Scientific Research Data Management for Soil-Vegetation-Atmosphere Data – The TR32DB

The implementation of a scientific research data management system is an important task within long-term, interdisciplinary research projects. Besides sustainable storage of data, including accurate descriptions with metadata, easy and secure exchange and provision of data is necessary, as well as backup and visualisation. The design of such a system poses challenges and problems that need to be solved.This paper describes the practical experiences gained by the implementation of a scientific research data management system, established in a large, interdisciplinary research project with focus on Soil-Vegetation-Atmosphere Data

Supporting the Interdisciplinary, Long-Term Research Project ‘Patterns in Soil-Vegetation-Atmosphere-Systems’ by Data Management Services

Science conducted in cross-institutional, interdisciplinary, long-term research projects requires active sharing of data, documents and further information. Thus, within the Collaborative Research Centre/Transregio 32 ‘Patterns in Soil-Vegetation-Atmosphere Systems’, funded by the German Research Foundation, research data management (RDM) services have been available since early 2007. These services were established to support all researchers during their entire individual research studies. They cover provision of general guidance, support and training for RDM. To fulfil the scientists’ needs and requests with regard to storage, backup, documentation, search and sharing of data with other project members, a project-specific RDM system was designed and implemented. This system was developed and continuously modified in collaboration with the scientists to facilitate their system acceptance. Besides the mentioned services, the system supports further common services such as controlled access to data, rights management, data publication with DOI and data statistics (on repository and single data level). All RDM services provided for the scientists are thus bundled and available to the users in one system: a ‘one-stop-shop’. After more than ten years of RDM service provision for the CRC/TR32, the repository statistics clearly visualize the use of the diverse RDM system services. Furthermore, it has been shown that an RDM adapted to the needs of interdisciplinary researchers can be fruitful and indispensable when scientists conduct their research study e.g. with a time lag. RDM services established at an early stage can contribute to a successful long-term research project

Design and Implementation of a Research Data Management System: The CRC/TR32 Project Database (TR32DB)

