cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7042352
title: A data set of monthly global ocean vertical velocity from 1950-2014

description: This data set provides monthly global ocean vertical velocity from 1950-2014. It was constructed from 41 CMIP6 models (historical experiment). It may be used for investigating the large-scale upwelling and downwelling. Note that this data set has not been widely tested. Please feel free to contact the author if you had any questions or concerns. It will be greatly appreciated if you could send the author an email when you used this data set, so that the author can better improve this data set, and more importantly, provide you with updated data sets or any modifications.
DOI: 10.5281/zenodo.7264408
title: Dataset CO2 Emission per Capita Forecast 2020-2100

description: The dataset includes Business As Usual (BAU) forecast of the world's global CO2 emissions per capita (CpC) for 2020-2100. The CO2 emission forecast is from the publication “<em>Dataset Global Warming Forecast using Acceleration Factors</em>” [3]. According to this publication, the CO2 emissions without international transport will change from 33,803 MtCO2/y in 2020 to 70,191 MtCO2/y in 2100, a 108% increase. The population forecast applies a parabolic trendline of the last 30 years. According to this calculation, the world population will change from 7,795 million in 2020 to 15,206 million in 2100, a 95% increase. CO2 emissions per capita (CpC) are calculated by dividing the CO2 emissions per year by the population in the same year. The world CpC was 4.3366 tCO2/y,cap in 2020. The CpC forecast for 2100 is 4.6160 tCO2/y,cap, 6.4% increase.
description: {"references": ["Hannah Ritchie, Max Roser, Edouard Mathieu, Bobbie Macdonald and Pablo Rosado - Data on CO2 and Greenhouse Gas Emissions by Our World in Data https://github.com/owid/co2-data#data-on-co2-and-greenhouse-gas-emissions-by-our-world-in-data", "Our World in Data, Cumulative CO2 emissions, 2020 https://ourworldindata.org/grapher/cumulative-co-emissions", "Dataset Global Warming Forecast using Acceleration Factors - Joseph Nowarski, DOI:10.5281/zenodo.7151890", "Dataset CO2 Emission per Capita 1800-2100 \u2013 Joseph Nowarski, DOI:10.5281/zenodo.7264407"]}
DOI: 10.5281/zenodo.7264409
title: Dataset CO2 Emission per Capita Forecast 2020-2100

description: The dataset includes Business As Usual (BAU) forecast of the world's global CO2 emissions per capita (CpC) for 2020-2100. The CO2 emission forecast is from the publication “<em>Dataset Global Warming Forecast using Acceleration Factors</em>” [3]. According to this publication, the CO2 emissions without international transport will change from 33,803 MtCO2/y in 2020 to 70,191 MtCO2/y in 2100, a 108% increase. The population forecast applies a parabolic trendline of the last 30 years. According to this calculation, the world population will change from 7,795 million in 2020 to 15,206 million in 2100, a 95% increase. CO2 emissions per capita (CpC) are calculated by dividing the CO2 emissions per year by the population in the same year. The world CpC was 4.3366 tCO2/y,cap in 2020. The CpC forecast for 2100 is 4.6160 tCO2/y,cap, 6.4% increase.
description: {"references": ["Hannah Ritchie, Max Roser, Edouard Mathieu, Bobbie Macdonald and Pablo Rosado - Data on CO2 and Greenhouse Gas Emissions by Our World in Data https://github.com/owid/co2-data#data-on-co2-and-greenhouse-gas-emissions-by-our-world-in-data", "Our World in Data, Cumulative CO2 emissions, 2020 https://ourworldindata.org/grapher/cumulative-co-emissions", "Dataset Global Warming Forecast using Acceleration Factors - Joseph Nowarski, DOI:10.5281/zenodo.7151890", "Dataset CO2 Emission per Capita 1800-2100 \u2013 Joseph Nowarski, DOI:10.5281/zenodo.7264407"]}
DOI: 10.5281/zenodo.7264414
title: Dataset CO2 Emission per GDP Forecast 2020-2100

