cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5695591.v1
title: True DMRs in 3 versus 3 sample comparison benchmark

description: True DMRs in 3 versus 3 sample comparison benchmark<br><br>This file contains the true differentially methylated regions (DMRs) for the 3 vs 3 comparison saved as a `.bed` (standard bed format). Each row corresponds to one of the 3000 simulated DMRs, and the first three columns denote their position: chromosome, start position, end position.<br><br>This benchmark data was created to mimic the properties of real WGBS data in order to evaluate the method dmrseq (described in the preprint at https://doi.org/10.1101/183210). This was done by splitting a set of biological replicates (controls) into two artificial groups and adding methylation differences to randomly selected regions. Differences are added between the first half of samples and the second half of samples. More methodological details and a script to reproduce the generation of this data file is available at the companion repository: https://github.com/kdkorthauer/dmrseqPaper.
DOI: 10.6084/m9.figshare.5695588.v1
title: True DMRs in 2 versus 2 sample comparison benchmark

description: True DMRs in 2 versus 2 sample comparison benchmark<br><br>This file contains the true differentially methylated regions (DMRs) for the 2 vs 2 comparison saved as a `.bed` (standard bed format). Each row corresponds to one of the 3000 simulated DMRs, and the first three columns denote their position: chromosome, start position, end position.<br><br>This benchmark data was created to mimic the properties of real WGBS data in order to evaluate the method dmrseq (described in the preprint at https://doi.org/10.1101/183210). This was done by splitting a set of biological replicates (controls) into two artificial groups and adding methylation differences to randomly selected regions. Differences are added between the first half of samples and the second half of samples. More methodological details and a script to reproduce the generation of this data file is available at the companion repository: https://github.com/kdkorthauer/dmrseqPaper.
DOI: 10.6084/m9.figshare.5695585
title: Benchmark WGBS data (sample 6 in 3 versus 3 sample comparison)

description: Benchmark WGBS data (sample 6 in 3 versus 3 sample comparison)<br><br>This file contains simulated benchmark WGBS data for 1 sample from a 3 vs 3 comparison saved as a `.txt.gz` (compressed text file). The tab-delimited text file contains four columns: chromosome, position, number of methylated reads, and total number of reads coverage.<br><br>This benchmark data was created to mimic the properties of real WGBS data in order to evaluate the method dmrseq (described in the preprint at https://doi.org/10.1101/183210). This was done by splitting a set of biological replicates (controls) into two artificial groups and adding methylation differences to randomly selected regions. Differences are added between the first half of samples and the second half of samples. More methodological details and a script to reproduce the generation of this data file is available at the companion repository: https://github.com/kdkorthauer/dmrseqPaper.
DOI: 10.6084/m9.figshare.5695579
title: Benchmark WGBS data (sample 5 in 3 versus 3 sample comparison)

description: Benchmark WGBS data (sample 5 in 3 versus 3 sample comparison)<br><br>This file contains simulated benchmark WGBS data for 1 sample from a 3 vs 3 comparison saved as a `.txt.gz` (compressed text file). The tab-delimited text file contains four columns: chromosome, position, number of methylated reads, and total number of reads coverage.<br><br>This benchmark data was created to mimic the properties of real WGBS data in order to evaluate the method dmrseq (described in the preprint at https://doi.org/10.1101/183210). This was done by splitting a set of biological replicates (controls) into two artificial groups and adding methylation differences to randomly selected regions. Differences are added between the first half of samples and the second half of samples. More methodological details and a script to reproduce the generation of this data file is available at the companion repository: https://github.com/kdkorthauer/dmrseqPaper.
DOI: 10.6084/m9.figshare.5695570
title: Benchmark WGBS data (sample 4 in 3 versus 3 sample comparison)

