cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27919132
title: Introducing β-Ti ductile-zones for enhancing both strength and ductility in heterostructured titanium alloy composites

description: Breaking trade-off barriers of strength-ductility in Ti alloys and their composites has been a key challenge for their engineering applications. Herein, we developed a new strategy of introducing β-Ti ductile zones in heterostructured (Cu@CNTs/TC4)+TC18 composites, which were synthesized using two-stepped ball milling, spark plasma sintering and hot rolling processes. Such composites are composed of TC18-coarse grain zones (CGZ) and (TiC+α'')-reinforced TC4-fine grain zones (FGZ). They achieve an exceptional high strength and high ductility, which are superior to those of TC4 alloys and homogeneous Cu@CNTs/TC4 composites. Heterogeneous interfacial microstructures of TC4-FGZ/TC18-CGZ play an important role in strengthening and toughening effects. The grain-coordinated deformation between TC18-CG and TC4-FG is also the key reason for achieving an exceptional ductility.
DOI: 10.6084/m9.figshare.27919132.v1
title: Introducing β-Ti ductile-zones for enhancing both strength and ductility in heterostructured titanium alloy composites

description: Breaking trade-off barriers of strength-ductility in Ti alloys and their composites has been a key challenge for their engineering applications. Herein, we developed a new strategy of introducing β-Ti ductile zones in heterostructured (Cu@CNTs/TC4)+TC18 composites, which were synthesized using two-stepped ball milling, spark plasma sintering and hot rolling processes. Such composites are composed of TC18-coarse grain zones (CGZ) and (TiC+α'')-reinforced TC4-fine grain zones (FGZ). They achieve an exceptional high strength and high ductility, which are superior to those of TC4 alloys and homogeneous Cu@CNTs/TC4 composites. Heterogeneous interfacial microstructures of TC4-FGZ/TC18-CGZ play an important role in strengthening and toughening effects. The grain-coordinated deformation between TC18-CG and TC4-FG is also the key reason for achieving an exceptional ductility.
DOI: 10.6084/m9.figshare.27868458
title: Polycrystalline Fe<sub>81</sub>Ga<sub>19</sub> alloy with magnetostriction comparable single-crystals prepared via laser powder bed fusion in-situ remelting

description: This study introduced the laser additive manufacturing strategy of an Fe-Ga alloy with magnetostriction of comparable to single crystal. The Fe<sub>81</sub>Ga<sub>19</sub> alloy is fabricated by in-situ laser remelting in the laser powder bed fusion. It is found that not only the direct precipitation of L60 nanoprecipitates is achieved by the laser remelting strategy, but also its distribution, density, and size are controlled by the optimized process. At the same time, remelting enhances the easy magnetization direction &lt;001&gt; orientation of the Fe<sub>81</sub>Ga<sub>19</sub> alloy. With this synergistic effect, the magnetostriction of the oriented polycrystalline Fe<sub>81</sub>Ga<sub>19</sub> alloy reaches 208 ppm.
DOI: 10.6084/m9.figshare.27868458.v1
title: Polycrystalline Fe<sub>81</sub>Ga<sub>19</sub> alloy with magnetostriction comparable single-crystals prepared via laser powder bed fusion in-situ remelting

description: This study introduced the laser additive manufacturing strategy of an Fe-Ga alloy with magnetostriction of comparable to single crystal. The Fe<sub>81</sub>Ga<sub>19</sub> alloy is fabricated by in-situ laser remelting in the laser powder bed fusion. It is found that not only the direct precipitation of L60 nanoprecipitates is achieved by the laser remelting strategy, but also its distribution, density, and size are controlled by the optimized process. At the same time, remelting enhances the easy magnetization direction &lt;001&gt; orientation of the Fe<sub>81</sub>Ga<sub>19</sub> alloy. With this synergistic effect, the magnetostriction of the oriented polycrystalline Fe<sub>81</sub>Ga<sub>19</sub> alloy reaches 208 ppm.
DOI: 10.6084/m9.figshare.27909800
title: Pulse current training induced marked improvement of soft magnetic properties in Fe-based amorphous alloys

