cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3950057
title: Geostatistical Analysis of SARS-CoV-2 in the United States

description: Geostatistics analyzes and predicts the values associated with spatial or spatio-temporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. It is a practical means of describing spatial patterns and interpolating values for locations where samples were not taken (and measures the uncertainty of those values, which is critical to informed decision making). This update to the Zenodo archive (version 3) consists of two archives consisting of geostatistical analyses of SARS-CoV-2 testing data: Archive #1: "1. Updated Geostatistical Analysis of SARS-CoV-2 in the United States (version 3 - July 17th 2020).zip" - a new set of results from geostatistical analyses performed against corrected COVID-19 case data (according to source-based errata documents) for the continental United States, spanning from March 24<sup>th</sup> through to July 13<sup>th</sup> 2020. Archive #2: "2. Initial Geostatistical Analysis of SARS-CoV-2 in the United States (version 2 - first published June 15th, 2020).zip" - the results from the geostatistical analyses performed for version 2 of this Zenodo archive which analyzed COVID-19 case data prior to any case correction step. These archives consist of map files (as both static images and as animations) and data files (including text files which contain the underlying data of said map files [where applicable]) which were generated when performing the following Geostatistical analyses: Hot Spot analysis (Getis-Ord Gi*), Cluster and Outlier analysis (Anselin Local Moran's I), and Spatial Autocorrelation (Global Moran's I). Note that the older archive also provides analytics that are not yet available in version 3, but will be incorporated in future releases of this archive (e.g. spatial-temporal analyses, and point-to-point comparisons with Kriging and Densification analysis). The Word document ("Description-of-Archive.Updated-Geostatistical-Analysis-of-SARS-CoV-2 (version 3 - published July 17th 2020).docx") describes in detail the contents of each file and folder within these two archives.
DOI: 10.7910/dvn/n2xypa/xuzzjz
title: detailed_erratum_finalized.pdf

description: :unav
DOI: 10.7910/dvn/n2xypa
title: Erratum: The Economic Consequences of Partisanship in a Polarized Era

description: This erratum corrects errors made in the article "The Economic Consequences of Partisanship in a Polarized Era," by Chris McConnell, Yotam Margalit, Neil Malhotra and Matthew Levendusky.
DOI: 10.12751/g-node.b4vh7k
title: PhyloBrain atlas: a cortical brain MRI atlas following a phylogenetic approach

description: Cortical atlases constitute a consistent division of the human cortex into areas that have common structural as well as meaningful and distinctive functional characteristics. The most widely used atlases follow the cytoarchitectonic and myeloarchitectonic characteristics of the cortex and have been combined to the standard anatomical nomenclature of gyri and sulci. More recently, common functional features depicted by resting state functional MRI have also guided the division of the cortical brain in functional regions of interest. However, to date, there are no atlases that divide the cortex considering the common evolutionary changes experienced by the mammalian cortex. Hence, the present study proposes the division of cortical areas into five main regions of interest (ROIs) following a phylogenetic approach: 1- archicortex, 2- paleocortex, 3- peri-archicortex, 4- proisocortex, 5-neocortex, and twelve neocortical sub-ROIs: 5.1.temporopolar, 5.2.post-central, 5.3.pre-central, 5.4.pericalcarine, 5.5.superior temporal, 5.6.middle temporal, 5.7.precuneus, 5.8.insular, 5.9.inferior parietal, 5.10.caudal anterior, 5.11.posterior cingulate, and 5.12.lingual gyrus. The segmentations were done using the T1-weighted MNI-ICBM152 non-linear 6th generation symmetric average brain MRI model.
DOI: 10.5281/zenodo.1414384
title: Mathematics Stack Exchange API Q&A Data

description: This dataset was compiled as part of the ESPRC project "Example-driven machine-human collaboration in mathematics", for the purpose of doing text-based analysis of mathematical discourse and for the construction of a conversational mathematics bot. It consists of approximately 1 million mathematics questions and their respective answers, as well as markers of interaction quality (such as user-provided scoring of question and answer quality) and social dynamics (reputation scores, badges, etc). The data was obtained from the StackExchange website, by querying the Stack Exchange API according to its documentation.
description: EPSRC funded project EP/P017320/1 "Example-driven machine-human collaboration in mathematics"
DOI: 10.5281/zenodo.1414383
title: Mathematics Stack Exchange API Q&A Data

