cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17044/scilifelab.25335448.v1
title: Data for the <i>Populus tremula</i> v2.2. genome project and associated genome-wide association study

description: <b>Background to the study:</b> We have produced a chromosome-scale genome assembly generated using long-read sequencing, optical and high-density genetic maps containing 39,894 annotated genes with functional annotations for 73,765 transcripts in 37,184 genes. We conducted whole-genome resequencing of the Umeå Aspen (UmAsp) collection comprising 227 aspen individuals. We utilised the assembly and existing whole genome re-sequencing data to perform genome-wide association<i> </i>analyses (GWAS) using Single Nucleotide Polymorphisms (SNPs) in the UmAsp, Swedish Aspen (SwAsp) and Scottish Aspen (ScotAsp) collections for leaf physiognomy phenotypes. We conducted Assay of Transposase Accessible Chromatin sequencing (ATAC-Seq) and identified genomic regions of accessible chromatin, and subset SNPs to these regions, which improved the GWAS detection rate. We identified candidate long non-coding RNAs in leaf samples and quantified their expression in an updated co-expression network, which we used to explore the functions of candidate genes identified from the GWAS.<br><br><b>This data set comprises: </b>the ATAC-Seq peaks from the ATAC-Sequencing of aspen leaves, '<b>Aspen leaf ATAC_Seq peaks</b><b>.zip'</b>; and the gene expression matrix of mean values per aspen genotype from the SwAsp collection, '<b>Gene_Expression_matrix_genotype_mean.tsv'</b>. We provide a zipped directory for each of '<b>ScotAsp.zip'</b>, '<b>SwAsp.zip'</b> and '<b>UmAsp.zip'</b> providing the raw leaf image scans, the cropped leaf images and raw data files from the LAMINA leaf shape analyses of these images, and the processed data files and genotypic BLUP values for each of these ScotAsp, SwAsp and UmAsp collections. We provide the GWAS associations of SNPs ranked by decreasing P-value until the 1000th gene for each of the 26 leaf physiognomy traits for each collection, i.e. '<b>ScotAsp top-ranked GWAS results'</b><b>, '</b><b>SwAsp top-ranked GWAS</b><b> results'</b> and '<b>Um</b><b>Asp top-ranked GWAS results'</b>. The single nucleotide polymorphism (SNP) data for each of the aspen collections is in <b>'</b><b>ScotAsp_biallelic_Het.HWE.recode.vcf</b><b>.gz</b><b>'</b><b>, 'SwAsp_AfterBatchRemoval_biallelic_Het.HWE.recode.vcf.gz' </b>and<b> 'UmAsp_biallelic_Het.MAF.HWE.recode_.vcf.gz'.</b>
DOI: 10.7483/opendata.cms.wuvw.4vcu
title: /TprimeBToTH_TLep_Hbb_LH_MT900_MH175_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.25348405
title: MINUTE-ChIP example data

description: This collection contains an example MINUTE-ChIP dataset to run minute pipeline on, provided as supporting material to help users understand the results of a MINUTE-ChIP experiment from raw data to a primary analysis that yields the relevant files for downstream analysis along with summarized QC indicators.
Example primary non-demultiplexed FASTQ files provided here were used to generate GSM5493452-GSM5493463 (H3K27m3) and GSM5823907-GSM5823918 (Input), deposited on GEO with the minute pipeline all together under series GSE181241.
For more information about MINUTE-ChIP, you can check the publication relevant to this dataset:
Kumar, Banushree, et al. "Polycomb repressive complex 2 shields naïve human pluripotent cells from trophectoderm differentiation." <em>Nature Cell Biology</em> 24.6 (2022): 845-857.
If you want more information about the minute pipeline, there is a public biorXiv and a GitHub repository and official documentation.
DOI: 10.17044/scilifelab.25348405.v1
title: MINUTE-ChIP example data