description: Benchmark WGBS data (sample 4 in 3 versus 3 sample comparison)<br><br>This file contains simulated benchmark WGBS data for 1 sample from a 3 vs 3 comparison saved as a `.txt.gz` (compressed text file). The tab-delimited text file contains four columns: chromosome, position, number of methylated reads, and total number of reads coverage.<br><br>This benchmark data was created to mimic the properties of real WGBS data in order to evaluate the method dmrseq (described in the preprint at https://doi.org/10.1101/183210). This was done by splitting a set of biological replicates (controls) into two artificial groups and adding methylation differences to randomly selected regions. Differences are added between the first half of samples and the second half of samples. More methodological details and a script to reproduce the generation of this data file is available at the companion repository: https://github.com/kdkorthauer/dmrseqPaper.
DOI: 10.6084/m9.figshare.5695570.v1
title: Benchmark WGBS data (sample 4 in 3 versus 3 sample comparison)

description: Benchmark WGBS data (sample 4 in 3 versus 3 sample comparison)<br><br>This file contains simulated benchmark WGBS data for 1 sample from a 3 vs 3 comparison saved as a `.txt.gz` (compressed text file). The tab-delimited text file contains four columns: chromosome, position, number of methylated reads, and total number of reads coverage.<br><br>This benchmark data was created to mimic the properties of real WGBS data in order to evaluate the method dmrseq (described in the preprint at https://doi.org/10.1101/183210). This was done by splitting a set of biological replicates (controls) into two artificial groups and adding methylation differences to randomly selected regions. Differences are added between the first half of samples and the second half of samples. More methodological details and a script to reproduce the generation of this data file is available at the companion repository: https://github.com/kdkorthauer/dmrseqPaper.
DOI: 10.6084/m9.figshare.5695567
title: Benchmark WGBS data (sample 3 in 3 versus 3 sample comparison)

description: Benchmark WGBS data (sample 3 in 3 versus 3 sample comparison)<br><br>This file contains simulated benchmark WGBS data for 1 sample from a 3 vs 3 comparison saved as a `.txt.gz` (compressed text file). The tab-delimited text file contains four columns: chromosome, position, number of methylated reads, and total number of reads coverage.<br><br>This benchmark data was created to mimic the properties of real WGBS data in order to evaluate the method dmrseq (described in the preprint at https://doi.org/10.1101/183210). This was done by splitting a set of biological replicates (controls) into two artificial groups and adding methylation differences to randomly selected regions. Differences are added between the first half of samples and the second half of samples. More methodological details and a script to reproduce the generation of this data file is available at the companion repository: https://github.com/kdkorthauer/dmrseqPaper.
DOI: 10.6084/m9.figshare.5695564
title: Benchmark WGBS data (sample 2 in 3 versus 3 sample comparison)

description: Benchmark WGBS data (sample 2 in 3 versus 3 sample comparison)<br><br>This file contains simulated benchmark WGBS data for 1 sample from a 3 vs 3 comparison saved as a `.txt.gz` (compressed text file). The tab-delimited text file contains four columns: chromosome, position, number of methylated reads, and total number of reads coverage.<br><br>This benchmark data was created to mimic the properties of real WGBS data in order to evaluate the method dmrseq (described in the preprint at https://doi.org/10.1101/183210). This was done by splitting a set of biological replicates (controls) into two artificial groups and adding methylation differences to randomly selected regions. Differences are added between the first half of samples and the second half of samples. More methodological details and a script to reproduce the generation of this data file is available at the companion repository: https://github.com/kdkorthauer/dmrseqPaper.
DOI: 10.6084/m9.figshare.5695561
title: Benchmark WGBS data (sample 1 in 3 versus 3 sample comparison)

description: Benchmark WGBS data (sample 1 in 3 versus 3 sample comparison)<br><br>This file contains simulated benchmark WGBS data for 1 sample from a 3 vs 3 comparison saved as a `.txt.gz` (compressed text file). The tab-delimited text file contains four columns: chromosome, position, number of methylated reads, and total number of reads coverage.<br><br>This benchmark data was created to mimic the properties of real WGBS data in order to evaluate the method dmrseq (described in the preprint at https://doi.org/10.1101/183210). This was done by splitting a set of biological replicates (controls) into two artificial groups and adding methylation differences to randomly selected regions. Differences are added between the first half of samples and the second half of samples. More methodological details and a script to reproduce the generation of this data file is available at the companion repository: https://github.com/kdkorthauer/dmrseqPaper.
DOI: 10.6084/m9.figshare.5695558
title: Benchmark WGBS data (sample 4 in 2 versus 2 sample comparison)

