cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10059050
title: EuroSDR RPAS benchmark datasets

description: Overview


The EuroSDR RPAS benchmark datasets were aqcuired in August 2021 as part of the EuroSDR benchmark initiative. This aims to evaluate the true geometric quality of real-world survey data generated from Remotely Piloted Aircraft System (RPAS) photogrammetry and lidar under different control configurations, focussing primarily on the geometric quality of data generated in the absence of ground control and local GNSS base station information.


Guided by a task force of National Mapping and Cadastral Agencies (NMCAs) experts and academics, in August 2021 Newcastle Geospatial Engineering team have established and surveyed a coordinated test field of independent checkpoints (CPs), test surfaces and profiles at the disused Wards Hill Quarry near Morpeth, Northumberland, UK. The 350 x 250 m study area was simultaneously surveyed using the following RPAS mounted instruments, each limited to a single flight to represent “real-world” operation:



	
DJI Phantom 4 RTK 
	
DJI ZENMUSE P1 
	
DJI ZENMUSE L1 
	
Routescene LidarPod 
	
Riegl MiniVUX



More information can be found here.


To facilitate and encourage wider use of the EuroSDR RPAS dataset, it has been made open access.


About us


We are the Geospatial Engineering research group in the School of Engineering at Newcastle University with a long history of research and teaching across geospatial disciplines. EuroSDR is a not-for-profit organisation linking National Mapping and Cadastral Agencies with Research Institutes and Universities in Europe for the purpose of applied research in spatial data provision, management and delivery.


 
description: Acknowledgements
EuroSDR funds this benchmark study. We would like to thank DJI, Heliguy and Routescene for assisting the data collection exercise, and Leica Geosystems for provision of surveying equipment used in the ground exercise. Newcastle University equipment used in this study was funded by UKCRIC - UK Collaboratorium for Research in Infrastructure & Cities: Newcastle Laboratories (EP/R010102/1).
DOI: 10.5281/zenodo.8309678
title: EuroSDR RPAS benchmark datasets

description: Overview


The EuroSDR RPAS benchmark datasets were aqcuired in August 2021 as part of the EuroSDR benchmark initiative. This aims to evaluate the true geometric quality of real-world survey data generated from Remotely Piloted Aircraft System (RPAS) photogrammetry and lidar under different control configurations, focussing primarily on the geometric quality of data generated in the absence of ground control and local GNSS base station information.


Guided by a task force of National Mapping and Cadastral Agencies (NMCAs) experts and academics, in August 2021 Newcastle Geospatial Engineering team have established and surveyed a coordinated test field of independent checkpoints (CPs), test surfaces and profiles at the disused Wards Hill Quarry near Morpeth, Northumberland, UK. The 350 x 250 m study area was simultaneously surveyed using the following RPAS mounted instruments, each limited to a single flight to represent “real-world” operation:



	
DJI Phantom 4 RTK 
	
DJI ZENMUSE P1 
	
DJI ZENMUSE L1 
	
Routescene LidarPod 
	
Riegl MiniVUX



More information can be found here.


To facilitate and encourage wider use of the EuroSDR RPAS dataset, it has been made open access.


About us


We are the Geospatial Engineering research group in the School of Engineering at Newcastle University with a long history of research and teaching across geospatial disciplines. EuroSDR is a not-for-profit organisation linking National Mapping and Cadastral Agencies with Research Institutes and Universities in Europe for the purpose of applied research in spatial data provision, management and delivery.


