cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4008233
title: Replication data for High-impact details of play and movements with higher trunk acceleration in female basketball game

description: This study aimed to identify the high-impact details of play and movements with higher acceleration and their frequency during a female basketball match. Trunk acceleration was measured during a simulated basketball game with eight female players. We categorized the various actions as details of play and movements generated at &gt;6 and 8G resultant accelerations using a video recording and an accelerometer attached to the players’ trunk. The frequency and ratio of the details of play and movements about all detected movements were calculated. A total of 1062 and 223 play actions were detected for the resultant acceleration thresholds of &gt;6 and 8G, respectively. For these acceleration thresholds, in terms of details of play, positioning on the half-court was the most frequently observed (29.6% and 23.8%, respectively). In the terms of movements, deceleration was the most frequently detected movement (21.5% and 23.3%, respectively), followed by landing (7.6 and 15.7%, respectively). The results also showed that characteristics of movements or playing style and position of play might have an effect on acceleration patterns during a basketball game. Monitoring the frequency and intensity of these movements and details of play can help to quantify the biomechanical load experienced by basketball players.
DOI: 10.5281/zenodo.4008232
title: Replication data for High-impact details of play and movements with higher trunk acceleration in female basketball game

description: This study aimed to identify the high-impact details of play and movements with higher acceleration and their frequency during a female basketball match. Trunk acceleration was measured during a simulated basketball game with eight female players. We categorized the various actions as details of play and movements generated at &gt;6 and 8G resultant accelerations using a video recording and an accelerometer attached to the players’ trunk. The frequency and ratio of the details of play and movements about all detected movements were calculated. A total of 1062 and 223 play actions were detected for the resultant acceleration thresholds of &gt;6 and 8G, respectively. For these acceleration thresholds, in terms of details of play, positioning on the half-court was the most frequently observed (29.6% and 23.8%, respectively). In the terms of movements, deceleration was the most frequently detected movement (21.5% and 23.3%, respectively), followed by landing (7.6 and 15.7%, respectively). The results also showed that characteristics of movements or playing style and position of play might have an effect on acceleration patterns during a basketball game. Monitoring the frequency and intensity of these movements and details of play can help to quantify the biomechanical load experienced by basketball players.
DOI: 10.6084/m9.figshare.6085097
title: Additional file 9: of Transcriptome and secretome analysis of Aspergillus fumigatus in the presence of sugarcane bagasse

description: Table S7. Proteins detected in the secretome of Aspergillus fumigatus grown in SEB by coupled system type LC-MS/MS. (XLSX 37 kb)
DOI: 10.6084/m9.figshare.6085097.v1
title: Additional file 9: of Transcriptome and secretome analysis of Aspergillus fumigatus in the presence of sugarcane bagasse

description: Table S7. Proteins detected in the secretome of Aspergillus fumigatus grown in SEB by coupled system type LC-MS/MS. (XLSX 37 kb)
DOI: 10.6084/m9.figshare.6085076
title: Additional file 7: of Transcriptome and secretome analysis of Aspergillus fumigatus in the presence of sugarcane bagasse

description: Table S6. A. fumigatus sugar transporters modulated in RNA-seq data. (XLSX 13 kb)
DOI: 10.6084/m9.figshare.6085076.v1
title: Additional file 7: of Transcriptome and secretome analysis of Aspergillus fumigatus in the presence of sugarcane bagasse

description: Table S6. A. fumigatus sugar transporters modulated in RNA-seq data. (XLSX 13 kb)
DOI: 10.6084/m9.figshare.6085064
title: Additional file 8: of Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

description: Table S5. Repeat motif and allelic amplification profiles of the SSR selected for genotyping. (XLSX 13 kb)
DOI: 10.6084/m9.figshare.6085064.v1
title: Additional file 8: of Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

description: Table S5. Repeat motif and allelic amplification profiles of the SSR selected for genotyping. (XLSX 13 kb)
DOI: 10.6084/m9.figshare.6085061
title: MOESM3 of Efficacy and safety of artemether–lumefantrine, artesunate–amodiaquine, and dihydroartemisinin–piperaquine for the treatment of uncomplicated Plasmodium falciparum malaria in three provinces in Angola, 2017

description: Additional file 3. Day 7 lumefantrine drug levels in patients treated with artemether-lumefantrine, by treatment outcome, Zaire, Angola.
DOI: 10.6084/m9.figshare.6085061.v1
title: MOESM3 of Efficacy and safety of artemether–lumefantrine, artesunate–amodiaquine, and dihydroartemisinin–piperaquine for the treatment of uncomplicated Plasmodium falciparum malaria in three provinces in Angola, 2017

