Data at scale

Alberto Traverso; Frank Dankers; Leonard Wee; Sander van Kuijk

doi:10.1007/978-3-319-99713-1_2

Data at scale

Alberto Traverso^*, Frank Dankers, Leonard Wee, Sander van Kuijk

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic

Abstract

Pre-requisites to better understand the chapter: basic knowledge of major sources of clinical data. Logical position of the chapter with respect to the previous chapter: in the previous chapter, you have learned what the major sources of clinical data are. In this chapter, we will dive into the main characteristics of presented data sources. In particular, we will learn how to distinguish and classify data according to its scale. Learning objectives: you will learn the major differences between data sources presented in previous chapters; how clinical data can be classified according to its scale. You will get familiar with the concept of ‘big’ clinical data; you will learn which are the major concerns limiting ‘big’ data exchange.

Original language	English
Title of host publication	Fundamentals of Clinical Data Science
Editors	Pieters Kubben, Michel Dumontier, Andre Dekker
Publisher	Springer
Chapter	2
Pages	11-17
Number of pages	7
ISBN (Electronic)	978-3-319-99713-1
ISBN (Print)	978-3-319-99712-4
DOIs	https://doi.org/10.1007/978-3-319-99713-1_2
Publication status	Published - 2019

Access to Document

10.1007/978-3-319-99713-1_2Licence: CC BY

Cite this

@inbook{75853542d0264e78b830e3f38ad86ed8,

title = "Data at scale",

abstract = "Pre-requisites to better understand the chapter: basic knowledge of major sources of clinical data. Logical position of the chapter with respect to the previous chapter: in the previous chapter, you have learned what the major sources of clinical data are. In this chapter, we will dive into the main characteristics of presented data sources. In particular, we will learn how to distinguish and classify data according to its scale. Learning objectives: you will learn the major differences between data sources presented in previous chapters; how clinical data can be classified according to its scale. You will get familiar with the concept of {\textquoteleft}big{\textquoteright} clinical data; you will learn which are the major concerns limiting {\textquoteleft}big{\textquoteright} data exchange.",

author = "Alberto Traverso and Frank Dankers and Leonard Wee and {van Kuijk}, Sander",

year = "2019",

doi = "10.1007/978-3-319-99713-1_2",

language = "English",

isbn = "978-3-319-99712-4",

pages = "11--17",

editor = "Pieters Kubben and Michel Dumontier and Andre Dekker",

booktitle = "Fundamentals of Clinical Data Science",

publisher = "Springer",

address = "United States",

}

TY - CHAP

T1 - Data at scale

AU - Traverso, Alberto

AU - Dankers, Frank

AU - Wee, Leonard

AU - van Kuijk, Sander

PY - 2019

Y1 - 2019

N2 - Pre-requisites to better understand the chapter: basic knowledge of major sources of clinical data. Logical position of the chapter with respect to the previous chapter: in the previous chapter, you have learned what the major sources of clinical data are. In this chapter, we will dive into the main characteristics of presented data sources. In particular, we will learn how to distinguish and classify data according to its scale. Learning objectives: you will learn the major differences between data sources presented in previous chapters; how clinical data can be classified according to its scale. You will get familiar with the concept of ‘big’ clinical data; you will learn which are the major concerns limiting ‘big’ data exchange.

AB - Pre-requisites to better understand the chapter: basic knowledge of major sources of clinical data. Logical position of the chapter with respect to the previous chapter: in the previous chapter, you have learned what the major sources of clinical data are. In this chapter, we will dive into the main characteristics of presented data sources. In particular, we will learn how to distinguish and classify data according to its scale. Learning objectives: you will learn the major differences between data sources presented in previous chapters; how clinical data can be classified according to its scale. You will get familiar with the concept of ‘big’ clinical data; you will learn which are the major concerns limiting ‘big’ data exchange.

U2 - 10.1007/978-3-319-99713-1_2

DO - 10.1007/978-3-319-99713-1_2

M3 - Chapter

SN - 978-3-319-99712-4

SP - 11

EP - 17

BT - Fundamentals of Clinical Data Science

A2 - Kubben, Pieters

A2 - Dumontier, Michel

A2 - Dekker, Andre

PB - Springer

ER -

Data at scale

Abstract

Access to Document

Fingerprint

Cite this