researchers in the machine learning field. URL: https://www.kaggle.com/datasets/daniaherzalla/tii-ssrc-23

The AppealCase dataset is the first large-scale resource specifically designed to support LegalAI research in appellate judgment scenarios. While prior work in LegalAI has focused heavily on one-shot trials, the appellate procedure—critical to ensuring fairness and correcting judicial errors—remains largely underexplored. To bridge this gap, we construct 10,000 pairs of matched first-instance and second-instance civil cases from China Judgments Online, covering 91 causes of action. We also design a structured annotation schema and define new tasks tailored to the appellate context.

The COVID-19 Posteroanterior Chest X-Ray fused (CPCXR) dataset is generated by the fusion of three publicly available datasets: COVID-19 cxr image, Radiological Society of North America (RSNA), and U.S. national library of medicine (USNLM) collected Montgomery country - NLM(MC). The dataset consists of samples of diseases labeled as COVID-19, Tuberculosis, Other pneumonia (SARS, MERS, etc.), and Normal. The dataset can be utilized to train an evaulate deep learning and machine learning models as binary and multi-class classification problem. Source: https://github.com/nspunn1993/COVID-19-PA-CXR-fused-dataset

CVE stands for Common Vulnerabilities and Exposures. CVE is a glossary that classifies vulnerabilities. The glossary analyzes vulnerabilities and then uses the Common Vulnerability Scoring System (CVSS) to evaluate the threat level of a vulnerability. A CVE score is often used for prioritizing the security of vulnerabilities.

Dataset DAPFAM See the accompanying paper: Ayaou et al., 2025 — “DAPFAM: A Domain‑Aware Patent Retrieval Dataset Aggregated at the Family Level” (arXiv:2506.22141). ## Summary DAPFAM provides 1 247 balanced query patent families and 45 336 target families with forward/backward‑citation relevance labels (≈ 50 K pairs). Each relevant link is explicitly marked in‑domain or out‑of‑domain according to IPC 3‑char overlap, enabling rigorous cross‑domain evaluation. * Full text (title · abstract · claims · description) plus rich metadata for every family. * Multi‑jurisdictional, English‑only text (families may originate in US, JP, EP, CN, …). * Parquet qrel file: qrels_all.parquet. ## Dataset Structure corpus.parquet # 45 336 rows, targets – every original column from the paper queries.parquet # 1 247 rows, queries – same columns + abstract_keywords qrels_all.parquet # (all | in | out) four‑column tables → query_id · relevant_id · relevance_score · domain_rel ## Citation @misc{ayaou2025dapfam, title={DAPFAM: A Domain-Aware Patent Retrieval Dataset Aggregated at the Family Level}, author={Iliass Ayaou and Denis Cavallucci and Hicham Chibane}, year={2025}, eprint={2506.22141}, archivePrefix={arXiv}, primaryClass={cs.CL} } ## Quick Stats * Queries: 1,247 * Corpus (targets): 45,336 * Qrels (all): 49,869 * Qrels (in): 19,736 * Qrels (out): 5,193

DeepParliament is a legal domain Benchmark Dataset that gathers bill documents and metadata and performs various bill status classification tasks. The dataset text covers a broad range of bills from 1986 to the present and contains richer information on parliament bill content. There are a total of 5329 documents where 4223 are in the train and 1106 are in the test dataset. Each bill document contains many sentences in both cases, and the document’s length varies greatly. Source: DeepParliament: A Legal domain Benchmark & Dataset for Parliament Bills Prediction Image Source: https://arxiv.org/pdf/2211.15424v1.pdf

It is a large-scale multimodal patent dataset with detailed captions for design patent figures. 💥 Our dataset includes half a million design patents comprising 3.61 million figures along with captions from patents granted by the United States Patent and Trademark Office USPTO over a 16-year period from 2007 to 2022. * Multimodality: We introduce a multimodal patent dataset that includes patent images, metadata, and detailed captions to support a variety of NLP, vision, and multimodal tasks. This dataset is also valuable for patent analysis tasks such as classification, retrieval, prior art searches, and design trend analysis. * Comprehensive dataset: We have compiled a collection of 435,101 patents spanning 16 years from U.S. design patent documents. This extensive collection includes a total of 3,609,805 drawing figures. Additionally, our dataset consists of eleven fields such as the title, patent ID, claims, date of publication, classification code, and extensive image-related information, including the number of images per patent and descriptions of the viewpoints. * Descriptive captions: To address the absence of descriptions about the designs, such as features and shapes, we generate elaborated captions by employing a vision-language model. It generates descriptive captions for the design figures, capturing details from the sketch. These captions, coupled with the images, enrich our dataset and becomes a valuable resource for advanced patent analysis and multimodal research applications.

