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We here provide the structure files, trajectories and topology files for analyzing and reproducing simulations performed in the publication: "Modulation of Multidrug Resistance Protein 1-mediated transport processes by the antiretroviral drug ritonavir". Classical molecular dynamics simulation of rat Mrp1 have been performed in complex with native substrates like GSH and GSSG or drug molecules like ritonavir, to study binding modes of different combinations. Structure and trajectory files (all 1 µs long) as well as recorded movies of the time progression are provided for the following simulation systems: 1. GSH bound to rat Mrp1 2. GSSG bound to rat Mrp1 3. Ritonavir bound to rat Mrp1 4. GSH + Ritonavir bound to rat Mrp1 5. GSSG + Ritonavir bound to rat Mrp1 Additionally itp files (GROMACS format) containing parameter information about the ligands GSH, GSSG and Ritonavir are provided for the force field CHARMM36m. Raw data of the homology modeling of rat Mrp1 by I-Tasser as well as Docking results produced via AutoDock4 are uploaded as zip directories.

Upward-looking still images as acquired by a photo camera (Tiger Shark, Imenco) with internal flash and 4 x zoom attached to a remotely operated vehicle (ROV) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020.

Cover percentages of vegetation layers within the Landklif plots, as assessed during the vegetation survey between mid-May and end of July 2019 (seven subplots, 10m2 sampling area per plot). LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.

We acknowledge funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through Project-ID 416229255 – SFB 1411 Design of Particulate Products (subprojects A01, C04 and D01) – and project INST 90-1123-1 FUGG. We gratefully acknowledge the scientific support and HPC resources provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). The hardware is funded by the DFG. This dataset contains simulation input files, experimental raw data and all processed data underlying the figures of the corresponding journal article. A comprehensive description of the presented data is compiled in the "README.txt" file.

A dataset of the metadata of 84,105 Rust packages on crates.io, 433 reported vulnerabilities, 300 vulnerable code repositories, and 218 vulnerability fix commits, spanning over 7 years in history.

This README file was generated on 2024-01-13 by Yingang Liu. **GENERAL INFORMATION** 1. Title of Dataset: Manufacturing of High Strength and High Conductivity Copper with Laser Powder Bed Fusion 2. Author Information ``` **A. Corresponding Author Contact Information** * Name: Ranming Niu * Institution: The University of Sydney * Email: ranming.niu@sydney.edu.au **B. Corresponding Author Contact Information** * Name: Christopher Hutchinson * Institution: Monash University * Email: christopher.hutchinson@monash.edu **C. Corresponding Author Contact Information** * Name: Ming-Xing Zhang * Institution: The University of Queensland * Email: mingxing.zhang@uq.edu.au **D. First Author Contact Information** * Name: Yingang Liu * Institution: The University of Queensland * Email: yingangliu@nwpu.edu.cn **E. First Author Contact Information** * Name: Jingqi Zhang * Institution: The University of Queensland * Email: jingqi.zhang@uq.edu.au ``` 3. Date of data collection (single date, range, approximate date): 2021-2023 4. Geographic location of data collection: The University of Queensland (Australia) 5. Information about funding sources that supported the collection of the data: ``` * Australian Research Council Discovery Project (DP210103162) ``` **SHARING/ACCESS INFORMATION** 1. Licenses/restrictions