The ICOS Carbon Portal as example of a  FAIR community data repository supporting scientific workflows

2021 ◽  
Author(s):  
Alex Vermeulen ◽  
Margareta Hellström ◽  
Oleg Mirzov ◽  
Ute Karstens ◽  
Claudio D'Onofrio ◽  
...  
Keyword(s):  
Observational Data ◽  
Eddy Covariance ◽  
Physical Geography ◽  
Scientific Workflows ◽  
Quality Data ◽  
Data Repository ◽  
Sparql Endpoint ◽  
Triple Store ◽  
Data Products

<p>The Integrated Carbon Observation System (ICOS) provides long term, high quality observations that follow (and cooperatively set) the global standards for the best possible quality data on the atmospheric composition for greenhouse gases (GHG), greenhouse gas exchange fluxes measured by eddy covariance and CO<sub>2</sub> partial pressure at water surfaces. The ICOS observational data feeds into a wide area of science that covers for example plant physiology, agriculture, biology, ecology, energy & fuels, forestry, hydrology, (micro)meteorology, environmental, oceanography, geochemistry, physical geography, remote sensing, earth-, climate-, soil- science and combinations of these in multi-disciplinary projects.<br>As ICOS is committed to provide all data and methods in an open and transparent way as free data, a dedicated system is needed to secure the long term archiving and availability of the data together with the descriptive metadata that belongs to the data and is needed to find, identify, understand and properly use the data, also in the far future, following the FAIR data principles. An added requirement is that the full data lifecycle should be completely reproducible to enable full trust in the observations and the derived data products.</p><p>In this presentation we will introduce the ICOS operational data repository named ICOS Carbon Portal that is based on the linked open data approach. All metadata is modelled in an ontology coded in OWL and based on a RDF triple store that is available through an open SparQL endpoint. The repository supports versioning, collections and models provenance through a simplified Prov-O ontology. All data objects are ingested under strict control for the identified data types on provision of the correct and sufficient (provenance) metadata, data format and data integrity. All data, including raw data, is stored in the long term trusted repository  B2SAFE with two replicates. On top of the triple store and SparQL endpoint we have built a series of services, APIs and graphical interfaces that allow machines to machine and user interaction with the data and metadata. Examples are a full faceted search with connected data cart and download facility, preview of higher level data products (time series of  point observations and spatial data), and cloud computing services like eddy covariance data processing and on demand atmospheric footprint calculations, all connected to the observational data from ICOS.  Another interesting development is the community support for scientific workflows using Jupyter notebook services that connect to our repository through a dedicated python library for direct metadata and data access.</p>

A NEO Physical Properties database within the NEOROCKS EU project

2020 ◽  
Author(s):  
Alessio Giunta ◽  
Marco Giardino ◽  
Ettore Perozzi ◽  
Gianluca Polenta ◽  
Angelo Zinzi ◽  
...  
Keyword(s):  
Physical Properties ◽  
Planetary Science ◽  
Physical Characterization ◽  
Data Repository ◽  
Physical Parameters ◽  
Wide Field ◽  
Horizon 2020 ◽  
Data Products ◽  
Eu Project

