The Clinical Research & Development (CRD) Solutions market, valued at $34,970 million in 2025, is projected to experience robust growth, driven by a Compound Annual Growth Rate (CAGR) of 6% from 2025 to 2033. This expansion is fueled by several key factors. The increasing prevalence of chronic diseases globally necessitates a greater investment in drug discovery and development, thereby boosting demand for CRD solutions. Furthermore, advancements in technology, particularly in areas like artificial intelligence (AI) and big data analytics, are significantly enhancing the efficiency and speed of clinical trials. The growing adoption of outsourcing strategies by pharmaceutical and biotechnology companies to reduce operational costs and focus on core competencies also contributes to market growth. Stringent regulatory requirements and increasing focus on patient safety are shaping the market landscape, demanding advanced and compliant CRD solutions. The competitive landscape is characterized by a mix of large multinational corporations and specialized niche players, with companies like IQVIA, ICON, and Wuxi Apptec holding significant market share. Despite the positive growth trajectory, the market faces certain challenges. These include the high costs associated with clinical trials, particularly for innovative therapies, which can limit access for smaller companies. The complexities of navigating global regulatory environments, with varying approvals and requirements across different regions, also pose hurdles. Competition is intensifying among CRD solution providers, requiring continuous innovation and adaptation to maintain a competitive edge. However, the long-term outlook for the CRD solutions market remains optimistic, driven by the enduring need for effective and efficient drug development processes to address unmet medical needs worldwide. The market's segmentation (though not provided) likely includes various services such as clinical trial management, data management, regulatory affairs, and other specialized services. This diversification provides ample opportunities for growth within specific niche areas.

Icon645 is a large-scale dataset of icon images that cover a wide range of objects: 645,687 colored icons 377 different icon classes These collected icon classes are frequently mentioned in the IconQA questions. In this work, we use the icon data to pre-train backbone networks on the icon classification task in order to extract semantic representations from abstract diagrams in IconQA. On top of pre-training encoders, the large-scale icon data could also contribute to open research on abstract aesthetics and symbolic visual understanding.

Atmospheric fields extracted from ICON-LEM DE simulations and regridded onto a regular lon-lat grid with 5km grid spacing. Two ICON experiments were performed: one with present-day aerosol radiative effects and one with aerosol absorption completely switched off

• The simulation domain covers the Germany

• The simulation setup is described in Heinze et al. (2017) & Stevens et al. (2020) &

• Native grid spacing is 312 m for DOM02 & 625 m for DOM01

• '20130502_CCN_rad' experiment: ** is the simulation dataset incl. aerosol absorption ** is derived from /hpss/arch/bm0834/k203095/ICON_LEM_DE_JUQUEEN/hdcp2_final_2dom/20130502_CCN_rad

• '20130502_semi_direct_effect' ** is the simulation dataset excl.. aerosol absorption; ** is derived from /hpss/arch/bm0834/k203095/ICON_LEM_DE_JUQUEEN/hdcp2_final_2dom/20130502_semi_direct_effect

References

Costa-Surós, M., Sourdeval, O., Acquistapace, C., Baars, H., Carbajal Henken, C., Genz, C., Hesemann, J., Jimenez, C., König, M., Kretzschmar, J., Madenach, N., Meyer, C. I., Schrödner, R., Seifert, P., Senf, F., Brueck, M., Cioni, G., Engels, J. F., Fieg, K., Gorges, K., Heinze, R., Siligam, P. K., Burkhardt, U., Crewell, S., Hoose, C., Seifert, A., Tegen, I., & Quaas, J. (2020). Detection and attribution of aerosol–cloud interactions in large-domain large-eddy simulations with the ICOsahedral Non-hydrostatic model. Atmos. Chem. Phys., 20(9), 5657–5678. https://doi.org/10.5194/acp-20-5657-2020

Heinze, R. et al. (2017), Large-eddy simulations over Germany using ICON: a comprehensive evaluation, Quart. J. Roy. Meteor. Soc., 143(702), 69–100.

Stevens, Bjorn and Acquistapace, C. and Hansen, A. and and Coauthors incl. Senf, F. (2020), Large-eddy and Storm Resolving Models for Climate Prediction The Added Value for Clouds and Precipitation, J. Meteor. Soc. Japan, doi:10.2151/jmsj.2020-021.

Context

Airline data holds immense importance as it offers insights into the functioning and efficiency of the aviation industry. It provides valuable information about flight routes, schedules, passenger demographics, and preferences, which airlines can leverage to optimize their operations and enhance customer experiences. By analyzing data on delays, cancellations, and on-time performance, airlines can identify trends and implement strategies to improve punctuality and mitigate disruptions. Moreover, regulatory bodies and policymakers rely on this data to ensure safety standards, enforce regulations, and make informed decisions regarding aviation policies. Researchers and analysts use airline data to study market trends, assess environmental impacts, and develop strategies for sustainable growth within the industry. In essence, airline data serves as a foundation for informed decision-making, operational efficiency, and the overall advancement of the aviation sector.

