This data set is an example data set for the data set used in the experiment of the paper "A Multilevel Analysis and Hybrid Forecasting Algorithm for Long Short-term Step Data". It contains two parts of hourly step data and daily step data

The raw data on behavior and physical fitness. The behavior for sampling worker before joining WE is on sheet behavior 31 and 62 Then, we show all data for behavior and physical fitness.

Each R script replicates all of the example code from one chapter from the book. All required data for each script are also uploaded, as are all data used in the practice problems at the end of each chapter. The data are drawn from a wide array of sources, so please cite the original work if you ever use any of these data sets for research purposes.

Dataset

This dataset was created by Arun Kumar

Contents

Context

This dataset was collected by me, along with my friends during my college days. The dataset mostly contains data from my friends and family members. This dataset has the survey data for the type of fitness practices that people follow.

Acknowledgements

This dataset wouldn't be here without the help of my friends. So, thanks to them!

What is in the dataset

1. Name of the person attending the survey
2. Gender of the person attending the survey
3. Age of the person attending the survey
4. How important is an exercise to you on the scale of 1 to 5
5. How do you describe your current level of fitness? - Perfect, Very good, Good, Average, Unfit
6. How often do you exercise? - Every day, 1 to 2 times a week, 2 to 3 times a week, 3 to 4 times a week, 5 to 6 times a week, never
7. What barriers, if any, prevent you from exercising more regularly? (Select all that applies) - I don't have enough time, I can't stay motivated, ill become too tired, I have an injury, I don't really enjoy exercising, I exercise regularly with no barriers
8. What forms of exercise do you currently participate in? (Select all that applies) - Walking or jogging, gym, swimming, yoga, Zumba dance, lifting weights, team sport, I don't really exercise
9. Do you exercise _? - Alone, With a friend, With a group, Within a class environment, I don't really exercise
10. What time of the day do you prefer to exercise? - Early morning, afternoon, evening
11. How long do you spend exercising per day? - 30 min, 1 hour, 2 hours, 3 hours and above, I don't really exercise
12. Would you say, you eat a healthy balanced diet? - Yes, No, Not always
13. What prevents you from eating a healthy balanced diet, if any? (Select all that applies) - Lack of time, Cost, Ease of access to fast food, Temptation, and cravings, I have a balanced diet
14. How healthy do you consider yourself on a scale of 1 to 5?
15. Have you recommended your friends to follow a fitness routine? - Yes, No
16. Have you ever purchased fitness equipment? - Yes, No
17. What motivates you to exercise? (Select all that applies) - I want to be fit, I want to increase muscle mass and strength, I want to lose weight, I want to be flexible, I want to relieve stress, I want to achieve a sporting goal, I'm not really interested in exercising.

Section 1: Introduction

Brief overview of dataset contents:

Current database contains anonymised data collected during exercise testing services performed on male and female participants (cycling, rowing, kayaking and running) provided by the Human Performance Laboratory, School of Medicine, Trinity College Dublin, Dublin 2, Ireland.

835 graded incremental exercise test files (285 cycling, 266 rowing / kayaking, 284 running)

Description file with each row representing a test file - COLUMNS: file name (AXXX), sport (cycling, running, rowing or kayaking)

Anthropometric data of participants by sport (age, gender, height, body mass, BMI, skinfold thickness,% body fat, lean body mass and haematological data; namely, haemoglobin concentration (Hb), haematocrit (Hct), red blood cell (RBC) count and white blood cell (WBC) count )

Test data (HR, VO2 and lactate data) at rest and across a range of exercise intensities

Derived physiological indices quantifying each individual’s endurance profile

Following a request from athletes seeking assessment by phone or e-mail the test protocol, risks, benefits and test and medical requirements, were explained verbally or by return e-mail. Subsequently, an appointment for an exercise assessment was arranged following the regulatory reflection period (7 days). Following this regulatory period each participant’s verbal consent was obtained pre-test, for participants under 18 years of age parent / guardian consent was obtained in writing. Ethics approval was obtained from the Faculty of Health Sciences ethics committee and all testing procedures were performed in compliance with Declaration of Helsinki guidelines.

