cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.16800212
title: Additional file 3 of Concatenation of paired-end reads improves taxonomic classification of amplicons for profiling microbial communities

description: Additional file 3. Table S1. Mock community sequence information and trimmed length positions. Primer information includes the name of each primer in the literature and the associated sequence, read lengths of paired reads, length of the reads after length-trimming by median Q20 score, SRA and Bioproject accessions, and citation. Table S2. Bacteria present within each mock community. Table S3. DADA2 statistics for each pipeline per mock community. Input represents the number of reads used after the trimming and merging/concatenating steps; Filtered is the amount of sequences remaining after DADA2 filtering based on a maxee=2; Denoised is the number of sequences remaining after DADA2 denoising; Merged and % of input merged applies only to pipelines with merging by DADA2 (NMd, LMd , QMd and QdMd); Non-chimeric output sequences were passed into the taxonomic classifier; % output is the percent of sequences from the input left for taxonomic classification. Table S4. Genera that were originally determined as false positives (FP) but have synonyms of taxa from mock communities, and therefore were renamed to match taxa from mock communities (modified names now represent true positives – TPs). Table S5. True positive (TP), false positive (FP), and false negative (FN) genera that are shared and unique between GG and SILVA reference databases. Mocks are grouped per study. Table S6. TP, FP, and FN counts for each pipeline per mock along with the calculated precision, recall, and F-measure. SILVA reference database and an ASV abundance threshold of 0.01% was used to filter out low abundant ASVs before these calculations were performed. Pipelines arranged by descending F-measure score per mock. Table S7. True positive (TP), false positive (FP), and false negative (FN) bacterial genera found in each mock per pipeline with SILVA database.
DOI: 10.6084/m9.figshare.16800160
title: Additional file 4 of Intraoperative radiation therapy induces immune response activity after pancreatic surgery

description: Additional file 4 Supplementary Table S4. Recurrence pattern of patients with pancreatic cancer.
DOI: 10.6084/m9.figshare.16800157
title: Additional file 3 of Intraoperative radiation therapy induces immune response activity after pancreatic surgery

description: Additional file 3 Supplementary Table S3. Cytokine levels in PF altered by IORT.
DOI: 10.6084/m9.figshare.16800154
title: Additional file 2 of Intraoperative radiation therapy induces immune response activity after pancreatic surgery

description: Additional file 2 Supplementary Table S2. Clinical characteristic of 30 patients.
DOI: 10.6084/m9.figshare.16800151
title: Additional file 1 of Intraoperative radiation therapy induces immune response activity after pancreatic surgery

description: Additional file 1 Supplementary Table S1. Summary of patients’ clinical characteristics.
DOI: 10.21421/d2/iwxcuj/v3up8d
title: GBS data of 122 forage type hybrid parents of pearl millet genotyped for forage quality traits.txt.gz

description: :unav
DOI: 10.21421/d2/iwxcuj/hdad0n
title: BMS code_105 genotypes used in GWAS_Final.tab

description: :unav
DOI: 10.21421/d2/iwxcuj
title: Genome wide SNPs data generated using GBS on forage type hybrid parents in Pearl millet

description: This data set contains single nucleotide polymorphism (SNP) markers identified by genotyping by sequencing (GBS) approach, and was used to identify marker-trait association for forage quality traits crude protein (CP) and in vitro organic matter digestibility (IVOMD) among 105 forage type hybrid parents. The DNA of 105 hybrid parents was genotyped using GBS by digesting DNA with ApeKI endonuclease restriction enzyme. PCR (Polymerase chain reaction) amplification of pooled amplicons was carried out before sequencing on Illumina Hiseq2500 platform. Raw sequencing reads and barcode information were processed for SNP identification from the published pearl millet reference genome using TASSEL v4.0 software. Barcode containing reads were retained and used for SNP calling. These reads were trimmed to 64 bp from barcode side, aligned against each other and used for SNP identification. The identified SNPs were assigned to each hybrid parent based on the information of the barcode sequence. Further, the SNP data were filtered with minor allele frequency (MAF) cut off of 0.10 (10%) and SNP with ≥25% missing data. These were further filtered for site coverage (90%), minor allele frequency (0.05), and maximum heterozygosity (50%); 34, 691 SNPs were identified and used for the GWAS analysis. One SNP was identified on LG4 for CP, and nine SNPs for IVOMD distributed across all linkage groups except on LG2. Results generated from this study could help breeders to improve the forage productivity in their breeding programs. Experiment location on Google Map Experiment location on Google Map
DOI: 10.6084/m9.figshare.16800925
title: Raw data for DeepImageTranslator V2

