cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5291/ill-data.4-06-18
title: Magnetic Dimensionality-Switching in Chains of High-Symmetry Lanthanide Single-Ion Magnets
DOI: 10.5291/ill-data.5-31-2940
title: Probing the ground state magnetic structures of LiNi(1-x)Fe(x)PO4.
DOI: 10.5291/ill-data.5-41-751
title: Single-crystal magnetic and Structural Analysis of anhydrous Ni-diiodide
DOI: 10.5291/ill-data.5-42-358
title: Criticality of the square lattice with 4-fold anisotropy in magnetic field
DOI: 10.5291/ill-data.5-51-571
title: The low-field susceptibility of the superconducting state of Sr2RuO4
DOI: 10.5291/ill-data.5-54-273
title: Polarised neutron diffraction study of Azurite
DOI: 10.5291/ill-data.5-54-252
title: Magnetic structure of a spin tube
DOI: 10.5291/ill-data.7-01-525
title: A New Low-Energy Excitation in Charge Density Wave Ordered YBa2Cu3O6.5
DOI: 10.5291/ill-data.7-02-159
title: Soft Modes and Phonon Anharmonicity in Thermoelectric GeTe
DOI: 10.5291/ill-data.dir-262
title: Impact of protons on particle-vibration couplings around 48Ca: the case of 51TI
DOI: 10.5291/ill-data.dir-183
title: THE NATURE OF THE SUPERCONDUCTING STATE IN SR2RUO4
DOI: 10.5291/ill-data.dir-236
title: Temperature-induced evolution of topological magnons in CrBr3
DOI: 10.5291/ill-data.dir-250
title: Field dependent crystal field excitations in ErI3
DOI: 10.5291/ill-data.dir-104
title: First Inelastic Neutron Scattering Study of a Spin-Incoherent Luttinger Liquid
DOI: 10.5291/ill-data.dir-113
title: Magnetic Ordering of the binary tri-halides of Vanadium, VX3 (X=Cl, Br)
DOI: 10.5291/ill-data.dir-115
title: Bose-Einstein condensation of multi-magnons into a multipolar quantum phase
DOI: 10.5291/ill-data.test-3199
title: Low-energy excitation in charge density wave YBa2Cu3O6.5
DOI: 10.5291/ill-data.test-2328
title: Crystal field excitations in Nd2Hf2O7
DOI: 10.5291/ill-data.uga-73
title: Partial saturation in granular media
DOI: 10.5281/zenodo.8026340
title: Enabling Roll-up and Drill-down Operations in News Exploration

description: This dataset contains 200k news articles. News entities are linked to DBPedia via named entity linking.
DOI: 10.5281/zenodo.8026339
title: Enabling Roll-up and Drill-down Operations in News Exploration

description: This dataset contains 200k news articles. News entities are linked to DBPedia via named entity linking.
DOI: 10.15468/qe2rms
title: Checklist of Flora Records at Asukese Forest Reserve in Ghana

description: The study areas for the checklist flora data collection were from Asukese Forest Reserve in the Ahafo Region of Ghana. Ecologically, the region falls within the moist semi deciduous forests zone of Ghana, which are characterized by high diversity of pioneer tree species. The information taken from this forest reserve was baseline biological and carbon survey data report.
DOI: 10.34740/kaggle/dsv/5906478
title: Elon Musk Tweets
DOI: 10.6084/m9.figshare.23500638
title: Supplementary Material for: Cerebral sinus vein thrombosis following sneezing - Case Report

description: Cerebral sinus vein thrombosis (CVT) is a relatively rare neurovascular entity, usually associated with acquired or genetic hypercoagulable states, and in many cases it remains idiopathic. Trauma is also associated with CVT among patients with major head or neck trauma, including penetrating injuries. However, CVT associated with acceleration trauma has only been described in few cases so far. We present an unusual case of a 19-year-old woman with no past medical history, admitted with an extensive CVT following sneezing. A thorough investigation did not reveal any other potential etiology or risk factor other than estrogen-containing oral contraceptives. The patient was treated with anti-coagulation and improved clinically with complete recanalization on follow-up imaging. This case suggests acceleration trauma may be a potential factor of risk for CVT.
DOI: 10.6084/m9.figshare.23500638.v1
title: Supplementary Material for: Cerebral sinus vein thrombosis following sneezing - Case Report