placed on the data: CC0 1.0 Universal (CC0 1.0) Public Domain 2. Links to publications that cite or use the data: ``` *Yingang Liu, Jingqi Zhang, Ranming Niu, Mohamad Bayat, Ying Zhou, Yu Yin, Qiyang Tan, Shiyang Liu, Jesper Henri Hattel, Miaoquan Li, Xiaoxu Huang, Julie Cairney, Yi-Sheng Chen, Mark Easton, Christopher Hutchinson, Ming-Xing Zhang. Manufacturing of High Strength and High Conductivity Copper with Laser Powder Bed Fusion, Nature Communications, (2024)* ``` 3. Links to other publicly accessible locations of the data: None 4. Links/relationships to ancillary data sets: None 5. Was data derived from another source? No A. If yes, list source(s): NA 6. Recommended citation for this dataset: ``` *Yingang Liu, Jingqi Zhang, Ranming Niu, Mohamad Bayat, Ying Zhou, Yu Yin, Qiyang Tan, Shiyang Liu, Jesper Henri Hattel, Miaoquan Li, Xiaoxu Huang, Julie Cairney, Yi-Sheng Chen, Mark Easton, Christopher Hutchinson, Ming-Xing Zhang (2024). Data from: Manufacturing of High Strength and High Conductivity Copper with Laser Powder Bed Fusion [Dataset]. Dryad. https://doi.org/10.5061/dryad.9cnp5hqrp* ``` **DATA & FILE OVERVIEW** 1. File List: ``` * Supporting_data_for_Fig._1b.xlsx * Supporting_data_for_Fig._3c.xlsx * Supporting_data_for_Fig._3e.xlsx * Supporting_data_for_Fig._4a.xlsx * Supporting_data_for_Fig._5a.xlsx * Supporting_data_for_Supplementary_Fig._4a.xlsx * Supporting_data_for_Supplementary_Fig._5c.xlsx * Supporting_data_for_Supplementary_Fig._7c.xlsx * Supporting_data_for_Supplementary_Fig._9a.xlsx * Supporting_data_for_Supplementary_Fig._9b.xlsx * Supporting_data_for_Supplementary_Fig._13b.xlsx * Supporting_data_for_Supplementary_Fig._13e.xlsx ``` 2. Relationship between files, if important: None 3. Additional related data collected that was not included in the current data package: None 4. Are there multiple versions of the dataset? No A. If yes, name of file(s) that was updated: NA i. Why was the file updated? NA ii. When was the file updated? NA \######################################################################### DATA-SPECIFIC INFORMATION FOR: Supporting_data_for_Fig._1b.xlsx This dataset was used to create Figure 1b. 1. Data name: Laser reflectivity of pure Cu, LaB6 and 1.0 wt% LaB6 nanoparticles doped Cu powder feedstock 2. Number of variables: 3 3. Number of cases/rows: 405. 4. Variable List: ``` * Pure Cu, LaB6 and 1.0 wt% LaB6 nanoparticles doped Cu: The sample for laser reflectivity test. * Laser wavelength [nm]: It is defined as the distance between successive peaks of a laser wave. * Laser reflectivity: It is defined as the ratio of reflected laser intensity to the incident laser intensity. ``` 5. Missing data codes: None 6. Specialized formats or other abbreviations used: ``` * Cu = Copper * LaB6 = Lanthanum hexaboride * wt% = Weight percent ``` \######################################################################### DATA-SPECIFIC INFORMATION FOR: Supporting_data_for_Fig._3c.xlsx This dataset was used to create Figure 3c. 1. Data name: The one-dimensional concentration profile for APT 2. Number of variables: 7 3. Number of cases/rows: 43. 4. Variable List: ``` * Distance [nm]: It is defined as the distance from the initial analysis point. * B [at.%]: It is defined as the concentration of the element B. * Cu [at.%]: It is defined as the concentration of the element Cu. * La [at.%]: It is defined as the concentration of the element La. * B [at.%] σ: It is defined as the standard deviation of concentration of the element B. * Cu [at.%] σ: It is defined as the standard deviation of concentration of the element Cu. * La [at.