INDATA, ENVISIONING & PROTOTYPING INFORMED BY KNOWLEDGE. OPEN-ACCESS DATA PLATFORM TO SUPPORT DESIGN SCENARIOS

2019 ◽  
Author(s):  
Ilaria Mariani ◽  
Stefano Parisi ◽  
Patrizia Bolzan ◽  
Mil Stepanovic ◽  
Michele Invernizzi ◽  
...  
Keyword(s):  
Open Access ◽  
Support Design ◽  
Open Access Data ◽  
Data Platform ◽  
Access Data

Open source riverscapes: Analyzing the river corridor of the Naryn River in Kyrgyzstan based on open access data

2021 ◽  
Author(s):  
Florian Betz ◽  
Magdalena Lauermann ◽  
Bernd Cyffka
Keyword(s):  
Remote Sensing ◽  
Open Access ◽  
Large Scale ◽  
Data Sets ◽  
Hierarchical Systems ◽  
Geomorphological Mapping ◽  
Open Access Data ◽  
Access Data ◽  
River Corridors ◽  
River Corridor

<p>In fluvial geomorphology as well as in freshwater ecology, rivers are commonly seen as nested hierarchical systems functioning over a range of spatial and temporal scales. Thus, for a comprehensive assessment, information on various scales is required. Over the past decade, remote sensing based approaches have become increasingly popular in river science to increase the spatial scale of analysis. However, data-scarce areas have been mostly ignored so far despite the fact that most remaining free flowing – and thus ecologically valuable – rivers worldwide are located in regions characterized by a lack of data sources like LiDAR or even aerial imagery. High resolution satellite data would be able to fill this data gap, but tends to be too costly for large scale applications what limits the ability for comprehensive studies on river systems in such remote areas. This in turn is a limitation for management and conservation of these rivers.</p><p>In this contribution, we suggest an approach for river corridor mapping based on open access data only in order to foster large scale geomorphological mapping of river corridors in data-scarce areas. For this aim, we combine advanced terrain analysis with multispectral remote sensing using the SRTM-1 DEM along with Landsat OLI imagery. We take the Naryn River in Kyrgyzstan as an example to demonstrate the potential of these open access data sets to derive a comprehensive set of parameters for characterizing this river corridor. The methods are adapted to the specific characteristics of medium resolution open access data sets and include an innovative, fuzzy logic based approach for riparian zone delineation, longitudinal profile smoothing based on constrained quantile regression and a delineation of the active channel width as needed for specific stream power computation. In addition, an indicator for river dynamics based on Landsat time series is developed. For each derived river corridor parameter, a rigor validation is performed. The results demonstrate, that our open access approach for geomorphological mapping of river corridors is capable to provide results sufficiently accurate to derive reach averaged information. Thus, it is well suited for large scale river characterization in data-scarce regions where otherwise the river corridors would remain largely unexplored from an up-to-date riverscape perspective. Such a characterization might be an entry point for further, more detailed research in selected study reaches and can deliver the required comprehensive background information for a range of topics in river science.</p>

scholarly journals Advances in studying brain morphology: The benefits of open-access data

2017 ◽  
Author(s):  
Christopher R Madan
Keyword(s):  
Open Access ◽  
Brain Structure ◽  
Large Scale ◽  
Open Data ◽  
Brain Morphology ◽  
Subcortical Structures ◽  
Open Access Data ◽  
Age Related ◽  
Neuroimaging Data ◽  
Access Data

Until recently, neuroimaging data for a research study needed to be collected within one’s own lab. However, when studying inter-individual differences in brain structure, a large sample of participants is necessary. Given the financial costs involved in collecting neuroimaging data from hundreds or thousands of participants, large-scale studies of brain morphology could previously only be conducted by well-funded laboratories with access to MRI facilities and to large samples of participants. With the advent of broad open-access data-sharing initiatives, this has recently changed–here the primary goal of the study is to collect large datasets to be shared, rather than sharing of the data as an afterthought. This paradigm shift is evident as increase in the pace of discovery, leading to a rapid rate of advances in our characterization of brain structure. The utility of open-access brain morphology data is numerous, ranging from observing novel patterns of age-related differences in subcortical structures to the development of more robust cortical parcellation atlases, with these advances being translatable to improved methods for characterizing clinical disorders (see Figure 1 for an illustration). Moreover, structural MRIs are generally more robust than functional MRIs, relative to potential artifacts and in being not task-dependent, resulting in large potential yields. While the benefits of open-access data have been discussed more broadly within the field of cognitive neuroscience elsewhere (Gilmore et al., 2017; Poldrack and Gorgolewski, 2014; Van Horn and Gazzaniga, 2013; Voytek, 2016), as well as in other fields (Ascoli et al., 2017; Choudhury et al., 2014; Davies et al., 2017), the current paper is focused specifically on the implications of open data to brain morphology research.

