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...

Academic journals indicators developed from the information contained in the Scopus database (Elsevier B.V.). These indicators can be used to assess and analyze scientific domains.

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.

This description is part of the blog post "Systematic Literature Review of teaching Open Science" https://sozmethode.hypotheses.org/839

According to my opinion, we do not pay enough attention to teaching Open Science in higher education. Therefore, I designed a seminar to teach students the practices of Open Science by doing qualitative research.About this seminar, I wrote the article ”Teaching Open Science and qualitative methods“. For the article ”Teaching Open Science and qualitative methods“, I started to review the literature on ”Teaching Open Science“. The result of my literature review is that certain aspects of Open Science are used for teaching. However, Open Science with all its aspects (Open Access, Open Data, Open Methodology, Open Science Evaluation and Open Science Tools) is not an issue in publications about teaching.

Based on this insight, I have started a systematic literature review. I realized quickly that I need help to analyse and interpret the articles and to evaluate my preliminary findings. Especially different disciplinary cultures of teaching different aspects of Open Science are challenging, as I myself, as a social scientist, do not have enough insight to be able to interpret the results correctly. Therefore, I would like to invite you to participate in this research project!

I am now looking for people who would like to join a collaborative process to further explore and write the systematic literature review on “Teaching Open Science“. Because I want to turn this project into a Massive Open Online Paper (MOOP). According to the 10 rules of Tennant et al (2019) on MOOPs, it is crucial to find a core group that is enthusiastic about the topic. Therefore, I am looking for people who are interested in creating the structure of the paper and writing the paper together with me. I am also looking for people who want to search for and review literature or evaluate the literature I have already found. Together with the interested persons I would then define, the rules for the project (cf. Tennant et al. 2019). So if you are interested to contribute to the further search for articles and / or to enhance the interpretation and writing of results, please get in touch. For everyone interested to contribute, the list of articles collected so far is freely accessible at Zotero: https://www.zotero.org/groups/2359061/teaching_open_science. The figure shown below provides a first overview of my ongoing work. I created the figure with the free software yEd and uploaded the file to zenodo, so everyone can download and work with it:

To make transparent what I have done so far, I will first introduce what a systematic literature review is. Secondly, I describe the decisions I made to start with the systematic literature review. Third, I present the preliminary results.

Systematic literature review – an Introduction

Systematic literature reviews “are a method of mapping out areas of uncertainty, and identifying where little or no relevant research has been done.” (Petticrew/Roberts 2008: 2). Fink defines the systematic literature review as a “systemic, explicit, and reproducible method for identifying, evaluating, and synthesizing the existing body of completed and recorded work produced by researchers, scholars, and practitioners.” (Fink 2019: 6). The aim of a systematic literature reviews is to surpass the subjectivity of a researchers’ search for literature. However, there can never be an objective selection of articles. This is because the researcher has for example already made a preselection by deciding about search strings, for example “Teaching Open Science”. In this respect, transparency is the core criteria for a high-quality review.

In order to achieve high quality and transparency, Fink (2019: 6-7) proposes the following seven steps:

1. Selecting a research question.
2. Selecting the bibliographic database.
3. Choosing the search terms.
4. Applying practical screening criteria.
5. Applying methodological screening criteria.
6. Doing the review.
7. Synthesizing the results.

I have adapted these steps for the “Teaching Open Science” systematic literature review. In the following, I will present the decisions I have made.

