A procedure to manage open access data for post-processing in GIS environment

DataBases (DB) are a widespread source of data, useful for many applications in different scientific fields. The present contribution describes an automatic procedure to access, download and store open access data from different sources, to be processed in a GIS environment. In particular, it refers to the specific need of the authors to manage both meteorological data (pressure and temperature) and GNSS (Global Navigation Satellite System) Zenith Total Delay (ZTD) estimates. Such data allow to produce Precipitable Water Vapor (PWV) maps, thanks to the so called GNSS for Meteorology(G4M) procedure, developed through GRASS GIS software ver. 7.4, for monitoring in time and interpreting severe meteorological events. Actually, the present version of the procedure includes the meteorological pressure and temperature data coming from NOAA’s Integrated Surface Database (ISD), whereas the ZTD data derive from the RENAG DB, that collects ZTD estimates for 181 GNSS Permanent Stations (PSs) from 1998 to 2015 in the French-Italian boundary region. Several Python scripts have been implemented to manage the download of data from NOAA and RENAG DBs, their import on a PostgreSQL/PostGIS geoDB, besides the data elaboration with GRASS GIS to produce PWV maps. The key features of the data management procedure are its scalability and versatility for different sources of data and different contexts. As a future development, a web-interface for the procedure will allow an easier interaction for the users both for post-processing and real-time data. The data management procedure repository is available at https://github.com/gtergeomatica/G4M-data

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Tracking open access journals evolution: Some considerations in open access data collection validation

Journal of the American Society for Information Science and Technology ◽

10.1002/asi.20639 ◽

2007 ◽

Vol 58 (11) ◽

pp. 1578-1585 ◽

Cited By ~ 14

Author(s):

Hajar Sotudeh ◽

Abbas Horri

Keyword(s):

Open Access ◽

Data Collection ◽

Open Access Data ◽

Open Access Journals ◽

Access Data

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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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A First High-Resolution Open Access Data and Open Source GIS Model-Prediction for the Globally Threatened Sarus Crane (Antigone antigone) in Nepal: Data Mining of 81 Predictors Support Evidence for Ongoing Declines in Distribution and Abundance

Hindu Kush-Himalaya Watersheds Downhill: Landscape Ecology and Conservation Perspectives ◽

10.1007/978-3-030-36275-1_28 ◽

2020 ◽

pp. 577-591

Author(s):

Dikpal Krishna Karmacharya ◽

Falk Huettmann ◽

Chunrong Mi ◽

Xuesong Han ◽

Rabita Duwal ◽

...

Keyword(s):

Data Mining ◽

High Resolution ◽

Open Access ◽

Open Source ◽

Model Prediction ◽

Gis Model ◽

Open Access Data ◽

Open Source Gis ◽

Access Data ◽

Support Evidence

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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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