cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6424205
title: Virus Finding Tools: current solutions and limitations - Synthetic datasets

description: The simulated RNA-seq datasets were built using FluxSimulator to generate paired-end reads. The first one contains thirty viral genomes from NCBI and the human genome (version GRCh38). The second dataset comprises five Human Rhinovirus A1, five Human Papillomavirus 16, and the human genome (version GRCh38). Concerning the first dataset, we chose the genomes to mix human, animal, and vegetable viruses for a fair analysis of each tool's reliability. Instead, the second dataset was created to understand the capability of each tool to assess the taxonomic level and the specificity of viral identification. We downloaded the genomes of the selected viruses from NCBI nucleotide, getting the FASTA file and the GFF3 annotation. Next, we used AGAT to transform the GFF3 files into GTF files. After that, we used FluxSimulator to generate a synthetic RNA-seq dataset for each species. Finally, we joined each simulated dataset to build a single RNA-seq sample. The number of reads generated for each species was chosen to act as a real sample. For each dataset, we include two compressed archives. The "*_fastq_files.tar" files contain the synthetic fastq files generated by FluxSimulator. The "*_raw_results.tar" files contain all the raw output produced by the tools employed in our benchmarking. The selected viruses for the first dataset are Human rhinovirus 1 strain ATCC VR-1559, Human Rhinovirus 3, Tomato mosaic virus, Molluscum contagiosum virus subtype 1, Apple mosaic virus RNA 3, Encephalomyocarditis virus, Human papillomavirus 52 isolate 52HB20, Hepatitis C virus genotype 1, Human papillomavirus type 31, Human papillomavirus type 54, JC polyomavirus, Marine RNA virus SF-2, Marine RNA virus JP-B, Hepatitis A virus, Human immunodeficiency virus 1, Anguillid herpes virus strain UK N080, Apis mellifera virus 14 isolate BFH508NG, Human enterovirus, Escherichia phage T7 isolate T7, Human herpesvirus 6B, Human measles virus, Cyprinid herpesvirus 3, Rotavirus C segment 8, Japanese encephalitis virus, Human papillomavirus 116, Influenza A virus (A/New York/392/2004(H3N2)) segment 4, Rotavirus RCU, Human parvovirus B19, Rous Sarcoma, Human papillomavirus 16. The selected strains and isolates for the second dataset are Human rhinovirus 1 strain ATCC VR-1559, Rhinovirus A1 strain 7A2, Rhinovirus A1 strain 5Q1, Rhinovirus A1 strain RvA1B/USA/2021/RJ9JKH, Rhinovirus A1 strain RvA1/USA/2021/L8MQLH, Human papillomavirus type 16, Human papillomavirus type 16 isolate 16CN37, Human papillomavirus type 16 isolate 16CN34, Human papillomavirus type 16 strain MML8, Human papillomavirus type 16 strain MML20. The tools employed in our benchmarking are VirusFinder, VirusSeq, VirTect, viGEN, VirDetect, DAMIAN, Metamap, Kraken2, Centrifuge.
DOI: 10.25345/c57940z39
title: MassIVE MSV000089226 - K48-Ubiquitin IP-MS of Jurkat leukemia cells treated with asparaginase
title: We have shown that drug-resistant leukemias rely on protein degradation through the ubiquitin proteasome pathway to survive during asparaginase therapy. Our studies support the model that this catabolic source of amino acids is upregulated in response to amino acid starvation. To investigate whether this involves bulk protein degradation or the degradation of specific protein targets, we analyzed K48-ubiquitinated proteins by immunoprecipitation-mass spectrometry proteomics from Jurkat cells after treatment with vehicle vs asparaginase. We found no enrichment of specific proteins after asparaginase treatment, consistent with the possibility that asparaginase therapy stimulates bulk protein degradation, although these results do not rule out degradation of specific proteins below the limit of detection of this assay.
DOI: 10.6084/m9.figshare.19554817
title: Statistical data I

description: Educational innovation study data
DOI: 10.5519/qd.1lxaam7p
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (802 records)
DOI: 10.5281/zenodo.6366323
title: Fast Calorimeter Simulation Challenge 2022 - Dataset 3