description: Additional file 3. Day 7 lumefantrine drug levels in patients treated with artemether-lumefantrine, by treatment outcome, Zaire, Angola.
DOI: 10.6084/m9.figshare.6085058
title: Additional file 1: of TBX21 predicts prognosis of patients and drives cancer stem cell maintenance via the TBX21â IL-4 pathway in lung adenocarcinoma

description: Table S1. Presenting 1027 inflammation-related genes measured in this study. Loza et al. [20] identified associations between novel variants in inflammation-related genes and several common diseases, especially for some cancers. Genetic studies also provided evidence that some cancers were associated with inflammatory pathways which impact the risk of cancer initiation, progression and severity. (XLSX 17 kb)
DOI: 10.6084/m9.figshare.6085058.v1
title: Additional file 1: of TBX21 predicts prognosis of patients and drives cancer stem cell maintenance via the TBX21â IL-4 pathway in lung adenocarcinoma

description: Table S1. Presenting 1027 inflammation-related genes measured in this study. Loza et al. [20] identified associations between novel variants in inflammation-related genes and several common diseases, especially for some cancers. Genetic studies also provided evidence that some cancers were associated with inflammatory pathways which impact the risk of cancer initiation, progression and severity. (XLSX 17 kb)
DOI: 10.6084/m9.figshare.6085052
title: Additional file 7: of Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

description: Table S4. Origin, location and primer sequences for the SSR markers used for genotyping. (XLSX 13 kb)
DOI: 10.6084/m9.figshare.6085052.v1
title: Additional file 7: of Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

description: Table S4. Origin, location and primer sequences for the SSR markers used for genotyping. (XLSX 13 kb)
DOI: 10.6084/m9.figshare.6085049
title: Additional file 6: of Transcriptome and secretome analysis of Aspergillus fumigatus in the presence of sugarcane bagasse

description: Table S5. CAZymes families in A. fumigatus genome and predicted peptide signal. (XLSX 32 kb)
DOI: 10.6084/m9.figshare.6085049.v1
title: Additional file 6: of Transcriptome and secretome analysis of Aspergillus fumigatus in the presence of sugarcane bagasse

description: Table S5. CAZymes families in A. fumigatus genome and predicted peptide signal. (XLSX 32 kb)
DOI: 10.6084/m9.figshare.6085046
title: MOESM2 of Efficacy and safety of artemether–lumefantrine, artesunate–amodiaquine, and dihydroartemisinin–piperaquine for the treatment of uncomplicated Plasmodium falciparum malaria in three provinces in Angola, 2017

description: Additional file 2. Microsatellite marker results, Angola therapeutic efficacy monitoring, 2017.
DOI: 10.6084/m9.figshare.6085046.v1
title: MOESM2 of Efficacy and safety of artemether–lumefantrine, artesunate–amodiaquine, and dihydroartemisinin–piperaquine for the treatment of uncomplicated Plasmodium falciparum malaria in three provinces in Angola, 2017

description: Additional file 2. Microsatellite marker results, Angola therapeutic efficacy monitoring, 2017.
DOI: 10.6084/m9.figshare.6085028
title: Additional file 5: of Transcriptome and secretome analysis of Aspergillus fumigatus in the presence of sugarcane bagasse

description: Table S4. Functional enrichment analysis with FunCat of up- and downregulated genes in Aspergillus fumigatus grown in SEB. (XLSX 12 kb)
DOI: 10.6084/m9.figshare.6085028.v1
title: Additional file 5: of Transcriptome and secretome analysis of Aspergillus fumigatus in the presence of sugarcane bagasse

description: Table S4. Functional enrichment analysis with FunCat of up- and downregulated genes in Aspergillus fumigatus grown in SEB. (XLSX 12 kb)
DOI: 10.21227/t970-nd64
title: NSS Mirai Dataset

description: This dataset is captured from a Mirai type botnet attack on an emulated IoT network in OpenStack.Detailed information on the dataset is depicted in the following work.Please cite it when you use this dataset for your research.Kalupahana Liyanage Kushan Sudheera, Dinil Mon Divakaran, Rhishi Pratap Singh, and Mohan Gurusamy, "ADEPT: Detection and Identification of Correlated Attack-Stages in IoT Networks," in IEEE Internet of Things Journal.
DOI: 10.21227/c6y0-2g84
title: NSS Mirai Dataset

