published journal article. This dataset is associated with the following publication: Schumacher, B., J. Zimmerman, J. Elliot, and G. Swanson. The Effect of Equilibration Time and Tubing Material on Soil Gas Measurements. SOIL AND SEDIMENT CONTAMINATION: AN INTERNATIONAL JOURNAL. CRC Press LLC, Boca Raton, FL, USA, 25(2): 151-163, (2016).

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 140 academic journals in which data papers or/and software papers could be published
• data_articles_journal_list_v5.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: 5th version - Information updated: number of journals, URL, document types associated to a specific journal.

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.

The data in this set was culled from the Directory of Open Access Journals (DOAJ), the Proquest database Library and Information Science Abstracts (LISA), and a sample of peer reviewed scholarly journals in the field of Library Science. The data include journals that are open access, which was first defined by the Budapest Open Access Initiative: By ‘open access’ to [scholarly] literature, we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. Starting with a batch of 377 journals, we focused our dataset to include journals that met the following criteria: 1) peer-reviewed 2) written in English or abstracted in English, 3) actively published at the time of..., Data Collection In the spring of 2023, researchers gathered 377 scholarly journals whose content covered the work of librarians, archivists, and affiliated information professionals. This data encompassed 221 journals from the Proquest database Library and Information Science Abstracts (LISA), widely regarded as an authoritative database in the field of librarianship. From the Directory of Open Access Journals, we included 144 LIS journals. We also included 12 other journals not indexed in DOAJ or LISA, based on the researchers’ knowledge of existing OA library journals. The data is separated into several different sets representing the different indices and journals we searched. The first set includes journals from the database LISA. The following fields are in this dataset:

Journal: title of the journal

Publisher: title of the publishing company

Open Data Policy: lists whether an open data exists and what the policy is

Country of publication: country where the journal is publ..., , # Open access practices of selected library science journals

The data in this set was culled from the Directory of Open Access Journals (DOAJ), the Proquest database Library and Information Science Abstracts (LISA), and a sample of peer reviewed scholarly journals in the field of Library Science.

The data include journals that are open access, which was first defined by the Budapest Open Access Initiative:Â

By ‘open access’ to [scholarly] literature, we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself.

Starting with a batch of 377 journals, we focused our dataset to include journals that met the following criteria: 1) peer-reviewed 2) written in Engli...

This collection contains five sets of datasets: 1) Publication counts from two multidisciplinary humanities data journals: the Journal of Open Humanities Data and Research Data in the Humanities and Social Sciences (RDJ_JOHD_Publications.csv); 2) A large dataset about the performance of research articles in HSS exported from dimensions.ai (allhumss_dims_res_papers_PUB_ID.csv); 3) A large dataset about the performance of datasets in HSS harvested from the Zenodo REST API (Zenodo.zip); 4) Impact and usage metrics from the papers published in the two journals above (final_outputs.zip); 5) Data from Twitter analytics on tweets from the @up_johd account, with paper DOI and engagement rate (twitter-data.zip).

Please note that, as requested by the Dimensions team, for 2 and 4, we only included the Publication IDs from Dimensions rather than the full data. Interested parties only need the Dimensions publications IDs to retrieve the data; even if they have no Dimensions subscription, they can easily get a no-cost agreement with Dimensions, for research purposes, in order to retrieve the data.

BackgroundThere is increasing interest to make primary data from published research publicly available. We aimed to assess the current status of making research data available in highly-cited journals across the scientific literature. Methods and ResultsWe reviewed the first 10 original research papers of 2009 published in the 50 original research journals with the highest impact factor. For each journal we documented the policies related to public availability and sharing of data. Of the 50 journals, 44 (88%) had a statement in their instructions to authors related to public availability and sharing of data. However, there was wide variation in journal requirements, ranging from requiring the sharing of all primary data related to the research to just including a statement in the published manuscript that data can be available on request. Of the 500 assessed papers, 149 (30%) were not subject to any data availability policy. Of the remaining 351 papers that were covered by some data availability policy, 208 papers (59%) did not fully adhere to the data availability instructions of the journals they were published in, most commonly (73%) by not publicly depositing microarray data. The other 143 papers that adhered to the data availability instructions did so by publicly depositing only the specific data type as required, making a statement of willingness to share, or actually sharing all the primary data. Overall, only 47 papers (9%) deposited full primary raw data online. None of the 149 papers not subject to data availability policies made their full primary data publicly available. ConclusionA substantial proportion of original research papers published in high-impact journals are either not subject to any data availability policies, or do not adhere to the data availability instructions in their respective journals. This empiric evaluation highlights opportunities for improvement.

