cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/ykvpjk42jw.1
title: Azohydromonas lata chemostat culture and metabolic model

description: A complementary experimental and modeling approach was used to analyze the production of PHB in chemostat cultures of A. lata DSM1123. The counterintuitive PHB accumulation under glucose-limited conditions, a situation where only biomass production should be observed, can be explained as the result of two internal metabolic constraints: a limited flow through the Krebs cycle and a maximum metabolically active protein. Given the predictive power and simplicity of the model, it may be used for assessing the impact of expressing synthetic pathways for target metabolites production, where the new enzymes must compete for the total protein pool.
DOI: 10.17632/wxzj3xkm6s.1
title: Interview to the Vice-Rector of Research in SER AGRICULTOR

description: Interview with the vice-rector of Research about ECOMETABOLIC
DOI: 10.17632/j8fmdwd4jp.3
title: Comparing methods for handling missing cost and outcome data in clinical trial-based cost-effectiveness analysis

description: Code for analysis of missing data
DOI: 10.17632/8gfwps2495.1
title: LINF_090006100

description: flagellar pocket cytoskeletal protein | BILBO1 | calmodulin-like protein containing EF hand domain; Leishmania infantum
DOI: 10.17632/66py5m4ghf.1
title: LINF_090005900

description: RNA-binding protein 5-like protein | RBP9; Leishmania infantum
DOI: 10.17632/fw4x6ytht8.1
title: LINF_090005500

description: N-acetyl-D-acetylglucosaminylphosphatidylinositol deacetylase | GPI12; Leishmania infantum
DOI: 10.17632/xnb8gnp67m.1
title: Cumulative incidence of recurrence data for lung adenocarcinoma in zhoushan hospital

description: Cumulative incidence of recurrence data for invasive lung adenocarcinoma in zhoushan hospital(2012-2018)
DOI: 10.17632/n5j8ht9wjz.1
title: LINF_090005200

description: hypothetical protein - conserved; Leishmania infantum
DOI: 10.17632/rmhxm26bpf.1
title: LINF_090005100

description: NSA1; Leishmania infantum
DOI: 10.17632/dw77ftghnf.1
title: Biogas user experience survey and fuel use in Halaba, Ethiopia

description: This data set provides the answers to two questionnaire based surveys with relevance to the paper "The mismatch between user-provision and biogas installation practices in Ethiopia: Impacts on quality and desirability". The surveys were conducted in Halaba, SNNP region, Ethiopia in September-November 2013. The spreadsheet contains 3 worksheets, the first of which gives information from a biogas user-experience survey (n=36), the second of which gives selected information from a wider survey on fuel and resource use (n=112) and the third gives a key explaining the column headings. The layout is convenient for reading into the R environment.
DOI: 10.17632/5bkgk7jzzs.1
title: Clothing detergent category data

description: The data provided was obtained using a questionnaire that required respondents to rank product profiles for clothing detergent products presented in the poster provided. Generic categories of clothing detergents consisting of five product attributes were presented to respondents for ranking in their order of importance. Using conjoint analysis techniques, the relative importance of each attribute was calculated as indicated in the manuscript.
DOI: 10.17632/nn53dskr7z.1
title: Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures

description: We have developed several autotuning benchmarks in CUDA that take into account performance-relevant source-code parameters and reach near peak-performance on various GPU architectures. We have used them during the development and evaluation of a novel search method for tuning space. With our framework Kernel Tuning Toolkit, freely available at Github, we measured computation times and hardware performance counters on several GPUs for the complete tuning spaces of five benchmarks. These data, which we provide here, might benefit research of search algorithms for the tuning spaces of GPU codes or research of relation between applied code optimization, hardware performance counters, and GPU kernels' performance. Moreover, we provide the scripts we used for robust evaluation of our searcher and comparison to others in detail. In particular, the script that simulates the tuning, i.e., replaces time-demanding compiling and executing the tuned kernels with a quick reading of the computation time from our measured data, makes it possible to inspect the convergence of tuning search over a large number of experiments. These scripts, freely available with our other codes, make it easier to experiment with search algorithms and compare them in a robust way. During our research, we generated models for predicting values of performance counters from values of tuning parameters of our benchmarks. Here, we provide the models themselves and describe the scripts we implemented for their training. These data might benefit researchers who want to reproduce or build on our research. For details on these data, see related article: J. Filipovič, J. Hozzová, A. Nezarat, J. Ol’ha, F. Petrovič, Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures, arXiv (2021). For details on the proposed search method using performance counters, see related article: J. Filipovič, J. Hozzová, A. Nezarat, J. Ol’ha, F. Petrovič, Using hardware performance counters to speed up autotuning convergence on GPUs, arXiv (2021).
DOI: 10.17632/p29mdksjvs.1
title: R/RStudio Software code for Evaluation of changes in some physico-chemical properties of bottled water exposed to sunlight in Bauchi State, Nigeria

description: This details the software code used in RStudio for analysing study (Evaluation of changes in some physico-chemical properties of bottled water exposed to sunlight in Bauchi State, Nigeria) data. The code is provided in .R, .docx and .pdf. For .R, it is accessible directly using RStudio. For .docx and .pdf, copy and paste the commands into RStudio. It includes codes for generating plots used in paper publication Note that you require the dataset used in the study which is accessible from the following DOI:
DOI: 10.17632/cpprtb4gmf.1
title: Supplemental Table S1

description: Metabolites identified from UPLC-MS/MS that exhibited significant changes in abundance in fermented milk during 21 days of refrigerated storage at 4 °C
DOI: 10.17632/949frmb8mw.1
title: Raw data of manuscript "Probing the sedimentary DOM in the deepest sector of Earth’s surface"

description: DOC, TOC, benthic O2 uptake, ,DOM optical measurement, and PARAFAC
DOI: 10.15468/ekr6q8
title: A predator-prey system: Phytoseiulus persimilis (Acari: Phytoseiidae) and Tetranychus urticae (Acari: Tetranychidae): worldwide occurrence datasets

description: This dataset contains the digitized treatments in Plazi based on the original journal article Migeon, Alain, Tixier, Marie-Stéphane, Navajas, Maria, Litskas, Vassilis D., Stavrinides, Menelaos C. (2019): A predator-prey system: Phytoseiulus persimilis (Acari: Phytoseiidae) and Tetranychus urticae (Acari: Tetranychidae): worldwide occurrence datasets. Acarologia 59 (3): 301-307, DOI: 10.24349/acarologia/20194322, URL: http://dx.doi.org/10.24349/acarologia/20194322
DOI: 10.5281/zenodo.4525141
title: Data set from Sturla F, Piatti F, Jaworek M, Lucherini F, Pluchinotta FR, Siryk SV, Giese D, Vismara R, Tasca G, Menicanti L, Redaelli A, Lombardi M. 4D Flow MRI hemodynamic benchmarking of surgical bioprosthetic valves. Magn Reson Imaging. 2020 May;68:18-29. doi: 10.1016/j.mri.2020.01.006. Epub 2020 Jan 22. PMID: 31981709.

