cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14273162.v1
title: MZ_0088_005_Teneriffia_aethiopica_female_60x_prodorsum-and-cupules

description: <b>Species:</b> <i>Teneriffia aethiopica </i>Zmudzinski, Skoracki & Friedrich<b>Source:</b> Ethiopia; Southern Nations, Nationalities and People’s Region, Debub Omo Zone, Woreda Hamer Bena, 50 km SE of Turmi, riverbed west of Chew Bahir (Stephanie Wildlife Sanctuary), 04°49'42"N 36°43'22"E, 700 m a.s. l., 2. April 2019, coll. F. Wachtel, extracted from soil substrate using Winkler's method (courtesy of Stefan Friedrich).<b>Deposition:</b> Female holotype and male paratype are deposited in the acarological collections of Department of Animal Morphology, Adam Mickiewicz University, Poznań, Poland.<br><b>Microscope:</b> Nikon Eclipse Ni-U<br><b>Camera:</b> Nikon DS-Ri2<b>Capture software:</b> NIS-Elements D<b>Optics:</b> CFI Apochromat NIR 60X W + DIC<br><b>Scale bars:</b> https://doi.org/10.6084/m9.figshare.c.4704977
DOI: 10.6084/m9.figshare.14199410
title: Salmonella Newport data

description: Salmonella Newport population genomics
DOI: 10.6084/m9.figshare.14198984.v7
title: Salmonella Infantis data

description: Population genomics - Salmonella Infantis
DOI: 10.6084/m9.figshare.14199410.v4
title: Salmonella Newport data

description: Salmonella Newport population genomics
DOI: 10.6084/m9.figshare.14198984.v8
title: Salmonella Infantis data

description: Population genomics - Salmonella Infantis
DOI: 10.6084/m9.figshare.14199410.v3
title: Salmonella Newport data

description: Salmonella Newport population genomics
DOI: 10.6084/m9.figshare.13148117.v1
title: Rawdata Baseline/Replication - Impact of Usability Mechanisms

description: Rawdata and R code for usability mechanism - analysis of impact.<br>Baseline (Marcelo).xlsx: rawdata of baseline experiment.Replication_1 (Herea).xlsx: rawdata of replication #1 of baseline experimentReplication_2 (Adriana).xlsx: rawdata of replication #2 of baseline experiment<br><br>Analysis.R: file containing the R code to analyze experiments.
DOI: 10.6084/m9.figshare.14273234.v1
title: WATER QUALITY PARAMETERS IN GAS FLARING AREA OF EBOCHA-OBRIKOM OF RIVERS STATE, NIGERIA.

description: As mounting evidence shows that populated Ebocha-Obrikom area of Rivers State struggle with water quality problems that affect human as well as ecosystem health <b>(</b>Morufu and Clinton, 2017; Raimi <i>et al.</i>, 2017; Olalekan <i>et al.,</i> 2018; Olalekan <i>et al.,</i> 2020). Typically, population density is highest along coastlines, where centralized sanitary systems serve only a small fraction of the population and decentralized agriculture leaves room for unregulated practices, resulting in nutrient, pathogen, and pesticide pollution.
DOI: 10.6084/m9.figshare.14273234
title: WATER QUALITY PARAMETERS IN GAS FLARING AREA OF EBOCHA-OBRIKOM OF RIVERS STATE, NIGERIA.

description: As mounting evidence shows that populated Ebocha-Obrikom area of Rivers State struggle with water quality problems that affect human as well as ecosystem health <b>(</b>Morufu and Clinton, 2017; Raimi <i>et al.</i>, 2017; Olalekan <i>et al.,</i> 2018; Olalekan <i>et al.,</i> 2020). Typically, population density is highest along coastlines, where centralized sanitary systems serve only a small fraction of the population and decentralized agriculture leaves room for unregulated practices, resulting in nutrient, pathogen, and pesticide pollution.
DOI: 10.6084/m9.figshare.14273213
title: Pensamiento informacional en Cuba

