Benthic and substrate cover data derived from photo-transect surveys in Lizard Island, Great Barrier Reef conducted on October 3-7, 2012

Underwater georeferenced photo-transect surveys were conducted on October 3-7, 2012 at various sections of the reef and lagoon at Lizard Island, Great Barrier Reef. For this survey a snorkeler swam while taking photos of the benthos at a set distance from the benthos using a standard digital camera and towing a GPS in a surface float which logged the track every five seconds. A Canon G12 digital camera was placed in a Canon underwater housing and photos were taken at 1 m height above the benthos. Horizontal distance between photos was estimated by three fin kicks of the survey snorkeler, which corresponded to a surface distance of approximately 2.0 - 4.0 m. The GPS was placed in a dry bag and logged the position at the surface while being towed by the photographer (Roelfsema, 2009). A total of 1,265 benthic photos were taken. Approximation of coordinates of each benthic photo was conducted based on the photo timestamp and GPS coordinate time stamp, using GPS Photo Link Software (www.geospatialexperts.com). Coordinates of each photo were interpolated by finding the GPS coordinates that were logged at a set time before and after the photo was captured. Benthic or substrate cover data was derived from each photo by randomly placing 24 points over each image using the Coral Point Count for Microsoft Excel program (Kohler and Gill, 2006). Each point was then assigned to 1 of 79 cover types, which represented the benthic feature beneath it. Benthic cover composition summary of each photo scores was generated automatically using CPCE program. The resulting benthic cover data of each photo was linked to GPS coordinates, saved as an ArcMap point shapefile, and projected to Universal Transverse Mercator WGS84 Zone 55 South.

Habitat Map for Lizard Island reef, Australia derived from a photo-transect survey field data collected in December 2011 and September/October 2012

An object based image analysis approach (OBIA) was used to create a habitat map of the Lizard Reef. Briefly, georeferenced dive and snorkel photo-transect surveys were conducted at different locations surrounding Lizard Island, Australia. For the surveys, a snorkeler or diver swam over the bottom at a depth of 1-2m in the lagoon, One Tree Beach and Research Station areas, and 7m depth in Watson's Bay, while taking photos of the benthos at a set height using a standard digital camera and towing a surface float GPS which was logging its track every five seconds. The camera lens provided a 1.0 m x 1.0 m footprint, at 0.5 m height above the benthos. Horizontal distance between photos was estimated by fin kicks, and corresponded to a surface distance of approximately 2.0 - 4.0 m. Approximation of coordinates of each benthic photo was done based on the photo timestamp and GPS coordinate time stamp, using GPS Photo Link Software (www.geospatialexperts.com). Coordinates of each photo were interpolated by finding the gps coordinates that were logged at a set time before and after the photo was captured. Dominant benthic or substrate cover type was assigned to each photo by placing 24 points random over each image using the Coral Point Count excel program (Kohler and Gill, 2006). Each point was then assigned a dominant cover type using a benthic cover type classification scheme containing nine first-level categories - seagrass high (>=70%), seagrass moderate (40-70%), seagrass low (<= 30%), coral, reef matrix, algae, rubble, rock and sand. Benthic cover composition summaries of each photo were generated automatically in CPCe. The resulting benthic cover data for each photo was linked to GPS coordinates, saved as an ArcMap point shapefile, and projected to Universal Transverse Mercator WGS84 Zone 56 South. The OBIA class assignment followed a hierarchical assignment based on membership rules with levels for "reef", "geomorphic zone" and "benthic community" (above).

Sediment core-top GDGT data from the Arctic, the North Pacific and the Southern Ocean

TEX86 (TetraEther indeX of tetraethers consisting of 86 carbon atoms) is a sea surface temperature (SST) proxy based on the distribution of archaeal isoprenoid glycerol dialkyl glycerol tetraethers (GDGTs). In this study, we appraise the applicability of TEX86 and TEX86L in subpolar and polar regions using surface sediments. We present TEX86 and TEX86L data from 160 surface sediment samples collected in the Arctic, the Southern Ocean and the North Pacific. Most of the SST estimates derived from both TEX86 and TEX86L are anomalously high in the Arctic, especially in the vicinity of Siberian river mouths and the sea ice margin, plausibly due to additional archaeal contributions linked to terrigenous input. We found unusual GDGT distributions at five sites in the North Pacific. High GDGT-0/crenarchaeol and GDGT-2/crenarchaeol ratios at these sites suggest a substantial contribution of methanogenic and/or methanotrophic archaea to the sedimentary GDGT pool here. Apart from these anomalous findings, TEX86 and TEX86L values in the surface sediments from the Southern Ocean and the North Pacific do usually vary with overlaying SSTs. In these regions, the sedimentary TEX86-SST relationship is similar to the global calibration, and the derived temperature estimates agree well with overlaying annual mean SSTs at the sites. However, there is a systematic offset between the regional TEX86L-SST relationships and the global calibration. At these sites, temperature estimates based on the global TEX86L calibration are closer to summer SSTs than annual mean SSTs. This finding suggests that in these subpolar settings a regional TEX86L calibration may be a more suitable equation for temperature reconstruction than the global calibration.

