cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/gxc4-aj16
title: Database for Cognitive Load Affect and Stress recognition

description: The CLAS (Cognitive Load, Affect and Stress) dataset was conceived as a freelyaccessible repository which is purposely developed to support research on the automated assessment of certain states of mind and the emotional condition of a person. This resource is intended to support RTD activities aiming at the development of intelligent human computer interaction (HCI) interfaces that incorporate functionalities allowing for the automated recognition of human emotions, the automated detection of stress conditions, the automated assessment of the degree of concentration, cognitive load, and momentary cognitive capacity, and can account for some personality traits related to the ability to quickly solve logical and mathematical problems under strict time constraints. The dataset consists of synchronized recordings of physiological signals, such as Electrocardiography (ECG), Plethysmography (PPG), ElectroDermal Activity (EDA), as well as accelerometer data, and metadata of 62 healthy volunteers, which were recorded while involved in three interactive tasks and two perceptive tasks. The interactive tasks aim to elicit different types of cognitive effort and included solving sequences of Math problems, Logic problems and the Stroop test. The perceptive tasks make use of images and audio-video stimuli, purposely selected to evoke emotions in the four quadrants of the arousalvalence space.
DOI: 10.5281/zenodo.3860313
title: Molecular Simulation of avb6 Integrin Inhibitors

description: Molecular dynamics trajectories for the simulation of an RGD mimetic, with a variety of aryl substituents, bound to avb6. DCD and PSF files are provided for five replicas of 10 ns simulation for each ligand derivative. Trajectories for simulations of the pro-domain of TGF-B1 in complex with avb6 are also provided.
description: E.E.G. and S.A.O. are supported by the Engineering and 841 Physical Sciences Research Council (EPSRC) under Glax- 842 oSmithKline CASE award schemes [EP/R512059/1 and EP/ 843 P510592/1, respectively]. We acknowledge the use of Athena 844 at HPC Midlands+, which was funded by the EPSRC on Grant 845 EP/P020232/1, in this research, as part of the HPC Midlands+ 846 consortium, and we thank EPSRC for funding via Prosperity 847 Partnership EP/S035990/1. We are grateful for access to the 848 University of Nottingham High Performance Computer.
DOI: 10.5281/zenodo.3860314
title: Molecular Simulation of avb6 Integrin Inhibitors

description: Molecular dynamics trajectories for the simulation of an RGD mimetic, with a variety of aryl substituents, bound to avb6. DCD and PSF files are provided for five replicas of 10 ns simulation for each ligand derivative. Trajectories for simulations of the pro-domain of TGF-B1 in complex with avb6 are also provided.
description: E.E.G. and S.A.O. are supported by the Engineering and 841 Physical Sciences Research Council (EPSRC) under Glax- 842 oSmithKline CASE award schemes [EP/R512059/1 and EP/ 843 P510592/1, respectively]. We acknowledge the use of Athena 844 at HPC Midlands+, which was funded by the EPSRC on Grant 845 EP/P020232/1, in this research, as part of the HPC Midlands+ 846 consortium, and we thank EPSRC for funding via Prosperity 847 Partnership EP/S035990/1. We are grateful for access to the 848 University of Nottingham High Performance Computer.
DOI: 10.18112/openneuro.ds002868.v1.0.0
title: CAMRI Mouse Brain MRI Data
DOI: 10.21227/nr6t-n969
title: Supplemental Materials for 'A Twofold Look-up Table Architecture for EfficientApproximation of Activation Functions'

description: This zip file includes supplemental materials for the research "A Twofold Look-up Table Architecture for Efficient Approximation of Activation Functions".
DOI: 10.21227/sjr9-a354
title: Supplemental Materials for 'A Twofold Look-up Table Architecture for EfficientApproximation of Activation Functions'

description: This zip file includes supplemental materials for the research "A Twofold Look-up Table Architecture for Efficient Approximation of Activation Functions".
DOI: 10.17632/th7fztbrv9.1
title: Data for: USING MACHINE LEARNING TO PREDICT OVARIAN CANCER

description: Supplementary data 1: It contains the original raw data. Supplementary data 2: It contains a list of biomarkers, their abbreviations, and their descriptions used in the study. Supplementary data 3: It contains the imputed version of the training data without the biomarker CA72-4. Supplementary data 4: It contains the raw training data. Supplementary data 5: It contains the raw test data.
DOI: 10.21227/3r39-cy56
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: 10.18112/openneuro.ds002870.v1.0.0
title: CAMRI Rat Brain MRI Data
DOI: 10.5281/zenodo.3866066
title: Stressors and Working From Home Questionnaire and Data

