cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14532255
title: Additional file 2 of Functional and genetic determinants of mutation rate variability in regulatory elements of cancer genomes

description: Additional file 2: Table S1A. Description of the open-chromatin sites of primary tumours and related normal tissues used in the analysis. Table S1B. Localised mutation frequencies in three classes of genomic sites across total mutations and subsets of mutations (total mutations, SBS signatures, ref./alt pairs). Table S1C. Localised mutation frequencies in TSSs and open-chromatin sites binned by mRNA abundance of target genes in matching tumour transcriptomes.Table S1D. Biological processes and molecular pathways with elevated mutation frequencies in TSSs. Table S1E. Localised mutation frequencies in CTCF sites grouped by conservation of CTCF binding in multiple cell lines. Table S1F. Localised mutation frequencies in CTCF sites grouped by motif presence and conservation of CTCF binding in multiple cell lines. Table S1G. List of hypermutated CTCF sites with constitutive binding and motifs (GRCh37). Table S1H. Interactions of genetic features (driver mutations, CNAs, genomic instability) and localised mutation frequencies. Table S1I. Genes in the identified CNA regions for which mRNA abundance increase is associated with copy number amplifications.
DOI: 10.6084/m9.figshare.14532255.v1
title: Additional file 2 of Functional and genetic determinants of mutation rate variability in regulatory elements of cancer genomes

description: Additional file 2: Table S1A. Description of the open-chromatin sites of primary tumours and related normal tissues used in the analysis. Table S1B. Localised mutation frequencies in three classes of genomic sites across total mutations and subsets of mutations (total mutations, SBS signatures, ref./alt pairs). Table S1C. Localised mutation frequencies in TSSs and open-chromatin sites binned by mRNA abundance of target genes in matching tumour transcriptomes.Table S1D. Biological processes and molecular pathways with elevated mutation frequencies in TSSs. Table S1E. Localised mutation frequencies in CTCF sites grouped by conservation of CTCF binding in multiple cell lines. Table S1F. Localised mutation frequencies in CTCF sites grouped by motif presence and conservation of CTCF binding in multiple cell lines. Table S1G. List of hypermutated CTCF sites with constitutive binding and motifs (GRCh37). Table S1H. Interactions of genetic features (driver mutations, CNAs, genomic instability) and localised mutation frequencies. Table S1I. Genes in the identified CNA regions for which mRNA abundance increase is associated with copy number amplifications.
DOI: 10.6084/m9.figshare.14532254
title: Additional file 1 of Trajectories through temporal networks

description: Additional file 1. This file is a Jupyter Notebook containing trajectory extraction and analysis code to reproduce the results in the “Passing sequences, shots, and goals” and “Multi-player tactics in association football” sections.
DOI: 10.6084/m9.figshare.14532257
title: Additional file 2 of Trajectories through temporal networks

description: Additional file 2. This is a comma-separated data file containing the trajectories extracted from the 2018 FIFA World Cup football match-event data as detailed in the “Experimental setup” section.
DOI: 10.6084/m9.figshare.14532254.v1
title: Additional file 1 of Trajectories through temporal networks

description: Additional file 1. This file is a Jupyter Notebook containing trajectory extraction and analysis code to reproduce the results in the “Passing sequences, shots, and goals” and “Multi-player tactics in association football” sections.
DOI: 10.6084/m9.figshare.14532257.v1
title: Additional file 2 of Trajectories through temporal networks

description: Additional file 2. This is a comma-separated data file containing the trajectories extracted from the 2018 FIFA World Cup football match-event data as detailed in the “Experimental setup” section.
DOI: 10.6084/m9.figshare.14532260
title: Additional file 3 of Trajectories through temporal networks

description: Additional file 3. This text file contains the program execution scripts used to extract weighted trajectories from the mobile money transaction data as detailed in the “Experimental setup” section.
DOI: 10.6084/m9.figshare.14532263
title: Additional file 4 of Trajectories through temporal networks

description: Additional file 4. This JSON file is the configuration file used in extracting weighted trajectories from the mobile money transaction data as detailed in the “Experimental setup” section.
DOI: 10.6084/m9.figshare.14532260.v1
title: Additional file 3 of Trajectories through temporal networks

