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Sharing data and research in a time of global pandemic, Part 2

[Header image from Fischer, E., Fischer, M., Grass, D., Henrion, I., Warren, W., Westman, E. (2020, August 07). Low-cost measurement of facemask efficacy for filtering expelled droplets during speech. Science Advances. https://advances.sciencemag.org/content/early/2020/08/07/sciadv.abd3083]

Back in March, just as things were rapidly shutting down across the United States, I wrote a post reflecting on how integral the practice of sharing and preserving research data would be to any solution to the crisis posed by COVID-19. While some of the language in that post seems a bit naive in retrospect (particularly the bit about RDAP’s annual meeting being one of the last in-person conferences of just the spring, as opposed to the entire calendar year!), the emphasis on the importance of rapid and robust data sharing has stood the test of time. In late June, the Research Data Alliance released a set of recommendations and guidelines for sharing research data under circumstances shaped by COVID-19, and a number of organizations, including the National Institutes of Health, have established portals for finding data related to the disease. Access to data has been forefront in the minds of many researchers.

Perhaps in response to this general sentiment (or maybe because folks haven’t been able to access their labs?!), we in the Libraries have seen a notable increase in the number of submissions to our Research Data Repository for data publication. These datasets have derived from a broad range of disciplines, spanning Environmental Sciences to Dermatology. I wanted to use this blog post as an opportunity to highlight a few of our accessions from the last several months.

One of our most prolific sources of data deposits has historically been the lab of Dr. Patrick Charbonneau, associate professor of Chemistry and Physics. Dr. Charbonneau’s lab investigates glass and its physical properties and contributes to a project known as The Simons Collaboration on Cracking the Glass Problem, which addresses issues like disorder, nonlinear response and far-from-equilibrium dynamics. The most recent contribution from Dr. Charbonneau’s research group, published just last week, is fairly characteristic of the materials we receive from Dr. Charbonneau’s group. It contains the raw binary observational data and scripts that were used to create the figures which appear in the researcher’s article. Making these research products available helps other scholars to repeat or reproduce (and thereby strengthen) the findings elucidated in an associated research publication.

Fig01 / Fig02b, Data from: Finite-dimensional vestige of spinodal criticality above the dynamical glass transition

 

Another recent data deposit—a first of its kind for the RDR—is a Q-sort concourse for the Human Dimensions of Large Marine Protected Areas project, which investigates the formulation of large marine protected areas (defined by the project as “any ocean area larger than 100,000 km² that has been designated for the purpose of conservation”) as a global movement. Q-methodology is a psychology and social sciences research method used to study viewpoints. In this study, 40 interviewees were asked to evaluate statements related to large-scale marine protected areas. Q-sorts can be particularly helpful when researchers wish to describe subjective viewpoints related to an issue.

Q sort record sheet from: Q-Sort Concourse and Data for the Human Dimensions of Large MPAs project

Finally, perhaps our most timely deposit has come from a group investigating an alternate method to evaluate the efficacy of masks to reduce the transmission of respiratory droplets during regular speech. “Low-cost measurement of facemask efficacy for filtering expelled droplets during speech,” published last week in Science Advances, is a proof-of-concept study that proposes an optical measurement technique that the group asserts is both inexpensive and easy to use. Because the topic of measuring mask efficiency is still both complex and unsettled, the group hopes this work will help improve evaluation in order to guide mask selection and policy decisions.

Screenshot of Speaker1_None_05.mp4, Video data from: Low-cost measurement of facemask efficacy for filtering expelled droplets during speech

The dataset consists of a series of movie recordings, that capture an operator wearing a face mask and speaking in the direction of an expanded laser beam inside a dark enclosure. Droplets that propagate through the laser beam scatter light, which is then recorded with a cell phone camera. The group tested 12 kinds of masks (see below), and recorded 2 sets of controls with no masks. 

Figure 2 from Low-cost measurement of facemask efficacy for filtering expelled droplets during speech

We hope to keep up the momentum our data management, curation, and publication program has gained over the last few months, but we need your help! For more information on using the Duke Research Data Repository to share and preserve your data, please visit our website, or drop up a line at datamangement@duke.edu. A full list of the datasets we’ve published since moving to fully remote operations in March is available below.

