scholarly journals Biogeochemical data from terrestrial and aquatic ecosystems in a periglacial catchment, West Greenland

2016 ◽  
Vol 8 (2) ◽  
pp. 439-459 ◽  
Author(s):  
Tobias Lindborg ◽  
Johan Rydberg ◽  
Mats Tröjbom ◽  
Sten Berglund ◽  
Emma Johansson ◽  
...  
Keyword(s):  
Feedback Mechanism ◽  
The Arctic ◽  
Data Sets ◽  
Laboratory Equipment ◽  
Data Set ◽  
West Greenland ◽  
Process Understanding ◽  
Extensive Field ◽  
Wide Range ◽  
Lake Catchment

Abstract. Global warming is expected to be most pronounced in the Arctic where permafrost thaw and release of old carbon may provide an important feedback mechanism to the climate system. To better understand and predict climate effects and feedbacks on the cycling of elements within and between ecosystems in northern latitude landscapes, a thorough understanding of the processes related to transport and cycling of elements is required. A fundamental requirement to reach a better process understanding is to have access to high-quality empirical data on chemical concentrations and biotic properties for a wide range of ecosystem domains and functional units (abiotic and biotic pools). The aim of this study is therefore to make one of the most extensive field data sets from a periglacial catchment readily available that can be used both to describe present-day periglacial processes and to improve predictions of the future. Here we present the sampling and analytical methods, field and laboratory equipment and the resulting biogeochemical data from a state-of-the-art whole-ecosystem investigation of the terrestrial and aquatic parts of a lake catchment in the Kangerlussuaq region, West Greenland. This data set allows for the calculation of whole-ecosystem mass balance budgets for a long list of elements, including carbon, nutrients and major and trace metals. The data set is freely available and can be downloaded from PANGAEA: doi:10.1594/PANGAEA.860961.

scholarly journals Biogeochemical data from terrestrial and aquatic ecosystems in a periglacial catchment, West Greenland

2016 ◽  
Author(s):  
Tobias Lindborg ◽  
Johan Rydberg ◽  
Mats Tröjbom ◽  
Sten Berglund ◽  
Emma Johansson ◽  
...  
Keyword(s):  
Feedback Mechanism ◽  
The Arctic ◽  
Data Sets ◽  
Laboratory Equipment ◽  
West Greenland ◽  
Process Understanding ◽  
Extensive Field ◽  
Wide Range ◽  
Lake Catchment ◽  
Fundamental Requirement

Abstract. Global warming is expected to be most pronounced in the Arctic where permafrost thaw and release of old carbon may provide an important feedback mechanism to the climate system. To better understand and predict climate effects and feedbacks on the cycling of elements within and between ecosystems in northern latitude landscapes, a thorough understanding of the processes related to transport and cycling of elements is required. A fundamental requirement to reach a better process understanding is to have access to high quality empirical data on chemical concentrations and biotic properties for a wide range of ecosystem domains and functional units (abiotic and biotic pools). The aim of this study is therefore to make one of the most extensive field data sets from a periglacial catchment readily available that can be used both to describe present day periglacial processes and to improve predictions of the future. Here we present the sampling and analytical methods, field and laboratory equipment and the resulting biogeochemical data from a state-of-the-art whole-ecosystem investigation of the terrestrial and aquatic parts of a lake catchment in the Kangerlussuaq region, West Greenland. This dataset allows for the calculation of whole-ecosystem mass balance budgets for long list of elements, including carbon, nutrients as well as major and trace metals. The dataset is freely available and can be downloaded from PANGAEA: https://doi.pangaea.de/10.1594/PANGAEA.860961

scholarly journals A 20-year record (1998–2017) of permafrost, active layer and meteorological conditions at a high Arctic permafrost research site (Bayelva, Spitsbergen)

