cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.8218997
title: Barcode sequence matrix

description: List of 572 barcode sequences used in statistical analyses. The region targeted is noted, along with the number of reads recovered for that sequence in each sample (n, 50). <br><br>Corresponding scientific paper: Boggs, L.M.; Scheible, M.K.; Machado, G.; Meiklejohn, K.A. Single Fragment or Bulk Soil DNA Metabarcoding: Which is Better for Characterizing Biological Taxa Found in Surface Soils for Sample Separation? <i>Genes</i> <b>2019</b>, <i>10</i>, 431
DOI: 10.6084/m9.figshare.8218997.v1
title: Barcode sequence matrix

description: List of 572 barcode sequences used in statistical analyses. The region targeted is noted, along with the number of reads recovered for that sequence in each sample (n, 50). <br><br>Corresponding scientific paper: Boggs, L.M.; Scheible, M.K.; Machado, G.; Meiklejohn, K.A. Single Fragment or Bulk Soil DNA Metabarcoding: Which is Better for Characterizing Biological Taxa Found in Surface Soils for Sample Separation? <i>Genes</i> <b>2019</b>, <i>10</i>, 431
DOI: 10.6084/m9.figshare.8218937
title: Dataset associated with Antczak et al., Environmental conditions shape the nature of a minimal bacterial genome published in Nature Communications 2019.

description: <br>Each file contains results obtained from a particular method for all the minimal genome proteins. Some of the results files are created by concatenation of separate protein or group of proteins results files.<br>Filename: 3DLigandSite.txtDescription: Structural models (in pdb format) generated by 3DLigandSite.<br>Filename: Argot2.5.txtDescription: Results (in csv format) containing Gene Ontology terms predicted by Argot2.5.<br>Filename: BLASTAgainstUniProt.txtDescription: Best BLAST for each of the proteins.<br>Filename: CATHFunFams.txtDescription: Results (in json format) containing CATH functional familes predicted by CATH FunFHMMer web server.<br>Filename: CombFunc.txtDescription: Results (in tsv format) containing Gene Ontology terms predicted by CombFunc.<br>Filename: DISOPRED3.txtDescription: Disorder ratio for each residue of all the proteins predicted by DISOPRED3.<br>Filename: eggNOG-Mapper.txtDescription: Results (in tsv format) containing Best Orthologous Groups, KEGG pathways, Gene Ontology terms, gene name, etc. predicted for all the proteins by eggNOG-Mapper.<br>Filename: FFPred3.txtDescription: Results containing Gene Ontology terms predicted by FFPred3.<br>Filename: InterPro.txtDescription: Results (in tsv format) containing families from all InterPro resources. Note interpro runs multiple domain/family based methods together so contains the results fro multiple individual programs<br>Filename: LipoP.txtDescription: Results containing predictions of lipoproteins.<br>Filename: LocTree3.txtDescription: Results (in csv format) containing Gene Ontology terms predicted by LocTree3.<br>Filename: Pfam.txtDescription: Domains predicted for each of the proteins by PfamScan.<br>Filename: Phyre2.zip (compressed txt file)Description: Results containing names of matching structural models and the alignment predicted by Phyre2.<br>Filename: TIGRFAM.txtDescription: Families of equivalogs predicted for each of the proteins by Hmmer3.<br>Filename: TMHMM.txtDescription: Results containing proteins topology predicted by TMHMM.<br>Filename: TrSSP.txtDescription: Results containing predictions of transporters and possible substrates made by TrSSP. <br>
DOI: 10.6084/m9.figshare.8218940
title: Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods :Spectroscopic limited maximum efficiency (SLME) data

