cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4244206
title: Different responses of CO2, CH4, and N2O fluxes to seasonally asymmetric warming in an alpine grassland of Tianshan Mountains

description: The measured CO2, CH4 and N2O fluxes and soil temperature and soil water content data.
DOI: 10.6084/m9.figshare.14740617.v1
title: Literature review data for 'Rethinking trophic discrimination factors (13C and 15N) for diet reconstruction'

description: Literature review of trophic discrimination factors for mammals.
DOI: 10.6084/m9.figshare.14740617
title: Literature review data for 'Rethinking trophic discrimination factors (13C and 15N) for diet reconstruction'

description: Literature review of trophic discrimination factors for mammals.
DOI: 10.6084/m9.figshare.8058902
title: Reconstructing Draft Genomes Using Genome Resolved Meta genomics Reveal Arsenic Metabolizing Genes and Secondary Metabolites in Fresh Water Lake in Eastern India - Main Data

description: We have taken water sample during winter and rainy season from Rabindra sarovar lake located at south kolkata and did metagenomic analysis. For metagenome analysis we followed genome resolved metagenomics approach. In this method samples are assembled after sequencing, then assembled samples are binned using binning tool to recover genome bins(n=27). Here in this repository we have uploaded recovered genome bins (.fna) and their respective 16S (.fa) as well as protein sequences (.faa) with annotation. A newick tree (.nwk) which infer phylogenetic position of 27 bins is addded here. This tree can edit anytime using ITOLv.4.In-addition we have also uploaded assembled data of both season(.fasta). <br>
DOI: 10.6084/m9.figshare.8058902.v5
title: Reconstructing Draft Genomes Using Genome-Resolved Meta- genomics to Decipher Fresh Water Lake Microbial Characteristics - Main Data

description: We have taken water sample during winter and rainy season from Rabindra sarovar lake located at south kolkata and did metagenomic analysis. For metagenome analysis we followed genome resolved metagenomics approach. In this method samples are assembled after sequencing, then assembled samples are binned using binning tool to recover genome bins(n=27). Here in this repository we have uploaded recovered genome bins (.fna) and their respective 16S (.fa) as well as protein sequences (.faa) with annotation. A newick tree (.nwk) which infer phylogenetic position of 27 bins is addded here. This tree can edit anytime using ITOLv.4.In-addition we have also uploaded assembled data of both season(.fasta). <br>
DOI: 10.6084/m9.figshare.9466571
title: Reconstructing Draft Genomes Using Genome Resolved Meta genomics Reveal Arsenic Metabolizing Genes and Secondary Metabolites in Fresh Water Lake in Eastern India - Supplementary Data

description: Supplementary information<br>
DOI: 10.6084/m9.figshare.9466571.v4
title: Reconstructing Draft Genomes Using Genome Resolved Meta genomics Reveal Arsenic Metabolizing Genes and Secondary Metabolites in Fresh Water Lake in Eastern India - Supplementary Data

description: Supplementary information<br>
DOI: 10.6084/m9.figshare.9466571.v3
title: Reconstructing draft genomes using genome-resolved meta- genomics to decipher fresh water lake microbial characteristics - Supplementary Data

description: Supplementary information<br>
DOI: 10.6084/m9.figshare.8796320.v3
title: Reconstructing Draft Genomes Using Genome Resolved Meta genomics Reveal Arsenic Metabolizing Genes and Secondary Metabolites in Fresh Water Lake in Eastern India - Junk Data

description: We have taken water sample during winter and rainy season from Rabindra sarovar lake located at south kolkata and did metagenomic analysis. For metagenome analysis we followed genome resolved metagenomics approach. In this method samples are assembled after sequencing, then assembled samples are binned using binning tool a total of 113 bins are reconstructed across the seasons out of which 27 bins are selected after refinement and der-eplication followed by quality assesment i.e completeness &gt;= 50% and contamination &lt;=10 % (CheckM analysis) . 27 bins are already uploaded in figshare under title "<b>Reconstructing Draft Genomes Using Genome Resolved Meta genomics Reveal Arsenic Metabolizing Genes and Secondary Metabolites in Fresh Water Lake in Eastern India - Main Data</b>" and rest 86 bins (.fa) are deposited under title <b>"Reconstructing Draft Genomes Using Genome Resolved Meta genomics Reveal Arsenic Metabolizing Genes and Secondary Metabolites in Fresh Water Lake in Eastern India - Junk Data"</b><br>
DOI: 10.6084/m9.figshare.8796320
title: Reconstructing Draft Genomes Using Genome Resolved Meta genomics Reveal Arsenic Metabolizing Genes and Secondary Metabolites in Fresh Water Lake in Eastern India - Junk Data