description: This is dataset 3 of the “Fast Calorimeter Simulation Challenge 2022”. It consists of four files with 50k GEANT4-simulated showers each of electrons with energies sampled from a log-uniform distribution ranging from 1 GeV to 1 TeV. The detector geometry is similar to dataset 2, but has a much higher granularity. Each of the 45 layer has now 18 radial and 50 angular bins, totalling 18x50x45=40500 voxels. This dataset was produced using the Par04 Geant4 example. dataset_3_1.hdf5 and dataset_3_2.hdf5 should be used for training, dataset_3_3.hdf5 and dataset_3_4.hdf5 can be used as reference in the evaluation. More details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at https://calochallenge.github.io/homepage/
DOI: 10.5281/zenodo.6366270
title: Fast Calorimeter Simulation Challenge 2022 - Dataset 2

description: This is dataset 2 of the “Fast Calorimeter Simulation Challenge 2022”. It consists of two files with 100k GEANT4-simulated showers each of electrons with energies sampled from a log-uniform distribution ranging from 1 GeV to 1 TeV. The detector has a concentric cylinder geometry with 45 layers, where each layer consists of active (silicon) and passive (tungesten) material. Each layer has 144 readout cells, 9 in radial and 16 in angular direction, yielding a total of 9x16x45 = 6480 voxels. dataset_2_1.hdf5 should be used for training, dataset_2_2.hdf5 can be used as reference in the evaluation. More details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at https://calochallenge.github.io/homepage/
DOI: 10.6084/m9.figshare.19554838
title: statistical data

description: Educational innovation study data
DOI: 10.6084/m9.figshare.19554838.v1
title: statistical data

description: Educational innovation study data
DOI: 10.6084/m9.figshare.19294346
title: Partition-Based Nonstationary Covariance Estimation Using the Stochastic Score Approximation

description: We introduce computational methods that allow for effective estimation of a flexible nonstationary spatial model when the field size is too large to compute the multivariate normal likelihood directly. In this method, the field is defined as a weighted spatially varying linear combination of a globally stationary process and locally stationary processes. Often in such a model, the difficulty in its practical use is in the definition of the boundaries for the local processes, and therefore, we describe one such selection procedure that generally captures complex nonstationary relationships. We generalize the use of a stochastic approximation to the score equations in this nonstationary case and provide tools for evaluating the approximate score in O(n log ⁡n) operations and <i>O</i>(<i>n</i>) storage for data on a subset of a grid. We perform various simulations to explore the effectiveness and speed of the proposed methods and conclude by predicting average daily temperature. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19294346.v2
title: Partition-Based Nonstationary Covariance Estimation Using the Stochastic Score Approximation

description: We introduce computational methods that allow for effective estimation of a flexible nonstationary spatial model when the field size is too large to compute the multivariate normal likelihood directly. In this method, the field is defined as a weighted spatially varying linear combination of a globally stationary process and locally stationary processes. Often in such a model, the difficulty in its practical use is in the definition of the boundaries for the local processes, and therefore, we describe one such selection procedure that generally captures complex nonstationary relationships. We generalize the use of a stochastic approximation to the score equations in this nonstationary case and provide tools for evaluating the approximate score in O(n log ⁡n) operations and <i>O</i>(<i>n</i>) storage for data on a subset of a grid. We perform various simulations to explore the effectiveness and speed of the proposed methods and conclude by predicting average daily temperature. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19294346.v1
title: Partition-Based Non-Stationary Covariance Estimation using the Stochastic Score Approximation

description: We introduce computational methods that allow for effective estimation of a flexible non-stationary spatial model when the field size is too large to compute the multivariate normal likelihood directly. In this method, the field is defined as a weighted spatially varying linear combination of a globally stationary process and locally stationary processes. Often in such a model, the difficulty in its practical use is in the definition of the boundaries for the local processes, and therefore we describe one such selection procedure that generally captures complex non-stationary relationships. We generalize the use of a stochastic approximation to the score equations in this non-stationary case and provide tools for evaluating the approximate score in O(n log n) operations and <i>O</i>(<i>n</i>) storage for data on a subset of a grid. We perform various simulations to explore the effectiveness and speed of the proposed methods and conclude by predicting average daily temperature.
DOI: 10.3886/e113169v1-114540
title: Replication data for: Vulnerable Growth

description: We study the conditional distribution of GDP growth as a function of economic and financial conditions. Deteriorating financial conditions are associated with an increase in the conditional volatility and a decline in the conditional mean of GDP growth, leading the lower quantiles of GDP growth to vary with financial conditions and the upper quantiles to be stable over time. Upside risks to GDP growth are low in most periods while downside risks increase as financial conditions become tighter. We argue that amplification mechanisms in the financial sector generate the observed growth vulnerability dynamics.
description: Real GDP growth and ficial conditions for the U.S.
DOI: 10.6073/pasta/c9b3da65a8c89cbfa6fc28d26f938c22
title: Six decades (1959-2021) of water quality in the upper San Francisco Estuary: an integrated database of 15 discrete monitoring surveys in the Sacramento San Joaquin Delta, Suisun Bay, Suisun Marsh, and San Francisco Bay