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).

Structural and Functional Analysis of the Caspase –dependent and –independent Domains of the X-linked Inhibitor of Apoptosis Protein in Inflammatory Breast Cancer Tumor Biology

Inflammatory breast cancer (IBC) is an extremely rare but highly aggressive form of breast cancer characterized by the rapid development of therapeutic resistance leading to particularly poor survival. Our previous work focused on the elucidation of factors that mediate therapeutic resistance in IBC and identified increased expression of the anti-apoptotic protein, X-linked inhibitor of apoptosis protein (XIAP), to correlate with the development of resistance to chemotherapeutics. Although XIAP is classically thought of as an inhibitor of caspase activation, multiple studies have revealed that XIAP can also function as a signaling intermediate in numerous pathways. Based on preliminary evidence revealing high expression of XIAP in pre-treatment IBC cells rather than only subsequent to the development of resistance, we hypothesized that XIAP could play an important signaling role in IBC pathobiology outside of its heavily published apoptotic inhibition function. Further, based on our discovery of inhibition of chemotherapeutic efficacy, we postulated that XIAP overexpression might also play a role in resistance to other forms of therapy, such as immunotherapy. Finally, we posited that targeting of specific redox adaptive mechanisms, which are observed to be a significant barrier to successful treatment of IBC, could overcome therapeutic resistance and enhance the efficacy of chemo-, radio-, and immuno- therapies. To address these hypotheses our objectives were: 1. to determine a role for XIAP in IBC pathobiology and to elucidate the upstream regulators and downstream effectors of XIAP; 2. to evaluate and describe a role for XIAP in the inhibition of immunotherapy; and 3. to develop and characterize novel redox modulatory strategies that target identified mechanisms to prevent or reverse therapeutic resistance.

Using various genomic and proteomic approaches, combined with analysis of cellular viability, proliferation, and growth parameters both in vitro and in vivo, we demonstrate that XIAP plays a central role in both IBC pathobiology in a manner mostly independent of its role as a caspase-binding protein. Modulation of XIAP expression in cells derived from patients prior to any therapeutic intervention significantly altered key aspects IBC biology including, but not limited to: IBC-specific gene signatures; the tumorigenic capacity of tumor cells; and the metastatic phenotype of IBC, all of which are revealed to functionally hinge on XIAP-mediated NFκB activation, a robust molecular determinant of IBC. Identification of the mechanism of XIAP-mediated NFκB activation led to the characterization of novel peptide-based antagonist which was further used to identify that increased NFκB activation was responsible for redox adaptation previously observed in therapy-resistant IBC cells. Lastly, we describe the targeting of this XIAP-NFκB-ROS axis using a novel redox modulatory strategy both in vitro and in vivo. Together, the data presented here characterize a novel and crucial role for XIAP both in therapeutic resistance and the pathobiology of IBC; these results confirm our previous work in acquired therapeutic resistance and establish the feasibility of targeting XIAP-NFκB and the redox adaptive phenotype of IBC as a means to enhance survival of patients.

SAFE: A Declarative Trust-Agile System with Linked Credentials

Secure Access For Everyone (SAFE), is an integrated system for managing trust

using a logic-based declarative language. Logical trust systems authorize each

request by constructing a proof from a context---a set of authenticated logic

statements representing credentials and policies issued by various principals

in a networked system. A key barrier to practical use of logical trust systems

is the problem of managing proof contexts: identifying, validating, and

assembling the credentials and policies that are relevant to each trust

decision.

SAFE addresses this challenge by (i) proposing a distributed authenticated data

repository for storing the credentials and policies; (ii) introducing a

programmable credential discovery and assembly layer that generates the

appropriate tailored context for a given request. The authenticated data

repository is built upon a scalable key-value store with its contents named by

secure identifiers and certified by the issuing principal. The SAFE language

provides scripting primitives to generate and organize logic sets representing

credentials and policies, materialize the logic sets as certificates, and link

them to reflect delegation patterns in the application. The authorizer fetches

the logic sets on demand, then validates and caches them locally for further

use. Upon each request, the authorizer constructs the tailored proof context

and provides it to the SAFE inference for certified validation.

Delegation-driven credential linking with certified data distribution provides

flexible and dynamic policy control enabling security and trust infrastructure

to be agile, while addressing the perennial problems related to today's

certificate infrastructure: automated credential discovery, scalable

revocation, and issuing credentials without relying on centralized authority.

