Dataset Search Results

HGT+ILS datasets from Davidson, R., Vachaspati, P., Mirarab, S., & Warnow, T. (2015). Phylogenomic species tree estimation in the presence of incomplete lineage sorting and horizontal gene transfer. BMC genomics, 16(10), 1-12. Contains model species trees, true and estimated gene trees, and simulated alignments.

This file contains the delta 15N values for leaf material collected from Cyathea rojasiana tree ferns before and after fertilization using ammonium -15N chloride solution to determine whether 15N update is possible from senescent leaves. Details of the experiment are provided in the online supplement to the published paper. Briefly, In February 2022 we selected three mature C. rojasiana individuals 1-1.5m in height that had leaves rooted in the soil and one new developing (but unexpanded) leaf. For each fern, two plastic pots (10 x 10 x 12 cm) were filled with a 50:50 mixture of washed river sand and soil from the Chorro watershed. For each pot, one senescent leaf that was rooted in the soil was carefully excavated and its roots transplanted into the pot. Pots were then fertilized by adding 30 ml of a 0.02 M 15N solution of ammonium-15N chloride (98% 15N; Sigma-Aldrich 299251; St Louis, MO) to yield a target concentration of 2 µg15N cm-3 of soil. After fertilization pots were carefully enclosed within thick plastic bags, and sealed around the senescent leaf rachis to prevent leaching any of 15N from the pot to the surrounding soil. At the time of N fertilization, pinnae of the youngest fully expanded leaf were collected from each fern. One pinna was collected from the base of the leaf and one from the distal end of the leaf. In March 2022, after 28 days the roots were removed from pots and two additional leaf pinnae sampled from each fern: one from the base and one from the distal end of the youngest (now fully expanded) leaf. Leaf samples were dried for 72 hours at 60 C and then leaf lamina tissue finely ground with a bead beater. The delta 15N for each leaf sample determined at the University of Illinois, Urbana-Champaign using a Thermo Delta V Advantage IRMS run in combination with a Costech 4010 Elemental Analyzer. Samples were run in continuous flow relative to laboratory standards that were calibrated with USGS 40, 41, and NBS 19 reference materials.

These are the responses to an open, convenience sample survey of residents of Illinois to understand their interactions with wild deer. The survey was available on REDCap between December 19, 2022 and December 19, 2023, and was publicized through listserves, Facebook groups, and media reporting. The file "COVID Deer Survey _ REDCap.pdf" contains the codebook for the survey, including the questions; all factor variables have ".factor" added to their name in the dataset. The file "DeerSurveyData.csv" contains the dataset. The file "Score_calculation_for_sharing.R" is the code to create the cleaned dataset used for analysis from the raw survey responses. Throughout, NA is used to represent null/not available/not applicable; this is most likely either a failure to answer the question or, in some cases, a question that was not presented as it is not relevant based on answers to previous questions.