description: We have taken water sample during winter and rainy season from Rabindra sarovar lake located at south kolkata and did metagenomic analysis. For metagenome analysis we followed genome resolved metagenomics approach. In this method samples are assembled after sequencing, then assembled samples are binned using binning tool a total of 113 bins are reconstructed across the seasons out of which 27 bins are selected after refinement and der-eplication followed by quality assesment i.e completeness &gt;= 50% and contamination &lt;=10 % (CheckM analysis) . 27 bins are already uploaded in figshare under title "<b>Reconstructing Draft Genomes Using Genome Resolved Meta genomics Reveal Arsenic Metabolizing Genes and Secondary Metabolites in Fresh Water Lake in Eastern India - Main Data</b>" and rest 86 bins (.fa) are deposited under title <b>"Reconstructing Draft Genomes Using Genome Resolved Meta genomics Reveal Arsenic Metabolizing Genes and Secondary Metabolites in Fresh Water Lake in Eastern India - Junk Data"</b><br>
DOI: 10.6084/m9.figshare.14597793
title: Magnetism Insights from the Thomas Young Experiment Data.xlsx

description: This is data that was collected for the paper "Magnetism: Insights from the Thomas Young Experiment" where it was proven that magnetism organizes itself into a wave no matter the disturbances. It must be something discrete before it organizes itself into a wave. It must be something discrete before it organizes into a wave formation. That discrete thing was named a khumson. Renamed a khumalon not to confuse it with a town in Uzbekistan.<br><br>The khumalon is a massless, low energy, highly unstable quantum phenomenon associated with regulating energy that gives a particle a charge. Its wave has a gaussian distribution. <br><br>All experiments involved measuring millimetre by millimetre the gauss readings in front of a metal slab with different configurations of slits. The metal slabs where themselves placed behind a powerful neodymium magnet. <br><br>As no such tool existed, a small rail system had to be created that could move millimetre by millimetre or less and the gauss meter sensor was given a place to be affixed nd thus turning a screw measure magnetic field and changes millimetre by millimetre. <br>
DOI: 10.6084/m9.figshare.14739426
title: COHD-COVID: Columbia Open Health Data for COVID-19 Research

description: Tab-delimited flat files of concept prevalence, concept co-occurrence, symptom prevalence, concept definitions, and concepts included in each symptom for "COHD-COVID: Columbia Open Health Data for COVID-19 Research"
DOI: 10.5281/zenodo.4890950
title: Claim Detection and Matching for Indian Languages

description: Two datasets are included in this repository: claim matching and claim detection datasets. The collections contain data in 5 languages: Bengali, English, Hindi, Malayalam and Tamil. The "claim detection" dataset contains textual claims from social media and fact-checking websites annotated for the "fact-check worthiness" of the claims in each message. Data points have one of the three labels of "Yes" (text contains one or more check-worthy claims), "No" and "Probably". The "claim matching" dataset is a curated collection of pairs of textual claims from social media and fact-checking websites for the purpose of automatic and multilingual claim matching. Pairs of data have one of the four labels of "Very Similar", "Somewhat Similar", "Somewhat Dissimilar" and "Very Dissimilar". All personally identifiable information (PII) including phone numbers, email addresses, license plate numbers and addresses have been replaced with general tags (e.g. &lt;PHONE#&gt;, &lt;ADDRESS&gt;, etc) to protect user anonymity. A detailed explanation on the curation and annotation process is provided in our ACL 2021 paper: <br> Kazemi, A.; Garimella, K.; Gaffney, D.; and Hale, S. A. 2021. Claim Matching Beyond English to Scale Global Fact-Checking. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics, ACL 2021.
DOI: 10.5281/zenodo.4890949
title: Claim Detection and Matching for Indian Languages