description: This dataset is captured from a Mirai type botnet attack on an emulated IoT network in OpenStack.Detailed information on the dataset is depicted in the following work.Please cite it when you use this dataset for your research.Kalupahana Liyanage Kushan Sudheera, Dinil Mon Divakaran, Rhishi Pratap Singh, and Mohan Gurusamy, "ADEPT: Detection and Identification of Correlated Attack-Stages in IoT Networks," in IEEE Internet of Things Journal.
DOI: 10.6084/m9.figshare.6085001
title: Additional file 4: of Transcriptome and secretome analysis of Aspergillus fumigatus in the presence of sugarcane bagasse

description: Table S3. Up and downregulated genes in SEB. (XLSX 314 kb)
DOI: 10.6084/m9.figshare.6085001.v1
title: Additional file 4: of Transcriptome and secretome analysis of Aspergillus fumigatus in the presence of sugarcane bagasse

description: Table S3. Up and downregulated genes in SEB. (XLSX 314 kb)
DOI: 10.6084/m9.figshare.6084980
title: Additional file 3: of Transcriptome and secretome analysis of Aspergillus fumigatus in the presence of sugarcane bagasse

description: Table S2. Sequence of primers from genes upregulated in RNA-seqdata selected for qRT-PCR. (XLSX 11 kb)
DOI: 10.6084/m9.figshare.6084980.v1
title: Additional file 3: of Transcriptome and secretome analysis of Aspergillus fumigatus in the presence of sugarcane bagasse

description: Table S2. Sequence of primers from genes upregulated in RNA-seqdata selected for qRT-PCR. (XLSX 11 kb)
DOI: 10.6084/m9.figshare.6084971
title: Additional file 3: of Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

description: Table S3. GenBank numbers for the sequences used in the phylogenetic analyses. (XLSX 12 kb)
DOI: 10.6084/m9.figshare.6084971.v1
title: Additional file 3: of Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

description: Table S3. GenBank numbers for the sequences used in the phylogenetic analyses. (XLSX 12 kb)
DOI: 10.6084/m9.figshare.6084965
title: Additional file 2: of Transcriptome and secretome analysis of Aspergillus fumigatus in the presence of sugarcane bagasse

description: Table S1. Raw Count matrix of genes found in RNA-seq. (XLSX 585 kb)
DOI: 10.6084/m9.figshare.6084965.v1
title: Additional file 2: of Transcriptome and secretome analysis of Aspergillus fumigatus in the presence of sugarcane bagasse

description: Table S1. Raw Count matrix of genes found in RNA-seq. (XLSX 585 kb)
DOI: 10.6084/m9.figshare.6084953
title: Additional file 2: of Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

description: Table S2. Nuclear genes used for the phylogenetic analyses. (XLSX 11 kb)
DOI: 10.6084/m9.figshare.6084953.v1
title: Additional file 2: of Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

description: Table S2. Nuclear genes used for the phylogenetic analyses. (XLSX 11 kb)
DOI: 10.6084/m9.figshare.6084926
title: Additional file 1: of Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

description: Table S1. Accessions used in this study, origin and characteristics. (XLSX 23 kb)
DOI: 10.6084/m9.figshare.6084926.v1
title: Additional file 1: of Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

description: Table S1. Accessions used in this study, origin and characteristics. (XLSX 23 kb)
DOI: 10.6084/m9.figshare.6084893
title: Additional file 5 of Multi-omics integration for neuroblastoma clinical endpoint prediction

description: Table S9. The table provides the assignment of the 145 subset patients into the two groups G1 and G2 identified with the deep learning approach and characterized by distinct survival curves. For clarity we report also the TR/TS assignment. (XLSX 7.46 kb)
DOI: 10.6084/m9.figshare.6084893.v1
title: Additional file 5 of Multi-omics integration for neuroblastoma clinical endpoint prediction

description: Table S9. The table provides the assignment of the 145 subset patients into the two groups G1 and G2 identified with the deep learning approach and characterized by distinct survival curves. For clarity we report also the TR/TS assignment. (XLSX 7.46 kb)
DOI: 10.6084/m9.figshare.6084878
title: MOESM2 of Conserved community structure and simultaneous divergence events in the fig wasps associated with Ficus benjamina in Australia and China

