Raw data supporting the Springer Nature Data Availability Statement (DAS) analysis in the State of Open Data 2024. SOOD_2024_special_analysis_DAS_SN.xlsx contains the DAS, DOI, publication date, DAS categories and related country by Insitution of any author.SOOD 2024_DAS_analysis_sharing.xlsx contains the summary data by country and data sharing type.Utilizing the Dimensions database, we identified articles containing key DAS identifiers such as “Data Availability Statement” or “Availability of Data and Materials” within their full text. Digital Object Identifiers (DOIs) of these articles were collected and matched against Springer Nature’s XML database to extract the DAS for each article. The extracted DAS were categorized into specific sharing types using text and data matching terms. For statements indicating that data are publicly available in a repository, we matched against a predefined list of repository identifiers, names, and URLs. The DAS were classified into the following categories:1. Data are available from the author on request. 2. Data are included in the manuscript or its supplementary material. 3. Some or all of the data are publicly available, for example in a repository.4. Figure source data are included with the manuscript. 5. Data availability is not applicable.6. Data are declared as not available by the author.7. Data available online but not in a repository.These categories are non-exclusive: more than one can apply to any one article. Publications outside the 2019–2023 range and non-article publication types (e.g., book chapters) that were initially included in the Dimensions search results were excluded from the final dataset. Articles were included in the final analysis after applying the exclusion criteria. Upon processing, it was found that only 370 results were returned for Botswana across the five-year period; due to this low number, Botswana was not included in the DAS focused country-level analysis. This analysis does not assess the accuracy of the DAS in the context of each individual article. There was no manual verification of the categories applied; as a result, terms used out of context could have led to misclassification. Approximately 5% of articles remained unclassified following text and data matching due to these limitations.

This dataset contains Data Availability Statements from 47,593 papers published in PLOS ONE between March 2014 (when the policy went into effect) and May 2016, analyzed for type of statement.

We analyzed all the samples using a stereomicroscope, Olympus C011 trinocular microscope, coupled with a CCD camera. All the samples were measured and photographed by the Infinity Capture software.The drawn was improved with a drawing table, Parblo A610 – Graphhic tablet using the program ImageJ (Public Dominic). The geographical location of the Araripe Basin was produced using the software QGIS Geographic Information System (version 3.12 – QGIS.org – Public Dominic) considering the coordinate system Datum – SIRGAS 200 from Instituto Brasileiro de Geografia e Estatística (IBGE, Brazil) and Companhia de Pesquisa de Recursos Minerais (CPRM, Brazil). The stratigraphy of the Santana group was drawn with program ImageJ (Public Dominic) to according with stratigraphy on Neumann & Cabreira, 1999 and Valença et al., 2003.

Two examples of input data from Universities of Glasgow and Leicester for UKRN led Open Research Indicators pilot 4. The overall aim of the pilot was to explore the co-creation of practical methods to monitor the prevalence of DAS in research articles and assess the quality of DAS and their usefulness.

This presentation was delivered on Thursday 13th February for Love Data Week 2025. It was presented by the research data librarians at Newcastle University and Northumbria University.Access the presentation slides in Northumbria University's repository.Data access statements are a cornerstone of responsible research, providing users with clear guidance on whether and how they can access the underlying research data that supports research findings. In the age of open science, these statements are more than just a funder requirement, they are an important tool for facilitating data sharing and ensuring reproducible research. By including a well-crafted data access statement in your publications, you demonstrate a commitment to transparency and rigour, helping to enhance your research profile and boost citations by fostering trust in your work.The session explored:The principles and importance of data access statements in research.Practical guidance on writing clear and impactful statements.Real-world examples and common pitfalls to avoid.Where to share research data.Resources and support to simplify the process.

Project Title: Add title here

Project Team: Add contact information for research project team members

Summary: Provide a descriptive summary of the nature of your research project and its aims/focal research questions.

Relevant publications/outputs: When available, add links to the related publications/outputs from this data.

Data availability statement: If your data is not linked on figshare directly, provide links to where it is being hosted here (i.e., Open Science Framework, Github, etc.). If your data is not going to be made publicly available, please provide details here as to the conditions under which interested individuals could gain access to the data and how to go about doing so.

Data collection details: 1. When was your data collected? 2. How were your participants sampled/recruited?

Sample information: How many and who are your participants? Demographic summaries are helpful additions to this section.

Research Project Materials: What materials are necessary to fully reproduce your the contents of your dataset? Include a list of all relevant materials (e.g., surveys, interview questions) with a brief description of what is included in each file that should be uploaded alongside your datasets.