description: Two datasets are included in this repository: claim matching and claim detection datasets. The collections contain data in 5 languages: Bengali, English, Hindi, Malayalam and Tamil. The "claim detection" dataset contains textual claims from social media and fact-checking websites annotated for the "fact-check worthiness" of the claims in each message. Data points have one of the three labels of "Yes" (text contains one or more check-worthy claims), "No" and "Probably". The "claim matching" dataset is a curated collection of pairs of textual claims from social media and fact-checking websites for the purpose of automatic and multilingual claim matching. Pairs of data have one of the four labels of "Very Similar", "Somewhat Similar", "Somewhat Dissimilar" and "Very Dissimilar". All personally identifiable information (PII) including phone numbers, email addresses, license plate numbers and addresses have been replaced with general tags (e.g. &lt;PHONE#&gt;, &lt;ADDRESS&gt;, etc) to protect user anonymity. A detailed explanation on the curation and annotation process is provided in our ACL 2021 paper: <br> Kazemi, A.; Garimella, K.; Gaffney, D.; and Hale, S. A. 2021. Claim Matching Beyond English to Scale Global Fact-Checking. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics, ACL 2021.
DOI: 10.21227/d4eq-t458
title: Jackal robot 7-class terrain dataset, vision and proprioception sensors

description: The Jackal UGV, from Clearpath Robotics, was used as the data collecting platform. This skid-steer four-wheel-drive vehicle, shown in Fig.1a, comes with an onboard IMU, two DC motors with encoders that measure wheel angular speeds, and current sensors that measure motor current outputs. On each side of the robot, the front wheel and back wheel are jointed with a gearbox and so spin together at the same rate and direction. The IMU provided vehicle attitude measurements in terms of Euler angles, as well as linear acceleration and angular rate of the vehicle body in three Euclidean axes. Camera systems such as the RS D435i and RS T265 were mounted to an aluminum frame attached to the top of the robot platform. While the tracking camera T265, faced forward, the D435i depth camera was positioned and tilted in a way that the camera had a clear visual of the terrain patch. The patch size was ~"680 mm × 340 mm" with a look ahead distance of 150 mm relative to the chassis of the vehicle. The D435i served as a regular RGB camera for this study and the depth images reconstructed by the D435i were not included in any of the data sets. The T265 camera was used as a Visual-Inertia Odometry (VIO) solution that provided ego-motion estimations of the vehicle. For this study, six different sensor signals were used: 1) current feedback, 2) wheel encoder readings from each side of the vehicle, 3) 6 DoF VIO measurements from the T265, 4) three-axis linear acceleration, 5) attitude measurement from the IMU, and 6) RGB images taken by the D435i. In addition to the RGB images, all other sensor signals were used as proprioceptive features. Seven terrain classes were investigated, including asphalt, brick road, grass, gravel, pavement, sand, and coated floors.
DOI: 10.5281/zenodo.4904848
title: The Challenges of Simulating SWE Beneath Forest Canopies are Reduced by Data Assimilation of Snow Depth

description: Intermittent snow depth observations can be leveraged with data assimilation (DA) to improve model estimates of snow water equivalent (SWE) at the point scale. A key consideration for scaling a DA system to the basin scale is its performance at locations with forest cover – where canopy-snow interactions affect snow accumulation and melt, yet are difficult to model and parameterize. We implement a particle filter (PF) assimilation technique to assimilate intermittent depth observations into the Flexible Snow Model (FSM2), and validate the output against snow density and SWE measurements across paired forest and open sites, at two locations with different climates and forest structures. Assimilation reduces depth error by 70-90%, density error by 5-30%, and SWE error by 50-70% at forest locations (relative to control model runs) and brings errors in-line with adjacent open sites. The PF correctly simulates the seasonal evolution of the snowpack under forest canopy, including cases where interception lowers SWE in the forest during accumulation, and shading reduces melt during the ablation season (relative to open sites). The snow model outputs are sensitive to canopy-related parameters, but DA reduces the range in depth and SWE estimates resulting from spatial variations or uncertainties in these parameters by more than 50%. The results demonstrate that the challenge of accurately measuring, estimating, or calibrating canopy-related parameters is reduced when snow depth observations are assimilated to improve SWE estimates.
DOI: 10.5281/zenodo.4904847
title: The Challenges of Simulating SWE Beneath Forest Canopies are Reduced by Data Assimilation of Snow Depth

