Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

To compare baseball player statistics effectively using visualization, we can create some insightful plots. Below are the steps to accomplish this in Python using libraries like Pandas and Matplotlib or Seaborn.

1. Load the Data

First, we need to load the judge.csv file into a DataFrame. This will allow us to manipulate and analyze the data easily.

2. Explore the Data

Before creating visualizations, it’s good to understand the data structure and identify the columns we want to compare. The relevant columns in your data include pitch_type, release_speed, game_date, and events.

3. Visualization

We can create various visualizations, such as: - A bar chart to compare the average release speed of different pitch types. - A line plot to visualize trends over time based on game dates. - A scatter plot to analyze the relationship between release speed and the outcome of the pitches (e.g., strikeouts, home runs).

Example Code

Here is a sample code to demonstrate how to create these visualizations using Matplotlib and Seaborn:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load the data
df = pd.read_csv('judge.csv')

# Display the first few rows of the dataframe
print(df.head())

# Set the style of seaborn
sns.set(style="whitegrid")

# 1. Average Release Speed by Pitch Type
plt.figure(figsize=(12, 6))
avg_speed = df.groupby('pitch_type')['release_speed'].mean().sort_values()
sns.barplot(x=avg_speed.values, y=avg_speed.index, palette="viridis")
plt.title('Average Release Speed by Pitch Type')
plt.xlabel('Average Release Speed (mph)')
plt.ylabel('Pitch Type')
plt.show()

# 2. Trends in Release Speed Over Time
# First, convert the 'game_date' to datetime
df['game_date'] = pd.to_datetime(df['game_date'])

plt.figure(figsize=(14, 7))
sns.lineplot(data=df, x='game_date', y='release_speed', estimator='mean', ci=None)
plt.title('Trends in Release Speed Over Time')
plt.xlabel('Game Date')
plt.ylabel('Average Release Speed (mph)')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

# 3. Scatter Plot of Release Speed vs. Events
plt.figure(figsize=(12, 6))
sns.scatterplot(data=df, x='release_speed', y='events', hue='pitch_type', alpha=0.7)
plt.title('Release Speed vs. Events')
plt.xlabel('Release Speed (mph)')
plt.ylabel('Event Type')
plt.legend(title='Pitch Type', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()

Explanation of the Code

• Data Loading: The CSV file is loaded into a Pandas DataFrame.
• Average Release Speed: A bar chart shows the average release speed for each pitch type.
• Trends Over Time: A line plot illustrates the trend in release speed over time, which can indicate changes in performance or strategy.
• Scatter Plot: A scatter plot visualizes the relationship between release speed and different events, providing insight into performance outcomes.

Conclusion

These visualizations will help you compare player statistics in a meaningful way. You can customize the plots further based on your specific needs, such as filtering data for specific players or seasons. If you have any specific comparisons in mind or additional data to visualize, let me know!

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly. While it was estimated at ***** zettabytes in 2025, the forecast for 2029 stands at ***** zettabytes. Thus, global data generation will triple between 2025 and 2029. Data creation has been expanding continuously over the past decade. In 2020, the growth was higher than previously expected, caused by the increased demand due to the coronavirus (COVID-19) pandemic, as more people worked and learned from home and used home entertainment options more often.

The Interactive LED Display for Business market is rapidly evolving, revolutionizing the way organizations engage with their audiences. These dynamic displays serve as multifaceted communication tools, enhancing presentations, advertisements, and customer interactions in retail spaces, corporate environments, educat

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. This catalogue entry provides post-processed ERA5 hourly single-level data aggregated to daily time steps. In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:

The daily aggregation statistic (daily mean, daily max, daily min, daily sum*) The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours) The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

*The daily sum is only available for the accumulated variables (see ERA5 documentation for more details). Users should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code, please see the documentation. For more details on the hourly data used to calculate the daily statistics, please refer to the ERA5 hourly single-level data catalogue entry and the documentation found therein.

Abstract To break with the traditional model of Basic Statistics classes in Higher Education, we sought on Statistical Literacy and Critical Education to develop an activity about graphic interpretation, which took place in a Virtual Learning Environment (VLE), as a complement to classroom meetings. Twenty-three engineering students from a public higher education institution in Rio de Janeiro took part in the research. Our objective was to analyze elements of graphic comprehension in an activity that consisted of identifying incorrect statistical graphs, conveyed by the media, followed by argumentation and interaction among students about these errors. The main results evidenced that elements of the Graphic Sense were present in the discussions and were the goal of the students' critical analysis. The VLE was responsible for facilitating communication, fostering student participation, and linguistic writing, so the use of digital technologies and activities favored by collaboration and interaction are important for statistical development, but such construction is a gradual process.

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License Creative Commons Attribution 4.0 International.

Related datasets

Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

You want to create your own football club named ‘ultralearnManral’. - Your club don't have a team yet. - Team will require to hire players for their roster. - You wants to make players selection decisions using past data.

Create some reports/kind of things which recommends data backed players for main team - To start with, a total 14-16 players are required. - Collected data contains information about players, clubs they are currently playing for and various performance measures.

