cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.4956299.v2
title: On the quantum differentiation of smooth real-valued functions

description: <b>Abstract</b>. Calculating the value of $C^{k\in\{1,\infty\}}$ class of smoothness real-valued function's derivative in point of $\mathbb{R}^+$ in radius of convergence of its Taylor polynomial (or series), applying an analog of Newton's binomial theorem and $q$-difference operator. $(P,q)$-power difference introduced in section 5. Additionally, by means of Newton's interpolation formula, the discrete analog of Taylor series, interpolation using $q$-difference and $p,q$-power difference is shown.<br> <b><br>arXiv</b>:1705.02516 <br><b>MSC 2010</b>: 26A24, 05A30, 41A58<br> <b>DOI</b>: 10.6084/m9.figshare.4956299 <br><b>Keywords</b>: derivative, differential calculus, differentiation, Taylor's theorem, Taylor's formula, Taylor's series, Taylor's polynomial, power function, Binomial theorem, smooth function, real calculus, Newton's interpolation formula, finite difference, q-derivative, Jackson derivative, q-calculus, quantum calculus, (p,q)-derivative, (p,q)-Taylor formula, mathematics, math, maths, science, arxiv, preprint, математика, наука, analysis, differential equations, quantum algebra, algebra, q-difference, algebra, analysis, calculus, open science
DOI: 10.6084/m9.figshare.12905747
title: Tissue differential expression profile of "De novo assembly of the Brown trout (Salmo trutta m. fario) brain and muscle transcriptome: transcript annotation, tissue differential expression profile and SNP discovery"

description: Muscle and Brain derived reads were mapped to ORF unigenes by TopHat. Tissue specific transcriptome expression analysis was performed by normalization of raw reads (FPKM, fragments per kilobase of exon per million fragments) obtained from both tissues, brain and muscle. Tissue expression threshold was set at FPKM≥0.5. This data set contains raw output from raw Cufflinks transcript expression analysis of brain (Raw_Cufflinks_Brain_transcript_expression.txt) and muscle (Raw_Cufflinks_Muscle_transcript_expression.txt) tissues, raw Cuffdiff transcript differential expression testing (Raw_Cuffdiff_Brain_Muscle_transcript_differential_expression_testing.txt) and procesed results of transcript differential expression testing at FDR&lt;0.01 (Differentialy expressed non-redundant ORF unigenes at FDR&lt;0.01.xlsx).
DOI: 10.6084/m9.figshare.4956284.v3
title: Metasequoia

description: Model Development and Evaluation of Mother Tree Height and Diameter at Breast Height of Mature Native Chinese Metasequoia
DOI: 10.6084/m9.figshare.12905747.v1
title: Tissue differential expression profile of "De novo assembly of the Brown trout (Salmo trutta m. fario) brain and muscle transcriptome: transcript annotation, tissue differential expression profile and SNP discovery"

description: Muscle and Brain derived reads were mapped to ORF unigenes by TopHat. Tissue specific transcriptome expression analysis was performed by normalization of raw reads (FPKM, fragments per kilobase of exon per million fragments) obtained from both tissues, brain and muscle. Tissue expression threshold was set at FPKM≥0.5. This data set contains raw output from raw Cufflinks transcript expression analysis of brain (Raw_Cufflinks_Brain_transcript_expression.txt) and muscle (Raw_Cufflinks_Muscle_transcript_expression.txt) tissues, raw Cuffdiff transcript differential expression testing (Raw_Cuffdiff_Brain_Muscle_transcript_differential_expression_testing.txt) and procesed results of transcript differential expression testing at FDR&lt;0.01 (Differentialy expressed non-redundant ORF unigenes at FDR&lt;0.01.xlsx).
DOI: 10.6084/m9.figshare.4956284.v2
title: 10.6084/m9.figshare.4956284

description: Model Development and Evaluation of Mother Tree Height and Diameter at Breast Height of Mature Native Chinese Metasequoia
DOI: 10.6084/m9.figshare.12902474
title: Supplementary Files for "De novo assembly of the Brown trout (Salmo trutta m. fario) brain and muscle transcriptome: transcript annotation, tissue differential expression profile and SNP discovery"

description: This data set contains two document files:Supplementary file1:Methodology descriptionSupplementary file2:Descriptive statistics of assembly and sequencing
DOI: 10.6084/m9.figshare.7326464
title: De novo assembled contigs

