Data accompanying manuscript Data of 7 runners during a Marathon is provided to accompany the manuscript “Quantifying and correcting for speed and stride frequency effects on running mechanics in fatiguing outdoor running”. For each runner the following files are provided: - Global Navigation Satellite System (GNSS) running speed - Inertial Measurement unit (IMU) running speed - Acceleration of foot sensor - Acceleration of lower leg/tibia sensor - Knee angles GNSS running speed GNSS running speed during the full marathon was based on different sports watches. Sampling frequencies between sports watches differed but was on average 0.7 (0.4) Hz. In line with the manuscript, missing latitude-longitude data was linearly interpolated before speed was computed as the distance between two latitude-longitude coordinates based on the Haversine formula. GNSS speeds above 20 km/h were deemed extremely unlikely and replaced with spline interpolation. GNSS speed was then resampled to 240 Hz to match the sampling frequency of the IMUs. Note that GNSS and IMU data are not yet time synchronized! - Filename: SubXX_gnss_speed.csv - Size of matrix: [1xN] IMU running speed IMU running speed was solely used for time synchronization of the GNSS running speed with IMU data. The scaled biomechanical model (as described in the manuscript) provided the velocity of the pelvis segment at 240 Hz. Pelvis IMU speed was then computed as the resultant pelvis IMU velocity. - Filename: SubXX_imu_speed.csv - Size of matrix: [Nx1] Acceleration of foot sensor Accelerations of the right foot were used for initial contact detection in the manuscript. 3D accelerations of a sensor on the right foot are provided in a sensor-fixed coordinate system. The sensor was placed on the midfoot within the shoes, the sensor was aligned with the long axis of the foot. The positive axis of the first dimension points towards the center of the ankle joint. The positive axis of the second dimension points to the right. The positive axis of the third dimensions is directed approximately upwards. - Filename: SubXX_rfoot_acc.csv - Size of matrix: [Nx3] Acceleration of lower leg/tibia sensor Accelerations of the lower leg were one of the quantities of interest in the manuscript. 1D acceleration of a sensor on the right lower leg at 240 Hz is provided in a sensor-fixed coordinate system. The sensor was aligned with the axial direction of the tibia. - Filename: SubXX_rtibia_acc.csv - Size of matrix: [Nx1] Knee angles Knee flexion/extension angles were one of the quantities of interest in the manuscript. Knee flexion/extension angles of the right lower leg at 240 Hz are provided. Knee flexion angles were defined 0° when the leg was fully extended during neutral standing. Flexion resulted in positive knee flexion angles. - Filename: SubXX_rknee_angle.csv - Size of matrix: [Nx1]

Marathon Systems Inc Company Export Import Records. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Radial Vectors quality controlled using HFRadarPyPython toolbox _NCProperties=version=2,netcdf=4.7.4,hdf5=1.10.6 acknowledgement=This data is provided by USF as part of the "Understand Gulf Ocean Systems (UGOS)" project. Funding is provided by the National Academies of Sciences, Engineering, and Medicine (NASEM). cdm_data_type=Swath comment=Site maintained by the University of South Florida (USF). For oi_* global attribute explanations, see references attribute contributor_name=Clifford Merz, Yonggang Liu, Michael Smith contributor_role=Principal Investigator/Hardware Maintenance, Data Analyst, Data QA/QC Conventions=CF-1.6, ACDD-1.3, COARDS date_metadata_modified=2020-12-08T14:30:31Z Easternmost_Easting=-78.9117493 geospatial_lat_max=24.8248904 geospatial_lat_min=22.8479397 geospatial_lat_units=degrees_north geospatial_lon_max=-78.9117493 geospatial_lon_min=-82.7076322 geospatial_lon_units=degrees_east geospatial_vertical_positive=down history=Hourly codar radial data quality controlled to QARTOD . infoUrl=https://www.nationalacademies.org/gulf/fellowships-and-grants/understanding-gulf-ocean-systems institution=Ocean Circulation Group - University of South Florida, College of Marine Science instrument=CODAR SeaSonde High Frequency Radar - MARA - Marathon, FL keywords_vocabulary=GCMD Science Keywords naming_authority=edu.rutgers.marine.rucool Northernmost_Northing=24.8248904 platform=UGOS HF Radar 5MHz Network processing_level=Level 3 project=Understanding Gulf Ocean Systems - Dry Tortugas and Lower Keys High Frequency Radars - High Frequency Radar Sea Surface Current Mapping references=https://www.nationalacademies.org/gulf/fellowships-and-grants/understanding-gulf-ocean-systems sea_name=Straits of Florida source=CODAR SeaSonde Surface Current Mapping Device sourceUrl=(local files) Southernmost_Northing=22.8479397 standard_name_vocabulary=CF Standard Name Table v41 subsetVariables=time, syntax_qc, radial_count_qc, operator_flag_qc, site time_coverage_end=2021-08-19T14:00:00Z time_coverage_start=2020-01-01T00:00:00Z Westernmost_Easting=-82.7076322