These are the datasets underlying the figures in the manuscript "Methods of active surveillance for hard ticks and associated tick-borne pathogens of public health importance in the contiguous United States: A Comprehensive Systematic Review". The review considered only publications reporting on active tick or tick-borne pathogen surveillance in the contiguous United States published between 1944 and 2018. For the purposes of this review, we were only concerned with studies of Ixodidae (hard ticks) and/or studies of tick-borne pathogens (in humans, animals, or hard ticks) of public health importance to humans. Study designs included cross-sectional, serological, epidemiological, ecological, or observational studies. Only peer-reviewed publications published in the English language were included. Studies were excluded if they focused on a tick that is not a vector of a human pathogen or on a pathogen that does not cause disease in humans, if the tick or tick-borne pathogen findings were incidental, or if they did not include quantitative surveillance data. For the purpose of this study, we defined surveillance data as information on ticks or pathogens provided through active sampling in natural areas; it should be noted that this does not match the strict definition used by the CDC, which requires sustained sampling efforts across time. Studies were also excluded if they: explored regions other than the contiguous US; focused on treatment, vaccine, or therapeutics development and/or diagnostics of human disease; focused on tick or pathogen genetics; focused on experimental studies with ticks or hosts; were tick control and/or management studies; performed only passive surveillance; were review articles; were not peer reviewed; were in a language other than English; the full text was not available; and if the disease was not a risk to the general public. In addition, for articles which reported data that had previously been published, we only included previously unreported information collected by the authors, and we referenced the specific period of collection for these data to ensure we were not double-recording data. Due to publication delays, we also performed a non-systematic review of the literature of articles published between 2019 – 2023 on tick and tickborne pathogen surveillance methods conducted in the contiguous United States. Keyword search was performed in PubMed Central and Web of Science Core Collection databases. The search algorithm keywords included tick(s), Amblyomma, Dermacentor, Ixodes, Rhipicephalus, Acari Ixodidea, tick host(s), Lyme disease, Rocky Mountain Spotted Fever, Spotted Fever Group, Rickettsiosis, Ehrlichiosis, Anaplasmosis, Borreliosis, Tularemia, Babesiosis, tick-borne pathogen, Powassan, Heartland, Bourbon, Colorado tick fever, Pacific Coast tick fever, tick surveillance, surveillance, (sero)epidemiology, prevalence, distribution, ecology, United States. The search algorithm utilized is provided as follows: TI= ((ticks OR Ixodes OR Amblyomma OR Dermacentor OR Rhipicephalus OR "Acari Ixodidi" OR "tick hosts" OR "tick host") OR ("Lyme Disease" OR "Rocky Mountain Spotted Fever" OR "Spotted Fever Group" OR Rickettsiosis OR Rickettsial OR Ehrlichiosis OR Anaplasmosis OR Borreliosis OR Tularemia OR Babesiosis OR Borrelia OR Ehrlichia OR Anaplasma OR Rickettsia OR Babesia OR "tick-borne pathogen" OR "tick borne pathogen")) AND TS= ("tick surveillance" OR surveillance OR epidemiology OR seroepidemiology OR ecology) AND CU=("United States of America" OR "USA" OR "United States" OR United-States). These datasets are the collated data underlying the figures in the manuscript. For more details, please see the publication. The following are explanations for variables used in all the CSV files: Tick: Species of tick collected Tick_Method: Method of collecting ticks Pathogen: Species of pathogen tested for Path_Method: Method of testing for pathogens Decade: Decade of publication n: Number of publications STATE: state in which study was conducted COUNTY: county in which study was conducted 1944 - 2018 (Was surveillance performed?): was there at least one publication included with a publication date within the 1944-2018 period in this geographic region? 2019 - 2023 (Was surveillance performed?): was there at least one publication included with a publication date within the 2019-2023 period in this geographic region?

This accompanying study is published under the title "Estimating soil N2O emissions induced by organic and inorganic fertilizer inputs using a Tier-2, regression-based meta-analytic approach for U.S. agricultural lands" at Science of the Total Environment. The study is authored by Dr. Yushu Xia, Dr. Hoyoung Kwon, and Dr. Michelle Wander. The DOI for this study is <a href="https://doi.org/10.1016/j.scitotenv.2024.171930">https://doi.org/10.1016/j.scitotenv.2024.171930</a>.

The organizations that contribute to the longevity of 67 long-lived molecular biology databases published in Nucleic Acids Research (NAR) between 1991-2016 were identified to address two research questions 1) which organizations fund these databases? and 2) which organizations maintain these databases? Funders were determined by examining funding acknowledgements in each database's most recent NAR Database Issue update article published (prior to 2017) and organizations operating the databases were determine through review of database websites.

