cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26050/wdcc/ar6.c6cdmimil
title: IPCC DDC: MIROC MIROC-ES2L model output prepared for CMIP6 CDRMIP

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CDRMIP.MIROC.MIROC-ES2L' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MIROC-ES2L climate model, released in 2018, includes the following components: aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T42; 128 x 64 longitude/latitude; 40 levels; top level 3 hPa), land: MATSIRO6.0+VISIT-e ver.1.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), ocnBgchem: OECO ver.2.0; NPZD-type with C/N/P/Fe/O cycles, seaIce: COCO4.9. The model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 500 km, atmos: 500 km, land: 500 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
DOI: 10.5281/zenodo.7359880
title: Design of Flame-Made ZnZrOx Catalysts for Sustainable Methanol Synthesis from CO2

description: Data set for publication entitled Design of Flame-Made ZnZrOx Catalysts for Sustainable Methanol Synthesis from CO2
DOI: 10.5281/zenodo.7788198
title: Raptors data

description: Raptors data from GBIF of all occurrences (longitude and latitude, IUCN status, Species scientific name and common name)
DOI: 10.5281/zenodo.8121108
title: Hydroclimatic and isotope data - Upper Selke

description: The data set contains .txt files of hydroclimatic timeseries and isotope (delta18O) data proposed in the work "Uncertainty in water transit time estimation with StorAge Selection functions and tracer data interpolation" by Borriero A., Kumar R., Nguyen T.V., Fleckenstein J.H. and Lutz S. accepted in Hydrology and Earth System Sciences.
DOI: 10.5281/zenodo.6012778
title: S21 | UATHTARGETS | University of Athens Target List

description: This is the collection associated with list S21 UATHTARGETS on the NORMAN Suspect List Exchange. https://www.norman-network.com/nds/SLE/ S21 | UATHTARGETS | <strong>University of Athens Target List </strong> Update 22/3/2020: added InChIKey file. Update 8/2/2022: new files from Dec 2021 with new compounds, NORMAN ID, classification and comments (provided by Maristina Nika). (v0.2.1 - attempted fix of CSV headers) Additional grant acknowledgement: Aristeia-Excellence: Transformation products of emerging pollutants in the aquatic environment (TREMEPOL project), 2012-2015, European Social Fund-Ministry of Education, http://tremepol.chem.uoa.gr/
DOI: 10.24381/cds.39c97304
title: Fish abundance and catch data for the Northwest European Shelf and Mediterranean Sea from 2006 to 2098 derived from climate projections
DOI: 10.5281/zenodo.6423524
title: Noise Intensity and Its Impact on The Perception and Concentration Level Among Forest Harvesting Workers in Industrial Forest Plantation, North Sumatera, Indonesia

description: Abstract <strong>Background:</strong> Noise has the potential to affect the comfort and health of workers. The objective of this research was to find out the effect of noise caused by timber harvesting process using chainsaw on the concentration of workers in an industrial forest plantation. Methods: This cross-sectional study included 20 respondents which consisted of 10 chainsaw operators/helpers and 10 non-chainsaw operators. All respondents were exposed to the sound of chainsaw in three different conditions (idle, half gas and racing conditions) with and without personal protective equipments (PPE). The sound intensity produced by the chainsaw and the noise received by the respondents were measured using sound level meter. Respondents’ perception toward noise was recorded using Likert scale. Respondents’ concentration level was assessed by giving 25 math-questions that must be answered within 10 minutes. Wilcoxon sign rank test was used to analysed paired data. Results: the average sound intensity received by respondents’ left and right ear were lower than the average of sound intensity produced by the chainsaw. The average sound intensity produced by the chainsaw in idle condition was 101.3 dB, while that received by the right ear was 81.36 dB and the left ear was 82.86 dB. When the chainsaw was in half gas condition, the average sound intensity produced was 111 dB while that received by the right ear was 95.83 dB and the left ear was 97.11 dB. In racing condition, the average sound intensity produced by the chainsaw was 124.7 dB while that received by the right ear was 106.2 dB and the left ear was 107.7 dB, respectively. The use of earmuff leads to better perception towards noise when compared with the use of earplug and the absent of any personal protective equipment (PPE). Wilcoxon test result showed that noise did not have significant impact on the concentration level of chainsaw operators, whilst the contratry result is true for non-chainsaw operators. Conclusion: The research indicated that although the noise produced by the chainsaw machine was considered as noisy for both chainsaw operators and non-chainsaw operator, it did not give significant effect on the concentration level of chainsaw operators and only affects the non-chainsaw operators. Therefore, given that the non-chainsaw operators were still affected by the noise, noise control measures are still needed to ensure occupational safety and health for the workers. Keywords: Chainsaw; forest harvesting; industrial forest plantation; noise
DOI: 10.6084/m9.figshare.20722270
title: Wolbachia_2050shocks

