More Like this

1. Data from: Effects of climate change on plant resource allocation and herbivore interactions in a Neotropical rainforest shrub

   https://doi.org/10.5281/zenodo.6885848  

   Maynard, Lauren; R., Susan Whitehead (January 2022, Zenodo)

   {"Abstract":["Data from "Effects of climate change on plant resource allocation and herbivore interactions in a Neotropical rainforest shrub" published in Ecology and Evolution. <\/em>doi.org/10.1002/ece3.9198<\/p>"]} 

   more » « less
2. 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"]} 

   more » « less
3. 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"]}"]} 

   more » « less
4. 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"]}"]} 

   more » « less
5. Gridded road network statistics and settlement age surfaces for core-based statistical areas in the conterminous U.S.

   https://doi.org/10.6084/m9.figshare.19593496.v1  

   Uhl, Johannes H.; Burghardt, Keith (January 2022, figshare)

   These geotiff files represent road network statistics for each core-based statistical area (CBSA) in the conterminous U.S., within grid cells of 1km x 1km. The road network statistics are based on the National transportation dataset (USGS-NTD) v2019. These statistics include: gridcell_stats_azimuthvariety_1km_all_cbsas.tif: The number of unique road angles (azimuth / orientation) in bins of 10 degrees per 1 sqkm grid cell. gridcell_stats_deadendrate_1km_all_cbsas.tif: The proportion of dead ends (nodes of degree 1) of all nodes per 1 sqkm grid cell. gridcell_stats_kmroad_1km_all_cbsas.tif: The approximate total road network length per 1 sqkm grid cell. This is based on the road segment length appended to each road segment centroid and may be biased for very long road segments. gridcell_stats_meandegree_1km_all_cbsas.tif: The average nodal degree of all nodes per 1 sqkm grid cell. gridcell_stats_meangriddedness_1km_all_cbsas.tif: The average griddedness of all nodes per 1 sqkm grid cell. gridcell_stats_nodedensity_1km_all_cbsas.tif: The number of nodes per 1 sqkm grid cell. gridcell_stats_nodesperkmroad_1km_all_cbsas.tif: The number of nodes per km road within each 1 sqkm grid cell. gridcell_stats_firstbuiltup_1km_all_cbsas.tif: The approximate settlement age per 1 sqkm grid cell. This layer is derived from the HISDAC-US First-built-up year (FBUY) layer, which is derived from Zillow's Transaction and Assessment Dataset (ZTRAX). The FBUY data is available here: Leyk, Stefan; Uhl, Johannes H., 2018, "FBUY.tar.gz", Historical settlement composite layer for the U.S. 1810 - 2015, https://doi.org/10.7910/DVN/PKJ90M/BOA5YC, Harvard Dataverse, V2  gridcell_stats_1km_all_cbsas_arcmap10.8.mxd: ESRI ArcMap 10.8 MXD file for quick visualization of the gridded surfaces. Spatial resolution: 1x1km Spatial reference: SR-ORG:7480, USA_Contiguous_Albers_Equal_Area_Conic_USGS_version Source data: USGS-NTD, HISDAC-US. File format: GeoTIFF. Spatial coverage of the road network metrics: All CBSAs in the conterminous U.S. Spatial coverage of the "first built-up year" surface: all U.S. counties that are covered by the HISDAC-US  historical settlement layers. This datasets includes around 2,700 U.S.  counties. In the remaining counties, construction year coverage in the  underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is  low. See Leyk & Uhl (2018) for details. All data created by Johannes H. Uhl, University of Colorado Boulder, USA. Code available at https://github.com/johannesuhl/USRoadNetworkEvolution. References: Burghardt, K., Uhl, J., Lerman, K.,  & Leyk, S. (2022). Road   Network Evolution in the Urban and Rural  United States Since 1900.   Computers, Environment and Urban Systems. Leyk, S., & Uhl, J. H. (2018). HISDAC-US, historical settlement   data  compilation for the conterminous United States over 200 years. Scientific data, 5(1), 1-14. DOI:  https://doi.org/10.1038/sdata.2018.175  

   more » « less