cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11733402.v1
title: Aneks 1. Wyniki badania liczby i wieku źródeł piśmienniczych przywoływanych w bibliografiach załącznikowych artykułów opublikowanych w czasopismach stanowiących przedmiot badania, z podziałem na dyscypliny, do których należały artykuły cytujące

description: Wyniki badania liczby i wieku źródeł piśmienniczych przywoływanych w bibliografiach załącznikowych artykułów opublikowanych w czasopismach stanowiących przedmiot badania, z podziałem na dyscypliny, do których należały artykuły cytujące
DOI: 10.6084/m9.figshare.11733354
title: Simulated significant wave height (3-hourly, 1850.01-2014.12) from FIO-ESM v2.0 historical experiment r1i1p1f1

description: Simulated significant wave height (3-hourly, 1850.01-2014.12) from FIO-ESM v2.0 historical experiment r1i1p1f1, which is branch from Jan 1, 301 of piControl experiment. The grid, named gx1v6, is the displaced pole (the North Pole displaced to the Greenland) grid with 1.1 degree in longitude and 0.27-0.54 degree in latitude.
DOI: 10.6084/m9.figshare.11733354.v1
title: Simulated significant wave height (3-hourly, 1850.01-2014.12) from FIO-ESM v2.0 historical experiment r1i1p1f1

description: Simulated significant wave height (3-hourly, 1850.01-2014.12) from FIO-ESM v2.0 historical experiment r1i1p1f1, which is branch from Jan 1, 301 of piControl experiment. The grid, named gx1v6, is the displaced pole (the North Pole displaced to the Greenland) grid with 1.1 degree in longitude and 0.27-0.54 degree in latitude.
DOI: 10.6084/m9.figshare.11733336
title: easoj011_ecoli_genomic_umi_ont_r10_g324hac

description: Genomic UMI consensus sequences from Escherichia coli str. K-12 substr. MG1655 (DSM 18039).
DOI: 10.6084/m9.figshare.11733336.v1
title: easoj011_ecoli_genomic_umi_ont_r10_g324hac

description: Genomic UMI consensus sequences from Escherichia coli str. K-12 substr. MG1655 (DSM 18039).
DOI: 10.6084/m9.figshare.11733321
title: Molecular and morphological data reveal hidden diversity in common North American <i>Frustulia</i> species (Amphipleuraceae)

description: <i>Frustulia</i> Rabenhorst is an established diatom genus that is common and widespread across North America. As for many diatom genera, taxa within <i>Frustulia</i> have been the subject of taxonomic confusion. Although recent studies have examined species from Europe and New Zealand, there exist no detailed genetic data for North American individuals. Using both molecular (<i>rbcL</i> and 18S rRNA sequences) and morphological (i.e., frustule characters and shape analysis) data, we investigated common taxa of <i>Frustulia</i> in North America. European and New Zealand taxa were also included to investigate how North American species were related. We recognized nine taxa from North America in this study including two unknowns. A new species, <i>Frustulia gibsonea</i> sp. nov. is described. <i>Frustulia gibsonea</i> was observed in previous studies and described as <i>F.</i> cf. <i>krammeri</i> based on morphology. The use of molecular characters in this study demonstrates that this taxon is a distinct species, and not a morph of <i>Frustulia krammeri</i>. Despite differences in <i>rbcL</i> and 18S, <i>F. gibsonea</i> and <i>F. krammeri</i> are quite similar morphologically, showing overlap with both traditional measurements and shape analysis. This suggests that the combination of molecular and morphological data can help in deciphering these morphologically cryptic taxa. <i>Frustulia saxonica</i> and <i>F. krammeri</i> could be weakly separated based on sequence divergences, although in this case morphological characters were more useful. As evidenced by the low sequence divergence values obtained between the two taxa, they are closely related. Future molecular research, focusing on less conserved markers, will be necessary to resolve the relationships of these taxonomic complexes.
DOI: 10.6084/m9.figshare.11733321.v1
title: Molecular and morphological data reveal hidden diversity in common North American <i>Frustulia</i> species (Amphipleuraceae)

