cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11875815
title: Additional file 9 of Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

description: Additional file 9. List of primers for quantitative real-time PCR.
DOI: 10.6084/m9.figshare.11875815.v1
title: Additional file 9 of Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

description: Additional file 9. List of primers for quantitative real-time PCR.
DOI: 10.6084/m9.figshare.11875806
title: Additional file 8 of Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

description: Additional file 8. The lncRNA-mRNA pairs in the lncRNA-mRNA co-expression network of plant hormone signal transduction.
DOI: 10.6084/m9.figshare.11875806.v1
title: Additional file 8 of Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

description: Additional file 8. The lncRNA-mRNA pairs in the lncRNA-mRNA co-expression network of plant hormone signal transduction.
DOI: 10.6084/m9.figshare.11875797
title: Additional file 6 of Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

description: Additional file 6. Transcription factors corresponding to differentially expressed lncRNAs in two genotypes.
DOI: 10.6084/m9.figshare.11875800
title: Additional file 7 of Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

description: Additional file 7. Differentially expressed lncRNAs in response to drought stress and re-watering.
DOI: 10.6084/m9.figshare.11875794
title: Additional file 5 of Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

description: Additional file 5. The lncRNA-mRNA pairs reflected the opposite expression trend in two genotypes.
DOI: 10.6084/m9.figshare.11875794.v1
title: Additional file 5 of Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

description: Additional file 5. The lncRNA-mRNA pairs reflected the opposite expression trend in two genotypes.
DOI: 10.6084/m9.figshare.11875797.v1
title: Additional file 6 of Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

description: Additional file 6. Transcription factors corresponding to differentially expressed lncRNAs in two genotypes.
DOI: 10.6084/m9.figshare.11875800.v1
title: Additional file 7 of Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

description: Additional file 7. Differentially expressed lncRNAs in response to drought stress and re-watering.
DOI: 10.6084/m9.figshare.11875788
title: Additional file 4 of Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

description: Additional file 4. LncRNA and their putative target mRNA based on the co-expression results.
DOI: 10.6084/m9.figshare.11875788.v1
title: Additional file 4 of Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

description: Additional file 4. LncRNA and their putative target mRNA based on the co-expression results.
DOI: 10.6084/m9.figshare.11875782
title: Additional file 3 of Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

description: Additional file 3. Relative expression data measured by qRT-PCRs and RNA-seq.
DOI: 10.6084/m9.figshare.11875782.v1
title: Additional file 3 of Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

description: Additional file 3. Relative expression data measured by qRT-PCRs and RNA-seq.
DOI: 10.6084/m9.figshare.11875770
title: Additional file 1 of Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

description: Additional file 1. RNA-seq data for 12 samples.
DOI: 10.6084/m9.figshare.11875773
title: Additional file 2 of Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

description: Additional file 2. RNA amount obtained from each treatment for RNA-seq analysis.
DOI: 10.6084/m9.figshare.11875773.v1
title: Additional file 2 of Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

description: Additional file 2. RNA amount obtained from each treatment for RNA-seq analysis.
DOI: 10.6084/m9.figshare.11875770.v1
title: Additional file 1 of Genome-wide analysis of long non-coding RNAs (lncRNAs) in two contrasting rapeseed (Brassica napus L.) genotypes subjected to drought stress and re-watering

description: Additional file 1. RNA-seq data for 12 samples.
DOI: 10.6084/m9.figshare.11875701
title: Additional file 6 of QTL mapping for plant height and fruit branch number based on RIL population of upland cotton

description: Additional file 6: Table S6. The genes annotation information in stable QTLs for PH and FBN.
DOI: 10.6084/m9.figshare.11875701.v1
title: Additional file 6 of QTL mapping for plant height and fruit branch number based on RIL population of upland cotton

description: Additional file 6: Table S6. The genes annotation information in stable QTLs for PH and FBN.
DOI: 10.6084/m9.figshare.11875692
title: Additional file 5 of QTL mapping for plant height and fruit branch number based on RIL population of upland cotton

description: Additional file 5: Table S5. The genes in stable QTLs for PH and FBN used for KEGG analysis.
DOI: 10.6084/m9.figshare.11875692.v1
title: Additional file 5 of QTL mapping for plant height and fruit branch number based on RIL population of upland cotton

description: Additional file 5: Table S5. The genes in stable QTLs for PH and FBN used for KEGG analysis.
DOI: 10.6084/m9.figshare.11875683
title: Additional file 4 of QTL mapping for plant height and fruit branch number based on RIL population of upland cotton