%] σ: It is defined as the standard deviation of concentration of the element La. ``` 5. Missing data codes: None 6. Specialized formats or other abbreviations used: ``` * B = Boron * Cu = Copper * LaB6 = Lanthanum * at.% = Atom percent * σ = Standard deviation ``` \######################################################################### DATA-SPECIFIC INFORMATION FOR: Supporting_data_for_Fig._3e.xlsx This dataset was used to create Figure 3e. 1. Data name: The one-dimensional concentration profile for APT 2. Number of variables: 7 3. Number of cases/rows: 43. 4. Variable List: ``` * Distance [nm]: It is defined as the distance from the initial analysis point. * B [at.%]: It is defined as the concentration of the element B. * Cu [at.%]: It is defined as the concentration of the element Cu. * La [at.%]: It is defined as the concentration of the element La. * B [at.%] σ: It is defined as the standard deviation of concentration of the element B. * Cu [at.%] σ: It is defined as the standard deviation of concentration of the element Cu. * La [at.%] σ: It is defined as the standard deviation of concentration of the element La. ``` 5. Missing data codes: None 6. Specialized formats or other abbreviations used: ``` * B = Boron * Cu = Copper * LaB6 = Lanthanum * at.% = Atom percent * σ = Standard deviation ``` \######################################################################### DATA-SPECIFIC INFORMATION FOR: Supporting_data_for_Fig._4a.xlsx This dataset was used to create Figure 4a. 1. Data name: Tensile stress-strain data of pure Cu and 1.0LaB6-Cu 2. Number of variables: 3 3. Number of cases/rows: It depends on the specimen. The maximum number of rows is 11470 (1.0LaB6-Cu). 4. Variable List: ``` * Pure Cu, 1.0LaB6-Cu and 1.0LaB6-Cu microparticle: The tensile sample. * Engineering strain [%]: It is defined as the change in length divided by the original length. * Engineering stress [MPa]: It is defined as the applied load divided by the original cross-sectional area of material. Here, MPa meams one million pascals (Pa). ``` 5. Missing data codes: None 6. Specialized formats or other abbreviations used: ``` * Cu = Copper * LaB6 = Lanthanum hexaboride * 1.0LaB6-Cu = 1.0 weight percent lanthanum hexaboride nanoparticles doped copper * 1.0LaB6-Cu microparticle = 1.0 weight percent lanthanum hexaboride microparticles doped copper ``` \######################################################################### DATA-SPECIFIC INFORMATION FOR: Supporting_data_for_Fig._5a.xlsx This dataset was used to create Figure 5a. 1. Data name: Compressive stress-strain data of sheet-based gyroid lattices made of pure Cu and 1.0LaB6-Cu 2. Number of variables: 3 3. Number of cases/rows: 30001. 4. Variable List: ``` * Pure Cu and 1.0LaB6-Cu: The sample used for compression test. * Engineering strain [%]: It is defined as the change in height divided by the original height. * Engineering stress [MPa]: It is defined as the applied load divided by the original cross-sectional area of material. MPa meams one million pascals (Pa). ``` 5. Missing data codes: None 6. Specialized formats or other abbreviations used: ``` * Cu = Copper * LaB6 = Lanthanum hexaboride * 1.0LaB6-Cu = 1.0 weight percent lanthanum hexaboride nanoparticles doped copper ``` \######################################################################### DATA-SPECIFIC INFORMATION FOR: Supporting_data_for_Supplementary_Fig._4a.xlsx This dataset was used to create Supplementary Figure 4a. 1. Data name: XRD Spectra 2. Number of variables: 3 3. Number of cases/rows: 4105 4. Variable List: ``` * Pure Cu and 1.0LaB6-Cu: The sample for XRD test. * 2θ [degree]: The angle between the incoming and outgoing beam directions. * Intensity: The intensity of an XRD peak. It is related to the number of atoms in the crystal that