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...

Database compilation of global and regional data sources used in the three case studies. Some of the sources included in this database were identified in collaboration with the regional stakeholders. This inventory of Open Access databases is relevant to multi-objective, multi-realm and multi-time planning in Europe.

The National Pollutant Release Inventory (NPRI) is Canada's public inventory of pollutant releases (to air, water and land), disposals and transfers for recycling. This database contains the full NPRI dataset from 1993 to the current reporting year. To help you navigate, a Microsoft Word file provides information on the database’s structure and schema. The database is available in Microsoft Access format (accdb). The data are in normalized or “list” format and are optimized for pivot table analyses. The data are also available in a CSV format : https://open.canada.ca/data/en/dataset/40e01423-7728-429c-ac9d-2954385ccdfb. Please consult the following resources to enhance your analysis: - Guide on using and Interpreting NPRI Data: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/using-interpreting-data.html - Access additional data from the NPRI, including datasets and mapping products: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/tools-resources-data/exploredata.html Supplemental Information This data is also available in non-proprietary CSV format on the Bulk Data page. http://open.canada.ca/data/en/dataset/40e01423-7728-429c-ac9d-2954385ccdfb These files contain data from 1993 to the latest reporting year available. These datasets are in normalized or ‘list’ format and are optimized for pivot table analyses. Supporting Projects: National Pollutant Release Inventory (NPRI)

The global market for academic research databases is experiencing robust growth, projected to reach $388.2 million in 2025. While the exact Compound Annual Growth Rate (CAGR) is not provided, considering the ongoing digitalization of research and education, a conservative estimate would place the CAGR in the range of 7-9% for the forecast period (2025-2033). This growth is fueled by several key drivers. The increasing reliance on digital resources by students, teachers, and researchers across all academic disciplines is a significant factor. Furthermore, the expanding volume of scholarly publications and the need for efficient access and management of research data are propelling market expansion. The rising adoption of cloud-based solutions and the development of sophisticated search and analytical tools within these databases are also contributing to this growth trajectory. The market segmentation highlights the diverse user base, with students, teachers, and experts representing major segments, each with varying needs and subscription models (charge-based or free access). The competitive landscape is characterized by established players like Scopus, Web of Science, and PubMed, alongside other significant contributors like ERIC, ProQuest, and IEEE Xplore, indicating a market with both established dominance and emerging players vying for market share. Geographic distribution shows a strong presence across North America and Europe, but with significant growth potential in Asia-Pacific regions. The market's future trajectory will likely be shaped by several trends. The increasing integration of artificial intelligence (AI) for enhanced search and data analysis capabilities will be a major factor. The ongoing development of open-access initiatives and the expansion of free databases will influence market dynamics, potentially impacting the revenue streams of subscription-based services. However, challenges such as data security concerns, the need for continuous content updates, and the varying levels of digital literacy across different user groups may act as restraints on market growth. Nevertheless, the overall outlook for the academic research database market remains positive, driven by the continued expansion of scholarly research and the growing demand for efficient and reliable access to research information globally.

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

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.

Research shows that natural environments have a beneficial effect on cognition and mental health. However, there is limited understanding of what properties within environments are most beneficial. Research in this area is limited by a lack of standardised, open-source, rated image databases dedicated to natural scenes.

In this study we will address this by having participants rate images from a database of 500 natural images on specific cognitive restoration properties (pleasantness, familiarity etc.) as well as approach-avoidance, arousal and valence. Physical characteristics of the images will also be reported. Participants will also provide demographic information to understand how individual differences might affect how people rate natural environments. The outcome of this will be a standardised database of rated images which are freely available to facilitate research in this area.

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 analysis, and 4) scoped to librarianship. The dataset presents an overview of the landscape of open access scholarly publishing in the LIS field during a very specific time period, spring and summer of 2023. Methods 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 published

Open ranking: details whether the journal is diamond, gold, and/or green

Open peer review: specifies if the journal does open peer review

Author retains copyright: explains copyright policy

Charges: Details whether there is an article processing charge

In DOAJ: details whether the journal is also published in the Directory of Open Access Journals

The second set includes similar information, but it includes the titles of journals listed in the DOAJ.

Journal: states the title of the journal

Publisher: title of the publishing company

Country: country where the journal is published

Open Data Policy: lists whether an open data exists

Open Data Notes: Details about the open data policy

OA since: lists when the journal became open access

Open ranking: details whether the journal is diamond, gold, and/or green

Open peer review: specifies if the journal does open peer review

Author Holds Copyright without Restriction: lists

APC: Details whether there is an article processing charge

Type of CC: lists the Creative Commons license applied to the journal articles

In LISA: details whether the journal is also published in the Library and Information Science Abstracts database

A third dataset includes twelve scholarly, peer reviewed journals focused on Library and Information Science but not included in the DOAJ or LISA.

