Topic: MARBLE - Unlock the Metadata from your Proprietary Binary Files
Abstract: This workshop introduces the new software tool MARBLE and is geared towards experimental researchers and scientific staff of all career levels. Using MARBLE you will learn to decipher and extract primary data and metadata encoded in proprietary binary files which are typically created by laboratory instruments. Disscuss with your peers about where proprietary file formats slow down your experimental workflows. Exchange directly with the MARBLE development team and tell us about your challenges around proprietary binary file encodings. Bring your own binary files and start to deciphering what matters to you most: your own data!
Motivation: Many instruments store primary data and related metadata in proprietary binary files. Even though export pipelines generally exists, they rarely output all metadata and primary data. Thus important parts of the primary data and metadata from your experiment remain inaccessible. Learn how to unlock the full potential of you metadata. During a mixture of lectures, discussions and hands-on sessions we will teach you how to use MARBLE to decipher data from proprietary binary files and store it in an open and accessible format.
You will learn:
how to use MARBLE on your own binary files
how to create translator files that allow you to batch-extract data from multiple files of the same type
about the legal background of the deciphering process
about data encoding in binary formats
about open and accessible data formats and the importance of standardized semantics for metadata
Date: 15th December 2022, from 09:00 AM to 12:00 PM
Registration: The registration will be open from 21st November - 9th December 2022. Click here.
A workshop jointly organized by HMC Hub Information & NFDI MatWerk located at Materials Data Science and Informatics (IAS-9) @ FZJ & the Institute of energy and climante research (IEK-2) @FZJ
The paper lab book is passé. Most of our research data are digital by design. The paper lab book is passé. The Electronic Lab Notebook (or ELN) is a digital version, a software platform, of the traditional lab notebook to record and store experimental results. Other obvious benefits of ELNs include data sharing, access, and analysis.
Several ELNs are already in active use in our NFDI-MatWerk community. In the online introductory event, six subject matter experts will present six different ELNs for 20 minutes each, using illustrative examples. Here, the basic functionalities of ELNs are illustrated using graph and image data from the MatWerk area.
You can exchange experiences with the experts on the following ELNs:
- eLabFTW (Nicolas Carpi, CEO of Deltablot)
- openBIS (Dr. Henry Lütcke, ETH Zurich)
- PASTA (Dr. Steffen Brinckmann, Forschungszentrum Jülich)
- Labfolder (not definied yet)
- Chemotion (Dr. Nicole Jung, KIT)
- NOMAD OASIS (Dr. Markus Scheidgen, FAIRmat, Universität zu Berlin)
We look forward to your active participation. If you have questions, feel welcome to contact:
Elisabeth Elschner, Task Area Community Interaction
Topic: Applying the FAIR Principles to Crystallography Data Publication – a use case for DAPHNE4NFDI?
Speaker: John R. Helliwell, Emeritus Professor of Chemistry (University of Manchester), Chairman of IUCr, and IUCr Representative to CODATA
Abstract: Crystallography is a discipline that has strived for decades to ensure the availability of its data with its publications. This has involved harnessing digital storage media at every stage of their development through punched cards, magnetic tapes, and disks and is exemplified today by ‘the cloud’. Crystallography has a highly developed database infrastructure which commenced with the Cambridge Structure Database in the 1960s and to the Protein Data Bank from 1971 onwards. There are community-agreed processed diffraction data and model validation checks that are routinely made, known as the Crystallographic Information Framework. Although this system is not perfect, it provides the best chance for ensuring reliability and thereby trust in what we do. This approach is summed up by the FAIR (Findable, Accessible, Interoperable, and Reusable) movement. More generally, the funding agencies, in their response to governments and taxpayers, also seek faster discoveries and, if possible, better value for their money. Thus, raw data could be released for use beyond the original research team, usually after an embargo period of typically 3 years. There is an expansion of the synchrotron, X-ray laser, and neutron facilities’ capacities to archive raw data. The colossal expansion of the raw data archives presents excellent opportunities to all scientists, including users of the photon and neutron facilities. In Germany, the National Research Data Infrastructure Germany (NFDI) is bringing proper data management tools and metadata harvesting to many science areas including the photon and neutron sciences (DAPHNE4NFDI, DAta from PHoton, and Neutron Experiments). DAPHNE4NFDI offers an exemplary approach to research raw data management strategy from proposal to data catalog to link to the publication.
