cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5255/ukda-sn-856117
title: Financial Crime Community Pulse, Romania, 2022

description: Our data were collected through a ten question questionnaire. The survey was distributed through various online channels and it was carried out in the second trimester of the year 2022. The valid responses of 1,856 Romanians are summarized, in order to capture their tax compliance and tax morale behaviours, perception of corruption, Anti Money Laundering skills and attitudes towards the Know Your Clients procedures, with respect to their age, gender, region, professional status and education. The objective was to reflect the Romanian perspective on financial crimes on various dimensions, in an accurate an up to date manner. The data may be used for various other multivariate data analysis techniques.
DOI: 10.25493/8xy5-bh7
title: Parcellation-based structural and resting-state functional whole-brain connectomes of 1000BRAINS cohort (v1.1)

description: This dataset provides the individual whole-brain connectomes for 261 subjects from the 1000BRAINS cohort of healthy adults. For this, 20 different state-of-the-art cortical parcellations were used in this dataset to reconstruct the region-based empirical structural connectivity (representing the anatomy of axonal tracts) and functional connectivity (representing the temporal correlation between neuronal activity of brain regions) from diffusion-weighted (dwMRI) and resting-state functional magnetic resonance imaging (fMRI) data, respectively. In addition, the regional blood-oxygen-level-dependent (BOLD) signals used for the calculation of the resting-state functional connectivity are also included in this dataset. Connectivity patterns of brain networks are of special interest in contemporary brain research, as they may reflect communication in the brain at the structural and functional levels. Their extraction, however, is a complex process that requires deep knowledge of MRI data processing methods. Furthermore, there is no consensus as to which parcellation of the brain is most suitable for a given analysis. The provided data can thus be used to investigate structural and functional human connectomes and their interrelations for varying brain parcellations. Accordingly, the investigations can be also extended to the whole-brain models for further analyses of brain structure and function.
DOI: 10.34740/kaggle/dsv/4724543
title: BBC News
DOI: 10.34740/kaggle/dsv/4724580
title: OntoUML/UFO Catalog
DOI: 10.5519/qd.3ocm48dj
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (6635 records)
DOI: 10.6084/m9.figshare.20097332
title: Additional file 11 of "WormTensor: a clustering method for time-series whole-brain activity data of C. elegans"

description: P-values of Hypergeometric test and q-value of FDR of all the clustering methods with the optimal numbers of clusters
DOI: 10.6084/m9.figshare.20097332.v5
title: Additional file 11 of "WormTensor: a clustering method for time-series whole-brain activity data of C. elegans"

description: P-values of Hypergeometric test and q-value of FDR of all the clustering methods with the optimal numbers of clusters
DOI: 10.6084/m9.figshare.20097332.v4
title: Additional file 11 of "WormTensor: a clustering method for time-series whole-brain activity data of C. elegans"

description: P-values of Hypergeometric test and q-value of FDR of all the clustering methods with the optimal numbers of clusters
DOI: 10.6084/m9.figshare.20097314
title: Additional file 2 of "WormTensor: a clustering method for time-series whole-brain activity data of C. elegans"

description: Cellular labels to interpret the clustering results (all the clustering methods with all the numbers of clusters (2 to 20))
DOI: 10.6084/m9.figshare.20097314.v3
title: Additional file 2 of "WormTensor: a clustering method for time-series whole-brain activity data of C. elegans"

description: Cellular labels to interpret the clustering results (all the clustering methods with all the numbers of clusters (2 to 20))
DOI: 10.6084/m9.figshare.20097314.v2
title: Additional file 2 of "WormTensor: a clustering method for time-series whole-brain activity data of C. elegans"

description: Cellular labels to interpret the clustering results (all the clustering methods with all the numbers of clusters (2 to 20))
DOI: 10.6084/m9.figshare.19368119
title: Additional file 1 of "WormTensor: a clustering method for time-series whole-brain activity data of C. elegans"

description: Cellular labels to interpret the clustering results
DOI: 10.6084/m9.figshare.19368119.v7
title: Additional file 1 of "WormTensor: a clustering method for time-series whole-brain activity data of C. elegans"

description: Cellular labels to interpret the clustering results
DOI: 10.6084/m9.figshare.19368119.v6
title: Additional file 1 of "WormTensor: a clustering method for time-series whole-brain activity data of C. elegans"

