Do you know that feeling, when you work on your research project after not having looked at it for a while (for example after receiving reviews) and you can’t remember how the different parts fit together?
And when you finally figure it out again, the results are somehow different. Oh no!
In this course we will set you up to never run into these kinds of issues again.
Let’s get your research project organized, under version control, stable, and published so that you can confidently say that your research is reproducible.
Topics
The goal of producing FAIR and reproducible research outputs
Working on research projects as a team
Get your project organized: organized folders, naming, documentation
Set up reproducible workflows: version control with git, stable computing environments, and automation
Make your project reproducible and FAIR for all: publication platforms, licensing, and more
Format
This is an online course. Each week you will:
watch the 📺weekly videos (~45 min),
review one of the 📖 booklet chapters,
have a short session with your 👤 accountability buddy* (15-20 minutes),
implement the ✍️ tasks of the week, and
discuss your progress in the 👩🏫 weekly online meeting with the instructor and fellow course participants (1-1.5 hours).
* You will choose your accountability buddy during the course. You and your buddy will help each other in implementing the tasks of each week.
Weekly Meetings
The course includes 5 Online Meetings, in which you will discuss the week’s contents with the instructor and fellow participants:
Meeting 1: Sep 25, 3:00 – 4:30 pm CEST Meeting 2: Oct 02,3:00 – 4:30 pm CEST Meeting 3: Oct 09, 3:00 – 4:30 pm CEST Meeting 4: Oct 16, 3:00 – 4:30 pm CEST Meeting 5: Oct 23, 3:00 – 4:30 pm CEST
Prerequisites
Basic programming knowledge (R, python, …)
Willingness to learn new technical skills
About the Instructor
Heidi Seiboldholds a PhD in Biostatistics with a focus on Machine Learning. Her research has been in the intersection of Data Science, Reproducibility and Medicine. She is a solopreneur in Open and Reproducible Research and an independent researcher at IGDORE. Heidi is the co-founder of the Digital Research Academy and Open Science Freelancers.
Do you know that feeling, when you work on your research project after not having looked at it for a while (for example after receiving reviews) and you can’t remember how the different parts fit together? And when you finally figure it out again, the results are somehow different. Oh no!
In this course we will set you up to never run into these kinds of issues again. Let’s get your research project organized, under version control, stable, and published so that you can confidently say that your research is reproducible.
Topics
The goal of producing FAIR and reproducible research outputs
Working on research projects as a team
Get your project organized: organized folders, naming, documentation
Set up reproducible workflows: version control with git, stable computing environments, and automation
Make your project reproducible and FAIR for all: publication platforms, licensing, and more
Format
This is an online course. Each week you will:
watch the weekly videos (~45 min),
review one of the booklet chapters,
have a short session with your accountability buddy* (15-20 minutes),
implement the tasks of the week, and
discuss your progress in the weekly online meeting with the instructor and fellow course participants (1-1.5 hours).
* You will choose your accountability buddy during the course. You and your buddy will help each other in implementing the tasks of each week.
Weekly Meetings:
Tuesday, October 17, 3 pm – 4:30 pm CET
Tuesday, October 24, 3 pm – 4:30 pm CET
Tuesday, October 31, NO MEETING
Tuesday, November 7, 3 pm – 4:30 pm CET
Tuesday, November 14, 3 pm – 4:30 pm CET
Prerequisites
Basic programming knowledge (R, python, …)
Willingness to learn new technical skills
About the Instructor
Heidi Seibold holds a PhD in Biostatistics with a focus on Machine Learning. Her research has been in the intersection of Data Science, Reproducibility and Medicine. She is a solopreneur in Open and Reproducible Research and an independent researcher at IGDORE. Heidi is the co-founder of the Digital Research Academy and Open Science Freelancers.
Am 24. und 25. November 2022 findet am ZEW Mannheim ein Workshop zum Thema „Big-Data-Analysen und neue Entwicklungen in Forschungsdatenzentren“ statt. Veranstaltet wird der Workshop vom IAB, dem ifo, dem ZEW sowie den zugehörigen Forschungsdatenzentren IAB-FDZ, EBDC (ifo) und ZEW-FDZ (einschließlich BERD@NFDI).
Das Ziel des Workshops besteht darin, Forschenden der Ökonomie die Möglichkeit zu geben, sich zu neuen Datentypen und Entwicklungen der Datennutzung auszutauschen. Das umfasst folgende Aspekte:
Big-Data-Analysen: Nutzung unstrukturierter Daten – aus: Internet (Stichwort: Webscraping), soziale Medien, Messdaten u. a. große Datenmengen – durch Umwandlung in strukturierte Forschungsdaten
Generierung von Forschungsdaten durch Verknüpfungen von administrativen und/oder Befragungsdaten, z. B. via Algorithmen maschinellen Lernens
Innovative Datenzugangswege in FDZ: z. B. FDZ-in-FDZ, Remote Access, Cloud
Die Vorträge des Workshops sollen zum einen methodische Themen aufgreifen. Zum anderen soll vorgestellt werden, welche Forschungsthemen mit neuen (großen) Daten(typen) adressiert werden, die zuvor nicht empirisch untersucht werden konnten. Ferner sollen rechtliche Probleme beim Umgang mit öffentlich verfügbaren Daten und administrativen Daten diskutiert werden (Stichworte: DSGVO und Urheberrecht).
Auf einer Podiumsdiskussion zum Thema „innovative Datenzugangswege“ mit Vertreterinnen und Vertretern von Forschungsdatendatenzentren (im NFDI-Verbund KonsortSWD) und Datennutzenden soll herausgearbeitet werden, wie rechtliche Vorgaben, technische Restriktionen und die Wünsche von Forschenden in Einklang gebracht werden können.
IAB, ifo und ZEW laden Forschende ein, sich mit inhaltlichen Beiträgen aus allen Bereichen der empirischen Wirtschaftsforschung zu beteiligen, die einen Bezug zu den Themen dieses Workshops haben, z. B. die Nutzung von unstrukturierten Daten im Rahmen eines Forschungsprojektes. Begrüßt werden ebenso Papiere zu methodischen Aspekten sowie Beiträge, die Anregungen für die künftige Generierung bzw. Sammlung von Daten bieten.
Es ist geplant, den Workshop in Präsenz durchzuführen, aber im Falle einer angespannten Pandemielage wird er bei Bedarf auch online abgehalten.
Die Sprache des Workshops wird vornehmlich Deutsch sein. Vorträge und Diskussionen können aber gerne auf Wunsch in englischer Sprache geführt werden.
This cross-disciplinary tutorial on tools and methods provides brief introductions to use cases from NFDI4Culture, Text+ and BERD@NFDI.
Presented use cases address capture, enrichment and dissemination of research data objects. Capture involves the creation of digital surrogates (e.g. with OCR) or the representation of existing artefacts in a digital representation (e.g. transcription). This is followed by the enrichment (e.g. annotation, tagging and association) of research data objects and is summarised with their dissemination, i.e. making them available and sharing them to support collaboration and reuse.
This Focused Tutorial will provide knowledge, methodological and technical expertise in the areas of data, metadata, taxonomies and standards with a view to the FAIR principles, and promotes a cross-disciplinary exchange and networking between the participating consortia.
Use Cases from Text+, NFDI4Culture and BERD@NFDI
free, In-Person, University Mannheim, via Zoom [Hybrid], no prerequisites, organized by UB Mannheim
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