cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3905396
title: Progesterone Electron Diffraction (MicroED) Datasets - Glacios TEM with a CETA-D

description: Electron diffraction datasets collected from nanometer sized crystals of progesterone (Sigma) applied to an electron microscopy grid covered in a pure carbon film. Data were collected using a Thermo Fisher Scientific Glacios Transmission Electron Microscope operated at 200 kV and -193 C using the software package Leginon. The beam diameter is ~600 nm and the total dose is ~6.8e-/A^2. The crystal was continuously rotated and diffraction data was collected with a CETA-D camera in rolling shutter mode. Data have been converted to SMV format with the addition of an offset value to remove negative pixel values. This offset value and a recommended pedestal value can be found in the image headers. An image of the crystal before centering on the small green dot is also included. Data from four crystals are provided, with 352382_1 being the best dataset. If processing in DIALS, you will first want to install the proper format class: dxtbx.install_format -u https://raw.githubusercontent.com/dials/dxtbx_ED_formats/master/FormatSMVCetaD_TUI.py
DOI: 10.5517/ccdc.csd.cc1kj62c
title: CCDC 1445532: Experimental Crystal Structure Determination

description: Related Article: Yingnan Zhao, Jie Ding, Xiao Han, Ting Geng, Xiuwen Zhou, Chen Hu, Yashu Wang, Guanjun Xiao, Bo Zou, Hongwei Hou|2020|Org.Chem.Front.|7|3853|doi:10.1039/D0QO00915F
DOI: 10.5517/ccdc.csd.cc1kn8xd
title: CCDC 1449464: Experimental Crystal Structure Determination

description: Related Article: Yingnan Zhao, Jie Ding, Xiao Han, Ting Geng, Xiuwen Zhou, Chen Hu, Yashu Wang, Guanjun Xiao, Bo Zou, Hongwei Hou|2020|Org.Chem.Front.|7|3853|doi:10.1039/D0QO00915F
DOI: 10.5517/ccdc.csd.cc1q2l3h
title: CCDC 1581406: Experimental Crystal Structure Determination

description: Related Article: Ning-Ning Shi, Xiao-Meng Yin, Wei-Song Gao, Jin-Miao Wang, Shi-Fu Zhang, Yu-Hua Fan, Mei Wang|2021|Inorg.Chim.Acta|526|120548|doi:10.1016/j.ica.2021.120548
DOI: 10.5517/ccdc.csd.cc1zlys9
title: CCDC 1835472: Experimental Crystal Structure Determination

description: Related Article: Ning-Ning Shi, Xiao-Meng Yin, Wei-Song Gao, Jin-Miao Wang, Shi-Fu Zhang, Yu-Hua Fan, Mei Wang|2021|Inorg.Chim.Acta|526|120548|doi:10.1016/j.ica.2021.120548
DOI: 10.5517/ccdc.csd.cc207vns
title: CCDC 1854595: Experimental Crystal Structure Determination

description: Related Article: Ning-Ning Shi, Xiao-Meng Yin, Wei-Song Gao, Jin-Miao Wang, Shi-Fu Zhang, Yu-Hua Fan, Mei Wang|2021|Inorg.Chim.Acta|526|120548|doi:10.1016/j.ica.2021.120548
DOI: 10.5281/zenodo.3937740
title: Teniposide Electron Diffraction (MicroED) Datasets - Glacios TEM with a CETA-D

description: Electron diffraction datasets collected from nanometer sized crystals of Teniposide (TCI) applied to an electron microscopy grid covered in a pure carbon film. Data were collected using a Thermo Fisher Scientific Glacios Transmission Electron Microscope operated at 200 kV and -193 C using the software package Leginon. The beam diameter is ~600 nm and the total dose is ~2.8e-/A^2. The crystals were continuously rotated and diffraction data was collected with a CETA-D camera in rolling shutter mode. Data have been converted to SMV format with the addition of an offset value to remove negative pixel values. This offset value can be found in the image headers. An image of the crystal before centering on the small green dot is also included. Data from six crystals are provided. If processing in DIALS, you will first want to install the proper format class: dxtbx.install_format -u https://raw.githubusercontent.com/dials/dxtbx_ED_formats/master/FormatSMVCetaD_TUI.py A final structure is deposited in the CCDC with code 2015361.
DOI: 10.5281/zenodo.3937739
title: Teniposide Electron Diffraction (MicroED) Datasets - Glacios TEM with a CETA-D

