Since the 1950s, the number of doctorate recipients has risen dramatically in the United States. In this paper, we investigate whether the longevity of doctorate recipients’ publication careers has changed. This is achieved by matching 1951–2010 doctorate recipients with rare names in astrophysics, chemistry, economics, genetics and psychology in the dissertation database ProQuest to their publications in the publication database Web of Science. Our study shows that pre-PhD publication careers have changed: the median year of first publication has shifted from after the PhD to several years before PhD in most of the studied fields. In contrast, post-PhD publication career spans have not changed much in most fields. The share of doctorate recipients who have published for more than twenty years has remained stable over time; the shares of doctorate recipients publishing for shorter periods also remained almost unchanged. Thus, though there have been changes in pre-PhD publication careers, post-PhD career spans remained quite stable.

Market Size statistics on the Database & Directory Publishing industry in United States

Summary of all data collected with corresponding sources and databases.

General descriptionThis dataset contains some markers of Open Science in the publications of the Chemical Biology Consortium Sweden (CBCS) between 2010 and July 2023. The sample of CBCS publications during this period consists of 188 articles. Every publication was visited manually at its DOI URL to answer the following questions.1. Is the research article an Open Access publication?2. Does the research article have a Creative Common license or a similar license?3. Does the research article contain a data availability statement?4. Did the authors submit data of their study to a repository such as EMBL, Genbank, Protein Data Bank PDB, Cambridge Crystallographic Data Centre CCDC, Dryad or a similar repository?5. Does the research article contain supplementary data?6. Do the supplementary data have a persistent identifier that makes them citable as a defined research output?VariablesThe data were compiled in a Microsoft Excel 365 document that includes the following variables.1. DOI URL of research article2. Year of publication3. Research article published with Open Access4. License for research article5. Data availability statement in article6. Supplementary data added to article7. Persistent identifier for supplementary data8. Authors submitted data to NCBI or EMBL or PDB or Dryad or CCDCVisualizationParts of the data were visualized in two figures as bar diagrams using Microsoft Excel 365. The first figure displays the number of publications during a year, the number of publications that is published with open access and the number of publications that contain a data availability statement (Figure 1). The second figure shows the number of publication sper year and how many publications contain supplementary data. This figure also shows how many of the supplementary datasets have a persistent identifier (Figure 2).File formats and softwareThe file formats used in this dataset are:.csv (Text file).docx (Microsoft Word 365 file).jpg (JPEG image file).pdf/A (Portable Document Format for archiving).png (Portable Network Graphics image file).pptx (Microsoft Power Point 365 file).txt (Text file).xlsx (Microsoft Excel 365 file)All files can be opened with Microsoft Office 365 and work likely also with the older versions Office 2019 and 2016. MD5 checksumsHere is a list of all files of this dataset and of their MD5 checksums.1. Readme.txt (MD5: 795f171be340c13d78ba8608dafb3e76)2. Manifest.txt (MD5: 46787888019a87bb9d897effdf719b71)3. Materials_and_methods.docx (MD5: 0eedaebf5c88982896bd1e0fe57849c2),4. Materials_and_methods.pdf (MD5: d314bf2bdff866f827741d7a746f063b),5. Materials_and_methods.txt (MD5: 26e7319de89285fc5c1a503d0b01d08a),6. CBCS_publications_until_date_2023_07_05.xlsx (MD5: 532fec0bd177844ac0410b98de13ca7c),7. CBCS_publications_until_date_2023_07_05.csv (MD5: 2580410623f79959c488fdfefe8b4c7b),8. Data_from_CBCS_publications_until_date_2023_07_05_obtained_by_manual_collection.xlsx (MD5: 9c67dd84a6b56a45e1f50a28419930e5),9. Data_from_CBCS_publications_until_date_2023_07_05_obtained_by_manual_collection.csv (MD5: fb3ac69476bfc57a8adc734b4d48ea2b),10. Aggregated_data_from_CBCS_publications_until_2023_07_05.xlsx (MD5: 6b6cbf3b9617fa8960ff15834869f793),11. Aggregated_data_from_CBCS_publications_until_2023_07_05.csv (MD5: b2b8dd36ba86629ed455ae5ad2489d6e),12. Figure_1_CBCS_publications_until_2023_07_05_Open_Access_and_data_availablitiy_statement.xlsx (MD5: 9c0422cf1bbd63ac0709324cb128410e),13. Figure_1.pptx (MD5: 55a1d12b2a9a81dca4bb7f333002f7fe),14. Image_of_figure_1.jpg (MD5: 5179f69297fbbf2eaaf7b641784617d7),15. Image_of_figure_1.png (MD5: 8ec94efc07417d69115200529b359698),16. Figure_2_CBCS_publications_until_2023_07_05_supplementary_data_and_PID_for_supplementary_data.xlsx (MD5: f5f0d6e4218e390169c7409870227a0a),17. Figure_2.pptx (MD5: 0fd4c622dc0474549df88cf37d0e9d72),18. Image_of_figure_2.jpg (MD5: c6c68b63b7320597b239316a1c15e00d),19. Image_of_figure_2.png (MD5: 24413cc7d292f468bec0ac60cbaa7809)

Data for publication in Data in Brief 2023

Datasets for publication: 'Measuring the excellence contribution at the journal level: An alternative to Garfield's Impact Factor'.