description: Solar-energy plays an important role in solving serious environmental problems and meeting high-energy demand. However, the lack of suitable materials hinders further progress of this technology. Here, we present the largest inorganic solar-cell material search to date using density functional theory (DFT) and machine-learning approaches. We calculated the spectroscopy limited maximum efficiency (SLME) using Tran-Blaha modified Becke-Johnson potential for 5097 non-metallic materials, and identified 1997 candidates with a SLME higher than 10%, including 934 candidates with a suitable convex-hull stability and effective carrier mass. Screening for 2D-layered cases, we found 58 potential materials and performed G<sub>0</sub>W<sub>0</sub> calculations on a subset to estimate the prediction-uncertainty. As the above DFT methods are still computationally expensive, we developed a high accuracy machine learning model to pre-screen efficient materials and applied it to over a million materials. Our results provide a general framework and universal strategy for the design of high-efficiency solar cell materials. The data and tools are publicly distributed at: https://www.ctcms.nist.gov/~knc6/JVASP.html, https://www.ctcms.nist.gov/jarvisml/, https://jarvis.nist.gov/ and https://github.com/usnistgov/jarvis .
DOI: 10.6084/m9.figshare.8218859
title: Integrative taxonomy of the cave-dwelling mysids of the genus <i>Hemimysis</i>

description: The genus <i>Hemimysis</i> (Malacostraca: Mysida: Mysidae) encompasses near-bottom, demersal and cave-dwelling mysids living in the marine, brackish and freshwater habitats around the European coast, from the Caspian Sea to the Scandinavian Peninsula. We conducted cladistic analysis of 52 morphological characters of all nine species and three subspecies of the genus <i>Hemimysis.</i> We also completed a molecular analysis based on three molecular markers of <i>Hemimysis lamornae</i> (Couch, 1856) found in the English Channel, the Mediterranean Sea, and the Black Sea. Both analyses did not support monophyly of <i>Hemimysis lamornae</i>. We thus consider the former subspecies <i>H. lamornae pontica</i> (Czerniavsky, 1882) and <i>H. lamornae mediterranea</i> Bacescu, 1936 as valid species. Analysis of mitochondrial cytochrome oxidase subunit I (COI) sequences of <i>H. pontica</i> shows no significant divergence between mysids living in the marine caves of Crimea and Bulgaria. Morphological trends in <i>Hemimysis</i> are discussed, <i>H. pontica</i> Czerniavsky, 1882 is redescribed, and a new key to all 11 species of the genus is given.
DOI: 10.6084/m9.figshare.8218889
title: Microbial and Metabolomic Profiles in Correlation with Depression and Anxiety Co-morbidities in Diarrhea-predominant IBS Patients. Urinal Metabolomic Data Negative Ions for 120 samples.xlsx

description: Urinal metabolomic profile of IBS-D patients and healthy controls using UHPLS-MS. ESI (-)
DOI: 10.6084/m9.figshare.8218937.v1
title: Argot2.5.txt

description: Results (in csv format) containing Gene Ontology terms predicted by Argot2.5 for the proteins encoded by the minimal bacterial genome<br><br>
DOI: 10.6084/m9.figshare.8218889.v1
title: Microbial and Metabolomic Profiles in Correlation with Depression and Anxiety Co-morbidities in Diarrhea-predominant IBS Patients. Urinal Metabolomic Data Negative Ions for 120 samples.xlsx

description: Urinal metabolomic profile of IBS-D patients and healthy controls using UHPLS-MS. ESI (-)
DOI: 10.6084/m9.figshare.8218859.v1
title: Integrative taxonomy of the cave-dwelling mysids of the genus <i>Hemimysis</i>

description: The genus <i>Hemimysis</i> (Malacostraca: Mysida: Mysidae) encompasses near-bottom, demersal and cave-dwelling mysids living in the marine, brackish and freshwater habitats around the European coast, from the Caspian Sea to the Scandinavian Peninsula. We conducted cladistic analysis of 52 morphological characters of all nine species and three subspecies of the genus <i>Hemimysis.</i> We also completed a molecular analysis based on three molecular markers of <i>Hemimysis lamornae</i> (Couch, 1856) found in the English Channel, the Mediterranean Sea, and the Black Sea. Both analyses did not support monophyly of <i>Hemimysis lamornae</i>. We thus consider the former subspecies <i>H. lamornae pontica</i> (Czerniavsky, 1882) and <i>H. lamornae mediterranea</i> Bacescu, 1936 as valid species. Analysis of mitochondrial cytochrome oxidase subunit I (COI) sequences of <i>H. pontica</i> shows no significant divergence between mysids living in the marine caves of Crimea and Bulgaria. Morphological trends in <i>Hemimysis</i> are discussed, <i>H. pontica</i> Czerniavsky, 1882 is redescribed, and a new key to all 11 species of the genus is given.
DOI: 10.6084/m9.figshare.8218859.v3
title: Integrative taxonomy of the cave-dwelling mysids of the genus <i>Hemimysis</i>