The journals’ author guidelines and/or editorial policies were examined on whether they take a stance with regard to the availability of the underlying data of the submitted article. The mere explicated possibility of providing supplementary material along with the submitted article was not considered as a research data policy in the present study. Furthermore, the present article excluded source codes or algorithms from the scope of the paper and thus policies related to them are not included in the analysis of the present article.

For selection of journals within the field of neurosciences, Clarivate Analytics’ InCites Journal Citation Reports database was searched using categories of neurosciences and neuroimaging. From the results, journals with the 40 highest Impact Factor (for the year 2017) indicators were extracted for scrutiny of research data policies. Respectively, the selection journals within the field of physics was created by performing a similar search with the categories of physics, applied; physics, atomic, molecular & chemical; physics, condensed matter; physics, fluids & plasmas; physics, mathematical; physics, multidisciplinary; physics, nuclear and physics, particles & fields. From the results, journals with the 40 highest Impact Factor indicators were again extracted for scrutiny. Similarly, the 40 journals representing the field of operations research were extracted by using the search category of operations research and management.

Journal-specific data policies were sought from journal specific websites providing journal specific author guidelines or editorial policies. Within the present study, the examination of journal data policies was done in May 2019. The primary data source was journal-specific author guidelines. If journal guidelines explicitly linked to the publisher’s general policy with regard to research data, these were used in the analyses of the present article. If journal-specific research data policy, or lack of, was inconsistent with the publisher’s general policies, the journal-specific policies and guidelines were prioritized and used in the present article’s data. If journals’ author guidelines were not openly available online due to, e.g., accepting submissions on an invite-only basis, the journal was not included in the data of the present article. Also journals that exclusively publish review articles were excluded and replaced with the journal having the next highest Impact Factor indicator so that each set representing the three field of sciences consisted of 40 journals. The final data thus consisted of 120 journals in total.

‘Public deposition’ refers to a scenario where researcher deposits data to a public repository and thus gives the administrative role of the data to the receiving repository. ‘Scientific sharing’ refers to a scenario where researcher administers his or her data locally and by request provides it to interested reader. Note that none of the journals examined in the present article required that all data types underlying a submitted work should be deposited into a public data repositories. However, some journals required public deposition of data of specific types. Within the journal research data policies examined in the present article, these data types are well presented by the Springer Nature policy on “Availability of data, materials, code and protocols” (Springer Nature, 2018), that is, DNA and RNA data; protein sequences and DNA and RNA sequencing data; genetic polymorphisms data; linked phenotype and genotype data; gene expression microarray data; proteomics data; macromolecular structures and crystallographic data for small molecules. Furthermore, the registration of clinical trials in a public repository was also considered as a data type in this study. The term specific data types used in the custom coding framework of the present study thus refers to both life sciences data and public registration of clinical trials. These data types have community-endorsed public repositories where deposition was most often mandated within the journals’ research data policies.

The term ‘location’ refers to whether the journal’s data policy provides suggestions or requirements for the repositories or services used to share the underlying data of the submitted works. A mere general reference to ‘public repositories’ was not considered a location suggestion, but only references to individual repositories and services. The category of ‘immediate release of data’ examines whether the journals’ research data policy addresses the timing of publication of the underlying data of submitted works. Note that even though the journals may only encourage public deposition of the data, the editorial processes could be set up so that it leads to either publication of the research data or the research data metadata in conjunction to publishing of the submitted work.

1The totals in this column equal the number of articles reporting a particular type of funding, minus instances of duplicate classification by type of company within funding category. These instances were: There was no information on funding for the article classified as both manufacturing and mining, and non-profit, non-governmental funding was used by the articles classified as both tobacco and transportation and both tobacco and alcohol. The overall column total is greater than the total number of included articles (N = 361) because some articles reported multiple types of funding.2Other funding sources include Blue Cross Blue Shield (4 tobacco articles), the World Health Organization (2 tobacco articles), and funding from a law firm (1 manufacturing article).3The totals in this row equal the total number of articles reporting funding for each type of company, minus instances where articles reported multiple types of funding, of which there are too many to list. The totals for the columns are therefore not equal to the sum of the classifications within the columns. The overall row total is greater than the total number of included articles (N = 361) because three articles were classified with two types of companies.