description: In these uncertain and difficult times caused by the spread of COVID-19, many of us have been forced to work from home and are required to complete job tasks on the same level of quality (as office). Several researchers argued that in any workplace, employees face positive and negative stressors. The questionnaire is focused on finding out if employees are facing the same stressors while working from home and to what extent are they aware of these stressors. The research is divided into two stages: 1) This questionnaire 2) A phone interview<br> The questionnaire takes approximately 5 minutes to complete. The responses will be used exclusively for the purpose of this study.
DOI: 10.5281/zenodo.3866067
title: Stressors and Working From Home Questionnaire and Data

description: In these uncertain and difficult times caused by the spread of COVID-19, many of us have been forced to work from home and are required to complete job tasks on the same level of quality (as office). Several researchers argued that in any workplace, employees face positive and negative stressors. The questionnaire is focused on finding out if employees are facing the same stressors while working from home and to what extent are they aware of these stressors. The research is divided into two stages: 1) This questionnaire 2) A phone interview<br> The questionnaire takes approximately 5 minutes to complete. The responses will be used exclusively for the purpose of this study.
DOI: 10.18112/openneuro.ds002864.v1.0.0
title: Put the name of your experiment here
DOI: 10.5281/zenodo.3866045
title: The Current State and 125 Kyr History of Permafrost in the Kara Sea Shelf: Modeling Constraints

description: The database for modeling the evolution of permafrost in the Kara shelf for the past 125 kyr presented in the manuscript https://www.the-cryosphere-discuss.net/tc-2019-112/
DOI: 10.5281/zenodo.3866046
title: The Current State and 125 Kyr History of Permafrost in the Kara Sea Shelf: Modeling Constraints

description: The database for modeling the evolution of permafrost in the Kara shelf for the past 125 kyr presented in the manuscript https://www.the-cryosphere-discuss.net/tc-2019-112/
DOI: 10.15468/5zuxt9
title: Updated checklist of marine fishes (Chordata: Craniata) from Portugal and the proposed extension of the Portuguese continental shelf

description: This dataset contains the digitized treatments in Plazi based on the original journal article Carneiro, Miguel, Martins, Rogélia, Landi, Monica, Costa Div-Rp, Filipe O. (2014): Updated checklist of marine fishes (Chordata: Craniata) from Portugal and the proposed extension of the Portuguese continental shelf. European Journal of Taxonomy 73: 1-73, DOI: http://dx.doi.org/10.5852/ejt.2014.73
DOI: 10.21227/v5qx-4w29
title: bardia article

description: I need this data set for my univercity project
DOI: 10.25345/c55125
title: MassIVE MSV000085514 - GNPS MMS1589 F1 RG/SB purification sous fraction N
title: adding metadata files here because can't find the F "dataset to update" of a field. Hope this one works
DOI: 10.5281/zenodo.3865786
title: Where does subduction initiate and cease? A global scale perspective (Supplementary material)

description: Supplementary material for Where does subduction initiate and cease? A global scale perspective
DOI: 10.5281/zenodo.3865787
title: Where does subduction initiate and cease? A global scale perspective (Supplementary material)

description: Supplementary material for Where does subduction initiate and cease? A global scale perspective
DOI: 10.5281/zenodo.3866460
title: IDTReeS 2020 Competition Data

description: Data provided by the Integrating Data science with Trees and Remote Sensing (IDTReeS) research group for use in the IDTReeS Competition. Geospatial and tabular data to be used in two data science tasks focused on using remote sensing data to quantify the locations, sizes and species identities of millions of trees and on determining how these methods generalize to other forests. Vector data are the geographic extents of Individual Tree Crown boundaries that have been identified by researchers in the IDTReeS group. The data were generated primarily by Sarah Graves, Sergio Marconi, and Benjamin Weinstein, with support from Stephanie Bohlman, Ethan White, and members of the IDTReeS group. Remote Sensing and Field data were generated by the National Ecological Observatory Network (NEON, Copyright © 2017 Battelle). Data were selected, downloaded, and packaged by Sergio Marconi. The most recent available data of the following products are provided: National Ecological Observatory Network. 2020. Data Product DP1.30010.001, High-resolution orthorectified camera imagery. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020. National Ecological Observatory Network. 2020. Data Product DP1.30003.001, Discrete return LiDAR point cloud. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020. National Ecological Observatory Network. 2020. Data Product DP1.10098.001, Woody plant vegetation structure. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020. National Ecological Observatory Network. 2020. Data Product DP3.30015.001, Ecosystem structure. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020. NEON has the following data policy: ‘The National Ecological Observatory Network is a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle Memorial Institute. This material is based in part upon work supported by the National Science Foundation through the NEON Program.’ THE NEON DATA PRODUCTS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE NEON DATA PRODUCTS BE LIABLE FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE NEON DATA PRODUCTS.
description: This data is supported by the National Science Foundation through grant 1926542 and by the Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative through grant GBMF4563 to E.P. White, and the NSF Dimension of Biodiversity program grant (DEB-1442280) and USDA/NIFA McIntire-Stennis program (FLA-FOR-005470).
DOI: 10.6082/uchicago.2204
title: Data and code for "Quantifying the deviation of the tropical upper tropospheric temperature response to surface warming from a moist adiabat"

description: Scripts and data to generate figures
DOI: 10.17632/th7fztbrv9.2
title: Data for: USING MACHINE LEARNING TO PREDICT OVARIAN CANCER

description: Supplementary data 1: It contains the original raw data. Supplementary data 2: It contains a list of biomarkers, their abbreviations, and their descriptions used in the study. Supplementary data 3: It contains the imputed version of the training data without the biomarker CA72-4. Supplementary data 4: It contains the raw training data. Supplementary data 5: It contains the raw test data.
DOI: 10.21227/prgx-bn83
title: Training Neural Networks for Loss Allocation in Power System

description: Power transmission system losses can typically represent from five to tenpercent of the total generation, a quantity worth millions ofdollars per year.The purpose of loss allocation in the context of pooldispatch is to assign to each individual generation and load theresponsibility of paying for part of the system transmissionlosses.Since the system losses arenon-separable, non-linear functions of the real powergeneration and loads, the allocation of transmission loss is achallenging and contentious issue in a fully deregulatedsystem. Loss allocation is a procedure for subdividing thesystem transmission losses into fractions, the cost of whichthen becomes the responsibilities of individual users of thepower system (Gencos, Discos, and Marketers). Loss allocationdoes not affect generation level or power flows; however, itmodifies the distribution of revenues and payments at thenetwork buses among suppliers and consumers.This dataset presents the data used in the paper titled "ANN Based Z-Bus Loss Allocation for PoolDispatch in Deregulated Power System" and also the data for the practical IEEE 30 bus system.Dataset 1 (fivebus.csv)FIve bus test system used in the paperDataset 2 (book30.csv)IEEE 30 bus system
DOI: 10.5281/zenodo.3866178
title: Academic Motivation Scale (AMS) Intrinsic Motivation (IM) Questionnaire and Data

description: The goal of this study is to investigate two things: 1) What motivates students to study at university? and 2) Have the recent COVID-19 social distancing measures demotivated students to continue studying in university? This survey will measure 3 types of motivations: Intrinsic, extrinsic, and amotivation. Intrinsic motivation is the level to which an individual engages in any given activity purely for enjoyment, satisfaction, or pleasure. Extrinsic motivation stems from an external source, such as deadlines, getting good grades, or the need to be praised/not punished. Lastly, amotivation is when an individual lacks any and all motivation, and the activity has no value or interest to them. This research is important because it could give Radboud University valuable knowledge and insights into why students study at all. Measuring amotivation can give insights about the potential lack in student motivation and their online content. This knowledge can be used to enhance their online content in the future, add new programs for students to use, give additional student support, and to foster greater student engagement and motivation. This survey consists of a total of 5 multiple choice questions regarding demographics. This is followed by a set of 24 statements to assess your intrinsic and extrinsic motivation to study. Then, a set of 4 statements to measure amotivation will be given twice. First in the context PRIOR to COVID-19 social distancing measures and again in the context AFTER COVID-19 social distancing measures were put in place. Lastly, there will be 4 questions which ask about your motivation levels and what you would recommend Radboud to do in the future. This will consist of a total of 32 statements and 9 questions. The survey takes approximately 5-10 minutes to complete.
DOI: 10.5281/zenodo.3866179
title: Academic Motivation Scale (AMS) Intrinsic Motivation (IM) Questionnaire and Data