description: Trinity predicted denovo transcripts. The FASTQ sequence reads were assembled using Trinity which is specifically designed for <i>de novo </i>assembly of transcriptomes. Trinity was run on the paired end sequences with the fixed default k-mer size of 25 and minimum contig length of 200. Assembly algorithm runs on three steps, Inchworm, Chrysalis and Butterfly. Inchworm first assembles overlapping sequences using a greedy extension and reports the unique portions of alternatively spliced transcripts (contigs). Chrysalis then clusters related contigs into components (i.e., compXXXX_c0) and models complexity using de Bruijn graphs for each of the clusters. Finally, Butterfly processes the graphs and reports subcomponents (i.e., compXXXX_c0_seq1, compXXXX_c0_seq2, etc.), roughly corresponding to genes that are made up of individual transcripts.
DOI: 10.6084/m9.figshare.7326464.v1
title: De novo assembled contigs

description: Trinity predicted denovo transcripts. The FASTQ sequence reads were assembled using Trinity which is specifically designed for <i>de novo </i>assembly of transcriptomes. Trinity was run on the paired end sequences with the fixed default k-mer size of 25 and minimum contig length of 200. Assembly algorithm runs on three steps, Inchworm, Chrysalis and Butterfly. Inchworm first assembles overlapping sequences using a greedy extension and reports the unique portions of alternatively spliced transcripts (contigs). Chrysalis then clusters related contigs into components (i.e., compXXXX_c0) and models complexity using de Bruijn graphs for each of the clusters. Finally, Butterfly processes the graphs and reports subcomponents (i.e., compXXXX_c0_seq1, compXXXX_c0_seq2, etc.), roughly corresponding to genes that are made up of individual transcripts.
DOI: 10.6084/m9.figshare.4955684
title: What's going on over there? Ventures into digital humanities at the University of Wollongong Library

description: A presentation delivered at the aaDH Digital Humanities Australasia Conference, Hobart, June 20-23 2016.<br><br>
DOI: 10.6084/m9.figshare.4955681
title: Yuri Kriel - article: Mode

description: NIRS (1sec), HR(1sec), mechanical power (1 sec) and VO2 (5sec), RPE and PACES data from 12 participants completing a maximal incremental exercise test to volitional cessation (MAX) and two experimental protocols: 1) cycling HIIT (HIITCYC) 2) running HIIT (HIITRUN)<br>
DOI: 10.6084/m9.figshare.4955669
title: OCC provenance data of all the responsible agents, as of April 26, 2017

description: This archive contains the dump of the OpenCitation Corpus (OCC, http://opencitations.net) provenance data about responsible agents, created regularly every month.<br><br>After unzipping the archive, Disk ARchive (DAR, http://dar.linux.free.fr/, a multi-platform archive tool for managing huge amount of data) is needed for recreating the whole structure. For extracting the DAR archive, please run the command<br><br>dar -x [archive-name]<br><br>Where "[archive-name"] is the name of the DAR file without final package number and extension. E.g.:<br><br>dar -x 2016-09-23-corpus_re<br><br>For further questions, comments, and suggestions please don't hesitate to contact Silvio Peroni at essepuntato@opencitations.net.
DOI: 10.6084/m9.figshare.4955681.v1
title: Yuri Kriel - PLoS article: Mode

description: NIRS (1sec), HR(1sec), mechanical power (1 sec) and VO2 (5sec), RPE and PACES data from 12 participants completing a maximal incremental exercise test to volitional cessation (MAX) and two experimental protocols: 1) cycling HIIT (HIITCYC) 2) running HIIT (HIITRUN)<br>
DOI: 10.6084/m9.figshare.4955666
title: Sarathchandra, Navin, Largent, and McCright Data

description: This dataset comes from an August 2016 pilot study of a new vaccine acceptance measurement instrument. Respondents were a convenience sample of 250 US adults recruited via Amazon Mechanical Turk.
DOI: 10.6084/m9.figshare.4955669.v1
title: OCC provenance data of all the responsible agents, as of April 26, 2017

description: This archive contains the dump of the OpenCitation Corpus (OCC, http://opencitations.net) provenance data about responsible agents, created regularly every month.<br><br>After unzipping the archive, Disk ARchive (DAR, http://dar.linux.free.fr/, a multi-platform archive tool for managing huge amount of data) is needed for recreating the whole structure. For extracting the DAR archive, please run the command<br><br>dar -x [archive-name]<br><br>Where "[archive-name"] is the name of the DAR file without final package number and extension. E.g.:<br><br>dar -x 2016-09-23-corpus_re<br><br>For further questions, comments, and suggestions please don't hesitate to contact Silvio Peroni at essepuntato@opencitations.net.
DOI: 10.6084/m9.figshare.4955684.v1
title: What's going on over there? Ventures into digital humanities at the University of Wollongong Library

