cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/csdw4b678n.1
title: همایش مطالعات و پژوهش های علمی علوم انسانی-موسسه قانون یار(سردبیر: دکتر بهنام اسدی) سال1397

description: اولین همایش بزرگ مطالعات و پژوهش های علمی علوم انسانی با مدیر مسئولی آقای دکتر بهنام اسدی در تاریخ 24 اسفند 1397 با همکاری دانشگاه آزاد اسلامی واحد کرمانشاه و تحت نظر موسسه قانون یار به شماره مجوز 78864 و شماره استاندارد بین المللی 3701-2538 در استان کرمانشاه با پذیرش 152 مقاله علمی در زمینه های و رشته های حقوق، فقه، مبانی اسلامی، علوم اجتماعی، ادبیات فارسی، علوم تربیتی و آموزشی و .... برگزار گردید. مقالات این همایش در تمامی پایگاه های علمی معتبر نمایه می گردد. • مرجع برگزار کننده : موسسه قانون یار- دانشگاه آزاد اسلامی کرمانشاه • رییس و دبیر علمی کنفرانس: دکتر بهنام اسدی • عضو شورای سیاستگذاری – راهبردی: شهلا رضایی-علی اشرف باقری فرد- محسن فاضلی-ناهید فرامرزی-امیر اسدی-زهرا اسدی • عضو کمیته علمی – هیات داوران: دکتر سعید خردمندی، دکتر بابک وثوقی فرد، دکتر پریسا گنجی، دکتر فرانک موسوی، دکتر میرحیدر میرنقی زاده، دکتر امین امیریان فارسانی، دکتر مختار فتاحی، • دبیر اجرایی: شهلا رضایی • مدیر اجرایی: حمزه ملکی • مسئول کمیته: دکتر امین امیریان فارسانی • مسئول دبیرخانه کنفرانس: محسن فاضلی • حوزه تحت پوشش: حوزه های تحت پوشش همایش و محورهای پذیرش مقالات پژوهشگران عبارتند از: (تمامی گرایش های رشته فقه، حقوق و علوم سیاسی) (حوزه های فقهی قضاوت و مطالعات قضایی) (علوم تربیتی، روانشناسی و علوم اجتماعی) (آموزش و پرورش و علوم رفتاری و جامعه شناسی) (حسابداری و تمامی رشته های مرتبط با گروه علوم انسانی ) • آدرس دبیرخانه مرکزی همایش: تهران، میدان انقلاب، خیابان منیری جاوید، بین خیابان لبافی نژاد و خیابان جمهوری، کوچه مینا، پلاک 29 طبقه سوم، دفتر مرکزی قانون یار، کدپستی: 1314973753
DOI: 10.6084/m9.figshare.8332706
title: Individual marker gene and MLSA phylogenies

description: CDS alignments and gene trees were extracted from the ‘41NeoPseudo’ Pantagruel database (Dataset https://doi.org/10.6084/m9.figshare.8320142) for the following genes atpD (gene family id: RHIZOC034665, OG id: 1), glnII (RHIZOC002590, 0), gyrB (RHIZOC002739, 0), recA (RHIZOC040013, 0) and rpoB (RHIZOC004093, 0). Note that R. marinum MGL06<sup>T</sup> sequence for glnII gene was missing in gene family RHIZOC002590, probably due to incomplete genome assembly. CDS alignments were concatenated and a tree was computed with MrBayes as described in Dataset https://doi.org/10.6084/m9.figshare.8320199.
DOI: 10.6084/m9.figshare.8332706.v4
title: Individual marker gene and MLSA phylogenies

description: CDS alignments and gene trees were extracted from the ‘41NeoPseudo’ Pantagruel database (Dataset https://doi.org/10.6084/m9.figshare.8320142) for the following genes atpD (gene family id: RHIZOC034665, OG id: 1), glnII (RHIZOC002590, 0), gyrB (RHIZOC002739, 0), recA (RHIZOC040013, 0) and rpoB (RHIZOC004093, 0). Note that R. marinum MGL06<sup>T</sup> sequence for glnII gene was missing in gene family RHIZOC002590, probably due to incomplete genome assembly. CDS alignments were concatenated and a tree was computed with MrBayes as described in Dataset https://doi.org/10.6084/m9.figshare.8320199.
DOI: 10.6084/m9.figshare.8332706.v3
title: Individual marker gene and MLSA phylogenies