description: This dataset was compiled as part of the ESPRC project "Example-driven machine-human collaboration in mathematics", for the purpose of doing text-based analysis of mathematical discourse and for the construction of a conversational mathematics bot. It consists of approximately 1 million mathematics questions and their respective answers, as well as markers of interaction quality (such as user-provided scoring of question and answer quality) and social dynamics (reputation scores, badges, etc). The data was obtained from the StackExchange website, by querying the Stack Exchange API according to its documentation.
description: EPSRC funded project EP/P017320/1 "Example-driven machine-human collaboration in mathematics"
DOI: 10.21227/39ss-sj38
title: OOK Data

description: This is the python code for our article
DOI: 10.5281/zenodo.3951607
title: Supplementary Material

description: Supplementary Material for Manuscript entitled “<em>Reframing Psychiatry for Precision Medicine</em>” by Elizabeth B Torres, PhD
DOI: 10.5281/zenodo.3951606
title: Supplementary Material

description: Supplementary Material for Manuscript entitled “<em>Reframing Psychiatry for Precision Medicine</em>” by Elizabeth B Torres, PhD
DOI: 10.5281/zenodo.3951447
title: 1H HRMAS NMR dataset of glioma and control samples

description: This repository contains the raw and processed 1H HRMAS NMR data to reproduce the results reported in the following preprint: Machine Learning Assisted Intraoperative Assessment of Brain Tumor Margins Using HRMAS NMR Spectroscopy (https://doi.org/10.1101/2020.02.24.20026955).
DOI: 10.5281/zenodo.3951366
title: Wikidata Dump politician

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.3951365
title: Wikidata Dump politician

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.21227/knqq-xx08
title: Coventry-2018

description: Coventry-2018 is a human activity recognition dataset captured by three Panasonic® Grid-EYE (AMG8833) infrared sensors in March 2018. The Grid-EYE sensors represent a 60 field of view scene by an 8 × 8 array named frame. The data streams are synchronized to 10 frames per second and saved as *.csv recordings using the LabVIEW® software. Two layouts are considered in this dataset with different geometry sizes: 1) small layout; and 2) large layout. The three sensors are located 1.5m from the area of interest in the small geometry size, while the distance grows to 2.5m for the large geometry size as illustrated in the dataset image. The dataset consists of 15 gestures distinguished in two categories: 1) single-subject gestures; and 2) double-subject gestures. There exist eight single-subject activities, and seven double-subject activities. Each single-subject gesture is repeated 10 times by three participants, while for double-subject gestures, the three possible combinations of the three subjects are used and similarly, the gestures are repeated 10 times. It needs to be emphasized that the single-subject activities are conducted in both layouts (small layout and large layout), while the double-subject activities are conducted only in the large layout.
DOI: 10.18119/n90s3v
title: RAS Pathway Proteomics
DOI: 10.5281/zenodo.3951726
title: Phytogenic additive from mesquite to improve nutrient digestibility, microbial protein, ruminal parameters and blood metabolites in sheep

description: Crude data of ingestive behavior of sheep.
DOI: 10.5281/zenodo.3951725
title: Phytogenic additive from mesquite to improve nutrient digestibility, microbial protein, ruminal parameters and blood metabolites in sheep

description: Crude data of ingestive behavior of sheep.
DOI: 10.6084/m9.figshare.3761562.v1546
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5281/zenodo.3951538
title: Using Resolution Proofs to Analyse CDCL SAT solvers

