cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3678386
title: Dataset related to the article-Physical Exercise Affects Adipose Tissue Profile and Prevents Arterial Thrombosis in BDNF Val66Met Mice

description: This record contains raw data related to the article "Physical Exercise Affects Adipose Tissue Profile and Prevents Arterial Thrombosis in BDNF Val66Met Mice"
description: This research was funded by the Italian Ministry of Health (Ricerca Corrente) grants number BIO35-2015: 2622789; BIO37-2016: 2613074; BIO37-2017: 2631213; MPP1.2A-2018: 2634597. Co-funding provided by the contribution of the Italian "5 × 1000" tax (2014, 2015 and 2016).
DOI: 10.5281/zenodo.3678387
title: Dataset related to the article-Physical Exercise Affects Adipose Tissue Profile and Prevents Arterial Thrombosis in BDNF Val66Met Mice

description: This record contains raw data related to the article "Physical Exercise Affects Adipose Tissue Profile and Prevents Arterial Thrombosis in BDNF Val66Met Mice"
description: This research was funded by the Italian Ministry of Health (Ricerca Corrente) grants number BIO35-2015: 2622789; BIO37-2016: 2613074; BIO37-2017: 2631213; MPP1.2A-2018: 2634597. Co-funding provided by the contribution of the Italian "5 × 1000" tax (2014, 2015 and 2016).
DOI: 10.17632/8x5tvtx5p5.1
title: Data for: Laser Ablation Split-Stream analysis of the Sm-Nd and U-Pb isotope compositions of monazite, titanite, and apatite - improvements, potential reference materials, and application to the Archean Saglek Block gneisses

description: Data for: Laser Ablation Split-Stream analysis of the Sm-Nd and U-Pb isotope compositions of monazite, titanite, and apatite
DOI: 10.21227/q3p2-9k28
title: Open Repository for the Evaluation of Ransomware Detection Tools

description: This repository contains the results of running more than 70 samples of ransomware, from different families, dating since 2015. It contains the network traffic (DNS and TCP) and the Input/Output (I/O) operations generated by the malware while encrypting a network shared directory. These data are contained in three files for each ransomware sample: one with the information from the DNS requests, other with the TCP connections another one containing the I/O operations. This information can be useful for testing new and old ransomware detection tools and compare their results.
DOI: 10.21227/8hdt-9r50
title: Open Repository for the Evaluation of Ransomware Detection Tools

description: This repository contains the results of running more than 70 samples of ransomware, from different families, dating since 2015. It contains the network traffic (DNS and TCP) and the Input/Output (I/O) operations generated by the malware while encrypting a network shared directory. These data are contained in three files for each ransomware sample: one with the information from the DNS requests, other with the TCP connections another one containing the I/O operations. This information can be useful for testing new and old ransomware detection tools and compare their results.
DOI: 10.5281/zenodo.3693572
title: Physiological parameters for three farm animal species (cattle, sheep, and swine) as the basis for the development of generic physiologically based kinetic models

description: <strong>IMPORTANT : PLEASE DISREGARD VERSION 1 OF THIS UPLOAD SINCE IT INCLUDES ERRONEOUS INFORMATION.</strong> This excel file (DOI: 10.5281/zenodo.3433224) provides physiological parameters and their inter-individual variability (mean, coefficient of variation, sample size) for three farm animal species: cattle (<em>Bos taurus</em>), sheep (<em>Ovis aries</em>), and swine (<em>Sus scrofa domesticus</em>). These physiological parameters were estimated based on the results of extensive literature searches and specific experimental data described in Lautz et al., (2020). This file is associated with R codes (DOI: 10.5281/zenodo.3432796) for generic PBK models, partition coefficient Quantitative Structure Activity Relationship (QSAR) models for each farm animal species and parameterisation of the model. The full data collection and implementation of the models using case studies are described in Lautz et al., 2020 (10.1016/j.toxlet.2019.10.008).
description: NL; XLSX; l.lautz@science.ru.nl
description: {"references": ["Leonie Lautz, S. Hoeks, R. Oldenkamp (2019). Development of generic physiologically based kinetic models for three farm animal species, cattle, sheep, and swine. DOI: 10.5281/zenodo.3432796.", "Leonie Lautz (2019). Physiological parameters for three farm animal species (cattle, sheep, and swine) as the basis for the development of generic physiologically based kinetic models. DOI: 10.5281/zenodo.3433224", "Leonie Lautz, Selwyn Hoeks, Rik Oldenkamp, Jan Hendriks, Jean-Lou Dorne, Ad Ragas (2020). Generic physiologically based kinetic modelling for farm animals: Part II. Predicting tissue concentrations of chemicals in swine, cattle, and sheep. Toxicology Letters. 318:50-56", "Leonie Lautz, Jean-Lou Dorne, Rik Oldenkamp, Jan Hendriks, Ad Ragas (2020). Generic physiologically based kinetic modelling for farm animals: Part I. Data collection of physiological parameters in swine, cattle and sheep. Toxicology Letters. 319:95-101."]}
DOI: 10.5281/zenodo.3693684
title: PROTON deliverable 5.1 dataset

