cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21149368.v2
title: concentration

description: the concentration of 51 aluminium alloy samples
DOI: 10.6084/m9.figshare.21149368.v1
title: concentration

description: the concentration of 51 aluminium alloy samples
DOI: 10.34820/fk2/ezax8l/dsj5ax
title: Mengukur - Menentukan letak pinggang.doc

description: :unav
DOI: 10.34820/fk2/ezax8l
title: Menentukan letak gris pinggang dalam mengukur

description: Menentukan garis pinggang dalam proses mengukur tubuh merupakan bagian penting dalam keakuratan mengukur tubuh.
DOI: 10.34740/kaggle/dsv/4224013
title: Elon Musk Tweets
DOI: 10.5281/zenodo.3993659
title: Resolved-EXIOBASE (REX) – A highly resolved MRIO database for analyzing supply-chain impacts (A. Code and data from 2006–2015)

description: This repository provides the code and the R-MRIO database for the years 2006–2015 of the study "A highly resolved MRIO database for analyzing environmental footprints and Green Economy Progress": https://doi.org/10.1016/j.scitotenv.2020.142587 The R-MRIO database for the years 1995–2005 is stored under the repository http://doi.org/10.5281/zenodo.3994795 The folder "R-MRIO_CODE" provides the code to resolve the spatial resolution of EXIOBASE3 from 44 countries and 5 Rest of the World (RoW) regions into 189 individual countries while keeping the high sectoral resolution (163 sectors) by the integration of data from Eora26, FAOSTAT and previous studies. It implements the environmental impact categories climate change impacts, particulate-matter related health impacts, water stress and land-use related biodiversity loss into EXIOBASE3, Eora26 and the resolved MRIO database.<br> The folder includes:<br> Exiobase_resolved.m: MATLAB code to resolve the EXIOBASE3 database according to the procedure described in Section 2.3–2.6 of the manuscript.<br> Folder ‘Files’: Includes all files required to run ‘Exiobase_resolved.m’, except for the MRIO tables from EXIOBASE3 and Eora26, which need to be downloaded from the EXIOBASE3 and Eora26 homepage and stored in the provided folder “Files/Exiobase/” and “Files/Eora/bp/”, respectively. These data can be downloaded from:<br> https://www.exiobase.eu/index.php/data-download/exiobase3mon<br> https://worldmrio.com/eora26/ The folders "Year_RMRIO" provide the R-MRIO database for each year from 2006–2015. Each folder contains the following files (<em>*.mat-files</em>):<br> A_RMRIO: the coefficient matrix<br> Y_RMRIO: the final demand matrix<br> Ext_RMRIO and Ext_hh_RMRIO: the satellite matrix of the economy and the final demand<br> TotalOut_RMRIO: the total output vector<br> The labels of the matrices are provided by the separate folder "Labels_RMRIO " A script for importing and indexing the RMRIO database files in Python as Pandas DataFrames can be found here: https://github.com/jbnsn/RMRIO-database-py-import
DOI: 10.5281/zenodo.3993658
title: Resolved-EXIOBASE (REX) – A highly resolved MRIO database for analyzing supply-chain impacts (A. Code and data from 2006–2015)