description: <i>Frustulia</i> Rabenhorst is an established diatom genus that is common and widespread across North America. As for many diatom genera, taxa within <i>Frustulia</i> have been the subject of taxonomic confusion. Although recent studies have examined species from Europe and New Zealand, there exist no detailed genetic data for North American individuals. Using both molecular (<i>rbcL</i> and 18S rRNA sequences) and morphological (i.e., frustule characters and shape analysis) data, we investigated common taxa of <i>Frustulia</i> in North America. European and New Zealand taxa were also included to investigate how North American species were related. We recognized nine taxa from North America in this study including two unknowns. A new species, <i>Frustulia gibsonea</i> sp. nov. is described. <i>Frustulia gibsonea</i> was observed in previous studies and described as <i>F.</i> cf. <i>krammeri</i> based on morphology. The use of molecular characters in this study demonstrates that this taxon is a distinct species, and not a morph of <i>Frustulia krammeri</i>. Despite differences in <i>rbcL</i> and 18S, <i>F. gibsonea</i> and <i>F. krammeri</i> are quite similar morphologically, showing overlap with both traditional measurements and shape analysis. This suggests that the combination of molecular and morphological data can help in deciphering these morphologically cryptic taxa. <i>Frustulia saxonica</i> and <i>F. krammeri</i> could be weakly separated based on sequence divergences, although in this case morphological characters were more useful. As evidenced by the low sequence divergence values obtained between the two taxa, they are closely related. Future molecular research, focusing on less conserved markers, will be necessary to resolve the relationships of these taxonomic complexes.
DOI: 10.6084/m9.figshare.11733333.v2
title: KZFP constraint scores

description: Constraint scores used in the manuscript "Ongoing evolution of KRAB zinc finger protein-coding genes in modern humans"
DOI: 10.6084/m9.figshare.11733333
title: KZFP constraint scores

description: Constraint scores used in the manuscript "Ongoing evolution of KRAB zinc finger protein-coding genes in modern humans"
DOI: 10.6084/m9.figshare.11733312
title: Hormone data from Despotic aggression in pre-moulting painted buntings

description: This file contains all data on DHEA we present in the manuscript
DOI: 10.6084/m9.figshare.11733312.v1
title: Hormone data from Despotic aggression in pre-moulting painted buntings

description: This file contains all data on DHEA we present in the manuscript
DOI: 10.6084/m9.figshare.11731824
title: Impacts of thermal energy storage on the management of variable demand and production in electricity and district heating systems: a Swedish case study

description: This study investigates how thermal energy storage (TES) influences the cost-optimal investment and operation of electricity and district heating (DH) systems in different scenarios. Greenfield energy system modelling for Year 2050 with a high time resolution shows that sensible TES strategies have a strong impact on the composition and operation of the DH system in all investigated scenarios. The introduction of TES displaces to a significant extent the heat-only boilers in all scenarios and can promote solar heating in small DH networks. The modelling shows that TES also promotes the use of power-to-heat processes and enables combined heat and power plants to increase full-load hours, with simultaneous adaptation to the variable production in the electricity system. A major benefit derived from TES is the ability to respond to rapid variations in the electricity system. Thus, the pit and tank storage systems with higher (dis)charging capacities are preferred over borehole storage.
DOI: 10.6084/m9.figshare.11731824.v1
title: Impacts of thermal energy storage on the management of variable demand and production in electricity and district heating systems: a Swedish case study

description: This study investigates how thermal energy storage (TES) influences the cost-optimal investment and operation of electricity and district heating (DH) systems in different scenarios. Greenfield energy system modelling for Year 2050 with a high time resolution shows that sensible TES strategies have a strong impact on the composition and operation of the DH system in all investigated scenarios. The introduction of TES displaces to a significant extent the heat-only boilers in all scenarios and can promote solar heating in small DH networks. The modelling shows that TES also promotes the use of power-to-heat processes and enables combined heat and power plants to increase full-load hours, with simultaneous adaptation to the variable production in the electricity system. A major benefit derived from TES is the ability to respond to rapid variations in the electricity system. Thus, the pit and tank storage systems with higher (dis)charging capacities are preferred over borehole storage.
DOI: 10.6084/m9.figshare.11730999
title: ShenzhenData