NOTE: As always assume budget for hiring players to be limited, team needs 18-22 possible players to choose from. - Formulating a report will help management/stack-holders make some decision regarding potential players.

Data from various sources are updated in the Statistical Information System of the City of Cologne. The annual statistical yearbook publishes these in tabular, graphic and cartographic form at the level of the city districts and districts. Furthermore, definitions and calculation bases are explained. Small-scale statistics at the level of the 86 districts can be obtained from the Cologne district information become. All levels of the local area structure are presented in this publication explained.

This statistical data catalogue supplements the range of small-scale data. Selected structural data can be called up here in compact tabular form at the level of the 570 statistical districts or the 86 districts. The two overviews provide information about which data is available and from which source it originates. The data itself is provided annually.

Notes:

• Data sources are indicated in the summary tables. When using the data, the data license Germany - attribution - version 2.0 must be observed.
• Some values ​​cannot be given to protect statistical confidentiality. For the data sets of the Federal Employment Agency, these are values ​​from 1 to < 10, for all further data records values ​​from 1 to < 5. This is marked in the data by a * .
• The differentiation of population figures by gender is currently made according to female and male residents. The case numbers of those who define themselves as non-binary/diverse are so low at a small-scale level that they cannot be reported for reasons of statistical confidentiality.
• The determination of residents with a migration background is carried out by combination various characteristics from the resident registration procedure. The data are to be interpreted as estimates. The statistical yearbook of the city of Cologne provides further details.
• The information on households comes from the household generation process. This is a statistical procedure in which residents within an address are assigned to a household as far as possible by querying certain criteria. If the procedure does not identify any connections, the allocation to single-person households takes place. The statistical yearbook of the city of Cologne provides further details.
• The data set pupils* at general schools (spatial location by place of residence) is available from 2013.
• The number of the statistical quarter or district is a spatial location and can be linked to the geodata (see related resource below).

Modularity describes the case where patterns of trait covariation are unevenly dispersed across traits. Specifically, trait correlations are high and concentrated within subsets of variables (modules), but the correlations between traits across modules are relatively weaker. For morphometric data sets, hypotheses of modularity are commonly evaluated using the RV coefficient, an association statistic used in a wide variety of fields. In this article, I explore the properties of the RV coefficient using simulated data sets. Using data drawn from a normal distribution where the data were neither modular nor integrated in structure, I show that the RV coefficient is adversely affected by attributes of the data (sample size and the number of variables) that do not characterize the covariance structure between sets of variables. Thus, with the RV coefficient, patterns of modularity or integration in data are confounded with trends generated by sample size and the number of variables, which limits biological interpretations and renders comparisons of RV coefficients across data sets uninformative. As an alternative, I propose the covariance ratio (CR) for quantifying modular structure and show that it is unaffected by sample size or the number of variables. Further, statistical tests based on the CR exhibit appropriate type I error rates and display higher statistical power relative to the RV coefficient when evaluating modular data. Overall, these findings demonstrate that the RV coefficient does not display statistical characteristics suitable for reliable assessment of hypotheses of modular or integrated structure and therefore should not be used to evaluate these patterns in morphological data sets. By contrast, the covariance ratio meets these criteria and provides a useful alternative method for assessing the degree of modular structure in morphological data.

Roller Coasters Of The World

Performance, Quality, and Innovation

By [source]

About this dataset

This dataset offers a comprehensive analysis of roller coaster performance quality. It contains detailed information about everything from seating arrangements and speeds to points awarded, rankings, and even awards won! The three key data files are Golden_Ticket_Award_Winners_Steel.csv, Golden_Ticket_Award_Winners_Wood.csv and roller coasters.csv - all of which provide statistical data or rankings that accurately catalog the roller coaster performances available today. This dataset features an array of columns covering all facets from length and speed to rank, name, location and material type allowing for a detailed look at the modern day roller coaster performance analysis like never before! Unlock the power this data holds in deciphering what makes up some of today's most thrilling amusement park rides worldwide – providing users with statistics that will leave them exhilarated yet awed!

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How to use the dataset

This dataset provides all the necessary information for you to get an insight into what roller coasters have received high awards and their individual scores. It is useful for finding out which roller coasters are most sought after and how each one was rated from an objective point of view. This information can be used in various ways, such as determining which amusement parks have the best rides, or looking up reviews and experiences from other people.

Research Ideas

• Creating a heatmap visualizing the number of award-winning roller coasters and the locations of amusement parks across different countries.
• Creating an interactive timeline to compare and track the changes in rankings and points awarded over time for different types of roller coasters, such as steel or wood.
• Creating a graph comparing speed, height and length for top-ranked roller coasters to show how their performance varies based on these parameters

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Golden_Ticket_Award_Winners_Steel.csv | Column name | Description | |:---------------|:-----------------------------------------------------------------| | Rank | The ranking of the roller coaster in the year. (Integer) | | Name | The name of the roller coaster. (String) | | Park | The amusement park where the roller coaster is located. (String) | | Location | The location of the amusement park. (String) | | Supplier | The manufacturer of the roller coaster. (String) | | Year Built | The year the roller coaster was built. (Integer) | | Points | The points awarded to the roller coaster. (Integer) |