description: This repository provides the code and the R-MRIO database for the years 2006–2015 of the study "A highly resolved MRIO database for analyzing environmental footprints and Green Economy Progress": https://doi.org/10.1016/j.scitotenv.2020.142587 The R-MRIO database for the years 1995–2005 is stored under the repository http://doi.org/10.5281/zenodo.3994795 The folder "R-MRIO_CODE" provides the code to resolve the spatial resolution of EXIOBASE3 from 44 countries and 5 Rest of the World (RoW) regions into 189 individual countries while keeping the high sectoral resolution (163 sectors) by the integration of data from Eora26, FAOSTAT and previous studies. It implements the environmental impact categories climate change impacts, particulate-matter related health impacts, water stress and land-use related biodiversity loss into EXIOBASE3, Eora26 and the resolved MRIO database.<br> The folder includes:<br> Exiobase_resolved.m: MATLAB code to resolve the EXIOBASE3 database according to the procedure described in Section 2.3–2.6 of the manuscript.<br> Folder ‘Files’: Includes all files required to run ‘Exiobase_resolved.m’, except for the MRIO tables from EXIOBASE3 and Eora26, which need to be downloaded from the EXIOBASE3 and Eora26 homepage and stored in the provided folder “Files/Exiobase/” and “Files/Eora/bp/”, respectively. These data can be downloaded from:<br> https://www.exiobase.eu/index.php/data-download/exiobase3mon<br> https://worldmrio.com/eora26/ The folders "Year_RMRIO" provide the R-MRIO database for each year from 2006–2015. Each folder contains the following files (<em>*.mat-files</em>):<br> A_RMRIO: the coefficient matrix<br> Y_RMRIO: the final demand matrix<br> Ext_RMRIO and Ext_hh_RMRIO: the satellite matrix of the economy and the final demand<br> TotalOut_RMRIO: the total output vector<br> The labels of the matrices are provided by the separate folder "Labels_RMRIO " A script for importing and indexing the RMRIO database files in Python as Pandas DataFrames can be found here: https://github.com/jbnsn/RMRIO-database-py-import
DOI: 10.5281/zenodo.3994795
title: Resolved-EXIOBASE (REX) – A highly resolved MRIO database for analyzing supply-chain impacts (B. Remaining data from 1995–2005)

description: This repository provides the R-MRIO database for the years 1995–2005 of the study "A highly resolved MRIO database for analyzing environmental footprints and Green Economy Progress". https://doi.org/10.1016/j.scitotenv.2020.142587 The code to resolve the database and the data for the years 2006–2015 are stored under the repository http://doi.org/10.5281/zenodo.3993659 The folders "R-MRIO_year" provide the following files (*.<em>mat-files</em>) for each year from 1995–2005:<br> A_RMRIO: the coefficient matrix<br> Y_RMRIO: the final demand matrix<br> Ext_RMRIO and Ext_hh_RMRIO: the satellite matrix of the economy and the final demand<br> TotalOut_RMRIO: the total output vector<br> The labels of the matrices are provided by the separate folder "Labels_RMRIO " A script for importing and indexing the RMRIO database files in Python as Pandas DataFrames can be found here: https://github.com/jbnsn/RMRIO-database-py-import
DOI: 10.5281/zenodo.3994794
title: Resolved-EXIOBASE (REX) – A highly resolved MRIO database for analyzing supply-chain impacts (B. Remaining data from 1995–2005)

description: This repository provides the R-MRIO database for the years 1995–2005 of the study "A highly resolved MRIO database for analyzing environmental footprints and Green Economy Progress". https://doi.org/10.1016/j.scitotenv.2020.142587 The code to resolve the database and the data for the years 2006–2015 are stored under the repository http://doi.org/10.5281/zenodo.3993659 The folders "R-MRIO_year" provide the following files (*.<em>mat-files</em>) for each year from 1995–2005:<br> A_RMRIO: the coefficient matrix<br> Y_RMRIO: the final demand matrix<br> Ext_RMRIO and Ext_hh_RMRIO: the satellite matrix of the economy and the final demand<br> TotalOut_RMRIO: the total output vector<br> The labels of the matrices are provided by the separate folder "Labels_RMRIO " A script for importing and indexing the RMRIO database files in Python as Pandas DataFrames can be found here: https://github.com/jbnsn/RMRIO-database-py-import
DOI: 10.6073/pasta/b8cd397da04fe71aed601c53ca1f6c50
title: Microclimate at Harvard Forest HEM, LPH and EMS Towers since 2005

