National Climate Targets Training Dataset – Climate Policy Radar

A dataset of climate targets made by national governments in their laws, policies and UNFCCC submissions which has been used to train a classifier. Text was sourced from the Climate Policy Radar database. We define a target as an aim to achieve a specific outcome, that is quantifiable and is given a deadline. This dataset distinguishes between different types of targets:

Reduction (a.k.a. emissions reduction): a… See the full description on the dataset page: https://huggingface.co/datasets/ClimatePolicyRadar/national-climate-targets.

Otter DUDe Dataset Card

Otter DUDe includes 1,452,568 instances of drug-target interactions.

  Dataset details





  DUDe

DUDe comprises a collection of 22,886 active compounds and their corresponding affinities towards 102 targets. For our study, we utilized a preprocessed version of the DUDe, which includes 1,452,568 instances of drug-target interactions. To prevent any data leakage, we eliminated the negative interactions and the overlapping triples with the TDC DTI… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/otter_dude.

As of December 2019, ** percent of adults in the United States do not want political campaigns to be able to micro-target them through digital ads. Respondents to a survey of U.S. adults reported that internet companies should make no information about its users available to political campaigns in order to target certain voters with online advertisements. Additionally, * percent of U.S. adults say that any information should be made available for a campaign's use.

As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment.

This dataset contains the cross-climate-model version fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2100 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 5 and the representative concentration pathway (RCP) used was RCP 8.5. More information about SSP and RCP can be referred to O'Neill et al. (2020).

Please be aware that in cases where a location contains multiple .EPW files, it indicates that there are multiple weather data collection points within that location.

More information about the six selected CMIP6 GCMs:

ACCESS-CM2 -
http://dx.doi.org/10.1071/ES19040
BCC-CSM2-MR -
https://doi.org/10.5194/gmd-14-2977-2021
CNRM-ESM2-1-
https://doi.org/10.1029/2019MS001791
MPI-ESM1-2-HR -
https://doi.org/10.5194/gmd-12-3241-2019
MRI-ESM2-0 -
https://doi.org/10.2151/jmsj.2019-051
NorESM2-MM -
https://doi.org/10.5194/gmd-13-6165-2020

Additional references:
O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.
Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0
Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228.

Please cite the following if this data is used in any research or project:

Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). “Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County.” The 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities and BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16, 2023. DOI: 10.1145/3600100.3626637

Cross-Model Version:

Representative Cities Version:

Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [<a

MealMe provides comprehensive grocery and retail SKU-level product data, including real-time pricing, from the top 100 retailers in the USA and Canada. Our proprietary technology ensures accurate and up-to-date insights, empowering businesses to excel in competitive intelligence, pricing strategies, and market analysis.

Retailers Covered: MealMe’s database includes detailed SKU-level data and pricing from leading grocery and retail chains such as Walmart, Target, Costco, Kroger, Safeway, Publix, Whole Foods, Aldi, ShopRite, BJ’s Wholesale Club, Sprouts Farmers Market, Albertsons, Ralphs, Pavilions, Gelson’s, Vons, Shaw’s, Metro, and many more. Our coverage spans the most influential retailers across North America, ensuring businesses have the insights needed to stay competitive in dynamic markets.