<p>The advent of new wide field, ground-based and multiwavelength space based sky surveys will lead to a large amount of data that needs to be efficiently processed, archived and disseminated. In addition, differently from astrometric observations which have a centralized data repository acting under IAU mandate (the MPC), the outcome of ground-based NEO observations devoted to NEO physical characterization are sparsely distributed. It appears then desirable to have data on NEO physical characterization available through a centralized access able to guarantee their long-term archiving, as well as to ensure the maintenance and the evolution of the corresponding data products.  </p> <p> </p> <p>Within the NEOROCKS EU project (“The NEO Rapid Observation, Characterization and Key Simulations” - SU-SPACE-23-SEC-2019 from the Horizon 2020), as part of WP5 (Data Management) activities, we propose the implementation of a unique NEO Physical Properties database hosting all different data products resulting from NEO observations devoted to physical characterization, in order to ensure an efficient data products dissemination and their short/long-term storage and availability. The NEOROCKS database, will be designed by means of an EPNCore derived data model (see [1]) ready for the EPN-TAP service implementation, and thus able to store, maintain and regularly update all different levels of processing, from raw data to final products (e.g. size, rotation, spectral type) beyond the duration of the project as an reliable source of services and data on NEO physical properties hosted at ASI SSDC.</p> <p> </p> <p>The NEOROCKS database will import NEO orbital elements from the Near-Earth Object Dynamics Site (NEODyS), while NEO physical parameters will be partly provided by NEOROCKS users, partly imported from external data source. In particular, the NEO physical properties database available at the ESA NEO Coordination Center, hosting since 2013 the legacy of the European Asteroid Research Node (EARN) and which will host Solar System Objects (SSO) NEO physical properties in the Gaia DR3 expected for the second half of 2021, will be imported and integrated into the NEOROCKS Physical Properties Database. Thus, a single query interface will allow to display both dynamical and physical properties of any given NEO, or to search for samples within the NEO population satisfying certain requirements (e.g. targets for astronomical observations and mission analysis).</p> <p> </p> <p><strong>Acknowledgements</strong>: The LICIACube team acknowledges financial support from Agenzia Spaziale Italiana (ASI, contract No. 2019-31-HH.0 CUP F84I190012600).</p> <p> </p> <p> </p> <p><strong>References</strong></p> <p> </p> <p>[1] Erard S., Cecconi B., Le Sidaner P., Berthier J., Henry F., Molinaro M., Giardino M., Bourrel N., Andre N., Gangloff M., Jacquey C., Topf F. 2014. The EPN-TAP protocol for the Planetary Science Virtual Observatory (2014). Astronomy And Computing, vol. 7-8, p. 52-61, ISSN: 2213-1337, doi: 10.1016/j.ascom.2014.07.008</p>

scholarly journals MODIS and VIIRS Calibration and Characterization in Support of Producing Long-Term High-Quality Data Products

2020 ◽  
Vol 12 (19) ◽  
pp. 3167
Author(s):  
Xiaoxiong Xiong ◽  
Amit Angal ◽  
Tiejun Chang ◽  
Kwofu Chiang ◽  
Ning Lei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Quality ◽  
Infrared Imaging ◽  
Quality Data ◽  
Earth Remote Sensing ◽  
Research Activities ◽  
The Earth ◽  
Data Products ◽  
Term Data

Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) have successfully operated since their launches in 1999 and 2002, respectively, and generated various data products to support the Earth remote sensing disciplines and users worldwide for their research activities and applications, including studies of the Earth system, and its changes over time and geographic regions. The MODIS data have also significantly contributed to the continuity of multi-decadal satellite data records and led to major advances in the Earth remote sensing field. The long-term data records from MODIS observations have been and will continue to be extended by the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments, currently operated aboard the Suomi-National Polar-Orbiting Partnership (NPP) and NOAA-20 satellites. The data quality of satellite instruments strongly depends on their calibration accuracy and stability. In order to help scientists and users gain a better understanding of MODIS and VIIRS data quality, this paper provides an overview of their on-orbit calibration methodologies, approaches, and results derived from instrument on-board calibrators and lunar observations, as well as select Earth view targets. What is also discussed is the calibration consistency between MODIS and VIIRS and its potential impact on producing multi-sensor long-term data records. As illustrated, the overall performance of both MODIS and VIIRS continues to meet their design requirements.

scholarly journals The Diamond League athletic series: does the air quality sparkle?

2021 ◽  
Author(s):  
James R. Hodgson ◽  
Lee Chapman ◽  
Francis D. Pope
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Performance Metrics ◽  
Urban Air Pollution ◽  
Quality Data ◽  
Performance Variation ◽  
Short And Long Term ◽  
Respiratory Conditions ◽  
Race Performance

AbstractUrban air pollution can have negative short- and long-term impacts on health, including cardiovascular, neurological, immune system and developmental damage. The irritant qualities of pollutants such as ozone (O3), nitrogen dioxide (NO2) and particulate matter (PM) can cause respiratory and cardiovascular distress, which can be heightened during physical activity and particularly so for those with respiratory conditions such as asthma. Previously, research has only examined marathon run outcomes or running under laboratory settings. This study focuses on elite 5-km athletes performing in international events at nine locations. Local meteorological and air quality data are used in conjunction with race performance metrics from the Diamond League Athletics series to determine the extent to which elite competitors are influenced during maximal sustained efforts in real-world conditions. The findings from this study suggest that local meteorological variables (temperature, wind speed and relative humidity) and air quality (ozone and particulate matter) have an impact on athletic performance. Variation between finishing times at different race locations can also be explained by the local meteorology and air quality conditions seen during races.