This data publication provides example video clips related to research on association among flight ability of juvenile songbirds at fledging and juvenile morphological traits (wing emergence, wing length, body condition, mass, and tarsus length. File names reflect the species dropped in each video. These videos are supplemental material for scientific publications by the authors and reflect an example subset of all videos collected form 2017-2018 as part of a larger study on the post-fledging ecology of grassland and shrubland birds in east-Central Illinois, USA. No birds were harmed/injured in the production of these videos and procedures were approved by the Illinois Institutional Animal Care and Use Committee (IACUC), protocol no. 18221. Individuals depicted in the videos have given consent for the videos to be shared (talent/model release form; <a href="https://publicaffairs.illinois.edu/resources/release/">https://publicaffairs.illinois.edu/resources/release/</a>)

The text file contains the original DNA nucleotide sequence data used in the phylogenetic analyses of Xue et al. (in review), comprising the 13 protein-coding genes and 2 ribosomal gene subunits of the mitochondrial genome. 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 six lines of the file identify the file as NEXUS, indicate that the file contains data for 30 taxa (species) and 13078 characters, indicate that the characters are DNA sequence, that gaps inserted into the DNA sequence alignment are indicated by a dash, and that missing data are indicated by a question mark. The positions of data partitions are indicated in the mrbayes block of commands for the phylogenetic program MrBayes (version 3.2.6) beginning near the end of the file. The mrbayes block also contains instructions for MrBayes on various non-default settings for that program. These are explained in the Methods section of the submitted manuscript. Two supplementary tables in the provided PDF file provide additional information on the species in the dataset, including the GenBank accession numbers for the sequence data (Table S1) and the DNA substitution models used for each of the individual mitochondrial genes and for different codon positions of the protein-coding genes used for analyses in the programs MrBayes and IQ-Tree (version 1.6.8) (Table S2). Full citations for references listed in Table S1 can be found by searching GenBank using the corresponding accession number. The supplemental tables will also be linked to the article upon publication at the journal website.

Bee movement between habitat patches in a naturally fragmented ecosystem depended on species, patch, and matrix variables. Using a mark-recapture methodology in the naturally fragmented Ozark glade ecosystem, we assessed the importance of bee size, nesting biology, the distance between patches (e.g., isolation), and nesting and floral resources in habitat patches and the surrounding matrix on bee movement. This dataset includes seven data files, three R code files, and a QGIS tool. Three of the data files include information collected at the study sites with regard to bees and matrix and patch characteristics. The other four data files are spatial files used to quantify the characteristics of the forest canopy between the study sites and the edge-to-edge distances between the study sites. R code in the R Markdown file recreates the analysis and data presentation for the associated publication. R script files contain processes for calculating some of the explanatory variables used in the analysis. The QGIS tool can be used as the first step to obtaining average values from a raster file where the cells are large relative to the areas of interest (AOI) that you would like to characterize. The second step is contained in one of the aforementioned R scripts. Detected effects included: Larger bees were more likely to move between patches. Bee movement was less likely as the distance between patches increased. However, relatively short distances (~50 m) inhibited movement more than our a priori expectations. Bees were unlikely to move away from home patches with abundant and diverse floral and below-ground nesting resources. When home patches were less resource-rich, bee movement depended on the characteristics of the away patch or the matrix. In these cases, bees were more likely to move to away patches with greater below-ground nesting and floral resources. Matrix habitats with more available floral and below-ground nesting resources appear to impede movement to neighboring patches, potentially because they already provide supplemental resources for bees.

This repository includes scripts and datasets for the paper, "Statistically consistent divide-and-conquer pipelines for phylogeny estimation using NJMerge." All data files in this repository are for analyses using the logdet distance matrix computed on the concatenated alignment. Data files for analyses using the average gene-tree internode distance matrix can be downloaded from the Illinois Data Bank (https://doi.org/10.13012/B2IDB-1424746_V1). The latest version of NJMerge can be downloaded from Github (https://github.com/ekmolloy/njmerge).<br /> <strong>List of Changes:</strong> &bull; Updated timings for NJMerge pipelines to include the time required to estimate distance matrices; this impacted files in the following folder: <strong>data.zip</strong> &bull; Replaced "Robinson-Foulds" distance with "Symmetric Difference"; this impacted files in the following folders: <strong> tools.zip; data.zip; scripts.zip</strong> &bull; Added some additional information about the java command used to run ASTRAL-III; this impacted files in the following folders: <strong>data.zip; astral64-trees.tar.gz (new)</strong>