description: Additional file 4: Table S4. The genes in stable QTL for PH and FBN used for GO analysis.
DOI: 10.6084/m9.figshare.11875683.v1
title: Additional file 4 of QTL mapping for plant height and fruit branch number based on RIL population of upland cotton

description: Additional file 4: Table S4. The genes in stable QTL for PH and FBN used for GO analysis.
DOI: 10.6084/m9.figshare.11875674
title: Additional file 3 of QTL mapping for plant height and fruit branch number based on RIL population of upland cotton

description: Additional file 3: Table S3. Distribution of the QTL clusters.
DOI: 10.6084/m9.figshare.11875671
title: Additional file 2 of QTL mapping for plant height and fruit branch number based on RIL population of upland cotton

description: Additional file 2: Table S2. The QTLs for agronomic traits identified in multiple environments.
DOI: 10.6084/m9.figshare.11875674.v1
title: Additional file 3 of QTL mapping for plant height and fruit branch number based on RIL population of upland cotton

description: Additional file 3: Table S3. Distribution of the QTL clusters.
DOI: 10.6084/m9.figshare.11875665
title: Additional file 1 of QTL mapping for plant height and fruit branch number based on RIL population of upland cotton

description: Additional file 1: Table S1. The ANOVA analysis and heritability analysis of PH and FBN traits.
DOI: 10.6084/m9.figshare.11875671.v1
title: Additional file 2 of QTL mapping for plant height and fruit branch number based on RIL population of upland cotton

description: Additional file 2: Table S2. The QTLs for agronomic traits identified in multiple environments.
DOI: 10.6084/m9.figshare.11875665.v1
title: Additional file 1 of QTL mapping for plant height and fruit branch number based on RIL population of upland cotton

description: Additional file 1: Table S1. The ANOVA analysis and heritability analysis of PH and FBN traits.
DOI: 10.6084/m9.figshare.12863996
title: DASS, Coping Australian Osteopathy Students

description: Dataset for Australian osteopathy students in relation to stress, anxiety, depression and coping strategies.
DOI: 10.6084/m9.figshare.12863996.v1
title: DASS, Coping Australian Osteopathy Students

description: Dataset for Australian osteopathy students in relation to stress, anxiety, depression and coping strategies.
DOI: 10.6084/m9.figshare.11875515
title: Additional file 9 of Establishment and characterization of patient-derived cancer models of malignant peripheral nerve sheath tumors

description: Additional file 9: Table S9. Cell viability after treatment with anti-cancer agents.
DOI: 10.6084/m9.figshare.11875515.v1
title: Additional file 9 of Establishment and characterization of patient-derived cancer models of malignant peripheral nerve sheath tumors

description: Additional file 9: Table S9. Cell viability after treatment with anti-cancer agents.
DOI: 10.6084/m9.figshare.11875509
title: Additional file 8 of Establishment and characterization of patient-derived cancer models of malignant peripheral nerve sheath tumors

description: Additional file 8: Table S8. Detection of depth by NCC Oncopanel.
DOI: 10.6084/m9.figshare.11875506
title: Additional file 7 of Establishment and characterization of patient-derived cancer models of malignant peripheral nerve sheath tumors

description: Additional file 7: Table S7. Detection of Mapping by NCC Oncopanel.
DOI: 10.6084/m9.figshare.11875506.v1
title: Additional file 7 of Establishment and characterization of patient-derived cancer models of malignant peripheral nerve sheath tumors

description: Additional file 7: Table S7. Detection of Mapping by NCC Oncopanel.
DOI: 10.6084/m9.figshare.11875500
title: Additional file 6 of Establishment and characterization of patient-derived cancer models of malignant peripheral nerve sheath tumors

description: Additional file 6: Table S6. Detection of fusion by NCC Oncopanel.
DOI: 10.6084/m9.figshare.11875500.v1
title: Additional file 6 of Establishment and characterization of patient-derived cancer models of malignant peripheral nerve sheath tumors

description: Additional file 6: Table S6. Detection of fusion by NCC Oncopanel.
DOI: 10.6084/m9.figshare.11875509.v1
title: Additional file 8 of Establishment and characterization of patient-derived cancer models of malignant peripheral nerve sheath tumors

description: Additional file 8: Table S8. Detection of depth by NCC Oncopanel.
DOI: 10.6084/m9.figshare.11875491
title: Additional file 5 of Establishment and characterization of patient-derived cancer models of malignant peripheral nerve sheath tumors

