cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21675272.v1
title: Table S7

description: <strong>Table S7. Differentially expressed (DE) genes identified by RNAseq in sample comparisons highlighting the effect of Niclosamide (NIC).</strong> <br> <strong>Entrez GeneID</strong> NCBI unique gene identifier <br> <strong>Gene Symbol</strong> Common abbreviated gene symbol <br> <strong>Aliases</strong> Alternate commonly used abbreviated gene symbols <br> <strong>description</strong> Gene name/Gene description <br> <strong>other_designations</strong> Alternate gene name/gene description <br> <strong>Base mean</strong> Mean of normalized counts <br> <strong>log2(FC)</strong> Log(base 2) fold change <br> <strong>StdErr</strong> Standard error <br> <strong>Wald-Stats</strong> Wald statistic <br> <strong>P-value</strong> P-value <br> <strong>P-adj</strong> Adjusted p-value (bonferroni correction) <br> <strong>Scaffold</strong> Scaffold where gene is located <br> <strong>Chromosome</strong> Chromosome where gene is located <br> <strong>Start</strong> Start of gene (bp position) <br> <strong>End</strong> End of gene (bp position) <br> <strong>Strand</strong> Strand gene is on <br> <strong>DE?(y/n)</strong> If gene is significantly differentially expressed (adjusted p-value ≤0.05, |log2(FC)|≥1) or not (y = yes, n=no) <br> <strong>Sample</strong> <strong>Description</strong> xx16 Vehicle treated no virus samples at 16 hours Nx16 Niclosamide treated no virus samples at 16 hours xx48 Vehicle treated no virus samples at 48 hours Nx48 Niclosamide treated no virus samples at 48 hours xV16 Vehicle treated virus samples at 16 hours NV16 Niclosamide treated virus samples at 16 hours xV48 Vehicle treated virus samples at 48 hours NV48 Niclosamide treated virus samples at 48 hours
DOI: 10.6084/m9.figshare.21675236
title: Table S6

description: <strong>Table S6. Differentially expressed (DE) genes identified by RNAseq in sample comparisons highlighting the effect of time</strong> <br> <br> <strong>Entrez GeneID</strong> NCBI unique gene identifier <br> <strong>Gene Symbol</strong> Common abbreviated gene symbol <br> <strong>Aliases</strong> Alternate commonly used abbreviated gene symbols <br> <strong>description</strong> Gene name/Gene description <br> <strong>other_designations</strong> Alternate gene name/gene description <br> <strong>Base mean</strong> Mean of normalized counts <br> <strong>log2(FC)</strong> Log(base 2) fold change <br> <strong>StdErr</strong> Standard error <br> <strong>Wald-Stats</strong> Wald statistic <br> <strong>P-value</strong> P-value <br> <strong>P-adj</strong> Adjusted p-value (bonferroni correction) <br> <strong>Scaffold</strong> Scaffold where gene is located <br> <strong>Chromosome</strong> Chromosome where gene is located <br> <strong>Start</strong> Start of gene (bp position) <br> <strong>End</strong> End of gene (bp position) <br> <strong>Strand</strong> Strand gene is on <br> <strong>DE?(y/n)</strong> If gene is significantly differentially expressed (adjusted p-value ≤0.05, |log2(FC)|≥1) or not (y = yes, n=no) <br> <strong>Sample</strong> <strong>Description</strong> xx0 Untreated samples at 0 hours xx16 Vehicle treated samples at 16 hours xx48 Vehicle treated samples at 48 hours Nx16 Niclosamide treated no virus samples at 16 hours Nx48 Niclosamide treated no virus samples at 48 hours xV16 Vehicle treated virus samples at 16 hours xV48 Vehicle treated virus samples at 48 hours
DOI: 10.6084/m9.figshare.21675236.v1
title: Table S6