All consenting participants were required to attend the laboratory on one occasion in a rested, carbohydrate loaded and well-hydrated state, and for male participants’ clean shaven in the facial region. All participants underwent a pre-test medical examination, including assessment of resting blood pressure, pulmonary function testing and haematological (Coulter Counter Act Diff, Beckmann Coulter, CA,US) review performed by a qualified medical doctor prior to exercise testing. Any person presenting with any cardiac abnormalities, respiratory difficulties, symptoms of cold or influenza, musculoskeletal injury that could impair performance, diabetes, hypertension, metabolic disorders, or any other contra-indicatory symptoms were excluded. In addition, participants completed a medical questionnaire detailing training history, previous personal and family health abnormalities, recent illness or injury, menstrual status for female participants, as well as details of recent travel and current vaccination status, and current medications, supplements and allergies. Barefoot height in metre (Holtain, Crymych, UK), body mass (counter balanced scales) in kilogram (Seca, Hamburg, Germany) and skinfold thickness in millimetre using a Harpenden skinfold caliper (Bath International, West Sussex, UK) were recorded pre-exercise.

Section 2: Testing protocols

2.1: Cycling

A continuous graded incremental exercise test (GxT) to volitional exhaustion was performed on an electromagnetically braked cycle ergometer (Lode Excalibur Sport, Groningen, The Netherlands). Participants initially identified a cycling position in which they were most comfortable by adjusting saddle height, saddle fore-aft position relative to the crank axis, saddle to handlebar distance and handlebar height. Participant’s feet were secured to the ergometer using their own cycling shoes with cleats and accompanying pedals. The protocol commenced with a 15-min warm-up at a workload of 120 Watt (W), followed by a 10-min rest. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a workload of 100 or 120 W for female and male participants, respectively, and subsequently increasing by a 20, 30 or 40 W incremental increase every 3-min depending on gender and current competition category. During assessment participants maintained a constant self-selected cadence chosen during their warm-up (permitted window was 5 rev.min−1 within a permitted absolute range of 75 to 95 rev.min−1) and the test was terminated when a participant was no longer able to maintain a constant cadence.

Heart rate (HR) data were recorded continuously by radio-telemetry using a Cosmed HR monitor (Cosmed, Rome, Italy). During the test, blood samples were collected from the middle finger of the right hand at the end of the second minute of each 3-min interval. The fingertip was cleaned to remove any sweat or blood and lanced using a long point sterile lancet (Braun, Melsungen, Germany). The blood sample was collected into a heparinised capillary tube (Brand, Wertheim, Germany) by holding the tube horizontal to the droplet and allowing transfer by capillary action. Subsequently, a 25μL aliquot of whole blood was drawn from the capillary tube using a YSI syringepet (YSI, OH, USA) and added into the chamber of a YSI 1500 Sport lactate analyser (YSI, OH, USA) for determination of non-lysed [Lac] in mmol.L−1. The lactate analyser was calibrated to the manufacturer’s requirements (± 0.05 mmol.L−1) before each test using a standard solution (YSI, OH, USA) of known concentration (5 mmol.L−1) and analyser linearity was confirmed using either a 15 or 30 mmol.L-1 standard solution (YSI, OH, USA).

Gas exchange variables including respiration rate (Rf in breaths.min-1), minute ventilation (VE in L.min-1), oxygen consumption (VO2 in L.min-1 and in mL.kg-1.min-1) and carbon dioxide production (VCO2 in L.min-1), were measured on a breath-by-breath basis throughout the test, using a cardiopulmonary exercise testing unit (CPET) and an associated software package (Cosmed, Rome, Italy). Participants wore a face mask (Hans Rudolf, KA, USA) which was connected to the CPET unit. The metabolic unit was calibrated prior to each test using ambient air and an alpha certified gas mixture containing 16% O2, 5% CO2 and 79% N2 (Cosmed, Rome, Italy). Volume calibration was performed using a 3L gas calibration syringe (Cosmed, Rome, Italy). Barometric pressure recorded by the CPET was confirmed by recording barometric pressure using a laboratory grade barometer.

Following testing mean HR and mean VO2 data at rest and during each exercise increment were computed and tabulated over the final minute of each 3-min interval. A graphical plot of [Lac], mean VO2 and mean HR versus cycling workload was constructed and analysed to quantify physiological endurance indices, see Data Analysis section. Data for VO2 peak in L.min-1 (absolute) and in mL.kg-1.min-1 (relative) and VE peak in L.min-1 were reported as the peak data recorded over any 10 consecutive breaths recorded during the last minute of the final exercise increment.