description: Data set from the article Sturla F, Piatti F, Jaworek M, Lucherini F, Pluchinotta FR, Siryk SV, Giese D, Vismara R, Tasca G, Menicanti L, Redaelli A, Lombardi M. 4D Flow MRI hemodynamic benchmarking of surgical bioprosthetic valves. Magn Reson Imaging. 2020 May;68:18-29. doi: 10.1016/j.mri.2020.01.006. Epub 2020 Jan 22. PMID: 31981709. <strong>Abstract</strong> <strong>Purpose: </strong>We exploited 4-dimensional flow magnetic resonance imaging (4D Flow), combined with a standardized in vitro setting, to establish a comprehensive benchmark for the systematic hemodynamic comparison of surgical aortic bioprosthetic valves (BPVs). <strong>Materials and methods: </strong>4D Flow analysis was performed on two small sizes of three commercialized pericardial BPVs (Trifecta™ GT, Carpentier-Edwards PERIMOUNT Magna and Crown PRT®). Each BPV was tested over a clinically pertinent range of continuous flow rates within an in vitro MRI-compatible system, equipped with pressure transducers. In-house 4D Flow post-processing of the post-valvular velocity field included the quantification of BPV effective orifice area (EOA), transvalvular pressure gradients (TPG), kinetic energy and viscous energy dissipation. <strong>Results: </strong>The 4D Flow technique effectively captured the 3-dimensional flow pattern of each device. Trifecta exhibited the lowest range of velocity and kinetic energy, maximized EOA (p &lt; 0.0001) and minimized TPGs (p ≤ 0.015) if compared with Magna and Crown, these reporting minor EOA difference s (p ≥ 0.042) and similar TPGs (p ≥ 0.25). 4D Flow TPGs estimations strongly correlated against ground-truth data from pressure transducers; viscous energy dissipation proved to be inversely proportional to the fluid jet penetration. <strong>Conclusion: </strong>The proposed 4D Flow analysis pinpointed consistent hemodynamic differences among BPVs, highlighting the not negligible effect of device size on the fluidynamic outcomes. The efficacy of non-invasive 4D Flow MRI protocol could shed light on how standardize the comparison among devices in relation to their actual hemodynamic performances and improve current criteria for their selection.
DOI: 10.5281/zenodo.4525140
title: Data set from Sturla F, Piatti F, Jaworek M, Lucherini F, Pluchinotta FR, Siryk SV, Giese D, Vismara R, Tasca G, Menicanti L, Redaelli A, Lombardi M. 4D Flow MRI hemodynamic benchmarking of surgical bioprosthetic valves. Magn Reson Imaging. 2020 May;68:18-29. doi: 10.1016/j.mri.2020.01.006. Epub 2020 Jan 22. PMID: 31981709.

description: Data set from the article Sturla F, Piatti F, Jaworek M, Lucherini F, Pluchinotta FR, Siryk SV, Giese D, Vismara R, Tasca G, Menicanti L, Redaelli A, Lombardi M. 4D Flow MRI hemodynamic benchmarking of surgical bioprosthetic valves. Magn Reson Imaging. 2020 May;68:18-29. doi: 10.1016/j.mri.2020.01.006. Epub 2020 Jan 22. PMID: 31981709. <strong>Abstract</strong> <strong>Purpose: </strong>We exploited 4-dimensional flow magnetic resonance imaging (4D Flow), combined with a standardized in vitro setting, to establish a comprehensive benchmark for the systematic hemodynamic comparison of surgical aortic bioprosthetic valves (BPVs). <strong>Materials and methods: </strong>4D Flow analysis was performed on two small sizes of three commercialized pericardial BPVs (Trifecta™ GT, Carpentier-Edwards PERIMOUNT Magna and Crown PRT®). Each BPV was tested over a clinically pertinent range of continuous flow rates within an in vitro MRI-compatible system, equipped with pressure transducers. In-house 4D Flow post-processing of the post-valvular velocity field included the quantification of BPV effective orifice area (EOA), transvalvular pressure gradients (TPG), kinetic energy and viscous energy dissipation. <strong>Results: </strong>The 4D Flow technique effectively captured the 3-dimensional flow pattern of each device. Trifecta exhibited the lowest range of velocity and kinetic energy, maximized EOA (p &lt; 0.0001) and minimized TPGs (p ≤ 0.015) if compared with Magna and Crown, these reporting minor EOA difference s (p ≥ 0.042) and similar TPGs (p ≥ 0.25). 4D Flow TPGs estimations strongly correlated against ground-truth data from pressure transducers; viscous energy dissipation proved to be inversely proportional to the fluid jet penetration. <strong>Conclusion: </strong>The proposed 4D Flow analysis pinpointed consistent hemodynamic differences among BPVs, highlighting the not negligible effect of device size on the fluidynamic outcomes. The efficacy of non-invasive 4D Flow MRI protocol could shed light on how standardize the comparison among devices in relation to their actual hemodynamic performances and improve current criteria for their selection.
DOI: 10.17632/xxrsf4nr7v.2
title: Spatial and latent memory data in PS2Tg2576 Alzheimer’s disease mouse model after memantine treatment