Journal: states the title of the journal

Publisher: title of the publishing company

Country: country where the journal is published

Open Data Policy: lists whether an open data exists

Open Data Notes: Details about the open data policy

Open ranking: details whether the journal is diamond, gold, and/or green

Open peer review: specifies if the journal does open peer review

Author Holds Copyright without Restriction: lists

APC: Details whether there is an article processing charge

Type of CC: lists the Creative Commons license applied to the journal articles

In LISA?: details whether the journal is also published in the Library and Information Science Abstracts database

Data Processing The researchers downloaded an Excel file from the publisher Proquest that listed the 221 journals included in LISA. From the DOAJ, the researchers searched and scoped to build an initial list. Thus, 144 journals were identified after limiting search results to English-language only journals and those whose scope fell under the following DOAJ search terms: librar* (to cover library, libraries, librarian, librarians, librarianship). Journals also needed to have been categorized within the DOAJ subject heading “Bibliography. Library science. Information resources. And for the journals that we analyzed that were in either index, those journals were included based on the researchers’ knowledge of current scholarly, peer-reviewed journals that would count toward tenure at their own university, an R1 university. Once the journals were identified, the researchers divided up the journals amongst each other and scoped them for the following criteria: 1) peer-reviewed 2) written in English or abstracted in English, 3) actively published at the time of analysis, and 4) scoped to librarianship. The end result was 134 journals that the researchers then explored on their individual websites to identify the following items: open data policies, open access publication options, country of origin, publisher, and peer review process. The researchers also looked for article processing costs, type of Creative Commons licensing (open licenses that allow users to redistribute and sometimes remix intellectual property), and whether the journals were included in either the DOAJ and/or LISA index. References: Budapest Open Access Initiative. (2002) http://www.soros.org/openaccess/

PatCID is a chemical-structure database automatically created from images in patent documents. It contains 13M unique molecules, 80M molecule images, and 1.2M annotated documents from the United States (USPTO), Europe (EPO), Japan (JPO), Korea (KIPO), and China (CNIPA).

Leveraging state-of-the-art document understanding models, PatCID enables accurate document and molecule retrieval in patents.

Examples of how to use PatCID can be found on the PatCID GitHub repository

PigBiobank is one of the most ambitious and extensive biobank projects globally, aimed at improving our understanding of diverse and complex traits in pigs. It is a large-scale research database that collects and stores a comprehensive range of genetic data and biological samples from thousands of participants. The database is periodically replenished with new data and is freely accessible to researchers. It contributes to a deeper insight into pig biology, genetics, and health, as well as its broader implications for human health and agriculture.

Historically, emerging and reemerging infectious diseases have caused large, deadly, and expensive multinational outbreaks. Often outbreak investigations aim to identify who infected whom by reconstructing the outbreak transmission tree, which visualizes transmission between individuals as a network with nodes representing individuals and branches representing transmission from person to person. We compiled a database, called OutbreakTrees, of 382 published, standardized transmission trees consisting of 16 directly transmitted diseases ranging in size from 2 to 286 cases. For each tree and disease, we calculated several key statistics, such as tree size, average number of secondary infections, the dispersion parameter, and the proportion of cases considered superspreaders, and examined how these statistics varied over the course of each outbreak and under different assumptions about the completeness of outbreak investigations. We demonstrated the potential utility of the database through 2 short analyses addressing questions about superspreader epidemiology for a variety of diseases, including Coronavirus Disease 2019 (COVID-19). First, we found that our transmission trees were consistent with theory predicting that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events. Additionally, we investigated patterns in how superspreaders are infected. Across trees with more than 1 superspreader, we found preliminary support for the theory that superspreaders generate other superspreaders. In sum, our findings put the role of superspreading in COVID-19 transmission in perspective with that of other diseases and suggest an approach to further research regarding the generation of superspreaders. These data have been made openly available to encourage reuse and further scientific inquiry.

The dataset contains all the data produced running the research software for the study:"Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta".

Disclaimer: these results are not considered to be representative, because we have fount that Mega Journals skewed significantly some of the data. The result datasets without Mega Journals are published here.