description: Data on the Cuban scientific output in Library and Information Science during the period 2005-2019.<br>
DOI: 10.6084/m9.figshare.14273213.v1
title: Pensamiento informacional en Cuba

description: Data on the Cuban scientific output in Library and Information Science during the period 2005-2019.<br>
DOI: 10.6084/m9.figshare.14208515.v1
title: CCC_graphium_colonna_SPAdes_preassembly.fasta

description: CCC_graphium_colonna_SPAdes_preassembly.fasta<br>
DOI: 10.6084/m9.figshare.14208515
title: CCC_graphium_colonna_SPAdes_preassembly.fasta

description: CCC_graphium_colonna_SPAdes_preassembly.fasta<br>
DOI: 10.6073/pasta/823cd9371ef9d6e58b6b129c37d38126
title: Plant community data at water sources, Mpala Research Centre, Kenya (2015-2017)

description: Data package contains four datasets of plant measurements taken at Mpala Research Centre, Laikipia County, Kenya from November 2015-September 2017. Additional code for data analysis is also provided as part of the publication `The effects of herbivore aggregations at water sources on savanna plants differ across soil and climate gradients`.
DOI: 10.6084/m9.figshare.14273312
title: Flow Data.xlsx

description: This is the flow data from the model simulation and the observation data used for validation
DOI: 10.6084/m9.figshare.3761562.v1815
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5281/zenodo.4632416
title: Appendix or supplementary files

description: The practice exercise of hazard perception test in this study
DOI: 10.21227/pg9m-dm51
title: January-26-Dong-Jiawei

description: January-26-Dong-Jiawei
DOI: 10.5281/zenodo.4632436
title: Instances for the Close Enough Traveling Salesman Problem

description: These are the instances used in the article titled 'Estimating the Tour Length for the Close Enough Traveling Salesman Problem.'
DOI: 10.5281/zenodo.4632435
title: Instances for the Close Enough Traveling Salesman Problem

description: These are the instances used in the article titled 'Estimating the Tour Length for the Close Enough Traveling Salesman Problem.'
DOI: 10.3886/e135741
title: Updated Romer-Romer Monetary Policy Shocks

description: The purpose of these files is to extend the Romer-Romer (2004) monetary policy shock series. Program and data are provided without any warranty. Please email jfwieland@ucsd.edu if you find any discrepancies.<br>When using this data in your work, cite Wieland-Yang (2020) along with Romer-Romer (2004) as a reference.<br>Thanks to:1. Yeji Sung for noting a missing match in an earlier version of the code and for alerting me to Philadelphia Fed’s dataset, which includes data for August-October 1972 that was missing in the original Romer-Romer dataset. The values I use differ slightly from the Philadelphia Fed’s. This is because I do not compound annualized growth rates in order to be consistent with the original Greenbook forecasts. 2. Pavel Kapinos for noting that the old target variable was shifted by one meeting from 2004-7.3. Michael McMahon for noting that the May 15th 2001 meeting was incorrectly coded as May 18.<br>Contents:1. RR_monetary_shock_update.doThis code file generates three Stata datasets, RR_monetary_shock_monthly.dta, RR_monetary_shock_quarterly.dta, and RR_monetary_shock_annual.dta. These correspond to the monetary shock series at monthly, quarterly, and annual frequency. Each Stata file contains four variables. The date variable "date", “resid” are the original Romer-Romer (2004) shocks, "resid_romer" are the monetary policy shocks based on the original Romer-Romer (2004) regression, and "resid_full" are the monetary policy shocks based on running the Romer-Romer (2004) regression on the full 1969-2007 sample.<br>2. RRimport.xlsThis is the original Romer-Romer (2004) dataset of Greenbook forecasts updated to 2007. Also includes recently-published data for August-October 1972 that was missing in the original Romer-Romer dataset (these are marked in yellow).<br>3. RRshock_Quarterly_1.txt and RRshock_Quarterly_2.txtThese are Greenbook forecasts downloaded from the FRED database. They are used to check the entries in RRimport.xls.<br>4. ForecastRelease.xlsxContains forecast release dates for meetings where FRED does not have the data.<br>5. RRshock_xls folderThese are the digitized Greenbook forecast from the Philadelphia Fed website. These are used to cross-check the data from FRED and the entries in RRimport.xls.
DOI: 10.3886/e135741v1
title: Updated Romer-Romer Monetary Policy Shocks

