Datasets

This upload includes the 16S.B.ALL in 100-HF condition (referred to as 16S.B.ALL-100-HF) used in Experiment 3 of the WITCH paper (currently accepted in principle by the Journal of Computational Biology). 100-HF condition refers to making sequences fragmentary with an average length of 100 bp and a standard deviation of 60 bp. Additionally, we enforced that all fragmentary sequences to have lengths > 50 bp. Thus, the final average length of the fragments is slightly higher than 100 bp (~120 bp). In this case (i.e., 16S.B.ALL-100-HF), 1,000 sequences with lengths 25% around the median length are retained as "backbone sequences", while the remaining sequences are considered "query sequences" and made fragmentary using the "100-HF" procedure. Backbone sequences are aligned using MAGUS (or we extract their reference alignment). Then, the fragmentary versions of the query sequences are added back to the backbone alignment using either MAGUS+UPP or WITCH. More details of the tar.gz file are described in README.txt.

This upload contains all datasets used in Experiment 2 of the EMMA paper (appeared in WABI 2023): Shen, Chengze, Baqiao Liu, Kelly P. Williams, and Tandy Warnow. "EMMA: A New Method for Computing Multiple Sequence Alignments given a Constraint Subset Alignment". The zip file has the following structure (presented as an example): salma_paper_datasets/ |_README.md |_10aa/ |_crw/ |_homfam/ |_aat/ | |_... |_... |_het/ |_5000M2-het/ | |_... |_5000M3-het/ ... |_rec_res/ Generally, the structure can be viewed as: [category]/[dataset]/[replicate]/[alignment files] # Categories: 1. 10aa: There are 10 small biological protein datasets within the `10aa` directory, each with just one replicate. 2. crw: There are 5 selected CRW datasets, namely 5S.3, 5S.E, 5S.T, 16S.3, and 16S.T, each with one replicate. These are the cleaned version from Shen et. al. 2022 (MAGUS+eHMM). 3. homfam: There are the 10 largest Homfam datasets, each with one replicate. 4. het: There are three newly simulated nucleotide datasets from this study, 5000M2-het, 5000M3-het, and 5000M4-het, each with 10 replicates. 5. rec\_res: It contains the Rec and Res datasets. Detailed dataset generation can be found in the supplementary materials of the paper. # Alignment files There are at most 6 `.fasta` files in each sub-directory: 1. `all.unaln.fasta`: All unaligned sequences. 2. `all.aln.fasta`: Reference alignments of all sequences. If not all sequences have reference alignments, only the sequences that have will be included. 3. `all-queries.unaln.fasta`: All unaligned query sequences. Query sequences are sequences that do not have lengths within 25% of the median length (i.e., not full-length sequences). 4. `all-queries.aln.fasta`: Reference alignments of query sequences. If not all queries have reference alignments, only the sequences that have will be included. 5. `backbone.unaln.fasta`: All unaligned backbone sequences. Backbone sequences are sequences that have lengths within 25% of the median length (i.e., full-length sequences). 6. `backbone.aln.fasta`: Reference alignments of backbone sequences. If not all backbone sequences have reference alignments, only the sequences that have will be included. >If all sequences are full-length sequences, then `all-queries.unaln.fasta` will be missing. >If fewer than two query sequences have reference alignments, then `all-queries.aln.fasta` will be missing. >If fewer than two backbone sequences have reference alignments, then `backbone.aln.fasta` will be missing. # Additional file(s) 1. `350378genomes.txt`: the file contains all 350,378 bacterial and archaeal genome names that were used by Prodigal (Hyatt et. al. 2010) to search for protein sequences.

This dataset contains the data for PASTA and UPP. PASTA data was used in the following articles: Mirarab, Siavash, Nam Nguyen, Sheng Guo, Li-San Wang, Junhyong Kim, and Tandy Warnow. “PASTA: Ultra-Large Multiple Sequence Alignment for Nucleotide and Amino-Acid Sequences.” Journal of Computational Biology 22, no. 5 (2015): 377–86. doi:10.1089/cmb.2014.0156. Mirarab, Siavash, Nam Nguyen, and Tandy Warnow. “PASTA: Ultra-Large Multiple Sequence Alignment.” Edited by Roded Sharan. Research in Computational Molecular Biology, 2014, 177–91. UPP data was used in: Nguyen, Nam-phuong D., Siavash Mirarab, Keerthana Kumar, and Tandy Warnow. “Ultra-Large Alignments Using Phylogeny-Aware Profiles.” Genome Biology 16, no. 1 (December 16, 2015): 124. doi:10.1186/s13059-015-0688-z.

