RDM Training Approach and Survey Topics for Research Data Management
Explore our innovative approach to Research Data Management (RDM) training based on the LERU framework. Discover the comprehensive agenda covering topics such as Data Management Planning, Working with Data, and Understanding Research Data. Dive into the survey topics including Carpentries and Mantra, designed to enhance your skills in handling research data effectively.
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Presentation Transcript
PARTNER SURVEY- UPB Catalin Negru
Agenda Our approach for RDM training TrainRDM survey Case study
Our approach for RDM training Based on: LERU1 - OpenScience and its role in Universities: A road map for cultural change; LERU Roadmap for Research Data (Matrix model) Structuring the courses on the three academic levels: Bachelor; Master; PhD and PostDoc Identification of thematic needs ( Research data & RDM) What we can offer and what we want to have The target audience at universities: students at the bachelor and masters levels, doctoral level, early career researchers, trainers The audience outside the university and other parties Training resources: platforms, hub, cloud, tools, repository, laboratory training possible?) Learning including or classroom, webinars, blended or not contexts, in-person distance, Studies programs/ Scientific disciplines/ areas or separate/distinctive (on Research data and RDM ) (is practical at 1LERU - LEAGUE OF EUROPEAN RESEARCH UNIVERSITIES: https://www.leru.org
RDM survey topics(Part II) Based on Carpentries1 and Mantra2: Understanding Research Data; Data Management Planning; Working with Data Sharing Data Archiving Data Working with Personal and Sensitive Data Data Cleaning with OpenRefine Visualising Data Data handling tutorials 1https://carpentries.org 2https://mantra.edina.ac.uk
Understanding Research Data What we have: - What we need: understand different types and sources of research data Skills sets: find research data, share and archive research data Target audience: master, phd students, early stage researchers Learning contexts: Master, PhD, Postgraduate studies Training resources: Moodle platform and university laboratories
Data Management Planning What we have: - What we need: learn how to write a DMP Skills sets: what is it and how to write a DMP DMP tools Target audience: phd students, early stage researchers Learning contexts: Master, PhD, Postgraduate studies Training resources: Moodle platform and university laboratories
Working with Data What we have: Master Introduction to Big Data course Cloud Computing Course Big Data processing frameworks: Apache Hadoop, Spark, Kafka... What we need: learn processing methods related to research data Skills sets: find open datasets; inspect data; clean data, proces data, store data, achive data Target audience: master, phd students, early stage researchers Learning contexts: Master, PhD, Postgraduate studies Training resources: Moodle platform and university laboratories
Working with Personal and Sensitive Data What we need: learn what is personal and sensitive data Skills sets: How to collect it, how to store it Available tools Issues regarding personal and sensitive data What we have: PhD Ethic course Target audience: master, phd students, early stage researchers Learning contexts: Master, PhD, Postgraduate studies Training resources: Moodle platform and university laboratories
Data Cleaning with OpenRefine What we have: - What we need: learn how to use OpenRefine Skills sets: data peprocessing tasks such as: exploring data, cleaning and transformation of data Target audience: master, phd students, early stage researchers Learning contexts: Master, PhD, Postgraduate studies Training resources: Moodle platform and university laboratories
Visualising Data What we have: BigData course: Tools: D3JS, What we need: learn visualization techniques for different types of research data Skills sets: Target audience: master, phd students, early stage researchers Learning contexts: Master, PhD, Postgraduate studies Training resources: Moodle platform and university laboratories
Data handling tutorials What we need: learn new techniques and methods for handling research data Skills sets: available tools for handling reseach data; issues related to research data handling; What we have: Bachelor Databases courses Web programming Target audience: master, phd students, early stage researchers Learning contexts: Master, PhD, Postgraduate studies Training resources: Moodle platform and university laboratories
Educational offer UPB Bachelor: Databases courses Master: Technical Writing Knowledge representation Introduction to Information Retrieval PhD: Ethic BigData processing technologies; Elements of Cloud Computing technology;
Case study Course: Introduction to Big Data Education level: Master Project requirements: Find a BigData open-source dataset Perform data cleaning operations Process the dataset (ETL) using BigData technologies such as: Hadoop, Spark, Storm, Kafka, HDFS: In order to extract valuable information Vizualize the dataset Extract relevant statistical information.
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