scholarly journals Towards an interoperability framework for observable property terminologies

2020 ◽  
Author(s):  
Barbara Magagna ◽  
Gwenaelle Moncoiffe ◽  
Anusuriya Devaraju ◽  
Pier Luigi Buttigieg ◽  
Maria Stoica ◽  
...  
Keyword(s):  
Working Group ◽  
Design Patterns ◽  
Best Practice ◽  
Scientific Data ◽  
Environmental Data ◽  
Environmental Research ◽  
Environmental Sciences ◽  
Data Annotation ◽  
Observable Property ◽  
Research Communities

<p>In October 2019, a new working group (InteroperAble Descriptions of Observable Property Terminology or I-ADOPT WG<sup>1</sup>) officially launched its 18-month workplan under the auspices of the Research Data Alliance (RDA) co-led by ENVRI-FAIR<sup>2</sup> project members. The goal of the group is to develop a community-wide, consensus framework for representing observable properties and facilitating semantic mapping between disjoint terminologies used for data annotation. The group has been active for over two years and comprises research communities, data centers, and research infrastructures from environmental sciences. The WG members have been heavily involved in developing or applying terminologies to semantically enrich the descriptions of measured, observed, derived, or computed environmental data. They all recognize the need to enhance interoperability between their efforts through the WG’s activities.</p><p>Ongoing activities of the WG include gathering user stories from research communities (Task 1), reviewing related terminologies and current annotation practices (Task 2) and - based on this - defining and iteratively refining requirements for a community-wide semantic interoperability framework (Task 3). Much like a generic blueprint, this framework will be a basis upon which terminology developers can formulate local design patterns while at the same time remaining globally aligned. This framework will assist interoperability between machine-actionable complex property descriptions observed across the environmental sciences, including Earth, space, and biodiversity science. The WG will seek to synthesize well-adopted but still disparate approaches into global best practice recommendations for improved alignment. Furthermore, the framework will help mediate between generic observation standards (O&M<sup>3</sup>, SSNO<sup>4</sup>, SensorML<sup>5</sup>, OBOE<sup>6</sup>, ..) and current community-led terminologies and annotation practices, fostering harmonized implementations of observable property descriptions. Altogether, the WG’s work will boost the Interoperability component of the FAIR principles (especially principle I3) by encouraging convergence and by enriching the terminologies with qualified references to other resources. We envisage that this will greatly enhance the global effectiveness and scope of tools operating across terminologies. The WG will thus strengthen existing collaborations and build new connections between terminology developers and providers, disciplinary experts, and representatives of scientific data user groups. </p><p>In this presentation, we introduce the working group to the EGU community, and invite them to join our efforts. We report the methodology applied, the results from our first three tasks and the first deliverable, namely a catalog of domain-specific terminologies in use in environmental research, which will enable us to systematically compare existing resources for building the interoperability framework. </p><p><sup>1</sup>https://www.rd-alliance.org/groups/interoperable-descriptions-observable-property-terminology-wg-i-adopt-wg<br><sup>2</sup>https://envri.eu/home-envri-fair/<br><sup>3</sup>https://www.iso.org/standard/32574.html<br><sup>4</sup>https://www.w3.org/TR/vocab-ssn/<br><sup>5</sup>https://www.opengeospatial.org/standards/sensorml<br><sup>6</sup>https://github.com/NCEAS/oboe/</p>

V-FOR-WaTer: A Virtual Research Environment for Environmental Research

2021 ◽  
Author(s):  
Marcus Strobl ◽  
Elnaz Azmi ◽  
Sibylle K. Hassler ◽  
Mirko Mälicke ◽  
Jörg Meyer ◽  
...  
Keyword(s):  
Data Access ◽  
Environmental Data ◽  
Environmental Research ◽  
Easy Access ◽  
Web Portal ◽  
Environmental Sciences ◽  
Research Environment ◽  
Data Services ◽  
Virtual Research Environment ◽  
The Web

