ACF Agency Wide resource Metadata-only record linking to the original dataset. Open original dataset below.

this is to show the Organizational Structure for development and employment fund

Ce jeu de données, rafraîchi une fois par jour, présente les données régionales consolidées depuis janvier 2021 et définitives (de janvier 2013 à décembre 2023) issues de l'application éCO2mix. Elles sont élaborées à partir des comptages et complétées par des forfaits. Les données sont dites consolidées lorsqu'elles ont été vérifiées et complétées (livraison en milieu de M+1). Elles deviennent définitives lorsque tous les partenaires ont transmis et vérifié l'ensemble des comptages, (livraison deuxième trimestre A+1).

Vous y trouverez au pas demi-heure:

La consommation réalisée. La production selon les différentes filières composant le mix énergétique. La consommation des pompes dans les Stations de Transfert d'Energie par Pompage (STEP). Le solde des échanges avec les régions limitrophes.Pour information, ci-dessous les définitions de TCO et TCH :TCO : le Taux de COuverture (TCO) d'une filière de production au sein d'une région représente la part de cette filière dans la consommation de cette régionTCH : le Taux de CHarge (TCH) ou facteur de charge (FC) d'une filière représente son volume de production par rapport à la capacité de production installée et en service de cette filière

Si vous souhaitez consulter les données régionales "temps réel" après la dernière consolidation, vous pouvez suivre ce lien : https://opendata.reseaux-energies.fr/explore/dataset/eco2mix-regional-tr

Pour en savoir plus, n'hésitez pas à consulter le site éCO2mix à cette adresse : http://www.rte-france.com/fr/eco2mix/eco2mix

Ce jeu de données est mis à jour automatiquement une fois par jour : en raison d'un nombre de téléchargements excessif des données éCO2mix régionales consolidées et définitives par des robots à des fréquences disproportionnées au regard de la fréquence de mise à jour du jeu de données, un quota de 50000 appels API par utilisateur et par mois a été mis en place. Si vous constatez des soucis d'accès aux données suite à la mise en place de ce quota, merci de nous contacter à l'adresse rte-opendata@rte-france.com

This is the data set that includes rankings of competing definitions of open government by a sample of Canadian journalists, parliamentarians and bloggers.

The Slovenian definition extraction training dataset DF_NDF_wiki_slo contains 38613 sentences extracted from the Slovenian Wikipedia. The first sentence of a term's description on Wikipedia is considered a definition, and all other sentences are considered non-definitions.

The corpus consists of the following files each containing one definition / non-definition sentence per line:

1. Definitions: df_ndf_wiki_slo_Y.txt with 3251 definition sentences.
2. Non-definitions: df_ndf_wiki_slo_N.txt with 14678 non-definition sentences which do not contain the term at the beginning of the sentence.
3. Non-definitions: df_ndf_wiki_slo_N1.txt with 20684 non-definition sentences which may also contain the term at the beginning of the sentence.

The dataset is described in more detail in Fišer et al. 2010. If you use this resource, please cite:

Fišer, D., Pollak, S., Vintar, Š. (2010). Learning to Mine Definitions from Slovene Structured and Unstructured Knowledge-Rich Resources. Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10). https://aclanthology.org/L10-1089/

Reference to training Transformer-based definition extraction models using this dataset: Tran, T.H.H., Podpečan, V., Jemec Tomazin, M., Pollak, Senja (2023). Definition Extraction for Slovene: Patterns, Transformer Classifiers and ChatGPT. Proceedings of the ELEX 2023: Electronic lexicography in the 21st century. Invisible lexicography: everywhere lexical data is used without users realizing they make use of a “dictionary”.

Related resources: Jemec Tomazin, M. et al. (2023). Slovenian Definition Extraction evaluation datasets RSDO-def 1.0, Slovenian language resource repository CLARIN.SI, http://hdl.handle.net/11356/1841

Dataset Overview

This dataset consists of 42,052 English words and their corresponding definitions. It is a comprehensive collection of words ranging from common terms to more obscure vocabulary. The dataset is ideal for Natural Language Processing (NLP) tasks, educational tools, and various language-related applications.

Key Features:

• Words: A diverse set of English words, including both rare and frequently used terms.
• Definitions: Each word is accompanied by a detailed definition that explains its meaning and contextual usage.

Total Number of Words: 42,052

Applications

This dataset is well-suited for a range of use cases, including:

• Natural Language Processing (NLP): Enhance text understanding models by providing contextual meaning and word associations.
• Vocabulary Building: Create educational tools or games that help users expand their vocabulary.
• Lexical Studies: Perform academic research on word usage, trends, and lexical semantics.
• Dictionary and Thesaurus Development: Serve as a resource for building dictionary or thesaurus applications, where users can search for words and definitions.

Data Structure

• Word: The column containing the English word.
• Definition: The column providing a comprehensive definition of the word.

Potential Use Cases

• Language Learning: This dataset can be used to develop applications or tools aimed at enhancing vocabulary acquisition for language learners.
• NLP Model Training: Useful for tasks such as word embeddings, definition generation, and contextual learning.
• Research: Analyze word patterns, rare vocabulary, and trends in the English language.

This version focuses on providing essential information while emphasizing the total number of words and potential applications of the dataset. Let me know if you'd like any further adjustments!