are capable of scattering X-rays. ``` 5. Missing data codes: None 6. Specialized formats or other abbreviations used: ``` * Cu = Copper * LaB6 = Lanthanum hexaboride * 1.0LaB6-Cu = 1.0 weight percent lanthanum hexaboride nanoparticles doped copper ``` \######################################################################### DATA-SPECIFIC INFORMATION FOR: Supporting_data_for_Supplementary_Fig._5c.xlsx This dataset was used to create Supplementary Figure 5c. 1. Data name: The one-dimensional concentration profile for APT 2. Number of variables: 7 3. Number of cases/rows: 112. 4. Variable List: ``` * Distance [nm]: It is defined as the distance from the initial analysis point. * B [at.%]: It is defined as the concentration of the element B. * Cu [at.%]: It is defined as the concentration of the element Cu. * La [at.%]: It is defined as the concentration of the element La. * B [at.%] σ: It is defined as the standard deviation of concentration of the element B. * Cu [at.%] σ: It is defined as the standard deviation of concentration of the element Cu. * La [at.%] σ: It is defined as the standard deviation of concentration of the element La. ``` 5. Missing data codes: None 6. Specialized formats or other abbreviations used: ``` * B = Boron * Cu = Copper * LaB6 = Lanthanum * at.% = Atom percent * σ = Standard deviation ``` \######################################################################### DATA-SPECIFIC INFORMATION FOR: Supporting_data_for_Supplementary_Fig._7c.xlsx This dataset was used to create Supplementary Figure 7c. 1. Data name: Tensile stress-strain data of 0.5LaB6-Cu, 1.0LaB6-Cu and 2.0LaB6-Cu 2. Number of variables: 3 3. Number of cases/rows: It depends on the specimen. The maximum number of rows is 11470 (1.0LaB6-Cu). 4. Variable List: ``` * 0.5LaB6-Cu, 1.0LaB6-Cu and 2.0LaB6-Cu: The tensile sample. * Engineering strain [%]: It is defined as the change in length divided by the original length. * Engineering stress [MPa]: It is defined as the applied load divided by the original cross-sectional area of material. Here, MPa meams one million pascals (Pa). ``` 5. Missing data codes: None 6. Specialized formats or other abbreviations used: ``` * Cu = Copper * LaB6 = Lanthanum hexaboride * 0.5LaB6-Cu = 0.5 weight percent lanthanum hexaboride nanoparticles doped copper * 1.0LaB6-Cu = 1.0 weight percent lanthanum hexaboride nanoparticles doped copper * 2.0LaB6-Cu = 2.0 weight percent lanthanum hexaboride nanoparticles doped copper ``` \######################################################################### DATA-SPECIFIC INFORMATION FOR: Supporting_data_for_Supplementary_Fig._9a.xlsx This dataset was used to create Supplementary Figure 9a. 1. Data name: Hardness of 1.0LaB6-Cu at elevated temperatures 2. Number of variables: 2 3. Number of cases/rows: 11 4. Variable List: ``` * Temperature [℃]: The temperature for heat treatment. * Hardness [HV]: It is defined as the resistance of a material to deformation. ``` 5. Missing data codes: None 6. Specialized formats or other abbreviations used: ``` * Cu = Copper * LaB6 = Lanthanum hexaboride * 1.0LaB6-Cu = 1.0 weight percent lanthanum hexaboride nanoparticles doped copper ``` \######################################################################### DATA-SPECIFIC INFORMATION FOR: Supporting_data_for_Supplementary_Fig._9b.xlsx This dataset was used to create Supplementary Figure 9b. 1. Data name: Tensile stress-strain data of 1.0LaB6-Cu and 1.0LaB6-Cu subjected to thermal exposure at 550℃ and 1,050℃ 2. Number of variables: 3 3. Number of cases/rows: It depends on the specimen. The maximum number of rows is 14070 (1.0LaB6-Cu 1050℃). 