description: Data for the article Janne I. Kokkala, Jakob Nordström: Using Resolution Proofs to Analyse CDCL SAT solvers, accepted to the 26th International Conference on Principles and Practice of Constraint Programming. Files: <strong>solver.tgz</strong> – Source code of the modified Glucose 3.0 used in the experiments <strong>instances.tar</strong> – All CNF instances used in the experiments (compressed individually using xz). Note that these are the formulas obtained after preprocessing, so they are not the same as used in the SAT races and competitions they are obtained from. <strong>data-instances.txt</strong> – List of all benchmark instance filenames and IDs used to refer to them in other data files. <strong>data-solvers.txt</strong> – Parameters used for each solver configuration (see the paper for explanation of where they were used). <strong>data-solverstats.txt</strong> – For each solver configuration and instance, some data of the run <strong>data-proofsizes.txt</strong> – For each solver configuration and instance, sizes of untrimmed proof, the trimmed solver proof, and the proof output by DRAT-trim, measured both in number of learnt clauses and in number of clause usages – note that for the clause usage counts, all unit clauses are considered to be used only once at the end (since that would result to a shorter resolution proof and is more related to the solver performance) <strong>data-features.txt</strong> – For the solver used in the clause feature experiments, this file contains for each instance the frequency distribution of each feature (both absolute and percentile rank) <strong>plots-features.pdf</strong> – Larger versions of the feature plots in the paper, including plots not shown in the paper. <strong>plots-proofsizes.pdf</strong> – Plots of the data for the pairwise solver proof size comparison experiments.
description: The computational experiments used resources provided by the Swedish National Infrastructure for Computing (SNIC) at the High Performance Computing Center North (HPC2N) at Umeå University. The authors were supported by the Swedish Research Council grant 2016-00782, and the second author also received funding from the Independent Research Fund Denmark (DFF) grant 9040-00389B.
DOI: 10.5281/zenodo.3951537
title: Using Resolution Proofs to Analyse CDCL SAT solvers

description: Data for the article Janne I. Kokkala, Jakob Nordström: Using Resolution Proofs to Analyse CDCL SAT solvers, accepted to the 26th International Conference on Principles and Practice of Constraint Programming. Files: <strong>solver.tgz</strong> – Source code of the modified Glucose 3.0 used in the experiments <strong>instances.tar</strong> – All CNF instances used in the experiments (compressed individually using xz). Note that these are the formulas obtained after preprocessing, so they are not the same as used in the SAT races and competitions they are obtained from. <strong>data-instances.txt</strong> – List of all benchmark instance filenames and IDs used to refer to them in other data files. <strong>data-solvers.txt</strong> – Parameters used for each solver configuration (see the paper for explanation of where they were used). <strong>data-solverstats.txt</strong> – For each solver configuration and instance, some data of the run <strong>data-proofsizes.txt</strong> – For each solver configuration and instance, sizes of untrimmed proof, the trimmed solver proof, and the proof output by DRAT-trim, measured both in number of learnt clauses and in number of clause usages – note that for the clause usage counts, all unit clauses are considered to be used only once at the end (since that would result to a shorter resolution proof and is more related to the solver performance) <strong>data-features.txt</strong> – For the solver used in the clause feature experiments, this file contains for each instance the frequency distribution of each feature (both absolute and percentile rank) <strong>plots-features.pdf</strong> – Larger versions of the feature plots in the paper, including plots not shown in the paper. <strong>plots-proofsizes.pdf</strong> – Plots of the data for the pairwise solver proof size comparison experiments.
description: The computational experiments used resources provided by the Swedish National Infrastructure for Computing (SNIC) at the High Performance Computing Center North (HPC2N) at Umeå University. The authors were supported by the Swedish Research Council grant 2016-00782, and the second author also received funding from the Independent Research Fund Denmark (DFF) grant 9040-00389B.
DOI: 10.3886/e116502v1-43220
title: Replication data for: The Impact of Insurance Provision on Household Production and Financial Decisions

description: This paper uses a natural experiment to study the impact of an agricultural insurance program on household production, borrowing, and saving behavior. The empirical strategy includes difference-in-differences and triple difference estimations. The results show that insurance provision increases the insured crop production by 16 percent and raises borrowing by 29 percent. Interestingly, it does not affect total household savings; however, it does affect the relative proportion of flexible-term savings. Furthermore, effects on production and savings persist in the long run, while effects on borrowing are significant in only the medium run. Lastly, calibration results suggest that the policy is both welfare improving and cost-effective. (JEL D13, D14, G22, O16, P32, P36, Q12)
DOI: 10.21227/bv3f-8r62
title: Device Fingerprinting for Access Control over a Campus and Isolated Network