HHS responsibly shares “open by default” data with the public to democratize access to information, demystify the Department, and increase transparency through data sharing. HHS Open Data is non-sensitive data, meaning thousands of health and human services datasets are publicly available to fuel new business models, enable emerging technologies like AI, accelerate scientific discoveries, and inspire American innovation. This top-1000 HHS Open Data websites and resources page, dynamically generated from the Digital Analytics Program (DAP) provided by the U.S. General Services Administration (GSA), is driven by near-real-time user demand. GSA’s DAP helps federal agencies and the public see how visitors find, access, and use government websites, data, and services online. The below list filters DAP for only resources from HHS and includes all HHS Divisions. You may filter by individual HHS Divisions and columns.

% of adults (aged 18+) classified as overweight or obese (new definition)

This map is used for this web feature layer hosted on ArcGIS Online.https://services.arcgis.com/uUvqNMGPm7axC2dD/arcgis/rest/services/webFACILITY/FeatureServer/0

Overall Violence (Violence Against Person Def.)

​Significant changes to the open data catalogue, including new datasets added, datasets renamed or retired, quarterly or annual updates to high-impact datasets, changes to data structure or definition. Smaller changes, such as adding or editing records or renaming a field in an existing dataset are not included. NoteThis log is published in the interest of transparency into the work of the open data program. You can subscribe to updates for a specific dataset by creating an account on the portal then clicking on the Follow button on the Information tab of any dataset. You can get updates by subscribing to our email newsletter. Data currency​New records will be added whenever a significant change is made to the open data catalogue.

ACF Children Bureau resource Metadata-only record linking to the original dataset. Open original dataset below.

When photographing the contents displayed on the digital screen, an inevitable frequency aliasing between the camera’s color filter array (CFA) and the screen’s LCD subpixel widely exists. The captured images are thus mixed with colorful stripes, named moiré patterns, which severely degrade the perceptual quality of images. Currently, efficiently removing moiré patterns from a single moiré image is still challenging and receives growing attention from the research community.

Ultra High-definition Demoiréing Dataset or UHDM is the first 4K resolution demoiréing dataset, consisting of 5,000 image pairs. This dataset includes diverse scenes, such as landscapes, sports, video clips, and documents. The moiré images are generated following practical routines, with different device combinations and viewpoints to produce diverse moiré patterns.

The dataset was collected by the authors of the paper Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoiréing.

To obtain the real-world 4K image pairs, the authors of the paper Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoiréing first collect high-quality images with resolutions ranging from 4K to 8K from the Internet. Given that Internet resources lack document scenes, which also constitute a vital application scenario (e.g., slides, papers), the authors manually generate high-quality text images and make sure they maintain 3000 dpi (Dots Per Inch). Finally, the collected moiré-free images cover a wide range of scenes, such as landscapes, sports, video clips, and documents. Given these high-quality images, the authors generate diverse real-world moiré patterns elaborated upon below.

1. First, in order to produce realistic moiré images and ease the difficulties of calibrations, the authors shoot the clean pictures displayed on the screen with a camera phone fixed on a DJI OM 5 smartphone gimbal, which makes it possible to conveniently and flexibly adjust the camera view through its control button.

2. Second, the authors note that the characteristics of moiré patterns highly are highly dependent upon the geometric relationship between the screen and the camera. Therefore, during the capturing process, the authors continuously adjust the viewpoint every ten shots to produce diverse moiré patterns.

3. Third, the authors adopt multiple < mobile phone, screen > (i.e., three mobile phones and three digital screens, see supplement for more details) combinations to cover various device pairs, since they will also have an impact on the styles of moiré patterns.

4. Finally, in order to obtain aligned pairs, the authors utilize the RANSAC algorithm to estimate the homography matrix between the original high-quality image and the captured moiré screen image. Since it is difficult to ensure accurate pixel-wise calibration due to the camera’s internal nonlinear distortions and perturbations of moiré artifacts, manual selection is performed to rule out severely misaligned image pairs, thereby ensuring quality.

@misc{
  2207.09935,
  Author = {Xin Yu and Peng Dai and Wenbo Li and Lan Ma and Jiajun Shen and Jia Li and Xiaojuan Qi},
  Title = {Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoireing},
  Year = {2022},
  Eprint = {arXiv:2207.09935},
}

View metadata for key information about this dataset.See also the related dataset L&I Violations.For questions about this dataset, contact ligisteam@phila.gov. For technical assistance, email maps@phila.gov.

A de-identified open dataset of 507 adults, capturing how people understand their core personal values, sense of meaning, life direction, fulfilment, behavioural alignment, and emotional wellbeing in the context of digital and AI-mediated environments.

Outlines surveillance case definitions for COVID-19 and provides instructions on reporting to the national level. Surveillance case definitions are provided for the purpose of standardized case classification and reporting to the Public Health Agency of Canada.

This Program Instruction (PI) provides clarifying guidance to title IV-E agencies on the parameters of short-term support that may be provided to kinship families with title IV-E kinship navigator funding under the flexibilities of Section 8, Division X of P.L. 116-260.

Metadata-only record linking to the original dataset. Open original dataset below.

This is the official Street Centerline dataset for the County of Sacramento and the incorporated cities within. The Street Range Index table is a distinct list of street names within the Centerline dataset along with the existing address range for each street by zip code.The Street Name Index table is a distinct list of street names within the Centerline dataset.

Parcel boundaries exported nightly from the Assessor's Office managed parcel fabric and joined with attributes related to owner information, values and size, the water source (Public Health database) and a link to the SmartGov public portal (permitting database). Most features are within 3 feet however some features can be up to 20 feet off. Please read the full data disclaimer when using this dataset.

This is the dataset of our EMNLP 2021 paper:

Graphine: A Dataset for Graph-aware Terminology Definition Generation.

Please read the "readme.md" in it for the format of the dataset.