description: The folder contains the flow velocity data (FlowObs.xlsx), climate forcing data (ClimateForcing.xlsx), DEM (dem.txt), soil type distribution (soil.txt), and two utilized land use maps (lu1979.txt and lu2014.txt) in Shenzhen, China. The flow velocity data were measured at the Guangming hydrological station in the Maozhou River for the year of 2017. The climate forcing data include the daily precipitation, maximum, and minimum temperature from the year of 2011 to 2017 recorded at the Zhuzilin Meteorological Station, Shenzhen. In the soil type file, a value of 1 and 2 represents rice paddy (alluvial) and latosoil (tropical weathered soil), respectively. In the land use files, a value of 1 and 2 represents rural and urban land use type, respectively.
DOI: 10.6084/m9.figshare.11729031
title: Dataset : Anti-Mullerian hormone levels in female cancer patients of reproductive age in Indonesia: A cross-sectional study

description: All raw data and demographic information obtained from subject during the present study.
DOI: 10.6084/m9.figshare.11729004
title: Supplementary files: Anti-Mullerian hormone levels in female cancer patients of reproductive age in Indonesia: a cross-sectional study

description: These are our supplementary files for Anti-Mullerian hormone levels in female cancer patients of reproductive age in Indonesia: a cross-sectional study.
DOI: 10.6084/m9.figshare.11728980
title: Aqualitas Database (full release)

description: <b>Aqualitas</b> database (full release) corresponding to 100 diatom taxa with about 100 specimens/taxon. The images have been taken with a 60X, NA=0.85 objective using oblique illumination. If you use this database please cite the following publications:<br>[1] Bueno, G. et al, “Automated Diatom Classification (Part A): Handcrafted feature approaches", Applied Sciences, 7, 753, pp. 1-22 (2017) doi: 10.3390/app7080753[2] Pedraza, A. et al, “Automated Diatom Classification (Part B): A Deep Learning Approach" Applied Sciences, 7, 460, pp. 1-25 (2017) doi: 10.3390/app7050460<br> <br>NOTE: For calibrating the images, use the attached image ruler. The distance between the large ticks is 100um.<br><b>Acknowledgements</b>This work was supported by the Spanish Government under the <b>Aqualitas</b> project (CTM2014-51907) during the period 2015-2019.
DOI: 10.6084/m9.figshare.11728980.v2
title: Aqualitas Database (full release)

description: AQUALITAS database (full release) corresponding to 100 diatom taxa with about 100 specimens/taxon. The images have been taken with a 60x NA=0.85 using oblique illumination. If you use this database please cite the following publications:<br>[1] Bueno, G. et al, “Automated Diatom Classification (Part A): Handcrafted feature approaches", Applied Sciences, 7, 753, pp. 1-22 (2017) doi: 10.3390/app7080753[2] Pedraza, A. et al, “Automated Diatom Classification (Part B): A Deep Learning Approach" Applied Sciences, 7, 460, pp. 1-25 (2017) doi: 10.3390/app7050460<br> <br>NOTE: For calibrating the images, use the attached image ruler. The distance between the large ticks is 0.01mm.
DOI: 10.6084/m9.figshare.11728980.v3
title: Aqualitas Database (full release)

description: AQUALITAS database (full release) corresponding to 100 diatom taxa with about 100 specimens/taxon. The images have been taken with a 60x NA=0.85 using oblique illumination. If you use this database please cite the following publications:<br>[1] Bueno, G. et al, “Automated Diatom Classification (Part A): Handcrafted feature approaches", Applied Sciences, 7, 753, pp. 1-22 (2017) doi: 10.3390/app7080753[2] Pedraza, A. et al, “Automated Diatom Classification (Part B): A Deep Learning Approach" Applied Sciences, 7, 460, pp. 1-25 (2017) doi: 10.3390/app7050460<br> <br>NOTE: For calibrating the images, use the attached image ruler. The distance between the large ticks is 0.01mm.
DOI: 10.6084/m9.figshare.11728980.v4
title: Aqualitas Database (full release)

