research data

Daniel S. Katz from the National Center for Supercomputing Applications (NCSA), University of Illinois

This talk will discuss the Journal of Open Source Software (JOSS), a diamond OA journal for open source research software. JOSS depends on volunteers for almost all of its functions, and most of these volunteers are those who do research software engineering (RSEng) as part of their job, and many of these people who do it for more than half of their job consider themselves research software engineers (RSEs). The talk will discuss how JOSS is run as an open-source community project, and how it interacts with the RSEng community. This includes that JOSS: runs on GitHub using a helpful bot to automate actions; has reviews that are open, collaborative, and constructive; and uses typical open-source mechanisms for communication. While JOSS reviews are checklist-driven, some of the checklist items are not purely binary; instead, they have values of bad, ok, and good, where ok and good are both sufficient to pass, but good is used to push the community to better
practices that those that are merely ok. JOSS communication using GitHub issues for discussion of reviews, Slack for internal team discussions, and traditional software project mechanisms including social media to announce publications and news, and the new JOSSCast to provide interviews with some paper authors. JOSS has been demonstrated to scale successfully in terms of both people and costs, and we look forward to continuing to support the research software community move towards more recognition and better practices.

You can find the slides from all the previous talks from the HiRSE Seminar series on zenodo and there’s a feedback form for you to tell us what you think about the seminar series and what other topics we should cover.

Institutions

• National Center for Supercomputing Applications

Oliver Knodel, Stefan Müller and David Pape from the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) will continue the HiRSE Seminar with their talk on Facilitating Research Data Management with HELIPORT.

Researchers rely on a variety of systems and tools when it comes to administering their research data. Processes involving research data management include proposal submission, data management planning, simulation campaigns, documentation during the experiment, and the creation and submission of journal and data publications. HELIPORT is a data management solution that aims at making all steps of the research experiment’s life cycle discoverable, accessible, interoperable and reusable according to the FAIR principles. This is done by linking to and interfacing with established tools and solutions, and exchanging metadata between systems involved in a project. The metadata are presented to the researchers through a web interface, but they are also accessible to computational agents via API and machine-readable landing pages. In this presentation, we will introduce the metadata project HELIPORT and what provided the impulse for the project, discuss the documentation of a real experiment in HELIPORT, and outline current developments and challenges.

The meeting will be held via Zoom. Meeting ID: 663 9759 7271 / Passcode: 442377

You can find the slides from all the previous talks from the HiRSE Seminar series on zenodo and there’s a feedback form for you to tell us what you think about the seminar series and what other topics we should cover.

Institutions

• National Center for Supercomputing Applications

What is AI Perspectives?

AI Perspectives is a new series of events that explores important questions in the field of AI addressing specialists of various disciplines from the natural sciences, the humanities and the arts.

Overview

AI is everywhere. Also at DESY. High time to launch its interdisciplinary research forum AI Perspectives focusing on AI and reflecting the trans- and interdisciplinary field that is Artificial Intelligence. We at DESY are interested in looking at the innovative field from a variety of different angles and discuss them with the public. This is why DESY sets up an interdisciplinary research forum on AI.

The research forum will be divided into two different events: an academic workshop seminar as well as a public panel discussion in the evening with the invited public. Participants are primarily young academics and artists, scientists and philosophers working in the field of AI. For the evening event the public is invited to join in the conclusions and preliminary results of the workshop session earlier in the day and pose their own pressing questions to the interdisciplinary group. The workshop seminar will be conducted in English, while the evening event will be in German.

In der Gesprächsreihe DDLitLab im Dialog gehen wir gemeinsam mit Expertinnen und Experten aktuellen Perspektiven, Projekten und Debatten über Daten- und Digitalkompetenzen im Zeichen innovativer Hochschullehre auf den Grund. Ziel ist es, eine offenen Raum zu schaffen, in dem die Teilnehmenden sich über ihre Erfahrungen und Gedanken mit ausgewählten Referent:innen zu aktuellen Entwicklungen austauschen können.   

