MY NASA DATA (MND) is a tool that allows anyone to make use of satellite data that was previously unavailable.Through the use of MND’s Live Access Server (LAS) a multitude of charts, plots and graphs can be generated using a wide variety of constraints. This site provides a large number of lesson plans with a wide variety of topics, all with the students in mind. Not only can you use our lesson plans, you can use the LAS to improve the ones that you are currently implementing in your classroom.

Overview:

This dataset provides a unique resource for researchers and data scientists interested in the global dynamics of the COVID-19 pandemic. It focuses on the impact of different SARS-CoV-2 variants and mutations on the duration of local epidemics. By combining variant information with epidemiological data, this dataset allows for a comprehensive analysis of factors influencing the trajectory of the pandemic.

Key Features:

• Global Coverage: Includes data from multiple countries.
• Variant-Specific Information: Detailed records for various SARS-CoV-2 variants.
• Epidemic Duration: Data on the duration of local epidemics, accounting for right-censoring.
• Epidemiological Variables: Includes mortality rates, a proxy for R0, transmission proxies, and other pertinent variables.
• Geographical characteristics: Include a continent variable for exploring geographical patterns
• Time varying variables: Include the number of waves and the number of variants in the different countries for more in-depth exploration.

Data Source: The data combines information from the Johns Hopkins University COVID-19 dataset (confirmed_cases.csv and deaths_cases.csv) and the covariants.org dataset (variants.csv). The dataset you see here is the combination of two datasets from Johns Hopkins University and covariants.org.

Questions to Inspire Users:

This dataset is designed for a diverse set of analytical questions. Here are some ideas to inspire the Kaggle community:

Survival Analysis:

1. How do different SARS-CoV-2 variants influence the duration of local epidemics?
2. Which factors (mortality, R0, etc.) are most strongly associated with shorter or longer epidemic durations?
3. Does the type of variant/mutation (mutation,S, Omicron, Delta, Other) have a significant impact on epidemic duration?
4. Is there a geographical pattern to the duration of epidemics?

Epidemiological Analysis:

1. How do local transmission rates (represented by our proxy of R0) affect the duration of an epidemic?
2. Do countries with higher mortality rates have different patterns of epidemic progression?
3. How can we predict the duration of an epidemic based on its initial characteristics?
4. How does the number of epidemic waves impact the duration of an epidemic?
5. Does the number of variants in a country affect the duration of an épidémie?

Data Science/Machine Learning:

1. Can we develop a machine learning model to predict the duration of an epidemic?
2. What features have the best predictive power ?
3. Can we identify clusters of variants/regions with similar epidemic patterns?
4. Are there interactions between variables that can explain the non-linearities that we have identified ?

Self-Serve Data Access Portals Market Outlook

According to our latest research, the global Self-Serve Data Access Portals market size reached USD 4.1 billion in 2024. The market is experiencing robust momentum, with a CAGR of 18.2% projected from 2025 to 2033. By the end of 2033, the market is forecasted to attain a valuation of USD 19.7 billion. This significant growth is being propelled by the increasing demand for democratized data access, the proliferation of big data analytics, and the widespread adoption of self-service business intelligence tools across diverse industry verticals. The market is also being shaped by the accelerating pace of digital transformation and the need for agile, data-driven decision-making processes within organizations.

A primary growth factor for the Self-Serve Data Access Portals market is the escalating need for organizations to empower non-technical users with seamless access to data. As enterprises strive to become more data-driven, there is a pronounced shift towards enabling business users to independently extract, analyze, and visualize data without relying on IT teams. This trend is particularly pronounced in sectors such as BFSI, healthcare, and retail, where timely insights are critical for operational efficiency and competitive advantage. The democratization of data is fostering a culture of self-service analytics, reducing bottlenecks, and accelerating the decision-making process. Furthermore, the integration of advanced analytics and AI-driven features within self-serve portals is enhancing user experience and broadening the scope of actionable insights, thereby fueling market expansion.

Another significant driver is the rapid adoption of cloud-based solutions, which has transformed the deployment landscape for self-serve data access portals. Cloud deployment offers scalability, flexibility, and cost-effectiveness, making it an attractive option for organizations of all sizes, especially small and medium enterprises (SMEs). The cloud enables seamless integration with various data sources, supports remote access, and ensures high availability and disaster recovery. As a result, cloud-based self-serve data access portals are gaining traction among enterprises seeking to modernize their data infrastructure and streamline operations. Additionally, the rise of hybrid and multi-cloud environments is further facilitating the adoption of self-serve portals, as organizations look to leverage the best features of different cloud platforms while maintaining data security and compliance.