Research data management (RDM) includes all processes and measures which ensure that research data are well-organised, documented, preserved, stored, backed up, accessible, available, and re-usable. Corresponding RDM systems or repositories form the technical framework to support the collection, accurate documentation, storage, back-up, sharing, and provision of research data, which are created in a specific environment, like a research group or institution. The required measures for the implementation of a RDM system vary according to the discipline or purpose of data (re-)use. In the context of RDM, the documentation of research data is an essential duty. This has to be conducted by accurate, standardized, and interoperable metadata to ensure the interpretability, understandability, shareability, and long-lasting usability of the data. RDM is achieving an increasing importance, as digital information increases. New technologies enable to create more digital data, also automatically. Consequently, the volume of digital data, including big data and small data, will approximately double every two years in size. With regard to e-science, this increase of data was entitled and predicted as the data deluge. Furthermore, the paradigm change in science has led to data intensive science. Particularly scientific data that were financed by public funding are significantly demanded to be archived, documented, provided or even open accessible by different policy makers, funding agencies, journals and other institutions. RDM can prevent the loss of data, otherwise around 80-90 % of the generated research data disappear and are not available for re-use or further studies. This will lead to empty archives or RDM systems. The reasons for this course are well known and are of a technical, socio-cultural, and ethical nature, like missing user participation and data sharing knowledge, as well as lack of time or resources. In addition, the fear of exploitation and missing or limited reward for publishing and sharing data has an important role. This thesis presents an approach in handling research data of the collaborative, multidisciplinary, long-term DFG-funded research project Collaborative Research Centre/Transregio 32 (CRC/TR32) “Patterns in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling, and Data Assimilation”. In this context, a RDM system, the so-called CRC/TR32 project database (TR32DB), was designed and implemented. The TR32DB considers the demands of the project participants (e.g. heterogeneous data from different disciplines with various file sizes) and the requirements of the DFG, as well as general challenges in RDM. For this purpose, a RDM system was established that comprises a well-described self-designed metadata schema, a file-based data storage, a well-elaborated database of metadata, and a corresponding user-friendly web interface. The whole system is developed in close cooperation with the local Regional Computing Centre of the University of Cologne (RRZK), where it is also hosted. The documentation of the research data with accurate metadata is of key importance. For this purpose, an own specific TR32DB Metadata Schema was designed, consisting of multi-level metadata properties. This is distinguished in general and data type specific (e.g. data, publication, report) properties and is developed according to the project background, demands of the various data types, as well as recent associated metadata standards and principles. Consequently, it is interoperable to recent metadata standards, such as the Dublin Core, the DataCite Metadata Schema, as well as core elements of the ISO19115:2003 Metadata Standard and INSPIRE Directive. Furthermore, the schema supports optional, mandatory, and automatically generated metadata properties, as well as it provides predefined, obligatory and self-established controlled vocabulary lists. The integrated mapping to the DataCite Metadata Schema facilitates the simple application of a Digital Object Identifier (DOI) for a dataset. The file-based data storage is organized in a folder system, corresponding to the structure of the CRC/TR32 and additionally distinguishes between several data types (e.g. data, publication, report). It is embedded in the Andrew File System hosted by the RRZK. The file system is capable to store and backup all data, is highly scalable, supports location independence, and enables easy administration by Access Control Lists. In addition, the relational database management system MySQL stores the metadata according to the previous mentioned TR32DB Metadata Schema as well as further necessary administrative data. A user-friendly web-based graphical user interface enables the access to the TR32DB system. The web-interface provides metadata input, search, and download of data, as well as the visualization of important geodata is handled by an internal WebGIS. This web-interface, as well as the entire RDM system, is self-developed and adjusted to the specific demands. Overall, the TR32DB system is developed according to the needs and requirements of the CRC/TR32 scientists, fits the demands of the DFG, and considers general problems and challenges of RDM as well. With regard to changing demands of the CRC/TR32 and technologic advances, the system is and will be consequently further developed. The established TR32DB approach was already successfully applied to another interdisciplinary research project. Thus, this approach is transferable and generally capable to archive all data, generated by the CRC/TR32, with accurately, interoperable metadata to ensure the re-use of the data, beyond the end of the project

Combining Multitemporal Microwave and Optical Remote Sensing Data. Mapping of Land Use / Land Cover, Crop Type, and Crop Traits