description: Biological dynamics of wMel-infected Ae.aegypti in response to future heat shocks for Cairns, Australia for 2050 scenarios.
DOI: 10.6084/m9.figshare.21126178
title: DATASET for Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia

description: This is the dataset for MV Mortality manuscript.
DOI: 10.6084/m9.figshare.23092196
title: Cloud Forest Bird Community Ecology Along Interacting Gradients of Elevation and Agricultural Land Use Change in the Peruvian Andes (Amazonas)

description: These data were collected as part of the project Aves del Bosque Montano Peruano (www.avesmontano.org) by graduate students, Ian Ausprey and Felicity Newell, at the University of Florida Department of Biology and Florida Museum of Natural History. The data are associated with several publications, which will be updated here as they are completed. <br> The repository includes community survey data based upon point counts, flock surveys, and mist netting surveys that were conducted across interacting gradients in elevation and agricultural land use change. It inclues (1) site-specific occurence data for all species, (2) abundance data collected by point counts using distance estimation, (3) functional and morphological traits for all species, and (4) a consensus phylogeny based on 100 random trees from the Jetz Bird Tree. <br> Please contact the authors if you plan to use these data. There are contextual issues regarding the survey design and sites that require consultation to understand. These data were extremely hard to collect, requiring 5 years of effort over a total of two years in the field. We ask that users be ethical in how they use these data and consider including us as authors if feasible. <br> Associated Papers: Ausprey, I. J., Newell, F. L., and Robinson, S. K.. 2021. Adaptations to light predict the foraging niche and disassembly of avian communities in tropical countrysides. <em>Ecology</em>. 102( 1):e03213. 10.1002/ecy.3213 Ausprey, I. J., Newell, F. L., & Robinson, S. K. (2022). Functional response traits and altered ecological niches drive the disassembly of cloud forest bird communities in tropical montane countrysides. <em>Journal of Animal Ecology</em>, 91, 2314– 2328. https://doi.org/10.1111/1365-2656.13816 <br> <br>
DOI: 10.25919/awsp-rq14
title: Radio-selected astronomical catalogue for Machine Learning based redshift estimates

description: Radio catalogues with optical (DES and/or SDSS), infrared (AllWISE), and spectroscopically measured redshifts for the training of machine learning algorithms for estimating redshifts.
DOI: 10.6084/m9.figshare.20965759.v5
title: Organic alkalinity as an important constituent of total alkalinity and the buffering system in coastal transition zones

description: This is the data reported in the manuscript, entitled " Organic alkalinity as an important constituent of total alkalinity and the buffer system in coastal transition zones", submitted to "JGR-Oceans" for consideration for publication.
DOI: 10.17632/k8kvh5b5ks.1
title: Evaluation of the effects of blockchain technology characteristics on supply chain performance attributes using an integrated fuzzy MCDM methodology (data)

description: This dataset contains calculations of the methods mentioned below. 
To determine the importance weights of the blockchain characteristics based on the supply chain, the interval type-2 fuzzy AHP method was used in the study. Then, interval type-2 fuzzy TOPSIS method was used to reveal the rank of importance of SCOR model supply chain performance attributes according to blockchain characteristics.
DOI: 10.26050/wdcc/ar6.c6cdmou0
title: IPCC DDC: MOHC UKESM1.0-LL model output prepared for CMIP6 CDRMIP