description: <strong>Table S6. Differentially expressed (DE) genes identified by RNAseq in sample comparisons highlighting the effect of time</strong> <br> <br> <strong>Entrez GeneID</strong> NCBI unique gene identifier <br> <strong>Gene Symbol</strong> Common abbreviated gene symbol <br> <strong>Aliases</strong> Alternate commonly used abbreviated gene symbols <br> <strong>description</strong> Gene name/Gene description <br> <strong>other_designations</strong> Alternate gene name/gene description <br> <strong>Base mean</strong> Mean of normalized counts <br> <strong>log2(FC)</strong> Log(base 2) fold change <br> <strong>StdErr</strong> Standard error <br> <strong>Wald-Stats</strong> Wald statistic <br> <strong>P-value</strong> P-value <br> <strong>P-adj</strong> Adjusted p-value (bonferroni correction) <br> <strong>Scaffold</strong> Scaffold where gene is located <br> <strong>Chromosome</strong> Chromosome where gene is located <br> <strong>Start</strong> Start of gene (bp position) <br> <strong>End</strong> End of gene (bp position) <br> <strong>Strand</strong> Strand gene is on <br> <strong>DE?(y/n)</strong> If gene is significantly differentially expressed (adjusted p-value ≤0.05, |log2(FC)|≥1) or not (y = yes, n=no) <br> <strong>Sample</strong> <strong>Description</strong> xx0 Untreated samples at 0 hours xx16 Vehicle treated samples at 16 hours xx48 Vehicle treated samples at 48 hours Nx16 Niclosamide treated no virus samples at 16 hours Nx48 Niclosamide treated no virus samples at 48 hours xV16 Vehicle treated virus samples at 16 hours xV48 Vehicle treated virus samples at 48 hours
DOI: 10.6084/m9.figshare.21674864
title: Table S5

description: <strong>Table S5. Differentially expressed genes identified by RNAseq in sample comparisons highlighting the effect of SARS-CoV-2.</strong> <br> <br> <strong>Entrez GeneID</strong> NCBI unique gene identifier <br> <strong>Gene Symbol</strong> Common abbreviated gene symbol <br> <strong>Aliases</strong> Alternate commonly used abbreviated gene symbols <br> <strong>description</strong> Gene name/Gene description <br> <strong>other_designations</strong> Alternate gene name/gene description <br> <strong>Base mean</strong> Mean of normalized counts <br> <strong>log2(FC)</strong> Log(base 2) fold change <br> <strong>StdErr</strong> Standard error <br> <strong>Wald-Stats</strong> Wald statistic <br> <strong>P-value</strong> P-value <br> <strong>P-adj</strong> Adjusted p-value (bonferroni correction) <br> <strong>Scaffold</strong> Scaffold where gene is located <br> <strong>Chromosome</strong> Chromosome where gene is located <br> <strong>Start</strong> Start of gene (bp position) <br> <strong>End</strong> End of gene (bp position) <br> <strong>Strand</strong> Strand gene is on <br> <strong>DE?(y/n)</strong> If gene is significantly differentially expressed (adjusted p-value ≤0.05, |log2(FC)|≥1) or not (y = yes, n=no) <br> <br> <br> <strong>Sample</strong> <strong>Description and codes</strong> xx16 Vehicle treated no virus samples at 16 hours xV16 Vehicle treated virus samples at 16 hours xx48 Vehicle treated no virus samples at 48 hours xV48 Vehicle treated virus samples at 48 hours Nx16 Niclosamide treated no virus samples at 16 hours NV16 Niclosamide treated virus samples at 16 hours Nx48 Niclosamide treated no virus samples at 48 hours NV48 Niclosamide treated virus samples at 48 hours
DOI: 10.6084/m9.figshare.21674864.v1
title: Table S5