description: Microclimatic data are collected continuously within footprint areas of the flux towers in order to (1) understand relationships between climatic and microclimatic conditions and carbon dioxide and water vapor exchange between forest and atmosphere, (2) to develop predictive models for the exchange rates of these gases, based on microclimate data, (3) detect long-term trends in climate and microclimate at the sites and (4) determine whether the response of carbon dioxide and water vapor exchange processes to climate or microclimate is changing over time. The primary climatic and microclimatic variables measured at these sites include: above-canopy photosynthetically active radiation (PAR), forest understory PAR at 1 m height, total net radiation exchange measured above the forest canopy, air temperature and relative humidity above the forest canopy, forest understory air temperature at 20 cm and 1 m above ground level (shielded but not ventilated thermocouple sensors, used only to estimate nighttime understory air temperature), soil temperature at 10 cm depth, and soil moisture.
DOI: 10.5281/zenodo.6687833
title: Replication package for: Optimal Environmental Targeting in the Amazon Rainforest

description: The package contains all the code necessary to replicate the figures and tables in Assuncao, J., McMillan, R., Murphy, J., and Souza-Rodrigues, E. (forthcoming). "Optimal Environmental Targeting in the Amazon Rainforest", Review of Economic Studies.
DOI: 10.5281/zenodo.6963994
title: Replication package for: Optimal Environmental Targeting in the Amazon Rainforest

description: The package contains all the code necessary to replicate the figures and tables in Assuncao, J., McMillan, R., Murphy, J., and Souza-Rodrigues, E. (forthcoming). "Optimal Environmental Targeting in the Amazon Rainforest", Review of Economic Studies.
DOI: 10.5281/zenodo.6687832
title: Replication package for: Optimal Environmental Targeting in the Amazon Rainforest

description: The package contains all the code necessary to replicate the figures and tables in Assuncao, J., McMillan, R., Murphy, J., and Souza-Rodrigues, E. (forthcoming). "Optimal Environmental Targeting in the Amazon Rainforest", Review of Economic Studies.
DOI: 10.5281/zenodo.7093043
title: Replication package for: Optimal Environmental Targeting in the Amazon Rainforest

description: The package contains all the code necessary to replicate the figures and tables in Assuncao, J., McMillan, R., Murphy, J., and Souza-Rodrigues, E. (forthcoming). "Optimal Environmental Targeting in the Amazon Rainforest", Review of Economic Studies.
DOI: 10.5281/zenodo.7093061
title: Replication package for: Optimal Environmental Targeting in the Amazon Rainforest

description: The package contains all the code necessary to replicate the figures and tables in Assuncao, J., McMillan, R., Murphy, J., and Souza-Rodrigues, E. (forthcoming). "Optimal Environmental Targeting in the Amazon Rainforest", Review of Economic Studies.
DOI: 10.5281/zenodo.7093069
title: Canopy gap sizes across the Brazilian Amazon

description: This data frame contains the raw gap data used to study the gap size-frequency distributions across the Brazilian Amazon in: Reis, C., Jackson, T., et al 2022. Forest disturbance and growth processes are reflected in the geographic distribution of large canopy gaps across the Brazilian Amazon. Journal of Ecology. They gaps were extracted from the EBA LiDAR data set https://zenodo.org/record/4968706#.YygSGHZKg5s
DOI: 10.5281/zenodo.7093068
title: Canopy gap sizes across the Brazilian Amazon

description: This data frame contains the raw gap data used to study the gap size-frequency distributions across the Brazilian Amazon in: Reis, C., Jackson, T., et al 2022. Forest disturbance and growth processes are reflected in the geographic distribution of large canopy gaps across the Brazilian Amazon. Journal of Ecology. They gaps were extracted from the EBA LiDAR data set https://zenodo.org/record/4968706#.YygSGHZKg5s
DOI: 10.5281/zenodo.7093049
title: LEMORTHO datasets september 2022