<p>The virtual research environment V-FOR-WaTer aims at simplifying data access for environmental sciences, fostering data publications and facilitating data analyses. By giving scientists from universities, research facilities and state offices easy access to data, appropriate pre-processing and analysis tools and workflows, we want to accelerate scientific work and facilitate the reproducibility of analyses.</p><p>The prototype of the virtual research environment consists of a database with a detailed metadata scheme that is adapted to water and terrestrial environmental data. Present datasets in the web portal originate from university projects and state offices. We are also finalising the connection of V-FOR-WaTer to GFZ Data Services, an established repository for geoscientific data. This will ease publication of data from the portal and in turn give access to datasets stored in this repository. Key to being compatible with GFZ Data Services and other systems is the compliance of the metadata scheme with international standards (INSPIRE, ISO19115).</p><p>The web portal is designed to facilitate typical workflows in environmental sciences. Map operations and filter options ensure easy selection of the data, while the workspace area provides tools for data pre-processing, scaling, and common hydrological applications. The toolbox also contains more specific tools, e.g. for geostatistics and soon for evapotranspiration. It is easily extendable and will ultimately also include user-developed tools, reflecting the current research topics and methodologies in the hydrology community. Tools are accessed through Web Processing Services (WPS) and can be joined, saved and shared as workflows, enabling more complex analyses and ensuring reproducibility of the results.</p>

The I-ADOPT Interoperability Framework: a proposal for FAIRer observable property descriptions

2021 ◽  
Author(s):  
Barbara Magagna ◽  
Gwenaelle Moncoiffe ◽  
Maria Stoica ◽  
Anusuriya Devaraju ◽  
Alison Pamment ◽  
...  
Keyword(s):  
Design Patterns ◽  
Management Practices ◽  
Spatial Scales ◽  
Biodiversity Loss ◽  
Building Blocks ◽  
Data Annotation ◽  
Observable Property ◽  
Metadata Standards ◽  
Research Infrastructures ◽  
Common Framework

<p>Global environmental challenges like climate change, pollution, and biodiversity loss are complex. To understand environmental patterns and processes and address these challenges, scientists require the observations of natural phenomena at various temporal and spatial scales and across many domains. The research infrastructures and scientific communities involved in these activities are often following their own data management practices which inevitably leads to a high degree of variability and incompatibility of approaches. Consequently, a variety of metadata standards and vocabularies have been proposed to describe observations and are actively used in different communities. However, this diversity in approaches now causes severe issues regarding the interoperability across datasets and hampers their exploitation as a common data source.</p><p>Projects like ENVRI-FAIR, FAIRsFAIR, FAIRplus are addressing this difficulty by working on the full integration of services across research infrastructures based on FAIR Guiding Principles supporting the EOSC vision towards an open research culture. Beyond these projects, we need collaboration and community consensus across domains to build a common framework for representing observable properties. The Research Data Alliance InteroperAble Descriptions of Observable Property Terminology Working Group (RDA I-ADOPT WG) was formed in October 2019 to address this need. Its membership covers an international representation of terminology users and terminology providers, including terminology developers, scientists, and data centre managers. The group’s overall objective is to deliver a common interoperability framework for observable property variables within its 18-month work plan. Starting with the collection of user stories from research scientists, terminology managers, and data managers or aggregators, we drafted a set of technical and content-related requirements. A survey of terminology resources and annotation practices provided us with information about almost one hundred terminologies, a subset of which was then analysed to identify existing conceptualisation practices, commonalities, gaps, and overlaps. This was then used to derive a conceptual framework to support their alignment. </p><p>In this presentation, we will introduce the I-ADOPT Interoperability Framework highlighting its semantic components. These represent the building blocks for specific ontology design patterns addressing different use cases and varying degrees of complexity in describing observed properties. We will demonstrate the proposed design patterns using a number of essential climate and essential biodiversity variables. We will also show examples of how the I-ADOPT framework will support interoperability between existing representations. This work will provide the semantic foundation for the development of more user-friendly data annotation tools capable of suggesting appropriate FAIR terminologies for observable properties.</p>

