ScholarWorks@UMass Amherst

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  Data for "Interplay Between Ion Transport, Applied Bias and Degradation under Illumination in Hybrid Perovskite p-i-n Devices"

  Emily C. Smith, Christie L.C. Ellis, Hamza Javaid, Lawrence A. Renna, Yao Liu, Thomas P. Russell, Monojit Bag, and Dhandapani Venkataraman

  We studied ion transport in hybrid organic inorganic perovskite p-i-n devices as a function of applied bias under device operating conditions. Using electrochemical impedance spectroscopy (EIS) and equivalent circuit modeling, we elucidated various resistive and capacitive elements in the device. We show that ion migration is predictably influenced by a low applied forward bias, characterized by an increased capacitance at the hole transporting (HTM) and electron transporting material (ETM) interfaces, as well as through the bulk. However, unlike observations in n-i-p devices, we found that there is a capacitive discharge leading to ion redistribution in the bulk at high forward biases. Furthermore, we show that a chemical double layer capacitance buildup as a result of ion accumulation impacts the electronic properties of the device, likely by either inducing charge pinning or charge screening, depending on the direction of the ion induced field. Lastly, we extrapolate ion diffusion coefficients (~10-7 cm2 s-1) and ionic conductivities (~10-7 S cm-1) from the Warburg mass (ion) diffusion response, and show that, as the device degrades, there is an overall depletion of capacitive effects coupled with an increased ion mobility.

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  Ramsey-Musolf According data

  Darrel Ramsey-Musolf

  This CSV file contains the data pertaining to my research on housing plan quality and low-income housing production that was published in Urban Science.

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  Species Potential Range Predictions (Out of the Weeds? Reduced Plant Invasion Risk with Climate Change in the Continental United States)

  Bethany Bradley and Jenica Allen

  This PDF file contains the binary potential range prediction maps for each species in the dataset under current climate. The prediction map for each species lists the species code (see Supplemental Online Table S2 for full species names), areas predicted to be climatically suitable/unsuitable under current climate, and the occurrence points for the species. See the main publication for model fitting details.

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  Anthropogenic Ignitions

  Emily J. Fusco, John Abatzoglou, Jennifer K. Balch, John T. Finn, and Bethany Bradley

  This dataset contains ignition points derived from the MODIS Burned Area Product (MCD45) from 2000-2012), It also contains a random subset of unburned points. Both ignition and unburned points have associated anthropogenic feature data.

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  The Evidence for Traffic (Data Tables)

  Eric E. Poehler

  The file, "Evidence_for_Traffic_at_Pompeii", is a spreadsheet containing the description of 1024 curbstones, guard stones, and stepping-stones, upon which the evidence for ancient traffic at Pompeii were inscribed. These data underlie and support the tables, figures, and arguments of Poehler, Eric. E. 2017 The Traffic System of Pompeii. (New York: Oxford University Press) and should be used in consultation with that work. Using tabs in the spreadsheet, these data are combined as a single table (Traffic_Evidence_All) and also divided into each object type. Field names are intended to be both human readable and in formats ready for use in other software environments. Copies of these data in more sustainable formats, such as .csv and .txt, are appended as additional files, but include only the combined data table.

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  Model of cheatgrass (Bromus tectorum) distribution across the Great Basin, USA

  Bethany Bradley

  A description of the methods associated with this model can be found in:

  Bradley, B.A., C.A. Curtis, E.J. Fusco, J.T. Abatzoglou, J.K. Balch, S. Dadashi, and M.N. Tuanmu. “Cheatgrass (Bromus tectorum) distribution in the intermountain western United States and its relationship to fire frequency, seasonality, and ignitions”, In Press, Biological Invasions

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  Ignition Cause

  Emily J. Fusco, John Abatzoglou, Jennifer K. Balch, John T. Finn, and Bethany Bradley

  This dataset contains ignition points derived from the MODIS Burned Area Product (MCD45) from 2000-2012), It also contains the determined cause for each ignition.

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  Lights, Camera...Citizen Science: Assessing the Effectiveness of Smartphone-based Video Training in Invasive Plant Identification dataset

  Jared Starr, Charles M. Schweik, Nathan Bush, Lena Fletcher, John T. Finn, Jennifer Fish, and Charles T. Bargeron

  The rapid growth and increasing popularity of smartphone technology is putting sophisticated data-collection tools in the hands of more and more citizens. This has exciting implications for the expanding field of citizen science. With smartphone-based applications (apps), it is now increasingly practical to remotely acquire high quality citizen-submitted data at a fraction of the cost of a traditional study. Yet, one impediment to citizen science projects is the question of how to train participants. The traditional “in-person” training model, while effective, can be cost prohibitive as the spatial scale of a project increases. To explore possible solutions, we analyze three training models: 1) in-person, 2) app-based video, and 3) app-based text/images in the context of invasive plant identification in Massachusetts. Encouragingly, we find that participants who received video training were as successful at invasive plant identification as those trained in-person, while those receiving just text/images were less successful. This finding has implications for a variety of citizen science projects that need alternative methods to effectively train participants when in-person training is impractical. This file is the raw data that accompanies the PLoS article.

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  Cheatgrass (Bromus tectorum) percent cover data

  Bethany Bradley

  A compilation of cheatgrass (Bromus tectorum) percent cover data across the western U.S. used to train a regional land cover map as well as assess relationships to fire.