Lecture

Publishing scientific articles & data

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

In course

ENV-444: Exploratory data analysis in environmental health

This course teaches how to apply exploratory spatial data analysis to health information. Teaching focuses on the role of GIS and spatial statistics in spatial epidemiology. It proposes a context to investigate the relationship between health, quality of l ...

Description

This lecture covers the structure of a standard scientific paper, including the title, authors, introduction, data, methods, results, discussion, conclusion, references, and contributions. It emphasizes the importance of correctly publishing texts and open data, providing insights on writing engaging titles, presenting data, analyzing methods, and interpreting results. The instructor also introduces tools like Zenodo for data repository, Zotero for reference management, and Overleaf and Authorea for collaborative writing.

Instructors (2)

Stéphane Joost

Expertise Geographic Information Science and spatial statisticsMolecular ecology, Landscape genomics, Seascape genomicsSpatial epidemiology, Health geographics Publications Publications- List of publications Teaching & PhD PhD Students Marco André Vieira Ruas, Manon Laetitia Pribille, Martine Valérie Besse Past EPFL PhD Students Estelle Rochat, Solange Catherine Gaillard, Oliver Michele Selmoni, Anaïs Ladoy, Annie Sandrine Guillaume Past EPFL PhD Students as codirector Sylvie Stucki, Kevin Leempoel, Martí

Idris Guessous

Official source

Related concepts (20)

Data analysis

Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

Scientific literature

Scientific literature comprises academic papers that report original empirical and theoretical work in the natural and social sciences. Within a field of research, relevant papers are often referred to as "the literature". Academic publishing is the process of contributing the results of one's research into the literature, which often requires a peer-review process. Original scientific research published for the first time in scientific journals is called the primary literature.

Publishing

Publishing is the activity of making information, literature, music, software, and other content available to the public for sale or for free. Traditionally, the term refers to the creation and distribution of printed works, such as books, newspapers, and magazines. With the advent of digital information systems, the scope has expanded to include digital publishing such as ebooks, digital magazines, websites, social media, music, and video game publishing.

Exploratory data analysis

In statistics, exploratory data analysis (EDA) is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling and thereby contrasts traditional hypothesis testing. Exploratory data analysis has been promoted by John Tukey since 1970 to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments.

Data management

Data management comprises all disciplines related to handling data as a valuable resource. The concept of data management arose in the 1980s as technology moved from sequential processing (first punched cards, then magnetic tape) to random access storage. Since it was now possible to store a discrete fact and quickly access it using random access disk technology, those suggesting that data management was more important than business process management used arguments such as "a customer's home address is stored in 75 (or some other large number) places in our computer systems.

Related lectures (58)

Data Science Essentials

Covers the essentials of data science, including data handling, visualization, and analysis, emphasizing practical skills and active engagement.

Data Analysis to AI and ML, Social Media

Explores the evolution from data analysis to AI and ML, emphasizing big data, machine learning, and social media interaction.

Introduction to Applied Data Analysis

Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.

Data Science Fundamentals

Covers the fundamentals of data science, the scientific method evolution, the role of a data scientist, and the significance of data as the new oil.

Data Management: FAIR Principles