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1. Data & Code: Quantum receiver enhanced by adaptive learning

   https://doi.org/10.5281/zenodo.7382847  

   Cui, Chaohan; Zhang, Zheshen (January 2022, Light Science Applications)

   This folder contains original data, data processing code, and demo code for the paper entitled "Quantum receiver enhanced by adaptive learning" published in Light: Science & Applications, DOI: 10.1038/s41377-022-01039-5. Please contact chaohancui@arizona.edu if you have questions or other concerns.  

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2. Data in: Aging power spectrum of membrane protein transport and other subordinated random walks

   https://doi.org/10.5281/zenodo.5528300  

   R., Zachary Fox; Barkai, Eli; Krapf, Diego (January 2021, Zenodo)

   {"Abstract":["Datasets generated in the report "Aging power spectrum of membrane protein transport and other subordinated random walks". Included data are:<\/p>\n\nNumerical simulations <\/strong>\nRWdata1.mat: 10,000 realizations, subordinated random walk with Hurst exponent, H<\/em>=0.3 and \\(\\alpha\\)=0.4.\nRWdata3.mat: 10,000 realizations, subordinated random walk with Hurst exponent, H<\/em>=0.7 and \\(\\alpha\\)=0.4.\nRWdata8.mat: 5,000 realizations, subordinated random walk with Hurst exponent, H<\/em>=0.75 and \\(\\alpha\\)=0.8.\nRWdataCTRW.mat: 10,000 realizations, continuous time random walk (CTRW), \\(\\alpha\\)=0.7.<\/p>\n\nSpectra of simulations<\/strong>\nPSDdata1.mat: Power spectral density (PSD) of a subordinated random walk with Hurst exponent, H<\/em>=0.3 and \\(\\alpha\\)=0.4. Five different realization times are used to compute the PDS: 2^8, 2^10, 2^12, 2^14, and 2^16.\nPSDdata3.mat: PSD of a subordinated random walk with Hurst exponent, H<\/em>=0.7 and \\(\\alpha\\)=0.4. Five different realization times are used to compute the PDS: 2^8, 2^10, 2^12, 2^14, and 2^16.\nPSDdata8.mat: PSD of a subordinated random walk with Hurst exponent, H<\/em>=0.75 and \\(\\alpha\\)=0.8. Four different realization times are used to compute the PDS: 2^15, 2^16, 2^17, and 2^18.\nPSDs_CTRW.mat: PSD of a continuous-time random walk (CTRW), \\(\\alpha\\)=0.7. Five different realization times are used to compute the PDS: 2^8, 2^10, 2^12, 2^14, and 2^16.<\/p>\n\nExperimental data of Nav1.6 channels in the soma of hippocampal neurons<\/strong>\nNavMSDtimes.csv: ensemble-averaged (EA) MSD and time-averaged (TA) MSD. The TA-MSD is measured for three observation times, 64, 128, and 256 frames (3.2, 6.4, and 12.8 s).\nNavPSD.csv: Power spectral density (PSD) measured for three observation times, 64, 128, and 256 frames.<\/p>"],"Other":["We acknowledge the support of the National Science Foundation grant 2102832 (to DK) and Israel Science Foundation grant 1898/17 (to EB).","{"references": ["Fox, Z.R., Barkai, E. & Krapf, D. Aging power spectrum of membrane protein transport and other subordinated random walks. Nat Commun 12, 6162 (2021). https://doi.org/10.1038/s41467-021-26465-8"]}"]} 

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3. Ixodes Scapularis monitoring data compiled from 6 studies

   https://doi.org/10.5281/zenodo.6540831  

   Christie, Rowan; Stack, Kaitlin Whitney; Perrone, Julia; Bahlai, Christie (January 2022, Zenodo)