Hack the Arctic: transforming data into solutions as a community  

2021 ◽  
Author(s):  
Stephany Buenrostro Mazon ◽  
Magdalena Brus ◽  
Katri Ahlgren ◽  
Alexander Mahura ◽  
Hanna K. Lappalainen ◽  
...  
Keyword(s):  
Scientific Data ◽  
Research Data ◽  
Environmental Data ◽  
Environmental Research ◽  
The Arctic ◽  
Observation System ◽  
Research Projects ◽  
System Research ◽  
Government Sector ◽  
International Projects

<p>A recurring question among research projects is how to optimize the use data that already exists and to identify its stakeholder’s needs, particularly in effort to bring services to a wider community outside academia. We propose a hackathon to allow the collaboration between civil, educational, business and governmental actors to address environmental challenges with the use of environment scientific data from international projects.</p><p>Hack the Arctic is co-organized by the Institute for Atmospheric and Earth System Research (INAR)/University of Helsinki, the Integrated Carbon Observation System Research Infrastructure (ICOS-ERIC) Headoffice, and the Environmental Research Infrastructures (ENVRI) Community. The hackathon event aims to enhance the usage and impact of environmental research data by and for society. The 48 hr event will gather multi-disciplinary teams through a public call to make use of existing environmental data from a network of research projects to develop services addressing the needs of different end-users. The participating teams will be mentored by researchers and data scientist in the use of the data. A panel of judges comprising of science mentors, innovation specialists and government sector actors will assess the implementation of the final pilot products at the end of the event.</p><p>We present Hack the Arctic as an up-and-coming alternative to expand the usage and visibility of research data and to make it widely accessible to a broader (nonacademic) audience by offering mentorship from data and scientific experts under one roof.</p>

scholarly journals What If the ‘Anthropocene’ Is Not Formalized as a New Geological Series/Epoch?

Quaternary ◽  
2018 ◽  
Vol 1 (3) ◽  
pp. 24 ◽  
Author(s):  
Valentí Rull
Keyword(s):  
Present State ◽  
Executive Committee ◽  
Working Group ◽  
Recent Literature ◽  
Special Issue ◽  
Environmental Sciences ◽  
International Union ◽  
Quaternary Stratigraphy ◽  
Wide Range ◽  
Geological Sciences

In the coming years, the Anthropocene Working Group (AWG) will submit its proposal on the ‘Anthropocene’ to the Subcommission of Quaternary Stratigraphy (SQS) and the International Commission on Stratigraphy (ICS) for approval. If approved, the proposal will be sent to the Executive Committee of the International Union of Geological Sciences (IUGS) for ratification. If the proposal is approved and ratified, then the ‘Anthropocene’ will be formalized. Currently, the ‘Anthropocene’ is a broadly used term and concept in a wide range of scientific and non-scientific situations, and, for many, the official acceptance of this term is only a matter of time. However, the AWG proposal, in its present state, seems to not fully meet the requirements for a new chronostratigraphic unit. This essay asks what could happen if the current ‘Anthropocene’ proposal is not formalized by the ICS/IUGS. The possible stratigraphic alternatives are evaluated on the basis of the more recent literature and the personal opinions of distinguished AWG, SQS, and ICS members. The eventual impact on environmental sciences and on non-scientific sectors, where the ‘Anthropocene’ seems already firmly rooted and de facto accepted as a new geological epoch, are also discussed. This essay is intended as the editorial introduction to a Quaternary special issue on the topic.

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

scholarly journals Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)

2021 ◽  
Vol 18 (3) ◽  
pp. 1333
Author(s):  
Ahmad R. Alsaber ◽  
Jiazhu Pan ◽  
Adeeba Al-Hurban 
Keyword(s):  
Air Quality ◽  
Missing Data ◽  
Random Forest ◽  
Missing Values ◽  
Imputation Method ◽  
Environmental Data ◽  
Environmental Research ◽  
Quality Data ◽  
Data Set ◽  
Air Quality Data

In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for NO2 (18.4%), CO (18.5%), PM10 (57.4%), SO2 (19.0%), and O3 (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.

scholarly journals Consensus for the General Use of Equine Water Treadmills for Healthy Horses

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 305
Author(s):  
Kathryn Nankervis ◽  
Carolyne Tranquille ◽  
Persephone McCrae ◽  
Jessica York ◽  
Morgan Lashley ◽  
...  
Keyword(s):  
Working Group ◽  
Best Practice ◽  
Treadmill Exercise ◽  
Good Practice ◽  
Belt Speed ◽  
Research Programmes ◽  
Potential Benefits ◽  
Water Treadmill ◽  
Selection Of