description: Cerebral sinus vein thrombosis (CVT) is a relatively rare neurovascular entity, usually associated with acquired or genetic hypercoagulable states, and in many cases it remains idiopathic. Trauma is also associated with CVT among patients with major head or neck trauma, including penetrating injuries. However, CVT associated with acceleration trauma has only been described in few cases so far. We present an unusual case of a 19-year-old woman with no past medical history, admitted with an extensive CVT following sneezing. A thorough investigation did not reveal any other potential etiology or risk factor other than estrogen-containing oral contraceptives. The patient was treated with anti-coagulation and improved clinically with complete recanalization on follow-up imaging. This case suggests acceleration trauma may be a potential factor of risk for CVT.
DOI: 10.25919/3g3t-p068
title: CVB: A Video Dataset of Cattle Visual Behaviors

description: Existing image/video datasets for cattle behavior recognition are mostly small, lack well-defined labels, or are collected in unrealistic controlled environments. This limits the utility of machine learning (ML) models learned from them. Therefore, we introduce a new dataset, called Cattle Visual Behaviors (CVB), that consists of 502 video clips, each fifteen seconds long, captured in natural lighting conditions, and annotated with eleven visually perceptible behaviors of grazing cattle. By creating and sharing CVB, our aim is to develop improved models capable of recognizing all important behaviors accurately and to assist other researchers and practitioners in developing and evaluating new ML models for cattle behavior classification using video data. The dataset is presented in the form of following three sub-directories. 1. raw_frames: contains 450 frames in each sub folder, representing 15 sec video, taking at a frames rate of 30 FPS, 2. annotations: contains the json files corresponding to the raw_frames folder. We have one json file for one video, containing the bounding box annotations for each cattle and their associated behaviors, and 3. CVB_in_AVA_format: contains the CVB data in the standard AVA dataset format which we have used to apply SlowFast model.
DOI: 10.6084/m9.figshare.19337105.v2
title: Biotic and abiotic predictors of potential N2O emissions from denitrification in Irish grasslands soils: a national-scale field studyItem

description: Data will be made available from July 2022.
DOI: 10.5281/zenodo.7305477
title: Dataset for "Open access books through open data sources: Assessing prevalence, providers, and preservation"

description: This dataset contains the raw collected data reported on in the manuscript titled "Open access books through open data sources: Assessing prevalence, providers, and preservation" which is available here: https://doi.org/10.5281/zenodo.7305490 One file contains the results of the digital object identifier queries, and the other data on which publication records were found to be included in which of the studied bibliometric data sources, and preservation services. The author is grateful to Alicia Wise and Ronald Snijder for assisting in the identification of available datasets and valuable feedback throughout the study. This research was commissioned by CLOCKSS, DOAB, and OAPEN.
DOI: 10.25934/pr00009005
title: Data challenge: respiratory tract infections; is it true resistance or does penicillin work 

description: Respiratory tract infections are one of the highest drivers of antibiotic use in the current healthcare system. These are common infections among children and adults. They do present as mild and severe infections; clinicians have a predisposition of prescribing antibiotics for these while the leading cause of these RTIs is viral. The symptoms usually are the same for bacterial and viral RTIs but there is limited diagnostic capability to distinguish the two; full hemogram is a possible laboratory test but there is reluctance to go through the whole process. Thus AMR results from increased consumption of antimicrobial drugs, improper prescribing of antimicrobial therapy due to their wide therapeutic index and initial prescription of a broad-spectrum drug that is unnecessary. These infections are present in outpatient and inpatient settings thus all problems associated with antibiotic prescriptions become escalated in the inpatient set ups since even viral RTIs in wards will be treated by the “big antibiotics” propagating the AMR epidemic. This goes on until there is no response to treatment with antibiotics. Then culture and sensitivity tests are ordered to show there was no bacterial pathogen. The fevers persist and bigger antibiotics are ordered, there is prolonged hospital stay and eventually patient gets better. The questions would be then; was there a bacterial cause initially? Was it there and was it treated or healing was natural? For those treated with several antibiotics was it true resistance or secondary infection?
This study aims to improve stewardship and patient health outcomes by looking at factors that determine resistance and develop a model, using regression techniques, that would predict future resistance patterns and moderating these factors to counter AMR. Use of high doses of some antibiotics is recommended for managing RTIs, would this be a solution for all the antibiotics used for RTI management?
DOI: 10.5281/zenodo.8026389
title: Reflection Of Video Assistant Referee Technology on Professional Football Discipline Board Decisions

