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published: 2021-03-31
 
This archive contains the datasets used in the paper "Recursive MAGUS: scalable and accurate multiple sequence alignment". - 16S.3, 16S.T, 16S.B.ALL - HomFam - RNASim These can also be found at https://sites.google.com/eng.ucsd.edu/datasets/alignment/pastaupp
published: 2021-03-23
 
DNN weights used in the evaluation of the ApproxTuner system. Link to paper: https://dl.acm.org/doi/10.1145/3437801.3446108
published: 2021-03-17
 
This dataset was developed as part of a study that assessed data reuse. Through bibliometric analysis, corresponding authors of highly cited papers published in 2015 at the University of Illinois at Urbana-Champaign in nine STEM disciplines were identified and then surveyed to determine if data were generated for their article and their knowledge of reuse by other researchers. Second, the corresponding authors who cited those 2015 articles were identified and surveyed to ascertain whether they reused data from the original article and how that data was obtained. The project goal was to better understand data reuse in practice and to explore if research data from an initial publication was reused in subsequent publications.
keywords: data reuse; data sharing; data management; data services; Scopus API
published: 2021-03-14
 
This dataset contains all the code, notebooks, datasets used in the study conducted to measure the spatial accessibility of COVID-19 healthcare resources with a particular focus on Illinois, USA. Specifically, the dataset measures spatial access for people to hospitals and ICU beds in Illinois. The spatial accessibility is measured by the use of an enhanced two-step floating catchment area (E2FCA) method (Luo & Qi, 2009), which is an outcome of interactions between demands (i.e, # of potential patients; people) and supply (i.e., # of beds or physicians). The result is a map of spatial accessibility to hospital beds. It identifies which regions need more healthcare resources, such as the number of ICU beds and ventilators. This notebook serves as a guideline of which areas need more beds in the fight against COVID-19. ## What's Inside A quick explanation of the components of the zip file * `COVID-19Acc.ipynb` is a notebook for calculating spatial accessibility and `COVID-19Acc.html` is an export of the notebook as HTML. * `Data` contains all of the data necessary for calculations:       * `Chicago_Network.graphml`/`Illinois_Network.graphml` are GraphML files of the OSMNX street networks for Chicago and Illinois respectively.       * `GridFile/` has hexagonal gridfiles for Chicago and Illinois       * `HospitalData/` has shapefiles for the hospitals in Chicago and Illinois       * `IL_zip_covid19/COVIDZip.json` has JSON file which contains COVID cases by zip code from IDPH       * `PopData/` contains population data for Chicago and Illinois by census tract and zip code.       * `Result/` is where we write out the results of the spatial accessibility measures       * `SVI/`contains data about the Social Vulnerability Index (SVI) * `img/` contains some images and HTML maps of the hospitals (the notebook generates the maps) * `README.md` is the document you're currently reading! * `requirements.txt` is a list of Python packages necessary to use the notebook (besides Jupyter/IPython). You can install the packages with `python3 -m pip install -r requirements.txt`
keywords: COVID-19; spatial accessibility; CyberGISX
published: 2021-03-10
 
The PhytoplasmasRef_Trivellone_etal.fas fasta file contains the original final sequence alignment used in the phylogenetic analyses of Trivellone et al. (Ecology and Evolution, in review). The 27 sequences (21 phytoplasma reference strains and 6 phytoplasmas strains from the present study) were aligned using the Muscle algorithm as implemented in MEGA 7.0 with default settings. The final dataset contains 952 positions of the F2n/R2 fragment of the 16S rRNA gene. The data analyses are further described in the cited original paper.
keywords: Hemiptera; Cicadellidae; Mollicutes; Phytoplasma; biorepository
published: 2021-03-08
 
These are abundance dynamics data and simulations for the paper "Higher-order interaction between species inhibits bacterial invasion of a phototroph-predator microbial community". In this V2, data were converted in Python, in addition to MATLAB and more information on how to work with the data was included in the Readme.
keywords: Microbial community; Higher order interaction; Invasion; Algae; Bacteria; Ciliate
published: 2021-03-08
 
