cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3866245
title: Trust and Acceptance of Lung Cancer Screening Strategies Questionnaire and Data

description: Lung cancer is one of the deadliest cancers today. Despite the widespread knowledge that early detection is crucial for cancer treatment, the majority of patients are diagnosed in an advanced stage with a significantly shorter 5-year survival rate. Lung cancer screening allows for early detection of cancer before symptoms arise. Currently, there are plans to introduce a lung cancer screening program in the Netherlands. The goal of this study is to gain knowledge about the effect of an individual's degree of trust on the acceptance level for different Lung Cancer Screening Strategies among people living in the Netherlands. This knowledge helps healthcare providers and professionals as well as the developers of screening software. Also, it supports the development of the best possible patient experience in the future. This survey consists of 30<strong> </strong>questions and should not take longer than 10 minutes to complete.
DOI: 10.5519/qd.xjdx4du4
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.5sp3mvt2
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2 records)
DOI: 10.5281/zenodo.3866267
title: Best practice templates for tephra collection, analysis, and correlation

description: Tephra is a unique volcanic product that has played an unparalleled role in the understanding of past eruptions, long-term behavior of volcanoes, effects of volcanism on climate and the environment. It provides spatially widespread, extremely high-resolution geologic time markers<strong>.</strong> We present comprehensive tephra domain-specific improvements in the data gathering framework, consisting of standardized field data collection, laboratory analysis, and correlation recommendations developed as templates. This new approach will facilitate better tephra documentation and parametrization, increased and improved communication, and more effective data sharing among diverse and to some degree disconnected scholarly communities. We hope that this will result in a synergism that benefits all researchers who work with tephra. This new standardized framework is critical to unlocking future advances that are emerging in tephrochronology, to more completely characterize eruptions and tephra transport, and to exploring the nature of potentially global-scale time-stratigraphic markers. For paleoclimatology, archeology, societal volcanic risk assessment, and volcanogenic environmental impact research, the framework will provide tools for consistent evaluation of tephra deposits on a global scale and across scientific disciplines.
DOI: 10.21227/hkgh-9y21
title: Survey on Reproducibility and Replicability in Remote Sensing

description: Modern science is build on systematic experimentation and observation. The reproducibility and replicability of the experiments and observations are central to science. However, reproducibility and replicability are not always guaranteed, sometimes referred to as 'crisis of reproducibility'. To analyze the extent of the crisis, we conducted a survey on the state of reproducibility in remote sensing. This survey was conducted as an online survey. The answers of the respondents are saved in this dataset in full-text CSV format.
DOI: 10.21227/ke46-8y92
title: Survey on Reproducibility and Replicability in Remote Sensing

description: Modern science is build on systematic experimentation and observation. The reproducibility and replicability of the experiments and observations are central to science. However, reproducibility and replicability are not always guaranteed, sometimes referred to as 'crisis of reproducibility'. To analyze the extent of the crisis, we conducted a survey on the state of reproducibility in remote sensing. This survey was conducted as an online survey. The answers of the respondents are saved in this dataset in full-text CSV format.
DOI: 10.5281/zenodo.3866340
title: Horizontal Scaling Media Transfer Performance Experiment Data

description: This dataset provides performance data rate measurements a 3GPP-compliant service-based architecture platform that demonstrates the concept of cloud-native service orchestration and routing for a media vertical sector application. Cloud-native service orchestration and routing is a complete end-to-end approach that enables virtualisation and management of multiple layers in the OSI model, which provides considerable flexibility and control to achieve delivery of QoS to users in the face of varying demand, at reasonable cost.
DOI: 10.5281/zenodo.3866341
title: Horizontal Scaling Media Transfer Performance Experiment Data

description: This dataset provides performance data rate measurements a 3GPP-compliant service-based architecture platform that demonstrates the concept of cloud-native service orchestration and routing for a media vertical sector application. Cloud-native service orchestration and routing is a complete end-to-end approach that enables virtualisation and management of multiple layers in the OSI model, which provides considerable flexibility and control to achieve delivery of QoS to users in the face of varying demand, at reasonable cost.
DOI: 10.17632/c2hpfxnw3j.1
title: Data for: The impact of MC-ICP-MS plasma conditions on the accuracy and precision of stable isotope measurements evaluated for barium isotopes