description: The genus <i>Hemimysis</i> (Malacostraca: Mysida: Mysidae) encompasses near-bottom, demersal and cave-dwelling mysids living in the marine, brackish and freshwater habitats around the European coast, from the Caspian Sea to the Scandinavian Peninsula. We conducted cladistic analysis of 52 morphological characters of all nine species and three subspecies of the genus <i>Hemimysis.</i> We also completed a molecular analysis based on three molecular markers of <i>Hemimysis lamornae</i> (Couch, 1856) found in the English Channel, the Mediterranean Sea, and the Black Sea. Both analyses did not support monophyly of <i>Hemimysis lamornae</i>. We thus consider the former subspecies <i>H. lamornae pontica</i> (Czerniavsky, 1882) and <i>H. lamornae mediterranea</i> Bacescu, 1936 as valid species. Analysis of mitochondrial cytochrome oxidase subunit I (COI) sequences of <i>H. pontica</i> shows no significant divergence between mysids living in the marine caves of Crimea and Bulgaria. Morphological trends in <i>Hemimysis</i> are discussed, <i>H. pontica</i> Czerniavsky, 1882 is redescribed, and a new key to all 11 species of the genus is given.
DOI: 10.6084/m9.figshare.8218937.v2
title: Dataset associated with Antczak et al., Environmental conditions shape the nature of a minimal bacterial genome published in Nature Communications 2019.

description: <br>Each file contains results obtained from a particular method for all the minimal genome proteins. Some of the results files are created by concatenation of separate protein or group of proteins results files.<br>Filename: 3DLigandSite.txtDescription: Structural models (in pdb format) generated by 3DLigandSite.<br>Filename: Argot2.5.txtDescription: Results (in csv format) containing Gene Ontology terms predicted by Argot2.5.<br>Filename: BLASTAgainstUniProt.txtDescription: Best BLAST for each of the proteins.<br>Filename: CATHFunFams.txtDescription: Results (in json format) containing CATH functional familes predicted by CATH FunFHMMer web server.<br>Filename: CombFunc.txtDescription: Results (in tsv format) containing Gene Ontology terms predicted by CombFunc.<br>Filename: DISOPRED3.txtDescription: Disorder ratio for each residue of all the proteins predicted by DISOPRED3.<br>Filename: eggNOG-Mapper.txtDescription: Results (in tsv format) containing Best Orthologous Groups, KEGG pathways, Gene Ontology terms, gene name, etc. predicted for all the proteins by eggNOG-Mapper.<br>Filename: FFPred3.txtDescription: Results containing Gene Ontology terms predicted by FFPred3.<br>Filename: InterPro.txtDescription: Results (in tsv format) containing families from all InterPro resources. Note interpro runs multiple domain/family based methods together so contains the results fro multiple individual programs<br>Filename: LipoP.txtDescription: Results containing predictions of lipoproteins.<br>Filename: LocTree3.txtDescription: Results (in csv format) containing Gene Ontology terms predicted by LocTree3.<br>Filename: Pfam.txtDescription: Domains predicted for each of the proteins by PfamScan.<br>Filename: Phyre2.txtDescription: Results containing names of matching structural models and the alignment predicted by Phyre2.<br>Filename: TIGRFAM.txtDescription: Families of equivalogs predicted for each of the proteins by Hmmer3.<br>Filename: TMHMM.txtDescription: Results containing proteins topology predicted by TMHMM.<br>Filename: TrSSP.txtDescription: Results containing predictions of transporters and possible substrates made by TrSSP. <br>
DOI: 10.6084/m9.figshare.8218859.v2
title: Integrative taxonomy of the cave-dwelling mysids of the genus <i>Hemimysis</i>

