cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5715310
title: Additional file 1: of Draft genome sequences of three fungal-interactive Paraburkholderia terrae strains, BS007, BS110 and BS437

description: Table S1 Sequencing statistics analysis of Paraburkholderia terrae BS007, BS110, and BS437. Table S2 Pan/core genome. Table S3 Metabolic profiles of compared bacterial strains (Based on a score from 0 to 1). Table S4.1 Secondary metabolites, BS007. Table S4.2 Secondary metabolites, BS110. Table S4.3 Secondary metabolites, BS437. Table S5.1 Nodulation genes. Table S5.2 Type 4 pili and biofilm formation systems. Table S5.3 Indole acetic acid (IAA) biosynthesis. Table S5.4 1-aminocyclopropane-1-carboxylate deaminase (ACC deaminase). Table S6.1 Type-1 secretion systems. Table S6.2 Type-2 secretion systems. Table S6.3 Type-3 secretion systems. Table S6.4 Type-4 secretion systems. Table S6.5 Type-6 secretion systems. Table S7 Homology of putative energy-generating gene clusters to P. terrae BS001. (XLSX 164 kb)
DOI: 10.6084/m9.figshare.5715310.v1
title: Additional file 1: of Draft genome sequences of three fungal-interactive Paraburkholderia terrae strains, BS007, BS110 and BS437

description: Table S1 Sequencing statistics analysis of Paraburkholderia terrae BS007, BS110, and BS437. Table S2 Pan/core genome. Table S3 Metabolic profiles of compared bacterial strains (Based on a score from 0 to 1). Table S4.1 Secondary metabolites, BS007. Table S4.2 Secondary metabolites, BS110. Table S4.3 Secondary metabolites, BS437. Table S5.1 Nodulation genes. Table S5.2 Type 4 pili and biofilm formation systems. Table S5.3 Indole acetic acid (IAA) biosynthesis. Table S5.4 1-aminocyclopropane-1-carboxylate deaminase (ACC deaminase). Table S6.1 Type-1 secretion systems. Table S6.2 Type-2 secretion systems. Table S6.3 Type-3 secretion systems. Table S6.4 Type-4 secretion systems. Table S6.5 Type-6 secretion systems. Table S7 Homology of putative energy-generating gene clusters to P. terrae BS001. (XLSX 164 kb)
DOI: 10.6084/m9.figshare.5715259
title: Raw data

description: Ammopiptanthus mongolicus
DOI: 10.6084/m9.figshare.5715259.v1
title: 111Untitled Item

description: Ammopiptanthus mongolicus
DOI: 10.6084/m9.figshare.5715259.v3
title: 111Untitled Item

description: Ammopiptanthus mongolicus
DOI: 10.6084/m9.figshare.5715259.v2
title: 111Untitled Item

description: Ammopiptanthus mongolicus
DOI: 10.6084/m9.figshare.5715196
title: Regularization for Machine Learning

description: Regularization for Machine Learning-RegML GUI
DOI: 10.6084/m9.figshare.5715196.v3
title: Regularization for Machine Learning

description: Regularization for Machine Learning-RegML GUI
DOI: 10.6084/m9.figshare.5715193
title: SineWave Oscilloscope

description: Sine Wave Oscilloscope for FUN
DOI: 10.6084/m9.figshare.5715193.v1
title: SineWave Oscilloscope

description: Sine Wave Oscilloscope for FUN
DOI: 10.6084/m9.figshare.5715181
title: Linear Feedback Shift Register

description: Linear Feedback Shift Register
DOI: 10.6084/m9.figshare.5715181.v2
title: Linear Feedback Shift Register

description: Linear Feedback Shift Register
DOI: 10.6084/m9.figshare.5715178
title: GameOfDotsAndBoxes

description: Game of Dots and Boxes
DOI: 10.6084/m9.figshare.5715178.v2
title: GameOfDotsAndBoxes