description: Additional file 3. This text file contains the program execution scripts used to extract weighted trajectories from the mobile money transaction data as detailed in the “Experimental setup” section.
DOI: 10.6084/m9.figshare.14532263.v1
title: Additional file 4 of Trajectories through temporal networks

description: Additional file 4. This JSON file is the configuration file used in extracting weighted trajectories from the mobile money transaction data as detailed in the “Experimental setup” section.
DOI: 10.6084/m9.figshare.14532266
title: Additional file 5 of Trajectories through temporal networks

description: Additional file 5. This HTML file displays the analysis code that produced the results in the “Use and circulation of mobile money” and “Building up e-money savings” sections.
DOI: 10.6084/m9.figshare.14532266.v1
title: Additional file 5 of Trajectories through temporal networks

description: Additional file 5. This HTML file displays the analysis code that produced the results in the “Use and circulation of mobile money” and “Building up e-money savings” sections.
DOI: 10.6084/m9.figshare.14532272
title: Additional file 2 of Deep sequencing of pre-translational mRNPs reveals hidden flux through evolutionarily conserved alternative splicing nonsense-mediated decay pathways

description: Additional file 2: Table S2. Comprehensive file of all intron locations annotated in RefSeq (hg38), Ensembl (GRCh38.p12), GENCODE (v29), and CHESS (v2.1).
DOI: 10.6084/m9.figshare.14532275
title: Additional file 3 of Deep sequencing of pre-translational mRNPs reveals hidden flux through evolutionarily conserved alternative splicing nonsense-mediated decay pathways

description: Additional file 3: Table S3. Locations and characteristics of highly conserved unannotated exon junctions identified in this study.
DOI: 10.6084/m9.figshare.14532272.v1
title: Additional file 2 of Deep sequencing of pre-translational mRNPs reveals hidden flux through evolutionarily conserved alternative splicing nonsense-mediated decay pathways

description: Additional file 2: Table S2. Comprehensive file of all intron locations annotated in RefSeq (hg38), Ensembl (GRCh38.p12), GENCODE (v29), and CHESS (v2.1).
DOI: 10.6084/m9.figshare.14532275.v1
title: Additional file 3 of Deep sequencing of pre-translational mRNPs reveals hidden flux through evolutionarily conserved alternative splicing nonsense-mediated decay pathways

description: Additional file 3: Table S3. Locations and characteristics of highly conserved unannotated exon junctions identified in this study.
DOI: 10.6084/m9.figshare.14532312
title: Additional file 1 of Food safety knowledge, attitude, and hygiene practices of street-cooked food handlers in North Dayi District, Ghana

description: Additional file 1.
DOI: 10.6084/m9.figshare.14532312.v1
title: Additional file 1 of Food safety knowledge, attitude, and hygiene practices of street-cooked food handlers in North Dayi District, Ghana

description: Additional file 1.
DOI: 10.6084/m9.figshare.14532341
title: Additional file 10 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data

description: Additional file 10. Reference Genes with Raw Counts Greater than 5 and R2 Values &gt; 0.9 in three separate RNA sequencing Studies.
DOI: 10.6084/m9.figshare.14532344
title: Additional file 11 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data

description: Additional file 11. Control Reference Data Files.
DOI: 10.6084/m9.figshare.14532341.v1
title: Additional file 10 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data

description: Additional file 10. Reference Genes with Raw Counts Greater than 5 and R2 Values &gt; 0.9 in three separate RNA sequencing Studies.
DOI: 10.6084/m9.figshare.14532344.v1
title: Additional file 11 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data

description: Additional file 11. Control Reference Data Files.
DOI: 10.6084/m9.figshare.14532350
title: Additional file 2 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data

description: Additional file 2. Summary of Statistical Measures used to Evaluate Gene Expression before and after Minimum Value Adjustment (MVA).
DOI: 10.6084/m9.figshare.14532356
title: Additional file 4 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data

description: Additional file 4. STRING db Pathway Identification Based on the Analysis of the Top 300 Genes Identified by the Designated Statistical Measures.
DOI: 10.6084/m9.figshare.14532350.v1
title: Additional file 2 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data