2.2: Running protocol

A continuous graded incremental exercise test (GxT) to volitional exhaustion was performed on a motorised treadmill (Powerjog, Birmingham, UK). The running protocol, performed at a gradient of 0%, commenced with a 15-min warm-up at a velocity (km.h-1) which was lower than the participant’s reported typical weekly long run (>60 min) on-road training velocity. Subsequently, the warm-up was followed by a 10 minute rest / dynamic stretching phase. From a safety perspective during all running GxT participants wore a suspended lightweight safety harness to minimise any potential falls risk. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a sub-maximal running velocity which was lower than the participant’s reported typical weekly long run (>60 min) on-road training velocity, and subsequently increased by ≥ 1 km.h-1 every 3-min depending on gender and current competition category. The test was terminated when a participant was no longer able to maintain the imposed treadmill.

Measurement variables, equipment and pre-test calibration procedures, timing and procedure for measurement of selected variables and subsequent data analysis were as outlined in Section 2.1.

2.3: Rowing / kayaking protocol

A discontinuous graded incremental exercise test (GxT) to volitional exhaustion was performed on a Concept 2C rowing ergometer (Concept, VA, US) in rowers or a Dansprint kayak ergometer (Dansprint, Hvidovre, Denmark) in flat-water kayakers. The protocol commenced with a 15-min low-intensity warm-up at a workload (W) dependent on gender, sport and competition category, followed by a 10-min rest. For rowing the flywheel damping (120, 125 or 130W) was set dependent on gender and competition category. For kayaking the bungee cord tension was adjusted by individual participants to suit their requirements. A discontinuous protocol of 3-min exercise at a targeted load followed by a 1-min rest phase to facilitate stationary earlobe capillary blood sample collection and resetting of ergometer display (Dansprint ergometer) was used. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a sub-maximal load 80 to 120 W for rowing, 50 to 90 W for kayaking and subsequently increased by 20,30 or 40 W every 3-min depending on gender, sport and current competition category. The test was terminated when a participant was no longer able to maintain the targeted workload.

Measurement variables, equipment and pre-test calibration procedures, timing and procedure for measurement of selected variables and subsequent data analysis were as outlined in Section 2.1.

3.1: Data analysis

Constructed graphical plots (HR, VO2 and [Lac] versus load / velocity) were analysed to quantify the following; load / velocity at TLac, HR at TLac, [Lac] at TLac, % of VO2 peak at TLac, % of HRmax at TLac, load / velocity and HR at a nominal [Lac] of 2 mmol.L-1, load / velocity, VO2 and [Lac} at a nominal HR of

1) Data Introduction • The Gym Members Exercise Dataset is a dataset built to systematically collect gym members' exercise routines, body information, exercise habits, and fitness indicators to analyze individual exercise patterns and health conditions.

2) Data Utilization (1) Gym Members Exercise Dataset has characteristics that: • This dataset contains various body and exercise related numerical and categorical variables such as age, gender, weight, height, body fat percentage, BMI, exercise type (e.g., aerobic, muscular, yoga, HIIT), exercise frequency, session time, heart rate (maximum, average, rest), calorie consumption, water intake, and experience level. (2) Gym Members Exercise Dataset can be used to: • Exercise effect analysis and customized fitness strategy: Various variables such as exercise type, frequency, session time and heart rate, calorie burn, body fat percentage, etc. can be analyzed and used to establish customized exercise plans for each member and optimize exercise effectiveness. • Healthcare and Member Characteristics Based Marketing: Based on demographics and exercise habit data such as age, gender, and experience level, it can be used to develop healthcare programs, segment members, and establish targeted marketing strategies.