description: The purpose of these files is to extend the Romer-Romer (2004) monetary policy shock series. Program and data are provided without any warranty. Please email jfwieland@ucsd.edu if you find any discrepancies.<br>When using this data in your work, cite Wieland-Yang (2020) along with Romer-Romer (2004) as a reference.<br>Thanks to:1. Yeji Sung for noting a missing match in an earlier version of the code and for alerting me to Philadelphia Fed’s dataset, which includes data for August-October 1972 that was missing in the original Romer-Romer dataset. The values I use differ slightly from the Philadelphia Fed’s. This is because I do not compound annualized growth rates in order to be consistent with the original Greenbook forecasts. 2. Pavel Kapinos for noting that the old target variable was shifted by one meeting from 2004-7.3. Michael McMahon for noting that the May 15th 2001 meeting was incorrectly coded as May 18.<br>Contents:1. RR_monetary_shock_update.doThis code file generates three Stata datasets, RR_monetary_shock_monthly.dta, RR_monetary_shock_quarterly.dta, and RR_monetary_shock_annual.dta. These correspond to the monetary shock series at monthly, quarterly, and annual frequency. Each Stata file contains four variables. The date variable "date", “resid” are the original Romer-Romer (2004) shocks, "resid_romer" are the monetary policy shocks based on the original Romer-Romer (2004) regression, and "resid_full" are the monetary policy shocks based on running the Romer-Romer (2004) regression on the full 1969-2007 sample.<br>2. RRimport.xlsThis is the original Romer-Romer (2004) dataset of Greenbook forecasts updated to 2007. Also includes recently-published data for August-October 1972 that was missing in the original Romer-Romer dataset (these are marked in yellow).<br>3. RRshock_Quarterly_1.txt and RRshock_Quarterly_2.txtThese are Greenbook forecasts downloaded from the FRED database. They are used to check the entries in RRimport.xls.<br>4. ForecastRelease.xlsxContains forecast release dates for meetings where FRED does not have the data.<br>5. RRshock_xls folderThese are the digitized Greenbook forecast from the Philadelphia Fed website. These are used to cross-check the data from FRED and the entries in RRimport.xls.
DOI: 10.5281/zenodo.4632439
title: Live Fuel Moisture Content Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict the moisture content in vegetation. The moisture is the ratio between the weight of water in vegetation and the weight of the dry part of vegetation. This is known as the live fuel moisture content (LFMC) and is an important variable as the risk of fire increases very rapidly as soon as the LFMC goes below 80%.This data is paired with one year of daily reflectance data at 7 spectral bands (459 nm to 2155 nm) before the LFMC sampling date from the Moderate Resolution Imaging Spectrometer (MODIS) satellite. This dataset contains 5003 time series obtained from researchers at Monash University with 7 dimensions, each corresponding to one spectral band.<br> <br> Data Donor<br> Liujun Zhu, liujun.zhu@monash.edu
DOI: 10.5281/zenodo.3902715
title: Live Fuel Moisture Content Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict the moisture content in vegetation. The moisture is the ratio between the weight of water in vegetation and the weight of the dry part of vegetation. This is known as the live fuel moisture content (LFMC) and is an important variable as the risk of fire increases very rapidly as soon as the LFMC goes below 80%.This data is paired with one year of daily reflectance data at 7 spectral bands (459 nm to 2155 nm) before the LFMC sampling date from the Moderate Resolution Imaging Spectrometer (MODIS) satellite. This dataset contains 5003 time series obtained from researchers at Monash University with 7 dimensions, each corresponding to one spectral band.<br> <br> Data Donor<br> Liujun Zhu, liujun.zhu@monash.edu
DOI: 10.3886/e114243v1-82082
title: Replication data for: Are the Effects of Monetary Policy Shocks Big or Small?

