cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12851349
title: Bias and High-Dimensional Adjustment in Observational Studies of Peer Effects

description: Peer effects, in which an individual’s behavior is affected by peers’ behavior, are posited by multiple theories in the social sciences. Randomized field experiments that identify peer effects, however, are often expensive or infeasible, so many studies of peer effects use observational data, which is expected to suffer from confounding. Here we show, in the context of information and media diffusion, that high-dimensional adjustment of a nonexperimental control group (660 million observations) using propensity score models produces estimates of peer effects statistically indistinguishable from those using a large randomized experiment (215 million observations). Compared with the experiment, naive observational estimators overstate peer effects by over 300% and commonly available variables (e.g., demographics) offer little bias reduction. Adjusting for a measure of prior behaviors closely related to the focal behavior reduces this bias by 91%, while models adjusting for over 3700 past behaviors provide additional bias reduction, reducing bias by over 97%, which is statistically indistinguishable from unbiasedness. This demonstrates how detailed records of behavior can improve studies of social influence, information diffusion, and imitation; these results are encouraging for the credibility of some studies but also cautionary for studies of peer effects in rare or new behaviors. More generally, these results show how large, high-dimensional datasets and statistical learning can be used to improve causal inference. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12851349.v6
title: Bias and High-Dimensional Adjustment in Observational Studies of Peer Effects

description: Peer effects, in which an individual’s behavior is affected by peers’ behavior, are posited by multiple theories in the social sciences. Randomized field experiments that identify peer effects, however, are often expensive or infeasible, so many studies of peer effects use observational data, which is expected to suffer from confounding. Here we show, in the context of information and media diffusion, that high-dimensional adjustment of a nonexperimental control group (660 million observations) using propensity score models produces estimates of peer effects statistically indistinguishable from those using a large randomized experiment (215 million observations). Compared with the experiment, naive observational estimators overstate peer effects by over 300% and commonly available variables (e.g., demographics) offer little bias reduction. Adjusting for a measure of prior behaviors closely related to the focal behavior reduces this bias by 91%, while models adjusting for over 3700 past behaviors provide additional bias reduction, reducing bias by over 97%, which is statistically indistinguishable from unbiasedness. This demonstrates how detailed records of behavior can improve studies of social influence, information diffusion, and imitation; these results are encouraging for the credibility of some studies but also cautionary for studies of peer effects in rare or new behaviors. More generally, these results show how large, high-dimensional datasets and statistical learning can be used to improve causal inference. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.14173410
title: The Effects of Stringent and Mild Interventions for Coronavirus Pandemic

description: The pandemic of COVID-19 has caused severe public health consequences around the world. Many interventions of COVID-19 have been implemented. It is of great public health and social importance to evaluate the effects of interventions in the pandemic of COVID-19. With the help of a synthetic control method, the regression discontinuity, and a state-space compartmental model, we evaluated the treatment and stagewise effects of the intervention policies. We found statistically significant treatment effects of broad stringent interventions in Wenzhou and mild interventions in Shanghai to subdue the epidemic’s spread. If those reduction effects were not activated, the expected number of positive individuals would increase by 2.18 times on February 5, 2020, for Wenzhou and 7.69 times on February 4, 2020, for Shanghai, respectively. Alternatively, regression discontinuity elegantly identified the stringent (<i>p</i>-value: &lt;0.001) and mild interventions (<i>p</i>-value: 0.024) lowered the severity of the epidemic. Under the compartmental modeling for different interventions, we understood the importance of implementing the interventions. The highest level alert to COVID-19 was practical and crucial at the early stage of the epidemic. Furthermore, the physical/social distancing policy was necessary once the spread of COVID-19 continued. If appropriate control measures were implemented, then epidemic would be under control effectively and early. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.14173410.v1
title: The Effects of Stringent and Mild Interventions for Coronavirus Pandemic