description: This collection contains an example MINUTE-ChIP dataset to run minute pipeline on, provided as supporting material to help users understand the results of a MINUTE-ChIP experiment from raw data to a primary analysis that yields the relevant files for downstream analysis along with summarized QC indicators.
Example primary non-demultiplexed FASTQ files provided here were used to generate GSM5493452-GSM5493463 (H3K27m3) and GSM5823907-GSM5823918 (Input), deposited on GEO with the minute pipeline all together under series GSE181241.
For more information about MINUTE-ChIP, you can check the publication relevant to this dataset:
Kumar, Banushree, et al. "Polycomb repressive complex 2 shields naïve human pluripotent cells from trophectoderm differentiation." <em>Nature Cell Biology</em> 24.6 (2022): 845-857.
If you want more information about the minute pipeline, there is a public biorXiv and a GitHub repository and official documentation.
DOI: 10.7483/opendata.cms.oe2m.1xfx
title: /TprimeBToTH_TLep_Hbb_LH_MT900_MH200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.6utj.fcar
title: /TprimeBToTH_TLep_Hbb_LH_MT900_MH200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.25441789
title: DBS-Pro PBMCs

description: This resource contains data generated using the method DBS-Pro (Droplet Barcode Sequencing for Protein analysis) for single cells for the manuscript "Identification of Major Immune Cell Lineages with DBS-Pro". The data was generated using peripheral blood mononuclear cells (PBMCs) extracted from a single healthy donor from which two libraries were prepared (P29859_1001, P29859_1002). The data quantifies surface proteins on single cells using a panel of six marker antibodies (CD3, CD4, CD8, CD19, CD14, CD45) and one isotype control antibody to quantify background signal. The antibodies are conjugated to oligonucleotides containing an antibody barcode and a UMI sequence. The stained cells are barcoded in droplet emulsions and then sequenced to get out quantitative information about the cells.The data includes raw FASTQ files (*.fastq.gz). The FASTQs were also run through the DBS-Pro pipeline (https://github.com/FrickTobias/DBS-Pro, v0.4) to generate *data.tsv.gz and *counts.h5ad files. The TSV files *data.tsv.gz contain the following columns: Barcode: The corrected droplet barcode Target: The antibody target (marker) name. E.g. CD3UMI: The UMI sequenceReadCount: Number of reads with this droplet barcode, target and UMI combinationSample: Sample nameFor convenience, [anndata](https://anndata.readthedocs.io/en/latest/index.html) `h5ad` files with count matrices are also generated for each sample. These can be used for downstream analysis using [Scanpy](https://scanpy.readthedocs.io/en/stable/). To import the data use the following code:```import scanpy as scadata = sc.read_h5ad("mysample.h5ad")adata```<br>
DOI: 10.17632/bgrjwbxr6z
title: Ultrastructure of the dung-beetle Johnston's organ

description: The enclosed TEM images were taken of the pedicel of the antenna of Kheper lamarcki – a South African, ball-rolling dung-beetle. These straight-line orientating beetles have been shown to use wind as a compass cue. Therefore, we hypothesised that the pedicel of these beetles contains the chordotonal Johnston’s organ, a mechanosensor known to support wind orientation. The pedicel of two dung-beetles were studied (LJ1.1. and LJ3.1., distinguished in the file names), at three levels of depth (denoted by I, II, or III in the file names). The samples were fixated in a modified Karnovsky fixative, post-fixated in osmium tetroxide, and embedded in AGAR. The embedded samples were then sectioned into 55-70 nm sections and stained with uranyl acetate and Reynold’s lead citrate. The images were viewed in ImageJ2 (v. 2.16.0). Indeed, the micrographs confirm the presence of the stretch receptive Johnston’s organ. The data reveals that the beetles’ Johnston’s organ is heterodynal and amphinematic, and its individual sensory units are arranged in approximately 110 clusters, with 1-5 units per cluster. Each sensory unit contains three sensory neurons, two of which display Type 1 cilia, and one with a Type 2 cilium.
DOI: 10.17632/bgrjwbxr6z.1
title: Ultrastructure of the dung-beetle Johnston's organ

