The peer-reviewed paper of AWARE dataset is published in ASEW 2021, and can be accessed through: http://doi.org/10.1109/ASEW52652.2021.00049. Kindly cite this paper when using AWARE dataset. Aspect-Based Sentiment Analysis (ABSA) aims to identify the opinion (sentiment) with respect to a specific aspect. Since there is a lack of smartphone apps reviews dataset that is annotated to support the ABSA task, we present AWARE: ABSA Warehouse of Apps REviews. AWARE contains apps reviews from three different domains (Productivity, Social Networking, and Games), as each domain has its distinct functionalities and audience. Each sentence is annotated with three labels, as follows: Aspect Term: a term that exists in the sentence and describes an aspect of the app that is expressed by the sentiment. A term value of “N/A” means that the term is not explicitly mentioned in the sentence. Aspect Category: one of the pre-defined set of domain-specific categories that represent an aspect of the app (e.g., security, usability, etc.). Sentiment: positive or negative. Note: games domain does not contain aspect terms. We provide a comprehensive dataset of 11323 sentences from the three domains, where each sentence is additionally annotated with a Boolean value indicating whether the sentence expresses a positive/negative opinion. In addition, we provide three separate datasets, one for each domain, containing only sentences that express opinions. The file named “AWARE_metadata.csv” contains a description of the dataset’s columns. How AWARE can be used? We designed AWARE such that it can be used to serve various tasks. The tasks can be, but are not limited to: Sentiment Analysis. Aspect Term Extraction. Aspect Category Classification. Aspect Sentiment Analysis. Explicit/Implicit Aspect Term Classification. Opinion/Not-Opinion Classification. Furthermore, researchers can experiment with and investigate the effects of different domains on users' feedback. Source: AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation

It contains manually verified 183K question-answer pairs about more than 18K persons and 24K images. The questions in this dataset require multi-entity, multi-relation and multi-hop reasoning over KG to arrive at an answer. To enable visual named entity linking, it also provides a support set containing reference images of 69K persons harvested from Wikidata as part of the dataset.

ROAD is designed to test an autonomous vehicle's ability to detect road events, defined as triplets composed by an active agent, the action(s) it performs and the corresponding scene locations. ROAD comprises videos originally from the Oxford RobotCar Dataset, annotated with bounding boxes showing the location in the image plane of each road event.

The REALY benchmark aims to introduce a region-aware evaluation pipeline to measure the fine-grained normalized mean square error (NMSE) of 3D face reconstruction methods from under-controlled image sets. Given the reconstructed mesh from the 2D image in REALY by a specific method, the REALY benchmark calculates the similarity of ground-truth scans on four regions (nose, mouth, forehead, cheek) with the predicted mesh.

The Audio Visual Scene-Aware Dialog (AVSD) dataset, or DSTC7 Track 3, is a audio-visual dataset for dialogue understanding. The goal with the dataset and track was to design systems to generate responses in a dialog about a video, given the dialog history and audio-visual content of the video. Source: The Eighth Dialog System Technology Challenge Image Source: http://workshop.colips.org/dstc7/papers/DSTC7_Task_3_overview_paper.pdf

