ALBERT

Description: DASF: Web is part of the Data Analytics Software Framework (DASF, https://git.geomar.de/digital-earth/dasf), developed at the GFZ German Research Centre for Geosciences (https://www.gfz-potsdam.de). It is funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/). DASF: Web collects all web components for the data analytics software framework DASF. It provides ready to use interactive data visualization components like time series charts, radar plots, stacked-parameter-relation (spr) and more, as well as a powerful map component for the visualization of spatio-temporal data. Moreover dasf-web includes the web bindings for the DASF RPC messaging protocol and therefore allows to connect any algorithm or method (e.g. via the dasf-messaging-python implementation) to the included data visualization components. Because of the component based architecture the integrated method could be deployed anywhere (e.g. close to the data it is processing), while the interactive data visualizations are executed on the local machine. dasf-web is implemented in Typescript and uses Vuejs/Vuetify, Openlayers and D3 as a technical basis.

Description: DASF: Progress API is part of the Data Analytics Software Framework (DASF, https://git.geomar.de/digital-earth/dasf), developed at the GFZ German Research Centre for Geosciences (https://www.gfz-potsdam.de). It is funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/). DASF: Progress API provides a light-weight tree-based structure to be sent via the DASF RPC messaging protocol. It's generic design supports deterministic as well as non-deterministic progress reports. While DASF: Messaging Python provides the necessary implementation to distribute the progress reports from the reporting backend modules, DASF: Web includes ready to use components to visualize the reported progress.

Description: The success of scientific projects increasingly depends on using data analysis tools and data in distributed IT infrastructures. Scientists need to use appropriate data analysis tools and data, extract patterns from data using appropriate computational resources, and interpret the extracted patterns. Data analysis tools and data reside on different machines because the volume of the data often demands specific resources for their storage and processing, and data analysis tools usually require specific computational resources and run-time environments. The data analytics software framework DASF, developed at the GFZ German Research Centre for Geosciences (https://www.gfz-potsdam.de) and funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/), provides a framework for scientists to conduct data analysis in distributed environments. The data analytics software framework DASF supports scientists to conduct data analysis in distributed IT infrastructures by sharing data analysis tools and data. For this purpose, DASF defines a remote procedure call (RPC) messaging protocol that uses a central message broker instance. Scientists can augment their tools and data with this protocol to share them with others. DASF supports many programming languages and platforms since the implementation of the protocol uses WebSockets. It provides two ready-to-use language bindings for the messaging protocol, one for Python and one for the Typescript programming language. In order to share a python method or class, users add an annotation in front of it. In addition, users need to specify the connection parameters of the message broker. The central message broker approach allows the method and the client calling the method to actively establish a connection, which enables using methods deployed behind firewalls. DASF uses Apache Pulsar (https://pulsar.apache.org/) as its underlying message broker. The Typescript bindings are primarily used in conjunction with web frontend components, which are also included in the DASF-Web library. They are designed to attach directly to the data returned by the exposed RPC methods. This supports the development of highly exploratory data analysis tools. DASF also provides a progress reporting API that enables users to monitor long-running remote procedure calls. One application using the framework is the Digital Earth Flood Event Explorer (https://git.geomar.de/digital-earth/flood-event-explorer). The Digital Earth Flood Event Explorer integrates several exploratory data analysis tools and remote procedures deployed at various Helmholtz centers across Germany.

Description: DASF: Messaging Python is part of the Data Analytics Software Framework (DASF, https://git.geomar.de/digital-earth/dasf), developed at the GFZ German Research Centre for Geosciences. It is funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/). DASF: Messaging Python is a RPC (remote procedure call) wrapper library for the python programming language. As part of the data analytics software framework DASF, it implements the DASF RPC messaging protocol. This message broker based RPC implementation supports the integration of algorithms and methods implemented in python in a distributed environment. It utilizes pydantic (https://pydantic-docs.helpmanual.io/) for data and model validation using python type annotations. Currently the implementation relies on Apache Pulsar (https://pulsar.apache.org/) as a central message broker instance.

Description: Irpinia seismic Array is part of the DEnse mulTi-paramEtriC observations and 4D high resoluTion imaging (DETECT) project focused on the acquisition of a unique multiparametric dataset and fosters collaboration among various institutions. The University of Naples Federico II (UniNa) and the German Research Centre for Geosciences (GFZ) are leading this effort carried out in collaboration with various local institutions and supported by the local municipalities. The DETECT project aims at exploiting very dense seismic networks deployed across a segmented fault system (Irpinia and Pergola-Melandro) to foster the development of scientific integrated methodologies for monitoring and imaging the fault behavior during the inter-seismic phase. The Irpinia seismic Array consists of a dense constellation of seismic antennas using more than 200 seismic stations deployed for one year. Each seismic antenna, with maximum aperture of ~2 km, uses one broad-band sensor, one short period sensor with 1 Hz and 8 with 4.5 Hz natural frequency. The antennas are deployed above and near the fault segments that generated during the last centuries many strong earthquakes in the southern Apennines. Waveform data are available from the GEOFON data centre, under network code ZK.