description: Game of Dots and Boxes
DOI: 10.6084/m9.figshare.5715175
title: Classic Snake Game

description: Classical Snake Game codded in JavaScript with P5js
DOI: 10.6084/m9.figshare.5715175.v2
title: Classic Snake Game

description: Classical Snake Game codded in JavaScript with P5js
DOI: 10.6084/m9.figshare.5715169
title: DeepLearning_from_scratch

description: Implementation of Deep Neural Network from scratch without other libraries<br><br>https://github.com/Nikeshbajaj/DeepLearning_from_scratch<br>
DOI: 10.6084/m9.figshare.5715169.v2
title: DeepLearning_from_scratch

description: Implementation of Deep Neural Network from scratch without other libraries<br><br>https://github.com/Nikeshbajaj/DeepLearning_from_scratch<br>
DOI: 10.6084/m9.figshare.5715166
title: Fast Fourier Transform GUI

description: MATLAB GUI for 8 -point FFT, with computation at all the stage. This is to help student to check their computation of FFT
DOI: 10.6084/m9.figshare.5715157
title: ILUMI_OGS1.2 kallisto RNA-Seq expression quantification (TPM)

description: Ignelater luminosus RNA-Seq reads were pseudoaligned to the ILUMI_OGS1.2 geneset CDS sequences using Kallisto (v0.44.0)(37) with 100 bootstraps (-b 100), producing transcripts-per-million reads (TPM).
DOI: 10.6084/m9.figshare.5715157.v1
title: ILUMI_OGS1.2 kallisto RNA-Seq expression quantification (TPM)

description: Ignelater luminosus RNA-Seq reads were pseudoaligned to the ILUMI_OGS1.2 geneset CDS sequences using Kallisto (v0.44.0)(37) with 100 bootstraps (-b 100), producing transcripts-per-million reads (TPM).
DOI: 10.6084/m9.figshare.5715151
title: Figure 5. PPYR_OGS1.1 + AQULA_OGS1.0 Sleuth / HE DE Edifferential expression Venn diagram analysis (BSN-TPM)

description: RScript plus results for between sample normalized expression quantification (BSN-TPM) with Kallisto/Sleuth + Bash script plus results for Highly expressed (HE), differentially expressed (DE), and annotated as Enzyme (E) analysis
DOI: 10.6084/m9.figshare.5715148
title: Single Cell RNA Sequencing of stem cell-derived retinal ganglion cells Supporting Tables

description: Single cell transcriptomes of 1,174 human embryonic stem cell-derived retinal ganglion cells (RGCs). These tables contain supplementary results from differential gene and pathway analysis.<br>
DOI: 10.6084/m9.figshare.5715151.v1
title: Figure 5. PPYR_OGS1.1 + AQULA_OGS1.0 Sleuth / HE DE Edifferential expression Venn diagram analysis (BSN-TPM)

description: RScript plus results for between sample normalized expression quantification (BSN-TPM) with Kallisto/Sleuth + Bash script plus results for Highly expressed (HE), differentially expressed (DE), and annotated as Enzyme (E) analysis
DOI: 10.6084/m9.figshare.5715148.v2
title: Single Cell RNA Sequencing of stem cell-derived retinal ganglion cells Supporting Tables

description: Single cell transcriptomes of 1,174 human embryonic stem cell-derived retinal ganglion cells (RGCs). These tables contain supplementary results from differential gene and pathway analysis.<br>
DOI: 10.6084/m9.figshare.5715148.v1
title: Single Cell RNA Sequencing of stem cell-derived retinal ganglion cells Supporting Tables

description: Single cell transcriptomes of 1,174 human embryonic stem cell-derived retinal ganglion cells (RGCs). These tables contain supplementary results from differential gene and pathway analysis.<br>
DOI: 10.6084/m9.figshare.5715142
title: AQULA_OGS1.0 kallisto RNA-Seq expression quantification (TPM)

