MOSAiC und weiter: Digitalisierung und nachhaltige Nutzung von Forschungsdaten in der Polarforschung

Die MOSAiC-Expedition war die größte Polarexpedition, die je durchgeführt wurde. Mehr als ein Jahr driftete das Forschungsschiff Polarstern durch den Arktischen Ozean und erhob dabei unzählige Forschungsdaten. Die Umsetzung stellte große logistische und technische Herausforderungen. Gleichzeitig setzte das Projekt Meilensteine in der Digitalisierung der MOSAiC-Daten. Das vorhandene Datenrepositorim PANGEA wurde als Datenbasis für die Abspeicherung der erhobenen und gewonnenen Daten genutzt. Das Datenmanagement hatte ein frühestmögliches Teilen der Daten zum Ziel. Außerdem stand von Anfang an das Datenmanagement als ein Teil von open science und einer frühen Datenzitierbarkeit. Ab 2023 sollen alle MOSAiC-Daten im Repositorium frei verfügbar sein. MOSAiC ist der bisher größte Anwendungsfall für das Projekt Nationale Forschungsdateninfrastruktur (NFDI)

MOSAiC und weiter: Digitalisierung und nachhaltige Nutzung von Forschungsdaten in der Polarforschung

Die MOSAiC-Expedition war die größte Polarexpedition, die je durchgeführt wurde. Mehr als ein Jahr driftete das Forschungsschiff Polarstern durch den Arktischen Ozean und erhob dabei unzählige Forschungsdaten. Die Umsetzung stellte große logistische und technische Herausforderungen. Gleichzeitig setzte das Projekt Meilensteine in der Digitalisierung der MOSAiC-Daten. Das vorhandene Datenrepositorim PANGEA wurde als Datenbasis für die Abspeicherung der erhobenen und gewonnenen Daten genutzt. Das Datenmanagement hatte ein frühestmögliches Teilen der Daten zum Ziel. Außerdem stand von Anfang an das Datenmanagement als ein Teil von open science und einer frühen Datenzitierbarkeit. Ab 2023 sollen alle MOSAiC-Daten im Repositorium frei verfügbar sein. MOSAiC ist der bisher größte Anwendungsfall für das Projekt Nationale Forschungsdateninfrastruktur (NFDI).The MOSAiC expedition was the largest polar expedition ever conducted. For more than a year, the research vessel Polarstern drifted through the Arctic Ocean collecting countless research data. The implementation posed major logistical and technical challenges. At the same time, the project set milestones in the digitization of MOSAiC data. The existing data repositoryim PANGEA was used as a database for storing the collected and acquired data. Data management aimed at sharing the data as early as possible. In addition, from the beginning, data management stood as a part of open science and early data citability. Starting in 2023, all MOSAiC data should be freely available in the repository. MOSAiC is the largest use case to date for the National Research Data Infrastructure (NFDI) project

Methodological considerations when translating "burnout"

No study has systematically examined how researchers address cross-cultural adaptation of burnout. We conducted an integrative review to examine how researchers had adapted the instruments to the different contexts. We reviewed the Content Validity Indexing scores for the Maslach Burnout Inventory-Human Services Survey from the 12-country comparative nursing workforce study, RN4CAST. In the integrative review, multiple issues related to translation were found in existing studies. In the cross-cultural instrument analysis, 7 out of 22 items on the instrument received an extremely low kappa score. Investigators may need to employ more rigorous cross-cultural adaptation methods when attempting to measure burnout

An integrative solution for managing, tracing and citing sensor-related information

In a data-driven scientific world, the need to capture information on sensors used in the data acquisition process has become increasingly important. Following the recommendations of the Open Geospatial Consortium (OGC), we started by adopting the SensorML standard for describing platforms, devices and sensors. However, it soon became obvious to us that understanding, implementing and filling such standards costs significant effort and cannot be expected from every scientist individually. So we developed a web-based sensor management solution (https://sensor.awi.de) for describing platforms, devices and sensors as hierarchy of systems which supports tracing changes to a system whereas hiding complexity. Each platform contains devices where each device can have sensors associated with specific identifiers, contacts, events, related online resources (e.g. manufacturer factsheets, calibration documentation, data processing documentation), sensor output parameters and geo-location. In order to better understand and address real world requirements, we have closely interacted with field-going scientists in the context of the key national infrastructure project “FRontiers in Arctic marine Monitoring ocean observatory” (FRAM) during the software development. We learned that not only the lineage of observations is crucial for scientists but also alert services using value ranges, flexible output formats and information on data providers (e.g. FTP sources) for example. Mostly important, persistent and citable versions of sensor descriptions are required for traceability and reproducibility allowing seamless integration with existing information systems, e.g. PANGAEA. Within the context of the EU-funded Ocean Data Interoperability Platform project (ODIP II) and in cooperation with 52north we are proving near real-time data via Sensor Observation Services (SOS) along with sensor descriptions based on our sensor management solution. ODIP II also aims to develop a harmonized SensorML profile for the marine community which we will be adopting in our solution as soon as available. In this presentation we will show our sensor management solution which is embedded in our data flow framework to offer out-of-the-box interoperability with existing information systems and standards. In addition, we will present real world examples and challenges related to the description and traceability of sensor metadata