description: Cellular labels to interpret the clustering results
DOI: 10.5281/zenodo.7439432
title: Chacterizing Toolkits for Platform Independent Chatbot Development

description: Context: With the increase in the use of conversational agents, especially those based on written language (chatbots), users can interact with machines through natural language. Problem: The growing demand for chatbots has raised problems related to building and deploying these conversational agents to different platforms, implying adaptation costs. Solution: We performed a systematic grey literature review to identify a set of DSL-supported tools for platform-independent chatbot development. IS Theory: In this research, we considered the Theory of Behavioral Decision scoping decision processing, the structure of choice, decision processes, and the Theory of Information Processing in what concerns data learning. Method: This research sought to list tools and DSLs for developing platform-independent chatbots, carried out through a review of the grey literature, addressing a qualitative analysis of primary studies. Summary of Results: After conducting the studies, we discovered 14 tools and 10 DSLs supporting the construction of platform-independent chatbots. Contributions and Impact in the IS area: A characterization of tools and DSLs in state of art supporting the construction of platform-independent chatbots. Protocolo de pesquisa, contém: String de Busca e Sinônimos Pequisas retornadas a partir da String de busca Execução dos critérios de inclusão e exclusão Avaliação da qualidade Extração de Dados
DOI: 10.5281/zenodo.7439431
title: Chacterizing Toolkits for Platform Independent Chatbot Development

description: Context: With the increase in the use of conversational agents, especially those based on written language (chatbots), users can interact with machines through natural language. Problem: The growing demand for chatbots has raised problems related to building and deploying these conversational agents to different platforms, implying adaptation costs. Solution: We performed a systematic grey literature review to identify a set of DSL-supported tools for platform-independent chatbot development. IS Theory: In this research, we considered the Theory of Behavioral Decision scoping decision processing, the structure of choice, decision processes, and the Theory of Information Processing in what concerns data learning. Method: This research sought to list tools and DSLs for developing platform-independent chatbots, carried out through a review of the grey literature, addressing a qualitative analysis of primary studies. Summary of Results: After conducting the studies, we discovered 14 tools and 10 DSLs supporting the construction of platform-independent chatbots. Contributions and Impact in the IS area: A characterization of tools and DSLs in state of art supporting the construction of platform-independent chatbots. Protocolo de pesquisa, contém: String de Busca e Sinônimos Pequisas retornadas a partir da String de busca Execução dos critérios de inclusão e exclusão Avaliação da qualidade Extração de Dados
DOI: 10.5281/zenodo.6385559
title: Model run scripts and data used for analyses in Zhang et. al. (2023, JAMES)

description: This archive contains run scripts for CLUBB single-column model (SCM) simulations and post-processed data and analysis scripts used in Zhang et al. (2023, JAMES) entitled "removing numerical pathologies in a turbulence parameterization through convergence testing". The versions of the CLUBB-SCM code used for the simulations can be found on Zenodo under 10.5281/zenodo.7439423, which includes two branches: 1. clubb_release-clubb_paper_base.zip: contains the version of the CLUBB code directly from the master branch, which is the standpoint of the code for our paper. 2. clubb_release-clubb_paper_code.zip: contains the version of the CLUBB code with all revisions discussed in our paper. These code changes have not been merged to CLUBB master branch during the publication period The code modifications in the above branches were based on the CLUBB’s source code available at https://github.com/larson-group/clubb_release.
description: This work was supported by the U.S. Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research (BER) via the Scientific Discovery through Advanced Computing (SciDAC) program. Project number: 70276.
DOI: 10.5281/zenodo.6385560
title: CLUBB single-column model output and scripts for analyses in Zhang et. al. (2022, JAMES)

description: This archive contains CLUBB single-column model (SCM) output and analysis scripts used in the study of "removing numerical pathologies in a turbulence parameterization through convergence testing" in Zhang et al. (2022, JAMES). Included are instantaneous output with a 600-second interval for various prognostic and diagnostic variables in CLUBB-SCM, and the analysis scripts to post-process and generate the figures for analyses in Zhang et al. (2022, JAMES). The CLUBB-SCM code used in the simulations can be found on Zenodo under 10.5281/zenodo.6385287. The CLUBB’s source code is available at https://github.com/larson-group/clubb_release.
description: This work was supported by the U.S. Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research (BER) via the Scientific Discovery through Advanced Computing (SciDAC) program. Project number: 70276.
DOI: 10.5281/zenodo.7439436
title: Model run scripts and data used for analyses in Zhang et. al. (2023, JAMES)

