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 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.

While art is omnipresent in human history, the neural mechanisms of how we perceive, value and differentiate art has only begun to be explored. Functional magnetic resonance imaging (fMRI) studies suggested that art acts as secondary reward, involving brain activity in the ventral striatum and prefrontal cortices similar to primary rewards such as food. However, potential similarities or unique characteristics of art-related neuroscience (or neuroesthetics) remain elusive, also because of a lack of adequate experimental tools: the available collections of art stimuli often lack standard image definitions and normative ratings. Therefore, we here provide a large set of well-characterized, novel art images for use as visual stimuli in psychological and neuroimaging research. The stimuli were created using a deep learning algorithm that applied different styles of popular paintings (based on artists such as Klimt or Hundertwasser) on ordinary animal, plant and object images which were drawn from established visual stimuli databases. The novel stimuli represent mundane items with artistic properties with proposed reduced dimensionality and complexity compared to paintings. In total, 2,332 novel stimuli are available open access as “art.pics” database at https://osf.io/BTWNQ/ with standard image characteristics that are comparable to other common visual stimuli material in terms of size, variable color distribution, complexity, intensity and valence, measured by image software analysis and by ratings derived from a human experimental validation study [n = 1,296 (684f), age 30.2 ± 8.8 y.o.]. The experimental validation study further showed that the art.pics elicit a broad and significantly different variation in subjective value ratings (i.e., liking and wanting) as well as in recognizability, arousal and valence across different art styles and categories. Researchers are encouraged to study the perception, processing and valuation of art images based on the art.pics database which also enables real reward remuneration of the rated stimuli (as art prints) and a direct comparison to other rewards from e.g., food or money.Key Messages: We provide an open access, validated and large set of novel stimuli (n = 2,332) of standardized art images including normative rating data to be used for experimental research. Reward remuneration in experimental settings can be easily implemented for the art.pics by e.g., handing out the stimuli to the participants (as print on premium paper or in a digital format), as done in the presented validation task. Experimental validation showed that the art.pics’ images elicit a broad and significantly different variation in subjective value ratings (i.e., liking, wanting) across different art styles and categories, while size, color and complexity characteristics remained comparable to other visual stimuli databases.

The global academic research database market is booming, projected to hit $388.2 million in 2025, with a robust CAGR driving growth. This in-depth analysis explores market size, key players (Scopus, Web of Science, PubMed), and future trends shaping this vital sector for researchers and educators.

This Excel sheet contains all published data to date on noble gas isotopes measured in natural diamonds. This database is open to the scientific community, which is invited to contribute and update data in the future. Cette feuille Excel contient toutes les données publiées sur les isotopes de gaz nobles mesurés dans les diamants naturels. Cette base de données est ouverte à la communauté scientifique, qui est invitée à contribuer et à mettre à jour les données à l'avenir.

The dataset shows the document types of open access items indexed in the database of Open-Access-Netzwerk. The dataset is presented in csv.

ST - DHS Public Access Database: Consistent with the 2013 OSTP Memorandum and the 2022 update, “Increasing Access to the Results of Federally Funded Scientific Research,” directed all agencies with greater than $100 million in R&D expenditures each year to prepare a plan for improving the public’s access to the results of federally funded research, specifically peer-reviewed scholarly publications and digital data. In response to the memorandum, DHS developed a DHS Public Access Plan, and intends to make available to the public digitally formatted scientific data that support the conclusions in peer-reviewed scholarly publications that are the results of DHS R&D funding. This data repository site with a customized DHS Storefront allows DHS to post releasable scientific digital data from peer-reviewed publications resulting from DHS-funded research. The data repository is configured to allow DHS users (and publishers acting on behalf of these users) to deposit data sets into the repository, making them available to the general public.

This dataset focuses on chamber-based methane (CH4) flux measurements in tidal wetlands across the Contiguous United States (CONUS)and is intended to serve as a community resource for Earth and environmental science research, climate change synthesis studies, and model evaluation. The database contains 35 contributed datasets with a total of 10,445 chamber-based CH4 flux observations across 41 years and 120 sites distributed across CONUS Atlantic and Pacific coasts and the Gulf of Mexico. Contributed datasets are converted to a standard format and units and organized hierarchically (site, chamber, chamber time series, porewater chemistry, and plant species) with metadata on contributors, geographic location, measurement conditions, and ancillary environmental variables. While focused on CH4 flux measurements, the database accommodates other greenhouse gas flux data (CO2 and N2O) as well as porewater profiles of various analytes, experimental treatments (e.g., fertilization, elevated CO2), and ecosystem disturbance classes (e.g., salinization, tidal restrictions, restoration). This database results from the Coastal Carbon Network’s (CCN) tidal wetland CH4 flux data synthesis. A description and analysis of the dataset are available in Arias-Ortiz et al. 2024, co-authored by members of the CCN Data Methane Working Group and data contributors.

