Introduction
"Open data is data that can be freely used, re-used and redistributed by anyone - subject only, at most, to the requirement to attribute and sharealike".
Source: https://opendatahandbook.org/guide/en/what-is-open-data/
What to expect from this Library Guide?
What to expect from this library guide open data search?
In this library guide you are following a research process of open data search. You will get more information about the data lifecycle, data repositories and the use of open datasets in your assignments.
The library guide consists of the following information:
- What data do I need?
- How do I find datasets?
- How can I use datasets?
- How do I evaluate datasets?
- How can I cite datasets?
For whom is the library guide useful?
The library guide open data search is useful for students of Hanze UAS in the economic or financial domain and might be the start of exploration of the search of open data. The guide is not comprehensive, but gives you a first insight in open data search.
Registry of Research Data Repositories
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re3data.org (Registry of Research Data Repositories)
Re3data is a global registry of research data repositories that covers research data repositories from different academic disciplines. It includes repositories that enable permanent storage of and access to data sets to researchers, funding bodies, publishers, and scholarly institutions. re3data.org promotes a culture of sharing, increased access and better visibility of research data.
Explanation of concepts
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GlossaryThe National Coordination Point Research Data Management (LCRDM) is a national network of experts in the field of research data management (RDM).
Data lifecycle
Steps in the data lifecyle:
- Creating data: collect data, the research itself, also the raw data
- Processing data: process the data, make it fit to results, everything from data cleaning, data wrangling, and data formatting to data compression, encrypt the raw data.
- Analysing data: all the computational and statistical techniques for analyzing data for some purpose: the algorithms and methods that underlie artificial intelligence (AI), data mining, machine learning, undefined and statistical inference, to gain knowledge or insights, build classifiers and predictors, or infer causality.
- Preserving data: to secure and safely storage the raw, processed and final data. Final data are deposited in data repositories as a safe place and services the managing of the data for further research.
- Giving access to data: to develop further research, it is important to know if research data on the topic is already available in a data repository.
- Re-using data: the research data can be re-used for further research.