Content

This dataset comprises diverse parameters relating to airline operations on a global scale. The dataset prominently incorporates fields such as Passenger ID, First Name, Last Name, Gender, Age, Nationality, Airport Name, Airport Country Code, Country Name, Airport Continent, Continents, Departure Date, Arrival Airport, Pilot Name, and Flight Status. These columns collectively provide comprehensive insights into passenger demographics, travel details, flight routes, crew information, and flight statuses. Researchers and industry experts can leverage this dataset to analyze trends in passenger behavior, optimize travel experiences, evaluate pilot performance, and enhance overall flight operations.

Dataset Glossary (Column-wise)

• Passenger ID - Unique identifier for each passenger
• First Name - First name of the passenger
• Last Name - Last name of the passenger
• Gender - Gender of the passenger
• Age - Age of the passenger
• Nationality - Nationality of the passenger
• Airport Name - Name of the airport where the passenger boarded
• Airport Country Code - Country code of the airport's location
• Country Name - Name of the country the airport is located in
• Airport Continent - Continent where the airport is situated
• Continents - Continents involved in the flight route
• Departure Date - Date when the flight departed
• Arrival Airport - Destination airport of the flight
• Pilot Name - Name of the pilot operating the flight
• Flight Status - Current status of the flight (e.g., on-time, delayed, canceled)

Structure of the Dataset

https://i.imgur.com/cUFuMeU.png" alt="">

Acknowledgement

The dataset provided here is a simulated example and was generated using the online platform found at Mockaroo. This web-based tool offers a service that enables the creation of customizable Synthetic datasets that closely resemble real data. It is primarily intended for use by developers, testers, and data experts who require sample data for a range of uses, including testing databases, filling applications with demonstration data, and crafting lifelike illustrations for presentations and tutorials. To explore further details, you can visit their website.

Cover Photo by: Kevin Woblick on Unsplash

Thumbnail by: Airplane icons created by Freepik - Flaticon

*** TYPE OF SURVEY AND METHODS *** The data set includes responses to a survey conducted by professionally trained interviewers of a social and market research company in the form of computer-aided telephone interviews (CATI) from 2017-02 to 2017-04. The target population was inhabitants of Germany aged 18 years and more, who were randomly selected by using the sampling approaches ADM eASYSAMPLe (based on the Gabler-Häder method) for landline connections and eASYMOBILe for mobile connections. The 1,331 completed questionnaires comprise 44.2 percent mobile and 55.8 percent landline phone respondents. Most questions had options to answer with a 5-point rating scale (Likert-like) anchored with ‘Fully agree’ to ‘Do not agree at all’, or ‘Very uncomfortable’ to ‘Very comfortable’, for instance. Responses by the interviewees were weighted to obtain a representation of the entire German population (variable ‘gewicht’ in the data sets). To this end, standard weighting procedures were applied to reduce differences between the sample and the entire population with regard to known rates of response and non-response depending on household size, age, gender, educational level, and place of residence. *** RELATED PUBLICATION AND FURTHER DETAILS *** The questionnaire, analysis and results will be published in the corresponding report (main text in English language, questionnaire in Appendix B in German language of the interviews and English translation). The report will be available as open access publication at KIT Scientific Publishing (https://www.ksp.kit.edu/). Reference: Orwat, Carsten; Schankin, Andrea (2018): Attitudes towards big data practices and the institutional framework of privacy and data protection - A population survey, KIT Scientific Report 7753, Karlsruhe: KIT Scientific Publishing. *** FILE FORMATS *** The data set of responses is saved for the repository KITopen at 2018-11 in the following file formats: comma-separated values (.csv), tapulator-separated values (.dat), Excel (.xlx), Excel 2007 or newer (.xlxs), and SPSS Statistics (.sav). The questionnaire is saved in the following file formats: comma-separated values (.csv), Excel (.xlx), Excel 2007 or newer (.xlxs), and Portable Document Format (.pdf). *** PROJECT AND FUNDING *** The survey is part of the project Assessing Big Data (ABIDA) (from 2015-03 to 2019-02), which receives funding from the Federal Ministry of Education and Research (BMBF), Germany (grant no. 01IS15016A-F). http://www.abida.de *** CONTACT *** Carsten Orwat, Karlsruhe Institute of Technology, Institute for Technology Assessment and Systems Analysis orwat@kit.edu Andrea Schankin, Karlsruhe Institute of Technology, Institute of Telematics andrea.schankin@kit.edu

MB "In-bigdata" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

KŪB BIGDATA FUND financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.