description: Raw data for "DeepImageTranslator V2: analysis of multimodal medical images using semantic segmentation maps generated through deep learning"
DOI: 10.5517/ccdc.csd.cc27jhdp
title: CCDC 2071432: Experimental Crystal Structure Determination

description: Related Article: Veronika Heinl, Monika Schmidt, Maria Eckhardt, Miriam Eberl, Andreas E. Seitz, Gábor Balázs, Michael Seidl, Manfred Scheer|2021|Chem.-Eur.J.|27|11649|doi:10.1002/chem.202101119
DOI: 10.5517/ccdc.csd.cc27l8rw
title: CCDC 2073148: Experimental Crystal Structure Determination

description: Related Article: Wouter Lindeboom, Duncan A. X. Fraser, Christopher B. Durr, Charlotte K. Williams|2021|Chem.-Eur.J.|27|12224|doi:10.1002/chem.202101140
DOI: 10.5517/ccdc.csd.cc27l8sx
title: CCDC 2073149: Experimental Crystal Structure Determination

description: Related Article: Wouter Lindeboom, Duncan A. X. Fraser, Christopher B. Durr, Charlotte K. Williams|2021|Chem.-Eur.J.|27|12224|doi:10.1002/chem.202101140
DOI: 10.5517/ccdc.csd.cc27vhwh
title: CCDC 2081057: Experimental Crystal Structure Determination

description: Related Article: Tobias Heitkemper, Leonard Naß, Christian P. Sindlinger|2021|Angew.Chem.,Int.Ed.|60|20055|doi:10.1002/anie.202107968
DOI: 10.5517/ccdc.csd.cc27vhxj
title: CCDC 2081058: Experimental Crystal Structure Determination

description: Related Article: Tobias Heitkemper, Leonard Naß, Christian P. Sindlinger|2021|Angew.Chem.,Int.Ed.|60|20055|doi:10.1002/anie.202107968
DOI: 10.5517/ccdc.csd.cc27vhyk
title: CCDC 2081059: Experimental Crystal Structure Determination

description: Related Article: Tobias Heitkemper, Leonard Naß, Christian P. Sindlinger|2021|Angew.Chem.,Int.Ed.|60|20055|doi:10.1002/anie.202107968
DOI: 10.5517/ccdc.csd.cc27vhzl
title: CCDC 2081060: Experimental Crystal Structure Determination

description: Related Article: Tobias Heitkemper, Leonard Naß, Christian P. Sindlinger|2021|Angew.Chem.,Int.Ed.|60|20055|doi:10.1002/anie.202107968
DOI: 10.5517/ccdc.csd.cc27vj0n
title: CCDC 2081061: Experimental Crystal Structure Determination

description: Related Article: Tobias Heitkemper, Leonard Naß, Christian P. Sindlinger|2021|Angew.Chem.,Int.Ed.|60|20055|doi:10.1002/anie.202107968
DOI: 10.5517/ccdc.csd.cc27vj1p
title: CCDC 2081062: Experimental Crystal Structure Determination

description: Related Article: Tobias Heitkemper, Leonard Naß, Christian P. Sindlinger|2021|Angew.Chem.,Int.Ed.|60|20055|doi:10.1002/anie.202107968
DOI: 10.5517/ccdc.csd.cc27ysdd
title: CCDC 2084204: Experimental Crystal Structure Determination

