Dataset Search Results

This data repository includes the features and the trained backbone parameters used in the ICLR 2022 Paper "On the Importance of Firth Bias Reduction in Few-Shot Classification". The code accompanying this data is open-source and available at https://github.com/ehsansaleh/firth_bias_reduction The code and the data have three modules: 1. The "code_firth" module (10 files) relates to the basic ResNet backbones and logistic classifiers (e.g., Figures 2 and 3 in the main paper). 2. The "code_s2m2rf" module (2 files) relates to the S2M2R feature backbones and cosine classifiers (e.g., Figure 4 in the main paper). 3. The "code_dcf" module (3 files) relates to the few-shot Distribution Calibration (DC) method (e.g., Table 1 in the main paper). The relevant files for each module have the module name as a prefix in their name. 1. For instance, the "code_dcf_features.tar" file should be placed at the "features" directory of the "code_dcf" module. 2. As another example, "code_firth_features_cifarfs_novel.tar" should be placed in the "features" directory of the "code_firth" module, and it includes the features extracted from the novel split of mini-ImageNet dataset. Each tar-ball should be extracted in its relevant directory, and the md5 check-sums of the extracted files are also provided in the open-source code repository for verification. Please note that the actual datasets of images are not included here (since we do not own those datasets). However, helper scripts for automatically downloading the original datasets are also provided in the every module and sub-directory of the GitHub code repository.

This dataset contains the full data used in the paper titled "Enabling High Precision Gradient Index Control in Subsurface Multiphoton Lithography," available at https://doi.org/10.1021/acsphotonics.2c01950 . The data used for Table 1 can be found in the dataset for the related Figure 8. Some supplemental figures' data can be found in the main figures data: Figure S2's data is contained in Figure 6. Figure S4 and Table S1 data is derived from Figure 6. Figure S9 is derived from Figure 7. Figure S10 is contained in Figure 7. Figure S12 is derived from Figure 6 and the Python code prism-fringe-analysis. Figures without a data file named after them do not have any data affiliated with them and are purely graphical representations.

Dataset to be for SocialMediaIE tutorial

This data set includes information used to determine patterns of mixing at three small confluences in East Central Illinois based on differences in the temperature or turbidity of the two confluent flows.

This dataset consists of various input data that are used in the GAMS model. All the data are in the format of .inc which can be read within GAMS or Notepad. Main data sources include: acreage data (acre), crop budget data ($/acre), crop yield data (e.g. bushel/acre), Soil carbon sequestration data (KgCO2/ha/yr). Model details can be found in the "Assessing the Additional Carbon Savings with Biofuel" and GAMS model package. ## File Description (1) GAMS Model.zip: This includes all the input files and scripts for running the model (2) Table*.csv: These files include the data from the tables in the manuscript (3) Figure2_3_4.csv: This contains the data used to create the figures in the manuscript (4) BaselineResults.csv: This includes a summary of the model results. (5) SensitivityResults_*.csv: Model results from the various sensitivity analyses performed (6) LUC_emission.csv: land use change emissions by crop reporting district for changes of pasturelands to annual crops.

Data on permanent plots at Fortuna and the Panama Canal Watershed, Republic of Panama, containing counts and percent of trees with one or more multiple stems >10cm diameter, with and without palms. Accompanying environmental data includes elevation, precipitation, soil type and soil chemical variables (pH, total N, NO3, NO4, resin P, mehlich Ca, K and Mg.

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.

Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying Our approach is described in our paper titled: Mishra, Sudhanshu, Shivangi Prasad, and Shubhanshu Mishra. 2020. “Multilingual Joint Fine-Tuning of Transformer Models for Identifying Trolling, Aggression and Cyberbullying at TRAC 2020.” In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC-2020). The source code for training this model and more details can be found on our code repository: https://github.com/socialmediaie/TRAC2020 NOTE: These models are retrained for uploading here after our submission so the evaluation measures may be slightly different from the ones reported in the paper.

The data provided herein is accelerometer and strain data taken from free vibration response of pre-tensioned, partially submerged steel beam specimens (modulus of elasticity assumed = 29,000 ksi). The specimens were subjected to various levels of pre-tension, and various levels of submersion in water. The purpose of the testing was to quantify the effects of partial submersion on the vibrating frequencies of pretensioned beams. Three specimens were tested, each with different cross section (but identical cross-sectional area). The different cross sections allow investigation of the effects of specimen width as the specimen vibrates through water. The testing procedure was as follows: 1) Apply a specified level of tension in the beam. Measure tension via 3 strain gages. 2) Submerge the specimens to a specified depth of water 3) Excite the beams with either a hammer impact or a pull-and-release method (physically pull the middle of the bar and quickly release) 4) Measure the free vibration of the beam with 2 accelerometers. Schematic drawings of the test setup and the test specimens are provided, as is a picture of the test setup.