description: Benchmark WGBS data (sample 4 in 2 versus 2 sample comparison)<br><br>This file contains simulated benchmark WGBS data for 1 sample from a 2 vs 2 comparison saved as a `.txt.gz` (compressed text file). The tab-delimited text file contains four columns: chromosome, position, number of methylated reads, and total number of reads coverage.<br><br>This benchmark data was created to mimic the properties of real WGBS data in order to evaluate the method dmrseq (described in the preprint at https://doi.org/10.1101/183210). This was done by splitting a set of biological replicates (controls) into two artificial groups and adding methylation differences to randomly selected regions. Differences are added between the first half of samples and the second half of samples. More methodological details and a script to reproduce the generation of this data file is available at the companion repository: https://github.com/kdkorthauer/dmrseqPaper.
DOI: 10.6084/m9.figshare.5695555
title: Development Group -- Wrist

description: This data is accelerometry data from devices participants in the development group wore on their wrists. The first six characters identify the participant. These files are output from an Actigraph native scoring of sleep (Cole Kripke). The first three rows are a header and should be skipped. After that the first column is date, the second time, the third Axis 1 counts. The final column is the sleep or wake determination. This column can be ignored or dropped and the data could be scored by our R based algorithm.
DOI: 10.6084/m9.figshare.5695555.v1
title: Development Group -- Wrist

description: This data is accelerometry data from devices participants in the development group wore on their wrists. The first six characters identify the participant. These files are output from an Actigraph native scoring of sleep (Cole Kripke). The first three rows are a header and should be skipped. After that the first column is date, the second time, the third Axis 1 counts. The final column is the sleep or wake determination. This column can be ignored or dropped and the data could be scored by our R based algorithm.
DOI: 10.6084/m9.figshare.5695552
title: Validation Group -- Wrist

description: This data is accelerometry data from devices participants in the test group wore on their wrists. The first six characters identify the participant. These files are output from an Actigraph native scoring of sleep (Cole Kripke). The first three rows are a header and should be skipped. After that the first column is date, the second time, the third Axis 1 counts. The final column is the sleep or wake determination. This column can be ignored or dropped and the data could be scored by our R based algorithm.
DOI: 10.6084/m9.figshare.5695552.v1
title: Test Group -- Wrist

description: This data is accelerometry data from devices participants in the test group wore on their wrists. The first six characters identify the participant. These files are output from an Actigraph native scoring of sleep (Cole Kripke). The first three rows are a header and should be skipped. After that the first column is date, the second time, the third Axis 1 counts. The final column is the sleep or wake determination. This column can be ignored or dropped and the data could be scored by our R based algorithm.
DOI: 10.6084/m9.figshare.5695543
title: Benchmark WGBS data (sample 3 in 2 versus 2 sample comparison)

description: Benchmark WGBS data (sample 3 in 2 versus 2 sample comparison)<br><br>This file contains simulated benchmark WGBS data for 1 sample from a 2 vs 2 comparison saved as a `.txt.gz` (compressed text file). The tab-delimited text file contains four columns: chromosome, position, number of methylated reads, and total number of reads coverage.<br><br>This benchmark data was created to mimic the properties of real WGBS data in order to evaluate the method dmrseq (described in the preprint at https://doi.org/10.1101/183210). This was done by splitting a set of biological replicates (controls) into two artificial groups and adding methylation differences to randomly selected regions. Differences are added between the first half of samples and the second half of samples. More methodological details and a script to reproduce the generation of this data file is available at the companion repository: https://github.com/kdkorthauer/dmrseqPaper.
DOI: 10.6084/m9.figshare.5695540
title: Validation Group -- Waist

description: This data is accelerometry data from devices participants in the test group wore on their waists. The file name identifies the participant. The first column is date, the second time, the third Axis 1 counts. <br>
DOI: 10.6084/m9.figshare.5695540.v2
title: Validation Group -- Waist

description: This data is accelerometry data from devices participants in the test group wore on their waists. The file name identifies the participant. The first column is date, the second time, the third Axis 1 counts. <br>
DOI: 10.6084/m9.figshare.5695531
title: Benchmark WGBS data (sample 2 in 2 versus 2 sample comparison)