Water treadmill exercise has become popular in recent years for the training and rehabilitation of equine athletes. In 2019, an equine hydrotherapy working group was formed to establish what was commonly considered to be best practice in the use of the modality. This article describes the process by which general guidelines for the application of water treadmill exercise in training and rehabilitation programmes were produced by the working group. The guidelines describe the consensus reached to date on (1) the potential benefits of water treadmill exercise, (2) general good practice in water treadmill exercise, (3) introduction of horses to the exercise, (4) factors influencing selection of belt speed, water depth and duration of exercise, and (5) monitoring movement on the water treadmill. The long-term goal is to reach a consensus on the optimal use of the modality within a training or rehabilitation programme. Collaboration between clinicians, researchers and experienced users is needed to develop research programmes and further guidelines regarding the most appropriate application of the modality for specific veterinary conditions.

scholarly journals Development of a Best Practice Statement on the Use of Ankle-Foot Orthoses Following Stroke in Scotland

2010 ◽  
Vol 34 (3) ◽  
pp. 245-253 ◽  
Author(s):  
Roy Bowers ◽  
Karyn Ross
Keyword(s):  
Health Service ◽  
Working Group ◽  
Best Practice ◽  
Foot Orthoses ◽  
Ankle Foot Orthoses ◽  
Expert Working Group ◽  
Health Service Quality ◽  
The Impact ◽  
Successes And Challenges

A National Health Service Quality Improvement Scotland (NHS QIS) scoping exercise in 2007 identified the use of ankle-foot orthoses (AFOs) following stroke as a clinical improvement priority, leading to the development of a best practice statement (BPS) on AFO use after stroke. This paper outlines the development process of the BPS which is available from NHS QIS. The authors were involved as part of a working group that included practitioners from the fields of orthotics, physiotherapy, stroke nursing and bioengineering, staff of NHS QIS and a patient representative. In consultation with an NHS QIS health services researcher, the authors undertook a systematic literature review to evidence where possible the recommendations made in the BPS. Where evidence was unavailable, consensus was reached by the expert working group. As the BPS was designed for the non-specialist and non-orthotic practitioner the authors also developed educational resources which were included within the BPS to aid the understanding of the principles underpinning orthotic design and prescription. The BPS has been widely distributed throughout the health service in Scotland and is available electronically at no cost via the NHS QIS website. As part of an ongoing evaluation of the impact of the BPS on the quality of orthotic provision, NHS QIS has invited feedback regarding successes and challenges to implementation.

scholarly journals The Science DMZ: A Network Design Pattern for Data-Intensive Science

2014 ◽  
Vol 22 (2) ◽  
pp. 173-185 ◽  
Author(s):  
Eli Dart ◽  
Lauren Rotman ◽  
Brian Tierney ◽  
Mary Hester ◽  
Jason Zurawski
Keyword(s):  
Network Design ◽  
Network Architecture ◽  
Design Patterns ◽  
Data Transfer ◽  
Scientific Discovery ◽  
High Capacity ◽  
Scientific Progress ◽  
Scientific Data ◽  
Data Intensive ◽  
And Performance

The ever-increasing scale of scientific data has become a significant challenge for researchers that rely on networks to interact with remote computing systems and transfer results to collaborators worldwide. Despite the availability of high-capacity connections, scientists struggle with inadequate cyberinfrastructure that cripples data transfer performance, and impedes scientific progress. The ScienceDMZparadigm comprises a proven set of network design patterns that collectively address these problems for scientists. We explain the Science DMZ model, including network architecture, system configuration, cybersecurity, and performance tools, that creates an optimized network environment for science. We describe use cases from universities, supercomputing centers and research laboratories, highlighting the effectiveness of the Science DMZ model in diverse operational settings. In all, the Science DMZ model is a solid platform that supports any science workflow, and flexibly accommodates emerging network technologies. As a result, the Science DMZ vastly improves collaboration, accelerating scientific discovery.

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