cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6550593
title: Sarcoramphus papa SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6550592
title: Sarcoramphus papa SPAdes preassembly

description: .
DOI: 10.5519/qd.awn7g53r
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4 records)
DOI: 10.6084/m9.figshare.17924324
title: Combination of emic and etic approach to parenting and attachment dataset

description: The study is part of the cross-national project „Combination of emic and etic approach to parenting and attachment” (CEE-PaAtt). Data from 758 mothers and their children aged between 8 and 12 years were collected in Poland, the Netherlands, and Turkey. <br>
DOI: 10.5281/zenodo.5654483
title: Plant tissue images

description: All raw imaging data generated during my postdoctoral fellowship with Jacques Dumais (2007-2011) and used for https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3076879/
DOI: 10.5281/zenodo.5654482
title: Plant tissue images

description: All raw imaging data generated during my postdoctoral fellowship with Jacques Dumais (2007-2011) and used for https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3076879/
DOI: 10.34740/kaggle/dsv/3639479
title: WallStreetBets 2022
DOI: 10.5281/zenodo.6550664
title: Erythroxylum rufum SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6550663
title: Erythroxylum rufum SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6550685
title: Erythroxylum reticulatum SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6550684
title: Erythroxylum reticulatum SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6550693
title: Erythroxylum haughtii SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6550692
title: Erythroxylum haughtii SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6550710
title: Erythroxylum echinodendron SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6550709
title: Erythroxylum echinodendron SPAdes preassembly

description: .
DOI: 10.6084/m9.figshare.19426253.v4
title: adult model

description: Adult model
DOI: 10.6084/m9.figshare.19426253.v5
title: adult model

description: Adult model
DOI: 10.6084/m9.figshare.18551483.v2
title: breast cancer model

description: This is an AdaBoost model trained on the train data.<br>Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image.<br>n the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].This database is also available through the UW CS ftp server:<br>ftp ftp.cs.wisc.edu<br>cd math-prog/cpo-dataset/machine-learn/WDBC/Also can be found on UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
DOI: 10.6084/m9.figshare.17931989.v2
title: iris_model.joblib

description: iris
DOI: 10.6084/m9.figshare.17696876.v2
title: avocado_rf.model

description: Avocado ML Model
DOI: 10.25345/c5nz80t77
title: MassIVE MSV000089474 - GNPS LC-MS/MS analysis of the crude extract of Trichoderma sp. R24
title: Untargeted LC-MS/MS data from the crude extract of Trichoderma sp.
DOI: 10.5281/zenodo.6550772
title: Buteo regalis SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6550771
title: Buteo regalis SPAdes preassembly

description: .
DOI: 10.34740/kaggle/dsv/3639895
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.34740/kaggle/dsv/3639948
title: 2021 World Population (updated daily)
DOI: 10.6084/m9.figshare.19602142.v1
title: Bird_outages_MA_data

description: The accompanying data used for an analysis of animal-related outages in the state of Massachusetts from 2013-2018. All data formating and analysis took place in R version 4.0.3. The original outage data comes from Eversource Energy, National Grid, and Unitil Corporation, made available through the MA office of Energy and Environmental Affairs. The outage dataset used in this analysis is available on Columbia University's International Research Institute (IRI) for Climate and Society Data Library at http://iridl.ldeo.columbia.edu/SOURCES/.EOEEA/. The original bird abundance data comes from the eBird Basic Dataset (May 2020) and the modeled relative abundance estimates for Massachusetts towns are also available on the IRI Data Library at http://iridl.ldeo.columbia.edu/SOURCES/.PRISM/.eBird/.derived/.detectionProbability/.
DOI: 10.6084/m9.figshare.17879585.v4
title: phishing_rf_model

description: The phishing url dataset contains synthetic data of urls - some regular and some used for phishing. This is a scikit-learn pipeline wrapping a random forest model trained on the training dataset.<br>The dataset is based on the project (https://github.com/Rohith-2/url_classification_dl) byRohith Ramakrishnan (https://www.linkedin.com/in/rohith-ramakrishnan-54094a1a0/) and others, accompanied bya blog post (https://medium.com/nerd-for-tech/url-feature-engineering-and-classification-66c0512fb34d&gt;).<br>The dataset is released under Creative Commons Zero v1.0 Universal (CC0 1.0).
DOI: 10.6084/m9.figshare.17696876.v3
title: avocado_rf.model