description: Related Article: Simon Trzmiel, Jan Langmann, Daniel Werner, Cäcilia Maichle-Mössmer, Wolfgang Scherer, Reiner Anwander|2021|Angew.Chem.,Int.Ed.|60|20049|doi:10.1002/anie.202106608
DOI: 10.5517/ccdc.csd.cc27ysff
title: CCDC 2084205: Experimental Crystal Structure Determination

description: Related Article: Simon Trzmiel, Jan Langmann, Daniel Werner, Cäcilia Maichle-Mössmer, Wolfgang Scherer, Reiner Anwander|2021|Angew.Chem.,Int.Ed.|60|20049|doi:10.1002/anie.202106608
DOI: 10.5517/ccdc.csd.cc27ysgg
title: CCDC 2084206: Experimental Crystal Structure Determination

description: Related Article: Simon Trzmiel, Jan Langmann, Daniel Werner, Cäcilia Maichle-Mössmer, Wolfgang Scherer, Reiner Anwander|2021|Angew.Chem.,Int.Ed.|60|20049|doi:10.1002/anie.202106608
DOI: 10.5517/ccdc.csd.cc27ysjj
title: CCDC 2084208: Experimental Crystal Structure Determination

description: Related Article: Simon Trzmiel, Jan Langmann, Daniel Werner, Cäcilia Maichle-Mössmer, Wolfgang Scherer, Reiner Anwander|2021|Angew.Chem.,Int.Ed.|60|20049|doi:10.1002/anie.202106608
DOI: 10.5517/ccdc.csd.cc27yskk
title: CCDC 2084209: Experimental Crystal Structure Determination

description: Related Article: Simon Trzmiel, Jan Langmann, Daniel Werner, Cäcilia Maichle-Mössmer, Wolfgang Scherer, Reiner Anwander|2021|Angew.Chem.,Int.Ed.|60|20049|doi:10.1002/anie.202106608
DOI: 10.5517/ccdc.csd.cc27ysll
title: CCDC 2084210: Experimental Crystal Structure Determination

description: Related Article: Simon Trzmiel, Jan Langmann, Daniel Werner, Cäcilia Maichle-Mössmer, Wolfgang Scherer, Reiner Anwander|2021|Angew.Chem.,Int.Ed.|60|20049|doi:10.1002/anie.202106608
DOI: 10.5517/ccdc.csd.cc27ysmm
title: CCDC 2084211: Experimental Crystal Structure Determination

description: Related Article: Simon Trzmiel, Jan Langmann, Daniel Werner, Cäcilia Maichle-Mössmer, Wolfgang Scherer, Reiner Anwander|2021|Angew.Chem.,Int.Ed.|60|20049|doi:10.1002/anie.202106608
DOI: 10.5517/ccdc.csd.cc27ysnn
title: CCDC 2084212: Experimental Crystal Structure Determination

description: Related Article: Simon Trzmiel, Jan Langmann, Daniel Werner, Cäcilia Maichle-Mössmer, Wolfgang Scherer, Reiner Anwander|2021|Angew.Chem.,Int.Ed.|60|20049|doi:10.1002/anie.202106608
DOI: 10.5517/ccdc.csd.cc27zhyp
title: CCDC 2084903: Experimental Crystal Structure Determination

description: Related Article: Tatiana Suárez-Rodríguez, Ángel L. Suárez-Sobrino, Alfredo Ballesteros|2021|Chem.-Eur.J.|27|13079|doi:10.1002/chem.202102534
DOI: 10.5517/ccdc.csd.cc280vqw
title: CCDC 2086198: Experimental Crystal Structure Determination

description: Related Article: Dominik Vítek, Libor Dostál, Aleš Růžička, Tomáš Mikysek, Roman Jambor|2021|Eur.J.Inorg.Chem.|2021|3301|doi:10.1002/ejic.202100523
DOI: 10.5517/ccdc.csd.cc280vrx
title: CCDC 2086199: Experimental Crystal Structure Determination