Original leaf gas exchange and absorptance data used in the Collison et al. (2020) Light, Not Age, Underlies the Q9 Maladaptation of Maize and Miscanthus Photosynthesis to Self-Shading - Frontiers in Plant Science doi: 10.3389/fpls.2020.00783

This repository includes scripts and datasets for Chapter 6 of my PhD dissertation, " Supertree-like methods for genome-scale species tree estimation," that had not been published previously. This chapter is based on the article: Molloy, E.K. and Warnow, T. "FastMulRFS: Fast and accurate species tree estimation under generic gene duplication and loss models." Bioinformatics, In press. https://doi.org/10.1093/bioinformatics/btaa444. The results presented in my PhD dissertation differ from those in the Bioinformatics article, because I re-estimated species trees using FastMulRF and MulRF on the same datasets in the original repository (https://doi.org/10.13012/B2IDB-5721322_V1). To re-estimate species trees, (1) a seed was specified when running MulRF, and (2) a different script (specifically preprocess_multrees_v3.py from https://github.com/ekmolloy/fastmulrfs/releases/tag/v1.2.0) was used for preprocessing gene trees (which were then given as input to MulRF and FastMulRFS). Note that this preprocessing script is a re-implementation of the original algorithm for improved speed (a bug fix also was implemented). Finally, it was brought to my attention that the simulation in the Bioinformatics article differs from prior studies, because I scaled the species tree by 10 generations per year (instead of 0.9 years per generation, which is ~1.1 generations per year). I re-simulated datasets (true-trees-with-one-gen-per-year-psize-10000000.tar.gz and true-trees-with-one-gen-per-year-psize-50000000.tar.gz) using 0.9 years per generation to quantify the impact of this parameter change (see my PhD dissertation or the supplementary materials of Bioinformatics article for discussion).

This dataset contains the ClonalKinetic dataset that was used in SimiC and its intermediate results for comparison. The Detail description can be found in the text file 'clonalKinetics_Example_data_description.txt' and 'ClonalKinetics_filtered.DF_data_description.txt'. The required input data for SimiC contains: 1. ClonalKinetics_filtered.clustAssign.txt => cluster assignment for each cell. 2. ClonalKinetics_filtered.DF.pickle => filtered scRNAseq matrix. 3. ClonalKinetics_filtered.TFs.pickle => list of driver genes. The results after running SimiC contains: 1. ClonalKinetics_filtered_L10.01_L20.01_Ws.pickle => inferred GRNs for each cluster 2. ClonalKinetics_filtered_L10.01_L20.01_AUCs.pickle => regulon activity scores for each cell and each driver gene. <b>NOTE:</b> “ClonalKinetics_filtered.rds” file which is mentioned in “ClonalKinetics_filtered.DF_data_description.txt” is an intermediate file and the authors have put all the processed in the pickle/txt file as described in the filtered data text.