The growing emphasis on regulatory compliance and data governance is also contributing to the expansion of the Self-Serve Data Access Portals market. Organizations are increasingly required to adhere to stringent data protection regulations such as GDPR, HIPAA, and CCPA, necessitating robust data access controls and audit trails. Modern self-serve portals are equipped with advanced security features, role-based access controls, and comprehensive logging capabilities, enabling organizations to maintain compliance while providing users with the freedom to explore and utilize data. This balance between accessibility and governance is driving adoption across highly regulated industries, further strengthening the market's growth trajectory.

From a regional perspective, North America continues to dominate the market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The mature IT infrastructure, high digital literacy, and early adoption of advanced analytics solutions in North America have positioned the region as a frontrunner. Meanwhile, Asia Pacific is emerging as a high-growth market, driven by rapid digitalization, expanding enterprise IT budgets, and increasing awareness of data-driven business strategies. The presence of a large SME sector and government initiatives promoting digital transformation are further accelerating market growth in the region. Europe, with its strong focus on data privacy and compliance, is also witnessing steady adoption of self-serve data access portals, particularly in the BFSI and healthcare sectors.

Component Analysis

The Self-Serve Data Access Portals market by component is segmented into software and services. The software segment comprises the core platforms and applications that facilitate self-service data access, analytics, and visualization. These solutions are designed to offer intuitive interfaces, robust data i

It has never been easier to solve any database related problem using any sequel language and the following gives an opportunity for you guys to understand how I was able to figure out some of the interline relationships between databases using Panoply.io tool.

I was able to insert coronavirus dataset and create a submittable, reusable result. I hope it helps you work in Data Warehouse environment.

The following is list of SQL commands performed on dataset attached below with the final output as stored in Exports Folder QUERY 1 SELECT "Province/State" As "Region", Deaths, Recovered, Confirmed FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Deaths>0 Description: How will we estimate where Coronavirus has infiltrated, but there is effective recovery amongst patients? We can view those places by having Recovery twice more than the Death Toll.

Query 2 SELECT country, sum(confirmed) as "Confirmed Count", sum(Recovered) as "Recovered Count", sum(Deaths) as "Death Toll" FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Confirmed>0 GROUP BY country

Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries

Query 3 SELECT country as "Countries where Coronavirus has reached" FROM "public"."coronavirus_updated" WHERE confirmed>0 GROUP BY country Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries.

Query 4 SELECT country, sum(suspected) as "Suspected Cases under potential CoronaVirus outbreak" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 AND confirmed=0 GROUP BY country ORDER BY sum(suspected) DESC

Description: Coronavirus is spreading at alarming rate. In order to know which countries are newly getting the virus is important because in these countries if timely measures are taken, it could prevent any causalities. Here is a list of suspected cases with no virus resulted deaths.

Query 5 SELECT country, sum(suspected) as "Coronavirus uncontrolled spread count and human life loss", 100*sum(suspected)/(SELECT sum((suspected)) FROM "public"."coronavirus_updated") as "Global suspected Exposure of Coronavirus in percentage" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 GROUP BY country ORDER BY sum(suspected) DESC Description: Coronavirus is getting stronger in particular countries, but how will we measure that? We can measure it by knowing the percentage of suspected patients amongst countries which still doesn’t have any Coronavirus related deaths. The following is a list.

The WZDx Specification enables infrastructure owners and operators (IOOs) to make harmonized work zone data available for third party use. The intent is to make travel on public roads safer and more efficient through ubiquitous access to data on work zone activity. Specifically, the project aims to get data on work zones into vehicles to help automated driving systems (ADS) and human drivers navigate more safely.

MCDOT leads the effort to aggregate and collect work zone data from the AZTech Regional Partners. A continuously updating archive of the WZDx feed data can be found at ITS WorkZone Data Sandbox. The live feed is currently compliant with WZDx specification version 1.1.