List of relevant datafile(s): If your project produces data that cannot be contained in a single file, list the names of each of the files here with a brief description of what parts of your research project each file is related to.

Data codebook: What is in each column of your dataset? Provide variable names as they are encoded in your data files, verbatim question associated with each response, response options, details of any post-collection coding that has been done on the raw-response (and whether that's encoded in a separate column).

Examples available at: https://www.thearda.com/data-archive?fid=PEWMU17 https://www.thearda.com/data-archive?fid=RELLAND14

Description

This dataset describes how datasets published in the research data repository RADAR are referenced, combining references extracted from Google Scholar, DataCite Event Data and the Data Citation Corpus.

DOIs assigned to RADAR datasets were retrieved from the RADAR API 2025-01-27. References in the three data sources were then identified using these DOIs. Each research output referencing a RADAR dataset was accessed to determine where the reference occurred in the full text. Author names and publication dates for datasets and referencing objects were added from OpenAlex and DataCite on 2025-02-10. Author names of datasets and referencing objects were compared to determine if data reuse occurred.

Columns

• from: DOI of the referencing object
• to: DOI of the RADAR dataset
• from_date: publication date of the referencing object
• to_date: publication date of the RADAR dataset
• source_gs: boolean indicating if the reference was found in Google Scholar
• source_dcc: boolean indicating if the reference was found in the Data Citation Corpus
• source_ded: boolean indicating if the reference was found in DataCite Event Data
• method_rl: boolean indicating if the dataset was referenced in the reference list
• method_das: boolean indicating if the dataset was referenced in the data availability statement
• method_fn: boolean indicating if the dataset was referenced in a footnote
• method_ft: boolean indicating if the dataset was referenced in other parts of the full text, for example in the methods section
• reuse_author: variable indicating if the reference is indicating data (overlap in the author names of dataset and referencing object) use or data reuse (no overlap)

The dataset contains World Bank Suicide mortality rate WDI (world development indicator) (2000-2019) world-wide data in original and processed form. In addition to the statistical data this dataset also contains bibliographic records of articles published on the topic of suicide in relation to individual countries during (2000-2019) in original and processed form.

The data consists of six archives:

1. World development indicator suicide mortality rate SH.STA.SUIC.P5. This archive contains suicide mortality rate of 159 countries during the period of 2000-2019 per 100,000 population including males and females as of November, 2023.
2. Web of science records country and suicide. This archive contains bibliographic records organized by country on the topic of suicide related to that country published during 2000-2019 as of November, 2023.
3. Suicide mortality rate statistics and keywords. This archive contains processed data of 1 and 2 archives in three files. The 'Countries suicide rates and WOS records' contains organized temporal suicide mortality rate data for each country and each year for males and females including counts of articles on suicide related in that country. The 'words and countries matrix' file contains information about how many times author and paper keywords from suicide related publications were seen in articles associated with each country. This data is organized as matrix in which rows are keywords, columns are countries and cells are counts of the keyword. The 'words and countries pairs' file contains same information only organized as keyword country pairs.
4. Suicide mortality rate clusters countries keywords titles. This archive contains bibliographic data organized by country clusters. These clusters group countries with similar suicide mortality rate dynamics in males and females shown in two included figures. Each folder of the cluster contains a section with bibliographic records; a section with keywords associated with each country; and a section in which each publication associated with the country has a separate filecontaining its title and keywords.
5. Suicide keywords embedding data. This archive contains word embedding vectors and metadata learned by recurrent neural network trained to classify countries from suicide related keywords of articles associated with those countries. Folder 'trained with keywords' contains embeddings learned in classifying countries in which training samples are keyword strings of publications. Folder 'trained with titles' contains embeddings learned in classifying countries in which training samples are strings containing titles of publication plus keywords.
6. Suicide keywords association rule mining. This archive contains files of subsets of keywords frequently mentioned together in suicide related publications. Folder 'Mining in clusters' has frequent keyword itemsets in country clusters. Folder 'Mining in individual countries' has frequent keyword itemsets in countries. Examples of keyword networks connecting clusters and networks connecting countries in individual clusters are included which helps to identify specific and shared keywords by country clusters and by countries in the individual clusters.

These datasets support a data availability statements for upcoming articles.

Description of coding categories and example statements.