 
description: Acknowledgements
EuroSDR funds this benchmark study. We would like to thank DJI, Heliguy and Routescene for assisting the data collection exercise, and Leica Geosystems for provision of surveying equipment used in the ground exercise. Newcastle University equipment used in this study was funded by UKCRIC - UK Collaboratorium for Research in Infrastructure & Cities: Newcastle Laboratories (EP/R010102/1).
DOI: 10.5281/zenodo.8309679
title: EuroSDR RPAS benchmark datasets

description: <strong>Overview</strong> The EuroSDR RPAS benchmark datasets were aqcuired in August 2021 as part of the EuroSDR benchmark initiative. This aims to evaluate the true geometric quality of real-world survey data generated from Remotely Piloted Aircraft System (RPAS) photogrammetry and lidar under different control configurations, focussing primarily on the geometric quality of data generated in the absence of ground control and local GNSS base station information. Guided by a task force of National Mapping and Cadastral Agencies (NMCAs) experts and academics, in August 2021 Newcastle Geospatial Engineering team have established and surveyed a coordinated test field of independent checkpoints (CPs), test surfaces and profiles at the disused Wards Hill Quarry near Morpeth, Northumberland, UK. The 350 x 250 m study area was simultaneously surveyed using the following RPAS mounted instruments, each limited to a single flight to represent “real-world” operation: DJI Phantom 4 RTK DJI ZENMUSE P1 DJI ZENMUSE L1 Routescene LidarPod Riegl MiniVUX More information can be found here. To facilitate and encourage wider use of the EuroSDR RPAS dataset, it has been made open access. <strong>About us</strong> We are the Geospatial Engineering research group in the School of Engineering at Newcastle University with a long history of research and teaching across geospatial disciplines. EuroSDR is a not-for-profit organisation linking National Mapping and Cadastral Agencies with Research Institutes and Universities in Europe for the purpose of applied research in spatial data provision, management and delivery.
description: Acknowledgements EuroSDR funds this benchmark study. We would like to thank DJI, Heliguy and Routescene for assisting the data collection exercise, and Leica Geosystems for provision of surveying equipment used in the ground exercise. Newcastle University equipment used in this study was funded by UKCRIC - UK Collaboratorium for Research in Infrastructure & Cities: Newcastle Laboratories (EP/R010102/1).
DOI: 10.5255/ukda-sn-856695
title: Thematic Analysis of Home Exercise Environments for Older Adults With Stroke, 2022-2023

description: Stroke has debilitative effects on the body and can hinder physical and cognitive function. Recovery after stroke depends on participating in a structured exercise program of meaningful activities. However, older adults with stroke find it challenging to maintain an optimal level of physical activity for brain recovery due to physical and cognitive impairments, lack of motivation and age-related factors such as frailty, losing muscle mass and depression. The current exercise setting for older adults with stroke is not feasible to encourage participation in physical activity. The main challenges are muscle spasticity and weakness, difficulty in grip, limb deformity and inappropriate exercise modes to strengthen the upper-body limbs. The main motivation for this project was to design an exercise system that could facilitate upper-body exercises and address personal constraints with minimum supervision. Thus, the aim of this study was to co-design an interactive and digital resistance exercise solution for community-dwelling older stroke survivors with moderate to severe impairments in the upper limbs. We used co-design and co-production procedures to address our aim. The method of data collection was qualitative and through semi-structured interviews in 2 phases: co-designing for idea generation and co-production for prototype evaluation. We non-randomly recruited 33 older adults with chronic stroke (6 months after stroke) from community rehabilitation centres to take part in this study. The interviews were in person or online via Zoom. The topics of the interview were the perceived barriers, the shape/size of user-friendly exercise equipment and how is it feasible to use the system at home. The design team made a system and the research team demonstrated to the participants for evaluation and recommendations for further changes. We used the thematic analysis method to analyse the qualitative data and grouped the raw data into main themes. The collection consists of excerpts of qualitative data from interviews.
DOI: 10.5517/ccdc.csd.cc142s99
title: CCDC 1045360: Experimental Crystal Structure Determination

description: Related Article: Adelew Estifanos Filkale, Manoj Kumar Gangwar, Indresh Kumar Pandey, Ray J. Butcher|2024|Inorg.Chim.Acta|559|121801|doi:10.1016/j.ica.2023.121801
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1j8kbp
title: CCDC 1408402: Experimental Crystal Structure Determination

