dataset_name stringlengths 2 128 | description stringlengths 1 9.7k | prompt stringlengths 59 185 |
|---|---|---|
ASR-ETeleCSC: An English Telephone Conversational Speech Corpus | This open-source dataset consists of 5.04 hours of transcribed English conversational speech beyond telephony, where 13 conversations were contained. | Provide a detailed description of the following dataset: ASR-ETeleCSC: An English Telephone Conversational Speech Corpus |
CaFFe | The temporal variability in calving front positions of marine-terminating glaciers permits inference on the frontal ablation. Frontal ablation, the sum of the calving rate and the melt rate at the terminus, significantly contributes to the mass balance of glaciers. Therefore, the glacier area has been declared as an Essential Climate Variable product by the World Meteorological Organization. The presented dataset provides the necessary information for training deep learning techniques to automate the process of calving front delineation. The dataset includes Synthetic Aperture Radar (SAR) images of seven glaciers distributed around the globe. Five of them are located in Antarctica: Crane, Dinsmoore-Bombardier-Edgeworth, Mapple, Jorum and the Sjörgen-Inlet Glacier. The remaining glaciers are the Jakobshavn Isbrae Glacier in Greenland and the Columbia Glacier in Alaska. Several images were taken for each glacier, forming a time series. The time series lie in the time span between 1995 and 2020. The images have different spatial resolutions, as they were captured by different satellites. The satellites used are Sentinel-1, TerraSAR-X, TanDEM-X, ENVISAT, European Remote Sensing Satellite 1&2, ALOS PALSAR, and RADARSAT-1. Along with the SAR images, two types of labels are provided so that deep learning techniques can be trained in a supervised manner. One label provides the position of the calving front. The other label shows the position of different landscape regions comprising glacier, rock outcrop, ocean including ice-melange, and an area where no information is available consisting of SAR shadows, layover regions, and areas outside the swath. The two labels allow different approaches to calving front delineation, as the calving front can be extracted from landscape region predictions during post-processing. As additional information for post-processing, the dataset includes bounding boxes for the dynamic calving front for each image. This bounding box excludes nearly static calving fronts also visible in the images, which are not of interest but would still be predicted as calving fronts by deep learning techniques. Hence, all front predictions outside this bounding box can be excluded during post-processing. To ensure the generalizability of the trained deep learning techniques to new unseen glaciers, the dataset is split into a training and an out-of-sample test set. The latter shall only be used to test the performance of the trained front delineation algorithm after all hyperparameters are optimized. The test set comprises the time series of Mapple and Columbia. More information on the dataset and how to use it can be found in the related paper.
- The dataset has four subfolders: bounding_boxes, fronts, sar_images, and zones.
- The bounding_boxes folder includes the bounding boxes for each image as separate text files.
- The fronts, sar_images, and zones folders are each divided into test and train subfolders.
- The sar_images folder holds the SAR images for training and testing as png files.
- The fronts and zones folders include the labels (fronts - calving front position and zones - position of landscape regions) for each of the images in the sar_images folder.
- The labels are png files with the same size and location as the corresponding SAR image.
- The naming scheme of all files is: Glacier_Date_Satellite_SpatialResolutionInMeter_QualityFactor_Orbit(_Modality).png
- The modality gives the type of label (front or zones).
- The quality factor (with 1 being the best and 6 the worst) is based on the expert's opinion, who labelled the data.
- Images with a quality factor of 6 were hard to interpret for the expert. Thus, the labels for these images may contain some inaccuracies.
### Dataset
Gourmelon, Nora; Seehaus, Thorsten; Braun, Matthias Holger; Maier, Andreas; Christlein, Vincent (2022): CaFFe (CAlving Fronts and where to Find thEm: a benchmark dataset and methodology for automatic glacier calving front extraction from sar imagery). PANGAEA, https://doi.org/10.1594/PANGAEA.940950
### Paper
Gourmelon, N., Seehaus, T., Braun, M., Maier, A., and Christlein, V.: Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery, Earth Syst. Sci. Data, 14, 4287–4313, https://doi.org/10.5194/essd-14-4287-2022, 2022. | Provide a detailed description of the following dataset: CaFFe |
CQ500 | Non-contrast head/brain CT of patients with head trauma or stroke symptoms. | Provide a detailed description of the following dataset: CQ500 |
ASR-RAMC-BIGCCSC: A CHINESE CONVERSATIONAL SPEECH CORPUS | A Rich Annotated Mandarin Conversational (RAMC) Speech Dataset, including 180 hours of Mandarin Chinese dialogue, 150, 10 and 20 hours for the training set, development set and test set respectively.
It contains 351 multi-turn dialogues, each of which is a coherent and compact conversation centered around one theme.
It covers 15 topics, including humanities, entertainment, sports, military, finance, religion, family life, politics, education, digital devices, environment, science, professional development, art and ordinary life.
It is suitable for exploring speech processing techniques in dialog scenarios. | Provide a detailed description of the following dataset: ASR-RAMC-BIGCCSC: A CHINESE CONVERSATIONAL SPEECH CORPUS |
Poisoned Water Detection using Smartphone embedded WiFi CSI data and Machine Learning Algorithms | This repository contains a dataset and machine learning algorithms to detect poisoned water from clean water via using equivalent Smartphone embedded Wi-Fi CSI data.
The machine learning algorithm (including k-NN, SVM, LSTM, and Ensemble) are written in MATLAB code!
The testbed is shown in img/p1.jpg
The equivalent Smartphone embedded Wi-Fi chipsets are shown in img/p2.jpg
The amplitude and phase measurements of the Wi-Fi CSI data are selected as vector features!
Each of these vectors includes 64 feature values, i.e. the amplitude vector has 64 values, and the phase vector has 64 values.
The method provides accurate classification results starting from 82% (via k-NN) up to 92% (via Ensemble)
This research and dataset are supported and created by a group of researchers from Koya University (https://www.koyauniversity.org/)
Note, any researchers or authors should cite the article, by using the following:
Maghdid, H.S., Salah, S.R., Hawre, A.T., Bayram, H.M., Sabir, A.T., Kaka, K.N., Taher, S.G., Abdulrahman, L.S., Al-Talabani, A.K., Asaad, S.M., & Asaad, A.T. (2023). A Novel Poisoned Water Detection Method Using Smartphone Embedded Wi-Fi Technology and Machine Learning Algorithms. | Provide a detailed description of the following dataset: Poisoned Water Detection using Smartphone embedded WiFi CSI data and Machine Learning Algorithms |
FS-Mol | A Few-Shot Learning Dataset of Molecules. | Provide a detailed description of the following dataset: FS-Mol |
YouTube Driving | YouTube Driving Dataset contains a massive amount of real-world driving frames with various conditions, from different weather, different regions, to diverse scene types | Provide a detailed description of the following dataset: YouTube Driving |
CCTSDB2021 | Traffic signs are one of the most important information that guide cars to travel, and the detection of traffic signs is an important component of autonomous driving and intelligent transportation systems. Constructing a traffic sign dataset with many samples and sufficient attribute categories will promote the development of traffic sign detection research. In this paper, we propose a new Chinese traffic sign detection benchmark, which adds more than 4,000 real traffic scene images and corresponding detailed annotations based on our CCTSDB 2017, and replaces many original easily-detected images with difficult samples to adapt to the complex and changing detection environment. Due to the increase of the number of difficult samples, the new benchmark can improve the robustness of the detection network to some extent compared to the old version. At the same time, we create new dedicated test sets and categorize them according to three aspects: category meanings, sign sizes, and weather conditions. Finally, we present a comprehensive evaluation of nine classic traffic sign detection algorithms on the new benchmark. Our proposed benchmark can help determine the future research direction of the algorithm and develop a more precise traffic sign detection algorithm with higher robustness and real-time performance. | Provide a detailed description of the following dataset: CCTSDB2021 |
PACE 2022 Exact | This is the set of graphs used in the [PACE 2022 challenge](https://pacechallenge.org/2022/) for computing the [Directed Feedback Vertex Set](https://en.wikipedia.org/wiki/Feedback_vertex_set), from the Exact track. It consists of 200 labelled directed graphs. The graphs range in size up to from N=512 up to N=131072 vertices, and up to 1315170 edges. The graphs are mostly not symmetric (an edge form u->v does not imply an edge from v->u), although some are symmetric. The graph labels are integers ranging from 1 to N.
There is the related [PACE 2022 Heuristic](https://paperswithcode.com/dataset/pace-2022-heuristic) dataset, which allowed for approximate solutions (feedback vertex sets that were not necessarily of minimum size). Those graphs are generally larger and denser, as approximate solutions were still accepted.
The [data format](https://pacechallenge.org/2022/tracks/#input-format) begins with one line `N E 0`, where N is the number of vertices, E is the number of edges, and 0 is the literal integer zero. The N subsequent lines are each a space-separated list of integers, such as `2 5 11 19`. If that appeared on line number 1 (the first after `N E 0`), it would indicate that there are edges from vertex 1 to each of the vertices 2, 5, 11, and 19. Some lines are blank, and these indicates vertices with outdegree zero. An example graph would be
```
4 4 0
2 3
3
1
```
The dataset can be downloaded [here](https://heibox.uni-heidelberg.de/f/75c18a4d83b642db9a58/?dl=1). The 100 instances that were available for public testing are precisely the odd-numbered ones in that link; the public instances can be downloaded on their own [here](https://heibox.uni-heidelberg.de/f/be4337d9e4234bca8606/?dl=1). | Provide a detailed description of the following dataset: PACE 2022 Exact |
PACE 2022 Heuristic | This is the set of graphs used in the [PACE 2022 challenge](https://pacechallenge.org/2022/) for computing the [Directed Feedback Vertex Set](https://en.wikipedia.org/wiki/Feedback_vertex_set), from the Heuristic track. It consists of 200 labelled directed graphs. The graphs are mostly not symmetric (an edge form u->v does not imply an edge from v->u), although some are symmetric. The graph labels are integers ranging from 1 to N.