Description of datasets:

SSH_Publications_in_OC_Meta_and_Open_Access_status.csv: containing information about OpenCitations Meta coverage of ERIH PLUS Journals as well as their Open Access availability. In this dataset, every row holds data for a Journal of ERIH PLUS also covered by OpenCitations Meta database. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "OC_omid", the internal OpenCitations Meta identifier for the venue; "issn", numbers of publications in each venue; "Open Access", a value to represent if the journal is OA or not, either "True" or "Unknown".

SSH_Publications_by_Discipline.csv: containing information about number of publications per discipline (in addition, number of journals per discipline are also included). The dataset has three columns, the first, labeled "Discipline", contains single disciplines of the ERIH classificaton, the second and the third, labeled "Journal_count" and "Publication_count", respectively, the number of Journals and the number of Publications counted for each discipline.

SSH_Publications_and_Journals_by_Country: containing information about number of publications and journals per country. The dataset has three columns, the first, labeled "Country", contains single countries of the ERIH classificaton, the second and the third, labeled "Journal_count" and "Publication_count", respectively, the number of Journals and the number of Publications counted for each discipline.

result_disciplines.json: the dictionary containing all disciplines as key and a list of related ERIH PLUS venue identifiers as value.

result_countries.json: the dictionary containing all countries as key and a list of related ERIH PLUS venue identifiers as value.

duplicate_omids.csv: a dataset containing the duplicated Journal entries in OpenCitations Meta, structured with two columns: "OC_omid", the internal OC Meta identifier; "issn", the issn values associated to that identifier

eu_data.csv: contains the data specific for European countries' SSH Journals covered in OCMeta. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "Original_Title", "Country_of_Publication","ERIH_PLUS_Disciplines", "disc_count", the number of disciplines per Journal.

eu_disciplines_count.csv: containing information about number of publications per discipline and number of journals per discipline of european countries. The dataset has three columns, the first, labeled "Discipline", contains single disciplines of the ERIH classificaton, the second and the third, labeled "Journal_count" and "Publication_count", respectively, the number of Journals and the number of Publications counted for each discipline.

meta_coverage_eu.csv: contains the data specific for European countries' SSH Journals covered in OCMeta. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "OC_omid", the internal OpenCitations Meta identifier for the venue; "issn", numbers of publications in each venue; "Open Access", a value to represent if the journal is OA or not, either "True" or "Unknown".

us_data.csv: contains the data specific for the United States' SSH Journals covered in OCMeta. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "Original_Title", "Country_of_Publication","ERIH_PLUS_Disciplines", "disc_count", the number of disciplines per Journal.

us_disciplines_count.csv: containing information about number of publications per discipline and number of journals per discipline of the United States. The dataset has three columns, the first, labeled "Discipline", contains single disciplines of the ERIH classificaton, the second and the third, labeled "Journal_count" and "Publication_count", respectively, the number of Journals and the number of Publications counted for each discipline.

meta_coverage_us.csv: contains the data specific for the United States' SSH Journals covered in OCMeta. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "OC_omid", the internal OpenCitations Meta identifier for the venue; "issn", numbers of publications in each venue; "Open Access", a value to represent if the journal is OA or not, either "True" or "Unknown".

Abstract of the research:

Purpose: this study aims to investigate the representation and distribution of Social Science and Humanities (SSH) journals within the OpenCitations Meta database, with a particular emphasis on their Open Access (OA) status, as well as their spread across different disciplines and countries. The underlying premise is that open infrastructures play a pivotal role in promoting transparency, reproducibility, and trust in scientific research. Study Design and Methodology: the study is grounded on the premise that open infrastructures are crucial for ensuring transparency, reproducibility, and fostering trust in scientific research. The research methodology involved the use of secondary data sources, namely the OpenCitations Meta database, the ERIH PLUS bibliographic index, and the DOAJ index. A custom research software was developed in Python to facilitate the processing and analysis of the data. Findings: the results reveal that 78.1% of SSH journals listed in the European Reference Index for the Humanities (ERIH-PLUS) are included in the OpenCitations Meta database. The discipline of Psychology has the highest number of publications. The United States and the United Kingdom are the leading contributors in terms of the number of publications. However, the study also uncovers that only 38% of the SSH journals in the OpenCitations Meta database are OA. Originality: this research adds to the existing body of knowledge by providing insights into the representation of SSH in open bibliographic databases and the role of open access in this domain. The study highlights the necessity for advocating OA practices within SSH and the significance of open data for bibliometric studies. It further encourages additional research into the impact of OA on various facets of citation patterns and the factors leading to disparity across disciplinary representation.