description: Avocado ML Model
DOI: 10.5281/zenodo.6550821
title: Effect of Water Level and Submergence Time on Leaf Growth, Stoichiometry and Homeostasis of Carex brevicuspis

description: Water level and submergence time are important factors influencing plant growth and plant C:N:P characteristics in freshwater wetlands. However, the effect of their interaction on plant stoichiometry and homeostasis is far from clear. Here, we performed a controlled experiment using four water levels (0, 10, 20, and 40 cm, relative to the soil surface) and under three submergence times (30, 60, and 90 days) with the aim of investigating the changes in plant growth and stoichiometric characteristics, as well as the strength of N, P, and N:P homeostasis in <em>C. brevicuspis</em> in the Dongting Lake, China. Results showed that biomass, density, height, and total carbon (TC) decreased significantly with increasing water level; however, the latter total nitrogen (TN) and total phosphorus (TP) increased significantly. The highest TN and TP were at 40 cm water level, while the highest C:N, C:P, and N:P ratios were at 0 cm water level. Furthermore, significant stoichiometric homeostasis of <em>C. brevicuspis</em> was found for N (<em>H</em><sub>N</sub>) and P (<em>H</em><sub>P</sub>) except for N:P (<em>H</em><sub>N:P</sub>). <em>H</em><sub>N</sub> and <em>H</em><sub>P </sub>decreased with increasing submergence time, and<em> H</em><sub>N</sub> was consistently greater than <em>H</em><sub>P</sub>, indicating that <em>C. brevicuspis</em> had a weak ability to maintain its internal P balance under long-term submergence. Therefore, the impact of water level and submergence time on plant growth and stoichiometric characteristics in this study, are applicable to the conservation and management of the wetlands dominated with <em>C. brevicuspis</em>.
DOI: 10.5281/zenodo.6550820
title: Effect of Water Level and Submergence Time on Leaf Growth, Stoichiometry and Homeostasis of Carex brevicuspis

description: Water level and submergence time are important factors influencing plant growth and plant C:N:P characteristics in freshwater wetlands. However, the effect of their interaction on plant stoichiometry and homeostasis is far from clear. Here, we performed a controlled experiment using four water levels (0, 10, 20, and 40 cm, relative to the soil surface) and under three submergence times (30, 60, and 90 days) with the aim of investigating the changes in plant growth and stoichiometric characteristics, as well as the strength of N, P, and N:P homeostasis in <em>C. brevicuspis</em> in the Dongting Lake, China. Results showed that biomass, density, height, and total carbon (TC) decreased significantly with increasing water level; however, the latter total nitrogen (TN) and total phosphorus (TP) increased significantly. The highest TN and TP were at 40 cm water level, while the highest C:N, C:P, and N:P ratios were at 0 cm water level. Furthermore, significant stoichiometric homeostasis of <em>C. brevicuspis</em> was found for N (<em>H</em><sub>N</sub>) and P (<em>H</em><sub>P</sub>) except for N:P (<em>H</em><sub>N:P</sub>). <em>H</em><sub>N</sub> and <em>H</em><sub>P </sub>decreased with increasing submergence time, and<em> H</em><sub>N</sub> was consistently greater than <em>H</em><sub>P</sub>, indicating that <em>C. brevicuspis</em> had a weak ability to maintain its internal P balance under long-term submergence. Therefore, the impact of water level and submergence time on plant growth and stoichiometric characteristics in this study, are applicable to the conservation and management of the wetlands dominated with <em>C. brevicuspis</em>.
DOI: 10.5281/zenodo.6550798
title: Simalia boeleni SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6550797
title: Simalia boeleni SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6550845
title: Circus hudsonius SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6550844
title: Circus hudsonius SPAdes preassembly