The dataset contains bibliographic information about scientific articles published by researchers from Norwegian research organizations and is an enhanced subset of data from the Cristin database. Cristin (current research information system in Norway) is a database with bibliographic records of all research articles with an Norwegian affiliation with a publicly funded research institution in Norway. The subset is limited to metadata about journal articles reported in the period 2013-2021 (186,621 records), and further limited to information of relevance for the study (see below). Article metadata are enhanced with open access status by several sources, particularly unpaywall, DOAJ and hybrid-information in case an article is part of a publish-and-read-deal.

ABSTRACT Here, we describe the percentage of non-citation in Brazilian public health journals, a field that, until now, had not been investigated nationally or internationally. We analyzed articles, published between 2008 and 2012, of eight public health journals indexed in the scopus database. The percentage of non-citation differs between journals (from 5.7% to 58.1%). We identified four statistically distinct groups: História, Ciência, Saúde – Manguinhos (58% uncited articles); Physis: Revista de Saúde Coletiva, Interface, and Saúde e Sociedade (32% to 37%); Ciência & Saúde Coletiva and Revista Brasileira de Epidemiologia (16% to 17%); and Cadernos de Saúde Pública and Revista de Saúde Pública (6%). The non-citation in the first three years post-publication also varies according to journal. Four journals have shown a clear decline of non-citation: Cadernos de Saúde Pública, Ciência & Saúde Coletiva, Revista Brasileira de Epidemiologia, and Physis. Another three (Revista de Saúde Pública, Saúde e Sociedade, and Interface) presented an oscillation in non-citation, but the rates of 2008 and 2012 are similar, with different magnitudes. In turn, the journal História, Ciência, Saúde – Manguinhos maintains high rates of non-citation. Multidisciplinary journals attract more citation, but a comprehensive citation model still needs to be formulated and tested.

International Journal of Scientific and Technology Research FAQ - ResearchHelpDesk - IJSTR - International Journal of Scientific & Technology Research is an open access international journal from diverse fields in sciences, engineering, and technologies Open Access that emphasizes new research, development, and applications. Papers reporting original research or extended versions of already published conference/journal papers are all welcomed. Papers for publication are selected through peer review to ensure originality, relevance, and readability. IJSTR ensures a wide indexing policy to make published papers highly visible to the scientific community. IJSTR is part of the eco-friendly community and favors e-publication mode for being an online 'GREEN journal'. IJSTR is an international peer-reviewed, electronic, online journal published monthly. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching, and research in the fields of engineering, science, and technology. Original theoretical work and application-based studies, which contribute to a better understanding of engineering, science, and technological challenges, are encouraged. IJSTR Publication Charges IJSTR covers the costs partially through article processing fees. IJSTR expenses are split among peer review administration and management, production of articles in PDF format, editorial costs, electronic composition and production, journal information system, manuscript management system, electronic archiving, overhead expenses, and administrative costs. Moreover, we are providing research paper publishing in minimum available costing such as there are no charges for rejected articles, no submission charges, and no surcharges based on the figures or supplementary data. IJSTR Publication Indexing IJSTR ​​​​​submit all published papers to indexing partners. Indexing totally depends on the content, indexing partner guidelines, and their indexing procedures. This is the reason sometimes indexing happens immediately and sometimes it takes time. Publication with IJSTR does not guarantee that paper will surely be added indexing partner website. The whole process for including any article (s) in the Scopus database is done by the Scopus team only. Journal or Publication House doesn't have any involvement in the decision whether to accept or reject a paper for the Scopus database and cannot influence the processing time of paper. International Journal of Scientific & Technology Research RG Journal Impact: 0.31 * *This value is calculated using ResearchGate data and is based on average citation counts from work published in this journal. The data used in the calculation may not be exhaustive. RG Journal impact history 2018 / 2019 0.31 2017 0.34 2016 0.33 2015 0.36 2014 0.19 Is Ijstr Scopus indexed? Yes IJSTR - International Journal of Scientific & Technology Research Journal is Scopus indexed. please visit for more details - IJSTR Scoups

List of Top Journals of Distributed Databases sorted by articles.

This document describes a dataset that aggregates information about 135 data journals.
Data journals focus on the publication of data papers -- a specialized publication type describing datasets, their collection and reuse potential that is peer-reviewed, citable and indexed.
This dataset includes a comprehensive list of data journals that was compiled by aggregating existing sources, as well as an overview of these sources.