description: It is difficult to fine-tune the atomic local structure and magnetic domain structure globally to evade the saturation magnetization (<i>B</i><sub>s</sub>)- coercivity (<i>H</i><sub>c</sub>) trade-off in amorphous alloys with high-Fe content. Herein, a unique pulse current training is introduced to optimize the soft magnetic properties of Fe-based amorphous alloys, achieving 1.78 T <i>B</i><sub>s</sub> and 1.6 A/m <i>H</i><sub>c</sub> in Fe<sub>83.3</sub>Si<sub>4</sub>B<sub>8</sub>P<sub>4</sub>Cu<sub>0.7</sub>. Structural analysis shows that low coercivity is due to the almost no pinning-sites magnetic domain structures despite a relative large grain size of 25 nm. Training accumulates energy to activate soft regions and rearrange structure, eliminating quenching-induced stresses and inhomogeneities to minimize reversal pinning. Pulse current training offers a promising and efficient approach to address the saturation magnetization-coercivity trade-off issue in Fe-based soft magnetic amorphous alloys by accumulating energy to gradually modulate microstructures.
DOI: 10.6084/m9.figshare.27909800.v2
title: Pulse current training induced marked improvement of soft magnetic properties in Fe-based amorphous alloys

description: It is difficult to fine-tune the atomic local structure and magnetic domain structure globally to evade the saturation magnetization (<i>B</i><sub>s</sub>)- coercivity (<i>H</i><sub>c</sub>) trade-off in amorphous alloys with high-Fe content. Herein, a unique pulse current training is introduced to optimize the soft magnetic properties of Fe-based amorphous alloys, achieving 1.78 T <i>B</i><sub>s</sub> and 1.6 A/m <i>H</i><sub>c</sub> in Fe<sub>83.3</sub>Si<sub>4</sub>B<sub>8</sub>P<sub>4</sub>Cu<sub>0.7</sub>. Structural analysis shows that low coercivity is due to the almost no pinning-sites magnetic domain structures despite a relative large grain size of 25 nm. Training accumulates energy to activate soft regions and rearrange structure, eliminating quenching-induced stresses and inhomogeneities to minimize reversal pinning. Pulse current training offers a promising and efficient approach to address the saturation magnetization-coercivity trade-off issue in Fe-based soft magnetic amorphous alloys by accumulating energy to gradually modulate microstructures.
DOI: 10.6084/m9.figshare.27680083
title: Accelerated composition-process-properties design of precipitation-strengthened copper alloys using machine learning based on Bayesian optimization

description: Designing new alloys with high performance is challenging due to the large search space for composition and process parameters. We propose an alloy design strategy based on machine learning algorithms for navigating the enormous search space. Specifically, feature engineering was applied to screen the major features, and a three-step alloy design strategy was employed to extract the required composition. The material design strategy for the multi-performance optimization of Cu-Ni-Si alloy through Bayesian optimization was proposed. This work provides novel insights into the comprehensive properties of Cu-Ni-Si alloys using machine learning with small data, with potential applicability to other materials systems. A machine learning strategy combining feature engineering with three-step alloy design is used to identify the optimal composition space for Cu-Ni-Si alloys with high strength, electrical conductivity, and elongation.
DOI: 10.6084/m9.figshare.27680083.v1
title: Accelerated composition-process-properties design of precipitation-strengthened copper alloys using machine learning based on Bayesian optimization

description: Designing new alloys with high performance is challenging due to the large search space for composition and process parameters. We propose an alloy design strategy based on machine learning algorithms for navigating the enormous search space. Specifically, feature engineering was applied to screen the major features, and a three-step alloy design strategy was employed to extract the required composition. The material design strategy for the multi-performance optimization of Cu-Ni-Si alloy through Bayesian optimization was proposed. This work provides novel insights into the comprehensive properties of Cu-Ni-Si alloys using machine learning with small data, with potential applicability to other materials systems. A machine learning strategy combining feature engineering with three-step alloy design is used to identify the optimal composition space for Cu-Ni-Si alloys with high strength, electrical conductivity, and elongation.
DOI: 10.6084/m9.figshare.27909800.v1
title: Pulse current training induced marked improvement of soft magnetic properties in Fe-based amorphous alloys

