cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.15079676
title: Additional file 13 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 13: Table S12. Using RandomForest, n = 274 ileal patients were randomly split into equal number of samples, n = 14 B2 and n = 14 B3. These were further aliquoted into test and train datasets for cross-validation of AUC with n = 13,777 ncRNAs, base mean greater than n &gt; 10.00. The input included n = 14 B2 and n = 14 B3 used to test disease versus controls status by evaluating model and test for accuracy based on sensitivities and specificity..
DOI: 10.6084/m9.figshare.15079676.v1
title: Additional file 13 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 13: Table S12. Using RandomForest, n = 274 ileal patients were randomly split into equal number of samples, n = 14 B2 and n = 14 B3. These were further aliquoted into test and train datasets for cross-validation of AUC with n = 13,777 ncRNAs, base mean greater than n &gt; 10.00. The input included n = 14 B2 and n = 14 B3 used to test disease versus controls status by evaluating model and test for accuracy based on sensitivities and specificity..
DOI: 10.6084/m9.figshare.15079679
title: Additional file 14 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 14: Table S13. Using RandomForest, n = 274 ileal patients were randomly split into equal number of samples, n = 14 B1 and n = 14 B3. These were further aliquoted into test and train datasets for cross-validation of AUC with n = 13,777 ncRNAs, base mean greater than n &gt; 10.00. The input included n = 14 B1 and n = 14 B3 used to test disease versus controls status by evaluating model and test for accuracy based on sensitivities and specificity.
DOI: 10.6084/m9.figshare.15079679.v1
title: Additional file 14 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 14: Table S13. Using RandomForest, n = 274 ileal patients were randomly split into equal number of samples, n = 14 B1 and n = 14 B3. These were further aliquoted into test and train datasets for cross-validation of AUC with n = 13,777 ncRNAs, base mean greater than n &gt; 10.00. The input included n = 14 B1 and n = 14 B3 used to test disease versus controls status by evaluating model and test for accuracy based on sensitivities and specificity.
DOI: 10.6084/m9.figshare.15079682
title: Additional file 15 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 15: Table S14. Using RandomForest, n = 274 ileal patients were randomly split into equal test and train datasets for cross-validation of AUC with n = 13,777 ncRNAs, base mean greater than n &gt; 10.00. The input included n = 20 non-inflamed and n = 20 inflamed (L1 = 10 and L3 = 10) used to test disease inflammation status by evaluating model and test for accuracy based on sensitivities and specificity.
DOI: 10.6084/m9.figshare.15079682.v1
title: Additional file 15 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 15: Table S14. Using RandomForest, n = 274 ileal patients were randomly split into equal test and train datasets for cross-validation of AUC with n = 13,777 ncRNAs, base mean greater than n &gt; 10.00. The input included n = 20 non-inflamed and n = 20 inflamed (L1 = 10 and L3 = 10) used to test disease inflammation status by evaluating model and test for accuracy based on sensitivities and specificity.
DOI: 10.6084/m9.figshare.15079685
title: Additional file 16 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 16: Table S15. Using RandomForest, n = 274 ileal patients were randomly split into equal test and train datasets for cross-validation of AUC with n = 13,777 ncRNAs, base mean greater than n &gt; 10.00. The input included n = 76 non-inflamed (+ L2 = 56) and n = 76 inflamed (L1 = 38, L3 = 38) used to test disease inflammation status by evaluating model and test for accuracy based on sensitivities and specificity.
DOI: 10.6084/m9.figshare.15079685.v1
title: Additional file 16 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 16: Table S15. Using RandomForest, n = 274 ileal patients were randomly split into equal test and train datasets for cross-validation of AUC with n = 13,777 ncRNAs, base mean greater than n &gt; 10.00. The input included n = 76 non-inflamed (+ L2 = 56) and n = 76 inflamed (L1 = 38, L3 = 38) used to test disease inflammation status by evaluating model and test for accuracy based on sensitivities and specificity.
DOI: 10.6084/m9.figshare.15079688.v1
title: Additional file 1 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 1: Table S1. List of fifteen types of non-coding RNAs categorization based upon their length of nucleotides.
DOI: 10.6084/m9.figshare.15079688
title: Additional file 1 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 1: Table S1. List of fifteen types of non-coding RNAs categorization based upon their length of nucleotides.
DOI: 10.6084/m9.figshare.15079705
title: Additional file 1 of Assessing the filtration efficiency and regulatory status of N95s and nontraditional filtering face-piece respirators available during the COVID-19 pandemic