Key Features: SKU-Level Granularity: Access detailed product-level data, including product descriptions, categories, brands, and variations. Real-Time Pricing: Monitor current pricing trends across major retailers for comprehensive market comparisons. Regional Insights: Analyze geographic price variations and inventory availability to identify trends and opportunities. Customizable Solutions: Tailored data delivery options to meet the specific needs of your business or industry. Use Cases: Competitive Intelligence: Gain visibility into pricing, product availability, and assortment strategies of top retailers like Walmart, Costco, and Target. Pricing Optimization: Use real-time data to create dynamic pricing models that respond to market conditions. Market Research: Identify trends, gaps, and consumer preferences by analyzing SKU-level data across leading retailers. Inventory Management: Streamline operations with accurate, real-time inventory availability. Retail Execution: Ensure on-shelf product availability and compliance with merchandising strategies. Industries Benefiting from Our Data CPG (Consumer Packaged Goods): Optimize product positioning, pricing, and distribution strategies. E-commerce Platforms: Enhance online catalogs with precise pricing and inventory information. Market Research Firms: Conduct detailed analyses to uncover industry trends and opportunities. Retailers: Benchmark against competitors like Kroger and Aldi to refine assortments and pricing. AI & Analytics Companies: Fuel predictive models and business intelligence with reliable SKU-level data. Data Delivery and Integration MealMe offers flexible integration options, including APIs and custom data exports, for seamless access to real-time data. Whether you need large-scale analysis or continuous updates, our solutions scale with your business needs.

Why Choose MealMe? Comprehensive Coverage: Data from the top 100 grocery and retail chains in North America, including Walmart, Target, and Costco. Real-Time Accuracy: Up-to-date pricing and product information ensures competitive edge. Customizable Insights: Tailored datasets align with your specific business objectives. Proven Expertise: Trusted by diverse industries for delivering actionable insights. MealMe empowers businesses to unlock their full potential with real-time, high-quality grocery and retail data. For more information or to schedule a demo, contact us today!

PURPOSE (possible uses)

Non-professional investors often try to find an interesting stock among those in an index (such as the Standard and Poor's 500, Nasdaq, etc.). They need only one company, the best, and they don't want to fail (perform poorly). So, the metric to optimize is accuracy, described as:

Accuracy = True Positives / (True Positives + False Positives)

And the predictive model can be a binary classifier.

The data covers the price and volume of shares of 31 NASDAQ companies in the year 2022.

Context

Every data set I found to predict a stock price (investing) aims to find the price for the next day, and only for that stock. But in practical terms, people like to find the best stocks to buy from an index and wait a few days hoping to get an increase in the price of this investment.

Content

Rows are grouped by companies and their age (newest to oldest) on a common date. The first column is the company. The following are the age, market, date (separated by year, month, day, hour, minute), share volume, various traditional prices of that share (close, open, high...), some price and volume statistics and target. The target is mainly defined as 1 when the closing price increases by at least 5% in 5 days (open market days). The target is 0 in any other case.

Complex features and target were made by executing: https://www.kaggle.com/code/luisandresgarcia/202307

Thanks

Many thanks to everyone who participates in scientific papers and Kaggle notebooks related to financial investment.

Help us provide the most useful data by completing our ODP User Feedback Survey for Child and Adult Care Food Program (CACFP) Data

About the Dataset
This data set contains claims information for meal reimbursement for sites participating in CACFP as child centers for the program year 2024-2025. This includes Child Care Centers, At-Risk centers, Head Start sites, Outside School Hours sites, and Emergency Shelters . The CACFP program year begins October 1 and ends September 30.

This dataset only includes claims submitted by CACFP sites operating as child centers.Sites can participate in multiple CACFP sub-programs. Each record (row) represents monthly meals data for a single site and for a single CACFP center sub-program.

To filter data for a specific CACFP center Program, select "View Data" to open the Exploration Canvas filter tools. Select the program(s) of interest from the Program field. A filtering tutorial can be found HERE

For meals data on CACFP participants operating as Day Care Homes, Adult Day Care Centers, or child care centers for previous program years, please refer to the corresponding “Child and Adult Care Food Programs (CACFP) – Meal Reimbursement” dataset for that sub-program available on the State of Texas Open Data Portal.

An overview of all CACFP data available on the Texas Open Data Portal can be found at our TDA Data Overview - Child and Adult Care Food Programs page.

An overview of all TDA Food and Nutrition data available on the Texas Open Data Portal can be found at our TDA Data Overview - Food and Nutrition Open Data page.