description: We systematically investigate the effect of matrix on the accuracy and precision of Ba isotope measurements under different but quantified plasma conditions. A NIST SRM 3104a Ba standard solution was doped with various amounts of major matrix elements (Ca and Sr) and analysed under six different plasma conditions. The results provide an improved understanding of the matrix-induced instrumental mass fractionation and define a matrix tolerance plasma state for Ba isotope analysis. Although these experiments were performed and calibrated for Ba isotopes, they are considered to be applicable for a wide variety of isotope measurements routinely performed on MC-ICP-MS.
DOI: 10.21227/acp2-f918
title: Aerial Imagery of PV Plants for boundary detection

description: Extracting the boundaries of Photovoltaic (PV) plants is essential in the process of aerial inspection and autonomous monitoring by aerial robots. This method provides a clear delineation of the utility-scale PV plants’ boundaries for PV developers, Operation and Maintenance (O&M) service providers for use in aerial photogrammetry, flight mapping, and path planning during the autonomous monitoring of PV plants.Preparing the data is the primary step for training a Fully Convolutional Network (FCN). Accordingly, the “Amir” dataset is developed for use in the autonomous monitoring of large-scale PV plants. This dataset includes a set of aerial imagery collected from different large-scale PV plants located in twelve countries from six continents. In the Amir dataset, all necessary data has been provided to train an accurate FCN model for boundary detection, including aerial imagery of PV plants and their masks. Amir covers many PV plants throughout the world. In addition, the aerial images are fully annotated to specify the boundaries precisely. Amir consists of 3580 aerial images from mainly large-scale PV plants. The Amir dataset has been developed for a research project entitled " smart and autonomous monitoring system for large-scale PV Plants''. The detail information about this dataset and its applications can be found in the paper entitled ''Automatic Boundary Extraction of Large-Scale Photovoltaic Plants using a Fully Convolutional Network on Aerial Imagery".
DOI: 10.21227/vvpv-q394
title: Aerial Imagery of PV Plants for boundary detection

description: Extracting the boundaries of Photovoltaic (PV) plants is essential in the process of aerial inspection and autonomous monitoring by aerial robots. This method provides a clear delineation of the utility-scale PV plants’ boundaries for PV developers, Operation and Maintenance (O&M) service providers for use in aerial photogrammetry, flight mapping, and path planning during the autonomous monitoring of PV plants.Preparing the data is the primary step for training a Fully Convolutional Network (FCN). Accordingly, the “Amir” dataset is developed for use in the autonomous monitoring of large-scale PV plants. This dataset includes a set of aerial imagery collected from different large-scale PV plants located in twelve countries from six continents. In the Amir dataset, all necessary data has been provided to train an accurate FCN model for boundary detection, including aerial imagery of PV plants and their masks. Amir covers many PV plants throughout the world. In addition, the aerial images are fully annotated to specify the boundaries precisely. Amir consists of 3580 aerial images from mainly large-scale PV plants. The Amir dataset has been developed for a research project entitled " smart and autonomous monitoring system for large-scale PV Plants''. The detail information about this dataset and its applications can be found in the paper entitled ''Automatic Boundary Extraction of Large-Scale Photovoltaic Plants using a Fully Convolutional Network on Aerial Imagery".
DOI: 10.6084/m9.figshare.12399008
title: Additional file 1 of QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping

description: Additional file 1. QAFFP construction. Statistics (ligand count and quality criteria $$q^{2}$$q2 and $$R_{0}^{'2}$$R0′2) of all 1360 trained models. Each model is given by the target ChEMBL ID and by an activity type (Ki, Kd, IC50 or EC50). 440 models used to construct the QAFFP fingerprint highlighted in green. Ligand data of 440 models (given as ligand ChEMBL ID, SMILES, activity type and activity value).
DOI: 10.6084/m9.figshare.12399008.v1
title: Additional file 1 of QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping

description: Additional file 1. QAFFP construction. Statistics (ligand count and quality criteria $$q^{2}$$q2 and $$R_{0}^{'2}$$R0′2) of all 1360 trained models. Each model is given by the target ChEMBL ID and by an activity type (Ki, Kd, IC50 or EC50). 440 models used to construct the QAFFP fingerprint highlighted in green. Ligand data of 440 models (given as ligand ChEMBL ID, SMILES, activity type and activity value).
DOI: 10.6084/m9.figshare.12399014
title: Additional file 2 of QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping

description: Additional file 2. Similarity searching task. The list of HET and HOM data sets including the number of actives and inactives.
DOI: 10.6084/m9.figshare.12399014.v1
title: Additional file 2 of QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping

description: Additional file 2. Similarity searching task. The list of HET and HOM data sets including the number of actives and inactives.
DOI: 10.21227/vsk2-dw53
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about "corona", "covid-19", "coronavirus" or the variants of "sars-cov-2". Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.Note: I started sharing the IDs of the tweets that contained the exact location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs (above 120+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-april29.* will contain tweet ID and sentiment data of the tweets that were tweeted between April 28, 2020, and April 29, 2020.Why are only tweet IDs being shared? It is Twitter's content redistribution policy that restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers to always pull fresh data using their API. Why? Here's my opinion. Maybe, some user might want to delete a particular tweet after a couple of minute(s)/hour(s)/day(s), and if the same tweet has already been pulled and is shared on a public domain, then it might make the user/community vulnerable to many inferences coming out of the shared data.
DOI: 10.21227/r83n-kw91
title: EnergyNow | IoT aplicada ao fomento da Indústria 4.0

description: A Indústria enfrenta desafios graves e fracassa sem competitividade. Atacando esta problemática, conferiu-se o oferecimento de maior eficiência a processos industriais para promover a produtividade, elevar a qualidade e impulsionar mudanças. A solução desenvolvida incluiu dispositivos com sensores não invasivos, simples de instalar, que contabilizam os itens sendo transportados em linhas de produção. De qualquer lugar e em tempo real, a plataforma conectada permite acesso a informações claras e confiáveis sobre a produtividade, mostram indicadores e métricas em gráficos com design intuitivo que aceleram a tomada de decisão. O profissional certo recebe alertas para resolver problemas específicos e algoritmos avançados apontam quando máquinas ou equipamentos possam interferir na produtividade. Acompanhando as tendências de digitalização de processos na manufatura avançada e da Indústria 4.0, o impacto compreende um mercado-alvo inicial de mais de seis mil fábricas; na região nordeste, o mercado possível tem mais de 75.000 fábricas; e o mercado disponível no Brasil soma mais de 600.000 fábricas. De fato, atendendo a um segmento que movimenta 907 bilhões de dólares no mundo. Com um modelo de acesso flexível, nas implantações, permite que seja possível incorporar evoluções contínuas dos serviços, com a adesão incluindo instalação dos dispositivos e treinamento especializado. A equipe dedicada tem ampla competência em uma variedade de metodologias de desenvolvimento de tecnologia, oportunamente, dispondo de engenheiros eletricistas e de produção, gerentes administrativos e de TI, arquitetos de nuvem, designer industrial, desenvolvedores full-stack, cientistas de dados especializados em inteligência artificial, além de consultores em produtividade. A proposição de valor do projeto tornou viável o fornecimento de uma solução de gerenciamento baseada em Internet das Coisas para impulsionar a produtividade, melhorar a competitividade e maximizar a eficiência da Indústria 4.0.
DOI: 10.21227/dfja-ed09
title: Hybrid Learning Aided Inactive Constraints Filtering Algorithm to Enhance AC OPF Solution Time

description: This dataset is used to train learners in our paper entitled: Hybrid Learning Aided Inactive Constraints Filtering Algorithm to Enhance AC OPF Solution Time
DOI: 10.21227/kege-qv50
title: Hybrid Learning Aided Inactive Constraints Filtering Algorithm to Enhance AC OPF Solution Time

description: This dataset is used to train learners in our paper entitled: Hybrid Learning Aided Inactive Constraints Filtering Algorithm to Enhance AC OPF Solution Time
DOI: 10.21227/x99t-7y84
title: EnergyNow | IoT aplicada ao fomento da Indústria 4.0

description: A Indústria enfrenta desafios graves e fracassa sem competitividade. Atacando esta problemática, conferiu-se o oferecimento de maior eficiência a processos industriais para promover a produtividade, elevar a qualidade e impulsionar mudanças. A solução desenvolvida incluiu dispositivos com sensores não invasivos, simples de instalar, que contabilizam os itens sendo transportados em linhas de produção. De qualquer lugar e em tempo real, a plataforma conectada permite acesso a informações claras e confiáveis sobre a produtividade, mostram indicadores e métricas em gráficos com design intuitivo que aceleram a tomada de decisão. O profissional certo recebe alertas para resolver problemas específicos e algoritmos avançados apontam quando máquinas ou equipamentos possam interferir na produtividade. Acompanhando as tendências de digitalização de processos na manufatura avançada e da Indústria 4.0, o impacto compreende um mercado-alvo inicial de mais de seis mil fábricas; na região nordeste, o mercado possível tem mais de 75.000 fábricas; e o mercado disponível no Brasil soma mais de 600.000 fábricas. De fato, atendendo a um segmento que movimenta 907 bilhões de dólares no mundo. Com um modelo de acesso flexível, nas implantações, permite que seja possível incorporar evoluções contínuas dos serviços, com a adesão incluindo instalação dos dispositivos e treinamento especializado. A equipe dedicada tem ampla competência em uma variedade de metodologias de desenvolvimento de tecnologia, oportunamente, dispondo de engenheiros eletricistas e de produção, gerentes administrativos e de TI, arquitetos de nuvem, designer industrial, desenvolvedores full-stack, cientistas de dados especializados em inteligência artificial, além de consultores em produtividade. A proposição de valor do projeto tornou viável o fornecimento de uma solução de gerenciamento baseada em Internet das Coisas para impulsionar a produtividade, melhorar a competitividade e maximizar a eficiência da Indústria 4.0.
DOI: 10.5281/zenodo.3866403
title: Particle Trace of a Milky Way Mass Galaxy from the EAGLE simulations

description: This repository contains .npy files, loaded like: <pre><code class="language-python">with open('EAGLE_MW_trace_coords.npy', 'rb') as f: coordinates = np.load(f) dmcoordinates = np.load(f) with open('EAGLE_MW_trace_redshifts.npy', 'rb') as f: redshifts = np.load(f)</code></pre> which contain the locations of particles (gas, stars and dark matter) which are within 30pkpc of the centre of a Milky Way stellar mass galaxy from the EAGLE suite of simulations. This dataset was primarily produced to look at the accretion of matter onto galaxies like the Milky Way, studying how they assemble over time, which makes for some quite pretty visualisations (explored in this repository). the file 'EAGLE_MW_trace_coords_downsampled_10.npy' contains the same data but for a downsampled set of particles (by a factor of 10).
DOI: 10.5281/zenodo.3866363
title: Evaluation set DCASE 2020 task 4 (for submissions)

description: This repo contains the dataset to download to submit results and be evaluated in task 4 of DCASE 2020. Please, check the submission package in order to follow the instruction to have a submission. *Note: some files are 5 mins long, so if your system is not suitable for this, make sure you aggegate the results.*
DOI: 10.5281/zenodo.3866364
title: Evaluation set DCASE 2020 task 4 (for submissions)

description: This repo contains the dataset to download to submit results and be evaluated in task 4 of DCASE 2020. Please, check the submission package in order to follow the instruction to have a submission. *Note: some files are 5 mins long, so if your system is not suitable for this, make sure you aggegate the results.*
DOI: 10.25345/c51b08
title: MassIVE MSV000085515 - Proteomic and Metabolomic Characterization of COVID-19 Patient Sera MRMHR
title: Targeted mass spectrometric assays for the 22 proteins in a second test cohort containing 19 COVID-19 patients&lt;ul&gt;&lt;li&gt;Dataset imported into MassIVE from &lt;a href="https://www.iprox.org/page/project.html?id=IPX0002171000"&gt;https://www.iprox.org/page/project.html?id=IPX0002171000&lt;/a&gt; on 05/30/20&lt;/li&gt;&lt;/ul&gt;
DOI: 10.5281/zenodo.3866410
title: MVs and FVs for Planck 2015 and 2018 temperature maps obtained by polyMV