description: CDS alignments and gene trees were extracted from the ‘41NeoPseudo’ Pantagruel database (Dataset https://doi.org/10.6084/m9.figshare.8320142) for the following genes atpD (gene family id: RHIZOC034665, OG id: 1), glnII (RHIZOC002590, 0), gyrB (RHIZOC002739, 0), recA (RHIZOC040013, 0) and rpoB (RHIZOC004093, 0). Note that R. marinum MGL06<sup>T</sup> sequence for glnII gene was missing in gene family RHIZOC002590, probably due to incomplete genome assembly. CDS alignments were concatenated and a tree was computed with MrBayes as described in Dataset https://doi.org/10.6084/m9.figshare.8320199.
DOI: 10.6084/m9.figshare.8335265
title: Pangenome gene alignments of 41 Pseudorhizobium and Neorhizobium strains

description: All predicted protein sequence were extracted from the 571Rhizob genome dataset and clustered into homologous gene families (with possibly multiple copies per genome) using MMSeqs2 release 2 (commit e5d64b2) (Steinegger & Söding 2017). Protein sequences from each gene family were aligned using ClustalOmega (Sievers et al. 2011) and reverse-translated into coding sequence (CDS) alignments using PAL2NAL (Suyama et al. 2006).This resulted in the extensive Dataset presented in https://doi.org/10.6084/m9.figshare.8343473. Alignment file set and sequence content were then restricted to sequences occurring in the the 41NeoPseudo sub-dataset, resulting into 6,714 gene family alignments.
DOI: 10.6084/m9.figshare.8335265.v1
title: Pangenome gene alignments of 41 Pseudorhizobium and Neorhizobium strains

description: All predicted protein sequence were extracted from the 571Rhizob genome dataset and clustered into homologous gene families (with possibly multiple copies per genome) using MMSeqs2 release 2 (commit e5d64b2) (Steinegger & Söding 2017). Protein sequences from each gene family were aligned using ClustalOmega (Sievers et al. 2011) and reverse-translated into coding sequence (CDS) alignments using PAL2NAL (Suyama et al. 2006).This resulted in the extensive Dataset presented in https://doi.org/10.6084/m9.figshare.8343473. Alignment file set and sequence content were then restricted to sequences occurring in the the 41NeoPseudo sub-dataset, resulting into 6,714 gene family alignments.
DOI: 10.6084/m9.figshare.8320199
title: Pangenome gene trees of 41 Pseudorhizobium and Neorhizobium strains

description: Gene trees were computed for each of the 6,714 homologous gene family of the 41-species pangenome with at least 4 sequences using MrBayes (Ronquist & Huelsenbeck 2003) under the GTR+4G+I model, running the Metropolis-coupled Markov chain Monte-Carlo (MCMCMC) for 2,000,000 generations, sampling a tree every 500 generations. Convergence of bipartition distribution in independent pairs of 4,000 sampled gene tree sets (‘chains’) was achieved for all gene families under these conditions.<br>Only the consensus of these tree chains (discarding the first 25% sampled trees as burn-in) are presented here.Correspondance of gene family and coding sequence identifiers with metadata (genome of origin, genome location, functional annotation, etc.) is available using the Pantagruel database provided in the related post (open with SQLite 3).
DOI: 10.6084/m9.figshare.8320199.v1
title: Pangenome gene trees of 41 Pseudorhizobium and Neorhizobium strains