There is the related [PACE 2022 Exact](https://paperswithcode.com/dataset/pace-2022-exact) dataset, which was for exact computation; those graphs are generally smaller and sparser, as only exact solutions were accepted.
The [data format](https://pacechallenge.org/2022/tracks/#input-format) begins with one line `N E 0`, where N is the number of vertices, E is the number of edges, and 0 is the literal integer zero. The N subsequent lines are each a space-separated list of integers, such as `2 5 11 19`. If that appeared on line number 1 (the first after `N E 0`), it would indicate that there are edges from vertex 1 to each of the vertices 2, 5, 11, and 19. Some lines are blank, and these indicates vertices with outdegree zero. An example graph would be
```
4 4 0
2 3
3
1
```
The dataset can be downloaded [here](https://heibox.uni-heidelberg.de/f/97634323e3cb4aab8291/?dl=1). The 100 instances that were available for public testing are precisely the odd-numbered ones in that link; the public instances can be downloaded on their own [here](https://heibox.uni-heidelberg.de/f/4fc21bd9748140bd8307/?dl=1). | Provide a detailed description of the following dataset: PACE 2022 Heuristic |
DigestPath | Introduced by Da et al. in [DigestPath: a Benchmark Dataset with Challenge Review for the Pathological Detection and Segmentation of Digestive-System](https://github.com/bupt-ai-cz/CAC-UNet-DigestPath2019/blob/main/papers/DigestPath-a-Benchmark-Dataset-with-Challenge-Review.pdf)
### [Grand-Challenge Page](https://digestpath2019.grand-challenge.org/)
### 1. Signet ring cell dataset
Signet ring cell carcinoma is a type of rare adenocarcinoma with poor prognosis. Early detection of such cells leads to huge improvement of patients' survival rate. However, there is no existing public dataset with annotations for studying the problem of signet ring cell detection.
This dataset has positive samples and negative samples. Training positive samples contain 77 images from 20 WSIs, with cell bounding boxes written in xml. Training negative samples contain 378 images from 79 WSIs.These negative WSIs have no signet ring, but could contain other kinds of tumor cells. Each signet ring cell is labeled by experienced pathologists with a rectangle bounding box tightly surrounding the cell. Each image is of size 2000X2000. The training images are from 2 organs, including gastric mucosa and intestine. Because of the difficulty of manual annotation, there exist some signet ring cells who are missed by pathologists. In other words, this dataset is a noisy dataset with its positive images not fully annotated.
All whole slide images were stained by hematoxylin and eosin and scanned at X40.
### 2. Colonoscopy tissue segment dataset
Colonoscopy pathology examination can find cells of early-stage colon tumor from small tissue slices. Pathologists need to daily examine hundreds of tissue slices, which is a time consuming and exhausting work. Here we propose a challenge task on automatic colonoscopy tissue segmentation and screening, aiming at automatic lesion segmentation and classification of the whole tissue (benign vs. malignant).
This dataset has positive samples and negative samples. Training positive samples contain 250 images of tissue from 93 WSIs, with pixel-level annotation in jpg format, where 0 means background and 255 for foreground (malignant lesion). You could simply get binary mask by a threshold 128. Training negative samples contain 410 images of tissue from 231 WSI. This negative images have no annotation because they don't have any malignant lesion.
The average size of all images are of 5000x5000 pixels, some of them are extremely huge. We will also provide another 152 patients' 212 tissues as the testing set, in which 90 images from 65 patients contain lesion. All whole slide images were stained by hematoxylin and eosin and scanned at X20.
Sign the DATABASE USE AGREEMENT first and download the dataset at the homepage!
```
Da Q, Huang X, Li Z, et al. DigestPath: a Benchmark Dataset with Challenge Review for the
Pathological Detection and Segmentation of Digestive-System[J].
Medical Image Analysis, 2022: 102485.
（https://doi.org/10.1016/j.media.2022.102485）
``` | Provide a detailed description of the following dataset: DigestPath |
AfriSenti | AfriSenti is the largest sentiment analysis dataset for under-represented African languages, covering 110,000+ annotated tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba). | Provide a detailed description of the following dataset: AfriSenti |
Jet Flavor dataset | Dataset for 'Jet Flavor Classification in High-Energy Physics with Deep Neural Networks' | Provide a detailed description of the following dataset: Jet Flavor dataset |
SPAVE-28G on NSF POWDER | This paper details the design of an autonomous alignment and tracking platform to mechanically steer directional horn antennas in a sliding correlator channel sounder setup for 28 GHz V2X propagation modeling. A pan-and-tilt subsystem facilitates uninhibited rotational mobility along the yaw and pitch axes, driven by open-loop servo units and orchestrated via inertial motion controllers. A geo-positioning subsystem augmented in accuracy by real-time kinematics enables navigation events to be shared between a transmitter and receiver over an Apache Kafka messaging middleware framework with fault tolerance. Herein, our system demonstrates a 3D geo-positioning accuracy of 17 cm, an average principal axes positioning accuracy of 1.1 degrees, and an average tracking response time of 27.8 ms. Crucially, fully autonomous antenna alignment and tracking facilitates continuous series of measurements, a unique yet critical necessity for millimeter wave channel modeling in vehicular networks. The power-delay profiles, collected along routes spanning urban and suburban neighborhoods on the NSF POWDER testbed, are used in pathloss evaluations involving the 3GPP TR38.901 and ITU M.2135 standards. Empirically, we demonstrate that these models fail to accurately capture the 28 GHz pathloss behavior in urban foliage and suburban radio environments. In addition to RMS direction-spread analyses for angles-of-arrival via the SAGE algorithm, we perform signal decoherence studies wherein we derive exponential characteristics of the spatial autocorrelation coefficient under distance and alignment effects. | Provide a detailed description of the following dataset: SPAVE-28G on NSF POWDER |
GSV-Cities | GSV-Cities is a large-scale dataset for training deep neural network for the task of Visual Place Recognition.
The dataset contains more than **530k** images:
* There are more than **62k** different places, spread across multiple cities around the globe.
* Each place is depited by at least 4 images (up to 20 images).
* All places are physically distant (at least 100 meters between any pair of places). | Provide a detailed description of the following dataset: GSV-Cities |
A collection of 131 CT datasets of pieces of modeling clay containing stones | This dataset contains a collection of 131 X-ray CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted, retrieved in the FleX-ray lab at CWI. The dataset consists of 5 parts. It is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection". The dataset can be used to set up other CT-based experiments concerning similar objects with variations in shape and composition. | Provide a detailed description of the following dataset: A collection of 131 CT datasets of pieces of modeling clay containing stones |
A collection of X-ray projections of 131 pieces of modeling clay containing stones for machine learning-driven object detection | This dataset contains a collection of 235800 X-ray projections of 131 pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The dataset is intended as an extensive and easy-to-use training dataset for supervised machine learning driven object detection. The ground truth locations of the stones are included. | Provide a detailed description of the following dataset: A collection of X-ray projections of 131 pieces of modeling clay containing stones for machine learning-driven object detection |
DIVOTrack | **DIVOTrack** is a cross-view multi-object tracking dataset for DIVerse Open scenes with dense tracking pedestrians in realistic and non-experimental environments. DIVOTrack has ten distinct scenarios and 550 cross-view tracks. | Provide a detailed description of the following dataset: DIVOTrack |
TT100K | Trainging and testing data: The original training set includes 6105 images, and the original testing set includes 3071 images.
Description: Although promising results have been achieved in the areas of traffic-sign detection and classification, few works have provided simultaneous solutions to these two tasks for realistic real world images. We make two contributions to this problem. Firstly, we have created a large traffic-sign benchmark from 100000 Tencent Street View panoramas, going beyond previous benchmarks. We call this benchmark Tsinghua-Tencent 100K. It provides 100000 images containing 30000 traffic-sign instances. These images cover large variations in illuminance and weather conditions. Each traffic-sign in the benchmark is annotated with a class label, its bounding box and pixel mask. Secondly, we demonstrate how a robust end-to-end convolutional neural network (CNN) can simultaneously detect and classify traffic-signs. Most previous CNN image processing solutions target objects that occupy a large proportion of an image, and such networks do not work well for target objects occupying only a small fraction of an image like the traffic-signs here. Experimental results show the robustness of our network and its superiority to alternatives. The benchmark, source code and the CNN model introduced in this paper is publicly available. | Provide a detailed description of the following dataset: TT100K |
jazznet | **jazznet** is a dataset of piano patterns for music audio machine learning research. The dataset comprises chords, arpeggios, scales, and chord progressions in all keys of an 88-key piano and in all the inversions, for a total of 162520 labeled piano patterns, resulting in 95GB of data and more than 26k hours of audio. The data is also accompanied by Python scripts to enable the easy generation of new piano patterns beyond those present in the dataset. The data is broken down into small, medium, and large subsets, comprising 21516, 30328, and 52360 patterns, respectively (with all the chords, arpeggios, and scales being present in all subsets). | Provide a detailed description of the following dataset: jazznet |
MO-Gymnasium | MO-Gymnasium is an open source Python library for developing and comparing multi-objective reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API. Essentially, the environments follow the standard [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) API, but return vectorized rewards as numpy arrays. | Provide a detailed description of the following dataset: MO-Gymnasium |
S-VED | The Sacrobosco Visual Elements Dataset (S-VED) is derived from 359 Sphaera editions, centered on the Tractatus de sphaera by Johannes de Sacrobosco (—1256) and printed between 1472 and 1650. The Sphaera editions were primarily used to teach geocentric astronomy to university students across Europe. Their visual elements, therefore, played an essential role in visualizing the ideas, messages, and concepts that the texts transmitted. As a precondition for studying the relation between text and visual elements, a time-consuming image labelling process was conducted as part of “The Sphere” project (https://sphaera.mpiwg-berlin.mpg.de) in order to extract and label the visual elements from the 76,000 pages of the corpus. This process resulted in the creation of the Extended Sacrobosco Visual Elements Dataset (S-VED𝑋) of which S-VED is a subset of. Due to copyright reasons only S-VED is made publicly available. S-VED consists of 4000 pages of which 2040 contain a total of 2927 visual elements. The visual elements are defined by bounding boxes and labels within a CSV file. For more information on the Sphaera corpus, feel free to check the project’s database at http://db.sphaera.mpiwg-berlin.mpg.de/. | Provide a detailed description of the following dataset: S-VED |
PACE 2018 Steiner Tree | This is the set of instances use in the [PACE 2018 competition](https://pacechallenge.org/2018/), of optimal Steiner Tree computation. The instances are grouped into three tracks of 200 instances each, except for the third track which is only 199 instances. Each instance is an undirected graph.