description: The enclosed TEM images were taken of the pedicel of the antenna of Kheper lamarcki – a South African, ball-rolling dung-beetle. These straight-line orientating beetles have been shown to use wind as a compass cue. Therefore, we hypothesised that the pedicel of these beetles contains the chordotonal Johnston’s organ, a mechanosensor known to support wind orientation. The pedicel of two dung-beetles were studied (LJ1.1. and LJ3.1., distinguished in the file names), at three levels of depth (denoted by I, II, or III in the file names). The samples were fixated in a modified Karnovsky fixative, post-fixated in osmium tetroxide, and embedded in AGAR. The embedded samples were then sectioned into 55-70 nm sections and stained with uranyl acetate and Reynold’s lead citrate. The images were viewed in ImageJ2 (v. 2.16.0). Indeed, the micrographs confirm the presence of the stretch receptive Johnston’s organ. The data reveals that the beetles’ Johnston’s organ is heterodynal and amphinematic, and its individual sensory units are arranged in approximately 110 clusters, with 1-5 units per cluster. Each sensory unit contains three sensory neurons, two of which display Type 1 cilia, and one with a Type 2 cilium.
DOI: 10.17044/scilifelab.25441789.v1
title: DBS-Pro PBMCs

description: This resource contains data generated using the method DBS-Pro (Droplet Barcode Sequencing for Protein analysis) for single cells for the manuscript "Identification of Major Immune Cell Lineages with DBS-Pro". The data was generated using peripheral blood mononuclear cells (PBMCs) extracted from a single healthy donor from which two libraries were prepared (P29859_1001, P29859_1002). The data quantifies surface proteins on single cells using a panel of six marker antibodies (CD3, CD4, CD8, CD19, CD14, CD45) and one isotype control antibody to quantify background signal. The antibodies are conjugated to oligonucleotides containing an antibody barcode and a UMI sequence. The stained cells are barcoded in droplet emulsions and then sequenced to get out quantitative information about the cells.The data includes raw FASTQ files (*.fastq.gz). The FASTQs were also run through the DBS-Pro pipeline (https://github.com/FrickTobias/DBS-Pro, v0.4) to generate *data.tsv.gz and *counts.h5ad files. The TSV files *data.tsv.gz contain the following columns: Barcode: The corrected droplet barcode Target: The antibody target (marker) name. E.g. CD3UMI: The UMI sequenceReadCount: Number of reads with this droplet barcode, target and UMI combinationSample: Sample nameFor convenience, [anndata](https://anndata.readthedocs.io/en/latest/index.html) `h5ad` files with count matrices are also generated for each sample. These can be used for downstream analysis using [Scanpy](https://scanpy.readthedocs.io/en/stable/). To import the data use the following code:```import scanpy as scadata = sc.read_h5ad("mysample.h5ad")adata```<br>
DOI: 10.17632/n9v4d68kwd
title: Data required for water resources carrying capacity of Yangtze River Economic Belt from 2010 to 2020

description: Data required for water resources carrying capacity of Yangtze River Economic Belt from 2010 to 2020
DOI: 10.17632/n9v4d68kwd.1
title: Data required for water resources carrying capacity of Yangtze River Economic Belt from 2010 to 2020

description: Data required for water resources carrying capacity of Yangtze River Economic Belt from 2010 to 2020
DOI: 10.7483/opendata.cms.sua7.s1hb
title: /TprimeBToTH_TLep_Hbb_LH_MT900_MH250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.25471861
title: <b>Exploring a pico-well based scRNA-seq method (HIVE</b><sup><strong>TM</strong></sup><b>) for simplified processing of equine bronchoalveolar lavage cells</b>