description: The upper San Francisco Estuary (SFE) is simultaneously a central hub of water delivery in California and home to commercially important and endangered fishes, such as Chinook Salmon, Green Sturgeon, and Delta and Longfin Smelt. Extensive ecological monitoring has been conducted for over 50 years, mainly under the auspices of the Interagency Ecological Program for the San Francisco Estuary (https://iep.ca.gov/). We integrated water quality data from 15 boat-based long-term monitoring surveys in the upper SFE. This integrated dataset includes measurements of temperature (surface and bottom), conductivity (surface), salinity (surface), Secchi depth, qualitative concentration of the toxic alga Microcystis (surface), Chlorophyll-a concentration (surface), nutrients (surface), and other parameters from 1959 - 2021. The component surveys range in sampling frequency from thrice weekly to monthly and range in duration from 5 – 60 years. Most component surveys sample at fixed stations, but the Enhanced Delta Smelt Monitoring survey uses random sites and some stations (with “EZ” in the station name) of the Environmental Monitoring Program follow the salinity field. It is highly recommended to inspect the documentation of the component surveys for more information on their methods.
DOI: 10.6084/m9.figshare.19554868
title: Buses telemetry dataset

description: This dataset presents a set of telemetries collected from buses in order to apply machine learning techniques to extract hidden patterns from them. It contains attributes collect from the CAM bus and kinetic attributes collected using a IMU sensor build-in an Arduino device installed on the buses.
DOI: 10.6084/m9.figshare.19554868.v1
title: Buses telemetry dataset

description: This dataset presents a set of telemetries collected from buses in order to apply machine learning techniques to extract hidden patterns from them. It contains attributes collect from the CAM bus and kinetic attributes collected using a IMU sensor build-in an Arduino device installed on the buses.
DOI: 10.25919/bmrm-dk47
title: Parkes observations for project P1069 semester 2020APRS_12

description: We propose to carry out Parkes follow-up observations of pulsar candidates identified in the Rapid ASKAP Continuum Survey (RACS). This is the first all-sky pulsar survey in the imaging domain in the southern hemisphere thanks to ASKAP. Eight sources have been selected because of (1) their very high fraction of circular polarisation; (2) their lack of optical counterparts; (3) strong variability, which might indicate either strong interstellar scintillation or eclipsing by companion stars if in binaries; (4) the non-detection of these relatively bright sources in previous pulsar surveys, which might indicate extreme nature of these candidates. High time and frequency resolution targeted search with UWL are proposed. Such observations will enable us to carry out deep searches of pulsars with very short spin periods or in compact binaries.
DOI: 10.6084/m9.figshare.19552066.v1
title: 50 Hz resampling data

description: 50 Hz resampling waveform data of the relatively large foreshocks preceding the Yangbi mainshock.
DOI: 10.6084/m9.figshare.19555009
title: Foreshock catalog

description: Foreshock catalog of the 2021 Yangbi earthquake
DOI: 10.6084/m9.figshare.19555009.v1
title: Foreshock catalog

description: Foreshock catalog of the 2021 Yangbi earthquake
DOI: 10.6084/m9.figshare.19552066
title: Foreshock waveform data

description: Waveform data of the relatively large foreshocks preceding the 2021 Yangbi mainshock.
DOI: 10.6084/m9.figshare.19552066.v3
title: Foreshock waveform data

description: Waveform data of the relatively large foreshocks preceding the 2021 Yangbi mainshock.
DOI: 10.6084/m9.figshare.19552066.v2
title: Foreshock waveform data

description: Waveform data of the relatively large foreshocks preceding the 2021 Yangbi mainshock.
DOI: 10.22003/xfel.eu-data-002806-00
title: Spectroscopic investigation of atomic physics in isochorically heated Cu
DOI: 10.22003/xfel.eu-data-002881-00
title: Direct observation of relativistic transparency in overcritical foam targets and impact of foam sub structure on laser energy coupling
DOI: 10.22003/xfel.eu-data-003212-00
title: test and setup RELAX (opt laser-only)
DOI: 10.22003/xfel.eu-data-002854-00
title: Kinetic instabilities in the interaction of ultra-intense ultra-short laser pulses with solids
DOI: 10.22003/xfel.eu-data-002734-00
title: Holographic Single Particle Imaging
DOI: 10.5281/zenodo.6426147
title: Virus Finding Tools: current solutions and limitations - Synthetic datasets