Track 1 is the "exact with low number of terminals" track, Track 2 is the "exact with low treewidth track", and Track 3 is the heuristic track. The exact tracks were intended to test solvers that need to provide a provably optimal solution; the heuristic track was intended for solvers that produce good but possibly suboptimal solutions. Details of the tracks and problem setup are on the [PACE problem description](https://pacechallenge.org/2018/steiner-tree/) page. The data format is specified in Appendix A of that page.
Graphs have sizes (number of vertices) ranging up to several thousand for exact tracks (1 and 2), and up to tens of thousands for the heuristic track. The exact tracks are typically very sparse; some of the heuristic instances are dense.
The official data download is [on the PACE GitHub](https://github.com/PACE-challenge/SteinerTree-PACE-2018-instances). | Provide a detailed description of the following dataset: PACE 2018 Steiner Tree |
PACE 2016 Feedback Vertex Set | This is the dataset used in the [PACE 2016 challenge](https://pacechallenge.org/2016/), Track B, which was computing minimal Feedback Vertex Set. This competition focused on exact solutions, i.e. provably minimal feedback vertex sets (and no heuristic solutions). This should not be confused with the PACE 2022 challenge, which focused on _directed_ feedback vertex set, and has its own entries on PapersWithCode ([exact](https://paperswithcode.com/dataset/pace-2022-exact) and [heuristic](https://paperswithcode.com/dataset/pace-2022-heuristic)).
The dataset can be downloaded [here](https://github.com/ckomus/PACE-fvs), and includes 100 instances that were released for practice (the `public/` folder) and 100 instances that were kept private (`hidden/`) until the competition evaluation. All 200 were used in the final evaluation. Each instance is an undirected graph, one edge per line, in the format `a b` indicating an edge between vertices `a` and `b`. Vertices are 1-indexed.
Final results of the competition were reported [in the PACE report](https://doi.org/10.4230/LIPIcs.IPEC.2016.30), and additional analysis of some top solutions was done [independently](https://arxiv.org/abs/1803.00925) by Kiljan and Pilipczuk. | Provide a detailed description of the following dataset: PACE 2016 Feedback Vertex Set |
Lot-insts | LoT-insts contains over 25k classes whose frequencies are naturally long-tail distributed. Its test set from four different subsets: many-, medium-, and few-shot sets, as well as a zero-shot open set. To our best knowledge, this is the first natural language dataset that focuses on this long-tailed and open classification problem. | Provide a detailed description of the following dataset: Lot-insts |
Weather2K | A multivariate spatio-temporal benchmark dataset for meteorological forecasting based on real-time observation data from ground weather stations. | Provide a detailed description of the following dataset: Weather2K |
ImageNet C-OOD (class-out-of-distribution) | This dataset was presented as part of the ICLR 2023 paper 𝘈 𝘧𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬 𝘧𝘰𝘳 𝘣𝘦𝘯𝘤𝘩𝘮𝘢𝘳𝘬𝘪𝘯𝘨 𝘊𝘭𝘢𝘴𝘴-𝘰𝘶𝘵-𝘰𝘧-𝘥𝘪𝘴𝘵𝘳𝘪𝘣𝘶𝘵𝘪𝘰𝘯 𝘥𝘦𝘵𝘦𝘤𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘪𝘵𝘴 𝘢𝘱𝘱𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘵𝘰 𝘐𝘮𝘢𝘨𝘦𝘕𝘦𝘵.
It is a framework that, based on this dataset (a subset of the ImageNet-21k dataset) is able to generate a C-OOD (AKA open-set recognition) benchmark that covers a variety of difficulty levels. these benchmarks are tailored to the evaluated model. This approach provides a more accurate representation of the model’s own performance.
The resulting difficulty levels of our framework allow benchmarking with respect to the difficulty levels most relevant to the task. For example, for a task with a high tolerance for
risk (e.g., a task for an entertainment application), the performance of a model on a median difficulty level might be more important than on the hardest difficulty level (severity 10).
The opposite might be true for some applications with a low tolerance for risk (e.g., medical applications), for which one requires the best performance to be attained even if the OOD is very hard to detect (severity 10).
The paper in which the framework was introduced showed that detection algorithms do not always improve performance on all inputs equally, and could even hurt performance for specific difficulty levels and models. Choosing the combination of (model, detection algorithm) based only on the detection performance on all data may yield sub-optimal results for our specific desired level of difficulty. | Provide a detailed description of the following dataset: ImageNet C-OOD (class-out-of-distribution) |
Performance Improving Code Edits (PIE) | **PIE** stands for Performance Improving Code Edits. PIE contains trajectories of programs, where a programmer begins with an initial, slower version and iteratively
makes changes to improve the program’s performance. | Provide a detailed description of the following dataset: Performance Improving Code Edits (PIE) |
OPT | Accurately tracking the six degree-of-freedom pose of an object in real scenes is an important task in computer vision and augmented reality with numerous applications. Although a variety of algorithms for this task have been proposed, it remains difficult to evaluate existing methods in the literature as oftentimes different sequences are used and no large benchmark datasets close to real-world scenarios are available. In this paper, we present a large object pose tracking benchmark dataset consisting of RGB-D video sequences of 2D and 3D targets with ground-truth information. The videos are recorded under various lighting conditions, different motion patterns and speeds with the help of a programmable robotic arm. We present extensive quantitative evaluation results of the state-of-the-art methods on this benchmark dataset and discuss the potential research directions in this field. | Provide a detailed description of the following dataset: OPT |
VEDAI | VEDAI is a dataset for Vehicle Detection in Aerial Imagery, provided as a tool to benchmark automatic target recognition algorithms in unconstrained environments. The vehicles contained in the database, in addition of being small, exhibit different variabilities such as multiple orientations, lighting/shadowing changes, specularities or occlusions. Furthermore, each image is available in several spectral bands and resolutions. A precise experimental protocol is also given, ensuring that the experimental results obtained by different people can be properly reproduced and compared. We also give the performance of some baseline algorithms on this dataset, for different settings of these algorithms, to illustrate the difficulties of the task and provide baseline comparisons. | Provide a detailed description of the following dataset: VEDAI |
RTB | The Robot Tracking Benchmark (RTB) is a synthetic dataset that facilitates the quantitative evaluation of 3D tracking algorithms for multi-body objects. It was created using the procedural rendering pipeline BlenderProc. The dataset contains photo-realistic sequences with HDRi lighting and physically-based materials. Perfect ground truth annotations for camera and robot trajectories are provided in the BOP format. Many physical effects, such as motion blur, rolling shutter, and camera shaking, are accurately modeled to reflect real-world conditions. For each frame, four depth qualities exist to simulate sensors with different characteristics. While the first quality provides perfect ground truth, the second considers measurements with the distance-dependent noise characteristics of the Azure Kinect time-of-flight sensor. Finally, for the third and fourth quality, two stereo RGB images with and without a pattern from a simulated dot projector were rendered. Depth images were then reconstructed using Semi-Global Matching (SGM).
The benchmark features six robotic systems with different kinematics, ranging from simple open-chain and tree topologies to structures with complex closed kinematics. For each robotic system, three difficulty levels are provided: easy, medium, and hard. In all sequences, the kinematic system is in motion. While for easy sequences the camera is mostly static with respect to the robot, medium and hard sequences feature faster and shakier motions for both the robot and camera. Consequently, motion blur increases, which also reduces the quality of stereo matching. Finally, for each object, difficulty level, and depth image quality, 10 sequences with 150 frames are rendered. In total, this results in 108.000 frames that feature different kinematic structures, motion patterns, depth measurements, scenes, and lighting conditions. In summary, the Robot Tracking Benchmark allows to extensively measure, compare, and ablate the performance of multi-body tracking algorithms, which is essential for further progress in the field. | Provide a detailed description of the following dataset: RTB |
NNID | We build what we name the Nearly-Nested Image Datasets (NNID) such that each dataset owns images of the same dimension, and each dataset is issued from a cropped version of the images belonging to the dataset with the biggest dimensions. This last dataset is named mother dataset and the images are named mother images.
By using NNID we ensure that the development is the same in all of the datasets. We also impose, as an additional constraint, that the difficulty of each dataset is the same. By the same difficulty, we mean that the distribution of costs is the same whatever the dataset. This additional constraint implies a specific way to crop the images, and most importantly, ensure that the experimental results obtained between the various dimension will be comparable since the source cost distribution of each dataset is the same. With the NNID we are able to avoid any impact of the development or the difficulty, on the experimental results; All the datasets are very similar except for the dimension.
We use, as a mother dataset, the LSSD dataset. LSSD is a mix of RAW images from ALASKA#2, BOSS, StegoApp DB, Wesaturate, RAISE, and Dresden datasets and uses a modified development script issued from the Alaska competition.
Given the NNID and an embedding algorithm, we focus on the average accuracy obtained by a classifier for each dataset. In order to obtain the relative payload size to embed for each dimension, we go by a dichotomous method, by running, for each dimension, multiple detections until finding the desired accuracy. | Provide a detailed description of the following dataset: NNID |
Video Localized Narratives | **Video Localized Narratives** is a new form of multimodal video annotations connecting vision and language. The annotations are created from videos with Localized Narratives, capturing even complex events involving multiple actors interacting with each other and with several passive objects. It contains annotations of 20k videos of the OVIS, UVO, and Oops datasets, totalling 1.7M words. | Provide a detailed description of the following dataset: Video Localized Narratives |
ArtiFact | The ArtiFact dataset is a large-scale image dataset that aims to include a diverse collection of real and synthetic images from multiple categories, including Human/Human Faces, Animal/Animal Faces, Places, Vehicles, Art, and many other real-life objects. The dataset comprises 8 sources that were carefully chosen to ensure diversity and includes images synthesized from 25 distinct methods, including 13 GANs, 7 Diffusion, and 5 other miscellaneous generators. The dataset contains 2,496,738 images, comprising 964,989 real images and 1,531,749 fake images.