More information about accessing and working with TDA data on the Texas Open Data Portal can be found on the SquareMeals.org website on the TDA Food and Nutrition Open Data page.

About Dataset Updates
TDA aims to post new program year data by December 15 of the active program year. Participants have 60 days to file monthly reimbursement claims. Dataset updates will occur daily until 90 days after the close of the program year. After 90 days from the close of the program year, the dataset will be updated at six months and one year from the close of the program year before becoming archived. Archived datasets will remain published but will not be updated. Any data posted during the active program year is subject to change.

About the Agency
The Texas Department of Agriculture administers 12 U.S. Department of Agriculture nutrition programs in Texas including the National School Lunch and School Breakfast Programs, the Child and Adult Care Food Programs (CACFP), and the summer meal programs. TDA’s Food and Nutrition division provides technical assistance and training resources to partners operating the programs and oversees the USDA reimbursements they receive to cover part of the cost associated with serving food in their facilities. By working to ensure these partners serve nutritious meals and snacks, the division adheres to its mission — Feeding the Hungry and Promoting Healthy Lifestyles.

For more information on these programs, please visit our website.
"

Wildlife Restoration funds (manufacturer’s federal excise taxes) generated from the sale of firearms, ammunition, and archery equipment, support the construction, operation, and maintenance of over 850 public target ranges in the United States. This represents a significant investment in safe, structured environments where the public may participate in all kinds of target shooting. In the last six months, there are 9 new ranges being built as well as 8 ranges being upgraded or expanded.This dataset is a compilation of the firearms, archery, and combined (both firearms & archery) ranges that have received funding through the Wildlife Restoration Act. The data is provided by each Region every six months to fulfill a Director's Request.Contact:Elena Campbell (elena_campbell@fws.gov)

As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment.

This dataset contains the cross-climate-model version fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2100 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 5 and the representative concentration pathway (RCP) used was RCP 8.5. More information about SSP and RCP can be referred to O'Neill et al. (2020).

Please be aware that in cases where a location contains multiple .EPW files, it indicates that there are multiple weather data collection points within that location.

More information about the six selected CMIP6 GCMs:

ACCESS-CM2 -
http://dx.doi.org/10.1071/ES19040
BCC-CSM2-MR -
https://doi.org/10.5194/gmd-14-2977-2021
CNRM-ESM2-1-
https://doi.org/10.1029/2019MS001791
MPI-ESM1-2-HR -
https://doi.org/10.5194/gmd-12-3241-2019
MRI-ESM2-0 -
https://doi.org/10.2151/jmsj.2019-051
NorESM2-MM -
https://doi.org/10.5194/gmd-13-6165-2020

Additional references:
O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.
Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0
Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228.

Please cite the following if this data is used in any research or project:

Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). “Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County.” The 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities and BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16, 2023. DOI: 10.1145/3600100.3626637

Cross-Model Version:

Representative Cities Version:

Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [<a

The RIPARIAS target species list is a species checklist dataset published by the Research Institute for Nature and Forest (INBO). It contains (1) the target species of the LIFE RIPARIAS project (LIFE19 NAT/BE/000953), all of them invasive alien species (IAS) of the Regulation (EU) 1143/2014 (https://ec.europa.eu/environment/nature/invasivealien/) and (2) the alert list species that currently do not occur in the LIFE RIPARIAS project area, but have proven to have negative impacts on biodiversity and need to be rapidly removed should they be encountered. The alert list was drafted within the LIFE RIPARIAS project following an evidence-based methodology involving climate matching and risk assessment (Branquart et al. 2022). By publishing this list on GBIF it can be used for general reference, early warning systems, data extractions, baseline reporting, project KPIs etc. Issues with the dataset can be reported at: https://github.com/riparias/riparias-target-list We have released this dataset to the public domain under a Creative Commons Zero waiver. We would appreciate it if you follow the INBO norms for data use (https://www.inbo.be/en/norms-data-use) when using the data. If you have any questions regarding this dataset, don't hesitate to contact us via the contact information provided in the metadata or via opendata@inbo.be. This dataset was published as open data for the LIFE RIPARIAS project (Reaching Integrated and Prompt Action in Response to Invasive Alien Species https://www.riparias.be/), with technical support provided by the Research Institute for Nature and Forest (INBO).