1: The Interstellar Boundary Explorer (IBEX) has operated in space since 2008 updating our knowledge of the outer heliosphere and its interaction with the local interstellar medium. Start-time: 2008-12-25. There are currently 16 releases of IBEX-HI and/or IBEX-LO data covering 2009-2019. 2: This data set is from the Release 7 (1 year-cadence) IBEX-Hi map data for the years 2009-2013 in the form of ram-directional ENA (hydrogen) fluxes with Compton-Getting correction (cg) of flux spectra for spacecraft motion and correction for ENA survival probability (sp) between 1 and 100 AU. 3. The data consist of all-sky maps in Solar Ecliptic Longitude (east and west) and Latitude angles for ENA (hydrogen) fluxes from IBEX-Hi energy bands 2-6 in numerical data form. Energy channels 2-6 have FWHM ranges of 0.52-0.95, 0.84-1.55, 1.36-2.50, 1.99-3.75, 3.13-6.00 keV, respectively. The corresponding center-point energies are 0.71, 1.11, 1.74, 2.73, and 4.29 keV. Details of the data and enabled science from Release 10 are given in the following journal publication: 4: McComas, D. J., et al. (2017), Seven Years of Imaging the Global Heliosphere with IBEX, Astrophys. J. Supp. Ser., 229(2), 41 (32 pp.), 5: http://doi.org/10.3847/1538-4365/aa66d8 6. The following codes are used to define dataset types:- cg = Compton-Getting corrections have been applied to the data to account for the speed of the spacecraft relative to the direction of arrival of the ENAs.- nocg = no Compton-Getting corrections- sp = survival probability corrections have been applied to the data to account for the loss of ENAs due to radiation pressure, photoionization and ionization via charge exchange with solar wind protons as they stream through the heliosphere. This correction scales the data out from IBEX at 1 AU to ~100 AU. In the original data this mode is denoted as Tabular.- noSP - no survival probability corrections have been applied to the data.- omni = data from all directions.- ram = data was collected when the spacecraft was ramming into the incoming ENAs.- antiram = data was collected when the spacecraft was moving away from the incoming ENAs. 7. The following list associates Release 16 map numbers (1-22) with mission year (1-9), orbits (11-471b), and dates (12/25/2008-12/26/2019):- Map 1: Map2009A, year 1, orbits 11-34, dates 12/25/2008-06/25/2009- Map 2: Map2009B, year 1, orbits 35-58, dates 06/25/2009-12/25/2009- Map 3: Map2010A, year 2, orbits 59-82, dates 12/25/2009-06/26/2010- Map 4: Map2010B, year 2, orbits 83-106, dates 06/26/2010-12/26/2010- Map 5: Map2011A, year 3, orbits 107-130a, dates 12/26/2010-06/25/2011- Map 6: Map2011B, year 3, orbits 130b-150a, dates 06/25/2011-12/24/2011- Map 7: Map2012A, year 4, orbits 150b-170a, dates 12/24/2011-06/22/2012- Map 8: Map2012B, year 4, orbits 170b-190b, dates 06/22/2012-12/26/2012- Map 9: Map2013A, year 5, orbits 191a-210b, dates 12/26/2012-06/26/2013- Map 10: Map2013B, year 5, orbits 211a-230b, dates 06/26/2013-12/26/2013- Map 11: Map2014A, year 6, orbits 231a-250b, dates 12/26/2013-06/26/2014- Map 12: Map2014B, year 6, orbits 251a-270b, dates 06/26/2014-12/24/2014- Map 13: Map2015A, year 7, orbits 271a-290b, dates 12/24/2014-06/24/2015- Map 14: Map2015B, year 7, orbits 291a-310b, dates 06/24/2015-12/23/2015- Map 15: Map2016A, year 8, orbits 311a-330b, dates 12/24/2015-06/23/2016- Map 16: Map2016B, year 8, orbits 331a-351a, dates 06/24/2016-12/26/2016- Map 17: Map2017A, year 9, orbits 351b-371a, dates 12/26/2016-06/24/2017- Map 18: Map2017B, year 9, orbits 371b-391a, dates 06/25/2017-12/25/2017- Map 19: Map2018A, year 10, orbits 391b-411b, dates 12/25/2017-06/28/2018- Map 20: Map2018B, year 10, orbits 412a-431b, dates 06/29/2018-12/26/2018- Map 21: Map2019A, year 11, orbits 432a-451b, dates 12/27/2018-06/27/2019- Map 22: Map2019B, year 11, orbits 452a-471b, dates 06/28/2019-12/26/2019* 8: This particular data set, denoted in the original ascii files as hvset_tabular_ram_cg_yearN for N=1,5, includes pixel map data from ram direction (ram-directional), CG, SP, 1 year cadence.

