cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7860651
title: Inferring the Astrophysical Population of Gravitational Wave Sources in the Presence of Noise Transients Data Release

description: Data release for paper "Inferring the Astrophysical Population of Gravitational Wave Sources in the Presence of Noise Transients." To read in one of the result files and obtain the posterior samples, type <pre><code class="language-python">import bilby result = bilby.core.result.read_in_result(filename="contaminant_0_result.json") samples = result.posterior</code></pre>
DOI: 10.5517/ccdc.csd.cc2d7z6v
title: CCDC 2212197: Experimental Crystal Structure Determination

description: Related Article: Milan Maďar, Lucie Koláčná, Filip Koucký, Jana Havlíčková, Juraj Kuchár, Jan Kotek, Vojtěch Kubíček, Jiří Ludvík, Petr Hermann|2023|J.Organomet.Chem.|989|122641|doi:10.1016/j.jorganchem.2023.122641
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.cc2d7z7w
title: CCDC 2212198: Experimental Crystal Structure Determination

description: Related Article: Milan Maďar, Lucie Koláčná, Filip Koucký, Jana Havlíčková, Juraj Kuchár, Jan Kotek, Vojtěch Kubíček, Jiří Ludvík, Petr Hermann|2023|J.Organomet.Chem.|989|122641|doi:10.1016/j.jorganchem.2023.122641
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.cc2d7z8x
title: CCDC 2212199: Experimental Crystal Structure Determination

description: Related Article: Milan Maďar, Lucie Koláčná, Filip Koucký, Jana Havlíčková, Juraj Kuchár, Jan Kotek, Vojtěch Kubíček, Jiří Ludvík, Petr Hermann|2023|J.Organomet.Chem.|989|122641|doi:10.1016/j.jorganchem.2023.122641
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.cc2d7z9y
title: CCDC 2212200: Experimental Crystal Structure Determination

description: Related Article: Milan Maďar, Lucie Koláčná, Filip Koucký, Jana Havlíčková, Juraj Kuchár, Jan Kotek, Vojtěch Kubíček, Jiří Ludvík, Petr Hermann|2023|J.Organomet.Chem.|989|122641|doi:10.1016/j.jorganchem.2023.122641
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.cc2d7zbz
title: CCDC 2212201: Experimental Crystal Structure Determination

description: Related Article: Milan Maďar, Lucie Koláčná, Filip Koucký, Jana Havlíčková, Juraj Kuchár, Jan Kotek, Vojtěch Kubíček, Jiří Ludvík, Petr Hermann|2023|J.Organomet.Chem.|989|122641|doi:10.1016/j.jorganchem.2023.122641
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: 10.5281/zenodo.581302
title: Supplementary Information For A Study Of Dft+U In The Context Of Bifeo3

description: Jupyter notebooks containing much of the analysis featured in an upcoming publication. The .xml files contain all of the structure information and input parameters necessary to completely reproduce the work. Examples of how to work with the .xml files using pymatgen, in order to extract and analyse the relevant results are given.

The notebook is easiest viewed using the nbviewer here:

https://nbviewer.jupyter.org/github/jks-science/BFO_dftu_SI/blob/master/dft_u_BFO_SI.ipynb

If you have any queries, don't hesitate to get in touch.
DOI: 10.5281/zenodo.2772161
title: susancheng/15N-data: First release of 15N data for Cheng et al. submitted to Biogeosciences

description: This release contains the first version of the data in Tables S1 and S3 in the manuscript by Cheng et al. submitted to Biogeosciences, titled "Decadal fates and impacts of nitrogen additions on temperate forest carbon storage: A data-model comparison."
DOI: 10.5281/zenodo.592305
title: Aph_Corpus V.1.0-Alpha

description: No description provided.
DOI: 10.15785/sbgrid/1022
title: X-Ray Diffraction data from Flavin-dependent halogenase Thal: N-terminally His-tagged quintuple mutant (NHis-Thal-RebH5), source of 7AQU structure

description: native data
description: An XDS.INP file is given as template.
DOI: 10.5281/zenodo.7865349
title: IODP Expedition 354 Alkalinity and pH

description: Alkalinity was determined by Gran titration with an autotitrator (Metrohm 794 basic Titrino) using 0.1 M HCl at 20 degrees C. Report includes alkalinity, correction factor (if applicable), and pH.
DOI: 10.5281/zenodo.7865348
title: IODP Expedition 354 Alkalinity and pH

