This dataset includes photochemical air quality modeling files for simulations of fire impacts on ground-level ozone cocnentrations in the U.S. during the summer of 2023. A data dictionary describes what is included in the each of the files. Detailed information on the model simulations and the file contents is included in a journal article documenting the dataset: Simon, H., Beidler, J., Baker, K.R., Henderson, B.H., Fox, L., Misenis, C., Campbell, P., Vukovich, J. Possiel, N., Eyth, E. Expediated Modeling of Burn Events Results (EMBER): A Screening-Level Dataset of 2023 Ozone Fire Impacts in the US, Data in Brief, https://doi.org/10.1016/j.dib.2024.111208 A web-based tool for browsing this dataset is also available at: https://www.epa.gov/air-quality-analysis/expedited-modeling-burn-events-results-ember. This dataset is associated with the following publication: Simon, H., J. Beidler, K. Baker, B. Henderson, L. Fox, C. Misenis, P. Campbell, J. Vukovich, N. Possiel, and A. Eyth. Expediated Modeling of Burn Events Results (EMBER): A Screening-Level Dataset of 2023 Ozone Fire Impacts in the US. Data in Brief. Elsevier B.V., Amsterdam, NETHERLANDS, 58: 111208, (2025).

ember-lab-berkeley/LeVERB-Bench-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

A complete list of live websites using the ember-data-model-fragments technology, compiled through global website indexing conducted by WebTechSurvey.

This ember load dataset represents the ember load index (ELI) per pixel, for a given pixel, based on surface and canopy fuel characteristics, climate, and topography within the pixel. The Ember Load Index (ELI) incorporates burn probability (BP). BP is incorporated into calculations of the ember production before the distribution of embers across the landscape to determine ember load. Given that ELI incorporates burn probability, this index can be used to identify where on the landscape hardening buildings may be needed to resist ignition and the priority for doing so according to the likelihood of the area being visited by fire.

Abstract

This public dataset contains key variables on energy consumption (primary energy, per capita, and growth rates), energy mix, electricity mix and other relevant metrics, made available by Our World in Data. Curated by Carnegie Mellon University Libraries.

Methodology

Additional data sources used by Our World in Data include:

• Energy Institute, https://www.energyinst.org/statistical-review
• U.S. Energy Information Administration, https://www.eia.gov/opendata/index.php#bulk-downloads
• The Shift Dataportal, https://www.theshiftdataportal.org/energy
• Ember, https://ember-climate.org/data-catalogue/yearly-electricity-data/
• HYDE version 3.2, https://www.pbl.nl/en/image/links/hyde
• Gapminder's Population version 7, https://www.gapminder.org/data/documentation/gd003/
• UN World Population Prospect, https://population.un.org/wpp/Download/Standard/Population/
• Gapminder's Systema Globalis, https://github.com/open-numbers/ddf--gapminder--systema_globalis
• World Bank's data on income groups
• Bolt, J., & van Zanden, J. L. (2020). Maddison Project Database, version 2020. https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2020

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Usage

License:

• Creative Commons BY
• See data sources (specified in Methodology) for additional license terms

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Codebook:

Please refer to the codebook for variable metadata (see the table named "codebook").

The ember transport model used in WildEST tracks the travel of embers from each source pixel to downwind receiving pixels. The relative number of embers landing on a given receiving pixel is summed across all potential source pixels. If the receiving pixel has a nonzero WRC Building Cover value (meaning the pixel is within 75 m of a qualifying building), then we separately sum the relative number of embers from the source pixel. The final SELB raster represents the expected annual relative ember production that lands on building cover across all weather types.

In recent years there has been a shift from heuristics-based malware detection towards machine learning, which proves to be more robust in the current heavily adversarial threat landscape. While we acknowledge machine learning to be better equipped to mine for patterns in the increasingly high amounts of similar-looking files, we also note a remarkable scarcity of the data available for similarity-targeted research. Moreover, we observe that the focus in the few related works falls on quantifying similarity in malware, often overlooking the clean data. This one-sided quantification is especially dangerous in the context of detection bypass. We propose to address the deficiencies in the space of similarity research on binary files, starting from EMBER — one of the largest malware classification data sets. We enhance EMBER with similarity information as well as malware class tags, to enable further research in the similarity space. Our contribution is threefold: (1) we publish EMBERSim, an augmented version of EMBER, that includes similarity-informed tags; (2) we enrich EMBERSim with automatically determined malware class tags using the open-source tool AVClass on VirusTotal data and (3) we describe and share the implementation for our class scoring technique and leaf similarity method.

