cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10635746
title: aaUNSFA

description: Dataset for network UNSFA-1 in project UNSFA
description: Network reference:
DOI: 10.6084/m9.figshare.22624432
title: Additional file 6 of Uterine luminal-derived extracellular vesicles: potential nanomaterials to improve embryo implantation

description: Additional file 6: Table S2. Relative abundance of proteins in the uterine luminal fluid-Evs.
DOI: 10.6084/m9.figshare.14248535
title: Super Resolution for Multi-Sources Image Stream Data Using Smooth and Sparse Tensor Completion and Its Applications in Data Acquisition of Additive Manufacturing

description: Recent developments of advanced imaging systems spur their applications in many areas, ranging from satellite remote sensing for geographic information to thermal imaging analysis for manufacturing process monitoring and control. Due to different specifications of imaging systems, the resulting image stream data (videos) have different spatial and temporal resolutions. This proposed work is based on the image stream data captured by multiple imaging systems for the same object with different but complementary spatial and temporal resolutions. For example, one system has high spatial but low temporal resolutions while the other one has opposite resolutions. The goal of this article is to develop a new super resolution method that integrates these different types of image stream data to improve both spatial and temporal resolutions, which is critical to obtaining more insightful information for more effective quality control of targeted processes or systems. To fulfill this goal, a new tensor completion model is developed by considering both smooth and sparse features simultaneously and is thus termed smooth and sparse tensor completion (SSTC). The results of the extensive case studies illustrate the superiority of our method over the elaborately selected benchmark methods.
DOI: 10.6084/m9.figshare.14248536
title: High-Dimensional Cost-constrained Regression Via Nonconvex Optimization

description: Budget constraints become an important consideration in modern predictive modeling due to the high cost of collecting certain predictors. This motivates us to develop cost-constrained predictive modeling methods. In this article, we study a new high-dimensional cost-constrained linear regression problem, that is, we aim to find the cost-constrained regression model with the smallest expected prediction error among all models satisfying a budget constraint. The nonconvex budget constraint makes this problem NP-hard. In order to estimate the regression coefficient vector of the cost-constrained regression model, we propose a new discrete first-order continuous optimization method. In particular, our method delivers a series of estimates of the regression coefficient vector by solving a sequence of 0-1 knapsack problems. Theoretically, we prove that the series of the estimates generated by our iterative algorithm converge to a first-order stationary point, which can be a globally optimal solution under some conditions. Furthermore, we study some extensions of our method that can be used for general statistical learning problems and problems with groups of variables. Numerical studies using simulated datasets and a real dataset from a diabetes study indicate that our proposed method can solve problems of fairly high dimensions with promising performance.
DOI: 10.6084/m9.figshare.14248541
title: Gaussian Process-Aided Function Comparison Using Noisy Scattered Data

description: This work proposes a nonparametric method to compare the underlying mean functions given two noisy datasets. The motivation for the work stems from an application of comparing wind turbine power curves. Comparing wind turbine data presents new problems, namely the need to identify the regions of difference in the input space and to quantify the extent of difference that is statistically significant. Our proposed method, referred to as funGP, estimates the underlying functions for different data samples using Gaussian process models. We build a confidence band using the probability law of the estimated function differences under the null hypothesis. Then, the confidence band is used for the hypothesis test as well as for identifying the regions of difference. This identification of difference regions is a distinct feature, as existing methods tend to conduct an overall hypothesis test stating whether two functions are different. Understanding the difference regions can lead to further practical insights and help devise better control and maintenance strategies for wind turbines. The merit of funGP is demonstrated by using three simulation studies and four real wind turbine datasets.
DOI: 10.6084/m9.figshare.6384404
title: Combination use of BMP2 and VEGF165 promotes osseointegration and stability of dental implants in irradiated bone

description: the data used in "Combination use of BMP2 and VEGF165 promotes
osseointegration and stability of dental implants in irradiated bone"<br>
DOI: 10.6084/m9.figshare.5335855
title: Quality assessment of positron emission tomography scans: recommendations for future multicentre trials