Systematic literature review – decisions I made

1. Research question: I am interested in the following research questions: How is Open Science taught in higher education? Is Open Science taught in its full range with all aspects like Open Access, Open Data, Open Methodology, Open Science Evaluation and Open Science Tools? Which aspects are taught? Are there disciplinary differences as to which aspects are taught and, if so, why are there such differences?
2. Databases: I started my search at the Directory of Open Science (DOAJ). “DOAJ is a community-curated online directory that indexes and provides access to high quality, open access, peer-reviewed journals.” (https://doaj.org/) Secondly, I used the Bielefeld Academic Search Engine (base). Base is operated by Bielefeld University Library and “one of the world’s most voluminous search engines especially for academic web resources” (base-search.net). Both platforms are non-commercial and focus on Open Access publications and thus differ from the commercial publication databases, such as Web of Science and Scopus. For this project, I deliberately decided against commercial providers and the restriction of search in indexed journals. Thus, because my explicit aim was to find articles that are open in the context of Open Science.
3. Search terms: To identify articles about teaching Open Science I used the following search strings: “teaching open science” OR teaching “open science” OR teach „open science“. The topic search looked for the search strings in title, abstract and keywords of articles. Since these are very narrow search terms, I decided to broaden the method. I searched in the reference lists of all articles that appear from this search for further relevant literature. Using Google Scholar I checked which other authors cited the articles in the sample. If the so checked articles met my methodological criteria, I included them in the sample and looked through the reference lists and citations at Google Scholar. This process has not yet been completed.
4. Practical screening criteria: I have included English and German articles in the sample, as I speak these languages (articles in other languages are very welcome, if there are people who can interpret them!). In the sample only journal articles, articles in edited volumes, working papers and conference papers from proceedings were included. I checked whether the journals were predatory journals – such articles were not included. I did not include blogposts, books or articles from newspapers. I only included articles that fulltexts are accessible via my institution (University of Kassel). As a result, recently published articles at Elsevier could not be included because of the special situation in Germany regarding the Project DEAL (https://www.projekt-deal.de/about-deal/). For articles that are not freely accessible, I have checked whether there is an accessible version in a repository or whether preprint is available. If this was not the case, the article was not included. I started the analysis in May 2019.
5. Methodological criteria: The method described above to check the reference lists has the problem of subjectivity. Therefore, I hope that other people will be interested in this project and evaluate my decisions. I have used the following criteria as the basis for my decisions: First, the articles must focus on teaching. For example, this means that articles must describe how a course was designed and carried out. Second, at least one aspect of Open Science has to be addressed. The aspects can be very diverse (FOSS, repositories, wiki, data management, etc.) but have to comply with the principles of openness. This means, for example, I included an article when it deals with the use of FOSS in class and addresses the aspects of openness of FOSS. I did not include articles when the authors describe the use of a particular free and open source software for teaching but did not address the principles of openness or re-use.
6. Doing the review: Due to the methodical approach of going through the reference lists, it is possible to create a map of how the articles relate to each other. This results in thematic clusters and connections between clusters. The starting point for the map were four articles (Cook et al. 2018; Marsden, Thompson, and Plonsky 2017; Petras et al. 2015; Toelch and Ostwald 2018) that I found using the databases and criteria described above. I used yEd to generate the network. „yEd is a powerful desktop application that can be used to quickly and effectively generate high-quality diagrams.” (https://www.yworks.com/products/yed) In the network, arrows show, which articles are cited in an article and which articles are cited by others as well. In addition, I made an initial rough classification of the content using colours. This classification is based on the contents mentioned in the articles’ title and abstract. This rough content classification requires a more exact, i.e., content-based subdivision and

Introduction: These datasets contain information about journals in the eight regions of the world based on United Nations SDG classification (Central& Southern Asia, Europe, Eastern &South Eastern Asia, Latin America, North Africa& Western Asia, Oceania, North America and Sub-Saharan Africa) that are indexed in Web of Science/Scopus and are available in Ulrich periodical directory. The datasets were created by matching Ulrich journal information with journal information from Web of Science and Scopus.

Data Creation: A single Web of Science master journal list was created for SSCI, SCI, AHCI and ESCI by combining and removing duplicate records from their lists; the Web of Science master journal contained 21,908 unique journals. Only active scholarly journals from Scopus were included in this study; i.e. duplicates, all inactive sources, trade journals, book series, monographs and conference proceedings were removed. 26,029 active journals of the 43,013 sources in Scopus were included. Journal lists from 239 countries were collected from Ulrich comprehensive periodical directory and analyzed by region. After removal of duplicates, this generated a database of 83,429 unique active academic journals. To compile regional and global datasets, duplicate journals in the regional and global levels, respectively, were removed. The master journal lists created from Web of Science, Scopus and Ulrich were transferred to an SQL database for querying. Journal matching was carried out in two steps. Firstly, the ISSN numbers of journals in Web of Science and Scopus were used to match journal records to Ulrich. In the second step, the remaining journals were then matched using their titles, and these matches were manually verified to reduce the chances of false positives. Using these two steps, we were able to match 20,255 (92.46%) of the journals in Web of Science, and 23,349 (89.70%) of the academic journals from Scopus, with Ulrichsweb journal list.