description: Electron diffraction datasets collected from nanometer sized crystals of Teniposide (TCI) applied to an electron microscopy grid covered in a pure carbon film. Data were collected using a Thermo Fisher Scientific Glacios Transmission Electron Microscope operated at 200 kV and -193 C using the software package Leginon. The beam diameter is ~600 nm and the total dose is ~2.8e-/A^2. The crystals were continuously rotated and diffraction data was collected with a CETA-D camera in rolling shutter mode. Data have been converted to SMV format with the addition of an offset value to remove negative pixel values. This offset value can be found in the image headers. An image of the crystal before centering on the small green dot is also included. Data from six crystals are provided. If processing in DIALS, you will first want to install the proper format class: dxtbx.install_format -u https://raw.githubusercontent.com/dials/dxtbx_ED_formats/master/FormatSMVCetaD_TUI.py A final structure is deposited in the CCDC with code 2015361.
DOI: 10.5281/zenodo.5195800
title: Training data for MaxQuant and Msstats TMT analysis in Galaxy

description: The files serve as input and intermediate results for a MaxQuant and MsstatsTMT training on lysine methyl transferase 9 knockdown and control cell proteomics (https://doi.org/10.1186/s12935-020-1141-2) in the Galaxy training network (https://training.galaxyproject.org). Input files: human FASTA protein database for Maxquant. MaxQuant experimental design template, MSstatsTMT annotation file Intermediate result files: MaxQuant protein groups and evidence
DOI: 10.5281/zenodo.5184772
title: Training data for MaxQuant and Msstats TMT analysis in Galaxy

description: The files serve as input and intermediate results for a MaxQuant and MsstatsTMT training on lysine methyl transferase 9 knockdown and control cell proteomics (https://doi.org/10.1186/s12935-020-1141-2) in the Galaxy training network (https://training.galaxyproject.org). Input files: human FASTA protein database for Maxquant. MaxQuant experimental design template, MSstatsTMT annotation file Intermediate result files: MaxQuant protein groups and evidence
DOI: 10.21227/4821-vf03
title: GEM HOUSE openData

description: Preliminary publication for testing purposes. Full delivery of many Gigabytes of data will be availavle by August 31, 2021. Please re-visit this place. This publication is part of a research and development project with the goal to provide households with data driven and user centered value added services around smart meter data in a native app. Fresh Energy GmbH publishes the GEM HOUSE openData package under the Open Data Commons Open Database License (ODbL). The delivery contains daily electricity consumption data for 955 households and additional information for a subsample of households, which includes meta data (such as a user-generated list of appliances and the number of occupants), high resolution data (0,5Hz) and results from non-intrusive appliance load monitoring (NILM), also known as algorithmic disaggregation. This paper shows descriptive data analysis with tables and graphs to provide the analyst with a first glance at the scope and depth of the content, as well as a codebook with variable definitions.
DOI: 10.6084/m9.figshare.15155658
title: Data for "Large dynamic scissoring mode displacements coupled to band gap opening in hybrid perovskites"

description: PDFs of the hybrid perovskites MAPbX3, used in the publication in the title.
DOI: 10.6084/m9.figshare.15155658.v1
title: Data for "Large dynamic scissoring mode displacements coupled to band gap opening in hybrid perovskites"

description: PDFs of the hybrid perovskites MAPbX3, used in the publication in the title.
DOI: 10.15161/oar.it/74021
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5281/zenodo.5195823
title: ESI Management of dark fermentation broth via biorefining and photo fermentation

description: Figure S1 FT-IR spectrums from optimization of the kind of HBD (a) Th; (b) M; (c) LC; (d) E used for effective removal of FF from the model fermentation broth and simultaneous formation of DES. Figure S2 FT-IR spectrums from optimization of the kind of HBD (a) Th; (b) M; (c) LC; (d) E used for effective removal of HMF from the model fermentation broth and simultaneous formation of DES. Figure S3 FT-IR spectrums from optimization of the kind of HBD (a) Th; (b) M; (c) LC; (d) E used for effective removal of Lev from the model fermentation broth and simultaneous formation of DES. Figure S4 FT-IR spectrums from optimization of the kind of HBD (a) Th; (b) M; (c) LC; (d) E used for effective removal of FA from the model fermentation broth and simultaneous formation of DES.
DOI: 10.5281/zenodo.5195822
title: ESI Management of dark fermentation broth via biorefining and photo fermentation