description: Device Fingerprinting for Access Control over a Campus and Isolated Network Device Fingerprinting (DFP) is a technique to identify devices using Inter-Arrival Time (IAT) of packets and without using any other unique identifier. Our experiments include generating graphs of IATs of 100 packets and using Convolutional Neural Network on the generated graphs to identify a device. We did two experiments where the first experiment was on Raspberri Pi and other experiment was on crawdad dataset. First Experiment: Raspberry Pi We developed a packet sniffer application to capture IAT of packets. Packet sniffer application was installed on Raspberry pi that was configured to work as router. We connceted two devices iPad4 and iPhone 7 Plus to the router and created IAT graphs for these two devices. Our scheme based on Convolution Neural Network (CNN) was able to identify the devices with accuracy of 86.7%. Second Experiment: Crawdad Dataset In the second experiment, we tested the scheme with Crawdad dataset. The proposed scheme achieved accuracy of 95.5% for GTID that is 3% higher than previous scheme for 14 devices and 5 device types on isolated network while 40% efficient in time to test a device fingerprint.
DOI: 10.4225/06/58c0ca27ec292
title: Chemical bonding at the metal-organic framework/metal oxide interface: simulated epitaxial growth of MOF-5 on rutile TiO2
DOI: 10.5281/zenodo.3942735
title: data

description: dataset
DOI: 10.5281/zenodo.3942734
title: data

description: dataset
DOI: 10.5519/qd.55juyf7n
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (105 records)
DOI: 10.21227/tz1h-3r31
title: CCAP

description: Chest CT of COVID-19
DOI: 10.5281/zenodo.3951777
title: Kamon Dataset and StarGAN Model

description: Dataset and resulting model of Japanese Kamon trained using StarGAN.
DOI: 10.5281/zenodo.3951776
title: Kamon Dataset and StarGAN Model

description: Dataset and resulting model of Japanese Kamon trained using StarGAN.
DOI: 10.5281/zenodo.3951779
title: San Serif Font Dataset and StarGAN model

description: Dataset and resulting model of san serif font trained using StarGAN.
DOI: 10.5281/zenodo.3951778
title: San Serif Font Dataset and StarGAN model

description: Dataset and resulting model of san serif font trained using StarGAN.
DOI: 10.21227/jg35-fk04
title: CCAP (A chest CT data set, which includes five categories: COVID-19, non-COVID-19 viral pneumonia (VP), bacterial pneumonia (BP), mycoplasma pneumonia (MP) and normal lung)

description: Chest CT of COVID-19
DOI: 10.25919/5f150ede3828d
title: Parkes observations for project P737 semester 2020APRS_05

description: The aim of this proposal is to carry out tests of the new backend instruments which cannot be carried out during standard maintenance periods. The scheduled time is used to measure beam shapes, determine polarisation calibration solutions and commission the National Facility observing modes for the new wide-band receiver.
DOI: 10.25919/5f150f507169e
title: Parkes observations for project P456 semester 2020APRS_04

description: The Parkes Pulsar Timing Array (PPTA) project has three primary goals: (a) detection of gravitational waves from astronomical sources, (b) establishment of a pulsar timescale, and (c) improvement of our understanding of Solar-system dynamics. The PPTA is the oldest of three international pulsar timing groups. We have the smallest telescope and the smallest group, but the best measurements and the best results. Our bound on the strength of the incoherent gravitational wave background is the only bound which significantly constrains theoretical models. We have maintained our leading position because: we have the southern sky; we have a small but well-focussed group; we have the best-calibrated receivers; and we have been able to observe with an almost regular cadence of 15 to 20 days over a wide bandwidth. With this proposal we aim to maintain our pre-eminent position in the field. Unlike most observing proposals, this is a continuing proposal for which the observations will continue to improve bounds on ultra-low-frequency gravitational waves until they are finally detected. Continued Parkes observations will remain valuable at least until the first stage of the SKA is able to improve on our sensitivity and observing cadence. Even after the gravitational wave background is detected we will want to continue observations in support of the nascent field of gravitational wave astronomy!
DOI: 10.21227/ftz6-ye22
title: CCAP (A chest CT data set, which includes five categories: COVID-19, non-COVID-19 viral pneumonia (VP), bacterial pneumonia (BP), mycoplasma pneumonia (MP) and normal lung)