description: Benchmark WGBS data (sample 2 in 2 versus 2 sample comparison)<br><br>This file contains simulated benchmark WGBS data for 1 sample from a 2 vs 2 comparison saved as a `.txt.gz` (compressed text file). The tab-delimited text file contains four columns: chromosome, position, number of methylated reads, and total number of reads coverage.<br><br>This benchmark data was created to mimic the properties of real WGBS data in order to evaluate the method dmrseq (described in the preprint at https://doi.org/10.1101/183210). This was done by splitting a set of biological replicates (controls) into two artificial groups and adding methylation differences to randomly selected regions. Differences are added between the first half of samples and the second half of samples. More methodological details and a script to reproduce the generation of this data file is available at the companion repository: https://github.com/kdkorthauer/dmrseqPaper.
DOI: 10.6084/m9.figshare.5695540.v1
title: Test Group -- Waist

description: This data is accelerometry data from devices participants in the test group wore on their waists. The file name identifies the participant. The first column is date, the second time, the third Axis 1 counts. <br>
DOI: 10.6084/m9.figshare.5695501
title: Benchmark WGBS data (sample 1 in 2 versus 2 sample comparison)

description: Benchmark WGBS data (sample 1 in 2 versus 2 sample comparison)<br><br>This file contains simulated benchmark WGBS data for 1 sample from a 2 vs 2 comparison saved as a `.txt.gz` (compressed text file). The tab-delimited text file contains four columns: chromosome, position, number of methylated reads, and total number of reads coverage.<br><br>This benchmark data was created to mimic the properties of real WGBS data in order to evaluate the method dmrseq (described in the preprint at https://doi.org/10.1101/183210). This was done by splitting a set of biological replicates (controls) into two artificial groups and adding methylation differences to randomly selected regions. Differences are added between the first half of samples and the second half of samples. More methodological details and a script to reproduce the generation of this data file is available at the companion repository: https://github.com/kdkorthauer/dmrseqPaper. <br>
DOI: 10.6084/m9.figshare.5695474
title: input_figshare_proteomics_121217

description: Proteomic analysis of skeletal muscle specifc carnitine palmitoyltransferase 1b (Cpt1b) knockout mice<br>
DOI: 10.6084/m9.figshare.5695471
title: R object with Benchmark WGBS data (3 samples per condition)

description: This file contains simulated benchmark WGBS data for 3 samples in two conditions saved as an `.rds` file for analysis with R, which contains a bsseq object as well as a Granges object denoting the true DMRs.<br><br>This benchmark data was created to mimic the properties of real WGBS data in order to evaluate the method dmrseq (described in the preprint at https://doi.org/10.1101/183210). This was done by splitting a set of biological replicates (controls) into two artificial groups and adding methylation differences to randomly selected regions. Differences are added between the first half of samples and the second half of samples. More methodological details and a script to reproduce the generation of this data file is available at the companion repository: https://github.com/kdkorthauer/dmrseqPaper. <br><br> The R object in this file is a named list with 5 elements: (1) gr.dmrs is a GenomicRanges object with 3000 ranges that represent the random DMRs added. (2) dmr.mncov is a numeric vector that contains the mean coverage in each simulated DMR. (3) dmr.L is a numeric vector that contains the number of CpGs in each simulated DMR. (4) bs is the BSseq object that contains the methylation data. (5) deltas is a numeric vector that contains the effect size (difference in proportion) used for each DMR.
DOI: 10.6084/m9.figshare.5695471.v1
title: R object with Benchmark WGBS data (3 samples per condition)