description: Additional file 5: Table S5. Detection of CNV by NCC Oncopanel.
DOI: 10.6084/m9.figshare.11875491.v1
title: Additional file 5 of Establishment and characterization of patient-derived cancer models of malignant peripheral nerve sheath tumors

description: Additional file 5: Table S5. Detection of CNV by NCC Oncopanel.
DOI: 10.6084/m9.figshare.11875482
title: Additional file 4 of Establishment and characterization of patient-derived cancer models of malignant peripheral nerve sheath tumors

description: Additional file 4: Table S4. Detection of SNV and indels by NCC Oncopanel.
DOI: 10.6084/m9.figshare.11875482.v1
title: Additional file 4 of Establishment and characterization of patient-derived cancer models of malignant peripheral nerve sheath tumors

description: Additional file 4: Table S4. Detection of SNV and indels by NCC Oncopanel.
DOI: 10.6084/m9.figshare.11875470
title: Additional file 3 of Establishment and characterization of patient-derived cancer models of malignant peripheral nerve sheath tumors

description: Additional file 3: Table S3. Summary of detections by NCC Oncopanel.
DOI: 10.6084/m9.figshare.11875470.v1
title: Additional file 3 of Establishment and characterization of patient-derived cancer models of malignant peripheral nerve sheath tumors

description: Additional file 3: Table S3. Summary of detections by NCC Oncopanel.
DOI: 10.6084/m9.figshare.11875458
title: Additional file 1 of Establishment and characterization of patient-derived cancer models of malignant peripheral nerve sheath tumors

description: Additional file 1: Table S1. Genes examined by NCC Oncopanel.
DOI: 10.6084/m9.figshare.11875461
title: Additional file 2 of Establishment and characterization of patient-derived cancer models of malignant peripheral nerve sheath tumors

description: Additional file 2: Table S2. Anti-cancer agents examined in this study.
DOI: 10.6084/m9.figshare.11875458.v1
title: Additional file 1 of Establishment and characterization of patient-derived cancer models of malignant peripheral nerve sheath tumors

description: Additional file 1: Table S1. Genes examined by NCC Oncopanel.
DOI: 10.6084/m9.figshare.11875461.v1
title: Additional file 2 of Establishment and characterization of patient-derived cancer models of malignant peripheral nerve sheath tumors

description: Additional file 2: Table S2. Anti-cancer agents examined in this study.
DOI: 10.6084/m9.figshare.11875449
title: Plant_Data.xlsx

description: Data for plant community composition in semi-natural grasslands formed on edges of rice fields in Tokyo, Japan.
DOI: 10.6084/m9.figshare.11875455
title: Interaction_Data.xlsx

description: Data for plant–insect interactions (herbivory) in semi-natural grasslands formed on edges of rice fields in Tokyo, Japan.
DOI: 10.6084/m9.figshare.11875455.v1
title: Interaction_Data.xlsx

description: Data for plant–insect interactions (herbivory) in semi-natural grasslands formed on edges of rice fields in Tokyo, Japan.
DOI: 10.6084/m9.figshare.11875449.v1
title: Plant_Data.xlsx

description: Data for plant community composition in semi-natural grasslands formed on edges of rice fields in Tokyo, Japan.
DOI: 10.6084/m9.figshare.11874825
title: Thamnaconus septentrionalis genome supporting data

description: This dataset includes the de novo transcriptome assembly, gene annotations and a high quality genome-wide Hi-C heatmap of Thamnaconus septentrionalis.
DOI: 10.6084/m9.figshare.11874831
title: Sleep-Disordered Breathing Does Not Impact Maternal Outcomes in Women with Hypertensive Disorders of Pregnancy

description: The relationship between sleep-disordered breathing and the severity of hypertensive disease during pregnancy.
DOI: 10.6084/m9.figshare.11874831.v1
title: Sleep-Disordered Breathing Does Not Impact Maternal Outcomes in Women with Hypertensive Disorders of Pregnancy

description: The relationship between sleep-disordered breathing and the severity of hypertensive disease during pregnancy.
DOI: 10.6084/m9.figshare.11874783
title: FB15k