   {"Abstract":["This dataset lists 289 blacklegged tick population datasets from 6 studies that record abundance. These datasets were found by inputing keywords Ixodes Scapularis<\/em> and tick <\/em>in data repositories including Long Term Ecological Research data portal, National Ecological Observatory Network data portal, Google Datasets, Data Dryad, and Data One. The types of tick data recorded from these studies include density (number per square meter for example), proportion of ticks, count of ticks found on people. The locations of the datasets range from New York, New Jersey, Iowa, Massachusetts, and Connecticut, and range from 9 to 24 years in length. These datasets vary in that some record different life stages, geographic scope (county/town/plot), sampling technique (dragging/surveying), and different study length. The impact of these study factors on study results is analyzed in our research.<\/p>\n\nFunding:<\/p>\n\nRMC is supported by the National Institute of General Medical Sciences of the National Institutes of the Health under Award Number R25GM122672. CAB, JP, and KSW are supported by the Office of Advanced Cyberinfrastructure in the National Science Foundation under Award Number #1838807. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the National Science Foundation.<\/p>"],"Other":["{"references": ["Ellison A. 2017. Incidence of Ticks and Tick Bites at Harvard Forest since 2006. Environmental Data Initiative. https://doi.org/10.6073/pasta/71f12a4ffb7658e71a010866d1805a84. Dataset accessed 6/25/2019", "New York State Department of Health Office of Public Health. 2019. Deer Tick Surveillance: Adults (Oct to Dec) excluding Powassan virus: Beginning 2008. https://health.data.ny.gov/Health/Deer-Tick-Surveillance-Nymphs-May-to-Sept-excludin/kibp-u2ip", "New York State Department of Health Office of Public Health. 2019. Access Nymph Deer Tick Collection Data by County (Excluding Powassan Virus). https://health.data.ny.gov/Health/Deer-Tick-Surveillance-Nymphs-May-to-Sept-excludin/kibp-u2ip", "Ostfeld RS, Levi T, Keesing F, Oggenfuss K, Canham CD (2018) Data from: Tick-borne disease risk in a forest food web. Dryad Digital Repository. https://doi.org/10.5061/dryad.d1c8046", "Oliver JD, Bennett SW, Beati L, Bartholomay LC (2017) Range Expansion and Increasing Borrelia burgdorferi Infection of the Tick Ixodes scapularis (Acari: Ixodidae) in Iowa, 1990\\u20132013. Journal of Medical Entomology 54(6): 1727-1734. https://doi.org/10.1093/jme/tjx121", "The Connecticut Agricultural Experiment Station. (n.d.). Summaries of tick testing. CT.gov. Retrieved May 12, 2022, from https://portal.ct.gov/CAES/Fact-Sheets/Tick-Summary/Summaries-of-Tick-Testing", "Jordan, R. A., & Egizi, A. (2019). The growing importance of lone star ticks in a Lyme disease endemic county: Passive tick surveillance in Monmouth County, NJ, 2006 - 2016. PloS one, 14(2), e0211778. https://doi.org/10.1371/journal.pone.0211778"]}"]} 

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4. NEON Tree Crowns Dataset

   https://doi.org/10.5281/zenodo.3765871  

   Weinstein, Ben; Marconi, Sergio; Zare, Alina; Bohlman, Stephanie; Graves, Sarah; Singh, Aditya; White, Ethan (January 2020, Zenodo)

