cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12813794.v1
title: data.rar

description: 1. AAIW is defined by the low salinity tongue, and spiciness is defined by salinity anomaly along isopycnal surfaces, so that one can use salinity or spiciness to trace AAIW. Here we use WOA18 data downloaded from https://www.nodc.noaa.gov/OC5/woa18/woa18data.html, and the results are shown in Figure 2, one can trace AAIW with different tracers, including the absolute salinity (panel a), the spiciness (panel b), the distance from the origin (defined by the temperature and salinity at 30<sup>o</sup>S and 50 m below the sea surface) (panel c), and the spicity (panel d).2. As observed in Schmitt (1981), density ratio was 1.89 and 1.95 for the South and North Center Water stations. Using World Ocean Circulation Experiment (WOCE) database (https://cchdo.ucsd.edu/), we find two stations: (17.4<sup>o</sup>S, 30.8<sup>o</sup>W) in Section A17 and (24.5<sup>o</sup>N, 36.7<sup>o</sup>W) in Section A05-2, nearest to those in Schmitt (1981), to calculate the density ratio.3. In the Arctic Ocean, we used temperature and salinity data acquired with Ice-Tethered Profiler (ITP), downloaded from https://www.whoi.edu/page.do?pid=28866. With the vertical resolution of 0.25 m, the data enables us to examine the small-scale structures, i.e. diffusive convection staircases. The ITP4 station is located at (79.0<sup>o</sup>N, 142.5<sup>o</sup>W) in the Canada Basin, where a stack of staircases exists in the upper layer of a water mass, called as Antarctic Water (Figure 3(a)) (Padman and Dillon, 1987).
DOI: 10.6084/m9.figshare.12813743.v1
title: Manuscript data.pdf

description: These data are tables of the in silico study part in the manuscript "In-vitro Cytotoxicity and In-silico Insights of the Multi-target Anticancer Candidates from Haplophyllum tuberculatum".
DOI: 10.6084/m9.figshare.12813669
title: Additional file 6 of Integrated Puncture Score: force–displacement weighted rind penetration tests improve stalk lodging resistance estimations in maize

description: Additional file 6. Example force-displacement data from a puncture test.
DOI: 10.6084/m9.figshare.12813663
title: Additional file 4 of Integrated Puncture Score: force–displacement weighted rind penetration tests improve stalk lodging resistance estimations in maize

description: Additional file 4. Example force-displacement data from a puncture test.
DOI: 10.6084/m9.figshare.12813666
title: Additional file 5 of Integrated Puncture Score: force–displacement weighted rind penetration tests improve stalk lodging resistance estimations in maize

description: Additional file 5. Example force-displacement data from a puncture test.
DOI: 10.6084/m9.figshare.12813657
title: Additional file 2 of Integrated Puncture Score: force–displacement weighted rind penetration tests improve stalk lodging resistance estimations in maize

description: Additional file 2. Example force-displacement data from a puncture test.
DOI: 10.6084/m9.figshare.12813660
title: Additional file 3 of Integrated Puncture Score: force–displacement weighted rind penetration tests improve stalk lodging resistance estimations in maize

description: Additional file 3. Example force-displacement data from a puncture test.
DOI: 10.6084/m9.figshare.12813663.v1
title: Additional file 4 of Integrated Puncture Score: force–displacement weighted rind penetration tests improve stalk lodging resistance estimations in maize

description: Additional file 4. Example force-displacement data from a puncture test.
DOI: 10.6084/m9.figshare.12813669.v1
title: Additional file 6 of Integrated Puncture Score: force–displacement weighted rind penetration tests improve stalk lodging resistance estimations in maize

description: Additional file 6. Example force-displacement data from a puncture test.
DOI: 10.6084/m9.figshare.12813654
title: Additional file 1 of Integrated Puncture Score: force–displacement weighted rind penetration tests improve stalk lodging resistance estimations in maize

description: Additional file 1. Matlab algorithm used to calculate the integrated puncture score.
DOI: 10.6084/m9.figshare.12813657.v1
title: Additional file 2 of Integrated Puncture Score: force–displacement weighted rind penetration tests improve stalk lodging resistance estimations in maize

description: Additional file 2. Example force-displacement data from a puncture test.
DOI: 10.6084/m9.figshare.12813666.v1
title: Additional file 5 of Integrated Puncture Score: force–displacement weighted rind penetration tests improve stalk lodging resistance estimations in maize

