Research Data & Outputs

Recent Submissions

Now showing 1 - 10 of 153

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  Microcat, Aquadopp, and ADCP data from the eastern mid-Atlantic ridge mooring array as part of Overturning in the Subpolar North Atlantic Program (OSNAP) from 2020-2022

  (Georgia Institute of Technology, 2025) Johns, William E.

  The University of Miami's OSNAP (Overturning in the subpolar North Atlantic Program) is an NSF funded project that is part of the international OSNAP array put in place to measure the full depth, basin-wide overturning circulation and associated transport of heat and freshwater (www.o-snap.org). The UM Eastern MAR array consists of a series of vertical sub-surface moorings deployed along the eastern slope of the Reykjanes Ridge and across the Iceland Basin near 58°N. This dataset contains data of 2-year deployment (2020-2022) of the Deep Western Boundary Current Array in the Iceland Basin. The data sets are time series of pressure, temperature, salinity and currents and have been fully processed, calibrated and quality controlled.
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  Raw Data for "Development of a novel point-of-care device to monitor arterial thrombosis"

  (Georgia Institute of Technology, 2025-01) Bresette, Christopher

  Raw data related to the paper, "Development of a novel point-of-care device to monitor arterial thrombosis", including Capillary Tube Diameters, Vacuum Pressure Measurements, Multiple Test Variability, End Volume vs Occlusion Time, Control End Volume vs Antiplatelet End Volume, Test Device vs PFA-100 correlation.
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  Data for "Decay and Solid-Liquid Partitioning of Mpox and Vaccinia Viruses in Primary Influent and Settled Solids to Guide Wastewater-Based Epidemiology Practices"

  (Georgia Institute of Technology, 2024) Phaneuf, Jacob R. ; Cha, Gyuhyon ; Hatt, Janet ; Konstantinidis, Kostas T. ; Graham, Katherine E.

  Data in support of the article "Decay and Solid-Liquid Partitioning of Mpox and Vaccinia Viruses in Primary Influent and Settled Solids to Guide Wastewater-Based Epidemiology Practices".
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  Data and CAD files for the article "AI-Driven Universal Lower-Limb Exoskeleton System for Community Ambulation"

  (Georgia Institute of Technology, 2024-11-14) Lee, Dawit ; Lee, Sanghyub ; Young, Aaron

  Research data files and Computer-Aided Design (CAD) files to accompany the article "AI-Driven Universal Lower-Limb Exoskeleton System for Community Ambulation"
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  Predator Induced and Non-Induced Eastern Oyster Shell Thickness Data

  (Georgia Institute of Technology, 2024-08) Roney, Sarah ; Dickinson, Gary ; Belgrad, Ben ; Weissburg, Marc J.

  This data is associated with the study "Eastern oysters minimize costs of inducible defenses by changing shell strengthening mechanism with age". This study tested which mechanism, hardness or thickness, juvenile eastern oysters use to strengthen their shells in response to chemical cues from predators. Data was collected from eastern oysters, Crassostrea virginica, grown in a nursery in Dauphin Island, AL with or without exposure to chemical cues from blue crabs, Callinectes sapidus. Two age groups (four-week and eight-week-old post-settlement) of juveniles were included in this study. Oyster shell thickness overall and within both shell layers was measured.
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  Predator Induced and Non-Induced Eastern Oyster Shell Hardness Data

  (Georgia Institute of Technology, 2024-08) Roney, Sarah ; Dickinson, Gary ; Belgrad, Ben ; Weissburg, Marc J.