description: This FREEBASE FB15k DATA consists of a collection of triplets (synset, relation_type, triplet) extracted from Freebase (http://www.freebase.com). This data set can be seen as a 3-mode tensor depicting ternary relationships between synsets. <br>All freebase_mtr100_mte100-*.txt files contain one triplet per line, with 2 mids (unique Freebase entity identifier) and relation type identifier in a tab separated format. The first element is the mid of the left hand side (of head) of the relation triple, the third one is the mid of the right hand side (or tail) and the second element is the name of the relationship between them.<br><br>There are 14,951 mids and 1,345 relation types among them. The training set contains 483,142 triplets, the validation set 50,000 and the test set 59,071.<br>All triplets are unique and we made sure that all synsets appearing in the validation or test sets were occurring in the training set.<br>The FB15k.zip file contains the other files, with more compression than the default "download all".
DOI: 10.6084/m9.figshare.11874792
title: SNP genotype data of 4234 accessions

description: SNP genotype data of 4234 accessions listed in Table S1. Jeong et al. (2019) Genetic diversity patterns and domestication origin of soybean. Theoretical and Applied Genetics 132:1179-1193. https://doi.org/10.1007/s00122-018-3271-7. Update details: Update of the 180K SoyaSNP array data genotyped from the 4,234 soybean samples: 10,572 of 170,223 high-quality SNPs were oriented in Glycine max Williams 82 genome assembly Glyma.Wm82.a2 relative to Glyma.Wm82.a1. Thus, the 10,572 SNP sites were corrected by replacing complementary base to each of those sites.
DOI: 10.6084/m9.figshare.11874792.v1
title: SNP genotype data of 4234 accessions

description: SNP genotype data of 4234 accessions listed in Table S1. Jeong et al. (2019) Genetic diversity patterns and domestication origin of soybean. Theoretical and Applied Genetics 132:1179-1193. https://doi.org/10.1007/s00122-018-3271-7. Update details: Update of the 180K SoyaSNP array data genotyped from the 4,234 soybean samples: 10,572 of 170,223 high-quality SNPs were oriented in Glycine max Williams 82 genome assembly Glyma.Wm82.a2 relative to Glyma.Wm82.a1. Thus, the 10,572 SNP sites were corrected by replacing complementary base to each of those sites.
DOI: 10.6084/m9.figshare.11874783.v1
title: FB15k

description: This FREEBASE FB15k DATA consists of a collection of triplets (synset, relation_type, triplet) extracted from Freebase (http://www.freebase.com). This data set can be seen as a 3-mode tensor depicting ternary relationships between synsets. <br>All freebase_mtr100_mte100-*.txt files contain one triplet per line, with 2 mids (unique Freebase entity identifier) and relation type identifier in a tab separated format. The first element is the mid of the left hand side (of head) of the relation triple, the third one is the mid of the right hand side (or tail) and the second element is the name of the relationship between them.<br><br>There are 14,951 mids and 1,345 relation types among them. The training set contains 483,142 triplets, the validation set 50,000 and the test set 59,071.<br>All triplets are unique and we made sure that all synsets appearing in the validation or test sets were occurring in the training set.
DOI: 10.6084/m9.figshare.11874432
title: tara_sample_nut_data

description: Sample and nutrient data for all tara amplicon data from surface and dcm with a lower filter threshold &lt;20 um.
DOI: 10.6084/m9.figshare.11874432.v1
title: tara_sample_nut_data

description: Sample and nutrient data for all tara amplicon data from surface and dcm with a lower filter threshold &lt;20 um.
DOI: 10.6084/m9.figshare.11874372
title: SNP_LOC_DATA.rda