description: The pandemic of COVID-19 has caused severe public health consequences around the world. Many interventions of COVID-19 have been implemented. It is of great public health and social importance to evaluate the effects of interventions in the pandemic of COVID-19. With the help of a synthetic control method, the regression discontinuity, and a state-space compartmental model, we evaluated the treatment and stagewise effects of the intervention policies. We found statistically significant treatment effects of broad stringent interventions in Wenzhou and mild interventions in Shanghai to subdue the epidemic’s spread. If those reduction effects were not activated, the expected number of positive individuals would increase by 2.18 times on February 5, 2020, for Wenzhou and 7.69 times on February 4, 2020, for Shanghai, respectively. Alternatively, regression discontinuity elegantly identified the stringent (p-value: &lt;0.001) and mild interventions (p-value: 0.024) lowered the severity of the epidemic. Under the compartmental modeling for different interventions, we understood the importance of implementing the interventions. The highest level alert to COVID-19 was practical and crucial at the early stage of the epidemic. Furthermore, the physical/social distancing policy was necessary once the spread of COVID-19 continued. If appropriate control measures were implemented, the epidemic would be under control effectively and early.
DOI: 10.6084/m9.figshare.12851349.v5
title: Bias and High-Dimensional Adjustment in Observational Studies of Peer Effects

description: Peer effects, in which an individual’s behavior is affected by peers’ behavior, are posited by multiple theories in the social sciences. Randomized field experiments that identify peer effects, however, are often expensive or infeasible, so many studies of peer effects use observational data, which is expected to suffer from confounding. Here we show, in the context of information and media diffusion, that high-dimensional adjustment of a nonexperimental control group (660 million observations) using propensity score models produces estimates of peer effects statistically indistinguishable from those using a large randomized experiment (215 million observations). Compared with the experiment, naive observational estimators overstate peer effects by over 300% and commonly available variables (e.g., demographics) offer little bias reduction. Adjusting for a measure of prior behaviors closely related to the focal behavior reduces this bias by 91%, while models adjusting for over 3700 past behaviors provide additional bias reduction, reducing bias by over 97%, which is statistically indistinguishable from unbiasedness. This demonstrates how detailed records of behavior can improve studies of social influence, information diffusion, and imitation; these results are encouraging for the credibility of some studies but also cautionary for studies of peer effects in rare or new behaviors. More generally, these results show how large, high-dimensional datasets and statistical learning can be used to improve causal inference. Supplementary materials for this article are available online.
DOI: 10.57702/jicom9g4
title: Japan Society of Radiological Technology Dataset

description: Japan Society of Radiological Technology dataset provides 247 X-ray images of 1024 × 1024 size, containing lung, heart, and clavicle labels.
DOI: 10.57702/3e2hyr3t
title: Harvard Whole Brain Atlas

description: The Harvard Whole Brain Atlas dataset contains MRI and PET images of patients with various brain conditions.
DOI: 10.57702/uxllq5eu
title: TeMO: Towards Text-Driven 3D Stylization for Multi-Object Meshes

description: A dataset of 3D object meshes and text prompts for text-driven 3D stylization for multi-object scenes.
DOI: 10.57702/lmvr0hok
title: Clip-nerf: Text-and-image driven manipulation of neural radiance fields

description: A dataset of 3D shapes and textures for text-driven 3D manipulation.
DOI: 10.57702/pa4431n8
title: ESC-50

description: The dataset used for training the CNN in cough detection is composed of various modified audio clips gathered from open-source online sources. Each of these audio files originally contained at least one cough event and are cropped to a length of five seconds with the full cough contained at some arbitrary point within.
DOI: 10.57702/yzddzrzs
title: ESC-10

description: The ESC-10 dataset is a subset of the ESC-50 dataset with 10 classes and 400 recordings.
DOI: 10.57702/4yzvdtr2
title: Learning the What and How of Annotation in Video Object Segmentation

description: Video object segmentation is crucial for several applications, from video editing to video data generation. Training a VOS model requires an abundance of manually labeled training videos. The de-facto traditional way of annotating objects requires humans to draw detailed segmentation masks on the target objects at each video frame. This annotation process, however, is tedious and time-consuming.
DOI: 10.57702/8imxc3e9
title: MOSE: A New Dataset for Video Object Segmentation in Complex Scenes

description: MOSE is a new dataset for video object segmentation in complex scenes. It contains 2149 videos, out of which only 1507 come with available ground-truth masks.
DOI: 10.57702/91wkzxun
title: Occluded Video Instance Segmentation (OVIS)