description: Chest CT of COVID-19
DOI: 10.5281/zenodo.3951825
title: Towards automated early detection of risks for a CO2 plume containment from permanent seismic monitoring data

description: This storage contains the training data for neural networks proposed in a manuscript 'Towards automated early detection of risks for a CO2 plume containment from permanent seismic monitoring data'. The data consists of output from reservoir simulations of a small-scale CO2 injection at CO2CRC Otway Project Stage 2C (Victoria, Australia). The output is presented as a set of images, where each pixel in a portable network graphics is a plume thickness for a particular injection scenario at a particular day after the injection has commenced. The format is unsigned integer 16-bit. The data set contains images of two major types: 1. REALISTIC: plumes are obtained from reservoir simulations in a complex geological model that was calibrated on an extensive set of geophysical surveys. File naming follows this convention 'plume_thick_real_scenario_%S_day_%N.png', where %S represents a string that encodes the injection scenario name and %N denotes day number after the injection started. 2. VANILLA: plumes are obtained from reservoir simulations in a simple model of a reservoir that reflects only few typical features of the Otway injection interval. 'plume_thick_vanilla_scenario_%S_day_%N.png', where %S represents a string that encodes the injection scenario name and %N denotes day number after the injection started.
DOI: 10.5281/zenodo.3951824
title: Towards automated early detection of risks for a CO2 plume containment from permanent seismic monitoring data

description: This storage contains the training data for neural networks proposed in a manuscript 'Towards automated early detection of risks for a CO2 plume containment from permanent seismic monitoring data'. The data consists of output from reservoir simulations of a small-scale CO2 injection at CO2CRC Otway Project Stage 2C (Victoria, Australia). The output is presented as a set of images, where each pixel in a portable network graphics is a plume thickness for a particular injection scenario at a particular day after the injection has commenced. The format is unsigned integer 16-bit. The data set contains images of two major types: 1. REALISTIC: plumes are obtained from reservoir simulations in a complex geological model that was calibrated on an extensive set of geophysical surveys. File naming follows this convention 'plume_thick_real_scenario_%S_day_%N.png', where %S represents a string that encodes the injection scenario name and %N denotes day number after the injection started. 2. VANILLA: plumes are obtained from reservoir simulations in a simple model of a reservoir that reflects only few typical features of the Otway injection interval. 'plume_thick_vanilla_scenario_%S_day_%N.png', where %S represents a string that encodes the injection scenario name and %N denotes day number after the injection started.
DOI: 10.21227/86wa-9r61
title: CCAP (A chest CT data set, which includes five categories: COVID-19, non-COVID-19 viral pneumonia (VP), bacterial pneumonia (BP), mycoplasma pneumonia (MP) and normal lung)

description: A chest CT data set from multi-centre hospitals, which includes five categories: COVID-19, non-COVID-19 viral pneumonia (VP), bacterial pneumonia (BP), mycoplasma pneumonia (MP) and normal lung.
DOI: 10.21227/7c41-m483
title: CCAP (A chest CT data set, which includes five categories: COVID-19, non-COVID-19 viral pneumonia (VP), bacterial pneumonia (BP), mycoplasma pneumonia (MP) and normal lung)

description: A chest CT data set from multi-centre hospitals, which includes five categories: COVID-19, non-COVID-19 viral pneumonia (VP), bacterial pneumonia (BP), mycoplasma pneumonia (MP) and normal lung.
DOI: 10.5281/zenodo.3951839
title: Dataset for CRC screening

description: Dataset for "Additional outreach effort of providing an opportunity to obtain a kit for fecal immunochemical test during the general health check-up to improve colorectal cancer screening rate in Japan: A longitudinal study"
DOI: 10.5281/zenodo.3951838
title: Dataset for CRC screening

description: Dataset for "Additional outreach effort of providing an opportunity to obtain a kit for fecal immunochemical test during the general health check-up to improve colorectal cancer screening rate in Japan: A longitudinal study"
DOI: 10.25919/5e8d61fa04b9f
title: Melampsora lini long-term genetic and phenotypic data

description: Melampsora lini data from long term surveys conducted in the Kiandra plain at sites Kiandra, P1 and P2. File includes SNP data, infectivity phenotypes and epidemiological data.