This is version 3.1.1.2020f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v3.1.0.2019f to include 2020 and so spans 1931-2020. The quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. The data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format "station_code"_HadISD_HadOBS_19310101-20210101_v3-1-1-2020f.nc. The station codes can be found under the docs tab. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height. To keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS. For more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/ References: When using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the "citable as" reference) : Dunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note. Dunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016. Dunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012 Smith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1 For a homogeneity assessment of HadISD please see this following reference Dunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. "Pairwise homogeneity assessment of HadISD." Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014.

Bangladesh number dataset provides contact information from trusted sources. We only collect phone numbers from reliable sources and define this information. To ensure transparency, we also provide the source URL to show where the information was collected from. In addition, we offer 24/7 support. If you have a question or need help, we’re always here. However, we care about accuracy, so we carefully collect the Bangladesh number dataset from trusted sources. You may rely on this data for business or personal use. With customer support, you’ll never have to wait when you need help or more information. We use opt-in data to respect privacy. This way, we contact only people who want to hear from you. Bangladesh phone data gives you access to contacts in Bangladesh. Here you can filter information by gender, age, and relationship status. This makes it easy to find exactly the people you want to connect with. We define this data by ensuring it follows all GDPR rules to keep it safe and legal. Our system works hard to remove any invalid data so you get only accurate and valid numbers. List to Data is a helpful website for finding important phone numbers quickly. Also, our Bangladesh phone data is suitable for doing business targeting specific groups. You can easily filter your list to focus on specific types of customers. Since we remove invalid data regularly, you don’t have to deal with old or useless numbers. We assure you that all data follows strict GDPR rules, so you can use it without any problems. Bangladesh phone number list is a collection of phone numbers from people in Bangladesh. We define this list by providing 100% correct and valid phone numbers that are ready to use. Also, we offer a replacement guarantee if you ever receive an invalid number. This means you will always have accurate data. We collect phone numbers that we provide based on customer’s permission. Moreover, we work hard to provide the best Bangladesh phone number list for businesses and personal use. We gather data correctly, so you won’t have to worry about getting outdated or incorrect information. Our replacement guarantee means you’ll always have valid numbers, so you can relax and feel confident.

Data Access Policy Management Market Outlook

According to our latest research, the global Data Access Policy Management market size in 2024 stands at USD 2.3 billion, reflecting the growing prioritization of data security and compliance across industries. The market is experiencing robust expansion, with a projected CAGR of 13.2% from 2025 to 2033. By 2033, the market is forecasted to reach an impressive USD 6.7 billion. This growth is primarily driven by increasing regulatory requirements, the rapid adoption of cloud technologies, and the ever-expanding digital footprint of organizations worldwide. As per our latest research, organizations are investing heavily in advanced data access policy management solutions to ensure secure, compliant, and efficient access to critical data assets.

A key growth factor for the Data Access Policy Management market is the intensifying regulatory landscape. With the introduction and enforcement of data protection regulations such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Health Insurance Portability and Accountability Act (HIPAA), organizations are under immense pressure to manage and monitor data access efficiently. These regulations mandate strict controls over who can access sensitive data, how access is granted, and how access activities are audited. Non-compliance can result in severe financial penalties and reputational damage, prompting organizations across sectors to invest in comprehensive data access policy management solutions. The demand for automated policy enforcement, real-time monitoring, and detailed audit trails is higher than ever, spurring innovation and adoption in this market.

Another significant driver is the accelerated adoption of cloud computing and hybrid IT environments. As organizations migrate their workloads to public and private clouds, the complexity of managing data access policies across diverse platforms increases exponentially. Traditional access management approaches often fall short in these dynamic environments, necessitating more sophisticated, centralized solutions that can enforce consistent policies regardless of where data resides. The need to support remote workforces and facilitate secure collaboration further amplifies the demand for robust data access policy management tools. These solutions not only help organizations maintain control over their data but also enhance operational agility by enabling secure, role-based access to information assets.

Furthermore, the proliferation of digital transformation initiatives is fueling market growth. Enterprises are leveraging big data, artificial intelligence, and Internet of Things (IoT) technologies to gain competitive advantage, resulting in a dramatic increase in data volume and diversity. Managing access to this expanding data landscape requires scalable and flexible policy management frameworks. Organizations are seeking solutions that can integrate seamlessly with existing identity and access management (IAM) systems, support granular policy definition, and provide real-time insights into access activities. The integration of advanced analytics and machine learning capabilities into data access policy management solutions is enabling proactive risk identification and policy optimization, further driving market expansion.