description: This file is used by MAGMA_Celltyping R package and is too large to store in github <br><br> tmpF1 = tempfile() tmpF2 = tempfile()<br> download.file("https://ctg.cncr.nl/software/MAGMA/ref_data/g1000_eur.zip",tmpF1)<br> library(R.utils)<br> unzip(tmpF1,exdir=dirname(tmpF1)) <br> library(data.table)<br> snpsALL = data.table::fread(sprintf("%s/g1000_eur.bim",dirname(tmpF1)))<br> g1000_snps = as.character(snpsALL$V2)<br> <br> #tmpF1 = tempfile()<br> #tmpF2 = tempfile()<br> #download.file("https://data.broadinstitute.org/alkesgroup/LDSCORE/w_hm3.snplist.bz2",tmpF1)<br> #library(R.utils)<br> #gunzip(tmpF1,tmpF2)<br> #whm3 = read.table(tmpF2,stringsAsFactors = FALSE)<br> #hm3_rsids = whm3[,1]<br> <br> # Get GRCh37 locations<br> #source("https://bioconductor.org/biocLite.R")<br> #BiocManager::install("SNPlocs.Hsapiens.dbSNP144.GRCh37")<br> library("SNPlocs.Hsapiens.dbSNP144.GRCh37")<br> snps #g1000_snps = g1000_snps[grep("^rs",g1000_snps)]<br> snp_locs = snpsById(snps, g1000_snps,ifnotfound="drop")<br> SNP = mcols(snp_locs)$RefSNP_id<br> CHR = as.character(seqnames(snp_locs)) #seqnames(snp_locs)<br> BP = pos(snp_locs)<br> SNP_DATA_GRCh37 = data.frame(SNP=SNP,CHR=CHR,BP=BP,Build="GRCh37",stringsAsFactors=FALSE)<br> #SNP_DATA_GRCh37 = data.table(SNP=SNP,CHR=CHR,BP=BP,Build="GRCh37")<br> #usethis::use_data(SNP_DATA_GRCh37,overwrite = TRUE)<br> <br> # Get GRCh38 locations<br> #source("https://bioconductor.org/biocLite.R")<br> #BiocManager::install("SNPlocs.Hsapiens.dbSNP144.GRCh38")<br> library(SNPlocs.Hsapiens.dbSNP144.GRCh38)<br> snps snp_locs = snpsById(snps, g1000_snps,ifnotfound="drop")<br> SNP = mcols(snp_locs)$RefSNP_id<br> CHR = as.character(seqnames(snp_locs))<br> BP = pos(snp_locs)<br> SNP_DATA_GRCh38 = data.frame(SNP=SNP,CHR=CHR,BP=BP,Build="GRCh38",stringsAsFactors=FALSE)<br> #usethis::use_data(SNP_DATA_GRCh38,overwrite = TRUE)<br> <br> # Save to package /data/<br> SNP_LOC_DATA = rbind(SNP_DATA_GRCh37,SNP_DATA_GRCh38)<br> usethis::use_data(SNP_LOC_DATA,overwrite = TRUE)
DOI: 10.6084/m9.figshare.11874357
title: Boyd Data

description: Data From: https://dx.doi.org/10.3389/fmicb.2016.01323 via interpersonal data request
DOI: 10.6084/m9.figshare.11874354
title: Elling Data

description: Data from: https://doi.org/10.1016/j.gca.2014.07.005 via interpersonal data request
DOI: 10.6084/m9.figshare.11874357.v1
title: Boyd Data

description: Data From: https://dx.doi.org/10.3389/fmicb.2016.01323 via interpersonal data request
DOI: 10.6084/m9.figshare.11874351
title: Batch Experiment Dataframe

description: Dataframe containing data on Sulfolobus acidocaldarius growth rate. Data from Cobban 2020 characterization experiments. Growth Curves are in an ordered text list with OD600 and sampling times seperated by a "/". RI is calculated including all GDGT's. Growth rates calculated with a custom script described in more detail elsewhere
DOI: 10.6084/m9.figshare.11874354.v1
title: Elling Data

description: Data from: https://doi.org/10.1016/j.gca.2014.07.005 via interpersonal data request
DOI: 10.6084/m9.figshare.11874291
title: Data in "Large-scale integration of artificial atoms in hybrid photonic circuits"

description: <b>Datasets </b><br><b>Manuscript:</b> Large-scale integration of artificial atoms in hybrid photonic circuits<br><b>Authors:</b> Noel H. Wan*, Tsung-Ju Lu**, Kevin C. Chen, Michael P. Walsh, Matthew E. Trusheim, Lorenzo De Santis, Eric A. Bersin, Isaac B. Harris, Sara L. Mouradian, Ian R. Christen, Edward S. Bielejec, and Dirk Englund***<br>*noelwan@mit.edu**tsungjul@mit.edu***englund@mit.edu<br><br>______________________________________________<b><br></b><b>Data: Experiment</b><br><b><br></b>Figure 3: https://figshare.com/s/e6930cd950711bbcc477Figure 4: https://figshare.com/s/d6ce61e336a135fc8cebFigure 5 & Extended Data Fig 6: https://figshare.com/s/d4259b9376dc0222cf86Extended Data Fig 4: https://figshare.com/s/02ff5993922dadec7a2b<br>Extended Data Fig 7: https://figshare.com/s/01dc1c0aa8b3289df154<br>Extended Data Fig 8: https://figshare.com/s/fcf126addbe10624bb14<br>Extended Data Fig 9: https://figshare.com/s/162698a8d1f857264826<br>Extended Data Table 1: https://figshare.com/s/683bf55a952d0caccea5<br><br>______________________________________________<br><br><b>Data: Simulation <br><br></b>Extended Data Fig 3: https://figshare.com/s/607a8c1eae618e7bc5e9<br><br><br><br>