description: The files located in <em>data_mvs.zip</em> are the tables of Multipole Vectors (MVs) for Planck 2015 and 2018 temperature maps (full sky and masked maps using the Common Temperature mask of PR3). The files located in <em>data_fvs.zip</em> are the tables of Fréchet vectors (FVs) for Planck 2015 and 2018 temperature maps. MVs and FVs were obtained using polyMV. Any publications making use of this data should cite this paper: R. A. Oliveira, T. S. Pereira, and M. Quartin, <strong>CMB statistical isotropy confirmation at all scales using multipole vectors</strong>, Phys. Dark Univ. 30 (2020) 100608 (arXiv:1812.02654 [astro-ph.CO]). Please refer to the above paper for further details.
description: All files are stored in the NumPy file format *.npy.
DOI: 10.21227/jw40-y336
title: Bandwidth Usage of University Campus

description: This dataset present the traffic volume (i.e., the bandwidth usage) of a university campus to the Internet during 6 months.
DOI: 10.21227/rm5v-3631
title: Bandwidth Usage of University Campus

description: This dataset present the traffic volume (i.e., the bandwidth usage) of a university campus to the Internet during 6 months.
DOI: 10.25345/c5wh8s
title: MassIVE MSV000085516 - Urine proteomics profiling of the COVID-19
title: Urine proteomics profiling from the mild and severe COVID-19 patients&lt;ul&gt;&lt;li&gt;Dataset imported into MassIVE from &lt;a href="https://www.iprox.org/page/project.html?id=IPX0002166000"&gt;https://www.iprox.org/page/project.html?id=IPX0002166000&lt;/a&gt; on 05/30/20&lt;/li&gt;&lt;/ul&gt;
DOI: 10.25919/5ed32ad20ca63
title: Parkes observations for project PX600 semester 2020APRS_06

description: The Galactic Center (GC) is a key target for the radio component of Breakthrough Listen (BL) program. Offering the largest amount of stars in any given direction in the sky, the GC is widely cited as a location believed to harbour advanced civilizations, and it is also the most energetic region in the Milky Way. We aim to conduct BL-GC Survey with Parkes radio telescope for around 350 hours. We will cover the frequency range from 700 MHz to 4 GHz, utilizing the newly installed ultra-wideband receiver. Our team has already leveraged both standard and bespoke tools to construct a flexible software stack to search data for signals of interest. We will look for signals from extraterrestrial intelligence (ETI) by searching for both simple narrow-band signals and complex modulated signals. Along with that, as an ancillary science, we will also search for accelerated pulsars — likely orbiting a supermassive black hole at the centre of the Milky Way Galaxy. Data products produced during this program will also be publically available to engage larger pulsar community
DOI: 10.5281/zenodo.3866461
title: IO Islamic 3441. Ḥusain Shâhî, A Detailed History of the Dynasty of the Durrânî Afghâns

description: IO Islamic 3441. Ḥusain Shâhî, A Detailed History of the Dynasty of the Durrânî Afghâns
DOI: 10.5281/zenodo.3866462
title: IO Islamic 3441. Ḥusain Shâhî, A Detailed History of the Dynasty of the Durrânî Afghâns

description: IO Islamic 3441. Ḥusain Shâhî, A Detailed History of the Dynasty of the Durrânî Afghâns
DOI: 10.5281/zenodo.3866463
title: IO Islamic 2224. Ḳiṣaṣ-alanbiyâ, History of the Prophets from Âdam to Muḥammad

description: IO Islamic 2224. Ḳiṣaṣ-alanbiyâ, History of the Prophets from Âdam to Muḥammad
DOI: 10.5281/zenodo.3866464
title: IO Islamic 2224. Ḳiṣaṣ-alanbiyâ, History of the Prophets from Âdam to Muḥammad

description: IO Islamic 2224. Ḳiṣaṣ-alanbiyâ, History of the Prophets from Âdam to Muḥammad