description: Figure S1 FT-IR spectrums from optimization of the kind of HBD (a) Th; (b) M; (c) LC; (d) E used for effective removal of FF from the model fermentation broth and simultaneous formation of DES. Figure S2 FT-IR spectrums from optimization of the kind of HBD (a) Th; (b) M; (c) LC; (d) E used for effective removal of HMF from the model fermentation broth and simultaneous formation of DES. Figure S3 FT-IR spectrums from optimization of the kind of HBD (a) Th; (b) M; (c) LC; (d) E used for effective removal of Lev from the model fermentation broth and simultaneous formation of DES. Figure S4 FT-IR spectrums from optimization of the kind of HBD (a) Th; (b) M; (c) LC; (d) E used for effective removal of FA from the model fermentation broth and simultaneous formation of DES.
DOI: 10.6084/m9.figshare.15152253.v1
title: Forest ditch GHG data

description: This is the data behind Peacock et al, 2021, Journal of Geophysical Research Biogeosciences, provisional title:<br>Emissions from forest drainage ditches should be included in anthropogenic greenhouse gas inventories <br>
DOI: 10.5258/soton/d1162
title: Data supporting the "Custer channel wing for Short Take-off and Landing of Unmanned Aircraft" article

description: Data supporting the article "Custer channel wing for Short Take-off and Landing of Unmanned Aircraft" article in the AIAA Journal of Aircraft
DOI: 10.5258/soton/wp00721
title: Ease of Social Distancing Index, mapped for urban areas in sub-Saharan Africa (single country update for Sierra Leone), version 1.1

description: Social distancing is a public health measure intended to reduce infectious disease transmission, by maintaining physical distance between individuals or households. In the context of the COVID-19 pandemic, populations in many countries around the world have been advised to maintain social distance (also referred to as physical distance), with distances of 6 feet or 2 metres commonly advised. Feasibility of social distancing is dependent on the availability of space and the number of people, which varies geographically. In locations where social distancing is difficult, a focus on alternative measures to reduce disease transmission may be needed.
DOI: 10.5281/zenodo.5193696
title: Data Set for 'Self-Supervised Machine Learning for Live Cell Imagery Segmentation'

description: <strong>Self-supervised machine learning code and data for segmenting live cell imagery (Matlab)</strong> <em>Running the Code</em> SSL_Demo_2.m : main program for self-supervised machine learning segmentation SSL_Declumping_2.m : main program for declumping application (applied to output of SSL_Demo_2.m) This Matlab code is designed to be used with time-resolved live cell microscopy images (tiffs) for the automated segmentation of cells from background. It is recommended you first run this code with its accompanying demo data (included in this package), keeping the current directory structure. Simply open SSL_Demo_2.m or SSL_Declumping_2.m in Matlab and hit Run. <em>Code Methodology</em> The principle of self-supervised machine learning is that you simply load your images and Run - no parameter tuning needed, no training imagery required. Run from start to finish, the SSL_Demo_2.m code uses consecutive pairs of images to generate training data of 'cells' and 'background' via dynamic feature vectors based on optical flow (unsupervised). These self-labeled pixels are then used to generate static feature vectors (entropy, gradient), which in turn are used to train a classifier model. The training data is updated every image in order to automatically adapt to temporal changes in cell morphologies or background illumination. The code was tested for high fidelity segmentation using five different modes of light microscopy: transmitted light, DIC, phase contrast, fluorescence and interference reflection microscopy. Six different cell lines were imaged to cover a range of morphologies and phenotypic dynamics using three cameras of differing resolutions. The associated manuscript for this work can be found here (although the latest version is under peer review as of this writing): https://www.biorxiv.org/content/10.1101/2021.01.07.425773v1 This code was tested on Matlab v2020a and v2021a using commercially available laptop computers running the Windows 10 operating system.
description: {"references": ["Robitaille, Michael et al; Robust Optical Flow Algorithm for General, Label-free Cell Segmentation; doi: https://doi.org/10.1101/2020.10.26.355958", "Robitaille, Michael, et al; A Self-Supervised Machine Learning Approach for Objective Live Cell Segmentation and Analysis; doi: https://doi.org/10.1101/2021.01.07.425773"]}
DOI: 10.5281/zenodo.5167317
title: Data Set for 'Self-Supervised Machine Learning for Live Cell Imagery Segmentation'