The data are provided to illustrate methods in evaluating systematic transactional data reuse in machine learning. A library account-based recommender system was developed using machine learning processing over transactional data of 383,828 transactions (or check-outs) sourced from a large multi-unit research library. The machine learning process utilized the FP-growth algorithm over the subject metadata associated with physical items that were checked-out together in the library. The purpose of this research is to evaluate the results of systematic transactional data reuse in machine learning. The analysis herein contains a large-scale network visualization of 180,441 subject association rules and corresponding node metrics.

Important Note: the raw transient files need to be downloaded through this separate link: https://uofi.box.com/s/oagdxhea1wi8tvfij4robj0z0w8wq7j4. Once downloaded, place the file within the within the .d folder in the unzipped 20210930_ShortTransient_S3_5 folder to perform reconstruction step. The minimal datasets to run the computational pipeline MEISTER introduced in the manuscript titled "Integrative Multiscale Biochemical Mapping of the Brain via Deep-Learning-Enhanced High-Throughput Mass Spectrometry". The key steps of our computational pipeline include (1) tissue mass spectrometry imaging (MSI) reconstruction; (2) multimodal image registration and 3D reconstruction; (3) regional analysis; and (4) single-cell and tissue data integration. Detailed protocols to reproduce our results in the manuscript are provided with an example data set shared for learning the protocols. Our computational processing codes are implemented mostly in Python as well as MATLAB (for image registration).

Data associated with the manuscript "Niche conservatism and spread explain hybridization and introgression between native and invasive fish" by Jordan H. Hartman, Joel B. Corush, Eric R. Larson, Jeremy S. Tiemann, Philip Willink, and Mark A. Davis. For this project, we combined results of ecological niche models (ENMs) and next-generation restriction site-associated DNA sequencing (RADseq) to test theories of niche conservatism and biotic resistance on the success of invasion, hybridization, and extent of introgression between native Western Banded Killifish and non-native Eastern Banded Killifish. This dataset provides the sampling locations and number of Banded Killifish in each population, accession numbers for RADseq from the National Center for Biotechnology Information Sequence Read Archive and the assignment of each Banded Killifish, the habitat associations of each population from the ENMs, and the occurrence points used to build the ENMs.

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.

This database provides estimates of agricultural and food commodity flows [in both tons and $US] between the US and China for the year 2017. Pairwise information is provided between US states and Chinese provinces, and US counties and Chinese provinces for 7 Standardized Classification of Transported Goods (SCTG) commodity categories. Additionally, crosswalks are provided to match Harmonized System (HS) codes and China's Multi-Regional Input Output (MRIO) commodity sectors to their corresponding SCTG commodity codes. The included SCTG commodities are: - SCTG 01: Iive animals and fish - SCTG 02: cereal grains - SCTG 03: agricultural products (except for animal feed, cereal grains, and forage products) - SCTG 04: animal feed, eggs, honey, and other products of animal origin - SCTG 05: meat, poultry, fish, seafood, and their preparations - SCTG 06: milled grain products and preparations, and bakery products - SCTG 07: other prepared foodstuffs, fats and oils For additional information, please see the related paper by Pandit et al. (2022) in Environmental Research Letters. ADD DOI WHEN RECEIVED

This is the dataset for the manuscript titled, "Differing physiological performance of coexisting cool- and warmwater fish species under heatwaves in the Midwestern United States"

