This slideshow is a sample of the "Data vizualisation" training available as video at LinkedIn Learning and presents a set of typical graphs reproducible with the free statistical software R.
This slideshow is a sample of the "Data vizualisation" training available as video at LinkedIn Learning and presents a set of typical graphs reproducible with the free statistical software R.
This document discusses different types of charts used to visualize qualitative and quantitative data, including histograms, bar charts, pie charts, frequency polygons, ogives, dotplots, and stem-and-leaf plots. It provides examples of how to construct each type of chart and compares their uses for different types of data. Key differences between chart types are outlined, such as histograms using bars to show frequencies and pie charts showing relative frequencies as wedges.
4 Ways to Create an Interactive Data Visualization StoryHitech BPO
Appropriate data, audience understanding & building a good story for meaningful & powerful visual dashboards that influence & engage the audience for actionable insights; is the key. This infographic is about storytelling with visualization.
A graph is a visual representation of data using points connected by lines or bars. It can show relationships between different types of data. Common types of graphs include bar graphs, pie charts, histograms, and line graphs. Bar graphs compare categories using vertical or horizontal bars of different heights or lengths. Pie charts show proportions using a circle divided into wedge-shaped slices. Histograms display distribution through bars of varying heights. Line graphs track changes over time by connecting data points with lines. Rene Descartes introduced the Cartesian coordinate system to precisely locate points on a graph using their x and y coordinates.
A graph is a visual representation of data using points connected by lines or bars. It can show relationships between different types of data. Common types of graphs include bar graphs, pie charts, histograms, and line graphs. Bar graphs compare categories using vertical or horizontal bars of different heights or lengths. Pie charts show proportions using a circle divided into wedge-shaped slices. Histograms display frequency distributions using rectangular bars of varying heights. Line graphs track changes over time by connecting data points with lines. Rene Descartes introduced the Cartesian coordinate system to precisely locate points on a graph using their x and y coordinates.
Graphs can effectively visualize data relationships but must be designed carefully.
Bar charts and pie charts are appropriate for discrete categorical data. Bar charts compare frequencies as proportional bar widths and heights. Pie charts show category proportions through wedge sizes.
Histograms and line graphs effectively display continuous and some ordinal data. Histograms use bar widths to represent value ranges. Line graphs connect data points to show trends over intervals.
However, graphs can mislead if they distort data scales, use inappropriate or confusing designs, or include non-data elements. Data representation and readability should always be prioritized in graph creation.
Visualisation de données (types de graphiques)📡 Vincent Isoz
Ce diaporama est un échantillon de la formation "Visualisation de données" disponible en vidéo chez LinkedIn Learning et présente un ensemble de graphiques typiques reproductibles avec le logiciel gratuit de statistiques R.
This slideshow is a sample of the "Data vizualisation" training available as video at LinkedIn Learning and presents a set of typical graphs reproducible with the free statistical software R.
This document discusses different types of charts used to visualize qualitative and quantitative data, including histograms, bar charts, pie charts, frequency polygons, ogives, dotplots, and stem-and-leaf plots. It provides examples of how to construct each type of chart and compares their uses for different types of data. Key differences between chart types are outlined, such as histograms using bars to show frequencies and pie charts showing relative frequencies as wedges.
4 Ways to Create an Interactive Data Visualization StoryHitech BPO
Appropriate data, audience understanding & building a good story for meaningful & powerful visual dashboards that influence & engage the audience for actionable insights; is the key. This infographic is about storytelling with visualization.
A graph is a visual representation of data using points connected by lines or bars. It can show relationships between different types of data. Common types of graphs include bar graphs, pie charts, histograms, and line graphs. Bar graphs compare categories using vertical or horizontal bars of different heights or lengths. Pie charts show proportions using a circle divided into wedge-shaped slices. Histograms display distribution through bars of varying heights. Line graphs track changes over time by connecting data points with lines. Rene Descartes introduced the Cartesian coordinate system to precisely locate points on a graph using their x and y coordinates.
A graph is a visual representation of data using points connected by lines or bars. It can show relationships between different types of data. Common types of graphs include bar graphs, pie charts, histograms, and line graphs. Bar graphs compare categories using vertical or horizontal bars of different heights or lengths. Pie charts show proportions using a circle divided into wedge-shaped slices. Histograms display frequency distributions using rectangular bars of varying heights. Line graphs track changes over time by connecting data points with lines. Rene Descartes introduced the Cartesian coordinate system to precisely locate points on a graph using their x and y coordinates.
Graphs can effectively visualize data relationships but must be designed carefully.
Bar charts and pie charts are appropriate for discrete categorical data. Bar charts compare frequencies as proportional bar widths and heights. Pie charts show category proportions through wedge sizes.
Histograms and line graphs effectively display continuous and some ordinal data. Histograms use bar widths to represent value ranges. Line graphs connect data points to show trends over intervals.
However, graphs can mislead if they distort data scales, use inappropriate or confusing designs, or include non-data elements. Data representation and readability should always be prioritized in graph creation.