description: Related Article: Dominik Vítek, Libor Dostál, Aleš Růžička, Tomáš Mikysek, Roman Jambor|2021|Eur.J.Inorg.Chem.|2021|3301|doi:10.1002/ejic.202100523
DOI: 10.5517/ccdc.csd.cc285jll
title: CCDC 2090689: Experimental Crystal Structure Determination

description: Related Article: Sarah Guerin, Sanaz Khorasani, Matthew Gleeson, Joseph O’Donnell, Rana Sanii, Reabetswe Zwane, Anthony M. Reilly, Christophe Silien, Syed A.M. Tofail, Ning Liu, Michael Zaworotko, Damien Thompson|2021|Cryst.Growth Des.|21|5818|doi:10.1021/acs.cgd.1c00702
DOI: 10.5517/ccdc.csd.cc288h24
title: CCDC 2093525: Experimental Crystal Structure Determination

description: Related Article: Craig S. Day, Alexander Fawcett, Ruchira Chatterjee, John F. Hartwig|2021|J.Am.Chem.Soc.|143|16184|doi:10.1021/jacs.1c07330
DOI: 10.6084/m9.figshare.14561613
title: Biodiversity_Function data

description: This dataset is used to support the paper "Ding J., Delgado-Baquerizo M., Wang JT., Eldridge D. 2021. Ecosystem functions are related to tree diversity in forests but soil biodiversity in open woodlands and shrublands. Journal of Ecology, https://doi.org/10.1111/1365-2745.13788"<br>We collected data at 126 sites from humid to arid areas in eastern Australia to explore the two-way relationships among aboveground (richness of trees and other plants) and belowground (richness of bacteria, fungi, invertebrate, protist) diversity and multiple ecosystem functions (multifunctionality, carbon and nutrient cycling, decomposition, plant biomass, soil stability).
DOI: 10.6084/m9.figshare.14561613.v1
title: Biodiversity_Function data

description: We collected data at 126 sites from humid to arid areas in eastern Australia to explore the two-way relationships among aboveground (richness of trees and other plants) and belowground (richness of bacteria, fungi, invertebrate, protist) diversity and multiple ecosystem functions (multifunctionality, carbon and nutrient cycling, decomposition, plant biomass, soil stability).
DOI: 10.6084/m9.figshare.14101349
title: Response of community structure to dryness_Data_Ding_Eldridge

description: This dataset is to support the paper published in Journal of Biogeography authored by Jingyi Ding and David Eldridge: "Ding, J., Eldridge, D. J. (2021). Community-level responses to increasing dryness vary with plant growth form across an extensive aridity gradient. Journal of Biogeography, 48, 1788-1796". <br>Methods: We measured the abundance and size distribution (median, skewness, variance) of perennial plant communities from different growth forms (trees, shrubs and grasses), the spatial arrangement of trees in the overstorey, and both biotic (competition) and abiotic (climate, soil properties) factors at 150 sites along an extensive aridity gradient from humid to arid areas. We used regression analyses and linear models to explore variation in community structure with increasing aridity, and key driving factors for different perennial plant communities. Results: Variation in community structure differed with growth form. As aridity increased, trees had wider canopies and were spatially aggregated, shrubs became miniaturised, but highly variable in size, and grasses comprised more larger individuals. Biotic and abiotic factors exerted different effects on different growth forms, with trees and shrubs consistently affected by competition and aridity, respectively, while grasses were weakly affected by aridity, summer rainfall and soil texture.
DOI: 10.6084/m9.figshare.14101349.v2
title: Response of community structure to dryness_Data_Ding_Eldridge