description: Additional file 2. Summary of Statistical Measures used to Evaluate Gene Expression before and after Minimum Value Adjustment (MVA).
DOI: 10.6084/m9.figshare.14532362
title: Additional file 6 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data

description: Additional file 6. STRING db Analysis of Intra-individual Positional Gene Rankings in 35 Control Samples Based On Range/Median, Range/Q3, Kurtosis and Q4/Q(2 + 3) Slope Calculations.
DOI: 10.6084/m9.figshare.14532368.v1
title: Additional file 8 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data

description: Additional file 8. STRING db Analysis of Intra-individual Positional Gene Rankings In 9 Archived Controls Based on Range/Median, Range/Q3, Kurtosis and Q4/Q(2 + 3) Slope Calculations.
DOI: 10.6084/m9.figshare.14532356.v1
title: Additional file 4 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data

description: Additional file 4. STRING db Pathway Identification Based on the Analysis of the Top 300 Genes Identified by the Designated Statistical Measures.
DOI: 10.6084/m9.figshare.14532368
title: Additional file 8 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data

description: Additional file 8. STRING db Analysis of Intra-individual Positional Gene Rankings In 9 Archived Controls Based on Range/Median, Range/Q3, Kurtosis and Q4/Q(2 + 3) Slope Calculations.
DOI: 10.6084/m9.figshare.14532371.v1
title: Additional file 9 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data

description: Additional file 9. STRING db Analysis of Intra-individual Positional Gene Rankings In 12 Archived Control Samples Based On Range/Median, Range/Q3, Kurtosis and Q4/Q(2 + 3) Slope Calculations.
DOI: 10.6084/m9.figshare.14532371
title: Additional file 9 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data

description: Additional file 9. STRING db Analysis of Intra-individual Positional Gene Rankings In 12 Archived Control Samples Based On Range/Median, Range/Q3, Kurtosis and Q4/Q(2 + 3) Slope Calculations.
DOI: 10.6084/m9.figshare.14532362.v1
title: Additional file 6 of Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data

description: Additional file 6. STRING db Analysis of Intra-individual Positional Gene Rankings in 35 Control Samples Based On Range/Median, Range/Q3, Kurtosis and Q4/Q(2 + 3) Slope Calculations.
DOI: 10.6084/m9.figshare.14411975
title: Te Waihora Groundwater Chemistry

description: A dataset of 40 hydrochemistry samples taken in groundwater and surface water around Te Waihora/Lake Ellesmere, a coastal lagoon in New Zealand. Samples were analysed for major ions, stable water isotopes, nutrients and trace metals.
DOI: 10.6084/m9.figshare.14532434.v1
title: Additional file 1 of Evolutionary pattern of karyotypes and meiosis in pholcid spiders (Araneae: Pholcidae): implications for reconstructing chromosome evolution of araneomorph spiders

description: Additional file 1: Table S1. Species under study, male karyotype data (including standard deviation). Abbreviations: EX = product of event X (X = number of event), phase = phase of mitotic/meiotic division used to obtain data on chromosome morphology (m mit = mitotic metaphase, m I = metaphase I, m II = metaphase II), plates = number of chromosome plates evaluated, parameters = parameters used to describe chromosome morphology [CI = centromeric index, RCL = relative chromosome length (% of TCL)]. Chromosome morphology is indicated by background colour of a box (pink: metacentric, brown: submetacentric, dark blue: subtelocentric, light blue: acrocentric, red: unknown).
DOI: 10.6084/m9.figshare.14532434
title: Additional file 1 of Evolutionary pattern of karyotypes and meiosis in pholcid spiders (Araneae: Pholcidae): implications for reconstructing chromosome evolution of araneomorph spiders

description: Additional file 1: Table S1. Species under study, male karyotype data (including standard deviation). Abbreviations: EX = product of event X (X = number of event), phase = phase of mitotic/meiotic division used to obtain data on chromosome morphology (m mit = mitotic metaphase, m I = metaphase I, m II = metaphase II), plates = number of chromosome plates evaluated, parameters = parameters used to describe chromosome morphology [CI = centromeric index, RCL = relative chromosome length (% of TCL)]. Chromosome morphology is indicated by background colour of a box (pink: metacentric, brown: submetacentric, dark blue: subtelocentric, light blue: acrocentric, red: unknown).
DOI: 10.6084/m9.figshare.14532557.v1
title: Additional file 2 of DNA methylation profiling for molecular classification of adult diffuse lower-grade gliomas