description: <b>Background:</b> Standardization protocols and guidelines for positron emission tomography (PET) in multicenter trials are available, despite a large variability in image acquisition and reconstruction parameters exist. In this study, we investigated the compliance of PET scans to the guidelines of the European Association of Nuclear Medicine (EANM). From these results, we provide recommendations for future multicenter studies using PET. <b>Material and methods:</b> Patients included in a multicenter randomized phase II study had repeated PET scans for early response assessment. Relevant acquisition and reconstruction parameters were extracted from the digital imaging and communications in medicine (DICOM) header of the images. The PET image parameters were compared to the guidelines of the EANM for tumor imaging version 1.0 recommended parameters. <b>Results:</b> From the 223 included patients, 167 baseline scans and 118 response scans were available from 15 hospitals. Scans of 19% of the patients had an uptake time that fulfilled the Uniform Protocols for Imaging in Clinical Trials response assessment criteria. The average quality score over all hospitals was 69%. Scans with a non-compliant uptake time had a larger standard deviation of the mean standardized uptake value (SUVmean) of the liver than scans with compliant uptake times. <b>Conclusions:</b> Although a standardization protocol was agreed on, there was a large variability in imaging parameters. For future, multicenter studies including PET imaging a prospective central quality review during patient inclusion is needed to improve compliance with image standardization protocols as defined by EANM.
DOI: 10.6084/m9.figshare.5335855.v1
title: Quality assessment of positron emission tomography scans: recommendations for future multicentre trials

description: <b>Background:</b> Standardization protocols and guidelines for positron emission tomography (PET) in multicenter trials are available, despite a large variability in image acquisition and reconstruction parameters exist. In this study, we investigated the compliance of PET scans to the guidelines of the European Association of Nuclear Medicine (EANM). From these results, we provide recommendations for future multicenter studies using PET. <b>Material and methods:</b> Patients included in a multicenter randomized phase II study had repeated PET scans for early response assessment. Relevant acquisition and reconstruction parameters were extracted from the digital imaging and communications in medicine (DICOM) header of the images. The PET image parameters were compared to the guidelines of the EANM for tumor imaging version 1.0 recommended parameters. <b>Results:</b> From the 223 included patients, 167 baseline scans and 118 response scans were available from 15 hospitals. Scans of 19% of the patients had an uptake time that fulfilled the Uniform Protocols for Imaging in Clinical Trials response assessment criteria. The average quality score over all hospitals was 69%. Scans with a non-compliant uptake time had a larger standard deviation of the mean standardized uptake value (SUVmean) of the liver than scans with compliant uptake times. <b>Conclusions:</b> Although a standardization protocol was agreed on, there was a large variability in imaging parameters. For future, multicenter studies including PET imaging a prospective central quality review during patient inclusion is needed to improve compliance with image standardization protocols as defined by EANM.
DOI: 10.6084/m9.figshare.5335855.v2
title: Quality assessment of positron emission tomography scans: recommendations for future multicentre trials

description: <b>Background:</b> Standardization protocols and guidelines for positron emission tomography (PET) in multicenter trials are available, despite a large variability in image acquisition and reconstruction parameters exist. In this study, we investigated the compliance of PET scans to the guidelines of the European Association of Nuclear Medicine (EANM). From these results, we provide recommendations for future multicenter studies using PET. <b>Material and methods:</b> Patients included in a multicenter randomized phase II study had repeated PET scans for early response assessment. Relevant acquisition and reconstruction parameters were extracted from the digital imaging and communications in medicine (DICOM) header of the images. The PET image parameters were compared to the guidelines of the EANM for tumor imaging version 1.0 recommended parameters. <b>Results:</b> From the 223 included patients, 167 baseline scans and 118 response scans were available from 15 hospitals. Scans of 19% of the patients had an uptake time that fulfilled the Uniform Protocols for Imaging in Clinical Trials response assessment criteria. The average quality score over all hospitals was 69%. Scans with a non-compliant uptake time had a larger standard deviation of the mean standardized uptake value (SUVmean) of the liver than scans with compliant uptake times. <b>Conclusions:</b> Although a standardization protocol was agreed on, there was a large variability in imaging parameters. For future, multicenter studies including PET imaging a prospective central quality review during patient inclusion is needed to improve compliance with image standardization protocols as defined by EANM.
DOI: 10.6084/m9.figshare.5335855.v3
title: Quality assessment of positron emission tomography scans: recommendations for future multicentre trials

