Compilation of 220 sediment core d13C data from the glacial Atlantic Ocean

We compare a compilation of 220 sediment core d13C data from the glacial Atlantic Ocean with three-dimensional ocean circulation simulations including a marine carbon cycle model. The carbon cycle model employs circulation fields which were derived from previous climate simulations. All sediment data have been thoroughly quality controlled, focusing on epibenthic foraminiferal species (such as Cibicidoides wuellerstorfi or Planulina ariminensis) to improve the comparability of model and sediment core carbon isotopes. The model captures the general d13C pattern indicated by present-day water column data and Late Holocene sediment cores but underestimates intermediate and deep water values in the South Atlantic. The best agreement with glacial reconstructions is obtained for a model scenario with an altered freshwater balance in the Southern Ocean that mimics enhanced northward sea ice export and melting away from the zone of sea ice production. This results in a shoaled and weakened North Atlantic Deep Water flow and intensified Antarctic Bottom Water export, hence confirming previous reconstructions from paleoproxy records. Moreover, the modeled abyssal ocean is very cold and very saline, which is in line with other proxy data evidence.

Radiocarbon ages, grain sizes, paleomagentic properties and XRF data of Lake Nam Co sediments, Tibetan Plateau

In September 2008 several cores (68 cm-115 cm length) (water depth: 93 m) were retrieved from Lake Nam Co (southern-central Tibetan Plateau; 4718 m a.s.l.). This study focuses on the interpretation of high-resolution (partly 0.2 cm) data from three gravity cores and the upper part of a 10.4 m long piston core, i.e., the past 4000 cal BP in terms of lake level changes, hydrological variations in the catchment area and consequently variations in monsoon strength. A wide spectrum of sedimentological, geochemical and mineralogical investigations was carried out. Results are presented for XRF core-scans, grain size distribution, XRD-measurements and SEM-image analyses. These data are complemented by an age-depth model using 210Pb and 137Cs analyses as well as eleven AMS-14C-ages. This model is supported by excellent agreement between secular variations determined on one of the gravity cores to geomagnetic field models. This is a significant improvement of the chronology as most catchments of lacustrine systems on the Tibetan Plateau contain carbonates resulting in an unknown reservoir effect for radiocarbon dates. The good correlation of our record to the geomagnetic field models confirms our age-depth model and indicates only insignificant changes in the reservoir effect throughout the last 4 ka. High (summer-) monsoonal activity, i.e. moist environmental conditions, was detected in our record between approximately 4000 and 1950 cal BP as well as between 1480 and 1200 cal BP. Accordingly, lower monsoon activity prevails in periods between the two intervals and thereafter. This pattern shows a good correlation to the variability of the Indian Ocean Summer Monsoon (IOSM) as recorded in a peat bog ~1000 km in NE direction from Lake Nam Co. This is the first time that such a supra regional homogenous monsoon activity is shown on the Tibetan Plateau and beyond. Finally our data show a significant lake level rise after the Little Ice Age (LIA) in Lake Nam Co which is suggested to be linked to glacier melting in consequence of rising temperatures occurring on the whole Tibetan Plateau during this time.

Dive depth profile and at surface behaviour data of emperor penguins from Pointe Géologie, Adélie Land, Antarctica, from expedition DDU 2005