description: This file contains simulated benchmark WGBS data for 3 samples in two conditions saved as an `.rds` file for analysis with R, which contains a bsseq object as well as a Granges object denoting the true DMRs.<br><br>This benchmark data was created to mimic the properties of real WGBS data in order to evaluate the method dmrseq (described in the preprint at https://doi.org/10.1101/183210). This was done by splitting a set of biological replicates (controls) into two artificial groups and adding methylation differences to randomly selected regions. Differences are added between the first half of samples and the second half of samples. More methodological details and a script to reproduce the generation of this data file is available at the companion repository: https://github.com/kdkorthauer/dmrseqPaper. <br><br> The R object in this file is a named list with 5 elements: (1) gr.dmrs is a GenomicRanges object with 3000 ranges that represent the random DMRs added. (2) dmr.mncov is a numeric vector that contains the mean coverage in each simulated DMR. (3) dmr.L is a numeric vector that contains the number of CpGs in each simulated DMR. (4) bs is the BSseq object that contains the methylation data. (5) deltas is a numeric vector that contains the effect size (difference in proportion) used for each DMR.
DOI: 10.6084/m9.figshare.5695444
title: input_figshare_metabolites_121217.xlsx

description: Metabolite changes in skeletal muscle specific carnitine palmitoyltransferase 1b (Cpt1b) knockout mice.<br>
DOI: 10.6084/m9.figshare.5695432
title: R object with Benchmark WGBS data (2 samples per condition)

description: This file contains simulated benchmark WGBS data for 3 samples in two conditions saved as an `.rds` file for analysis with R, which contains a bsseq object as well as a Granges object denoting the true DMRs.<br><br>This benchmark data was created to mimic the properties of real WGBS data in order to evaluate the method dmrseq (described in the preprint at https://doi.org/10.1101/183210). This was done by splitting a set of biological replicates (controls) into two artificial groups and adding methylation differences to randomly selected regions. Differences are added between the first half of samples and the second half of samples. More methodological details and a script to reproduce the generation of this data file is available at the companion repository: https://github.com/kdkorthauer/dmrseqPaper. <br><br> The R object in this file is a named list with 5 elements: (1) gr.dmrs is a GenomicRanges object with 3000 ranges that represent the random DMRs added. (2) dmr.mncov is a numeric vector that contains the mean coverage in each simulated DMR. (3) dmr.L is a numeric vector that contains the number of CpGs in each simulated DMR. (4) bs is the BSseq object that contains the methylation data. (5) deltas is a numeric vector that contains the effect size (difference in proportion) used for each DMR. <br>
DOI: 10.6084/m9.figshare.5695414
title: Cartesian planar breakout

description: A simulation of a shock breakout from a planar atmosphere<br>
DOI: 10.6084/m9.figshare.5695399
title: Dataset for Novel magnetic resonance technique for characterizing mesoscale structure of trabecular bone

description: This dataset contains ratio metric values used to obtain results in "Novel magnetic resonance technique for characterizing mesoscale structure of trabecular bone". It is a Matlab workspace consisting of three tables - ratio_metric_healthy contains baseline and eroded ratio metric values for the healthy bone samples, ratio_metric_op contains thresholded ratio metric values for the osteoporotic samples, and ratio_metric_10_pt contains ratio metric values for healthy samples computed using a narrower high-frequency band. Values for the healthy samples are reported as n x 25 arrays, where n is the number of VOIs in a dataset and the columns represent an average over an increasing number of 1 mm x 1 mm x 5 mm samples within a VOI (i.e. the first column is the ratio metric value for one 1x1x5 sample, the second column is the ratio metric for two samples averaged together, the last column is the ratio metric for all 25 samples - the whole VOI - averaged together). The table for the osteoporotic samples contains only the ratio metric values obtained by averaging over all 25 samples in a VOI.
DOI: 10.6084/m9.figshare.5695414.v1
title: Cartesian planar breakout

description: A simulation of a shock breakout from a planar atmosphere<br>
DOI: 10.6084/m9.figshare.5695387
title: Development Group -- Waist

description: This data is accelerometry data from devices participants in the development group wore on their waists. The file name identifies the participant. The first column is date, the second time, the third Axis 1 counts.
DOI: 10.6084/m9.figshare.5695387.v1
title: Development Group -- Waist

description: This data is accelerometry data from devices participants in the development group wore on their waists. The file name identifies the participant. The first column is date, the second time, the third Axis 1 counts.
DOI: 10.6084/m9.figshare.5695351
title: ConFindr Example Data

description: Example data to run ConFindr on.<br>
DOI: 10.6084/m9.figshare.5695351.v1
title: ConFindr Example Data

description: Example data to run ConFindr on.<br>