The list is continually updated on GitHub, where additional information on data journals (URLs of data journal homepages) is provided: https://github.com/MaxiKi/data-journals

This dataset contains free to read/open access status of scholarly journal articles from Stockholm University (Sweden) published between 2012-2017. The data published in xlsx and csv format. Only journal articles with a known DOI are included. The status of free/open access of the articles were checked manually and then compared to the Unpaywall/oaDOi database (https://unpaywall.org/data) in February 2018. The data was fetched with the help of the Unpaywall/oaDOI API: https://unpaywall.org/api/v2 Definitions of the columns in the data file: Article:DOI: DOI id of the ariclesu:DIVA PID: id of the article in the Stockholm University publication database (DiVA: http://su.diva-portal.org/)Journal: Name of the artcleYear: Publication year Manually checked data:Free to read at publisher homepage: 1 if the full-text of the article is free to read without registration at the publisher's homepageOA: 1 if the article has some kind of OA licenseLicense: Specification of the OA license (type of Creative Commons license, or "Other license"Gold OA journal: 1 if the journal is fully open accessPublisher: name of the publisher Data from oaDOIDOI found in oaDOI: 1 if the DOI is found in the oaDOI databaseoaDOI found something open: 1 if the oaDOI database found an open version availableFree to read at publisher homepage according to oaDOI: 1 if there is a free to read available version at the publisher's homepage according to oaDOIOA at publisher according to oaDOI best locationoaDOI best location: best free location according to oaDOIoaDOI data_standard: 1 or 2 according to oaDOI oaDOI license: license from the oaDOI databaseoaDOI license (standardized format): license type converted to the format of the column "License". License types other than Creative Commons are categorized as "Other license". Note: since many things were checked manually/half-automatically, some errors are inevitable. Furthermore all data was only accurate at the time of the check.

The top 10 journals having FOC-related publications (N = 743).

WhenArthritis Researchwas launched in 1999 the publishers and ourselves took the innovative decision to make all primary research articles available to everyone for free through the journal's own websitehttp://arthritis-research.comas well as through PubMed Centralhttp://www.pubmedcentral.nih.gov/, the National Institute of Health's repository for biomedical research articles, and through the BioMed Central (BMC) websitehttp://biomedcentral.com. We now feel that it is time to lead the way forward again by only publishing research articles in full online beginning with volume 4 number 4 ofArthritis Research.

This dataset was developed to create a census of sufficiently documented molecular biology databases to answer several preliminary research questions. Articles published in the annual Nucleic Acids Research (NAR) “Database Issues” were used to identify a population of databases for study. Namely, the questions addressed herein include: 1) what is the historical rate of database proliferation versus rate of database attrition?, 2) to what extent do citations indicate persistence?, and 3) are databases under active maintenance and does evidence of maintenance likewise correlate to citation? An overarching goal of this study is to provide the ability to identify subsets of databases for further analysis, both as presented within this study and through subsequent use of this openly released dataset.

Citation metrics are widely used and misused. We have created a publicly available database of top-cited scientists that provides standardized information on citations, h-index, co-authorship adjusted hm-index, citations to papers in different authorship positions and a composite indicator (c-score). Separate data are shown for career-long and, separately, for single recent year impact. Metrics with and without self-citations and ratio of citations to citing papers are given. Scientists are classified into 22 scientific fields and 174 sub-fields according to the standard Science-Metrix classification. Field- and subfield-specific percentiles are also provided for all scientists with at least 5 papers. Career-long data are updated to end-of-2022 and single recent year data pertain to citations received during calendar year 2022. The selection is based on the top 100,000 scientists by c-score (with and without self-citations) or a percentile rank of 2% or above in the sub-field. This version (6) is based on the October 1, 2023 snapshot from Scopus, updated to end of citation year 2022. This work uses Scopus data provided by Elsevier through ICSR Lab (https://www.elsevier.com/icsr/icsrlab). Calculations were performed using all Scopus author profiles as of October 1, 2023. If an author is not on the list it is simply because the composite indicator value was not high enough to appear on the list. It does not mean that the author does not do good work.

PLEASE ALSO NOTE THAT THE DATABASE HAS BEEN PUBLISHED IN AN ARCHIVAL FORM AND WILL NOT BE CHANGED. The published version reflects Scopus author profiles at the time of calculation. We thus advise authors to ensure that their Scopus profiles are accurate. REQUESTS FOR CORRECIONS OF THE SCOPUS DATA (INCLUDING CORRECTIONS IN AFFILIATIONS) SHOULD NOT BE SENT TO US. They should be sent directly to Scopus, preferably by use of the Scopus to ORCID feedback wizard (https://orcid.scopusfeedback.com/) so that the correct data can be used in any future annual updates of the citation indicator databases.