description: <strong>Table S5. Differentially expressed genes identified by RNAseq in sample comparisons highlighting the effect of SARS-CoV-2.</strong> <br> <br> <strong>Entrez GeneID</strong> NCBI unique gene identifier <br> <strong>Gene Symbol</strong> Common abbreviated gene symbol <br> <strong>Aliases</strong> Alternate commonly used abbreviated gene symbols <br> <strong>description</strong> Gene name/Gene description <br> <strong>other_designations</strong> Alternate gene name/gene description <br> <strong>Base mean</strong> Mean of normalized counts <br> <strong>log2(FC)</strong> Log(base 2) fold change <br> <strong>StdErr</strong> Standard error <br> <strong>Wald-Stats</strong> Wald statistic <br> <strong>P-value</strong> P-value <br> <strong>P-adj</strong> Adjusted p-value (bonferroni correction) <br> <strong>Scaffold</strong> Scaffold where gene is located <br> <strong>Chromosome</strong> Chromosome where gene is located <br> <strong>Start</strong> Start of gene (bp position) <br> <strong>End</strong> End of gene (bp position) <br> <strong>Strand</strong> Strand gene is on <br> <strong>DE?(y/n)</strong> If gene is significantly differentially expressed (adjusted p-value ≤0.05, |log2(FC)|≥1) or not (y = yes, n=no) <br> <br> <br> <strong>Sample</strong> <strong>Description and codes</strong> xx16 Vehicle treated no virus samples at 16 hours xV16 Vehicle treated virus samples at 16 hours xx48 Vehicle treated no virus samples at 48 hours xV48 Vehicle treated virus samples at 48 hours Nx16 Niclosamide treated no virus samples at 16 hours NV16 Niclosamide treated virus samples at 16 hours Nx48 Niclosamide treated no virus samples at 48 hours NV48 Niclosamide treated virus samples at 48 hours
DOI: 10.57967/hf/0159
title: my-wonderful-dataset
DOI: 10.5517/ccdc.csd.cc1lq1gt
title: CCDC 1480946: Experimental Crystal Structure Determination

description: Related Article: Christian H. Müller, Christian Rösner, Constantin G. Daniliuc, Ulrich Hennecke|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dfjt6
title: CCDC 2217548: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, J.D. Kennedy, M.J. Carr|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dfjv7
title: CCDC 2217549: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, J.D. Kennedy, M.J. Carr|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dfjyb
title: CCDC 2217552: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, J.D. Kennedy, J. Bould, C.A. Kilner, M.G.S. Londesborough, C. O'Dowd|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dfk0f
title: CCDC 2217554: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, J.D. Kennedy, J. Bould, U. Doerfler|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dnq4y
title: CCDC 2224440: Experimental Crystal Structure Determination

description: Related Article: Idamalarselvi R, Priyanka J, Priscilla R, Ramachandraraja C|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5519/qd.8szu18t8
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1067815 records)
DOI: 10.48366/r266339
title: The machine learning methods implemented environmental features detection

description: This comparison summarizes the characteristics of the machine learning methods implemented in the localization works in terms of the environmental features detection or the localization of passengers.
DOI: 10.5281/zenodo.7401623
title: japetella_diaphana_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.7401622
title: japetella_diaphana_decontaminated_gx.fasta.gz

description: .
DOI: 10.6084/m9.figshare.14896881
title: A dataset of overground walking full-body kinematics and kinetics in individuals with Parkinson’s disease

description: The data set comprises raw and processed (both in c3d and csv formats) full-body kinematics and kinetics signals of 26 individuals with Parkinson's disease walking in overground in ON and OFF medication. The data were collected using a three-dimensional (3D) motion-capture system while the subjects walked barefoot. A file of metadata (xls), including anthropometrics and information related to each file in the data set is also provided. In addition, a model file (mdh) and a pipeline file (v3s) for the Visual 3D software program are also provided. The data collection was performed in the <strong>Laboratory of Biomechanics and Motor Control at the Federal University of ABC, Brazil</strong>. Standard gait analysis was collected using a motion-capture system that had 12 cameras (Raptor-4; Motion Analysis Corporation, Santa Rosa, CA, USA), five force platforms (three 40 × 60-cm Optima models; AMTI, Watertown, MA, USA; two 40 × 60-cm 9281EA models; Kistler, Winterthur, Switzerland) embedded in the floor. The marker-set protocol adopted for this study comprised: (a) in the lower limb, 26 anatomical reflective markers (Leardini et al., 2007); and (b) trunk and upper limb, 18 anatomical reflective markers following the recommendations of the International Society of Biomechanics (Wu et al., 2002;Wu et al., 2005); totaling 44 anatomical reflective markers <br>
DOI: 10.6084/m9.figshare.14896881.v3
title: A dataset of overground walking full-body kinematics and kinetics in individuals with Parkinson’s disease