description: AQUALITAS database (full release) corresponding to 100 diatom taxa with about 100 specimens/taxon. The images have been taken with a 60x NA=0.85 using oblique illumination. If you use this database please cite the following publications:<br>[1] Bueno, G. et al, “Automated Diatom Classification (Part A): Handcrafted feature approaches", Applied Sciences, 7, 753, pp. 1-22 (2017) doi: 10.3390/app7080753[2] Pedraza, A. et al, “Automated Diatom Classification (Part B): A Deep Learning Approach" Applied Sciences, 7, 460, pp. 1-25 (2017) doi: 10.3390/app7050460<br> <br>NOTE: For calibrating the images, use the attached image ruler. The distance between the large ticks is 0.01mm.
DOI: 10.6084/m9.figshare.11728980.v5
title: Aqualitas Database (full release)

description: AQUALITAS database (full release) corresponding to 100 diatom taxa with about 100 specimens/taxon. The images have been taken with a 60x NA=0.85 using oblique illumination. If you use this database please cite the following publications:<br>[1] Bueno, G. et al, “Automated Diatom Classification (Part A): Handcrafted feature approaches", Applied Sciences, 7, 753, pp. 1-22 (2017) doi: 10.3390/app7080753[2] Pedraza, A. et al, “Automated Diatom Classification (Part B): A Deep Learning Approach" Applied Sciences, 7, 460, pp. 1-25 (2017) doi: 10.3390/app7050460<br> <br>NOTE: For calibrating the images, use the attached image ruler. The distance between the large ticks is 0.01mm.
DOI: 10.6084/m9.figshare.11728980.v6
title: Aqualitas Database (full release)

description: AQUALITAS database (full release) corresponding to 100 diatom taxa with about 100 specimens/taxon. The images have been taken with a 60x NA=0.85 using oblique illumination. If you use this database please cite the following publications:<br>[1] Bueno, G. et al, “Automated Diatom Classification (Part A): Handcrafted feature approaches", Applied Sciences, 7, 753, pp. 1-22 (2017) doi: 10.3390/app7080753[2] Pedraza, A. et al, “Automated Diatom Classification (Part B): A Deep Learning Approach" Applied Sciences, 7, 460, pp. 1-25 (2017) doi: 10.3390/app7050460<br> <br>NOTE: For calibrating the images, use the attached image ruler. The distance between the large ticks is 0.01mm.
DOI: 10.6084/m9.figshare.11728980.v7
title: Aqualitas Database (full release)

description: AQUALITAS database (full release) corresponding to 100 diatom taxa with about 100 specimens/taxon. The images have been taken with a 60x NA=0.85 using oblique illumination. If you use this database please cite the following publications:<br>[1] Bueno, G. et al, “Automated Diatom Classification (Part A): Handcrafted feature approaches", Applied Sciences, 7, 753, pp. 1-22 (2017) doi: 10.3390/app7080753[2] Pedraza, A. et al, “Automated Diatom Classification (Part B): A Deep Learning Approach" Applied Sciences, 7, 460, pp. 1-25 (2017) doi: 10.3390/app7050460<br> <br>NOTE: For calibrating the images, use the attached image ruler. The distance between the large ticks is 0.01mm.
DOI: 10.6084/m9.figshare.11728980.v8
title: Aqualitas Database (full release)

description: AQUALITAS database (full release) corresponding to 100 diatom taxa with about 100 specimens/taxon. The images have been taken with a 60x NA=0.85 using oblique illumination. If you use this database please cite the following publications:<br>[1] Bueno, G. et al, “Automated Diatom Classification (Part A): Handcrafted feature approaches", Applied Sciences, 7, 753, pp. 1-22 (2017) doi: 10.3390/app7080753[2] Pedraza, A. et al, “Automated Diatom Classification (Part B): A Deep Learning Approach" Applied Sciences, 7, 460, pp. 1-25 (2017) doi: 10.3390/app7050460<br> <br>NOTE: For calibrating the images, use the attached image ruler. The distance between the large ticks is 0.01mm.
DOI: 10.6084/m9.figshare.11728980.v9
title: Aqualitas Database (full release)