In order to analyse specific features of data papers, we established a representative sample of data journals, based on lists from the European FOSTER Plus project , the German wiki forschungsdaten.org hosted by the University of Konstanz and two French research organizations.The complete list consists of 82 data journals, i.e. journals which publish data papers. They represent less than 0,5% of academic and scholarly journals. For each of these 82 data journals, we gathered information about the discipline, the global business model, the publisher, peer reviewing etc. The analysis is partly based on data from ProQuest’s Ulrichsweb database, enriched and completed by information available on the journals’ home pages.One part of the data journals are presented as “pure” data journals stricto sensu , i.e. journals which publish exclusively or mainly data papers. We identified 28 journals of this category (34%). For each journal, we assessed through direct search on the journals’ homepages (information about the journal, author’s guidelines etc.) the use of identifiers and metadata, the mode of selection and the business model, and we assessed different parameters of the data papers themselves, such as length, structure, linking etc.The results of this analysis are compared with other research journals (“mixed” data journals) which publish data papers along with regular research articles, in order to identify possible differences between both journal categories, on the level of data papers as well as on the level of the regular research papers. Moreover, the results are discussed against concepts of knowledge organization.

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.

Academic Search Complete is the world's most valuable and comprehensive scholarly, multi-disciplinary full-text database, with more than 8,500 full-text periodicals, including more than 7,300 peer-reviewed journals.

The global market for academic research databases is experiencing robust growth, projected to be valued at $259.3 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 5.9% from 2025 to 2033. This expansion is driven by several key factors. The increasing digitization of scholarly publications and the growing reliance on online research resources across universities, research institutions, and corporations are significant contributors. Furthermore, the expanding availability of open-access journals and repositories, while presenting challenges to some established players, ultimately broadens the overall market by increasing accessibility and usage. The rising demand for advanced search functionalities, data analytics tools integrated within these databases, and robust citation management systems also fuels market growth. Different subscription models, including free and charge-based access, cater to diverse user needs – students, teachers, experts, and others – further driving market segmentation and overall growth. The North American market currently holds a significant share due to the presence of major research institutions and established database providers. However, increasing research activities in Asia-Pacific and other regions are poised to fuel future growth, with a potentially significant increase in the market share in these regions over the forecast period. Competition remains intense among established players like Scopus, Web of Science, and PubMed, alongside newer entrants. Differentiation through superior indexing, advanced search capabilities, and specialized content areas is vital for success in this competitive landscape. The market segmentation by application (Student, Teacher, Expert, Others) and type of access (Charge, Free) provides valuable insights into the diverse user base and revenue streams. The "charge" segment is expected to maintain a significant market share, driven by the demand for comprehensive and specialized research content requiring paid subscriptions. However, the "free" segment, fueled by the increasing availability of open-access resources, will also show considerable growth, broadening accessibility and market penetration. Regional growth patterns will likely reflect existing research infrastructure and investments in higher education and research across different geographic areas. Continued technological advancements and innovation in areas such as artificial intelligence-powered search and data analysis will further shape the market landscape, leading to more sophisticated and efficient research tools in the years to come.

Database of bibliographic citations of multidisciplinary areas that covers various journals of medical, scientific, and social sciences including humanities.Publisher independent global citation database.

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.

This dataset was compiled as part of a study on Barriers and Opportunities in the Discoverability and Indexing of Student-led Academic Journals. The list of student journals and their details is compiled from public sources. This list is used to identify the presence of Canadian student journals in Google Scholar as well as in select indexes and databases: DOAJ, Scopus, Web of Science, Medline, Erudit, ProQuest, and HeinOnline. Additionally, journal publishing platform is recorded to be used for a correlational analysis against Google Scholar indexing results. For further details see README.

In order to analyse specific features of data papers, we established a representative sample of data journals, based on lists from the European FOSTER Plus project , the German wiki forschungsdaten.org hosted by the University of Konstanz and two French research organizations.
The complete list consists of 82 data journals, i.e. journals which publish data papers. They represent less than 0,5% of academic and scholarly journals. For each of these 82 data journals, we gathered information about the discipline, the global business model, the publisher, peer reviewing etc. The analysis is partly based on data from ProQuest’s Ulrichsweb database, enriched and completed by information available on the journals’ home pages.
One part of the data journals are presented as “pure” data journals stricto sensu , i.e. journals which publish exclusively or mainly data papers. We identified 28 journals of this category (34%). For each journal, we assessed through direct search on the journals’ homepages (information about the journal, author’s guidelines etc.) the use of identifiers and metadata, the mode of selection and the business model, and we assessed different parameters of the data papers themselves, such as length, structure, linking etc.
The results of this analysis are compared with other research journals (“mixed” data journals) which publish data papers along with regular research articles, in order to identify possible differences between both journal categories, on the level of data papers as well as on the level of the regular research papers. Moreover, the results are discussed against concepts of knowledge organization.