This dataset provides information about the number of properties, residents, and average property values for Ember Lane cross streets in Montello, WI.

Dataset

This dataset was created by Vivekananda Bharupati

Released under Data files © Original Authors

Contents

BRAIN Initiative data archive for multi-modal neurophysiological and behavioral data, supporting the Brain Behavior Quantification and Synchronization (BBQS) Program. Accessible and versatile data archive for storage, processing, and curation of multimodal neurophysiological and behavioral datasets. EMBER extends established BRAIN Initiative data infrastructure, provides new data harmonization and synchronization capabilities, and supports scalable integrations for data coordination and AI driven batch processing to enable the goals of the BBQS program.

A labeled benchmark dataset for training machine learning models to statically detect malicious Windows portable executable files. The dataset includes features extracted from 1.1M binary files: 900K training samples (300K malicious, 300K benign, 300K unlabeled) and 200K test samples (100K malicious, 100K benign).

Due to its novelty and scale, the EMBER project is a key study within the prescribed burning evidence base. However, it has several significant but overlooked methodological flaws. In this paper, we outline and discuss these flaws. In doing so, we aim to highlight the current paucity of evidence relating to prescribed burning impacts on ecosystem services within the British uplands. We show that the results of the EMBER project are currently unreliable because: it used a correlative space‐for‐time approach; treatments were located within geographically separate and environmentally distinct sites; environmental differences between sites and treatments were not accounted for during statistical analysis; and, peat surface temperature results are suggestive of measurement error. Policy Implications. Given the importance of the EMBER project, our findings suggest that (a) government agencies and policymakers need to re‐examine the strengths and limitations of the prescribed burning evidence base; and, (b) future work needs to control for site‐specific differences so that prescribed burning impacts on ecosystem services can be reliably identified.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This dataset contains monthly generation, emissions and demand data for 85 geographies representing more than 90% of global power demand. Data is collected from multi-country datasets (EIA, Eurostat, BP, UN) as well as national sources (e.g China data from the National Bureau of Statistics).

credit: Nicolas Fulghum https://ember-climate.org/data-catalogue/monthly-electricity-data/

Ember js açık kaynak kodlu web uygulama çatısı Ember js uygulama geliştiricilerine ölçeklenebilir tek sayfalık web uygul

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

In order to have a better understanding of statistical distribution of firebrands' mass, size (projected area), and traveling distance, full-scale firebrand generation experiments were conducted. Full-scale structural components (fence, corner, and roof) and their assemblies were built from typical residential building construction materials. The samples were ignited and exposed to realistic gusty wind traces in a wind tunnel facility in the Insurance Institute for Business & Home Safety (IBHS) Research Center located in Richburg, South Carolina. Water pans were placed downwind to quench the flying firebrands immediately after landing. The distance between the center of the water pans in which the firebrands' landed and the front location of the burning sample was defined as the traveling distance. The firebrands were collected from the water pans and placed in an oven to reach zero percent moisture content. Dried firebrands were scattered on a white sheet. High-resolution pictures were captures of each sheet using a digital camera (Nikon D5600). Following that, an automated image processing algorithm using MATLAB was developed and employed to measure firebrand projected area. Using a digital balance (Sartorius H51, resolution of ±0.0001 gram), firebrand mass was measured. Experiments and raw data collection for this study were conducted from 2016-2017. The result was 50,571 firebrands collected and measured, with 24,149 from structural components and 26,422 from structural assemblies.The collected firebrands from previous firebrand production experiments using full-scale building components and their assemblies varied between 50 and 1000 firebrands. The sample size of this study is significantly larger than any existing firebrand data sets. This work was based on a statistics-based framework for the sampling and measurement processes in firebrand generation experiments so that the obtained firebrand data can achieve the desired level of statistical reliability. These firebrand data sets are useful in understanding the characteristics and distribution of firebrands generated from various structural fuels. They can be used for developing and training predictive models for the firebrand phenomenon (generation, transport, and ignition), models to predict fire spread in the wildland and wildland-urban interface, and models to estimate risks from wildfire. They are also useful for wildfire mitigation strategies or guidelines to minimize threat and damage from firebrand attacks.Original metadata was published on 05/20/2020. On 09/01/2021 the data embargo was lifted and the data for this publication became available.

Ember Technologies Inc Company Export Import Records. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

This dataset provides information about the number of properties, residents, and average property values for Ember Way cross streets in Ann Arbor, MI.