The colloquium is an initiative of the seven consortia FAIRmat, MaRDi, NFDIMatWerk, NFDI4Chem, NFDI4Cat, PUNCH4NFDI, and DAPHNE4NFDI is intended to address all interested parties and offer the opportunity to exchange ideas in a relaxed atmosphere - even across NFDI borders.
Date: 07th December 2022, 10:30 am
Location: Deutsches Elektronen-Synchrotron DESY, Notkestrasse 85, 22607 Hamburg, Building 5, DESY Auditorium
The event is organized in Hamburg. If you just want to join the lecture, you can join online after registration.
Registration: https://events.hifis.net/event/572/
Topic: Designing Future Computational Notebooks for Collaboration and Learning
Speaker: Ph.D. April Wang
Abstract: Computing technologies allow people to work, learn, and socialize remotely but seamlessly together, particularly over complex computational tasks like programming and data science. Yet, collaboration in data science is often hard. Since data science is highly exploratory, the artifact and analysis often iterate fast. It is difficult to maintain a shared understanding across various collaborators. On the other hand, tools like computational notebooks provide a convenient approach for data scientists to run, document,
and share analysis in a storytelling way. However, there are still many open-ended questions about how to improve the collaboration experience by designing better collaborative data science tools. For example, data scientists often neglect to keep updated documentation during rapid exploration, which results in computational notebooks that are messy and difficult to read; without strategic planning, working together in a shared notebook may block each other's work. My research draws upon human-centered design techniques to identify barriers in real-world data science programming practices and explore the design space of collaborative data science environments through tool-building.
April Wang is a Ph.D. candidate at the School of Information at the University of Michigan, advised by professor Steve Oney and professor Christopher Brooks. With the growing complexity and interdisciplinary of the data science field, data science workers must embrace effective collaboration to improve the quality and efficiency of the work. Her work aims to understand different collaboration needs and challenges around data science and design better programming tools to support collaborative data science. Her
work has been published at top-tier HCI venues (e.g., CHI, TOCHI, CSCW), and has received several paper awards.
Date: 23th November 2022, 03:00 PM
Registration: Zoom Meeting: https://us06web.zoom.us/j/83320449768?pwd=YkQ3azQ2MG1ldHlxZENKbUtHdTY2
Meeting-ID: 833 2044 9768
Kenncode: 510511
Topic: Strategy for and psychosocial aspects of realizing FAIR data processes
Speaker: Dr. Nancy Washton, Catalysis Science Group Lead, Physical Sciences Division, Pacific Northwest National Laboratory
Abstract: The last decade has given rise to increased scrutiny on the need for domains in physical sciences to generate robust and reusable data that can be accessed by the broader research community. Adoption of FAIR principles has allowed research communities that rely on large, shared data sets to approach data equity and thereby advance inclusion and knowledge across the globe as they speed discovery. However, the entirety of physical sciences has yet to embrace FAIR principles, with specific subdomains at various stages of moving toward full adoption. Chemistry and many of its subdomains lag materials science and physics in mounting a concerted communal effort for adopting processes that allow FAIR data principles and structures to be realized. Barriers to adoption are technical and psychosocial, and the interplay between them. This presentation will discuss a strategy that addresses both key point: technical and psychosocial. In the absence of a robust conceptual and technical framework that allows researchers to easily record, aggregate and store the various parts of a coherent data set, and an understanding of positive and negative beliefs related to shared data, the goal of FAIR data available in the public domain will not be achieved.
The colloquium is organised by NFDI4Cat, and a collaboation of several NFDI consortia Daphne4NFDI, FAIRmat, maRDI4NFDI, NFDI-MatWerk, NFDI4Chem, Punch4NFDI.
Date: 10 November 2022, 05:30 pm
Registration: Click here.
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