description: The genus <i>Hemimysis</i> (Malacostraca: Mysida: Mysidae) encompasses near-bottom, demersal and cave-dwelling mysids living in the marine, brackish and freshwater habitats around the European coast, from the Caspian Sea to the Scandinavian Peninsula. We conducted cladistic analysis of 52 morphological characters of all nine species and three subspecies of the genus <i>Hemimysis.</i> We also completed a molecular analysis based on three molecular markers of <i>Hemimysis lamornae</i> (Couch, 1856) found in the English Channel, the Mediterranean Sea, and the Black Sea. Both analyses did not support monophyly of <i>Hemimysis lamornae</i>. We thus consider the former subspecies <i>H. lamornae pontica</i> (Czerniavsky, 1882) and <i>H. lamornae mediterranea</i> Bacescu, 1936 as valid species. Analysis of mitochondrial cytochrome oxidase subunit I (COI) sequences of <i>H. pontica</i> shows no significant divergence between mysids living in the marine caves of Crimea and Bulgaria. Morphological trends in <i>Hemimysis</i> are discussed, <i>H. pontica</i> Czerniavsky, 1882 is redescribed, and a new key to all 11 species of the genus is given.
DOI: 10.6084/m9.figshare.8218937.v3
title: Dataset associated with Antczak et al., Environmental conditions shape the nature of a minimal bacterial genome published in Nature Communications 2019.

description: <br>Each file contains results obtained from a particular method for all the minimal genome proteins. Some of the results files are created by concatenation of separate protein or group of proteins results files.<br>Filename: 3DLigandSite.txtDescription: Structural models (in pdb format) generated by 3DLigandSite.<br>Filename: Argot2.5.txtDescription: Results (in csv format) containing Gene Ontology terms predicted by Argot2.5.<br>Filename: BLASTAgainstUniProt.txtDescription: Best BLAST for each of the proteins.<br>Filename: CATHFunFams.txtDescription: Results (in json format) containing CATH functional familes predicted by CATH FunFHMMer web server.<br>Filename: CombFunc.txtDescription: Results (in tsv format) containing Gene Ontology terms predicted by CombFunc.<br>Filename: DISOPRED3.txtDescription: Disorder ratio for each residue of all the proteins predicted by DISOPRED3.<br>Filename: eggNOG-Mapper.txtDescription: Results (in tsv format) containing Best Orthologous Groups, KEGG pathways, Gene Ontology terms, gene name, etc. predicted for all the proteins by eggNOG-Mapper.<br>Filename: FFPred3.txtDescription: Results containing Gene Ontology terms predicted by FFPred3.<br>Filename: InterPro.txtDescription: Results (in tsv format) containing families from all InterPro resources. Note interpro runs multiple domain/family based methods together so contains the results fro multiple individual programs<br>Filename: LipoP.txtDescription: Results containing predictions of lipoproteins.<br>Filename: LocTree3.txtDescription: Results (in csv format) containing Gene Ontology terms predicted by LocTree3.<br>Filename: Pfam.txtDescription: Domains predicted for each of the proteins by PfamScan.<br>Filename: Phyre2.txtDescription: Results containing names of matching structural models and the alignment predicted by Phyre2.<br>Filename: TIGRFAM.txtDescription: Families of equivalogs predicted for each of the proteins by Hmmer3.<br>Filename: TMHMM.txtDescription: Results containing proteins topology predicted by TMHMM.<br>Filename: TrSSP.txtDescription: Results containing predictions of transporters and possible substrates made by TrSSP. <br>
DOI: 10.6084/m9.figshare.8218937.v4
title: Dataset associated with Antczak et al., Environmental conditions shape the nature of a minimal bacterial genome published in Nature Communications 2019.