description: The aim of this study is to understand how Video Assistant Referee (VAR) technology is reflected in the decisions of Professional Football Disciplinary Board (PFDK) and to examine whether this technology creates any change. In accordance with this purpose, comparison of disciplinary violations detected in Super League competitions in general and direct send-off decisions to PFDK decisions was carried out within the scope of 2018-2019 and 2019-2020 seasons, the first two seasons where VAR technology was used in Super League, and 2016-2017 and 2017-2018 seasons, the last two seasons where VAR technology was not used. PFDK decisions for four seasons between 2016-2020 were collected by document analysis.
DOI: 10.5281/zenodo.8026388
title: Reflection Of Video Assistant Referee Technology on Professional Football Discipline Board Decisions

description: The aim of this study is to understand how Video Assistant Referee (VAR) technology is reflected in the decisions of Professional Football Disciplinary Board (PFDK) and to examine whether this technology creates any change. In accordance with this purpose, comparison of disciplinary violations detected in Super League competitions in general and direct send-off decisions to PFDK decisions was carried out within the scope of 2018-2019 and 2019-2020 seasons, the first two seasons where VAR technology was used in Super League, and 2016-2017 and 2017-2018 seasons, the last two seasons where VAR technology was not used. PFDK decisions for four seasons between 2016-2020 were collected by document analysis.
DOI: 10.6084/m9.figshare.14444612
title: Moghaddaszadeh et al_Biomedical Materials

description: Moghadaszadeh et al_JBMR, Dataset
DOI: 10.6084/m9.figshare.14444612.v1
title: Moghaddaszadeh et al_Biomedical Materials

description: Moghadaszadeh et al_JBMR, Dataset
DOI: 10.5281/zenodo.7751674
title: Data for: A Modular Double Electrode Flow Cell with Exchangeable Generator and Detector Electrodes

description: Raw data and processed data shown in figures of the publication titled: "A Modular Double Electrode Flow Cell with Exchangeable Generator and Detector Electrodes" DOI: 10.1002/celc.202300126 by Frederik J. Stender<sup>[a]</sup>, Keisuke Obata<sup>[b]</sup>, Max Baumung<sup>[a,c]</sup>, Fatwa F. Abdi<sup>[b]</sup>, Marcel Risch<sup>[a,c]</sup> [a] Frederik Johannes Stender, Max Baumung, Dr. Marcel Risch<br> Institut für Material Physik<br> Georg-August-Universität Göttingen<br> Friedrich-Hund-Platz 1, 37085 Göttingen<br> E-mail: mrisch@material.physik.uni-goettingen.de [b] Dr. Keisuke Obata, Dr. Fatwa Firdaus Abdi<br> Institut für Solare Brennstoffe<br> Helmholtz-Zentrum Berlin für Materialien und Energie GmbH<br> Hahn-Meitner-Platz 1, 14109 Berlin [c] Dr. Marcel Risch<br> Nachwuchsgruppe Gestaltung des Sauerstoffentwicklungsmechanismus<br> Helmholtz-Zentrum Berlin für Materialien und Energie GmbH<br> Hahn-Meitner-Platz 1, 14109 Berlin<br> E-mail: marcel.risch@helmholtz-berlin.de
DOI: 10.5281/zenodo.7751673
title: Data for: A Modular Double Electrode Flow Cell with Exchangeable Generator and Detector Electrodes

description: Raw data and processed data shown in figures of the publication titled: "A Modular Double Electrode Flow Cell with Exchangeable Generator and Detector Electrodes" DOI: 10.1002/celc.202300126 by Frederik J. Stender<sup>[a]</sup>, Keisuke Obata<sup>[b]</sup>, Max Baumung<sup>[a,c]</sup>, Fatwa F. Abdi<sup>[b]</sup>, Marcel Risch<sup>[a,c]</sup> [a] Frederik Johannes Stender, Max Baumung, Dr. Marcel Risch<br> Institut für Material Physik<br> Georg-August-Universität Göttingen<br> Friedrich-Hund-Platz 1, 37085 Göttingen<br> E-mail: mrisch@material.physik.uni-goettingen.de [b] Dr. Keisuke Obata, Dr. Fatwa Firdaus Abdi<br> Institut für Solare Brennstoffe<br> Helmholtz-Zentrum Berlin für Materialien und Energie GmbH<br> Hahn-Meitner-Platz 1, 14109 Berlin [c] Dr. Marcel Risch<br> Nachwuchsgruppe Gestaltung des Sauerstoffentwicklungsmechanismus<br> Helmholtz-Zentrum Berlin für Materialien und Energie GmbH<br> Hahn-Meitner-Platz 1, 14109 Berlin<br> E-mail: marcel.risch@helmholtz-berlin.de
DOI: 10.5281/zenodo.8026422
title: Models and data for "Guaranteed safe controller synthesis for switched systems using analytical solutions"