  This data is associated with the study "Eastern oysters minimize costs of inducible defenses by changing shell strengthening mechanism with age". Data was collected from eastern oysters, Crassostrea virginica, of two age groups (four-week and eight-week-old) that were induced with chemical cues from blue crabs, Callinectes sapidus, or not induced. Measurements included the Vickers hardness values of foliated and prismatic oyster shell layers, as well as the number and length of cracks resulting from hardness tests.
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  Data for Low-water-permeability foils based on bio-renewable cellulose derivatives

  (Georgia Institute of Technology, 2024-06) Hickman, Tanner J. ; Li, Tao ; Stingelin, Natalie ; Meredith, J. Carson

  Packaging is one of the largest contributors to plastic waste. Hence, polymers produced from renewable sources have become attractive to substitute or fully replace petroleum-based plastics in packaging materials. However, the properties of some of the prime candidates [e.g., cellulose and its derivatives] rapidly deteriorate already at a modest relative humidity rendering them impractical for use in packaging products. Here, we show on the example of carboxymethyl cellulose that chemical crosslinking with citric acid can be exploited to precisely control the moisture sensitivity of cellulose-based structures. Specifically, we demonstrate that the water vapor transmission rate (WVTR) of carboxymethyl cellulose can be manipulated in a controlled fashion over three orders of magnitude. Thereby, the lowest WVTR value, obtained for an optimal crosslinker content, is one order of magnitude lower than that measured for poly(ethylene terephthalate) even at a relatively humidity of up to 65%. Our work, thus, clearly illustrates that cellulose-based materials can be made insensitive to humidity, which is not only of great importance for providing a solution towards more sustainable plastic packaging but, generally, for expanding the scope of applications of cellulose and its derivatives, allowing us to leverage their natural abundance, chemical versatility, and biodegradability. Dataset embargo expires October 1, 2024.
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  Kinematic improvement differs between transradial versus partial hand prosthesis use following interlimb transfer

  (Georgia Institute of Technology, 2024-06) Alterman, Bennett ; Ali, Saif ; Keeton, Emily ; Hendrix, William ; Lee, Jade ; Binkley, Kartina ; Johnson, John ; Wang, Shuo ; Kling, James ; Wheaton, Lewis A.

  Developing empirical approaches to functional rehabilitation during the acute and sub-acute stages following amputation remains an area of need. For persons with unilateral limb loss, interlimb training (ILT) is a potentially attractive approach, as it may allow for prosthesis use learning on the unaffected side while awaiting fitting with the prosthesis on the affected side. Understanding the possible benefits of ILT for functional adaptation with prostheses will be beneficial to our understanding of its utility, particularly across levels of upper-extremity amputation. Persons with sound limbs performed simple and complex reach-to-grasp tasks while wearing either a transradial or partial-hand prosthesis simulator in a 5-day ILT paradigm. We hypothesized that participating in ILT would result in motor improvements, particularly for partial hand device use and during increased task complexity. ILT yielded modest effects for both groups, showing significant increases in reach peak velocity, while only partial-hand users showed decreases in reach duration. Overall, the most notable and consistent effects were seen in partial hand users. These results suggest interlimb training may provide tangible benefit as an indicator of future prosthesis adaptation during early-stage amputation rehabilitation, especially with partial hand loss.
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  Comparison of High-Throughput and Conventional Tensile Testing for 3D-Printed Polymers

  (Georgia Institute of Technology, 2024) Rolle, Javaz T. ; Shoukat, Daniyal ; Park, Jay H. ; Meredith, J. Carson ; Orbey, Nese

  Data files with ultimate tensile strength of 3D printed free standing films at various conditions using the HTMECH as the testing machine.
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  Data for Process-aware part retrieval for cyber manufacturing using unsupervised deep learning

  (Georgia Institute of Technology, 2023-07) Yan, Xiaoliang ; Wang, Zhichao ; Bjorni, Jacob ; Zhao, Changxuan ; Dinar, Mahmoud ; Rosen, David ; Melkote, Shreyes

  Cyber manufacturing service, which connects end users with manufacturers over the internet, is significantly hampered by the lack of an automated part retrieval method. The state-of-the-art is focused on automatic shape retrieval, which does not consider manufacturing process requirements, such as material properties. This paper proposes a manufacturing process-aware part retrieval method using deep unsupervised learning that considers both part shape and material properties. Part retrieval results show that the proposed method yields 93.0% process and function class label matching precision, which outperforms the shape-only part retrieval model and supervised learning models trained with process, function, or both labels.