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4MM+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

Techsalerator’s Business Funding Data for North America is an extensive and insightful resource designed for businesses, investors, and financial analysts who need a deep understanding of the Asian funding landscape. This dataset meticulously captures and categorizes critical information about the funding activities of companies across the continent, providing valuable insights into the financial health and investment trends within various sectors.

What the Dataset Includes: Funding Rounds: Detailed records of funding rounds for companies in North America, including the size of the round, the date it occurred, and the stages of investment (Seed, Series A, Series B, etc.).

Investment Sources: Information on the sources of investment, such as venture capital firms, private equity investors, angel investors, and corporate investors.

Financial Milestones: Key financial achievements and benchmarks reached by companies, including valuation increases, revenue milestones, and profitability metrics.

Sector-Specific Data: Insights into how different sectors are performing, with data segmented by industry verticals such as technology, healthcare, finance, and consumer goods.

Geographic Breakdown: An overview of funding trends and activities specific to each North America country, allowing users to identify regional patterns and opportunities.

EU Countries Included in the Dataset: Antigua and Barbuda Bahamas Barbados Belize Canada Costa Rica Cuba Dominica Dominican Republic El Salvador Grenada Guatemala Haiti Honduras Jamaica Mexico Nicaragua Panama Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Trinidad and Tobago United States

Benefits of the Dataset: Informed Decision-Making: Investors and analysts can use the data to make well-informed investment decisions by understanding funding trends and financial health across different regions and sectors. Strategic Planning: Businesses can leverage the insights to identify potential investors, benchmark against industry peers, and plan their funding strategies effectively. Market Analysis: The dataset helps in analyzing market dynamics, identifying emerging sectors, and spotting investment opportunities across North America. Techsalerator’s Business Funding Data for North America is a vital tool for anyone involved in the financial and investment sectors, offering a granular view of the funding landscape and enabling more strategic and data-driven decisions.

This description provides a more detailed view of what the dataset offers and highlights the relevance and benefits for various stakeholders.

About this dataset

Context

The dataset tabulates the Argos population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Argos. The dataset can be utilized to understand the population distribution of Argos by age. For example, using this dataset, we can identify the largest age group in Argos.

Key observations

The largest age group in Argos, IN was for the group of age 30 to 34 years years with a population of 204 (11.20%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Argos, IN was the 85 years and over years with a population of 6 (0.33%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Argos is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Argos total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Argos Population by Age. You can refer the same here

These geospatial data and their accompanying report outline many areas of coal in the United States beneath more than 3,000 ft of overburden. Based on depth, these areas may be targets for injection and storage of supercritical carbon dioxide. Additional areas where coal exists beneath more than 1,000 ft of overburden are also outlined; these may be targets for geologic storage of carbon dioxide in conjunction with enhanced coalbed methane production. These areas of deep coal were compiled as polygons into a shapefile for use in a geographic information system (GIS). The coal-bearing formation names, coal basin or field names, geographic provinces, coal ranks, coal geologic ages, and estimated individual coalbed thicknesses (if known) of the coal-bearing formations were included. An additional point shapefile, coal_co2_projects.shp, contains the locations of pilot projects for carbon dioxide injection into coalbeds. This report is not a comprehensive study of deep coal in the United States. Some areas of deep coal were excluded based on geologic or data-quality criteria, while others may be absent from the literature and still others may have been overlooked by the authors.