Description: Py4HIP is an open-source software tool for Heat-In-Place calculations implemented as a self-explanatory Jupyter notebook written in Python (Py4HIP.ipynb) Calculating the Heat In Place (HIP) is a standard method for assessing the geothermal potential for a defined geological unit (e.g., Nathenson, 1975; Muffler and Cataldi, 1978; Garg and Combs, 2015). The respective implementation in Py4HIP is based on a volumetric quantification of contained energy after Muffler and Cataldi (1978), where the geological unit at hand is considered spatially variable in terms of its temperature, thickness, porosity, density and volumetric heat capacity of its solid and fluid (brine) components. The energy values

Description: The data result from a sedimentological and geochemical multiproxy approach to study a Holocene palaeolake record north of Tayma, NW Saudi Arabia. The lacustrine, partly varved record was analysed in the frame of the DFG founded project CLEAR “Holocene climatic events in Northern Arabia - Environmental changes and human response”. The Tayma palaeolake record comprises continuous lacustrine sediments covering the early to mid-Holocene. The dataset allows detailed palaeoenvironmental and palaeoclimate interpretations from the early Holocene humid period and subsequent dryer conditions during the mid-Holocene. The dataset is part of the supplementary material to “Neugebauer et al. (submitted)” where further details about the locality, core composite, age model, sampling and analytical methods and data processing are given. The data are provided in individual xlsx-files per type of data. The different files include sedimentological and geochemical data determined on the ca. 6 m long master core from the sediment cores (Tay 220/221 and Tay 253/254/255/256): (i) sediment core microfacies data, (ii) bulk total organic carbon (TOC) and carbonate delta13C_carb and delta18O_carb data, (iii) single aragonite laminae delta13C_arag and delta18O_arag data, (iv) concentrations of n-alkanes n-C29 and n-C31 and hydrogen isotope composition deltaD, (v) XRF core scanning data. All data are provided on composite depths and age scales (based on Bayesian age modelling of radiocarbon dates, varve counting and one tephrochronological anchor; see details in the Supplementary material of Neugebauer et al., submitted).

Description: This dataset was collected during field-based monitoring in the Kali Gandaki River catchment be-tween the years 2013 and 2017. The monitoring aims to understand the hydrological fluxes and feedback with weathering and erosion processes across the mountain range. The Kali Gandaki River sources its water in the North and traverses through the Himalayan Mountain Range, along a north-south transect. The field-based monitoring comprises targeted field campaigns to revisit locations at different years and seasons in order to constrain the annual and intra-annual variability. This is complemented by permanent installations and routine river and rain sampling at two loca-tions, Lete and Purtighat. Lete is situated at the orographic barrier, at ~2500 m asl. and the up-stream catchment integrates the northern part of the Himalayan Range as well as some of the southern edge of the Tibetan Plateau. Purtighat is located further south and integrates the north-ern part as well as south-facing flanks of the Higher and Lower Himalayas. At both locations, auto-mated river monitoring is installed as well as a trained station ward for daily routine sampling. At Lete, rainfall samples are obtained on a daily resolution during the monsoon. This sampling was not feasible at Purtighat for logistic reasons. Instead, rain was sampled daily in Kathmandu. This dataset contains five tables of stable water isotope analysis. One containing grab samples from the Kali Gandaki river in its vicinities and 4 tables with time series sampling from the Kali Gandaki River and from rainfall.

Description: The scope of the Science Plan is to describe the scientific background, applications, and activities of the Environmental Mapping and Analysis Program (EnMAP) imaging spectroscopy mission. Primarily, this document addresses scientists and funding institutions, but it may also be of interest to environmental stakeholders and governmental agencies. It is designed to be a living document that will be updated throughout the entire mission lifetime. Chapter 1 provides a brief overview of the principles and current state of imaging spectroscopy. This is followed by an introduction to the EnMAP mission, including its objectives and impact on international programs as well as major environmental and societal challenges. Chapter 2 describes the EnMAP system together with data products and access, calibration/validation, and synergies with other missions. Chapter 3 gives an overview of the major fields of application such as vegetation and forests, geology and soils, coastal and inland waters, cryosphere, urban areas, atmosphere and hazards. Finally, Chapter 4 outlines the scientific exploitation strategy, which includes the strategy for community building and training, preparatory flight campaigns and software developments. A list of abbreviations is provided in the annex to this document and an extended glossary of terms and abbreviations is available on the EnMAP website.

Description: The stations are part of a seismic network in the Helsinki capital area of Finland in 2020. The stations recorded the response to a second stimulation of a ∼ 6 km deep enhanced geothermal system in the Otaniemi district of Espoo that followed on the first larger stimulation in 2018. The second stimulation from 6 May to 24 May 2020 established a geothermal doublet system. The Institute of Seismology, University of Helsinki (ISUH), installed the 70 GIPP-provided geophones in addition to surface broadband sensors, ISUH-owned short-period instruments, and a borehole satellite network deployed by the operating company. The data set consists of raw CUBE-recorder data and converted MSEED data. The data set has been collected to underpin a wide range of seismic analysis techniques for complementary scientific studies of the evolving reservoir processes and the induced event properties. These should inform the legislation and educate the public for transparent decision making around geothermal power generation in Finland. The full 2020 network and with it the deployment of the CUBE stations is described in a Seismological Research Letter Data Mine Column by A. Rintamäki et al. (2021).