We provide data on an Excel file, with absolute differences in beta values between replicate samples for each probe provided in different tabs for raw data and different normalization methods.

Additional file 2 Data set (excel file). The excel data file data_set_of_extracted_data_Buchka_et_al.xlsx contains the data from our bibliographical survey.

The following data entry sheets are designed to quantify salinity normalized seawater total alkalinity anomalies (∆nTA) from inputs of offshore and coral reef total alkalinity (TA) and salinity (S) data while taking into account the various sources of uncertainty associated with these data normalizations and calculations to estimate the CDP for each reef observation (for details see Courtney et al., 2021). Only cells blocked in white should be modified on the "Data Entry" sheet and all cells blocked in gray are locked to protect the formulas from being modfied. Data for at least one offshore TA and S sample and one coral reef TA and S sample must be entered to display the ∆nTA and CDP for the given reef system. The equations herein will average all offshore TA and S data to calculate the ∆nTA to leverage all possible data. Additionally, the spreadsheets allow for the reference S to be set to the mean offshore or mean coral reef S and are calculated for a range of freshwater TA endmembers, including the option for a user defined value. ∆nTA is calculated as per the following equations from Courtney et al (2021). The CDP summary page also provides a number of summary graphs to visualize (1) whether there are apparent relationships between coral reef TA and S, (2) how the ∆nTA of the inputted data compares to global coral reef ∆TA data from Cyronak et al. (2018), (3) how the ∆nTA data varies spatially across the reef locations, and (4) how well the ∆nTA data covers a complete diel cycle. For further details on the uncertainties associated with the salinity normalization of coral reef data and relevant equations, please see the following publication: Courtney TA, Cyronak T, Griffin AJ, Andersson AJ (2021) Implications of salinity normalization of seawater total alkalinity in coral reef metabolism studies. PLOS One 16(12): e0261210. https://doi.org/10.1371/journal.pone.0261210 Please cite as: Courtney TA & Andersson AJ (2022) Calcification Dissolution Potential Tool for Excel: Version 1. https://doi.org/10.5281/zenodo.7051628

We provide the data used for this research in both Excel (one file with one matrix per sheet, 'Allmatrices.xlsx'), and CSV (one file per matrix).

Patent applications (Patent_applications.csv) Patent applications from residents and no residents per million inhabitants. Data obtained from the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

High-tech exports (High-tech_exports.csv) The proportion of exports of high-level technology manufactures from total exports by technology intensity, obtained from the Trade Structure by Partner, Product or Service-Category database (Lall, 2000; UNCTAD, 2019)

Expenditure on education (Expenditure_on_education.csv) Per capita government expenditure on education, total (2010 US$). The data was obtained from the government expenditure on education (total % of GDP), GDP (constant 2010 US$), and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Scientific publications (Scientific_publications.csv) Scientific and technical journal articles per million inhabitants. The data were obtained from the scientific and technical journal articles and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Expenditure on R&D (Expenditure_on_R&D.csv) Expenditure on research and development. Data obtained from the research and development expenditure (% of GDP), GDP (constant 2010 US$), and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Two centuries of GDP (GDP_two_centuries.csv) GDP per capita that accounts for inflation. Data obtained from the Maddison Project Database, version 2018 (Inklaar et al. 2018), and available from the Open Numbers community (open-numbers.github.io).

Inklaar, R., de Jong, H., Bolt, J., & van Zanden, J. (2018). Rebasing “Maddison”: new income comparisons and the shape of long-run economic development (GD-174; GGDC Research Memorandum). https://www.rug.nl/research/portal/files/53088705/gd174.pdf

Lall, S. (2000). The Technological Structure and Performance of Developing Country Manufactured Exports, 1985‐98. Oxford Development Studies, 28(3), 337–369. https://doi.org/10.1080/713688318

Unctad. 2019. “Trade Structure by Partner, Product or Service-Category.” 2019. https://unctadstat.unctad.org/EN/.

World Bank. (2020). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators

Version: 6

Date of data collection: May 2025

General description: Publication of datasets according to the FAIR principles could be reached publishing a data paper (and/or a software paper) in data journals as well as 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_v6.xlsx: full list of 177 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v6.csv: full list of 177 academic journals in which data papers or/and software papers could be published
- readme_v6.txt, with a detailed descritption of the dataset and its variables.

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: 6th version
- Information updated: number of journals (17 were added and 4 were deleted), URL, document types associated to a specific journal.
- Information added: diamond journals were identified.

Version: 5

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2023/09/05

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_v5.xlsx: full list of 162 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v5.csv: full list of 162 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: 5th version
- Information updated: number of journals, URL, document types associated to a specific journal.
163 journals (excel y csv)

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.

This spreadsheet implements the FA normalization technique for analyzing a set of male Drosophila cuticular hydrocarbons. It is intended for GC-FID output. Sample data is included. New data can be copied into the file to apply the normalization. (0.07 MB DOC)

In this study, blood proteome characterization in face transplantation using longitudinal serum samples from six face transplant patients was carried out with SOMAscan platform. Overall, 24 serum samples from 13 no-rejection, 5 nonsevere rejection and 6 severe rejection episodes were analyzed.Files attached:HMS-16-007.20160218.adat - raw SomaScan dataset presented in adat format.HMS-16-007_SQS_20160218.pdf - technical validation report on the dataset.HMS-16-007.HybNorm.20160218.adat - SomaScan dataset after hybridization control normalization presented in adat format.HMS-16-007.HybNorm.MedNorm.20160218.adat - SomaScan dataset after hybridization control normalization and median signal normalization presented in adat format.HMS-16-007.HybNorm.MedNorm.Cal.20160218.adat - SomaScan dataset after hybridization control normalization, median signal normalization, and calibration presented in adat format.HMS-16-007.HybNorm.MedNorm.Cal.20160218.xls - SomaScan dataset after hybridization control normalization, median signal normalization, and calibration presented in Microsoft Excel Spreadsheet format.Patients_metadata.txt – metadata file containing patients’ demographic and clinical information presented in tab-delimited text format. Metadata is linked to records in the SomaScan dataset via ‘SampleType’ column.SciData_R_script.R – this script is given as an example of a downstream statistical analysis of the HMS-16-007.HybNorm.MedNorm.Cal.20160218.adat dataset.SciData_R_script_SessionInfo - Session information for SciData_R_script.R script.