From a regional perspective, North America continues to dominate the Data Access Policy Management market, owing to the presence of leading technology providers, stringent regulatory requirements, and high awareness of data security best practices. Europe follows closely, driven by strong regulatory enforcement and increasing digitalization across industries. The Asia Pacific region is witnessing the fastest growth, propelled by rapid economic development, increasing digital adoption, and evolving regulatory frameworks. Latin America and the Middle East & Africa are also emerging as promising markets, as organizations in these regions ramp up their investments in data security and compliance infrastructure. The global nature of data flows and the interconnectedness of business ecosystems underscore the importance of robust data access policy management across all regions.

Component Analysis

The Data Access Policy Management market is segmented by component into software and services, each playing a pivotal role in the overall value proposition. The software segment encompasses standalone policy management platforms as well

US B2B Contact Database | 200M+ Verified Records | 95% Accuracy | API/CSV/JSON Elevate your sales and marketing efforts with America's most comprehensive B2B contact data, featuring over 200M+ verified records of decision-makers, from CEOs to managers, across all industries. Powered by AI and refreshed bi-weekly, this dataset ensures you have access to the freshest, most accurate contact details available for effective outreach and engagement.

Key Features & Stats:

200M+ Decision-Makers: Includes C-level executives, VPs, Directors, and Managers.

95% Accuracy: Email & Phone numbers verified for maximum deliverability.

Bi-Weekly Updates: Never waste time on outdated leads with our frequent data refreshes.

50+ Data Points: Comprehensive firmographic, technographic, and contact details.

Core Fields:

Direct Work Emails & Personal Emails for effective outreach.

Mobile Phone Numbers for cold calls and SMS campaigns.

Full Name, Job Title, Seniority for better personalization.

Company Insights: Size, Revenue, Funding data, Industry, and Tech Stack for a complete profile.

Location: HQ and regional offices to target local, national, or international markets.

Top Use Cases:

Cold Email & Calling Campaigns: Target the right people with accurate contact data.

CRM & Marketing Automation Enrichment: Enhance your CRM with enriched data for better lead management.

ABM & Sales Intelligence: Target the right decision-makers and personalize your approach.

Recruiting & Talent Mapping: Access CEO and senior leadership data for executive search.

Instant Delivery Options:

JSON – Bulk downloads via S3 for easy integration.

REST API – Real-time integration for seamless workflow automation.

CRM Sync – Direct integration with your CRM for streamlined lead management.

Enterprise-Grade Quality:

SOC 2 Compliant: Ensuring the highest standards of security and data privacy.

GDPR/CCPA Ready: Fully compliant with global data protection regulations.

Triple-Verification Process: Ensuring the accuracy and deliverability of every record.

Suppression List Management: Eliminate irrelevant or non-opt-in contacts from your outreach.

US Business Contacts | B2B Email Database | Sales Leads | CRM Enrichment | Verified Phone Numbers | ABM Data | CEO Contact Data | US B2B Leads | US prospects data

AI-Ready Data Pipelines Market Outlook

According to our latest research, the AI-Ready Data Pipelines market size reached USD 8.4 billion in 2024, reflecting robust adoption across industries globally. The market is experiencing a significant surge, propelled by the growing demand for scalable and efficient data infrastructure, with a recorded CAGR of 21.7% from 2025 to 2033. By 2033, the AI-Ready Data Pipelines market is forecasted to achieve a value of USD 59.7 billion, driven by advancements in artificial intelligence, big data analytics, and the urgent need for real-time data processing capabilities across diverse sectors as per the latest research findings.

The exponential growth in the AI-Ready Data Pipelines market is primarily fueled by the explosive increase in data volumes generated by digital transformation initiatives, IoT devices, and advanced enterprise applications. Organizations are rapidly shifting toward AI-driven decision-making, necessitating highly reliable, scalable, and automated data pipelines that can seamlessly ingest, process, and deliver data for analytics and machine learning models. This surge in demand is further amplified by the proliferation of cloud computing and hybrid environments, which require robust solutions for data integration, transformation, and governance. As businesses strive to remain competitive in the digital era, the need for AI-ready data infrastructure becomes a critical success factor, propelling market growth.

Another key growth driver for the AI-Ready Data Pipelines market is the increasing complexity of enterprise data ecosystems. Modern organizations are dealing with a multitude of data sources, formats, and storage environments, making manual data preparation and management both inefficient and error-prone. AI-ready data pipelines automate these processes, ensuring data quality, consistency, and compliance with regulatory standards. The integration of advanced technologies such as machine learning, natural language processing, and real-time analytics within data pipelines enables organizations to derive actionable insights faster and with greater accuracy. These capabilities are especially crucial for sectors like BFSI, healthcare, and retail, where timely and high-quality data is essential for customer experience, risk management, and operational efficiency.