description: Additional file 5. Example force-displacement data from a puncture test.
DOI: 10.6084/m9.figshare.12813654.v1
title: Additional file 1 of Integrated Puncture Score: force–displacement weighted rind penetration tests improve stalk lodging resistance estimations in maize

description: Additional file 1. Matlab algorithm used to calculate the integrated puncture score.
DOI: 10.6084/m9.figshare.12813660.v1
title: Additional file 3 of Integrated Puncture Score: force–displacement weighted rind penetration tests improve stalk lodging resistance estimations in maize

description: Additional file 3. Example force-displacement data from a puncture test.
DOI: 10.6084/m9.figshare.12813598
title: Additional file 3 of Is combined peritoneal dialysis and hemodialysis redundant? A nationwide study from Taiwan

description: Additional file 3: Figure S2. (A) Distribution of the duration of PD at the start of combined therapy (years). (B) Distribution of the duration between the onset of combined therapy to the end, including transfer to HD, death, kidney transplantation, or the end of follow-up (years).
DOI: 10.6084/m9.figshare.12813598.v1
title: Additional file 3 of Is combined peritoneal dialysis and hemodialysis redundant? A nationwide study from Taiwan

description: Additional file 3: Figure S2. (A) Distribution of the duration of PD at the start of combined therapy (years). (B) Distribution of the duration between the onset of combined therapy to the end, including transfer to HD, death, kidney transplantation, or the end of follow-up (years).
DOI: 10.6084/m9.figshare.12813541
title: Additional file 5 of Genome-wide characterization and expression analysis of geranylgeranyl diphosphate synthase genes in cotton (Gossypium spp.) in plant development and abiotic stresses

description: Additional file 5: Table S5. List of primers for qRT-PCR. (ODS 13 kb)
DOI: 10.6084/m9.figshare.12813538
title: Additional file 4 of Genome-wide characterization and expression analysis of geranylgeranyl diphosphate synthase genes in cotton (Gossypium spp.) in plant development and abiotic stresses

description: Additional file 4: Table S4. Transcriptomic data of tissue specific expression and abiotic stress responses of GhGGPPS gene family. (ODS 26 kb)
DOI: 10.6084/m9.figshare.12813541.v1
title: Additional file 5 of Genome-wide characterization and expression analysis of geranylgeranyl diphosphate synthase genes in cotton (Gossypium spp.) in plant development and abiotic stresses

description: Additional file 5: Table S5. List of primers for qRT-PCR. (ODS 13 kb)
DOI: 10.6084/m9.figshare.12813532
title: Additional file 2 of Genome-wide characterization and expression analysis of geranylgeranyl diphosphate synthase genes in cotton (Gossypium spp.) in plant development and abiotic stresses

description: Additional file 2: Table S2. Prediction of biophysical properties of 25 GhGGPPS genes. (ODS 19 kb)
DOI: 10.6084/m9.figshare.12813529
title: Additional file 1 of Genome-wide characterization and expression analysis of geranylgeranyl diphosphate synthase genes in cotton (Gossypium spp.) in plant development and abiotic stresses

description: Additional file 1: Table S1. Renaming of GGPPS genes from 18 species. (ODS 14 kb)
DOI: 10.6084/m9.figshare.12813535
title: Additional file 3 of Genome-wide characterization and expression analysis of geranylgeranyl diphosphate synthase genes in cotton (Gossypium spp.) in plant development and abiotic stresses

description: Additional file 3: Table S3. Orthologous/paralogous gene pairs and gene duplication types of GhGGPPS gene family. (ODS 14 kb)
DOI: 10.6084/m9.figshare.12813538.v1
title: Additional file 4 of Genome-wide characterization and expression analysis of geranylgeranyl diphosphate synthase genes in cotton (Gossypium spp.) in plant development and abiotic stresses

description: Additional file 4: Table S4. Transcriptomic data of tissue specific expression and abiotic stress responses of GhGGPPS gene family. (ODS 26 kb)
DOI: 10.6084/m9.figshare.12813532.v1
title: Additional file 2 of Genome-wide characterization and expression analysis of geranylgeranyl diphosphate synthase genes in cotton (Gossypium spp.) in plant development and abiotic stresses

description: Additional file 2: Table S2. Prediction of biophysical properties of 25 GhGGPPS genes. (ODS 19 kb)
DOI: 10.6084/m9.figshare.12813529.v1
title: Additional file 1 of Genome-wide characterization and expression analysis of geranylgeranyl diphosphate synthase genes in cotton (Gossypium spp.) in plant development and abiotic stresses