description: <strong>Abstract</strong>
Single-cell RNA sequencing (scRNA-seq) is a valuable tool for investigating cellular heterogeneity in diseases such as equine asthma (EA). This study evaluates the HIVE™ scRNA-seq method, a pico-well-based technology, for processing bronchoalveolar lavage (BAL) cells from horses with EA. The HIVE method offers practical advantages, including compatibility with both field and clinical settings, as well as a gentle workflow suited for handling sensitive cells.Our results show that the major cell types in equine BAL were successfully identified; however, the proportions of T cells and macrophages deviated from cytological expectations, with macrophages being overrepresented and T cells underrepresented. Despite these limitations, the HIVE method confirmed previously identified T cell and macrophage subpopulations and defined other BAL cell subsets. However, compared to previous studies, T helper subsets were less clearly defined. Additionally, consistent with previous scRNA-seq studies, the HIVE method detected fewer granulocytes and mast cells than anticipated in the total BAL samples. Nevertheless, applying the method to purified mast cells recovered an expected number of cells. A small set of eosinophils were also detected which have not been characterized in earlier studies. In summary these findings suggest that while the HIVE method shows promise for certain applications, further optimization is needed to improve the accuracy of cell type representation, particularly for granulocytes and mast cells, in BAL samples.
The HIVE™ (name, not acronym) libraries were prepared with the single-cell RNA-seq Processing Kit v1 (Honeycomb Biotechnologies) and sequenced on a NovaSeq 6000 v 1.5. Raw reads were processed and converted to count matrices of gene expression values using the custom software BeeNet<sup>TM</sup> (Honeycomb Biotechnologies).
The R code herein and associated Seurat-objects (.rds files) may be used to reproduce the data analysis in the manuscript.
<br>
DOI: 10.17632/wj65h5tnp4.2
title: Bridging the Gap in Hypertension Management: Evaluating Blood Pressure Control and Associated Risk Factors in a Resource-Constrained Setting

description: Dataset Description

This dataset contains a simulated collection of 10,000 patient records, designed to explore hypertension management in resource-constrained settings. It provides comprehensive data for analyzing blood pressure control rates, associated risk factors, and complications. The dataset is ideal for predictive modeling, risk analysis, and treatment optimization, offering insights into demographic, clinical, and treatment-related variables.

Dataset Structure

1. Dataset Volume

 • Size: 10,000 records.
 • Features: 19 variables, categorized into Sociodemographic, Clinical, Complications, and Treatment/Control groups.

2. Variables and Categories

A. Sociodemographic Variables

 1. Age:
 • Continuous variable in years.
 • Range: 18–80 years.
 • Mean ± SD: 49.37 ± 12.81.
 2. Sex:
 • Categorical variable.
 • Values: Male, Female.
 3. Education:
 • Categorical variable.
 • Values: No Education, Primary, Secondary, Higher Secondary, Graduate, Post-Graduate, Madrasa.
 4. Occupation:
 • Categorical variable.
 • Values: Service, Business, Agriculture, Retired, Unemployed, Housewife.
 5. Monthly Income:
 • Categorical variable in Bangladeshi Taka.
 • Values: &lt;5000, 5001–10000, 10001–15000, &gt;15000.
 6. Residence:
 • Categorical variable.
 • Values: Urban, Sub-urban, Rural.

B. Clinical Variables

 7. Systolic BP:
 • Continuous variable in mmHg.
 • Range: 100–200 mmHg.
 • Mean ± SD: 140 ± 15 mmHg.
 8. Diastolic BP:
 • Continuous variable in mmHg.
 • Range: 60–120 mmHg.
 • Mean ± SD: 90 ± 10 mmHg.
 9. Elevated Creatinine:
 • Binary variable (\geq 1.4 \, \text{mg/dL}).
 • Values: Yes, No.
 10. Diabetes Mellitus:
 • Binary variable.
 • Values: Yes, No.
 11. Family History of CVD:
 • Binary variable.
 • Values: Yes, No.
 12. Elevated Cholesterol:
 • Binary variable (\geq 200 \, \text{mg/dL}).
 • Values: Yes, No.
 13. Smoking:
 • Binary variable.
 • Values: Yes, No.