Furthermore, the increasing emphasis on data governance and security is shaping the evolution of the AI-Ready Data Pipelines market. With stringent regulatory frameworks such as GDPR, HIPAA, and CCPA, enterprises are prioritizing solutions that not only streamline data flows but also ensure compliance and robust data protection. AI-ready data pipelines are equipped with advanced features for data lineage, auditing, and access controls, making them indispensable for organizations operating in highly regulated industries. The convergence of data privacy, security, and operational efficiency is creating new opportunities for vendors to innovate and differentiate their offerings, further accelerating market expansion.

Regionally, North America continues to dominate the AI-Ready Data Pipelines market due to its mature technology landscape, high adoption of AI and analytics, and presence of major industry players. However, Asia Pacific is emerging as a high-growth region, driven by rapid digitalization, expanding cloud infrastructure, and increasing investments in AI and big data technologies. Europe is also witnessing steady growth, supported by strong regulatory frameworks and a focus on data-driven innovation. The Middle East & Africa and Latin America are gradually catching up, with governments and enterprises investing in digital transformation initiatives to enhance competitiveness and service delivery.

Component Analysis

The component segment of the AI-Ready Data Pipelines market comprises software, hardware, and services, each playing a pivotal role in enabling seamless data flow for AI and analytics applications. Software sol

Data in the Classroom is an online curriculum to foster data literacy. This Investigating Sea Level Using Data in the Classroom module is geared towards grades 6 - 12. Visit Data in the Classroom for more information.This application is the Investigating Sea Level module.This module was developed to engage students in increasingly sophisticated modes of understanding and manipulation of data. It was completed prior to the release of the Next Generation Science Standards (NGSS)* and has recently been adapted to incorporate some of the innovations described in the NGSS.Each level of the module provides learning experiences that engage students in the three dimensions of the NGSS Framework while building towards competency in targeted performance expectations. Note: this document identifies the specific practice, core idea and concept directly associated with a performance expectation (shown in parentheses in the tables) but also includes additional practices and concepts that can help students build toward a standard.*NGSS Lead States. 2013. Next Generation Science Standards: For States, By States. Washington, DC: The National Academies Press. Next Generation Science Standards is a registered trademark of Achieve. Neither Achieve nor the lead states and partners that developed the Next Generation Science Standards was involved in the production of, and does not endorse, this product.

OpenStreetMap (OSM) is a free, editable map & spatial database of the whole world. This dataset is an extract of OpenStreetMap data for 21 Pacific Island Countries, in a GIS-friendly format. The OSM data has been split into separate layers based on themes (buildings, roads, points of interest, etc), and it comes bundled with a QGIS project and styles, to help you get started with using the data in your maps. This OSM product will be updated weekly and contains data for Cook Islands, Federated States of Micronesia, Fiji, Kiribati, Republic of the Marshall Islands, Nauru, Niue, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu, Guam, Northern Mariana Islands, French Polynesia, Wallis and Futuna, Tokelau, American Samoa as well as data on the Pacific region. The goal is to increase awareness among Pacific GIS users of the richness of OpenStreetMap data in Pacific countries, as well as the gaps, so that they can take advantage of this free resource, become interested in contributing to OSM, and perhaps join the global OSM community.

Techsalerator’s Location Sentiment Data for the Netherlands

Techsalerator’s Location Sentiment Data for the Netherlands provides a robust dataset offering insights into public sentiment across different geographical locations. This dataset is crucial for businesses, policymakers, and researchers aiming to understand regional opinions, customer feedback, and sentiment trends throughout the Netherlands.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Techsalerator’s Location Sentiment Data for the Netherlands

Techsalerator’s Location Sentiment Data for the Netherlands offers a structured analysis of sentiment variations across cities, rural areas, and key commercial hubs. The dataset is invaluable for market research, brand reputation analysis, and urban development strategies.

Top 5 Key Data Fields

• Geographical Location – Captures sentiment data based on specific cities, regions, and neighborhoods, allowing precise sentiment analysis.
• Sentiment Score – Provides a quantified measure of positive, neutral, and negative sentiment across different locations.
• Source of Sentiment – Identifies whether the sentiment originates from social media, reviews, news articles, or surveys.
• Time-Based Trends – Tracks sentiment fluctuations over time, highlighting seasonal or event-driven sentiment shifts.
• Demographic Insights – Breaks down sentiment data by age, gender, and other demographics for deeper audience analysis.