Overview. Overview of the number of journals, publications, excellent publications and multidisciplinarity for each category considered.

ALL. Journal indicators for all the document types by JCR category.

ALL_JCR. Journal indicators for all the document types by JCR category (only journals indexed in the JCR category are taken into account).

AR. Journal indicators for only articles and reviews by JCR category.

AR_JCR. Journal indicators for only articles and reviews by JCR category (only journals indexed in the JCR category are taken into account).

The dataset contains information about surgical journals included in the SCOPUS database used in our research. The following information is present in the data file:

Title

Journal sub-specialty

Country Origin

Continent

SJR

Publisher

Types of Publisher

Publication Model

Language

Protein-Protein, Genetic, and Chemical Interactions for Chen X (2002):TTD: Therapeutic Target Database. curated by BioGRID (https://thebiogrid.org); ABSTRACT: A number of proteins and nucleic acids have been explored as therapeutic targets. These targets are subjects of interest in different areas of biomedical and pharmaceutical research and in the development and evaluation of bioinformatics, molecular modeling, computer-aided drug design and analytical tools. A publicly accessible database that provides comprehensive information about these targets is therefore helpful to the relevant communities. The Therapeutic Target Database (TTD) is designed to provide information about the known therapeutic protein and nucleic acid targets described in the literature, the targeted disease conditions, the pathway information and the corresponding drugs/ligands directed at each of these targets. Cross-links to other databases are also introduced to facilitate the access of information about the sequence, 3D structure, function, nomenclature, drug/ligand binding properties, drug usage and effects, and related literature for each target. This database can be accessed at http://xin.cz3.nus.edu.sg/group/ttd/ttd.asp and it currently contains entries for 433 targets covering 125 disease conditions along with 809 drugs/ligands directed at each of these targets. Each entry can be retrieved through multiple methods including target name, disease name, drug/ligand name, drug/ligand function and drug therapeutic classification.

Including the Science and Technology Pavilion journals, publications, and other materials.

Two Datasets: works_published and works_cited for year 2022 from OpenAlex database.Check license https://github.com/ourresearch/openalex-docs/blob/main/license.md "OpenAlex data is made available under the CC0 license. That means it's in the public domain, and free to use in any way you like. We appreciate attribution where it's convenient, but it's not at all necessary. There is one exception: the MAG Format snapshot is released under ODC-BY, as per the original MAG license applied by Microsoft (it reuses their schema). See the LICENSE.txt file in the MAG format snapshot distribution for attribution requirement details."Data Quality Considerations:OpenAlex has improved the accuracy of the data with helps from algorithms and institutions.Our current data quality assessment showed the precision and recall 95%+.The first dataset "works_published", as constructed in the provided sources, refers to the publications authored by individuals affiliated with the University of Arizona (UArizona). The data is retrieved using the OpenAlexR package by querying the OpenAlex database with UArizona's Research Organization Registry (ROR) ID (03m2x1q45) and specific publication date ranges. Key aspects of this dataset:Scope: It contains records of scholarly works associated with UArizona authors, including various publication types such as journals, repositories (like PubMed and arXiv), and others. It is also possible to filter the results to include only "journal" type publications using the primary_location.source.type = "journal" parameter in the oa_fetch function.Temporal Coverage: The sources demonstrate fetching data for specific years (e.g., 2019, 2020, 2021, 2022, 2023).Data Retrieval: The process involves using the oa_fetch function from the openalexR package with the entity="works" parameter and specifying the institutions.ror.Data Structure: Each record in this dataset represents a publication and includes various fields. Certain fields are data frames.Usage: This dataset is used as a starting point for various data analyses and data mining.The second dataset "works_cited", refers to scholarly works cited by the publications within the works_published dataset. It is created by extracting the OpenAlex IDs from the $referenced_works field of the works_published data and then using the oa_fetch function to retrieve the full metadata for these cited works. Key aspects of this dataset:Scope: It includes metadata for a wide range of scholarly works that have been cited by UArizona-affiliated publications. This can encompass articles, books, preprints, book chapters, and other types of scholarly outputs.Data Derivation: The dataset is derived from the referenced_works field of the works_published dataset.Data Structure: Each record in this dataset represents a cited work and contains various fields retrieved by the OpenAlex API.The third file (institution_publications.r) is the source code to get the above dataset.Note the code retrieves additional years in addition to 2022.Usage: Both datasets are crucial for performing publication and citation analysis and mining, including:Identifying the most frequently cited works and journals.Analyzing the journal usage and publisher distribution of cited works.Understanding the scholarly landscape influencing UArizona research.Identifying potential resources for library collections based on citation frequency.Investigating the presence and frequency of citations from specific publishers or to specific works.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.eduThis item is part of University of Arizona authors' scholarly works published and cited works

This database compiles systematic reviews (SRs) of animal studies (i.e., reviews that focused exclusively on non-human animal research, or reviews that included animal studies along with human studies). This database was developed using a rigorous, systematic approach and it covers a broad range of research fields: preclinical research, toxicology, environmental health, and veterinary medicine. The goals of this database are to: (1) provide a comprehensive collection of animal study SRs to advance systematic review methods development; (2) enable researchers to avoid duplication of effort and, thus, reduce research waste by identifying published SRs of animal studies that may already address a research question; and (3) aid in the creation of evidence maps, usually designed as interactive figures of study characteristics.