description: .
DOI: 10.25345/c5j67913t
title: MassIVE MSV000089475 - pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level enabling the functional exploration of site-specific glycosylation
title: Interpreting large-scale glycoproteomic data for intact glycopeptide identification has been tremendously advanced by software tools. However, software tools for quantitative analysis of intact glycopeptides remain lagging behind, which greatly hinders exploring the differential expression and functions of site-specific glycosylation in organisms. Here, we report pGlycoQuant, a generic software tool for quantitative intact glycopeptide analysis, supporting both primary and tandem mass spectrometry quantitation for multiple quantitative strategies. pGlycoQuant advances in glycopeptide evidence matching through applying a deep learning model that reduces missing values for glycopeptide quantification by over 60% compared with Byologic, MSFragger-Glyco and Skyline, as well as an optional function of Match-In-Run (MIR) algorithm for more quantitative coverage of glycopeptides, thus greatly expanding the quantitative function of several powerful search engines, currently including pGlyco 2.0, pGlyco3, Byonic and MSFragger-Glyco. The pGlycoQuant-based site-specific N-glycoproteomic study conducted here quantifies 6435 intact N-glycopeptides in three hepatocellular carcinoma cell lines with different metastatic potentials and, together with in vitro molecular biology experiments, illustrates core fucosylation at site 979 of the L1 cell adhesion molecule (L1CAM) as a potential regulator of HCC metastasis. pGlycoQuant is freely available at https://github.com/Power-Quant/pGlycoQuant/release. We have demonstrated pGlycoQuant to be a powerful tool for the quantitative analysis of site-specific glycosylation and the exploration of potential glycosylation-related biomarker candidates, and we expect further applications in glycoproteomic studies.
DOI: 10.34740/kaggle/dsv/3640190
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.6550862
title: Cyprinella lepida SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6550861
title: Cyprinella lepida SPAdes preassembly

description: .
DOI: 10.6084/m9.figshare.19426253
title: adult model

description: Adult model
DOI: 10.6084/m9.figshare.19426253.v6
title: adult model

description: Adult model
DOI: 10.6084/m9.figshare.17696876.v4
title: avocado_rf.model

description: Avocado ML Model
DOI: 10.6084/m9.figshare.18551483
title: breast cancer model

description: This is an AdaBoost model trained on the train data.<br>Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image.<br>n the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].This database is also available through the UW CS ftp server:<br>ftp ftp.cs.wisc.edu<br>cd math-prog/cpo-dataset/machine-learn/WDBC/Also can be found on UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
DOI: 10.6084/m9.figshare.18551483.v3
title: breast cancer model

description: This is an AdaBoost model trained on the train data.<br>Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image.<br>n the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].This database is also available through the UW CS ftp server:<br>ftp ftp.cs.wisc.edu<br>cd math-prog/cpo-dataset/machine-learn/WDBC/Also can be found on UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
DOI: 10.6084/m9.figshare.17931989
title: iris_model.joblib

description: iris
DOI: 10.6084/m9.figshare.17931989.v3
title: iris_model.joblib

description: iris
DOI: 10.6084/m9.figshare.17879585
title: phishing_rf_model

description: The phishing url dataset contains synthetic data of urls - some regular and some used for phishing. This is a scikit-learn pipeline wrapping a random forest model trained on the training dataset.<br>The dataset is based on the project (https://github.com/Rohith-2/url_classification_dl) byRohith Ramakrishnan (https://www.linkedin.com/in/rohith-ramakrishnan-54094a1a0/) and others, accompanied bya blog post (https://medium.com/nerd-for-tech/url-feature-engineering-and-classification-66c0512fb34d&gt;).<br>The dataset is released under Creative Commons Zero v1.0 Universal (CC0 1.0).
DOI: 10.6084/m9.figshare.17879585.v5
title: phishing_rf_model

description: The phishing url dataset contains synthetic data of urls - some regular and some used for phishing. This is a scikit-learn pipeline wrapping a random forest model trained on the training dataset.<br>The dataset is based on the project (https://github.com/Rohith-2/url_classification_dl) byRohith Ramakrishnan (https://www.linkedin.com/in/rohith-ramakrishnan-54094a1a0/) and others, accompanied bya blog post (https://medium.com/nerd-for-tech/url-feature-engineering-and-classification-66c0512fb34d&gt;).<br>The dataset is released under Creative Commons Zero v1.0 Universal (CC0 1.0).
DOI: 10.5281/zenodo.6550923
title: PatchGastricADC22