description: The dataset includes Business as Usual (BAU) forecast of world global CO2 emissions per GDP (Cp$) for 2020-2100. The CO2 emission forecast is from the publication “<em>Dataset Global Warming Forecast using Acceleration Factors</em>” [6]. According to this publication, the CO2 emissions without international transport will change from 33,803 MtCO2/y in 2020 to 70,191 MtCO2/y in 2100, a 108% increase. The GDP forecast applies a parabolic trendline of the last 30 years. According to this calculation, the world GDP will change from 126.3 MM$/y in 2020 to 728.1 MM$/y in 2100, a 476% increase. CO2 emissions per GDP (Cp$) are calculated by dividing the CO2 emissions per year by the GDP in the same year. The world 0.000268 tCO2/$GDP Cp$ in 2020 will decrease by 64% in 2100 to 0.000096 tCO2/$GDP.
description: {"references": ["Hannah Ritchie, Max Roser, Edouard Mathieu, Bobbie Macdonald and Pablo Rosado - Data on CO2 and Greenhouse Gas Emissions by Our World in Data https://github.com/owid/co2-data#data-on-co2-and-greenhouse-gas-emissions-by-our-world-in-data", "Our World in Data, Cumulative CO2 emissions, 2020 https://ourworldindata.org/grapher/cumulative-co-emissions", "GDP, PPP (constant 2017 international $) - World Bank, International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme", "Cumulative CO2 Emissions of International Transport \u2013 Joseph Nowarski, DOI:10.5281/zenodo.7114110", "Dataset Cumulative CO2 Emissions of International Transport \u2013 Joseph Nowarski, DOI:10.5281/zenodo.7114087", "Dataset Global Warming Forecast using Acceleration Factors - Joseph Nowarski, DOI:10.5281/zenodo.7151890", "CO2 Emission per GDP Forecast 2020-2100 - Joseph Nowarski, DOI:10.5281/zenodo.7264413"]}
DOI: 10.5281/zenodo.7264415
title: Dataset CO2 Emission per GDP Forecast 2020-2100

description: The dataset includes Business as Usual (BAU) forecast of world global CO2 emissions per GDP (Cp$) for 2020-2100. The CO2 emission forecast is from the publication “<em>Dataset Global Warming Forecast using Acceleration Factors</em>” [6]. According to this publication, the CO2 emissions without international transport will change from 33,803 MtCO2/y in 2020 to 70,191 MtCO2/y in 2100, a 108% increase. The GDP forecast applies a parabolic trendline of the last 30 years. According to this calculation, the world GDP will change from 126.3 MM$/y in 2020 to 728.1 MM$/y in 2100, a 476% increase. CO2 emissions per GDP (Cp$) are calculated by dividing the CO2 emissions per year by the GDP in the same year. The world 0.000268 tCO2/$GDP Cp$ in 2020 will decrease by 64% in 2100 to 0.000096 tCO2/$GDP.
description: {"references": ["Hannah Ritchie, Max Roser, Edouard Mathieu, Bobbie Macdonald and Pablo Rosado - Data on CO2 and Greenhouse Gas Emissions by Our World in Data https://github.com/owid/co2-data#data-on-co2-and-greenhouse-gas-emissions-by-our-world-in-data", "Our World in Data, Cumulative CO2 emissions, 2020 https://ourworldindata.org/grapher/cumulative-co-emissions", "GDP, PPP (constant 2017 international $) - World Bank, International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme", "Cumulative CO2 Emissions of International Transport \u2013 Joseph Nowarski, DOI:10.5281/zenodo.7114110", "Dataset Cumulative CO2 Emissions of International Transport \u2013 Joseph Nowarski, DOI:10.5281/zenodo.7114087", "Dataset Global Warming Forecast using Acceleration Factors - Joseph Nowarski, DOI:10.5281/zenodo.7151890", "CO2 Emission per GDP Forecast 2020-2100 - Joseph Nowarski, DOI:10.5281/zenodo.7264413"]}
DOI: 10.5281/zenodo.4011179
title: Dataset regarding the « Reasons for concern » about climate change from figures in IPCC and related publications

