Open-Access Data and Toolbox for Tracking COVID-19 Impact on Power Systems

Intervention policies against COVID-19 have caused large-scale disruptions globally, and led to a series of pattern changes in the power system operation. Analyzing these pandemic-induced patterns is imperative to identify the potential risks and impacts of this extreme event. With this purpose, we developed an open-access data hub (COVID-EMDA+), an open-source toolbox (CoVEMDA), and a few evaluation methods to explore what the U.S. power systems are experiencing during COVID-19. These resources could be broadly used for research, policy making, or educational purposes. Technically, our data hub harmonizes a variety of raw data such as generation mix, demand profiles, electricity price, weather observations, mobility, confirmed cases and deaths. Several support methods and metrics are then implemented in our toolbox, including baseline estimation, regression analysis, and scientific visualization. Based on these, we conduct three empirical studies on the U.S. power systems and markets to introduce some new solutions and unexpected findings. This conveys a more complete picture of the pandemic's impacts, and also opens up several attractive topics for future work. Python, Matlab source codes, and user manuals are all publicly shared on a Github repository.

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Open-Access Data and Toolbox for Tracking COVID-19 Impact on Power Systems

10.36227/techrxiv.17156354.v1 ◽

2021 ◽

Author(s):

Guangchun Ruan ◽

Zekuan Yu ◽

Shutong Pu ◽

Songtao Zhou ◽

Haiwang Zhong ◽

...

Keyword(s):

Open Access ◽

Power Systems ◽

Large Scale ◽

Extreme Event ◽

Research Policy ◽

Empirical Studies ◽

Open Access Data ◽

Unexpected Findings ◽

Access Data ◽

The U.S

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

10.5194/egusphere-egu21-4735 ◽

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 &#8211; and thus ecologically valuable &#8211; 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>

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

10.7287/peerj.preprints.3010v1 ◽

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.

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

10.7287/peerj.preprints.3010 ◽

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., in press; Poldrack and Gorgolewski, 2014; Van Horn and Gazzaniga, 2013; Van Horn and Toga, 2014; Vogelstein et al., 2016; Voytek, 2016), as well as in other fields (Ascoli et al., 2017; Choudhury et al., 2014; Davies et al., 2017), this opinion paper is focused specifically on the implications of open data to brain morphology research.

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Review of: "Finding citations for PubMed: A large-scale comparison between five open access data sources"

Qeios ◽

10.32388/xclpb8 ◽

2021 ◽

Author(s):

Silvio Peroni

Keyword(s):

Open Access ◽

Large Scale ◽

Data Sources ◽

Open Access Data ◽

Access Data

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Open Source Riverscapes: Analyzing the Corridor of the Naryn River in Kyrgyzstan Based on Open Access Data

Remote Sensing ◽

10.3390/rs12162533 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2533 ◽

Cited By ~ 1

Author(s):

Florian Betz ◽

Magdalena Lauermann ◽

Bernd Cyffka

Keyword(s):

Remote Sensing ◽

Open Access ◽

Large Scale ◽

Scale Analysis ◽

Hierarchical Systems ◽

Stream Power ◽

Spatial And Temporal Scales ◽

Open Access Data ◽

Access Data ◽

River Corridor

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 widely ignored so far, even if most remaining free flowing rivers are located in such areas. In this study, we suggest an approach for river corridor mapping based on open access data only, in order to foster large-scale analysis of river systems in data-scarce areas. We take the more than 600 km long Naryn River in Kyrgyzstan as an example, and demonstrate the potential of the SRTM-1 elevation model and Landsat OLI imagery in the automated mapping of various riverscape parameters, like the riparian zone extent, distribution of riparian vegetation, active channel width and confinement, as well as stream power. For each parameter, a rigor validation is performed to evaluate the performance of the applied datasets. The results demonstrate that our approach to riverscape mapping is capable of providing sufficiently accurate results for reach-averaged parameters, and is thus well-suited to large-scale river corridor assessment in data-scarce regions. Rather than an ultimate solution, we see this remote sensing approach as part of a multi-scale analysis framework with more detailed investigation in selected study reaches.

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

10.7287/peerj.preprints.3010v2 ◽

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., in press; Poldrack and Gorgolewski, 2014; Van Horn and Gazzaniga, 2013; Van Horn and Toga, 2014; Vogelstein et al., 2016; Voytek, 2016), as well as in other fields (Ascoli et al., 2017; Choudhury et al., 2014; Davies et al., 2017), this opinion paper is focused specifically on the implications of open data to brain morphology research.

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