description: Alkalinity was determined by Gran titration with an autotitrator (Metrohm 794 basic Titrino) using 0.1 M HCl at 20 degrees C. Report includes alkalinity, correction factor (if applicable), and pH.
DOI: 10.34740/kaggle/dsv/5523260
title: #ChatGPT 1000 Daily 🐦 Tweets
DOI: 10.5281/zenodo.1287600
title: Fwilhelmi/Potential_Pitfalls_Mabs_Spatial_Reuse: Submission To Jnca

description: This repository contains the Code and the LaTeX files used for the journal article "Potential and Pitfalls of Multi-Armed Bandits for Decentralized Spatial Reuse in WLANs", which has been sent to "Journal of Network and Computer Applications".
description: The dataset has been updated to include the results provided to the reviewed version of the article.
DOI: 10.5281/zenodo.1345229
title: Yeast 1-hybrid screens for upstream regulators of A. thaliana AGO1, AGO7, and AGO10: raw data and R code

description: These files document a yeast 1-hybrid experiments and associated analyses described in a paper by Hoyer et al. (2019): https://doi.org/10.1002/pld3.102 This release corresponds to the fourth version of the Zenodo record. None of the code or data files changed from record 1472704 (the version linked in the paper); I simply added a link to the Plant Direct paper to the ReadMe file.
DOI: 10.5281/zenodo.7779765
title: Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts.

description: <em><strong>Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts.</strong></em> <em><strong>Version 3: Updated 2023-04-25</strong></em> These Residual-UNet model data are based on RGB (red, green, and blue) images of coasts and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7384242 Classes: {0=other, 1=water} <strong>File descriptions</strong> For each model, there are 5 files with the same root name: 1. <strong>'.json' </strong>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.<strong> '.h5'</strong> 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.<strong> '_modelcard.json'</strong> 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. <strong> '_model_history.npz'</strong> 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. <strong> '.png'</strong> 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> *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 ** Buscombe, D. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7384242
DOI: 10.5281/zenodo.7779766
title: Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts.

description: <em><strong>Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts.</strong></em> <em><strong>Version 2: Updated 2023-03-28</strong></em> These Residual-UNet model data are based on RGB (red, green, and blue) images of coasts and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7384242 Classes: {0=other, 1=water} <strong>File descriptions</strong> For each model, there are 5 files with the same root name: 1. <strong>'.json' </strong>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.<strong> '.h5'</strong> 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.<strong> '_modelcard.json'</strong> 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. <strong> '_model_history.npz'</strong> 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. <strong> '.png'</strong> 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> *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 ** Buscombe, D. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7384242
DOI: 10.5281/zenodo.7865364
title: Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts.

description: <em><strong>Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts.</strong></em> <em><strong>Version 3: Updated 2023-04-25</strong></em> These Residual-UNet model data are based on RGB (red, green, and blue) images of coasts and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7384242 Classes: {0=other, 1=water} <strong>File descriptions</strong> For each model, there are 5 files with the same root name: 1. <strong>'.json' </strong>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.<strong> '.h5'</strong> 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.<strong> '_modelcard.json'</strong> 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. <strong> '_model_history.npz'</strong> 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. <strong> '.png'</strong> 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> *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 ** Buscombe, D. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7384242
DOI: 10.15785/sbgrid/1023
title: X-Ray Diffraction data from Flavin-dependent halogenase Thal: N-terminally His-tagged quintuple mutant (NHis-Thal-RebH5), source of 7AQV structure

description: native data
description: An XDS.INP file is given as template.
DOI: 10.17909/kkvb-cj86
title: HZ 4 Flux Event
DOI: 10.25345/c5gh9bk63
title: MassIVE MSV000091795 - Bile acid modifications MS/MS spectral resource (all spectra from MassQL queries)
title: Bile acids are important signaling molecules with impact on host health and metabolism. However, the diversity of bile acids furnished by both the host and microbes is incompletely characterized. To address this knowledge gap, we created a reusable resource of candidate tandem mass spectrometry (MS/MS) spectra by filtering approximately 1.2 billion publicly available MS/MS spectra from 2,706 untargeted metabolomics projects for bile acid-specific MS/MS ion patterns. Provided here are two MGFs/mzMLs consisting of 594,431 MS/MS spectra directly obtained from the public data on GNPS/MassIVE using MassQL queries designed for non-, mono-, di-, tri-, tetra- and penta-hydroxylated bile acids and MS/MS spectra from synthetic standards of 38 amino acids and 28 polyamines conjugated to bile acids.
DOI: 10.34943/30bdd50e-4c48-4ffe-ac17-97ceeb1b8451
title: China Creek Conductivity Temperature Depth Deployed 2021-09-24