This is a collection of 31 quasi-linear convective system (QLCS) mesovortices (MVs) that were manually identified and analyzed using the lowest elevation scan of the nearest relevant Weather Surveillance Radar–1988 Doppler (WSR-88D) during the two years (springs of 2022 and 2023) of the Propagation, Evolution, and Rotation in Linear Storms (PERiLS) field campaign. Throughout the two years of PERiLS, a total of nine intensive observing periods (IOPs) occurred (see https://catalog.eol.ucar.edu/perils_2022/missions and https://catalog.eol.ucar.edu/perils_2023/missions for exact IOP dates/times). However, only six of these IOPs (specifically, IOPs 2, 3, and 4 from both years) are included in this dataset. The inclusion criteria were based on the presence of strictly QLCS MVs within the C-band On Wheels (COW) domain, one of the research radars deployed in the field for the PERiLS project. Further details on how MVs were identified are provided below. This analysis was completed using the Gibson Ridge radar-viewing software (GR2Analyst). Each MV had to be produced by a QLCS, defined as a continuous area of 35 dBZ radar reflectivity over at least 100 km when viewed from the lowest elevation scan. The MVs analyzed also had to pass through/near the COW’s domain at some point during their lifetimes to allow for additional analysis using the COW data. Tornadic (TOR), wind-damaging (WD), and non-damaging (ND) MVs were analyzed. ND MVs were ones that usually had a tornado warning placed on them but did not produce any damage and persisted for five or more radar scans; this was done to target the strongest MVs that forecasters thought could be tornadic. The QLCS MVs were identified using objective criteria, which included the existence of a circulation with a maximum differential velocity (dV; i.e., the difference between the maximum outbound and minimum inbound velocities at a constant range) of at least 20 kt over a distance ≤ 7 km. The following radar-based characteristics were catalogued for each QLCS MV at the lowest elevation angle of the nearest WSR-88D: latitude and longitude locations of the MV, the genesis to decay time of the MV, the maximum dV across the MV, the maximum rotational velocity (Vrot; i.e., dV divided by two), diameter of the MV, the range from the radar of the MV center, and the height above radar level of the MV center. In the Excel sheet, there are a total of 37 sheets. 32 of the 37 sheets are for each MV that was examined. One of those MVs (sheet titled 'EFU_tor_iop3') was not included in the final count of MVs (31). This MV produced an EFU tornado and only tornadoes that were given ratings were used to calculate MV statistics. The 31 MV sheets that were used to calculate MV statistics are labeled following the convention 'mv#_iop#_qlcs'. ‘mv#’ is the unique number that was assigned to each MV for clear identification, 'iop#' is the IOP in which the MV occurred, 'qlcs' denotes that the MV was produced by a QLCS, and the 2023 IOPs are denoted by ‘_2023’ after ‘qlcs’ in the sheet name. In these sheets, there are notes on what was visually seen in the radar data, damage associated with each MV (using the National Centers for Environmental Information (NCEI) database), and the characteristics of the MV at each time step of its lifetime. The yellow rows in each of the sheets indicate the last row of data included in the pretornadic, predamaging (wind damage), and pre-nondamaging statistics. The orange boxes in the notes column indicate any reports that were in NCEI but not in GR2Analyst. There are also sheets that examine pretornadic and predamaging diameter trends, box and whisker plot statistics of the overall characteristics of the different types of MVs, and the overall characteristics of each MV, with one Excel sheet (‘combined_qlcs_mvs’) examining the characteristics of each MV over its entire lifetime and one Excel sheet (‘combined_qlcs_mvs_before_report’) examining the characteristics of each MV before it first produced damage or had a tornado warning placed on it.

This dataset consists of all the figure files that are part of the main text and supplementary of the manuscript titled "Optical manipulation of the charge density wave state in RbV3Sb5". For detailed information on the individual files refer to the readme file.

This is the raw topographies (without linear background subtraction) related to the publication: https://www.nature.com/articles/s41586-024-07519-5

This repository includes scripts and datasets for the paper, "FastMulRFS: Fast and accurate species tree estimation under generic gene duplication and loss models." Note: The results from estimating species trees with ASTRID-multi (included in this repository) are *not* included in the FastMulRFS paper. We estimated species trees with ASTRID-multi in the fall of 2019, but ASTRID-multi had an important bug fix in January 2020. Therefore, the ASTRID-multi species trees in this repository should be ignored.

This data describes habitat use, availability, landscape level influences, and daily movement of dabbling ducks in the Wabash River Valley of southeastern Illinois and southwestern Indiana. It contains triangulated locations of individual ducks, associated habitat assignments of those locations, flood survey data to determine water availability, and randomly generated points to assess landscape level questions.

Dataset associated with Jones et al FE-2019-01175 submission: Does the size and developmental stage of traits at fledging reflect juvenile flight ability among songbirds? Excel CSV files with all of the data used in analyses and file with descriptions of each column. The flight ability variable in this dataset was derived from fledgling drop tests, examples of which can be found in the related dataset: Jones, Todd M.; Benson, Thomas J.; Ward, Michael P. (2019): Flight Ability of Juvenile Songbirds at Fledgling: Examples of Fledgling Drop Tests. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2044905_V1.

This dataset provides files for use in analysis of human land preference across Australasia, and in a localized analysis of land preference in Laos and Vietnam. All files can be imported into ArcGIS for visualization, and re-analyzed using the open source Maxent species distribution modeling program. CSV files contain known human presence sites for model validation. ASC files contain geographically coded environmental data for mean annual temperature and mean annual precipitation during the Last Glacial Maximum, as well as downward slope data. All ASC files are in the WGS 1984 Mercator map projection for visualization in ArcGIS and can be opened as text files in text editors supporting large file sizes.