description: Gene trees were computed for each of the 6,714 homologous gene family of the 41-species pangenome with at least 4 sequences using MrBayes (Ronquist & Huelsenbeck 2003) under the GTR+4G+I model, running the Metropolis-coupled Markov chain Monte-Carlo (MCMCMC) for 2,000,000 generations, sampling a tree every 500 generations. Convergence of bipartition distribution in independent pairs of 4,000 sampled gene tree sets (‘chains’) was achieved for all gene families under these conditions.<br>Only the consensus of these tree chains (discarding the first 25% sampled trees as burn-in) are presented here.Correspondance of gene family and coding sequence identifiers with metadata (genome of origin, genome location, functional annotation, etc.) is available using the Pantagruel database provided in the related post (open with SQLite 3).
DOI: 10.34740/kaggle/dsv/2376858
title: 2021 World Population (updated daily)
DOI: 10.6084/m9.figshare.14865732
title: DATA_REF - Castillo, Carmona et al., 2021.xlsx

description: Dataset for Castillo, Carmona et al. (2021)<br>
DOI: 10.6084/m9.figshare.14865732.v1
title: DATA_REF - Castillo, Carmona et al., 2021.xlsx

description: Dataset for Castillo, Carmona et al. (2021)<br>
DOI: 10.5281/zenodo.5036609
title: Temperate Oligocene surface ocean conditions offshore of Cape Adare, Ross Sea, Antarctica

description: Datasets accompanying Temperate Oligocene surface ocean conditions offshore of Cape Adare, Ross Sea, Antarctica by Hoem et al., 2021. Climate of the past. Supplementary table S1: Demagnetization data results of DSDP Site 274, cores 21-43 Supplementary table S2: Concentrations of GDGTs at DSDP Site 274, cores 21-43. Supplementary table S3: Total palynomorph assemblage data, total counts, DSDP Site 274, cores 21-43 Supplementary table S4: Correspondence analysis (CA) scores of palynological data DSDP Site 274, cores 21-43.
DOI: 10.5281/zenodo.5036608
title: Temperate Oligocene surface ocean conditions offshore of Cape Adare, Ross Sea, Antarctica

description: Datasets accompanying Temperate Oligocene surface ocean conditions offshore of Cape Adare, Ross Sea, Antarctica by Hoem et al., 2021. Climate of the past. Supplementary table S1: Demagnetization data results of DSDP Site 274, cores 21-43 Supplementary table S2: Concentrations of GDGTs at DSDP Site 274, cores 21-43. Supplementary table S3: Total palynomorph assemblage data, total counts, DSDP Site 274, cores 21-43 Supplementary table S4: Correspondence analysis (CA) scores of palynological data DSDP Site 274, cores 21-43.
DOI: 10.34740/kaggle/dsv/2376899
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.6084/m9.figshare.14823678.v1
title: Diffraction data for: Striping of orbital-order with charge-disorder in optimally doped manganites

description: The data set contains the high resolution powder diffraction data collected at ID22, ESRF at 80 and 700K on the solid solution AMn7O12 (A=Na1-xCax and La1-xCax).Wavelength for the datasets is 0.39990 A
DOI: 10.5281/zenodo.5036687
title: Training data for "Identification of allelic variants in SARS-CoV-2 from deep sequencing reads"

description: Effectively monitoring global infectious disease crises, such as the COVID-19 pandemic, requires capacity to generate and analyze large volumes of sequencing data in near real time. These data have proven essential for monitoring the emergence and spread of new variants, and for understanding the evolutionary dynamics of the virus. Two sequencing platforms in combination with several established library preparation strategies are predominantly used to generate SARS-CoV-2 sequence data. However, data alone do not equal knowledge: they need to be analyzed. The Galaxy community developed analysis workflows to support the <strong>identification of allelic variants (AVs) in SARS-CoV-2 from deep sequencing reads</strong>. These workflows allow one to identify AVs and lineages in SARS-CoV-2 genomes with variant allele frequencies ranging from 5% to 100% (i.e., they detect variants with intermediate frequencies as well. In this tutorial we will see how to run these workflows for the different types of input data: Single end data derived from Illumina-based RNAseq experiments Paired end data derived from Illumina-based RNAseq experiments Paired-end data generated with Illumina-based Ampliconic (ARTIC) protocols ONT fastq files generated with Oxford nanopore (ONT)-based Ampliconic (ARTIC) protocols To illustrate the tutorial, we took some example datasets (paired-end data generated with Illumina-based Ampliconic (ARTIC) protocols) from COG-UK, the COVID-19 Genomics UK Consortium.
DOI: 10.5281/zenodo.5036686
title: Training data for "Identification of allelic variants in SARS-CoV-2 from deep sequencing reads"

