The datasets containing simulation performance results during the current study, in addition to the code to replicate the simulation study in its entirety, are available here. See the README file for a description the Stata do-files, R-script files, tips to run the code, and the performance result dataset dictionaries.

The Data Analytics Market size was valued at USD 41.05 USD billion in 2023 and is projected to reach USD 222.39 USD billion by 2032, exhibiting a CAGR of 27.3 % during the forecast period. Data Analytics can be defined as the rigorous process of using tools and techniques within a computational framework to analyze various forms of data for the purpose of decision-making by the concerned organization. This is used in almost all fields such as health, money matters, product promotion, and transportation in order to manage businesses, foresee upcoming events, and improve customers’ satisfaction. Some of the principal forms of data analytics include descriptive, diagnostic, prognostic, as well as prescriptive analytics. Data gathering, data manipulation, analysis, and data representation are the major subtopics under this area. There are a lot of advantages of data analytics, and some of the most prominent include better decision making, productivity, and saving costs, as well as the identification of relationships and trends that people could be unaware of. The recent trends identified in the market include the use of AI and ML technologies and their applications, the use of big data, increased focus on real-time data processing, and concerns for data privacy. These developments are shaping and propelling the advancement and proliferation of data analysis functions and uses. Key drivers for this market are: Rising Demand for Edge Computing Likely to Boost Market Growth. Potential restraints include: Data Security Concerns to Impede the Market Progress . Notable trends are: Metadata-Driven Data Fabric Solutions to Expand Market Growth.

De marktomvang van de markt voor sensorische analyse-apparaten is gecategoriseerd op basis van type (e-nose, geursynthesizer) en applicatie (entertainment, onderwijs, gezondheidszorg, voedsel en drank, communicatie) en geografische regio's (Noord-Amerika, Europa, Azië-Pacific, Zuid-Amerika en Afrika) segmenten.

This repository provides the raw data, analysis code, and results generated during a systematic evaluation of the impact of selected experimental protocol choices on the metagenomic sequencing analysis of microbiome samples. Briefly, a full factorial experimental design was implemented varying biological sample (n=5), operator (n=2), lot (n=2), extraction kit (n=2), 16S variable region (n=2), and reference database (n=3), and the main effects were calculated and compared between parameters (bias effects) and samples (real biological differences). A full description of the effort is provided in the associated publication.

Data on Forest Inventory and Analysis (FIA) includes information on Palau's forests 2013-2014. The Pacific Northwest Forest Inventory and Analysis (PNW-FIA) program measures and compiles data on plots in coastal Alaska, California, Hawaii, Oregon, Washington, and U.S.- affiliated Pacific Islands. Most data are available in Access databases and can be downloaded by clicking one of the links below. PNW data are combined with data from all states in the U.S. and stored in the national FIADB. Data for any state can be accessed on the national website (see links to national tools below). Please be aware that some documents may be very large. The PNW-FIA Program shifted from a periodic to an annual inventory system in 2001. Periodic inventories sampled primarily timberland plots outside of national forests and most reserved areas, in a single state within a 2- or 3-year window. Typically, re-assessments occurred every ten years in the West. For the annual inventory in the Pacific Northwest all forested plots are now sampled, with one-tenth of the plots in any given state being visited annually. A full annual inventory cycle is complete in ten years. To download and use the FIA Database, follow this link https://www.fs.fed.us/pnw/rma/fia-topics/inventory-data

When studying the impacts of climate change, there is a tendency to select climate data from a small set of arbitrary time periods or climate windows (e.g., spring temperature). However, these arbitrary windows may not encompass the strongest periods of climatic sensitivity and may lead to erroneous biological interpretations. Therefore, there is a need to consider a wider range of climate windows to better predict the impacts of future climate change. We introduce the R package climwin that provides a number of methods to test the effect of different climate windows on a chosen response variable and compare these windows to identify potential climate signals. climwin extracts the relevant data for each possible climate window and uses this data to fit a statistical model, the structure of which is chosen by the user. Models are then compared using an information criteria approach. This allows users to determine how well each window explains variation in the response variable and compare model support between windows. climwin also contains methods to detect type I and II errors, which are often a problem with this type of exploratory analysis. This article presents the statistical framework and technical details behind the climwin package and demonstrates the applicability of the method with a number of worked examples.

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 ?

La taille du marché du marché de l'analyse commerciale et des logiciels d'entreprise est classé en fonction de type (logiciels d'outils, de manware) et d'application (Communial, Governments, d'autres) et des régions géographiques (Amérique du Nord, Europe, Asie-Pacifique, Amérique du Sud et Moyen-Orient et Afrique).

