cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48366/r576864
title: Comparing Explainable Diabetic Retinopathy (ExplainDR) to the state-of-the-art

description: The comparison table below presents the system presented by the authors (ExplainDR) named by considering ablation study or not, compared to the state-of-the-art research.
DOI: 10.6084/m9.figshare.22303321.v1
title: Table_S6_Prevalence_Phecodes.xlsx

description: Prevalence of all Phecode phenotypes, in the total-, male- and female population. Shows number of individuals in the case group and the control group, as well as the prevalence in percentage.
DOI: 10.5281/zenodo.7751893
title: Exposing the CSI - S3

description: This dataset has been used to study a CSI-based activity-recognition framework in the paper "Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations". The dataset is split into several archives, one for each experiment considered in the paper. A description of all the experiments can be found in the paper, together with a map of the lab reporting the location of all the nodes and target positions.
DOI: 10.5281/zenodo.7741704
title: Raptor@GenomeBiology: Query Files

description: Filename Compressed size Uncompressed size query_file.1K.fa.zst 7.0 MiB 28 MiB query_file.1K.fastq.zst 7.7 MiB 52 MiB query_file.1M.fa.zst 70 MiB 278 MiB query_file.1M.fastq.zst 77 MiB 519 MiB query_file.5M.fa.zst 348 MiB 1.4 GiB query_file.5M.fastq.zst 381 MiB 2.6 GiB query_file.10M.fa.zst 615 MiB 2.8 GiB query_file.10M.fastq.zst 670 MiB 5.1 GiB Filename SHA256 compressed query_file.1K.fa.zst 087b030d2018d66e0580e76739041b6e5e5bba92836c940425c32a83bebd4079 query_file.1K.fastq.zst 6735a16521aac4aa490b891c1c86d930f028247b0fc79d9ea258edbf74cad251 query_file.1M.fa.zst 6e75c9ae3a773591f397979dfcf9fbd9f1283b37a681055aa6074576783dfd0f query_file.1M.fastq.zst 558dea66e348eff1adc030f3fc8989af476ef9a92aa587085bd22825a8af576d query_file.5M.fa.zst 4071b288434169379fb046bc49f7ee9f01a45972e333b7146f51b6e3dc00138c query_file.5M.fastq.zst 37f8db71ee07833f51031d92c0ac8da1c9b70f39369672e1fe4b503cf8f1279d query_file.10M.fa.zst fa22332e5227b2d3cc9c3c6f954269d89726e446a8bde7011be281006e9dd1bb query_file.10M.fastq.zst 8f4f0238cfaf05f4580ca4bf80c462f03c4870f32f336b94af31904799d33a7d Filename SHA256 uncompressed query_file.1K.fa.zst dfbbb7b4d37ab4fe406ad2a37583de4deabc8ee67f1d62e50fa27124ea821bec query_file.1K.fastq.zst f8b0f17f04db748cff73cdbdbb8aef227ba239acd6a5c99faae0e3b74d06cd1e query_file.1M.fa.zst 845ad455550fa11de737f5f36cd81842a9949dd6e86a1d56987feb448155cb81 query_file.1M.fastq.zst 55553b21eda74c4f5bf9a9960640ec0c80144a9123354b826c186ebd7438f30b query_file.5M.fa.zst 5e8e4c625f37c2419bf81ca876c94e799efa7c5783240ace15b78c045fcbb9a3 query_file.5M.fastq.zst 65ff2c129cc5209d492e4af84fef3091379dd52ae7bc325a6fa3cd4ca3208606 query_file.10M.fa.zst 2f19cd74c0b85cb2746a9c65ac2930201e3498ba4750e2a4e1e1c376c7e23af9 query_file.10M.fastq.zst 1f4dc264d4069cfbf0a86f5b0acdbcb1b11733e1a050697279729a21b632d071
DOI: 10.5281/zenodo.7741703
title: Raptor@GenomeBiology: Query Files

description: Filename Compressed size Uncompressed size query_file.1K.fa.zst 7.0 MiB 28 MiB query_file.1K.fastq.zst 7.7 MiB 52 MiB query_file.1M.fa.zst 70 MiB 278 MiB query_file.1M.fastq.zst 77 MiB 519 MiB query_file.5M.fa.zst 348 MiB 1.4 GiB query_file.5M.fastq.zst 381 MiB 2.6 GiB query_file.10M.fa.zst 615 MiB 2.8 GiB query_file.10M.fastq.zst 670 MiB 5.1 GiB Filename SHA256 compressed query_file.1K.fa.zst 087b030d2018d66e0580e76739041b6e5e5bba92836c940425c32a83bebd4079 query_file.1K.fastq.zst 6735a16521aac4aa490b891c1c86d930f028247b0fc79d9ea258edbf74cad251 query_file.1M.fa.zst 6e75c9ae3a773591f397979dfcf9fbd9f1283b37a681055aa6074576783dfd0f query_file.1M.fastq.zst 558dea66e348eff1adc030f3fc8989af476ef9a92aa587085bd22825a8af576d query_file.5M.fa.zst 4071b288434169379fb046bc49f7ee9f01a45972e333b7146f51b6e3dc00138c query_file.5M.fastq.zst 37f8db71ee07833f51031d92c0ac8da1c9b70f39369672e1fe4b503cf8f1279d query_file.10M.fa.zst fa22332e5227b2d3cc9c3c6f954269d89726e446a8bde7011be281006e9dd1bb query_file.10M.fastq.zst 8f4f0238cfaf05f4580ca4bf80c462f03c4870f32f336b94af31904799d33a7d Filename SHA256 uncompressed query_file.1K.fa.zst dfbbb7b4d37ab4fe406ad2a37583de4deabc8ee67f1d62e50fa27124ea821bec query_file.1K.fastq.zst f8b0f17f04db748cff73cdbdbb8aef227ba239acd6a5c99faae0e3b74d06cd1e query_file.1M.fa.zst 845ad455550fa11de737f5f36cd81842a9949dd6e86a1d56987feb448155cb81 query_file.1M.fastq.zst 55553b21eda74c4f5bf9a9960640ec0c80144a9123354b826c186ebd7438f30b query_file.5M.fa.zst 5e8e4c625f37c2419bf81ca876c94e799efa7c5783240ace15b78c045fcbb9a3 query_file.5M.fastq.zst 65ff2c129cc5209d492e4af84fef3091379dd52ae7bc325a6fa3cd4ca3208606 query_file.10M.fa.zst 2f19cd74c0b85cb2746a9c65ac2930201e3498ba4750e2a4e1e1c376c7e23af9 query_file.10M.fastq.zst 1f4dc264d4069cfbf0a86f5b0acdbcb1b11733e1a050697279729a21b632d071
DOI: 10.5281/zenodo.7751892
title: Exposing the CSI - S3

description: This dataset has been used to study a CSI-based activity-recognition framework in the paper "Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations". The dataset is split into several archives, one for each experiment considered in the paper. A description of all the experiments can be found in the paper, together with a map of the lab reporting the location of all the nodes and target positions.
DOI: 10.34740/kaggle/dsv/5200698
title: Egypt Gold Prices | Daily Updated
DOI: 10.5281/zenodo.7750953
title: MUSES Leaf Area Index (LAI) Monthly Global 500m SIN Grid in 2002

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 500m spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2002. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2001</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2003</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2002 Spatial Resolution: 500m Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7750952
title: MUSES Leaf Area Index (LAI) Monthly Global 500m SIN Grid in 2002

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 500m spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2002. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2001</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2003</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2002 Spatial Resolution: 500m Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.6084/m9.figshare.22303429
title: Effects of polyethylene microplastics and fresh/aged biochar on soil CO2 and N2O emissions regulated by soil aggregation, dissolved organic matter structure and the formation of necromass-derived soil organic carbon

description: Raw data for all the figures drawn in the manuscript
DOI: 10.25678/0005v2
title: Data and Code for: Spread of networked populations is determined by the interplay between dispersal behavior and habitat configuration

description: Predicting the spread of populations across fragmented habitats is vital if we are to manage their persistence in the long term. We applied network theory with a model and an experiment to show that spread rate is jointly defined by the configuration of habitat networks (i.e., the arrangement and length of connections between habitat fragments) and the movement behavior of individuals. We found that population spread rate in the model was well predicted by algebraic connectivity of the habitat network. A multi-generation experiment with the microarthropod Folsomia candida validated this model prediction. Realized habitat connectivity and spread rate were determined by the interaction between dispersal behavior and habitat configuration, such that the network configurations that facilitated the fastest spread changed depending on the shape of the species’ dispersal kernel. Predicting the spread rate of populations in fragmented landscapes requires combining knowledge of species-specific dispersal kernels and the spatial configuration of habitat networks. This information can be used to design landscapes to manage the spread and persistence of species in fragmented habitats.
DOI: 10.5517/ccdc.csd.cc29xpc9
title: CCDC 2142731: Experimental Crystal Structure Determination

description: Related Article: Zhicheng Zhang, Linzi Wen, Shihai Xu, Yu Tang, Xiaohui Cao, Pengju Feng|2023|Green Chemistry|25|2287|doi:10.1039/D3GC00124E
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.cc2bg1gb
title: CCDC 2158451: Experimental Crystal Structure Determination

description: Related Article: Minghui Wei, Chengkou Liu, Chang-Sheng Wang, Yuguang Li, Peng Qiu, Quanxiao Dong, Zhao Yang, Zheng Fang, Kai Guo|2023|Green Chemistry|25|2453|doi:10.1039/D2GC04491A
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.cc2d2ws5
title: CCDC 2207317: Experimental Crystal Structure Determination

description: Related Article: Wilfred T. Diment, Gloria Rosetto, Noura Ezaz-Nikpay, Ryan W. F. Kerr, Charlotte K. Williams|2023|Green Chemistry|25|2262|doi:10.1039/D2GC04580J
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.cc2d2wt6
title: CCDC 2207318: Experimental Crystal Structure Determination

description: Related Article: Wilfred T. Diment, Gloria Rosetto, Noura Ezaz-Nikpay, Ryan W. F. Kerr, Charlotte K. Williams|2023|Green Chemistry|25|2262|doi:10.1039/D2GC04580J
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.cc2dfbz5
title: CCDC 2217367: Experimental Crystal Structure Determination

description: Related Article: Yasuyuki Yamada, Yusuke Miwa, Yuka Toyoda, Quan Manh Phung, Kin-ichi Oyama, Kentaro Tanaka|2023|Cat.Sci.Tech.|13|1725|doi:10.1039/D2CY01980A
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: 10.34740/kaggle/dsv/5200795
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.6084/m9.figshare.22303462
title: Magnetic Field Data

description: Magnetic field data taken in March 2022 on the PEPL RMFv2 thruster operating at 45 sccm Xe flow, 2 kA pk-pk RMF current, 180 G peak bias field strength, 125 us pulse duration
DOI: 10.6084/m9.figshare.21534843
title: Cognitive functions and symptoms predicting later use of psychiatric services following mild traumatic brain injury in school-age

description: To investigate whether neuropsychological test performance or presence of some specific injury symptoms at 1–3 months following pediatric mild traumatic brain injury (mTBI) can help to identify the children at risk for developing post-traumatic psychiatric symptoms. Data from 120 children and adolescents aged 7–15 years, treated at Turku University Hospital between 2010 and 2016 due to mTBI, and who had undergone neuropsychological evaluation at 1–3 months following injury, were enrolled from the hospital records. Neuropsychological test performancesand injury symptom reports were retrospectively retrieved from the patient files. Slow information processing speed (<i>p</i> = 0.044), emotion regulation deficit (<i>p</i> = 0.014), impulsivity (<i>p</i> = 0.013), verbal processing difficulties (<i>p</i> = 0.042) and headache (<i>p</i> = 0.026) were independent predictors for having later contact in psychiatric care. Neuropsychological examination containing measure of information processing speed, injury symptom interview, and parental questionnaires on behavioural issues of the child at 1–3 months following mTBI seems to be useful in detecting children with risk for post traumatic psychiatric symptoms. Targeted support and guidance for this group of children and adolescents and their families are recommended to prevent the development of an unfavorable psychosocial outcome.
DOI: 10.6084/m9.figshare.21534843.v1
title: Cognitive functions and symptoms predicting later use of psychiatric services following mild traumatic brain injury in school-age

description: To investigate whether neuropsychological test performance or presence of some specific injury symptoms at 1–3 months following pediatric mild traumatic brain injury (mTBI) can help to identify the children at risk for developing post-traumatic psychiatric symptoms. Data from 120 children and adolescents aged 7–15 years, treated at Turku University Hospital between 2010 and 2016 due to mTBI, and who had undergone neuropsychological evaluation at 1–3 months following injury, were enrolled from the hospital records. Neuropsychological test performancesand injury symptom reports were retrospectively retrieved from the patient files. Slow information processing speed (<i>p</i> = 0.044), emotion regulation deficit (<i>p</i> = 0.014), impulsivity (<i>p</i> = 0.013), verbal processing difficulties (<i>p</i> = 0.042) and headache (<i>p</i> = 0.026) were independent predictors for having later contact in psychiatric care. Neuropsychological examination containing measure of information processing speed, injury symptom interview, and parental questionnaires on behavioural issues of the child at 1–3 months following mTBI seems to be useful in detecting children with risk for post traumatic psychiatric symptoms. Targeted support and guidance for this group of children and adolescents and their families are recommended to prevent the development of an unfavorable psychosocial outcome.
DOI: 10.5281/zenodo.7752565
title: Experimental data set for the article entitled "Mathematical Model of Steam Reforming in the Anode Channel of a Molten Carbonate Fuel Cell"