In diesem Semester starten wir mit Prof. Dr. Christopher Buschow (Hamburg Media School) und Dr. Daniel Possler (Universität Würzburg) mit ihrem Seminarkonzept zum Thema „Data Driven Business Models“. Im Rahmen eines Seminars „Data Driven Business Model Generation“ und einer entsprechenden Ringvorlesung am Institut für Journalistik und Kommunikationsforschung (IJK) der Hochschule für Musik, Theater und Medien Hannover haben sie die veränderten Anforderungen an Medienunternehmen und die Grundlagen datengetriebener Geschäftsmodellen vertieft. Welche Datensätze werden in (Medien)Unternehmen gesammelt und wie werden sie aufbereitet? Wie werden Geschäftsmodelle rund um diese Daten entwickelt? Welche technologischen, ethischen und gesamtgesellschaftlichen Aspekte werden bei datenbasierten Geschäftsmodellen beleuchtet?

Sowohl Mitglieder der UHH als auch externe Gäste sind herzlich dazu eingeladen, sich aktiv an unseren Gesprächsrunden zu beteiligen!

Präsenz und via Zoom. Die Zugangsdaten für die digitale Teilnahme via Zoom erhalten Sie nach Ihrer Anmeldung kurz vor dem Termin per E-Mail.

Institutions

• DDLitLab, ISA-Zentrum

On September, 25th to 27th, 2023 the third MLE Days on machine learning in engineering will take place on the campus of the Hamburg University of Technology (TUHH). This year, it combines a summer school with a one-day startup-challenge. Participation is free of charge.

The Summer School teaches insights into the world of machine learning with a focus on engineering. It provides sessions about fundamentals of machine learning, concrete application examples, as well as hands-on sessions to try out and consolidate lessons learned. Keynote talks complete the program. Three parallel tracks are provided to allow participants to choose contributions adapted to their interest and machine learning experience. The use cases range from sensor and image processing, to electrical engineering and materials science, to aviation and maritime logistics. A poster session and an elevator pitch event allow participants to present their own work on machine learning topics. The best posters and pitches will be selected by a jury and awarded prizes. A networking event allows attendees to establish contacts with selected corporate partners and sponsors from start-ups and medium-sized businesses to large corporations. In the startup-challenge attendees learn how to turn machine learning ideas into a business.

The MLE Days are organized by the Machine Learning in Engineering research initiative of the TUHH (MLE@TUHH) in collaboration with the Helmholtz Center Hereon, DASHH, and the Career Center of TUHH, the AI.Startup.Hub, and AI.HAMBURG. The MLE initiative joins the competencies in the field of machine learning at the Hamburg University of Technology with the goal of transferring knowledge towards business and industry. Students, PhD students, postdocs, and professors from all disciplines of the TUHH are engaged together with colleagues from the Helmholtz Center Hereon to make methods and applications of machine learning known, to network, and to foster scientific exchange.

Join us to discover the new applications of machine learning in engineering practice!

Institutions

• Hamburg University of Technology (TUHH) 

In the digital transformation of science, research data is an essential basis of the research process and as diverse as the subject cultures in which they are created and used. In data-based or data-driven research, computational methods can be used to scale and transform the research process.
As a scientist, you should store your research data in a secure technical environment using established standards and compliance with data protection regulations. When using computer-aided methods - from statistics to artificial intelligence - and in interdisciplinary research and use of innovative digital methods, you need advice from an expert.
What does this mean in concrete terms for scientific practice? The experts give concrete tips and answer your questions. In addition, the participants have the opportunity to exchange perspectives, challenges and best practice examples and to be inspired.
All interested parties are invited to find out more about the services of the Center for Sustainable Research Data Management (CRDM) and - this time also present - the House of Computing and Data Science (HCDS) at Universität Hamburg from 12 p.m.
The information event is open to the university. Registration is free. The event will take place in German.