Physical fitness is a key element of a healthy life, and being overweight or lacking physical exercise will lead to health problems. Therefore, assessing an individual’s physical health status from a non-medical, cost-effective perspective is essential. This paper aimed to evaluate the national physical health status through national physical examination data, selecting 12 indicators to divide the physical health status into four levels: excellent, good, pass, and fail. The existing challenge lies in the fact that most literature on physical fitness assessment mainly focuses on the two major groups of sports athletes and school students. Unfortunately, there is no reasonable index system has been constructed. The evaluation method has limitations and cannot be applied to other groups. This paper builds a reasonable health indicator system based on national physical examination data, breaks group restrictions, studies national groups, and hopes to use machine learning models to provide helpful health suggestions for citizens to measure their physical status. We analyzed the significance of the selected indicators through nonparametric tests and exploratory statistical analysis. We used seven machine learning models to obtain the best multi-classification model for the physical fitness test level. Comprehensive research showed that MLP has the best classification effect, with macro-precision reaching 74.4% and micro-precision reaching 72.8%. Furthermore, the recall rates are also above 70%, and the Hamming loss is the smallest, i.e., 0.272. The practical implications of these findings are significant. Individuals can use the classification model to understand their physical fitness level and status, exercise appropriately according to the measurement indicators, and adjust their lifestyle, which is an important aspect of health management.

The relationship between physical activity and mental health, especially depression, is one of the most studied topics in the field of exercise science and kinesiology. Although there is strong consensus that regular physical activity improves mental health and reduces depressive symptoms, some debate the mechanisms involved in this relationship as well as the limitations and definitions used in such studies. Meta-analyses and systematic reviews continue to examine the strength of the association between physical activity and depressive symptoms for the purpose of improving exercise prescription as treatment or combined treatment for depression. This dataset covers 27 review articles (either systematic review, meta-analysis, or both) and 365 primary study articles addressing the relationship between physical activity and depressive symptoms. Primary study articles are manually extracted from the review articles. We used a custom-made workflow (Fu, Yuanxi. (2022). Scopus author info tool (1.0.1) [Python]. https://github.com/infoqualitylab/Scopus_author_info_collection that uses the Scopus API and manual work to extract and disambiguate authorship information for the 392 reports. The author information file (author_list.csv) is the product of this workflow and can be used to compute the co-author network of the 392 articles. This dataset can be used to construct the inclusion network and the co-author network of the 27 review articles and 365 primary study articles. A primary study article is "included" in a review article if it is considered in the review article's evidence synthesis. Each included primary study article is cited in the review article, but not all references cited in a review article are included in the evidence synthesis or primary study articles. The inclusion network is a bipartite network with two types of nodes: one type represents review articles, and the other represents primary study articles. In an inclusion network, if a review article includes a primary study article, there is a directed edge from the review article node to the primary study article node. The attribute file (article_list.csv) includes attributes of the 392 articles, and the edge list file (inclusion_net_edges.csv) contains the edge list of the inclusion network. Collectively, this dataset reflects the evidence production and use patterns within the exercise science and kinesiology scientific community, investigating the relationship between physical activity and depressive symptoms. FILE FORMATS 1. article_list.csv - Unicode CSV 2. author_list.csv - Unicode CSV 3. Chinese_author_name_reference.csv - Unicode CSV 4. inclusion_net_edges.csv - Unicode CSV 5. review_article_details.csv - Unicode CSV 6. supplementary_reference_list.pdf - PDF 7. README.txt - text file 8. systematic_review_inclusion_criteria.csv - Unicode CSV UPDATES IN THIS VERSION COMPARED TO V3 (Clarke, Caitlin; Lischwe Mueller, Natalie; Joshi, Manasi Ballal; Fu, Yuanxi; Schneider, Jodi (2023): The Inclusion Network of 27 Review Articles Published between 2013-2018 Investigating the Relationship Between Physical Activity and Depressive Symptoms. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4614455_V3) - We added a new file systematic_review_inclusion_criteria.csv.

Context

This is a dummy dataset which was created with an aim to make user understand the relationship between multiple datasets. This dataset can be used for Exploratory Data Analysis, Data Visualization, understanding the concepts of merge and joins.

Content

Data has four tables as mentioned in data details.

Acknowledgements

Not Applicable

Matlab codes for the solution of exercises which can be found in the book "Tensor Calculus and Differential Geometry for Engineers"

Related publications:

Shahab Sahraee, Peter Wriggers: Tensor Calculus and Differential Geometry for Engineers, Springer, Berlin, to be published.

The two primary goals of this workshop are: (1) to present an example of working with data that uses one of the files available through the Data Liberation Initiative (DLI); and (2) to provide a hands-on computing exercise that introduces some basic approaches to quantitative analysis. The study chosen for this example is the National Survey of Literacy Skills Used in Daily Activities conducted in 1989. In completing this example, three general strategies for performing quantitative analysis will be discussed.

