cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.4272620
title: RVF Outbreak Data

description: Surveillance data from the ProMED-mail electronic surveillance system for reported RVF outbreaks between 1998 and 2016.<br>
DOI: 10.6084/m9.figshare.4272620.v1
title: RVF Outbreak Data

description: Surveillance data from the ProMED-mail electronic surveillance system for reported RVF outbreaks between 1998 and 2016.<br>
DOI: 10.6084/m9.figshare.4272620.v2
title: RVF Outbreak Data

description: Surveillance data from the ProMED-mail electronic surveillance system for reported RVF outbreaks between 1998 and 2016.<br>
DOI: 10.6084/m9.figshare.4272506
title: Tissue Classification Data

description: This data is part of the DIPY (Diffusion Imaging in Python) Tissue Classification project. It is a T1-weighted MRI image used as an example file to perform tissue classification and separate between tissue types. The T1-weighted image comes in a raw and a denoised version. It also contains an Anisotropic Power map from the same subject for the purpose of also testing segmentation algorithms on it. Total of three nifti images.
DOI: 10.6084/m9.figshare.4272446
title: Kudos dataset

description: The Kudos dataset (extracted from Kudos in February 2016) is analysed in the research article with the title "Analysing researchers' outreach efforts and the association with publication metrics: A case study of Kudos". This research paper is a result of a joint research collaboration between Kudos and CHESS, Nanyang Technological University, Singapore. Kudos made funds available to CHESS to perform the study and also provided the dataset used for the analysis.<br>In recent years, social media and scholarly collaboration networks have become increasingly accepted as effective tools for discovering and sharing research. Altmetrics are also becoming more common, as they reflect impact fast, are openly accessible and represent both academic and lay audiences, unlike traditional metrics such as citation counts. As a researcher, it still remains challenging to know whether the efforts to increase the visibility and outreach of your research on social media are associated with improved publication metrics.<br>In this paper, we analyse the effectiveness of common online channels used for sharing publications using Kudos (https://www.growkudos.com, launched in May 2014), a web-based service that aims to help researchers increase the outreach of their publications, as a case study. We extracted a dataset from Kudos of 20,775 unique publications that had been claimed by authors, and for which actions had been taken to explain or share via Kudos. For 4,867 of these, full text download data from publishers was available. Our findings show that researchers are most likely to share their work on Facebook, but links shared on Twitter are most likely to be clicked on. A Mann-Whitney U test revealed that a treatment group (publications having actions in Kudos) had a significantly higher median average of 149 full text downloads (23.1% more) per publication as compared to a control group (having no actions in Kudos) with a median average of 121 full text downloads per publication. These findings suggest that performing actions on publications, such as sharing, explaining, or enriching, could help to increase the number of full text downloads of a publication.<br>The DOIs of the publications in the dataset have been anonymised to protect the privacy of the users in Kudos. <br>A readme text file is provided describing the data fields of the four datasets.<br>All fields in the CSV file should be imported (e.g., into Excel) as text values.<br><br>
DOI: 10.6084/m9.figshare.4272443
title: MatLab Scripts for the EDM Test Bearings

description: MatLab Scripts to view data and produce journal figures
DOI: 10.6084/m9.figshare.4272446.v1
title: Kudos dataset

description: The Kudos dataset (extracted from Kudos in February 2016) is analysed in the research article with the title "Analysing researchers' outreach efforts and the impact on publication metrics: A case study of Kudos". This research paper is a result of a joint research collaboration between Kudos and CHESS, Nanyang Technological University, Singapore. Kudos made funds available to CHESS to perform the study and also provided the dataset used for the analysis.<br>In recent years, social media and scholarly collaboration networks have become increasingly accepted as effective tools for discovering and sharing research. Altmetrics are also becoming more common, as they reflect impact fast, are openly accessible and represent both academic and lay audiences, unlike traditional metrics such as citation counts. As a researcher, it still remains challenging to know whether the efforts to increase the visibility of your research on social media lead to improved publication metrics.<br>In this paper, we analyse the effectiveness of common online channels used for sharing publications using Kudos (https://www.growkudos.com, launched in May 2014), a web-based service that aims to help researchers increase the impact of their publications, as a case study. We extracted a dataset from Kudos of 20,775 unique publications that had been claimed by authors, and for which actions had been taken to explain or share via Kudos. For 4,867 of these, full text download data from publishers was available. Our findings show that researchers are most likely to share their work on Facebook, but links shared on LinkedIn are most likely to be clicked on. A Mann-Whitney U test revealed that a treatment group (publications having actions in Kudos) had a significantly higher median average of 149 full text downloads (23.1% more) per publication as compared to a control group (having no actions in Kudos) with a median average of 121 full text downloads per publication. These findings suggest that performing actions on publications, such as sharing, explaining, or enriching, could help to improve the impact of a publication.<br>The DOIs of the publications in the dataset have been anonymised to protect the privacy of the users in Kudos. <br>A readme text file is provided describing the data fields of the four datasets.<br>All fields in the CSV file should be imported (e.g., into Excel) as text values.<br><br>
DOI: 10.6084/m9.figshare.4272446.v3
title: Kudos dataset

description: The Kudos dataset (extracted from Kudos in February 2016) is analysed in the research article with the title "Analysing researchers' outreach efforts and the association with publication metrics: A case study of Kudos". This research paper is a result of a joint research collaboration between Kudos and CHESS, Nanyang Technological University, Singapore. Kudos made funds available to CHESS to perform the study and also provided the dataset used for the analysis.<br>In recent years, social media and scholarly collaboration networks have become increasingly accepted as effective tools for discovering and sharing research. Altmetrics are also becoming more common, as they reflect impact fast, are openly accessible and represent both academic and lay audiences, unlike traditional metrics such as citation counts. As a researcher, it still remains challenging to know whether the efforts to increase the visibility and outreach of your research on social media are associated with improved publication metrics.<br>In this paper, we analyse the effectiveness of common online channels used for sharing publications using Kudos (https://www.growkudos.com, launched in May 2014), a web-based service that aims to help researchers increase the outreach of their publications, as a case study. We extracted a dataset from Kudos of 20,775 unique publications that had been claimed by authors, and for which actions had been taken to explain or share via Kudos. For 4,867 of these, full text download data from publishers was available. Our findings show that researchers are most likely to share their work on Facebook, but links shared on Twitter are most likely to be clicked on. A Mann-Whitney U test revealed that a treatment group (publications having actions in Kudos) had a significantly higher median average of 149 full text downloads (23.1% more) per publication as compared to a control group (having no actions in Kudos) with a median average of 121 full text downloads per publication. These findings suggest that performing actions on publications, such as sharing, explaining, or enriching, could help to increase the number of full text downloads of a publication.<br>The DOIs of the publications in the dataset have been anonymised to protect the privacy of the users in Kudos. <br>A readme text file is provided describing the data fields of the four datasets.<br>All fields in the CSV file should be imported (e.g., into Excel) as text values.<br><br>
DOI: 10.6084/m9.figshare.4272446.v2
title: Kudos dataset

description: The Kudos dataset (extracted from Kudos in February 2016) is analysed in the research article with the title "Analysing researchers' outreach efforts and the impact on publication metrics: A case study of Kudos". This research paper is a result of a joint research collaboration between Kudos and CHESS, Nanyang Technological University, Singapore. Kudos made funds available to CHESS to perform the study and also provided the dataset used for the analysis.<br>In recent years, social media and scholarly collaboration networks have become increasingly accepted as effective tools for discovering and sharing research. Altmetrics are also becoming more common, as they reflect impact fast, are openly accessible and represent both academic and lay audiences, unlike traditional metrics such as citation counts. As a researcher, it still remains challenging to know whether the efforts to increase the visibility of your research on social media lead to improved publication metrics.<br>In this paper, we analyse the effectiveness of common online channels used for sharing publications using Kudos (https://www.growkudos.com, launched in May 2014), a web-based service that aims to help researchers increase the impact of their publications, as a case study. We extracted a dataset from Kudos of 20,775 unique publications that had been claimed by authors, and for which actions had been taken to explain or share via Kudos. For 4,867 of these, full text download data from publishers was available. Our findings show that researchers are most likely to share their work on Facebook, but links shared on LinkedIn are most likely to be clicked on. A Mann-Whitney U test revealed that a treatment group (publications having actions in Kudos) had a significantly higher median average of 149 full text downloads (23.1% more) per publication as compared to a control group (having no actions in Kudos) with a median average of 121 full text downloads per publication. These findings suggest that performing actions on publications, such as sharing, explaining, or enriching, could help to improve the impact of a publication.<br>The DOIs of the publications in the dataset have been anonymised to protect the privacy of the users in Kudos. <br>A readme text file is provided describing the data fields of the four datasets.<br>All fields in the CSV file should be imported (e.g., into Excel) as text values.<br><br>
DOI: 10.6084/m9.figshare.4272440
title: 90Deg EDM Bearing Experimental Test Data

description: Measured vibration of a defective bearing with an EDM defect on the outer raceway of the bearing. Entry and exit slope of 90 degrees and 9.7mm long with an appiled load of 2.5-10kN and a shaft speed of 5-12.5Hz.
DOI: 10.6084/m9.figshare.4272437
title: OSN citations.paj

description: This is a citation network represented in Pajek format. The network consists of 2,291 nodes which are linked with citations. Each node is a research article in the online social network research field. The label for each article begins with the last name of the 1st author followed by the 1st letters of the co-authors’ last name and ended with the publishing year of the article. For each citation link, the arrow directs from the cited article to the citing article.<br><br>
DOI: 10.6084/m9.figshare.4272437.v1
title: OSN citations.paj

description: This is a citation network represented in Pajek format. The network consists of 2,291 nodes which are linked with citations. Each node is a research article in the online social network research field. The label for each article begins with the last name of the 1st author followed by the 1st letters of the co-authors’ last name and ended with the publishing year of the article. For each citation link, the arrow directs from the cited article to the citing article.<br><br>
DOI: 10.6084/m9.figshare.4272440.v1
title: 90Deg EDM Bearing Experimental Test Data

description: Measured vibration of a defective bearing with an EDM defect on the outer raceway of the bearing. Entry and exit slope of 90 degrees and 9.7mm long with an appiled load of 2.5-10kN and a shaft speed of 5-12.5Hz.
DOI: 10.6084/m9.figshare.4272392
title: International Journal of Systematic and Evolutionary Microbiology (IJSEM) phenotypic database

description: The International Journal of Systematic and Evolutionary Microbiology (IJSEM) is the official publication of the International Committee on Systematics of Prokaryotes and the Bacteriology and Applied Microbiology Division of the International Union of Microbiological Societies, and the official journal of record for novel bacterial and archaeal taxa (http://ijs.microbiologyresearch.org/content/journal/ijsem). We manually searched IJSEM articles to extract phenotypic, metabolic and environmental tolerance data of bacterial strains from 2004 to 2014.<br><br> We focused on the most recent entries as they presumably used standardized and state-of-the-art methods, up-to-date taxonomic nomenclature, most strains had easily retrievable 16S rRNA gene sequence data, and many strains also had publicly available genome sequence data available. Data was manually collected using Google Forms as variable structure of the articles and inconsistent reporting of relevant information precluded the use of automatic text parsing algorithms. <br>
DOI: 10.6084/m9.figshare.4272356
title: 45Deg EDM Bearing Experimental Test Data

description: Measured vibration of a defective bearing with an EDM defect on the outer raceway of the bearing. Entry and exit slope of 45 degrees and 9.7mm long with an appiled load of 2.5-10kN and a shaft speed of 5-12.5Hz.
DOI: 10.6084/m9.figshare.4272353
title: 30Deg EDM Bearing Experimental Test Data