In a set of field studies across four years, the effect of self-shading on photosynthetic performance in lower canopy sorghum leaves was studied at sites in Champaign County, IL. Photosynthetic parameters in upper and lower canopy leaves, carbon assimilation, electron transport, stomatal conductance, and activity of three C4-specific photosynthetic enzymes, were compared within a genetically diverse range of accessions varying widely in canopy architecture and thereby in the degree of self-shading. Accessions with erect leaves and high light transmission through the canopy are henceforth referred to as ‘erectophile’ and those with low leaf erectness, ‘planophile’. In the final year of the study, bundle sheath leakiness in erectophile and planophile accessions was also compared.
keywords: Sorghum; Photosynethic Performance; Leaf Inclination
published: 2021-03-06
 
This dataset consists of raw ADC readings from a 3 transmitter 4 receiver 77GHz FMCW radar, together with synchronized RGB camera and depth (active stereo) measurements. The data is grouped into 4 distinct radar configurations: - "indoor" configuration with range <14m - "30m" with range <38m - "50m" with range <63m - "high_res" with doppler resolution of 0.043m/s # Related code https://github.com/moodoki/radical_sdk # Hardware Project Page https://publish.illinois.edu/radicaldata
keywords: radar; FMCW; sensor-fusion; autonomous driving; dataset; RGB-D; object detection; odometry
published: 2021-03-05
 
Datasets that accompany Beilke, Blakey, and O'Keefe 2021 publication (Title: Bats partition activity in space and time in a large, heterogeneous landscape; Journal: Ecology and Evolution).
keywords: spatiotemporal; chiroptera
published: 2021-03-05
 
Adey_Larson_Behavior.csv: Results of behavioral assays for rusty crayfish Faxonius rusticus collected from six lakes in Vilas County, Wisconsin in summer 2018. Crayfish_ID is an individual crayfish ID or identifier that matches to individuals in Adey_Larson_Isotope. Collection is how organisms were collected (trapped = baited trapping, snorkel = by hand). Lake is the study lake crayfish were collected from. Length is crayfish carapace length in mm. CPUE is crayfish catch-per-unit effort from baited trapping in that lake during summer 2018. Shelter_Occupancy, Exploration, Feeding_Snail, Feeding_Detritus, Feeding_Crayfish, and Aggressiveness are behavioral assay scores for individual crayfish. Shelter_Occupancy is frequency of observation intervals (12 maximum) in which crayfish were observed in shelter over a 12 hour period. Exploration is time for crayfish to explore a new area measured in seconds (maximum possible time 1200 seconds or 20 minutes). Feeding_Snail, Feeding_Detritus, and Feeding_Crayfish is the time for crayfish to take a food item (snail, detritus, or snail in the presence of another crayfish) measured in seconds (maximum possibe time 1200 seconds or 20 minutes). Aggressiveness is the response to an approach with a novel object scored as a fast retreat (-2), slow retreat (-1), no visible response (0), approach without threat display (1), approach with threat display (2), interaction with closed chelae (3), or interaction with open chelae (4). Three repeated aggressiveness measures were made per individual (Aggresiveness1, Aggresiveness2, Aggresiveness3), which were summed for inclusion in subsequent analyses (Aggresiveness_Sum). More detailed behavioral assay methods can be found in Adey 2019 Masters thesis. Adey_Larson_Isotope.csv: Stable isotope (13C, 15N) values for rusty crayfish Faxonius rusticus and snail or mussel primary consumers from six lakes in Vilas County, Wisconsin collected during summer 2018. Crayf is an individual crayfish ID or identifier that matches to the same individual crayfish in Adey_Larson_Behavior. Lake is the study lake. Collection is how organisms were collected (trapped = baited trapping, snorkel = by hand). Sample type indicates whether isotope values are for crayfish, snail, or mussel. d13C and d15N are stable isotope values.
keywords: individual specialization; intraspecific competition; behavior; diet; stable isotopes; crayfish; invasive species; limnology; Faxonius rusticus
published: 2021-02-26
 