Humanity has changed the earth’s surface to a dramatic extent. This is especially true for the area used for agricultural production. Against the background of a growing world population and the associated increased demand for food, it is precisely this area that will become even more important in the future. In order not to have to allocate even more land to agricultural use, optimization and intensification is the only way out of the dilemma. In this context, precise Geoinformation of the agriculturally used area is of central importance. It is utilized for improving land use, producing yield forecasts for more stable food security, and optimizing agricultural management. Rapid developments in the field of satellite-based remote sensing sensors make it possible to monitor agricultural areas with increased spatial, spectral and temporal resolution. However, to retrieve the needed information from this data, new methods are needed. Furthermore, the quality of the data has to be verified. Only then can the presented geodata help to grow crops more sustainably and more efficiently. This thesis develops new approaches for monitoring agricultural areas using the technology of microwave remote sensing in combination with optical remote sensing and existing geodata. It is framed by the overall objective to obtain knowledge on how this combination of data can provide the necessary geoinformation for land use studies, precision farming, and agricultural monitoring systems. Hundreds of remote sensing images from more than eight different satellites were analyzed in six research studies from two different Areas of Interest (AOIs). The studies guide through various spatial scales. First, the general Land Use / Land Cover (LULC) on a regional level in a multi-sensor scenario is derived, evaluating different sensor combinations of varying resolutions. Next, an innovative method is proposed, through which the high geometric accuracy of radar-imaging satellite sensors is exploited to update the spatial accuracy of any external geodata of lower spatial accuracy. Such external data is then used in the next two studies, which focus on cost-effective crop type mapping using Synthetic Aperture Radar (SAR) images. The resulting enhanced LULC maps present the annually changing crop types of the region alongside external, official geoinformation that is not retrievable from remote sensing sensors. The last two research studies deal with a single maize field, on which high resolution optical WorldView-2 images and experimental bistatic SAR observations from TanDEM-X are assessed and combined with ground measurements. As a result, this thesis shows that, depending on the AOI and the application, different resolution demands need to be fulfilled before LULC, crop type, and crop traits mapping can be performed with adequate accuracy. The spatial resolution needs to be adapted to the particularities of the AOI. Evaluation of the sensors showed that SAR sensors proved beneficial for the study objective. Processing the SAR images is complicated, and the images are unintuitive at first sight. However, the advantage of SAR sensors is that they work even in cloudy conditions. This results in an increased temporal resolution, which is particularly important for monitoring the highly dynamic agricultural area. Furthermore, the high geometric accuracy of the SAR images proved ideal for implementing the Multi-Data Approach (MDA). Thus information-rich external geodata could be used to lower the remote sensing resolution needs, improve the accuracy of the LULC-maps, and to provide enhanced LULC-maps. The first study of the maize field demonstrates the potential of the WorldView-2 data in predicting in-field biomass variations, and its increased accuracy when fused with plant height measurements. The second study shows the potential of the TanDEM-X Constellation (TDM) to retrieve plant height from space. LULC, crop type and information on the spatial distribution of biomass can thus be derived efficiently and with high accuracy from the combination of SAR, optical satellites and external geodata. The shown analyses for acquiring such geoinformation represent a high potential for helping to solve the future challenges of agricultural production

A comprehensive dataset of vegetation states, fluxes of matter and energy, weather, agricultural management, and soil properties from intensively monitored crop sites in western Germany

Data description paperThe development and validation of hydroecological land-surface models to simulate agricultural areas require extensive data on weather, soil properties, agricultural management, and vegetation states and fluxes. However, these comprehensive data are rarely available since measurement, quality control, documentation, and compilation of the different data types are costly in terms of time and money. Here, we present a comprehensive dataset, which was collected at four agricultural sites within the Rur catchment in western Germany in the framework of the Transregional Collaborative Research Centre 32 (TR32) "Patterns in Soil-Vegetation-Atmosphere Systems: Monitoring, Modeling and Data Assimilation". Vegetation-related data comprise fresh and dry biomass (green and brown, predominantly per organ), plant height, green and brown leaf area index, phenological development state, nitrogen and carbon content (overall > 17 000 entries), and masses of harvest residues and regrowth of vegetation after harvest or before planting of the main crop (> 250 entries). Vegetation data including LAI were collected in frequencies of 1 to 3 weeks in the years 2015 until 2017, mostly during overflights of the Sentinel 1 and Radarsat 2 satellites. In addition, fluxes of carbon, energy, and water (> 180 000 half-hourly records) measured using the eddy covariance technique are included. Three flux time series have simultaneous data from two different heights. Data on agricultural management include sowing and harvest dates as well as information on cultivation, fertilization, and agrochemicals (27 management periods). The dataset also includes gap-filled weather data (> 200 000 hourly records) and soil parameters (particle size distributions, carbon and nitrogen content; > 800 records). These data can also be useful for development and validation of remote-sensing products. The dataset is hosted at the TR32 database (https://www.tr32db.uni-koeln.de/data.php?dataID=1889, last access: 29 September 2020) and has the DOI https://doi.org/10.5880/TR32DB.39 (Reichenau et al., 2020).Peer reviewe

Uncertainties in Digital Elevation Models: Evaluation and Effects on Landform and Soil Type Classification