Adult male and female emperor penguins (Aptenodytes forsteri) were fitted with satellite transmitters at Pointe-Géologie (Adélie Land), Dumont d'Urville Sea coast, in November 2005. Nine of 30 data sets were selected for analyses to investigate the penguins' diving behaviour at high resolution (doi:10.1594/PANGAEA.633708, doi:10.1594/PANGAEA.633709, doi:10.1594/PANGAEA.633710, doi:10.1594/PANGAEA.633711). The profiles are in synchrony with foraging trips of the birds during austral spring (doi:10.1594/PANGAEA.472171, doi:10.1594/PANGAEA.472173, doi:10.1594/PANGAEA.472164, doi:10.1594/PANGAEA.472160, doi:10.1594/PANGAEA.472161). Corresponding high resolution winter data (n = 5; archived elsewhere) were provided by A. Ancel, Centre d'Ecologie et Physiologie Energétiques, CNRS, Strasbourg, France. Air-breathing divers tend to increase their overall dive duration with increasing dive depth. In most penguin species, this occurs due to increasing transit (descent and ascent) durations but also because the duration of the bottom phase of the dive increases with increasing depth. We interpreted the efficiency with which emperor penguins can exploit different diving depths by analysing dive depth profile data of nine birds studied during the early and late chick-rearing period in Adélie Land, Antarctica. Another eight datasets of dive depth and duration frequency recordings (doi:10.1594/PANGAEA.472150, doi:10.1594/PANGAEA.472152, doi:10.1594/PANGAEA.472154, doi:10.1594/PANGAEA.472155, doi:10.1594/PANGAEA.472142, doi:10.1594/PANGAEA.472144, doi:10.1594/PANGAEA.472146, doi:10.1594/PANGAEA.472147), which backup the analysed high resolution depth profile data, and dive depth and duration frequency recordings of another bird (doi:10.1594/PANGAEA.472156, doi:10.1594/PANGAEA.472148) did not match the requirement of high resolution for analyses. Eleven additional data sets provide information on the overall foraging distribution of emperor penguins during the period analysed (doi:10.1594/PANGAEA.472157, doi:10.1594/PANGAEA.472158, doi:10.1594/PANGAEA.472162, doi:10.1594/PANGAEA.472163, doi:10.1594/PANGAEA.472166, doi:10.1594/PANGAEA.472167, doi:10.1594/PANGAEA.472168, doi:10.1594/PANGAEA.472170, doi:10.1594/PANGAEA.472172, doi:10.1594/PANGAEA.472174, doi:10.1594/PANGAEA.472175).

A review of road structure data in six European countries

The European Union has expanded significantly in recent years. Sustainable trade within the Union, leading to economic growth to the benefit of the ‘old’ and ‘new’ member states is thus extremely important. The road infrastructure is strategic and vital to such development since an uneven transport infrastructure, in terms of capacity and condition, has the potential to reinforce uneven development trends and hinder economic convergence of old and new member states. In the decades since their design and construction, loading conditions have significantly changed for many major highway infrastructure elements/networks owing primarily to increased freight volumes and vehicle sizes. This, coupled with the gradual deterioration of a significant number of highway structures due to their age, and the absence of a pan-European assessment framework, can be expected to affect the smooth functioning of the infrastructure in its as-built condition. Increased periods of reduced flow can be expected owing to planned and unplanned interventions for repair/rehabilitation. This paper reports the findings of a survey regarding the current status of the highway infrastructure elements in six countries within the European Union as reported by the owners/operators. The countries surveyed include a cross-section of ‘existing’ older countries and ‘new’ member states. The current situations for bridges, culverts, tunnels and retaining walls are reported, along with their potential replacement costs. The findings act as a departure point for further studies in support of a centralised and/or synchronised EU approach to infrastructure maintenance management. Information in the form presented in this paper is central to any future decision-making frameworks in terms of trade route choice and operations, monetary investment, optimised maintenance, management and rehabilitation of the built infrastructure and the economic integration of the newly joined member states.

Methods for Imputing Missing Values and Synthesizing Confidential Values for Continuous and Magnitude Data

Abstract

Continuous variable is one of the major data types collected by the survey organizations. It can be incomplete such that the data collectors need to fill in the missingness. Or, it can contain sensitive information which needs protection from re-identification. One of the approaches to protect continuous microdata is to sum them up according to different cells of features. In this thesis, I represents novel methods of multiple imputation (MI) that can be applied to impute missing values and synthesize confidential values for continuous and magnitude data.

The first method is for limiting the disclosure risk of the continuous microdata whose marginal sums are fixed. The motivation for developing such a method comes from the magnitude tables of non-negative integer values in economic surveys. I present approaches based on a mixture of Poisson distributions to describe the multivariate distribution so that the marginals of the synthetic data are guaranteed to sum to the original totals. At the same time, I present methods for assessing disclosure risks in releasing such synthetic magnitude microdata. The illustration on a survey of manufacturing establishments shows that the disclosure risks are low while the information loss is acceptable.

The second method is for releasing synthetic continuous micro data by a nonstandard MI method. Traditionally, MI fits a model on the confidential values and then generates multiple synthetic datasets from this model. Its disclosure risk tends to be high, especially when the original data contain extreme values. I present a nonstandard MI approach conditioned on the protective intervals. Its basic idea is to estimate the model parameters from these intervals rather than the confidential values. The encouraging results of simple simulation studies suggest the potential of this new approach in limiting the posterior disclosure risk.

