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HBO/WO

MBO

2 maart 2024

Data management

Introductie op research data management, met onder meer aandacht voor Open Science, FAIR data management, een aantal praktische tips en persoonlijke data. De slides zijn voorzien van het UvA-logo, en een aantal slides hebben betrekking op de lokale situatie van de UvA.

Presentatie

Casper Fermin, Joost Oosterhuis, Ira Lenters

1 januari 2025

Data-analyse-protocol voor de mkb-accountant

Geschikt voor vraagstukken rond het adviseren van het midden- en kleinbedrijf (mkb) over stuurdata ten aanzien van bedrijfsvoering. Het ontwikkelde protocol is geschikt voor ondernemers vanaf circa € 0,5 miljoen omzet en die willen starten met data-analyse en het inrichten van dashboards van de belangrijkste performance indicatoren.

Document

Kees van Montfort, Frank Jan de Graaf, Matthijs Karssen, Maaike Lycklama

6 november 2021

Julia Data Science

This is an open-source and open access book on how to do Data Science using Julia. The book describes the basics of the Julia programming language DataFrames.jl for data manipulation and Makie.jl for data visualization. You will learn to: Read CSV and Excel data into Julia Process data in Julia, that is, learn how to answer data questions Filter and subset data Handle missing data Join multiple data sources together Group and summarize data Export data out of Julia to CSV and Excel files Plot data with different Makie.jl backends Save visualizations in several formats such as PNG or PDF Use different plotting functions to make diverse data visualizations Customize visualizations with attributes Use and create new plotting themes Add LaTeX elements to plots Manipulate color and palettes Create complex figure layouts

Open (Access) Tekst Boek

Jose Storopoli, Rik Huijzer, Lazaro Alonso

10 augustus 2023

Research Data Management: Data classification

Data classification While writing your dissertation you may (re)use existing data or create new data. These data sets may contain sensitive data. Sensitive data is frequently classified as: public, internal, confidential or secret. The data classification is important as it determines how you store, share and publish your data. Example of classification of sensitive data. Sharing data Sensitive data identifies individuals, species, objects or locations, and carries a risk of causing discrimination, harm or unwanted attention. Related.

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Erasmus University Library

10 augustus 2023

Research Data Management: Open Data

What is Open Data? Open Data is data that anyone can access, use and share. Open Data challenges intelligent and creative minds to provide societal value. Open Data applications allow us to make better use of existing resources and create new products and services. Open Data is also one of the cornerstones of Open Science, a concept central to science practitioners and policy makers of today, promoting (re)use of research data and results amongst scientists, the business community and society as a whole. Open Data and academic research In academia, Open Data are used in humanities and cultural heritage research communities (e.g. Journal of Open Humanities Data (JOHD), Europeana); Open Data are also common in the life sciences (e.g. Elixer) and in economics (e.g. World Bank). Some open datasets can be useful to explore societally relevant questions relevant to current policy debates and topics of interest to specific demographics. Open Data is also important for educational purposes (e.g., see Bishop & Kuula-Luumi, 2017). Open Data applications Open Data helps to create (new) value for societies, citizens and businesses. There are now several examples of successful startups with innovative applications of Open Data. Appealing examples are available in health, transport, energy and the environmental sector (see ODINE for examples). I want to work with Open Data. Where to start, who to contact? A good starting point for information and inspiration on “all things” Open Data is the Open Data Handbook. This online handbook provides case studies, learning resources and networked information on Open Data. Related. Support.

Website

Erasmus University Library

31 mei 2023

Data Privacy Handbook

The Data Privacy Handbook is a practical guide on handling personal data in scientific research, primarily written for Utrecht University researchers and research support staff in the Netherlands. It is an initiative of Research Data Management Support, in collaboration with privacy and data experts at Utrecht University. The Data Privacy Handbook consists of: A knowledge base which explains how the EU General Data Protection Regulation (GDPR, Dutch: Algemene Verordening Gegevensbescherming) applies to scientific research, including guidelines and good practices in carrying out GDPR-compliant scientific research; An overview of privacy-enhancing techniques & tools and practical guidance on their implementation; Use Cases in the form of research projects with privacy-related issues, for which a reusable solution (e.g., tool, workflow) is shared.

Website

Universiteitsbibliotheek Utrecht

10 augustus 2023

Research Data Management: Research data management

Research data management Research data management (RDM) is an important topic with increasing relevance for academics. Due to recent examples of researchers committing fraud by falsifying or manipulating research data, this topic has attracted a lot of attention. These examples of fraud resulted in a Taskforce Scientific Integrity at the EUR, which recommended the introduction of research data management for researchers. For students, it has led to stricter supervision and attention to plagiarism. Benefits Basic elements of data management In each stage of the research life cycle different requirements are identified. Related. Suggested.

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Erasmus University Library

10 augustus 2023

Research Data Management: Citing data

Citing data In this module we'll explain why and how to cite datasets. Once you've establised a link between your publication and the underlying data anyone reading the publication will be able to find the dataset. Also, anyone looking at your data is able to find your publication. How to cite data So which elements are essential for other people to identify and retrieve your data? Sometimes additional elements may be requested, but be sure to include at least the following items. Digital Object Identifier (DOI) A DOI is a digital identifier of an object. It enables an object to be identified and found on the Internet. The metadata of the object are stored together with the DOI and includes information about where the object is located on the Internet. If the object is moved to a new location, the metadata can be updated with the new URL of the object. A DOI therefore provides a more long-lasting and reliable reference for finding an online publication than a URL. Related.

Website

Erasmus University Library

1 januari 2019

Finding data

A LibGuide that will help you to find data you need for your research. It has general information on translating your research question into data requirements, and gives information on useful sources for specific types of data.

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Universiteitsbibliotheek van de Vrije Universiteit

23 augustus 2023

Lecture: Data Preparation and Exploration

To be able to chose the most suited data preparation method based on the case prepare data in practice (handle missing values, create new derived features) Data enrichment Ethical: anonymisation and problems with this (identification possible in indirect ways) - again, there should be some examples out there Imputation – mention that it can introduce bias and that this needs to be kept in mind New feature creation – loss of proper semantics Ethical: remove bias from the dataset Parallels and differences between sampling of data in statistics and acquisition of data (including big data) for ML and AI

Website

HCAIM Consortium