description: Additional file 1: Analysis of concordance between results obtained from filtration efficiency testing at academic laboratory and ICS laboratories (n = 10).
DOI: 10.6084/m9.figshare.15079705.v1
title: Additional file 1 of Assessing the filtration efficiency and regulatory status of N95s and nontraditional filtering face-piece respirators available during the COVID-19 pandemic

description: Additional file 1: Analysis of concordance between results obtained from filtration efficiency testing at academic laboratory and ICS laboratories (n = 10).
DOI: 10.6084/m9.figshare.15079694
title: Additional file 3 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 3: Table S2. The cohort Metadata Chi-squares: Chi-squared of patient characteristics such as sex, age, disease type, disease behavior, and inflammatory status in both ileal (n = 345), and rectal (n = 390) datasets.
DOI: 10.6084/m9.figshare.15079694.v1
title: Additional file 3 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 3: Table S2. The cohort Metadata Chi-squares: Chi-squared of patient characteristics such as sex, age, disease type, disease behavior, and inflammatory status in both ileal (n = 345), and rectal (n = 390) datasets.
DOI: 10.6084/m9.figshare.15079697.v1
title: Additional file 4 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 4: Table S3. The case-control DE analysis results on ileal (n = 345) and rectal (n = 390) biopsies, by comparing disease status (CD = 274 vs. CTRL = 71) in ileal and rectal (CD = 329 vs. CTRL=61) datasets. All FDR significant DEncRNAs and nominally significant (P &lt; 0.05) obtained from both ileal (n=3520) and rectal (n = 1447) are provided Ileum Gene Ontology: Using TopGO with input of 89 DEncRNAs in ileal samples from CD versus controls, 136 pathways were observed. Of which, 36 were molecular function, 79 cellular components, and 21 biological processes.
DOI: 10.6084/m9.figshare.15079697
title: Additional file 4 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 4: Table S3. The case-control DE analysis results on ileal (n = 345) and rectal (n = 390) biopsies, by comparing disease status (CD = 274 vs. CTRL = 71) in ileal and rectal (CD = 329 vs. CTRL=61) datasets. All FDR significant DEncRNAs and nominally significant (P &lt; 0.05) obtained from both ileal (n=3520) and rectal (n = 1447) are provided Ileum Gene Ontology: Using TopGO with input of 89 DEncRNAs in ileal samples from CD versus controls, 136 pathways were observed. Of which, 36 were molecular function, 79 cellular components, and 21 biological processes.
DOI: 10.6084/m9.figshare.15079700
title: Additional file 5 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 5: Table S4. Ileum Gene Ontology: Using TopGO with input of 89 DEncRNAs in ileal samples from CD versus controls, 136 pathways were observed. Of which, 36 were molecular function, 79 cellular components, and 21 biological processes.
DOI: 10.6084/m9.figshare.15079700.v1
title: Additional file 5 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 5: Table S4. Ileum Gene Ontology: Using TopGO with input of 89 DEncRNAs in ileal samples from CD versus controls, 136 pathways were observed. Of which, 36 were molecular function, 79 cellular components, and 21 biological processes.
DOI: 10.6084/m9.figshare.15079714
title: Additional file 4 of Assessing the filtration efficiency and regulatory status of N95s and nontraditional filtering face-piece respirators available during the COVID-19 pandemic

description: Additional file 4: Filtration efficiency raw data; depicted in Fig. 3.
DOI: 10.6084/m9.figshare.15079714.v1
title: Additional file 4 of Assessing the filtration efficiency and regulatory status of N95s and nontraditional filtering face-piece respirators available during the COVID-19 pandemic