The third method is for imputing missing values in continuous and categorical variables. It is extended from a hierarchically coupled mixture model with local dependence. However, the new method separates the variables into non-focused (e.g., almost-fully-observed) and focused (e.g., missing-a-lot) ones. The sub-model structure of focused variables is more complex than that of non-focused ones. At the same time, their cluster indicators are linked together by tensor factorization and the focused continuous variables depend locally on non-focused values. The model properties suggest that moving the strongly associated non-focused variables to the side of focused ones can help to improve estimation accuracy, which is examined by several simulation studies. And this method is applied to data from the American Community Survey.

Mining the Online Social Network Data: Influence, Summarization, and Organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

San Francisco Coastal System grain size and geochemical data for sand provenance study

Over 150 million cubic meter of sand-sized sediment has disappeared from the central region of the San Francisco Bay Coastal System during the last half century. This enormous loss may reflect numerous anthropogenic influences, such as watershed damming, bay-fill development, aggregate mining, and dredging. The reduction in Bay sediment also appears to be linked to a reduction in sediment supply and recent widespread erosion of adjacent beaches, wetlands, and submarine environments. A unique, multi-faceted provenance study was performed to definitively establish the primary sources, sinks, and transport pathways of beach sized-sand in the region, thereby identifying the activities and processes that directly limit supply to the outer coast. This integrative program is based on comprehensive surficial sediment sampling of the San Francisco Bay Coastal System, including the seabed, Bay floor, area beaches, adjacent rock units, and major drainages. Analyses of sample morphometrics and biological composition (e.g., Foraminifera) were then integrated with a suite of tracers including 87Sr/86Sr and 143Nd/144Nd isotopes, rare earth elements, semi-quantitative X-ray diffraction mineralogy, and heavy minerals, and with process-based numerical modeling, in situ current measurements, and bedform asymmetry to robustly determine the provenance of beach-sized sand in the region.

Model simulation output data (CCSM3) related to uplift of Africa and Himalaya/Tibet Plateau and the influence on tropical African vegetation

Hominid evolution in the late Miocene has long been hypothesized to be linked to the retreat of the tropical rainforest in Africa. One cause for the climatic and vegetation change often considered was uplift of Africa, but also uplift of the Himalaya and the Tibetan Plateau was suggested to have impacted rainfall distribution over Africa. Recent proxy data suggest that in East Africa open grassland habitats were available to the common ancestors of hominins and apes long before their divergence and do not find evidence for a closed rainforest in the late Miocene. We used the coupled global general circulation model CCSM3 including an interactively coupled dynamic vegetation module to investigate the impact of topography on African hydro-climate and vegetation. We performed sensitivity experiments altering elevations of the Himalaya and the Tibetan Plateau as well as of East and Southern Africa. The simulations confirm the dominant impact of African topography for climate and vegetation development of the African tropics. Only a weak influence of prescribed Asian uplift on African climate could be detected. The model simulations show that rainforest coverage of Central Africa is strongly determined by the presence of elevated African topography. In East Africa, despite wetter conditions with lowered African topography, the conditions were not favorable enough to maintain a closed rainforest. A discussion of the results with respect to other model studies indicates a minor importance of vegetation-atmosphere or ocean-atmosphere feedbacks and a large dependence of the simulated vegetation response on the land surface/vegetation model.

E-RUN version 1.1: Observational gridded runoff estimates for Europe, link to data in NetCDF format (69 MB)

River runoff is an essential climate variable as it is directly linked to the terrestrial water balance and controls a wide range of climatological and ecological processes. Despite its scientific and societal importance, there are to date no pan-European observation-based runoff estimates available. Here we employ a recently developed methodology to estimate monthly runoff rates on regular spatial grid in Europe. For this we first assemble an unprecedented collection of river flow observations, combining information from three distinct data bases. Observed monthly runoff rates are first tested for homogeneity and then related to gridded atmospheric variables (E-OBS version 12) using machine learning. The resulting statistical model is then used to estimate monthly runoff rates (December 1950 - December 2015) on a 0.5° x 0.5° grid. The performance of the newly derived runoff estimates is assessed in terms of cross validation. The paper closes with example applications, illustrating the potential of the new runoff estimates for climatological assessments and drought monitoring.