2018 ◽  
Vol 10 (1) ◽  
pp. 355-390 ◽  
Author(s):  
Julia Boike ◽  
Inge Juszak ◽  
Stephan Lange ◽  
Sarah Chadburn ◽  
Eleanor Burke ◽  
...  
Keyword(s):  
Time Series ◽  
Energy Balance ◽  
Observational Data ◽  
Positive Feedback ◽  
Global Climate ◽  
Feedback Mechanism ◽  
The Arctic ◽  
Data Sets ◽  
Data Quality Control ◽  
Data Set

Abstract. Most permafrost is located in the Arctic, where frozen organic carbon makes it an important component of the global climate system. Despite the fact that the Arctic climate changes more rapidly than the rest of the globe, observational data density in the region is low. Permafrost thaw and carbon release to the atmosphere are a positive feedback mechanism that can exacerbate global warming. This positive feedback functions via changing land–atmosphere energy and mass exchanges. There is thus a great need to understand links between the energy balance, which can vary rapidly over hourly to annual timescales, and permafrost, which changes slowly over long time periods. This understanding thus mandates long-term observational data sets. Such a data set is available from the Bayelva site at Ny-Ålesund, Svalbard, where meteorology, energy balance components and subsurface observations have been made for the last 20 years. Additional data include a high-resolution digital elevation model (DEM) that can be used together with the snow physical information for snowpack modeling and a panchromatic image. This paper presents the data set produced so far, explains instrumentation, calibration, processing and data quality control, as well as the sources for various resulting data sets. The resulting data set is unique in the Arctic and serves as a baseline for future studies. The mean permafrost temperature is −2.8 °C, with a zero-amplitude depth at 5.5 m (2009–2017). Since the data provide observations of temporally variable parameters that mitigate energy fluxes between permafrost and atmosphere, such as snow depth and soil moisture content, they are suitable for use in integrating, calibrating and testing permafrost as a component in earth system models.The presented data are available in the Supplement for this paper (time series) and through the PANGAEA and Zenodo data portals: time series (https://doi.org/10.1594/PANGAEA.880120, https://zenodo.org/record/1139714) and HRSC-AX data products (https://doi.org/10.1594/PANGAEA.884730, https://zenodo.org/record/1145373).

scholarly journals A Visual and VAE Based Hierarchical Indoor Localization Method

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3406
Author(s):  
Jie Jiang ◽  
Yin Zou ◽  
Lidong Chen ◽  
Yujie Fang
Keyword(s):  
Image Retrieval ◽  
Indoor Localization ◽  
Data Sets ◽  
Indoor Environments ◽  
Global Features ◽  
Data Set ◽  
Data Annotation ◽  
Wide Range ◽  
Annotation Costs ◽  
Global And Local

Precise localization and pose estimation in indoor environments are commonly employed in a wide range of applications, including robotics, augmented reality, and navigation and positioning services. Such applications can be solved via visual-based localization using a pre-built 3D model. The increase in searching space associated with large scenes can be overcome by retrieving images in advance and subsequently estimating the pose. The majority of current deep learning-based image retrieval methods require labeled data, which increase data annotation costs and complicate the acquisition of data. In this paper, we propose an unsupervised hierarchical indoor localization framework that integrates an unsupervised network variational autoencoder (VAE) with a visual-based Structure-from-Motion (SfM) approach in order to extract global and local features. During the localization process, global features are applied for the image retrieval at the level of the scene map in order to obtain candidate images, and are subsequently used to estimate the pose from 2D-3D matches between query and candidate images. RGB images only are used as the input of the proposed localization system, which is both convenient and challenging. Experimental results reveal that the proposed method can localize images within 0.16 m and 4° in the 7-Scenes data sets and 32.8% within 5 m and 20° in the Baidu data set. Furthermore, our proposed method achieves a higher precision compared to advanced methods.

scholarly journals From pole to pole: 33 years of physical oceanography onboard R/V <i>Polarstern</i>