Top 5 Sentiment Trends in the Netherlands

• Urban vs. Rural Sentiment Disparities – Cities like Amsterdam and Rotterdam often have more dynamic sentiment trends compared to rural areas.
• Tourism Impact on Sentiment – Sentiment shifts based on tourist influx, with seasonal peaks in cities like Utrecht and The Hague.
• Economic and Political Sentiment – Public opinion fluctuates with economic conditions, policy changes, and local government decisions.
• Consumer Brand Perception – Businesses leverage sentiment data to measure public opinion on products, services, and customer experiences.
• Environmental Concerns – Sentiment data highlights public discussions on climate change, sustainability, and urban development policies.

Top 5 Applications of Location Sentiment Data in the Netherlands

• Market Research and Consumer Behavior – Businesses use sentiment insights to refine marketing strategies and product offerings.
• Urban Planning and Development – City planners assess public sentiment to enhance infrastructure, transportation, and public spaces.
• Brand Reputation Management – Companies monitor regional sentiment to address concerns and improve customer satisfaction.
• Political and Social Analysis – Governments and researchers study public opinion on policies, elections, and social issues.
• Crisis and Risk Management – Real-time sentiment monitoring aids in responding to crises, protests, and emergency situations.

Accessing Techsalerator’s Location Sentiment Data

To obtain Techsalerator’s Location Sentiment Data for the Netherlands, contact info@techsalerator.com with your specific requirements. Techsalerator provides customized datasets based on requested fields, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Geographical Location
• Sentiment Score
• Source of Sentiment
• Time-Based Trends
• Demographic Insights
• Industry-Specific Sentiment (Retail, Hospitality, Finance, etc.)
• Social Media and Online Reviews Sentiment
• Public Opinion on Key Issues
• Event-Based Sentiment Analysis
• Contact Information

For organizations and researchers looking to analyze sentiment trends across the Netherlands, Techsalerator’s dataset is a powerful tool for data-driven decision-making and strategic insights.

Context

EveryPolitician is a project with the goal of providing data about every politician in the world. They collect open data on as many politicians as they can find and these datasets are just a small sample of the data available at http://www.everypolitician.org.

Content

Each country has their own governmental structure and EveryPolitician provides data for as many countries as possible. At the time of publishing, there was information on politicians from 233 countries. I chose to publish JSON files for these 10 countries:

• Australia
• Brazil
• China
• France
• India
• Nigeria
• Russia
• South_Africa
• UK
• US

These JSON files follow the POPOLO format

Acknowledgements

These data were collected from http://everypolitician.org/. Their website has more data than I have published here - this is a small sample.

First I'm give credits to Raviiloveyou who create the original Taxi trip fare predictor data set. Modify the Taxi Set to included taxi fares from Philadelphia, PA. The following costs are calculations have been updated in the dataset to include all fares for taxis
First 1/10 mile (flag drop) or fraction thereof: $2.70 Each additional 1/10 mile or fraction thereof: $0.25 Each 37.6 seconds of wait time: $0.25 Include speed of the taxis in KPH (Kilometers per Hour)

Columns are the following: Trip Duration in second (part of the original data set)

Trip Duration in minutes

Trip Duration in Hours

Distance Traveled in Kilometers (part of the original data set)

KPH speed of the taxis in Kilometers per Hour

Wait Time Cost: Each 37.6 seconds of wait time: $0.25 is taxi time used to get the person to the location

Distance Cost: Each additional 1/10 mile (.1 mile = 0.160934 KM) or fraction thereof: $0.25

Fare w Flag: starting cost is $2.70 added into Wait Time Cost plus Distance Cost

TIP: how much money did the taxi drive get for the trip (part of the original data set)

Miscellaneous fees: part of the original data set

Total Fare New: is the total cost of the trip

Num of passengers: is the number of passengers Note there is no addition cost per passenger for Philadelphia, PA Taxis.

surge applied: (part of the original data set)

The BOREAS TF-10 team collected tower flux and meteorological data at two sites, a fen and a young jack pine forest, near Thompson, Manitoba, Canada, as part of BOREAS. A preliminary data set was assembled in August 1993 while field testing the instrument packages, and at both sites data were collected from 15-Aug to 31-Aug. The main experimental period was in 1994, when continuous data were collected from 08-Apr to 23-Sept at the fen site. A very limited experiment was run in the spring/summer of 1995, when the fen site tower was operated from 08-Apr to 14-Jun in support of a hydrology experiment in an adjoining, feeder basin. Upon examination of the 1994 data set, it became clear that the behavior of the heat, water, and carbon dioxide fluxes throughout the whole growing season was an important scientific question, and that the 1994 data record was not sufficiently long to capture the character of the seasonal behavior of the fluxes. Thus, the fen site was operated in 1996 in order to collect data from spring melt to autumn freeze-up. Data were collected from 29-Apr to 05-Nov at the fen site. All variables are presented as 30-minute averages.