For benchmarking, please refer to its variant UPFD-POL and UPFD-GOS. The dataset has been integrated with Pytorch Geometric (PyG) and Deep Graph Library (DGL). You can load the dataset after installing the latest versions of PyG or DGL. The UPFD dataset includes two sets of tree-structured graphs curated for evaluating binary graph classification, graph anomaly detection, and fake/real news detection tasks. The dataset is dumped in the form of Pytorch-Geometric dataset object. You can easily load the data and run various GNN models using PyG. The dataset includes fake&real news propagation (retweet) networks on Twitter built according to fact-check information from Politifact and Gossipcop. The news retweet graphs were originally extracted by FakeNewsNet. Each graph is a hierarchical tree-structured graph where the root node represents the news; the leaf nodes are Twitter users who retweeted the root news. A user node has an edge to the news node if he/she retweeted the news tweet. Two user nodes have an edge if one user retweeted the news tweet from the other user. We crawled near 20 million historical tweets from users who participated in fake news propagation in FakeNewsNet to generate node features in the dataset. We incorporate four node feature types in the dataset, the 768-dimensional bert and 300-dimensional spacy features are encoded using pretrained BERT and spaCy word2vec, respectively. The 10-dimensional profile feature is obtained from a Twitter account's profile. You can refer to profile_feature.py for profile feature extraction. The 310-dimensional content feature is composed of a 300-dimensional user comment word2vec (spaCy) embedding plus a 10-dimensional profile feature. The dataset statistics is shown below: | Data | #Graphs | #Fake News| #Total Nodes | #Total Edges | #Avg. Nodes per Graph | |-------|--------|--------|--------|--------|--------| | Politifact | 314 | 157 | 41,054 | 40,740 | 131 | | Gossipcop | 5464 | 2732 | 314,262 | 308,798 | 58 | Please refer to the paper for more details about the UPFD dataset. Due to the Twitter policy, we could not release the crawled user's historical tweets publicly. To get the corresponding Twitter user information, you can refer to the news lists under \data in our github repo and map the news id to FakeNewsNet. Then, you can crawl the user information by following the instruction on FakeNewsNet. In the UPFD project, we use Tweepy and Twitter Developer API to get the user information.

Syntax-Aware Fill-in-the-Middle (SAFIM) is a benchmark for evaluating Large Language Models (LLMs) on the code Fill-in-the-Middle (FIM) task. SAFIM has three subtasks: Algorithmic Block Completion, Control-Flow Expression Completion, and API Function Call Completion. SAFIM is sourced from code submitted from April 2022 to January 2023 to minimize the impact of data contamination on evaluation results. - Authors: Linyuan Gong, Sida Wang, Mostafa Elhoushi, Alvin Cheung - Paper: https://arxiv.org/abs/2403.04814 - Huggingface Dataset: https://huggingface.co/datasets/gonglinyuan/safim - Leaderboard: https://safimbenchmark.com - Code & Submission Instructions: https://github.com/gonglinyuan/safim The SAFIM benchmark is partially derived from problem descriptions and code solutions from https://codeforces.com. According to the license of CodeForces, you may publish the texts of Codeforces problems in any open sources, but you must preserve a direct link to the site.

FRMT is a dataset and evaluation benchmark for Few-shot Region-aware Machine Translation, a type of style-targeted translation. The dataset consists of human translations of a few thousand English Wikipedia sentences into regional variants of Portuguese and Mandarin. Source documents are selected to enable detailed analysis of phenomena of interest, including lexically distinct terms and distractor terms. Source:

BAFMD contains images posted on Twitter during the pandemic from around the world with more images from underrepresented race and age groups to mitigate the problem for the face mask detection task. Source: Bias-Aware Face Mask Detection Dataset Image Source: https://arxiv.org/pdf/2211.01207v1.pdf

EMOTyDA is a multimodal Emotion aware Dialogue Act dataset collected from open-sourced dialogue datasets.

The Gossipcop variant of the UPFD dataset for benchmarking. Please refer to the UPFD dataset for more details of the data.

JamALT is a revision of the JamendoLyrics dataset (80 songs in 4 languages), adapted for use as an automatic lyrics transcription (ALT) benchmark. The lyrics have been revised according to the newly compiled annotation guide, which include rules about spelling, punctuation, and formatting. The audio is identical to the JamendoLyrics dataset. However, only 79 songs are included, as one of the 20 French songs has been removed due to concerns about potentially harmful content.

The PolitiFact variant of the UPFD dataset for benchmarking. Please refer to the UPFD dataset for more details of the data.

CoRAL is a language and culturally aware Croatian Abusive dataset covering phenomena of implicitness and reliance on local and global context.

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Dataset of Stack Overflow questions about Terraform with cost-related keywords.

Dataset of commit messages and issues containing evidence of cost awareness.