2017 ◽  
Vol 9 (1) ◽  
pp. 211-220 ◽  
Author(s):  
Amelie Driemel ◽  
Eberhard Fahrbach ◽  
Gerd Rohardt ◽  
Agnieszka Beszczynska-Möller ◽  
Antje Boetius ◽  
...  
Keyword(s):  
Data Collection ◽  
Water Cycle ◽  
Heat Budget ◽  
Ocean Dynamics ◽  
The Arctic ◽  
Sensor Calibration ◽  
Data Sets ◽  
Calibration Data ◽  
Data Set ◽  
Different Characteristics

Abstract. Measuring temperature and salinity profiles in the world's oceans is crucial to understanding ocean dynamics and its influence on the heat budget, the water cycle, the marine environment and on our climate. Since 1983 the German research vessel and icebreaker Polarstern has been the platform of numerous CTD (conductivity, temperature, depth instrument) deployments in the Arctic and the Antarctic. We report on a unique data collection spanning 33 years of polar CTD data. In total 131 data sets (1 data set per cruise leg) containing data from 10 063 CTD casts are now freely available at doi:10.1594/PANGAEA.860066. During this long period five CTD types with different characteristics and accuracies have been used. Therefore the instruments and processing procedures (sensor calibration, data validation, etc.) are described in detail. This compilation is special not only with regard to the quantity but also the quality of the data – the latter indicated for each data set using defined quality codes. The complete data collection includes a number of repeated sections for which the quality code can be used to investigate and evaluate long-term changes. Beginning with 2010, the salinity measurements presented here are of the highest quality possible in this field owing to the introduction of the OPTIMARE Precision Salinometer.

scholarly journals Global whole-rock geochemical database compilation

2019 ◽  
Author(s):  
Matthew Gard ◽  
Derrick Hasterok ◽  
Jacqueline Halpin
Keyword(s):  
Database Management ◽  
Temporal Trends ◽  
Physical Property ◽  
Database Management System ◽  
Geochemical Data ◽  
Data Sets ◽  
Unique Contribution ◽  
Data Set ◽  
Wide Range ◽  
Geochemical Indices

Abstract. Dissemination and collation of geochemical data are critical to promote rapid, creative and accurate research and place new results in an appropriate global context. To this end, we have assembled a global whole-rock geochemical database, with other associated sample information and properties, sourced from various existing databases and supplemented with numerous individual publications and corrections. Currently the database stands at 1,023,490 samples with varying amounts of associated information including major and trace element concentrations, isotopic ratios, and location data. The distribution both spatially and temporally is quite heterogeneous, however temporal distributions are enhanced over some previous database compilations, particularly in terms of ages older than ~ 1000 Ma. Also included are a wide range of computed geochemical indices, physical property estimates and naming schema on a major element normalized version of the geochemical data for quick reference. This compilation will be useful for geochemical studies requiring extensive data sets, in particular those wishing to investigate secular temporal trends. The addition of physical properties, estimated by sample chemistry, represents a unique contribution to otherwise similar geochemical databases. The data is published in .csv format for the purposes of simple distribution but exists in a format acceptable for database management systems (e.g. SQL). One can either manipulate this data using conventional analysis tools such as MATLAB®, Microsoft® Excel, or R, or upload to a relational database management system for easy querying and management of the data as unique keys already exist. This data set will continue to grow, and we encourage readers to contact us or other database compilations contained within about any data that is yet to be included. The data files described in this paper are available at https://doi.org/10.5281/zenodo.2592823 (Gard et al., 2019).

scholarly journals Panoramic stitching of heterogeneous single-cell transcriptomic data

2018 ◽  
Author(s):  
Brian Hie ◽  
Bryan Bryson ◽  
Bonnie Berger
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Data Sets ◽  
Cell Type ◽  
Data Set ◽  
Wide Range ◽  
Data Set Integration ◽  
Biological Patterns ◽  
Insight Into ◽  
Comprehensive Reference