description: Intermittent snow depth observations can be leveraged with data assimilation (DA) to improve model estimates of snow water equivalent (SWE) at the point scale. A key consideration for scaling a DA system to the basin scale is its performance at locations with forest cover – where canopy-snow interactions affect snow accumulation and melt, yet are difficult to model and parameterize. We implement a particle filter (PF) assimilation technique to assimilate intermittent depth observations into the Flexible Snow Model (FSM2), and validate the output against snow density and SWE measurements across paired forest and open sites, at two locations with different climates and forest structures. Assimilation reduces depth error by 70-90%, density error by 5-30%, and SWE error by 50-70% at forest locations (relative to control model runs) and brings errors in-line with adjacent open sites. The PF correctly simulates the seasonal evolution of the snowpack under forest canopy, including cases where interception lowers SWE in the forest during accumulation, and shading reduces melt during the ablation season (relative to open sites). The snow model outputs are sensitive to canopy-related parameters, but DA reduces the range in depth and SWE estimates resulting from spatial variations or uncertainties in these parameters by more than 50%. The results demonstrate that the challenge of accurately measuring, estimating, or calibrating canopy-related parameters is reduced when snow depth observations are assimilated to improve SWE estimates.
DOI: 10.6073/pasta/5c10782bcb305dccfc8c154a831e5732
title: High resolution stream temperature, pressure, and estimated depth from transducers in streams in the Lake Sunapee watershed, New Hampshire, USA 2010-2018

description: Seven in-stream HOBO pressure transducers and one reference HOBO pressure transducer have been deployed within the Lake Sunapee, NH, USA watershed. Six of the transducers have been in operation since 2010 and an additional transducer was added in 2016. The transducers record data every 15 minutes, and data are downloaded approximately three times per year (early Spring, mid Summer and late Fall). Shortly after download, the data are processed to estimate stream depth using HOBOware's Barometric Compensation Assistant and converted to a .csv file in HOBOware. The data have been QAQC'd to recode obviously errant data to NA using R programming language. No data transformation has occurred beyond basic QAQC of the data to remove known data issues and obviously errant data. The barometric pressure data from the reference transducer located on land are also included in this data package.
DOI: 10.6084/m9.figshare.14740725
title: Fig3ACompareBestDescRevision

description: Data for Fig 3A comparing the best number of descriptors to include in the Inflammation Index. Plotting 'InfInd' by 'Treatment' split by 'No. Descriptors'. Updated for manuscript revision.<br>
DOI: 10.6084/m9.figshare.14740725.v1
title: Fig3ACompareBestDescRevision

description: Data for Fig 3A comparing the best number of descriptors to include in the Inflammation Index. Plotting 'InfInd' by 'Treatment' split by 'No. Descriptors'. Updated for manuscript revision.<br>
DOI: 10.6084/m9.figshare.14740731
title: Fig3CDietComparisonRevision

description: Data for Fig 3C diet comparison plot comparing the column 'InfInd' across 'Diet'. Updated for manuscript revision.<br>
DOI: 10.6084/m9.figshare.14740731.v1
title: Fig3CDietComparisonRevision

description: Data for Fig 3C diet comparison plot comparing the column 'InfInd' across 'Diet'. Updated for manuscript revision.<br>
DOI: 10.6084/m9.figshare.14740740
title: Fig5AInflammationIndexStability

description: Data for Fig 5 plot of inflammation index value after an 18 minute gap between imaging of cells.<br>
DOI: 10.6084/m9.figshare.14740740.v1
title: Fig5AInflammationIndexStability

description: Data for Fig 5 plot of inflammation index value after an 18 minute gap between imaging of cells.<br>
DOI: 10.25345/c5tc14
title: MassIVE MSV000087577 - Quantitative proteomic analyses reveal the dynamics of protein and amino acid accumulation in soybean seed from early to late stages of seed filling
title: Global analyses of protein profiling during seed development in soybean is paramount to understand the metabolic processes that correspond to the differential protein accumulation and hence seed quality in soybean. Using high throughput tandem mass tag (TMT) based tagging techniques, we identified 4,172 proteins in three stages namely early, mid, and late seed filling. We mapped the identified metabolic pathways associated with seed filling. An elevated level of several kinases was observed from the early to mid-stages of seed filling, indicating that protein phosphorylation was a major event that occurred during this period. The early to late stages of seed filling was characterized by an increased level of proteins associated with the cell wall, oil, and vacuolar-related processes. Twenty-five seed storage protein genes located on 12 different chromosomes were identified. Among the seed storage proteins, 7S (B-subunit) and 11S (Gy3, Gy4, Gy5) exhibited steadily increased abundance from early to late-stage seed development, whereas, 2S albumin exhibited decreased abundance during the same period. An increased abundance of proteases, senescence-associated proteins and, oil synthesis proteins was observed from the mid to late stages of seed filling. The mid to late stages of seed filling were also characterized by a lower abundance of transferases, transporters, Kunitz family trypsin, and protease inhibitors. Two enzymes associated with methionine synthesis exhibited lower abundance from early to late stages. This study unveiled the expression of several key enzymes/proteins associated with amino acid and protein syntheses and their accumulation during seed development which will assist scientists and breeders to develop new value-added soybeans with improved protein quality.
DOI: 10.6084/m9.figshare.14740734.v1
title: BASE_PACE_MODU_cases_description.xlsx