description: This paper studies the small estimated effects of monetary policy shocks from standard VARs versus the large effects from the Romer and Romer (2004) approach. The differences are driven by three factors: the different contractionary impetus, the period of reserves targeting, and lag length selection. Accounting for these factors, the real effects of policy shocks are consistent across approaches and most likely medium. Alternative monetary policy shock measures from estimated Taylor rules also yield medium-sized real effects and indicate that the historical contribution of monetary policy shocks to real fluctuations has been significant, particularly during the 1970s and early 1980s. (JEL E32, E43, E52)
DOI: 10.5281/zenodo.3902637
title: Appliances Energy Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict total energy usage in kWh of a house. This dataset contains 138 time series obtained from the Appliances Energy Prediction dataset from the UCI repository. The time series has 24 dimensions. This includes temperature and humidity measurements of 9 rooms in a house, monitored with a ZigBee wireless sensor network. It also includes weather and climate data such as temperature, pressure, humidity, wind speed, visibility and dewpoint measured from Chievres airport. The data set is averaged for 10 minutes period and spanning 4.5 months. Please refer to https://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction for more details<br> <br> Relevant papers<br> Luis M. Candanedo, Veronique Feldheim, Dominique Deramaix, Data driven prediction models of energy use of appliances in a low-energy house, Energy and Buildings, Volume 140, 1 April 2017, Pages 81-97, ISSN 0378-7788<br> <br> Citation request<br> Luis M. Candanedo, Veronique Feldheim, Dominique Deramaix, Data driven prediction models of energy use of appliances in a low-energy house, Energy and Buildings, Volume 140, 1 April 2017, Pages 81-97, ISSN 0378-7788
DOI: 10.5281/zenodo.3902636
title: Appliances Energy Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict total energy usage in kWh of a house. This dataset contains 138 time series obtained from the Appliances Energy Prediction dataset from the UCI repository. The time series has 24 dimensions. This includes temperature and humidity measurements of 9 rooms in a house, monitored with a ZigBee wireless sensor network. It also includes weather and climate data such as temperature, pressure, humidity, wind speed, visibility and dewpoint measured from Chievres airport. The data set is averaged for 10 minutes period and spanning 4.5 months. Please refer to https://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction for more details<br> <br> Relevant papers<br> Luis M. Candanedo, Veronique Feldheim, Dominique Deramaix, Data driven prediction models of energy use of appliances in a low-energy house, Energy and Buildings, Volume 140, 1 April 2017, Pages 81-97, ISSN 0378-7788<br> <br> Citation request<br> Luis M. Candanedo, Veronique Feldheim, Dominique Deramaix, Data driven prediction models of energy use of appliances in a low-energy house, Energy and Buildings, Volume 140, 1 April 2017, Pages 81-97, ISSN 0378-7788
DOI: 10.25387/g3.14067449
title: Supplemental Material for Castillo and Almeida, 2021

description: Supplementary Figures and Tables for manuscript G3-2021-402135R1
DOI: 10.5281/zenodo.4630690
title: Supplementary material for "A Massively Parallel Time-Domain Coupled Electrodynamics-Micromagnetics Solver"