The c-score focuses on impact (citations) rather than productivity (number of publications) and it also incorporates information on co-authorship and author positions (single, first, last author). If you have additional questions, please read the 3 associated PLoS Biology papers that explain the development, validation and use of these metrics and databases. (https://doi.org/10.1371/journal.pbio.1002501, https://doi.org/10.1371/journal.pbio.3000384 and https://doi.org/10.1371/journal.pbio.3000918).

Finally, we alert users that all citation metrics have limitations and their use should be tempered and judicious. For more reading, we refer to the Leiden manifesto: https://www.nature.com/articles/520429a

Journal of Big Data Impact Factor 2024-2025 - ResearchHelpDesk - The Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research. The journal examines the challenges facing big data today and going forward including, but not limited to: data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing platforms; distributed file systems and databases; and scalable storage systems. Academic researchers and practitioners will find the Journal of Big Data to be a seminal source of innovative material. All articles published by the Journal of Big Data are made freely and permanently accessible online immediately upon publication, without subscription charges or registration barriers. As authors of articles published in the Journal of Big Data you are the copyright holders of your article and have granted to any third party, in advance and in perpetuity, the right to use, reproduce or disseminate your article, according to the SpringerOpen copyright and license agreement. For those of you who are US government employees or are prevented from being copyright holders for similar reasons, SpringerOpen can accommodate non-standard copyright lines.

This dataset contains data collected during a study ("Towards High-Value Datasets determination for data-driven development: a systematic literature review") conducted by Anastasija Nikiforova (University of Tartu), Nina Rizun, Magdalena Ciesielska (Gdańsk University of Technology), Charalampos Alexopoulos (University of the Aegean) and Andrea Miletič (University of Zagreb) It being made public both to act as supplementary data for "Towards High-Value Datasets determination for data-driven development: a systematic literature review" paper (pre-print is available in Open Access here -> https://arxiv.org/abs/2305.10234) and in order for other researchers to use these data in their own work.

The protocol is intended for the Systematic Literature review on the topic of High-value Datasets with the aim to gather information on how the topic of High-value datasets (HVD) and their determination has been reflected in the literature over the years and what has been found by these studies to date, incl. the indicators used in them, involved stakeholders, data-related aspects, and frameworks. The data in this dataset were collected in the result of the SLR over Scopus, Web of Science, and Digital Government Research library (DGRL) in 2023.

Methodology

To understand how HVD determination has been reflected in the literature over the years and what has been found by these studies to date, all relevant literature covering this topic has been studied. To this end, the SLR was carried out to by searching digital libraries covered by Scopus, Web of Science (WoS), Digital Government Research library (DGRL).

These databases were queried for keywords ("open data" OR "open government data") AND ("high-value data*" OR "high value data*"), which were applied to the article title, keywords, and abstract to limit the number of papers to those, where these objects were primary research objects rather than mentioned in the body, e.g., as a future work. After deduplication, 11 articles were found unique and were further checked for relevance. As a result, a total of 9 articles were further examined. Each study was independently examined by at least two authors.

To attain the objective of our study, we developed the protocol, where the information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information.

Test procedure Each study was independently examined by at least two authors, where after the in-depth examination of the full-text of the article, the structured protocol has been filled for each study. The structure of the survey is available in the supplementary file available (see Protocol_HVD_SLR.odt, Protocol_HVD_SLR.docx) The data collected for each study by two researchers were then synthesized in one final version by the third researcher.

Description of the data in this data set

Protocol_HVD_SLR provides the structure of the protocol Spreadsheets #1 provides the filled protocol for relevant studies. Spreadsheet#2 provides the list of results after the search over three indexing databases, i.e. before filtering out irrelevant studies

The information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information

Descriptive information
1) Article number - a study number, corresponding to the study number assigned in an Excel worksheet 2) Complete reference - the complete source information to refer to the study 3) Year of publication - the year in which the study was published 4) Journal article / conference paper / book chapter - the type of the paper -{journal article, conference paper, book chapter} 5) DOI / Website- a link to the website where the study can be found 6) Number of citations - the number of citations of the article in Google Scholar, Scopus, Web of Science 7) Availability in OA - availability of an article in the Open Access 8) Keywords - keywords of the paper as indicated by the authors 9) Relevance for this study - what is the relevance level of the article for this study? {high / medium / low}

Approach- and research design-related information 10) Objective / RQ - the research objective / aim, established research questions 11) Research method (including unit of analysis) - the methods used to collect data, including the unit of analy-sis (country, organisation, specific unit that has been ana-lysed, e.g., the number of use-cases, scope of the SLR etc.) 12) Contributions - the contributions of the study 13) Method - whether the study uses a qualitative, quantitative, or mixed methods approach? 14) Availability of the underlying research data- whether there is a reference to the publicly available underly-ing research data e.g., transcriptions of interviews, collected data, or explanation why these data are not shared? 15) Period under investigation - period (or moment) in which the study was conducted 16) Use of theory / theoretical concepts / approaches - does the study mention any theory / theoretical concepts / approaches? If any theory is mentioned, how is theory used in the study?