description: Additional file 1: Table S1. Renaming of GGPPS genes from 18 species. (ODS 14 kb)
DOI: 10.6084/m9.figshare.12813535.v1
title: Additional file 3 of Genome-wide characterization and expression analysis of geranylgeranyl diphosphate synthase genes in cotton (Gossypium spp.) in plant development and abiotic stresses

description: Additional file 3: Table S3. Orthologous/paralogous gene pairs and gene duplication types of GhGGPPS gene family. (ODS 14 kb)
DOI: 10.6084/m9.figshare.12813407
title: Gene database of Sulfolobus tokodaii strain 7

description: <i>Sulfolobus tokodaii </i>is a thermophilic, obligately aerobe archaeon able to live in acidic environment. This resource sought to provide fundamental information about the genetic repertoire of <i>Sulfolobus tokodaii</i> strain 7 by parsing the annotated genome sequence of the archaeon from Genbank. Comprising gene name, gene function, and gene sequence, the resource should find use in functional genomics workflows seeking to understand the molecular machinery and metabolic capabilities of this archaeon<i>.</i>
DOI: 10.6084/m9.figshare.12813413
title: tidsQualityAssessment.h5

description: Model and weights file for TIDS-based image quality assessment using a ResNet architecture.
DOI: 10.6084/m9.figshare.12813395
title: JVASP-37273 to JVASP-37900

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813413.v1
title: tidsQualityAssessment.h5

description: Model and weights file for TIDS-based image quality assessment using a ResNet architecture.
DOI: 10.6084/m9.figshare.12813389
title: JVASP-36108 to JVASP-36668

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813392
title: JVASP-36669 to JVASP-37272

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813386
title: JVASP-35347 to JVASP-36105

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813392.v1
title: JVASP-36669 to JVASP-37272

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813395.v1
title: JVASP-37273 to JVASP-37900

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813344
title: JVASP-31186 to JVASP-34336

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813347
title: JVASP-34339 to JVASP-35346

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813389.v1
title: JVASP-36108 to JVASP-36668

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813341
title: JVASP-29808 to JVASP-31185

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813347.v1
title: JVASP-34339 to JVASP-35346

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813386.v1
title: JVASP-35347 to JVASP-36105

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813338
title: JVASP-28908 to JVASP-31185

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813341.v1
title: JVASP-29808 to JVASP-31185

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813344.v1
title: JVASP-31186 to JVASP-34336

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813329
title: 2.18_Marker_SNPs.hmp.txt

description: This constitutes a dense and exhaustive coverage of the soybean genome using a dual genotyping approach combining both GBS and WGS, for a core set of 137 Canadian soybean lines.The entire panel was characterized via 56K GBS-derived SNPs. A Canadian soybean reference panel (102 lines, of which 56 were also present in the association panel) of 4.3M WGS-derived SNPs was used to perform imputation of untyped loci in the first catalogue. This led to a final catalogue of 2.18M SNPs (MAF ≥ 0.05) for a mean density of 1 SNP every 435 bp across the soybean genome.
DOI: 10.6084/m9.figshare.12813329.v1
title: 2.18_Marker_SNPs.hmp.txt

description: This constitutes a dense and exhaustive coverage of the soybean genome using a dual genotyping approach combining both GBS and WGS, for a core set of 137 Canadian soybean lines.The entire panel was characterized via 56K GBS-derived SNPs. A Canadian soybean reference panel (102 lines, of which 56 were also present in the association panel) of 4.3M WGS-derived SNPs was used to perform imputation of untyped loci in the first catalogue. This led to a final catalogue of 2.18M SNPs (MAF ≥ 0.05) for a mean density of 1 SNP every 435 bp across the soybean genome.
DOI: 10.6084/m9.figshare.12813338.v1
title: JVASP-28908 to JVASP-31185