This data set is related to a SoyFACE experiment conducted in 2004, 2006, 2007, and 2008 with the soybean cultivars Loda and HS93-4118. The experiment looked at how seed elements were affected by elevated CO2 and yield. In this V2, 2 new files were added per journal requirement. Total there are 5 data files in text format within the digrado_et_al_gcb_data_V2 and 1 readme file. The name of files are listed below. Details about headers are explained in the readme.txt file. <b>1. ionomic_data.txt file</b> contains the ionomic data (mg/kg) for the two cultivars. The file contains all six technical replicates for each plot. The cultivar, year, treatment, and the plot from which the samples were collected are given for each entry. <b>2. yield_data.txt file</b> contains the yield data for the two cultivars (seed yield in kg/ha, seed yield in bu/a, Protein (%), Oil (%)). The file contains yield data for every plot. The cultivar, year, treatment, and the plot from which the samples were collected are given for each entry. <b>3. mineral_pro_oil_yield.txt file</b> contains the yield per hectare for each mineral (g/ha) along with the yield per hectare for protein and oil (t/ha). This was obtained by multiplying the seed content of each element (minerals, protein, and oil) by the total seed yield. The file contains yield data for every plots. The cultivar, year, treatment, and the plot from which the samples were collected are given for each entry. <b>4. economic_assessment.txt file</b> contains data used to assess the financial impact of altered seed oil content on soybean oil production. <b>5. meteorological_data.txt file</b> contains the meteorological data recorded by a weather station located ~ 3km from the experimental site (Willard Airport Champaign). Data covering the period between May 28 and September 24 were used for 2004; between May 25 and September 24 were used in 2006; between May 23 and September 17 in 2007; and between June 16 and October 24 in 2008.

Coups d'Ètat 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 as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e., realized, unrealized, or conspiracy) the type of actor(s) who initiated the coup (i.e., military, rebels, etc.), as well as the fate of the deposed leader. Version 2.1.3 adds 19 additional coup events to the data set, corrects the date of a coup in Tunisia, and reclassifies an attempted coup in Brazil in December 2022 to a conspiracy. Version 2.1.2 added 6 additional coup events that occurred in 2022 and updated the coding of an attempted coup event in Kazakhstan in January 2022. Version 2.1.1 corrected a mistake in version 2.1.0, where the designation of “dissident coup” had been dropped in error for coup_id: 00201062021. Version 2.1.1 fixed this omission by marking the case as both a dissident coup and an auto-coup. Version 2.1.0 added 36 cases to the data set and removed two cases from the v2.0.0 data. This update also added actor coding for 46 coup events and added executive outcomes to 18 events from version 2.0.0. A few other changes were made to correct inconsistencies in the coup ID variable and the date of the event. Version 2.0.0 improved several aspects of the previous version (v1.0.0) and incorporated additional source material to include: • Reconciling missing event data • Removing events with irreconcilable event dates • Removing events with insufficient sourcing (each event needs at least two sources) • Removing events that were inaccurately coded as coup events • Removing variables that fell below the threshold of inter-coder reliability required by the project • Removing the spreadsheet ‘CoupInventory.xls’ because of inadequate attribution and citations in the event summaries • Extending the period covered from 1945-2005 to 1945-2019 • Adding events from Powell and Thyne’s Coup Data (Powell and Thyne, 2011) <br> <b>Items in this Dataset</b> 1. <i>Cline Center Coup d'État Codebook v.2.1.3 Codebook.pdf</i> - This 15-page document describes the Cline Center Coup d’État Project dataset. The first section of this codebook provides a summary of the different versions of the data. The second section provides a succinct definition of a coup d’état used by the Coup d'État Project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The third section describes the methodology used to produce the data. <i>Revised February 2024</i> 2. <i>Coup Data v2.1.3.csv</i> - This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup d’État Project. It contains 29 variables and 1000 observations. <i>Revised February 2024</i> 3. <i>Source Document v2.1.3.pdf</i> - This 325-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 that particular event. <i>Revised February 2024</i> 4. <i>README.md</i> - This file contains useful information for the user about the dataset. It is a text file written in markdown language. <i>Revised February 2024</i> <br> <b> Citation Guidelines</b> 1. To cite the codebook (or any other documentation associated with the Cline Center Coup d’État Project Dataset) please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Scott Althaus. 2024. “Cline Center Coup d’État Project Dataset Codebook”. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7 2. To cite data from the Cline Center Coup d’État Project Dataset please use the following citation (filling in the correct date of access): Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Emilio Soto. 2024. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7