description: Models and data for the paper "Guaranteed safe controller synthesis for switched systems using analytical solutions" presented at the 7th IEEE Conference on Control Technology and Applications, Barbados, August 16-18, 2023.
description: This work is partly supported by the Villum Synergy project CLAIRE and the Villum Investigator project 4SOS.
DOI: 10.5281/zenodo.8026421
title: Models and data for "Guaranteed safe controller synthesis for switched systems using analytical solutions"

description: Models and data for the paper "Guaranteed safe controller synthesis for switched systems using analytical solutions" presented at the 7th IEEE Conference on Control Technology and Applications, Barbados, August 16-18, 2023.
description: This work is partly supported by the Villum Synergy project CLAIRE and the Villum Investigator project 4SOS.
DOI: 10.6084/m9.figshare.23500695
title: Centrifuge test results (shallow foundation test)

description: Centrifuge test results in KAIST
DOI: 10.5281/zenodo.6526783
title: Public Dataset for "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter"

description: Dataset for the "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter" paper. The full text of the paper can be found here. The folder "Tweet_IDs" contains the complete list of the 152,514,929 tweet IDs (together with their timestamps) which we used for the analysis in the study: "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter" by Nikos Salamanos, Michael J. Jensen, Costas Iordanou and Michael Sirivianos We have split the tweets into separate .zip files based on the date listed in their timestamps. The crawling took place from September 21 to November 7, 2016 (47 days; we did not collect data on 02/10/2016). Each "tweet_day_X.zip" file contains the file "tweet_day_X.csv", where X in [1,2,...,47]. For instance, the file "tweets_day_1.zip" contains the tweets of the 1st day: 09/21/2016. Please cite the paper in any published work that uses any of these resources. @misc{nikos_salamanos_2021_4699959,<br> author = {Nikos Salamanos and<br> Michael J. Jensen and<br> Costas Iordanou and<br> Michael Sirivianos},<br> title = {{Did State-sponsored Trolls Shape the 2016 US <br> Presidential Election Discourse? Quantifying<br> Influence on Twitter}},<br> month = apr,<br> year = 2021,<br> publisher = {Zenodo},<br> version = 3,<br> doi = {10.5281/zenodo.4699959},<br> url = {https://doi.org/10.5281/zenodo.4699959}<br> }
DOI: 10.5281/zenodo.6526782
title: Public Dataset for "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter"

description: Dataset for the "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter" paper. The full text of the paper can be found here. The folder "Tweet_IDs" contains the complete list of the 152,514,929 tweet IDs (together with their timestamps) which we used for the analysis in the study: "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter" by Nikos Salamanos, Michael J. Jensen, Costas Iordanou and Michael Sirivianos We have split the tweets into separate .zip files based on the date listed in their timestamps. The crawling took place from September 21 to November 7, 2016 (47 days; we did not collect data on 02/10/2016). Each "tweet_day_X.zip" file contains the file "tweet_day_X.csv", where X in [1,2,...,47]. For instance, the file "tweets_day_1.zip" contains the tweets of the 1st day: 09/21/2016. Please cite the paper in any published work that uses any of these resources. @misc{nikos_salamanos_2021_4699959,<br> author = {Nikos Salamanos and<br> Michael J. Jensen and<br> Costas Iordanou and<br> Michael Sirivianos},<br> title = {{Did State-sponsored Trolls Shape the 2016 US <br> Presidential Election Discourse? Quantifying<br> Influence on Twitter}},<br> month = apr,<br> year = 2021,<br> publisher = {Zenodo},<br> version = 3,<br> doi = {10.5281/zenodo.4699959},<br> url = {https://doi.org/10.5281/zenodo.4699959}<br> }
DOI: 10.14473/csda/tllryk/1entec
title: PR04_V2211.pdf

description: :unav
DOI: 10.14473/csda/tllryk/iybw8l
title: PR03_V2211.pdf

description: :unav
DOI: 10.14473/csda/tllryk/ghadf8
title: PR05_V2211.pdf

description: :unav
DOI: 10.14473/csda/tllryk/liv6sg
title: PR02_V2211.pdf

description: :unav
DOI: 10.14473/csda/tllryk/udj12n
title: PR06_V2211.pdf

description: :unav
DOI: 10.14473/csda/tllryk/9xdhvk
title: TI_V2211.pdf

description: :unav
DOI: 10.14473/csda/tllryk/8pv4je
title: D_V2211.pdf

description: :unav
DOI: 10.14473/csda/tllryk/7hwvgk
title: PR01_V2211.pdf

description: :unav
DOI: 10.14473/csda/tllryk/wygodc
title: OM_V2211.pdf

description: :unav
DOI: 10.14473/csda/tllryk/7tcvbz
title: NS_2211_DataFile-FINAL_CVVM.tab