description: Related Article: Adelew Estifanos Filkale, Manoj Kumar Gangwar, Indresh Kumar Pandey, Ray J. Butcher|2024|Inorg.Chim.Acta|559|121801|doi:10.1016/j.ica.2023.121801
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1lg4fm
title: CCDC 1473350: Experimental Crystal Structure Determination

description: Related Article: Adelew Estifanos Filkale, Manoj Kumar Gangwar, Indresh Kumar Pandey, Ray J. Butcher|2024|Inorg.Chim.Acta|559|121801|doi:10.1016/j.ica.2023.121801
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.21227/mcqt-xh17
title: Hyperspectral Remote Sensing Benchmark Database for Oil Spill Detection with an Isolation Forest-Guided Unsupervised Detector

description: In the wake of marine oil exploration and transportation, the accidents of oil spills have occurredfrequently around the world, which leads to the severe pollution of the marine environment and thehuge damage of coastal species [1–6]. On April 20, 2010, the explosion of Deepwater Horizon oildrilling platform led to a severe leakage. Million barrels of oil polluted the Gulf of Mexico with thearea of about 10,000 square kilometers [7, 8]. Due to this accident, the marine ecosystems, such as fishand seabirds, have been seriously destroyed. On June 4, 2011, the Penglai 19-3 oilfield in Bohai Bay,Northeast China, witnessed a serious oil spill incident, which caused the leak of more than 7 thousandtons of oil into the sea [9]. The polluted area was almost 6200 square kilometers. If the oil would notbe timely monitored after the leakage, the oil slick would be washed onto the coast by the sea waves.This situation would pose a huge threat to coastal aquaculture fishery resources and human health.Therefore, it is of great importance to effectively detect oil spills on the sea surface to monitor thedistribution, impact, and volume of oil spills. Hyperspectral data, which provides rich spectral informationfrom the visible to the infrared spectrum, are a good candidate for oil spill detection.
DOI: 10.13023/kgs.data.11.01.2023
title: DVGQ Joint Data

description: A compilation of digitally vectorized joint data from the U.S. Geological Survey geologic quadrangle maps. Joint data was collected by USGS and KGS geologists using a compass during geologic mapping of the 7.5-minute quadrangle. Field notes of mappers are not available, therefore the orientation of joints was determined by a script written by KGS personnel during the digitization process.
description: The joint data was collected during the geologic mapping of the area from 1962 to 1977. The orientation of the joint measurements was determined during the digitization process. This data was then compiled and served on the KGS Map Service.
DOI: 10.5281/zenodo.7632173
title: Enzyme kinetics: the initial rate dogma revisited

description: Dataset of Excel files and Matlab scripts used for the publication.
DOI: 10.5281/zenodo.10059024
title: The measurement of true initial rates is not always absolutely necessary to estimate enzyme kinetic parameters

description: Dataset of Excel files used for the publication.
DOI: 10.5281/zenodo.7528423
title: The measurement of true initial rates is not always absolutely necessary to estimate enzyme kinetic parameters

description: Dataset of Excel files used for the publication.
DOI: 10.5281/zenodo.7528424
title: Enzyme kinetics: the initial rate dogma revisited

description: Dataset of Excel files and Matlab scripts used for the publication.
DOI: 10.5281/zenodo.8327846
title: Enzyme kinetics: the initial rate dogma revisited

description: Dataset of Excel files and Matlab scripts used for the publication.
DOI: 10.6084/m9.figshare.24470038
title: UVC short description and overview models.xlsx