4. Variable List: ``` * 1.0LaB6-Cu, 1.0LaB6-Cu 550℃ and 1.0LaB6-Cu 1050℃: The tensile sample. * Engineering strain [%]: It is defined as the change in length divided by the original length. * Engineering stress [MPa]: It is defined as the applied load divided by the original cross-sectional area of material. Here, MPa meams one million pascals (Pa). ``` 5. Missing data codes: None 6. Specialized formats or other abbreviations used: ``` * Cu = Copper * LaB6 = Lanthanum hexaboride * 1.0LaB6-Cu = 1.0 weight percent lanthanum hexaboride nanoparticles doped copper * 1.0LaB6-Cu 550℃ = 1.0 weight percent lanthanum hexaboride nanoparticles doped copper subjected to thermal exposure at 550℃ * 1.0LaB6-Cu 1050℃ = 1.0 weight percent lanthanum hexaboride nanoparticles doped copper subjected to thermal exposure at 1050℃ ``` \######################################################################### DATA-SPECIFIC INFORMATION FOR: Supporting_data_for_Supplementary_Fig._13b.xlsx This dataset was used to create Supplementary Figure 13b. 1. Data name: Laser reflectivity of pure Cu and 1.0 wt% TiB2 nanoparticles doped Cu powder feedstock 2. Number of variables: 3 3. Number of cases/rows: 405. 4. Variable List: ``` * Pure Cu and 1.0 wt% TiB2 nanoparticles doped Cu: The sample for laser reflectivity test. * Laser wavelength [nm]: It is defined as the distance between successive peaks of a laser wave. * Laser reflectivity: It is defined as the ratio of reflected laser intensity to the incident laser intensity. ``` 5. Missing data codes: None 6. Specialized formats or other abbreviations used: ``` * Cu = Copper * TiB2 = Titanium diboride * wt% = Weight percent ``` \######################################################################### DATA-SPECIFIC INFORMATION FOR: Supporting_data_for_Supplementary_Fig._13e.xlsx This dataset was used to create Supplementary Figure 13e. 1. Data name: Tensile stress-strain data of pure Cu and 1.0TiB2-Cu 2. Number of variables: 3 3. Number of cases/rows: It depends on the specimen. The maximum number of rows is 19150 (1.0TiB2-Cu). 4. Variable List: ``` * Pure Cu and 1.0TiB2-Cu: The tensile sample. * Engineering strain [%]: It is defined as the change in length divided by the original length. * Engineering stress [MPa]: It is defined as the applied load divided by the original cross-sectional area of material. Here, MPa meams one million pascals (Pa). ``` 5. Missing data codes: None 6. Specialized formats or other abbreviations used: ``` * Cu = Copper * TiB2 = Titanium diboride * 1.0TiB2-Cu = 1.0 weight percent titanium diboride nanoparticles doped copper ``` \######################################################################### Additive manufacturing (AM), known as 3D printing, enables rapid fabrication of geometrically complex copper (Cu) components for electrical conduction and heat management applications. However, pure Cu or Cu alloys produced by 3D printing often suffer from either low strength or low conductivity at room and elevated temperatures. Here, we demonstrate a design strategy for 3D printing of high strength, high conductivity Cu by uniformly dispersing a minor portion of lanthanum hexaboride (LaB6) nanoparticles in pure Cu through laser powder bed fusion (L-PBF). We show that trace additions of LaB6 to pure Cu result in an improved L-PBF processability, an enhanced strength, and improved thermal stability, all whilst maintaining a high conductivity. The presented strategy could expand the applicability of 3D-printed Cu components to more demanding conditions where high strength, high conductivity, and thermal stability are required. These datasets were collected at University of Queensland (Australia) from 2021 to 2023. Details for each dataset are provided in the README file.