DA2 is a large-scale Dual-Arm Dexterity-Aware (DA2) grasp dataset, including a total of about 9M parallel-jaw grasp pairs for more than 6000 different meshes. The grasp pairs are labeled with multiple grasp dexterity measures by fully analyzing the grasp matrix. The dataset constitutes a standardized data source filling the gap in vision-guided dual-arm grasping of arbitrary objects.

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

The DAVIDE dataset consists of synchronized blurred, depth, and sharp videos. The dataset comprises 90 video sequences divided into 69 for training, 7 for validation, and 14 for testing. The test set includes annotations of seven content attributes categorized by: 1) environment (indoor/outdoor), 2) motion (camera motion/camera and object motion), and 3) scene proximity (close/mid/far). These annotations aim to facilitate further analysis into scenarios where depth information could be more beneficial.

We present the first large-scale energy-aware benchmark that allows studying AutoML methods to achieve better trade-offs between performance and search energy consumption, named EA-HAS-Bench. EA-HAS-Bench provides a large-scale architecture/hyperparameter joint search space, covering diversified configurations related to energy consumption. Furthermore, we propose a novel surrogate model specially designed for large joint search space, which proposes a Bezier curve-based model to predict learning curves with unlimited shape and length.

Risk-Aware Planning is a dataset that contains the overhead images and their semantic segmentation captured by a drone from the CityEnviron environment in AirSim simulator.

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The Situational Awareness Database for Instruct-Tuning (SAD-Instruct) is a dataset for dynamic task guidance. It contains situationally aware instructions for performing everyday tasks or completing scenarios in 3D environments. The dataset provides step-by-step instructions for these scenarios grounded in the situation's context. This context is defined through a scenario-specific scene graph that captures the objects, attributes, and environmental relations. The dataset is designed to enable research in grounded language learning, instruction following, and situated dialogue.

TeleSim is a network-aware hardware-in-the-loop dataset designed to evaluate the performance of telerobotic systems under varying network conditions. It includes 300 fine-manipulation trials using a 6-DoF robotic arm and simulated networks in OMNeT++. Each trial captures: - Network configuration (bandwidth, latency, jitter, packet loss) - Measured network metrics (throughput, delay, jitter, real packet loss) - Video quality (PSNR, SSIM) - Task performance (completion time, success rate) Motivation: Existing teleoperation datasets often ignore the impact of real-world network degradation. TeleSim fills this gap by systematically evaluating how adverse conditions affect visual perception and task execution. Use cases: - Benchmarking teleoperation performance - Designing adaptive control algorithms - Evaluating network resilience of robotic systems

The volumetric representation of human interactions is one of the fundamental domains in the development of immersive media productions and telecommunication applications. Particularly in the context of the rapid advancement of Extended Reality (XR) applications, this volumetric data has proven to be an essential technology for future XR elaboration. In this work, we present a new multimodal database to help advance the development of immersive technologies. Our proposed database provides ethically compliant and diverse volumetric data, in particular 27 participants displaying posed facial expressions and subtle body movements while speaking, plus 11 participants wearing head-mounted displays (HMDs). The recording system consists of a volumetric capture (VoCap) studio, including 31 synchronized modules with 62 RGB cameras and 31 depth cameras. In addition to textured meshes, point clouds, and multi-view RGB-D data, we use one Lytro Illum camera for providing light field (LF) data simultaneously. Finally, we also provide an evaluation of our dataset employment with regard to the tasks of facial expression classification, HMDs removal, and point cloud reconstruction. The dataset can be helpful in the evaluation and performance testing of various XR algorithms, including but not limited to facial expression recognition and reconstruction, facial reenactment, and volumetric video. HEADSET and its all associated raw data and license agreement will be publicly available for research purposes.

The dataset contains 105,811 information-seeking conversations converted from MS MARCO. This dataset is constructed to relieve the data scarcity problem of conversational search to an extent. Considering the multi-intent problem and contextual information, this large-scale intent-oriented and context-aware dataset is automatically constructed based on the web search session data in MS MARCO. This dataset can be used to train and evaluate conversational search systems.