description: This archive contains run scripts for CLUBB single-column model (SCM) simulations and post-processed data and analysis scripts used in Zhang et al. (2023, JAMES) entitled "removing numerical pathologies in a turbulence parameterization through convergence testing". The versions of the CLUBB-SCM code used for the simulations can be found on Zenodo under 10.5281/zenodo.7439423, which includes two branches: 1. clubb_release-clubb_paper_base.zip: contains the version of the CLUBB code directly from the master branch, which is the standpoint of the code for our paper. 2. clubb_release-clubb_paper_code.zip: contains the version of the CLUBB code with all revisions discussed in our paper. These code changes have not been merged to CLUBB master branch during the publication period The code modifications in the above branches were based on the CLUBB’s source code available at https://github.com/larson-group/clubb_release.
description: This work was supported by the U.S. Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research (BER) via the Scientific Discovery through Advanced Computing (SciDAC) program. Project number: 70276.
DOI: 10.26181/21588276.v1
title: The data set for testing cellCounts

description: The 10x Chromium single-cell RNA sequencing technology is a powerful gene expression profiling platform, which is capable of profiling expression of thousands of genes in tens of thousands of cells simultaneously. This platform can produce hundreds of million reads in a single experiment, making it a very challenging task to quantify expression levels of genes in individual cells due to the massive data volume. Here we present cellCounts, a new tool for efficient and accurate quanti-fication of 10x Chromium. cellCounts employs the seed-and-vote strategy to align reads to a refer-ence genome, collapses reads to UMIs (Unique Molecular Identifier) and then assigns UMIs to genes based on the featureCounts program. Using multiple real datasets, we showed that cell-Counts is ~3 times faster than cellRanger, a popular quantification program developed by 10x. Using simulation and real datasets with built-in ground truth, we demonstrated that cellCounts is markedly more accurate than cellRanger, cellCounts is implemented in R, making it easily inte-grated with other R programs for analysing Chromium data.
DOI: 10.5519/qd.p2eqo3ew
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.7439463
title: Wavelet decomposition program

description: the signal is decomposed into global trend and fluctuation components using wavelet decomposition
DOI: 10.5281/zenodo.7439462
title: Wavelet decomposition program

description: the signal is decomposed into global trend and fluctuation components using wavelet decomposition
DOI: 10.34740/kaggle/dsv/4724746
title: 💥📰 Daily Breaking News Twitter Dataset
DOI: 10.5281/zenodo.7429932
title: Investigation of machine learning algorithms for taxonomic classification of marine metagenomes

description: Training, testing, and blind datasets used for machine learning algorithms for taxonomic classification of marine metagenomes: <strong>K12.kmers.txt</strong> - 12bp k-mer vocabulary constructed by Jellyfish v1.1.11 from 47,894 genomes in GTDB release 202 <strong>MarRef_1.6.tsv</strong> - Metadata file downloaded from MarRef v1.6 <strong>MarRef.genustrain.fasta</strong> - Training set from MarRef v1.6 (seed=808) used for genus classification <strong>MarRef.genustest.fasta</strong> - Testing set from MarRef v1.6 (seed=747) used for genus classification <strong>MarRef.speciestrain.fasta</strong> - Training set from MarRef v1.6 (seed=808) used for species classification <strong>MarRef.speciestest.fasta</strong> - Testing set from MarRef v1.6 (seed=747) used for species classification <strong>MarRef.traintest.key.tsv</strong> - Table containing MarRef accession, GenBank accession, GenBank taxonomy ID, taxonomic information, and labels used for species and genus testing and training <strong>anonymous_reads_*.fq</strong> - Blind datasets (1-10) in interleaved fastq format <strong>reads_mapping_*.tsv</strong> - Key for blind datasets 1-10. Each sequence header is mapped to its corresponding MarRef accession and NCBI taxonomic ID.
DOI: 10.5281/zenodo.7429931
title: Investigation of machine learning algorithms for taxonomic classification of marine metagenomes

