Publications

[1]
C. Jay et al., “Theory-Software Translation: Research Challenges and Future Directions,” arXiv:1910.09902 [cs], Oct. 2019 [Online]. Available: http://arxiv.org/abs/1910.09902. [Accessed: 23-Oct-2019]
[1]
E. A. Huerta et al., “Enabling real-time multi-messenger astrophysics discoveries with deep learning,” Nat Rev Phys, vol. 1, no. 10, pp. 600–608, Oct. 2019 [Online]. Available: https://www.nature.com/articles/s42254-019-0097-4. [Accessed: 03-Oct-2019]
[1]
A. E. Ahmed et al., “Managing genomic variant calling workflows with Swift/T,” PLOS ONE, vol. 14, no. 7, p. e0211608, Jul. 2019 [Online]. Available: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0211608. [Accessed: 10-Jul-2019]
[1]
D. S. Katz, K. McHenry, C. Reinking, and R. Haines, “Research Software Development Management in Universities: Case Studies from Manchester’s RSDS Group, Illinois’ NCSA, and Notre Dame’s CRC,” in 2019 IEEE/ACM 14th International Workshop on Software Engineering for Science (SE4Science), 2019, pp. 17–24.
[1]
D. S. Katz, S. Druskat, R. Haines, C. Jay, and A. Struck, “The State of Sustainable Research Software: Learning from the Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE5.1),” Journal of Open Research Software, vol. 7, no. 1, p. 11, Apr. 2019 [Online]. Available: http://openresearchsoftware.metajnl.com/articles/10.5334/jors.242/. [Accessed: 02-Apr-2019]
[1]
E. A. Huerta, R. Haas, S. Jha, M. Neubauer, and D. S. Katz, “Supporting High-Performance and High-Throughput Computing for Experimental Science,” Comput Softw Big Sci, vol. 3, no. 1, p. 5, Feb. 2019 [Online]. Available: https://doi.org/10.1007/s41781-019-0022-7. [Accessed: 08-Feb-2019]
[1]
D. Sholler, K. Ram, C. Boettiger, and D. S. Katz, “Enforcing public data archiving policies in academic publishing: A study of ecology journals,” Big Data & Society, vol. 6, no. 1, p. 2053951719836258, Jan. 2019 [Online]. Available: https://doi.org/10.1177/2053951719836258. [Accessed: 01-Apr-2019]
[1]
M. Barker et al., “The global impact of science gateways, virtual research environments and virtual laboratories,” Future Generation Computer Systems, 2019 [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0167739X18314018. [Accessed: 04-Jan-2019]
[1]
Y. Babuji et al., “Scalable Parallel Programming in Python with Parsl,” in Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (Learning), New York, NY, USA, 2019, pp. 22:1–22:8 [Online]. Available: http://doi.acm.org/10.1145/3332186.3332231. [Accessed: 04-Sep-2019]
[1]
Y. Babuji et al., “Parsl: Pervasive Parallel Programming in Python,” in Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing, New York, NY, USA, 2019, pp. 25–36 [Online]. Available: http://doi.acm.org/10.1145/3307681.3325400. [Accessed: 13-Jul-2019]
[1]
D. S. Katz et al., “The principles of tomorrow’s university,” F1000Research, vol. 7, p. 1926, Dec. 2018 [Online]. Available: https://f1000research.com/articles/7-1926/v1. [Accessed: 04-Jan-2019]
[1]
M. D. Hildreth et al., “HEP Software Foundation Community White Paper Working Group - Data and Software Preservation to Enable Reuse,” arXiv:1810.01191 [physics], Oct. 2018 [Online]. Available: http://arxiv.org/abs/1810.01191. [Accessed: 09-Oct-2018]
[1]
S. Druskat and D. S. Katz, “Mapping the Research Software Sustainability Space,” in 2018 IEEE 14th International Conference on e-Science (e-Science), 2018, pp. 25–30.
[1]
J. Cohen, D. S. Katz, M. Barker, R. Haines, and N. C. Hong, “Building a Sustainable Structure for Research Software Engineering Activities,” in 2018 IEEE 14th International Conference on e-Science (e-Science), 2018, pp. 31–32.