description: We present data on whether memantine, an adamantane derivative and medical NMDA-receptor antagonist, improves spatial and latent learning deficits in amyloid precursor protein/presenilin 2 double-transgenic mice (PS2Tg2576 mice). PS2Tg2576 mice were subjected to Morris water maze (MWM) test for spatial memory and the water-finding test for latent memory testing at ages 3 and 5-6 months. At 3 months of age, the learning performance of PS2Tg2576 mice was normal compared to wild-type mice in both tasks. At 5-6 months of age, cognitive deficits were observed in the MWM and water-finding test; 3-4 weeks of memantine treatment (30 mg/kg/day, p.o.) significantly reverted the learning deficits in the MWM; however, had little effect on those in the water-finding test. The information provided in this report adds to the literature and can be used for the selection of animal models and behavioral paradigms for Alzheimer's disease (AD) research.
DOI: 10.17632/2g3c836mh3.1
title: Synthetic datasets of adversarial images

description: We build synthetic datasets of images with embedded adversarial text to improve the robustness of OCR-based spam detectors. The datasets were used in our project (https://github.com/niddal-imam/Post-OCR-Correction).
DOI: 10.17632/3n2vrw6rdk.1
title: Effect of MIP on Students Understanding of Mechanical Properties of Steel

description: Data Used to Study Effect of Multimedia Instructional Package on Students Understanding of Mechanical Properties of Steel
DOI: 10.5281/zenodo.4500613
title: PharmKG: A Dedicated Knowledge Graph Benchmark for Biomedical Data Mining

description: Biomedical knowledge graphs, which can help with the understanding of complex biological systems and pathologies, have begun to play a critical role in medical practice and research. However, challenges remain in their embedding and use due to their complex nature and the specific demands of their construction. Existing studies often suffer from problems such as sparse and noisy datasets, insufficient modeling methods, and non-uniform evaluation metrics. In this work,<strong> </strong>we established a comprehensive knowledge graph (KG) system for the biomedical field in an attempt to bridge the gap. Here we introduced PharmKG, a multi-relational, attributed biomedical knowledge graph, composed of more than 500,000 individual interconnections between genes, drugs, and diseases, with 29 relation types over a vocabulary of ~8,000 disambiguated entities. Each entity in PharmKG is attached with heterogeneous, domain-specific information obtained from multi-omics data, i.e. gene expression, chemical structure, and disease word embedding while preserving the semantic and biomedical features. For baselines, we offered 9 state-of-the-art knowledge graph embedding (KGE) approaches and a new biological, intuitive, graph neural network-based KGE method that uses a combination of both global network structure and heterogeneous domain features. Based on the proposed benchmark, we conducted extensive experiments to assess these KGE models using multiple evaluation metrics. Finally, we discussed our observations across various downstream biological tasks and provide insights and guidelines for how to use a knowledge graph in biomedicine. We hope that the unprecedented quality and diversity of PharmKG will lead to advances in biomedical knowledge graph construction, embedding, and application.
description: [Update New Version]: We have updated the PharmKG-8k dataset we used in the paper, including the training/validation/test set. If you found this dataset useful for your research, please cite our paper: PharmKG: A Dedicated Knowledge Graph Benchmark for Biomedical Data Mining.
DOI: 10.7910/dvn/myd6fx
title: Congressional Attention to Economic Problems

description: Provides yearly counts of terms/n-grams spoken about 11 economic problems in Congress from 1995-2016
DOI: 10.5281/zenodo.4524952
title: Dataset related to "Comprehensive Phenotyping of Dendritic Cells in Cancer Patients by Flow Cytometry"