description: Effectively monitoring global infectious disease crises, such as the COVID-19 pandemic, requires capacity to generate and analyze large volumes of sequencing data in near real time. These data have proven essential for monitoring the emergence and spread of new variants, and for understanding the evolutionary dynamics of the virus. Two sequencing platforms in combination with several established library preparation strategies are predominantly used to generate SARS-CoV-2 sequence data. However, data alone do not equal knowledge: they need to be analyzed. The Galaxy community developed analysis workflows to support the <strong>identification of allelic variants (AVs) in SARS-CoV-2 from deep sequencing reads</strong>. These workflows allow one to identify AVs and lineages in SARS-CoV-2 genomes with variant allele frequencies ranging from 5% to 100% (i.e., they detect variants with intermediate frequencies as well. In this tutorial we will see how to run these workflows for the different types of input data: Single end data derived from Illumina-based RNAseq experiments Paired end data derived from Illumina-based RNAseq experiments Paired-end data generated with Illumina-based Ampliconic (ARTIC) protocols ONT fastq files generated with Oxford nanopore (ONT)-based Ampliconic (ARTIC) protocols To illustrate the tutorial, we took some example datasets (paired-end data generated with Illumina-based Ampliconic (ARTIC) protocols) from COG-UK, the COVID-19 Genomics UK Consortium.
DOI: 10.6084/m9.figshare.14852253
title: Figure 7 - A549 (53BP1+PCNA) - Cisplatin Pulsed Cells - Raw LIF

description: A549 Lung Adenocarcinoma cells were engineered to co-express a PCNA chromobody tagged with mNeonGreen (green) and a truncated version of 53BP1 tagged with mApple (red). Cells were synchronised with 1mM thymidine and pulsed with 5µg/ml cisplatin for 2 hr prior to release to enrich cells in early S phase. Cells were then imaged using a Leica SP8 confocal microscopy, using HyD detectors a WLL and 63X 1.4 NA objective. A 10-µm-thick z-stack (1 µm slice, 1AU) was taken every 30 min for 72 hr, with the maximum projection raw LIF and compressed MP4 files included here.<br>This data relates to Figure 7 of the published article:Rajal AG, Marzec KA, McCloy RA, Nobis M, Chin V, Hastings JF, Lai K, Kennerson M, Hughes WE, Vaghjiani V, Timpson P, Cain JE, Watkins DN, Croucher DR, Burgess A (2021) A non-genetic, cell cycle dependent mechanism of platinum resistance in lung adenocarcinoma. Elife 10:e65234. https://doi.org/10.7554/elife.65234
DOI: 10.6084/m9.figshare.14852253.v1
title: Figure 7 - A549 (53BP1+PCNA) - Cisplatin Pulsed Cells - Raw LIF

description: A549 Lung Adenocarcinoma cells were engineered to co-express a PCNA chromobody tagged with mNeonGreen (green) and a truncated version of 53BP1 tagged with mApple (red). Cells were synchronised with 1mM thymidine and pulsed with 5µg/ml cisplatin for 2 hr prior to release to enrich cells in early S phase. Cells were then imaged using a Leica SP8 confocal microscopy, using HyD detectors a WLL and 63X 1.4 NA objective. A 10-µm-thick z-stack (1 µm slice, 1AU) was taken every 30 min for 72 hr, with the maximum projection raw LIF and compressed MP4 files included here.<br>This data relates to Figure 7 of the published article:Rajal AG, Marzec KA, McCloy RA, Nobis M, Chin V, Hastings JF, Lai K, Kennerson M, Hughes WE, Vaghjiani V, Timpson P, Cain JE, Watkins DN, Croucher DR, Burgess A (2021) A non-genetic, cell cycle dependent mechanism of platinum resistance in lung adenocarcinoma. Elife 10:e65234. https://doi.org/10.7554/elife.65234
DOI: 10.5281/zenodo.4709003
title: Dataset for 'Inherent variability of geotechnical properties for Finnish clay soils'

