13,752 research outputs found
GIS Readiness Survey 2014
The GIS Readiness Survey 2014 is a follow-up to the corresponding survey that was carried out among public institutions in Denmark in 2009. The present survey thus provides an updated image of status and challenges in relation to the use of spatial information, the construction of the common infrastructure for spatial information, and the work related to the further development of the foundation for the digital administration. One of the thought-provoking trends is that INSPIRE seems to be discussed less in the organisations. On the other hand, there is no doubt that standards continue to be considered of great significance, not least in relation to metadata, data quality and data specifications, just as spatial data are clearly being communicated more and more
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Open Science principles for accelerating trait-based science across the Tree of Life.
Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles-open data, open source and open methods-is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges
Scalable Model-Based Management of Correlated Dimensional Time Series in ModelarDB+
To monitor critical infrastructure, high quality sensors sampled at a high
frequency are increasingly used. However, as they produce huge amounts of data,
only simple aggregates are stored. This removes outliers and fluctuations that
could indicate problems. As a remedy, we present a model-based approach for
managing time series with dimensions that exploits correlation in and among
time series. Specifically, we propose compressing groups of correlated time
series using an extensible set of model types within a user-defined error bound
(possibly zero). We name this new category of model-based compression methods
for time series Multi-Model Group Compression (MMGC). We present the first MMGC
method GOLEMM and extend model types to compress time series groups. We propose
primitives for users to effectively define groups for differently sized data
sets, and based on these, an automated grouping method using only the time
series dimensions. We propose algorithms for executing simple and
multi-dimensional aggregate queries on models. Last, we implement our methods
in the Time Series Management System (TSMS) ModelarDB (ModelarDB+). Our
evaluation shows that compared to widely used formats, ModelarDB+ provides up
to 13.7 times faster ingestion due to high compression, 113 times better
compression due to the adaptivity of GOLEMM, 630 times faster aggregates by
using models, and close to linear scalability. It is also extensible and
supports online query processing.Comment: 12 Pages, 28 Figures, and 1 Tabl
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