description: This is the set of simulations used for the deliverable 5.1 in the PROTON project, for the organized crime. The simulation is available on github. In detail: data used for section 2.7.1 data used for sections 2.7.2 and 2.7.4 data used for section 2.7.3 Reference videos: https://www.youtube.com/watch?v=zJJY0-qj6qs https://www.youtube.com/watch?v=rA3aKLaVlR4
DOI: 10.5281/zenodo.3693595
title: Biogeochemical model projections years 1982-2007 in North East Atlantic

description: Marine enviromental data from Biogeochemical models in paper "Can we project changes in fish abundance and distribution in response to climate?" at Global Change Biology journal
DOI: 10.5281/zenodo.3693596
title: Biogeochemical model projections years 1982-2007 in North East Atlantic

description: Marine enviromental data from Biogeochemical models in paper "Can we project changes in fish abundance and distribution in response to climate?" at Global Change Biology journal
DOI: 10.5281/zenodo.3693608
title: Dataset for: Quantum cascade lasers with discrete and non equidistant extended tuning tailored by simulated annealing

description: Dataset used for article 10.1364/OE.27.026701.
DOI: 10.5281/zenodo.3693609
title: Dataset for: Quantum cascade lasers with discrete and non equidistant extended tuning tailored by simulated annealing

description: Dataset used for article 10.1364/OE.27.026701.
DOI: 10.5281/zenodo.3693685
title: Unified Bug Dataset

description: <strong>A Public Unified Bug Dataset for Java and its Assessment Regarding Metrics and Bug Prediction.</strong> Online appendix for the paper submitted to the <strong><em>Software Quality Journal</em></strong>. Bug datasets have been created and used by many researchers to build and validate novel bug prediction models. In this work, our aim is to collect existing public source code metrics based bug datasets and unify their contents. Furthermore, we wish to assess the plethora of collected metrics and the capabilities of the unified bug dataset in bug prediction.<br> We considered 5 public datasets and we downloaded the corresponding source code for each system in the datasets and performed source code analysis to obtain a common set of source code metrics. This way, we produced a unified bug dataset at class and file level as well. We investigated the diversion of metric definitions and values of the different bug datasets. Finally, we used a decision tree algorithm to show the capabilities of the dataset in bug prediction.<br> We found that there are statistically significant differences in the values of the original and the newly calculated metrics, furthermore, notations and definitions can severely differ. We compared the bug prediction capabilities of the original and the extended metric suites (within-project learning). Afterwards, we merged all classes (and files) into one large dataset which consists of 47,618 elements (43,744 for files) and we evaluated the bug prediction model build on this large dataset as well. Finally, we also investigated cross-project capabilities of the bug prediction models and datasets. We made the unified dataset publicly available for everyone. By using a public unified dataset as an input for different bug prediction related investigations, researchers can make their studies reproducible, thus able to be validated and verified.
DOI: 10.26302/sshade/experiment_bs_20191214_001
title: Vis-NIR spectra of NH4-Montmorillonite, Antigorite, Dolomite and Graphite and their mixtures with different grain sizes and temperatures (140-300 K)

description: Vis-NIR spectra of NH4-Montmorillonite (0-25µm / 180-290K; 25-50µm / 200K), Antigorite (25-50µm / 200K; 50-100µm / 180-290K), Dolomite (0-25µm / 140-200K; 50-100µm / 200K), and Graphite (&gt;100µm / 290K) and 5 mixtures: #1: Mo6%:An5%:Do5%:Gr84% (0-25µm / 290K); #2: Mo17%:An24%:Do57%:Gr2% (50-100µm / 290K); #3a: Mo9%:An19%:Do64%:Gr8% (50-100µm / 290K); #3b: Mo9%:An19%:Do60%:Gr12% (50-100µm / 290K); #4: Mo28%:An36%:Do18%:Gr18% (0-25, 25-50, 50-100µm / 200, 300K)
DOI: 10.5281/zenodo.3635922
title: Sub-pixel accuracy in electron detection using a convolutional neural network

