cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3836042
title: UM experiments for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"

description: NetCDF4 files containing UM vn 11.1 data used in Lambert et al., 2020, Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection, submitted to Journal of Advances in Modeling Earth Systems. Key: "summaryday.nc" contain eleven months of data in each year, excluding either February or March. "summarydat2.nc" contain one month of data in each year, either February or March. "last5" indicates that for this simulation only the last five years of data are available. "llcs" are simulations with Lambert-Lewis. "gr" are simulations with Gregory-Rowntree. "llcsemu" are simulations with the Lambert-Lewis emulator. "gremu" are simulations with the Gregory-Rowntree emulator. "llcsemu_llcs" is the test simulation wherein the LLCS emulator is run equatorward of 30 degrees and the original LLCS convection scheme is run poleward of 30 degrees. "4xco2" have 4 x pre-industrial atmospheric carbon dioxide concentration. (Others have 1 x pre-industrial atmospheric carbon dioxide concentration.) "rh0.7" and "rh0.9" have LLCS RHCRIT set to 70% and 90% respectively. "30day" are one month simulations for July for which daily output are available. Other data are monthly mean only.
DOI: 10.5281/zenodo.3870486
title: Otros Libros

description: Otros Libros
DOI: 10.25919/5ed3edb15f654
title: Parkes observations for project P885 semester 2020APRS_05

description: We propose to observe the magnetars 1E 1547.0-5408 and PSR J1622-4950 approximately weekly, to track their rotation, flux density, and polarisation, as we have long been doing through this project. The revolutionary UWL capabilities promise new opportunities to disentangle still mysterious radio emission mechanisms in magnetars. This promise is supported by the recent follow up of XTE J1810-197, again a radio magnetar after 10 years dormant, for which our observations with the UWL receiver are providing unique and tantalizing measurements of its radio spectrum and polarisation.
DOI: 10.5281/zenodo.3837626
title: Statistical model training data for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"

description: Gzipped CSV files containing convection scheme inputs and outputs used for training. Column format of each file: THETA_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,Q_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DTHETA_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DQ_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28 where THETA_IN are input values of potential temperature [K], Q_IN are input values of specific humidity [kg/kg], DTHETA are changes in potential temperature due to convection [K], DQ are changes in specific humidity due to convection [kg/kg]. Key: "llcs" are simulations with Lambert-Lewis. "gr" are simulations with Gregory-Rowntree. "4xco2" have 4 x pre-industrial atmospheric carbon dioxide concentration. (Others have 1 x pre-industrial atmospheric carbon dioxide concentration.) "rh0.7" and "rh0.9" have LLCS RHCRIT set to 70% and 90% respectively. All are 30 day simulations either for January "jan" or July "jul".
DOI: 10.5281/zenodo.3837627
title: Statistical model training data for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"

description: Gzipped CSV files containing convection scheme inputs and outputs used for training. Column format of each file: THETA_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,Q_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DTHETA_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DQ_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28 where THETA_IN are input values of potential temperature [K], Q_IN are input values of specific humidity [kg/kg], DTHETA are changes in potential temperature due to convection [K], DQ are changes in specific humidity due to convection [kg/kg]. Key: "llcs" are simulations with Lambert-Lewis. "gr" are simulations with Gregory-Rowntree. "4xco2" have 4 x pre-industrial atmospheric carbon dioxide concentration. (Others have 1 x pre-industrial atmospheric carbon dioxide concentration.) "rh0.7" and "rh0.9" have LLCS RHCRIT set to 70% and 90% respectively. All are 30 day simulations either for January "jan" or July "jul".
DOI: 10.5519/qd.fxv7397u
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.3870509
title: eHealth-KD 2020

description: Docs, data and development scripts for the eHealth-KD Challenge at IberLEF 2020
DOI: 10.21227/qtmk-j795
title: Benchmark Dataset for the Paper "A Hybrid Optimization Method Combining Network Expansion Planning and Switching State Optimization"

description: A Hybrid Optimization Method Combining Network Expansion Planning and Switching State OptimizationGrid Planning Benchmark Dataset for the Paper "A Hybrid Optimization Method Combining Network Expansion Planning and Switching State Optimization"
DOI: 10.5281/zenodo.3868690
title: The role of storms in sand wave formation: A numerical modeling study

description: The data is mainly the results from the simulation for manuscript "The role of storms in sand wave formation: A numerical modeling study".
DOI: 10.5281/zenodo.3868691
title: The role of storms in sand wave formation: A numerical modeling study