description: A. lateralis RNA-Seq reads were pseudoaligned to the AQULA_OGS1.0 geneset mRNAs using Kallisto (v0.43.1)(37) with 100 bootstraps (-b 100), producing transcripts-per-million reads (TPM).
DOI: 10.6084/m9.figshare.5715142.v1
title: AQULA_OGS1.0 kallisto RNA-Seq expression quantification (TPM)

description: A. lateralis RNA-Seq reads were pseudoaligned to the AQULA_OGS1.0 geneset mRNAs using Kallisto (v0.43.1)(37) with 100 bootstraps (-b 100), producing transcripts-per-million reads (TPM).
DOI: 10.6084/m9.figshare.5715139
title: PPYR_OGS1.1 kallisto RNA-Seq expression quantification (TPM)

description: P. pyralis, RNA-Seq reads were pseudoaligned to Ppyr_OGS1.1 geneset CDSs using Kallisto (v0.44.0)(37) with 100 bootstraps (-b 100), producing transcripts-per-million reads (TPM).
DOI: 10.6084/m9.figshare.5715136
title: OrthoFinder protein clustering analysis (Orthogroups)

description: Peptides from the PpyrOGS1.1, AlatOGS1.0, and IlumiOGS1.2 genebuilds, the D. melanogaster (NCBI GCF_000001215.4) and T. castaneum (NCBI GCF_000002335.3) reference protein gene sets were clustered into orthogroups using the Orthofinder (v2.1.6) pipeline.
DOI: 10.6084/m9.figshare.5715136.v1
title: OrthoFinder protein clustering analysis (Orthogroups)

description: Peptides from the PpyrOGS1.1, AlatOGS1.0, and IlumiOGS1.2 genebuilds, the D. melanogaster (NCBI GCF_000001215.4) and T. castaneum (NCBI GCF_000002335.3) reference protein gene sets were clustered into orthogroups using the Orthofinder (v2.1.6) pipeline.
DOI: 10.6084/m9.figshare.5715103
title: gpu-cuda-spinodal-9pt

description: Solution to the CHiMaD Phase Field benchmark problem on spinodal decomposition using CUDA, with a 9-point discrete Laplacian stencil
DOI: 10.6084/m9.figshare.5715103.v1
title: gpu-cuda-spinodal-9pt

description: Solution to the CHiMaD Phase Field benchmark problem on spinodal decomposition using CUDA, with a 9-point discrete Laplacian stencil
DOI: 10.6084/m9.figshare.5715097
title: Murray Cod Larval Database Murrumbidgee 2011-2013

description: Data on larval sampling, otolith microchemistry, water biogeochem, other abiotic factors in the Murrumbidgee River in 2011-2013. This provides the spatio-temporal data to accompany the associated SNP data for individual larvae (https://figshare.com/s/3198588f10133d9737d6).<br>This data allowed identification of hybridisation between Murray cod and Trout cod. (https://peerj.com/articles/2593/) The snp data also allowed identification of a mating strategy (pair-bonding) in Murray cod.
DOI: 10.6084/m9.figshare.5715097.v1
title: Murray Cod Larval Database Murrumbidgee 2011-2013

description: Data on larval sampling, otolith microchemistry, water biogeochem, other abiotic factors in the Murrumbidgee River in 2011-2013. This provides the spatio-temporal data to accompany the associated SNP data for individual larvae (https://figshare.com/s/3198588f10133d9737d6).<br>This data allowed identification of hybridisation between Murray cod and Trout cod. (https://peerj.com/articles/2593/) The snp data also allowed identification of a mating strategy (pair-bonding) in Murray cod.
DOI: 10.17608/k6.auckland.5715058
title: Micro-CT Visualisation of a Jet Injected Tissue Sample

description: Visualisation of a 3D reconstructed, jet injected sample using micro-CT. Videos are supplementary to Chapter 7 of a PhD thesis titled "Large Volume Jet Inejction" by James Mckeage.
DOI: 10.17608/k6.auckland.5715058.v1
title: Micro-CT Visualisation of a Jet Injected Tissue Sample

