데이터셋 탐색

ATOMIC is an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge. Compared to existing resources that center around taxonomic knowledge, ATOMIC focuses on inferential knowledge organized as typed if-then relations with variables (e.g., "if X pays Y a compliment, then Y will likely return the compliment"). Source: ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning Image Source: https://homes.cs.washington.edu/~msap/atomic/

AVA is a project that provides audiovisual annotations of video for improving our understanding of human activity. Each of the video clips has been exhaustively annotated by human annotators, and together they represent a rich variety of scenes, recording conditions, and expressions of human activity. There are annotations for: - Kinetics (AVA-Kinetics) - a crossover between AVA and Kinetics. In order to provide localized action labels on a wider variety of visual scenes, authors provide AVA action labels on videos from Kinetics-700, nearly doubling the number of total annotations, and increasing the number of unique videos by over 500x. - Actions (AvA Actions) - the AVA dataset densely annotates 80 atomic visual actions in 430 15-minute movie clips, where actions are localized in space and time, resulting in 1.62M action labels with multiple labels per human occurring frequently. - Spoken Activity (AVA ActiveSpeaker, AVA Speech). AVA ActiveSpeaker: associates speaking activity with a visible face, on the AVA v1.0 videos, resulting in 3.65 million frames labeled across ~39K face tracks. AVA Speech densely annotates audio-based speech activity in AVA v1.0 videos, and explicitly labels 3 background noise conditions, resulting in ~46K labeled segments spanning 45 hours of data. Image Source: https://www.researchgate.net/profile/Paolo_Napoletano/publication/309327222/figure/fig1/AS:419620126248965@1477056642346/Sample-images-from-the-Aesthetic-Visual-Analysis-AVA-database-sorted-by-their-aesthetic.png

HAA500 is a manually annotated human-centric atomic action dataset for action recognition on 500 classes with over 591k labeled frames. Unlike existing atomic action datasets, where coarse-grained atomic actions were labeled with action-verbs, e.g., "Throw", HAA500 contains fine-grained atomic actions where only consistent actions fall under the same label, e.g., "Baseball Pitching" vs "Free Throw in Basketball", to minimize ambiguities in action classification. HAA500 has been carefully curated to capture the movement of human figures with less spatio-temporal label noises to greatly enhance the training of deep neural networks. Source: HAA500: Human-Centric Atomic Action Dataset with Curated Videos

WikiAtomicEdits is a corpus of 43 million atomic edits across 8 languages. These edits are mined from Wikipedia edit history and consist of instances in which a human editor has inserted a single contiguous phrase into, or deleted a single contiguous phrase from, an existing sentence. Source: WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse Image Source: https://arxiv.org/pdf/1808.09422v1.pdf

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in rehabilitation research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires significant neuroanatomical expertise. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N=1271), a larger dataset of T1w stroke MRIs and manually segmented lesion masks that includes training (public. n=655), test (masks hidden, n=300), and generalizability (completely hidden, n=316) data. Algorithm development using this larger sample should lead to more robust solutions, and the hidden test and generalizability datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke rehabilitation research. Official Paper: https://www.nature.com/articles/s41597-022-01401-7

Quality, diversity, and size of training dataset are critical factors for learning-based gaze estimators. We create two datasets satisfying these criteria for near-eye gaze estimation under infrared illumination: a synthetic dataset using anatomically-informed eye and face models with variations in face shape, gaze direction, pupil and iris, skin tone, and external conditions (two million images at 1280x960), and a real-world dataset collected with 35 subjects (2.5 million images at 640x480). Using our datasets, we train a neural network for gaze estimation, achieving 2.06 (+/- 0.44) degrees of accuracy across a wide 30 x 40 degrees field of view on real subjects excluded from training and 0.5 degrees best-case accuracy (across the same field of view) when explicitly trained for one real subject. We also train a variant of our network to perform pupil estimation, showing higher robustness than previous methods. Our network requires fewer convolutional layers than previous networks, achieving sub-millisecond latency.

This dataset is part of the publication "A bi-atrial statistical shape model for large-scale in silico studies of human atria: Model development and application to ECG simulations" by Nagel et al. (https://doi.org/10.1016/j.media.2021.102210). It includes a bi-atrial statistical shape model built based on 47 MR and CT images (Left atrium segmentation challenge (Tobon-Gomez, 2015), Left atrium fibrosis and scar segmentation challenge (Karim, 2013), Left atrial wall thickness challenge (Karim, 2018)). ScalismoLab (https://scalismo.org) was used for parts of the model generation. Further Details are explained in the paper. The SSM is available as an h5 file including information about the mean shape's vertex locations and their triangulation as well as the eigenvectors and -values. 100 random instances derived from the model are available. Each zip file contains the volumetric bi-atrial geometry as vtk file, which was augmented in a post-processing step with a homogeneous wall thickness, fiber orientation, intra-atrial bridges and material tags so that they are ready to use for electrophysiological simulations of atrial signals. Furthermore, the scalar field resulting from computing the gradient of the Laplace equation with the boundary conditions described by Piersanti et al. (Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations, Computer Methods in Applied Mechanics and Engineering, 2020, https://doi.org/10.1016/j.cma.2020.113468) are available on the left and the right atrial instances. Furthermore, 95 geometries with uniformly distributed left atrial volumes are available in LAE_geometries.zip.