description: This record contains data related to article “Comprehensive Phenotyping of Dendritic Cells in Cancer Patients by Flow Cytometry" Dendritic cells (DCs) play a crucial role in the complex interplay between tumor cells and the immune system. During the elimination phase of cancer immunoediting, immunostimulatory DCs are critical for the control of tumor growth. During the escape phase, regulatory DCs sustain tumor tolerance and contribute to the development of the immunosuppressive tumor microenvironment that characterizes this phase. Moreover, increasing evidence indicates that DCs are also critical for the success of cancer immunotherapy. Hence, there is increasing need to fully characterize DC subsets and their activatory/inhibitory profile in cancer patients. In this review, we describe the role played by different DC subsets in the different phases of cancer immunoediting, the function exerted by different activatory and inhibitory molecules expressed on DC surface, and the cytokines produced by distinct DC subsets, in order to provide an overview on the DC features that may be useful to be assessed when dealing with the flow cytometric characterization of DCs in cancer patients. Note: files in uploaded dataset are in ".fcs" format. the following softwares are able to open them: FlowJo, BD FACSDiva.
DOI: 10.5281/zenodo.4081339
title: Research data for "Revisiting neutron scattering data from deuterated milk" (Food Hydrocolloids, doi:10.1016/j.foodhyd.2020.106511)

description: USANS and SESANS data used for the analysis in this paper. Data are either two- or three-column data (as specified below). Data were extracted from the figures in the papers using the macOS application GraphClick. USANS and SANS data were originally published in "Protein aggregate structure under high pressure" by Andrew J. Jackson and Duncan J. McGillivray (<em>Chem. Commun.</em>, 2011, <strong>47</strong>, 487-489, doi:10.1039/C0CC02314K). USANS data are three-column *.txt files [Q in Å<sup>–1</sup>, I(Q) in cm<sup>–1</sup>, uncertainty in I(Q) in cm<sup>–1</sup>]. SANS data (numbered 1, 2, and 3 to denote different instrument configurations) are two-column *.txt files [Q in Å<sup>–1</sup>, I(Q) in cm<sup>–1</sup>]. SESANS data were originally published in "Milk Gelation Studied with Small Angle Neutron Scattering Techniques and Monte Carlo Simulations" by Léon F. van Heijkamp, Ignatz M. de Schepper, Markus Strobl, R. Hans Tromp, Jouke R. Heringa, and Wim G. Bouwman (<em>J. Phys. Chem. A</em>, 2010, <strong>114</strong>, 2412–2426, doi:10.1021/jp9067735). SESANS data are three-column *.csv files [Z in Å, normalized SESANS signal in cm<sup>–1</sup> Å<sup>–2</sup>, uncertainty in normalized SESANS signal in cm<sup>–1</sup> Å<sup>–2</sup>].
DOI: 10.5281/zenodo.4081338
title: Research data for "Revisiting neutron scattering data from deuterated milk" (Food Hydrocolloids, doi:10.1016/j.foodhyd.2020.106511)

description: USANS and SESANS data used for the analysis in this paper. Data are either two- or three-column data (as specified below). Data were extracted from the figures in the papers using the macOS application GraphClick. USANS and SANS data were originally published in "Protein aggregate structure under high pressure" by Andrew J. Jackson and Duncan J. McGillivray (<em>Chem. Commun.</em>, 2011, <strong>47</strong>, 487-489, doi:10.1039/C0CC02314K). USANS data are three-column *.txt files [Q in Å<sup>–1</sup>, I(Q) in cm<sup>–1</sup>, uncertainty in I(Q) in cm<sup>–1</sup>]. SANS data (numbered 1, 2, and 3 to denote different instrument configurations) are two-column *.txt files [Q in Å<sup>–1</sup>, I(Q) in cm<sup>–1</sup>]. SESANS data were originally published in "Milk Gelation Studied with Small Angle Neutron Scattering Techniques and Monte Carlo Simulations" by Léon F. van Heijkamp, Ignatz M. de Schepper, Markus Strobl, R. Hans Tromp, Jouke R. Heringa, and Wim G. Bouwman (<em>J. Phys. Chem. A</em>, 2010, <strong>114</strong>, 2412–2426, doi:10.1021/jp9067735). SESANS data are three-column *.csv files [Z in Å, normalized SESANS signal in cm<sup>–1</sup> Å<sup>–2</sup>, uncertainty in normalized SESANS signal in cm<sup>–1</sup> Å<sup>–2</sup>].
DOI: 10.5281/zenodo.4077337
title: PharmKG: A Dedicated Knowledge Graph Benchmark for Biomedical Data Mining