description: Visualisation of a 3D reconstructed, jet injected sample using micro-CT. Videos are supplementary to Chapter 7 of a PhD thesis titled "Large Volume Jet Inejction" by James Mckeage.
DOI: 10.6084/m9.figshare.5715052
title: Literary Alzheimer’s: A qualitative feasibility study of dementia-friendly book groups

description: Despite the estimated 47 million people living with dementia and despite reading being our mostpopular cultural activity, there is lack of research around how people with dementia experiencereading and what tools can be used to help them to enjoy a good read. Current IFLA Guidelines forLibrary Services to Persons with Dementia are unhelpful, as they encourage the stigmatisation andinfantalisation of the adult with dementia. There have been few strategic efforts by New Zealandlibraries to engage those living with dementia to continue reading. Nelson and Canterbury librariesare some exceptions, providing ‘dementia-friendly’ book groups.This report summarises the findings of a qualitative study, which asks how people living withdementia experience reading literary fiction and how such activity is best facilitated for creativityand enjoyment. The findings of the study refute Ribot’s ‘regression hypothesis’ of dementia. Whatwas striking in both focus groups was the preserved command and appreciation of language, andthe stimulation of sharing the “wonderful words” of the featured author Charles Dickens. To supportmemory deficits, while preserving the stimulation of the original language, the following modificationswere found to be helpful: providing a cast of characters, reducing the amount of text, andregularly repeating referents.
DOI: 10.6084/m9.figshare.5715040
title: Single-cell RNA-seq data from Smart-seq2 sequencing of FACS sorted cells

description: Gene-count tables for FACS sorted cells sequenced with Smart-Seq2 from 20 organs of 7 mice. Cells are grouped by tissue of origin.<br><br> Includes data for 53,760 cells, 44,879 of which passed a QC cutoff of at least 500 genes and 50,000 reads.<br>Cell annotations using the Cell Ontology [1] controlled vocabulary are in a separate csv.<br>[1] http://purl.obolibrary.org/obo/cl.owl<br>
DOI: 10.6084/m9.figshare.5715037
title: adult_birds_RNA-seq_to_tan_reference.vcf

description: Contains variants identified by mapping RNA-Seq of 11 white and nine tan birds to the tan reference genome in NCBI. This is a supplementary file in Sun et al. (2018).
DOI: 10.6084/m9.figshare.5715037.v2
title: adult_birds_RNA-seq_to_tan_reference.vcf

description: Contains variants identified by mapping RNA-Seq of 11 white and nine tan birds to the tan reference genome in NCBI. This is a supplementary file in Sun et al. (2018).
DOI: 10.6084/m9.figshare.5715025
title: Single-cell RNA-seq data from microfluidic emulsion

description: Gene-count files and metadata files for single cells from different organs of mice processed on the 10X Genomics Platform. The counts are given using the .mtx file output by the CellRanger program, with one folder per run.<br>Includes data for 422,803 droplets, 55,656 of which passed a QC cutoff of 500 genes and 1000 UMI.<br>Cell annotations using the Cell Ontology [1] controlled vocabulary are in a separate csv.<br>[1] http://purl.obolibrary.org/obo/cl.owl<br>
DOI: 10.17608/k6.auckland.5715001
title: East Polynesian Monumental Architecture Classification

description: This is a presence/absence data set of monumental structures (e.g., marae, moai, heiau) from East Polynesia forming the dataset analysed in Cochrane 2015 (Journal of the Polynesian Society). All data colated from various published sources. First columns identify the structures by island, publication, site name, etc. Subsequent columns are presence/absence of particular architectural features. For full description see Cochrane 2015
DOI: 10.17608/k6.auckland.5715001.v1
title: East Polynesian Monumental Architecture Classification

