SimuGPT/2bus-delete-block-all-formats dataset hosted on Hugging Face and contributed by the HF Datasets community

We introduced the task of acoustic question answering (AQA) in https://arxiv.org/abs/1811.10561.A second version of the dataset was introduced in https://arxiv.org/abs/2106.06147This dataset aim to promote research in the acoustic reasoning area.It comprise Acoustic Scenes and multiple questions/answers for each of them.Each question is accompanied by a functional program which describe the reasoning steps needed in order to answer it. The dataset is constitued is separated in 3 sets :

First results based in the All-Clear workshop dataset [1] used on the zooniverse's Sunspotter project. Volunteers had to choose the most complex active region of a pair based on a random selection of the least classified images within each binned group.

The dataset is composed of four files:

lookup_timesfits.csv: lists the filenames and the date of the data acquisition.

lookup_properties.csv: lists the properties about the active region observed in each frame to be classified. Some of the properties as measured by SMART [2]

classifications.csv: lists each classification made by the volunteers.

rankings.csv: lists the final ranking on complexity.

The score provided on the rankings file follows the Elo rating system. However, a new score following other selection mechanism is possible using the data available on the classification file.

Though the user's information has been removed, the classifications keep an index to differentiate classifications made by different users.

Some software to ingest the tables into a sqlite database and to obtain some preliminary results are available on GitHub.

[1] DOI: 10.3847/0004-637X/829/2/89

[2] DOI: 10.1016/j.asr.2010.06.024

If there exist changes in the dataset, it is recommended to delete all files in this folder and execute the main function from the start.

ERASE Trash Detection

## Overview

ERASE  Trash Detection is a dataset for object detection tasks - it contains Recyclable Trash annotations for 2,521 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset of 1158 aged domains 20+ years old scheduled for deletion on 2025-06-03. These domains can be acquired through domain backorder services.

Dataset of 545 aged .ORG domains 10-20 years old scheduled for deletion on 2025-06-02. These domains can be acquired through domain backorder services.

Dataset of 3000 aged .COM domains 10-20 years old scheduled for deletion on 2025-06-04 (Part 2 of 3). These domains can be acquired through domain backorder services.

CLEAR is a continual image classification benchmark dataset with a natural temporal evolution of visual concepts in the real world that spans a decade (2004-2014). CLEAR is built from existing large-scale image collections (YFCC100M) through a novel and scalable low-cost approach to visio-linguistic dataset curation. The pipeline makes use of pretrained vision language models (e.g. CLIP) to interactively build labeled datasets, which are further validated with crowd-sourcing to remove errors and even inappropriate images (hidden in original YFCC100M). The major strength of CLEAR over prior CL benchmarks is the smooth temporal evolution of visual concepts with real-world imagery, including both high-quality labeled data along with abundant unlabeled samples per time period for continual semi-supervised learning.