C. Complications

 14. LVH (Left Ventricular Hypertrophy):
 • Binary variable (ECG diagnosis).
 • Values: Yes, No.
 15. IHD (Ischemic Heart Disease):
 • Binary variable.
 • Values: Yes, No.
 16. CVD (Cerebrovascular Disease):
 • Binary variable.
 • Values: Yes, No.
 17. Retinopathy:
 • Binary variable.
 • Values: Yes, No.

D. Treatment and Control

 18. Treatment:
 • Categorical variable indicating therapy type.
 • Values: Single Drug, Combination Drugs.
 19. Control Status:
 • Binary variable.
 • Values: Controlled, Uncontrolled.

Dataset Applications

 1. Predictive Modeling:
 • Develop models to predict blood pressure control status using demographic and clinical data.
 2. Risk Analysis:
 • Identify significant factors influencing hypertension control and complications.
 3. Severity Scoring:
 • Quantify hypertension severity for patient risk stratification.
 4. Complications Prediction:
 • Forecast complications like IHD, LVH, and CVD for early intervention.
 5. Treatment Guidance:
 • Analyze therapy efficacy to recommend optimal treatment strategies.
DOI: 10.17044/scilifelab.25471861.v1
title: <b>Exploring a pico-well based scRNA-seq method (HIVE</b><sup><strong>TM</strong></sup><b>) for simplified processing of equine bronchoalveolar lavage cells</b>

description: <strong>Abstract</strong>
Single-cell RNA sequencing (scRNA-seq) is a valuable tool for investigating cellular heterogeneity in diseases such as equine asthma (EA). This study evaluates the HIVE™ scRNA-seq method, a pico-well-based technology, for processing bronchoalveolar lavage (BAL) cells from horses with EA. The HIVE method offers practical advantages, including compatibility with both field and clinical settings, as well as a gentle workflow suited for handling sensitive cells.Our results show that the major cell types in equine BAL were successfully identified; however, the proportions of T cells and macrophages deviated from cytological expectations, with macrophages being overrepresented and T cells underrepresented. Despite these limitations, the HIVE method confirmed previously identified T cell and macrophage subpopulations and defined other BAL cell subsets. However, compared to previous studies, T helper subsets were less clearly defined. Additionally, consistent with previous scRNA-seq studies, the HIVE method detected fewer granulocytes and mast cells than anticipated in the total BAL samples. Nevertheless, applying the method to purified mast cells recovered an expected number of cells. A small set of eosinophils were also detected which have not been characterized in earlier studies. In summary these findings suggest that while the HIVE method shows promise for certain applications, further optimization is needed to improve the accuracy of cell type representation, particularly for granulocytes and mast cells, in BAL samples.
The HIVE™ (name, not acronym) libraries were prepared with the single-cell RNA-seq Processing Kit v1 (Honeycomb Biotechnologies) and sequenced on a NovaSeq 6000 v 1.5. Raw reads were processed and converted to count matrices of gene expression values using the custom software BeeNet<sup>TM</sup> (Honeycomb Biotechnologies).
The R code herein and associated Seurat-objects (.rds files) may be used to reproduce the data analysis in the manuscript.
<br>
DOI: 10.17632/wj65h5tnp4.1
title: Bridging the Gap in Hypertension Management: Evaluating Blood Pressure Control and Associated Risk Factors in a Resource-Constrained Setting

description: Dataset Description

This dataset contains a simulated collection of 10,000 patient records, designed to explore hypertension management in resource-constrained settings. It provides comprehensive data for analyzing blood pressure control rates, associated risk factors, and complications. The dataset is ideal for predictive modeling, risk analysis, and treatment optimization, offering insights into demographic, clinical, and treatment-related variables.