description: <b>Background:</b> Standardization protocols and guidelines for positron emission tomography (PET) in multicenter trials are available, despite a large variability in image acquisition and reconstruction parameters exist. In this study, we investigated the compliance of PET scans to the guidelines of the European Association of Nuclear Medicine (EANM). From these results, we provide recommendations for future multicenter studies using PET. <b>Material and methods:</b> Patients included in a multicenter randomized phase II study had repeated PET scans for early response assessment. Relevant acquisition and reconstruction parameters were extracted from the digital imaging and communications in medicine (DICOM) header of the images. The PET image parameters were compared to the guidelines of the EANM for tumor imaging version 1.0 recommended parameters. <b>Results:</b> From the 223 included patients, 167 baseline scans and 118 response scans were available from 15 hospitals. Scans of 19% of the patients had an uptake time that fulfilled the Uniform Protocols for Imaging in Clinical Trials response assessment criteria. The average quality score over all hospitals was 69%. Scans with a non-compliant uptake time had a larger standard deviation of the mean standardized uptake value (SUVmean) of the liver than scans with compliant uptake times. <b>Conclusions:</b> Although a standardization protocol was agreed on, there was a large variability in imaging parameters. For future, multicenter studies including PET imaging a prospective central quality review during patient inclusion is needed to improve compliance with image standardization protocols as defined by EANM.
DOI: 10.25386/genetics.7957376
title: Supplemental Material for Kottler et al., 2020

description: Figure S1 contains pictures of melanic and non-melanic <i>G. holbrooki</i>. Figure S2 presents the results of the RAD-sequencing analysis. Figure S3 represents a molecular phylogeny of fish <i>sox9</i>. Figure S4 are alignments of male <i>G. holbrooki </i><i>gipc1 </i>and <i>sox9b </i>alleles and protein sequences. Figure S5 are multiple species alignments of Gipc1 and Sox9 protein sequences. Figure S6 contains PCR results demonstrating that <i>sox9bY </i>was generated by allelic diversification. Table S1 gives information on the fish used for pool sequencing. Table S2 contains the annotated loci within the 100 kb windows with the highest density of sex-specific SNPs. Table S3 presents the results of the allele-specific PCRs. Table S4 contains the sex chromosome genotypes of F<sub>1 </sub>fish (pedigree WLC20074) from a cross between a <i>G. affinis </i>female (WLC1313) and a <i>G. holbrooki </i>male (WLC2514) determined by PCR. Table S5 gives information on the number of offspring of non-melanic females and non-melanic males derived from melanic strains.
DOI: 10.25386/genetics.7957376.v1
title: Supplemental Material for Kottler et al., 2020

description: Figure S1 contains pictures of melanic and non-melanic <i>G. holbrooki</i>. Figure S2 presents the results of the RAD-sequencing analysis. Figure S3 represents a molecular phylogeny of fish <i>sox9</i>. Figure S4 are alignments of male <i>G. holbrooki </i><i>gipc1 </i>and <i>sox9b </i>alleles and protein sequences. Figure S5 are multiple species alignments of Gipc1 and Sox9 protein sequences. Figure S6 contains PCR results demonstrating that <i>sox9bY </i>was generated by allelic diversification. Table S1 gives information on the fish used for pool sequencing. Table S2 contains the annotated loci within the 100 kb windows with the highest density of sex-specific SNPs. Table S3 presents the results of the allele-specific PCRs. Table S4 contains the sex chromosome genotypes of F<sub>1 </sub>fish (pedigree WLC20074) from a cross between a <i>G. affinis </i>female (WLC1313) and a <i>G. holbrooki </i>male (WLC2514) determined by PCR. Table S5 gives information on the number of offspring of non-melanic females and non-melanic males derived from melanic strains.
DOI: 10.6084/m9.figshare.7957427
title: Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata

description: Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone.
DOI: 10.6084/m9.figshare.7957427.v1
title: Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata

description: Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone.
DOI: 10.5517/ccdc.csd.cc2hn7hx
title: CCDC 2313359: Experimental Crystal Structure Determination

description: Related Article: Jie Zhang, Xin Wang, Wen-Qi Wang, Xiangyuan Deng, Cheng-Yang Yue, Xiao-Wu Lei, Zhongliang Gong|2024|Inorg.Chem.|63|2647|doi:10.1021/acs.inorgchem.3c03924
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: 10.5281/zenodo.15198883
title: S2BMS - Sentinel-2 UK Butterfly Monitoring Scheme data set (Van der Plas et al., 2025, CVPR FGVC)

description: S2BMS (Sentinel-2 UK Butterfly Monitoring Scheme) data set

This data set consists of cloud-free Sentinel-2 (RGB+NIR, 10 m resolution) satellite images and aggregated measurements of butterfly occurrence for 1,329 locations in the UK. 

The code (to load the data) is available at https://github.com/vdplasthijs/PECL/, and in particular please see the DataSetImagePresence data loader class. The data is formatted as 1 satellite image tif file per location and 1 (geo-referenced) csv file with the species presence data for all locations.

The Sentinel-2 data was acquired from Google Earth Engine. The UKBMS data was acquired from GBIF: https://doi.org/10.15468/dl.u4rzfc

For more information, please see our upcoming paper at the 2025 CVPR FGVC12 workshop, titled:



Van der Plas, Law, Pocock, Predicting butterfly species presence from satellite imagery using soft contrastive regularisation, CVPRW proceedings (2025). 


This data is provided under a CC-BY-NC 4.0 license; please cite the above publication when using the data in publications. Thank you!
DOI: 10.5281/zenodo.15198884
title: S2BMS - Sentinel-2 UK Butterfly Monitoring Scheme data set (Van der Plas et al., 2025, CVPR FGVC)

description: S2BMS (Sentinel-2 UK Butterfly Monitoring Scheme) data set

This data set consists of cloud-free Sentinel-2 (RGB+NIR, 10 m resolution) satellite images and aggregated measurements of butterfly occurrence for 1,329 locations in the UK. 

The code (to load the data) is available at https://github.com/vdplasthijs/PECL/, and in particular please see the DataSetImagePresence data loader class. The data is formatted as 1 satellite image tif file per location and 1 (geo-referenced) csv file with the species presence data for all locations.

The Sentinel-2 data was acquired from Google Earth Engine. The UKBMS data was acquired from GBIF: https://doi.org/10.15468/dl.u4rzfc

For more information, please see our upcoming paper at the 2025 CVPR FGVC12 workshop, titled:



Van der Plas, Law, Pocock, Predicting butterfly species presence from satellite imagery using soft contrastive regularisation, CVPRW proceedings (2025). 


This data is provided under a CC-BY-NC 4.0 license; please cite the above publication when using the data in publications. Thank you!
DOI: 10.48328/tudatalib-644
title: Supplementary Information to ‘Experiment for Validation of Numerical Models of Coupled Heat and Mass Transfer around Energy Cables'

description: The increasing decentralization of electrical energy production as well as an increasing number of fluctuating regenerative energy sources require significant investments in grid expansion. Among other assessments, an exact prediction of the thermal behavior of the cable environment is necessary to be able to design cable routes both technically and economically sufficient. To investigate the coupled thermal and hydraulic processes around a cable like heat source with high temporal and spatial resolution at defined boundary conditions, a cylindrical laboratory test was designed and experiments with two soils conducted by Verschaffel-Drefke et al. (2021). The data collected can be used to validate models of coupled heat and mass transfer around power cables. Both the experimentally determined soil properties and the measured data of the three conducted test series are provided.
DOI: 10.15156/bio/sh0993904.09fu
title: SH0993904.09FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0993905.09fu
title: SH0993905.09FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.