SEED-Data-Edit

SEED-Data-Edit is a hybrid dataset for instruction-guided image editing with a total of 3.7 image editing pairs, which comprises three distinct types of data: Part-1: Large-scale high-quality editing data produced by automated pipelines (3.5M editing pairs). Part-2: Real-world scenario data collected from the internet (52K editing pairs). Part-3: High-precision multi-turn editing data annotated by humans (95K editing pairs, 21K multi-turn rounds with a maximum… See the full description on the dataset page: https://huggingface.co/datasets/AILab-CVC/SEED-Data-Edit.

Dataset Card for HQ-EDIT

HQ-Edit, a high-quality instruction-based image editing dataset with total 197,350 edits. Unlike prior approaches relying on attribute guidance or human feedback on building datasets, we devise a scalable data collection pipeline leveraging advanced foundation models, namely GPT-4V and DALL-E 3. HQ-Edit’s high-resolution images, rich in detail and accompanied by comprehensive editing prompts, substantially enhance the capabilities of existing image editing… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/HQ-Edit-data-demo.

Comprehensive dataset containing 1,866 verified Video editing service businesses in Brazil with complete contact information, ratings, reviews, and location data.

OmniEdit

In this paper, we present OMNI-EDIT, which is an omnipotent editor to handle seven different image editing tasks with any aspect ratio seamlessly. Our contribution is in four folds: (1) OMNI-EDIT is trained by utilizing the supervision from seven different specialist models to ensure task coverage. (2) we utilize importance sampling based on the scores provided by large multimodal models (like GPT-4o) instead of CLIP-score to improve the data quality. 📃Paper | 🌐Website |… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/OmniEdit-Filtered-1.2M.

This report focuses on the editing and statistical imputation procedures that were applied to respondent data for the 2017 NSDUH. Logical editing uses data from elsewhere within the same respondent's record to reduce the occurrence of missing or ambiguous data or to resolve inconsistencies between related variables. Imputation is defined as the replacement of missing values with valid, nonmissing values. Statistical imputation usually involves some randomness to preserve the natural variability in the data.

50,000 Sets - Image Editing Dataset includes high-quality image pairs and annotations for object removal, addition, modification, and replacement. Editing targets span people, animals, products, plants, and landscapes across diverse real-world scenes. Each set includes clearly labeled annotations marking the regions and changes required based on editing instructions. This dataset is ideal for tasks such as image synthesis, AI-based photo editing, virtual scene generation, data augmentation, inpainting, and training image manipulation models. All data has been quality tested and complies with global privacy standards, including GDPR, CCPA, and PIPL.

Comprehensive dataset containing 1 verified Video Editing locations in United States with complete contact information, ratings, reviews, and location data.

Many statistical organizations collect data that are expected to satisfy linear constraints; as examples, component variables should sum to total variables, and ratios of pairs of variables should be bounded by expert-specified constants. When reported data violate constraints, organizations identify and replace values potentially in error in a process known as edit-imputation. To date, most approaches separate the error localization and imputation steps, typically using optimization methods to identify the variables to change followed by hot deck imputation. We present an approach that fully integrates editing and imputation for continuous microdata under linear constraints. Our approach relies on a Bayesian hierarchical model that includes (i) a flexible joint probability model for the underlying true values of the data with support only on the set of values that satisfy all editing constraints, (ii) a model for latent indicators of the variables that are in error, and (iii) a model for the reported responses for variables in error. We illustrate the potential advantages of the Bayesian editing approach over existing approaches using simulation studies. We apply the model to edit faulty data from the 2007 U.S. Census of Manufactures. Supplementary materials for this article are available online.