Related resources:

Ghasempouri S., Ghiotto M., & Giacomini S. (2023). Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - RESEARCH ARTICLE. https://doi.org/10.5281/zenodo.8263908

Ghasempouri, S., Ghiotto, M., Giacomini, S., (2023). Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - DATA MANAGEMENT PLAN (Version 4). Zenodo. https://doi.org/10.5281/zenodo.8174644

Ghasempouri, S., Ghiotto, M., Giacomini, S. (2023e). Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - PROTOCOL. V.5. (https://dx.doi.org/10.17504/protocols.io.5jyl8jo1rg2w/v5)

The database includes the classification of 966 active nitrogen-relevant policies from South Asia (including Afghanistan, Pakistan, India, Nepal, Bhutan, Bangladesh, the Maldives and Sri Lanka). The collection during 2020 and 2021 focuses on national level policies; some subnational policies were also collected. Data collection involved building on an existing open access global database developed by Kanter et al., 2020 that contained 51 policies for South Asia established to 2017 sourced by the environmental law ECOLEX database. Further policies were collected mostly from online sources: such as international policy databases: FAOLEX and national government and ministry websites. A protocol for policy collection and classification was established and followed to ensure consistent and thorough collections across the eight countries. Policies were classified according to a variety of parameters including the sink (air, water etc.) and sector (agriculture, industry etc.) they address and by type of policy. Policies were clustered if they had a central node policy in place and if a ‘subordinate policy’ (including amendments) did not offer anything new in terms of content related to Nitrogen management. This data was collected as part of a collective partnership that brings together leading organisations from across South Asia and the UK to reduce the adverse global impacts of nitrogen pollution on the environment, health, and wellbeing. More specifically providing a resource for both SANH partners and the wider scientific and policy community to understand the nitrogen policy landscape in the south Asian region. Furthermore, this research contributes to efforts in building a nitrogen policy arena promoting sustainable management of nitrogen, mitigating adverse effects. The dataset provides a thorough overview of available nitrogen related policies in South Asia but does not provide a complete set of all the nitrogen relevant policies available in each country. In some cases, this was due to our dependency on policy availability online, and some websites were not maintained. In addition, we excluded policies established post 2020 to avoid policy responses to COVID19 and to align more closely with the original global study. Repealed policies were omitted from the 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.

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.

Geochemical fingerprinting of artefacts and sources has proven to be the most effective way to use material evidence in order to reconstruct strategies of raw material procurement, exchange systems, and mobility patterns among past societies. In order to facilitate access to this growing body of data and to promote comparability and reproducibility in provenance studies, we designed Pofatu, the first online and open-access database presenting geochemical compositions and contextual information for archaeological sources and artefacts.

The data repository includes a compilation of geochemical data and supporting analytical metadata, as well as the archaeological provenance and context for each sample. All information on Samples related to sources and artefacts can be accessed on this platform or downloaded from Zenodo or GitHub.

While most prehistoric quarries and surface procurement sources used in the past have yet to be identified, provenance studies must also integrate wide and reliable geological data. For this reason, we advise Pofatu users to also consult other open-access repositories focusing specifically on geological samples, such as GeoRoc and EarthChem.

Overview of all included and excluded databases for clinical variant annotation in cancer with the help of open-access databases. Databases and web links were last accessed on January 28, 2021.

Dataset belonging to the report: Publication cultures and Dutch research output: a quantitative assessment

On the report:

Research into publication cultures commissioned by VSNU and carried out by Utrecht University Library has detailed university output beyond just journal articles, as well as the possibilities to assess open access levels of these other output types. For all four main fields reported on, the use of publication types other than journal articles is indeed substantial. For Social Sciences and Arts & Humanities in particular (with over 40% and over 60% of output respectively not being regular journal articles) looking at journal articles only ignores a significant share of their contribution to research and society. This is not only about books and book chapters, either: book reviews, conference papers, reports, case notes (in law) and all kinds of web publications are also significant parts of university output.

Analyzing all these publication forms and especially determining to what extent they are open access is currently not easy. Even combining some the largest citation databases (Web of Science, Scopus and Dimensions) leaves out a lot of non-article content and in some fields even journal articles are only partly covered. Lacking metadata like affiliations and DOIs (either in the original documents or in the scholarly search engines) makes it even harder to analyze open access levels by institution and field. Using repository-harvesting databases like BASE and NARCIS in addition to the main citation databases improves understanding of open access of non-article output, but these routes also have limitations. The report has recommendations for stakeholders, mostly to improve metadata and coverage and apply persistent identifiers.

The dataset shows the subjects of open access items (2012) indexed in the database of Open-Access-Netzwerk. The dataset is presented in csv.

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to

establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data

Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered.
Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review:

Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection.
Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation.

See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt