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17 results for “Publication trends”
TRENDS AND PATTERNS OF RESEARCH PUBLICATIONS AT KYAMBOGO UNIVERSITY BETWEEN 2003 TO 2020
<p>Data for the study was sourced from: Google Scholar, Emerald, Ebscohost, Taylor and Francis and Kyambogo University Scholars’ Space. This took place between June 2019 and June 2020. Data sourced was based on two specific criteria: having acknowledged to being a staff member of the University and having published within the period 2003 to 2020.</p>
Model agreement and trend analysis data associated to the publication: "Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050"
<p>This dataset is associated with the following publication:</p> <p>Haslebacher, C., Demory, M.-E., Demory, B.-O., Sarazin, M., and Vidale, P. L., “Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions”, <em>Astronomy and Astrophysics</em>, vol. 665, 2022. doi:10.1051/0004-6361/202142493.</p> <p>In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with:</p> <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> <p>Pickle files ending with '_d_obs_ERA5.pkl' contain in situ data and ERA5 data. Pickle files ending with 'd_model.pkl' contain PRIMAVERA model data. A few explanations:<br> - 'ds_sel': contains monthly timeseries of selected intersecting data<br> - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10)<br> - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10)<br> - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10) </p> <p>The subfolder 'median_nc_u_v_t' contains NETCDF files with the median and interquartile range of the wind speed in u and v direction, the temperature and geopotential height. This was used for Figs. G1-G8 and to calculate the refractive index structure constant Cn2.</p> <p>The subfolder 'skill_score_classification' contains csv files with the sorted skill score classifications. The column headers are: model_name, skill score, correlation coefficient, standard deviation, centred root mean square error.</p> <p>The folder 'trend_analysis' contains for each variable csv files of ERA5 and PRIMAVERA monthly time series used for trend analysis, pdf files of analysis summaries, csv files of Bayesian analysis results and png files of longitude-latitude maps of trends (analysed with linear regression). Additionally, there is a csv file of averaged in situ pressures.</p> <p>Code that generated and used this data is available on github: <a href="https://github.com/CarolineHaslebacher/Astroclimate-future-project">https://github.com/CarolineHaslebacher/Astroclimate-future-project</a> </p> <p> </p>
Data for Analyses presented in associated publication: Investigating trends in those who experience menstrual bleeding changes after SARS CoV-2 vaccination
<p>This is the data used for analyses presented in our associated publication (details to be provided later). There are three datasets, because we analyzed by specific, biologically relevant subgroups (pre-menopausal people with predictable menstrual cycles, pre-menopausal people who usually do not menstruate, and post-menopausal people). The data provided here has been cleaned and aggregated as described in the associated publication. Specifically,</p> <ul> <li>here we have only included the variables that are included in our analyses</li> <li>we collapsed specific identities (gender, race, ethnicity) into binarized categories (as used in the publication) in order to reduce the likelihood someone could be re-identified from this de-identified data</li> <li>this dataset also does not include unique identifiers for individuals, but instead has row numbers for each dataset (starting at 1 for each data subset). There are no repeated individuals between datasets, as these are mutually exclusive subgroups</li> <li>Additional information regarding the survey and data processes can be found at https://osf.io/6rvxk/?view_only=f91f1247658f49e3bbf59b2f6cfd3898</li> </ul>
Resources for publication "Meteorologically normalised long-term trends of atmospheric ammonia (NH3) in Switzerland/Liechtenstein and the explanatory role of gas-aerosol partitioning"
<p>Resources for publication "Meteorologically normalised long-term trends of atmospheric ammonia (NH3) in Switzerland/Liechtenstein and the explanatory role of gas-aerosol partitioning".</p>
Data mining tool to discover DevOps trends from public repositories: Predicting Release Candidates with gthbmining.rc