description: Datasets necessary to reproduce the LEMORTHO benchmark. The dataset used as the reference on LEMOrtho is a customized version of the Orthobench revisited study (https://dx.doi.org/10.1093/gbe/evaa211 , https://dx.doi.org/10.1002/bies.201100062 ), from which we removed a few problematic genes: <code>ENSCAFP00000022898,ENSCINP00000001707, ENSCINP00000002932, ENSCINP00000010295, ENSCINP00000018374, ENSCINP00000025546, ENSCINP00000025552, ENSCINP00000027090, ENSCINP00000035497, ENSDARP00000002330, ENSDARP00000076594, ENSDARP00000131597, ENSDARP00000156542, ENSMODP00000012647, ENSMUSP00000033502, ENSP00000316782, ENSP00000358380, ENSP00000359057, ENSP00000365858, ENSP00000371923, ENSP00000463957, ENSP00000474456, ENSP00000477509, ENSP00000477979, ENSP00000478609, ENSP00000478752, ENSP00000479545, ENSP00000479693, ENSP00000480818, ENSP00000481542, ENSP00000481835, ENSP00000498781, ENSPTRP00000001775, ENSPTRP00000040053, ENSPTRP00000056359, ENSPTRP00000058531, ENSPTRP00000086593, ENSPTRP00000092368, ENSPTRP00000092968, ENSRNOP00000038111, ENSRNOP00000067336, ENSTNIP00000010123, ENSTNIP00000017372, FBpp0073951, FBpp0075520, FBpp0082643, FBpp0082970, FBpp0291496, FBpp0291497, FBpp0309618, WBGene00001184.1, WBGene00001249.1, WBGene00006925.1, WBGene00006926.1, WBGene00006927.1, WBGene00006928.1, WBGene00006929.1, WBGene00006930.1, WBGene00010139.1, WBGene00015237.</code> To use these datasets with the standalone LEMORTHO pipeline, place each dataset in the folder $LEM_ORTHO_ROOT/benchmark/datasets/
DOI: 10.5281/zenodo.7093048
title: LEMORTHO datasets september 2022

description: Datasets necessary to reproduce the LEMORTHO benchmark. The dataset used as the reference on LEMOrtho is a customized version of the Orthobench revisited study (https://dx.doi.org/10.1093/gbe/evaa211 , https://dx.doi.org/10.1002/bies.201100062 ), from which we removed a few problematic genes: <code>ENSCAFP00000022898,ENSCINP00000001707, ENSCINP00000002932, ENSCINP00000010295, ENSCINP00000018374, ENSCINP00000025546, ENSCINP00000025552, ENSCINP00000027090, ENSCINP00000035497, ENSDARP00000002330, ENSDARP00000076594, ENSDARP00000131597, ENSDARP00000156542, ENSMODP00000012647, ENSMUSP00000033502, ENSP00000316782, ENSP00000358380, ENSP00000359057, ENSP00000365858, ENSP00000371923, ENSP00000463957, ENSP00000474456, ENSP00000477509, ENSP00000477979, ENSP00000478609, ENSP00000478752, ENSP00000479545, ENSP00000479693, ENSP00000480818, ENSP00000481542, ENSP00000481835, ENSP00000498781, ENSPTRP00000001775, ENSPTRP00000040053, ENSPTRP00000056359, ENSPTRP00000058531, ENSPTRP00000086593, ENSPTRP00000092368, ENSPTRP00000092968, ENSRNOP00000038111, ENSRNOP00000067336, ENSTNIP00000010123, ENSTNIP00000017372, FBpp0073951, FBpp0075520, FBpp0082643, FBpp0082970, FBpp0291496, FBpp0291497, FBpp0309618, WBGene00001184.1, WBGene00001249.1, WBGene00006925.1, WBGene00006926.1, WBGene00006927.1, WBGene00006928.1, WBGene00006929.1, WBGene00006930.1, WBGene00010139.1, WBGene00015237.</code> To use these datasets with the standalone LEMORTHO pipeline, place each dataset in the folder $LEM_ORTHO_ROOT/benchmark/datasets/
DOI: 10.5281/zenodo.6362734
title: Single cell whole genome sequencing from Funnell, O'Flanagan, Williams et al

description: This repository provides the processed data neccessary to reproduce the results from: "<em>The impacts of single cell genomic variation induced by mutational processes in cancer Funnell, O’Flanagan, Williams et al</em>"
DOI: 10.5281/zenodo.6407098
title: Single cell whole genome sequencing from Funnell, O'Flanagan, Williams et al