AbstractResearchers are generating single-cell RNA sequencing (scRNA-seq) profiles of diverse biological systems1–4 and every cell type in the human body.5 Leveraging this data to gain unprecedented insight into biology and disease will require assembling heterogeneous cell populations across multiple experiments, laboratories, and technologies. Although methods for scRNA-seq data integration exist6,7, they often naively merge data sets together even when the data sets have no cell types in common, leading to results that do not correspond to real biological patterns. Here we present Scanorama, inspired by algorithms for panorama stitching, that overcomes the limitations of existing methods to enable accurate, heterogeneous scRNA-seq data set integration. Our strategy identifies and merges the shared cell types among all pairs of data sets and is orders of magnitude faster than existing techniques. We use Scanorama to combine 105,476 cells from 26 diverse scRNA-seq experiments across 9 different technologies into a single comprehensive reference, demonstrating how Scanorama can be used to obtain a more complete picture of cellular function across a wide range of scRNA-seq experiments.

Characterising RDF data sets

2017 ◽  
Vol 44 (2) ◽  
pp. 203-229 ◽  
Author(s):  
Javier D Fernández ◽  
Miguel A Martínez-Prieto ◽  
Pablo de la Fuente Redondo ◽  
Claudio Gutiérrez
Keyword(s):  
Data Structures ◽  
Large Scale ◽  
Open Data ◽  
Structural Features ◽  
Data Sets ◽  
Data Set ◽  
Wide Range ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description

The publication of semantic web data, commonly represented in Resource Description Framework (RDF), has experienced outstanding growth over the last few years. Data from all fields of knowledge are shared publicly and interconnected in active initiatives such as Linked Open Data. However, despite the increasing availability of applications managing large-scale RDF information such as RDF stores and reasoning tools, little attention has been given to the structural features emerging in real-world RDF data. Our work addresses this issue by proposing specific metrics to characterise RDF data. We specifically focus on revealing the redundancy of each data set, as well as common structural patterns. We evaluate the proposed metrics on several data sets, which cover a wide range of designs and models. Our findings provide a basis for more efficient RDF data structures, indexes and compressors.

Descriptive Statistics and Cross-National Comparisons of Arctic Power Projection

2020 ◽  
pp. 56-80
Author(s):  
Jonathan N. Markowitz
Keyword(s):  
Descriptive Statistics ◽  
The Arctic ◽  
Military Force ◽  
Data Sets ◽  
Data Set ◽  
Power Projection ◽  
Resource Rents ◽  
Before And After ◽  
Military Activity ◽  
Events Data

Chapter 4 employs data from three new data sets, the Arctic Military Activity Events Data Set, the Arctic Bases Data Set, and the Icebreaker and Ice-Hardened Warships Data Set. These new data enable a systematic comparison of each state’s Arctic military forces and deployments before and after the 2007 climate shock. The data offer a corrective to both sensationalist media accounts that suggest that all states are scrambling to fight over Arctic resources and those who downplay real changes in states’ Arctic military capabilities and presence. Confirming Rent-Addition’s Theory’s predictions, the descriptive statistical comparisons reveal that the states that were most economically dependent on resource rents, Norway and Russia, were the most willing to back their claims by projecting military force to disputed areas and investing in Arctic bases, ice-hardened warships, and icebreakers.

scholarly journals Revising the retrieval technique of a long-term stratospheric HNO<sub>3</sub> data set: from a constrained matrix inversion to the optimal estimation algorithm

2011 ◽  
Vol 29 (7) ◽  
pp. 1317-1330 ◽  
Author(s):  
I. Fiorucci ◽  
G. Muscari ◽  
R. L. de Zafra
Keyword(s):  
Optimal Estimation ◽  
Matrix Inversion ◽  
The Arctic ◽  
Inversion Method ◽  
Data Sets ◽  
Vertical Profiles ◽  
Mixing Ratio ◽  
Data Set ◽  
The Antarctic