Dataset Structure

1. Dataset Volume

 • Size: 10,000 records.
 • Features: 19 variables, categorized into Sociodemographic, Clinical, Complications, and Treatment/Control groups.

2. Variables and Categories

A. Sociodemographic Variables

 1. Age:
 • Continuous variable in years.
 • Range: 18–80 years.
 • Mean ± SD: 49.37 ± 12.81.
 2. Sex:
 • Categorical variable.
 • Values: Male, Female.
 3. Education:
 • Categorical variable.
 • Values: No Education, Primary, Secondary, Higher Secondary, Graduate, Post-Graduate, Madrasa.
 4. Occupation:
 • Categorical variable.
 • Values: Service, Business, Agriculture, Retired, Unemployed, Housewife.
 5. Monthly Income:
 • Categorical variable in Bangladeshi Taka.
 • Values: &lt;5000, 5001–10000, 10001–15000, &gt;15000.
 6. Residence:
 • Categorical variable.
 • Values: Urban, Sub-urban, Rural.

B. Clinical Variables

 7. Systolic BP:
 • Continuous variable in mmHg.
 • Range: 100–200 mmHg.
 • Mean ± SD: 140 ± 15 mmHg.
 8. Diastolic BP:
 • Continuous variable in mmHg.
 • Range: 60–120 mmHg.
 • Mean ± SD: 90 ± 10 mmHg.
 9. Elevated Creatinine:
 • Binary variable (\geq 1.4 \, \text{mg/dL}).
 • Values: Yes, No.
 10. Diabetes Mellitus:
 • Binary variable.
 • Values: Yes, No.
 11. Family History of CVD:
 • Binary variable.
 • Values: Yes, No.
 12. Elevated Cholesterol:
 • Binary variable (\geq 200 \, \text{mg/dL}).
 • Values: Yes, No.
 13. Smoking:
 • Binary variable.
 • Values: Yes, No.

C. Complications

 14. LVH (Left Ventricular Hypertrophy):
 • Binary variable (ECG diagnosis).
 • Values: Yes, No.
 15. IHD (Ischemic Heart Disease):
 • Binary variable.
 • Values: Yes, No.
 16. CVD (Cerebrovascular Disease):
 • Binary variable.
 • Values: Yes, No.
 17. Retinopathy:
 • Binary variable.
 • Values: Yes, No.

D. Treatment and Control

 18. Treatment:
 • Categorical variable indicating therapy type.
 • Values: Single Drug, Combination Drugs.
 19. Control Status:
 • Binary variable.
 • Values: Controlled, Uncontrolled.

Dataset Applications

 1. Predictive Modeling:
 • Develop models to predict blood pressure control status using demographic and clinical data.
 2. Risk Analysis:
 • Identify significant factors influencing hypertension control and complications.
 3. Severity Scoring:
 • Quantify hypertension severity for patient risk stratification.
 4. Complications Prediction:
 • Forecast complications like IHD, LVH, and CVD for early intervention.
 5. Treatment Guidance:
 • Analyze therapy efficacy to recommend optimal treatment strategies.
DOI: 10.7483/opendata.cms.tjf0.qcmb
title: /TprimeBToTH_TLep_Hbb_LH_MT900_MH250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.wk34.uzyu
title: /TprimeBToTH_TLep_Hbb_LH_MT900_MH25_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.v6j9.pago
title: /TprimeBToTH_TLep_Hbb_LH_MT900_MH25_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.15468/dl.6ayq2c
title: Occurrence Download

description: A dataset containing 13708 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Living Specimen, Occurrence evidence)",
 "Country is one of (Canada, United States of America)",
 "TaxonKey is Larix laricina (Du Roi) K.Koch",
 "Year 1990-2025"
 ]
}

 The dataset includes 13708 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071221-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.cms.kvah.edzd
title: /TprimeBToTH_TLep_Hbb_LH_MT900_MH350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.eaj6.g3vd
title: /TprimeBToTH_TLep_Hbb_LH_MT900_MH350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM