cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7700430
title: Doodleverse/Segmentation Zoo Res-UNet models for v2 PCMSC/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images

description: Doodleverse/Segmentation Zoo Res-UNet models for v2 PCMSC/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images These Residual-UNet models have been created using Segmentation Gym* Image size used by model: 1024 x 768 x 3 pixels classes: water other <br> <strong>File descriptions</strong> For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU <strong>References</strong><br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym
DOI: 10.5281/zenodo.7700429
title: Doodleverse/Segmentation Zoo Res-UNet models for v2 PCMSC/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images

description: Doodleverse/Segmentation Zoo Res-UNet models for v2 PCMSC/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images These Residual-UNet models have been created using Segmentation Gym* Image size used by model: 1024 x 768 x 3 pixels classes: water other <br> <strong>File descriptions</strong> For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU <strong>References</strong><br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym
DOI: 10.6084/m9.figshare.22217461
title: Test suites (TS)

description: In this project, we introduce the data that we generated for our experiment, and explain how readers can duplicate our experiment using the data. Index Terms—Test suites, test suite effectiveness, syntactic coverage, semantic coverage, detector sets, absolute correctness, relative correctness, partial correctness, total correctness.
DOI: 10.6084/m9.figshare.22217461.v1
title: Test suites (TS)

description: In this project, we introduce the data that we generated for our experiment, and explain how readers can duplicate our experiment using the data. Index Terms—Test suites, test suite effectiveness, syntactic coverage, semantic coverage, detector sets, absolute correctness, relative correctness, partial correctness, total correctness.
DOI: 10.6084/m9.figshare.22217470
title: Compute Similarity Measure

description: In this project, we introduce the data that we generated for our experiment, and explain how readers can duplicate our experiment using the data.
DOI: 10.6084/m9.figshare.22217470.v1
title: Compute Similarity Measure

description: In this project, we introduce the data that we generated for our experiment, and explain how readers can duplicate our experiment using the data.
DOI: 10.6084/m9.figshare.22217473
title: Generate subset Relations

description: Generate subset Relations
DOI: 10.6084/m9.figshare.22217473.v1
title: Generate subset Relations

description: Generate subset Relations
DOI: 10.6084/m9.figshare.22217479
title: Semcovgenerator

description: Semcovgenerator
DOI: 10.6084/m9.figshare.22217479.v1
title: Semcovgenerator

description: Semcovgenerator
DOI: 10.6084/m9.figshare.22217482
title: Source

description: Test suits. <br>
DOI: 10.6084/m9.figshare.22217482.v1
title: Source

description: Test suits. <br>
DOI: 10.6084/m9.figshare.22217506.v1
title: TOT25

description: Mutant 25
DOI: 10.6084/m9.figshare.22217506
title: TOT25

description: Mutant 25
DOI: 10.34740/kaggle/dsv/5112016
title: Polish-Ukrainian translations from Tatoeba
DOI: 10.6084/m9.figshare.22217518
title: TOT50

description: Mutant 50
DOI: 10.6084/m9.figshare.22217518.v1
title: TOT50

description: Mutant 50
DOI: 10.6084/m9.figshare.22217536
title: MUT1

description: MUT1
DOI: 10.6084/m9.figshare.22217536.v1
title: MUT1

description: MUT1
DOI: 10.6084/m9.figshare.22217542
title: MUT2

description: MUT2.
DOI: 10.6084/m9.figshare.22217542.v1
title: MUT2

description: MUT2.
DOI: 10.5281/zenodo.7679678
title: Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express

description: Bow shock crossings at Venus identified manually from the ASPERA-4 and MAG instruments onboard Venus Express for the full mission from 2006 to 2014. A detailed description of the dataset can be found in the paper "Influence of solar wind variations on the shapes of Venus’ plasma boundaries based on Venus Express observations" by Signoles et al. The boundary crossings by Venus Express are determined from combining the measurements of both ion, electron and magnetic field measurements. The bow shock is identified from the sharp increase in the magnetic field magnitude, and the increase in electron and ion temperature. The ion composition boundary is identified from the decrease in magnetosheath protons and electrons, and the appearance of planetary heavy ions. The dataset contains 5193 identified bow shock crossings and 2679 identified ion composition boundary crossings. For more information on the dataset contact: M. Persson, moaperssonphd at gmail.com
DOI: 10.5281/zenodo.7679677
title: Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express

description: Bow shock crossings at Venus identified manually from the ASPERA-4 and MAG instruments onboard Venus Express for the full mission from 2006 to 2014. A detailed description of the dataset can be found in the paper "Influence of solar wind variations on the shapes of Venus’ plasma boundaries based on Venus Express observations" by Signoles et al. The boundary crossings by Venus Express are determined from combining the measurements of both ion, electron and magnetic field measurements. The bow shock is identified from the sharp increase in the magnetic field magnitude, and the increase in electron and ion temperature. The ion composition boundary is identified from the decrease in magnetosheath protons and electrons, and the appearance of planetary heavy ions. The dataset contains 5193 identified bow shock crossings and 2679 identified ion composition boundary crossings. For more information on the dataset contact: M. Persson, moaperssonphd at gmail.com
DOI: 10.6084/m9.figshare.20443173
title: Global mean sea level rise inferred from ocean salinity and temperature changes

description: Barystatic sea level rise caused by the addition of freshwater to the ocean from melting ice can in principle be recorded by a reduction in seawater salinity, but detection of this signal has been hindered by sparse data coverage and the small trends compared to natural variability. Here, we develop an autoregressive machine learning method to estimate salinity changes in the global ocean from 2001-2019 that reduces uncertainties in ocean freshening trends by a factor of four compared to previous estimates. We find that the ocean mass rose by 13,000±3,000 Gt from 2001-2019, implying a barystatic sea level rise of 2.0±0.5 mm/yr. Combined with sea level rise of 1.3±0.1 mm/yr due to ocean thermal expansion, these results suggest that global mean sea level rose by 3.4±0.6 mm/yr from 2001-2019. These results provide an important validation of remote-sensing measurements of ocean mass changes, global sea level rise, and global ice budgets.
DOI: 10.6084/m9.figshare.20443173.v1
title: Global mean sea level rise inferred from ocean salinity and temperature changes

description: Global warming causes sea level rise via thermal expansion of the oceans and the addition of freshwater from melting ice. Barystatic sea level rise caused by the addition of freshwater to the ocean from melting ice can in principle be recorded by a reduction in seawater salinity, but robust detection of this signal has been hindered by sparse data coverage and the small magnitude of the changes compared to natural variability. Here, we develop an autoregressive machine learning method to estimate salinity changes in the global ocean from 2001-2019 that reduces uncertainties in ocean freshening trends by a factor of four compared to previous estimates. We find that the ocean mass rose by 13±3 Tt from 2001-2019, implying a barystatic sea level rise of 2.0±0.5 mm/yr. Combined with sea level rise of 1.3±0.1 mm/yr due to ocean thermal expansion, these results suggest that global mean sea level rose by 3.4±0.6 mm/yr from 2001-2019. These results provide an important validation of remote-sensing measurements of ocean mass changes, global sea level rise, and global ice budgets.
DOI: 10.5517/ccdc.csd.cc2ckdw7
title: CCDC 2191479: Experimental Crystal Structure Determination

description: Related Article: Qin Wang, Ya-Ting Yu, Ji-Lei Wang, Jia-Nian Li, Ning-Fang Li, Xinrong Fan, Yan Xu|2023|Inorg.Chem.|62|3162|doi:10.1021/acs.inorgchem.2c04065
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.cc2ckdx8
title: CCDC 2191480: Experimental Crystal Structure Determination

description: Related Article: Qin Wang, Ya-Ting Yu, Ji-Lei Wang, Jia-Nian Li, Ning-Fang Li, Xinrong Fan, Yan Xu|2023|Inorg.Chem.|62|3162|doi:10.1021/acs.inorgchem.2c04065
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.cc2d8wbx
title: CCDC 2213069: Experimental Crystal Structure Determination

description: Related Article: Lavleen Mader, Sarah K. I. Watt, Harish R. Iyer, Linh Nguyen, Harpreet Kaur, Jeffrey W. Keillor|2023|RSC Med. Chem.|14|277|doi:10.1039/D2MD00378C
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.cc2cl9fq
title: CCDC 2192333: Experimental Crystal Structure Determination

description: Related Article: Guanping Li, Olaf Stefanczyk, Kunal Kumar, Koji Nakabayashi, Shin-ichi Ohkoshi|2023|Inorg.Chem.|62|3278|doi:10.1021/acs.inorgchem.2c04382
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.cc2cl9gr
title: CCDC 2192334: Experimental Crystal Structure Determination

description: Related Article: Guanping Li, Olaf Stefanczyk, Kunal Kumar, Koji Nakabayashi, Shin-ichi Ohkoshi|2023|Inorg.Chem.|62|3278|doi:10.1021/acs.inorgchem.2c04382
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.cc2cl9hs
title: CCDC 2192335: Experimental Crystal Structure Determination

description: Related Article: Guanping Li, Olaf Stefanczyk, Kunal Kumar, Koji Nakabayashi, Shin-ichi Ohkoshi|2023|Inorg.Chem.|62|3278|doi:10.1021/acs.inorgchem.2c04382
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.cc2cl9jt
title: CCDC 2192336: Experimental Crystal Structure Determination

description: Related Article: Guanping Li, Olaf Stefanczyk, Kunal Kumar, Koji Nakabayashi, Shin-ichi Ohkoshi|2023|Inorg.Chem.|62|3278|doi:10.1021/acs.inorgchem.2c04382
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: 10.6084/m9.figshare.22002578
title: Supplementary Table 1_Substantial role of check dams in sediment trapping and carbon sequestration on the Chinese Loess Plateau.xlsx

description: Geographic information and sampling information of 86 dam-controlled catchments
DOI: 10.6084/m9.figshare.22002578.v2
title: Supplementary Table 1_Substantial role of check dams in sediment trapping and carbon sequestration on the Chinese Loess Plateau.xlsx