description: Additional file 2: Table S1. This table presents the clinical and molecular characteristics of diffuse lower-grade glioma cases with low methylation class/subclass prediction scores.
DOI: 10.6084/m9.figshare.14532557
title: Additional file 2 of DNA methylation profiling for molecular classification of adult diffuse lower-grade gliomas

description: Additional file 2: Table S1. This table presents the clinical and molecular characteristics of diffuse lower-grade glioma cases with low methylation class/subclass prediction scores.
DOI: 10.6084/m9.figshare.14532567
title: Additional file 2 of Sex differences associated with adverse drug reactions resulting in hospital admissions

description: Additional file 2.
DOI: 10.6084/m9.figshare.14532567.v1
title: Additional file 2 of Sex differences associated with adverse drug reactions resulting in hospital admissions

description: Additional file 2.
DOI: 10.6084/m9.figshare.14528817.v1
title: Illustrative data file to give background on analysis strategy from Quantifying the cost of decision fatigue: suboptimal risk decisions in finance

description: Making decisions over extended periods of time is cognitively taxing and can lead to decision fatigue, which is linked to a preference for the ‘default’ option, namely whatever decision involves relatively little cognitive effort. Such effects have been demonstrated across a number of applied settings, including forensic and clinical contexts. Previous research, however, has not quantified the cost of such suboptimal decisions. We assessed the magnitude of the negative consequences of decision fatigue in the finance sector. Using 26 501 credit loan applications evaluated by credit officers of a major bank, we show that in this real-life financial risk taking context credit loan approvals across the course of a day decreased during midday compared to early or later in the workday, reflecting a preference for the default option. To quantify the economic loss associated with such decision variability, we then modelled the bank's additional credit collection if all decisions had been made during early morning levels of approval. This would have resulted in $509 023 extra revenue for the bank, for one month. Thus, we provide further evidence that is consistent with a pattern of decision fatigue, and that it can have a substantial negative impact in the finance sector that warrants considerations to counteract it.
DOI: 10.6084/m9.figshare.14528817
title: Illustrative data file to give background on analysis strategy from Quantifying the cost of decision fatigue: suboptimal risk decisions in finance

description: Making decisions over extended periods of time is cognitively taxing and can lead to decision fatigue, which is linked to a preference for the ‘default’ option, namely whatever decision involves relatively little cognitive effort. Such effects have been demonstrated across a number of applied settings, including forensic and clinical contexts. Previous research, however, has not quantified the cost of such suboptimal decisions. We assessed the magnitude of the negative consequences of decision fatigue in the finance sector. Using 26 501 credit loan applications evaluated by credit officers of a major bank, we show that in this real-life financial risk taking context credit loan approvals across the course of a day decreased during midday compared to early or later in the workday, reflecting a preference for the default option. To quantify the economic loss associated with such decision variability, we then modelled the bank's additional credit collection if all decisions had been made during early morning levels of approval. This would have resulted in $509 023 extra revenue for the bank, for one month. Thus, we provide further evidence that is consistent with a pattern of decision fatigue, and that it can have a substantial negative impact in the finance sector that warrants considerations to counteract it.
DOI: 10.6084/m9.figshare.14532617
title: Additional file 1 of “Strengthening data quality and reporting from small-scale surveys in humanitarian settings: a case study from Yemen, 2011–2019”

description: Additional file 1. Estimated standard error for CDR and U5DR based on reported confidence limits
DOI: 10.6084/m9.figshare.14532617.v1
title: Additional file 1 of “Strengthening data quality and reporting from small-scale surveys in humanitarian settings: a case study from Yemen, 2011–2019”

description: Additional file 1. Estimated standard error for CDR and U5DR based on reported confidence limits
DOI: 10.6084/m9.figshare.14532618
title: Additional file 2 of Anna Karenina and the subgingival microbiome associated with periodontitis