E-RUN version 1.0: Observational gridded runoff estimates for Europe, link to data in NetCDF format (68 MB)

River runoff is an essential climate variable as it is directly linked to the terrestrial water balance and controls a wide range of climatological and ecological processes. Despite its scientific and societal importance, there are to date no pan-European observation-based runoff estimates available. Here we employ a recently developed methodology to estimate monthly runoff rates on regular spatial grid in Europe. For this we first collect an unprecedented collection of river flow observations, combining information from three distinct data bases. Observed monthly runoff rates are first tested for homogeneity and then related to gridded atmospheric variables (E-OBS version 11) using machine learning. The resulting statistical model is then used to estimate monthly runoff rates (December 1950-December 2014) on a 0.5° × 0.5° grid. The performance of the newly derived runoff estimates is assessed in terms of cross validation. The paper closes with example applications, illustrating the potential of the new runoff estimates for climatological assessments and drought monitoring.

Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, specially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.

Diatom isotope data from ODP Hole 178-1098A for the last deglaciation (12.2-12.8 ka BP)

Understanding the response of the Antarctic ice sheets during the rapid climatic change that accompanied the last deglaciation has implications for establishing the susceptibility of these regions to future 21st Century warming. A unique diatom d18O record derived from a high-resolution deglacial seasonally laminated core section off the west Antarctic Peninsula (WAP) is presented here. By extracting and analysing single species samples from individual laminae, season-specific isotope records were separately generated to show changes in glacial discharge to the coastal margin during spring and summer months. As well as documenting significant intra-annual seasonal variability during the deglaciation, with increased discharge occurring in summer relative to spring, further intra-seasonal variations are apparent between individual taxa linked to the environment that individual diatom species live in. Whilst deglacial d18O are typically lower than those for the Holocene, indicating glacial discharge to the core site peaked at this time, inter-annual and inter-seasonal alternations in excess of 3 per mil suggest significant variability in the magnitude of these inputs. These deglacial variations in glacial discharge are considerably greater than those seen in the modern day water column and would have altered both the supply of oceanic warmth to the WAP as well as regional marine/atmospheric interactions. In constraining changes in glacial discharge over the last deglaciation, the records provide a future framework for investigating links between annually resolved records of glacial dynamics and ocean/climate variability along the WAP.

Advanced Business Simulations-Incorporating business and process execution data

Key Performance Indicators (KPIs) and their predictions are widely used by the enterprises for informed decision making. Nevertheless , a very important factor, which is generally overlooked, is that the top level strategic KPIs are actually driven by the operational level business processes. These two domains are, however, mostly segregated and analysed in silos with different Business Intelligence solutions. In this paper, we are proposing an approach for advanced Business Simulations, which converges the two domains by utilising process execution & business data, and concepts from Business Dynamics (BD) and Business Ontologies, to promote better system understanding and detailed KPI predictions. Our approach incorporates the automated creation of Causal Loop Diagrams, thus empowering the analyst to critically examine the complex dependencies hidden in the massive amounts of available enterprise data. We have further evaluated our proposed approach in the context of a retail use-case that involved verification of the automatically generated causal models by a domain expert.

A Data Driven Tool supported CLD Creation Approach

The creation of Causal Loop Diagrams (CLDs) is a major phase in the System Dynamics (SD) life-cycle, since the created CLDs express dependencies and feedback in the system under study, as well as, guide modellers in building meaningful simulation models. The cre-ation of CLDs is still subject to the modeller's domain expertise (mental model) and her ability to abstract the system, because of the strong de-pendency on semantic knowledge. Since the beginning of SD, available system data sources (written and numerical models) have always been sparsely available, very limited and imperfect and thus of little benefit to the whole modelling process. However, in recent years, we have seen an explosion in generated data, especially in all business related domains that are analysed via Business Dynamics (BD). In this paper, we intro-duce a systematic tool supported CLD creation approach, which analyses and utilises available disparate data sources within the business domain. We demonstrate the application of our methodology on a given business use-case and evaluate the resulting CLD. Finally, we propose directions for future research to further push the automation in the CLD creation and increase confidence in the generated CLDs.

Data Science Seminar: Generic Big Data Processing for Advancing Situation Awareness and Decision-Support

The generation of heterogeneous big data sources with ever increasing volumes, velocities and veracities over the he last few years has inspired the data science and research community to address the challenge of extracting knowledge form big data. Such a wealth of generated data across the board can be intelligently exploited to advance our knowledge about our environment, public health, critical infrastructure and security. In recent years we have developed generic approaches to process such big data at multiple levels for advancing decision-support. It specifically concerns data processing with semantic harmonisation, low level fusion, analytics, knowledge modelling with high level fusion and reasoning. Such approaches will be introduced and presented in context of the TRIDEC project results on critical oil and gas industry drilling operations and also the ongoing large eVacuate project on critical crowd behaviour detection in confined spaces.