Abstract: Gravity data collected during KN182-03 were combined with the gravity data from Fujiwara et al. (2003) and processed as described by Smith et al (2008). The free-air gravity data were reduced to obtain the mantle Bouguer and the residual mantle Bouguer anomalies (MBA and RMBA, respectively) following standard procedures (Kuo and Forsyth, 1988; Prince and Forsyth, 1988). The effects of the water-crust and crust-mantle interfaces were subtracted from the free-air gravity anomaly along tracks to obtain MBA using the method of Parker (1973), and assuming a crustal thickness of 5 km. Densities of water, crust, and mantle were assumed to be 1030, 2750, and 3300 kg/m3, respectively (Escartin and Cannat, 1999). The gravity thermal effect due to spreading was calculated using a passive upwelling model and the ridge geometry (Phipps Morgan and Forsyth, 1988). The calculated thermal effect was removed from the MBA along tracks to obtain the RMBA, which was then gridded. The data were gridded for 13°-15°N and the grid covers the Mid-Atlantic Ridge between the Fifteen-Twenty and Marathon fracture zones. The file presented here is in GMT-compatible netCDF grid format.

De Maria B, Lucini D, Gois MO, Catai AM, Perego F, Malacarne M, Pagani M, Porta A, Dalla Vecchia LA. Improvement of Sympathovagal Balance by Regular Exercise May Counteract the Ageing Process. A Study by the Analysis of QT Variability. Front Physiol. 2022 Apr 20;13:880250. doi: 10.3389/fphys.2022.880250. PMID: 35514344; PMCID: PMC9065681.

Abstract

QT interval (QT) variability analysis provides pathophysiological and prognostic information utilized in cardiac and non-cardiac diseases, complementary to those obtained from the analysis of heart period (HP) variability. An increased QT variability has been associated to a higher risk for cardiac events and poorest prognosis. Autonomic cardiovascular adaptation to internal and external challenges, such those occurring in athletes exposed to high levels of physical stress and in ageing could also be deepen by analyzing QT variability, searching for early prognostic signatures. The aim of the study was to analyze the QT variability and cardiac control complexity in a group of middle-aged half-marathon runners at baseline (B) and at a 10-year follow-up (FU). We found that the overall QT variability decreased at FU, despite the inescapable increase in age (52.3 ± 8.0 years at FU). This change was accompanied by an increase of the HP variability complexity without changes of the QT variability complexity. Of notice, over the years, the group of athletes maintained their regular physical activity by switching to a moderate intensity rather than strenuous. In conclusion, regular and moderate exercise over the years was beneficial for this group of athletes, as reflected by the decreased overall QT variability that is known to be associated to lower cardiovascular risk. The concomitant enhanced cardiac control complexity also suggests a trend opposite to what usually occurs with ageing, resulting in a more flexible cardiac control, typical of younger people.

The global marathon conveyors market, encompassing various applications across food & tobacco, transport & logistics, printing & packaging, textiles, and agriculture, is experiencing robust growth. While precise market sizing data isn't provided, industry reports suggest a substantial market, likely exceeding $5 billion in 2025, considering the individual segment sizes and the high demand for efficient material handling solutions. A conservative Compound Annual Growth Rate (CAGR) of 6% is projected for the forecast period (2025-2033), driven by factors such as increasing automation in manufacturing and logistics, a rise in e-commerce leading to higher package handling needs, and a growing focus on improving operational efficiency across diverse industries. The increasing adoption of advanced conveyor technologies, including those with integrated sensors and automated controls, further fuels market expansion. However, challenges like high initial investment costs for advanced systems and potential supply chain disruptions can act as restraints. The market is segmented by conveyor type (chain belt, steel belt, slider bed), with chain belt conveyors currently holding the largest market share due to their versatility and cost-effectiveness. Geographically, North America and Europe are currently the dominant regions, but significant growth opportunities exist in rapidly developing economies of Asia-Pacific, particularly China and India, fueled by industrialization and infrastructural development. The competitive landscape features both established players like Vecoplan and Key Technology, alongside regional and specialized manufacturers. Success in this market relies on offering customized solutions tailored to specific industry needs, integrating advanced technologies for enhanced efficiency and safety, and establishing robust distribution and after-sales service networks. The increasing adoption of sustainable practices and energy-efficient conveyor systems also presents a significant opportunity for manufacturers to gain a competitive edge. Looking ahead, the market is expected to see further consolidation, with larger players potentially acquiring smaller companies to expand their product portfolios and geographical reach. The focus on digitalization, including predictive maintenance and real-time data analytics, will become increasingly important for improving operational efficiency and reducing downtime.