description: This repository provides the processed data neccessary to reproduce the results from: "<em>The impacts of single cell genomic variation induced by mutational processes in cancer Funnell, O’Flanagan, Williams et al</em>"
DOI: 10.5281/zenodo.6416046
title: Single cell whole genome sequencing from Funnell, O'Flanagan, Williams et al

description: This repository provides the processed data neccessary to reproduce the results from: "<em>The impacts of single cell genomic variation induced by mutational processes in cancer Funnell, O’Flanagan, Williams et al</em>"
DOI: 10.5281/zenodo.6727780
title: Single cell whole genome sequencing from Funnell, O'Flanagan, Williams et al

description: This repository provides the processed data neccessary to reproduce the results from: "Single cell genomic variation induced by mutational processes in cancer<strong> </strong><em>Funnell, O’Flanagan, Williams et al</em>"
DOI: 10.5281/zenodo.6362733
title: Single cell whole genome sequencing from Funnell, O'Flanagan, Williams et al

description: This repository provides the processed data necessary to reproduce the results from: "Single cell genomic variation induced by mutational processes in cancer<strong> </strong><em>Funnell, O’Flanagan, Williams et al</em>" This includes the following: Single cell whole genome sequencing Allele specific copy number profiles SNV counts per cell Structural variant counts per cell QC metrics clone assignments phylogenetic trees computed with sitka benchmarking results vs other methods bulk whole genome sequencing copy number profiles SNVs 10X single cell RNA sequencing count matrices seurat Rdata objects analysis tables downstream processed results used to generate figures oxford nanopore phasing results For further information please feel free to get in touch with Marc Williams (william1 [at] mskcc.org)
DOI: 10.5281/zenodo.6788394
title: Single cell whole genome sequencing from Funnell, O'Flanagan, Williams et al

description: This repository provides the processed data neccessary to reproduce the results from: "Single cell genomic variation induced by mutational processes in cancer<strong> </strong><em>Funnell, O’Flanagan, Williams et al</em>"
DOI: 10.5281/zenodo.6998936
title: Single cell whole genome sequencing from Funnell, O'Flanagan, Williams et al

description: This repository provides the processed data necessary to reproduce the results from: "Single cell genomic variation induced by mutational processes in cancer<strong> </strong><em>Funnell, O’Flanagan, Williams et al</em>" This includes the following: Single cell whole genome sequencing Allele specific copy number profiles SNV counts per cell Structural variant counts per cell QC metrics clone assignments phylogenetic trees computed with sitka benchmarking results vs other methods bulk whole genome sequencing copy number profiles SNVs 10X single cell RNA sequencing count matrices seurat Rdata objects analysis tables downstream processed results used to generate figures oxford nanopore phasing results For further information please feel free to get in touch with Marc Williams (william1 [at] mskcc.org)
DOI: 10.34740/kaggle/dsv/4224067
title: Face Mask Detection Dataset Images
DOI: 10.6084/m9.figshare.19248266.v1
title: Functional thresholds alter the relationship of plant resistance and recovery to drought

description: Data and Code from<br> "Ingrisch J, Umlauf N, Bahn M, Functional thresholds alter the relationship of plant resistance and recovery to drought" <br> <br> Data and Code are organised as an R-Project. Code is supplied as .r and .rmd files. In case of .rmd (r-markdown) script output is also supplied as .html.<br> <br> ./Data/Treatment_Info.txt: treatment information to each mesocosm (plot), Plant (Dac = Dactylis, Pla = Plantago), Rep (running number) and Water = target water content (in % of field capacity).<br> ./Data/01_compiled/: contains raw data used for analysis<br> ./Data/02_pro/: contains output from threshold regression analysis used for subsequent analysis<br> ./Data/03_output/: contains model output used in the manuscript<br> <br> ./Figs/ contains figures produced in analysis and used in manuscript<br> <br> ./R-Scripts/<br> /01_ThresholdRegression_ModelCalculation.r: calculates threshold regression models for drought and recovery data and multiple paramters, stores output<br> /02_ThresholdIdentification.rmd: uses model output from 01..., selects best models and creates figure<br> /03_GAMs.r: calculates response surface for productivity measures and bivariate response scheme.<br> 03_LWC-Responses.rmd: produces figure showing response in relation to relative leaf water deficit measured at peak drought.<br> 03_Microclimate.r: creates figure of microclimatic conditions during experiment.<br> <br>
DOI: 10.5281/zenodo.7093134
title: Water Isotherms of Adsorbent Materials Evaluated in the Hycool Project