Digital elevation models (DEMs) are a widely used source for the digital representation of the Earth's surface in a wide range of scientific, industrial and military applications. Since many processes on Earth are influenced by the shape of the relief, a variety of different applications rely on accurate information about the topography. For instance, DEMs are used for the prediction of geohazards, climate modelling, or planning-relevant issues, such as the identification of suitable locations for renewable energies. Nowadays, DEMs can be acquired with a high geometric resolution and over large areas using various remote sensing techniques, such as photogrammetry, RADAR, or laser scanning (LiDAR). However, they are subject to uncertainties and may contain erroneous representations of the terrain. The quality and accuracy of the topographic representation in the DEM is crucial, as the use of an inaccurate dataset can negatively affect further results, such as the underestimation of landslide hazards due to a too flat representation of relief in the elevation model. Therefore, it is important for users to gain more knowledge about the accuracy of a terrain model to better assess the negative consequences of DEM uncertainties on further analysis results of a certain research application. A proper assessment of whether the purchase or acquisition of a highly accurate DEM is necessary or the use of an already existing and freely available DEM is sufficient to achieve accurate results is of great qualitative and economic importance. In this context, the first part of this thesis focuses on extending knowledge about the behaviour and presence of uncertainties in DEMs concerning terrain and land cover. Thus, the first two studies of this dissertation provide a comprehensive vertical accuracy analysis of twelve DEMs acquired from space with spatial resolutions ranging from 5 m to 90 m. The accuracy of these DEMs was investigated in two different regions of the world that are substantially different in terms of relief and land cover. The first study was conducted in the hyperarid Chilean Atacama Desert in northern Chile, with very sparse land cover and high elevation differences. The second case study was conducted in a mid-latitude region, the Rur catchment in the western part of Germany. This area has a predominantly flat to hilly terrain with relatively diverse and dense vegetation and land cover. The DEMs in both studies were evaluated with particular attention to the influence of relief and land cover on vertical accuracy. The change of error due to changing slope and land cover was quantified to determine an average loss of accuracy as a function of slope for each DEM. Additionally, these values were used to derive relief-adjusted error values for different land cover classes. The second part of this dissertation addresses the consequences that different spatial resolutions and accuracies in DEMs have on specific applications. These implications were examined in two exemplary case studies. In a geomorphometric case study, several DEMs were used to classify landforms by different approaches. The results were subsequently compared and the accuracy of the classification results with different DEMs was analysed. The second case study is settled within the field of digital soil mapping. Various soil types were predicted with machine learning algorithms (random forest and artificial neural networks) using numerous relief parameters derived from DEMs of different spatial resolutions. Subsequently, the influence of high and low resolution DEMs with the respectively derived land surface parameters on the prediction results was evaluated. The results on the vertical accuracy show that uncertainties in DEMs can have diverse reasons. Besides the spatial resolution, the acquisition technique and the degree of improvements made to the dataset significantly impact the occurrence of errors in a DEM. Furthermore, the relief and physical objects on the surface play a major role for uncertainties in DEMs. Overall, the results in steeper areas show that the loss of vertical accuracy is two to three times higher for a 90 m DEM than for DEMs of higher spatial resolutions. While very high resolution DEMs of 12 m spatial resolution or higher only lose about 1 m accuracy per 10° increase in slope steepness, 30 m DEMs lose about 2 m on average, and 90 m DEMs lose more than 3 m up to 6 m accuracy. However, the results also show significant differences for DEMs of identical spatial resolution depending on relief and land cover. With regard to different land cover classes, it can be stated that mid-latitude forested and water areas cause uncertainties in DEMs of about 6 m on average. Other tested land cover classes produced minor errors of about 1 – 2 m on average. The results of the second part of this contribution prove that a careful selection of an appropriate DEM is more crucial for certain applications than for others. The choice of different DEMs greatly impacted the landform classification results. Results from medium resolution DEMs (30 m) achieved up to 30 % lower overall accuracies than results from high resolution DEMs with a spatial resolution of 5 m. In contrast to the landform classification results, the predicted soil types in the second case study showed only minor accuracy differences of less than 2 % between the usage of a spatial high resolution DEM (15 m) and a low resolution 90 m DEM. Finally, the results of these two case studies were compared and discussed with other results from the literature in other application areas. A summary and assessment of the current state of knowledge about the impact of a particular chosen terrain model on the results of different applications was made. In summary, the vertical accuracy measures obtained for each DEM are a first attempt to determine individual error values for each DEM that can be interpreted independently of relief and land cover and can be better applied to other regions. This may help users in the future to better estimate the accuracy of a tested DEM in a particular landscape. The consequences of elevation model selection on further results are highly dependent on the topic of the study and the study area's level of detail. The current state of knowledge on the impact of uncertainties in DEMs on various applications could be established. However, the results of this work can be seen as a first step and more work is needed in the future to extend the knowledge of the effects of DEM uncertainties on further topics that have not been investigated to date