description: Occluded Video Instance Segmentation (OVIS) is a benchmark for evaluating video object segmentation models in occluded scenes.
DOI: 10.57702/16dzbxj9
title: COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss

description: COVID-19 CT dataset for automated diagnosis and severity assessment of COVID-19
DOI: 10.57702/352h0rfu
title: Open-Sora Plan

description: The dataset used in this paper for text-to-video generation, consisting of short video clips.
DOI: 10.57702/juzm3om4
title: Diverse instance discovery: Vision-Transformer for instance-aware multi-label image recognition

description: Multi-label image recognition is a practical and challenging computer vision task. The authors propose a method to leverage the advantages of Transformer with long-range dependency modeling to circumvent the disadvantages of CNNs limited to local receptive fields.
DOI: 10.57702/ohcf6mxe
title: Stanford AIMI Coronary Calcium (COCA) dataset

description: The Stanford AIMI Coronary Calcium (COCA) dataset is used for heart CTCA images. COCA contains 787 3D coronary CT images. Each 3D image has a different number (ranging from 27 to 156) of 2D slices on the axial plane, and they add up to 39,281 axial slices in total.
DOI: 10.57702/zyfqipwy
title: DAVIS2017 dataset

description: The dataset used in the paper for video compressive sensing.
DOI: 10.57702/gw8d57zz
title: LDD: A Grape Diseases Dataset

description: A new dataset for grape diseases object detection and instance segmentation.
DOI: 10.57702/h3xifxgl
title: A grapevine leaves dataset for early detection and classification of esca disease

description: A dataset for grapevine leaves for early detection and classification of esca disease.
DOI: 10.57702/3vosy2g5
title: Leaf disease segmentation and detection in apple orchards

description: A dataset for leaf disease segmentation and detection in apple orchards.
DOI: 10.57702/vwwj19a7
title: TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers

description: Mixup is a commonly adopted data augmentation technique for image classification. Recent advances in mixup methods primarily focus on mixing based on saliency.
DOI: 10.57702/hf1zexdy
title: ImageNet-21k and ImageNet-1k

description: The ImageNet-21k and ImageNet-1k datasets are two large-scale image classification datasets.
DOI: 10.57702/dgk0yrtw
title: MIMIC-ABN

description: Radiology report generation dataset
DOI: 10.57702/17m5yrcs
title: CE5-CDA

description: A newly annotated dataset of real lunar images collected by the Chang’e 5 Landing Camera, containing off-nadir view angles.
DOI: 10.57702/2idlgyoq
title: CRESENT

description: A dataset of rendered images of the lunar surface, with crater rims supervised by a bounding ellipse.
DOI: 10.57702/2dyeg2bv
title: LRO-NAC

description: A manually supervised dataset of 20 images from the LRO NAC, containing over 20,000 annotated craters.
DOI: 10.57702/jxl2envc
title: DiffDA: a Diffusion model for weather-scale Data Assimilation

description: DiffDA: a Diffusion model for weather-scale Data Assimilation
DOI: 10.57702/d4wuhg0g
title: DEEPA: A DEEP NEURAL ANALYZER FOR SPEECH AND SINGING VOCODING

description: The proposed neural analyzer, DeepA, that is based on a VAE architecture.
DOI: 10.57702/6pl51aab
title: NHSS: A speech and singing parallel database

description: NHSS: A speech and singing parallel database.
DOI: 10.57702/hik2iqpx
title: EEG dataset for BCI classification

description: The dataset used in this paper is a collection of EEG signals recorded from 6 healthy males using dry electrodes. The dataset is used for training and testing a convolutional neural network for EEG-based brain-computer interface (BCI) classification.
DOI: 10.57702/aouh4nyw
title: Face Video Dataset

description: A face video dataset containing 16300 face videos from the Internet.
DOI: 10.57702/wbj8kgsn
title: ProSky: NEAT Meets NOMA-mmWave

description: The dataset used in this paper is a 3D downlink cellular network that covers an area A of L x L units in which a UAV serves a total of 2N users, uniformly distributed ground users, NUE.
DOI: 10.57702/4jvg1qo0
title: SIMMC: Situated Interactive Multi-Modal Conversational Data Collection and Evaluation Platform