description: Training, testing, and blind datasets used for machine learning algorithms for taxonomic classification of marine metagenomes: <strong>K12.kmers.txt</strong> - 12bp k-mer vocabulary constructed by Jellyfish v1.1.11 from 47,894 genomes in GTDB release 202 <strong>MarRef_1.6.tsv</strong> - Metadata file downloaded from MarRef v1.6 <strong>MarRef.genustrain.fasta</strong> - Training set from MarRef v1.6 (seed=808) used for genus classification <strong>MarRef.genustest.fasta</strong> - Testing set from MarRef v1.6 (seed=747) used for genus classification <strong>MarRef.speciestrain.fasta</strong> - Training set from MarRef v1.6 (seed=808) used for species classification <strong>MarRef.speciestest.fasta</strong> - Testing set from MarRef v1.6 (seed=747) used for species classification <strong>MarRef.traintest.key.tsv</strong> - Table containing MarRef accession, GenBank accession, GenBank taxonomy ID, taxonomic information, and labels used for species and genus testing and training <strong>anonymous_reads_*.fq</strong> - Blind datasets (1-10) in interleaved fastq format <strong>reads_mapping_*.tsv</strong> - Key for blind datasets 1-10. Each sequence header is mapped to its corresponding MarRef accession and NCBI taxonomic ID.
DOI: 10.34740/kaggle/dsv/4724799
title: 🇺🇦 Ukraine Conflict Twitter Dataset
DOI: 10.6084/m9.figshare.21729026
title: Hyperglycemia

description: Hyperglycemia and HIV death
DOI: 10.48577/jpl.k0xsjd
title: JPL's Foundry Furnace: web-based concurrent engineering for formulation

description: No abstract available.
DOI: 10.6084/m9.figshare.21729035
title: arv_death

description: ARV therapy for children and predictors of mortality
DOI: 10.5281/zenodo.7013087
title: IMC datasets for HCC mouse models

description: Raw MCD files associated with published study: Profiling of mouse syngeneic HCC tumor models as a framework to understand anti-PD-1 sensitive tumor microenvironments (https://doi.org/10.1002/hep.32707)
DOI: 10.5281/zenodo.7013086
title: IMC datasets for HCC mouse models

description: Raw MCD files associated with published study: Profiling of mouse syngeneic HCC tumor models as a framework to understand anti-PD-1 sensitive tumor microenvironments (https://doi.org/10.1002/hep.32707)
DOI: 10.5281/zenodo.7439428
title: IMC dataset for CCA mouse model study

description: Imaging Mass Cytometry files acquired at Johns Hopkins Mass Cytometry Facility Contents of zipped file, "Raw_MCDFiles_Manuscript.zip": Raw MCD files for cholangiocarcinoma mouse model study Metadata for ROIs Also included, "IMC - Mouse Panel": panel of antibodies used Notes: ROI D2 was split into two ROIs: D2-split1 and D2-split2 Non-experimental ROIs are labelled as either “null” or “Energy Test”
DOI: 10.5281/zenodo.7439427
title: IMC dataset for CCA mouse model study

description: Imaging Mass Cytometry files acquired at Johns Hopkins Mass Cytometry Facility Contents of zipped file, "Raw_MCDFiles_Manuscript.zip": Raw MCD files for cholangiocarcinoma mouse model study Metadata for ROIs Also included, "IMC - Mouse Panel": panel of antibodies used Notes: ROI D2 was split into two ROIs: D2-split1 and D2-split2 Non-experimental ROIs are labelled as either “null” or “Energy Test”
DOI: 10.25919/x1d1-pz44
title: Persistent water within the MDB from MIM

description: Thirty-three years of minimum monthly images of surface water extent have been used to identify persistent water within the Murray-Darling Basin (MDB). The multi-index method (MIM) (refer to Ticehurst, Teng and Sengupta 2022 in the Related Links for a description of the method) was used for mapping surface water across the MDB based on Landsat surface reflectance data available in Digital Earth Australia. Persistent water layers are provided for 1988 to 2020, 1990 to 1999, 2000 to 2009 and 2010 to 2019. The data were produced as part of the Murray-Darling Basin Ecosystem Function (MDB-EF) project. Refer to the metadata file (see Supporting files) for information about the method, data format and limitations of this product.
DOI: 10.21227/qbt8-fn98
title: Influence of Neuromuscular Electrical Stimulation Waveforms and Durations on Functional Corticomuscular Coupling and Brain Functional Connectivity Network 