File: Golden_Ticket_Award_Winners_Wood.csv | Column name | Description | |:---------------|:-----------------------------------------------------------------| | Rank | The ranking of the roller coaster in the year. (Integer) | | Name | The name of the roller coaster. (String) | | Park | The amusement park where the roller coaster is located. (String) | | Location | The location of the amusement park. (String) | | Supplier | The manufacturer of the roller coaster. (String) | | Year Built | The year the roller coaster was built. (Integer) | | Points | The points awarded to the roller coaster. (Integer) |

File: roller_coasters.csv | Column name | Description | |:-------------------|:--------------------------------------------------------------------| | name | The name of the roller coaster. (String) | | material_type | The type of material used to construct the roller coaster. (String) | | **seating...

Multilocus sequence data provide far greater power to resolve species limits than the single locus data typically used for broad surveys of clades. However, current statistical methods based on a multispecies coalescent framework are computationally demanding, because of the number of possible delimitations that must be compared and time-consuming likelihood calculations. New methods are therefore needed to open up the power of multilocus approaches to larger systematic surveys. Here, we present a rapid and scalable method that introduces 2 new innovations. First, the method reduces the complexity of likelihood calculations by decomposing the tree into rooted triplets. The distribution of topologies for a triplet across multiple loci has a uniform trinomial distribution when the 3 individuals belong to the same species, but a skewed distribution if they belong to separate species with a form that is specified by the multispecies coalescent. A Bayesian model comparison framework was developed and the best delimitation found by comparing the product of posterior probabilities of all triplets. The second innovation is a new dynamic programming algorithm for finding the optimum delimitation from all those compatible with a guide tree by successively analyzing subtrees defined by each node. This algorithm removes the need for heuristic searches used by current methods, and guarantees that the best solution is found and potentially could be used in other systematic applications. We assessed the performance of the method with simulated, published, and newly generated data. Analyses of simulated data demonstrate that the combined method has favorable statistical properties and scalability with increasing sample sizes. Analyses of empirical data from both eukaryotes and prokaryotes demonstrate its potential for delimiting species in real cases.

The 18-22 inch industrial display market is a dynamic segment within the broader display technology landscape, catering to a variety of industries including manufacturing, healthcare, transportation, and retail. These displays are integral for applications such as process controls, data visualization, and user inter

Men's Mile Run World Record Progression History (1861-Present)

Examining the Athlete, Nationality and Venue Influence on Race Times

By Ben Jones [source]

About this dataset

This remarkable dataset chronicles the world record progression of the men's mile run, containing detailed information on each athlete's time, their name, nationality, date of their accomplishment and the location of their event. It allows us to look back in history and get a comprehensive overview of how this track event has progressed over time. Analyzing this information can help us understand how training and technology have improved the event over the years; as well as study different athletes' performances and learn how some athletes have pushed beyond their limits or fell short. This valuable resource is an essential source for anyone intrigued by the cutting edge achievements in men's mile running world records. Discovering powerful insights from this dataset can allow us to gain perspective into not only our own personal goals but also uncover ideas on how we could continue pushing our physical boundaries by watching past successes. Explore and comprehend for yourself what it means to be a true athlete at heart!

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How to use the dataset

This guide provides an introduction on how best to use this dataset in order to analyze various aspects involving the men’s mile run world records. We will focus on analyzing specific fields such as date, athlete name & nationality, time taken for completion and auto status by using statistical methods and graphical displays of data.

In order to use this data effectively it is important that you understand what each field measures: • Time: The amount of time it took for an athlete to finish a race - measured in minutes and seconds (example: 3:54).
• Auto: Whether or not a pacemaker was used during a specific race (example ; yes/no).
• Athlete Name & Nationality: The name and nationality associated with an athlete who set \record(example; Usain Bolt - Jamaica).
• Date : Year representing when a specific record was set by an individual( example-2021 ). •Venue : Location at which the record is set.(example; London Olympic Stadium )

Now that you understand which fields measure what let’s discuss various ways that you can use these datasets features. Analyzing trends in historical sporting performances has long been utilized as means for understanding changes brought about by new training methods/technologies etc., over time . This can be done with our dataset by using basic statistical displays like bar graphs & average analysis or more advanced methods such as regression analysis or even Bayesian approaches etc..The first thing anyone interested should do when dealing with this sort of data is inspect any wacky outliers before beginning more rigorous analysis; if one discovers any potential unreasonable values it would be best to discard them before building after models or readings based off them (this sort of elimination is common practice).After cleaning your work space let’s move onto building interactive visual display through graphics ,plotting different columns against one another e.g., – plotting time against date allows us see changes overtime from 1861 until now . Additionally plotting time vs Auto allows us see any

Research Ideas

• Comparing individual athletes and identifying those who have consistently pushed the event to higher levels of performance.
• Analyzing national trends related to improvement in track records over time, based on differences in training and technology.
• Creating a heatmap to visualize the progression of track records around the world and locate regions with a particularly strong historical performance in this event

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. -...