description: This directory contains the inputs, outputs, job submission scripts and github hashes used to generate perform our simulations and create the figures presented in "A Massively Parallel Time-Domain Coupled Electrodynamics-Micromagnetics Solver" by Yao et al. and submitted to "The International Journal of High Performance Computing Applications." The source code for ARTEMIS is here https://github.com/ECP-WarpX/artemis.git (Commit hash used : 64e32d56767e6f8027de81a8c4897b808f211267) and AMReX code is here : https://github.com/AMReX-Codes/amrex.git (Commit hash : 7c3e95210a1877c67139ea57c62fbcc717acc1c3) The following modules were loaded for performing simulations on NERSC Cori GPU: 1) cgpu 2) gcc 3) cuda/10.2.89 4)mvapich2 and on NERSC Haswell Partition 1) modules/3.2.11.4<br> 2) altd/2.0<br> 3) darshan/3.2.1<br> 4) craype-network-aries<br> 5) gcc/8.3.0<br> 6) craype/2.6.2<br> 7) cray-mpich/7.7.10<br> 8) craype-haswell<br> 9) craype-hugepages2M<br> 10) cray-libsci/19.06.1<br> 11) udreg/2.3.2-7.0.1.1_3.47__g8175d3d.ari<br> 12) ugni/6.0.14.0-7.0.1.1_7.49__ge78e5b0.ari<br> 13) pmi/5.0.14<br> 14) dmapp/7.1.1-7.0.1.1_4.61__g38cf134.ari<br> 15) gni-headers/5.0.12.0-7.0.1.1_6.36__g3b1768f.ari<br> 16) xpmem/2.2.20-7.0.1.1_4.20__g0475745.ari<br> 17) job/2.2.4-7.0.1.1_3.47__g36b56f4.ari<br> 18) dvs/2.12_2.2.165-7.0.1.1_14.4__ge967908e<br> 19) alps/6.6.58-7.0.1.1_6.19__g437d88db.ari<br> 20) rca/2.2.20-7.0.1.1_4.61__g8e3fb5b.ari<br> 21) atp/2.1.3<br> 22) PrgEnv-gnu/6.0.5 The input files, datasets, and jupyter notebooks (StrongScaling/strong_scaling_analysis.ipynb and WeakScaling/weak_scaling_plots.ipynb) used to generate the weak and strong scaling for CPU and GPU performance, shown in Figures 2, 3(a) and 3(b), are in the directory named "InputFilesAndDataSetForScalingStudies". The simulation data sets and analysis scripts for Figures 5, 6, and 7 in the paper are in the directory named "DataSetForEmptyWaveguideAndTunableFilter.tar.gz". The runs for the waveguide simulations used to produce Figures 5, 6, and 7 were run on a local LBNL work-station with a single NVIDIA Quadro GV100 GPU. The input files are located in the ARTEMIS repository (https://github.com/ECP-WarpX/artemis.git) at Examples/Waveguide/.
DOI: 10.5281/zenodo.4630689
title: Supplementary material for "A Massively Parallel Time-Domain Coupled Electrodynamics-Micromagnetics Solver"