description: <strong>Abstract</strong> Modern direct electron detectors (DEDs) provided a giant leap in the use of cryogenic electron microscopy (cryo-EM) to study the structures of macromolecules and complexes thereof. However, the currently available commercial DEDs, all based on the monolithic active pixel sensor, still require relative long exposure times and their best results have been obtained at 300 keV. There is a need for pixelated electron counting detectors that can be operated at a broader range of energies, at higher throughput and higher dynamic range. Hybrid Pixel Detectors (HPDs) of the Medipix family were reported to be unsuitable for cryo-EM at energies above 80 keV as those electrons would affect too many pixels. Here we show that the Timepix3, part of the Medipix family, can be used for cryo-EM applications at higher energies. We tested Timepix3 detectors on a 200 keV FEI Tecnai Arctica microscope and a 300 keV FEI Tecnai G2 Polara microscope. A correction method was developed to correct for per-pixel differences in output. Timepix3 data were simulated for individual electron events using the package Geant4Medipix. Global statistical characteristics of the simulated detector response were in good agreement with experimental results. A convolutional neural network (CNN) was trained using the simulated data to predict the incident position of the electron within a pixel cluster. After training, the CNN predicted, on average, .39 pixel and 0.42 pixel from the incident electron position for 200 keV and 300 keV electrons respectively. The CNN improved the MTF of experimental data at half Nyquist from 0.39 to 0.70 at 200 keV, and from 0.06 to 0.65 at 300 keV respectively. We illustrate that the useful dose-lifetime of a protein can be measured within a 1 second exposure using Timepix3. <strong>Data description</strong> Data has been split up in experimental data, simulations, neural net models and ToT correction results. In general: each directory contains individual READMEs with steps how to reproduce the data. <strong>Experimental data</strong> For each type of data at 200 kV or 300 kV only the input raw data has been added and the resulting image file. Intermediate files have been left out. <strong>Models</strong> The models are the CNN models generated at 200 and 300 kV. <strong>Simulated data</strong> The simulated data consists of the dataset used for training the neural network and the indepedently simulated validation set. <strong>ToT correction</strong> The ToT correction file only contain the resulting correction matrix. The experimental flat field data has been left out, due to its volume. It's about 300 GiB of data for both 200 and 300 kV. <strong>Software </strong> The software used can be found as related identifiers to this deposit.
DOI: 10.7910/dvn/8xsazd/9g0z9j
title: evs_weimar.csv

description: :unav
DOI: 10.7910/dvn/8xsazd/b8m2fu
title: elections_2017.csv

description: :unav
DOI: 10.21227/8fwk-h267
title: Twitter Sentiment Analysis Data

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live[1]. The last column, viz. sentiment score, is not the score estimated by the model. The LSTM model is still in the pre-alpha phase. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [2] has been appended as the last column. The sentiment scores produced by our model will be made public after the project is completely open-sourced.####status/latest addition: Jan 31, 2020####Tweets containing the term "republic day": 0.37 million-plusTweets containing the term "samsung": 1.20 million-plusTweets containing the term "facebook": 0.77 million-plusTweets containing the term "android": 0.88 million-plusTweets containing the term "oneplus": 15 thousand-plusTweets containing the term "messi": 225 thousand-plusTweets containing the term "housefull 4": 346 thousand-plusTweets containing the term "the joker": 1 million-plusTweets containing the term "iphone 11": 2 million-plus##################References:[1]https://sentiment.live/ [2]https://textblob.readthedocs.io/en/dev/
DOI: 10.5281/zenodo.3693633
title: How Magic Is Zero? An Empirical Analysis of Initial Development Releases in Three Software Package Distributions

description: This is the replication package for "How Magic Is Zero? An Empirical Analysis of Initial Development Releases in<br> Three Software Package Distributions" accepted for publication in SoHeal 2020. The notebooks in "notebooks/" require the dependencies specified in "requirements.txt" to be installed.<br> They rely on data files in "data/". These data can be obtained by running the "convert.py" scripts in that folder.<br> The script requires data files from "data-raw/". These files can be generated by running "extract.py" in that folder.<br> This script requires the libraries.io data dump, as explained in the README file contained in this folder. If you don't have these data, or if you don't want to download them, you can ask for the required data/*.csv.gz files by email.
DOI: 10.5281/zenodo.3693634
title: How Magic Is Zero? An Empirical Analysis of Initial Development Releases in Three Software Package Distributions

description: This is the replication package for "How Magic Is Zero? An Empirical Analysis of Initial Development Releases in<br> Three Software Package Distributions" accepted for publication in SoHeal 2020. The notebooks in "notebooks/" require the dependencies specified in "requirements.txt" to be installed.<br> They rely on data files in "data/". These data can be obtained by running the "convert.py" scripts in that folder.<br> The script requires data files from "data-raw/". These files can be generated by running "extract.py" in that folder.<br> This script requires the libraries.io data dump, as explained in the README file contained in this folder. If you don't have these data, or if you don't want to download them, you can ask for the required data/*.csv.gz files by email.
DOI: 10.21227/kjwm-wf83
title: Twitter Sentiment Analysis Data

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live[1]. The last column, viz. sentiment score, is not the score estimated by the model. The LSTM model is still in the pre-alpha phase. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [2] has been appended as the last column. The sentiment scores produced by our model will be made public after the project is completely open-sourced.####status/latest addition: March 02, 2020####Tweets containing the term "corona": 2.3 million-plus (first collection)Tweets containing the term "republic day": 0.37 million-plusTweets containing the term "samsung": 1.20 million-plusTweets containing the term "facebook": 0.77 million-plusTweets containing the term "android": 0.88 million-plusTweets containing the term "oneplus": 15 thousand-plusTweets containing the term "messi": 225 thousand-plusTweets containing the term "housefull 4": 346 thousand-plusTweets containing the term "the joker": 1 million-plusTweets containing the term "iphone 11": 2 million-plus##################References:[1]https://sentiment.live/ [2]https://textblob.readthedocs.io/en/dev/
DOI: 10.17632/hs58h3fdtt.3
title: Data for: Estimation of thermophysical properties of a hydrocarbon fuel at a supercritical pressure

description: The raw data of inverse estimation of thermophysical properties of n-decane at a supercritical pressure of 5 MPa. The generality of proposed method and the effects of initial value, relaxation factor, function category and measurement error on inverse identification are investigated.
DOI: 10.18716/nmrshiftdb2/75300
title: NMR data for InChI=1S/C14H19N3O3/c1-2-20-14(19)9(8-15)12-13(18)17-11-7-5-3-4-6-10(11)16-12/h10-11,16H,2-7H2,1H3,(H,17,18)/b12-9-
DOI: 10.5519/qd.s0ofm1so
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (45 records)
DOI: 10.18716/nmrshiftdb2/75310
title: NMR data for InChI=1S/C11H16N2O3/c1-2-16-10(15)11-5-3-7-12-9(11)13-8(14)4-6-11/h2-7H2,1H3,(H,12,13,14)
DOI: 10.18716/nmrshiftdb2/75312
title: NMR data for InChI=1S/C11H16N2O3/c1-3-16-11(15)7-5-4-6-8-9(7)10(14)13(2)12-8/h7,14H,3-6H2,1-2H3
DOI: 10.15129/fd7f5ae7-1216-4877-b49f-d772e5261cf0
title: Data for: "Hemodynamics in the pulmonary bifurcation in relation to adults with congenital heart disease"

description: This dataset contain graphics data of simplified geometries of the pulmonary bifurcation, in .igs format, which was created for the purposes of computational fluid dynamic simulations. The .igs files are graphics files based on the Initial Graphics Exchange Specification (IGES) and can be read by most computer aided design (CAD) software, e.g. SolidWorks, GiD, ANSA, Autodesk etc.
DOI: 10.18716/nmrshiftdb2/75298
title: NMR data for ethyl (E)-2-cyano-3-methoxyprop-2-enoate
DOI: 10.5258/2cjc-sw27
title: Dataset for: The shadow Effect on the Ground Surface due to Vibration Transmission from a Railway Tunnel

description: Dataset for: Jin, Q., Thompson, D., Lurcock, D., & Ntotsios, E. (2020). The shadow effect on the ground surface due to vibration transmission from a railway tunnel. Transportation Geotechnics, 23, [100335]. DOI: DOI: 10.1016/j.trgeo.2020.100335
DOI: 10.5281/zenodo.3693643
title: Dataset for the article "Optomechanics of Chiral Dielectric Metasurfaces"

