Last Version: 4

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2022/12/15

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v4.xlsx: full list of 140 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v4.csv: full list of 140 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 4th version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Journal Citation Reports (JCR) and/or Scimago Journal and Country Rank (SJR), Scopus and Web of Science (WOS), Journal Master List.

Version: 3

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2022/10/28

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v3.xlsx: full list of 124 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_3.csv: full list of 124 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 3rd version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Journal Citation Reports (JCR) and/or Scimago Journal and Country Rank (SJR).

Erratum - Data articles in journals Version 3:

Botanical Studies -- ISSN 1999-3110 -- JCR (JIF) Q2
Data -- ISSN 2306-5729 -- JCR (JIF) n/a
Data in Brief -- ISSN 2352-3409 -- JCR (JIF) n/a

Version: 2

Author: Francisco Rubio, Universitat Politècnia de València.

Date of data collection: 2020/06/23

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v2.xlsx: full list of 56 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v2.csv: full list of 56 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 2nd version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Scimago Journal and Country Rank (SJR)

Total size: 32 KB

Version 1: Description

This dataset contains a list of journals that publish data articles, code, software articles and database articles.

The search strategy in DOAJ and Ulrichsweb was the search for the word data in the title of the journals.
Acknowledgements:
Xaquín Lores Torres for his invaluable help in preparing this dataset.

BackgroundPrevious studies have shown that the frailty index based on laboratory tests (FI-Lab) can identify older adults at increased risk of adverse health outcomes. This study aimed to determine whether the FI-Lab is associated with mortality risk and can provide incremental improvements in risk stratification of patients with critical acute myocardial infarction (AMI).Materials and methodsWe conducted a secondary analysis of data from the Medical Information Mart for Intensive Care (MIMIC)-IV database. A 33-item FI-Lab was constructed. Outcomes of interest were in-hospital and 1-year mortality. Logistic regression models were used to investigate the association between the FI-Lab and outcomes. For the assessment of the incremental predictive value, the FI-Lab was added to several risk stratification scoring systems for critically ill patients, and the following indices were calculated: Δ C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI).ResultsOut of 2,159 patients, 477 died in hospital (22.1%), and 898 died during the 1-year follow-up period. After adjustment for confounders, the FI-Lab was associated with increased in-hospital mortality [odds ratio (OR) = 1.06, 95% confidence interval (CI): 1.05–1.07] and 1-year mortality (OR = 1.05, 95% CI: 1.04–1.06) when assessed as a continuous variable (per 0.01-score increase). When assessed as a categorical variable, the FI-Lab was associated with in-hospital mortality (2nd Quartile: OR = 1.89, 95% CI: 1.18–3.03; 3rd Quartile: OR = 3.46, 95% CI: 2.20–5.46; and 4th Quartile: OR = 5.79, 95% CI: 3.61–9.28 compared to 1st Quartile) as well as 1-year mortality (2nd Quartile: OR = 1.66, 95% CI: 1.23–2.24; 3rd Quartile: OR = 2.40, 95% CI: 1.76–3.26; and 4th Quartile: OR = 3.76, 95% CI: 2.66–5.30 compared to 1st Quartile) after adjustment for confounders. The addition of the FI-Lab to all disease severity scores improved discrimination and significantly reclassified in-hospital and 1-year mortality risk.ConclusionThe FI-Lab was a strong predictor of short- and long-term mortality in patients with critical AMI. The FI-Lab improved the ability to predict mortality in patients with critical AMI and therefore might be useful in the clinical decision-making process.

The data shows house sales and prices by Glasgow data zones between 1993 to 2012. Residential property transactions are recorded by Registers of Scotland. More information can be found here. The field names used were generated by the Land Value Information Unit at The University of the West of Scotland and The Scottish Government. The number of sales, median price and lower quartile price are based on new build and second hand sales that are recorded/registered whether they are cash purchases or funded by mortgages. The mean price is based on new build and second hand sales between 20,000 and 1,000,000. More information can be found in the metadata which is among the resources. (c) Crown copyright, 2014. Data supplied by Scottish Neighbourhood Statistics Website Licence: None

BackgroundNon-alcoholic fatty liver disease (NAFLD) is independently associated with atrial fibrillation (AF) risk. The uric acid (UA) to high-density lipoprotein cholesterol (HDL-C) ratio (UHR) has been shown to be closely associated with cardiovascular disease (CVD) and NAFLD. The aim of this study is to clarify whether elevated UHR is associated with the occurrence of AF in patients with NAFLD and to determine whether UHR predicted AF.MethodsPatients diagnosed with NAFLD in the Department of Cardiovascular Medicine of the Second Hospital of Shanxi Medical University from January 1, 2020, to December 31, 2021, were retrospectively enrolled in this study. The study subjects were categorized into AF group and non-AF group based on the presence or absence of combined AF. Logistic regression was performed to evaluate the correlation between UHR and AF. Sensitivity analysis and subgroup interaction analysis were performed to verify the robustness of the study results. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal cutoff value for UHR to predict the development of AF in patients with NAFLD.ResultsA total of 421 patients with NAFLD were included, including 171 in the AF group and 250 in the non-AF group. In the univariate regression analysis, NAFLD patients with higher UHR were more likely to experience AF, and the risk of AF persisted after confounding factors were adjusted for (OR: 1.010, 95%CI: 1.007–1.013, P<0.001). AF risk increased with increasing UHR quartile (P for trend < 0.001). Despite normal serum UA and HDL-C, UHR was still connected with AF in patients with NAFLD. All subgroup variables did not interact significantly with UHR in the subgroup analysis. The ROC curve analysis showed that the areas under the curve for UA, HDL-C, and UHR were 0.702, 0.606, and 0.720, respectively, suggesting that UHR has a higher predictive value for AF occurrence in NAFLD patients compared to HDL-C or UA alone.ConclusionIncreased UHR level was independently correlated with a high risk of AF in NAFLD patients.

ΔHRexercise: difference between heart-rate at peak exercise and resting heart-rateΔHRrecovery: difference between heart-rate at peak exercise and after 1 min recoveryΔHR5year: difference between resting heart-rate recorded at year 5 and at baseline§The analysis is restricted to 5139 subjects, see methodsRelative risk that was associated with a heart-rate measurement is given for the second, third and fourth quartile, taken the first quartile as reference. Relative risks were estimated with the Cox proportional-hazard model. CI denotes confidence interval.Symbols of p-values for trend test: ***: p