[1]
M. L. Mondelli et al., “BioWorkbench: a high-performance framework for managing and analyzing bioinformatics experiments,” PeerJ, vol. 6, p. e5551, Aug. 2018 [Online]. Available: https://peerj.com/articles/5551. [Accessed: 29-Aug-2018]
[1]
D. S. Katz and N. P. C. Hong, “Software Citation in Theory and Practice,” in Mathematical Software – ICMS 2018, 2018, pp. 289–296 [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-319-96418-8_34. [Accessed: 21-Jul-2018]
[1]
H. S. Foundation et al., “HEP Software Foundation Community White Paper Working Group - Training, Staffing and Careers,” arXiv:1807.02875 [hep-ex, physics:physics], Jul. 2018 [Online]. Available: http://arxiv.org/abs/1807.02875. [Accessed: 10-Jul-2018]
[1]
S. Druskat and D. S. Katz, “Mapping the research software sustainability space,” arXiv:1807.01772 [cs], Jul. 2018 [Online]. Available: http://arxiv.org/abs/1807.01772. [Accessed: 10-Jul-2018]
[1]
D. S. Katz et al., “Fourth Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE4),” Journal of Open Research Software, vol. 6, no. 1, Feb. 2018 [Online]. Available: http://openresearchsoftware.metajnl.com/articles/10.5334/jors.184/. [Accessed: 15-Feb-2018]
[1]
A. M. Smith et al., “Journal of Open Source Software (JOSS): design and first-year review,” PeerJ Comput. Sci., vol. 4, p. e147, Feb. 2018 [Online]. Available: https://peerj.com/articles/cs-147. [Accessed: 13-Feb-2018]
[1]
D. S. Katz, K. E. Niemeyer, and A. M. Smith, “Publish Your Software: Introducing the Journal of Open Source Software (JOSS),” Computing in Science & Engineering, vol. 20, no. 3, pp. 84–88, 2018 [Online]. Available: https://ieeexplore.ieee.org/document/8358035/. [Accessed: 21-May-2018]
[1]
J. C. Carver, S. Gesing, D. S. Katz, K. Ram, and N. Weber, “Conceptualization of a US Research Software Sustainability Institute (URSSI),” Computing in Science & Engineering, vol. 20, no. 3, pp. 4–9, 2018 [Online]. Available: https://ieeexplore.ieee.org/document/8358036/. [Accessed: 21-May-2018]
[1]
Y. Babuji et al., “Parsl: Scalable Parallel Scripting in Python,” in 10th International Workshop on Science Gateways (IWSG 2018), Edinburgh, Scotland, 2018 [Online]. Available: http://ceur-ws.org/Vol-2357/paper11.pdf
[1]
D. S. Katz et al., “Community Organizations: Changing the Culture in Which Research Software Is Developed and Sustained,” Computing in Science Engineering, pp. 1–1, 2018.
[1]
D. S. Katz, “Software Citations and the ACAT Community,” J. Phys.: Conf. Ser., vol. 1085, no. 2, p. 022010, 2018 [Online]. Available: http://stacks.iop.org/1742-6596/1085/i=2/a=022010. [Accessed: 30-Oct-2018]
[1]
B. Couturier et al., “HEP Software Foundation Community White Paper Working Group - Software Development, Deployment and Validation,” arXiv:1712.07959 [hep-ex, physics:physics], Dec. 2017 [Online]. Available: http://arxiv.org/abs/1712.07959
[1]
A. A. Alves Jr et al., “A Roadmap for HEP Software and Computing R&D for the 2020s,” arXiv:1712.06982 [hep-ex, physics:physics], Dec. 2017 [Online]. Available: http://arxiv.org/abs/1712.06982
[1]
J. P. Tennant et al., “A multi-disciplinary perspective on emergent and future innovations in peer review,” F1000Research, vol. 6, p. 1151, Nov. 2017 [Online]. Available: https://f1000research.com/articles/6-1151/v3
[1]
U. Nangia and D. S. Katz, “Track 1 Paper: Surveying the U.S. National Postdoctoral Association Regarding Software Use and Training in Research.” 30-Aug-2017 [Online]. Available: https://figshare.com/articles/Track_1_Paper_Surveying_the_U_S_National_Postdoctoral_Association_Regarding_Software_Use_and_Training_in_Research/5328442