Visualisation de données (types de graphiques)📡 Vincent Isoz
Ce diaporama est un échantillon de la formation "Visualisation de données" disponible en vidéo chez LinkedIn Learning et présente un ensemble de graphiques typiques reproductibles avec le logiciel gratuit de statistiques R.
Visualisation de données (types de graphiques) v15.0📡 Vincent Isoz
Ce diaporama est un échantillon de la formation "Visualisation de données" disponible en vidéo chez LinkedIn Learning et présente un ensemble de graphiques typiques reproductibles avec le logiciel gratuit de statistiques R.
Scientific Evolution LLS Services Catalog v5.0 (2020)📡 Vincent Isoz
This document provides an introduction to the Scientific Evolution Sàrl company and their quantitative seminar programs. The company aims to be a leader in quantitative scientific management through their comprehensive range of open enrollment seminars focused on quantitative techniques. The seminars are designed for high-potential employees and cover topics like probabilities, statistics, forecasting, finance, data analysis, and more. Participants will learn practical applications of quantitative methods through case studies and real-world examples to help solve business challenges.
Visualisation de données (types de graphiques) v3.0📡 Vincent Isoz
Ce diaporama est un échantillon de la formation "Visualisation de données" disponible en vidéo chez LinkedIn Learning et présente un ensemble de graphiques typiques reproductibles avec le logiciel gratuit de statistiques R.
Excellent E-book sur GGPlot 2 en français et en couleurs de mon collègue Daname Kolani! Cet e-book est un échantillon des sujets que nous traîtons dans les formations R et GGPlot 2 que nous dispensons.
The document provides instructions for installing and configuring Scientific Linux 7.1 in a VirtualBox virtual machine. It describes how to download and install Scientific Linux, configure various networking and security settings, and install programming environments and applications like Qt, Eclipse, XML Copy Editor, Umbrello, Apache, MySQL, PHP, phpMyAdmin, MariaDB, Tomcat and more. The document is a guide for setting up a development environment for tasks like C++, Java, PHP and database programming on Scientific Linux.
Petite présentation PowerPoint de la solution proposée par la société Scientific Evolution Sàrl pour la génération et l'analyse scientifique de tests de compétences, de quiz, de sondages et de cartes de révision.
The document advertises quantitative training programs offered by Scientific Evolution Sàrl to improve skills in mathematics, modeling, simulation, and auditing. The training programs focus on high-level applied mathematics and quantitative techniques to analyze complex business and industrial problems. They are inspired by postgraduate programs at top universities and aim to provide professionals and teams with the quantitative skills needed to make effective decisions. The document emphasizes applying scientific methodologies and using state-of-the-art tools to solve real-world problems.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Visualisation de données (types de graphiques) v15.0📡 Vincent Isoz
Ce diaporama est un échantillon de la formation "Visualisation de données" disponible en vidéo chez LinkedIn Learning et présente un ensemble de graphiques typiques reproductibles avec le logiciel gratuit de statistiques R.
Scientific Evolution LLS Services Catalog v5.0 (2020)📡 Vincent Isoz
This document provides an introduction to the Scientific Evolution Sàrl company and their quantitative seminar programs. The company aims to be a leader in quantitative scientific management through their comprehensive range of open enrollment seminars focused on quantitative techniques. The seminars are designed for high-potential employees and cover topics like probabilities, statistics, forecasting, finance, data analysis, and more. Participants will learn practical applications of quantitative methods through case studies and real-world examples to help solve business challenges.
Visualisation de données (types de graphiques) v3.0📡 Vincent Isoz
Ce diaporama est un échantillon de la formation "Visualisation de données" disponible en vidéo chez LinkedIn Learning et présente un ensemble de graphiques typiques reproductibles avec le logiciel gratuit de statistiques R.
Excellent E-book sur GGPlot 2 en français et en couleurs de mon collègue Daname Kolani! Cet e-book est un échantillon des sujets que nous traîtons dans les formations R et GGPlot 2 que nous dispensons.
The document provides instructions for installing and configuring Scientific Linux 7.1 in a VirtualBox virtual machine. It describes how to download and install Scientific Linux, configure various networking and security settings, and install programming environments and applications like Qt, Eclipse, XML Copy Editor, Umbrello, Apache, MySQL, PHP, phpMyAdmin, MariaDB, Tomcat and more. The document is a guide for setting up a development environment for tasks like C++, Java, PHP and database programming on Scientific Linux.
Petite présentation PowerPoint de la solution proposée par la société Scientific Evolution Sàrl pour la génération et l'analyse scientifique de tests de compétences, de quiz, de sondages et de cartes de révision.
The document advertises quantitative training programs offered by Scientific Evolution Sàrl to improve skills in mathematics, modeling, simulation, and auditing. The training programs focus on high-level applied mathematics and quantitative techniques to analyze complex business and industrial problems. They are inspired by postgraduate programs at top universities and aim to provide professionals and teams with the quantitative skills needed to make effective decisions. The document emphasizes applying scientific methodologies and using state-of-the-art tools to solve real-world problems.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).