description: <br>Each file contains results obtained from a particular method for all the minimal genome proteins. Some of the results files are created by concatenation of separate protein or group of proteins results files.<br>Filename: 3DLigandSite.txtDescription: Structural models (in pdb format) generated by 3DLigandSite.<br>Filename: Argot2.5.txtDescription: Results (in csv format) containing Gene Ontology terms predicted by Argot2.5.<br>Filename: BLASTAgainstUniProt.txtDescription: Best BLAST for each of the proteins.<br>Filename: CATHFunFams.txtDescription: Results (in json format) containing CATH functional familes predicted by CATH FunFHMMer web server.<br>Filename: CombFunc.txtDescription: Results (in tsv format) containing Gene Ontology terms predicted by CombFunc.<br>Filename: DISOPRED3.txtDescription: Disorder ratio for each residue of all the proteins predicted by DISOPRED3.<br>Filename: eggNOG-Mapper.txtDescription: Results (in tsv format) containing Best Orthologous Groups, KEGG pathways, Gene Ontology terms, gene name, etc. predicted for all the proteins by eggNOG-Mapper.<br>Filename: FFPred3.txtDescription: Results containing Gene Ontology terms predicted by FFPred3.<br>Filename: InterPro.txtDescription: Results (in tsv format) containing families from all InterPro resources. Note interpro runs multiple domain/family based methods together so contains the results fro multiple individual programs<br>Filename: LipoP.txtDescription: Results containing predictions of lipoproteins.<br>Filename: LocTree3.txtDescription: Results (in csv format) containing Gene Ontology terms predicted by LocTree3.<br>Filename: Pfam.txtDescription: Domains predicted for each of the proteins by PfamScan.<br>Filename: Phyre2.zip (compressed txt file)Description: Results containing names of matching structural models and the alignment predicted by Phyre2.<br>Filename: TIGRFAM.txtDescription: Families of equivalogs predicted for each of the proteins by Hmmer3.<br>Filename: TMHMM.txtDescription: Results containing proteins topology predicted by TMHMM.<br>Filename: TrSSP.txtDescription: Results containing predictions of transporters and possible substrates made by TrSSP. <br>
DOI: 10.6084/m9.figshare.8218703
title: Repeated gain and loss of a single gene modulates the evolution of vascular pathogen lifestyles

description: All supplemental code and datasets for "Repeated gain and loss of a single gene modulates the evolution of vascular pathogen lifestyles", including trimmed alignments, optimal and constrained maximum likelihood tree files, majority rule consensus species tree files, orthogroup assignments, Perl scripts used for genomic neighborhood comparisons.
DOI: 10.6084/m9.figshare.8218703.v1
title: Repeated gain and loss of a single gene modulates the evolution of vascular pathogen lifestyles

description: All supplemental code and datasets for "Repeated gain and loss of a single gene modulates the evolution of vascular pathogen lifestyles", including trimmed alignments, optimal and constrained maximum likelihood tree files, majority rule consensus species tree files, orthogroup assignments, Perl scripts used for genomic neighborhood comparisons.
DOI: 10.6084/m9.figshare.8218670
title: nonlinear-16-grams-number

description: 16-grams
DOI: 10.6084/m9.figshare.8218664
title: nonlinear-16-grams

description: 16-grams
DOI: 10.6084/m9.figshare.8218664.v1
title: nonlinear-16-grams

description: 16-grams
DOI: 10.6084/m9.figshare.8218649
title: Cophyla_multigene_21Jan2019_partitioned.nex

description: Alignment of four mitochondrial genes (16S, 12S, cox1, cob) forspecies of the frog genus Cophyla from Madagascar, used for phylogenetic analysis. <br>
DOI: 10.6084/m9.figshare.8218655
title: Pediatric electric bicycle injuries and comparison to other pediatric traffic injuries