description: This is a presence/absence data set of monumental structures (e.g., marae, moai, heiau) from East Polynesia forming the dataset analysed in Cochrane 2015 (Journal of the Polynesian Society). All data colated from various published sources. First columns identify the structures by island, publication, site name, etc. Subsequent columns are presence/absence of particular architectural features. For full description see Cochrane 2015
DOI: 10.17608/k6.auckland.5714968
title: Archaeological ceramic ppm data from Fiji

description: PPM data from LAICPMS analyses of intact archaeological sherds from Fiji. Columns are ppms and rows are individual sherds described by excavation year, provenience, and sherd number (e.g, 1991-1011-53int equal excavated in 1991, context 10111, sherd 53 (intact)).
DOI: 10.17608/k6.auckland.5714968.v1
title: Archaeological ceramic ppm data from Fiji

description: PPM data from LAICPMS analyses of intact archaeological sherds from Fiji. Columns are ppms and rows are individual sherds described by excavation year, provenience, and sherd number (e.g, 1991-1011-53int equal excavated in 1991, context 10111, sherd 53 (intact)).
DOI: 10.6084/m9.figshare.5714944
title: Vanderbilt Water Conservation Index with State and MSA Covariates

description: Vanderbilt Water Conservation Index with covariates at city and state level, as analyzed in J.M. Gilligan <i>et al</i>. "Urban Water Conservation Policies in the United States" <i>Earth's Future</i> (in press).<br>The basic data is contained in msa_data.csv (water conservation indices and MSA-level covariates, which corresponds to Dataset S1 in the supporting information) and state_covariates.csv (state-level covariates, which corresponds to Dataset S3). <br><br>The variables are described in msa_data_codebook.csv (which corresponds to Dataset S2) and state_covariates_codebook.csv (which corresponds to Dataset S4).<br>Table_S1.csv, Table_S2.csv, Table_S15.csv, Table_S16.csv, and Table_S17.csv are large tables from the supporting information for the article Urban Water Conservation Policies.<br>
DOI: 10.6084/m9.figshare.5714812
title: Photinus pyralis orthomyxo-like virus 2 sequence and annotation

description: Photinus pyralis orthomyxo-like virus 2 sequence and annotation
DOI: 10.6084/m9.figshare.5714812.v1
title: Photinus pyralis orthomyxo-like virus 2 sequence and annotation

description: Photinus pyralis orthomyxo-like virus 2 sequence and annotation
DOI: 10.6084/m9.figshare.5714806
title: Photinus pyralis orthomyxo-like virus 1 sequence and annotation

description: Photinus pyralis orthomyxo-like virus 1 sequence and annotation
DOI: 10.6084/m9.figshare.5714806.v1
title: Photinus pyralis orthomyxo-like virus 1 sequence and annotation

description: Photinus pyralis orthomyxo-like virus 1 sequence and annotation
DOI: 10.6084/m9.figshare.5714326
title: Working directory for de novo genome assembly tutorial

description: This is the working directory for data used in a de novo genome assembly tutorial: https://astrobiomike.github.io/genomics/de_novo_assembly
DOI: 10.6084/m9.figshare.5714326.v1
title: Working directory for de novo genome assembly tutorial

description: This is the working directory for data used in a de novo genome assembly tutorial: https://astrobiomike.github.io/genomics/de_novo_assembly
DOI: 10.6084/m9.figshare.5713507
title: Results directory for the de novo genome assembly tutorial

description: Directory contains results files from a de novo genome assembly tutorial: https://astrobiomike.github.io/genomics/de_novo_assembly
DOI: 10.6084/m9.figshare.5713507.v1
title: Results directory for the de novo genome assembly tutorial

description: Directory contains results files from a de novo genome assembly tutorial: https://astrobiomike.github.io/genomics/de_novo_assembly
DOI: 10.6084/m9.figshare.5712292.v1
title: Extended Data Table 1 - Genomic sequencing library statistics