description: This dataset is to support the paper published in Journal of Biogeography authored by Jingyi Ding and David Eldridge: "Ding, J., Eldridge, D. J. (2021). Community-level responses to increasing dryness vary with plant growth form across an extensive aridity gradient. Journal of Biogeography, 48, 1788-1796". <br>Methods: We measured the abundance and size distribution (median, skewness, variance) of perennial plant communities from different growth forms (trees, shrubs and grasses), the spatial arrangement of trees in the overstorey, and both biotic (competition) and abiotic (climate, soil properties) factors at 150 sites along an extensive aridity gradient from humid to arid areas. We used regression analyses and linear models to explore variation in community structure with increasing aridity, and key driving factors for different perennial plant communities. Results: Variation in community structure differed with growth form. As aridity increased, trees had wider canopies and were spatially aggregated, shrubs became miniaturised, but highly variable in size, and grasses comprised more larger individuals. Biotic and abiotic factors exerted different effects on different growth forms, with trees and shrubs consistently affected by competition and aridity, respectively, while grasses were weakly affected by aridity, summer rainfall and soil texture.
DOI: 10.6084/m9.figshare.14101349.v1
title: Plan community responses to aridity_Data_Ding_Eldridge

description: This dataset is to support the paper published in Journal of Biogeography: "Community-level responses to increasing dryness vary with plant growth form across an extensive aridity gradient". <br>Methods: We measured the abundance and size distribution (median, skewness, variance) of perennial plant communities from different growth forms (trees, shrubs and grasses), the spatial arrangement of trees in the overstorey, and both biotic (competition) and abiotic (climate, soil properties) factors at 150 sites along an extensive aridity gradient from humid to arid areas. We used regression analyses and linear models to explore variation in community structure with increasing aridity, and key driving factors for different perennial plant communities. Results: Variation in community structure differed with growth form. As aridity increased, trees had wider canopies and were spatially aggregated, shrubs became miniaturised, but highly variable in size, and grasses comprised more larger individuals. Biotic and abiotic factors exerted different effects on different growth forms, with trees and shrubs consistently affected by competition and aridity, respectively, while grasses were weakly affected by aridity, summer rainfall and soil texture. Main conclusions: Our study highlights the idiosyncratic adaptation strategies used by trees, shrubs and grasses in response to drying climates at the community level through their effect on the size distribution or spatial aggregation. The structure of different perennial growth forms was influenced by different effects from either biotic (competition) or abiotic (climate, soil) factors. Under forecasted drier climates, canopy expansion and greater aggregation of trees might enhance resource sinks and shelter for diverse biota, potentially shielding plant communities against predicted aridification.<br><br>
DOI: 10.6084/m9.figshare.16697954.v1
title: Raw data from Vocal plasticity in harbour seal pups

description: Vocal plasticity can occur in response to environmental and biological factors, including conspecifics' vocalizations and noise. Pinnipeds are one of the few mammalian groups capable of vocal learning, and are therefore relevant to understanding the evolution of vocal plasticity in humans and other animals. Here, we investigate the vocal plasticity of harbour seals (<i>Phoca vitulina</i>), a species with vocal learning abilities observed in adulthood but not puppyhood. To evaluate early mammalian vocal development, we tested 1–3 weeks-old seal pups. We tailored noise playbacks to this species and age to induce seal pups to shift their fundamental frequency (f0), rather than adapt call amplitude or temporal characteristics. We exposed individual pups to low- and high-intensity bandpass-filtered noise, which spanned—and masked—their typical range of f0; simultaneously, we recorded pups' spontaneous calls. Unlike most mammals, pups modified their vocalizations by lowering their f0 in response to increased noise. This modulation was precise and adapted to the particular experimental manipulation of the noise condition. In addition, higher levels of noise induced less dispersion around the mean f0, suggesting that pups may have actively focused their phonatory efforts to target lower frequencies. Noise did not seem to affect call amplitude. However, one seal showed two characteristics of the Lombard effect known for human speech in noise: significant increase in call amplitude and flattening of spectral tilt. Our relatively low noise levels may have favoured f0 modulation while inhibiting amplitude adjustments. This lowering of f0 is unusual, as most animals commonly display no such f0 shift. Our data represent a relatively rare case in mammalian neonates, and have implications for the evolution of vocal plasticity and vocal learning across species, including humans.This article is part of the theme issue ‘Voice modulation: from origin and mechanism to social impact (Part I)’.
DOI: 10.6084/m9.figshare.16697954
title: Raw data from Vocal plasticity in harbour seal pups