description: The establishment of university venture capital (UVC) models has gained prominence as a means to address the funding gap faced by university spin-offs (USOs) during their early life stages. However, limited research exists regarding the various UVC models and their potential impact on UVC operations. This paper aims to address this research gap by studying UVC models existing in Europe. Through an analysis of 57 European UVCs, this study identifies nine distinct models based on UVC management and entities involved. The identified models provide valuable insights into the structuring of UVCs. The results show four crucial dimensions that universities must consider when establishing UVCs: control, funding capabilities, signaling, and logic. When establishing UVCs, universities must carefully navigate trade-offs between the four dimensions, as each dimension carries its own set of advantages and potential drawbacks. This research makes a contribution to academic literature by expanding the current understanding of UVCs and shedding light on their various models. Moreover, it provides practical implications for universities, UVCs, and USOs by offering insights into the different UVC models present in Europe. This knowledge can assist stakeholders in making decisions regarding the establishment and operation of UVCs, ultimately aiding in the successful development of USOs.
DOI: 10.6084/m9.figshare.24470038.v1
title: UVC short description and overview models.xlsx

description: The establishment of university venture capital (UVC) models has gained prominence as a means to address the funding gap faced by university spin-offs (USOs) during their early life stages. However, limited research exists regarding the various UVC models and their potential impact on UVC operations. This paper aims to address this research gap by studying UVC models existing in Europe. Through an analysis of 57 European UVCs, this study identifies nine distinct models based on UVC management and entities involved. The identified models provide valuable insights into the structuring of UVCs. The results show four crucial dimensions that universities must consider when establishing UVCs: control, funding capabilities, signaling, and logic. When establishing UVCs, universities must carefully navigate trade-offs between the four dimensions, as each dimension carries its own set of advantages and potential drawbacks. This research makes a contribution to academic literature by expanding the current understanding of UVCs and shedding light on their various models. Moreover, it provides practical implications for universities, UVCs, and USOs by offering insights into the different UVC models present in Europe. This knowledge can assist stakeholders in making decisions regarding the establishment and operation of UVCs, ultimately aiding in the successful development of USOs.
DOI: 10.5281/zenodo.8297585
title: Biome classification influences the projected rate of future biome transitions

description: Aim. Biome classification schemes are widely used to map biogeographic patterns of vegetation formations on large spatial scales. Future climate change will influence  biome patterns, and vegetation models can be used to assess the susceptibility of biomes to experience  transitions. However, biome classification is not unique, and various classification schemes and biome maps exist. Here, we aimed to assess how the choice of biome classification schemes influences current and projected future biome patterns.


Location. Africa, Australia, Tropical Asia


Time period. 2000-2099


Major taxa studied. Tropical vegetation


Methods. We used adaptive dynamic global vegetation model version 2 (aDGVM2) to simulate vegetation in the study region. We classified vegetation into biomes using (1) a classification scheme based on the cover of  functional types, (2) a cluster analysis based on the cover of  functional types, and (3) a cluster analysis based on trait patterns simulated by the aDGVM2. We compared the resulting biome maps to multiple observation-based biome maps and quantified differences in projected biome changes under the RCP8.5 scenario for the different classification schemes.


Results. As expected, biome patterns were strongly related to the scheme used for biome classification. The highest data-model agreement was derived for a cluster analysis using 21 simulated traits. Traits related to size were most important for classification. Considering all classification schemes, the area projected to undergo biome transitions under climate change varied between 16.5% and 32.1%. Despite this variability, different schemes consistently showed that grassland and savanna areas are most susceptible to climate change, whereas tropical forests and deserts are stable. Our results demonstrate that traits simulated by aDGVM2 are appropriate to delimit biomes.


Main conclusions.  Studies projecting biome patterns and transitions under current and future climate should consider applying different biome classification schemes to avoid biases in such projections caused by biome classification schemes.
DOI: 10.5281/zenodo.7923067
title: Biome classification influences the projected rate of future biome transitions

description: Aim. Biome classification schemes are widely used to map biogeographic patterns of vegetation formations on large spatial scales. Future climate change will influence  biome patterns, and vegetation models can be used to assess the susceptibility of biomes to experience  transitions. However, biome classification is not unique, and various classification schemes and biome maps exist. Here, we aimed to assess how the choice of biome classification schemes influences current and projected future biome patterns.


