cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27239177.v1
title: RAVEN: development of a novel robotic additive manufacturing system towards small-scale fabrication of complex scaffolds for regenerative medicine

description: Recent technological advances in the field of Additive Manufacturing (AM) and the increasing need in Regenerative Medicine (RM) for devices that better and better mimic native tissue architecture are showing limitations in the current scaffolds fabrication techniques. A switch from the typical layer-by-layer approach is needed to achieve precise control on fibres orientation and pores dimension and morphology. In this work a new AM apparatus, the RAVEN (Robot-Assisted Volumetric ExtrusioN) system, is presented. RAVEN is based on a 7-DOF robotic arm and an extruder and allows for volumetric extrusion of polymeric filaments. The development process, namely the robotic motion optimisation, the optimisation towards small-scale trajectories, the custom-made hardware/software interfaces, and the different printing capabilities are hereby presented. The successful results are promising towards future advanced applications in which the ability of the robot to change its configuration while printing will be crucial.
DOI: 10.6084/m9.figshare.25379658
title: AM-SegNet for additive manufacturing <i>in situ</i> X-ray image segmentation and feature quantification

description: Synchrotron X-ray imaging has been utilised to detect the dynamic behaviour of molten pools during the metal additive manufacturing (AM) process, where a substantial amount of imaging data is generated. Here, we develop an efficient and robust deep learning model, AM-SegNet, for segmenting and quantifying high-resolution X-ray images and prepare a large-scale database consisting of over 10,000 pixel-labelled images for model training and testing. AM-SegNet incorporates a lightweight convolution block and a customised attention mechanism, capable of performing semantic segmentation with high accuracy (∼96%) and processing speed (&lt; 4 ms per frame). The segmentation results can be used for quantification and multi-modal correlation analysis of critical features (e.g. keyholes and pores). Additionally, the application of AM-SegNet to other advanced manufacturing processes is demonstrated. The proposed method will enable end-users in the manufacturing and imaging domains to accelerate data processing from collection to analytics, and provide insights into the processes’ governing physics.
DOI: 10.6084/m9.figshare.25379658.v1
title: AM-SegNet for additive manufacturing <i>in situ</i> X-ray image segmentation and feature quantification

description: Synchrotron X-ray imaging has been utilised to detect the dynamic behaviour of molten pools during the metal additive manufacturing (AM) process, where a substantial amount of imaging data is generated. Here, we develop an efficient and robust deep learning model, AM-SegNet, for segmenting and quantifying high-resolution X-ray images and prepare a large-scale database consisting of over 10,000 pixel-labelled images for model training and testing. AM-SegNet incorporates a lightweight convolution block and a customised attention mechanism, capable of performing semantic segmentation with high accuracy (∼96%) and processing speed (&lt; 4 ms per frame). The segmentation results can be used for quantification and multi-modal correlation analysis of critical features (e.g. keyholes and pores). Additionally, the application of AM-SegNet to other advanced manufacturing processes is demonstrated. The proposed method will enable end-users in the manufacturing and imaging domains to accelerate data processing from collection to analytics, and provide insights into the processes’ governing physics.
DOI: 10.6084/m9.figshare.26779777
title: Magnetic field-assisted 3D printing of magnetic self-powered sensors

description: Triply periodic minimal surface (TPMS) based lattices have drawn great attention in recent years due to their lightweight, design flexibility and compatibility with 3D printing. However, more effort should be made in the functionality enhancement of engineered sensors, especially with field-assisted 3D printing methods. In this study, magnetic field-assisted photopolymerisation (MF-VP) 3D printing technique was employed to fabricate magnetic Schwarz primitive TPMS (P-TPMS) lattice. Based on the fabricated magnetic P-TPMS lattices, a self-powered sensor was designed to achieve the velocity-to-electrical signal conversion process without the need for external power supply. MF-VP 3D printing technique significantly improved the mechanical compressive performance of the magnetic P-TPMS lattice in the carbonyl iron powder alignment direction, magnetic properties and sensing capabilities. The magnetic P-TPMS lattice self-powered sensor exhibited outstanding stability and durability, maintaining highly stable relative output voltage signals after 10,000 compression cycles. The developed sensor successfully detected toy car impact processes and foot movement states (stop/slow/fast), highlighting the practical application potential of the self-powered sensor.
DOI: 10.6084/m9.figshare.26779777.v1
title: Magnetic field-assisted 3D printing of magnetic self-powered sensors