description: The data set comprises raw and processed (both in c3d and csv formats) full-body kinematics and kinetics signals of 26 individuals with Parkinson's disease walking in overground in ON and OFF medication. The data were collected using a three-dimensional (3D) motion-capture system while the subjects walked barefoot. A file of metadata (xls), including anthropometrics and information related to each file in the data set is also provided. In addition, a model file (mdh) and a pipeline file (v3s) for the Visual 3D software program are also provided.<br>
DOI: 10.21227/mvem-c267
title: Gate voltage tunable temperature coefficient of resistance of WSe2 for thermal sensing applications

description: Two-dimensional (2D) materials have layered structure with unique properties. One of the properties of interest is the temperature coefficient of resistance (TCR) which should be large for fast thermal sensors. The TCR is the calculation of the relative change in resistance per degree of temperature change. Taking a step further, tunable TCR is a concept that involves the control of TCR by the gate voltage. Here we have shown that in a field effect transistor device, the 2D semiconductor material WSe2 has a TCR, which can be controlled by varying the applied gate voltage. We also compared it with MoS2 and found that the WSe2 TCR is approximately six times that of MoS2 and 19 times that of metallic thin films. Also, WSe2 TCR could be controlled to 300% of its value within 10V of applied gate voltage. Specific high values of TCR can be tuned using the gate voltage as WSe2 TCR can be selected to -8.7 %K-1. This has applications in bolometers, thermal sensors, and accelerometers.
DOI: 10.5281/zenodo.7388132
title: Supporting data for GP_2021_3

description: This package contains raw and processed datasets used to support the conclusions of "Length Biases in Single-Cell RNA Sequencing of pre-mRNA" by Gennady Gorin and Lior Pachter. GP_2021_3_raw_data.tar.gz: raw <em>loom</em> files. GP_2021_3_fits.tar.gz: fits generated with <em>Monod</em>. GP_2021_3-zenodo.zip: copy of the GitHub repository (as of Dec 1, 2022) with all analysis scripts and notebooks.
DOI: 10.25390/caryinstitute.11801052.v51
title: Cary Institute Environmental Monitoring Program Current Year Data 2022

description: The Cary Institute of Ecosystem Studies Environmental Monitoring Program is a long-term data collection program designed to understand how the environment changes over time. The program includes monitoring of climate including temperature and precipitation, as well as variables related to air pollution, such as acid deposition and ozone, and water pollution and other streamwater chemistry. Our solar radiation monitoring includes diffuse and global photosynthetically active radiation (PAR), diffuse and global shortwave radiation, net radiation and UV. Long-term monitoring of solar radiation provides us with an understanding of atmospheric energy dynamics, which can affect natural and human systems. The Cary Institute of Ecosystem Studies, Environmental Monitoring Program furnishes data under the following conditions: The data have received quality assurance scrutiny by our program, and, although we are confident of the accuracy of these data, Cary Institute will not be held liable for errors in these data. Data are subject to change resulting from updates in data screening or models used. Data citation: please click on the Cite button in the Figshare repository.<br>Those wishing to publish data from Cary Institute of Ecosystem Studies, Environmental Monitoring Program are encouraged to contact data manager Vicky Kelly, kellyv@caryinstitute.org.<br>For complete data for previous years and for complete metadata, please see appropriate items for the Cary Institute Environmental Monitoring Program at https://caryinstitute.figshare.com/
DOI: 10.6073/pasta/053dd151fa7adb4015f919f8a3a7189e
title: Phenologies of the Tabonuco Forest trees and shrubs

description: These data are being used to, among other things, (1) determine the seasonality of flowering and fruiting in Tabonuco forest and test hypotheses concerning the causation of seasonality (or lack thereof), (2) test the effect of annual variation in rainfall and other climatic variables on seed and fruit production of individual species, and (3) compare the relative dispersal of species on the Luquillo Forest Dynamics Plot by applying information on the spatial distribution of canopy trees to the data on seed and fruit fall. These data also serve as background information on the flowering and fruiting of individual species. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
DOI: 10.18112/openneuro.ds004197.v1.0.6
title: Bimodal dataset on Inner speech
DOI: 10.25500/edata.bham.00000893
title: Research data supporting "Monitoring and apportioning sources of indoor air quality using low-cost particulate matter sensors"
DOI: 10.5281/zenodo.7392105
title: A single-cell transcriptional gradient in human cutaneous memory T cells restricts Th17/Tc17 identity