description: AQUALITAS database (full release) corresponding to 100 diatom taxa with about 100 specimens/taxon. The images have been taken with a 60X, NA=0.85 objective using oblique illumination. If you use this database please cite the following publications:<br>[1] Bueno, G. et al, “Automated Diatom Classification (Part A): Handcrafted feature approaches", Applied Sciences, 7, 753, pp. 1-22 (2017) doi: 10.3390/app7080753[2] Pedraza, A. et al, “Automated Diatom Classification (Part B): A Deep Learning Approach" Applied Sciences, 7, 460, pp. 1-25 (2017) doi: 10.3390/app7050460<br> <br>NOTE: For calibrating the images, use the attached image ruler. The distance between the large ticks is 0.01mm.
DOI: 10.6084/m9.figshare.11728980.v10
title: Aqualitas Database (full release)

description: AQUALITAS database (full release) corresponding to 100 diatom taxa with about 100 specimens/taxon. The images have been taken with a 60X, NA=0.85 objective using oblique illumination. If you use this database please cite the following publications:<br>[1] Bueno, G. et al, “Automated Diatom Classification (Part A): Handcrafted feature approaches", Applied Sciences, 7, 753, pp. 1-22 (2017) doi: 10.3390/app7080753[2] Pedraza, A. et al, “Automated Diatom Classification (Part B): A Deep Learning Approach" Applied Sciences, 7, 460, pp. 1-25 (2017) doi: 10.3390/app7050460<br> <br>NOTE: For calibrating the images, use the attached image ruler. The distance between the large ticks is 0.01mm.
DOI: 10.6084/m9.figshare.11728980.v11
title: Aqualitas Database (full release)

description: AQUALITAS database (full release) corresponding to 100 diatom taxa with about 100 specimens/taxon. The images have been taken with a 60X, NA=0.85 objective using oblique illumination. If you use this database please cite the following publications:<br>[1] Bueno, G. et al, “Automated Diatom Classification (Part A): Handcrafted feature approaches", Applied Sciences, 7, 753, pp. 1-22 (2017) doi: 10.3390/app7080753[2] Pedraza, A. et al, “Automated Diatom Classification (Part B): A Deep Learning Approach" Applied Sciences, 7, 460, pp. 1-25 (2017) doi: 10.3390/app7050460<br> <br>NOTE: For calibrating the images, use the attached image ruler. The distance between the large ticks is 0.01mm.
DOI: 10.6084/m9.figshare.11728980.v12
title: Aqualitas Database (full release)

description: AQUALITAS database (full release) corresponding to 100 diatom taxa with about 100 specimens/taxon. The images have been taken with a 60X, NA=0.85 objective using oblique illumination. If you use this database please cite the following publications:<br>[1] Bueno, G. et al, “Automated Diatom Classification (Part A): Handcrafted feature approaches", Applied Sciences, 7, 753, pp. 1-22 (2017) doi: 10.3390/app7080753[2] Pedraza, A. et al, “Automated Diatom Classification (Part B): A Deep Learning Approach" Applied Sciences, 7, 460, pp. 1-25 (2017) doi: 10.3390/app7050460<br> <br>NOTE: For calibrating the images, use the attached image ruler. The distance between the large ticks is 0.01mm.
DOI: 10.6084/m9.figshare.11728980.v13
title: Aqualitas Database (full release)