  • Zhang, Y. (2020). Data from: Contributions of World Regions to the Global Tropospheric Ozone Burden Change from 1980 to 2010. Duke Research Data Repository. https://doi.org/10.7924/r40p13p11
  • Campbell, L. M., Gray, N., & Gruby, R. (2020). Data from: Q-Sort Concourse and Data for the Human Dimensions of Large MPAs project. Duke Research Data Repository. https://doi.org/10.7924/r4j38sg3b
  • Berthier, L., Charbonneau, P., & Kundu, J. (2020). Data from: Finite-dimensional vestige of spinodal criticality above the dynamical glass transition. Duke Research Data Repository. https://doi.org/10.7924/r4jh3m094
  • Fischer, E., Fischer, M., Grass, D., Henrion, I., Warren, W., Westman, E. (2020). Video data files from: Low-cost measurement of facemask efficacy for filtering expelled droplets during speech. Duke Research Data Repository. V2 https://doi.org/10.7924/r4ww7dx6q
  • Lin, Y., Kouznetsova, T., Chang, C., Craig, S. (2020). Data from: Enhanced polymer mechanical degradation through mechanochemically unveiled lactonization. Duke Research Data Repository. V2 https://doi.org/10.7924/r4fq9x365
  • Chavez, S. P., Silva, Y., & Barros, A. P. (2020). Data from: High-elevation monsoon precipitation processes in the Central Andes of Peru. Duke Research Data Repository. V2 https://doi.org/10.7924/r41n84j94
  • Jeuland, M., Ohlendorf, N., Saparapa, R., & Steckel, J. (2020). Data from: Climate implications of electrification projects in the developing world: a systematic review. Duke Research Data Repository. https://doi.org/10.7924/r42n55g1z
  • Cardones, A. R., Hall, III, R. P., Sullivan, K., Hooten, J., Lee, S. Y., Liu, B. L., Green, C., Chao, N., Rowe Nichols, K., Bañez, L., Shah, A., Leung, N., & Palmeri, M. L. (2020). Data from: Quantifying skin stiffness in graft-versus-host disease, morphea and systemic sclerosis using acoustic radiation force impulse imaging and shear wave elastography. Duke Research Data Repository. https://doi.org/10.7924/r4h995b4q
  • Caves, E., Schweikert, L. E., Green, P. A., Zipple, M. N., Taboada, C., Peters, S., Nowicki, S., & Johnsen, S. (2020). Data and scripts from: Variation in carotenoid-containing retinal oil droplets correlates with variation in perception of carotenoid coloration. Duke Research Data Repository. https://doi.org/10.7924/r4jw8dj9h
  • DiGiacomo, A. E., Bird, C. N., Pan, V. G., Dobroski, K., Atkins-Davis, C., Johnston, D. W., Ridge, J. T. (2020). Data from: Modeling salt marsh vegetation height using Unoccupied Aircraft Systems and Structure from Motion. Duke Research Data Repository. https://doi.org/10.7924/r4w956k1q
  • Hall, III, R. P., Bhatia, S. M., Streilein, R. D. (2020). Data from: Correlation of IgG autoantibodies against acetylcholine receptors and desmogleins in patients with pemphigus treated with steroid sparing agents or rituximab. Duke Research Data Repository. https://doi.org/10.7924/r4rf5r157
  • Jin, Y., Ru, X., Su, N., Beratan, D., Zhang, P., & Yang, W. (2020). Data from: Revisiting the Hole Size in Double Helical DNA with Localized Orbital Scaling Corrections. Duke Research Data Repository. https://doi.org/10.7924/r4k072k9s
  • Kaleem, S. & Swisher, C. B. (2020). Data from: Electrographic Seizure Detection by Neuro ICU Nurses via Bedside Real-Time Quantitative EEG. Duke Research Data Repository. https://doi.org/10.7924/r4mp51700
  • Yi, G. & Grill, W. M. (2020). Data and code from: Waveforms optimized to produce closed-state Na+ inactivation eliminate onset response in nerve conduction block. Duke Research Data Repository. https://doi.org/10.7924/r4z31t79k
  • Flanagan, N., Wang, H., Winton, S., Richardson, C. (2020). Data from: Low-severity fire as a mechanism of organic matter protection in global peatlands: thermal alteration slows decomposition. Duke Research Data Repository. https://doi.org/10.7924/r4s46nm6p
  • Gunsch, C. (2020). Data from: Evaluation of the mycobiome of ballast water and implications for fungal pathogen distribution. Duke Research Data Repository. https://doi.org/10.7924/r4t72cv5v
  • Warnell, K., & Olander, L. (2020). Data from: Opportunity assessment for carbon and resilience benefits on natural and working lands in North. Carolina. Duke Research Data Repository. https://doi.org/10.7924/r4ww7cd91

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