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CDRMIP.MOHC.UKESM1-0-LL' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The UKESM1.0-N96ORCA1 climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), atmosChem: UKCA-StratTrop, land: JULES-ES-1.0, ocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: MEDUSA2, seaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
DOI: 10.5517/cc14pxnd
title: CCDC 1063754: Experimental Crystal Structure Determination

description: Related Article: M. A. Uvarova, A. A. Ageshina, M. A. Golubnichaya, and S. E. Nefedov|2015|Zh.Neorg.Khim.|8|1032|
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.8199371
title: Repetitive elements of Erebia and Carex and their genomic proportions

description: Datasets of repetitive elements in the genomes of <em>Erebia </em>and <em>Carex</em>, detected and annotated using RepeatExplorer2 (Novák et al., 2010, 2020) using low-coverage (0.1X) short read sequencing data. Data from the article "Holocentric repeat landscapes: from microevolutionary patterns to macroevolutionary associations with karyotype evolution": Cornet, C., Mora, P., Augustijnen, H., Nguyen, P., Escudero, M., & Lucek, K. (2023). Holocentric repeat landscapes: From micro-evolutionary patterns to macro-evolutionary associations with karyotype evolution. Molecular Ecology, 00, 1–19. https://doi.org/10.1111/ mec.17100 47 <em>Erebia </em>and 14 <em>Carex </em>species were analysed in genus-level analyses ("Erebia" and "Carex" folders).<br> In addition, individuals of 4 <em>Erebia </em>species ("Erebia cassioides", "Erebia tyndarus", "Erebia nivalis" and "Erebia pronoe" folders) from different populations were analysed in species-level analyses. Subfolders "Individuals" and "Comparative" represent the two modes in which RepeatExplorer2 was run: the individual mode identifies repeats in each sample separately, and the comparative mode identifies repeats in all samples simultaneously, allowing comparisons between individuals and species. Files named "CLUSTER_TABLE..." are the raw output of RepeatExplorer2 and represent the overall number of reads in each cluster of repetitive element, and their annotation.<br> Files named "COMPARATIVE_ANALYSIS_COUNTS..." are the raw output of RepeatExplorer2 in comparative mode, representing the number of reads in each cluster for each sample included in the analysis. <br> Files named "Genome_proportion..." are the genomic proportion of each repeat annotation, calculated as the proportion of reads with the same annotation. Refer to Cornet et al. (2023) in Molecular Ecology for more details on how the data was generated, the downstream analyses and the sample names (see Tables S1, S2 and S3). References: Novák, P., Neumann, P., & Macas, J. (2010). Graph-based clustering and characterization of repetitive sequences in next-generation sequencing data. BMC Bioinformatics, 11(1), 378. https://doi.org/10.1186/1471-2105-11-378 Novák, P., Neumann, P., & Macas, J. (2020). Global analysis of repetitive DNA from unassembled sequence reads using RepeatExplorer2. Nature Protocols, 15(11), Article 11. https://doi.org/10.1038/s41596-020-0400-y
description: Additional funding: ME was supported by FEDER/MICINN – AEI (PID2021-122715NB-I00). PN was supported by the 23-06455S grant of the Czech Science Foundation. PM thanks the University of Jaén for its "Convocatoria de Recualificación del Sistema Universitario Español-Margarita Salas" postdoctoral grant under the "Plan de Recuperación Transformación" program funded by the Spanish Ministry of Universities with European Union's NextGenerationEU funds (grant no. UJAR10MS).
DOI: 10.5281/zenodo.8199370
title: Repetitive elements of Erebia and Carex and their genomic proportions