description: Additional file 2: Figure S2. Bayesian consensus phylogeny for COI data. Phylogenetic tree. Figure S3. Bayesian consensus phylogeny for ITS data. Phylogenetic tree.
DOI: 10.6084/m9.figshare.6084878.v1
title: MOESM2 of Conserved community structure and simultaneous divergence events in the fig wasps associated with Ficus benjamina in Australia and China

description: Additional file 2: Figure S2. Bayesian consensus phylogeny for COI data. Phylogenetic tree. Figure S3. Bayesian consensus phylogeny for ITS data. Phylogenetic tree.
DOI: 10.6084/m9.figshare.6084875
title: Additional file 13: of Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

description: Table S9. Observed heterozygosity (Ho) in Aeschynomene species (XLSX 14 kb)
DOI: 10.6084/m9.figshare.6084875.v1
title: Additional file 13: of Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

description: Table S9. Observed heterozygosity (Ho) in Aeschynomene species (XLSX 14 kb)
DOI: 10.6084/m9.figshare.6084872
title: Additional file 4 of Multi-omics integration for neuroblastoma clinical endpoint prediction

description: Tables S5–S8. The tables highlight the cases in which INF performs similarly or better than both juxt and rSNF. Each sheet represents one classifier (sheet S5: RF; sheet S6: RF KBest; sheet S7: LSVM; sheet S8: LSVM KBest). The information is coded as follows. Bold: INF performs better than both juxt and rSNF in terms of MCC CV, MCC val and Nfeat. Black (not bold): MCC INF − MCC juxt &lt; 0.1 and MCC INF − MCC rSNF &lt;0.1. Light gray: failed run or INF performs worse in either CV or validation. (XLSX 15.7 kb)
DOI: 10.6084/m9.figshare.6084872.v1
title: Additional file 4 of Multi-omics integration for neuroblastoma clinical endpoint prediction

description: Tables S5–S8. The tables highlight the cases in which INF performs similarly or better than both juxt and rSNF. Each sheet represents one classifier (sheet S5: RF; sheet S6: RF KBest; sheet S7: LSVM; sheet S8: LSVM KBest). The information is coded as follows. Bold: INF performs better than both juxt and rSNF in terms of MCC CV, MCC val and Nfeat. Black (not bold): MCC INF − MCC juxt &lt; 0.1 and MCC INF − MCC rSNF &lt;0.1. Light gray: failed run or INF performs worse in either CV or validation. (XLSX 15.7 kb)
DOI: 10.6084/m9.figshare.6084854
title: Additional file 12: of Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

description: Table S8. Scoring of mean allele number per SSR and species (XLSX 15 kb)
DOI: 10.6084/m9.figshare.6084854.v1
title: Additional file 12: of Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

description: Table S8. Scoring of mean allele number per SSR and species (XLSX 15 kb)
DOI: 10.6084/m9.figshare.6084845
title: Additional file 3 of Multi-omics integration for neuroblastoma clinical endpoint prediction

description: Tables S3 and S4. INF performances (MCC CV, MCC val, Nfeat) color coded to highlight cases in which INF performs better than juxt or rSNF integration approaches (sheet S3 :juxt vs. INF; sheet S4: rSNF vs. INF). Green cell background: MCC CV (or MCC val or Nfeat) is equal between INF and juxt or rSNF; yellow cell background: MCC INF−MCC juxt &lt;0.1 or MCC INF−MCC rSNF &lt;0.1; NP: not performed; NA: not available (see main text); SF: the top feature set is too small for reliable classification. (XLSX 10.5 kb)
DOI: 10.6084/m9.figshare.6084845.v1
title: Additional file 3 of Multi-omics integration for neuroblastoma clinical endpoint prediction

description: Tables S3 and S4. INF performances (MCC CV, MCC val, Nfeat) color coded to highlight cases in which INF performs better than juxt or rSNF integration approaches (sheet S3 :juxt vs. INF; sheet S4: rSNF vs. INF). Green cell background: MCC CV (or MCC val or Nfeat) is equal between INF and juxt or rSNF; yellow cell background: MCC INF−MCC juxt &lt;0.1 or MCC INF−MCC rSNF &lt;0.1; NP: not performed; NA: not available (see main text); SF: the top feature set is too small for reliable classification. (XLSX 10.5 kb)
DOI: 10.6084/m9.figshare.6084830
title: Additional file 11: of Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

description: Table S7. Cross-species transferability of the SSR markers (XLSX 13 kb)
DOI: 10.6084/m9.figshare.6084830.v1
title: Additional file 11: of Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