description: Experimental data for the article: Szablowski, L.; Dybinski, O.; Szczesniak, A.; Milewski, J. Mathematical Model of Steam Reforming in the Anode Channel of a Molten Carbonate Fuel Cell. Energies 2022, 15, 608. The experiments were performed by the first two authors.<br> These data set refer to experiments carried out on a stand used to test high-temperature fuel cells. The subject of the study was a molten carbonate fuel cell fueled with a mixture of methane and steam with steam to carbon ratio of 2.0, 2.5, 3.0 and 3.5 and at the cell operating temperature of 550°C and 650°C. Additionally, in the anode channel of the cell, there was a catalyst in the amount of 2 g. The active area of the cell was 20.25 cm<sup>2</sup>. The article that uses these research results is published in an open access journal with a CC-BY license. This research was funded by the National Science Center, Poland (Grant number 2020/39/D/ST8/02021).
description: This research was funded by the National Science Center, Poland (Grant number 2020/39/D/ST8/02021)
DOI: 10.5281/zenodo.7752564
title: Experimental data set for the article entitled "Mathematical Model of Steam Reforming in the Anode Channel of a Molten Carbonate Fuel Cell"

description: Experimental data for the article: Szablowski, L.; Dybinski, O.; Szczesniak, A.; Milewski, J. Mathematical Model of Steam Reforming in the Anode Channel of a Molten Carbonate Fuel Cell. Energies 2022, 15, 608. The experiments were performed by the first two authors.<br> These data set refer to experiments carried out on a stand used to test high-temperature fuel cells. The subject of the study was a molten carbonate fuel cell fueled with a mixture of methane and steam with steam to carbon ratio of 2.0, 2.5, 3.0 and 3.5 and at the cell operating temperature of 550°C and 650°C. Additionally, in the anode channel of the cell, there was a catalyst in the amount of 2 g. The active area of the cell was 20.25 cm<sup>2</sup>. The article that uses these research results is published in an open access journal with a CC-BY license. This research was funded by the National Science Center, Poland (Grant number 2020/39/D/ST8/02021).
description: This research was funded by the National Science Center, Poland (Grant number 2020/39/D/ST8/02021)
DOI: 10.6084/m9.figshare.19610091
title: Appendix 13.1.xlsx

description: Supplementary data from Samuels and Schap (2023). Samuels, J.X., and J.A. Schap. 2023. Regional topography and climate influence the nature and timing of changes in the structure of rodent and lagomorph communities through the Cenozoic of North America. <em>Evolution of Cenozoic Land Mammal Faunas and Ecosystems. 25 years of the NOW database of fossil mammals</em>. Vertebrate Paleobiology and Paleoanthropology Book Series. <br> Appendix 13.1. Comparison of the fossil records of rodents and lagomorphs across nine regions of North America through time, including the number of localities with identified rodent and lagomorph fossils and species richness with differing crown heights at each time interval. Occurrence data are derived from Samuels and Hopkins (2017) Appendix A with updates as described in the text.
DOI: 10.6084/m9.figshare.19610091.v1
title: Samuels and Schap 2022 Appendix A1.xlsx

description: Supplementary data from Samuels and Schap (2022). Samuels, J.X., and J.A. Schap. 2022. Regional topography and climate influence the nature and timing of changes in the structure of rodent and lagomorph communities through the Cenozoic of North America. <em>Evolution of Cenozoic Land Mammal Faunas and Ecosystems. 25 years of the NOW database of fossil mammals</em>. Vertebrate Paleobiology and Paleoanthropology Book Series. <br> Appendix A1. Comparison of the fossil records of rodents and lagomorphs across nine regions of North America through time, including the number of localities with identified rodent and lagomorph fossils and species richness with differing crown heights at each time interval. Occurrence data are derived from Samuels and Hopkins (2017) Appendix A with updates as described in the text.
DOI: 10.25500/edata.bham.00000933
title: LC-FAIMS-MS/MS Proteomics data for analysis of SUM52 breast cancer cells with FGFR inhibitor drugs