<p>Public repositories have been performing an essential role in bring- ing software and services to technical communities and general users. Most of the cases, public repositories have a DevOps tool, with a live and historical database behind it, to support delivering and all steps this software or service should adopt before going to production. This paper introduces gthbmining, a data mining set of tools to discover DevOps trends from public repositories, and presents the module gthbmining.rc. Considering the premise of a GitHub public repository, the main contribution here is pre- dicting release candidates, an important label a software release has. The methodology, architecture, components and interfaces are explained, as well as potential users. The results show a reliable and flexible tool, as classifiers metrics and graphics are provided, along with the possibility to add new data mining algorithms in the open source module presented. Related works are also supplied, and a conclusion shows the outcomes gthbmining.rc can provide.</p>
Learning Analytics for Personalized Learning Environments: Visualizing Journal Publication Trends
<p>Data collected from Scopus database for the "Learning Analytics for Personalized Learning Environments: Visualizing Journal Publication Trends" entitled research.</p>
Artifacts for the 2023 Trends in Functional Programming Publication: Versatile and Flexible Modelling of the RISC-V Instruction Set Architecture
<p>This dataset contains the artifacts for the performance evaluation conducted in the publication <em>Versatile and Flexible Modelling of the RISC-V Instruction Set Architecture</em> which will be published in the proceedings of the 2023 <em>Trends in Functional Programming</em> conference. The provided artifacts contain a <a href="https://docker.io">Docker</a> container for executing <a href="https://embench.org">Embench</a> benchmarks with LibRISCV, RISC-V VP, Grift and Forvis. A pre-built version of the container is included.</p>
Publication Database on Recent Trends and Perspectives of Collaborative Robotics in Work 4.0
<p>This publication database is part of a literature review project aimed at providing a comprehensive and up-to-date (31st of January 2022) overview of recent trends and perspectives of robotics in the working world 4.0. The related journal article can be found here: DOI (https://doi.org/10.3390/robotics12030084). The database contains bibliographic information of all the publications that were included in the literature review.</p> <p> </p> <p>This publication database (including data and complementary documents) is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) by INSTITUTE OF MECHANISM THEORY, MACHINE DYNAMICS AND ROBOTICS - RWTH AACHEN UNIVERSITY.</p>
Datasets used in the publication "Trends in Winter Light Environment Over the Arctic Ocean..."
<p>These are datasets accompanying the publication DOI:10.1029/2020GL089037.</p> <p>For each year and for every pixel in a daily time series of OC-CCI v3.1 Chlorophyll at 4 km resolution, the first day (Ts) (in Day of Year) when a valid chlorophyll value appears in the data set is identified. A valid pixel implies that the satellite sensor has detected open water with clear skies. Te denotes the last day in the year when there is a valid chlorophyll value in the data set. For each year, the open‐water duration for the pixel is computed as D = Te − Ts.</p> <p>The file <strong>Ts_Te_D_statistics_OC-CCI.nc</strong> contains all data needed to generate figures 1-2, and panels A-C in figure 3.</p> <p>The file <strong>Ts_Te_D_statistics_SeaIce-CCI.n</strong>c contains all data needed to generate panels D-F in figure 3.</p> <p>The file <strong>PAR_Chl_first_last_day.nc</strong> contains all data needed to generate figure 4</p>
Dataset for: Faculty Publication Trends in a Japanese National University: A Diachronic Document Analysis
<p>Dataset for paper titled: Faculty Publication Trends in a Japanese National University: A Diachronic Document Analysis</p>
The trend of morbidity and mortality of Coronavirus disease 2019 under the first-level public health emergency response in China
<p>This is our data for the article titled "The trend of morbidity and mortality of Coronavirus disease 2019 under the first-level public health emergency response in China". All the data were publically available. In detail, data in China were collected from the daily epidemic reports released by Chinese national and provincial health commissions (http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml.). Outside China, data were collected from daily situation reports by World Health Organization including cumulative confirmed and death cases (https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/.).</p>
Data of the ER 2020 Publication: Past Trends and Future Prospects in Conceptual Modeling - A Bibliometric Analysis
<p>Related Publication:</p> <p>Härer, Felix, Fill, Hans-Georg (2020): Past Trends and Future Prospects in Conceptual Modeling - A Bibliometric Analysis. Accepted for: 39th International Conference on Conceptual Modeling, ER 2020.</p> <p>Contents:</p> <ul> <li>The directory <em>Descriptive Analysis</em> contains all publications of the analysis database used for the descriptive analysis.</li> <li>The directory <em>Bibliometric Analysis</em> contains the NLP and analysis processes for RapidMiner with stopwords and synonyms.</li> </ul>
Industrial relations in central public administration: recent trends and features
<p>This report maps current developments in industrial relations systems in the central public administration secrtor across the Member States, covering all EU Member States except Croatia, which joined the EU in mid-2013. The report explores to what extent there is formal industrial relations processes within the sector, including the organisation of unions and employer representatives, and the role of collective bargaining and consultation. It also examines the specific features of the sector in terms of both industrial relations processes and worker outcomes and charts reforms that have been carried out since 2008: changes in collective bargaining and in the balance of negotiation versus unilateral imposition of change by the employer, and the responses of trade unions. Finally, the report addresses the role of the social partners regarding the outcomes for employees of the recent austerity measures on issues such as wages, job security, working time and pensions.</p> <p>[Google](http://www.google.com)<br /> <br /> http://www.eurofound.europa.eu/eiro/studies/tn1307019s/index.htm </p>
Supplementary material 2 from: Reyes-Puig C, Mancero E (2022) Beyond the species name: an analysis of publication trends and biases in taxonomic descriptions of rainfrogs (Amphibia, Strabomantidae, Pristimantis). ZooKeys 1134: 73-100. https://doi.org/10.3897/zookeys.1134.91348
Complete translation of the manuscript
Supplementary material 1 from: Reyes-Puig C, Mancero E (2022) Beyond the species name: an analysis of publication trends and biases in taxonomic descriptions of rainfrogs (Amphibia, Strabomantidae, Pristimantis). ZooKeys 1134: 73-100. https://doi.org/10.3897/zookeys.1134.91348
Complete database of Pristimantis descriptions
Data mining tool to discover DevOps trends from public repositories: Predicting Release Candidates with gthbmining.rc
<p>Public repositories have been performing an essential role in bring- ing software and services to technical communities and general users. Most of the cases, public repositories have a DevOps tool, with a live and historical database behind it, to support delivering and all steps this software or service should adopt before going to production. This paper introduces gthbmining, a data mining set of tools to discover DevOps trends from public repositories, and presents the module gthbmining.rc. Considering the premise of a GitHub public repository, the main contribution here is pre- dicting release candidates, an important label a software release has. The methodology, architecture, components and interfaces are explained, as well as potential users. The results show a reliable and flexible tool, as classifiers metrics and graphics are provided, along with the possibility to add new data mining algorithms in the open source module presented. Related works are also supplied, and a conclusion shows the outcomes gthbmining.rc can provide.</p>
Data for publication "CMIP6 trends of oceanic variables (2015-2100)"
<p>These netCDF files are made available as part of the paper: G. Ibarra-Berastegui, J. Sáenz, A. Ulazia, S. J. González-Rojí & G. Esnaola (2024) "CMIP6 trends of oceanic variables (2015-2100)", submitted to Ocean Engineering. The dataset holds the trends of several variables needed to reproduce the results included in the manuscript.</p> <p>The files are named following the same structure: <em>${Variable}</em>_trend_<em>${SSP}</em>_<em>${Season}</em>perz.nc</p> <ul> <li>Variable: This dataset includes the trends (in % with respect to current-day values) of four variables for the period 2015-2100: wave energy flux (WEF), wind speed (U), mean wave period (mwp; Tm in the paper) and significant wave height (swh; Hs in the paper). *</li> <li>SSP: The analysis is performed for two Shared Socioeconomic Pathways (SSP): the lowest and highest GHG emission climate scenarios, SSP 1-2.6 and SSP 5-8.5 respectively.</li> <li>Season: The trend analysis is performed for the entire year (YE) or for each season: spring (SP), summer (SU), autumn (AU) or winter (WI). </li> </ul> <p>* Note that, as explained in the manuscript, the current-day oceanic values have been obtained from ERA5 global averages corresponding to the 1985-2014 period. The ERA5 data can be downloaded from the official website of the ECMWF. </p>
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