Two news datasets (KINNEWS and KIRNEWS) for multi-class classification of news articles in Kinyarwanda and Kirundi, two low-resource African languages. The two languages are mutually intelligible. Source: KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi

The ROAD dataset is made up of observations from the Low Frequency Array (LOFAR) telescope. LOFAR is comprised of 52 stations across Europe, where each station is an array of 96 dual polarisation low-band antennas (LBA) in the 10–90 MHz range and 48 or 96 dual polarisation high-band antenna antennas (HBA) in the 110–250 MHz range. The data are four dimensional, with the dimensions corresponding to time, frequency, polarisation, and station. dictate the array configuration (i.e. the number of stations used), the number of frequency channels (Nf), the time sampling, as well as the overall integration time (Nt) of the observing session. Furthermore, the dual-polarisation of the antennas results in a correlation product (Npol) of size 4. The ROAD dataset contains ten classes that describe various system-wide phenomena and anomalies from data obtained by the LOFAR telescope. These classes are categorised into four groups: data processing system failures, electronic anomalies, environmental effects, and unwanted astronomical events as shown by the table below. | Category | Description | Band | Polarisation | Occurrence rate | ** Num Samples** | |--------------------------------|-------------------------------------------------------------|-------------------|---------------------------|------------------------------|--------------------------| | Normal | All non-characterised effects | Both | All | - | 4687 | | % Electric fence | RFI emitted from electric fences | Low | Cross | 64 | | Data processing | | | | | | | First order data loss | Data loss from consecutive time and/or frequency channels | Both | All | 0.02 | 146 | | Second order data loss | Data loss from single frequency and/or single time channels | Both | All | 0.04 | 283 | | Electronic systems | | | | | | | High noise element | High power disturbances caused by miscellaneous events | Both | All | 0.01 | 88 | | Oscillating tile | Amplifier going into oscillation | High | All | 0.01 | 56 | | Astronomical events | | | | | | | Source in side-lobes | A-team source passing through side-lobes | High | All | 0.06 | 446 | | Galactic plane | Galactic plane passing through the main lobe of the antenna | Both | Cross | 0.08 | 550 | | Solar storm | Strong emissions from the sun | Low | All | 0.02 | 147 | | Environmental effects | | | | | | | Lightning | Lightning storm | Both | All | 0.06 | 389 | | Ionospheric RFI reflections | RFI reflected from the ionosphere | Low | All | 0.04 | 261 |

SF-MASK is a collection made from 20k low-resolution images exported from diverse and heterogeneous datasets, ranging from 7 x 7 to 64 x 64 pixel resolution. An accurate visualization of this collection, through counting grids, made it possible to highlight gaps in the variety of poses assumed by the heads of the pedestrians. Source: A Masked Face Classification Benchmark Image Source: https://arxiv.org/pdf/2211.13061v1.pdf

SmokEng is a dataset of 3144 tweets, which are selected based on the presence of colloquial slang related to smoking and analyze it based on the semantics of the tweet. Source: SmokEng: Towards Fine-grained Classification of Tobacco-related Social Media Text

This dataset is comprised of the dynamic analysis reports generated by CAPEv2, from both malware and goodware. We source the goodware as they do in Dambra et al. (https://arxiv.org/abs/2307.14657), where trough the community-maintained packages of Chocolatey they create a dataset that spans 2012 to 2020. The malware are sourced from VirusTotal, namely samples of Portable Executable from 2017 - 2020 that they release for academic purposes. In total, the dataset we assembled contains 26,200 PE samples: 8,600 (33%) goodware and 17,675 (67%) malware. All samples were executed in a series of Windows 7 VMware virtual machines and without network connectivity due to policy/ethical constraints. These were orchestrated through CAPEv2, a maintained open-source successor of Cuckoo. This framework is widely used for analyzing and detecting potentially malicious binaries by executing them in an isolated environment and observing their behavior without risking the security of the host system. Every sample was executed for 150 seconds, an empirical lower bound on the time required to gather the full behavior of samples. We tried to mitigate evasive checks by running the VMwareCloak script to remove well-known artifacts introduced by VMware. Moreover, we populated the filesystem with documents and other common types of files, to resemble a legitimate desktop workstation that malware may identify as a valuable target. The dynamic analysis output is a detailed report with information on the syscalls invoked by the binary and all the relative flags and arguments, as well as all interactions with the file system, registry, network, and other key elements of the operating system. The final dataset contains only the dynamic analysis reports, without the original binaries.

Eduge news classification dataset provided by Bolorsoft LLC. Used to train the Eduge.mn production news classifier 75K news articles in 9 categories: урлаг соёл, эдийн засаг, эрүүл мэнд, хууль, улс төр, спорт, технологи, боловсрол and байгал орчин Download train/test data via: train test