description: Biomedical knowledge graphs, which can help with the understanding of complex biological systems and pathologies, have begun to play a critical role in medical practice and research. However, challenges remain in their embedding and use due to their complex nature and the specific demands of their construction. Existing studies often suffer from problems such as sparse and noisy datasets, insufficient modeling methods, and non-uniform evaluation metrics. In this work,<strong> </strong>we established a comprehensive knowledge graph (KG) system for the biomedical field in an attempt to bridge the gap. Here we introduced PharmKG, a multi-relational, attributed biomedical knowledge graph, composed of more than 500,000 individual interconnections between genes, drugs, and diseases, with 29 relation types over a vocabulary of ~8,000 disambiguated entities. Each entity in PharmKG is attached with heterogeneous, domain-specific information obtained from multi-omics data, i.e. gene expression, chemical structure, and disease word embedding while preserving the semantic and biomedical features. For baselines, we offered 9 state-of-the-art knowledge graph embedding (KGE) approaches and a new biological, intuitive, graph neural network-based KGE method that uses a combination of both global network structure and heterogeneous domain features. Based on the proposed benchmark, we conducted extensive experiments to assess these KGE models using multiple evaluation metrics. Finally, we discussed our observations across various downstream biological tasks and provide insights and guidelines for how to use a knowledge graph in biomedicine. We hope that the unprecedented quality and diversity of PharmKG will lead to advances in biomedical knowledge graph construction, embedding, and application.
description: [Update New Version]: We have updated the PharmKG-8k dataset we used in the paper, including the training/validation/test set. If you found this dataset useful for your research, please cite our paper: PharmKG: A Dedicated Knowledge Graph Benchmark for Biomedical Data Mining.
DOI: 10.5517/ccdc.csd.cc21dsy6
title: CCDC 1889138: Experimental Crystal Structure Determination

description: Related Article: Jiu-Fu Lu, Juan Song, Mei-Ling Liu, Cai-Bin Zhao, Shao-Bo Guo, Quan Liu, Pei Huang, Qin Wang, Xiao-Hu Yu, Ling-Xia Jin, Hong-Guang Ge|2021|Polyhedron|196|114922|doi:10.1016/j.poly.2020.114922
DOI: 10.5517/ccdc.csd.cc245d5x
title: CCDC 1971388: Experimental Crystal Structure Determination

description: Related Article: Christina Wegeberg, Mathias L. Skavenborg, Andrea Liberato, James N. McPherson, Wesley R. Browne, Erik D. Hedegård, Christine J. McKenzie|2021|Inorg.Chem.|60|1975|doi:10.1021/acs.inorgchem.0c03441
DOI: 10.5517/ccdc.csd.cc24bvly
title: CCDC 1976640: Experimental Crystal Structure Determination

description: Related Article: Marsel Z. Shafikov, Ross Martinscroft, Craig Hodgson, Anna Hayer, Armin Auch, Valery N. Kozhevnikov|2021|Inorg.Chem.|60|1780|doi:10.1021/acs.inorgchem.0c03251
DOI: 10.5517/ccdc.csd.cc24bvmz
title: CCDC 1976641: Experimental Crystal Structure Determination

description: Related Article: Marsel Z. Shafikov, Ross Martinscroft, Craig Hodgson, Anna Hayer, Armin Auch, Valery N. Kozhevnikov|2021|Inorg.Chem.|60|1780|doi:10.1021/acs.inorgchem.0c03251