description: <strong>Self-supervised machine learning code and data for segmenting live cell imagery (Matlab)</strong> <em>Running the Code</em> SSL_Demo_2.m : main program for self-supervised machine learning segmentation SSL_Declumping_2.m : main program for declumping application (applied to output of SSL_Demo_2.m) This Matlab code is designed to be used with time-resolved live cell microscopy images (tiffs) for the automated segmentation of cells from background. It is recommended you first run this code with its accompanying demo data (included in this package), keeping the current directory structure. Simply open SSL_Demo_2.m or SSL_Declumping_2.m in Matlab and hit Run. <em>Code Methodology</em> The principle of self-supervised machine learning is that you simply load your images and Run - no parameter tuning needed, no training imagery required. Run from start to finish, the SSL_Demo_2.m code uses consecutive pairs of images to generate training data of 'cells' and 'background' via dynamic feature vectors based on optical flow (unsupervised). These self-labeled pixels are then used to generate static feature vectors (entropy, gradient), which in turn are used to train a classifier model. The training data is updated every image in order to automatically adapt to temporal changes in cell morphologies or background illumination. The code was tested for high fidelity segmentation using five different modes of light microscopy: transmitted light, DIC, phase contrast, fluorescence and interference reflection microscopy. Six different cell lines were imaged to cover a range of morphologies and phenotypic dynamics using three cameras of differing resolutions. The associated manuscript for this work can be found here (although the latest version is under peer review as of this writing): https://www.biorxiv.org/content/10.1101/2021.01.07.425773v1 This code was tested on Matlab v2020a and v2021a using commercially available laptop computers running the Windows 10 operating system.
description: {"references": ["Robitaille, Michael et al; Robust Optical Flow Algorithm for General, Label-free Cell Segmentation; doi: https://doi.org/10.1101/2020.10.26.355958", "Robitaille, Michael, et al; A Self-Supervised Machine Learning Approach for Objective Live Cell Segmentation and Analysis; doi: https://doi.org/10.1101/2021.01.07.425773"]}
DOI: 10.5281/zenodo.5195621
title: Coastal erosion and accretion areas along the Beaufort Sea and Laptev Sea Coasts based on Landsat 1999 - 2014

description: The dataset covers the Laptev Sea coast from 120 to 168 E and Alaska and Canadian Beaufort Sea Coast from 130 to 168 W. Probabilities of erosion and accretion (change of land to water and visa versa) have been derived from Landsat for the time period 1999–2014. A probability threshold of 50% was applied to separate erosion and accretion areas which are provided as polygons (shape files). Further information regarding the algorithm is available in Bartsch et al. (2020).
description: The authors acknowledge financial support by the HORIZON2020 (BG-2017-1) project Nunataryuk, ESA's DUE GlobPermafrost project (Contract Number 4000116196/15/INB) and ESA's CCI+ Permafrost (4000123681/18/I-NB) project.
description: {"references": ["Nitze, I., Grosse, G., Jones, B., Arp, C., Ulrich, M., Fedorov, A., et al. (2017). Landsat-based trend analysis of lake dynamics across northern permafrost regions. Remote Sens. 9:640. doi: 10.3390/rs9070640", "Nitze, I., Grosse, G., Jones, B. M., Romanovsky, V. E., and Boike, J. (2018). Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic. Nat. Commun. 9:5423. doi: 10.1038/s41467-018-07663-3", "Bartsch A, Ley S, Nitze I, Pointner G and Vieira G (2020) Feasibility Study for the Application of Synthetic Aperture Radar for Coastal Erosion Rate Quantification Across the Arctic. Front. Environ. Sci. 8:143. doi: 10.3389/fenvs.2020.00143"]}
DOI: 10.5281/zenodo.5195620
title: Coastal erosion and accretion areas along the Beaufort Sea and Laptev Sea Coasts based on Landsat 1999 - 2014

description: The dataset covers the Laptev Sea coast from 120 to 168 E and Alaska and Canadian Beaufort Sea Coast from 130 to 168 W. Probabilities of erosion and accretion (change of land to water and visa versa) have been derived from Landsat for the time period 1999–2014. A probability threshold of 50% was applied to separate erosion and accretion areas which are provided as polygons (shape files). Further information regarding the algorithm is available in Bartsch et al. (2020).
description: The authors acknowledge financial support by the HORIZON2020 (BG-2017-1) project Nunataryuk, ESA's DUE GlobPermafrost project (Contract Number 4000116196/15/INB) and ESA's CCI+ Permafrost (4000123681/18/I-NB) project.
description: {"references": ["Nitze, I., Grosse, G., Jones, B., Arp, C., Ulrich, M., Fedorov, A., et al. (2017). Landsat-based trend analysis of lake dynamics across northern permafrost regions. Remote Sens. 9:640. doi: 10.3390/rs9070640", "Nitze, I., Grosse, G., Jones, B. M., Romanovsky, V. E., and Boike, J. (2018). Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic. Nat. Commun. 9:5423. doi: 10.1038/s41467-018-07663-3", "Bartsch A, Ley S, Nitze I, Pointner G and Vieira G (2020) Feasibility Study for the Application of Synthetic Aperture Radar for Coastal Erosion Rate Quantification Across the Arctic. Front. Environ. Sci. 8:143. doi: 10.3389/fenvs.2020.00143"]}
DOI: 10.6084/m9.figshare.15164133
title: Spatio-Temporal-EEG-Analysis