description: :unav
DOI: 10.5281/zenodo.7956360
title: Breast MRI molecular cancer subtype

description: This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns the classification of breast cancer molecular subtypes on dynamic contrast-enhanced magnetic resonance imaging (MRI) and was derived from Duke Hospital. The data set is a subset of the data originally introduced and described by Saha et al. (2018), with no additional images or patient information. Data was restructured in compliance with the AUC23 challenge format. The dataset is a single-institutional, retrospective collection of 737 biopsy-confirmed patients from 1 January 2000 to 23 March 2014 with invasive breast cancer and available pre-operative MRI at Duke Hospital. Images are 3D tensors: 0: 3D T1-subtraction dynamic contrast-enhanced MRI Classification labels: 0: Luminal A, estrogen-receptor (ER) and/or progesterone-receptor (PR) positive<strong>,</strong> human epidermal growth factor receptor 2 (HER2) negative 1: Luminal B, ER and/or PR negative, HER2 positive 2: HER2, ER and PR negative, HER2 positive 3: Triple negative, ER, PR, and HER2 negative Folder structure: imagesTr (root folder with all patients and studies)<br> ├── Breast_MRI_0001_0000.mha (3D T1-subtraction MRI imaging for study 0001)<br> ├── Breast_MRI_0003_0000.mha (3D T1-subtraction MRI imaging for study 0003)<br> ├── ... Please cite the following article if you are using the Duke-Breast-Cancer-MRI Dataset: <pre><code>A. Saha, M. R. Harowicz, L. J. Grimm, C. E. Kim, S. V. Ghate, R. Walsh, M. A. Mazurowski, "A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features". Br J Cancer. 2018 Aug;119(4):508-516. doi: 10.1038/s41416-018-0185-8. Epub 2018 Jul 23. PMID: 30033447; PMCID: PMC6134102. </code></pre>
description: {"references": ["Saha A, Harowicz MR, Grimm LJ, Kim CE, Ghate SV, Walsh R, Mazurowski MA. A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features. Br J Cancer. 2018 Aug;119(4):508-516. doi: 10.1038/s41416-018-0185-8. Epub 2018 Jul 23. PMID: 30033447; PMCID: PMC6134102."]}
DOI: 10.5281/zenodo.7956359
title: Breast MRI molecular cancer subtype

description: This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns the classification of breast cancer molecular subtypes on dynamic contrast-enhanced magnetic resonance imaging (MRI) and was derived from Duke Hospital. The data set is a subset of the data originally introduced and described by Saha et al. (2018), with no additional images or patient information. Data was restructured in compliance with the AUC23 challenge format. The dataset is a single-institutional, retrospective collection of 737 biopsy-confirmed patients from 1 January 2000 to 23 March 2014 with invasive breast cancer and available pre-operative MRI at Duke Hospital. Images are 3D tensors: 0: 3D T1-subtraction dynamic contrast-enhanced MRI Classification labels: 0: Luminal A, estrogen-receptor (ER) and/or progesterone-receptor (PR) positive<strong>,</strong> human epidermal growth factor receptor 2 (HER2) negative 1: Luminal B, ER and/or PR negative, HER2 positive 2: HER2, ER and PR negative, HER2 positive 3: Triple negative, ER, PR, and HER2 negative Folder structure: imagesTr (root folder with all patients and studies)<br> ├── Breast_MRI_0001_0000.mha (3D T1-subtraction MRI imaging for study 0001)<br> ├── Breast_MRI_0003_0000.mha (3D T1-subtraction MRI imaging for study 0003)<br> ├── ... Please cite the following article if you are using the Duke-Breast-Cancer-MRI Dataset: <pre><code>A. Saha, M. R. Harowicz, L. J. Grimm, C. E. Kim, S. V. Ghate, R. Walsh, M. A. Mazurowski, "A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features". Br J Cancer. 2018 Aug;119(4):508-516. doi: 10.1038/s41416-018-0185-8. Epub 2018 Jul 23. PMID: 30033447; PMCID: PMC6134102. </code></pre>
description: {"references": ["Saha A, Harowicz MR, Grimm LJ, Kim CE, Ghate SV, Walsh R, Mazurowski MA. A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features. Br J Cancer. 2018 Aug;119(4):508-516. doi: 10.1038/s41416-018-0185-8. Epub 2018 Jul 23. PMID: 30033447; PMCID: PMC6134102."]}