description: Additional file 1: Supplemental Table 1. Genus and species-level taxa that differed between chronic, localized and generalized aggressive periodontitis are shown. Differences in detection frequency (Fisher's exact test), differential abundance (DESeq2 along with log (2) fold differences, p-value, and FDR adjusted p-value (Wald’s Test), drivers of the class separation (SIMPER), species that were identified in ≥ 80% of individuals (common core microbiome) are shown.
DOI: 10.6084/m9.figshare.14532618.v1
title: Additional file 2 of Anna Karenina and the subgingival microbiome associated with periodontitis

description: Additional file 1: Supplemental Table 1. Genus and species-level taxa that differed between chronic, localized and generalized aggressive periodontitis are shown. Differences in detection frequency (Fisher's exact test), differential abundance (DESeq2 along with log (2) fold differences, p-value, and FDR adjusted p-value (Wald’s Test), drivers of the class separation (SIMPER), species that were identified in ≥ 80% of individuals (common core microbiome) are shown.
DOI: 10.6084/m9.figshare.14532621
title: Additional file 3 of Anna Karenina and the subgingival microbiome associated with periodontitis

description: Additional file 2: Supplemental Table 2. Same players, different teams. Network statistics of co-occurrence plots are shown.
DOI: 10.6084/m9.figshare.14532627
title: Additional file 5 of Anna Karenina and the subgingival microbiome associated with periodontitis

description: Additional file 4: Supplemental Table 4. GAP-a microbial chimera. Genes that were shared by all three diseases, as well as between generalized and localized aggressive periodontitis and generalized aggressive periodontitis and chronic periodontitis.
DOI: 10.6084/m9.figshare.14532627.v1
title: Additional file 5 of Anna Karenina and the subgingival microbiome associated with periodontitis

description: Additional file 4: Supplemental Table 4. GAP-a microbial chimera. Genes that were shared by all three diseases, as well as between generalized and localized aggressive periodontitis and generalized aggressive periodontitis and chronic periodontitis.
DOI: 10.6084/m9.figshare.14532624
title: Additional file 4 of Anna Karenina and the subgingival microbiome associated with periodontitis

description: Additional file 3: Supplemental Table 3. Periodontitis and the Anna Karenina principle. Genes that differed between chronic, localized and generalized aggressive periodontitis are shown, along with log (2) fold differences, p-value, and FDR adjusted p-value (Wald’s Test).
DOI: 10.6084/m9.figshare.14532624.v1
title: Additional file 4 of Anna Karenina and the subgingival microbiome associated with periodontitis

description: Additional file 3: Supplemental Table 3. Periodontitis and the Anna Karenina principle. Genes that differed between chronic, localized and generalized aggressive periodontitis are shown, along with log (2) fold differences, p-value, and FDR adjusted p-value (Wald’s Test).
DOI: 10.6084/m9.figshare.14532621.v1
title: Additional file 3 of Anna Karenina and the subgingival microbiome associated with periodontitis

description: Additional file 2: Supplemental Table 2. Same players, different teams. Network statistics of co-occurrence plots are shown.
DOI: 10.6084/m9.figshare.14532635
title: Additional file 1 of Learning analytics in higher education: a preponderance of analytics but very little learning?

description: Additional file 1. Learning Analytics higher education 2013–2019.
DOI: 10.6084/m9.figshare.14532666
title: Additional file 2 of The Coronavirus Network Explorer: mining a large-scale knowledge graph for effects of SARS-CoV-2 on host cell function

description: Additional file 2. Jupyter notebook with example code and data.
DOI: 10.6084/m9.figshare.14532666.v1
title: Additional file 2 of The Coronavirus Network Explorer: mining a large-scale knowledge graph for effects of SARS-CoV-2 on host cell function

description: Additional file 2. Jupyter notebook with example code and data.
DOI: 10.6084/m9.figshare.14391400
title: Additional file 1 of Under pressure: phenotypic divergence and convergence associated with microhabitat adaptations in Triatominae

description: Additional file 1: Table S1. Populations, specimen details and haplotype codes of 106 Rhodnius ecuadoriensis bugs used in morphometric and/or molecular analyses. A summary table with the numbers of bugs used in each analysis is also provided.