To ensure diversity across different sources, the real images of the dataset are randomly sampled from source datasets containing numerous categories, whereas synthetic images are generated within the same categories as the real images. Captions and image masks from the COCO dataset are utilized to generate images for text2image and inpainting generators, while normally distributed noise with different random seeds is used for noise2image generators. The dataset is further processed to reflect real-world scenarios by applying random cropping, downscaling, and JPEG compression, in accordance with the [IEEE VIP Cup 2022 standards](https://grip-unina.github.io/vipcup2022/).
The ArtiFact dataset is intended to serve as a benchmark for evaluating the performance of synthetic image detectors under real-world conditions. It includes a broad spectrum of diversity in terms of generators used and syntheticity, providing a challenging dataset for image detection tasks.
* Total number of images: 2,496,738
* Number of real images: 964,989
* Number of fake images: 1,531,749
* Number of generators used for fake images: 25 (including 13 GANs, 7 Diffusion, and 5 miscellaneous generators)
* Number of sources used for real images: 8
* Categories included in the dataset: Human/Human Faces, Animal/Animal Faces, Places, Vehicles, Art, and other real-life objects
* Image Resolution: 200 x 200 | Provide a detailed description of the following dataset: ArtiFact |
MVTec LOCO AD | **MVTec Logical Constraints Anomaly Detection (MVTec LOCO AD)** dataset is intended for the evaluation of unsupervised anomaly localization algorithms. The dataset includes both structural and logical anomalies. It contains 3644 images from five different categories inspired by real-world industrial inspection scenarios. Structural anomalies appear as scratches, dents, or contaminations in the manufactured products. Logical anomalies violate underlying constraints, e.g., a permissible object being present in an invalid location or a required object not being present at all. The dataset also includes pixel-precise ground truth data for each anomalous region. | Provide a detailed description of the following dataset: MVTec LOCO AD |
VoiceBank+DEMAND | VoiceBank+DEMAND is a noisy speech database for training speech enhancement algorithms and TTS models. The database was designed to train and test speech enhancement methods that operate at 48kHz. A more detailed description can be found in the paper associated with the database. Some of the noises were obtained from the Demand database, available here: http://parole.loria.fr/DEMAND/ . The speech database was obtained from the Voice Banking Corpus, available here: http://homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz . | Provide a detailed description of the following dataset: VoiceBank+DEMAND |
FetReg: Largescale Multi-centre Fetoscopy Placenta Dataset | Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge. Through FetReg2021 challenge, we released the first large-scale multi-centre dataset of fetoscopy laser photocoagulation procedure. The dataset contains 2,718 pixel-wise annotated images (for background, vessel, fetus, tool classes) from 24 different in vivo TTTS fetoscopic surgeries and 24 unannotated video clips video clips containing 9,616 frames for training and testing. The dataset is useful for the development of generalized and robust semantic segmentation and video mosaicking algorithms for long duration fetoscopy videos. | Provide a detailed description of the following dataset: FetReg: Largescale Multi-centre Fetoscopy Placenta Dataset |
V-PCCD | A simulated dataset built in Unreal Engine 4 with AirSim. Designed for visual point cloud change detection. Including GT point clouds before changes and after changes. Besides, 4 trajectories with stereo camera and IMU data are recorded for change detection task. | Provide a detailed description of the following dataset: V-PCCD |
Press Briefing Claim Dataset | # Press Briefing Claim Dataset
The dataset contains a total of 53 press briefings from a time span of over four years (2017-2021). While, on average, one press briefing per month is held, the distribution is highly skewed towards recent years.
The Press briefings can be categorized into five main thematic categories: Climate, Energy and Mobility, Medicine, Technology and Science. The press briefings are unevenly distributed among the five categories with a major focus on medical press briefings, which also reflects the COVID-19 pandemic.
In total, 177 speakers, excluding the hosts, were detected in the dataset. Most of the time the speakers are invited experts. Excluding the top 10 percentile press briefings as outliers, a press briefing had four guests on average.
The press briefings consist of 3066 passages, 1719 from guests and journalists and 1294 from hosts. Regarding the sentences, 25040 sentences were collected in total, 5955 from hosts and 18942 from guests. | Provide a detailed description of the following dataset: Press Briefing Claim Dataset |
VIS30K | We present the VIS30K dataset, a collection of 29,689 images that represents 30 years of figures and tables from each track
of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST). VIS30K’s comprehensive coverage of the scientific literature in
visualization not only reflects the progress of the field but also enables researchers to study the evolution of the state-of-the-art and to find
relevant work based on graphical content. We describe the dataset and our semi-automatic collection process, which couples
convolutional neural networks (CNN) with curation. Extracting figures and tables semi-automatically allows us to verify that no images are
overlooked or extracted erroneously. To improve quality further, we engaged in a peer-search process for high-quality figures from early
IEEE Visualization papers. | Provide a detailed description of the following dataset: VIS30K |
UEA time-series datasets | Five datasets used in NeurTraL-AD paper: \textit{RacketSports (RS).} Accelerometer and gyroscope recording of players playing four different racket sports. Each sport is designated as a different class. \textit{Epilepsy (EPSY).} Accelerometer recording of healthy actors simulating four different activity classes, one of them being an epileptic shock. \textit{Naval air training and operating procedures standardization (NAT).} Positions of sensors mounted on different body parts of a person performing activities. There are six different activity classes in the dataset. \textit{Character trajectories (CT).} Velocity trajectories of a pen on a WACOM tablet. There are $20$ different characters in this dataset. \textit{Spoken Arabic Digits (SAD).} MFCC features of ten arabic digits spoken by $88$ different speakers. | Provide a detailed description of the following dataset: UEA time-series datasets |
ProofNet | **ProofNet** is a benchmark for autoformalization and formal proving of undergraduate-level mathematics. The ProofNet benchmarks consists of 371 examples, each consisting of a formal theorem statement in Lean 3, a natural language theorem statement, and natural language proof. The problems are primarily drawn from popular undergraduate pure mathematics textbooks and cover topics such as real and complex analysis, linear algebra, abstract algebra, and topology. | Provide a detailed description of the following dataset: ProofNet |
CMU Panoptic Dataset 2.0 | The field of biomechanics is at a turning point, with marker-based motion capture set to be replaced by portable and inexpensive hardware, rapidly improving markerless tracking algorithms, and open datasets that will turn these new technologies into field-wide team projects. To expedite progress in this direction, we have collected the CMU Panoptic Dataset 2.0, which contains 86 subjects captured with 140 VGA cameras, 31 HD cameras, and 15 IMUs, performing on average 6.5 min of activities, including range of motion activities and tasks of daily living. | Provide a detailed description of the following dataset: CMU Panoptic Dataset 2.0 |
Dubbing Test Set | **Dubbing Test Set** consists of two subsets extracted from the En→De test set of COVOST-2, a large-scale multilingual speech translation corpus based on Common Voice. Specifically, the first subset is created by randomly sampling 91 sentences (test91), while the second is randomly sampled 101 sentences from the longest 10% of the De part of the test set (test101). | Provide a detailed description of the following dataset: Dubbing Test Set |
Regensburg Pediatric Appendicitis Dataset | This dataset was acquired in a retrospective study from a cohort of pediatric patients admitted with abdominal pain to Children’s Hospital St. Hedwig in Regensburg, Germany. Multiple abdominal B-mode ultrasound images were acquired for most patients, with the number of views varying from 1 to 15. The images depict various regions of interest, such as the abdomen’s right lower quadrant, appendix, intestines, lymph nodes and reproductive organs. Alongside multiple US images for each subject, the dataset includes information encompassing laboratory tests, physical examination results, clinical scores, such as Alvarado and pediatric appendicitis scores, and expert-produced ultrasonographic findings. Lastly, the subjects were labeled w.r.t. three target variables: diagnosis (appendicitis vs. no appendicitis), management (surgical vs. conservative) and severity (complicated vs. uncomplicated or no appendicitis). The study was approved by the Ethics Committee of the University of Regensburg (no. 18-1063-101, 18-1063_1-101 and 18-1063_2-101) and was performed following applicable guidelines and regulations. | Provide a detailed description of the following dataset: Regensburg Pediatric Appendicitis Dataset |
PS4 | A dataset of 18,731 proteins with their PDB code, index of the first residue in their respective DSSP file, their residue sequence and 9-category secondary structure sequence (including polyproline helices). | Provide a detailed description of the following dataset: PS4 |
Persian-ATIS | The PATIS is a Persian language dataset for intent detection and slot filling. | Provide a detailed description of the following dataset: Persian-ATIS |
OpenD5 | **OpenD5** is a a meta-dataset which aggregates 675 open-ended problems ranging across business, social sciences, humanities, machine learning, and health, and uses a set of unified evaluation metrics: validity, relevance, novelty, and significance. It is designed for the new task, D5, that automatically discovers differences between two large corpora in a goal-driven way. | Provide a detailed description of the following dataset: OpenD5 |
DELIVER | **DELIVER** is an arbitrary-modal segmentation benchmark, covering Depth, LiDAR, multiple Views, Events, and RGB. Aside from this, the dataset is also used in four severe weather conditions as well as five sensor failure cases to exploit modal complementarity and resolve partial outages. It is designed for the tasks of arbitrary-modal semantic segmentation. | Provide a detailed description of the following dataset: DELIVER |
MCubeS | Multimodal material segmentation (MCubeS) dataset contains 500 sets of images from 42 street scenes. Each scene has images for four modalities: RGB, angle of linear polarization (AoLP), degree of linear polarization (DoLP), and near-infrared (NIR). The dataset provides annotated ground truth labels for both material and semantic segmentation for every pixel. The dataset is divided training set with 302 image sets, validation set with 96 image sets, and test set with 102 image sets. Each image has 1224 x 1024 pixels and a total of 20 class labels per pixel. | Provide a detailed description of the following dataset: MCubeS |
MuscleMap136 | **MuscleMap136** is a dataset for video-based Activated Muscle Group Estimation (AMGE) aiming at identifying currently activated muscular regions of humans performing a specific activity. Video-based AMGE is an important yet overlooked problem. To this intent, the MuscleMap136 dataset features 15K video clips with 136 different activities and 20 labeled muscle groups. | Provide a detailed description of the following dataset: MuscleMap136 |
Data for: Employing Partial Least Squares Regression with Discriminant Analysis for Bug Prediction | For creating, optimizing, and evaluating our statistical model, we used the Public Unified Bug Dataset for Java. It contains the data entries of 5 different public bug datasets (PROMISE, Eclipse Bug Dataset, Bug Prediction Dataset, Bugcatchers Bug Dataset, and GitHub Bug Dataset) in a unified manner.