description: A presentation delivered at the aaDH Digital Humanities Australasia Conference, Hobart, June 20-23 2016.<br>Accompanying notes are also included.
DOI: 10.6084/m9.figshare.4955684.v3
title: What's going on over there? Ventures into digital humanities at the University of Wollongong Library

description: A presentation delivered at the aaDH Digital Humanities Australasia Conference, Hobart, June 20-23 2016.<br><br>
DOI: 10.6084/m9.figshare.4955648.v1
title: Data files for Hammer et al. "Caterpillars lack a resident gut microbiome"

description: See readme for description of each file
DOI: 10.6084/m9.figshare.4955648
title: Data files for Hammer et al. "Caterpillars lack a resident gut microbiome"

description: See readme for description of each file
DOI: 10.6084/m9.figshare.4955648.v2
title: Data files for Hammer et al. "Caterpillars lack a resident gut microbiome"

description: See readme for description of each file
DOI: 10.6084/m9.figshare.4955513
title: Read counts for most abundant microbial agent in patient samples - (Item G)

description: Vitreous was analyzed using metagenomics using two DNA isolation methods (QIAamp DNA Mini Kit, QIA; QIAamp UCP Pathogen Mini kit, UCP) and the taxonomic affiliation of reads was determined using Kraken, Bracken, and BLASTn analysis. <br>The read counts for the most abundant microbial agent in endophthalmitis patients are listed. <br><br><b>Reference</b> <b>Kirstahler P, Bjerrum SS, Friis-Møller A, </b><b>la Cour M, Aarestrup FM, Westh H., and Pamp SJ. (2018) </b>Genomics-Based Identification of Microorganisms in Human Ocular Body Fluid.<b> </b><b>Scientific Reports</b>, doi:10.1038/s41598-018-22416-4.
DOI: 10.6084/m9.figshare.4955513.v2
title: Read counts for most abundant microbial agent in patient samples - (Item G)

description: Vitreous was analyzed using metagenomics using two DNA isolation methods (QIAamp DNA Mini Kit, QIA; QIAamp UCP Pathogen Mini kit, UCP) and the taxonomic affiliation of reads was determined using Kraken, Bracken, and BLASTn analysis. <br>The read counts for the most abundant microbial agent in endophthalmitis patients are listed. <br><br><b>Reference</b> <b>Kirstahler P, Bjerrum SS, Friis-Møller A, </b><b>la Cour M, Aarestrup FM, Westh H., and Pamp SJ. (2018) </b>Genomics-Based Identification of Microorganisms in Human Ocular Body Fluid.<b> </b><b>Scientific Reports</b>, doi:10.1038/s41598-018-22416-4.
DOI: 10.6084/m9.figshare.4955483
title: Epi-data from a cross-sectional survey

description: Reversal of gender ratio in medicine and the social impacts survey amongst Chinese physicians using a cross-sectional survey<b></b>
DOI: 10.6084/m9.figshare.4955513.v1
title: Read counts for most abundant microbial agent in patient samples - (Item E)

description: Vitreous was analyzed using metagenomics using two DNA isolation methods (QIAamp DNA Mini Kit, QIA; QIAamp UCP Pathogen Mini kit, UCP) and the taxonomic affiliation of reads was determined using Kraken, Bracken, and BLASTn analysis. <br>The read counts for the most abundant microbial agent in endophthalmitis patients are listed. <br><br><b>Reference</b> <b>Kirstahler P, Bjerrum SS, Friis-Møller A, </b><b>la Cour M, Aarestrup FM, Westh H., and Pamp SJ. (2017) </b>Genomics-Based Identification of Microorganisms in Human Ocular Body Fluid.<b> Journal </b>Vol:Issue page-page.
DOI: 10.6084/m9.figshare.4955465
title: Global forest loss disproportionately erodes biodiversity in intact landscapes (data and code)

description: Main data set and code for our analysis of how the effects of forest loss vary depending on current forest cover. Consists of the following files:<br>1. species_data_simple.csvData set for our main analysis containing data on mammals, amphibians, and birds. Data set consists of taxonomic information, whether or not the species is tropical, was listed as threatened under range-related issues, geographic region, IUCN Red List category/trend/change, location of geographic range centroid, and averages across ranges of the following variables: 2000 forest cover, 2000-2012 forest gain, 2000-2014 forest loss, historic forest loss, and human footprint.<br>2. model_everything_simple_serial.RCode to fit our primary spatial models using logistic regression with spatial autocovariates. Models are fit separately for each response variable (category/trend/change) and level of forest dependency.<br>3. plot_models_simple.RCode to plot the primary results (logistic regression standardized coefficients). The plot produced is used in Figure 2 of our manuscript.<br>
DOI: 10.6084/m9.figshare.4955483.v1
title: Epi-data from a cross-sectional survey