Analysis of ‘Combined Exercises ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/605219f1454ae37cc68d6a16 on 18 January 2022.

--- Dataset description provided by original source is as follows ---

Costs and Personnel involved in Exercises combined by Armed Forces Branch.

--- Original source retains full ownership of the source dataset ---

Physical fitness is a key element of a healthy life, and being overweight or lacking physical exercise will lead to health problems. Therefore, assessing an individual’s physical health status from a non-medical, cost-effective perspective is essential. This paper aimed to evaluate the national physical health status through national physical examination data, selecting 12 indicators to divide the physical health status into four levels: excellent, good, pass, and fail. The existing challenge lies in the fact that most literature on physical fitness assessment mainly focuses on the two major groups of sports athletes and school students. Unfortunately, there is no reasonable index system has been constructed. The evaluation method has limitations and cannot be applied to other groups. This paper builds a reasonable health indicator system based on national physical examination data, breaks group restrictions, studies national groups, and hopes to use machine learning models to provide helpful health suggestions for citizens to measure their physical status. We analyzed the significance of the selected indicators through nonparametric tests and exploratory statistical analysis. We used seven machine learning models to obtain the best multi-classification model for the physical fitness test level. Comprehensive research showed that MLP has the best classification effect, with macro-precision reaching 74.4% and micro-precision reaching 72.8%. Furthermore, the recall rates are also above 70%, and the Hamming loss is the smallest, i.e., 0.272. The practical implications of these findings are significant. Individuals can use the classification model to understand their physical fitness level and status, exercise appropriately according to the measurement indicators, and adjust their lifestyle, which is an important aspect of health management.

This dataset was acquired during Cyber Czech – a hands-on cyber defense exercise (Red Team/Blue Team) held in March 2019 at Masaryk University, Brno, Czech Republic. Network traffic flows and a high variety of event logs were captured in an exercise network deployed in the KYPO Cyber Range Platform.

Contents

The dataset covers two distinct time intervals, which correspond to the official schedule of the exercise. The timestamps provided below are in the ISO 8601 date format.

Day 1, March 19, 2019

Start: 2019-03-19T11:00:00.000000+01:00

End: 2019-03-19T18:00:00.000000+01:00

Day 2, March 20, 2019

Start: 2019-03-20T08:00:00.000000+01:00

End: 2019-03-20T15:30:00.000000+01:00

The captured and collected data were normalized into three distinct event types and they are stored as structured JSON. The data are sorted by a timestamp, which represents the time they were observed. Each event type includes a raw payload ready for further processing and analysis. The description of the respective event types and the corresponding data files follows.

cz.muni.csirt.IpfixEntry.tgz – an archive of IPFIX traffic flows enriched with an additional payload of parsed application protocols in raw JSON.

cz.muni.csirt.SyslogEntry.tgz – an archive of Linux Syslog entries with the payload of corresponding text-based log messages.

cz.muni.csirt.WinlogEntry.tgz – an archive of Windows Event Log entries with the payload of original events in raw XML.

Each archive listed above includes a directory of the same name with the following four files, ready to be processed.

data.json.gz – the actual data entries in a single gzipped JSON file.

dictionary.yml – data dictionary for the entries.

schema.ddl – data schema for Apache Spark analytics engine.

schema.jsch – JSON schema for the entries.

Finally, the exercise network topology is described in a machine-readable NetJSON format and it is a part of a set of auxiliary files archive – auxiliary-material.tgz – which includes the following.

global-gateway-config.json – the network configuration of the global gateway in the NetJSON format.

global-gateway-routing.json – the routing configuration of the global gateway in the NetJSON format.

redteam-attack-schedule.{csv,odt} – the schedule of the Red Team attacks in CSV and ODT format. Source for Table 2.

redteam-reserved-ip-ranges.{csv,odt} – the list of IP segments reserved for the Red Team in CSV and ODT format. Source for Table 1.

topology.{json,pdf,png} – the topology of the complete Cyber Czech exercise network in the NetJSON, PDF and PNG format.

topology-small.{pdf,png} – simplified topology in the PDF and PNG format. Source for Figure 1.