These data were used in the survival and cause-specific mortality analyses of translocated nuisance American black bear in Wisconsin published in Animal Conservation (Bauder, J.M., N.M. Roberts, D. Ruid, B. Kohn, and M.L. Allen. Accepted. Lower survival of nuisance American black bears (Ursus americanus) is not due to translocation. Animal Conservation). Included are CSV files including each bear's capture history and associated covariates and meta-data for each CSV file. Also included is an example R script of how to conduct the analyses (this R script is also included as supporting information with the published paper).
keywords: black bear; survival; translocation; nuisance wildlife management
published: 2021-02-25
 
Total nitrogen leaching rates were calculated over the Mississippi Atchafalaya River Basin (MARB) using an integrated economic-biophysical modeling approach. Land allocation for corn production and total nitrogen application rates were calculated for crop reporting districts using the Biofuel and Environmental Policy Analysis Model (BEPAM) for 5 RFS2 policy scenarios. These were used as input in the Integrated BIosphere Simulator-Agricultural Version (Agro-IBIS) and the Terrestrial Hydrologic Model with Biogeochemistry (THMB) to calculate the nitrogen loss. Land allocation and total nitrogen application simulations were simulated for the period 2016-2030 for 303 crop reporting districts (https://www.nass.usda.gov/Data_and_Statistics/County_Data_Files/Frequently_Asked_Questions/county_list.txt). The final 2030 values are reported here. Both are stored in csv files. Units for land allocation are million ha and nitrogen application are million kg. The nitrogen leaching rates were modeled with a spatial resolution of 5' x 5' using the North American Datum of 1983 projection and stored in NetCDF files. The 30-year average is calculated over the last 30 years of the 45 years being simulated. Leaching rates are calculated in kg-N/ha.
keywords: nitrogen leaching, bioethanol, bioenergy crops
published: 2021-02-24
 
This dataset contains model output from the Community Earth System Model, Version 2 (CESM2; Danabasoglu et al. 2020). These data were used for analysis in Impacts of Large-Scale Soil Moisture Anomalies in Southeastern South America, published in the Journal of Hydrometeorology (DOI: 10.1175/JHM-D-20-0116.1). See this publication for details of the model simulations that created these data. Four NetCDF (.nc) files are included in this dataset. Two files correspond to the control simulation (FHIST_SP_control) and two files correspond to a simulation with a dry soil moisture anomaly imposed in southeastern South America (FHIST_SP_dry; see the publication mentioned in the preceding paragraph for details on the spatial extent of the imposed anomaly). For each simulation, one file corresponds to output from the atmospheric model (file names with "cam") of CESM2 and the other to the land model (file names with "clm2"). These files are raw CESM output concatenated into a single file for each simulation. All files include data from 1979-01-02 to 2003-12-31 at a daily resolution. The spatial resolution of all files is about 1 degree longitude x 1 degree latitude. Variables included in these files are listed or linked below. Variables in atmosphere model output: Vertical velocity (omega) Convective precipitation Large-scale precipitation Surface pressure Specific humidity Temperature (atmospheric profile) Reference temperature (temp. at reference height, 2 meters in this case) Zonal wind Meridional wind Geopotential height Variables in land model output: See https://www.cesm.ucar.edu/models/cesm1.2/clm/models/lnd/clm/doc/UsersGuide/history_fields_table_40.xhtml Note that not all of the variables listed at the above link are included in the land model output files in this dataset. This material is based upon work supported by the National Science Foundation under Grant No. 1454089. We acknowledge high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. The CESM project is supported primarily by the National Science Foundation. We thank all the scientists, software engineers, and administrators who contributed to the development of CESM2. References Danabasoglu, G., and Coauthors, 2020: The Community Earth System Model Version 2 (CESM2). Journal of Advances in Modeling Earth Systems, 12, e2019MS001916, https://doi.org/10.1029/2019MS001916.
keywords: Climate modeling; atmospheric science; hydrometeorology; hydroclimatology; soil moisture; land-atmosphere interactions
published: 2021-02-23
 