Successful Short-Term Volcanic Eruption Forecasting Using Seismic Features, Suplementary Material

by Rey-Devesa (1,2), Benítez (3), Prudencio, Ligdamis Gutiérrez (1,2), Cortés (1,2), Titos (3), Koulakov (4,5), Zuccarello (6) and Ibáñez (1,2).

Institutions associated:

(1) Department of Theoretical Physics and Cosmos. Science Faculty. Avd. Fuentenueva s/n. University of Granada. 18071. Granada. Spain.

(2) Andalusian Institute of Geophysiscs. Campus de Cartuja. University of Granada. C/Profesor Clavera 12. 18071. Granada. Spain.

(3) Department of Signal Theory, Telematics and Communication. University of Granada. Informatics and Telecommunication School. 18071. Granada. Spain.

(4) Trofimuk Institute of Petroleum Geology and Geophysics SB RAS, Prospekt Koptyuga, 3, 630090 Novosibirsk, Russia

(5) Institute of the Earth’s Crust SB RAS, Lermontova 128, Irkutsk, Russia

(6) Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa (INGV-Pisa), via Cesare Battisti, 53, 56125, Pisa, Italy.

Acknowledgment:

This study was partially supported by the Spanish FEMALE project (PID2019-106260GB-I00).
P. Rey-Devesa was funded by the Ministerio de Ciencia e Innovación del Gobierno de España (MCIN),
Agencia Estatal de Investigación (AEI), Fondo Social Europeo (FSE),
and Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+I Ayudas para contratos predoctorales para la formación de doctores 2020 (PRE2020-092719).
Ivan Koulakov was supported by the Russian Science Foundation (Grant No. 20-17-00075).
Luciano Zuccarello was supported by the INGV Pianeta Dinamico 2021 Tema 8 SOME project (grant no. CUP D53J1900017001)
funded by the Italian Ministry of University and Research
“Fondo finalizzato al rilancio degli investimenti delle amministrazioni centrali dello Stato e allo sviluppo del Paese, legge 145/2018”.
English language editing was performed by Tornillo Scientific, UK.

Data availability statement:

1.- Seismic data from Kilauea, Augustine, Bezymianny (2007), and Mount St. Helens are available from the IRIS data repository (http://ds.iris.edu/seismon/index.phtml).
(An example of the Python code to access the data is described below.)
2.- Seismic data from Bezymianny (2017-2018) are available from Ivan Koulakov (ivan.science@gmail.com) upon request.
3.- Seismic data from Mt. Etna are available from INGV-Italy upon request (http://terremoti.ingv.it/en/help),
also available from the Zenodo data repository (https://doi.org/10.5281/zenodo.6849621).

Access code in Python to download the records of Kilauea, Augustine and Mount St. Helens volcanoes, from the IRIS data repository.

'''To access the raw signals please first install ObsPy and then execute following commands in a python console: '''

Example:

from obspy.core import UTCDateTime
from obspy.clients.fdsn import Client
import obspy.io.mseed
client = Client('IRIS')
t1 = UTCDateTime('2006-01-10T00:00:00')
t2 = UTCDateTime('2006-01-12T00:00:00')
raw_data = client.get_waveforms(
network='AV',
station='AUH',
location='',
channel='HHZ',
starttime=t1,
endtime=t2)

'''To further download station information execute: '''

xml = client.get_stations(network='AV',station='AUH',
channel='HHZ',starttime=t1,endtime=t2,level='response')

''' 'To scale the data using the station’s meta-data: '''

data = raw_data.remove_response(inventory=xml)

''' To filter, trim and plot the data execute: '''

data.write("Augustine.mseed", format="MSEED")

data.filter('bandpass',freqmin=1.0,freqmax=20)
data.trim(t1+60,t2-60)
data.plot()

Contents:

6 different Matlab codes. The principal code is called FeatureExtraction.
The codes rsac.m and ReadMSEEDFast.m are for reading different format of data. (Not developed by the group)
Seismic Data from Mt. Etna for using as an example.

This is the template for datasets analysed as part of United Kingdom Reproducability Network (UKRN) Research Indicators Project, pilot 4 - the prevalence and quality of data availability statements.