description: Reversal of gender ratio in medicine and the social impacts survey amongst Chinese physicians using a cross-sectional survey<b></b>
DOI: 10.6084/m9.figshare.4955465.v1
title: Global forest loss disproportionately erodes biodiversity in intact landscapes

description: Main data set and code for our analysis of how the effects of forest loss vary depending on current forest cover. Consists of the following files:<br>1. species_data_simple.csvData set for our main analysis containing data on mammals, amphibians, and birds. Data set consists of taxonomic information, whether or not the species is tropical, was listed as threatened under range-related issues, geographic region, IUCN Red List category/trend/change, location of geographic range centroid, and averages across ranges of the following variables: 2000 forest cover, 2000-2012 forest gain, 2000-2014 forest loss, historic forest loss, and human footprint.<br>2. model_everything_simple_serial.RCode to fit our primary spatial models using logistic regression with spatial autocovariates. Models are fit separately for each response variable (category/trend/change) and level of forest dependency.<br>3. plot_models_simple.RCode to plot the primary results (logistic regression standardized coefficients). The plot produced is used in Figure 2 of our manuscript.<br>
DOI: 10.6084/m9.figshare.4955465.v2
title: Data and code for: Global forest loss disproportionately erodes biodiversity in intact landscapes

description: Main data set and code for our analysis of how the effects of forest loss vary depending on current forest cover. Consists of the following files:<br>1. species_data_simple.csvData set for our main analysis containing data on mammals, amphibians, and birds. Data set consists of taxonomic information, whether or not the species is tropical, was listed as threatened under range-related issues, geographic region, IUCN Red List category/trend/change, location of geographic range centroid, and averages across ranges of the following variables: 2000 forest cover, 2000-2012 forest gain, 2000-2014 forest loss, historic forest loss, and human footprint.<br>2. model_everything_simple_serial.RCode to fit our primary spatial models using logistic regression with spatial autocovariates. Models are fit separately for each response variable (category/trend/change) and level of forest dependency.<br>3. plot_models_simple.RCode to plot the primary results (logistic regression standardized coefficients). The plot produced is used in Figure 2 of our manuscript.<br>
DOI: 10.6084/m9.figshare.4955450
title: List of pairs/triplets of genes with the same and divergent microRNA binding sites patterns in Brassica rapa

description: <b>Table S5:</b> List of pairs/triplets of genes with the same and divergent microRNA binding sites patterns in <i>Brassica rapa</i>. LF, MF1 and MF2 indicate three different subgenomes derived from the whole genome triplication event, respectively. The symbol of ‘-’ represents the absence of a clearly detectable homeolog. The syntenic gene information of three subgenomes is obtained from (Cheng et al. 2012).http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0036442<br>
DOI: 10.6084/m9.figshare.4955447
title: microRNA targets of Arabidopsis thaliana

description: MicroRNA binding site prediction dataset and the experimental dataset of <i>Arabidopsis thaliana</i>. The microRNA-target interactions on the list of the prediction dataset are those identified by at least two out of three prediction tools. The microRNA-target interactions on the list of the experimental dataset are manually collected from the combination of multiple publications and public databases.
DOI: 10.6084/m9.figshare.4955450.v1
title: List of pairs/triplets of genes with the same and divergent microRNA binding sites patterns in Brassica rapa

description: <b>Table S5:</b> List of pairs/triplets of genes with the same and divergent microRNA binding sites patterns in <i>Brassica rapa</i>. LF, MF1 and MF2 indicate three different subgenomes derived from the whole genome triplication event, respectively. The symbol of ‘-’ represents the absence of a clearly detectable homeolog. The syntenic gene information of three subgenomes is obtained from (Cheng et al. 2012).http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0036442<br>
DOI: 10.6084/m9.figshare.4955465.v3
title: Global forest loss disproportionately erodes biodiversity in intact landscapes (data and code)

description: Main data set and code for our analysis of how the effects of forest loss vary depending on current forest cover. Consists of the following files:<br>1. species_data_simple.csvData set for our main analysis containing data on mammals, amphibians, and birds. Data set consists of taxonomic information, whether or not the species is tropical, was listed as threatened under range-related issues, geographic region, IUCN Red List category/trend/change, location of geographic range centroid, and averages across ranges of the following variables: 2000 forest cover, 2000-2012 forest gain, 2000-2014 forest loss, historic forest loss, and human footprint.<br>2. model_everything_simple_serial.RCode to fit our primary spatial models using logistic regression with spatial autocovariates. Models are fit separately for each response variable (category/trend/change) and level of forest dependency.<br>3. plot_models_simple.RCode to plot the primary results (logistic regression standardized coefficients). The plot produced is used in Figure 2 of our manuscript.<br>
DOI: 10.6084/m9.figshare.4955414.v1
title: S8 Fig