description: The data is mainly the results from the simulation for manuscript "The role of storms in sand wave formation: A numerical modeling study".
DOI: 10.5281/zenodo.3870835
title: Information System for Cycling Navigation for Aveiro, Portugal - Database and ArcGIS Graph

description: This data was collected within the scope of a master's thesis in mechanical engineering from University of Aveiro, Portugal. The goal was to develop a information system for cycling navigation for an urban area of the portuguese city of Aveiro. Therefore, data collection was achieved through an instrumented alluminium bicycle equipped with a 1) GNSS Data Logger, 2) wireless heart rate recorder device and 3) video camera. 120 km were covered to collect 8h of video and about 100000 second by second data points, which were analyzed and organized through an 449 link-map built in ArcGIS. Seven different bikeability indicators were built: travel time, energy expenditure, effort distribution, infrastructure performance, safety, comfort and emissions hotspots of two pollutants (CO2 and NOx). The spreadsheet dataset is divided into three sections: Final Attributes - all the collected and treated data of each link, both used in the indicators and in another set for system' network analysis; Attributes Statistics - reveals some statistics of the weighted final atrributes achieved, such as mean, standard error, standard deviation, range, confidence level, among others; Link Type Statistics - reveals the distribution of the different link-types along the map and the respective average speeds recorded. In order to associate the FID numbers with the respective links, the developed ArcGIS graph was also made available.
DOI: 10.5281/zenodo.3870836
title: Information System for Cycling Navigation for Aveiro, Portugal - Database and ArcGIS Graph

description: This data was collected within the scope of a master's thesis in mechanical engineering from University of Aveiro, Portugal. The goal was to develop a information system for cycling navigation for an urban area of the portuguese city of Aveiro. Therefore, data collection was achieved through an instrumented alluminium bicycle equipped with a 1) GNSS Data Logger, 2) wireless heart rate recorder device and 3) video camera. 120 km were covered to collect 8h of video and about 100000 second by second data points, which were analyzed and organized through an 449 link-map built in ArcGIS. Seven different bikeability indicators were built: travel time, energy expenditure, effort distribution, infrastructure performance, safety, comfort and emissions hotspots of two pollutants (CO2 and NOx). The spreadsheet dataset is divided into three sections: Final Attributes - all the collected and treated data of each link, both used in the indicators and in another set for system' network analysis; Attributes Statistics - reveals some statistics of the weighted final atrributes achieved, such as mean, standard error, standard deviation, range, confidence level, among others; Link Type Statistics - reveals the distribution of the different link-types along the map and the respective average speeds recorded. In order to associate the FID numbers with the respective links, the developed ArcGIS graph was also made available.
DOI: 10.5281/zenodo.3797130
title: A chromosome-level genome assembly of the woolly apple aphid, Eriosoma lanigerum (Hausman) (Hemiptera: Aphididae)

description: <strong><em>Eriosoma lanigerum</em> v1.0 frozen release</strong> Genome assembly: Eriosoma_lanigerum.v1.0.scaffolds.fa.gz BRAKER2 gene models: Eriosoma_lanigerum.v1.0.scaffolds.gff BRAKER2 protein sequences: Eriosoma_lanigerum.v1.0.scaffolds.gff.aa.fa BRAKER2 protein sequences (longest transcript per gene only): Eriosoma_lanigerum.v1.0.scaffolds.gff.aa.LTPG.fa BRAKER2 coding sequences: Eriosoma_lanigerum.v1.0.scaffolds.gff.cds.fa <em>Buchnera aphidicola</em> scaffolds: Buchnera_aphidicola.scaffolds.fa <strong>Aphid orthogroups</strong> OrthoFinder run files (see for details https://github.com/davidemms/OrthoFinder/blob/master/OrthoFinder-manual.pdf): OrthoFinder_run.tar.gz
DOI: 10.5281/zenodo.3797131
title: A chromosome-level genome assembly of the woolly apple aphid, Eriosoma lanigerum (Hausman) (Hemiptera: Aphididae)

description: <strong><em>Eriosoma lanigerum</em> v1.0 frozen release</strong> Genome assembly: Eriosoma_lanigerum.v1.0.scaffolds.fa.gz BRAKER2 gene models: Eriosoma_lanigerum.v1.0.scaffolds.gff BRAKER2 protein sequences: Eriosoma_lanigerum.v1.0.scaffolds.gff.aa.fa BRAKER2 protein sequences (longest transcript per gene only): Eriosoma_lanigerum.v1.0.scaffolds.gff.aa.LTPG.fa BRAKER2 coding sequences: Eriosoma_lanigerum.v1.0.scaffolds.gff.cds.fa <em>Buchnera aphidicola</em> scaffolds: Buchnera_aphidicola.scaffolds.fa <strong>Aphid orthogroups</strong> OrthoFinder run files (see for details https://github.com/davidemms/OrthoFinder/blob/master/OrthoFinder-manual.pdf): OrthoFinder_run.tar.gz
DOI: 10.21227/mtqj-c319
title: PV Array Reconfiguration Based Converter-less MPPT Method for Rural Area Applications