scholarly journals Chances and challenges of a long-term data repository in multiple sclerosis: 20th birthday of the German MS registry

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lisa-Marie Ohle ◽  
David Ellenberger ◽  
Peter Flachenecker ◽  
Tim Friede ◽  
Judith Haas ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Sociodemographic Factors ◽  
Healthcare Research ◽  
Structure Design ◽  
Data Repository ◽  
Research Projects ◽  
Real World Data ◽  
Data Repositories ◽  
Term Data

AbstractIn 2001, the German Multiple Sclerosis Society, facing lack of data, founded the German MS Registry (GMSR) as a long-term data repository for MS healthcare research. By the establishment of a network of participating neurological centres of different healthcare sectors across Germany, GMSR provides observational real-world data on long-term disease progression, sociodemographic factors, treatment and the healthcare status of people with MS. This paper aims to illustrate the framework of the GMSR. Structure, design and data quality processes as well as collaborations of the GMSR are presented. The registry’s dataset, status and results are discussed. As of 08 January 2021, 187 centres from different healthcare sectors participate in the GMSR. Following its infrastructure and dataset specification upgrades in 2014, more than 196,000 visits have been recorded relating to more than 33,000 persons with MS (PwMS). The GMSR enables monitoring of PwMS in Germany, supports scientific research projects, and collaborates with national and international MS data repositories and initiatives. With its recent pharmacovigilance extension, it aligns with EMA recommendations and helps to ensure early detection of therapy-related safety signals.

scholarly journals The on-premise data sharing infrastructure e!DAL: Foster FAIR data for faster data acquisition

GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
Author(s):  
Daniel Arend ◽  
Patrick König ◽  
Astrid Junker ◽  
Uwe Scholz ◽  
Matthias Lange
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Best Practice ◽  
Research Process ◽  
Research Data ◽  
Primary Data ◽  
Data Repository ◽  
Plant Genomics ◽  
Quality Of Data

Abstract Background The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. Although the ELIXIR Core Data Resources and other established infrastructures provide comprehensive and long-term stable services and platforms for FAIR data management, a large quantity of research data is still hidden or at risk of getting lost. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, e.g., time series of images or high-resolution hyper-spectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institional boundaries. Results To share these potentially dark data in a FAIR way and master these challenges the ELIXIR Germany/de.NBI service Plant Genomic and Phenomics Research Data Repository (PGP) implements a “bring the infrastructure to the data” approach, which allows research data to be kept in place and wrapped in a FAIR-aware software infrastructure. This article presents new features of the e!DAL infrastructure software and the PGP repository as a best practice on how to easily set up FAIR-compliant and intuitive research data services. Furthermore, the integration of the ELIXIR Authentication and Authorization Infrastructure (AAI) and data discovery services are introduced as means to lower technical barriers and to increase the visibility of research data. Conclusion The e!DAL software matured to a powerful and FAIR-compliant infrastructure, while keeping the focus on flexible setup and integration into existing infrastructures and into the daily research process.

scholarly journals Broken Worlds: Towards an Archaeology of the Shatter Zone

2021 ◽  
Author(s):  
Ben Raffield
Keyword(s):  
Eighteenth Century ◽  
Global Scale ◽  
Physical Geography ◽  
Trigger Factors ◽  
Eastern Woodlands ◽  
Term Change ◽  
Anthropological Research ◽  
Historic Change

AbstractIn recent years, archaeological studies of long-term change and transformation in the human past have often been dominated by the discussion of dichotomous processes of ‘collapse’ and ‘resilience’. These discussions are frequently framed in relatively narrow terms dictated by specialist interests that place an emphasis on the role of single ‘trigger’ factors as motors for historic change. In order to address this issue, in this article I propose that the study of the ‘shatter zone’—a term with origins in physical geography and geopolitics that has been more recently harnessed in anthropological research—has the potential to facilitate multi-scalar, interdisciplinary analyses of the ways in which major historical changes unfold across both space and time, at local, regional, and inter-regional levels. This article unpacks the concept of the shatter zone and aligns this with existing archaeological frameworks for the study of long-term adaptive change. I then situate these arguments within the context of recent studies of colonial interaction and conflict in the Eastern Woodlands of North America during the sixteenth to eighteenth century. The study demonstrates how a more regulated approach to the shatter zone has the potential to yield new insights on the ways in which populations mitigate and react to instability and change while also facilitating comparative studies of these processes on a broader, global scale.