Ce rapport fournit des informations sur la taille du marché et les prévisions de la valeur du marché, exprimée dans USD Million, à travers ces segments définis.

This data release presents the results of analyses of biota and water samples collected on multiple dates from 2007 to 2014 at 3 locations in Lake Mead National Recreation Area. Data are presented in 3 spreadsheets containing sample analyses for (1) stable isotopes in biota (2007-2014), (2) synthetic organic compounds in biota (2013-2014), and (3) synthetic organic compounds in water (2013-2014)

A course on data analysis/ in particular variance analysis. For secondary analysis a survey is used evaluating new institutions of teacher training.

We include the gene sets that are used in the experimental part of our research paper.

AutoTrain Dataset for project: imdb-sentiment-analysis

  Dataset Description

This dataset has been automatically processed by AutoTrain for project imdb-sentiment-analysis.

  Languages

The BCP-47 code for the dataset's language is en.

  Dataset Structure







  Data Instances

A sample from this dataset looks as follows: [ { "text": "Me neither, but this flick is unfortunately one of those movies that are too bad to be good and… See the full description on the dataset page: https://huggingface.co/datasets/linktimecloud/autotrain-data-imdb-sentiment-analysis.

ITS data collected as part of Comparison of statistical methods used to meta-analyse results from interrupted time series studies: an empirical study.

Code used to analyse the ITS studies.

This contains the South American portion of the Hydrologic Derivatives for Modeling and Analysis (HDMA) database. The HDMA database provides comprehensive and consistent global coverage of raster and vector topographically derived layers, including raster layers of digital elevation model (DEM) data, flow direction, flow accumulation, slope, and compound topographic index (CTI); and vector layers of streams and catchment boundaries. The coverage of the data is global (-180º, 180º, -90º, 90º) with the underlying DEM being a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) and the Shuttle Radar Topography Mission (SRTM). For most of the globe south of 60º North, the raster resolution of the data is 3-arc-seconds, corresponding to the resolution of the SRTM. For the areas North of 60º, the resolution is 7.5-arc-seconds (the smallest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30-arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information.

According to Cognitive Market Research, the global Next Generation Sequencing Data Analysis market size will be USD 1234.76 million in 2025. It will expand at a compound annual growth rate (CAGR) of 23.60% from 2025 to 2033.

North America held the major market share for more than 40% of the global revenue with a market size of USD 456.86 million in 2025 and will grow at a compound annual growth rate (CAGR) of 21.8% from 2025 to 2033.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 358.08 million.
APAC held a market share of around 23% of the global revenue with a market size of USD 296.34 million in 2025 and will grow at a compound annual growth rate (CAGR) of 26.7% from 2025 to 2033.
South America has a market share of more than 5% of the global revenue with a market size of USD46.92 million in 2025 and will grow at a compound annual growth rate (CAGR) of 24.4% from 2025 to 2033.
The Middle East had a market share of around 2% of the global revenue and was estimated at a market size of USD 49.39 million in 2025 and will grow at a compound annual growth rate (CAGR) of 25.1% from 2025 to 2033.
Africa had a market share of around 1% of the global revenue and was estimated at a market size of USD 27.16 million in 2025 and will grow at a compound annual growth rate (CAGR) of 23.9% from 2025 to 2033.
NGS Commercial Software category is the fastest growing segment of the Next Generation Sequencing Data Analysis industry

Market Dynamics of Next Generation Sequencing Data Analysis Market

Key Drivers for Next Generation Sequencing Data Analysis Market

Growing Demand For Personalized Medicine Boosts NGS Data Analysis To Boost Market Growth

The growing demand for personalized medicine is a key factor driving the Next Generation Sequencing (NGS) Data Analysis Market. Personalized medicine focuses on providing tailored treatments based on a patient’s genetic profile, helping to improve accuracy and effectiveness. NGS plays a vital role in identifying genetic variations and mutations, which helps healthcare providers design customized treatment plans. As more patients and doctors adopt this approach for better health outcomes, the need for accurate and advanced NGS data analysis tools continues to rise. This demand is further fueled by the increasing prevalence of chronic diseases such as cancer and genetic disorders, where personalized medicine is crucial. Consequently, the market for NGS data analysis is witnessing significant growth. For instance, in March 2022, Thermo Fisher Scientific launched the CE-IVD marked Ion Torrent Genexus Dx Integrated Sequencer, an automated, next-generation sequencing (NGS) platform. The platform automates library preparation, sequencing, analysis, and variant calling on one instrument using pre-filled reagents.