description: Triply periodic minimal surface (TPMS) based lattices have drawn great attention in recent years due to their lightweight, design flexibility and compatibility with 3D printing. However, more effort should be made in the functionality enhancement of engineered sensors, especially with field-assisted 3D printing methods. In this study, magnetic field-assisted photopolymerisation (MF-VP) 3D printing technique was employed to fabricate magnetic Schwarz primitive TPMS (P-TPMS) lattice. Based on the fabricated magnetic P-TPMS lattices, a self-powered sensor was designed to achieve the velocity-to-electrical signal conversion process without the need for external power supply. MF-VP 3D printing technique significantly improved the mechanical compressive performance of the magnetic P-TPMS lattice in the carbonyl iron powder alignment direction, magnetic properties and sensing capabilities. The magnetic P-TPMS lattice self-powered sensor exhibited outstanding stability and durability, maintaining highly stable relative output voltage signals after 10,000 compression cycles. The developed sensor successfully detected toy car impact processes and foot movement states (stop/slow/fast), highlighting the practical application potential of the self-powered sensor.
DOI: 10.6084/m9.figshare.26484550
title: Stabilising mechanism of cathode jet and droplet transfer in hybrid-laser–GMAW-based directed energy deposition of titanium alloy

description: A hybrid laser–GMAW-based directed energy deposition (DED) was developed to stabilise the cathode jet and droplet transfer in DED of Ti alloy. High-speed photography, spectroscopic diagnostic, theoretical calculation, and numerical simulation were employed to investigate the stabilising mechanisms. At a high laser energy density, the highest temperature of the molten pool was located in the laser-irradiated zone, generating a stable cathode jet. Ti atoms easily ionised into Ti ions at high temperatures (&gt;8200 K); therefore, the cathode jet was primarily composed of Ti ions. The laser-induced metal vapour attracted and contracted the arc, resulting in an increase in the arc temperature, and strong evaporation near the wire during the peak-current stage. Therefore, the distribution areas of Ti I and Ti II were enlarged. Introducing the laser inhibited the obstructive effect of the cathode jet on the droplet, thereby enhancing the stability of droplet transfer.
DOI: 10.6084/m9.figshare.26484550.v1
title: Stabilising mechanism of cathode jet and droplet transfer in hybrid-laser–GMAW-based directed energy deposition of titanium alloy

description: A hybrid laser–GMAW-based directed energy deposition (DED) was developed to stabilise the cathode jet and droplet transfer in DED of Ti alloy. High-speed photography, spectroscopic diagnostic, theoretical calculation, and numerical simulation were employed to investigate the stabilising mechanisms. At a high laser energy density, the highest temperature of the molten pool was located in the laser-irradiated zone, generating a stable cathode jet. Ti atoms easily ionised into Ti ions at high temperatures (&gt;8200 K); therefore, the cathode jet was primarily composed of Ti ions. The laser-induced metal vapour attracted and contracted the arc, resulting in an increase in the arc temperature, and strong evaporation near the wire during the peak-current stage. Therefore, the distribution areas of Ti I and Ti II were enlarged. Introducing the laser inhibited the obstructive effect of the cathode jet on the droplet, thereby enhancing the stability of droplet transfer.
DOI: 10.6084/m9.figshare.27239704
title: Autonomous intelligent additive manufacturing of continuous fiber-reinforced composites: data-enhanced knowledgebase and multi-sensor fusion

description: Additive manufacturing (AM) are an emerging technique to generate complex structures of composite. However, the instability of the AM process leads to adverse manufacturing effects, including dimensional inaccuracies and poor mechanical performance in CFRP composites. Online monitoring and adaptive control based on autonomous cognition are suggested to ensure the accuracy of as-fabricated parts and enhance their quality upon manufacturing. In this study, a method of online monitoring and self-adaptive control based on multi-sensor fusion is proposed to predict and adjust the process parameters during AM of CFRP. The force sensor, visual camera, and thermal camera are employed to obtain the multiple signals upon manufacturing and then realize the autonomous perception, cognition, and decision features. Herein, the quantitative correlations between local defects and multi-sensing features are established to guide the decision-making of closed-loop adjustment. Besides, an empirical surrogate model between the misalignment of fiber bundles and input parameters is built to predict the proper parameters. Moreover, the temperature difference and contact force are selected as the controlled features, which are acquired using a thermal camera and a force sensor. The proposed system offers a novel framework for bolstering both the stability of the AM process and the quality of fabricated components.
DOI: 10.6084/m9.figshare.27239704.v1
title: Autonomous intelligent additive manufacturing of continuous fiber-reinforced composites: data-enhanced knowledgebase and multi-sensor fusion

