Atipico1/symbol-data-large dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

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.

Author: Behrooz Keshtgar, behrooz.keshtgar@kit.edu

This archive contains the post-processed data used to generate the figures and the code repository for the publication "Uncertainties in cloud-radiative heating within an idealized extratropical cyclone" by Behrooz Keshtgar, Aiko Voigt, Bernhard Mayer and Corinna Hoose.

Description of the data:

figure1.nc: precipitation rate, cloud cover, surface pressure, and cloud classes on day 4.5 of the ICON-NWP baroclinic life cycle simulation.

figure2.nc: spatially and temporally averaged profiles of cloud water, ice mass content, and cloud fractions from ICON-LEM simulations.

figure4.nc: spatially and temporally averaged cloud-radiative heating profiles from ICON-LEM simulations and offline radiation calculations for each LEM domain.

figure5.nc: cross-section of radiative heating rates for 3D and 1D radiative transfer calculations in the shallow cumulus domain.

figure6.nc: spatially averaged cloud-radiative heating profiles from 3D and 1D radiation calculations for each LEM domain.

figure7.nc: cross-section of cloud-radiative heating calculated with the ice optics of Fu and Baum_ghm in the WCB ascent region.

figure8.nc: spatially and temporally averaged profiles of cloud-radiative heating from 1D radiation calculations with different ice optics for each LEM domain.

figure9.nc: spatially and temporally averaged profiles of cloud-radiative heating from 1D radiation calculations with LEM and NWP clouds for each LEM domain.

figure10.nc: spatially and temporally averaged density and cloud-radiative heating profiles from different offline radiation calculations for each LEM domain.

figure11.nc: profiles of the mean absolute difference of cloud-radiative heating from different offline radiation calculations at different resolutions for each LEM domain.

The keshtgar-etal-2024-cyclone-crh-uncertainties-main.zip is the copy of the published git repository for the model run and analysis scripts. The repository contains

- Scripts for the ICON model simulations

- Scripts for the offline radiative transfer calculations with LibRadTran and the post-processing routine

- Python scripts and Jupyter Notebooks for the analysis in the paper

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.

*** 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

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

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Warning: Large file size (over 1GB). Each monthly data set is large (over 4 million rows), but can be viewed in standard software such as Microsoft WordPad (save by right-clicking on the file name and selecting 'Save Target As', or equivalent on Mac OSX). It is then possible to select the required rows of data and copy and paste the information into another software application, such as a spreadsheet. Alternatively, add-ons to existing software, such as the Microsoft PowerPivot add-on for Excel, to handle larger data sets, can be used. The Microsoft PowerPivot add-on for Excel is available from Microsoft http://office.microsoft.com/en-gb/excel/download-power-pivot-HA101959985.aspx Once PowerPivot has been installed, to load the large files, please follow the instructions below. Note that it may take at least 20 to 30 minutes to load one monthly file. 1. Start Excel as normal 2. Click on the PowerPivot tab 3. Click on the PowerPivot Window icon (top left) 4. In the PowerPivot Window, click on the "From Other Sources" icon 5. In the Table Import Wizard e.g. scroll to the bottom and select Text File 6. Browse to the file you want to open and choose the file extension you require e.g. CSV Once the data has been imported you can view it in a spreadsheet. What does the data cover? General practice prescribing data is a list of all medicines, dressings and appliances that are prescribed and dispensed each month. A record will only be produced when this has occurred and there is no record for a zero total. For each practice in England, the following information is presented at presentation level for each medicine, dressing and appliance, (by presentation name): - the total number of items prescribed and dispensed - the total net ingredient cost - the total actual cost - the total quantity The data covers NHS prescriptions written in England and dispensed in the community in the UK. Prescriptions written in England but dispensed outside England are included. The data includes prescriptions written by GPs and other non-medical prescribers (such as nurses and pharmacists) who are attached to GP practices. GP practices are identified only by their national code, so an additional data file - linked to the first by the practice code - provides further detail in relation to the practice. Presentations are identified only by their BNF code, so an additional data file - linked to the first by the BNF code - provides the chemical name for that presentation.

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

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