description: Description of building parts in three case study buildings to study the effects interdependent parts have on life cycle material flows and associated environmental impact of the building. This data is related to fictitious buildings (pavilions).<br>This excel file contains three datasets for three fictitious cases used in the analysis of interdependencies:- BASE: the base case - PACE: a pace-layered variation of the BASE case - MODU: a modular variation of the BASE case<br>The three cases can be visualized in the associated paper (DOI will be provided in References when known).
DOI: 10.5281/zenodo.4902841
title: Levantamento Bibliográfico Sistemático: Administração e Direito Administrativo - Políticas Públicas e Serviços Sociais

description: Levantamento Bibliográfico Sistemático: Administração Pública e Direito Administrativo - Políticas Públicas e Serviços Sociais Compilação
DOI: 10.5281/zenodo.4902840
title: Levantamento Bibliográfico Sistemático: Administração e Direito Administrativo - Políticas Públicas e Serviços Sociais

description: Levantamento Bibliográfico Sistemático: Administração Pública e Direito Administrativo - Políticas Públicas e Serviços Sociais Compilação
DOI: 10.6084/m9.figshare.14740749
title: RawData.xlsx

description: Raw data used to establish the life history results
DOI: 10.6084/m9.figshare.14740749.v1
title: RawData.xlsx

description: Raw data used to establish the life history results
DOI: 10.6084/m9.figshare.14740752
title: OrganizedRawData.xlsx

description: Raw data in a more structure form used in data analysis
DOI: 10.6084/m9.figshare.14740752.v1
title: OrganizedRawData.xlsx

description: Raw data in a more structure form used in data analysis
DOI: 10.5281/zenodo.4904876
title: 3D Evrard SPH input data

description: Input data for Evrard test case https://github.com/unibas-dmi-hpc/SPH-EXA_mini-app
DOI: 10.5281/zenodo.4904875
title: 3D Evrard SPH input data

description: Input data for Evrard test case https://github.com/unibas-dmi-hpc/SPH-EXA_mini-app
DOI: 10.6084/m9.figshare.14740755.v1
title: GrowthRate.xlsx

description: Growth rate data of each individual
DOI: 10.6084/m9.figshare.14740755
title: GrowthRate.xlsx

description: Growth rate data of each individual
DOI: 10.6084/m9.figshare.14740719
title: Fig3ACompareBestMasksRevision

description: Data for Fig 3A comparing mask size values across the columns 'InfInd', 'Treatment' and split by 'TCSValue'. Updated for the revised manuscript.<br>
DOI: 10.6084/m9.figshare.14740719.v2
title: Fig3ACompareBestMasksRevision

description: Data for Fig 3A comparing mask size values across the columns 'InfInd', 'Treatment' and split by 'TCSValue'. Updated for the revised manuscript.<br>
DOI: 10.6084/m9.figshare.14325866.v1
title: National Database of Medical Students from Argentina

description: This Database comes from a national survey of Medical Students from Argentina. If you would like to work with this database, please contact Maximo Rousseau Portalis (maximorp@campus.fmed.uba.ar).<br>We will be honored to share the information and collaborate with you.<br>Cheers.
DOI: 10.5281/zenodo.4904647
title: Botanicals for eco-management of aphids

description: The data presented here provide information on the effect of selected botanical insecticides and field margin vegetation on aphids and their natural enemies’ in dolichos bean, <em>Lablab purpureus</em>, under field conditions.
DOI: 10.5281/zenodo.4904646
title: Botanicals for eco-management of aphids

description: The data presented here provide information on the effect of selected botanical insecticides and field margin vegetation on aphids and their natural enemies’ in dolichos bean, <em>Lablab purpureus</em>, under field conditions.
DOI: 10.5281/zenodo.4902866
title: Table9Apks

description: These are some sample apps mentioned in table 9 of the paper.
DOI: 10.5281/zenodo.4902865
title: Table9Apks

description: These are some sample apps mentioned in table 9 of the paper.
DOI: 10.5281/zenodo.4903794
title: Data of CP in COVID-19 patients

description: assessment of in hospital mortality rate
DOI: 10.5281/zenodo.4903793
title: Data of CP in COVID-19 patients

description: assessment of in hospital mortality rate