Here, the authors performed an in-silico analysis on a meta-dataset including gene-expression data from 5,342 clinically defined estrogen receptor-positive/ human epidermal growth factor receptor 2-negative (ER+/HER2-) breast cancers (BC), and DNA copy number/mutational and proteomic data, to determine whether the therapeutic response of ER+/HER2- breast cancers differs according to the molecular basal or luminal subtype.Data access: The dataset Breast_cancer_classifications.csv supporting figure 1, table 1, and supplementary tables 1-3 is publicly available in the figshare repository as part of this data record. This study used and analysed 36 publicly available datasets that are all listed in Supplementary table 8 and are cited from the data availability statement of the published article.Study aims and methodology: To evaluate the response and/or potential vulnerability to hormone treatment (HT) and other systemic therapies of BC, and to assess the degree of difference between basal and luminal breast cancer subtypes, the authors performed an in-silico analysis of a meta-dataset including gene expression data from 8,982 non-redundant BCs and DNA copy number/mutational and proteomic data from TCGA. The aim was to compare the Basal versus Luminal samples. Out of the 8,982 samples of the database, 6,563 were defined as ER+ (5,342 according to immunohistochemistry (IHC) and 1,221 according to inferred stratus).The authors analysed breast cancer gene expression data pooled from 36 public datasets (the publicly available datasets are listed in supplementary table 8), comprising 8,982 invasive primary BCs. The pre-analytic data processing was done as described previously in https://doi.org/10.1038/s41416-018-0309-1. Please refer to the published article for more details on the methodology and statistical analysis.Data supporting the figures, tables and supplementary tables in the published article: Data supporting figure 1, table 1, and supplementary tables 1-3: Dataset Breast_cancer_classifications.csv is in .csv file format. The dataset includes histo-clinical and molecular data of the tumors analysed in study, and is part of this data record.Data supporting supplementary table 4: Dataset genome.wustl.edu_BRCA.IlluminaGA_DNASeq.Level_2.3.2.0.tar.gz.1 is a tar archive gz compressed of maf format files. This dataset was accessed through the Genomic Data Commons (GDC) Data Portal and can be downloaded directly here: https://api.gdc.cancer.gov/data/afaf2790-04d4-453a-8c1b-75cf42ffd35f.Data supporting supplementary table 5: Dataset gdc_manifest.txt consists of gz archives of txt format files. The file was accessed through the GDC Data Portal here : https://portal.gdc.cancer.gov/repository?facetTab=files&filters={"op":"and","content":[{"op":"in","content":{"field":"cases.project.project_id","value":["TCGA-BRCA"]}},{"op":"in","content":{"field":"files.access","value":["open"]}},{"op":"in","content":{"field":"files.analysis.workflow_type","value":["HTSeq - Counts"]}},{"op":"in","content":{"field":"files.experimental_strategy","value":["RNA-Seq"]}}]}&searchTableTab=filesData supporting supplementary table 6: Dataset Table S5_Revised.xlsx is in .xlsx file format and is part of the supplementary information files of the published article.Data supporting supplementary table 7: Dataset BRCA.RPPA.Level_3.tar is a tar archive of txt format files. The file was accessed through the GDC Data Portal and can be downloaded directly here: https://api.gdc.cancer.gov/data/85988e1b-4f7d-493e-96ae-9eee61ac2833.

In this reference study, blood samples of 127 healthy individuals were analyzed with a wide range of -omics technologies, resulting in the most comprehensive -omics
profiling data set that is publicly available. The molecular measurements that are available here, can be used as reference values for any future (multi-)omics studyies. Along with phenotypic information (Sex, Age, BMI etc. and measured cell types levels) on the healthy subjects, the following data types are included:

• Targeted metabolomics (acylcarnitines, amino acids and very long chain fatty acids)
• Lipidomics (negative and positive ionization modes)
• Proteomics
• mRNA-seq
• miRNA-seq
• miRNA qRT-PCR
• Enzymation Methylation sequencing

The pre-processed mult-omics data can be accessed here in the shape of a MultiAssayExperiment object (Ramos et al. 2017). Instructions on how to read the object into R can be found here: Read_MultiAssayExperiment.

A similar object for Python (MuData) including the same data will be added later.

DATA AVAILABILITY STATEMENT:

Full data related to the EATRIS-Plus multiomic cohort are available in the ClinData repository (https://clindata.imtm.cz) and include full phenotypic information, physical and laboratory examinations, multiomic data from white blood cells (whole genome sequencing, enzymatic methylation DNA sequencing, mRNA sequencing, miRNA sequencing) or plasma (miRNA qPCR profiling, proteomics, targeted metabolomics, untargeted lipidomics, Raman spectroscopy profiling). However, access is restricted due to legal, ethical, scientific and/or commercial reasons. Access to the data is subject to approval and a data sharing transfer agreement. For data access please contact data.access@imtm.cz.