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

This database table contains the current target list of the X-ray Multi-Mirror Newton (XMM-Newton) mission including those in (i) the routine calibration plan, (ii) the Guaranteed Time Observation (GTO) program, (iii) the triggered target candidates or ToOs accepted for the First through the Twentieth-Fourth Announcements of Opportunity (AO-1 through AO-24) programs, (iv) the AO-1 through AO-24 Guest Observer (GO) program targets with priority A or B, (v) the AO-1 through AO-19 GO program targets with priority C which have been observed, (vi) the AO-20 through AO-24 GO program targets with priority C, and (vii) the targets granted by agreement of the ESA Director of Science and the National Space Agency of Japan, following the loss of the original Astro-E spacecraft. For complete and authoritative information on the XMM-Newton mission, policies, and data archive, refer to the web pages of the European Space Agency's (ESA's) XMM-Newton Science Operations Center at http://xmm.esac.esa.int/ and of NASA's XMM-Newton Guest Observer Facility at http://heasarc.gsfc.nasa.gov/docs/xmm/xmmgof.html Notice that all priority C targets from AOs 1 through 19 which were never observed by XMM-Newton (and hence have expired) have been removed from this table. To check which targets have either already been observed by XMM-Newton or are on the short-term schedule to be observed in the next few weeks, users should examine the XMMMASTER table which is also contained in Browse. To find out which targets are currently scheduled to be observed in the next three months, the user should check the XMM-Newton Advanced Plan at http://xmm.esac.esa.int/external/xmm_sched/advance_plan.shtml While abstracts are available for most proposals, there are a number of targets for which the HEASARC lacks the corresponding abstracts: e.g., the abstracts for AO-2 Guest Observer targets which have non-US PIs are not available. This database table was last updated by the HEASARC in December 2024, when AO-24's accepted targets were added.

AO-23's accepted targets were added in November 2023.

AO-22's accepted targets were added in November 2022.

AO-21's accepted targets were added and AO-19's unobserved priority C targets were removed in December 2021.

AO-20's accepted targets were added and AO-18's unobserved priority C targets were removed in December 2020.

AO-19's accepted targets were added and AO-17's unobserved priority C targets were removed in April 2020.

In June 2019, many duplicate entries were removed.

AO-18's accepted targets were added and AO-16's unobserved priority C targets were removed in November 2018.

AO-17's accepted targets were added and AO-15's unobserved priority C targets were removed in November 2017.

AO-16's accepted targets were added and AO-14's unobserved priority C targets were removed in December 2016.

AO-15's accepted targets were added and AO-13's unobserved priority C targets were removed in December 2015.

In August 2015, proposal titles were added.

AO-14's accepted targets were added and AO-12's unobserved priority C targets were removed in December 2014.

AO-13's accepted targets were added and AO-11's unobserved priority C targets were removed in December 2013.

AO-12's accepted targets were added and AO-10's unobserved priority C targets were removed in December 2012.

AO-11's accepted targets were added and AO-9's unobserved priority C targets were removed in December 2011.

AO-10's accepted targets were added and AO-8's unobserved priority C targets were removed in December 2010.

AO-9's accepted targets were added and AO-7's unobserved priority C targets were removed in January 2010.

AO-8's accepted targets were added and AO-6's unobserved priority C targets were removed in December 2008.

AO-7's accepted targets were added and AO-5's unobserved priority C targets were removed in January 2008.

AO-6's accepted targets were added in January 2007, and, in November 2006, an effort was made to remove most of the unobserved priority C targets from previous AOs. This is a service provided by NASA HEASARC .

This dataset was derived from tracked biopsy sessions using the Artemis biopsy system, many of which included image fusion with MRI targets. Patients received a 3D transrectal ultrasound scan, after which nonrigid registration (e.g. “fusion”) was performed between real-time ultrasound and preoperative MRI, enabling biopsy cores to be sampled from MR regions of interest. Most cases also included sampling of systematic biopsy cores using a 12-core digital template. The Artemis system tracked targeted and systematic core locations using encoder kinematics of a mechanical arm, and recorded locations relative to the Ultrasound scan. MRI biopsy coordinates were also recorded for most cases. STL files and biopsy overlays are available and can be visualized in 3D Slicer with the SlicerHeart extension. Spreadsheets summarizing biopsy and MR target data are also available. See the Detailed Description tab below for more information.