Description The molecules were randomly drawn from the largest set of isomers in the QM9 dataset [1] which consists of molecules with a fixed composition of atoms (C7O2H10) arranged in different chemically valid structures. It is an extension of the ismoer MD data used in [2]. The database was generated from molecular dynamics simulations using the Fritz-Haber Institute ab initio simulation package (FHI-aims)[3]. The simulations were carried out using the standard quantum chemistry computational method density functional theory (DFT) in the generalized gradient approximation (GGA) with the Perdew-Burke-Ernzerhof (PBE) functional[4] and the Tkatchenko-Scheffler (TS) van der Waals correction method [5]. The database consist of 129 molecules each containing 5,000 conformational geometries, energies and forces with a resolution of 1 femtosecond in the molecular dynamics trajectories. ### Format The data is stored in ASE sqlite format with the total energy in eV under the key total energy and the atomic_forces under the key atomic_forces in eV/Ang. The following Python snippet iterates over the first 10 entries of the dataset located at path_to_db: python from ase.db import connect with connect(path_to_db) as conn: for row in conn.select(limit=10): print(row.toatoms()) print(row['total_energy']) print(row.data['atomic_forces']) ### Partitions The data is partitioned as used in the SchNet paper [6]: reference.db - 80% of steps of 80% of MD trajectories reference_eq.db - equilibrium conformations of those molecules test_within.db - remaining 20% unseen steps of reference trajectories test_other.db - remaining 20% unseen MD trajectories test_eq.db - equilibrium conformations of test trajectories In the paper, we split the reference data (reference.db) into 400k training examples and 4k validation examples. The indices are given in the files train_ids.txt and validation_idx.txt, respectively. ### Benchmarks Model Energy (within) [eV] Force (within) [eV/A] Energy (other) [eV] Force (other) [eV/A] SchNet [6] 0.016 0.043 0.104 0.095 ### Download Available here: data/iso17.tar.gz (799.7 MB) ### How to cite When using this dataset, please make sure to cite the following papers: K.T. Schütt, P.-J. Kindermans, H.E. Sauceda, S. Chmiela, A. Tkatchenko, K.-R. Müller. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. Advances in Neural Information Processing System. 2017. K.T. Schütt, F. Arbabzadah, S. Chmiela, K.R. Müller, A. Tkatchenko. Quantum-chemical insights from deep tensor neural networks. Nature Communications, 8, 13890. 2017. R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. von Lilienfeld. Quantum chemistry structures and properties of 134 kilo molecules. Scientific Data, 1, 2014. References [1] R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. von Lilienfeld. Quantum chemistry structures and properties of 134 kilo molecules. Scientific Data, 1, 2014. [2] Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., & Tkatchenko, A. (2017). Quantum-chemical insights from deep tensor neural networks. Nature Communications, 8, 13890. [3] Blum, V.; Gehrke, R.; Hanke, F.; Havu, P.; Havu, V.; Ren, X.; Reuter, K.; Scheffler, M. Ab Initio Molecular Simulations with Numeric Atom-Centered Orbitals. Comput. Phys. Commun. 2009, 180 (11), 2175–2196. [4] Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized Gradient Approximation Made Simple. Phys. Rev. Lett. 1996, 77 (18), 3865–3868. [5] Tkatchenko, A.; Scheffler, M. Accurate Molecular Van Der Waals Interactions from Ground-State Electron Density and Free-Atom Reference Data. Phys. Rev. Lett. 2009, 102 (7), 73005. [6] Schütt, K. T., Kindermans, P. J., Sauceda, H. E., Chmiela, S., Tkatchenko, A., & Müller, K. R. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. Advances in Neural Information Processing System (accepted). 2017.

Dataset used in "Machine Learning for Analyzing Atomic Force Microscopy (AFM) Images Generated from Polymer Blends". Contains AFM images in raw ".ibw" format.

RTP Dataset (Reactant–Transition State–Product Dataset) The RTP dataset forms the core training and evaluation set and consists of 35,087 molecular geometries sampled from 11,961 unique elementary reactions. For each reaction, three critical geometries are included: the optimized reactant, transition state (TS), and product. Each geometry is labeled with its corresponding DFT-computed potential energy, atomic forces, and Hessian matrix, calculated at the wb97xd/6-31g(d) level of theory. This dataset represents stationary points (critical points) on the potential energy surface and serves as the foundation for training the MLIPs to reproduce energies, gradients, and curvatures. ### IRC Dataset (Intrinsic Reaction Coordinate Dataset) To assess the extrapolation performance of the trained MLIPs along continuous reaction pathways, a dataset of 34,248 geometries was compiled from 600 Intrinsic Reaction Coordinate (IRC) paths, each corresponding to a distinct elementary reaction in the RTP dataset. These geometries were obtained by following the minimum energy path (MEP) from the transition state to both reactant and product wells using quantum chemistry calculations at the wb97xd/6-31g(d) level of theory. While these geometries are not explicitly used in training, they provide a rigorous benchmark for evaluating the ability of MLIPs to generalize beyond training data and accurately model transition state connectivity and reaction dynamics. ### NMS Dataset (Normal Mode Sampling Dataset) To evaluate MLIP robustness on off-equilibrium, perturbed structures, 62,527 geometries were generated via Normal Mode Sampling (NMS). These structures are derived by displacing intermediate IRC geometries along their vibrational modes with random amplitudes, simulating thermal fluctuations and non-equilibrium distortions. The properties of these perturbed structures were calculated at the wb97xd/6-31g(d) level of theory. This dataset allows for testing the model's stability and accuracy in more realistic, noisy molecular environments as encountered in molecular dynamics simulations or under experimental conditions. These datasets were used in the training and testing of Machine Learning Interatomic Potentials (MLIPs) as part of the work represented in the article titled Does Hessian Data Improve the Performance of Machine Learning Potentials?.

The dataset contains standard contexts of the lattices of all atomic lattices in the Concept Explorer format.

Projection of RibFrac CT dataset to a 2D plane to imitate X-Ray data for a total of 880 images with multi-label segmentation masks. The dataset contains fine-grained 92 individual labels of anatomical structures, which, when including super-classes, lead to a total of 166 labels in both lateral and frontal view.