scholarly journals Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam principles)

2011 ◽  
Vol 5 (11-12) ◽  
pp. 580-589 ◽  
Author(s):  
Christopher R. Kinsinger ◽  
James Apffel ◽  
Mark Baker ◽  
Xiaopeng Bian ◽  
Christoph H. Borchers ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Open Access ◽  
Data Quality ◽  
Quality Metrics ◽  
Mass Spectrometry Data ◽  
Open Access Data ◽  
Data Quality Metrics ◽  
Access Data

scholarly journals Recommendations for Mass Spectrometry Data Quality Metrics for Open Access Data (Corollary to the Amsterdam Principles)

2011 ◽  
Vol 11 (2) ◽  
pp. 1412-1419 ◽  
Author(s):  
Christopher R. Kinsinger ◽  
James Apffel ◽  
Mark Baker ◽  
Xiaopeng Bian ◽  
Christoph H. Borchers ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Open Access ◽  
Data Quality ◽  
Quality Metrics ◽  
Mass Spectrometry Data ◽  
Open Access Data ◽  
Data Quality Metrics ◽  
Access Data

scholarly journals Recommendations for Mass Spectrometry Data Quality Metrics for Open Access Data (Corollary to the Amsterdam Principles)

2011 ◽  
Vol 10 (12) ◽  
pp. O111.015446 ◽  
Author(s):  
Christopher R. Kinsinger ◽  
James Apffel ◽  
Mark Baker ◽  
Xiaopeng Bian ◽  
Christoph H. Borchers ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Open Access ◽  
Data Quality ◽  
Quality Metrics ◽  
Mass Spectrometry Data ◽  
Open Access Data ◽  
Data Quality Metrics ◽  
Access Data

scholarly journals Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam principles)

PROTEOMICS ◽  
2011 ◽  
Vol 12 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Christopher R. Kinsinger ◽  
James Apffel ◽  
Mark Baker ◽  
Xiaopeng Bian ◽  
Christoph H. Borchers ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Open Access ◽  
Data Quality ◽  
Quality Metrics ◽  
Mass Spectrometry Data ◽  
Open Access Data ◽  
Data Quality Metrics ◽  
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.

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

An Algebraic Approach to Data Quality Metrics for Entity Resolution over Large Datasets

2008 ◽  
pp. 3067-3084
Author(s):  
John Talburt ◽  
Richard Wang ◽  
Kimberly Hess ◽  
Emily Kuo
Keyword(s):  
Data Quality ◽  
Algebraic Approach ◽  
Entity Resolution ◽  
Quality Metrics ◽  
Quality Literature ◽  
Intrinsic Quality ◽  
Recognition Systems ◽  
Entity Identification ◽  
Data Quality Metrics

This chapter introduces abstract algebra as a means of understanding and creating data quality metrics for entity resolution, the process in which records determined to represent the same real-world entity are successively located and merged. Entity resolution is a particular form of data mining that is foundational to a number of applications in both industry and government. Examples include commercial customer recognition systems and information sharing on “persons of interest” across federal intelligence agencies. Despite the importance of these applications, most of the data quality literature focuses on measuring the intrinsic quality of individual records than the quality of record grouping or integration. In this chapter, the authors describe current research into the creation and validation of quality metrics for entity resolution, primarily in the context of customer recognition systems. The approach is based on an algebraic view of the system as creating a partition of a set of entity records based on the indicative information for the entities in question. In this view, the relative quality of entity identification between two systems can be measured in terms of the similarity between the partitions they produce. The authors discuss the difficulty of applying statistical cluster analysis to this problem when the datasets are large and propose an alternative index suitable for these situations. They also report some preliminary experimental results, and outlines areas and approaches to further research in this area.

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