scholarly journals Combined Correlation and Cluster Analysis for Long-Term Power Quality Data from Virtual Power Plant

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 641
Author(s):  
Michał Jasiński
Keyword(s):  
Cluster Analysis ◽  
Power Plant ◽  
Power Quality ◽  
Quality Data ◽  
Virtual Power ◽  
Distributed Resources ◽  
Virtual Power Plant ◽  
And Cluster Analysis ◽  
Global Indicators

Analysis of the connection between different units that operate in the same area assures always interesting results. During this investigation, the concerned area was a virtual power plant (VPP) that operates in Poland. The main distributed resources included in the VPP are a 1.25 MW hydropower plant and an associated 0.5 MW energy storage system. The mentioned VPP was a source of synchronic, long-term, multipoint power quality (PQ) data. Then, for five related measurement points, the conclusion about the relation in point of PQ was performed using correlation analysis, the global index approach, and cluster analysis. Global indicators were applied in place of PQ parameters to reduce the amount of analyzed data and to check the correlation between phase values. For such a big dataset, the occurrence of outliers is certain, and outliers may affect the correlation results. Thus, to find and exclude them, cluster analysis (k-means algorithm, Chebyshev distance) was applied. Finally, the correlation between PQ global indicators of different measurement points was performed. It assured general information about VPP units’ relation in point of PQ. Under the investigation, both Pearson’s and Spearman’s rank correlation coefficients were considered.

scholarly journals Modeling Building Stock Development

2021 ◽  
Vol 13 (2) ◽  
pp. 723
Author(s):  
Antti Kurvinen ◽  
Arto Saari ◽  
Juhani Heljo ◽  
Eero Nippala
Keyword(s):  
Model Development ◽  
Housing Demand ◽  
Quality Data ◽  
Building Stock ◽  
Test Arena ◽  
Climate Targets ◽  
Wide Range ◽  
Key Variables ◽  
The Impact

It is widely agreed that dynamics of building stocks are relatively poorly known even if it is recognized to be an important research topic. Better understanding of building stock dynamics and future development is crucial, e.g., for sustainable management of the built environment as various analyses require long-term projections of building stock development. Recognizing the uncertainty in relation to long-term modeling, we propose a transparent calculation-based QuantiSTOCK model for modeling building stock development. Our approach not only provides a tangible tool for understanding development when selected assumptions are valid but also, most importantly, allows for studying the sensitivity of results to alternative developments of the key variables. Therefore, this relatively simple modeling approach provides fruitful grounds for understanding the impact of different key variables, which is needed to facilitate meaningful debate on different housing, land use, and environment-related policies. The QuantiSTOCK model may be extended in numerous ways and lays the groundwork for modeling the future developments of building stocks. The presented model may be used in a wide range of analyses ranging from assessing housing demand at the regional level to providing input for defining sustainable pathways towards climate targets. Due to the availability of high-quality data, the Finnish building stock provided a great test arena for the model development.

scholarly journals Analyzing Individual-Level Secondary Data with Instrumental Variable Methods Is Useful for Studying the Effects of Air Pollution on Dementia

2021 ◽  
Vol 79 (1) ◽  
pp. 15-23
Author(s):  
Kelly C. Bishop ◽  
Sehba Husain-Krautter ◽  
Jonathan D. Ketcham ◽  
Nicolai V. Kuminoff ◽  
Corbett Schimming
Keyword(s):  
Air Pollution ◽  
Instrumental Variable ◽  
Secondary Data ◽  
Quality Data ◽  
Long Term Effects ◽  
Individual Level ◽  
Medicare Claims Data ◽  
Advance Knowledge ◽  
Air Quality Data

We hypothesize that analyzing individual-level secondary data with instrumental variable (IV) methods can advance knowledge of the long-term effects of air pollution on dementia. We discuss issues in measurement using secondary data and how IV estimation can overcome biases due to measurement error and unmeasured variables. We link air-quality data from the Environmental Protection Agency’s monitors with Medicare claims data to illustrate the use of secondary data to document associations. Additionally, we describe results from a previous study that uses an IV for pollution and finds that PM2.5’s effects on dementia are larger than non-causal associations.

Sign in / Sign up

Export Citation Format

Share Document