description: This data corresponds to the 'burning ember' diagrams from IPCC reports and related publications (IPCC TAR, Smith et al. 2009 for AR4-related embers, AR5 and SR15). It was used to build figure 3 of Zommers et al. 2020 (<em>Burning Embers: Towards more transparent and robust climate change risk assessments</em>. Accepted for publication in Nature Reviews Earth & Environment). The data provided here is the result of extraction of information from the original figures, as presented in the related technical document 10.5281/zenodo.3992856. As explained in the Supplementary Information of Zommers et al. 2020 and the technical document, this is not data from the IPCC. The provided values are approximations of the global mean temperature increase corresponding to each change in risk in the original diagrams. The rigour of the preparation process and the limitations of the dataset are explained in the technical document.
DOI: 10.6084/m9.figshare.24223345
title: Supplementary Data 2

description: This file contains the raw data for calculations done for the manuscript "The complementarity of nutrient density and disease burden for nutritional Life Cycle Assessments".
DOI: 10.6084/m9.figshare.24223345.v1
title: Supplementary Data 2

description: This file contains the raw data for calculations done for the manuscript "The complementarity of nutrient density and disease burden for nutritional Life Cycle Assessments".
DOI: 10.6084/m9.figshare.22262491.v2
title: Rhinoceros3D files and Grasshopper3D definitions used in the paper EPiC Grasshopper: A bottom-up parametric tool to quantify life cycle embodied environmental flows of buildings and infrastructure assets

description: This dataset contains: <br> The Rhinoceros3D file used as a standard residential building case study for Australia <br> The Grasshopper3D definitions (×3) used to quantify the life cycle embodied environmental flows of the case study building as well as a parametric case study building and a road sections <br> The Grasshopper3D definitions require EPiC Grasshopper to run. Download it from: https://doi.org/10.6084/m9.figshare.22773653.v1
DOI: 10.34820/fk2/i41vnr/yp8ozq
title: S2212567112003152.html

description: :unav
DOI: 10.34820/fk2/i41vnr
title: Barrier factors and potential solutions for Indonesian SMEs

description: This study seeks to identify the potency of SMEs and reasons for inability in competing in global market and the outcome will be obtained in measurement form consisting several barrier factors faced by SMEs.
DOI: 10.25739/vv79-4p87
title: Carolyn_Lawrence_Dill_GOMAP_Sorghum_DOE-JGI_v3.1.1_March_2023_v1.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in Sorghum bicolor v3.1.1 genome annotation with a median of 10 annotations per gene model with 100% coverage. It was created in March of 2023.
DOI: 10.25739/ecz3-pj55
title: Carolyn_Lawrence_Dill_GOMAP_Soybean_LIS_Wm82_IGA1008.gnm1.ann1.FGN6_May_2023_v1.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in Glycine max cultivar William 82 genome annotation with a median of 10 annotations per gene model with 100% coverage. It was created in May of 2023.
DOI: 10.25739/79sp-4r24
title: Carolyn_Lawrence_Dill_GOMAP_Stiff_brome_DOE-JGI_Bd21_v3.2_May_2023_v1.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in Brachypodium distachyon Bd21 v3.2 genome annotation with a median of 10 annotations per gene model with 100% coverage. It was created in May of 2023.
DOI: 10.25739/3wm1-xd35
title: Carolyn_Lawrence_Dill_GOMAP_Sugar_Pine_TreeGenes_v1.5_June_2023_v2.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in Pinus lambertiana v1.5 genome annotation with a median of 9 annotations per gene model with 100% coverage. This dataset is an updated version of the previously generated dataset https://doi.org/10.25739/jvs4-xr88. It was created in June of 2023.
DOI: 10.25739/j4g2-gg60
title: Carolyn_Lawrence_Dill_GOMAP_Tea_Teabase_CSS_ChrLev_20200506_June_2023_v1.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in Camellia sinensis CSS_ChrLev_20200506 Cultivar Shuchazao (accession no. GS2002008) genome annotation with a median of 10 annotations per gene model with 99.99% coverage. It was created in June of 2023.
DOI: 10.25739/pzdc-g192
title: Carolyn_Lawrence_Dill_GOMAP_Tomato_SGN_SL4.0_July_2023_v2.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in Solanum lycopersicum SL4.0 genome annotation with a median of 10 annotations per gene model with 100% coverage. This dataset is an updated version of the previously generated dataset https://doi.org/10.25739/zh2v-4p15. It was created in July of 2023.
DOI: 10.25739/66k7-yb12
title: Carolyn_Lawrence_Dill_GOMAP_Wheat_URGI_IWGSC_RefSeq_v2.1_May_2023_v1.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in Triticum aestivum IWGSC RefSeq v2.1 genome annotation with a median of 10 annotations per gene model with 100% coverage. It was created in May of 2023.
DOI: 10.25739/wk2r-1t44
title: Carolyn_Lawrence_Dill_GOMAP_Wild_Tomato_SGN_LA716_June_2023_v2.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in Solanum pennellii LA716 genome annotation with a median of 9 annotations per gene model with 100% coverage. This dataset is an updated version of the previously generated dataset https://doi.org/10.25739/fhr4-cx67. It was created in June of 2023.
DOI: 10.21227/92hg-ad62
title: TWICE Dataset: Digital twin of test scenarios in a controlled environment