description: SIMMC is an extension to ParlAI for multi-modal conversational data collection and system evaluation. It simulates an immersive setup, where crowd workers interact with environments constructed in AI Habitat or Unity while engaging in a conversation.
DOI: 10.5281/zenodo.13831402
title: MaskedProtEnT_Datasets
DOI: 10.5281/zenodo.13831403
title: MaskedProtEnT_Datasets
DOI: 10.5281/zenodo.14257235
title: MaskedProtEnT_Datasets
DOI: 10.57702/40by4p8y
title: MVTec ITODD

description: The MVTec ITODD dataset is a dataset for 3D object recognition in industry.
DOI: 10.57702/ov9339ty
title: PoseCNN

description: The PoseCNN dataset is a dataset for 6D object pose estimation in cluttered scenes.
DOI: 10.57702/soj3yw86
title: iShape

description: The iShape dataset is a dataset for irregular shape instance segmentation.
DOI: 10.57702/ywqswszg
title: DPOD

description: The DPOD dataset is a dataset for 6D object pose estimation.
DOI: 10.57702/ailzfspn
title: TCGA_LGG

description: The TCGA_LGG database is a repository of brain images for the study of lower grade gliomas.
DOI: 10.57702/67tsc1m2
title: Tiny-ImageNet6

description: The dataset used for transfer learning tasks.
DOI: 10.57702/vc761c7f
title: VisDrone2019-Det

description: Object detection with Unmanned Aerial Vehicles (UAVs) has attracted much attention in the research field of computer vision. However, not easy to accurately detect objects with data obtained from UAVs, which capture images from very high altitudes, making the image dominated by small object sizes, that difficult to detect.
DOI: 10.57702/u9fuiwda
title: Hybrid-S2S: Video Object Segmentation with Recurrent Networks and Correspondence Matching

description: One-shot Video Object Segmentation (VOS) is the task of pixel-wise tracking an object of interest within a video sequence, where the segmentation mask of the ﬁrst frame is given at inference time.
DOI: 10.5281/zenodo.14262754
title: Raw landmarks related to the paper, "Evolution under intensive industrial breeding: skull size and shape comparison between historic and modern pig lineages'

description: Raw coordinates (p x k = 82 x 3) of domestic and wild pig skulls that form the dataset for the paper, "­Evolution under intensive industrial breeding: skull size and shape comparison between historic and modern pig lineages
DOI: 10.57702/5ue6lm5a
title: PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning

description: Patch-based attacks introduce a perceptible but localized change to the input that induces misclassiﬁcation. A limitation of cur- rent patch-based black-box attacks is that they perform poorly for tar- geted attacks, and even for the less challenging non-targeted scenarios, they require a large number of queries.
DOI: 10.57702/47yooc1a
title: MaskPlus: Improving Mask Generation for Instance Segmentation

description: Instance segmentation is a promising yet challenging topic in computer vision. Recent approaches such as Mask R-CNN typically divide this problem into two parts – a detection component and a mask generation branch, and mostly focus on the improvement of the detection part. In this paper, we present an approach that extends Mask R-CNN with five novel techniques for improving the mask generation branch and reducing the conflicts between the mask branch and the detection component in training.
DOI: 10.57702/pakkri6f
title: MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features

description: Instance segmentation by refining object detection with semantic and direction features.
DOI: 10.57702/esw62qln
title: Human Pose Transfer by Adaptive Hierarchical Deformation

description: Human pose transfer, as a misaligned image generation task, is very challenging. Existing methods cannot effectively utilize the input information, which often fail to preserve the style and shape of hair and clothes.
DOI: 10.57702/yel000vt
title: SWaT dataset

description: The dataset used in the research paper for testing the robustness of autoencoder-based anomaly detectors to adversarial poisoning attacks.
DOI: 10.57702/6qvb3f54
title: Conceptual Captions

description: The dataset used in the paper "Scaling Laws of Synthetic Images for Model Training". The dataset is used for supervised image classification and zero-shot classification tasks.
DOI: 10.57702/5hyjshzh
title: Movie dataset

description: The Movie dataset is a graph dataset constructed from the Movielens-2k dataset. The Movielens-2k dataset contains movies information such as actors, genres, and tags information. The feature of each movie is the tags information. The task is to predict the genres of the movies.
DOI: 10.57702/i6u8lefh
title: ILSVRC2012 (ImageNet 1K)