description: Description of the proposed method is presented with the support of experimental videos.
DOI: 10.21227/z7y4-r960
title: PV Array Reconfiguration Based Converter-less MPPT Method for Rural Area Applications

description: Description of the proposed method is presented with the support of experimental videos.
DOI: 10.5281/zenodo.3870524
title: LG-covid19-HOTP: Literature Graph of Scholarly Articles Relevant to COVID-19 Study

description: Parallel to the dataset CORD-19 of scholarly articles, we provide the literature graph LG-covid19-HOTP composed of not only articles (graph nodes) that are relevant to the study of coronavirus, but also in and out citation links (directed graph edges) to base navigation and search among the articles. The article records are related and connected, not isolated. The graph has been updated weekly since March 26, 2020. The current graph includes <strong>27,161</strong> hot-off-the-press (HOTP) articles since January 2020. It contains <strong>265,689</strong> articles and <strong>2,127,399</strong> links. The link-to-node ratio is remarkably higher than some other existing literature graphs. In addition to the dataset we provide more functionalities at lg-covid-19-hotp.cs.duke.edu such as new articles, weekly meta-data analysis in terms of publication growth over time, ranking by citation, and statistical near-neighbor embedding maps by similarity in co-citation, and similarity in co-reference. Since April 11, we have enabled a novel functionality - self-navigated surf-search over the maps. At the site we also take courtesy input of COVID-19 articles that are missing from the current collection.
description: {"references": ["Semantic Scholar Open Research Corpus. 2019. Version 2019-11-01. Retrieved from http://s2-public-api-prod.us-west-2.elasticbeanstalk.com/corpus/download/. Accessed 2019-12-06.", "Elsevier Scopus Citation Overview API. Accessed 2020-03-25.", "COVID-19 Open Research Dataset (CORD-19). 2020. Version 2020-03-20. Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2020-03-26. 10.5281/zenodo.3727291", "Crossref REST API. Available at www.crossref.org. Accessed 2020-03-25."]}
DOI: 10.5281/zenodo.3870499
title: Excel data

description: excel data
DOI: 10.5281/zenodo.3870500
title: Excel data

description: excel data
DOI: 10.5281/zenodo.3871042
title: An Experimental Study of Operator Choices in the (1+(λ,λ)) Genetic Algorithm

description: This dataset contains experimental results that accompanies the same-named paper accepted to the MOTOR conference and scheduled to be published in a CCIS volume. The following data files are a part of this dataset, whose meaning should become clear after reading the paper: w1-runs.csv: the results of each of the runs, each of the configurations on the OneMax problem; w2-runs.csv: same for the LinInt<sub>2</sub> problem; w5-runs.csv: same for the LinInt<sub>5</sub> problem; ms-runs.csv: same for the easy random MAX-SAT instances; w1-stats.csv: the quartile statistics for the OneMax problem; w2-stats.csv: same for the LinInt<sub>2</sub> problem; w5-stats.csv: same for the LinInt<sub>5</sub> problem; ms-stats.csv: same for the easy random MAX-SAT instances; tunings-static.csv: the outcomes of irace for the static configurations (with fixed λ); tunings-dynamic.csv: the outcomes of irace for the dynamic configurations (with self-adjusting λ). Apart from that, the file pictures.pdf presents the contents of *-stats.csv files visually in a concide way.
DOI: 10.5281/zenodo.3871043
title: An Experimental Study of Operator Choices in the (1+(λ,λ)) Genetic Algorithm