Sensitivity of land-atmosphere coupling strength in dependence of land cover and atmospheric thermodynamics over Europe

Biogeophysical feedbacks between the land surface and the atmosphere have been identified to heavily control the climate system. Land-atmosphere (L-A) coupling strength is a concept to quantify the feedback processes. However, the quantification is still subject to uncertainties, in particular, in the context of land surface influences on local convective precipitation. On the one hand, feedback processes are the result of a chain of complex interactions between various components in the L-A system all exhibiting spatiotemporal variability. On the other hand, L-A coupling strength is not a directly measurable quantity. It can be assessed with different scientific approaches, which makes the quantification dependent on the methodology and the availability of suitable data sets. The aim of this doctoral thesis is to investigate the impact of changes in the vegetation cover and the atmospheric thermodynamic conditions on the long-term coupling signal between the land surface and the triggering of deep moist convection during the European summer. The convective triggering potential low-level humidity index framework, which is a commonly used L-A coupling metric, classifies a day in favor for L-A coupling or not, based on the prevailing thermodynamic conditions in the atmosphere. The daily classifications are used to measure the frequency of days with favorable conditions during the study period, and to identify regions with high frequencies of favorable conditions as coupling hot spots. The framework is applied to model output from regional climate model (RCM) simulations with WRF-NoahMP with diverging land cover conducted over the historical period 1986-2015 for the Euro-CORDEX domain. Impacts of changes in vegetation cover are analyzed by comparing the L-A coupling strength from two sensitivity experiments with idealized extreme land use and land cover changes (LULCCs) against a simulation with realistic land cover. A posteriori modifications to the temperature and moisture output fields of the simulation with realistic land cover were implemented to analyze impacts of systematic changes in the atmospheric thermodynamic conditions. A potential coupling hot spot with predominantly positive feedbacks was identified over Eastern Europe. In Southern Europe and Europes coastal areas, the coupling is regularly inhibited by very dry, very wet or stable conditions in the atmosphere. The location of the hot spot appeared insensitive to LULCCs and changes in the thermodynamic conditions. None of the sensitivity tests within a realistic range of temperature and moisture modifications for a recent climate period, led to a disappearance of the hot spot or to overcome the causes for inhibiting coupling in the respective areas in summer. Nevertheless, the experiments demonstrated also considerable variance of the coupling strength within the hot spot region. LULCCs changed the turbulent heat fluxes from the land surface, and thus the atmospheric boundary layer (ABL) heating and moistening. This impacted the boundary layer development of each day. It also caused changes in the average thermodynamic characteristics during the study period, which changed the frequency of favorable pre-conditioning for convection triggering and enhanced the variance in the coupling strength in the hot spot. Both effects were identified to influence the land surface control on the occurrence of convective precipitation. Furthermore, the sensitivity tests with a posteriori modifications revealed uncertainties in the predominant atmospheric response to differently wet surfaces around the Black Sea, shown by a disagreement in the predominant coupling pathway between the modification cases. The findings further indicate uncertainty in whether the hot spot expands over Central Europe, as the feedback signal was sensitive to changes in temperature and moisture. Additionally, the model has a warm and dry bias in this area, which suggests an overestimation of the humidity deficit. The large humidity deficit, in turn, was the inhibiting factor for a high frequency of occurrence of favorable pre-conditions for deep moist convection. The analyses reveal a sensitivity of the L-A coupling strength and atmospheric response to the prevailing land surface and atmospheric conditions in the hot spot. This highlights the need to consider both the land surface state and its impact on L-A coupling strength with respect to predictions of convective precipitation events in strongly coupled regions (and