We envision SAFE as a new foundation for building secure network systems. We

used SAFE to build secure services based on case studies drawn from practice:

(i) a secure name service resolver similar to DNS that resolves a name across

multi-domain federated systems; (ii) a secure proxy shim to delegate access

control decisions in a key-value store; (iii) an authorization module for a

networked infrastructure-as-a-service system with a federated trust structure

(NSF GENI initiative); and (iv) a secure cooperative data analytics service

that adheres to individual secrecy constraints while disclosing the data. We

present empirical evaluation based on these case studies and demonstrate that

SAFE supports a wide range of applications with low overhead.

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.

Retrieval of grassland biophysical parameters using multitemporal optical and radar satellite data

The amount and quality of available biomass is a key factor for the sustainable livestock industry and agricultural management related decision making. Globally 31.5% of land cover is grassland while 80% of Ireland’s agricultural land is grassland. In Ireland, grasslands are intensively managed and provide the cheapest feed source for animals. This dissertation presents a detailed state of the art review of satellite remote sensing of grasslands, and the potential application of optical (Moderate–resolution Imaging Spectroradiometer (MODIS)) and radar (TerraSAR-X) time series imagery to estimate the grassland biomass at two study sites (Moorepark and Grange) in the Republic of Ireland using both statistical and state of the art machine learning algorithms. High quality weather data available from the on-site weather station was also used to calculate the Growing Degree Days (GDD) for Grange to determine the impact of ancillary data on biomass estimation. In situ and satellite data covering 12 years for the Moorepark and 6 years for the Grange study sites were used to predict grassland biomass using multiple linear regression, Neuro Fuzzy Inference Systems (ANFIS) models. The results demonstrate that a dense (8-day composite) MODIS image time series, along with high quality in situ data, can be used to retrieve grassland biomass with high performance (R2 = 0:86; p < 0:05, RMSE = 11.07 for Moorepark). The model for Grange was modified to evaluate the synergistic use of vegetation indices derived from remote sensing time series and accumulated GDD information. As GDD is strongly linked to the plant development, or phonological stage, an improvement in biomass estimation would be expected. It was observed that using the ANFIS model the biomass estimation accuracy increased from R2 = 0:76 (p < 0:05) to R2 = 0:81 (p < 0:05) and the root mean square error was reduced by 2.72%. The work on the application of optical remote sensing was further developed using a TerraSAR-X Staring Spotlight mode time series over the Moorepark study site to explore the extent to which very high resolution Synthetic Aperture Radar (SAR) data of interferometrically coherent paddocks can be exploited to retrieve grassland biophysical parameters. After filtering out the non-coherent plots it is demonstrated that interferometric coherence can be used to retrieve grassland biophysical parameters (i. e., height, biomass), and that it is possible to detect changes due to the grass growth, and grazing and mowing events, when the temporal baseline is short (11 days). However, it not possible to automatically uniquely identify the cause of these changes based only on the SAR backscatter and coherence, due to the ambiguity caused by tall grass laid down due to the wind. Overall, the work presented in this dissertation has demonstrated the potential of dense remote sensing and weather data time series to predict grassland biomass using machine-learning algorithms, where high quality ground data were used for training. At present a major limitation for national scale biomass retrieval is the lack of spatial and temporal ground samples, which can be partially resolved by minor modifications in the existing PastureBaseIreland database by adding the location and extent ofeach grassland paddock in the database. As far as remote sensing data requirements are concerned, MODIS is useful for large scale evaluation but due to its coarse resolution it is not possible to detect the variations within the fields and between the fields at the farm scale. However, this issue will be resolved in terms of spatial resolution by the Sentinel-2 mission, and when both satellites (Sentinel-2A and Sentinel-2B) are operational the revisit time will reduce to 5 days, which together with Landsat-8, should enable sufficient cloud-free data for operational biomass estimation at a national scale. The Synthetic Aperture Radar Interferometry (InSAR) approach is feasible if there are enough coherent interferometric pairs available, however this is difficult to achieve due to the temporal decorrelation of the signal. For repeat-pass InSAR over a vegetated area even an 11 days temporal baseline is too large. In order to achieve better coherence a very high resolution is required at the cost of spatial coverage, which limits its scope for use in an operational context at a national scale. Future InSAR missions with pair acquisition in Tandem mode will minimize the temporal decorrelation over vegetation areas for more focused studies. The proposed approach complements the current paradigm of Big Data in Earth Observation, and illustrates the feasibility of integrating data from multiple sources. In future, this framework can be used to build an operational decision support system for retrieval of grassland biophysical parameters based on data from long term planned optical missions (e. g., Landsat, Sentinel) that will ensure the continuity of data acquisition. Similarly, Spanish X-band PAZ and TerraSAR-X2 missions will ensure the continuity of TerraSAR-X and COSMO-SkyMed.

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.