description: It is difficult to fine-tune the atomic local structure and magnetic domain structure globally to evade the saturation magnetization (<i>B</i><sub>s</sub>)- coercivity (<i>H</i><sub>c</sub>) trade-off in amorphous alloys with high-Fe content. Herein, a unique pulse current training is introduced to optimize the soft magnetic properties of Fe-based amorphous alloys, achieving 1.78 T <i>B</i><sub>s</sub> and 1.6 A/m <i>H</i><sub>c</sub> in Fe<sub>83.3</sub>Si<sub>4</sub>B<sub>8</sub>P<sub>4</sub>Cu<sub>0.7</sub>. Structural analysis shows that low coercivity is due to the almost no pinning-sites magnetic domain structures despite a relative large grain size of 25 nm. Training accumulates energy to activate soft regions and rearrange structure, eliminating quenching-induced stresses and inhomogeneities to minimize reversal pinning. Pulse current training offers a promising and efficient approach to address the saturation magnetization-coercivity trade-off issue in Fe-based soft magnetic amorphous alloys by accumulating energy to gradually modulate microstructures.
DOI: 10.6084/m9.figshare.27765241
title: Hardening mechanism and plastic deformation behavior in super-hard AlCrNbSiTiN high-entropy nitride nanocomposite coatings

description: The rapid development of industrial applications places severe demands on the mechanical properties of protective coatings. Unraveling the hardening mechanism and deformation behavior is essential for the design of advanced hard coatings. In this paper, a novel AlCrNbSiTiN high-entropy nitride (HEN) coating with an extremely low wear rate of 6.3 × 10<sup>−7</sup> mm<sup>3 </sup>N<sup>−1 </sup>m<sup>−1</sup> and super-hardness of 46 GPa was deposited. The variation of ion-bombardment energy induces the transformation of the nanocomposite structure and hardening mechanism. The plastic deformation behavior was revealed using <i>Ex situ</i> TEM observation and the MeN-Si<sub>3</sub>N<sub>4</sub> nanocomposite structure is a critical contributor to the outstanding mechanical properties and resistance to deformation. Alterations of Si<sub>3</sub>N<sub>4</sub> induce the transition of nanocomposite structure and hardening mechanism, and <i>Ex situ</i> TEM reveals MeN-Si<sub>3</sub>N<sub>4</sub> is essential for excellent mechanical properties and resistance to the deformation of AlCrNbSiTiN coatings.
DOI: 10.6084/m9.figshare.27765241.v1
title: Hardening mechanism and plastic deformation behavior in super-hard AlCrNbSiTiN high-entropy nitride nanocomposite coatings

description: The rapid development of industrial applications places severe demands on the mechanical properties of protective coatings. Unraveling the hardening mechanism and deformation behavior is essential for the design of advanced hard coatings. In this paper, a novel AlCrNbSiTiN high-entropy nitride (HEN) coating with an extremely low wear rate of 6.3 × 10<sup>−7</sup> mm<sup>3 </sup>N<sup>−1 </sup>m<sup>−1</sup> and super-hardness of 46 GPa was deposited. The variation of ion-bombardment energy induces the transformation of the nanocomposite structure and hardening mechanism. The plastic deformation behavior was revealed using <i>Ex situ</i> TEM observation and the MeN-Si<sub>3</sub>N<sub>4</sub> nanocomposite structure is a critical contributor to the outstanding mechanical properties and resistance to deformation. Alterations of Si<sub>3</sub>N<sub>4</sub> induce the transition of nanocomposite structure and hardening mechanism, and <i>Ex situ</i> TEM reveals MeN-Si<sub>3</sub>N<sub>4</sub> is essential for excellent mechanical properties and resistance to the deformation of AlCrNbSiTiN coatings.
DOI: 10.34740/kaggle/dsv/10903586
title: Contrastive Language Image Pretraining by openai
DOI: 10.6084/m9.figshare.28521731
title: Narwhal_Dentine_Stable_Isotope_Data.csv from δ<sup>15</sup>N and δ<sup>13</sup>C cycles in narwhal (<i>Monodon monoceros</i>) embedded teeth reveal seasonal variation in ecology and/or physiology

description: Dataset supporting this article have been uploaded as a part of supplementary material.
DOI: 10.6084/m9.figshare.28521701
title: Dataset for OTDR-Integrated D-shaped Optical Fiber Sensor for Refractive Index Detection

