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  • 1
    Publikationsdatum: 2022-02-15
    Beschreibung: Abstract
    Beschreibung: This data repository contains the spatial distribution of the direct financial loss computed expected for the residential building stock of Metropolitan Lima (Peru) after the occurrence of six decoupled earthquake and tsunami risk scenarios (Gomez-Zapata et al., 2021a; Harig and Rakowsky, 2021). These risk scenarios were independently calculated making use of the DEUS (Damage Exposure Update Service) available in https://github.com/gfzriesgos/deus. The reader can find documentation about this programme in (Brinckmann et al, 2021) where the input files required by DEUS and outputs are comprehensively described. Besides the spatially distributed hazard intensity measures (IM), other inputs required by DEUS to computed the decoupled risk loss estimates comprise: spatially aggregated building exposure models classified in every hazard-dependent scheme. Each class must be accompanied by their respective fragility functions, and financial consequence model (with loss ratios per involved damage state). The collection of inputs is presented in Gomez-Zapata et al. (2021b). The risk estimates are computed for each spatial aggregation areas of the exposure model. For such a purpose, the initial damage state of the buildings is upgraded from undamaged (D0) to any progressive damage state permissible by the fragility functions. The resultant outputs are spatially explicit .JSON files that use the same spatial aggregation boundaries of the initial building exposure models. An aggregated direct financial loss estimate is reported for each cell after every hazard scenario. It is reported one seismic risk loss distribution outcome for each of the 2000 seismic ground motion fields (GMF) per earthquake magnitude (Gomez-Zapata et al., 2021a). Therefore, 1000 seismic risk estimates from uncorrelated GMF are stored in “Clip_Mwi_uncorrelated” and 1000 seismic risk estimates from spatially cross-correlated GMF (using the model proposed by Markhvida et al. (2018)) are stored in “Clip_ Mwi_correlated”. It is worth noting that the prefix “clip” of these folders refers to the fact that, all of the seismic risk estimates were clipped with respect to the geocells were direct tsunami risk losses were obtained. This spatial compatibility in the losses obtained for similar areas and Mw allowed the construction of the boxplots that are presented in Figure 16 in Gomez-Zapata et al., (2021). The reader should note that folder “All_exposure_models_Clip_8.8_uncorrelated_and_correlated” also contains another folder entitled “SARA_entire_Lima_Mw8.8” where the two realisations (with and without correlation model) selected to produce Figure 10 in Gomez-Zapata et al., (2021) are stored. Moreover, the data to produce Figure 9 (boxplots comparing the variability in the seismic risk loss estimates for this specific Mw 8.8, are presented in the following .CSV file: “Lima_Mw_8.8_direct_finantial_loss_distributions_all_spatial_aggregations_Corr_and_NoCorr.csv”. Naturally, 1000 values emulating the 1000 realisations are the values that compose the variability expressed in that figure. Since that is a preliminary study (preprint version), the reader is invited to track the latest version of the actually published (if so) journal paper and check the actual the definitive numeration of the aforementioned figures.
    Schlagwort(e): tsunami risk ; earthquake risk ; risk scenario ; physical vulnerability ; loss ; deterministic risk ; fragility function ; RIESGOS ; Scenario-based multi-risk assessment in the Andes region ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 TSUNAMIS ; EARTH SCIENCE SERVICES 〉 HAZARDS MANAGEMENT
    Materialart: Dataset , Dataset
    Standort Signatur Erwartet Verfügbarkeit
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  • 2
    Publikationsdatum: 2022-09-27
    Beschreibung: Abstract
    Beschreibung: 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 RCP 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.
    Beschreibung: TechnicalInfo
    Beschreibung: Copyright 2021 Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany / DASF Data Analytics Software Framework Licensed under the Apache License, Version 2.0 (the "License"); you may not use these files except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
    Beschreibung: Other
    Beschreibung: The data analytics software framework DASF, developed at the GFZ German Research Centre for Geosciences 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. 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 (RCP) 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 RCP 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.
    Schlagwort(e): DASF ; Data Analytics Software Framework ; RCP ; remote procedure call ; interactive visualization ; web components ; typescript ; vuetify ; openlayers ; d3 ; EARTH SCIENCE SERVICES 〉 DATA ANALYSIS AND VISUALIZATION 〉 VISUALIZATION/IMAGE PROCESSING
    Materialart: Software , Software
    Standort Signatur Erwartet Verfügbarkeit
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  • 3
    Publikationsdatum: 2022-09-27
    Beschreibung: Abstract
    Beschreibung: 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 (RCP) 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 RCP 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.