description: <b>Objective:</b> The objective of this study was to conduct a comprehensive analysis of demographics, injury characteristics and hospital resource utilization of significant pediatric electric bicycle (e-bike) injuries leading to hospitalization following an emergency department visit in comparison to pediatric injuries caused by other traffic related mechanisms. <b>Methods:</b> A retrospective review of all pediatric traffic injury hospitalizations following an emergency department visit to a level I trauma center between October 2014 and September 2016 was conducted. Data regarding age, sex, number of computed tomography (CT) scans obtained, number of major procedures, length of hospital stay (LOS), Injury Severity Score (ISS), and number of injuries per patient were collected and compared between e-bike injuries and other traffic injuries. <b>Results:</b> Three hundred thirty-seven admissions were analyzed: 46 (14%) were due to e-bike injuries (29% of patients &gt;12 years). Age, proportion of brain injuries, and use of CT were significantly increased compared to mechanical bicycle injuries (13.1 ± 3.4 vs. 10.6 ± 3.6, 13% vs. 3%, 1 [0–3] vs. 1 [0–1], <i>P</i> &lt; .01, <i>P</i> = .03, <i>P</i> = .05). Age, LOS, and use of CT were significantly increased compared to injuries caused to automobile passengers (13.1 ± 3.4 vs. 7.4 ± 5.3, 1 [1–3] vs. 1 [1–2], 1 [0–3] vs. 0 [0–1], <i>P</i> &lt; .01, <i>P</i> = .03, <i>P</i> = .01), as well as ISS and number of injuries per patient (<i>P</i> = .04, <i>P</i> &lt; .01). Injuries caused by e-bikes were similar to injuries caused to pedestrians, except for age (13.1 ± 3.4 vs. 8.5 ± 3.7, <i>P</i> &lt; .01). Multivariable analysis revealed a significant association between mechanism of injury and ISS, with increased ISS among e-bike injuries compared to mecahnical bike injuries (OR 2.56, CI 1.1–5.88, <i>P</i> = 0.03) and automobile injuries (OR 4.16, CI 1.49–12.5, (<i>P</i> &lt; .01). <b>Conclusion:</b> E-bikes are a significant cause of severe injury in children compared to most other traffic injuries, particularly in older children.
DOI: 10.6084/m9.figshare.8218649.v1
title: Cophyla_multigene_21Jan2019_partitioned.nex

description: Alignment of four mitochondrial genes (16S, 12S, cox1, cob) forspecies of the frog genus Cophyla from Madagascar, used for phylogenetic analysis. <br>
DOI: 10.6084/m9.figshare.8218655.v1
title: Pediatric electric bicycle injuries and comparison to other pediatric traffic injuries

description: <b>Objective:</b> The objective of this study was to conduct a comprehensive analysis of demographics, injury characteristics and hospital resource utilization of significant pediatric electric bicycle (e-bike) injuries leading to hospitalization following an emergency department visit in comparison to pediatric injuries caused by other traffic related mechanisms. <b>Methods:</b> A retrospective review of all pediatric traffic injury hospitalizations following an emergency department visit to a level I trauma center between October 2014 and September 2016 was conducted. Data regarding age, sex, number of computed tomography (CT) scans obtained, number of major procedures, length of hospital stay (LOS), Injury Severity Score (ISS), and number of injuries per patient were collected and compared between e-bike injuries and other traffic injuries. <b>Results:</b> Three hundred thirty-seven admissions were analyzed: 46 (14%) were due to e-bike injuries (29% of patients &gt;12 years). Age, proportion of brain injuries, and use of CT were significantly increased compared to mechanical bicycle injuries (13.1 ± 3.4 vs. 10.6 ± 3.6, 13% vs. 3%, 1 [0–3] vs. 1 [0–1], <i>P</i> &lt; .01, <i>P</i> = .03, <i>P</i> = .05). Age, LOS, and use of CT were significantly increased compared to injuries caused to automobile passengers (13.1 ± 3.4 vs. 7.4 ± 5.3, 1 [1–3] vs. 1 [1–2], 1 [0–3] vs. 0 [0–1], <i>P</i> &lt; .01, <i>P</i> = .03, <i>P</i> = .01), as well as ISS and number of injuries per patient (<i>P</i> = .04, <i>P</i> &lt; .01). Injuries caused by e-bikes were similar to injuries caused to pedestrians, except for age (13.1 ± 3.4 vs. 8.5 ± 3.7, <i>P</i> &lt; .01). Multivariable analysis revealed a significant association between mechanism of injury and ISS, with increased ISS among e-bike injuries compared to mecahnical bike injuries (OR 2.56, CI 1.1–5.88, <i>P</i> = 0.03) and automobile injuries (OR 4.16, CI 1.49–12.5, (<i>P</i> &lt; .01). <b>Conclusion:</b> E-bikes are a significant cause of severe injury in children compared to most other traffic injuries, particularly in older children.