This dataset contains five files. (i) open_citations_jan2024_pub_ids.csv.gz, open_citations_jan2024_iid_el.csv.gz, open_citations_jan2024_el.csv.gz, and open_citation_jan2024_pubs.csv.gz represent a conversion of Open Citations to an edge list using integer ids assigned by us. The integer ids can be mapped to omids, pmids, and dois using the open_citation_jan2024_pubs.csv and open_citations_jan2024_pub_ids.scv files. The network consists of 121,052,490 nodes and 1,962,840,983 edges. Code for generating these data can be found https://github.com/chackoge/ERNIE_Plus/tree/master/OpenCitations. (ii) The fifth file, baseline2024.csv.gz, provides information about the metadata of PubMed papers. A 2024 version of PubMed was downloaded using Entrez and parsed into a table restricted to records that contain a pmid, a doi, and has a title and an abstract. A value of 1 in columns indicates that the information exists in metadata and a zero indicates otherwise. Code for generating this data: https://github.com/illinois-or-research-analytics/pubmed_etl

This dataset provides the data for Nguyen, Nam-phuong, et al. "TIPP: taxonomic identification and phylogenetic profiling." Bioinformatics 30.24 (2014): 3548-3555.

This dataset includes measurements taken during the experiments on patterns of alluvial cover over bedrock. The dataset includes an hour worth of timelapse images taken every 10s for eight different experimental conditions. It also includes the instantaneous water surface elevations measured with eTapes at a frequency of 10Hz for each experiment. The 'Read me Data.txt' file explains in more detail the contents of the dataset.

Sample data from one typical phantom test and one deidentified shunt patient test (shown in Fig. 8 of the MRM paper), with the corresponding analysis code for the Shunt-FENSI technique. For the MRM paper “Measuring CSF Shunt Flow with MRI Using Flow Enhancement of Signal Intensity (FENSI)”

Contains scattering data obtained for (TaSe4)2I at the Advanced Photon Source at Argonne National Laboratory. Beamline 6ID-D was used with a beam energy of 64.8 keV in a transmission geometry. Data was obtained at temperatures between 28 and 300 K. See the readme.txt file for more information.

Scripts and example data for AIDData (aiddata.org) processing in support of forthcoming Nakamura dissertation. This dataset includes two sets of scripts and example data files from an aiddata.org data dump. Fuller documentation about the functionality for these scripts is within the readme file. Additional background information and description of usage will be in the forthcoming Nakamura dissertation (link will be added when available). Data originally supplied by Nakamura. Python code and this readme file created by Wickes. Data included within this deposit are examples to demonstrate execution. Roughly, there are two python scripts in here: keyword_search.py, designed to assist in finding records matching specific keywords, and matching_tool.ipynb, designed to assist in detection of which records are and are not contained within a keyword results file and an aiddata project data file.

This dataset is for characterizing chemical short-range-ordering in CrCoNi medium entropy alloys. It has three sub-folders: 1. code, 2. sample WQ, 3. sample HT. The software needed to run the files is Gatan Microscopy Suite® (GMS). Please follow the instruction on this page to install the DM3 GMS: <a href="https://www.gatan.com/installation-instructions#Step1">https://www.gatan.com/installation-instructions#Step1</a> 1. Code folder contains three DM scripts to be installed in Gatan DigitalMicrograph software to analyze scanning electron nanobeam diffraction (SEND) dataset: Cepstrum.s: need [EF-SEND_sampleWQ_cropped_aligned.dm3] in Sample WQ and the average image from [EF-SEND_sampleWQ_cropped_aligned.dm3]. Same for Sample HT folder. log_BraggRemoval.s: same as above. Patterson.s: Need refined diffuse patterns in Sample HT folder. 2. Sample WQ and 3. Sample HT folders both contain the SEND data (.ser) and the binned SEND data (.dm3) as well as our calculated strain maps as the strain measurement reference. The Sample WQ folder additionally has atomic resolution STEM images; the Sample HT folder additionally has three refined diffuse patterns as references for diffraction data processing. * Only .ser file is needed to perform the strain measurement using imToolBox as listed in the manuscript. .emi file contains the meta data of the microscope, which can be opened together with .ser file using FEI TIA software.