Quality- and relevance- related information
17) Quality concerns - whether there are any quality concerns (e.g., limited infor-mation about the research methods used)? 18) Primary research object - is the HVD a primary research object in the study? (primary - the paper is focused around the HVD determination, sec-ondary - mentioned but not studied (e.g., as part of discus-sion, future work etc.))

HVD determination-related information
19) HVD definition and type of value - how is the HVD defined in the article and / or any other equivalent term? 20) HVD indicators - what are the indicators to identify HVD? How were they identified? (components & relationships, “input -> output") 21) A framework for HVD determination - is there a framework presented for HVD identification? What components does it consist of and what are the rela-tionships between these components? (detailed description) 22) Stakeholders and their roles - what stakeholders or actors does HVD determination in-volve? What are their roles? 23) Data - what data do HVD cover? 24) Level (if relevant) - what is the level of the HVD determination covered in the article? (e.g., city, regional, national, international)

Format of the file .xls, .csv (for the first spreadsheet only), .odt, .docx

Licenses or restrictions CC-BY

For more info, see README.txt

This set of data and programs were used to analyze the relationship between authors’ ties with professional editors and the publications of these authors in the editors’ journals. We assessed whether this relationship was related to three aspects of authors’ reputation in the eyes of the editor: a past collaboration with the editor, an affiliation to one of the editor’s former research affiliations, and whether the author had already published in the journal of the editor. We collected all published articles recorded in the PubMed database up to December 2020; job offers for editorial positions at Nature journals between December 2020 and December 2021 published on the Nature website; editors' scientific and editorial experiences from LinkedIn complemented by Google searches. Data extraction was built of three steps to identify the past collaborators of editors, whether these collaborators published in the editor’s journal before or after the editor’s appointment at the journal, and whether an author had a track record in the journal at a given date. Statistical analyses were performed with the output of these steps. The study was complemented by the analysis of job offers for editorial positions issued by the editors’ journal and by retracted publications in the editors' journal. Content description: The file entitled "Neveu et al additional methods.pdf" provides additional details of the method described in the main text of the article and the Supplementary online published alongside the article. The file entitled "Neveu et al additional results.pdf" provides results of the robustness analyses. These analyses aimed at checking that the results reported in the main text and the Supplementary online of the article were not biased by the parameters mentioned in the Method section of the article. The compressed file "DataAndPrograms.zip" contains all programs used to extract the data used in the analyses of the article. These programs are in the folder "Preprocessing of data". Inside this folder, the folders "1st level", "2nd Level", "3rd level" and "4th level" contain programs which must be run in the order suggested by the names of the folders. In each of these folders, .sh programs are for running the Matlab program of the folder on grid computing. The Matlab programs which are in the "Functions" folder are all the functions which are called by the programs which are in the other folders of the folder "Preprocessing of data". The Matlab programs which are in the folder "Output analysis" are all the functions which are used once the four levels have been run. The Matlab programs which are in the "Tools" folder are a set of functions used for the management of the output of each of the four levels especially when the grid computation crashed. Data are in the folders "Raw data" and "Extracted data". Data in the "Extracted data" are the output of the aforementioned four levels of the preprocessing. The programs used for the statistical analyses are in the folder "Statistical analyses". They are sorted according to the sets of results reported in the article. This folder includes Excel files of the data used in the linear mixed models used. The programs are R and Matlab ones. The programs of the folder "Robustness analyses" are for the robustness analyses of the results obtained with programs of the "Statistical analyses" folder. The folder "Supplementary analyses" contains data and programs which were used to run analyses. Results of these analyses are reported in the Supplementary materials of the article. The compressed file “Supplementary prints of websites referenced in the article.zip” contains screenshots of the websites cited in the main text of the article. These screenshots were renewed in 2024. The first version is already included in the file “Prints of websites referenced in the article.zip”.