Coups d'état are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup D'état Project (CDP) as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e. realized or successful coups, unrealized coup attempts, or thwarted conspiracies) the type of actor(s) who initiated the coup (i.e. military, rebels, etc.), as well as the fate of the deposed leader. This is version 2.0.0 of this dataset. The first version, <a href="https://clinecenter.illinois.edu/project/research-themes/democracy-and-development/coup-detat-project-cdp ">v.1.0.0</a>, was released in 2013. Since then, the Cline Center has taken several steps to improve on the previously-released data. These changes include: <ol> <li>Filling in missing event data values</li> <li>Removing events with no identifiable dates</li> <li>Reconciling event dates from sources that have conflicting information</li> <li>Removing events with insufficient sourcing (each event now has at least two sources)</li> <li>Removing events that were inaccurately coded and did not meet our definition of a coup event</li> <li>Extending the time period covered from 1945-2005 to 1945-2019</li> <li>Removing certain variables that fell below the threshold of inter-coder reliability required by the project</li> <li>The spreadsheet ‘CoupInventory.xls’ was removed because of inadequate attribution and citation in the event summaries</li></ol> <b>Items in this Dataset</b> 1. <i>CDP v.2.0.2 Codebook.pdf</i> <ul><li>This 14-page document provides a description of the Cline Center Coup D’état Project Dataset. The first section of this codebook provides a succinct definition of a coup d’état used by the CDP and an overview of the categories used to differentiate the wide array of events that meet the CDP definition. It also defines coup outcomes. The second section describes the methodology used to produce the data. <i>Created November 2020. Revised February 2021 to add some additional information about how the Cline Center edited some values in the COW country codes."</i> </li></ul> 2. <i>Coup_Data_v2.0.0.csv</i> <ul><li>This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup D’etat Project. It contains 29 variables and 943 observations. <i>Created November 2020</i></li></ul> 3. <i>Source Document v2.0.0.pdf</i> <ul><li>This 305-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify each particular event. <i>Created November 2020</i> </li></ul> 4. <i>README.md</i> <ul><li>This file contains useful information for the user about the dataset. It is a text file written in mark down language. <i>Created November 2020</i> </li></ul> <br> <b> Citation Guidelines</b> 1) To cite this codebook please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, and Jonathan Bonaguro. 2021. “Cline Center Coup D’état Project Dataset Codebook”. Cline Center Coup D’état Project Dataset. Cline Center for Advanced Social Research. V.2.0.2. February 23. University of Illinois Urbana-Champaign. doi: <a href="https://doi.org/10.13012/B2IDB-9651987_V2">10.13012/B2IDB-9651987_V3</a> 2) To cite the data please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, and Jonathan Bonaguro. 2020. Cline Center Coup D’état Project Dataset. Cline Center for Advanced Social Research. V.2.0.0. November 16. University of Illinois Urbana-Champaign. doi: <a href="https://doi.org/10.13012/B2IDB-9651987_V2">10.13012/B2IDB-9651987_V3</a>
keywords: Coup d'état; event data; Cline Center; Cline Center for Advanced Social Research; political science
published: 2021-02-18
 
Increasingly pervasive location-aware sensors interconnected with rapidly advancing wireless network services are motivating the development of near-real-time urban analytics. This development has revealed both tremendous challenges and opportunities for scientific innovation and discovery. However, state-of-the-art urban discovery and innovation are not well equipped to resolve the challenges of such analytics, which in turn limits new research questions from being asked and answered. Specifically, commonly used urban analytics capabilities are typically designed to handle, process, and analyze static datasets that can be treated as map layers and are consequently ill-equipped in (a) resolving the volume and velocity of urban big data; (b) meeting the computing requirements for processing, analyzing, and visualizing these datasets; and (c) providing concurrent online access to such analytics. To tackle these challenges, we have developed a novel cyberGIS framework that includes computationally reproducible approaches to streaming urban analytics. This framework is based on CyberGIS-Jupyter, through integration of cyberGIS and real-time urban sensing, for achieving capabilities that have previously been unavailable toward helping cities solve challenging urban informatics problems. The files included in this dataset functions as follows: 1) Spatial_interpolation.ipynb is a python based Jupyter notebook that enables users to conduct spatial interpolation with AoT data; 2) Urban_Informatics.ipynb is a Jupyter notebook that helps to explore the AoT dataset; 3) chicago-complete.weekly.2019-09-30-to-2019-10-06.tar includes all the high-frequency urban sensing data from AoT sensors from 2019 September 30th to 2019 October 6th collected in Chicago, US; 4) sensors.csv is a processed dataset including information about the temperature in Chicago, and it is used in Spatial_interpolation.ipynb.
keywords: CyberGIS; Urban informatics; Array of Things
published: 2021-02-16
 
Data from census of peer-reviewed papers discussing nosZ and published from 2013 to 2019. These data were reported in the manuscript titled, "Beyond denitrification: the role of microbial diversity in controlling nitrous oxide reduction and soil nitrous oxide emissions" published in Global Change Biology as an Invited Report.
keywords: atypical nosZ; Clade II nosZ; denitrification; nitrous oxide; N2O reduction; non-denitrifier; nosZ; nosZ-II; nosZ Clade II; soil N2O emissions
published: 2021-02-15
 
The file contains biomass and count data of food items encountered in the digestive tract of collected green-winged teal from the Illinois River Valley during spring 2016-2018. The file also contains biomass of food items collected from core samples collected at sites where the green-winged teal were collected. Together, the consumed and availability food data are used to calculate diet selection. The data also contains information on the teal, collection, sites, and other covariates used in analysis. Lastly, the dataset contains biomass of food items collected in medium (#35) and small (#60) sieves for 2018 core samples.
keywords: Anas crecca; food selection; green-winged teal; Illinois River Valley; moist-soil plants; spring migration; stopover ecology
published: 2021-05-01
 
This is the first version of the dataset. This dataset contains anonymize data collected during the experiments mentioned in the publication: “I can show what I really like.”: Eliciting Preferences via Quadratic Voting that would appear in April 2021. Once the publication link is public, we would provide an update here. These data were collected through our open-source online systems that are available at (experiment1)[https://github.com/a2975667/QV-app] and (experiment 2)[https://github.com/a2975667/QV-buyback] There are two folders in this dataset. The first folder (exp1_data) contains data collected during experiment 1; the second folder (exp2_data) contains data collected during experiment 2.
keywords: Quadratic Voting; Likert scale; Empirical studies; Collective decision-making
published: 2021-02-10
 
This dataset consists of microclimatic temperature and vegetation structure maps at a 3-meter spatial resolution across the Great Smoky Mountains National Park. Included are raster models for sub-canopy, near-surface, minimum and maximum temperature averaged across the study period, season, and month during the growing season months of March through November from 2006-2010. Also available are the topographic and vegetation inputs developed for the microclimate models, including LiDAR-derived vegetation height, LiDAR-derived vegetation structure within four height strata, solar insolation, distance-to-stream, and topographic convergence index (TCI).
keywords: microclimate buffering; forest vegetation structure; temperature; Appalachian Mountains; climate downscaling; understory; LiDAR
published: 2021-08-28
 
Metabolite identifications and profiles of liver samples from 22 day old male and female pigs from gilt that exposed to porcine reproductive and respiratory syndrome virus (P) or not (C) that were weaned at 21 days of age (W) or not (N). Profiles were obtained by University of Illinois Carver Metabolomics Center. Spectrum for each sample was acquired using a gas chromatography mass spectrometry system consisting of an Agilent 7890 gas chromatograph, an Agilent 5975 MSD, and an HP 7683B auto sampler.
keywords: gas chromatography; mass spectrometry; maternal immune activation; weaning; liver
published: 2021-03-15
 