description: This dataset contains the laboratory test results used in a paper titled “Inherent variability of geotechnical properties for Finnish clay soils” presented in 18th International Probabilistic Workshop (Löfman and Korkiala-Tanttu, 2021b). The data originates from four clay soil deposits located in Finland: Haarajoki, Suurpelto, Tolsa, and POKO. This dataset was used to derive statistics for inherent variability of geotechnical properties within nominally homogeneous soil layers. The types of laboratory tests include: Classification tests like water content determination, density, grain size Undrained shear strengths from Swedish fall cone test Incrementally loaded and constant-rate-of strain oedometer tests Two dataset files are provided in Excel-format: Laboratory_data.xlsx Oedometer_increments_creep_and_cv.xlsx In the first dataset file, each row represents laboratory test results for one soil specimen (i.e. parameters like water content, compression index). In the second dataset file, each row represents results for one load increment in an incrementally loaded oedometer test (i.e. coefficient of consolidation and coefficient of creep). Both dataset files contain “info” sheets that explain what each data column contains. Further information about the data and the studied sites can be found from Löfman and Korkiala-Tanttu (2021a, 2021b). Some of the oedometer test results included in this dataset have also been published in a database called FI-CLAY/14/856 (Löfman and Korkiala-Tanttu, 2021a). Some of the data included in this database was not utilized in the paper “Inherent variability of geotechnical properties for Finnish clay soils”. This ‘extra’ data was included in the dataset regardless, in case future analyses can benefit from them. The compilation of this dataset was funded by the Finnish Transport Infrastructure Agency. References: Löfman, Monica S. and Leena Korkiala-Tanttu. 2021a. Transformation models for the compressibility properties of Finnish clays using a multivariate database. <em>Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards. </em>https://doi.org/10.1080/17499518.2020.1864410 Löfman, Monica S., and Leena K. Korkiala-Tanttu, 2021b. Inherent variability of geotechnical properties for Finnish clay soils. In the Proceedings of 18th International Probabilistic Workshop (IPW2020), 12-14 May 2021, Guimarães, Portugal. Springer. Lecture Notes in Civil Engineering, vol 153. Springer, Cham. https://doi.org/10.1007/978-3-030-73616-3_32
DOI: 10.5281/zenodo.4709002
title: Dataset for 'Inherent variability of geotechnical properties for Finnish clay soils'