Automatic data quality control for understanding extreme climate event

The understanding of extreme events strongly depends on knowledge gained from data. Data integration of mul-tiple sources, scales and earth compartments is the fo-cus of the project Digital Earth, which also join efforts on the quality control of data. Automatic quality control is embedded in the ingest component of the O2A, the ob-servation-to-archive data flow framework of the Alfred-Wegener-Institute. In that framework, the O2A-Sensor provides observation properties to the O2A-Ingest, which delivers quality-flagged data to the O2A-dash-board. The automatic quality control currently follows a procedural approach, where modules are included to implement formulations found in the literature and other operational observatory networks. A set of plausibility tests including range, spike and gradient tests are cur-rently operational. The automatic quality control scans the ingesting data in near-real-time (NRT) format, builds a table of devices, and search - either by absolute or derivative values - for correctness and validity of obser-vations. The availability of observation properties, for in-stance tests parameters like physical or operation ranges, triggers the automatic quality control, which in turn iterates through the table of devices to set the qual-ity flag for each sample and observation. To date, the quality flags in use are sequential and qualitative, i.e. it describes a level of quality in the data. A new flagging system is under development to include a descriptive characteristic that will comprise technical and user inter-pretation. Within Digital Earth, data on flood and drought events along the Elbe River and methane emissions in the North Sea are to be reviewed using automatic qual-ity control. Fast and scalable automatic quality control will disentangle uncertainty raised by quality issues and thus improve our understanding of extreme events in those cases

IceGIS: A near real time ice information system for FS Polarstern

To support ship navigation, station planning, and scientific data evaluation, a new near real time sea-ice information system was installed onboard FS Polarstern during summer 2016

O2A - Data Flow Framework from Sensor Observations to Archives

The Alfred Wegener Institute coordinates German polar research and is one of the most productive polar research institutions worldwide with scientists working in both Polar Regions – a task that can only be successful with the help of excellent infrastructure and logistics. Conducting research in the Arctic and Antarctic requires research stations staffed throughout the year as the basis for expeditions and data collection. It needs research vessels, aircrafts and long-term observatories for large-scale measurements as well as sophisticated technology. In this sense, the AWI also provides this infrastructure and competence to national and international partners. To meet the challenge the AWI has been progressively developing and sustaining an e-Infrastructure for coherent discovery, visualization, dissemination and archival of scientific information and data. Most of the data originates from research activities being carried out in a wide range of sea-, airand land-based operating research platforms. Archival and publishing in PANGAEA repository along with DOI assignment to individual datasets is a pursued end-of-line step. Within AWI, a workflow for data acquisition from vessel-mounted devices along with ingestion procedures for the raw data into the institutional archives has been well established. However, the increasing number of ocean-based stations and respective sensors along with heterogeneous project-driven requirements towards satellite communication, sensor monitoring, quality control and validation, processing algorithms, visualization and dissemination has recently lead us to build a more generic and cost-effective framework, hereafter named O2A (observations to archives). The main strengths of our framework (https://www.awi.de/en/data-flow) are the seamless flow of sensor observation to archives and the fact that it complies with internationally used OGC standards and assuring interoperability in international context (e.g. SOS/SWE, WMS, WFS, etc.). O2A comprises several extensible and exchangeable modules (e.g. controlled vocabularies and gazetteers, file type and structure validation, aggregation solutions, processing algorithms, etc.) as well as various interoperability services. We are providing integrated tools for standardized platform, device and sensor descriptions following SensorML (https://sensor.awi.de), automated near-real time and “big data” data streams supporting SOS and O&M and dashboards allowing data specialists to monitor their data streams for trends and early detection of malfunction of sensors (https://dashboard.awi.de). Also in the context of the “Helmholtz Data Federation” with outlook towards the European Open Science Cloud we are developing a cloud-based workspace providing user-friendly solutions for data storage on petabyte-scale and state-of-the-art computing solutions (Hadoop, Spark, Notebooks, rasdaman, etc.) to support scientists in collaborative data analysis and visualization activities including geo-information systems (http://maps.awi.de). Our affiliated repositories offer archival and long-term preservation as well as publication solutions for data, data products, publications, presentations and field reports (https://www.pangaea.de, https://epic.awi.de)