description: Computational histopathology has made significant strides in the past few years, slowly getting closer to clinical adoption. One area of benefit would be the automatic generation of diagnostic reports from H\&E-stained whole slide images which would further increase the efficiency of the pathologists' routine diagnostic workflows. In this study, we compiled a dataset (PatchGastricADC22) of histopathological captions of stomach adenocarcinoma endoscopic biopsy specimens, which we extracted from diagnostic reports and paired with patches extracted from the associated whole slide images. The dataset contains a variety of gastric adenocarcinoma subtypes. We trained a baseline attention-based model to predict the captions from features extracted from the patches and obtained promising results. We make the captioned dataset of 262K patches publicly available.
DOI: 10.5281/zenodo.6550925
title: PatchGastricADC22

description: Computational histopathology has made significant strides in the past few years, slowly getting closer to clinical adoption. One area of benefit would be the automatic generation of diagnostic reports from H\&E-stained whole slide images which would further increase the efficiency of the pathologists' routine diagnostic workflows. In this study, we compiled a dataset (PatchGastricADC22) of histopathological captions of stomach adenocarcinoma endoscopic biopsy specimens, which we extracted from diagnostic reports and paired with patches extracted from the associated whole slide images. The dataset contains a variety of gastric adenocarcinoma subtypes. We trained a baseline attention-based model to predict the captions from features extracted from the patches and obtained promising results. We make the captioned dataset of 262K patches publicly available.
DOI: 10.5281/zenodo.6021441
title: PatchGastricADC22

description: Computational histopathology has made significant strides in the past few years, slowly getting closer to clinical adoption. One area of benefit would be the automatic generation of diagnostic reports from H\&E-stained whole slide images which would further increase the efficiency of the pathologists' routine diagnostic workflows. In this study, we compiled a dataset (PatchGastricADC22) of histopathological captions of stomach adenocarcinoma endoscopic biopsy specimens, which we extracted from diagnostic reports and paired with patches extracted from the associated whole slide images. The dataset contains a variety of gastric adenocarcinoma subtypes. We trained a baseline attention-based model to predict the captions from features extracted from the patches and obtained promising results. We make the captioned dataset of 262K patches publicly available.
DOI: 10.5281/zenodo.6550916
title: An accurate binding free energy method from end-state MD simulations (ANI_LIE test files)

description: Required files are added.
DOI: 10.5281/zenodo.6550915
title: An accurate binding free energy method from end-state MD simulations (ANI_LIE test files)

description: Required files are added.
DOI: 10.6084/m9.figshare.19768321
title: Effect of Iron Fortification on Anaemia and risk of Malaria among Ghanaian Pre-School Children with Haemoglobinopathies and different ABO blood groups

description: This is the data set, consenting form and ethical approvals for this study
DOI: 10.5281/zenodo.6550945
title: Cyprinella lutrensis SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6550944
title: Cyprinella lutrensis SPAdes preassembly

description: .
DOI: 10.5519/qd.i4lie76u
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (28 records)
DOI: 10.5281/zenodo.6550975
title: Cyprinella venusta SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6550974
title: Cyprinella venusta SPAdes preassembly

description: .
DOI: 10.34848/xbgbhb/0joyah
title: Agencia de Renovación del Territorio - Respuesta al radicado 20222300034721.pdf

description: :unav
DOI: 10.34848/xbgbhb/bcqxxu
title: Procesamiento de datos - Información iniciativas PDET implementadas por privados.tab

description: :unav
DOI: 10.5281/zenodo.6550801
title: Visualizing Eigen/Zundel cations and their interconversion in monolayer water on metal surfaces

description: Here lies the experimental and theoretical data for the manuscript titled " Visualizing Eigen/Zundel cations and their interconversion in monolayer water on metal surfaces" to be published in Science.
DOI: 10.5281/zenodo.6550800
title: Visualizing Eigen/Zundel cations and their interconversion in monolayer water on metal surfaces

description: Here lies the experimental and theoretical data for the manuscript titled " Visualizing Eigen/Zundel cations and their interconversion in monolayer water on metal surfaces" to be published in Science.
DOI: 10.5281/zenodo.6551010
title: Erythroxylum alaternifolium SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6551009
title: Erythroxylum alaternifolium SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6010468
title: LatentJam Dataset