The review aimed to address this knowledge gap by determining the effectiveness of and the parameters underpinning group exercise interventions among community-dwelling older persons in Southeast Asia. The outcomes of interest were physical function as measured using Time Up and Go test (TUG), activities of daily living (ADL), the physical performance included balance, muscle strength and gait speed, and fall-related outcomes. Data from selected studies were pooled to calculate the effect size through the mean and standard deviations obtained from outcome measures post-intervention for experimental and control groups. The chi-squared test and the I2 statistic were employed to evaluate heterogeneity between studies. The random modal effect model was utilized if I2 exceeded 50% and p-value > 0.1. Mean differences (MD) and 95% confidence intervals (CI) were used to analyse the studies. The value of p < 0.05 was considered statistically significant.

Data include information about metabolic requirements for incremental shuttle walking tests and the same protocol performed on a treadmill walking test.

Homework 1 assignment for Data Analysis. This program is part of assignment 1 in Data Analysis. The first exercise is to simply find the sum of 2 plus 2. The second exercise is to create an vector. The overall intent is also to document the work following best practices.

Exercise data set for the SAS book by Uehlinger. Sample of individual variables and cases from the data set of ZA Study 0757 (political ideology).

Topics: most important political problems of the country; political interest; party inclination; beha

Practice Analytics Software Market Outlook

The global practice analytics software market size was valued at approximately USD 1.2 billion in 2023 and is anticipated to reach around USD 3.5 billion by 2032, exhibiting a remarkable CAGR of 12.5% during the forecast period. The significant growth in this market can be attributed to the increasing need for data-driven decision-making in various professional domains such as healthcare, legal, and financial services. The growing adoption of digital solutions to enhance operational efficiency and improve service delivery is also driving the market forward.

The rising demand for practice analytics software is primarily driven by the healthcare sector, where the need for efficient patient data management and advanced analytics to improve clinical outcomes is paramount. Healthcare providers are increasingly leveraging practice analytics tools to streamline administrative processes, enhance patient care, and comply with regulatory requirements. Furthermore, the shift towards value-based care and the increasing adoption of electronic health records (EHRs) are fueling the demand for robust analytics solutions capable of deriving actionable insights from vast amounts of healthcare data.

In the legal sector, the adoption of practice analytics software is on the rise as law firms seek to improve case management, optimize resource allocation, and enhance client satisfaction. The ability to analyze historical case data, track litigation trends, and predict case outcomes is invaluable for legal practitioners. Additionally, the growing complexity of legal cases and the need for efficient document management systems are further propelling the demand for advanced analytics solutions in this field. This trend is expected to continue, contributing significantly to the market's growth over the forecast period.

Financial services are also witnessing a surge in the adoption of practice analytics software, driven by the need for real-time data analysis, risk management, and regulatory compliance. Financial institutions are increasingly relying on analytics tools to gain insights into customer behavior, optimize investment strategies, and mitigate risks. The advent of advanced technologies such as artificial intelligence (AI) and machine learning (ML) is further enhancing the capabilities of practice analytics software, enabling financial firms to make more informed decisions and achieve better financial outcomes.

Regionally, North America remains the dominant market for practice analytics software, owing to the region's advanced healthcare infrastructure, high adoption of digital technologies, and the presence of major market players. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by increasing investments in healthcare IT, growing awareness about the benefits of analytics, and the rapid adoption of digital solutions across various sectors. The expanding middle-class population and the increasing demand for quality healthcare and legal services are also contributing to the market's growth in this region.

Component Analysis

The practice analytics software market is segmented by components into software and services. The software segment comprises various solutions designed to collect, analyze, and report data to aid decision-making. This segment is witnessing significant growth due to the rising demand for comprehensive analytics solutions that offer real-time insights and predictive analytics capabilities. The increasing integration of AI and ML in these software solutions is further enhancing their functionalities, making them indispensable tools for various applications, including healthcare, legal, and financial services.

Within the software segment, solutions are tailored to meet the specific needs of different industries. For instance, healthcare analytics software often includes features like patient data management, clinical decision support, and population health management. In contrast, legal analytics software focuses on case management, document review, and litigation analysis. The ability of these solutions to provide industry-specific insights is driving their adoption across various sectors, thereby fueling the overall growth of the software segment.

The services segment includes professional services such as implementation, training, consulting, and support and maintenance. As organizations increasingly adopt practice analytics software, the demand for these services is also on the rise. Implementation servic