description: AQUALITAS database (full release) corresponding to 100 diatom taxa with about 100 specimens/taxon. The images have been taken with a 60X, NA=0.85 objective using oblique illumination. If you use this database please cite the following publications:<br>[1] Bueno, G. et al, “Automated Diatom Classification (Part A): Handcrafted feature approaches", Applied Sciences, 7, 753, pp. 1-22 (2017) doi: 10.3390/app7080753[2] Pedraza, A. et al, “Automated Diatom Classification (Part B): A Deep Learning Approach" Applied Sciences, 7, 460, pp. 1-25 (2017) doi: 10.3390/app7050460<br> <br>NOTE: For calibrating the images, use the attached image ruler. The distance between the large ticks is 0.01mm.
DOI: 10.6084/m9.figshare.11728980.v14
title: Aqualitas Database (full release)

description: AQUALITAS database (full release) corresponding to 100 diatom taxa with about 100 specimens/taxon. The images have been taken with a 60X, NA=0.85 objective using oblique illumination. If you use this database please cite the following publications:<br>[1] Bueno, G. et al, “Automated Diatom Classification (Part A): Handcrafted feature approaches", Applied Sciences, 7, 753, pp. 1-22 (2017) doi: 10.3390/app7080753[2] Pedraza, A. et al, “Automated Diatom Classification (Part B): A Deep Learning Approach" Applied Sciences, 7, 460, pp. 1-25 (2017) doi: 10.3390/app7050460<br> <br>NOTE: For calibrating the images, use the attached image ruler. The distance between the large ticks is 0.01mm.
DOI: 10.6084/m9.figshare.11728980.v15
title: Aqualitas Database (full release)

description: AQUALITAS database (full release) corresponding to 100 diatom taxa with about 100 specimens/taxon. The images have been taken with a 60X, NA=0.85 objective using oblique illumination. If you use this database please cite the following publications:<br>[1] Bueno, G. et al, “Automated Diatom Classification (Part A): Handcrafted feature approaches", Applied Sciences, 7, 753, pp. 1-22 (2017) doi: 10.3390/app7080753[2] Pedraza, A. et al, “Automated Diatom Classification (Part B): A Deep Learning Approach" Applied Sciences, 7, 460, pp. 1-25 (2017) doi: 10.3390/app7050460<br> <br>NOTE: For calibrating the images, use the attached image ruler. The distance between the large ticks is 0.01mm.
DOI: 10.6084/m9.figshare.11728878
title: The bioinformatics wealth of nations

description: Analysis of literature performance metrics for the field of bioinformatics, across countries (up to 31-Dec-2019).<br>Data supplement for the manuscript "The bioinformatics wealth of nations" by Anastasia Chasapi, Vasilis J. Promponas, Christos A. Ouzounis (bioinformatics/btaa132 Bioinformatics: 36, 2963) (2020).<br><br>Legends: Table S1 <br> List of countries and territories (column A) with their output in terms of publications (B), citations (C), citations/publication (D) and the H-index (E) for bioinformatics (WoS query: 'all fields'). Reported by sorting values in column E. <br> Table S2 <br> List of 40 top-producer countries (A) for bioinformatics, with 7 indices: - four primary indices: publications (B, and rank: C), citations (D, and rank: E), citations/ publication (F, and rank: G), H-index (H, and rank: I) - rank of countries with top 1% highly cited publications (J) - two population (K, in thousand inhabitants) normalized indices: publications per million (L=1000*B/K, and rank: M) H-index per million (N=1000*H/K, and rank: O) <br>
DOI: 10.6084/m9.figshare.11728980.v16
title: Aqualitas Database (full release)

description: AQUALITAS database (full release) corresponding to 100 diatom taxa with about 100 specimens/taxon. The images have been taken with a 60X, NA=0.85 objective using oblique illumination. If you use this database please cite the following publications:<br>[1] Bueno, G. et al, “Automated Diatom Classification (Part A): Handcrafted feature approaches", Applied Sciences, 7, 753, pp. 1-22 (2017) doi: 10.3390/app7080753[2] Pedraza, A. et al, “Automated Diatom Classification (Part B): A Deep Learning Approach" Applied Sciences, 7, 460, pp. 1-25 (2017) doi: 10.3390/app7050460<br> <br>NOTE: For calibrating the images, use the attached image ruler. The distance between the large ticks is 0.01mm.