[1]
A. M. Smith et al., “Journal of Open Source Software (JOSS): design and first-year review,” arXiv:1707.02264 [cs], Jul. 2017 [Online]. Available: http://arxiv.org/abs/1707.02264
[1]
R. C. Jiménez et al., “Four simple recommendations to encourage best practices in research software,” F1000Research, vol. 6, p. 876, Jun. 2017 [Online]. Available: https://f1000research.com/articles/6-876/v1
[1]
D. S. Katz et al., “Report on the Fourth Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE4),” arXiv:1705.02607 [cs], May 2017 [Online]. Available: http://arxiv.org/abs/1705.02607
[1]
S. Jha, D. S. Katz, A. Luckow, N. Chue Hong, O. Rana, and Y. Simmhan, “Introducing distributed dynamic data-intensive (D3) science: Understanding applications and infrastructure,” Concurrency Computat.: Pract. Exper., vol. 29, no. 8, p. n/a-n/a, Apr. 2017 [Online]. Available: http://onlinelibrary.wiley.com/doi/10.1002/cpe.4032/abstract
[1]
T. Clark, H. Cousijn, D. S. Katz, and M. Fenner, “Open Science strategies for NIH data management, sharing, and citation,” PeerJ Preprints, e2836v1, Mar. 2017 [Online]. Available: https://peerj.com/preprints/2836. [Accessed: 12-Jul-2017]
[1]
K. Chard, S. Caton, K. Kugler, O. Rana, and D. S. Katz, “A social content delivery network for e-Science,” Concurrency Computat.: Pract. Exper., vol. 29, no. 4, p. n/a-n/a, Feb. 2017 [Online]. Available: http://onlinelibrary.wiley.com/doi/10.1002/cpe.3854/abstract
[1]
J. M. Wozniak, J. Ozik, D. S. Katz, and M. Wilde, “Streaming supercomputing needs workflow-enabled programming-in-the-large,” arXiv:1702.07425 [cs], Feb. 2017 [Online]. Available: http://arxiv.org/abs/1702.07425
[1]
K. Maheshwari, D. Katz, S. D. Olabarriaga, J. Wozniak, and D. Thain, “Report on the first workshop on negative and null results in eScience,” Concurrency Computat.: Pract. Exper., vol. 29, no. 2, p. n/a-n/a, Jan. 2017 [Online]. Available: http://onlinelibrary.wiley.com/doi/10.1002/cpe.3908/abstract
[1]
U. Nangia and D. S. Katz, “Understanding Software in Research: Initial Results from Examining Nature and a Call for Collaboration,” 2017, pp. 486–487 [Online]. Available: http://ieeexplore.ieee.org/document/8109183/. [Accessed: 05-Dec-2017]
[1]
M. Turilli et al., “Evaluating Distributed Execution of Workloads,” 2017, pp. 276–285 [Online]. Available: http://ieeexplore.ieee.org/document/8109146/. [Accessed: 05-Dec-2017]
[1]
E. A. Huerta et al., “BOSS-LDG: A Novel Computational Framework That Brings Together Blue Waters, Open Science Grid, Shifter and the LIGO Data Grid to Accelerate Gravitational Wave Discovery,” 2017, pp. 335–344 [Online]. Available: http://ieeexplore.ieee.org/document/8109152/. [Accessed: 05-Dec-2017]
[1]
A. Allen et al., “Engineering Academic Software (Dagstuhl Perspectives Workshop 16252),” Dagstuhl Manifestos, vol. 6, no. 1, pp. 1–20, 2017 [Online]. Available: http://drops.dagstuhl.de/opus/volltexte/2017/7146
[1]
A. Marshall-Colon et al., “Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform,” Front. Plant Sci., vol. 8, 2017 [Online]. Available: http://journal.frontiersin.org/article/10.3389/fpls.2017.00786/full. [Accessed: 03-Jun-2017]
[1]
D. S. Katz, K. E. Niemeyer, and A. M. Smith, “Strategies for biomedical software management, sharing, and citation,” PeerJ Preprints, e2640v1, Dec. 2016 [Online]. Available: https://peerj.com/preprints/2640. [Accessed: 12-Jul-2017]
[1]