Here are a few use cases for this project:

1. Marathon Management: This model can be used in analyzing photos or videos from marathons or races. By recognizing bibs and participants, it can automate the task of identifying participants, ranking them, and detecting any possible issues like race fraud.

2. Retail and Fashion Industry: The ability to accurately identify clothing items such as shirts, hats, and shoes can assist in creating AI-driven fashion apps. Users could use these apps to find similar items for purchase, or even virtually try on items.

3. Sport Event Analytics: This model can be implemented in video analysis systems to distinguish between different roles in sports events. For example, it could recognize a referee by their specific bib, helping to automate the process of gathering game statistics.

4. Surveillance Systems: The detection of specific items such as clothing, hats, bibs, and shoes can improve the information obtained from security camera footage. It can be used for crowd control, identification of individuals, or anomaly detection in video surveillance.

5. Content Categorising in Social Media Platforms: This AI can be used by social media platforms for content categorizing and ad targeting. For example, recognizing a person's clothing and accessories in their photos can give indicators about their fashion preferences, leading to more personalized advertising.

The global Jones counter market, encompassing electronic and mechanical devices used in various applications such as bike races and marathons, is experiencing robust growth. While the precise market size for 2025 isn't explicitly provided, considering a plausible CAGR of 8% (a reasonable estimate based on growth in related sporting goods markets) and assuming a 2019 market size of $200 million, we can project a 2025 market value of approximately $300 million. This growth trajectory is anticipated to continue through 2033, driven by factors such as increased participation in cycling and running events, technological advancements leading to more accurate and feature-rich counters, and a growing preference for electronic devices over mechanical ones. The market is segmented by type (electronic vs. mechanical) and application (bike races, marathons, and others), with electronic Jones counters expected to capture a larger market share due to their enhanced precision, data logging capabilities, and user-friendly interfaces. Key players such as Shimano, Garmin, and SRAM are driving innovation and expanding their product portfolios to cater to the rising demand. Geographical regions like North America and Europe currently hold significant market shares, but emerging markets in Asia-Pacific are exhibiting strong growth potential due to the rising popularity of sports and increased disposable income. However, the market's growth is not without challenges. Constraints such as the relatively high cost of electronic Jones counters compared to mechanical ones and the potential for market saturation in mature regions could slightly impede growth. Nevertheless, ongoing product development focusing on affordability, enhanced functionality, and integration with other fitness tracking devices is expected to mitigate these challenges and sustain market expansion throughout the forecast period. The anticipated CAGR of 8% suggests a significant opportunity for stakeholders across the entire value chain, from manufacturers and distributors to retailers and event organizers. The focus on integrating these counters into broader fitness ecosystems and data analytics will continue to propel innovation and market growth in the long term. This comprehensive report provides an in-depth analysis of the global Jones Counter market, projecting a market value exceeding $250 million by 2028. We examine key trends, competitive landscapes, and growth drivers influencing this specialized segment of the sporting goods industry. This report is crucial for businesses involved in manufacturing, distribution, and utilization of Jones Counters across various applications, including cycling and marathon races. Search terms such as electronic Jones counter market, bike race timing systems, marathon race technology, sports timing equipment, and Jones counter production are strategically incorporated for enhanced search engine optimization.

This dataset contains some data used to evaluate the performance of the Syntalos data acquisition platform in various scenarios.

The "Marathon" dataset contains one run over multiple hours to evaluate data synchrony after a long experiment, while the "LaunchSyncVarSteps*" sets contain Syntalos starting and stopping the same experiment many times to determine the inherent start latency jitter.

For every run, Syntalos was tasked with acquiring a variety of data from an electrophysiology amplifier, UCLA Miniscope and two different types of cameras, while also responding to incoming periodic events via a Python script.

Used PC Systems for data acquisition:

• HP DV7-7147SG laptop with an Intel i5-3210M and 8 GB of memory
• Lenovo E495 laptop
• PC with an AMD Ryzen 5 5600G CPU and 16.0 GB of RAM

Data Acquisition Hardware:

• Intan Technologies RHD USB Interface Board with an RHD 64-Channel Recording headstage (Intan Technologies, Los Angeles, California)
• The Imaging Source Cameras: DFK 37BUX462 and DFK 37BUX290 (The Imaging Source LLC, Charlotte, United States)
• Basler ace Classic acA1920-25um camera (Basler AG, Ahrensburg, Germany)
• UVC Webcam, AnkePower Computer Webcam V-24 1080p (generic camera Amazon online shop)
• UCLA Miniscope v4 (Los Angeles, California)
• Arduino Uno R3
• Raspberry Pi Pico