description: Additional file 4: Filtration efficiency raw data; depicted in Fig. 3.
DOI: 10.6084/m9.figshare.15079703
title: Additional file 6 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 6: Table S5. Using TopGO with input of 41 DEncRNAs in rectal samples from CD versus controls, 36 pathways were observed. Of which, 7 were molecular function, 12 were cellular, and 17 biological processes.
DOI: 10.6084/m9.figshare.15079703.v1
title: Additional file 6 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 6: Table S5. Using TopGO with input of 41 DEncRNAs in rectal samples from CD versus controls, 36 pathways were observed. Of which, 7 were molecular function, 12 were cellular, and 17 biological processes.
DOI: 10.6084/m9.figshare.15079706.v1
title: Additional file 7 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 7: Table S6. Ileum Disease Behavior DEncRNAs. DE analysis results for ileal (n = 345) biopsies by comparing disease behavior against controls and among CD disease behaviors. All FDR significant DEncRNAs with log2FC &gt; 1 and nominally significant (P &lt; 0.05) are listed, B1 versus controls (n = 70), B2 versus controls (n = 124), and B3 versus controls (n = 22).
DOI: 10.6084/m9.figshare.15079709.v1
title: Additional file 8 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 8: Table S7. Rectum Disease Behavior DEncRNAs: DE analysis results for rectal (n = 390) biopsies by comparing disease behavior against controls and among CD disease behaviors. All FDR significant DEncRNAs and nominally significant (P &lt; 0.05) are listed, B1 versus controls (n = 23), B2 versus controls (n = 9), and B3 versus controls (n = 14).
DOI: 10.6084/m9.figshare.15079706
title: Additional file 7 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 7: Table S6. Ileum Disease Behavior DEncRNAs. DE analysis results for ileal (n = 345) biopsies by comparing disease behavior against controls and among CD disease behaviors. All FDR significant DEncRNAs with log2FC &gt; 1 and nominally significant (P &lt; 0.05) are listed, B1 versus controls (n = 70), B2 versus controls (n = 124), and B3 versus controls (n = 22).
DOI: 10.6084/m9.figshare.15079709
title: Additional file 8 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 8: Table S7. Rectum Disease Behavior DEncRNAs: DE analysis results for rectal (n = 390) biopsies by comparing disease behavior against controls and among CD disease behaviors. All FDR significant DEncRNAs and nominally significant (P &lt; 0.05) are listed, B1 versus controls (n = 23), B2 versus controls (n = 9), and B3 versus controls (n = 14).
DOI: 10.6084/m9.figshare.15079712
title: Additional file 9 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 9: Table S8. Intra-Disease Behavior DEncRNAs: DE analysis results for ileal (n = 345) biopsies by comparing among disease behaviors. All FDR significant DEncRNAs and nominally significant (P
DOI: 10.6084/m9.figshare.15079712.v1
title: Additional file 9 of Profiling non-coding RNA levels with clinical classifiers in pediatric Crohn’s disease

description: Additional file 9: Table S8. Intra-Disease Behavior DEncRNAs: DE analysis results for ileal (n = 345) biopsies by comparing among disease behaviors. All FDR significant DEncRNAs and nominally significant (P
DOI: 10.6084/m9.figshare.15079966
title: Additional file 6 of Simulation studies to optimize genomic selection in honey bees

description: Additional file 6. Genetic gain, $${R}_{GS}$$ R GS , in the initial selection cycle of different breeding schemes applying CBS and GPS. The table presents the configuration of the breeding schemes shown in Fig. 4, as well as 9 reruns of the optimal breeding scheme with ssGBLUPBQ, and 1765 other runs chosen at even spacing to represent the remaining schemes. Equations (18), (19), and (20) were used to calculate $${R}_{GS}$$ R GS in the scenarios described in Table 1 for different ratios of preselected BQ and DPQ, with the prediction accuracy adjusted to the proportion on preselected BQ in all scenarios. Genetic gain is given in the units of the selection criterion. The parameter setting MOD was used. The standard deviations of the true breeding values $${\sigma }_{pW}$$ σ pW (worker groups from year 8) and $${\sigma }_{uQ}$$ σ uQ (queens from year 9) are shown in Additional file 5. The 1765 remaining schemes were picked by the numbers of dams of DPQ chosen for GPS, ( $${N}_{DPQ}^{GPS}$$ N DPQ GPS ), dams of BQ chosen for GPS ( $${N}_{BQ}^{GPS}$$ N BQ GPS ), candidate DPQ per dam for GPS ( $${n}_{DPQ}^{GPS}$$ n DPQ GPS ), and candidate BQ per dam for GPS ( $${n}_{BQ}^{GPS}$$ n BQ GPS ) with the step widths 10, 20, 8, and 8, respectively.
DOI: 10.6084/m9.figshare.15079966.v1
title: Additional file 6 of Simulation studies to optimize genomic selection in honey bees

