Mendeley Data Showcase

This Python script visualizes data related to the brand value of the Indian Premier League (IPL), its constituent teams, and a comparison with other major football leagues. It uses matplotlib for creating the plots and numpy for numerical operations. The script generates four key visualizations: IPL Brand Value Over Time: A line plot illustrating the growth of the IPL's brand value in billion dollars from 2009 to 2024. This chart shows the increasing financial strength and popularity of the league over the years. Top 10 IPL Team Brand Values (2024): A bar chart displaying the brand values (in million dollars) of the top 10 IPL teams in 2024. This visualization allows for a comparison of the brand strength of different teams within the league. The numerical value of each bar is displayed on top of the bar. Growth Rate of IPL Team Brand Values (2024): Another bar chart presenting the growth rates (in percentage) of the top 10 IPL teams' brand values in 2024. This plot highlights the teams with the highest growth potential and increasing popularity. The growth rate in percentage is displayed on top of the bar. Comparison of IPL and Football League Brand Values: A bar chart comparing the brand values (in billion dollars) of the IPL (top 5 teams), Bundesliga, and English Premier League (EPL). This visualization contextualizes the IPL's financial strength in comparison to other prominent sports leagues worldwide.

IPL DATA SET 2014-2020

The data were acquired through the manual collection of scores and statistics during the matches of the Indian Premier League (IPL) 2022 based on the fantasy points system. It is a sports performance dataset acquired through observation and recording of game events followed by manual entry of the recorded scores and statistics into a spreadsheet or database. The data points in this dataset were generated based on the fantasy points system, which assigns points to players based on their performance during the match. The inclusion criteria for the data points in this dataset would likely be based on the performance of players who participated in the IPL 2022 matches. The exclusion criteria would depend on the specific research question or analysis being conducted with the data.

Replication materials for Mors & Waguespack, forthcoming 2021, "Fast success and slow failure: the process speed of dispersed research teams", Research Policy For further information see "readme.pdf"

environmenral factors and proxies of CL and IPL GDGTs in lake surface sediments, SPM and soils.

This dataset contains the code and data to reproduce the experiments of the paper "A Comparative Study of Fourier Transform and CycleGAN as Domain Adaptation Techniques for Weed Segmentation". The data comes from the ROSE Challenge, a benchmarking competition of agricultural robots focused on autonomous weed destruction. The ROSE Challenge has been organized by the National Laboratory of Metrology and Testing (LNE) and the National Research Institute for Agriculture, Food, and the Environment (INRAE). Four teams participated in the ROSE field campaigns with different robots and camera systems. The teams' names are BIPBIP, PEAD, ROSEAU, and WeedElec. The images used in our experiments were collected in the years 2019 and 2021 in an experimental field at the INRAE research center located in Montoldre, France. The teams scanned maize (Zea mays) and bean (Phaseolus vulgaris) plants with four kinds of weeds (Lolium perenne, Sinapis arvensis, Chenopodium album, Matricaria chamomilla) under natural daylight conditions. The dataset is composed of RGB images (with different resolutions) and semantic segmentation masks to distinguish the crop, weed, and background classes. There are 1000 labeled images in total (125 per team and crop type) per year.

Data tracking KPIs for numerous global virtual teams.

This is a test data set for teams

Abstract: We present a replication of Bartling et al. (2014) who developed an experiment to measure the intrinsic value of decision rights. We can confirm their seminal results for individuals with remarkable accuracy, thus strongly confirming the prior of a robust result. Motivated by the observation that team decision making is ubiquitous, we then extend their design by studying how teams value decision rights. In the aggregate, we find no differences be- tween individuals and teams. However, in our exploratory analysis, we uncover an im- portant heterogeneity: teams with a smooth decision making process have much lower intrinsic values of decision rights than individuals, but teams with internal conflicts have much higher values, thus distorting decisions. The published article can be found at https://doi.org/10.1016/j.jebo.2023.03.019

Images of TeamStation AI platform in action building nearshore teams