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813209
title: Data from: Evaluation of alternative prey-, predator-, and ratio-dependent functional response models in a zooplankton microcosm

description: There is strenuous debate among ecologists regarding the inclusion of predator density into the originally prey- dependent functional response. We provided comprehensive empirical comparisons of alternative functional response models for the predatory ostracod <i>Heterocypris incongruens</i> (Ramdohr, 1808) and the rotifer <i>Brachionus calyciflorus</i> (Pallas, 1766) as its prey in small freshwater microcosms. Prey killed was measured at factorial combinations of four predator densities and five prey densities, and was recorded at 3 min intervals over 60 min experiments. To support the potential effect of predator interference on per capita kill rate, we recorded ostracod activity and aggression. Kill rate increased following a saturating function with increasing prey density and decreased with increasing predator density. Model evaluation using an information–theoretic approach indicated that the Arditi–Ginzburg type II ratio-dependent model performed best, followed by the Arditi–Akcakaya and Beddington–DeAngelis type II predator-dependent models, suggesting that predator interference was important in predicting kill rates. Interference among predators increased and their activity decreased with increasing predator density, providing confirmation that interference was responsible for the predator-dependent effect. By combining a microcosm experiment and behavioral observations, our results suggest that predator interference at realistic population densities influences ostracod kill rates and this form of interference was best accommodated by predator-dependent models.<br><br>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>The ‘well’ is the arena in which the predator-prey microcosm experiments we conducted. The total volume of water held in the well (well_vol) was 2.75 ml.Date: Date the trial was completedWell ID: Row and Column of Plate<br>The dates and wells could be combined to create a unique trial ID in most cases.<br>P.well: Number of ostracods per wellP: Ostracod density, individuals/mlN.well: Number of rotifers per wellN: Rotifer density, individuals/mlkilled: Number of Rotifers killed during observation<br>I would count the number of rotifers consistently throughout the hour trial period. The interval between counting was about 3 mins. Thus, there is a minimum time that this depletion could have occurred (min.time) or a maximum time (max.time) based on the time since I last checked the well (multiple wells during an hour had predator and prey combinations, so I was checking a multiple in a given hour). When the time seems high that does not mean that the wells were not checked, it means that depletion didn’t occur until that interval. An average time was used for the paper, to calculate the kill per min, per predator.<br>
DOI: 10.6084/m9.figshare.12813209.v1
title: Data from: Evaluation of alternative prey-, predator-, and ratio-dependent functional response models in a zooplankton microcosm

description: There is strenuous debate among ecologists regarding the inclusion of predator density into the originally prey- dependent functional response. We provided comprehensive empirical comparisons of alternative functional response models for the predatory ostracod <i>Heterocypris incongruens</i> (Ramdohr, 1808) and the rotifer <i>Brachionus calyciflorus</i> (Pallas, 1766) as its prey in small freshwater microcosms. Prey killed was measured at factorial combinations of four predator densities and five prey densities, and was recorded at 3 min intervals over 60 min experiments. To support the potential effect of predator interference on per capita kill rate, we recorded ostracod activity and aggression. Kill rate increased following a saturating function with increasing prey density and decreased with increasing predator density. Model evaluation using an information–theoretic approach indicated that the Arditi–Ginzburg type II ratio-dependent model performed best, followed by the Arditi–Akcakaya and Beddington–DeAngelis type II predator-dependent models, suggesting that predator interference was important in predicting kill rates. Interference among predators increased and their activity decreased with increasing predator density, providing confirmation that interference was responsible for the predator-dependent effect. By combining a microcosm experiment and behavioral observations, our results suggest that predator interference at realistic population densities influences ostracod kill rates and this form of interference was best accommodated by predator-dependent models.<br><br>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>The ‘well’ is the arena in which the predator-prey microcosm experiments we conducted. The total volume of water held in the well (well_vol) was 2.75 ml.Date: Date the trial was completedWell ID: Row and Column of Plate<br>The dates and wells could be combined to create a unique trial ID in most cases.<br>P.well: Number of ostracods per wellP: Ostracod density, individuals/mlN.well: Number of rotifers per wellN: Rotifer density, individuals/mlkilled: Number of Rotifers killed during observation<br>I would count the number of rotifers consistently throughout the hour trial period. The interval between counting was about 3 mins. Thus, there is a minimum time that this depletion could have occurred (min.time) or a maximum time (max.time) based on the time since I last checked the well (multiple wells during an hour had predator and prey combinations, so I was checking a multiple in a given hour). When the time seems high that does not mean that the wells were not checked, it means that depletion didn’t occur until that interval. An average time was used for the paper, to calculate the kill per min, per predator.<br>
DOI: 10.6084/m9.figshare.12813113
title: DATA_IllegalActivityBugoma

description: These data are the number of unique illegal activities sites, per month per year, recorded by the Bugoma Primate Conservation Project staff between 2017 to 2020.
DOI: 10.6084/m9.figshare.12813113.v1
title: DATA_IllegalActivityBugoma