description: The goal of this study is to investigate two things: 1) What motivates students to study at university? and 2) Have the recent COVID-19 social distancing measures demotivated students to continue studying in university? This survey will measure 3 types of motivations: Intrinsic, extrinsic, and amotivation. Intrinsic motivation is the level to which an individual engages in any given activity purely for enjoyment, satisfaction, or pleasure. Extrinsic motivation stems from an external source, such as deadlines, getting good grades, or the need to be praised/not punished. Lastly, amotivation is when an individual lacks any and all motivation, and the activity has no value or interest to them. This research is important because it could give Radboud University valuable knowledge and insights into why students study at all. Measuring amotivation can give insights about the potential lack in student motivation and their online content. This knowledge can be used to enhance their online content in the future, add new programs for students to use, give additional student support, and to foster greater student engagement and motivation. This survey consists of a total of 5 multiple choice questions regarding demographics. This is followed by a set of 24 statements to assess your intrinsic and extrinsic motivation to study. Then, a set of 4 statements to measure amotivation will be given twice. First in the context PRIOR to COVID-19 social distancing measures and again in the context AFTER COVID-19 social distancing measures were put in place. Lastly, there will be 4 questions which ask about your motivation levels and what you would recommend Radboud to do in the future. This will consist of a total of 32 statements and 9 questions. The survey takes approximately 5-10 minutes to complete.
DOI: 10.5519/qd.cnbo72o9
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (86 records)
DOI: 10.25919/5ed3038b0ad34
title: Parkes observations for project P1041 semester 2020APRS_06

description: We recently discovered five millisecond pulsars (MSPs) in the globular cluster Omega Centauri. This is the first pulsar discovery in this globular cluster. Four of these pulsars are isolated with spin periods of 4.1, 4.2, 4.6 and 6.8ms. The fifth has a spin period of 4.8ms and is in an eclipsing binary system with an orbital period of 2.1 hours. We propose to carry out a timing campaign of these new pulsars in order to study the dynamics in the core of Omega Centauri, to reveal the origin of Gamma-ray emission from the core, and to probe the ionised interstellar medium in Omega Centauri. Our proposed observations will also enable us to carry out deeper searches of pulsars in the cluster.
DOI: 10.5281/zenodo.3866224
title: Control over personal workspace and Work-related stress level Questionnaire and Data

description: Studies have found that the physical environment affects the level of job stress experienced by workers. The gap between the actual physical environment and workers’ expectations causes job stress because of additional efforts to tailor the workspace to personal needs. The goal of this research is to provide insights into how control over a personal workspace environment has impacted the level of stress experienced during the Coronavirus pandemic time. These insights are especially relevant in the recent situation when many office workers globally have to work virtually from their living space. Though only broadly adopted recently to cope with the Coronavirus health crisis, there is a likelihood that, if productivity is achieved, working from home can become a new norm. This survey takes approximately 15 minutes to complete.
DOI: 10.5281/zenodo.3866225
title: Control over personal workspace and Work-related stress level Questionnaire and Data

description: Studies have found that the physical environment affects the level of job stress experienced by workers. The gap between the actual physical environment and workers’ expectations causes job stress because of additional efforts to tailor the workspace to personal needs. The goal of this research is to provide insights into how control over a personal workspace environment has impacted the level of stress experienced during the Coronavirus pandemic time. These insights are especially relevant in the recent situation when many office workers globally have to work virtually from their living space. Though only broadly adopted recently to cope with the Coronavirus health crisis, there is a likelihood that, if productivity is achieved, working from home can become a new norm. This survey takes approximately 15 minutes to complete.
DOI: 10.25625/f8gjbu
title: Coffee Lectures zum Forschungsdatenmanagement an der SUH, Sommersemester 2020 (digital).

description: Ankündigungsposter zu den online stattfindenden Coffee Lectures zum Forschungsdatenmanagement im Sommersemester 2020 während der Covid-19 Pandemie.
DOI: 10.25625/f8gjbu/pglxqz
title: as_SoSe 2020_Coffee Lecture Corona UPDATE (Poster).pdf

description: :unav
DOI: 10.15161/oar.it/23697
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.3866244
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.