The Delt_Comb.NEX text file contains the original data used in the phylogenetic analyses of Zahniser & Dietrich, 2013 (European Journal of Taxonomy, 45: 1-211). The text file is marked up according to the standard NEXUS format commonly used by various phylogenetic analysis software packages. The file will be parsed automatically by a variety of programs that recognize NEXUS as a standard bioinformatics file format. The first nine lines of the file indicate the file type (Nexus), that 152 taxa were analyzed, that a total of 3971 characters were analyzed, the format of the data, and specification for two symbols used in the dataset. There are four datasets separated into blocks, one each for: 28S rDNA gene, Histone H3 gene, morphology, and insertion/deletion characters scored based on the alignment of the 28S rDNA dataset. Descriptions of the morphological characters and more details on the species and specimens included in the dataset are provided in the publication using this dataset. A text file, Delt_morph_char.txt, is available here that states the morphological characters and characters states that were scored in the Delt_Comb.NEX dataset. The original DNA sequence data are available from NCBI GenBank under the accession numbers indicated in publication. Chromatogram files for each sequencing read are available from the first author upon request.

This is the dataset used in the Landscape Ecology publication of the same name. This dataset consists of the following files: NWCA_Int_Veg.txt NWCA_Reg_Veg.txt NWCA_Site_Attributes.txt NWCA_Int_Veg.txt is a site and plot by species matrix. Column labeled SITES consists of site IDs. Column labeled Plots consist of Plot ID numbers. All other columns represent species abundances (estimates of percent cover, summed across five plots). NWCA_Reg_Veg.txt is a site by species matrix of species abundances. Column labeled SITES consist of site IDs. All other columns represent species abundances (estimates of percent cover within individual plots). NWCA_Site_Attributes.txt is a matrix of site attributes. Column labeled SITES consist of site IDs. Column labeled AA_CENTER_LAT consist of latitudinal coordinates for the Assessment Area center point in decimal degrees. Column labeled AA_CENTER_LONG consist of longitudinal coordinates for the Assessment Area center point in decimal degrees. Column REFPLUS_NWCA represents disturbance gradient classes including MIN (minimally disturbed), L (least disturbed), I (intermediate), M (most disturbed). Column REFPLUS_NWCA2 represents revised disturbance gradient classes based on protocols described in the article. These revised classes were used for analysis. Column labeled STRESS_HEAVYMETAL represents heavy metal stressor classes, used to ascertain which wetlands were missing soil data. Classes in the STRESS_HEAVYMETAL column include Low, Moderate, High, and Missing. Sites with Missing STRESS_HEAVYMETAL classes were removed from analysis. More information about this dataset: All of the data used in this analysis was gathered from the National Wetlands Condition Assessment. Wetland surveys were conducted from 4/4/2011 to 11/2/2011. The entire National Wetlands Condition Assessment Dataset, which includes 3640 unique taxonomic identities of plants, can be found at: https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys

The XSEDE program manages the database of allocation awards for the portfolio of advanced research computing resources funded by the National Science Foundation (NSF). The database holds data for allocation awards dating to the start of the TeraGrid program in 2004 to present, with awards continuing through the end of the second XSEDE award in 2021. The project data include lead researcher and affiliation, title and abstract, field of science, and the start and end dates. Along with the project information, the data set includes resource allocation and usage data for each award associated with the project. The data show the transition of resources over a fifteen year span along with the evolution of researchers, fields of science, and institutional representation.

These data were collected for an experiment examining effects of neonicotinoid (clothianidin) presence on hover fly (Diptera: Syrphidae) behavior. Hover flies of two species (Eristalis arbustorum and Toxomerus marginatus) were offered a choice to feed on artificial flowers laced with sucrose solution that was either contaminated (CLO) or not contaminated (CON) with clothianidin. Two different concentrations of clothianidin in 0.5 M sucrose solution were tested: 2.5 ppb and 150 ppb. We conducted four sets of 10 trials, each trial set examining a different combination of species and clothianidin dose. Across 6 hours of video for each trial we recorded 1) number of visits to each flower that resulted in feeding, and 2) amount of time spent feeding during each visit. We found that while neither species fed significantly longer on either of the solutions, E. arbustorum appeared to avoid flowers with clothianidin particularly at high rates. In the paper, we attribute this avoidance response, partially, to hover fly-visible spectral differences between the two flower choices and discuss potential implications for field and lab-based studies. In the enclosed zip file we have included all data for this project and code scripts from R. * Note: Data folder contains 4 files (instead of 6 as mentioned in Readme): e.tenax_photoreceptors.csv; hoverfly_data_UPDATE.csv; number_visits_UPDATE.csv; and Original 2018 hover fly choice test data_Clem2020.xlsx

This data set shows how density effects have an important influence on mixing at a small river confluence. The data consist of results of simulations using a detached eddy simulation model.