description: The simulated RNA-seq datasets were built using FluxSimulator to generate paired-end reads. The first one contains thirty viral genomes from NCBI and the human genome (version GRCh38). The second dataset comprises five Human Rhinovirus A1, five Human Papillomavirus 16, and the human genome (version GRCh38). Concerning the first dataset, we chose the genomes to mix human, animal, and vegetable viruses for a fair analysis of each tool's reliability. Instead, the second dataset was created to understand the capability of each tool to assess the taxonomic level and the specificity of viral identification. We downloaded the genomes of the selected viruses from NCBI nucleotide, getting the FASTA file and the GFF3 annotation. Next, we used AGAT to transform the GFF3 files into GTF files. After that, we used FluxSimulator to generate a synthetic RNA-seq dataset for each species. Finally, we joined each simulated dataset to build a single RNA-seq sample. The number of reads generated for each species was chosen to act as a real sample. For each dataset, we include two compressed archives. The "*_fastq_files.tar" files contain the synthetic fastq files generated by FluxSimulator. The "*_raw_results.tar" files contain all the raw output produced by the tools employed in our benchmarking. The selected viruses for the first dataset are Human rhinovirus 1 strain ATCC VR-1559, Human Rhinovirus 3, Tomato mosaic virus, Molluscum contagiosum virus subtype 1, Apple mosaic virus RNA 3, Encephalomyocarditis virus, Human papillomavirus 52 isolate 52HB20, Hepatitis C virus genotype 1, Human papillomavirus type 31, Human papillomavirus type 54, JC polyomavirus, Marine RNA virus SF-2, Marine RNA virus JP-B, Hepatitis A virus, Human immunodeficiency virus 1, Anguillid herpes virus strain UK N080, Apis mellifera virus 14 isolate BFH508NG, Human enterovirus, Escherichia phage T7 isolate T7, Human herpesvirus 6B, Human measles virus, Cyprinid herpesvirus 3, Rotavirus C segment 8, Japanese encephalitis virus, Human papillomavirus 116, Influenza A virus (A/New York/392/2004(H3N2)) segment 4, Rotavirus RCU, Human parvovirus B19, Rous Sarcoma, Human papillomavirus 16. The selected strains and isolates for the second dataset are Human rhinovirus 1 strain ATCC VR-1559, Rhinovirus A1 strain 7A2, Rhinovirus A1 strain 5Q1, Rhinovirus A1 strain RvA1B/USA/2021/RJ9JKH, Rhinovirus A1 strain RvA1/USA/2021/L8MQLH, Human papillomavirus type 16, Human papillomavirus type 16 isolate 16CN37, Human papillomavirus type 16 isolate 16CN34, Human papillomavirus type 16 strain MML8, Human papillomavirus type 16 strain MML20. The tools employed in our benchmarking are VirusFinder, VirusSeq, VirTect, viGEN, VirDetect, DAMIAN, Metamap, Kraken2, Centrifuge.
DOI: 10.5281/zenodo.6424204
title: Virus Finding Tools: current solutions and limitations - Synthetic datasets