This document, Innovating the Data Ecosystem: An Update of The Federal Big Data Research and Development Strategic Plan, updates the 2016 Federal Big Data Research and Development Strategic Plan. This plan updates the vision and strategies on the research and development needs for big data laid out in the 2016 Strategic Plan through the six strategies areas (enhance the reusability and integrity of data; enable innovative, user-driven data science; develop and enhance the robustness of the federated ecosystem; prioritize privacy, ethics, and security; develop necessary expertise and diverse talent; and enhance U.S. leadership in the international context) to enhance data value and reusability and responsiveness to federal policies on data sharing and management.

The PE²rr corpus contains source language texts from different domains along with their automatically generated translations into several morphologically rich languages, their post-edited versions, and error annotations of the performed post-edit operations. The main advantage of the corpus is the fusion of post-editing and error classification tasks, which have usually been seen as two independent tasks, although naturally they are not.

Data collection and Editing workflows using ArcGIS ProOutline: ArcGIS Pro is a new desktop mapping and analysis application available to schools across Canada. This webinar will provide an overview of common editing workflows and demonstrate how you can create and edit data interactively in addition. You will also learn how to prepare your data for use in upcoming field work. This session is aimed at faculty and students who are currently using ArcGIS software tools at universities and colleges, and who are keen to learn more about how they can quickly migrate their desktop work to ArcGIS Pro. Software: ArcGIS Pro, ArcGIS Online, Collector for ArcGISVideo: https://youtu.be/LtvETpH1ZLs

Comprehensive dataset containing 31 verified Video editing service businesses in New Mexico, United States with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 26 verified Video editing service businesses in Rhode Island, United States with complete contact information, ratings, reviews, and location data.

finbarr/rlvr-code-data-cpp-code-edit dataset hosted on Hugging Face and contributed by the HF Datasets community

Directory of suggested edits to information in NYC Street Tree Map. Users can suggest a different species, diameter, or other notes about the tree. Edits are reviewed by a NYC Street Tree Map administrator before they are incorporated into the Map. This directory tracks the content and status of each suggested edit.

The GCMD database holds more than 30,000 descriptions of Earth science data sets and services covering all aspects of Earth and environmental sciences. The mission of the GCMD is to (1) Assist the scientific community in the discovery of Earth science data, related services, and ancillary information (platforms, instruments, projects, data centers/service providers); and (2) Provide discovery/collection-level metadata of Earth science resources and provide scientists a comprehensive and high quality database to reduce overall expenditures for scientific data collection and dissemination.

Text-guided image editing using diffusion models

This report focuses on the editing and statistical imputation procedures that were applied to respondent data for the 2013 NSDUH. Logical editing uses data from elsewhere within the same respondent's record to reduce the occurrence of missing or ambiguous data or to resolve inconsistencies between related variables. Imputation is defined as the replacement of missing values with valid, nonmissing values. Statistical imputation usually involves some randomness to preserve the natural variability in the data.

This report focuses on the editing and statistical imputation procedures that were applied to respondent data for the 2018 NSDUH. Logical editing uses data from elsewhere within the same respondent's record to reduce the occurrence of missing or ambiguous data or to resolve inconsistencies between related variables. Imputation is defined as the replacement of missing values with valid, nonmissing values. Statistical imputation usually involves some randomness to preserve the natural variability in the data.

Learn about the editing and statistical imputation procedures that were applied to respondent data for the 2021 National Survey on Drug Use and Health (NSDUH). Logical editing resolves inconsistencies or ambiguous data based on a respondent’s answers to other questions in the survey. Statistical imputation uses mathematical techniques to assign values when they are missing in the data.Introductory Chapters:An introduction, including a discussion of changes from the 2020 to 2021 survey.A description of the procedures and general principles for editing the NSDUH data.A description of the general imputation procedures used in NSDUH.Remaining chapters are descriptions of the editing and imputation for the following types of variables:Front-end demographics.Back-end demographics.Substance use.Special drugs and substance use disorder.Additional substance use, including treatment and emerging issues.Substance use risk and protective factors.Physical and mental health.Roster variables.Income.Health insurance.Pair variables.