The dataset contains 47,618 Java Classes altogether, from which 8,780 contain at least one bug, while 38,838 are bug-free. The total number of bugs recorded in the dataset is 17,365, which means that each bugged Java Class contains 1.98 bugs in average (with standard deviation of 2.39).
Unfortunately, the PLS-DA implementation in PLS_Toolbox was too slow due to the tremendous amount of administrative calculations it performs. Therefore, we have developed and used a much faster PLS-DA script independently from PLS_Toolbox. According to the literature, there is no obvious way to choose the fastest and most accurate algorithm. Thus, we had to find the right balance between speed and accuracy, and chose the bidiag2stab method for our implementation. For tuning the model parameters and finding the best possible classification, we performed many model training runs, thus a very fast PLS core implementation was essential. With our PLS-DA Matlab script, we generated a classification using data splitting of 80% training, 10% validation and 10% test sets. | Provide a detailed description of the following dataset: Data for: Employing Partial Least Squares Regression with Discriminant Analysis for Bug Prediction |
RuWorldTree | RuWorldTree is a QA dataset with multiple-choice elementary-level science questions, which evaluate the understanding of core science facts.
**Motivation**
The WorldTree dataset starts the triad of the Reasoning and Knowledge tasks. The data includes the corpus of factoid utterances of various kinds, complex factoid questions and a corresponding causal chain of facts from the corpus resulting in a correct answer.
The WorldTree design was originally proposed in (Jansen et al., 2018).
An example in English for illustration purposes:
```{
'question': 'A bottle of water is placed in the freezer. What property of water will change when the water reaches the freezing point? (A) color (B) mass (C) state of matter (D) weight',
'answer': 'C',
'exam_name': 'MEA',
'school_grade': 5,
'knowledge_type': 'NO TYPE',
'perturbation': 'ru_worldtree',
'episode': [18, 10, 11]
}```
**Data Fields**
- text: a string containing the sentence text
- answer: a string with a candidate for the coreference resolution
- options: a list of all the possible candidates present in the text
- reference: a string containing an anaphor (a word or phrase that refers back to an earlier word or phrase)
- homonymia_type: a float corresponding to the type of the structure with syntactic homonymy
- label: an integer, either 0 or 1, indicating whether the homonymy is resolved correctly or not
perturbation: a string containing the name of the perturbation applied to text. If no perturbation was applied, the dataset name is used
- episode: a list of episodes in which the instance is used. Only used for the train set
**Data Splits**
The dataset consists of a training set with labeled examples and a test set in two configurations:
- raw data: includes the original data with no additional sampling
- episodes: data is split into evaluation episodes and includes several perturbations of test for robustness evaluation
We use the same splits of data as in the original English version.
**Test Perturbations**
Each training episode in the dataset corresponds to seven test variations, including the original test data and six adversarial test sets, acquired through the modification of the original test through the following text perturbations:
- ButterFingers: randomly adds noise to data by mimicking spelling mistakes made by humans through character swaps based on their keyboard distance
- Emojify: replaces the input words with the corresponding emojis, preserving their original meaning
- EDAdelete: randomly deletes tokens in the text
- EDAswap: randomly swaps tokens in the text
- BackTranslation: generates variations of the context through back-translation (ru -> en -> ru)
- AddSent: replaces one or more choice options with a generated one | Provide a detailed description of the following dataset: RuWorldTree |
RuOpenBookQA | RuOpenBookQA is a QA dataset with multiple-choice elementary-level science questions which probe the understanding of core science facts.
**Motivation**
RuOpenBookQA is mainly based on the work of (Mihaylov et al., 2018): it is a QA dataset with multiple-choice elementary-level science questions, which probe the understanding of 1k+ core science facts.
Very similar to the pipeline of the RuWorldTree, the dataset includes a corpus of factoids, factoid questions and correct answer. Only one fact is enough to find the correct answer, so this task can be considered easier.
```{
'ID': '7-674',
'question': 'If a person walks in the direction opposite to the compass needle, they are going (A) west (B) north (C) east (D) south',
'answer': 'D',
'episode': [11],
'perturbation': 'ru_openbook'
}```
**Data Fields**
- ID: a string containing a unique question id
- question: a string containing question text with answer options
- answer: a string containing the correct answer key (A, B, C or D)
- perturbation: a string containing the name of the perturbation applied to text. If no perturbation was applied, the dataset name is used
- episode: a list of episodes in which the instance is used. Only used for the train set
**Data Splits**
The dataset consists of a training set with labeled examples and a test set in two configurations:
- raw data: includes the original data with no additional sampling
- episodes: data is split into evaluation episodes and includes several perturbations of test for robustness evaluation
**Test Perturbations**
Each training episode in the dataset corresponds to seven test variations, including the original test data and six adversarial test sets, acquired through the modification of the original test through the following text perturbations:
- ButterFingers: randomly adds noise to data by mimicking spelling mistakes made by humans through character swaps based on their keyboard distance
- Emojify: replaces the input words with the corresponding emojis, preserving their original meaning
- EDAdelete: randomly deletes tokens in the text
- EDAswap: randomly swaps tokens in the text
- BackTranslation: generates variations of the context through back-translation (ru -> en -> ru)
- AddSent: replaces one or more choice options with a generated one | Provide a detailed description of the following dataset: RuOpenBookQA |
MultiQ | MultiQ is a multi-hop QA dataset for Russian, suitable for general open-domain question answering, information retrieval, and reading comprehension tasks.
**Motivation**
Question-answering has been an essential task in natural language processing and information retrieval. However, certain areas in QA remain quite challenging for modern approaches, including the multi-hop one, which is traditionally considered an intersection of graph methods, knowledge representation, and SOTA language modeling.
Multi-hop reasoning has been the least addressed QA direction for Russian. The task is represented by the MuSeRC dataset (Fenogenova et al., 2020) and only a few dozen questions in SberQUAD (Efimov et al., 2020) and RuBQ (Rybin et al., 2021). In response, we have developed a semi-automatic pipeline for multi-hop dataset generation based on Wikidata.
An example in English for illustration purposes:
```{
'support_text': 'Gerard McBurney (b. June 20, 1954, Cambridge) is a British arranger, musicologist, television and radio presenter, teacher, and writer. He was born in the family of American archaeologist Charles McBurney and secretary Anna Frances Edmonston, who combined English, Scottish and Irish roots. Gerard's brother Simon McBurney is an English actor, writer, and director. He studied at Cambridge and the Moscow State Conservatory with Edison Denisov and Roman Ledenev.',
'main_text': 'Simon Montague McBurney (born August 25, 1957, Cambridge) is an English actor, screenwriter, and director.
Biography.
Father is an American archaeologist who worked in the UK. Simon graduated from Cambridge with a degree in English Literature. After his father's death (1979) he moved to France, where he studied theater at the Jacques Lecoq Institute. In 1983 he created the theater company "Complicity". Actively works as an actor in film and television, and acts as a playwright and screenwriter.',
'question': 'Where was Gerard McBurney's brother born?',
'bridge_answers': [{'label': 'passage', 'length': 14, 'offset': 300, 'segment': 'Simon McBurney'}],
'main_answers': [{'label': 'passage', 'length': 9, 'offset': 47, 'segment': Cambridge'}],
'episode': [15],
'perturbation': 'multiq'
}```
**Data Fields**
- question: a string containing the question text
- support_text: a string containing the first text passage relating to the question
- main_text: a string containing the main answer text
- bridge_answers: a list of entities required to hop from the support text to the main text
- main_answers: a list of answers to the question
- perturbation: a string containing the name of the perturbation applied to text. If no perturbation was applied, the dataset name is used
- episode: a list of episodes in which the instance is used. Only used for the train set
**Data Splits**
The dataset consists of a training set with labeled examples and a test set in two configurations:
raw data: includes the original data with no additional sampling
- episodes: data is split into evaluation episodes and includes several perturbations of test for robustness evaluation Test and train data sets are disjoint with respect to individual - questions, but may include overlaps in support and main texts.
**Test Perturbations**
Each training episode in the dataset corresponds to seven test variations, including the original test data and six adversarial test sets, acquired through the modification of the original test through the following text perturbations:
- ButterFingers: randomly adds noise to data by mimicking spelling mistakes made by humans through character swaps based on their keyboard distance
- Emojify: replaces the input words with the corresponding emojis, preserving their original meaning
- EDAdelete: randomly deletes tokens in the text
- EDAswap: randomly swaps tokens in the text
- BackTranslation: generates variations of the context through back-translation (ru -> en -> ru)
- AddSent: generates an extra sentence at the end of the text | Provide a detailed description of the following dataset: MultiQ |
CheGeKa | CheGeKa is a Jeopardy!-like Russian QA dataset collected from the official Russian quiz database ChGK.