description: The dataset used in the paper is ILSVRC2012 (ImageNet 1K), a large-scale image classification dataset.
DOI: 10.57702/tfzzptol
title: Synapse Multi-Organ Segmentation

description: Medical image segmentation dataset
DOI: 10.57702/wfuqq42w
title: ISIC 2017

description: Skin lesion segmentation dataset
DOI: 10.57702/h3lzs9h7
title: PointDreamer: Zero-shot 3D Textured Mesh Reconstruction from Colored Point Cloud by 2D Inpainting

description: Reconstructing textured meshes from colored point clouds is an important but challenging task in 3D graphics and vision. Most existing methods predict colors as implicit functions in 3D or UV space, suffering from blurry textures or the lack of generalization capability.
DOI: 10.57702/kvs59r5t
title: DEtection FRee (DEFR) HOI recognition

description: Human-Object Interaction (HOI) recognition at image level without using supervisions of object location and human pose.
DOI: 10.57702/8ixmh9mp
title: HICO dataset

description: HICO dataset contains 600 HOI categories which have 117 unique verbs and 80 COCO object classes.
DOI: 10.57702/4jvow8k6
title: Roadway Capacity-driven Graph Convolution Network for Network-Wide Traffic Prediction

description: The Roadway Capacity-driven Graph Convolution Network (RCDGCN) model integrates static and dynamic roadway capacity attributes in spatio-temporal settings to predict network-wide traffic states.
DOI: 10.57702/o1bmnws3
title: ICM-495 highway network and Manhattan urban network

description: The ICM-495 highway network and an urban network in Manhattan, New York City.
DOI: 10.57702/sxyklvld
title: LAION5B

description: The LAION5B dataset contains images with various objects and scenes.
DOI: 10.57702/t7nafdsi
title: Adolescent Brain Cognitive Development (ABCD) dataset

description: The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. The Human Connectome Project Young Adult (HCP) dataset.
DOI: 10.57702/iz475e0p
title: Human Connectome Project Young Adult (HCP) dataset

description: The Human Connectome Project Young Adult (HCP) dataset.
DOI: 10.57702/gk36g9no
title: LIDC Dataset

description: The Lung Image Database Consortium (LIDC) image collection dataset contains images of the lung.
DOI: 10.57702/vep8pnz2
title: SceneGenie: Scene Graph Guided Diffusion Models for Image Synthesis

description: Text-conditioned image generation has made significant progress in recent years with generative adversarial networks and more recently, diffusion models. The proposed guidance approach for the sampling process in the diffusion model that leverages bounding box and segmentation map information at inference time without additional training data.
DOI: 10.57702/80fd7gyj
title: UAH-DriveSet

description: UAH-DriveSet is a driving behavior analysis dataset containing car driving histories of six drivers simulating three different driving patterns.
DOI: 10.57702/ulowlwj9
title: Multi-Agent Particle Environment

description: The Multi-Agent Particle Environment is a medium-size collaborative problem where a defensive escort team of agents is learning to maintain an optimal formation around the VIP to protect it from possible physical attacks.
DOI: 10.57702/fxo0uhxk
title: Unrel

description: The Unrel dataset is a benchmark for visual relationship prediction, containing atypical relations that are not expected to occur in the Visual Genome benchmark.
DOI: 10.57702/dyfivy9u
title: RCS dataset

description: A large dataset of 9,600 Electromagnetic (EM) RCS signatures collected using the automated system
DOI: 10.57702/tx3duntn
title: Query-guided Attention in Vision Transformers for Localizing Objects Using a Single Sketch

description: Sketch-based object localization in natural images, where given a crude hand-drawn sketch of an object, the goal is to localize all the instances of the same object on the target image.
DOI: 10.57702/qkbjfq4q
title: Sketchy

description: Sketchy is a large collection of sketch-photo pairs. The dataset consists of images from 125 different classes, with 100 photos each.
DOI: 10.57702/dry79duc
title: Dynamics of the Loop Current in US Waters Study

description: The dataset used in this study was produced by the observational program Dynamics of the Loop Current in US Waters Study, which provides a 4D mapping of the current speed and density structure in the LC region from a high-density array of 149 moored instruments over a two-and-a-half-year period.