This dataset shows:

• the 500 customers that send the most applications to us
• the number and type of applications we receive and complete
• how many requests for information we send to customers

It includes all types of requests for information but excludes:

• telephone requests for information
• bankruptcy applications
• bulk applications
• applications that we've received but not yet completed

Geographic coverage

England and Wales

License statement

The data is available free of charge for use and re-use under the Open Government Licence (OGL). Make sure that you understand the terms of the OGL before using the data. If you use or publish this data, you must add the following statement:

Contains HM Land Registry data © Crown copyright and database rights [year of supply or data of publication]. This data is licensed under the Open Government Licence v3.0.

You must also provide a link in the data you publish to this explanation of the dataset.

Frequency of update

Every three months

As of 2025, around ** percent of enterprises already have workloads in the public cloud, with * percent planning to move additional workloads to the cloud in the next 12 months. In addition, ** percent of respondents reported having data stored on the public cloud.

IMOPE is the reference database for buildings at national level. To date and on a daily basis, it supports nearly 20,000 public and private actors and more than 800 territories (in operational context: fight against unworthy housing, fight against vacancy, energy renovation, OPAH-RU, PIG, VOC,...) wanting to know and transform the French building sector.

Resulting from public research conducted at Mines Saint-Etienne (Institut Mines Télécom), this breakthrough innovation, the methods of which have been patented by the Ministry of the Economy, Industrial and Digital Sovereignty, brings together all the data of interest (+ 250 items of information) on each of the 20 million existing buildings.

⁇ Consult the news of the ONB and the national IMOPE database ⁇ ACTU ONB/IMOPE

IMOPE has been co-built, since its creation in 2016, with and for the actors of the territories (ALEC, operators ANAH, ADIL, DDT, ADEME, EPCI, urban planning agencies ...) in order to meet the multiple challenges of the building sector. Issues on which we can cite:energy renovation, combating vacancy, precariousness and unsanitary conditions, attrition of housing, home support, adaptation to climate change, etc.

The sourcing of merged and reprocessed data: A single and multiple sourcing to increase knowledge and merging in particular: - Open Data: BAN, BDTOPO, DVF, DPE (ADEME), consumption data (ENEDIS, GRDF), RPLS, QPV, Georisks, permanent equipment base, SITADEL, socio-economic data (RP, FiLoSoFi, INSEE), OPAH, ... - "Conventional" data: Land files enriched by Cerema (source DGFiP DGALN), LOVAC, non-anonymised data of owners, RNIC (ANAH) - Local or business data: devices, FSL, LHI, orders, procedures, reporting, planning permission, rental permit, ANAH aid, ... - "Enriched" data: Machine Learning and Deep Learning (DVF, DPE, power source and heating type predictions)

A strong commitment to the commons: U.R.B.S, spin-off of Mines Saint-Etienne, maintains, develops and improves on a clean background and since 2019 the IMOPE database. With a view to mutualisation and openness, U.R.B.S. invites the entire building community (architects, public decision-makers, insurers, artisans, diagnosticians, researchers, citizens, design offices, etc.) to disseminate and reuse widely internally as well as externally, natively or with post-processing, the data contained in the IMOPE database.

It is driven by this philosophy of sharing that we have deployed the**National Building Observatory** (ONB). The**ONB** is a citizen geo-common. As a decision-making tool providing knowledge of the building stock, it makes it easier for everyone to access the information contained in the national IMOPE database.

Convinced that together we will go further, the ONB and IMOPE are initiatives led by civil society. Civil society of which we are part and which, we are convinced, is the keystone for achieving the energy, climate and social objectives of the building sector.

⁇ For more information: https://www.urbs.fr ⁇ To contact us: contact@urbs.fr ⁇ To access the ONB: https://app.urbs.fr/onb/connection

⁇ To access the data catalogue, click here