description: The level of non-eukaryote contamination of the raw read data for each P. pyralis library was assessed using kraken v1.0 using a dust-masked minikraken database to eliminate comparison with repetitive sequences. Overall contamination levels were low, in agreement with a low level of contamination in our final assembly. On average, contamination was 3.5% in the PacBio reads (whole body) and 1.6% in the Illumina reads (only thorax). There was no support for Wolbachia in any of the P. pyralis libraries, with the exception of a single read from a single library which had a kraken hit to Wolbachia. QUAST version 4.3, was used to calculate genome quality statistics for comparison and optimization of assembly methods. BUSCO (v3.0.2) was used to estimate the percentage of expected single copy conserved orthologs captured in our assemblies and a subset of previously published beetle genome assemblies. The endopterygota_odb9 (metamorphosing insects) BUSCO set was used. The bacteria_odb9 gene set was used to identify potential contaminants by screening contigs and scaffolds for conserved bacterial genes. For genome predictions from beetles, the parameter “--species tribolium2012” was used to improve the BUSCO internal Augustus gene predictions. For non-beetle insect genome predictions, “--species=fly” was used. <br>
DOI: 10.6084/m9.figshare.5712178
title: Decision tree models to classify nanomaterials according to the <i>DF4nanoGrouping</i> scheme

description: To keep pace with its rapid development an efficient approach for the risk assessment of nanomaterials is needed. Grouping concepts as developed for chemicals are now being explored for its applicability to nanomaterials. One of the recently proposed grouping systems is <i>DF4nanoGrouping</i> scheme. In this study, we have developed three structure-activity relationship classification tree models to be used for supporting this system by identifying structural features of nanomaterials mainly responsible for the surface activity. We used data from 19 nanomaterials that were synthesized and characterized extensively in previous studies. Subsets of these materials have been used in other studies (short-term inhalation, protein carbonylation, and intrinsic oxidative potential), resulting in a unique data set for modeling. Out of a large set of 285 possible descriptors, we have demonstrated that only three descriptors (size, specific surface area, and the quantum-mechanical calculated property ‘lowest unoccupied molecular orbital’) need to be used to predict the endpoints investigated. The maximum number of descriptors that were finally selected by the classification trees (CT) was very low– one for intrinsic oxidative potential, two for protein carbonylation, and three for NOAEC. This suggests that the models were well-constructed and not over-fitted. The outcome of various statistical measures and the applicability domains of our models further indicate their robustness. Therefore, we conclude that CT can be a useful tool within the <i>DF4nanoGrouping</i> scheme that has been proposed before.
DOI: 10.6084/m9.figshare.5712178.v1
title: Decision tree models to classify nanomaterials according to the <i>DF4nanoGrouping</i> scheme

description: To keep pace with its rapid development an efficient approach for the risk assessment of nanomaterials is needed. Grouping concepts as developed for chemicals are now being explored for its applicability to nanomaterials. One of the recently proposed grouping systems is <i>DF4nanoGrouping</i> scheme. In this study, we have developed three structure-activity relationship classification tree models to be used for supporting this system by identifying structural features of nanomaterials mainly responsible for the surface activity. We used data from 19 nanomaterials that were synthesized and characterized extensively in previous studies. Subsets of these materials have been used in other studies (short-term inhalation, protein carbonylation, and intrinsic oxidative potential), resulting in a unique data set for modeling. Out of a large set of 285 possible descriptors, we have demonstrated that only three descriptors (size, specific surface area, and the quantum-mechanical calculated property ‘lowest unoccupied molecular orbital’) need to be used to predict the endpoints investigated. The maximum number of descriptors that were finally selected by the classification trees (CT) was very low– one for intrinsic oxidative potential, two for protein carbonylation, and three for NOAEC. This suggests that the models were well-constructed and not over-fitted. The outcome of various statistical measures and the applicability domains of our models further indicate their robustness. Therefore, we conclude that CT can be a useful tool within the <i>DF4nanoGrouping</i> scheme that has been proposed before.