Use Case "dCache"

_posts/2022/07/2022-07-15-use-case-dCache.md

+---
+title: "Mass Storage for Machine Learning in Seismology"
+title_image: globe.jpeg
+data: 2022-07-15
+authors:
+  - klaffki
+  - "Münchmeyer, Jannes"
+layout: blogpost
+categories:
+  - Use Case
+tags:
+  - DESY Storage (HDF)
+  - Helmholtz AI
+  - Machine Learning
+  - Seismology
+excerpt: >
+    The DESY Storage (HDF) provides seismologists with a repository to share data on earthquakes via SeisBench, a toolbox for machine learning in seismology.
+---
+
+## From dCache to DESY Storage (HDF) — HIFIS mass storage from DESY for Helmholtz
+
+<figure>
+    <img src="{% link assets/img/posts/2022-06-29-use-case-dCache/dCache-logo.svg %}" alt="Raven as logo for dCache" style="float:right;width:15%;min-width:120px;padding:5px 5px 20px 20px;">
+</figure>
+[dCache](https://www.dcache.org) is an open source project which developed a system for storing and retrieving large amounts of data, providing world-wide access. 
+It has been built and is further developed by Deutsches Elektronen-Synchrotron ([DESY](https://desy.de)),
+the Fermi National Accelerator Laboratory ([FNAL](http://www.fnal.gov/)) 
+and the Nordic e-Infrastructure collaboration ([NeIC](https://neic.no/nt1/)). 
+
+Thus, the system was a perfect candidate for DESY to provide mass storage Helmholtz-wide via HIFIS.
+Actually, it has been one of the first services connected to the Helmholtz AAI in early 2020 as a demonstrator.
+Now, it's becoming a regular service and will be available via the Helmholtz Cloud Portal in August 2022, branded as "DESY Storage (HDF)", HDF being short for Helmholtz Data Federation.
+
+
+## SeisBench — A toolbox for machine learning in seismology
+The first users of the then prototype are seismologists from the REPORT-DL project:
+[Rapid Earthquake Phase Analysis of Ocean-bottom, Regional and Teleseismic events with Deep Learning](https://www.geomar.de/en/fb4-gdy/projects/translate-to-english-report-dl).
+This was funded in 2019 by [Helmholtz AI](https://www.helmholtz.ai/themenmenue/you-helmholtz-ai/funding-lines/funded-projects/index.html), another Helmholtz Incubator plattform, and within this context, [SeisBench](https://github.com/seisbench/seisbench) was developed
+— A toolbox for machine learning in seismology.
+
+<figure>
+    <img src="{% link assets/img/posts/2022-06-29-use-case-dCache/seisbench_logo.svg %}" alt="seisBench logo" style="float:left;width:25%;padding:20px 20px 20px 5px;">
+</figure>
+SeisBench is an open-source python toolbox, aiming to standardise access to datasets and models for seismic waveform processing with deep learning.
+This way, SeisBench both reduces the overhead for developers of such models and bridges the gap between model developers and seismic practitioners. 
+
+Key part of SeisBench is the ability to directly access benchmark datasets and pretrained models. 
+To facilitate the sharing of this data, they use the DESY Storage (HDF). 
+This service equips them with a high-performance repository, enabling the comfortable sharing of datasets of several hundred gigabytes. 
+Additional functionality provided through webDAV allows to implement convenience functions, 
+such as the possibility to enumerate available model weights. 
+More detailed information can be found in the project's [documentation](https://seisbench.readthedocs.io/en/stable/).
+
+<figure>
+    <img src="{% link assets/img/posts/2022-06-29-use-case-dCache/schematic_fig_seisbench.svg %}" alt="Schematic diagram of SeisBench." style="float:right;width:100%;">
+    <figcaption>Schematic diagram of SeisBench. By Jack Woollam, license: GPLv3</figcaption>
+</figure>
+
+Within the nine months since publication, SeisBench has grown an active user base of almost 200 users. 
+These users access the DESY Storage (HDF) repository around 5000 times per month. Users are located internationally, 
+including researchers at world-leading institutions (e.g. Harvard, Cambridge, Cornell). 
+The majority of users come from outside the Helmholtz community, which highlights the importance to grant world-wide and easy access to such contents. 
+
+In addition to the infrastructure, DESY Storage (HDF) offers the SeisBench team detailed statistics on usage patterns. 
+This allows them to identify which parts of their software are most used by the community, e.g., 
+which models are of the largest interest. They use this information for planning future focuses in the development of SeisBench.
+
+
+## How to use DESY Storage (HDF) via HIFIS for _your_ projects
+The storage service is usable for any user group with central Helmholtz stakeholders, but not intended for single users. 
+Users shall please briefly apply via HIFIS support, providing 
+a main contact (if multiple users are involved), the purpose of usage (brief description), including approximate ressources needed, 
+the number of users (approximately), the Helmholtz centres / other organisations of the user(s) and the envisioned time frame of usage
+in order to set up the service optimally.
+
+## Get in contact
+For dCache / DESY Storage (HDF): [Christian Voss](mailto:christian.voss@desy.de?subject="desy-storage"), [Paul Millar](mailto:paul.millar@desy.de?subject="desy-storage")
+
+For SeisBench: [Jannes Münchmeyer](mailto:munchmej@gfz-potsdam.de?subject="SeisBench"), [Jack Woollam](mailto:jack.woollam@kit.edu?subject="SeisBench"), [Andreas Rietbrock](mailto:andreas.rietbrock@kit.edu?subject="SeisBench")
+
+For HIFIS: [HIFIS Support](mailto:support@hifis.net?subject="desy-storage")

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