Poster presented at the Research Data Alliance 5th Plenary Meeting, March 2015. To best encourage data publishing by scientific researchers, the burden of submission needs to be low. Data archiving at the time of and in conjunction with article publication can be an effective means, by catching authors when they’re motivated and tying data submission into an already-familiar publication process. Here we share Dryad’s experiences with integrating journals using various workflows.

This dataset contains information about the open data policies of library and information science journals. Journal requirements for public data archiving and data availability statements, collected from journal websites, are included for 201 LIS publications.

The dataset consist of visiting data for 23 journals from Universitetsforlaget in the period 2014-2019. 11 journals were subscription based journals that converted to open access in 2017, an event which also is the topic of the study. The other 12 journals are subscription based journals, used for control in the study. Data is used in the publication "Attracting new users or business as usual? A case study of converting academic subscription based journals to open access ". The dataset also includes R-code for parsing, analysis and plots.

Research data to accommodate the article "Overlay journals: a study of the current landscape" (https://doi.org/10.1177/09610006221125208)

Identifying the sample of overlay journals was an explorative process (occurring during April 2021 to February 2022). The sample of investigated overlay journals were identified by using the websites of Episciences.org (2021), Scholastica (2021), Free Journal Network (2021), Open Journals (2021), PubPub (2022), and Wikipedia (2021). In total, this study identified 34 overlay journals. Please see the paper for more details about the excluded journal types.

The journal ISSN numbers, manuscript source repositories, first overlay volumes, article volumes, publication languages, peer-review type, licence for published articles, author costs, publisher types, submission policy, and preprint availability policy were observed by inspecting journal editorial policies and submission guidelines found from journal websites. The overlay journals’ ISSN numbers were identified by examining journal websites and cross-checking this information with the Ulrich’s periodicals database (Ulrichsweb, 2021). Journals that published review reports, either with reviewers’ names or anonymously, were classified as operating with open peer-review. Publisher types defined by Laakso and Björk (2013) were used to categorise the findings concerning the publishers. If the journal website did not include publisher information, the editorial board was interpreted to publish the journal.

The Organisation for Economic Co-operation and Development (OECD) field of science classification was used to categorise the journals into different domains of science. The journals’ primary OECD field of sciences were defined by the authors through examining the journal websites.

Whether the journals were indexed in the Directory of Open Access Journals (DOAJ), Scopus, or Clarivate Analytics’ Web of Science Core collection’s journal master list was examined by searching the services with journal ISSN numbers and journal titles.

The identified overlay journals were examined from the viewpoint of both qualitative and quantitative journal metrics. The qualitative metrics comprised the Nordic expert panel rankings of scientific journals, namely the Finnish Publication Forum, the Danish Bibliometric Research Indicator and the Norwegian Register for Scientific Journals, Series and Publishers. Searches were conducted from the web portals of the above services with both ISSN numbers and journal titles. Clarivate Analytics’ Journal Citation Reports database was searched with the use of both ISSN numbers and journal titles to identify whether the journals had a Journal Citation Indicator (JCI), Two-Year Impact Factor (IF) and an Impact Factor ranking (IF rank). The examined Journal Impact Factors and Impact Factor rankings were for the year 2020 (as released in 2021).

The principal goal of the research study is to analyze the transparency of a selection of academic journals based on an analysis model with 20 indicators grouped into 6 parameters. Given the evident interest in and commitment to transparency among quality academic journals and researchers’ difficulties in choosing journals that meet a set of criteria, we present indicators that may help researchers choose journals while also helping journals to consider what information from the editorial process to publish, or not, on their websites to attract authors in the highly competitive environment of today’s scholarly communication. To test the validity of the indicators, we analyze a small sample: the Spanish Communications and Library and Information Science journals listed in the Scimago Journal Rank. The results confirm that our analysis model is valid and can be extrapolated to other disciplines and journals.

Database providing access to quality controlled Open Access Journals. For a journal to be included it should exercise quality control on submitted papers through an editor, editorial board and/or a peer-review system. It is not be limited to particular languages or subject areas. Offering free online access to high quality full text content, plus excellent search tools, the portal enables researchers to find, use and re-use a vast range of materials with ease. The content of DOAJ will be even more visible and disseminated through this portal. The aim of the Directory is to increase the visibility and ease of use of open access scientific and scholarly journals thereby promoting their increased usage and impact. As of April 2014, DOAJ has 9,709 journals, 5,624 journals searchable at article level, 133 Countries and 1,600,991 articles. The database may be browsed by title or subject, or searched through the interface to for journals or articles.

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”.