description: Datasets of repetitive elements in the genomes of <em>Erebia </em>and <em>Carex</em>, detected and annotated using RepeatExplorer2 (Novák et al., 2010, 2020) using low-coverage (0.1X) short read sequencing data. Data from the article "Holocentric repeat landscapes: from microevolutionary patterns to macroevolutionary associations with karyotype evolution": Cornet, C., Mora, P., Augustijnen, H., Nguyen, P., Escudero, M., & Lucek, K. (2023). Holocentric repeat landscapes: From micro-evolutionary patterns to macro-evolutionary associations with karyotype evolution. Molecular Ecology, 00, 1–19. https://doi.org/10.1111/ mec.17100 47 <em>Erebia </em>and 14 <em>Carex </em>species were analysed in genus-level analyses ("Erebia" and "Carex" folders).<br> In addition, individuals of 4 <em>Erebia </em>species ("Erebia cassioides", "Erebia tyndarus", "Erebia nivalis" and "Erebia pronoe" folders) from different populations were analysed in species-level analyses. Subfolders "Individuals" and "Comparative" represent the two modes in which RepeatExplorer2 was run: the individual mode identifies repeats in each sample separately, and the comparative mode identifies repeats in all samples simultaneously, allowing comparisons between individuals and species. Files named "CLUSTER_TABLE..." are the raw output of RepeatExplorer2 and represent the overall number of reads in each cluster of repetitive element, and their annotation.<br> Files named "COMPARATIVE_ANALYSIS_COUNTS..." are the raw output of RepeatExplorer2 in comparative mode, representing the number of reads in each cluster for each sample included in the analysis. <br> Files named "Genome_proportion..." are the genomic proportion of each repeat annotation, calculated as the proportion of reads with the same annotation. Refer to Cornet et al. (2023) in Molecular Ecology for more details on how the data was generated, the downstream analyses and the sample names (see Tables S1, S2 and S3). References: Novák, P., Neumann, P., & Macas, J. (2010). Graph-based clustering and characterization of repetitive sequences in next-generation sequencing data. BMC Bioinformatics, 11(1), 378. https://doi.org/10.1186/1471-2105-11-378 Novák, P., Neumann, P., & Macas, J. (2020). Global analysis of repetitive DNA from unassembled sequence reads using RepeatExplorer2. Nature Protocols, 15(11), Article 11. https://doi.org/10.1038/s41596-020-0400-y
description: Additional funding: ME was supported by FEDER/MICINN – AEI (PID2021-122715NB-I00). PN was supported by the 23-06455S grant of the Czech Science Foundation. PM thanks the University of Jaén for its "Convocatoria de Recualificación del Sistema Universitario Español-Margarita Salas" postdoctoral grant under the "Plan de Recuperación Transformación" program funded by the Spanish Ministry of Universities with European Union's NextGenerationEU funds (grant no. UJAR10MS).
DOI: 10.5281/zenodo.8242661
title: Synthetic gene expression data with underlying gene network

description: This is the synthetic gene expression data along with the underlying gene network used in the simulation studies of Hu and Szymczak (2023) for evaluating network-guided random forest. In this dataset we consider the situation of 1000 genes and 1000 samples each for training and testing sets. Each file contains a list of 100 replications of the considered scenario which can be identified via the file name. In particular, we consider 6 different scenarios depending on the number of disease modules and how are the effects of disease genes distributed within the disease module. When there are disease genes, we also consider 3 different levels of effect sizes. The binary responses are then generated via a logistic regression model. More details on these scenarios and the data generation mechanism can be found in Hu and Szymczak (2023). The data is generated by the function <em>gen_data</em> in R package <em>networkRF</em> which can be accessed at https://github.com/imbs-hl/networkRF. To obtain the datasets with 3000 genes, which is the other part of the data used in the simulation studies of Hu and Szymczak (2023), simply modify the <em>num.var</em> argument of the function <em>gen_data.</em> More descriptions on the implementation and the format of the output can be found in the help page of the R package.
description: {"references": ["Hu and Szymczak (2023). Evaluation of network-guided random forest for disease gene discovery. arXiv:2308.01323"]}
DOI: 10.5281/zenodo.8242660
title: Synthetic gene expression data with underlying gene network

description: This is the synthetic gene expression data along with the underlying gene network used in the simulation studies of Hu and Szymczak (2023) for evaluating network-guided random forest. In this dataset we consider the situation of 1000 genes and 1000 samples each for training and testing sets. Each file contains a list of 100 replications of the considered scenario which can be identified via the file name. In particular, we consider 6 different scenarios depending on the number of disease modules and how are the effects of disease genes distributed within the disease module. When there are disease genes, we also consider 3 different levels of effect sizes. The binary responses are then generated via a logistic regression model. More details on these scenarios and the data generation mechanism can be found in Hu and Szymczak (2023). The data is generated by the function <em>gen_data</em> in R package <em>networkRF</em> which can be accessed at https://github.com/imbs-hl/networkRF. To obtain the datasets with 3000 genes, which is the other part of the data used in the simulation studies of Hu and Szymczak (2023), simply modify the <em>num.var</em> argument of the function <em>gen_data.</em> More descriptions on the implementation and the format of the output can be found in the help page of the R package.
description: {"references": ["Hu and Szymczak (2023). Evaluation of network-guided random forest for disease gene discovery. arXiv:2308.01323"]}