Dataset associated with "Hiding in plain sight: genetic confirmation of putative Louisiana Fatmucket Lampsilis hydiana in Illinois" as submitted to Freshwater Mollusk Biology and Conservation by Stodola et al. Images are from cataloged specimens from the Illinois Natural History Survey (INHS) Mollusk Collection in Champaign, Illinois that were used for genetic research. File names indicate the species as confirmed in Stodola et al. (i.e., Lampsilis siliquoidea or Lampsilis hydiana) followed by the INHS Mollusk Collection catalog number, followed by the individual specimen number, followed by shell view (interior or exterior). If no specimen number is noted in the file name, there is only one specimen for that catalog number. For example: Lsiliquoidea_46515_1_2_3_exterior. Images were created by photographing specimens on a metric grid in an OrTech Photo-e-Box Plus with a Nikon D610 single lens reflex camera using a 60mm lens. Post-processing of images (cropping, image rotation, and auto contrast) occurred in Adobe Photoshop and saved as TIFF files using no image compression, interleaved pixel order, and IBM PC Byte Order. One additional partial lot, INHS Mollusk Catalog No. 37059 (shown with both interior and exterior view in one image), is included for reference but was not genetically sequenced. A .csv file contains an index of all specimens photographed. SPECIES: species confirmed using genetic analyses GENE: cox1 or nad1 mitochondrial gene ACCESSION: GenBank accession number INHS CATALOG NO: Illinois Natural History Survey Mollusk Collection Catalog number WATERBODY: waterbody where specimen was collected PUTATIVE SPECIES: species determination based on morphological characters prior to genetic analysis Phylogenetic sequence data (.nex files) were aligned using BioEdit (Hall, T.A. 1999. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symposium Series 41:95-98.). Pertinent methodology for the analysis are contained within the manuscript submittal for Stodola et al. to Freshwater Mollusk Biology and Conservation. In these files, "N" is a standard symbol for an unknown base.
keywords: Lampsilis hydiana; Lampsilis siliquoidea; unionid; Louisiana Fatmucket; Fatmucket; genetic confirmation
published: 2021-02-01
 
These datasets provide the basis of our analysis in the paper - The Potential Impact of a Clean Energy Society On Air Quality. All datasets here are from the model output (CAM4-chem). All the simulations were run to steady-state and only the outputs used in the analysis are archived here.
keywords: clean energy; ozone; particulates
published: 2021-01-27
 