description: Ensuring the safe and reliable operation of autonomous vehicles under adverse weather remains a significant challenge. To address this, we have developed a comprehensive dataset composed of sensor data acquired in a real test track and reproduced in the laboratory for the same test scenarios.The provided dataset includes camera, radar, LiDAR, inertial measurement unit (IMU), and GPS data recorded under adverse weather conditions (rainy, night-time, and snowy conditions). We recorded test scenarios using objects of interest such as car, cyclist, truck and pedestrian -- some of which are inspired by EURONCAP (European New Car Assessment Programme). The sensor data generated in the laboratory is acquired by the execution of simulation-based tests in hardware-in-the-loop environment with the digital twin of each real test scenario. The dataset contains more than 2 hours of recording, which totals more than 280GB of data.Therefore, it is a valuable resource for researchers in the field of autonomous vehicles to test and improve their algorithms in adverse weather conditions, as well as explore the simulation-to-reality gap. The dataset is available for download at: \texttt{https://twicedataset.github.io/site/}
DOI: 10.5281/zenodo.8393831
title: All Raw Data of Camptostemon philippinensis in Balikpapan Bay

description: It contains all raw data of the Camptostemon philippinensis Project in Balikpapan Bay
DOI: 10.6084/m9.figshare.24195054
title: Barcoded overexpression screens in gut Bacteroidales identify genes with new roles in carbon utilization and stress resistance"

description: Data downloads for "Barcoded overexpression screens in gut Bacteroidales identify genes with new roles in carbon utilization and stress resistance"genomes.tar.gz includes the genomes and annotations for Bacteroides thetaiotaomicron VPI-5482 and for the six other source organisms. orgId.names explains which assembly identifier goes with which insert library.mapping_*.tar.gz has the final results of the mapping script, with the 05-BC_and_genes_dfs subdirectory for each library.The scripts for mapping are in Boba-seq.tar.gz<br>Also see<br>https://github.com/OGalOz/Boba-seq<br>(which may have an updated version)bobaseq.R has the R functions for computing strain fitness values and identifying statistically significant and biologically consistent hits. Also see<br>https://github.com/morgannprice/BobaseqFitness<br>(which may have an updated version of the script)fit2.R has the commands to build the R image, using the routines in bobaseq.RBtcomb.image has the resulting R image.bobaseq_figs.R has R commands for making the figures.The Boba-seq samples are described in Bt_bobaseq_exps_2023Sep11.tsvfaa.hits has the results of all-versus-all protein similarity (from blastp) for these 7 genomes. Also see psim in the R image. These are used to help identify biologically consistent hits.The *.gb files are vector maps for pNBU2-repA1, pNBU2-repA2, and pNBU2-repA3, pNBU2-repA1-barcoded, pFD340, and pKSH24.AH*.fq.gz are paired-end RNASeq data for B. thetaiotaomicron carrying two different plasmids with 3 replicates each. AH001, AH002, and AH003 are the empty vector samples. AH004, AH005, and AH006 are the samples with the ssb protein ("fragment 2") in the insert.
DOI: 10.17909/zssq-wr33
title: Data for Modeling Circumstellar Gas Emission around a White Dwarf Using Cloudy

description: HST/COS NUV data for GaiaJ0611-6931
DOI: 10.5281/zenodo.8008672
title: BQE WIM Data Year 4 Project (Implementation and Effectiveness of Autonomous Enforcement of Overweight Trucks in an Urban Infrastructure Environment)