Discover the booming academic research databases market! This comprehensive analysis reveals key trends, growth drivers, and leading players (Scopus, Web of Science, PubMed, etc.) impacting this multi-billion dollar industry from 2019-2033. Explore market size, CAGR, regional insights, and future forecasts.

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)

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

This dataset is for the paper "An Open-access Database for the Evaluation of Cardio-mechanical Signals from Patients with Valvular Heart Diseases" published to Frontiers in Physiology. Please cite "Yang C, Fan F, Aranoff N, Green P, Li Y, Liu C and Tavassolian N (2021) An Open-Access Database for the Evaluation of Cardio-Mechanical Signals From Patients With Valvular Heart Diseases. Front. Physiol. 12:750221. doi: 10.3389/fphys.2021.750221" when using this database.

The archive comprises SCG and GCG recordings sourced from and processed at multiple sites worldwide, including Columbia University Medical Center and Stevens Institute of Technology in the USA, as well as Southeast University, Nanjing Medical University, and the first affiliated hospital of Nanjing Medical University in China. It includes electrocardiogram (ECG), SCG, and GCG recordings collected from 100 patients with various conditions of valvular heart diseases, such as aortic and mitral stenosis. The recordings were collected from clinical environments with the same types of wearable sensor patch. Besides the raw recordings of ECG, SCG and GCG signals, a set of hand-corrected fiducial point annotations is provided by manually checking the results of the annotated algorithm. The database also includes relevant echocardiogram parameters associated with each subject such as ejection fraction, valve area, and mean gradient pressure.

ScienceCentral is a free or open access full-text archive of scientific society journal literature hosted by the Korean Federation of Science and Technology Societies. It was launched in December 2013. We analyzed the number of articles deposited, page views by period, country of visitors, number of visitors, and entry point of visits. Descriptive statistics were presented. We also hypothesized that visitors accessed ScienceCentral mostly through Google and Google Scholar since ScienceCentral allows Googlebot to index it. The number of deposited articles was 19,419 from 124 journals in December 2016. The number of page views per month was 20,228 in December 2016. The top countries of visitors were South Korea (39.9%), the United States (13.26%), India (4.2%), China (3.4%), and Russia (3.2%). The average number of page views per article a month in December 2016 was 1.0. Google and Google Scholar were powerful referral sites to ScienceCentral. Except for direct visits to ScienceCentral, seven out of the top ten access sites to ScienceCentral were Google or Google Scholar sites from a variety of countries. Although the number of visitors and page views has increased continuously, the average number of page views per article a month has not increased.

The code using these data to reproduce the figures in Taube et al. "An open-access database of infectious disease transmission trees enables exploration of superspreader epidemiology" can be found on Github at https://github.com/DrakeLab/taube-transmission-trees. OutbreakTrees, the database of transmission trees underlying these data, can be found at https://outbreaktrees.ecology.uga.edu/.

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.

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

The global academic research databases market is booming, projected to reach $259.3 million in 2025, with a CAGR of 5.9% through 2033. Discover key drivers, trends, and regional insights from this comprehensive market analysis covering Scopus, Web of Science, and more. Explore market segmentation by access type and user application.

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.

Scientific Data Management (SDM) Program shares and manages scientific and scientific program information systems in ways that support the mission and business of the NWFSC. We strive to bring quality information, in the right form, to the right people at the right time to support necessary decisions and generate ideas. Multi-FMC annual project planning (for budget, people, and operational costs) and data set tracking (for data entry to feed InPort/NCEI/Data.gov) TEST CASE TWO.

The COVID-19 Open Research Dataset is an extensive machine-readable resource of over 45,000 scholarly articles, including over 33,000 with full text, about COVID-19 and the coronavirus family of viruses for use by the global research community. This dataset is intended to mobilize researchers to apply recent advances in natural language processing to generate new insights in support of the fight against this infectious disease.

The dataset is updated weekly and contains all COVID-19 and coronavirus-related research (e.g., SARS, MERS) from the following sources: PubMed's PMC open access corpus (using this query: COVID-19 and coronavirus research), additional COVID-19 research articles from a corpus maintained by the World Health Organization (WHO), and bioRxiv and medRxiv pre-prints (using this query: COVID-19 and coronavirus research). Also available is a comprehensive metadata file of 44,000 coronavirus and COVID-19 research articles with links to PubMed, Microsoft Academic, and the WHO COVID-19 database of publications (includes articles without open access full text).