NHERI

The Impact of Data Reuse

Scholarly Citations of DesignSafe


Another way of measuring the impact of DesignSafe is by identifying research papers that cite the use of DesignSafe or the data available at DesignSafe. Table 1 lists DesignSafe citations since 2018 as determined from papers identified via Google Alerts. The first column represents papers that make any reference to DesignSafe through citation of the DesignSafe marker paper (Rathje et al. 2017) or through the acknowledgements. The next column represents papers in which a researcher cites their own data in DesignSafe as a part of the original research project, and the third column represents papers that re-use data available in DesignSafe after the original project is over. Note that a paper may contribute to multiple columns in Table 1. For instance, a data re-use paper may also reference the marker paper, or a paper may cite more than one dataset. There is a meaningful number of total citations that reference the use of DesignSafe and the data published in DesignSafe. While Google Alerts may not capture all of the citations and mentions of DesignSafe datasets that are available in the literature, the positive trend highlights the value of publishing data, the importance of citing data in the references using DOIs, and the types of research being conducted using data published in DesignSafe.

Year

DesignSafe
Citation

Primary
Data Use

Subsequent
Data Reuse

Totals

2021

24

45

37

112

2020

52

74

61

187

2019

21

25

30

76

2018

26

31

13

70

Data Reuse Case Studies


Data reuse can take many forms: expanding work on established lines of research, validating numerical models, testing new hypotheses, and more. To learn more about the motivations for data reuse we interview DesignSafe data consumers and develop Data Reuse Stories. The researchers interviewed give us insight into not only the variety of data available from DesignSafe that has been reused, but also the distinct ways those data can be reused across different fields within the NHERI community. Furthermore, these researchers demonstrate that DesignSafe can act as an invaluable platform for collaboration across institutions and fields of interests within natural hazards research, facilitated by the easy access to published datasets and works-in-progress in the Data Depot. These research groups valued the overall completeness of the datasets and the metadata offered within the Data Depot platform.


AI AND MACHINE LEARNING

Structural Health Monitoring Framework (Sajedi and Liang 2019)

Recent work by researchers from the University of Buffalo has shown the value of continuing to preserve and provide access to legacy data from NEES through the DesignSafe Data Depot. Seyed Omid Sajedi and Xiao Liang (2019) repurposed shake table experimental data from “Seismic Performance Assessment and Retrofit of Non-Ductile RC Frames with Infill Walls” (Shing et. al. 2007) to evaluate the effectiveness of a structural health monitoring framework they developed. This framework identifies the existence, probable location, and severity of damage in a structure following an earthquake, and has the potential to provide information about building damage accurately, dependably, and quickly for the affected communities. Read more >>

 

Predicting Wind Pressure Coefficients (Tian et al. 2020)

DesignSafe resources, especially the Data Depot publication pipeline, have proven invaluable to wind researchers. Scholars out of the University of Florida and Clarkson University (Tian et al. 2020) were able to reuse experimental data from “Upwind Terrain Effects on Low-Rise Building Pressure Loading Observed in the Boundary Layer Wind Tunnel'' (Fernández-Cabán and Masters 2018) that was published in the Data Depot. Read more >>

 


VALIDATION OF NUMERICAL APPROACH

Integrated seismic response predictions (Erazo and Nagarajaiah 2019)

Dr. Kalil Erazo and his colleagues from Rice University and Tufts University have repeatedly used data from the experimental project “Shake Table Response of Full Scale Reinforced Concrete Building Slice” (Panagiotou et al. 2013, https://doi.org/10.4231/D35T3G04T) to validate a Bayesian-modeling approach that couples experimental measurements with a numerical model of the system. Read more >>

 

Hysteretic finite element model of structural beams (Amir et al. 2020)

Legacy data from the NEES cyberinfrastructure available in DesignSafe continues to prove useful for current NHERI researchers, including those seeking to validate new computational modeling frameworks. Researchers out of Penn State’s Institute for Computer and Data Sciences (Amir et al. 2020) reused experimental data on buckling at connections in steel structures from “Ultra-Low Cycle Fatigue and Fracture in Steel Structures” (Kanvinde et al. 2005) to validate one component of their hysteretic beam finite-element model. Read more >>

 

Seismic Response of Cut and Cover Tunnels (Sadiq et al. 2019)

Availability of open, curated datasets are fundamental to those researchers who do not have the resources to conduct large-scale experiments. Such is the case for Dr. Duhee Park and his students at Hanyang University in South Korea. Their research group studies various effects of earthquakes on tunnels, and they were looking for experimental data regarding the seismic response of a tunnel system. In their published research (Sadiq et al. 2019) they used published data from Gillis et al. (2014) from a geotechnical centrifuge test that investigated the seismic response of a shallow cut and cover tunnel in sand to validate a widely used Equivalent linear (EQL) analysis approach for seismic analysis of tunnels. Read more >>

 


MODEL DEVELOPMENT

Wind Effects on Elevated Buildings (Kim et al. 2020)

The DesignSafe Data Depot not only serves the NHERI community by providing access to scholarship and datasets, but also by acting as a means for researchers to collaborate through sharing and combining datasets. Elaina Sutley of the University of Kansas and Arindam Chowdhury of Florida International University, along with other colleagues (Kim et al. 2020), merged field damage survey data and experimental data collected separately to gain a better understanding of wind effects on coastal elevated buildings. Read more >>

 

Observed Hurricane Damage to Manufactured Homes (Sutley et al. 2020)

Wind researchers have used DesignSafe to access and merge their own data with the data of others to produce new works that fill substantial gaps in the existing literature. Researchers from the University of Kansas and University of Alabama (Sutley et al. 2020) merged subsets of field reconnaissance data from two separate hurricanes from “RAPID: Assessing the Performance of Elevated Wood Buildings, including Manufactured Housing, in Florida during 2018 Hurricane Michael'' (Sutley et al. 2019) and “RAPID: A Coordinated Structural Engineering Response to Hurricane Irma (in Florida)” (Kijewski-Correa et. al. 2018). Read more >>

 

Statistical models for shear wave velocity profiles (Bahrampouri et al. 2019)

Field characterization datasets such as the subsurface geotechnical dataset from the DesignSafe project “Dynamic Characterization of Wellington, New Zealand" project (Cox and Vantassel 2018, https://doi.org/10.17603/DS24M6J) can be exploited for a variety of purposes. Although the data collected by Cox and Vantassel (2018) was collected to understanding earthquake shaking in Wellington, New Zealand from the 2016 Kaikoura earthquake, Dr. Rodriguez-Marek and his colleagues at Virginia Tech used the data to represent the statistical uncertainty of the shear wave velocity (Vs) at a site for use in ground response analysis. Read more >>