description: This LatentJam dataset contains 12 music latent representations (9 content-based and 3 collaborative-filtering) for 29 275 music tracks from Jamendo. It is released as part of the publication "Similarity of Nearest-Neighbor Query Results in Deep Latent Spaces". For more details on the individual models used for extraction, refer to the text of the paper. The example code that uses this dataset and reproduces the experiments and analysis done in the paper is available at https://github.com/philtgun/compare-embeddings. See the README for more details on how to use this dataset and individual files. The content-based representations have been extracted with the Essentia library (essentia-tensorflow 2.1b6.dev374) during the internship of the first author in the Jamendo in 2021 as part of the MIP-Frontiers project. The collaborative filtering representations have been computed from data provided by Jamendo with the Implicit library (AlternatingLeastSquares algorithm, implicit 0.4.4). The Python version used is 3.7.13.<br> This dataset is released under CC BY-NC-SA 4.0 License. Please cite the publication if you use this dataset: <pre><code>@inproceedings{tovstogan_similarity_2009, title = {Similarity of nearest-neighbor query results in deep latent spaces}, author = {Tovstogan, Philip and Serra, Xavier and Bogdanov, Dmitry}, booktitle = {Proceedings of the 19th Sound and Music Computing Conference ({SMC})}, year = {2022} }</code></pre>
DOI: 10.5281/zenodo.6010467
title: LatentJam Dataset

description: This LatentJam dataset contains 12 music latent representations (9 content-based and 3 collaborative-filtering) for 29 275 music tracks from Jamendo. It is released as part of the publication "Similarity of Nearest-Neighbor Query Results in Deep Latent Spaces". For more details on the individual models used for extraction, refer to the text of the paper. The example code that uses this dataset and reproduces the experiments and analysis done in the paper is available at https://github.com/philtgun/compare-embeddings. See the README for more details on how to use this dataset and individual files. The content-based representations have been extracted with the Essentia library (essentia-tensorflow 2.1b6.dev374) during the internship of the first author in the Jamendo in 2021 as part of the MIP-Frontiers project. The collaborative filtering representations have been computed from data provided by Jamendo with the Implicit library (AlternatingLeastSquares algorithm, implicit 0.4.4). The Python version used is 3.7.13.<br> This dataset is released under CC BY-NC-SA 4.0 License. Please cite the publication if you use this dataset: <pre><code>@inproceedings{tovstogan_similarity_2009, title = {Similarity of nearest-neighbor query results in deep latent spaces}, author = {Tovstogan, Philip and Serra, Xavier and Bogdanov, Dmitry}, booktitle = {Proceedings of the 19th Sound and Music Computing Conference ({SMC})}, year = {2022} }</code></pre>
DOI: 10.6084/m9.figshare.19768498.v1
title: Supplementary files: The global prevalence and association between the risk of myocarditis and mRNA-based COVID-19 vaccination: A network meta-analysis

description: Supplementary files: The global prevalence and association between the risk of myocarditis and mRNA-based COVID-19 vaccination: A network meta-analysis
DOI: 10.5517/ccdc.csd.cc23sbv3
title: CCDC 1959815: Experimental Crystal Structure Determination

description: Related Article: Taku Shoji, Akari Yamazaki, Ryuzi Katoh, Konomi Shimamura, Rina Sakai, Masafumi Yasunami, Tetsuo Okujima, Shunji Ito|2022|J.Org.Chem.|87|5827|doi:10.1021/acs.joc.2c00133
DOI: 10.5517/ccdc.csd.cc298fnp
title: CCDC 2123272: Experimental Crystal Structure Determination

description: Related Article: Yanfeng Gao, Yuanhao Mao, Zhiwei Miao|2022|Org.Lett.|24|3064|doi:10.1021/acs.orglett.2c01037
DOI: 10.5517/ccdc.csd.cc298w8r
title: CCDC 2123694: Experimental Crystal Structure Determination

description: Related Article: Weiqiao Lan, Fangyi Liu, Jintao Hu, Jiatong Zhu, Shan Hu, Jie-Ping Wan, Lihua Liao|2022|J.Org.Chem.|87|5592|doi:10.1021/acs.joc.1c02873
DOI: 10.5517/ccdc.csd.cc299yq9
title: CCDC 2124731: Experimental Crystal Structure Determination

description: Related Article: Jianwei Yan, Linxia Zheng, Jiangfei Wang, Xiaomin Liu, Youhong Hu|2022|J.Org.Chem.|87|6347|doi:10.1021/acs.joc.1c03120