D. Katz et al., “Report on the Third Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE3),” Journal of Open Research Software, vol. 4, no. 1, Oct. 2016 [Online]. Available: http://openresearchsoftware.metajnl.com/article/10.5334/jors.118/. [Accessed: 04-Jun-2017]
[1]
K. E. Niemeyer, A. M. Smith, and D. S. Katz, “The Challenge and Promise of Software Citation for Credit, Identification, Discovery, and Reuse,” J. Data and Information Quality, vol. 7, no. 4, pp. 16:1–16:5, Oct. 2016 [Online]. Available: http://doi.acm.org/10.1145/2968452
[1]
A. M. Smith, D. S. Katz, and K. E. Niemeyer, “Software citation principles,” PeerJ Comput. Sci., vol. 2, p. e86, Sep. 2016 [Online]. Available: https://peerj.com/articles/cs-86. [Accessed: 04-Jun-2017]
[1]
M. Gamell et al., “Evaluating Online Global Recovery with Fenix Using Application-Aware In-Memory Checkpointing Techniques,” in 2016 45th International Conference on Parallel Processing Workshops (ICPPW), 2016, pp. 346–355 [Online]. Available: https://doi.org/10.1109/ICPPW.2016.56
[1]
Y. Perez-Riverol et al., “Ten Simple Rules for Taking Advantage of Git and GitHub,” PLOS Computational Biology, vol. 12, no. 7, p. e1004947, Jul. 2016 [Online]. Available: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004947. [Accessed: 04-Jun-2017]
[1]
Z. Zhang, D. S. Katz, A. Merzky, M. Turilli, S. Jha, and Y. Nand, “Application Skeleton: Generating Synthetic Applications for Infrastructure Research,” The Journal of Open Source Software, vol. 1, no. 1, May 2016 [Online]. Available: http://joss.theoj.org/papers/10.21105/joss.00017
[1]
“Sustaining Research Software: an SC18 Panel.” [Online]. Available: https://arxiv.org/abs/1902.08942. [Accessed: 27-Feb-2019]

ISDA

[1]
S. P. Satheesan et al., “Extensible Framework for Analysis of Farm Practices and Programs,” PEARC, 2019.
[1]
S. Puthanveetil, A. Craig, and Y. Zhang, “A Historical Big Data Analysis to Understand the Social Construction of Juvenile Delinquency in the United States,” eScience, 2019.
[1]
C. Wang et al., “Social Media Intelligence and Learning Environment: an Open Source Framework for Social Media Data Collection, Analysis and Curation,” eScience, 2019.
[1]
C. Willis, M. Lambert, R. Kalyanam, K. Coakley, K. McHenry, and C. Kirkpatrick, “Labs Workbench: A Scalable Platform for Research Data Access, Education, and Training,” SCGI, 2019.
[1]
P. Kumar, L. Marini, M. Pitcel, L. Keefer, and K. McHenry, “Design of an Observatory Data System and Open Source Sharing with Active Curation and Machine Learning,” GSA, 2019.
[1]
K. Chard et al., “Implementing Computational Reproducibility in the Whole Tale Environment,” P-RECS’19, 2019.
[1]
D. Katz, K. McHenry, C. Reinking, and R. Haines, “Research Software Development & Management in Universities: Case Studies from Manchester’s RSDS Group, Illinois’ NCSA, and Notre Dame’s CRC,” ACM/IEEE International Conference on Software Engineering, International Workshop on Software Engineering for Science (SE4Science), 2019.
[1]
P. Nguyen et al., “Bracelet: Edge-Cloud Microservice Infrastructure for Aging Scientific Instruments,” International Conference on Computing, Networking and Communications (ICNC), 2019.
[1]
R. Kooper et al., “Developing and Adapting Data Management Services Across Multiple Virtual Observatories,” AGU, 2018.
[1]
L. Marini et al., “Using Docker to Ease the Sharing and Running of Code in the PEcAn Project,” AGU, 2018.
[1]
M. Ruiz et al., “A Prediction System for Vector-Borne Diseases : a Use Case for Weekly Estimation of West Nile Virus Risk,” AGU, 2018.
