The biostatistics software market is experiencing robust growth, driven by the increasing adoption of data-driven approaches in pharmaceutical research, clinical trials, and academic studies. The market, valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the rising volume of complex biological data necessitates sophisticated software solutions for analysis and interpretation. Secondly, advancements in machine learning and artificial intelligence are enhancing the capabilities of biostatistics software, enabling more accurate and efficient data processing. Thirdly, regulatory pressures demanding robust data analysis in the pharmaceutical and healthcare sectors are boosting demand for validated and compliant biostatistics tools. The market is segmented by software type (general-purpose versus specialized) and end-user (pharmaceutical companies, academic institutions, and others). Pharmaceutical companies represent a significant portion of the market due to their extensive reliance on clinical trial data analysis. However, the academic and research segments are also exhibiting strong growth due to increased research activities and funding. Geographically, North America and Europe currently dominate the market, but Asia-Pacific is expected to witness substantial growth in the coming years due to increasing healthcare spending and technological advancements in the region. The competitive landscape is characterized by a mix of established players offering comprehensive suites and specialized niche vendors. While leading players like IBM SPSS Statistics and Minitab enjoy significant market share based on their brand recognition and established user bases, smaller companies specializing in specific statistical methods or user interfaces are gaining traction by catering to niche demands. This competitive dynamic will likely drive innovation and further segmentation within the market, resulting in specialized software offerings tailored to particular research areas and user requirements. The challenges the market faces include the high cost of software licensing, the need for specialized training for effective utilization, and the potential integration complexities with existing data management systems. However, the overall growth trajectory remains positive, driven by the inherent need for sophisticated biostatistical analysis in various sectors.

Public health-related decision-making on policies aimed at controlling the COVID-19 pandemic outbreak depends on complex epidemiological models that are compelled to be robust and use all relevant available data. This data article provides a new combined worldwide COVID-19 dataset obtained from official data sources with improved systematic measurement errors and a dedicated dashboard for online data visualization and summary. The dataset adds new measures and attributes to the normal attributes of official data sources, such as daily mortality, and fatality rates. We used comparative statistical analysis to evaluate the measurement errors of COVID-19 official data collections from the Chinese Center for Disease Control and Prevention (Chinese CDC), World Health Organization (WHO) and European Centre for Disease Prevention and Control (ECDC). The data is collected by using text mining techniques and reviewing pdf reports, metadata, and reference data. The combined dataset includes complete spatial data such as countries area, international number of countries, Alpha-2 code, Alpha-3 code, latitude, longitude, and some additional attributes such as population. The improved dataset benefits from major corrections on the referenced data sets and official reports such as adjustments in the reporting dates, which suffered from a one to two days lag, removing negative values, detecting unreasonable changes in historical data in new reports and corrections on systematic measurement errors, which have been increasing as the pandemic outbreak spreads and more countries contribute data for the official repositories. Additionally, the root mean square error of attributes in the paired comparison of datasets was used to identify the main data problems. The data for China is presented separately and in more detail, and it has been extracted from the attached reports available on the main page of the CCDC website. This dataset is a comprehensive and reliable source of worldwide COVID-19 data that can be used in epidemiological models assessing the magnitude and timeline for confirmed cases, long-term predictions of deaths or hospital utilization, the effects of quarantine, stay-at-home orders and other social distancing measures, the pandemic’s turning point or in economic and social impact analysis, helping to inform national and local authorities on how to implement an adaptive response approach to re-opening the economy, re-open schools, alleviate business and social distancing restrictions, design economic programs or allow sports events to resume.

The global biostatistical consulting services market is experiencing robust growth, driven by the increasing complexity of clinical trials and the rising demand for data-driven insights in the pharmaceutical and medical device industries. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by several key factors, including the expanding adoption of advanced statistical techniques, the surge in outsourcing of biostatistical activities by pharmaceutical and biotech companies, and the growing prevalence of chronic diseases necessitating extensive clinical research. The pharmaceutical companies segment currently dominates the market due to their heavy reliance on robust data analysis for drug development and regulatory submissions. However, the medical device and contract research organization (CRO) segments are also showing significant growth, contributing to the overall market expansion. The increasing availability of large datasets and advancements in data analytics technologies further enhance the demand for specialized biostatistical expertise. Geographic segmentation reveals a strong presence in North America, driven by the high concentration of pharmaceutical and biotech companies and robust regulatory frameworks. Europe follows closely, with strong growth anticipated in Asia-Pacific, particularly in India and China, owing to the rising investment in healthcare infrastructure and clinical research activities. While the market faces challenges such as data privacy concerns and the need for skilled biostatisticians, the overall outlook remains positive, underpinned by sustained innovation within the pharmaceutical and healthcare sectors. The emergence of artificial intelligence and machine learning in biostatistics further presents exciting growth opportunities in the coming years. The competition among the numerous established players and emerging companies ensures continued innovation and service improvements, further enhancing the growth trajectory of the biostatistical consulting services market.