MRI targets were defined using multiparametric MRI, e.g. t2-weighted, diffusion-weighted, and perfusion-weighted sequences, and scored on a Likert-like scale with close correspondence to PIRADS version 2. t2-weighted MRI was used to trace ROI contours, and is the only sequence provided in this dataset. MR imaging was performed on a 3 Tesla Trio, Verio or Skyra scanner (Siemens, Erlangen, Germany). A transabdominal phased array was used in all cases, and an endorectal coil was used in a subset of cases. The majority of pulse sequences are 3D T2:SPC, with TR/TE 2200/203, Matrix/FOV 256 × 205/14 × 14 cm, and 1.5mm slice spacing. Some cases were instead 3D T2:TSE with TR/TE 3800–5040/101, and a small minority were imported from other institutions (various T2 protocols.)

Ultrasound scans were performed with Hitachi Hi-Vision 5500 7.5 MHz or the Noblus C41V 2-10 MHz end-fire probe. 3D scans were acquired by rotation of the end-fire probe 200 degrees about its axis, and interpolating to resample the volume with isotropic resolution.

Patients with suspicion of prostate cancer due to elevated PSA and/or suspicious imaging findings were consecutively accrued. Any consented patient who underwent or had planned to receive a routine, standard-of-care prostate biopsy at the UCLA Clark Urology Center was included.

Note: Some Private Tags in this collection are critical to properly displaying the STL surface and the Prostate anatomy. Private Tag (1129,"Eigen, Inc",1016) DS VoxelSize is especially important for multi-frame US cases.

Retail Sales in the United States increased 0.50 percent in July of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States - Hires: Total Private was 3.60000 Rate in June of 2025, according to the United States Federal Reserve. Historically, United States - Hires: Total Private reached a record high of 7.10000 in May of 2020 and a record low of 3.10000 in June of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Hires: Total Private - last updated from the United States Federal Reserve on September of 2025.

This dataset provides the values used to develop the figures within the manuscript "Incorporating upstream emissions into electric sector nitrogen oxide reduction targets". Here is the abstract from that manuscript: Electricity production is a major source of air pollutants in the U.S. Policies to reduce these emissions can result in the power industry choosing to apply controls or switch to fuels with lower combustion emissions. However, the life cycle emissions associated with various fuels can differ considerably, potentially impacting the effectiveness of fuel switching. Life cycle emissions, which include emissions from extracting, processing, transporting, and distributing fuels, as well as manufacturing and constructing new generating capacity, have received less consideration in policy-making. Life cycle analysis allows quantification of these emissions such that they can be considered in decision-making. We examine a hypothetical electric sector emission reduction target for nitrogen oxides using the Global Change Assessment Model with U.S. state-level resolution. When only power plant emissions are considered in setting an emission reduction target, fuel switching leads to an increase in upstream emissions that offsets a portion of the targeted reductions. When fuel extraction, processing, and transport emissions are included under the reduction target, the resulting control strategy meets the required reductions and does so at lower cost. However, manufacturing and construction emissions increase, indicating that it may be beneficial to consider these sources as well. In the real world, life cycle-based approaches could be implemented by allowing industry to earn reduction credits by reducing upstream emissions. We discuss some of the limitations of such an approach, including the difficulty in identifying the _location of upstream emissions, which may occur across regulatory authorities or even outside of the U.S. This dataset is associated with the following publication: Babaee, S., D. Loughlin, and O. Kaplan. Incorporating upstream emissions into electric sector nitrogen oxide reduction targets. Cleaner Engineering and Technology. Elsevier B.V., Amsterdam, NETHERLANDS, 1: 100017, (2020).