[1]
W. Thomas et al., “Petabytes in Practice: Working with Collections as Data at Scale,” Workshop on Cyberinfrastructure and Machine Learning for Digital Libraries and Archives, JCDL, 2018.
[1]
S. Satheesan et al., “Brown Dog: Making the Digital World a Better Place, a Few Files at a Time,” PEARC, 2018.
[1]
L. Marini et al., “Clowder: Open Source Data Management for Long Tail Data,” PEARC, 2018.
[1]
M. Burnette et al., “TERRA-REF Data Processing Infrastructure,” PEARC, 2018.
[1]
M. Simeone, C. Morris, K. McHenry, and R. Markley, “The Canoe and the Superpixel: Image Analysis of the Changing Shorelines on Historical Maps of the Great Lakes M. Simeone, C. Morris, K. McHenry, and R. Markley,” Journal18, 2018 [Online]. Available: http://www.journal18.org/issue5/the-canoe-and-the-superpixel-image-analysis-of-the-changing-shorelines-on-historical-maps-of-the-great-lakes/
[1]
D. Li, B. Deal, X. Zhou, M. Slavenas, and W. Sullivan, “Moving Beyond the Neighborhood: Daily Exposure to Nature and Adolescents’ Mood,” Landscape and Urban Planning, 2018 [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0169204618300318
[1]
P. Suppakittpaisarn, B. Jiang, M. Slavenas, and W. Sullivan, “Does Density of Green Infrastructure Predict Preference?,” Urban Forestry & Urban Greening, 2018 [Online]. Available: https://www.researchgate.net/publication/323514146_DOES_DENSITY_OF_GREEN_INFRASTRUCTURE_PREDICT_PREFERENCE
[1]
R. Kooper, M. Burnette, J. Maloney, and D. LeBauer, “Data Flow for the TERRA-REF Project,” presented at the AGU, 2017.
[1]
S. Satheesan et al., “Brown Dog: A Data Transformation Ecosystem for Research - Advancing from Beta to 1.0,” presented at the AGU, 2017.
[1]
I. Gutierrez-Polo et al., “Monitoring Water Quality in the Great Lakes leveraging Geo-Temporal Cyberinfrastructure,” presented at the IEEE eScience, 2017.
[1]
A. Langmead, P. Rodriguez, S. Puthanveetil Satheesan, and A. Craig, “Extracting Meaningful Data from Decomposing Bodies,” presented at the PEARC, 2017.
[1]
A. Langmead, P. Rodriguez, S. Puthanveetil Satheesan, and A. Craig, “Extracting Meaningful Data from Decomposing Bodies,” in Workshop on Computer Vision in Digital Humanities, Digital Humanities, 2017.
[1]
C. Willis, M. Lambert, K. McHenry, and C. Kirkpatrick, “Container-Based Analysis Environments for Low-Barrier Access to Research Data,” PEARC, 2017.
[1]
P. Nguyen et al., “4CeeD: Real-time Acquisition and Analysis Framework for Materials-related Cyber-Physical Environments,” 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2017.
[1]
N. Kenyon et al., “Timing of Mesenchymal Stem Cell Infusions Affects Rejection Free and Overall Islet Allograft Survival,” presented at the American Transplant Congress, 2017.
[1]
P. Rodriguez, S. Puthanveetil Satheesan, J. Will, E. Wuerffel, and A. Craig, “Extracting, Assimilating, and Sharing the Results of Image Analysis on the FSA/OWI Photography Collection,” presented at the PEARC, 2017.
[1]
C. Sophocleous, L. Marini, R. Georgiou, M. Elfarargy, and K. McHenry, “Medici 2: A Scalable Content Management System for Cultural Heritage Datasets,” Code4Lib, 2017 [Online]. Available: http://journal.code4lib.org/articles/12317
[1]
J. Cutcher-Gershenfeld et al., “Five ways consortia can catalyse open science,” Nature, 2017 [Online]. Available: http://www.nature.com/news/five-ways-consortia-can-catalyse-open-science-1.21706
[1]
M. Elag, P. Kumar, L. Marini, J. Myers, M. Hedstrom, and B. Plale, “Identification and characterization of information-networks in long-tail data collections,” Environmental Modelling & Software, 2017.