https://corporate.thermofisher.com/us/en/index/newsroom/press-releases/2022/Mar/29-Thermo-Fisher-Scientific-Launches-CE-IVD-Marked-Next-Generation-Sequencing-Instrument-for-Use-in-Clinical-Labs.html

Rising Prevalence Of Cancer Drives The Adoption Of NGS Technology To Boost Market Growth

The rising prevalence of cancer is a major driver for the Next Generation Sequencing (NGS) Data Analysis Market. Cancer diagnosis and treatment require precise genetic insights, which NGS technology provides by detecting mutations and genetic variations linked to different cancer types. This helps in early diagnosis, targeted therapy, and personalized treatment plans, improving patient outcomes. As the global burden of cancer increases, healthcare providers and researchers are turning to NGS for more reliable solutions. NGS data analysis plays a crucial role in interpreting the large volume of sequencing data generated, making it essential for cancer research and clinical applications. The growing focus on precision medicine in oncology further accelerates the adoption of NGS technology, boosting market growth.

Restraint Factor for the Next Generation Sequencing Data Analysis Market

High Cost Of NGS Data Analysis Tools And Services Limits Adoption, Will Limit Market Growth

The high cost of Next Generation Sequencing (NGS) data analysis tools and services limits the market’s growth. Advanced software, high-performance computing systems, and cloud-based storage required for analyzing large genomic datasets come with a significant price. These expenses are often difficult for smaller labs, star...

Document Layout Analysis

## Overview

Document Layout Analysis is a dataset for instance segmentation tasks - it contains Tables Images Titles Textblocks annotations for 1,403 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

CEOS Analysis Ready Data for Land (CARD4L) are satellite data that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets [1]. In this paper, key input data (e.g. aerosol optical depth, precipitable water, BRDF parameters) needed for atmospheric and BRDF corrections of Landsat data are identified and a sensitivity analysis is conducted using outputs of a physics based atmospheric and BRDF model. The results show that aerosol impacts more on the visible bands where the average variation of reflectance could reach 0.05 of reflectance unit. The variation over dark targets can be much higher so that it is a critical parameter for aquatic applications. By contrast, precipitable water (water vapor in the rest of the paper) only impacts the near-infrared (NIR) and shortwave (SWIR) bands and the extent of change is much smaller. BRDF parameters impact time series most on winter and summer images of highly anisotropic areas and when they are normalized to 45º solar angle. Different BRDF levels for different spectrum ranges not only impact the magnitude of reflectance, but also the signature for these areas. It seems that it is necessary to normalize surface BRDF to ensure time series consistency of the Landsat ARD product. Abstract presented at 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

La taille du marché du marché intelligent de la solution d'analyse vidéo est classé en fonction du type (logiciel, du matériel) et de l'application (détection des incidents, gestion des intrusions, comptage des foules, surveillance du trafic, reconnaissance automatique des plaques d'immatriculation, reconnaissance faciale, autres) et les régions géographiques (Amérique du Nord, ce rapport, Asie-Pacifique, Amérique du Sud, et le marché et les prévisions). Million USD, à travers ces segments définis.

BackgroundThe conduction and report of network meta-analysis (NMA), including the presentation of the network-plot, should be transparent. We aimed to propose metrics adapted from graph theory and social network-analysis literature to numerically describe NMA geometry.MethodsA previous systematic review of NMAs of pharmacological interventions was performed. Data on the graph’s presentation were collected. Network-plots were reproduced using Gephi 0.9.1. Eleven geometric metrics were tested. The Spearman test for non-parametric correlation analyses and the Bland-Altman and Lin’s Concordance tests were performed (IBM SPSS Statistics 24.0).ResultsFrom the 477 identified NMAs only 167 graphs could be reproduced because they provided enough information on the plot characteristics. The median nodes and edges were 8 (IQR 6–11) and 10 (IQR 6–16), respectively, with 22 included studies (IQR 13–35). Metrics such as density (median 0.39, ranged 0.07–1.00), median thickness (2.0, IQR 1.0–3.0), percentages of common comparators (median 68%), and strong edges (median 53%) were found to contribute to the description of NMA geometry. Mean thickness, average weighted degree and average path length produced similar results than other metrics, but they can lead to misleading conclusions.ConclusionsWe suggest the incorporation of seven simple metrics to report NMA geometry. Editors and peer-reviews should ensure that guidelines for NMA report are strictly followed before publication.