periods). Given that L-A coupling provides predictive skill for climate projections and seasonal forecasts, improved understanding about causes of variability in L-A coupling strength is crucial for improvements therein.Biogeophysikalische Rückkopplungsprozesse zwischen Landoberfläche und Atmosphäre haben einen großen Einfluss auf das Klimasystem. Allerdings unterliegt ihre Quantifizierung, allen voran des Einflusses der Landoberflächen auf die Auslösung konvektiver Niederschläge, weiterhin großen Unsicherheiten. Ursachen dafür sind die Komplexität der Interaktionen im Land-Atmosphären (L-A)-System unter Beteiligung vieler verschiedener Komponenten, die alle unterschiedlich starker räumlich-zeitlicher Variabilität unterliegen. Zudem ist die L-A Kopplungsstärke keine direkt messbare, sondern eine diagnostische Größe, die noch dazu mit verschiedenen wissenschaftlichen Ansätzen untersucht wird, sodass Ergebnisse sowohl von derWahl der Metrik, als auch von der Qualität und dem Zugang zu geeigneten Datensätzen abhängt. Das Ziel dieser Doktorarbeit ist die Untersuchung, ob und wie sich Änderungen in der Landnutzung und den thermodynamischen Bedingungen der Atmosphäre auf die potentielle Kopplungsstärke zwischen Landoberflächenfeuchte und dem Auslösen von hochreichender Konvektion in den europäischen Sommermonaten auswirken. Dafür wurden drei Klimasimulationen mit dem regionalen Klimamodel WRF-NoahMP für den historischen Zeitraum 1986-2015 für die Euro-CORDEX Domain durchgeführt, die sich in der Landbedeckung unterscheiden. Die Kopplungsstärke wurde mit Hilfe der L-A-Kopplungsmetrik Convective triggering potential low-level humidity index Framework analysiert, welche die Häufigkeit von förderlichen Bedingungen für lokal ausgelöste Konvektion in der Atmosphäre quantifiziert. Durch den Vergleich der Ergebnisse der Kopplungsmetrik für die Simulationen mit verschiedener Landbedeckung konnten die Einflüsse von Änderungen in der Vegetation analysiert werden. Weitere systematische Änderungen in den thermodynamischen Bedingungen und deren Auswirkungen auf die Kopplungsstärke konnten mit Hilfe von nachträglichen Modifikationen der Temperatur- und Feuchtefelder der Simulation mit realistischer Landbedeckung erfasst werden. Sämtliche Analysen zeigten einen Kopplungshotspot über Ost- und Nordosteuropa, wo vorwiegend positive Rückkopplungen zwischen Landoberfläche und konvektiven Niederschlägen auftreten. Die Lage des Hotspots wir nicht durch Änderungen der Landbedeckung oder der Atmosphärenstruktur beeinflusst. Keine der Temperatur- und Feuchteänderungen, deren Spektrum einen realistischen Rahmen für das gegenwärtige Klima abdecken, konnten ein Verschwinden des Hotspots herbeiführen oder die Ursachen für die Unterdrückung von Rückkopplungen (zu starke Trockenheit, Feuchte oder Stabilität in der Atmosphäre) über Südeuropa und in Küstennähe beseitigen. Allerdings zeigen die Experimente und Sensitivitätstests eine deutliche Varianz in der Kopplungsstärke in der Hotspotregion. Landnutzungsänderungen modifizieren die Aufteilung der Wärmeflüsse an der Landoberfläche und beeinflussen, ob die Grenzschicht vorwiegend feuchter oder aufgeheizt wird. Dadurch wird die Grenzschichtentwicklung jedes Tages beeinflusst, aber auch die mittleren thermodynamischen Eigenschaften der Atmosphäre, welche direkt mit förderlichen Vorbedingungen für das Auslösen von hochreichender Konvektion in Verbindung stehen und diese verändern. Beides wirkt sich auf den Einfluss der Landoberfläche auf das Auftreten konvektiver Niederschläge aus. Zusätzlich zeigten die Sensitivitätstests Unsicherheiten in der Reaktion der Atmosphäre auf die Variabilität der Landoberflächenfeuchte um das Schwarze Meer, und der Ausdehnung des Hotspots über Zentraleuropa. Die Ausdehnung wird von den Temperatur- und Feuchtemodifikationen beeinflusst, und im Modell wird das Feuchtedefizit in dieser Region überschätzt. Das regelmäßig hohe Feuchtedefizit ist die Hauptursache für das Verhindern von Rückkopplungen in dieser Region. Sämtliche Analysen zeigen eine Sensitivität der L-A Kopplungsstärke und der Reaktion der Atmosphäre auf die Landoberflächen- und Atmosphärenbedingungen im Hotspot. Daher ist es notwendig, sowohl die Landoberflächenbedingungen selber, als auch deren Einfluss auf die Kopplungsstärke zu berücksichtigen, um konvektiven Niederschlag akkurat vorhersagen zu können, vor allem in stark gekoppelten Regionen bzw. Zeiträumen. Da L-A Kopplung auch einen prognostischen Wert für Klimaprojektionen und saisonale Vorhersagen hat, trägt ein erhöhtes Verständnis über Ursachen für Variabilität in L-A Kopplungsstärke zu deren Verbesserung bei