Curated networks and clustering output from the manuscript: Well-Connected Communities in Real-World Networks https://arxiv.org/abs/2303.02813

This upload contains one additional set of datasets (RNASim10k, ten replicates) used in Experiment 2 of the EMMA paper (appeared in WABI 2023): Shen, Chengze, Baqiao Liu, Kelly P. Williams, and Tandy Warnow. "EMMA: A New Method for Computing Multiple Sequence Alignments given a Constraint Subset Alignment". The zipped file has the following structure: 10k |__R0 |__unaln.fas |__true.fas |__true.tre |__R1 ... # Alignment files: 1. `unaln.fas`: all unaligned sequences. 2. `true.fas`: the reference alignment of all sequences. 3. `true.tre`: the reference tree on all sequences. For other datasets that uniquely appeared in EMMA, please refer to the related dataset (which is linked below): Shen, Chengze; Liu, Baqiao; Williams, Kelly P.; Warnow, Tandy (2022): Datasets for EMMA: A New Method for Computing Multiple Sequence Alignments given a Constraint Subset Alignment. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2567453_V1

This dataset contains the data for 16S and 23S rRNA alignments including their reference trees. The original alignments are from the Gutell Lab CRW, currently located at https://crw-site.chemistry.gatech.edu/DAT/3C/Alignment/.

This dataset contains the data for SATe-I. SATe-I data was used in the following article: K. Liu, S. Raghavan, S. Nelesen, C. R. Linder, T. Warnow, "Rapid and Accurate Large-Scale Coestimation of Sequence Alignments and Phylogenetic Trees," Science, vol. 324, no. 5934, pp. 1561-1564, 19 June 2009.

Data sets to reproduce the results provided by the tutorial in paper "Data-Driven and Machine Learning Based Framework for Image-Guided Single-Cell Mass Spectrometry"

This repository contains the training dataset associated with the 2023 Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics (DGM-Image Challenge), hosted by the American Association of Physicists in Medicine. This dataset contains more than 100,000 8-bit images of size 512x512. These images emulate coronal slices from anthropomorphic breast phantoms adapted from the VICTRE toolchain [1], with assigned X-ray attenuation coefficients relevant for breast computed tomography. Also included are the labels indicating the breast type. The challenge has now concluded. More information about the challenge can be found here: <a href="https://www.aapm.org/GrandChallenge/DGM-Image/">https://www.aapm.org/GrandChallenge/DGM-Image/</a>. * New in V3: we added a CSV file containing the image breast type labels and example images (PNG).

Traces created using DeathStarBench (https://github.com/delimitrou/DeathStarBench) benchmark of microservice applications with injected failures on containers. Failures consist of disk/CPU/memory failures.

This dataset is the raw data including SEM, TEM, PINEM images and FDTD simulation as well as pairwise interaction calculation results.

This dataset accompanies the paper "STREETS: A Novel Camera Network Dataset for Traffic Flow" at Neural Information Processing Systems (NeurIPS) 2019. Included are: *Over four million still images form publicly accessible cameras in Lake County, IL. The images were collected across 2.5 months in 2018 and 2019. *Directed graphs describing the camera network structure in two communities in Lake County. *Documented non-recurring traffic incidents in Lake County coinciding with the 2018 data. *Traffic counts for each day of images in the dataset. These counts track the volume of traffic in each community. *Other annotations and files useful for computer vision systems. Refer to the accompanying "readme.txt" or "readme.pdf" for further details.

This dataset is the result of three crawls of the web performed in May 2018. The data contains raw crawl data and instrumentation captured by OpenWPM-Mobile, as well as analysis that identifies which scripts access mobile sensors, which ones perform some of browser fingerprinting, as well as clustering of scripts based on their intended use. The dataset is described in the included README.md file; more details about the methodology can be found in our ACM CCS'18 paper: Anupam Das, Gunes Acar, Nikita Borisov, Amogh Pradeep. The Web's Sixth Sense: A Study of Scripts Accessing Smartphone Sensors. In Proceedings of the 25th ACM Conference on Computer and Communications Security (CCS), Toronto, Canada, October 15–19, 2018. (Forthcoming)

Provides RNASim-VS2 datasets used in Gillian's Master's thesis.

Thank you for using these datasets! These files contain trees and reference alignments, as well as the selected query sequences for testing phylogenetic placement methods against and within the SCAMPP framework. There are four datasets from three different sources, each containing their source alignment and "true" tree, any estimated trees that may have been generated, and any re-estimated branch lengths that were created to be used with their requisite phylogenetic placement method. Three biological datasets (16S.B.ALL, PEWO/LTP_s128_SSU, and PEWO/green85) and one simulated dataset (nt78) is contained. See README.txt in each file for more information.