description: This dataset contains experimental results that accompanies the same-named paper accepted to the MOTOR conference and scheduled to be published in a CCIS volume. The following data files are a part of this dataset, whose meaning should become clear after reading the paper: w1-runs.csv: the results of each of the runs, each of the configurations on the OneMax problem; w2-runs.csv: same for the LinInt<sub>2</sub> problem; w5-runs.csv: same for the LinInt<sub>5</sub> problem; ms-runs.csv: same for the easy random MAX-SAT instances; w1-stats.csv: the quartile statistics for the OneMax problem; w2-stats.csv: same for the LinInt<sub>2</sub> problem; w5-stats.csv: same for the LinInt<sub>5</sub> problem; ms-stats.csv: same for the easy random MAX-SAT instances; tunings-static.csv: the outcomes of irace for the static configurations (with fixed λ); tunings-dynamic.csv: the outcomes of irace for the dynamic configurations (with self-adjusting λ). Apart from that, the file pictures.pdf presents the contents of *-stats.csv files visually in a concide way.
DOI: 10.14457/mu.res.2016.36
title: Synthesis of cyclic polyesters and copolymers using ligated metal complexes containing alkoxy side chain for drug delivery systems
DOI: 10.15161/oar.it/23699
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.14457/mu.res.1974.1
title: A system-wide analysis of transcriptional regulation in hybrid plants in response to temperature change
DOI: 10.14457/mu.res.2003.40
title: Interaction of prostaglandins with rat renal organic anion transporter 1
DOI: 10.14457/mu.res.2016.37
title: Insights into the structure and function of the binary toxin: a mosquito control agent produced from Lysinibacillus sphaericus
DOI: 10.14457/mu.res.2016.38
title: Investigation of changes in conformation and oligomeric state of the nucleotide excision repair proteins during repair reaction
DOI: 10.14457/mu.res.2016.39
title: Identification of genes causing hereditary cardiomyopathies in Thai population
DOI: 10.14457/mu.res.2017.45
title: Regulation of gene expression and functional analyses of chloroplastlocalized chaperonin 60 under environmental stresses in the model unicellular green alga Chlamydomonas reinhardtii
DOI: 10.17632/jxyznh57kz.1
title: Data for: Relative entropy and minimum-variance pricing kernel in asset pricing model evaluation

description: Using all stocks listed in the Australian Securities Exchange and macroeconomic data for Australia, the dataset comprises the following series: 1. Monthly returns for 20 size-price to cash flow portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database) 2. Monthly returns for 25 size-book to market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database) 3. Monthly returns for 41 industry portfolios. (Raw data source: Datastream database) 4. Private final consumption expenditure, in national currency and constant prices, non-seasonally adjusted, for Australia. (Raw data source: OECD) 5. Fama and French (1993) factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database) 6. Fama and French (2015) factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology. (Raw data source: Datastream database) 7. Three-month interest rate of the Treasury Bill for Australia. (Raw data source: OECD) We have produced all return series using the following data from Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) price-to-cash flow ratio (PC series), (v) primary SIC codes, and (vi) tax rate (WC08346 series). We use the rules suggested by Griffin, Kelly, & Nardari (2010) for excluding non-common equity securities from Datastream data. REFERENCES: Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277.
DOI: 10.17632/jg88fmnnvh.1
title: Data for: 3614069

description: Demographic data used as an input of the modified leslie model was obtained from a city in JangSu province, China. Demographic data consist of three parts, annual fertility rate of different age groups, annual mortality rate of different age groups and the basic population of different age groups.
DOI: 10.17632/p9sx97fpg6.2
title: Economic assessment and community management of Prosopis juliflora invasion in Swemeh village- Jordan

description: This data set provides information about a study conducted in Swemeh -Jordan to assess the economic impacts of a Prosopis juliflora invasion and gauge community preferences for management plans from 203 households. The data set includes information about the benefits and costs gained from Prosopis juliflora invasion, besides to the households' preferences between two policy responses: P. juliflora management or complete eradication.
DOI: 10.17632/r844v3dzt3.1
title: Dataset of Digital Business from 1978 to 2019

description: The digital business research & publication dataset, which was indexed by Scopus from 1978 to 2019. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, Correspondence Address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.
DOI: 10.17632/zs5h47xsxc.1
title: Metabogenomics reveals four candidate regions involved in the pathophysiology of Equine Metabolic Syndrome.
DOI: 10.21227/ztd3-7815
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about "corona", "covid-19", "coronavirus" or the variants of "sars-cov-2". Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.Note: I started sharing the IDs of the tweets that contained the exact location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs (above 140+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-april29.* will contain tweet ID and sentiment data of the tweets that were tweeted between April 28, 2020, and April 29, 2020.Why are only tweet IDs being shared? It is Twitter's content redistribution policy that restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers to always pull fresh data using their API. Why? Here's my opinion. Maybe, some user might want to delete a particular tweet after a couple of minute(s)/hour(s)/day(s), and if the same tweet has already been pulled and is shared on a public domain, then it might make the user/community vulnerable to many inferences coming out of the shared data.
DOI: 10.17632/4htdxdzvzw.1
title: Data for: The Role of Capital Structure on Firm’s Profitability of Listed Cement Sector in Pakistan Stock Exchange

description: This data is collect from the Pakistan listed stock of Cement sectors