*This is the third version of the dataset*. New changes in this 3rd version: <i>1.replaces simulations where the initial condition consists of a sinusoidal channel with topographic perturbations with simulations where the initial condition consists of a sinusoidal channel without topographic perturbations. These simulations better illustrate the transformation of a nondendritic network into a dendritic one. 2. contains two additional simulations showing how total domain size affects the landscape's dynamism. 3. changes dataset title to reflect the publication's title</i> This dataset contains data from 18 simulations using a landscape evolution model. A landscape evolution model simulates how uplift and rock incision shape the Earth's (or other planets) surface. To date, most landscape evolution models exhibit "extreme memory" (paper: https://doi.org/10.1029/2019GL083305 and dataset: https://doi.org/10.13012/B2IDB-4484338_V1). Extreme memory in landscape evolution models causes initial conditions to be unrealistically preserved. This dataset contains simulations from a new landscape evolution model that incorporates a sub-model that allows bedrock channels to erode laterally. With this addition, the landscapes no longer exhibit extreme memory. Initial conditions are erased over time, and the landscapes tend towards a dynamic steady state instead of a static one. The model with lateral erosion is named LEM-wLE (Landscape Evolution Model with Lateral Erosion) and the model without lateral erosion is named LEM-woLE (Landscape Evolution Model without Lateral Erosion). There are 16 folders in total. Here are the descriptions: <i>>LEM-woLE_simulations:</i> This folder contains simulations using LEM-woLE. Inside the folder are 5 subfolders containing 100 elevation rasters, 100 drainage area rasters, and 100 plots showing the slope-area relationship. Elevation depicts the height of the landscape, and drainage area represents a contributing area that is upslope. Each folder corresponds to a different initial condition. Driver files and code for these simulations can be found at https://github.com/jeffskwang/LEM-wLE. <i>>MOVIE_S#_data:</i> There are 13 data folders that contain raster data for 13 simulations using LEM-wLE. Inside each folder are 1000 elevation rasters, 1000 drainage area rasters, and 1000 plots showing the slope-area relationship. Driver files and code for these simulations can be found at https://github.com/jeffskwang/LEM-wLE. <i>>movies_mp4_format:</i> For each data folder there are 3 movies generated that show elevation (a), drainage area (b), and erosion rates (c). These files are formatted in the mp4 format and are best viewed using VLC media player (https://www.videolan.org/vlc/index.html). <i>>movies_wmv_format:</i> This folder contains the same movies as the "movies_mp4_format" folder, but they are in a wmv format. These movies can be viewed using Windows media player or other Windows platform movie software. Here are the captions for the 13 movies: Movie S1. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Sinusoidal channel without randomized perturbations. Boundary Condition: 1 open boundary at the bottom of the domain, and 3 closed boundaries elsewhere. KL/KV = 1. Movie S2. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Inclined with small, randomized perturbations. Boundary Condition: 1 open boundary at the bottom of the domain, and 3 closed boundaries elsewhere. KL/KV = 1. Movie S3. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Inclined with large, randomized perturbations. Boundary Condition: 1 open boundary at the bottom of the domain, and 3 closed boundaries elsewhere. KL/KV = 1. Movie S4. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: V-shaped valley with randomized perturbations. Boundary Condition: 1 open boundary at the bottom of the domain, and 3 closed boundaries elsewhere. KL/KV = 1. Movie S5. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Sinusoidal channel with randomized perturbations. Boundary Condition: 1 open boundary at the bottom of the domain, and 3 closed boundaries elsewhere. KL/KV = 1. Movie S6. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Sinusoidal channel without randomized perturbations. Boundary Condition: 1 open boundary at the bottom of the domain, and 3 closed boundaries elsewhere. KL/KV = 0.25. Movie S7. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Sinusoidal channel without randomized perturbations. Boundary Condition: 1 open boundary at the bottom of the domain, and 3 closed boundaries elsewhere. KL/KV = 0.5. Movie S8. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Sinusoidal channel without randomized perturbations. Boundary Condition: 1 open boundary at the bottom of the domain, and 3 closed boundaries elsewhere. KL/KV = 0.75. Movie S9. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Flat with randomized perturbations. Boundary Condition: 1 open boundary at the bottom of the domain, and 3 closed boundaries elsewhere. KL/KV = 1. Movie S10. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Flat with randomized perturbations. Boundary Condition: 2 open boundaries at the top and bottom of the domain, and 2 closed boundaries on the left and right sides. KL/KV = 1. Movie S11. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Flat with randomized perturbations. Boundary Condition: 4 open boundaries. KL/KV = 1. Movie S12. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Flat with randomized perturbations. Boundary Condition: 4 open boundaries. KL/KV = 1. Compared to Movie S11, the length of the domain is 50% shorter, decreasing the total domain area. Movie S13. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Flat with randomized perturbations. Boundary Condition: 4 open boundaries. KL/KV = 1. Compared to Movie S11, the length of the domain is 50% longer, increasing the total domain area. The associated publication for this dataset has not yet been published, and we will update this description with a link when it is.
keywords: landscape evolution; drainage networks; lateral migration; geomorphology
published: 2021-01-25
 