    Schlagwort(e): DASF ; RCP ; Python ; Progress ; Data Analytics Software Framework ; EARTH SCIENCE SERVICES 〉 DATA ANALYSIS AND VISUALIZATION ; EARTH SCIENCE SERVICES 〉 DATA ANALYSIS AND VISUALIZATION 〉 STATISTICAL APPLICATIONS ; EARTH SCIENCE SERVICES 〉 DATA MANAGEMENT/DATA HANDLING ; EARTH SCIENCE SERVICES 〉 DATA MANAGEMENT/DATA HANDLING 〉 DATA NETWORKING/DATA TRANSFER TOOLS
    Materialart: Software , Software
    Standort Signatur Erwartet Verfügbarkeit
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  • 4
    Publikationsdatum: 2022-09-27
    Beschreibung: Abstract
    Beschreibung: 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 RCP (remote procedure call) wrapper library for the python programming language. As part of the data analytics software framework DASF, it implements the DASF RCP messaging protocol. This message broker based RCP 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.
    Beschreibung: TechnicalInfo
    Beschreibung: Copyright 2021 Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany / DASF Data Analytics Software Framework Licensed under the Apache License, Version 2.0 (the "License"); you may not use these files except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
    Beschreibung: Other
    Beschreibung: 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. 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 (RCP) 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 RCP 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.
    Schlagwort(e): DASF ; Data Analytics Software Framework ; RCP ; remote procedure call ; message broker ; distributed analysis ; python ; EARTH SCIENCE SERVICES 〉 DATA MANAGEMENT/DATA HANDLING ; EARTH SCIENCE SERVICES 〉 DATA MANAGEMENT/DATA HANDLING 〉 DATA NETWORKING/DATA TRANSFER TOOLS
    Materialart: Software , Software
    Standort Signatur Erwartet Verfügbarkeit
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  • 5
    Publikationsdatum: 2022-09-27
    Beschreibung: Abstract
    Beschreibung: 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 RCP 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.
    Beschreibung: TechnicalInfo
    Beschreibung: Copyright 2021 Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany / DASF Data Analytics Software Framework Licensed under the Apache License, Version 2.0 (the "License"); you may not use these files except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
    Beschreibung: Other
    Beschreibung: 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. 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 (RCP) 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 RCP 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.
    Schlagwort(e): DASF ; RCP ; Python ; Progress ; Data Analytics Software Framework ; EARTH SCIENCE SERVICES 〉 DATA MANAGEMENT/DATA HANDLING ; EARTH SCIENCE SERVICES 〉 DATA MANAGEMENT/DATA HANDLING 〉 DATA NETWORKING/DATA TRANSFER TOOLS
    Materialart: Software , Software
    Standort Signatur Erwartet Verfügbarkeit
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  • 6
    Publikationsdatum: 2022-11-23
    Beschreibung: Abstract
    Beschreibung: The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS (Remote Rapid Visual Screening) methodology. It is composed by 604 randomly distributed buildings in the urban area of Valparaiso and Viña del Mar (Chile). The survey has been carried out between November and December 2018 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images from Google StreetView (vintage: December 2018) and footprints from OpenStreetMap (OSM). The buildings were inspected by local structural engineers from the Chilean Research Centre for Integrated Disaster Risk Management (CIGIDEN) while collecting their attribute values in terms of the GEM v.2.0 taxonomy
    Schlagwort(e): taxonomy ; RRVS ; GEM ; risk exposure ; attributes ; survey ; Valparaiso ; RIESGOS ; Scenario-based multi-risk assessment in the Andes region
    Materialart: Dataset , Dataset
    Standort Signatur Erwartet Verfügbarkeit
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  • 7
    Publikationsdatum: 2022-11-23
    Beschreibung: Abstract
    Beschreibung: This data publication is composed by two main folders: (1) “Top-down_exposure_modelling_Lima” and (2) “Vulnerability_models_Lima/”. The first one contains a complete collection of data models used to represent the residential building portfolio of Lima and Callao (Peru) using a top-down approach (census-based desktop study). Therein, the reader can find a comprehensive description of the procedure of how the exposure models were constructed. This includes python scripts and postprocessed geodatasets to represent these building stock into predefined and separate classes for earthquake and tsunami physical vulnerabilities. The second folder contains sets of fragility functions for these building classes and the assumed economic consequence model. These models are suplement material of a submitted paper (Gomez-Zapata et al., 2021b). Please note it is an unpublished preprint version at the time of writing this document. The reader is strongly advised to look for the definitive version once (if so) it is accepted and published.
    Schlagwort(e): exposure modelling ; physical vulnerability ; consequence model ; fragility function ; earthquake vulnerability ; tsnami vulnerability ; occupancy types ; residential building ; RIESGOS ; Scenario-based multi-risk assessment in the Andes region ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 TSUNAMIS
    Materialart: Dataset , Dataset
    Standort Signatur Erwartet Verfügbarkeit
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