Explore the booming biostatistics consulting market, driven by innovation in pharmaceuticals and medical devices. Discover key growth drivers, trends, and regional insights for 2025-2033.

Biology students’ understanding of statistics is incomplete due to poor integration of these two disciplines. In some cases, students fail to learn statistics at the undergraduate level due to poor student interest and cursory teaching of concepts, highlighting a need for new and unique approaches to the teaching of statistics in the undergraduate biology curriculum. The most effective method of teaching statistics is to provide opportunities for students to apply concepts, not just learn facts. Opportunities to learn statistics also need to be prevalent throughout a student’s education to reinforce learning. The purpose of developing and implementing curriculum that integrates a topic in biology with an emphasis on statistical analysis was to improve students’ quantitative thinking skills. Our lesson focuses on the change in the richness of native species for a specified area with the aid of iNaturalist and the capacity for analysis afforded by Google Sheets. We emphasized the skills of data entry, storage, organization, curation and analysis. Students then had to report their findings, as well as discuss biases and other confounding factors. Pre- and post-lesson assessment revealed students’ quantitative thinking skills, as measured by a paired-samples t test, improved. At the end of the lesson, students had an increased understanding of basic statistical concepts, such as bias in research and making data-based claims, within the framework of biology.

Primary Image: Website screenshot of an iNaturalist observation (Clasping Milkweed – Asclepias amplexicalis). This image is an example of a data entry on iNaturalist. The data students export from iNaturalist is made up of hundreds, or even thousands, of observations like this one. This image is licensed under Creative Commons Attribution - Share Alike 4.0 International license. Source: Observation by cassi saari, 2014.

The biostatistics software market is booming, projected to reach $6.38 billion by 2033 with a 12% CAGR. Discover key trends, regional insights, leading companies, and growth drivers shaping this dynamic sector. Analyze market segmentation, competitive landscape, and future forecasts in this comprehensive report.

Data analysis of the detection of intron 3 and intron 12 retaining MAPT species by digital droplet PCR in frontal lateral cortex, hippocampal and cerebellum samples of non-demented individuals and Alzheimer's disease patients. Each retention was detected in a different fluorophore. There are species that retain both introns TIR3+12-MAPT (Fam +, Hex +) which would be translated into DW-Tau (double intron retention), species that retain only intron 3 TIR3-MAPT (Fam +, Hex -) which would be translated into NW-Tau (Nt truncation) or species that retain only intron 12 TIR12-MAPT (Fam -, Hex +) which would be translated into CW-Tau (Ct truncation). Additionally Tubulin-beta and total-MAPT levels were measured by ddPCR to perform normalization.

Dataset Overview:

This dataset contains simulated (hypothetical) but almost realistic (based on AI) data related to sleep, heart rate, and exercise habits of 500 individuals. It includes both pre-exercise and post-exercise resting heart rates, allowing for analyses such as a dependent t-test (Paired Sample t-test) to observe changes in heart rate after an exercise program. The dataset also includes additional health-related variables, such as age, hours of sleep per night, and exercise frequency.

The data is designed for tasks involving hypothesis testing, health analytics, or even machine learning applications that predict changes in heart rate based on personal attributes and exercise behavior. It can be used to understand the relationships between exercise frequency, sleep, and changes in heart rate.

File: Filename: heart_rate_data.csv File Format: CSV

- Features (Columns):

Age: Description: The age of the individual. Type: Integer Range: 18-60 years Relevance: Age is an important factor in determining heart rate and the effects of exercise.

Sleep Hours: Description: The average number of hours the individual sleeps per night. Type: Float Range: 3.0 - 10.0 hours Relevance: Sleep is a crucial health metric that can impact heart rate and exercise recovery.

Exercise Frequency (Days/Week): Description: The number of days per week the individual engages in physical exercise. Type: Integer Range: 1-7 days/week Relevance: More frequent exercise may lead to greater heart rate improvements and better cardiovascular health.

Resting Heart Rate Before: Description: The individual’s resting heart rate measured before beginning a 6-week exercise program. Type: Integer Range: 50 - 100 bpm (beats per minute) Relevance: This is a key health indicator, providing a baseline measurement for the individual’s heart rate.

Resting Heart Rate After: Description: The individual’s resting heart rate measured after completing the 6-week exercise program. Type: Integer Range: 45 - 95 bpm (lower than the "Resting Heart Rate Before" due to the effects of exercise). Relevance: This variable is essential for understanding how exercise affects heart rate over time, and it can be used to perform a dependent t-test analysis.

Max Heart Rate During Exercise: Description: The maximum heart rate the individual reached during exercise sessions. Type: Integer Range: 120 - 190 bpm Relevance: This metric helps in understanding cardiovascular strain during exercise and can be linked to exercise frequency or fitness levels.

Potential Uses: Dependent T-Test Analysis: The dataset is particularly suited for a dependent (paired) t-test where you compare the resting heart rate before and after the exercise program for each individual.