description: This data is a collection of water adsorption equilibria measured on a selection of water adsorbent materials.<br> They were produced by E. Piccoli (Empa) and revised by V. Brancato and A. Frazzica (CNR-ITAE) and by S. Galmarini (Empa).<br> The methods used to characterize the materials are described in [1], using a DVS Endeveour instrument from SMS. More details can be found in [2].<br> This work was performed in the framework of the project "HyCool: Industrial Cooling through Hybrid system based on Solar Heat", funded by the European Commission H2020 Programme under Grant Agreement No. 792073.
DOI: 10.5281/zenodo.7093133
title: Water Isotherms of Adsorbent Materials Evaluated in the Hycool Project

description: This data is a collection of water adsorption equilibria measured on a selection of water adsorbent materials.<br> They were produced by E. Piccoli (Empa) and revised by V. Brancato and A. Frazzica (CNR-ITAE) and by S. Galmarini (Empa).<br> The methods used to characterize the materials are described in [1], using a DVS Endeveour instrument from SMS. More details can be found in [2].<br> This work was performed in the framework of the project "HyCool: Industrial Cooling through Hybrid system based on Solar Heat", funded by the European Commission H2020 Programme under Grant Agreement No. 792073.
DOI: 10.5281/zenodo.7093078
title: OOPSLA 2022 Artifact for "A Fast in-place Interpreter for WebAssembly"

description: This archive includes source-code snapshots of 3 Web Engines for executing WebAssembly 3 non-Web engines: Wasm3, the WebAssembly Micro-Runtime, and the Wizard Research Engine binary builds for Linux x86-64 build instructions PolyBenchC benchmark Wasm binaries benchmarking setup and scripts data collected from experiments included in the OOPSLA 2022 paper instructions for running the benchmarks on Linux systems
description: {"references": ["Titzer, Ben L. (2022). A fast in-place interpreter for WebAssembly. ArXiv:2205.01183", "Titzer, Ben L. (2022). A fast in-place interpreter for WebAssembly. In PACMPL 2022, To appear."]}
DOI: 10.3886/e113055v1-127440
title: Replication data for: No Price Like Home: Global House Prices, 1870-2012

description: How have house prices evolved over the long run? This paper presents annual house prices for 14 advanced economies since 1870. We show that real house prices stayed constant from the nineteenth to the mid-twentieth century, but rose strongly and with substantial cross-country variation in the second half of the twentieth century. Land prices, not replacement costs, are the key to understanding the trajectory of house prices. Rising land prices explain about 80 percent of the global house price boom that has taken place since World War II. Our findings have implications for the evolution of wealth-to-income ratios, the growth effects of agglomeration, and the price elasticity of housing supply.
DOI: 10.21227/yaxm-6273
title: Human arm motion tracking by fusion of IMU modules and SLAM method compared to VICON

description: Human arm motion data including forearm, upper-arm, and scapula link IMU modules beside SLAM reference position measurements compared to VICON as ground trouth.
DOI: 10.6084/m9.figshare.20066642
title: Dataset of drag-reduction experiments using different types of coatings

description: This is the data related to the following manuscript: <em><strong>Surface coatings to reduce skin friction: a dataset from a rotating-disk experiment</strong></em> <br> The data is shared in csv format and excel format. In the excel file (data_excel.xlsx), the data related to all figures with numerical series is shared. In the csv files, the specific data related to all figures with numerical series is shared. For example, the file fig1_one_layer.csv, contains the data related to the one layer configuration belonging to figure 1. All the experimental data in the excel and csv files have been averaged over four observations. In the file error_bars_data.csv, the data related to the error bars is summarized. The error bars were computed using the standard deviation over four observations. <br> <br>