description: This dataset contains the laboratory test results used in a paper titled “Inherent variability of geotechnical properties for Finnish clay soils” presented in 18th International Probabilistic Workshop (Löfman and Korkiala-Tanttu, 2021b). The data originates from four clay soil deposits located in Finland: Haarajoki, Suurpelto, Tolsa, and POKO. This dataset was used to derive statistics for inherent variability of geotechnical properties within nominally homogeneous soil layers. The types of laboratory tests include: Classification tests like water content determination, density, grain size Undrained shear strengths from Swedish fall cone test Incrementally loaded and constant-rate-of strain oedometer tests Two dataset files are provided in Excel-format: Laboratory_data.xlsx Oedometer_increments_creep_and_cv.xlsx In the first dataset file, each row represents laboratory test results for one soil specimen (i.e. parameters like water content, compression index). In the second dataset file, each row represents results for one load increment in an incrementally loaded oedometer test (i.e. coefficient of consolidation and coefficient of creep). Both dataset files contain “info” sheets that explain what each data column contains. Further information about the data and the studied sites can be found from Löfman and Korkiala-Tanttu (2021a, 2021b). Some of the oedometer test results included in this dataset have also been published in a database called FI-CLAY/14/856 (Löfman and Korkiala-Tanttu, 2021a). Some of the data included in this database was not utilized in the paper “Inherent variability of geotechnical properties for Finnish clay soils”. This ‘extra’ data was included in the dataset regardless, in case future analyses can benefit from them. The compilation of this dataset was funded by the Finnish Transport Infrastructure Agency. References: Löfman, Monica S. and Leena Korkiala-Tanttu. 2021a. Transformation models for the compressibility properties of Finnish clays using a multivariate database. <em>Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards. </em>https://doi.org/10.1080/17499518.2020.1864410 Löfman, Monica S., and Leena K. Korkiala-Tanttu, 2021b. Inherent variability of geotechnical properties for Finnish clay soils. In the Proceedings of 18th International Probabilistic Workshop (IPW2020), 12-14 May 2021, Guimarães, Portugal. Springer. Lecture Notes in Civil Engineering, vol 153. Springer, Cham. https://doi.org/10.1007/978-3-030-73616-3_32
DOI: 10.6084/m9.figshare.14865798
title: Proband_SNP_Indel_ANNO.xlsx

description: WES data of an Iranian patient with LVWM
DOI: 10.6084/m9.figshare.14865798.v1
title: Proband_SNP_Indel_ANNO.xlsx

description: WES data of an Iranian patient with LVWM
DOI: 10.5281/zenodo.5036700
title: Code and data for "Sensitivity of Northern Hemisphere climate to ice-ocean interface heat flux parameterizations"

description: This repository provides the source code and modeled data for three different ice-ocean heat flux<br> parameterizations of a 1-D idealized model, as well as 3-D climate models including CICE,<br> MPIOM and COSMOS, which are used in a GMD manuscript called "Sensitivity of Northern Hemisphere climate to ice-ocean interface heat flux parameterizations". The NCL-based scripts for plotting the figures are also provided. <br> The file all.tar.gz consists of 4 folders as the following: 1. 1-D<br> In the 1-D folder one can find the matlab source code for the 1-D idealized model with main.m being the main script and the others sub-scripts for calculating seasonal changes of different forcings, involving the surface albedo (albedo.m), shortwave fluxes (shortwave.m) and all other kinds of fluxes (otherfluxes.m). 2. code<br> The code folder provides the source code of the three models used in our study: CICE, MPIOM and COSMOS. The most important code in terms of the ice-ocean heat flux in CICE can be found at code/cice/source/ice_therm_vertical.F90. The switch of the options of the three parameterizations can be achieved by changing the parameter "oceanic_heat" (1 for icebath, 2 for 2eq and 3 for 3eq) in the namelist when running the model. The mpiom folder contains the three different set of MPIOM source code for the three ice-ocean heat flux parameterizations respectively. In cosmos, one could find 4 sub-folders, with the folder echam5 containing the source code for the atmosphere module ECHAM5, and the other 3 folders containing the MPIOM source code incorporating with the three different ice-ocean heat flux parameterizations, similar as the mpiom folder. 3. data<br> This folder provides the simulated output from the three models: CICE, MPIOM and COSMOS. Each model folder contains three sub-folders called 2eq, 3eq and icebath, representing the modelled data for the 2eq, 3eq and icebath parameterizations respectively. For CICE, we upload the modeled results of the last 10 years. For MPIOM and COSMOS, as the original data set are too large, here we upload the climatology of the data from the last 100 simulation years. Note that in cosmos, there are some additional variables which are listed seperately, namely the AMOC (amoc.nc), the sea surface pressure (slp.nc) as well as the surface temperature (tsurf.nc). reg.nc contains ocean temperature and salinity which have been interpolated onto a 1x1 regular grid. 4. plot_figures<br> This folder gives the scripts based on NCL to plot the figures in the manuscript, with the *.ncl files being the plotting scripts and the *.eps being the figures. <br> All the code, data, scripts can be used by anyone who has interest.