**Motivation**
The task can be considered the most challenging in terms of reasoning, knowledge and logic, as the task implies the QA pairs with a free response form (no answer choices); however, a long chain of causal relationships between facts and associations forms the correct answer.
The original corpus of the CheGeKa game was introduced in Mikhalkova (2021).
An example in English for illustration purposes:
```{
'question_id': 3665,
'question': 'THIS MAN replaced John Lennon when the Beatles got together for the last time.',
'answer': 'Julian Lennon',
'topic': 'The Liverpool Four',
'author': 'Bayram Kuliyev',
'tour_name': 'Jeopardy!. Ashgabat-1996',
'tour_link': 'https://db.chgk.info/tour/ash96sv',
'episode': [16],
'perturbation': 'chegeka'
}```
**Data Fields**
- question_id: an integer corresponding to the question id in the database
- question: a string containing the question text
- answer: a string containing the correct answer to the question
- topic: a string containing the question category
- author: a string with the full name of the author
- tour_name: a string with the title of a tournament
- tour link: a string containing the link to a tournament (None for the test set)
- perturbation: a string containing the name of the perturbation applied to text. If no perturbation was applied, the dataset name is used
- episode: a list of episodes in which the instance is used. Only used for the train set
**Data Splits**
The dataset consists of a training set with labeled examples and a test set in two configurations:
- raw data: includes the original data with no additional sampling
- episodes: data is split into evaluation episodes and includes several perturbations of test for robustness evaluation
**Test Perturbations**
Each training episode in the dataset corresponds to seven test variations, including the original test data and six adversarial test sets, acquired through the modification of the original test through the following text perturbations:
- ButterFingers: randomly adds noise to data by mimicking spelling mistakes made by humans through character swaps based on their keyboard distance
- Emojify: replaces the input words with the corresponding emojis, preserving their original meaning
- EDAdelete: randomly deletes tokens in the text
- EDAswap: randomly swaps tokens in the text
- BackTranslation: generates variations of the context through back-translation (ru -> en -> ru)
- AddSent: generates extra words or a sentence at the end of the question | Provide a detailed description of the following dataset: CheGeKa |
Ethics 2 | Ethics2 (per ethics) dataset is created to test the knowledge of the basic concepts of morality. The task is to predict human ethical judgments about diverse text situations in a multi-label classification setting. The main objective of the task is to evaluate the positive or negative implementation of five concepts in normative with ‘yes’ and ‘no’ ratings. The included concepts are as follows: virtue, law, moral, justice, and utilitarianism.
**Motivation**
There are a multitude of approaches to evaluating ethics in machine learning. The Ethics dataset for Russian is created from scratch for the first time, relying on the design compatible with (Hendrycks et al., 2021).
Our Ethics dataset would go through community validation and discussion as it is the first ethics dataset for Russian based on the established methodology. We acknowledge that the work (Hendrycks et al., 2021) has flaws; thus, we do not reproduce the generative approach. We construct the dataset using a similar annotation scheme: we avoid the direct question of whether the deed is good or bad. Instead, we make annotations according to five criteria that describe the aspects of the annotators' attitude to the deed.
An example in English for illustration purposes:
`{
'source': 'gazeta',
'text': '100-year-old Greta Ploech gave handmade cookies to a toddler who helped her cross a busy highway at a pedestrian crossing. The video was posted on the Readers Channel.',
'sit_virtue': 1,
'sit_moral': 0,
'sit_law': 0,
'sit_justice': 1,
'sit_util': 1,
'episode': [5],
'perturbation': 'sit_ethics'
}`
**Data Fields**
- text: a string containing the body of a news article or a fiction text
- source: a string containing the source of the text
- per_virtue: an integer, either 0 or 1, indicating whether virtue standards are violated in the text
- per_moral: an integer, either 0 or 1, indicating whether moral standards are violated in the text
- per_law: an integer, either 0 or 1, indicating whether any laws are violated in the text
- per_justice: an integer, either 0 or 1, indicating whether justice norms are violated in the text
- per_util: an integer, either 0 or 1, indicating whether utilitarianism norms are violated in the text
- perturbation: a string containing the name of the perturbation applied to text. If no perturbation was applied, the dataset name is used
- episode: a list of episodes in which the instance is used. Only used for the train set
**Data Splits**
The dataset consists of a training set with labeled examples and a test set in two configurations:
- raw data: includes the original data with no additional sampling
- episodes: data is split into evaluation episodes and includes several perturbations of test for robustness evaluation
**Test Perturbations**
Each training episode in the dataset corresponds to seven test variations, including the original test data and six adversarial test sets, acquired through the modification of the original test through the following text perturbations:
- ButterFingers: randomly adds noise to data by mimicking spelling mistakes made by humans through character swaps based on their keyboard distance
- Emojify: replaces the input words with the corresponding emojis, preserving their original meaning
- EDAdelete: randomly deletes tokens in the text
- EDAswap: randomly swaps tokens in the text
- BackTranslation: generates variations of the context through back-translation (ru -> en -> ru)
- AddSent: generates an extra sentence at the end of the text | Provide a detailed description of the following dataset: Ethics 2 |
Winograd Automatic | The Winograd schema challenge composes tasks with syntactic ambiguity, which can be resolved with logic and reasoning.
**Motivation**
The dataset presents an extended version of a traditional Winograd challenge (Levesque et al., 2012): each sentence contains unresolved homonymy, which can be resolved based on commonsense and reasoning. The Winograd scheme is extendable with the real-life sentences filtered out of the National Corpora with a set of 11 syntactic queries, extracting sentences like "Katya asked Masha if she..." (two possible references to a pronoun), "A change of scenery that..." (Noun phrase & subordinate clause with "that" in the same gender and number), etc. The extraction pipeline can be adjusted to various languages depending on the set of ambiguous syntactic constructions possible.
An example in English for illustration purposes:
`{
‘text’: ‘But then I was glad, because in the end the singer from Turkey who performed something national, although in a modern version, won.’,
‘answer’: ‘singer’,
‘label’: 1,
‘options’: [‘singer’, ‘Turkey’],
‘reference’: ‘who’,
‘homonymia_type’: ‘1.1’,
episode: [15],
‘perturbation’ : ‘winograd’
}`
**Data Fields**
- text: a string containing the sentence text
- answer: a string with a candidate for the coreference resolution
- options: a list of all the possible candidates present in the text
- reference: a string containing an anaphor (a word or phrase that refers back to an earlier word or phrase)
- homonymia_type: a float corresponding to the type of the structure with syntactic homonymy
- label: an integer, either 0 or 1, indicating whether the homonymy is resolved correctly or not
- perturbation: a string containing the name of the perturbation applied to text. If no perturbation was applied, the dataset name is used
- episode: a list of episodes in which the instance is used. Only used for the train set
**Data Splits**
The dataset consists of a training set with labeled examples and a test set in two configurations:
- raw data: includes the original data with no additional sampling
- episodes: data is split into evaluation episodes and includes several perturbations of test for robustness evaluation
The train and test sets are disjoint with respect to the sentence-candidate answer pairs but may include overlaps in individual sentences and homonymy type.
**Test Perturbations**
Each training episode in the dataset corresponds to six test variations, including the original test data and five adversarial test sets, acquired through the modification of the original test through the following text perturbations:
- ButterFingers: randomly adds noise to data by mimicking spelling mistakes made by humans through character swaps based on their keyboard distance
- Emojify: replaces the input words with the corresponding emojis, preserving their original meaning
- EDAdelete: randomly deletes tokens in the text
- EDAswap: randomly swaps tokens in the text
- AddSent: generates extra words or a sentence at the end of the text | Provide a detailed description of the following dataset: Winograd Automatic |
AdvNet | AdvNet is a dataset of traffic signs images. Specifically, it includes adversarial traffic sign images (i.e., pictures of traffic signs with stickers on their surface) that can fool state-of-the-art neural network-based perception systems and clean traffic sign images without any stickers on them.
If you use AdvNet, please cite the following paper:
Y. Kantaros, T. Carpenter, K. Sridhar, I. Lee, J. Weimer: `Real-Time Detectors for Adversarial Digital and Physical Inputs to Perception Systems', 12th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), 2021 | Provide a detailed description of the following dataset: AdvNet |
Cam-CAN | The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) is a large-scale collaborative research project at the University of Cambridge, launched in October 2010, with substantial initial funding from the Biotechnology and Biological Sciences Research Council (BBSRC), followed by support from the Medical Research Council (MRC) Cognition & Brain Sciences Unit (CBU) and the European Union Horizon 2020 LifeBrain project. The Cam-CAN project uses epidemiological, cognitive, and neuroimaging data to understand how individuals can best retain cognitive abilities into old age.