description: We demonstrated an Optical Time Domain Reflectometer-based refractive index (RI) sensor for RI detection. A bare fiber and a D-shaped fiber structure were utilized as a sensing probe. Both probes were connected to an OTDR via a single-mode fiber before the probe was immersed in a solution with different RIs. Results show a bare fiber owns a sensing sensitivity of 17.226 dB/RIU, while a D-shaped fiber has a sensitivity of 12.835 dB/RIU. The D-shaped fiber probe has a higher linearity of 0.9985 compared to 0.9341 for bare fiber, indicating the D-shaped fiber is more reliable for RI detection.
DOI: 10.15468/dl.sm3qew
title: Occurrence Download

description: A dataset containing 55 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Dipterocarpus crinitus Dyer"
 ]
}

 The dataset includes 55 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003323-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dqrf2q
title: Occurrence Download

description: A dataset containing 360616 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Scorzoneroides autumnalis (L.) Moench)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 360616 records from 837 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003272-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-03.3056036/2529796
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056037/2529797
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056039/2529798
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056040/2529799
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056042/2529800
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056043/2529801
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056044/2529802
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056045/2529803
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056046/2529804
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056048/2529805
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056049/2529806
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056050/2529807
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056051/2529808
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056052/2529809
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056053/2529810
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056054/2529811
title: Data for EMSL Project 51287 from March 2024
DOI: 10.15468/dl.btckfa
title: Occurrence Download

description: A dataset containing 823 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Stenoglottis Lindl."
 ]
}

 The dataset includes 823 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003837-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-03.3056055/2529812
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056056/2529813
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056057/2529814
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056058/2529815
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056059/2529816
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056060/2529817
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056061/2529818
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056062/2529819
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056063/2529820
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056064/2529821
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056065/2529822
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056066/2529823
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056067/2529824
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056068/2529825
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056069/2529826
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056070/2529827
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056071/2529828
title: Data for EMSL Project 51287 from March 2024
DOI: 10.15468/dl.fhnah9
title: Occurrence Download

description: A dataset containing 6955 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Observation, Human Observation, Material sample, Living Specimen, Occurrence evidence)",
 "OccurrenceStatus is Present",
 "TaxonKey is Ochotona princeps (Richardson, 1828)"
 ]
}

 The dataset includes 6955 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003838-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-03.3056072/2529829
title: Data for EMSL Project 51287 from March 2024
DOI: 10.15468/dl.uy4vex
title: Occurrence Download

description: A dataset containing 94 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is China",
 "Geometry POLYGON((73.93509 8.47918,134.06491 8.47918,134.06491 32.52082,73.93509 32.52082,73.93509 8.47918))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Alcedo hercules Laubmann, 1917"
 ]
}

 The dataset includes 94 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003839-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-03.3056073/2529830
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056076/2529831
title: Data for EMSL Project 51287 from March 2024
DOI: 10.15468/dl.uh6fc5
title: Occurrence Download

description: A dataset containing 217 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pterorhinus waddelli (Dresser, 1905)"
 ]
}

 The dataset includes 217 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003840-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-03.3056077/2529832
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056078/2529833
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056079/2529834
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056080/2529835
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056081/2529836
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056082/2529837
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056083/2529838
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056084/2529839
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056085/2529840
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056086/2529841
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056087/2529842
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056088/2529843
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056089/2529844
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056090/2529845
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056091/2529846
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056092/2529847
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056093/2529848
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056094/2529849
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056095/2529850
title: Data for EMSL Project 51287 from March 2024
DOI: 10.15468/dl.wqz9ss
title: Occurrence Download

description: A dataset containing 17947 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Holbrookia maculata Girard, 1851"
 ]
}

 The dataset includes 17947 records from 109 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003841-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-03.3056096/2529851
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056097/2529852
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056098/2529853
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056099/2529854
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056100/2529855
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056101/2529856
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056102/2529857
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056103/2529858
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056104/2529859
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056105/2529860
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056106/2529861
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056107/2529862
title: Data for EMSL Project 51287 from March 2024
DOI: 10.25582/data.2024-03.3056108/2529863
title: Data for EMSL Project 51287 from March 2024