Exploratory Data Analysis (EDA):Investigate relationships between sleep, exercise frequency, and changes in heart rate. Potential analyses include correlations between sleep hours and resting heart rate improvement, or regression analyses to predict heart rate after exercise.

Machine Learning: Use the dataset for predictive modeling, and build a beginner regression model to predict post-exercise heart rate using age, sleep, and exercise frequency as features.

Health and Fitness Insights: This dataset can be useful for studying how different factors like sleep and age influence heart rate changes and overall cardiovascular health.

License: Choose an appropriate open license, such as:

CC BY 4.0 (Attribution 4.0 International).

Inspiration for Kaggle Users: How does exercise frequency influence the reduction in resting heart rate? Is there a relationship between sleep and heart rate improvements post-exercise? Can we predict the post-exercise heart rate using other health variables? How do age and exercise frequency interact to affect heart rate?

Acknowledgments: This is a simulated dataset for educational purposes, generated to demonstrate statistical and machine learning applications in the field of health analytics.

Simulated datasets used in the supplement with large group size of 30, log-scale noise levels and zero proportions.The settings are reflected in the title of the data:(1) large: group size = 30; (2) 0.5, 1, 1.5: the log-scale noise level(3) 0.3, 0.4, 0.5: the extra zero proportion(4) The last number is the index of replicates. There are 50 replicates for each setting

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 3.0 Combined Fee, Designation, Easement feature class (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to remove overlaps, avoiding overestimation in protected area statistics and to support user needs. A Python scripted process ("PADUS3_0_CreateVectorAnalysisFileScript.zip") associated with this data release prioritized overlapping designations (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation status (e.g. GAP Status Code 1 over 2), public access values (in the order of Closed, Restricted, Open, Unknown), and geodatabase load order (records are deliberately organized in the PAD-US full inventory with fee owned lands loaded before overlapping management designations, and easements). The Vector Analysis File ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") associated item of PAD-US 3.0 Spatial Analysis and Statistics ( https://doi.org/10.5066/P9KLBB5D ) was clipped to the Census state boundary file to define the extent and serve as a common denominator for statistical summaries. Boundaries of interest to stakeholders (State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative) were incorporated into separate geodatabase feature classes to support various data summaries ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip") and Comma-separated Value (CSV) tables ("PADUS3_0SummaryStatistics_TabularData_CSV.zip") summarizing "PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip" are provided as an alternative format and enable users to explore and download summary statistics of interest (Comma-separated Table [CSV], Microsoft Excel Workbook [.XLSX], Portable Document Format [.PDF] Report) from the PAD-US Lands and Inland Water Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). In addition, a "flattened" version of the PAD-US 3.0 combined file without other extent boundaries ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") allow for other applications that require a representation of overall protection status without overlapping designation boundaries. The "PADUS3_0VectorAnalysis_State_Clip_CENSUS2020" feature class ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.gdb") is the source of the PAD-US 3.0 raster files (associated item of PAD-US 3.0 Spatial Analysis and Statistics, https://doi.org/10.5066/P9KLBB5D ). Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types represented in some manner, while work continues to maintain updates and improve data quality (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, changes in protected area status between versions of the PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://www.fgdc.gov/ngda-reports/NGDA_Datasets.html ), agencies are the best source of their lands data.

Crime isn't a topic most people want to use mental energy to think about. We want to avoid harm, protect our loved ones, and hold on to what we claim is ours. So how do we remain vigilant without digging too deep into the filth that is crime? Data, of course. The focus of our study is to explore possible trends between crime and communities in the city of Calgary. Our purpose is visualize Calgary criminal behaviour in order to help increase awareness for both citizens and law enforcement. Through the use of our visuals, individuals can make more informed decisions to improve the overall safety of their lives. Some of the main concerns of the study include: how crime rates increase with population, which areas in Calgary have the most crime, and if crime adheres to time-sensative patterns.

This graph shows how the impact factor of ^ is computed. The left axis depicts the number of papers published in years X-1 and X-2, and the right axis displays their citations in year X.

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

PRISMA Checklist. (DOC)

These tables will be updated monthly. Data were previously published in the Supplement to the Federal Reserve Bulletin, which ceased publication in December 2008.

Statistics from the analysis datasets.

Observed factor level counts (for which dvt = 1) for binary risk factors in each study of the DVT case study. Entries are left blank for studies that did not measure the corresponding factor.

File S1 includes Appendix S1, Appendix S2, Appendix S3, Appendix S4. Appendix S1: Search terms used to identify studies of one year mortality on antiretroviral therapy. Appendix S2: Full citations for studies reviewed. Appendix S3: Illustration of a distribution used to impute CD4 count with bands. Appendix S4: CD4 coefficient (bottom) and model fit (F-statistic – top) for the relationship between one year mortality on ART and baseline CD4 count using varying assumptions about the amount of mortality among those lost to follow-up. (DOCX)