description: Geographic information and sampling information of 86 dam-controlled catchments
DOI: 10.6084/m9.figshare.22002578.v1
title: Supplementary Table 1_Substantial role of check dams in sediment trapping and carbon sequestration on the Chinese Loess Plateau.xlsx

description: Geographic information and sampling information of 86 dam-controlled catchments
DOI: 10.6084/m9.figshare.20400729.v2
title: Leech genomes

description: genome sequences and gene annotation of three leeches
DOI: 10.5281/zenodo.7680963
title: Detection and Quantification of Cotton Trichomes by Deep Learning Algorithm

description: This file includes three image datasets for singled and clustered trichomes: the original image dataset, the black-and-white image dataset, and the enhanced image dataset. In addition, the codes of the four models are included. The details are in the article "Detection and Quantification of Cotton Trichomes by Deep Learning Algorithm".
DOI: 10.5281/zenodo.7680962
title: Detection and Quantification of Cotton Trichomes by Deep Learning Algorithm

description: This file includes three image datasets for singled and clustered trichomes: the original image dataset, the black-and-white image dataset, and the enhanced image dataset. In addition, the codes of the four models are included. The details are in the article "Detection and Quantification of Cotton Trichomes by Deep Learning Algorithm".
DOI: 10.6084/m9.figshare.22217596
title: Antiangiogenic project data, images, tables, raw data, and ARRIVE

description: 1. Figures. zip: figures all figures in the article 2. Tables.zip: Tables 1 and 2 3. Raw data.zip: containing raw data for: <br> a. figure 3 (Spectrophotometer analysis) b. GCMS supporting table 1 and Figure 7) c. Antiangiogenic activity for Table 2 evaluation d. FTIR f. TEM (raw images annotated) g. XRD <br> 4. Raw imges uncropped and without notation 5. ARRIVE
DOI: 10.6084/m9.figshare.22217596.v1
title: Antiangiogenic project data, images, tables, raw data, and ARRIVE

description: 1. Figures. zip: figures all figures in the article 2. Tables.zip: Tables 1 and 2 3. Raw data.zip: containing raw data for: <br> a. figure 3 (Spectrophotometer analysis) b. GCMS supporting table 1 and Figure 7) c. Antiangiogenic activity for Table 2 evaluation d. FTIR f. TEM (raw images annotated) g. XRD <br> 4. Raw imges uncropped and without notation 5. ARRIVE
DOI: 10.5281/zenodo.7700452
title: Scaled RNA sequencing counts accompaining the publication Singh and Gollapalli 2023

description: RNA-Seq analysis of WT and taurine-deficient osteoblasts. RNA was extracted from the mouse primary calvarial osteoblasts prepared from day 3–5 neonates of WT or <em>Slc6a6</em>-/- genotypes using the RNeasy extraction kit (Qiagen). RNA sequencing libraries were prepared with Illumina-compatible NEBNext Ultra<sup>TM</sup> II Directional RNA library kit at Genotypic Technology, Inc. The raw data were trimmed for adapter sequences and low-quality bases (&lt;q30) using Cutadapt with default parameters [DOI:10.14806/ej.17.1.200] and checked for quality using FastQC [Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data]. Mouse raw reads were aligned to the mouse reference genome (mm9) using Hisat2(<em>24</em>) with default parameters. HTSeq [G Putri, S Anders, PT Pyl, JE Pimanda, F Zanini Analyzing high-throughput sequencing data with HTSeq 2.0 arXiv:2112.00939 (2021)] was used to estimate gene abundance. Downstream analyses were done using the analysis framework tidybulk (<em>25, 26</em>). The filterByExpr functionality of edgeR (<em>27</em>) was used to identify the abundant gene-transcripts included in downstream analyses using default parameters and the knock-out phenotype as the factor of interest. The algorithm trimmed mean of M valued (TMM) values identified sample-wise scaling to compensate for differences in sequencing depth across samples (<em>28</em>). For aiding data exploration, we reduced the data dimensionality using principal component analysis (PCA). Differential expression analyses were performed using the robust likelihood ratio implementation of edgeR, testing for differences in gene transcript abundance greater than 1 log-fold (<em>29, 30</em>). The ppcseq method was used to investigate the presence of outlier observation among the significant results to avoid biases in the statistics (<em>31</em>). The ggplot2 software produced most of the data visualizations, whereas tidyHeatmap was used for heatmap visualization (<em>32-34</em>). We performed gene-set enrichment analyses using the MSigDB C2 experimentally derived gene set, the clusterProfiler algorithm, and the visualization tool enrichplot (cran.r-project.org/) (https://rdrr.io/cran/msigdbr/) (<em>35</em>). An aging signature was composed of a union of gene sets from MSigDB and a set of genes derived from aging datasets identified in this study through gene set enrichment analysis and published literature (<em>36</em>). The <em>p</em>-values of gene set analyses were corrected for multiple testing using the Benjamini Hochberg correction (<em>37</em>).