description: Vocal plasticity can occur in response to environmental and biological factors, including conspecifics' vocalizations and noise. Pinnipeds are one of the few mammalian groups capable of vocal learning, and are therefore relevant to understanding the evolution of vocal plasticity in humans and other animals. Here, we investigate the vocal plasticity of harbour seals (<i>Phoca vitulina</i>), a species with vocal learning abilities observed in adulthood but not puppyhood. To evaluate early mammalian vocal development, we tested 1–3 weeks-old seal pups. We tailored noise playbacks to this species and age to induce seal pups to shift their fundamental frequency (f0), rather than adapt call amplitude or temporal characteristics. We exposed individual pups to low- and high-intensity bandpass-filtered noise, which spanned—and masked—their typical range of f0; simultaneously, we recorded pups' spontaneous calls. Unlike most mammals, pups modified their vocalizations by lowering their f0 in response to increased noise. This modulation was precise and adapted to the particular experimental manipulation of the noise condition. In addition, higher levels of noise induced less dispersion around the mean f0, suggesting that pups may have actively focused their phonatory efforts to target lower frequencies. Noise did not seem to affect call amplitude. However, one seal showed two characteristics of the Lombard effect known for human speech in noise: significant increase in call amplitude and flattening of spectral tilt. Our relatively low noise levels may have favoured f0 modulation while inhibiting amplitude adjustments. This lowering of f0 is unusual, as most animals commonly display no such f0 shift. Our data represent a relatively rare case in mammalian neonates, and have implications for the evolution of vocal plasticity and vocal learning across species, including humans.This article is part of the theme issue ‘Voice modulation: from origin and mechanism to social impact (Part I)’.
DOI: 10.6084/m9.figshare.16801174.v1
title: Live Sightings Database

description: This excel database contains live sightings of huemul documented in Bernardo O'Higgins National Park during surveys from 2014-2020.
DOI: 10.6084/m9.figshare.16801174
title: Live Sightings Database

description: This excel database contains live sightings of huemul documented in Bernardo O'Higgins National Park during surveys from 2014-2020.
DOI: 10.6084/m9.figshare.12941525
title: Woody allometry site-level dataset_Ding_Travers_Eldridge.xlsx

description: This dataset is used to support the results presented in the paper "Ding, J., Travers, S. K., Eldridge, D. J. (2021). Grow wider canopies or thicker stems: variable response of woody plants to increasing dryness. Global Ecology and Biogeography, 30, 183-195." authored by Jingyi Ding, Samantha Travers, David Eldridge.
DOI: 10.6084/m9.figshare.12941525.v1
title: Woody allometry site-level dataset_Ding_Travers_Eldridge.xlsx

description: This dataset is used to support the results presented in the paper "Ding, J., Travers, S. K., Eldridge, D. J. (2021). Grow wider canopies or thicker stems: variable response of woody plants to increasing dryness. Global Ecology and Biogeography, 30, 183-195." authored by Jingyi Ding, Samantha Travers, David Eldridge.
DOI: 10.6084/m9.figshare.14129438
title: Soil surface condition_microbial diversity_data

description: We collect data on soil bacterial and fungal communities, 14 field-based soil surface attributes at 173 sites along a woody cover gradient to determine the relationship between soil surface condition and soil microbes.<br>This dataset is used to support the paper "Ding J., Travers S., Eldridge D. 2021. Microbial communities are associated with indicators of soil surface condition across a continental gradient. Geoderma, https://doi.org/10.1016/j.geoderma.2021.115439" authored by Jingyi Ding, Samantha Travers, David Eldridge <br>
DOI: 10.6084/m9.figshare.14129438.v1
title: Soil surface condition_microbe diversity_data

description: We collect data on soil bacterial and fungal communities, 14 field-based soil surface attributes at 173 sites along a woody cover gradient to determine the relationship between soil surface condition and soil microbes.<br>This dataset is used to support the paper "Microbial communities are associated with indicators of soil surface condition across a continental gradient" authored by Jingyi Ding, Samanthan Travers, David Eldridge <br>