description: Additive manufacturing (AM) are an emerging technique to generate complex structures of composite. However, the instability of the AM process leads to adverse manufacturing effects, including dimensional inaccuracies and poor mechanical performance in CFRP composites. Online monitoring and adaptive control based on autonomous cognition are suggested to ensure the accuracy of as-fabricated parts and enhance their quality upon manufacturing. In this study, a method of online monitoring and self-adaptive control based on multi-sensor fusion is proposed to predict and adjust the process parameters during AM of CFRP. The force sensor, visual camera, and thermal camera are employed to obtain the multiple signals upon manufacturing and then realize the autonomous perception, cognition, and decision features. Herein, the quantitative correlations between local defects and multi-sensing features are established to guide the decision-making of closed-loop adjustment. Besides, an empirical surrogate model between the misalignment of fiber bundles and input parameters is built to predict the proper parameters. Moreover, the temperature difference and contact force are selected as the controlled features, which are acquired using a thermal camera and a force sensor. The proposed system offers a novel framework for bolstering both the stability of the AM process and the quality of fabricated components.
DOI: 10.6084/m9.figshare.26400892
title: 3D concrete printing of self-supported filaments via entrained cables: constructing formwork-free spanning structures

description: 3D concrete printing (3DCP) enhances design flexibility, reduces construction costs and lowers environmental impact. Traditionally used for wall fabrication, this study introduces a system for printing self-supporting spanning structures using reinforced concrete filaments with tensioned cables, eliminating the need for formwork. The research involved conceptual design and prototyping to integrate cables into the printing process, as well as structural testing of a small-scale model consisting of a reinforced filament. Numerical analysis using the concrete damage plasticity model (CDP) and the traction-separation model simulated the filament's non-linear behaviour and damage. Compared with experimental data, numerical analysis showed good accuracy. Reinforced filaments exhibited a significant increase in flexural strength, from 1.2 kgf·m to 5.0 kgf·m, compared to non-reinforced filaments. Results confirm the feasibility of the proposed method, though challenges remain in ensuring long-term functionality and scalability. Improving the bond between concrete and cables, refining printing parameters and exploring alternative materials are key aspects. While this study focuses on reinforced filaments as proof of concept, future work will address multifilament and multilayer elements like slabs. HighlightsA process for 3D concrete printing spanning structures without formwork was developed.A custom device unwinds cables and prints the concrete filament on top, making it self-supported.Kevlar cables were shown to be compatible with the process, while basalt cables presented difficulties.The flexural strength of cable-entrained filaments improves significantly compared to those without cables. A process for 3D concrete printing spanning structures without formwork was developed. A custom device unwinds cables and prints the concrete filament on top, making it self-supported. Kevlar cables were shown to be compatible with the process, while basalt cables presented difficulties. The flexural strength of cable-entrained filaments improves significantly compared to those without cables.
DOI: 10.6084/m9.figshare.26400892.v1
title: 3D concrete printing of self-supported filaments via entrained cables: constructing formwork-free spanning structures

description: 3D concrete printing (3DCP) enhances design flexibility, reduces construction costs and lowers environmental impact. Traditionally used for wall fabrication, this study introduces a system for printing self-supporting spanning structures using reinforced concrete filaments with tensioned cables, eliminating the need for formwork. The research involved conceptual design and prototyping to integrate cables into the printing process, as well as structural testing of a small-scale model consisting of a reinforced filament. Numerical analysis using the concrete damage plasticity model (CDP) and the traction-separation model simulated the filament's non-linear behaviour and damage. Compared with experimental data, numerical analysis showed good accuracy. Reinforced filaments exhibited a significant increase in flexural strength, from 1.2 kgf·m to 5.0 kgf·m, compared to non-reinforced filaments. Results confirm the feasibility of the proposed method, though challenges remain in ensuring long-term functionality and scalability. Improving the bond between concrete and cables, refining printing parameters and exploring alternative materials are key aspects. While this study focuses on reinforced filaments as proof of concept, future work will address multifilament and multilayer elements like slabs. HighlightsA process for 3D concrete printing spanning structures without formwork was developed.A custom device unwinds cables and prints the concrete filament on top, making it self-supported.Kevlar cables were shown to be compatible with the process, while basalt cables presented difficulties.The flexural strength of cable-entrained filaments improves significantly compared to those without cables. A process for 3D concrete printing spanning structures without formwork was developed. A custom device unwinds cables and prints the concrete filament on top, making it self-supported. Kevlar cables were shown to be compatible with the process, while basalt cables presented difficulties. The flexural strength of cable-entrained filaments improves significantly compared to those without cables.
DOI: 10.6084/m9.figshare.26202858
title: Development of a porcine decellularized extracellular matrix (DECM) bioink for 3D bioprinting of meniscus tissue engineering: formulation, characterisation and biological evaluation