   {"Abstract":["Abstract<\/strong><\/p>\n\nThe NeonTreeCrowns dataset is a set of individual level crown estimates for 100 million trees at 37 geographic sites across the United States surveyed by the National Ecological Observation Network\u2019s Airborne Observation Platform. Each rectangular bounding box crown prediction includes height, crown area, and spatial location. <\/p>\n\nHow can I see the data?<\/strong><\/p>\n\nA web server to look through predictions is available through idtrees.org<\/p>\n\nDataset Organization<\/strong><\/p>\n\nThe shapefiles.zip contains 11,000 shapefiles, each corresponding to a 1km^2 RGB tile from NEON (ID: DP3.30010.001). For example "2019_SOAP_4_302000_4100000_image.shp" are the predictions from "2019_SOAP_4_302000_4100000_image.tif" available from the NEON data portal: https://data.neonscience.org/data-products/explore?search=camera. NEON's file convention refers to the year of data collection (2019), the four letter site code (SOAP), the sampling event (4), and the utm coordinate of the top left corner (302000_4100000). For NEON site abbreviations and utm zones see https://www.neonscience.org/field-sites/field-sites-map. <\/p>\n\nThe predictions are also available as a single csv for each file. All available tiles for that site and year are combined into one large site. These data are not projected, but contain the utm coordinates for each bounding box (left, bottom, right, top). For both file types the following fields are available:<\/p>\n\nHeight: The crown height measured in meters. Crown height is defined as the 99th quartile of all canopy height pixels from a LiDAR height model (ID: DP3.30015.001)<\/p>\n\nArea: The crown area in m^{2<\/sup> of the rectangular bounding box.<\/p>\n\n}Label: All data in this release are "Tree".<\/p>\n\nScore: The confidence score from the DeepForest deep learning algorithm. The score ranges from 0 (low confidence) to 1 (high confidence)<\/p>\n\nHow were predictions made?<\/strong><\/p>\n\nThe DeepForest algorithm is available as a python package: https://deepforest.readthedocs.io/. Predictions were overlaid on the LiDAR-derived canopy height model. Predictions with heights less than 3m were removed.<\/p>\n\nHow were predictions validated?<\/strong><\/p>\n\nPlease see<\/p>\n\nWeinstein, B. G., Marconi, S., Bohlman, S. A., Zare, A., & White, E. P. (2020). Cross-site learning in deep learning RGB tree crown detection. Ecological Informatics<\/em>, 56<\/em>, 101061.<\/p>\n\nWeinstein, B., Marconi, S., Aubry-Kientz, M., Vincent, G., Senyondo, H., & White, E. (2020). DeepForest: A Python package for RGB deep learning tree crown delineation. bioRxiv<\/em>.<\/p>\n\nWeinstein, Ben G., et al. "Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks." Remote Sensing<\/em> 11.11 (2019): 1309.<\/p>\n\nWere any sites removed?<\/strong><\/p>\n\nSeveral sites were removed due to poor NEON data quality. GRSM and PUUM both had lower quality RGB data that made them unsuitable for prediction. NEON surveys are updated annually and we expect future flights to correct these errors. We removed the GUIL puerto rico site due to its very steep topography and poor sunangle during data collection. The DeepForest algorithm responded poorly to predicting crowns in intensely shaded areas where there was very little sun penetration. We are happy to make these data are available upon request.<\/p>\n\n# Contact<\/p>\n\nWe welcome questions, ideas and general inquiries. The data can be used for many applications and we look forward to hearing from you. Contact ben.weinstein@weecology.org. <\/p>"],"Other":["Gordon and Betty Moore Foundation: GBMF4563"]} 

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5. The Gulf of Mexico Eddy Dataset (GOMED), a census of statistically significant eddy-like events from all available surface drifter data

   https://doi.org/10.5281/zenodo.4453875  

   M., Jonathan Lilly; Pérez-Brunius, Paula (January 2021, Zenodo)

   {"Abstract":["This dataset uses trajectory data from a large set of drifters to extract and analyze displacement signals associated with coherent eddies in the Gulf of Mexico, using a multivariate wavelet ridge analysis as presented in Lilly and Pérez-Brunius (2021). The data includes eddy displacement signals for all ridges, as well as the time-varying ellipse parameters and estimated ellipse center location. The instantaneous frequency is also included, as is the instantaneous bias estimate derived by Lilly and Olhede (2012). The data are organized as appended trajectory data that can be readily separated through the use of the "ids" field.  The ridge length (\\(L\\)),and ridge-averaged circularity (\\(\\overline{\\xi}\\))  are also included, as is measure of statistical significance denoted by (\\(\\rho\\)). The dataset is available for download as a NetCDF file.<\/p>\n\nLilly, J. M. and P. Pérez-Brunius (2021).  Extracting statistically significant eddy signals from large Lagrangian datasets using wavelet ridge analysis, with application to the Gulf of Mexico. Nonlinear Processes in Geophysics<\/em>, 28: 181\u2013212. https://doi.org/10.5194/npg-28-181-2021. <\/p>\n\nLilly, J. M. and Olhede, S. C.: Analysis of modulated multivariate oscillations, IEEE T. Signal Proces., 60, 600\u2013612, 2012. 10.1109/TSP.2011.2173681<\/p>"],"Other":["The GOMED database is a product of the Gulf of Mexico Research Consortium (CIGoM) and was partially funded by the CONACYT-SENER-Hydrocarbons Sector Fund, Mexico, project 201441.See database webpage with additional information, as well as request for download form (https://giola.cicese.mx/database/GOMED)","{"references": ["Lilly, J. M. and Olhede, S. C.: Higher-order properties of analytic wavelets, IEEE T. Signal Proces., 57, 146\\u2013160,\\u00a0https://doi.org/10.1109/TSP.2008.2007607, 2009.", "Lilly, J. M. and Olhede, S. C.: Analysis of modulated multivariate oscillations, IEEE T. Signal Proces., 60, 600\\u2013612, 2012."]}"]} 

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