description: The simulated RNA-seq datasets were built using FluxSimulator to generate paired-end reads. The first one contains thirty viral genomes from NCBI and the human genome (version GRCh38). The second dataset comprises five Human Rhinovirus A1, five Human Papillomavirus 16, and the human genome (version GRCh38). Concerning the first dataset, we chose the genomes to mix human, animal, and vegetable viruses for a fair analysis of each tool's reliability. Instead, the second dataset was created to understand the capability of each tool to assess the taxonomic level and the specificity of viral identification. We downloaded the genomes of the selected viruses from NCBI nucleotide, getting the FASTA file and the GFF3 annotation. Next, we used AGAT to transform the GFF3 files into GTF files. After that, we used FluxSimulator to generate a synthetic RNA-seq dataset for each species. Finally, we joined each simulated dataset to build a single RNA-seq sample. The number of reads generated for each species was chosen to act as a real sample. For each dataset, we include two compressed archives. The "*_fastq_files.tar" files contain the synthetic fastq files generated by FluxSimulator. The "*_raw_results.tar" files contain all the raw output produced by the tools employed in our benchmarking. The selected viruses for the first dataset are Human rhinovirus 1 strain ATCC VR-1559, Human Rhinovirus 3, Tomato mosaic virus, Molluscum contagiosum virus subtype 1, Apple mosaic virus RNA 3, Encephalomyocarditis virus, Human papillomavirus 52 isolate 52HB20, Hepatitis C virus genotype 1, Human papillomavirus type 31, Human papillomavirus type 54, JC polyomavirus, Marine RNA virus SF-2, Marine RNA virus JP-B, Hepatitis A virus, Human immunodeficiency virus 1, Anguillid herpes virus strain UK N080, Apis mellifera virus 14 isolate BFH508NG, Human enterovirus, Escherichia phage T7 isolate T7, Human herpesvirus 6B, Human measles virus, Cyprinid herpesvirus 3, Rotavirus C segment 8, Japanese encephalitis virus, Human papillomavirus 116, Influenza A virus (A/New York/392/2004(H3N2)) segment 4, Rotavirus RCU, Human parvovirus B19, Rous Sarcoma, Human papillomavirus 16. The selected strains and isolates for the second dataset are Human rhinovirus 1 strain ATCC VR-1559, Rhinovirus A1 strain 7A2, Rhinovirus A1 strain 5Q1, Rhinovirus A1 strain RvA1B/USA/2021/RJ9JKH, Rhinovirus A1 strain RvA1/USA/2021/L8MQLH, Human papillomavirus type 16, Human papillomavirus type 16 isolate 16CN37, Human papillomavirus type 16 isolate 16CN34, Human papillomavirus type 16 strain MML8, Human papillomavirus type 16 strain MML20. The tools employed in our benchmarking are VirusFinder, VirusSeq, VirTect, viGEN, VirDetect, DAMIAN, Metamap, Kraken2, Centrifuge.
DOI: 10.5517/ccdc.csd.cc2bgm8q
title: CCDC 2159003: Experimental Crystal Structure Determination

description: Related Article: Daniel Franz, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgm9r
title: CCDC 2159004: Experimental Crystal Structure Determination

description: Related Article: Daniel Franz, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgmbs
title: CCDC 2159005: Experimental Crystal Structure Determination

description: Related Article: Daniel Franz, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgmct
title: CCDC 2159006: Experimental Crystal Structure Determination

description: Related Article: Daniel Franz, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgmdv
title: CCDC 2159007: Experimental Crystal Structure Determination

description: Related Article: Daniel Franz, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgmfw
title: CCDC 2159008: Experimental Crystal Structure Determination

description: Related Article: Daniel Franz, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgfpy
title: CCDC 2158830: Experimental Crystal Structure Determination

description: Related Article: Claas Hoffend, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgfqz
title: CCDC 2158831: Experimental Crystal Structure Determination

description: Related Article: Claas Hoffend, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgfr0
title: CCDC 2158832: Experimental Crystal Structure Determination

description: Related Article: Claas Hoffend, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bgjx8
title: CCDC 2158930: Experimental Crystal Structure Determination

description: Related Article: Adelina Reichert, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bkc2c
title: CCDC 2161632: Experimental Crystal Structure Determination

description: Related Article: A.E.M. Abdallah, G.H. Elgemeie, P.G. Jones|2022|IUCrData|7|x220332|doi:10.1107/S2414314622003327
DOI: 10.5517/ccdc.csd.cc2bkxd7
title: CCDC 2162169: Experimental Crystal Structure Determination

description: Related Article: J. Song, X. Jiang, Z. Wang, J. Pei, H. Li|2022|IUCrData|7|x220342|doi:10.1107/S241431462200342X
DOI: 10.5517/ccdc.csd.cc2bl85d
title: CCDC 2162503: Experimental Crystal Structure Determination

description: Related Article: E.V. Nikitina, S.T. Çelikesir, M. Akkurt, S.K. Petkevich, E.A. Akishina, V.N. Khrustalev, S. Mlowe|2022|Acta Crystallogr.,Sect.E:Cryst.Commun.|78|453|doi:10.1107/S2056989022003450
DOI: 10.21227/093p-b328
title: Twitter Dataset - Over 200,000 Tweets containing the word "Vaccine" for research porpuses

description: This dataset contains 220,085 tweets containing the word vaccine between December 9th and December 18th 2021 at different times during each day, extracted using the Twitter API v2. Each tweet was extracted at least 3 days after its initial posting time in order to register 3 days of engagements, and it doesn't include retweets.Includes:Tweet IDTextAuthor IDDateLike countRetweet countQuote countReply countUser data (Followers, Following, Tweet count, Account creation date, Verified status)Usernames are hidden for privacy reasons