CRC806-Database: A semantic e-Science infrastructure for an interdisciplinary research centre

Well designed information infrastructure improves the conduct of research, and can connect researchers and projects across disciplines to facilitate collaboration. The topic of this thesis is the design and development of an information infrastructure for a large interdisciplinary research project, the DFG-funded Collaborative Research Centre 806 (CRC 806). Under the name CRC806-Database the presented infrastructure was developed in the frame of the subproject "Z2: Data Management and Data Services", a so-called INF project, which is responsible for the research data management within a DFG funded CRC. During the design, development and implementation of the CRC806-Database, the complex requirements for sound data management in the context of a large interdisciplinary research project were considered theoretically, as well as practically during the implementation. The presented infrastructure design is mainly based on the requirements for research data management in CRC's, that is mainly the secure storage of primary research data for at least ten years, as well as on the further recommendations, that are about support and improvement of research and facilitation of Web-based collaboration, for information infrastructure by the DFG. The CRC806-Database semantic e-Science infrastructure consists of three main components, i.) the CRC806-RDM component that implements the research data management, including a data catalog and a publication database, ii.) the CRC806-SDI component that provides a Spatial Data Infrastructure (SDI) for Web-based management of spatial data, and additionally, iii.) the CRC806-KB component that implements a collaborative virtual research environment and knowledgebase. From a technical perspective, the infrastructure is based on the application of existing Open Source Software (OSS) solutions, that were customized to adapt to the specific requirements were necessary. The main OSS products that were applied for the development of the CRC806-Database are; Typo3, CKAN, GeoNode and Semantic MediaWiki. As integrative technical and theoretical basis of the infrastructure, the concept of Semantic e-Science was implemented. The term e-Science refers to a scientific paradigm that describes computationally intensive science carried out in networked environments. The prefix "Semantic" extends this concept with the application of Semantic Web technologies. A further applied conceptual basis for the development of CRC806-Database, is known under the name "Open Science", that includes the concepts of "Open Access", "Open Data" and "Open Methodology". These concepts have been implemented for the CRC806-Database semantic e-Science infrastructure, as described in the course of this thesis