SENSOR.awi.de: Management of heterogeneous platforms and sensors

SENSOR.awi.de is a component of our data flow framework designed to enable a semi-automated flow of sensor observations to archives (acronym O2A). The dramatic increase in the number and type of platforms and respective sensors operated by Alfred Wegener Institute along with complex project-driven requirements in terms of satellite communication, sensor monitoring, quality control and validation, processing pipelines, visualization, and archival under FAIR principles, led us to build a generic and cost-effective data flow framework. Most important, all components and services which make up this framework are extensible and exchangeble, were built using open access technologies (e.g. elastic search) and vocabularies (SeaVox NERC 2.0 vocabulary) and are compliant with various interoperability standards recommended by the international community. In this poster we illustrate the SENSOR.awi.de component which is the first step in the data acquisition process. In this component we have adopted the OGC standard SensorML 2.0 in oder to describe not only sensor-specific information (provenance/lineage metadata, data governance, physical characteristics, sensor positioning within the platform, parameter accuracy, etc) but also related events (e.g. station numbers as assigned in the data acquisition system on board along with actions such as deployment and recovery) and digital resources relevant for documenting the scientific workflows (e.g. calibration certificates). For this sake we have developed an AWI-specific SensorML profile and are sharing the model and as parters in the EU-funded project ODIP II we are contributing towards the generic Marine Sensor Profile. We have also been systematically sharing our experience in the RDA "Martina data Harmonization" Interest Group. In SENSOR.awi.de we are not only keen to describe sensors but also to create a sustainable identificaiton solution. Because the payload of various platforms change with time and sensor calibration may affect the data streams, it is important to keep track of these changes. For this sake we set up an audit trail history solution which allows scientists to create and identify an individual version/instance of a sensor. Each individual sensor instance and version gets assigned a handle as persistent identifier. These persistent identifiers enable the creation of citations for individual sensor instances at a given timestamp which can be used in publications and as part of the metadata associated with the final dataset and/or data product archived, e.g., in PANGAEA. To date, ~1300 sensor have been described in SENSOR.awi.de. Scientific Discipline/Research Area: Sensor Registry, Persistent Identification of Instruments, Provenance Metadata Relevance/Link to RDA: Input to/from RDA-Groups: "Persistent Identification of Instruments", "Data Citation" and "PID Kernel Information" Working Groups and "Vocabulary Services" Interest Group

DASHBOARD.awi.de: Streaming and monitoring solutions for near real-time data

DASHBOARD.awi.de is a component of our data flow framework designed to enable a semi-automated flow of sensor observations to archives (acronym O2A). The dramatic increase in the number and type of platforms and respective sensors operated by Alfred Wegener Institute along with complex project-driven requirements in terms of satellite communication, sensor monitoring, quality control and validation, processing pipelines, visualization, and archival under FAIR principles, led us to build a generic and cost-effective data flow framework. Most important, all components and services which make up this framework are extensible and exchangeble, were built using open access technologies (e.g. elastic search) and vocabularies (SeaVox NERC 2.0 vocabulary) and are compliant with various interoperability standards recommended by the international community. In this poster we illustrate the DASHBOARD.awi.de component which is a web-based monitoring environment for supporting scientists in the graphing, mapping and simple analysis of time series. With a set of fit-for-purpose widgets including data download, scientists are able to identify gaps and outliers in the streamed data. Morover, we are in the process of building alerting solutions for individual parameters using the parameter properties available from SENSOR.awi.de (e.g. min/max parameter range). The streaming services attached to this component are using the near real-time data transfered from remote field sites to local databases and storage systems. For this sake, we are participating in the RDA "Array Database Assessment" Working Group and "Big Data" Interest Group. The graphing solutions built within DASHBOARD.awi.de can be easily re-used in other context (e.g. project web pages) . Our solutions support the OGC standard Observations and Measurements (O&M), JSON and CSV as exchange data format. To date, ~ 150 individual parameters are being transfered in near real-time from remote sites to our onshore storage systems and monitored by scientists. In the week of the 11th RDA Plenary Berlin, we will be able to interact with live data from Polarstern crossing the Drake Passage, on its way to Antarctica. Scientific Discipline/Research Area: Data flow, standardized vocabulary, Visualisation, Streaming. Relevance/Link to RDA: The streaming services attached to this component are using the near real-time data transfered from remote field sites to local databases and storage systems. For this sake, we are participating in the RDA "Array DB Assesment" Working Group and "Big Data" Interest Group