We used European geometrid moths (> 630 species) as a model group to investigate how life history traits linked to larval host plant use (i.e., diet breadth and host-plant growth form) and seasonal life cycle (i.e., voltinism, overwintering stage, and caterpillar phenology) are related to adult body size in holometabolous insect herbivores. To do so, we applied phylogenetic comparative methods to account for shared evolutionary history among herbivore species. We further categorised larval diet breadth based on the phylogenetic structure of utilised host plant genera. Our results indicate that species associated with woody plants are, on average, larger than herb feeders and increase in size with increasing diet breadth. Obligatorily univoltine species are larger than multivoltine species, and attain larger sizes when their larvae are restricted to the early season. Furthermore, adult body size is significantly smaller in species that overwinter in the pupal stage compared to those that overwinter as egg or caterpillar. In summary, our results indicate that the ecological niche of an holometabolous insect herbivore is strongly interrelated with its size at maturity. We compiled information on overwintering stage (546 species) and caterpillar phenology (138 species) for European geometrid moths (Lepidoptera: Geometridae). Information was compiled based on available literature and reliable online sources.

In 2020, we conducted fieldwork in some limestone areas where there were no previous records of Petrocosmea. On August 5th, a small population of an unknown Petrocosmea species was discovered on the stone wall of a small sinkhole in a limestone valley occupied by Pinus yunnanensis Franch. in the northern part of Shiping County, Yunnan. Morphologically, the plant closely resembles P. sericea, but with an extremely short corolla tube (only 2 mm, the shortest in all known species of Petrocosmea) and glabrous and straight filaments. We describe this species as P. wangii. On September 19th, another unknown species of Petrocosmea was collected in Mojiang County. The individuals we planted in the greenhouse in December of that year produced flowers similar to P. forrestii, and we subsequently went to the same locality during flowering to confirm the prevalence of the flower morphology. Morphologically, it most closely resembles P. forrestii, but its filaments are adnate to the base of the corolla tube for about 2 mm, which easily differentiate it from all other known species of Petrocosmea, and we describe this species as P. yei. # Type specimen information of *Petrocosmea wangii* and *P. yei* Information on the type specimens of two new species of *Petrocosmea* from Yunnan Province, China. A Microsoft Excel file contains information such as the locality where the type specimens of these two new species were collected, the date of collection, collector, the collection number, and the herbarium where they are stored. For the purpose of conservation of the new species described in the article, we do not provide coordinates of the collection site.

This dataset includes measured and calculated data over the epi-mesopelagic layer (0-450 m depth) of 29 stations with 4 stations within a cyclonic eddy nearby Brava island, 5 stations within a cyclonic eddy nearby Sal island, and one station within an anticyclonic eddy around the Cabo Verde islands in the eastern Tropical North Atlantic during the M160 cruise on the RV Meteor from November 22nd to December 20th, 2019. Duplicate samples for dissolved organic carbon (DOC) and total dissolved organic nitrogen (TDN) were filtered through 0.45 μm GMF GD/X filters (Whatman, GE Healthcare Life 248 Sciences, UK) and collected in combusted glass ampoules. Syringes and filters were rinsed with 25 and 50 mL of sample, respectively in order to remove potential contaminations. Samples were acidified with 20 μL of hydrochloric acid (30%) and stored at 4°C until analysis by high−temperature combustion using a Shimadzu TOC−VCSH after Engel & Galgani (2016). Values of TDN were corrected with dissolved inorganic nitrogen (nitrate+nitrite) and thereafter referred to as dissolved organic nitrogen (DON). To estimate the fraction of semi-labile dissolved organic carbon and semi-labile dissolved organic nitrogen, we determined high-molecular-weight (>1 kDa) dissolved combined carbohydrates (dCCHO) based on Engel and Händel (2011, https://doi.org/10.1093/plankt/fbq122) and dissolved hydrolysable amino acids (dHAA) based on Lindroth and Mopper (1979, https://doi.org/10.1021/ac50047a019) and Dittmar et al, (2009). The analysis of DCCHO detected 11 monomers: and the dHAA analysis classified 13 monomers. The calculations for the carbon and nitrogen contents of dCCHO and dHAA were based on carbon and nitrogen atoms contained in the identified monomers. The sum of dCCHO and dHAA carbon and nitrogen contents are referred to as SL-DOC and SL-DON respectively. Size class of virus abundance measured by flow cytometry.