description: Additonal files for training the models presented in https://github.com/VDelv/Spatio-Temporal-EEG-Analysis.<br>/!\ The signals are adapted version of two datasets. Thus it is necessary to approve the licences from https://figshare.com/articles/dataset/Multi-channel_EEG_recordings_during_a_sustained-attention_driving_task/6427334 and https://zenodo.org/record/4558990.
DOI: 10.5281/zenodo.5053123
title: Genetic variation influencing DNA methylation provides new insights into the molecular pathways regulating genomic function - Selected Supplementary Tables

description: <strong>Selected Supplementary Tables - ST5, 7, 8 and 9</strong> <strong>Supplementary Table 5. Cosmopolitan results. </strong>Cosmopolitan SNP-CpG associations identified through genome-wide association amongst Europeans and South Asians. <strong>Supplementary Table 7. Cross-tissue replication.</strong> Results for further testing of the 11,165,559 cosmopolitan SNP-CpG associations identified (by genome-wide association in blood), in 4 isolated white cell subsets (CD4+ lymphocytes, CD8+ lymphocytes, monocytes and neutrophils), in adipocytes isolated from subcutaneous adipose tissue or visceral adipose tissue, and in whole adipose tissue. <strong>Supplementary Table 8. Conditional analysis.</strong> Results of conditional analysis to identify SNPs independently associated with each of the ~360K CpG sites tested. <strong>Supplementary Table 9. Sentinel SNPs and CpGs.</strong> Results of R2 pruning and locus merging to identify discrete genetic and methylation loci that are associated, and their respective sentinel SNPs and sentinel CpG sites. Other files (e.g. annotation files and 'intermediate' processing files) referenced in our code are also provided.
DOI: 10.23729/9b15aadf-bfc3-4199-9fca-679dfbf88516
title: Data for "Water disinfection with bentonite-geopolymer composite foam containing silver nanoparticles"

description: This dataset contains data used in the article "Water disinfection with bentonite-geopolymer composite foam containing silver nanoparticles".
DOI: 10.5281/zenodo.5190125
title: RAW SEM images mosaics dataset for the HRDIC strain localization study in shot peened Ni superalloy

description: We used a FEI Magellan HR 400L FE-SEM with a theoretical resolution of ≤ 0.9 nm at &lt;1kV and ≤ 0.8 nm at ≥ 5kV to take backscattered electron images of the fine, homogeneous distributed gold speckle pattern obtained by remodelling of a thin gold layer previously deposited on the polished sample surface. The images were obtained at a working distance of 3.5 mm, 5 kV and 0.8 nA beam current. Mosaics of 30x15 images were used to cover 950x420 µm<sup>2</sup>. Each image contains 2048 x 1768 pixels and has a horizontal field of view of 43 µm. The images were overlapped by 20% to enable easy stitching prior to the digital image correlation. We obtained 7 mosaics, one before tensile testing and 6 after each deformation step. This set of images at different strain steps is coupled with the EBSD data set in https://doi.org/10.5281/zenodo.4730184, the HRDIC strain maps in http://doi.org/10.5281/zenodo.4728016 and data visualisation scripts in http://doi.org/10.5281/zenodo.4727939 0_def corresponds to the undeformed sample, while 1_def to 6_def were obtained after each deformation step.
DOI: 10.5281/zenodo.5190124
title: RAW SEM images mosaics dataset for the HRDIC strain localization study in shot peened Ni superalloy

description: We used a FEI Magellan HR 400L FE-SEM with a theoretical resolution of ≤ 0.9 nm at &lt;1kV and ≤ 0.8 nm at ≥ 5kV to take backscattered electron images of the fine, homogeneous distributed gold speckle pattern obtained by remodelling of a thin gold layer previously deposited on the polished sample surface. The images were obtained at a working distance of 3.5 mm, 5 kV and 0.8 nA beam current. Mosaics of 30x15 images were used to cover 950x420 µm<sup>2</sup>. Each image contains 2048 x 1768 pixels and has a horizontal field of view of 43 µm. The images were overlapped by 20% to enable easy stitching prior to the digital image correlation. We obtained 7 mosaics, one before tensile testing and 6 after each deformation step. This set of images at different strain steps is coupled with the EBSD data set in https://doi.org/10.5281/zenodo.4730184, the HRDIC strain maps in http://doi.org/10.5281/zenodo.4728016 and data visualisation scripts in http://doi.org/10.5281/zenodo.4727939 0_def corresponds to the undeformed sample, while 1_def to 6_def were obtained after each deformation step.