description: Measured vibration of a defective bearing with an EDM defect on the outer raceway of the bearing. Entry and exit slope of 30 degrees and 9.7mm long with an appiled load of 2.5-10kN and a shaft speed of 5-12.5Hz.
DOI: 10.6084/m9.figshare.4272260
title: Laboratory submissions for PRRS 2007-2014 in Denmark

description: Weekly number of swine herds tested in Denmark for PRRS from January 2007 to December 2014.
DOI: 10.6084/m9.figshare.4272260.v1
title: Laboratory submissions for PRRS 2007-2014 in Denmark

description: Weekly number of swine herds tested in Denmark for PRRS from January 2007 to December 2014.
DOI: 10.6084/m9.figshare.4272353.v2
title: 30Deg EDM Bearing Experimental Test Data

description: Measured vibration of a defective bearing with an EDM defect on the outer raceway of the bearing. Entry and exit slope of 30 degrees and 9.7mm long with an appiled load of 2.5-10kN and a shaft speed of 5-12.5Hz.
DOI: 10.6084/m9.figshare.4272353.v1
title: 20Deg EDM Bearing Experimental Test Data

description: Measured vibration of a defective bearing with an EDM defect on the outer raceway of the bearing. Entry and exit slope of 30 degrees and 9.7mm long with an appiled load of 2.5-10kN and a shaft speed of 5-12.5Hz.
DOI: 10.6084/m9.figshare.4272203
title: Neural network predictions of segregation on AuPd(111) surfaces

description: Supporting information for the manuscript "Neural network predictions of segregation on AuPd(111) surfaces." This includes the supporting information file as submitted for publication along with figures and data discussed in that manuscript.
DOI: 10.6084/m9.figshare.4272200
title: Flegl, M. and Andrade, L. 2016. Rio 2016 - Olympic Sport Economic Data

description: This dataset includes important economic, demographic and sport data related to Summer Olympic games in Rio de Janeiro 2016. Dataset includes variables such as: GDP, GDP per capita, Inflation, Population total, Population 15-64, Economic Active Population, Corruption Perception Index, Medal rankings, and World Bank's country classification by income. Dataset can be used for any Rio 2016 Olympic games related analysis and any classical economic models.
DOI: 10.6084/m9.figshare.4272200.v1
title: FleglAndrade_Rio2016_OlympicSportEconomicData.xlsx

description: This dataset includes important economic, demographic and sport data related to Summer Olympic games in Rio de Janeiro 2016. Dataset includes variables such as: GDP, GDP per capita, Inflation, Population total, Population 15-64, Economic Active Population, Corruption Perception Index, Medal rankings, and World Bank's country classification by income. Dataset can be used for any Rio 2016 Olympic games related analysis and any classical economic models.
DOI: 10.6084/m9.figshare.4272203.v1
title: Neural network predictions of segregation on AuPd(111) surfaces

description: Supporting information for the manuscript "Neural network predictions of segregation on AuPd(111) surfaces." This includes the supporting information file as submitted for publication along with figures and data discussed in that manuscript.
DOI: 10.6084/m9.figshare.4272200.v2
title: FleglAndrade_Rio2016_OlympicSportEconomicData.xlsx

description: This dataset includes important economic, demographic and sport data related to Summer Olympic games in Rio de Janeiro 2016. Dataset includes variables such as: GDP, GDP per capita, Inflation, Population total, Population 15-64, Economic Active Population, Corruption Perception Index, Medal rankings, and World Bank's country classification by income. Dataset can be used for any Rio 2016 Olympic games related analysis and any classical economic models.
DOI: 10.6084/m9.figshare.4272068
title: Response Inhibition of handball experts

description: Response inhibition of handball experts (<i>n</i> = 30) and recreational athletes (<i>n</i> = 30) with feet and hands. Shorter SSRTs indicate higher inhibitory control. Each datapoint respresents the SSRT of one participant.<br> Figure available at http://bit.ly/ssrt_hb under CC license https://creativecommons.org/licenses/by/4.0/. <br>
DOI: 10.6084/m9.figshare.4270202
title: Online Appendix S2 from Medeiros et al. 2017: Seaweed tissue chemistry data

description: Raw ICP-OES data from Medeiros et al. 2017 (published in <i>Rhodora</i>).<br><br>
DOI: 10.6084/m9.figshare.4270247
title: Online Appendix S3 from Medeiros et al. 2017: Callahan Mine floristic data

description: Historical and contemporary seaweed floristic data for Callahan Mine area. Contemporary records mainly generated by Arthur C. Mathieson.
DOI: 10.6084/m9.figshare.4270202.v1
title: Online Appendix S2 from Medeiros et al. 2017: Seaweed tissue chemistry data

description: Raw ICP-OES data from Medeiros et al. 2017 (published in <i>Rhodora</i>).<br><br>
DOI: 10.6084/m9.figshare.4270247.v1
title: Online Appendix S3 from Medeiros et al. 2017: Callahan Mine floristic data

description: Historical and contemporary seaweed floristic data for Callahan Mine area. Contemporary records mainly generated by Arthur C. Mathieson.
DOI: 10.6084/m9.figshare.4270178
title: Physical activity levels determine exercise-induced changes in brain excitability

description: Physical activity levels determine exercise-induced changes in brain excitability
DOI: 10.6084/m9.figshare.4270151
title: S3 Table. The numerical value of the 89 quantitative image features extracted from RepeatCT.xlsx

description: S3 Table. The numerical value of the 89 quantitative image features extracted from RepeatCT
DOI: 10.6084/m9.figshare.4270151.v1
title: S3 Table. The numerical value of the 89 quantitative image features extracted from RepeatCT.xlsx

description: S3 Table. The numerical value of the 89 quantitative image features extracted from RepeatCT
DOI: 10.6084/m9.figshare.4270178.v1
title: group_averages_all_data.xlsx

description: Physical activity levels determine exercise-induced changes in brain excitability
DOI: 10.6084/m9.figshare.4270178.v2
title: Physical activity levels determine exercise-induced changes in brain excitability

description: Physical activity levels determine exercise-induced changes in brain excitability
DOI: 10.6084/m9.figshare.4270136
title: Sea surface salinity Hovmöller diagram

description: Sea surface salinity Hovmöller diagram generated from a FOAM reanalysis (http://www.metoffice.gov.uk/research/models/ocean-models)<br>
DOI: 10.6084/m9.figshare.4270112
title: PowerPoint Slides for: Distinct in vitro Complement Activation by Various Intravenous Iron Preparations

description: <b><i>Background:</i></b> Intravenous (IV) iron preparations are widely used in the treatment of anemia in patients undergoing hemodialysis (HD). All IV iron preparations carry a risk of causing hypersensitivity reactions. However, the pathophysiological mechanism is poorly understood. We hypothesize that a relevant number of these reactions are mediated by complement activation, resulting in a pseudo-anaphylactic clinical picture known as complement activation-related pseudo allergy (CARPA). <b><i>Methods:</i></b> First, the in-vitro complement-activating capacity was determined for 5 commonly used IV iron preparations using functional complement assays for the 3 pathways. Additionally, the preparations were tested in an ex-vivo model using the whole blood of healthy volunteers and HD patients. Lastly, in-vivo complement activation was tested for one preparation in HD patients. <b><i>Results:</i></b> In the in-vitro assays, iron dextran, and ferric carboxymaltose caused complement activation, which was only possible under alternative pathway conditions. Iron sucrose may interact with complement proteins, but did not activate complement in-vitro. In the ex-vivo assay, iron dextran significantly induced complement activation in the blood of healthy volunteers and HD patients. Furthermore, in the ex-vivo assay, ferric carboxymaltose and iron sucrose only caused significant complement activation in the blood of HD patients. No in-vitro or ex-vivo complement activation was found for ferumoxytol and iron isomaltoside. IV iron therapy with ferric carboxymaltose in HD patients did not lead to significant in-vivo complement activation. <b><i>Conclusion:</i></b> This study provides evidence that iron dextran and ferric carboxymaltose have complement-activating capacities in-vitro, and hypersensitivity reactions to these drugs could be CARPA-mediated.