scholarly journals Erratum: Korkovelos, A., et al. The Role of Open Access Data in Geospatial Electrification Planning and the Achievement of SDG7. An OnSSET-Based Case Study for Malawi. Energies 2019, 12(7), 1395

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5044
Author(s):  
Alexandros Korkovelos ◽  
Babak Khavari ◽  
Andreas Sahlberg ◽  
Mark Howells ◽  
Christopher Arderne ◽  
...  
Keyword(s):  
Open Access ◽  
Open Access Data ◽  
Access Data

The authors wish to make a change in author names (adding new author—Dimitrios Mentis) to this paper [...]

scholarly journals The Workflow Trace Archive: Open-Access Data From Public and Private Computing Infrastructures

2020 ◽  
Vol 31 (9) ◽  
pp. 2170-2184
Author(s):  
Laurens Versluis ◽  
Roland Matha ◽  
Sacheendra Talluri ◽  
Tim Hegeman ◽  
Radu Prodan ◽  
...  
Keyword(s):  
Open Access ◽  
Public And Private ◽  
Open Access Data ◽  
Trace Archive ◽  
Access Data

scholarly journals Cascadia fore arc seismic survey: Open-access data available

Eos ◽  
2012 ◽  
Vol 93 (50) ◽  
pp. 521-522 ◽  
Author(s):  
W. Steven Holbrook ◽  
Graham Kent ◽  
Katie Keranen ◽  
H. Paul Johnson ◽  
Anne Trehu ◽  
...  
Keyword(s):  
Open Access ◽  
Seismic Survey ◽  
Open Access Data ◽  
Access Data

The Mid-Atlantic Watershed Atlas (MAWA): Open access data search & watershed-based community building

2010 ◽  
Vol 25 (6) ◽  
pp. 808-812 ◽  
Author(s):  
Patrick M. Reed ◽  
Brian Bills ◽  
Michael Anderson ◽  
Blake Ketchum ◽  
Michael Piasecki
Keyword(s):  
Open Access ◽  
Community Building ◽  
Open Access Data ◽  
Data Search ◽  
Access Data

scholarly journals Estimation and Mapping of Forest Structure Parameters from Open Access Satellite Images: Development of a Generic Method with a Study Case on Coniferous Plantation

2019 ◽  
Vol 11 (11) ◽  
pp. 1275 ◽  
Author(s):  
David Morin ◽  
Milena Planells ◽  
Dominique Guyon ◽  
Ludovic Villard ◽  
Stéphane Mermoz ◽  
...  
Keyword(s):  
Feature Selection ◽  
Open Access ◽  
Forest Structure ◽  
Data Sources ◽  
Machine Learning Techniques ◽  
Structure Parameters ◽  
Alos Palsar ◽  
Open Access Data ◽  
Coniferous Plantation ◽  
Access Data

Temperate forests are under climatic and economic pressures. Public bodies, NGOs and the wood industry are looking for accurate, current and affordable data driven solutions to intensify wood production while maintaining or improving long term sustainability of the production, biodiversity, and carbon sequestration. Free tools and open access data have already been exploited to produce accurate quantitative forest parameters maps suitable for policy and operational purposes. These efforts have relied on different data sources, tools, and methods that are tailored for specific forest types and climatic conditions. We hypothesized we could build on these efforts in order to produce a generic method suitable to perform as well or better in a larger range of settings. In this study we focus on building a generic approach to create forest parameters maps and confirm its performance on a test site: a maritime pine (Pinus pinaster) forest located in south west of France. We investigated and assessed options related with the integration of multiple data sources (SAR L- and C-band, optical indexes and spatial texture indexes from Sentinel-1, Sentinel-2 and ALOS-PALSAR-2), feature extraction, feature selection and machine learning techniques. On our test case, we found that the combination of multiple open access data sources has synergistic benefits on the forest parameters estimates. The sensibility analysis shows that all the data participate to the improvements, that reach up to 13.7% when compared to single source estimates. Accuracy of the estimates is as follows: aboveground biomass (AGB) 28% relative RMSE, basal area (BA) 27%, diameter at breast height (DBH) 20%, age 17%, tree density 24%, and height 13%. Forward feature selection and SVR provided the best estimates. Future work will focus on validating this generic approach in different settings. It may prove beneficial to package the method, the tools, and the integration of open access data in order to make spatially accurate and regularly updated forest structure parameters maps effortlessly available to national bodies and forest organizations.

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