Dataset associated with Zenzal et al. Oikos submission: Retreat, detour, or advance? Understanding the movements of birds confronting the Gulf of Mexico. https://doi.org/10.1111/oik.07834 Four CSV files were used for analysis and are related to the following subsections under the “Statistics” heading in the “Materials and Methods” section of the journal article: 1. Departing the Edge = “AIC Analysis.csv” 2. Comparing Retreating to Advancing = “Advance and Retreat Analysis.csv” and “Wind Data at Departure.csv” 3. Food Abundance = “Fruit Data.csv” and “Arthropod Data.csv” <b>Description of variables:</b> Year: the year in which data were collected. Departure: the direction in which an individual departed the Bon Secour National Wildlife Refuge. “North” indicates an individual that departed ≥315° or <45°; “Circum” indicates an individual that departed east (45 – 134°) or west ( 225 – 314°); “Trans” indicates an individual that departed south (135 – 224°). Age: the age of an individual at capture. Individuals were aged as hatch year (HY) or after hatch year (AHY) according to Pyle (1997; see related article for full citation). Fat: the fat score of an individual at capture. Individuals were scored on a 6-point scale ranging from 0-5 following Helms and Drury (1960; see related article for full citation). Species: the standardized four letter alphabetic code used as an abbreviation for English common names of North American Birds. SWTH: Catharus ustulatus; REVI: Vireo olivaceus; INBU: Passerina cyanea; WOTH: Hylocichla mustelina; RTHU: Archilochus colubris. FTM_SD: stopover duration or number of days between first capture and departure from automated radio telemetry system coverage at the Bon Secour National Wildlife Refuge. TMB_SD: stopover duration or number of days between first and last detection from automated radio telemetry systems north of Mobile Bay, AL, USA. Mean speed north (km/hr): the northbound travel speed of individuals retreating from the Bon Secour National Wildlife Refuge by determining the time when the signal strength indicated the bird was directly east or west of the automated telemetry system and dividing the amount of time it took for an individual to move in an assumed straight path between the Refuge systems and those north of Mobile Bay, AL, USA. Mean speed south (km/hr): the southbound travel speed of individuals advancing from north of Mobile Bay, AL, USA by determining the time when the signal strength indicated the bird was directly east or west of the automated telemetry system and dividing the amount of time it took for an individual to move in an assumed straight path between the Refuge systems and those north of Mobile Bay, AL, USA. LN_FTM_DEP_TIME: the natural log of departure time from the Bon Secour National Wildlife Refuge. Departure time is defined as the number of hours before or after civil twilight. LN_TMB_DEP_TIME: the natural log of departure time from north of Mobile Bay, AL, USA. Departure time is defined as the number of hours before or after civil twilight. Paired_FTM_DEP_TIME: the departure time or number of hours before or after civil twilight from Bon Secour National Wildlife Refuge. Paired_TMB_DEP_TIME: the departure time or number of hours before or after civil twilight from north of Mobile Bay, AL, USA. Wind Direction: the direction from which the wind originated at the Bon Secour National Wildlife Refuge on nights when individuals were departing. “N” indicates winds from the north (≥315° or <45°); “E” indicates winds from the east (45 – 134°); “W” indicates winds from the west ( 225 – 314°); “S” indicates winds from the south (135 – 224°). Wind Speed (m/s): the wind speed on nights when individuals were departing the Bon Secour National Wildlife Refuge. Group: the direction the bird was traveling under specific wind conditions. Northbound individuals traveled north from Bon Secour National Wildlife Refuge. Southbound individuals traveled south from habitats north of Mobile Bay, AL, USA. Fruit: weekly mean number of ripe fruit per meter. Site: the site from which the data were collected. FTM is located within the Bon Secour National Wildlife Refuge. TMB is located within the Jacinto Port Wildlife Management Area. DOY: number indicating day of year (i.e., 1 January = 001….31 December = 365). Arthropod Biomass: estimated mean arthropod biomass from each sampling period. <b>Note:</b> Empty cells indicate unavailable data where applicable.
keywords: migratory birds; migration; automated telemetry; Gulf of Mexico
published: 2021-01-23
 
Data sets from "Comparing Methods for Species Tree Estimation With Gene Duplication and Loss." It contains data simulated with gene duplication and loss under a variety of different conditions.
keywords: gene duplication and loss; species-tree inference;