description: Two additional example allocentric units in which spatial receptive fields are tuned to sound source location in the world coordinate frame. Data shown as in Figure 4 of main text with line plots showing mean ± s.e.m.
DOI: 10.6084/m9.figshare.4955405.v1
title: S7 Fig

description: <b>Example allocentric raster plot</b>Raster plots showing spiking responses of an example allocentric unit (Fig 4.c in the main manuscript).
DOI: 10.6084/m9.figshare.4955402.v1
title: S5 Fig

description: <b>Example egocentric raster plot</b><br>Raster plot showing spiking responses of an example egocentric unit (Fig. 4b in the main manuscript).
DOI: 10.6084/m9.figshare.4955390.v1
title: S1 Fig

description: <b>Allocentric tuning can generate a wide variety of tuning curve shapes and tuned locations. </b><b>a</b>, Variation in tuning shape generated by allocentric simulations tuned to different regions of space and implemented using different spike probability distributions: Simulations (left to right) based on logistic (red), Gaussian (black), Laplace (blue) and uniform (grey) probability density functions. <b>b</b>, Shifts in tuning simulated by varying world-based position of peak spike probability. Plots generated from logistic probability density functions.<b></b>
DOI: 10.6084/m9.figshare.4955363
title: Re-calibration of Arctic sea ice extent datasets using Arctic surface air temperature records

description: A new seasonal and annual dataset describing Arctic sea ice extents for 1901–2015 was constructed by individually re-calibrating sea ice data sources from the three Arctic regions (North American, Nordic and Siberian) using the corresponding surface air temperature trends for the pre-satellite era (1901–1978), so that the strong relationship between seasonal sea ice extent and surface air temperature observed for the satellite era (1979-present) also applies to the pre-satellite era. According to this new dataset, the recent period of Arctic sea ice retreat since the 1970s followed a period of sea ice growth after the mid-1940s, which in turn followed a period of sea ice retreat after the 1910s. Arctic sea ice is a key component of the Arctic hydrological cycle, through both its freshwater storage role and its influence on oceanic and atmospheric circulation. Therefore, these new insights have significance for our understanding of Arctic hydrology.<b>EDITOR</b> D. Koutsoyiannis<b>ASSOCIATE EDITOR</b> not assigned <b>EDITOR</b> D. Koutsoyiannis <b>ASSOCIATE EDITOR</b> not assigned
DOI: 10.6084/m9.figshare.4955363.v1
title: Re-calibration of Arctic sea ice extent datasets using Arctic surface air temperature records

description: A new seasonal and annual dataset describing Arctic sea ice extents for 1901–2015 was constructed by individually re-calibrating sea ice data sources from the three Arctic regions (North American, Nordic and Siberian) using the corresponding surface air temperature trends for the pre-satellite era (1901–1978), so that the strong relationship between seasonal sea ice extent and surface air temperature observed for the satellite era (1979-present) also applies to the pre-satellite era. According to this new dataset, the recent period of Arctic sea ice retreat since the 1970s followed a period of sea ice growth after the mid-1940s, which in turn followed a period of sea ice retreat after the 1910s. Arctic sea ice is a key component of the Arctic hydrological cycle, through both its freshwater storage role and its influence on oceanic and atmospheric circulation. Therefore, these new insights have significance for our understanding of Arctic hydrology.<b>EDITOR</b> D. Koutsoyiannis<b>ASSOCIATE EDITOR</b> not assigned <b>EDITOR</b> D. Koutsoyiannis <b>ASSOCIATE EDITOR</b> not assigned
DOI: 10.6084/m9.figshare.4955318
title: Clinical Features Fusobacterium Infections

description: Clinical features and outcomes of Fusobacterium species infections in a ten-year follow-up<br>
DOI: 10.6084/m9.figshare.4955318.v1
title: Clinical Features Fusobacterium Infections

description: @page { margin: 2cm } p { margin-bottom: 0.25cm; direction: ltr; line-height: 120%; text-align: left; widows: 2; orphans: 2; page-break-after: avoid } a:link { so-language: zxx } Clinical features and outcomes of Fusobacterium species infections in a ten-year follow-up<br>