description: Table S7. Cross-species transferability of the SSR markers (XLSX 13 kb)
DOI: 10.6084/m9.figshare.6084824
title: Additional file 2 of Multi-omics integration for neuroblastoma clinical endpoint prediction

description: Table S2. Information relative to the match of aCGH samples and RNA-Seq/microarray samples included in the study. (XLSX 9.23 kb)
DOI: 10.6084/m9.figshare.6084824.v1
title: Additional file 2 of Multi-omics integration for neuroblastoma clinical endpoint prediction

description: Table S2. Information relative to the match of aCGH samples and RNA-Seq/microarray samples included in the study. (XLSX 9.23 kb)
DOI: 10.6084/m9.figshare.6084803
title: Additional file 10: of Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

description: Table S6. Allelic diversity of the SSR markers (XLSX 22 kb)
DOI: 10.6084/m9.figshare.6084803.v1
title: Additional file 10: of Naturally occurring variations in the nod-independent model legume Aeschynomene evenia and relatives: a resource for nodulation genetics

description: Table S6. Allelic diversity of the SSR markers (XLSX 22 kb)
DOI: 10.6084/m9.figshare.6084800
title: Additional file 1 of Multi-omics integration for neuroblastoma clinical endpoint prediction

description: Table S1. Clinical characteristics of the patients included in the study. (XLSX 27.9 kb)
DOI: 10.6084/m9.figshare.6084800.v1
title: Additional file 1 of Multi-omics integration for neuroblastoma clinical endpoint prediction

description: Table S1. Clinical characteristics of the patients included in the study. (XLSX 27.9 kb)
DOI: 10.6084/m9.figshare.6084755
title: MOESM8 of Control charts for monitoring mood stability as a predictor of severe episodes in patients with bipolar disorder

description: Additional file 8. Sensitivity and PPV of any Shewhart’s control rule’ for predicting manic and depressive episodes within the next four weeks, when a depressive episode is defined as a QIDS score ≥ 11 and a manic episode is defined as an ASRM score ≥ 6.
DOI: 10.6084/m9.figshare.6084755.v1
title: MOESM8 of Control charts for monitoring mood stability as a predictor of severe episodes in patients with bipolar disorder

description: Additional file 8. Sensitivity and PPV of any Shewhart’s control rule’ for predicting manic and depressive episodes within the next four weeks, when a depressive episode is defined as a QIDS score ≥ 11 and a manic episode is defined as an ASRM score ≥ 6.
DOI: 10.6084/m9.figshare.6084731
title: MOESM7 of Control charts for monitoring mood stability as a predictor of severe episodes in patients with bipolar disorder

description: Additional file 7. Shewhartâ s control rules sensitivity and PPV for predicting manic episodes within the next eight weeks.
DOI: 10.6084/m9.figshare.6084731.v1
title: MOESM7 of Control charts for monitoring mood stability as a predictor of severe episodes in patients with bipolar disorder

description: Additional file 7. Shewhartâ s control rules sensitivity and PPV for predicting manic episodes within the next eight weeks.
DOI: 10.6084/m9.figshare.6084719
title: MOESM6 of Control charts for monitoring mood stability as a predictor of severe episodes in patients with bipolar disorder

description: Additional file 6. Sensitivity and PPV of run sum procedure for predicting depressive episodes within the next four weeks.
DOI: 10.6084/m9.figshare.6084719.v1
title: MOESM6 of Control charts for monitoring mood stability as a predictor of severe episodes in patients with bipolar disorder

description: Additional file 6. Sensitivity and PPV of run sum procedure for predicting depressive episodes within the next four weeks.
DOI: 10.6084/m9.figshare.6084710
title: Additional file 8: of Negative and positive control ranges in the bacterial reverse mutation test: JEMS/BMS collaborative study

description: Figure S7. Comparison of the negative and positive control counts for each strain with or without S9 mix. The doses shown in Figs. 6, 7, 8, 9, and 10 were used for comparison. The R2 values indicate the correlation coefficients of the linear regression lines. (ODP 62Â kb)
DOI: 10.6084/m9.figshare.6084710.v1
title: Additional file 8: of Negative and positive control ranges in the bacterial reverse mutation test: JEMS/BMS collaborative study

description: Figure S7. Comparison of the negative and positive control counts for each strain with or without S9 mix. The doses shown in Figs. 6, 7, 8, 9, and 10 were used for comparison. The R2 values indicate the correlation coefficients of the linear regression lines. (ODP 62Â kb)