Abstract. The Ground-Based Millimeter-wave Spectrometer (GBMS) was designed and built at the State University of New York at Stony Brook in the early 1990s and since then has carried out many measurement campaigns of stratospheric O3, HNO3, CO and N2O at polar and mid-latitudes. Its HNO3 data set shed light on HNO3 annual cycles over the Antarctic continent and contributed to the validation of both generations of the satellite-based JPL Microwave Limb Sounder (MLS). Following the increasing need for long-term data sets of stratospheric constituents, we resolved to establish a long-term GMBS observation site at the Arctic station of Thule (76.5° N, 68.8° W), Greenland, beginning in January 2009, in order to track the long- and short-term interactions between the changing climate and the seasonal processes tied to the ozone depletion phenomenon. Furthermore, we updated the retrieval algorithm adapting the Optimal Estimation (OE) method to GBMS spectral data in order to conform to the standard of the Network for the Detection of Atmospheric Composition Change (NDACC) microwave group, and to provide our retrievals with a set of averaging kernels that allow more straightforward comparisons with other data sets. The new OE algorithm was applied to GBMS HNO3 data sets from 1993 South Pole observations to date, in order to produce HNO3 version 2 (v2) profiles. A sample of results obtained at Antarctic latitudes in fall and winter and at mid-latitudes is shown here. In most conditions, v2 inversions show a sensitivity (i.e., sum of column elements of the averaging kernel matrix) of 100 ± 20 % from 20 to 45 km altitude, with somewhat worse (better) sensitivity in the Antarctic winter lower (upper) stratosphere. The 1σ uncertainty on HNO3 v2 mixing ratio vertical profiles depends on altitude and is estimated at ~15 % or 0.3 ppbv, whichever is larger. Comparisons of v2 with former (v1) GBMS HNO3 vertical profiles, obtained employing the constrained matrix inversion method, show that v1 and v2 profiles are overall consistent. The main difference is at the HNO3 mixing ratio maximum in the 20–25 km altitude range, which is smaller in v2 than v1 profiles by up to 2 ppbv at mid-latitudes and during the Antarctic fall. This difference suggests a better agreement of GBMS HNO3 v2 profiles with both UARS/ and EOS Aura/MLS HNO3 data than previous v1 profiles.

scholarly journals Near real-time adjusted reanalysis forcing data for hydrology

2017 ◽  
Author(s):  
Peter Berg ◽  
Chantal Donnelly ◽  
David Gustafsson
Keyword(s):  
Real Time ◽  
Arctic Region ◽  
Analysis Data ◽  
Global Scale ◽  
Current Data ◽  
The Arctic ◽  
Data Sets ◽  
Initial State ◽  
Time Step ◽  
Data Set

Abstract. Updating climatological forcing data to near current data are compelling for impact modelling, e.g. to update model simulations or to simulate recent extreme events. Hydrological simulations are generally sensitive to bias in the meteorological forcing data, especially relative to the data used for the calibration of the model. The lack of daily resolution data at a global scale has previously been solved by adjusting re-analysis data global gridded observations. However, existing data sets of this type have been produced for a fixed past time period, determined by the main global observational data sets. Long delays between updates of these data sets leaves a data gap between present and the end of the data set. Further, hydrological forecasts require initialisations of the current state of the snow, soil, lake (and sometimes river) storage. This is normally conceived by forcing the model with observed meteorological conditions for an extended spin-up period, typically at a daily time step, to calculate the initial state. Here, we present a method named GFD (Global Forcing Data) to combine different data sets in order to produce near real-time updated hydrological forcing data that are compatible with the products covering the climatological period. GFD resembles the already established WFDEI method (Weedon et al., 2014) closely, but uses updated climatological observations, and for the near real-time it uses interim products that apply similar methods. This allows GFD to produce updated forcing data including the previous calendar month around the 10th of each month. We present the GFD method and different produced data sets, which are evaluated against the WFDEI data set, as well as with hydrological simulations with the HYPE model over Europe and the Arctic region. We show that GFD performs similarly to WFDEI and that the updated period significantly reduces the bias of the reanalysis data, although less well for the last two months of the updating cycle. For real-time updates until the current day, extending GFD with operational meteorological forecasts, a large drift is present in the hydrological simulations due to the bias of the meteorological forecasting model.

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