description: Additional file 6. Genetic gain, $${R}_{GS}$$ R GS , in the initial selection cycle of different breeding schemes applying CBS and GPS. The table presents the configuration of the breeding schemes shown in Fig. 4, as well as 9 reruns of the optimal breeding scheme with ssGBLUPBQ, and 1765 other runs chosen at even spacing to represent the remaining schemes. Equations (18), (19), and (20) were used to calculate $${R}_{GS}$$ R GS in the scenarios described in Table 1 for different ratios of preselected BQ and DPQ, with the prediction accuracy adjusted to the proportion on preselected BQ in all scenarios. Genetic gain is given in the units of the selection criterion. The parameter setting MOD was used. The standard deviations of the true breeding values $${\sigma }_{pW}$$ σ pW (worker groups from year 8) and $${\sigma }_{uQ}$$ σ uQ (queens from year 9) are shown in Additional file 5. The 1765 remaining schemes were picked by the numbers of dams of DPQ chosen for GPS, ( $${N}_{DPQ}^{GPS}$$ N DPQ GPS ), dams of BQ chosen for GPS ( $${N}_{BQ}^{GPS}$$ N BQ GPS ), candidate DPQ per dam for GPS ( $${n}_{DPQ}^{GPS}$$ n DPQ GPS ), and candidate BQ per dam for GPS ( $${n}_{BQ}^{GPS}$$ n BQ GPS ) with the step widths 10, 20, 8, and 8, respectively.
DOI: 10.6084/m9.figshare.15079990
title: Additional file 1 of Transcriptomic analysis to reveal the differentially expressed miRNA targets and their miRNAs in response to Ralstonia solanacearum in ginger species

description: Additional file 1. List of miRTGs with their corresponding miRNAs.
DOI: 10.6084/m9.figshare.15079990.v1
title: Additional file 1 of Transcriptomic analysis to reveal the differentially expressed miRNA targets and their miRNAs in response to Ralstonia solanacearum in ginger species

description: Additional file 1. List of miRTGs with their corresponding miRNAs.
DOI: 10.6084/m9.figshare.15080002
title: Additional file 1 of Spatiotemporal variation in the fecal microbiota of mule deer is associated with proximate and future measures of host health

description: Additional file 1: Table S1. Rarefied (13,000) OTU’s with counts per sample. Table S2. Primer Barcodes and Relevant Metadata for All Samples. Table S3. Weighted and unweighted Adonis tables for MaxFat, LoinThickness, Age, and RumpFat. Table S4. Phylum level correlations for taxa (December samples only) with March health outcomes in repeat deer. Table S5. Order level correlations for taxa (December samples only) with March health outcomes in repeat deer. Table S6. Family level correlations for taxa (December samples only) with March health outcomes in repeat deer. Table S7. Genus level correlations for taxa (December samples only) with March health outcomes in repeat deer. Table S8. ASV level correlations for taxa (December samples only) with March health outcomes in repeat deer. Table S9. Phylum level correlations for taxa (all samples) with the health measures of deer at time of sample collection. Table S10. Order level correlations for taxa (all samples) with the health measures of deer at time of sample collection. Table S11. Family level correlations for taxa (all samples) with the health measures of deer at time of sample collection. Table S12. Genus level correlations for taxa (all samples) with the health measures of deer at time of sample collection. Table S13. ASV level correlations for taxa (all samples) with the health measures of deer at time of sample collection.
DOI: 10.6084/m9.figshare.15080002.v1
title: Additional file 1 of Spatiotemporal variation in the fecal microbiota of mule deer is associated with proximate and future measures of host health