https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/ | Provide a detailed description of the following dataset: Cam-CAN |
TRR360D | **TRR360D** is based on the ICDAR2019MTD modern table detection dataset, it refers to the annotation format of the DOTA dataset. The training set contains 600 rotated images and 977 annotated instances, and the test set contains 240 rotated images and 499 annotated instances. | Provide a detailed description of the following dataset: TRR360D |
UESTC-MMEA-CL | UESTC-MMEA-CL is a new multi-modal activity dataset for continual egocentric activity recognition, which is proposed to promote future studies on continual learning for first-person activity recognition in wearable applications. Our dataset provides not only vision data with auxiliary inertial sensor data but also comprehensive and complex daily activity categories for the purpose of continual learning research. UESTC-MMEA-CL comprises 30.4 hours of fully synchronized first-person video clips, acceleration stream and gyroscope data in total. There are 32 activity classes in the dataset and each class contains approximately 200 samples. We divide the samples of each class into the training set, validation set and test set according to the ratio of 7:2:1. For the continual learning evaluation, we present three settings of incremental steps, i.e., the 32 classes are divided into {16, 8, 4} incremental steps and each step contains {2, 4, 8} activity classes, respectively. | Provide a detailed description of the following dataset: UESTC-MMEA-CL |
IoT-23 | IoT-23 is a dataset of network traffic from Internet of Things (IoT) devices. It has 20 malware captures executed in IoT devices, and 3 captures for benign IoT devices traffic. It was first published in January 2020, with captures ranging from 2018 to 2019. These IoT network traffic was captured in the Stratosphere Laboratory, AIC group, FEL, CTU University, Czech Republic. Its goal is to offer a large dataset of real and labeled IoT malware infections and IoT benign traffic for researchers to develop machine learning algorithms. This dataset and its research was funded by Avast Software. The malware was allow to connect to the Internet. | Provide a detailed description of the following dataset: IoT-23 |
Spring | **Spring** is a large, high-resolution and high-detail, computer-generated benchmark for scene flow, optical flow, and stereo. Based on rendered scenes from the open-source Blender movie "Spring", it provides photo-realistic HD datasets with state-of-the-art visual effects and ground truth training data. | Provide a detailed description of the following dataset: Spring |
FluidLab | **FluidLab** is a simulation environment with a diverse set of manipulation tasks involving complex fluid dynamics. These tasks address interactions between solid and fluid as well as among multiple fluids. | Provide a detailed description of the following dataset: FluidLab |
OpenASL | Large-scale American Sign Language (ASL) - English dataset collected from online video sites (e.g., YouTube). OpenASL contains 288 hours of ASL videos in multiple domains from over 200 signers. | Provide a detailed description of the following dataset: OpenASL |
ChatGPT Software Testing Study | **ChatGPT Software Testing Study Dataset** contains questions from a well-known software testing book by Ammann and Offutt. It uses all the textbook questions in Chapters 1 to 5 that have solutions available on the book’s official website. These solutions are made publicly available to help students learn. Questions that are not in the student solution are omitted because publishing our results might expose answers that the authors of the book do not intend to make public. | Provide a detailed description of the following dataset: ChatGPT Software Testing Study |
VTQA | VTQA is a dataset containing open-ended questions about image-text pairs. This dataset requires the model to align multimedia representations of the same entity to implement multi-hop reasoning between image and text and finally use natural language to answer the question. The aim of this dataset is to develop and benchmark models that are capable of multimedia entity alignment, multi-step reasoning and open-ended answer generation.
VTQA dataset consists of 10,238 image-text pairs and 27,317 questions. The images are real images from [MSCOCO](https://cocodataset.org/) dataset, containing a variety of entities. The annotators are required to first annotate relevant text according to the image, and then ask questions based on the image-text pair, and finally answer the question open-ended. | Provide a detailed description of the following dataset: VTQA |
CCEDD | We construct a largest publicly Cervical Cell Edge Detection Dataset (CCEDD) based on our Local Label Point Correction (LLPC). Our dataset is ten times larger than the previous datasets, which greatly facilitates the development of overlapping cell edge detection.
Paper: Local Label Point Correction for Edge Detection of Overlapping Cervical Cells
Paper link: https://www.frontiersin.org/articles/10.3389/fninf.2022.895290/full
Code link: https://github.com/nachifur/LLPC | Provide a detailed description of the following dataset: CCEDD |
NTLNP | This is an image dataset for object detection of wildlife in the mixed coniferous broad-leaved forest.
A total of 25,657 images in this dataset were generated from video clips taken by infrared cameras in the Northeast Tiger and Leopard National Park, including 17 main species (15 wild animals and 2 major domestic animals): Amur tiger, Amur leopard, wild boar, roe deer, sika deer, Asian black bear, red fox, Asian badger, raccoon dog, musk deer, Siberian weasel, sable, yellow-throated marten, leopard cat, Manchurian hare, cow, and dog.
All images were labeled in Pascal VOC format.
The image resolution is 1280 × 720 or 1600 × 1200 pixels. | Provide a detailed description of the following dataset: NTLNP |
UR5 Tool Dataset | In this dataset UR5 robot used 6 tools: metal-scissor, metal-whisk, plastic-knife, plastic-spoon, wooden-chopstick, and wooden-fork to perform 6 behaviors: look, stirring-slow, stirring-fast, stirring-twist, whisk, and poke. The robot explored 15 objects: cane-sugar, chia-seed, chickpea, detergent, empty, glass-bead, kidney-bean, metal-nut-bolt, plastic-bead, salt, split-green-pea, styrofoam-bead, water, wheat, and wooden-button kept cylindrical containers. The robot performed 10 trials on each object using a tool, resulting in 5,400 interactions (6 tools x 6 behaviors x 15 objects x 10 trials). The robot records multiple sensory data (audio, RGB images, depth images, haptic, and touch images) while interacting with the objects. | Provide a detailed description of the following dataset: UR5 Tool Dataset |
3DOH50K | 3DOH50K is the first real 3D human dataset for the problem of human reconstruction and pose estimation in occlusion scenarios. It contains 51600 images with accurate 2D pose and 3D pose, SMPL parameters, and binary mask. | Provide a detailed description of the following dataset: 3DOH50K |
Jung | Dataset for document shadow removal | Provide a detailed description of the following dataset: Jung |
VNAT | This dataset is a collection of labelled PCAP files, both encrypted and unencrypted, across 10 applications, as well as a pandas dataframe in HDF5 format containing detailed metadata summarizing the connections from those files. It was created to assist the development of machine learning tools that would allow operators to see the traffic categories of both encrypted and unencrypted traffic flows. In particular, features of the network packet traffic timing and size information (both inside of and outside of the VPN) can be leveraged to predict the application category that generated the traffic. | Provide a detailed description of the following dataset: VNAT |
Kligler | Dataset for document shadow removal | Provide a detailed description of the following dataset: Kligler |
AutoFR Dataset | **AutoFR Dataset** is broken down by each site that we crawl within a zip file. It contains multiple different experiments that we conducted in our paper. The overall dataset contains 1042 sites that we crawled where we detected ads within the Top-5K. | Provide a detailed description of the following dataset: AutoFR Dataset |
FMD (materials) | Sharan, Lavanya, Ruth Rosenholtz, and Edward Adelson.
"Material perception: What can you see in a brief glance?."
Journal of Vision 9.8 (2009): 784-784.
http://people.csail.mit.edu/celiu/CVPR2010/FMD/FMD.zip | Provide a detailed description of the following dataset: FMD (materials) |
MobileBrick | Generate high-quality 3D ground-truth shapes for reconstruction evaluation is extremely challenging because even 3D scanners can only generate pseudo ground-truth shapes with artefacts. We propose a novel data capturing and 3D annotation pipeline to obtain precise 3D ground-truth shapes without relying on expensive 3D scanners. The key to creating the precise 3D ground-truth shapes is using LEGO models, which are made of LEGO bricks with known geometry. The MobileBrick dataset provides a unique opportunity for future research on high-quality 3D reconstruction thanks to two distinctive features: 1) A large number of RGBD sequences with precise 3D ground-truth annotations. 2) The RGBD images were captured using mobile devices so algorithms can be tested in a realistic setup for mobile AR applications. | Provide a detailed description of the following dataset: MobileBrick |
X-Humans | **X-Humans** consists of 20 subjects (11 males, 9 females) with various clothing types and hair style. The collection of this dataset has been approved by an internal ethics committee. For each subject, we split the motion sequences into a training and test set. In total, there are 29,036 poses for training and 6,439 test poses. X-Humans also contains ground-truth SMPL-X parameters, obtained via a custom SMPL-X registration pipeline specifically designed to deal with low-resolution body parts. | Provide a detailed description of the following dataset: X-Humans |
IoTCheck Dataset | https://github.com/uci-plrg/iotcheck-data | Provide a detailed description of the following dataset: IoTCheck Dataset |
OVRseen | https://athinagroup.eng.uci.edu/projects/ovrseen/ | Provide a detailed description of the following dataset: OVRseen |
PubChem18 | A.2.1 AN OPEN, LARGE-SCALE DATASET FOR ZERO-SHOT DRUG DISCOVERY DERIVED FROM PUBCHEM
We constructed a large public dataset extracted from PubChem (Kim et al., 2019; Preuer et al., 2018), an open chemistry
database, and the largest collection of readily available chemical data. We take assays ranging from 2004 to 2018-05.
It initially comprises 224,290,250 records of molecule-bioassay activity, corresponding to 2,120,854 unique molecules
and 21,003 unique bioassays. We find that some molecule-bioassay pairs have multiple activity records, which may not
all agree. We reduce every molecule-bioassay pair to exactly one activity measurement by applying majority voting.
Molecule-bioassay pairs with ties are discarded. This step yields our final bioactivity dataset, which features 223,219,241 records of molecule-bioassay activity, corresponding to 2,120,811 unique molecules and 21,002 unique bioassays ranging
from AID 1 to AID 1259411. Molecules range up to CID 132472079. The dataset has 3 different splitting schemes. | Provide a detailed description of the following dataset: PubChem18 |
LPSC | This data set contains annotated text versions of 1635 two-page abstracts published at the Lunar and Planetary Science Conference from 1998 to 2020 of relevance to four Mars missions. The annotations were generated using named entity recognition and relation extraction provided by the MTE processing pipeline (available at https://github.com/wkiri/MTE), followed by manual review. Annotated entities include Element, Mineral, Property, and Target. Annotated relations include Contains(Target, Element | Mineral) and HasProperty(Target, Property). The extracted information (without full texts) is also available as a database (stored in .csv files) at https://pds-geosciences.wustl.edu/missions/mte/mte.htm .