Dataset description

The datasets include mobility market indicators and macroeconomic indicators for Austria, which were used to calculate the Mobility as a Service (MaaS) Status Index (MSI). The MSI evaluates the readiness and potential for implementing Mobility as a Service (MaaS) in Austria. The datasets cover two distinct periods: 2017-2022 (T1) and 2023-2028 (T2). The indicators include annual revenues, vehicle costs, number of users, market shares, GDP per capita, urbanization rates, and investments in transportation infrastructure, among others.

Context and methodology

Each indicator is represented by the average annual growth rate, a mean value, and a normalized mean value (min-max-normalization) for period T1 and T2. The data were sourced from Statista (2024)

Technical details

The dataset contains two Microsoft Excel files (one for mobility market indicators, one for macroeconomic indicators). Other than Microsoft Excel, there is no additional software needed to investigate the data.

Additional file 1. Hyperparameter tuning. Full results of the hyperparameter-tuning process, performed over the training set in 6-fold cross-validation. The file is an \emph{Open Document Format} table (.ods), which can be viewed and analysed with spreadsheet applications like MS Excel or LibreOffice Calc.

Expression (normalized read count) for breast cancer specific 79 fusion-protein and 419 3′-truncated protein transcripts. Expression is the normalized RNA-Seq read counts as estimated using RSEM and followed by upper quartile normalization. File contains expression data for breast cancer specific fusion-protein and 3′-truncated protein transcripts only. The first sheet in the excel file contains the data columns and a key describing the data is on the second excel sheet. (XLSX 33 kb)

Dataset S2. MiRNA expression in pancreatic cancer samples. (Microsoft Excel spreadsheet) (XLSX 355 kb)

Excel file of ECM proteins analyzed with mass spectrometry for Matrigel, Native, B-ECM and B-ECM hydrogel groups. (XLSX)

1. Mass lists.File name format: Sample_(sample number)_(mass spectrometer used).txt) Archive: 'Mass lists.zip'Basic set:Sample_1_maXis.txt Sample_2_maXis.txt Sample_3_maXis.txt Sample_1_ Apex_Ultra.txt Sample_2_ Apex_Ultra.txt Sample_3_ Apex_Ultra.txt Sample_1_ OrbiTrap_Elite.txt Sample_2_ OrbiTrap_Elite.txt Sample_3_ OrbiTrap_Elite.txt Sample_1_ micrOTOF-Q.txt Sample_2_ micrOTOF-Q.txt Sample_3_ micrOTOF-Q.txt Sample_1_ IFunnel_Q-ToF.txt Sample_2_ IFunnel_Q-ToF.txt Sample_3_ IFunnel_Q-ToF.txt Additional set:Sample_3_maXis_Wide_range.txt Sample_3_maXis_High_range.txt 2. Raw mass spectra in mzXL format.File name format: Sample_(sample number)_(mass spectrometer used).mzXLArchive: 'Mass spectra.zip'3. Data for the basic set of mass spectra (file ‘matrix.mat’). Data are presented in Matlab workspace format and include the following:mz, m/z values for mass peak intensities;intensities, mass peak intensities, where columns correspond to mass lists (basic set) and rows correspond to m/z values;normalization_curves, normalization curves for mass lists, where columns correspond to mass lists (basic set) and rows correspond to m/z values;standardized_intensities, standardized_intensities for mass lists, where columns correspond to mass lists (basic set) and rows correspond to m/z values. 4. Data for additional set of mass spectra (file ‘matrix_ad.mat’). Data are presented in Matlab workspace format and include the following:mz, m/z values for mass peak intensities;intensities, mass peak intensities, where columns correspond to mass lists (additional set) and rows correspond to m/z values;normalization_curves, normalization curves for mass lists, where columns correspond to mass lists (additional set) and rows correspond to m/z values;standardized_intensities, standardized_intensities for mass lists, where columns correspond to mass lists (additional set) and rows correspond to m/z values. 5. Dataset for Figure 1 (file ‘Dataset for Figure 1.xlsx’).The file is in the Microsoft Excel program format and includes data for Figure 1. 6. Dataset for Figure 3 (file ‘Dataset data for Figure 3.xlsx’).The file is in the Microsoft Excel program format and includes data for Figure 3. 7. Source code for the SantaOmics algorithm and the data to run it.

The source code is presented as a Matlab script (file ‘SantaOmics.m’). Data are presented as a saved Matlab workspace (file ‘workspace.mat’). To run the SantaOmics algorithm, the workspace should be loaded in the Matlab program, and ‘SantaOmics.m’ should be evaluated in the Matlab environment. Mass peak intensities of the initial mass spectra (presented in variable ‘intensities’; n = 15) should be standardized and written as variable ‘standardized_intensities’. Depending on the power of the computer, the algorithm may take from several to tens of minutes to complete.