description: The AML CTD Metrec X 50154 was deployed on 2021-09-24 at China Creek. China Creek is located in the Alberni Inlet, which is a long, narrow inlet on Vancouver Island. This device is a Conductivity Temperature Depth. Conductivity Temperature Depth (CTD) is an instrument package that contains sensors for measuring the conductivity, temperature, and pressure of seawater. Salinity, sound velocity, depth and density are variables that can be derived from sensor measurements. CTDs can carry additional instruments and sensors such as oxygen sensors, turbidity sensors and fluorometers. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/a4ecc9e9-8289-4c17-b0fa-dc8e67fc8fb7
title: China Creek Conductivity Temperature Depth Deployed 2021-09-24

description: The AML CTD Metrec X 50154 was deployed on 2021-09-24 at China Creek. China Creek is located in the Alberni Inlet, which is a long, narrow inlet on Vancouver Island. This device is a Conductivity Temperature Depth. Conductivity Temperature Depth (CTD) is an instrument package that contains sensors for measuring the conductivity, temperature, and pressure of seawater. Salinity, sound velocity, depth and density are variables that can be derived from sensor measurements. CTDs can carry additional instruments and sensors such as oxygen sensors, turbidity sensors and fluorometers. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 2.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/58842c63-39dd-4bfc-a5d1-02e927e7bedb
title: China Creek Oxygen Sensor Deployed 2021-09-24

description: The Aanderaa Optode 4531A (S/N 171) was deployed on 2021-09-24 at China Creek. China Creek is located in the Alberni Inlet, which is a long, narrow inlet on Vancouver Island. This device is a Oxygen Sensor. Oxygen sensors measure dissolved oxygen concentration in seawater. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/be549b37-7c4e-49ef-9f51-a7079dc5fe9f
title: China Creek Oxygen Sensor Deployed 2021-09-24

description: The Aanderaa Optode 4531A (S/N 171) was deployed on 2021-09-24 at China Creek. China Creek is located in the Alberni Inlet, which is a long, narrow inlet on Vancouver Island. This device is a Oxygen Sensor. Oxygen sensors measure dissolved oxygen concentration in seawater. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 2.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/814f7ff3-1fcc-4fd3-8983-610202dd188e
title: China Creek Fluorometer Deployed 2021-09-24

description: The WET Labs ECO FLRT 5958 was deployed on 2021-09-24 at China Creek. China Creek is located in the Alberni Inlet, which is a long, narrow inlet on Vancouver Island. This device is a Fluorometer. Fluorometers measure the amount of stimulated electromagnetic radiation (fluorescence) produced by pulses of electromagnetic radiation emitted into the water column. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/cc064a64-be2f-40e0-8866-3d976a37b420
title: China Creek Fluorometer Deployed 2021-09-24

description: The WET Labs ECO FLRT 5958 was deployed on 2021-09-24 at China Creek. China Creek is located in the Alberni Inlet, which is a long, narrow inlet on Vancouver Island. This device is a Fluorometer. Fluorometers measure the amount of stimulated electromagnetic radiation (fluorescence) produced by pulses of electromagnetic radiation emitted into the water column. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 2.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.5281/zenodo.1036009
title: Khillion/Galaxyxml-Analysis: F1000 Submission

description: Several analysis on Galaxy XML or test on ToolDog.
DOI: 10.3886/e188721
title: Data and code for: Robots and Female Employment in German Manufacturing

description: We analyze the impact of robot adoption on female employment. Our analysis is based on novel micro data on robot use of German manufacturing establishments linked with social security records. An event-study analysis for robot adoption shows increased churning among female workers. Whereas hiring rises significantly at robot adoption, separations increase with a smaller magnitude one year later. Overall, employment effects are modestly positive and strongest for medium-qualified women. We find no adverse employment effects for female workers in any of our broad qualification groups. This is code accompanying the article.
DOI: 10.3886/e188721v1
title: Data and code for: Robots and Female Employment in German Manufacturing

description: We analyze the impact of robot adoption on female employment. Our analysis is based on novel micro data on robot use of German manufacturing establishments linked with social security records. An event-study analysis for robot adoption shows increased churning among female workers. Whereas hiring rises significantly at robot adoption, separations increase with a smaller magnitude one year later. Overall, employment effects are modestly positive and strongest for medium-qualified women. We find no adverse employment effects for female workers in any of our broad qualification groups. This is code accompanying the article.