The description above is quoted from the source: https://zenodo.org/record/7066107#.ZAo4VOzMIW8 | Provide a detailed description of the following dataset: LPSC |
1DSfM | The 1DSfM Landmarks is a collection of community-based image reconstruction by Kyle Wilson and is comprised of 14 datasets with comparison to bundler ground truth. Notredame is provided separately. Datasets (tar.gz, 642 MB) Alamo images (tar, 2.0 GB) Ellis Island images (tar, 1.6 GB) Madrid Metropolis images (tar, 0.7 GB) Montreal Notre Dame images (tar, 1.6 GB) NYC_Library images (tar, 1.6 GB) Piazza del Popolo images (tar, 1.5 GB) Piccadilly images (tar, 3.7 GB) Roman Forum images (tar, 1.5 GB) Tower of London images (tar, 1.1 GB) Trafalgar images (tar, 8.5 GB) Union Square images (tar, 3.6 GB) Vienna Cathedral images (tar, 3.3 GB) Yorkminster images (tar, 2.2 GB) Gendarmenmarkt images (tar, 1.0 GB) References Robust Global Translations with 1DSfM Kyle Wilson and Noah Snavely, ECCV 2014 | Provide a detailed description of the following dataset: 1DSfM |
CCv2 | **Casual Conversations v2 (CCv2)** is composed of over 5,567 participants (26,467 videos) and intended mainly to be used for assessing the performance of already trained models in computer vision and audio applications for the purposes permitted in our data license agreement. The videos feature paid individuals who agreed to participate in the project and explicitly provided Age, Gender, Language/Dialect, Geo-location, Disability, Physical adornments, Physical attributes labels themselves. The videos were recorded in Brazil, India, Indonesia, Mexico, Philippines, United States, and Vietnam with a diverse set of adults in various categories. A group of trained annotators labeled the participants’ apparent skin tone using the Fitzpatrick scale and Monk Scale, in addition to annotations of Voice timbre, Activity and Recording setups. Spoken words in all videos are either scripted (a sample paragraph from The Idiot by Fyodor Dostoevsky provided with the dataset) or nonscripted (answering one of five predetermined questions). | Provide a detailed description of the following dataset: CCv2 |
COCO-MLT | The COCO-MLT is created from MS COCO-2017, containing 1,909 images from 80 classes. The maximum of training number per class is 1,128 and the minimum is 6. We use the test set of COCO2017 with 5,000 for evaluation. The ratio of head, medium, and tail classes is 22:33:25 in COCO-MLT. | Provide a detailed description of the following dataset: COCO-MLT |
VOC-MLT | We construct the long-tailed version of VOC from its 2012 train-val set. It contains 1,142 images from 20 classes, with a maximum of 775 images per class and a minimum of 4 images per class. The ratio of head, medium, and tail classes after splitting is 6:6:8. We evaluate the performance on VOC2007 test set with 4952 images. | Provide a detailed description of the following dataset: VOC-MLT |
CHAD | # CHAD: Charlotte Anomaly Dataset
CHAD is high-resolution, multi-camera dataset for surveillance video anomaly detection. It includes bounding box, Re-ID, and pose annotations, as well as frame-level anomaly labels, dividing all frames into two groups of anomalous or normal. You can find the paper with all the details in the following link: [**CHAD: Charlotte Anomaly Dataset**](https://arxiv.org/abs/2212.09258 "CHAD Paper"). Please refer to the page of the dataset for more information. | Provide a detailed description of the following dataset: CHAD |
FEE Corridor | The data set contains point cloud data captured in an indoor environment with precise localization and ground truth mapping information.
Two ”stop-and-go” data sequences of a robot with mounted Ouster OS1-128 lidar are provided.
This data-capturing strategy allows recording lidar scans that do not suffer from an error caused
by sensor movement. Individual scans from static robot positions are recorded. Additionally, point clouds recorded with the Leica BLK360 scanner are provided as mapping ground-truth data. | Provide a detailed description of the following dataset: FEE Corridor |
CLOTH3D | This work presents CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightness and fabric. Clothes are simulated on top of thousands of different pose sequences and body shapes, generating realistic cloth dynamics. We provide the dataset with a generative model for cloth generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on graph convolutions (GCVAE) to learn garment latent spaces. This allows for realistic generation of 3D garments on top of SMPL model for any pose and shape. | Provide a detailed description of the following dataset: CLOTH3D |
Fashion-MNIST-H | We provide multiple human annotations for each test image in Fashion-MNIST. This can be used as soft labels or probabilistic labels instead of the usual hard (single) labels. | Provide a detailed description of the following dataset: Fashion-MNIST-H |
MGTAB | **MGTAB** is the first standardized graph-based benchmark for stance and bot detection. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. For more details, please refer to the MGTAB paper. | Provide a detailed description of the following dataset: MGTAB |
ATM’22 | **ATM'22** is a multi-site, multi-domain dataset for pulmonary airway segmentation. It contains large-scale CT scans with detailed pulmonary airways annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID19 CTs with ground-glass opacity and consolidation. | Provide a detailed description of the following dataset: ATM’22 |
Med-EASi | Med-EASi (Medical dataset for Elaborative and Abstractive Simplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical texts. It contains 1979 expert-simple text pairs in medical domain, spanning a total of 4478 UMLS concepts across all text pairs. The dataset is annotated with four textual transformations: replacement, elaboration, insertion and deletion. | Provide a detailed description of the following dataset: Med-EASi |
Real-World Stereo Color and Sharpness Mismatch Dataset | A real-world stereo video dataset, containing 1200 frame pairs with real-world color and sharpness mismatches caused by beam splitter.
Color and sharpness mismatches between views of the stereoscopic 3D video can decrease the overall video quality and may cause viewer discomfort and headaches. To eliminate this problem, there are correction methods that aim to make the views consistent.
We propose a new real-world dataset of stereoscopic videos for evaluating color-mismatch-correction methods. We collected it using a beam splitter and three cameras. A beam splitter introduces real-world mismatches between stereopair views. Similar mismatches can appear in stereoscopic movies filmed with a beam-splitter rig. Our approach used a beam splitter to set a zero stereobase between the left camera and the left ground-truth camera, allowing us to create a distorted ground-truth data pair. | Provide a detailed description of the following dataset: Real-World Stereo Color and Sharpness Mismatch Dataset |
DeSmoke-LAP dataset | The laparoscopic surgery dataset is associated with our International Journal of Computer Assisted Radiology and Surgery (IJCARS) publication titled “DeSmoke-LAP: Improved Unpaired Image-to-Image Translation for Desmoking in Laparoscopic Surgery”. The training model of the proposed method is available as an open source on Github. We propose DeSmoke-LAP, a new method for removing smoke from real robotic laparoscopic hysterectomy videos. The proposed method is based on the unpaired image-to-image cycle-consistent generative adversarial network in which two novel loss functions, namely, inter-channel discrepancies and dark channel prior.
The dataset contains frames and video clips from 10 robot-assisted laparoscopic hysterectomy procedure videos. The original videos were decomposed into frames at 1 fps. From each video, 300 hazy images and 300 clear images were manually selected by observing the electrocauterisation. A short video clip of 50 frames from each procedure was also selected that was utilised for testing. 5-fold cross-validation was performed for all methods under comparison. Quantitative evaluation was done using referenceless metrics and qualitative evaluation was performed through a survey filled out by end-users (surgeons). | Provide a detailed description of the following dataset: DeSmoke-LAP dataset |
SESYD Dataset | SESYD "Systems Evaluation SYnthetic Documents" is a database of synthetical documents with groundtruth. This database targets two main research problems in the document image analysis field (i) symbol recognition and spotting in line drawing images (floorplans and electrical diagrams) (ii) character segmentation and recognition in geographical maps. The database is composed of eleven collections for performance evaluation containing 284k images, 190k symbols and 284k characters (k for thousand). SESYD is today a key database in the document image analysis field published in 2010 and referred by one hundred of citations into research papers.
Please, cite the following paper [1] if you are using this database.
[1] M. Delalandre, E. Valveny, T. Pridmore and D. Karatzas. Generation of Synthetic Documents for Performance Evaluation of Symbol Recognition & Spotting Systems. International Journal on Document Analysis and Recognition (IJDAR), 13(3):187-207, 2010. [http://mathieu.delalandre.free.fr/publications/IJDAR2010.pdf](http://mathieu.delalandre.free.fr/publications/IJDAR2010.pdf) | Provide a detailed description of the following dataset: SESYD Dataset |
VisA | The VisA dataset contains 12 subsets corresponding to 12 different objects as shown in the above figure. There are 10,821 images with 9,621 normal and 1,200 anomalous samples. Four subsets are different types of printed circuit boards (PCB) with relatively complex structures containing transistors, capacitors, chips, etc. For the case of multiple instances in a view, we collect four subsets: Capsules, Candles, Macaroni1 and Macaroni2. Instances in Capsules and Macaroni2 largely differ in locations and poses. Moreover, we collect four subsets including Cashew, Chewing gum, Fryum and Pipe fryum, where objects are roughly aligned. The anomalous images contain various flaws, including surface defects such as scratches, dents, color spots or crack, and structural defects like misplacement or missing parts. | Provide a detailed description of the following dataset: VisA |
Uncertainty and Concept Drift | AI-based digital twins are at the leading edge of theIndustry 4.0 revolution, which are technologically empowered bythe Internet of Things and real-time data analysis. Information
collected from industrial assets is produced in a continuous fashion, yielding data streams that must be processed under stringent timing constraints. Such data streams are usually subject to
non-stationary phenomena, causing that the data distribution of the streams may change, and thus the knowledge captured by models used for data analysis may become obsolete (leading to the so-called concept drift effect). The early detection of thechange (drift) is crucial for updating the model’s knowledge, which is challenging especially in scenarios where the ground truth associated to the stream data is not readily available. Among many other techniques, the estimation of the model’s confidence has been timidly suggested in a few studies as a criterion for detecting drifts in unsupervised settings. The goal of this manuscript is to confirm and expose solidly the connection between the model’s confidence in its output and the presence of a concept drift, showcasing it experimentally and advocating for a major consideration of uncertainty estimation in comparative studies to be reported in the future. | Provide a detailed description of the following dataset: Uncertainty and Concept Drift |
V2V4Real | **V2V4Real** is a large-scale real-world multi-modal dataset for V2V perception. The data is collected by two vehicles equipped with multi modal sensors driving together through diverse scenarios. It covers a driving area of 410 km comprising 20K LiDAR frames, 40K RGB frames, 240K annotated 3D bounding boxes for 5 classes, and HDMaps that cover all the driving routes. | Provide a detailed description of the following dataset: V2V4Real |
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