Read me file for the data repository ******************************************************************************* This repository has raw data for the publication "Enhancing Carrier Mobility In Monolayer MoS2 Transistors With Process Induced Strain". We arrange the data following the figure in which it first appeared. For all electrical transfer measurement, we provide the up-sweep and down-sweep data, with voltage units in V and conductance unit in S. All Raman modes have unit of cm^-1. ******************************************************************************* How to use this dataset All data in this dataset is stored in binary Numpy array format as .npy file. To read a .npy file: use the Numpy module of the python language, and use np.load() command. Example: suppose the filename is example_data.npy. To load it into a python program, open a Jupyter notebook, or in the python program, run: import numpy as np data = np.load("example_data.npy") Then the example file is stored in the data object. *******************************************************************************

BAM files for founding strain (MG1655-motile) as well as evolved strains from replicate motility selection experiments in low-viscosity agar plates containing either rich medium (LB) or minimal medium (M63+0.18mM galactose)

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.

These datasets represent first-time collaborations between first and last authors (with mutually exclusive publication histories) on papers with 2 to 5 authors in years [1988,2009] in PubMed. Each record of each dataset captures aspects of the similarity, nearness, and complementarity between two authors about the paper marking the formation of their collaboration.

This deposit contains all raw data and analysis from the paper "In-cell titration of small solutes controls protein stability and aggregation". Data is collected into several types: 1) analysis*.tar.gz are the analysis scripts and the resulting data for each cell. The numbers correspond to the numbers shown in Fig.S1. (in publication) 2) scripts.tar.gz contains helper scripts to create the dataset in bash format. 3) input.tar.gz contains headers and other information that is fed into bash scripts to create the dataset. 4) All rawData*.tar.gz are tarballs of the data of cells in different solutes in .mat files readable by matlab, as follows: - Each experiment included in the publication is represented by two matlab files: (1) a calibration jump under amber illumination (_calib.mat suffix) (2) a full jump under blue illumination (FRET data) - Each file contains the following fields: &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;coordleft - coordinates of cropped and aligned acceptor channel on the original image &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;coordright - coordinates of cropped and aligned donor channel on the original image] &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dataleft - a 3d 12-bit integer matrix containing acceptor channel flourescence for each pixel and time step. Not available in _calib files &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dataright - a 3d 12-bit integer matrix containing donor channel flourescence for each pixel and time step. This will be mCherry in _calib files and AcGFP in data files. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;frame1 - original image size &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;imgstd - cropped dimensions &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numFrames - number of frames in dataleft and dataright &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;videos - a structure file containing camera data. Specifically, videos.TimeStamp includes the time from each frame.

The files are plain text and contain the original data used in phylogenetic analyses of of Typhlocybinae (Bin, Dietrich, Yu, Meng, Dai and Yang 2022: Ecology & Evolution, in press). The three files with extension .phy are text files with aligned DNA sequences in the standard PHYLIP format and correspond to Matrix 1 (amino acid alignment), Matrix 2 (nucleotide alignment of first two codon positions of protein-coding genes) and Matrix 3 (nucleotide alignment of protein-coding genes plus 2 ribosomal genes) described in the Methods section. An additional text file in NEXUS format (.nex extension) contains the morphological character data used in the ancestral state reconstruction (ASCR) analysis described in the Methods. NEXUS is a standard format used by various phylogenetic analysis software. For more information on data file content, see the included "readme" files.

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.