description: BQE (Brooklyn-Queens Expressway) WIM Data for QB (Queens Bound) and SIB (Staten Island Bound)
DOI: 10.5281/zenodo.8008673
title: BQE WIM Data Year 4 Project (Implementation and Effectiveness of Autonomous Enforcement of Overweight Trucks in an Urban Infrastructure Environment)

description: BQE (Brooklyn-Queens Expressway) WIM Data for QB (Queens Bound) and SIB (Staten Island Bound)
DOI: 10.6084/m9.figshare.24223396
title: Proteostasis related gene sets for eukaryotic model species and Prokaryotes (manual curation)

description: The files include gene sets related to known components of proteostasis network, such as protein folding, quality control and degradation, autophagy and relevant signaling pathways. Eight gene lists are included for specific Eukaryotes (<i>Homo sapiens, Gallus gallus, Danio rerio, Xenopus tropicalis, Caenorhabditis elegans, Drosophila melanogaster, Saccharomyces cerevisiae and Arabidopsis Thaliana</i>) and a generic gene list was created for the whole prokaryotic domain.
DOI: 10.6084/m9.figshare.24223396.v1
title: Proteostasis related gene sets for eukaryotic model species and Prokaryotes (manual curation)

description: The files include gene sets related to known components of proteostasis network, such as protein folding, quality control and degradation, autophagy and relevant signaling pathways. Eight gene lists are included for specific Eukaryotes (<i>Homo sapiens, Gallus gallus, Danio rerio, Xenopus tropicalis, Caenorhabditis elegans, Drosophila melanogaster, Saccharomyces cerevisiae and Arabidopsis Thaliana</i>) and a generic gene list was created for the whole prokaryotic domain.
DOI: 10.34820/fk2/udzqht/uhgoyp
title: QUALITY MANAGEMENT FOR LEATHER INDUSTRY.pdf

description: :unav
DOI: 10.34820/fk2/udzqht
title: QUALITY MANAGEMENT FOR LEATHER INDUSTRY TO INCREASE COMPETITIVENESS IN THE GLOBAL MARKET

description: The Indonesian government is targeting leather products, especially from West Java to become one of the export commodities that will encourage national economic growth. The existence of a leather industry particularly in the city of Garut has attracted many Indonesian researchers over the years to investigate the condition through many fields of research. However, the latest research identified several barriers related to the leather business development in Garut city, which was related to the optimization ratio of its performance and product quality. This study aimed to design a quality management model and key performance indicators to make this sector competitive in the global market. The research method used was inductive with a qualitative approach and a constructivist grounded theory in developing a quality management model. Theoretical and snowball sampling was chosen in identifying potential participants. The findings of this study contribute to the knowledge and practice of quality management models with key performance indicators that can be used to measure current conditions as a foundation for future improvement in the leather industry to produce competitive products that meet global standard. KPIs were formulated with certain criteria and should be applied by business actors.
DOI: 10.34820/fk2/85qxmv/kk1svq
title: Potential and Barriers SMEs.pdf

description: :unav
DOI: 10.34820/fk2/85qxmv
title: Barrier Factors and Potential Solutions for Indonesian SMEs

description: Facts that Indonesian SMEs have a great potency related with the ability to survive in crisis times do not lead to products capability. Moreover, several factors became reasons why the image of Indonesian SMEs does not merge significantly; inability in competing in global market and the outcome will be obtained in measurement form consisting several barrier factors faced by SMEs. The methodology adopted in this research is critical literature reviews, quantitative and qualitative data collection through questionnaires to 200 respondents and in-depth interviews with each of them. The research howed there are ten major barriers faced by SMEs: competition barriers; financial access; price of energy; technology; inefficient production cost; economic factors; management skill; process; limitation of sales; and raw material. Most SMEs agreed that government should dedicate more efforts in certain actions to eliminate SMEs barriers, like security standards improvement, development programmes, good financing policy, rational energy policy, continuous performance evaluation, commitment of corruption termination, and many supporting program needed by SMEs. Hereinafter, this research could contribute government programmes for empowering SMEs as the pillars of Indonesian economy.
DOI: 10.34740/kaggle/dsv/6573428
title: Global Crypto Currency Price Database
DOI: 10.34820/fk2/lgmia9/wqdabo
title: Implementing_Technology_in_Creative_Indu.pdf

description: :unav