Simulated sequences provide a way to evaluate multiple sequence alignment (MSA) methods where the ground truth is exactly known. However, the realism of such simulated conditions often comes under question compared to empirical datasets. In particular, simulated data often does not display heterogeneity in the sequence lengths, a common feature in biological datasets. In order to imitate sequence length heterogeneity, we here present a set of data that are evolved under a mixture model of indel lengths, where indels have an occasional chance of being promoted to long indels (emulating large insertion/deletion events, e.g., domain-level gain/loss). This dataset is otherwise (e.g., in GTR parameters) analogous to the 1000M condition as presented in the SATe paper (doi: 10.1126/science.1171243) but with 5000 sequences and simulated with INDELible (http://abacus.gene.ucl.ac.uk/software/indelible/). For more information, see README.txt. For the INDELible control files, see https://github.com/ThisBioLife/5000M-234-het.

Agriculture has substantial socioeconomic and environmental impacts that vary between crops. However, information on how the spatial distribution of specific crops has changed over time across the globe is relatively sparse. We introduce the Probabilistic Cropland Allocation Model (PCAM), a novel algorithm to estimate where specific crops have likely been grown over time. Specifically, PCAM downscales annual and national-scale data on the crop-specific area harvested of 17 major crops to a global 0.5-degree grid from 1961-2014. The resulting database presented here provides annual global gridded likelihood estimates of crop-specific areas. Both mean and standard deviations of grid cell fractions are available for each of the 17 crops. Each netCDF file contains an individual year of data with an additional variable ("crs") that defines the coordinate reference system used. Our results provide new insights into the likely changes in the spatial distribution of major crops over the past half-century. For additional information, please see the related paper by Jackson et al. (2019) in Environmental Research Letters (https://doi.org/10.1088/1748-9326/ab3b93).

These datasets are for the four-dimensional scanning transmission electron microscopy (4D-STEM) and electron energy loss spectroscopy (EELS) experiments for cathode nanoparticles at different cutoff voltages and in different electrolytes. The raw 4D-STEM experiment datasets were collected by TEM image & analysis software (FEI) and were saved as SER files. The raw 4D-STEM datasets of SER files can be opened and viewed in MATLAB using our analysis software package of imToolBox available at <a href="https://github.com/flysteven/imToolBox">https://github.com/flysteven/imToolBox</a>. The raw EELS datasets were collected by DigitalMicrograph software and were saved as DM4 files. The raw EELS datasets can be opened and viewed in DigitalMicrograph software or using our analysis codes available at <a href="https://github.com/chenlabUIUC/OrientedPhaseDomain">https://github.com/chenlabUIUC/OrientedPhaseDomain</a>. All the datasets are from the work "Formation and impact of nanoscopic oriented phase domains in electrochemical crystalline electrodes" (2022). The 4D-STEM experiment data include four example datasets for cathode nanoparticles collected at different cutoff voltages and in different electrolytes as described below. Each dataset contains a stack of diffraction patterns collected at different probe positions scanned across the cathode nanoparticle. 1. Pristine cathode particle: "Pristine particle 4D-STEM.ser" 2. Cathode particle at the cutoff voltage of 0.09V during discharge at C/10 in the aqueous electrolyte: "Intermediate cutoff0_09V discharge (aqueous) 4D-STEM.ser" 3. Fully discharged cathode particle at C/10 in the aqueous electrolyte: "Fully discharged particle 4D-STEM.ser" 4. Fully discharged cathode particle at C/10 in the dry organic electrolyte: "Fully discharge particle (dry organic electrolyte).ser" The EELS experiment data includes three example datasets for cathode nanoparticles collected at different cutoff voltages during discharge in the aqueous electrolyte (in "EELS datasets.zip") as described below. Each EELS dataset contains the zero-loss and core-loss EELS spectra collected at different probe positions scanned across the cathode nanoparticle. 1. Pristine cathode particle: "Pristine particle EELS.zip" 2. Cathode particle at the cutoff voltage of 0.09V during discharge at C/10 in the aqueous electrolyte: "intermediate discharge (aqueous) EELS.zip" 3. Fully discharged cathode particle at C/10 in the aqueous electrolyte: "fully discharge (aqueous) EELS.zip" The details of the software package and codes that can be used to analyze the 4D-STEM datasets and EELS datasets are available at: https://github.com/chenlabUIUC/OrientedPhaseDomain. Once our paper is formally published, we will update the relationship of these datasets with our paper.