description: In our manuscript, we utilized scRNA-seq libraries we generated from: -8 human psoriatic skin samples and 7 healthy control skin samples (ZIST.rds) -3 human psoriatic skin samples before and after tildrakizumab treatment (three_tildra_Trm1.rds) These *rds files are the Seurat objects for these data sets post-filtering and integration. For raw sequencing data corresponding to these samples, access can be found under accession number EGA: S00001005271. We also utilized bulk RNAseq data generated from CRISPR-Cas9 knockout of ZFP36L2 in human CD4 T cells from 3 different donors. The count matrices for each of these individual samples is uploaded here alongside a key explaining what each of the samples are with filenames that also correspond to the raw fastq files submitted at the European Genome-Phenome Archive (EGA), under accession number EGA: S00001005271. There are two replicates for each sample. All methods underlying the generation and analysis of these datasets can be found in the original manuscript: Cook CP, Taylor M, Liu Y, et al. A single-cell transcriptional gradient in human cutaneous memory T cells restricts Th17/Tc17 identity. <em>Cell Rep Med</em>. 2022;3(8):100715. doi:10.1016/j.xcrm.2022.100715 <br> Any additional questions or information requests can be addressed to Jeffrey.cheng@ucsf.edu or cook.675@berkeley.edu
DOI: 10.5281/zenodo.7242346
title: A single-cell transcriptional gradient in human cutaneous memory T cells restricts Th17/Tc17 identity

description: In our manuscript, we utilized scRNA-seq libraries we generated from: -8 human psoriatic skin samples and 7 healthy control skin samples (ZIST.rds) -3 human psoriatic skin samples before and after tildrakizumab treatment (three_tildra_Trm1.rds) These *rds files are the Seurat objects for these data sets post-filtering and integration. For raw sequencing data corresponding to these samples, access can be found under accession number EGA: S00001005271. We also utilized bulk RNAseq data generated from CRISPR-Cas9 knockout of ZFP36L2 in human CD4 T cells from 3 different donors. The count matrices for each of these individual samples is uploaded here alongside a key explaining what each of the samples are with filenames that also correspond to the raw fastq files submitted at the European Genome-Phenome Archive (EGA), under accession number EGA: S00001005271. There are two replicates for each sample. All methods underlying the generation and analysis of these datasets can be found in the original manuscript: Cook CP, Taylor M, Liu Y, et al. A single-cell transcriptional gradient in human cutaneous memory T cells restricts Th17/Tc17 identity. <em>Cell Rep Med</em>. 2022;3(8):100715. doi:10.1016/j.xcrm.2022.100715 <br> Any additional questions or information requests can be addressed to Jeffrey.cheng@ucsf.edu or cook.675@berkeley.edu
DOI: 10.6084/m9.figshare.21537646
title: Role of traversing speed and axial load on the properties of friction stir welded dissimilar AA6101-T6 AND AA1350 aluminium alloys

description: Traditionally, AA6101-T6 and AA1350 aluminum alloys find prominent place in many industrial applications like electrical bus bar utilities for the sake of better conductivity. Friction Stir Welding is a non-traditional solid-state welding technique that was invented and developed by The Welding Institute. In this analysis, AA6101-T6 and AA1350 alloys have been put on experimentation with friction stir welding by varying axial load (5 kN to 9 kN) and traversing speed (60 mm/min to 120 mm/min). In order to examine the weld quality, properties such as tensile, wear and microstructural analyses have been carried out. From the results, it was observed that the weldment made with 7 kN axial load and 90 mm/min traversing speed exhibited better joint properties. Similarly, the weldment made with 5 kN axial load and 120 mm/min traversing speed exhibited poor joint properties. This has been attained due to the variations in heat input, metals softening and grain coarsening at weld nugget. Taguchi L<sub>9</sub> orthogonal array with ANOVA and Signal to Noise ratio have been adopted to understand the contribution of axial load and traversing speed on the properties of weldments. Results reveal that traversing speed is statistically significant on the properties of weldment, when compared to axial load.