Location. Africa, Australia, Tropical Asia


Time period. 2000-2099


Major taxa studied. Tropical vegetation


Methods. We used adaptive dynamic global vegetation model version 2 (aDGVM2) to simulate vegetation in the study region. We classified vegetation into biomes using (1) a classification scheme based on the cover of  functional types, (2) a cluster analysis based on the cover of  functional types, and (3) a cluster analysis based on trait patterns simulated by the aDGVM2. We compared the resulting biome maps to multiple observation-based biome maps and quantified differences in projected biome changes under the RCP8.5 scenario for the different classification schemes.


Results. As expected, biome patterns were strongly related to the scheme used for biome classification. The highest data-model agreement was derived for a cluster analysis using 21 simulated traits. Traits related to size were most important for classification. Considering all classification schemes, the area projected to undergo biome transitions under climate change varied between 16.5% and 32.1%. Despite this variability, different schemes consistently showed that grassland and savanna areas are most susceptible to climate change, whereas tropical forests and deserts are stable. Our results demonstrate that traits simulated by aDGVM2 are appropriate to delimit biomes.


Main conclusions.  Studies projecting biome patterns and transitions under current and future climate should consider applying different biome classification schemes to avoid biases in such projections caused by biome classification schemes.
DOI: 10.5281/zenodo.7923068
title: Biome classification influences the projected rate of future biome transitions

description: Aim. Biome classification schemes are widely used to map biogeographic patterns of vegetation formations on large spatial scales. Future climate change will influence vegetation dynamics, and vegetation models can be used to assess the susceptibility of biomes to experience biome transitions. However, biome classification is not unique, and various classification schemes and biome maps exist. Here, we aimed to assess how the choice of biome classification schemes influences modeled rates of future biome changes.


Location. Africa, Australia, Tropical Asia Time period. 2000-2099


Major taxa studied. Tropical vegetation


Methods. We used aDGVM2 to simulate vegetation in the study region. We classified vegetation into biomes using (1) a classification scheme based on the cover of different functional types, (2) a cluster analysis based on the cover of different functional types, and (3) a cluster analysis based on trait patterns simulated by the aDGVM2. We compared the resulting biome maps to multiple observation-based biome and land cover products and quantified differences in projected biome changes under the RCP8.5 scenario for the different classification schemes. 


Results. As expected, biome patterns were strongly related to the scheme used for biome classification. The highest data-model agreement was derived for a cluster analysis using simulated trait patterns. The area projected to undergo biome transitions under climate change varied between 16.5% and 32.1% for different classification schemes. Despite this variability, different schemes consistently showed that grassland and savanna areas are most susceptible to climate change, whereas tropical forests and deserts are stable. Our results demonstrate that traits simulated by aDGVM2 are appropriate to delimit biomes. 


Main conclusions. Studies projecting biome transitions under climate change should consider applying different biome classification schemes to avoid biases in such projections caused by biome classification schemes.
DOI: 10.7910/dvn/cwzvks
title: Replication Data for: "Dos intrépidos gaúchos aos responsáveis homens de camisa azul: moralidade, sociabilidade e hierarquia na sociedade do agronegócio"

description: Data from the article "Dos intrépidos gaúchos aos responsáveis homens de camisa azul: moralidade, sociabilidade e hierarquia na sociedade do agronegócio", by Gustavo Meyer and Cleyton Gerhardt, published in Revista DADOS, v. 67, n. 4 de 2024.
DOI: 10.5281/zenodo.10059062
title: TrackFM Datasets
DOI: 10.5281/zenodo.10059096
title: Clean-Clean ER Datasets with FastText embeddings

description: Please see version 5 for more datasets. This one contains only a print-out of the DBPedia dataset in CSV format.
Each line corresponds to a different entity profile and has the following structure (where n is number of attributes):
numerical_id , uri , n ,  aname_0 , aval_0 , aname_1 , aval_1 ,...aname_n , aval_n  
That is, the separator is "space,space".
Special thanks to moemode for providing the dataset.