description: This study aims to present an easily scalable, cost-efficient process of dECM extraction from porcine meniscus, dedicated to bioink preparation and 3D bioprinting. Due to its cartilage like structure and mechanical robustness, the meniscus is an exceptionally demanding tissue for extraction and decellularisation of its ECM. Its processing poses a great difficulty and renders the methods previously developed for soft tissues useless. A process, combining homogenisation, hydrolysis, supercritical CO2 (scCO2) extraction and lyophilisation, was developed to meet this challenge. This protocol allows for retaining its native compounds and biocompatibility while offering good printability and providing a stimulatory environment for cell proliferation and differentiation towards a meniscus-like phenotype. Also, this process is economically and ecologically friendly since it doesn't require the use of high amounts of solvents, detergents or expensive enzymes (DNase). The decellularisation process has been meticulously studied, demonstrating a substantial reduction in DNA content but still exceeding accepted thresholds. The study further explores the biocompatibility of the dECM, demonstrating no detrimental effects of remnant DNA on cell survival during extended in vitro culture, indicating excellent biocompatibility. These findings challenge the current definition of decellularisation effectiveness based solely on DNA content, proposing a broader assessment of biological effects.
DOI: 10.6084/m9.figshare.26202858.v1
title: Development of a porcine decellularized extracellular matrix (DECM) bioink for 3D bioprinting of meniscus tissue engineering: formulation, characterisation and biological evaluation

description: This study aims to present an easily scalable, cost-efficient process of dECM extraction from porcine meniscus, dedicated to bioink preparation and 3D bioprinting. Due to its cartilage like structure and mechanical robustness, the meniscus is an exceptionally demanding tissue for extraction and decellularisation of its ECM. Its processing poses a great difficulty and renders the methods previously developed for soft tissues useless. A process, combining homogenisation, hydrolysis, supercritical CO2 (scCO2) extraction and lyophilisation, was developed to meet this challenge. This protocol allows for retaining its native compounds and biocompatibility while offering good printability and providing a stimulatory environment for cell proliferation and differentiation towards a meniscus-like phenotype. Also, this process is economically and ecologically friendly since it doesn't require the use of high amounts of solvents, detergents or expensive enzymes (DNase). The decellularisation process has been meticulously studied, demonstrating a substantial reduction in DNA content but still exceeding accepted thresholds. The study further explores the biocompatibility of the dECM, demonstrating no detrimental effects of remnant DNA on cell survival during extended in vitro culture, indicating excellent biocompatibility. These findings challenge the current definition of decellularisation effectiveness based solely on DNA content, proposing a broader assessment of biological effects.
DOI: 10.15468/dl.9x3yz8
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2001",
 "Year is less than or equal to 2020",
 "Geometry POLYGON ((11.78914 47.56768, 11.78914 47.58816, 11.81788 47.58816, 11.81788 47.56768, 11.78914 47.56768))"
 ]
}

 The dataset includes 15 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001430-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14030742
title: Processed species-level data for "Global Biodiversity Loss from Outsourced Deforestation"

description: This repository holds processed species-level data for the following paper:



Wiebe, R.A.* and Wilcove, D.S. 2025. Global Biodiversity Loss from Outsourced Deforestation.


*rwiebe@princeton.edu, Guyot Hall, Princeton University, Princeton, NJ

In this analysis, we quantified the range loss to forest-dwelling vertebrates that is attributable to demand for agricultural and forestry products by developed countries in 2001-2015.

We used several datasets in this analysis. Data on forest cover and forest loss were sourced from Hansen et al. and publicly available to download online: https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.7.html. Data on range maps for species were sourced from the IUCN and downloaded from the IUCN’s API (https://www.iucnredlist.org/resources/spatial-data-download) and its partner BirdLife International’s API (http://datazone.birdlife.org/species/requestdis). These datasets are made available for academic research on request, and an API key is necessary for their downloads. We sourced data on land use attribution from Nguyen Tien Hoang and Keiichiro Kanemoto, from their 2021 publication “Mapping the deforestation footprint of nations reveals growing threat to tropical forests.” These data are not publicly available, but the authors kindly provided the data on request.

Code necessary to replicate the analysis can be found in a public repository at: https://github.com/AlexWiebe/Outsourced-Biodiversity-Loss.
DOI: 10.5281/zenodo.14710643
title: raw metagenomic data

description: Raw metagenomic data
DOI: 10.5281/zenodo.14710644
title: raw metagenomic data

description: Raw metagenomic data
DOI: 10.5281/zenodo.14719942
title: Novel biomarkers in bone turnover metabolism
DOI: 10.5281/zenodo.14719943
title: Novel biomarkers in bone turnover metabolism
DOI: 10.15468/dl.xunckt
title: Occurrence Download

description: A dataset containing 28014 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2001",
 "Year is less than or equal to 2020",
 "Geometry POLYGON ((175.2081 -40.49993, 175.2081 -40.46327, 175.2576 -40.46327, 175.2576 -40.49993, 175.2081 -40.49993))"
 ]
}

 The dataset includes 28014 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001431-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jfzysq
title: Occurrence Download

description: A dataset containing 572616 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Knautia L.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 572616 records from 2120 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001413-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.