description: This folder contains the raw data from which the graphs in the paper "Optomechanics of Chiral Dielectric Metasurfaces", DOI 10.1002/adom.201901507 have been obtained.
DOI: 10.5281/zenodo.3693644
title: Dataset for the article "Optomechanics of Chiral Dielectric Metasurfaces"

description: This folder contains the raw data from which the graphs in the paper "Optomechanics of Chiral Dielectric Metasurfaces", DOI 10.1002/adom.201901507 have been obtained.
DOI: 10.18716/nmrshiftdb2/75304
title: NMR data for tert-butyl 2-oxo-3-prop-2-ynylimidazolidine-1-carboxylate
DOI: 10.18716/nmrshiftdb2/75308
title: NMR data for 1-[[(1R,2R)-2-(2-hydroxyethyl)cyclopropyl]methyl]-5-methylpyrimidine-2,4-dione
DOI: 10.18716/nmrshiftdb2/75316
title: NMR data for ethyl 4-[(E)-2-phenylethenyl]furo[3,2-c]pyridine-6-carboxylate
DOI: 10.5258/soton/d1260
title: Data set for "The influence of sleeper material characteristics on railway track behaviour: concrete vs composite sleeper"

description: Data to create figures from the article: Ferro, E., Harkness, J., & Le Pen, L. (2020). The influence of sleeper material characteristics on railway track behaviour: concrete vs composite sleeper. Transportation Geotechnics.
DOI: 10.7488/4da76240-5de1-4c1d-9c54-be360d3f6dd1
title: Bridging the Gap: Crannog

description: Bridging the Gap (BtG) is the only talent initiative for documentary in the UK. It helps emerging filmmakers to hone their storytelling skills and work closely with industry professionals. The concept was created by Noe Mendelle who has run the scheme for the last 16 years. Crannog is an example of the many films produced through BtG. It is directed by Isa Rao and the executive producer is Noe Mandelle. This dataset is held in the Edinburgh DataVault, directly accessible only to authorised University of Edinburgh users. External users may request access to a copy of the data by contacting the Principal Investigator, Contact Person or Data Manager named on this page. University of Edinburgh users who wish to have direct access should consult the information about retrieving data from the DataVault at: http://www.ed.ac.uk/is/research-support/datavault.
description: Alexis, who has a life threatening disease, spends her time on her farm taking care of dozens of sick, wounded and dying animals. She feels nobody should die alone. Crannog is a 15’ documentary produced in the context of the Bridging the Gap scheme. It demonstrates how it is possible to film a simple story yet weave deep philosophical questions into the narrative. It was premiered at Edinburgh International Film Festival, June 2018 (UK). ## Access ## This dataset is held in the Edinburgh DataVault, directly accessible only to authorised University of Edinburgh users. External users may request access to a copy of the data by contacting the Principal Investigator, Contact Person or Data Manager named on this page or email CAHSS REF: cahss.ref@ed.ac.uk. University of Edinburgh users who wish to have direct access should consult the information about retrieving data from the DataVault at: http://www.ed.ac.uk/is/research-support/datavault .
DOI: 10.18716/nmrshiftdb2/75314
title: NMR data for InChI=1S/C11H16N2O3/c1-12-9(11(15)16-2)7-13-6-4-3-5-8(13)10(12)14/h7-8H,3-6H2,1-2H3
DOI: 10.18716/nmrshiftdb2/75318
title: NMR data for InChI=1S/C24H24O7/c1-13(25)29-22-20-19-17(30-23(20)31-24(22,2)3)12-16(28-4)18(21(19)27)15(26)11-10-14-8-6-5-7-9-14/h5-12,20,22-23,27H,1-4H3/b11-10+
DOI: 10.18716/nmrshiftdb2/75322
title: NMR data for 2,6-dibromo-3,5-difluoropyridine-4-carboxylic acid
DOI: 10.5281/zenodo.3693623
title: Supporting data for "Towards an ultra-high-speed combustion pyrometer" paper

description: Model and experimental data presented in "Towards an ultra-high-speed combustion pyrometer" paper for the <em>International Journal of Turbomachinery, Propulsion and Power </em>and at the 9th EVI-GTI International Gas Turbine Instrumentation Conference on 20-21/11/2019 in Graz (Austria). Each file contains data relative to a figure/table in the journal paper.
description: This work was funded through the European Metrology research Programme (EMRP) Project 17IND07 DynPT. The EMRP is jointly funded by EMRP participating countries within EURAMET and the European Union.