[1]
P. Jiang, M. Elaga, P. Kumar, S. Peckham, L. Marini, and R. Liu, “A service-oriented architecture for coupling web service models using the Basic Model Interface (BMI),” Environmental Modelling & Software, 2017 [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1364815216311525
[1]
K. McHenry et al., “DIBBs Brown Dog - The Need for and Challenges of a Science Driven Data Transformation Service,” in DIBBs PI Workshop, 2017.
[1]
Y. Zhao et al., “Automatic Glomerulus Extraction in Whole Slide Images Towards Computer Aided Diagnosis,” 2016 IEEE 12th International Conference on e-Science (e-Science), 2016.
[1]
S. Padhy et al., “An Architecture for Automatic Deployment of Brown Dog Services At Scale into Diverse Computing Infrastructures,” in XSEDE, 2016.
[1]
N. Makhoul, C. Navarro, J. Lee, and A. Abi-Youness, “Assessment of seismic damage to buildings in resilient Byblos City,” International Journal of Disaster Risk Reduction, 2016 [Online]. Available: http://www.sciencedirect.com/science/article/pii/S221242091530217X
[1]
M. Slavenas, E. Wuerffel, P. Rodriguez, J. Will, and A. Craig, “Image Analysis and Infrastructure Support for Mining the Farm Security Administration – Office of War Information Photography Collection,” in XSEDE, 2016.
[1]
G. Jansen, R. Marciano, S. Padhy, and K. McHenry, “Designing Scalable Cyberinfrastructure for Meta- data Extraction in Billion-Record Archives,” in iPRES, 2016.
[1]
K. McHenry et al., “Brown Dog - A Science Driven Data Transformation Service,” in XSEDE Gateways, 2016.
[1]
S. Padhy et al., “Autocuration Cyberinfrastrucutre for Scientific Discovery and Preservation,” in IEEE eScience, 2015.
[1]
S. Padhy et al., “Brown Dog: Leveraging Everything Towards Autocuration,” in IEEE Big Data, 2015.
[1]
V. Kuhn, A. Craig, M. Simeone, S. P. Satheesan, and L. Marini, “The VAT: Enhanced Video Analysis,” in XSEDE, 2015.
[1]
M. Poole, N. Lambert, S. Satheesan, A. Das, A. Yahja, and M. Hasegawa-Johnson, GroupScope: A Framework and Tools for Large Scale Study of Social Processes. 2015.
[1]
N. Kenyon et al., “Mesenchymal Stem Cells Enhance Rejection Free and Overall Islet Allograft Survival,” in American Transplant Congress, 2015.
[1]
M. Dietze et al., “Chasing the long tail of environmental data: PEcAn is nuts about Brown Dog,” in AGU, 2015.
[1]
M. Dietze et al., “A Quantitative Assessment of a Terrestrial Biosphere Model’s Data Needs Across North American Biomes,” Journal of Geophysical Research - Biogeosciences, 2014.
[1]
L. Diesendruck, R. Kooper, L. Marini, and K. McHenry, “Using Lucene to Index and Search the Digitized 1940 US Census,” Concurrency and Computation: Practice and Experience, 2014.
[1]
E. Black et al., “Using Hidden Markov Models to Determine Changes in Subject Data Over Time, Studying the Immunoregulatory Effect of Mesenchymal Stem Cells,” in IEEE eScience, 2014 [Online]. Available: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4461877/
[1]
E. Cowdery, R. Kooper, D. LeBauer, A. Desai, J. Mantooth, and M. Dietze, “The PEcAn Project: Accessible Tools for On-demand Ecosystem Modeling,” in AGU, San Francisco, 2014.
[1]
S. Rivera et al., “Proposing a Framework for Crowd-Sourced Green Infrastructure Design,” in International Environmental Modelling and Software Society, 2014.
[1]
D. Berman et al., “Multiple Mesenchymal Stem Cell (MSC) Infusions May Be Required to Enhance Engraftment of Allogeneic Islets in Nonhuman Primates,” in Cutting Edge of Transplantation, 2014.