description: These data are the number of unique illegal activities sites, per month per year, recorded by the Bugoma Primate Conservation Project staff between 2017 to 2020.
DOI: 10.6084/m9.figshare.12813176.v1
title: A dataset on occurrence of ten potential host animals of SARS-CoV-2

description: This dataset provides 5429 geo-referenced occurrence records in 2158 unique locations of 10 potential animal hosts, mainly concerning the wild mammals in the geographic scope of China and adjacent countries.
DOI: 10.6084/m9.figshare.12813014
title: Datasets of building a physical proxy for gas-phase sulfuric acid concentration based on its budget analysis

description: Dataset including gaseous sulfuric acid and related parameters measured at SORPES(118°57′E,32°07′N) during winter (from December 11, 2017 to January 17, 2018), spring (from April 13, 2018 to April 23, 2018), summer (form July 11, 2018 to August 9, 2018) and autumn (November 2018), respectively.
DOI: 10.6084/m9.figshare.12813014.v1
title: Datasets of building a physical proxy for gas-phase sulfuric acid concentration based on its budget analysis

description: Dataset including gaseous sulfuric acid and related parameters measured at SORPES(118°57′E,32°07′N) during winter (from December 11, 2017 to January 17, 2018), spring (from April 13, 2018 to April 23, 2018), summer (form July 11, 2018 to August 9, 2018) and autumn (November 2018), respectively.
DOI: 10.6084/m9.figshare.12812984
title: JVASP-27753 to JVASP-28905

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12812978
title: JVASP-22901 to JVASP-25384

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12812981
title: JVASP-25349 to JVASP-27738

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12812978.v1
title: JVASP-22901 to JVASP-25384

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12812972
title: JVASP-20574 to JVASP-21608

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12812981.v1
title: JVASP-25349 to JVASP-27738

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12812984.v1
title: JVASP-27753 to JVASP-28905

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12812975
title: JVASP-21609 to JVASP-22894

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12812879
title: Slab-controlled progressive evolution of the Kudi back-arc ophiolite in response to the rollback of the Proto-Tethys oceanic slab, in Western Kunlun, NW Tibet Plateau

description: <b>Table S1 </b>Zircon U-Pb ages of three Kudi ophiolite samples.<br> <b>Table S2 </b>Results of zircons Hf isotope compositions of samples. <b>Table S3</b> Results of whole-rock major (wt.%) and trace (ppm) data on the Kudi ophiolite. <b>Table S4 </b>Results of the whole-rock Sr-Nd-B isotope compositions of the Kudi ophiolite. <b> </b> <b>Table S5 </b>Major elements of olivine from the Kudi ophiolite. <b>Table S6 </b>Major elements of spinel from the Kudi ophiolite. <b>Table S7 </b>Major elements of amphibole from the Kudi ophiolite.
DOI: 10.6084/m9.figshare.12812972.v1
title: JVASP-20574 to JVASP-21608

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12812879.v1
title: Slab-controlled progressive evolution of the Kudi back-arc ophiolite in response to the rollback of the Proto-Tethys oceanic slab, in Western Kunlun, NW Tibet Plateau

description: <b>Table S1 </b>Zircon U-Pb ages of three Kudi ophiolite samples.<br> <b>Table S2 </b>Results of zircons Hf isotope compositions of samples. <b>Table S3</b> Results of whole-rock major (wt.%) and trace (ppm) data on the Kudi ophiolite. <b>Table S4 </b>Results of the whole-rock Sr-Nd-B isotope compositions of the Kudi ophiolite. <b> </b> <b>Table S5 </b>Major elements of olivine from the Kudi ophiolite. <b>Table S6 </b>Major elements of spinel from the Kudi ophiolite. <b>Table S7 </b>Major elements of amphibole from the Kudi ophiolite.
DOI: 10.6084/m9.figshare.12812975.v1
title: JVASP-21609 to JVASP-22894

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12812801
title: Some experimental data of the paper

description: The experimental data and procedures used in the paper "A New Three-Dimensional Noise Modeling Method based on Singular Value Decomposition and Its Application to CMONOC GNSS Network".
DOI: 10.6084/m9.figshare.12812801.v1
title: Some experimental data of the paper

description: The experimental data and procedures used in the paper "A New Three-Dimensional Noise Modeling Method based on Singular Value Decomposition and Its Application to CMONOC GNSS Network".
DOI: 10.6084/m9.figshare.12812746
title: Additional file 2 of Hospital characteristics associated with low-value care in public hospitals in New South Wales, Australia

description: Additional file 2. Summary of the hospital characteristics for each low-value procedure.