description: This directory contains the inputs, outputs, job submission scripts and github hashes used to generate perform our simulations and create the figures presented in "A Massively Parallel Time-Domain Coupled Electrodynamics-Micromagnetics Solver" by Yao et al. and submitted to "The International Journal of High Performance Computing Applications." The source code for ARTEMIS is here https://github.com/ECP-WarpX/artemis.git (Commit hash used : 64e32d56767e6f8027de81a8c4897b808f211267) and AMReX code is here : https://github.com/AMReX-Codes/amrex.git (Commit hash : 7c3e95210a1877c67139ea57c62fbcc717acc1c3) The following modules were loaded for performing simulations on NERSC Cori GPU: 1) cgpu 2) gcc 3) cuda/10.2.89 4)mvapich2 and on NERSC Haswell Partition 1) modules/3.2.11.4<br> 2) altd/2.0<br> 3) darshan/3.2.1<br> 4) craype-network-aries<br> 5) gcc/8.3.0<br> 6) craype/2.6.2<br> 7) cray-mpich/7.7.10<br> 8) craype-haswell<br> 9) craype-hugepages2M<br> 10) cray-libsci/19.06.1<br> 11) udreg/2.3.2-7.0.1.1_3.47__g8175d3d.ari<br> 12) ugni/6.0.14.0-7.0.1.1_7.49__ge78e5b0.ari<br> 13) pmi/5.0.14<br> 14) dmapp/7.1.1-7.0.1.1_4.61__g38cf134.ari<br> 15) gni-headers/5.0.12.0-7.0.1.1_6.36__g3b1768f.ari<br> 16) xpmem/2.2.20-7.0.1.1_4.20__g0475745.ari<br> 17) job/2.2.4-7.0.1.1_3.47__g36b56f4.ari<br> 18) dvs/2.12_2.2.165-7.0.1.1_14.4__ge967908e<br> 19) alps/6.6.58-7.0.1.1_6.19__g437d88db.ari<br> 20) rca/2.2.20-7.0.1.1_4.61__g8e3fb5b.ari<br> 21) atp/2.1.3<br> 22) PrgEnv-gnu/6.0.5 The input files, datasets, and jupyter notebooks (StrongScaling/strong_scaling_analysis.ipynb and WeakScaling/weak_scaling_plots.ipynb) used to generate the weak and strong scaling for CPU and GPU performance, shown in Figures 2, 3(a) and 3(b), are in the directory named "InputFilesAndDataSetForScalingStudies". The simulation data sets and analysis scripts for Figures 5, 6, and 7 in the paper are in the directory named "DataSetForEmptyWaveguideAndTunableFilter.tar.gz". The runs for the waveguide simulations used to produce Figures 5, 6, and 7 were run on a local LBNL work-station with a single NVIDIA Quadro GV100 GPU. The input files are located in the ARTEMIS repository (https://github.com/ECP-WarpX/artemis.git) at Examples/Waveguide/.
DOI: 10.6084/m9.figshare.14273549
title: Ribosomal protein database of Staphylococcus aureus Newman strain

description: This work presents the ribosomal protein database of <i>Staphylococcus aureus </i>Newman strain. Original data for the work came from the annotated proteome data of the bacterium downloaded from UniProt. Using an in-house MATLAB ribosomal protein database analysis software, the original proteome data file was parsed to extract protein name and amino acid sequence of all ribosomal proteins in the species. The database also includes calculated variables such as number of residues, molecular weight, and nucleotide sequence. Overall, the presented database could serve as a ribosomal protein mass fingerprint for use in microbial identification, or it could be used in fundamental studies seeking to uncover new insights into ribosomal protein biology.
DOI: 10.25387/g3.14067449.v2
title: Supplemental Material for Castillo and Almeida, 2021

description: Supplementary Figures and Tables for manuscript G3-2021-402135R1
DOI: 10.25387/g3.14067449.v1
title: Supplemental Material for Castillo and Almeida, 2021

description: Supplementary Figures and Tables for manuscript G3-2021-402135R1
DOI: 10.6084/m9.figshare.14273549.v1
title: Ribosomal protein database of Staphylococcus aureus Newman strain

description: This work presents the ribosomal protein database of <i>Staphylococcus aureus </i>Newman strain. Original data for the work came from the annotated proteome data of the bacterium downloaded from UniProt. Using an in-house MATLAB ribosomal protein database analysis software, the original proteome data file was parsed to extract protein name and amino acid sequence of all ribosomal proteins in the species. The database also includes calculated variables such as number of residues, molecular weight, and nucleotide sequence. Overall, the presented database could serve as a ribosomal protein mass fingerprint for use in microbial identification, or it could be used in fundamental studies seeking to uncover new insights into ribosomal protein biology.
DOI: 10.5281/zenodo.3902654
title: Australia Rainfall Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict the total daily rainfall using 24 hours of temperature measurements. This is useful as temperature sensors are much cheaper and easy to maintain as compared to rain gauges. This dataset contains 160,267 time series obtained from a dataset released by the Australian Bureau of Meteorology (BOM).The time series has 3 dimensions, measuring the average hourly temperature, minimum hourly temperature and maximum hourly temperature from 518 weather stations throughout all of Australia. <br> Please refer to https://data.gov.au/data/dataset/weather-forecasting-verification-data-2015-05-to-2016-04 for more details