description: Additional file 1: Table S1. Rarefied (13,000) OTU’s with counts per sample. Table S2. Primer Barcodes and Relevant Metadata for All Samples. Table S3. Weighted and unweighted Adonis tables for MaxFat, LoinThickness, Age, and RumpFat. Table S4. Phylum level correlations for taxa (December samples only) with March health outcomes in repeat deer. Table S5. Order level correlations for taxa (December samples only) with March health outcomes in repeat deer. Table S6. Family level correlations for taxa (December samples only) with March health outcomes in repeat deer. Table S7. Genus level correlations for taxa (December samples only) with March health outcomes in repeat deer. Table S8. ASV level correlations for taxa (December samples only) with March health outcomes in repeat deer. Table S9. Phylum level correlations for taxa (all samples) with the health measures of deer at time of sample collection. Table S10. Order level correlations for taxa (all samples) with the health measures of deer at time of sample collection. Table S11. Family level correlations for taxa (all samples) with the health measures of deer at time of sample collection. Table S12. Genus level correlations for taxa (all samples) with the health measures of deer at time of sample collection. Table S13. ASV level correlations for taxa (all samples) with the health measures of deer at time of sample collection.
DOI: 10.6084/m9.figshare.15080131
title: Additional file 1 of Influenza vaccine supply, 2005–2006: did we come up short?

description: Additional file 1: Table 1 flu surveys summary 3.13.07. (RTF 87 KB)
DOI: 10.6084/m9.figshare.15080131.v1
title: Additional file 1 of Influenza vaccine supply, 2005–2006: did we come up short?

description: Additional file 1: Table 1 flu surveys summary 3.13.07. (RTF 87 KB)
DOI: 10.25345/c5153p
title: MassIVE MSV000087921 - Fungi from Brazilian marine sponge
title: Aspergillus welwitschiae FMPV 28 was obtained from marine sponge Taedania sp. collected in Ponta Verde Alagoas State, Brazil
DOI: 10.6084/m9.figshare.15080218
title: Additional file 2 of Ensemble completeness in conformer sampling: the case of small macrocycles

description: Additional file 2. Text file with 3D coordinates of starting conformers in SDF file format.
DOI: 10.6084/m9.figshare.15080218.v1
title: Additional file 2 of Ensemble completeness in conformer sampling: the case of small macrocycles

description: Additional file 2. Text file with 3D coordinates of starting conformers in SDF file format.
DOI: 10.6084/m9.figshare.15080233
title: Additional file 3 of A comprehensive approach for microbiota and health monitoring in mouse colonies using metagenomic shotgun sequencing

description: Raw data form NGS analyses. The different lines represent the different species identified with the normalized counts. For each species there are 8 columns that describe the taxonomy (superkingdom, phylum, class, order, family, genus, species, Species ID). The following columns identify the reads of each sample.
DOI: 10.6084/m9.figshare.15080233.v1
title: Additional file 3 of A comprehensive approach for microbiota and health monitoring in mouse colonies using metagenomic shotgun sequencing

description: Raw data form NGS analyses. The different lines represent the different species identified with the normalized counts. For each species there are 8 columns that describe the taxonomy (superkingdom, phylum, class, order, family, genus, species, Species ID). The following columns identify the reads of each sample.
DOI: 10.6084/m9.figshare.15080236
title: Additional file 4 of A comprehensive approach for microbiota and health monitoring in mouse colonies using metagenomic shotgun sequencing

description: Raw data form NGS analysis of the negative control. The different lines represent the different species identified with the normalized counts. For each species there are 8 columns that describe the taxonomy (superkingdom, phylum, class, order, family, genus, species, Species ID). The following columns identify the reads of the negative control sample and the average reads of the fecal samples.
DOI: 10.6084/m9.figshare.15080236.v1
title: Additional file 4 of A comprehensive approach for microbiota and health monitoring in mouse colonies using metagenomic shotgun sequencing

description: Raw data form NGS analysis of the negative control. The different lines represent the different species identified with the normalized counts. For each species there are 8 columns that describe the taxonomy (superkingdom, phylum, class, order, family, genus, species, Species ID). The following columns identify the reads of the negative control sample and the average reads of the fecal samples.
DOI: 10.6084/m9.figshare.15080239
title: Additional file 5 of A comprehensive approach for microbiota and health monitoring in mouse colonies using metagenomic shotgun sequencing

description: FELASA list of pathogens investigated in the course of the study: comparison between standard vs NGS analyses. Each different line represents the species included in the FELASA list of pathogens. For each species there are 6 columns that describe a short taxonomy (superkingdom, NCBI Taxon ID, species); the last two columns indicates the number of positive mice identified by standard or NGS analyses as described in Matherial and Methods.