description: Each trial included 1 s of wrist flexing, 5 s of wrist holding, 1 s of wrist relaxing, and the last 3 s were reserved for rest .
DOI: 10.34740/kaggle/dsv/4724925
title: Michelin Guide Restaurants
DOI: 10.25584/pnnl.data/1902325
title: Multi-Species Complex and Standard Metabolomic Samples with Verified Truth Annotations Dataset

description: This dataset contains 4523251 (~6.35 GB) metabolite-spectra matches following identification with CoreMS. Data were manually curated as true positives, true negatives, or unknowns. Calculations for spectral similarity scores were carried out with two methods for a total of ~12.7 GB (6.35 * 2) of data. They are all .tsv files, though can easily be changed to .txt. The file types are: * human cerebrospinal fluid (CSF), human blood plasma human urine: already published here https://www.nature.com/articles/s41597-021-00894-y, • purchased FAMES standards • fungi species (A. niger, A. nidulans, T. reesei) • soil crust
DOI: 10.17632/4m54tfbv34.1
title: Transcripts:  Selection of excerpts from Dan Andrews' media conferences in 2020

description: This data set contains the transcripts of excerpts from a selection of media conferences by the Hon Dan Andrews MLA, Premier of Victoria, held between August and November 2020.  These media conferences were held in the context of the lockdown of Melbourne and the wider state of Victoria due to COVID-19.  The excerpts are the portion of the media conference in which the Premier presented his prepared remarks.  The excerpts do not include the questions and answer session with journalists which followed his remarks.  

The rationale for the selection of transcripts for analysis is also available here.
DOI: 10.6084/m9.figshare.21724334
title: Neuroimage Demo

description: The supplement data of <em>Learning of irrelevant stimulus-response associations modulates cognitive control </em>
DOI: 10.6084/m9.figshare.21724334.v1
title: Neuroimage Demo

description: The supplement data of <em>Learning of irrelevant stimulus-response associations modulates cognitive control </em>
DOI: 10.26186/100521
title: Australian Hydrochemistry Data

description: Geoscience Australia and its predecessors have analysed hydrochemistry of water sampled from boreholes (both pore water and groundwater), surface features, and rainwater. Sampling was undertaken during drilling or monitoring projects, and this dataset represents a significant subset of stored analyses. Water chemistry including isotopic data is essential to better understand groundwater origins, ages and dynamics, processes such as recharge and inter-aquifer connectivity and for informing conceptual and numerical groundwater models. This GA dataset underpins a nationally consistent data delivery tool and web-based mapping to visualise, analyse and download groundwater chemistry and environmental isotope data. This dataset is a spatially-enabled groundwater hydrochemistry database based on hydrochemistry data from projects completed in Geoscience Australia. The database includes information on physical-chemical parameters (EC, pH, redox potential, dissolved oxygen), major and minor ions, trace elements, nutrients, pesticides, isotopes and organic chemicals. Basic calculations for piper plots colours are derived from Peeters, 2013 - A Background Color Scheme for Piper Plots to Spatially Visualize Hydrochemical Patterns - Groundwater, Volume 52(1) . Upon loading the data to the database, all hydrochemistry data are assessed for reliability using Quality Assurance/Quality Control procedures and all datasets were standardised. This data is made accessible with open geospatial consortium (OGC) web services and is discoverable via the Geoscience Australia Portal (https://portal.ga.gov.au/). This dataset is published with the permission of the CEO, Geoscience Australia.
DOI: 10.26186/100802
title: GIS dataset of Onshore Seismic Surveys (updated May 2019)

description: This GIS dataset includes onshore seismic surveys undertaken by Geoscience Australia and its predecessors in collaboration with the State and Territory Geological Surveys, National Research Facility for Earth Sounding (ANSIR), AuScope Earth Imaging, Universities and other industry and research partners from 1976 to present. This Shapefile enables users to display seismic lines on a map and contains links to data packages available for free download. The data are also available as a web service (see Related Links).