The SRs included in the database were identified using a comprehensive search strategy (see data) in MEDLINE (via PubMed), Embase (via Ovid), and Web of Science. The records included in the animal studies SR database meet the following eligibility criteria: 1. The reference aims to systematically review the literature. The title or abstract states this aim using terminology such as “literature review,” “literature overview,” “systematic review,” “systematic survey,” or “meta-analysis.” 2. The reference summarizes the results of studies in laboratory or experimental animals to investigate human or animal health. 3. The reference reports the eligibility criteria for the primary studies, specifies search terms, and the search is performed in at least one specified database/electronic source (e.g., PubMed). 4. A full text version of the reference is publicly available.

There were no restrictions in language or publication date.

Version 1.0 covers data through 13 February 2018 Version 1.1 covers data through 18 June 2019

Employment statistics on the Database, Storage & Backup Software Publishing industry in the US

IVIS data from the four experiments used in this publication. Refer to the sample data file to what mouse is what condition. QUT Research Data Respository Dataset Resource available for download

The database represents emissions rates quantified by Carbon Mapper across landfills observed and reported in Cusworth et al. (2024). Carbon Mapper leverages hyperspectral imaging spectroscopy to detect, locate, and quantify localized methane emissions. The database contains three sheets: (1) Unique plume detections: this dataset includes individual emission detections (latitude, longitude, emission rate, emission rate uncertainty, datetime) as well as the corresponding landfill’s GHGRP ID. (2) Daily-Average Landfill Emission: daily averaged landfill emissions derived by spatial aggregation and averaging of individual landfill emissions following the methods described in Cusworth et al. (2021). (3) Time-averaged Source List: Average emission rates across all dates of observation. This dataset is associated with the following publication: Cusworth, D.H., R.M. Duren, A.K. Ayasse, R. Jiorle, K. Howell, A. Aubrey, R.O. Green, M.L. Eastwood, J.W. Chapman, A.K. Thorpe, J. Heckler, G.P. Asner, M.L. Smith, E. Thoma, M. Krause, D. Heins, and S. Thorneloe-Howard. Quantifying methane emissions from United States landfills. SCIENCE. American Association for the Advancement of Science (AAAS), Washington, DC, USA, 383(6690): 1499-1504, (2024).

This dataset is about books. It has 1 row and is filtered where the book is Advanced database techniques. It features 7 columns including author, publication date, language, and book publisher.

The global full-text database market is projected to grow from XXX million in 2025 to XXX million by 2033, at a CAGR of XX% during the forecast period. The growth is attributed to increasing demand for information retrieval, advancements in technology, and rising need for efficient research and development. Key drivers of the market include growing adoption of digital libraries, rising demand for personalized content, and increasing focus on research and development. Key trends in the full-text database market include the emergence of artificial intelligence (AI) and machine learning (ML) technologies, the growth of open access publishing, and the increasing adoption of cloud-based solutions. The market is segmented by application (academic research, corporate research, legal research, and others) and by type (bibliographic, full-text, and abstract). Major players in the market include John Wiely & Sons, ICPSR, IEEE, EBSCO, UMI, Blackwell, Springer Link, Elsevier Science, Apache Solr, Elastic N.V., CNKI, China Science and Technology Journal Database, Wanfang Data Knowledge Service Platform, China Science Citation Database, and Chinese, Western, Japanese and Russian Journals Joint Directory Database. The market is expected to witness significant growth in emerging economies, such as China and India, due to rising literacy rates and increasing demand for information access.

The present Dataset containes the data collected for the gEneSys Systematic Literature Review on the nexus between gender and energy transition. Data have been collected from 152 papers published between 2000 and 2023. The publications have been identified through an hoc research query and retrieved from the Web of Science Database.

The dataset inscludes the following variables:

Title of the publication, category of the categorization of Bell et al., 2020 (Political, Economic, Socio-Ecological, Technological).

Cluster in which the publication has been included.

Parts of the publication’s results about the nexus between gender and energy.

Parts of the publication’s text about the gender gap assessed by the publication.

Parts of the publication’s text about the gender gap identified to be bridged by future research.

The type of the gender issue/s addressed by the publication.

The type of the gender issue/s addressed by the publication.

Technology/ies mentioned in the publication.

The name of the country or countries studied by the publication.

World Bank classification of the level of income of the country or countries studied by the publication.

World Bank classification of the region of the country or countries studied by the publication.

Spatial Context (e.g. international, national, inner-country, peri-urban, rural) of the country or countries studied by the publication.

Research method employed in the publication (qualitative, quantitative, mixed).

Specific qualitative, quantitative or mixed method or methods employed in the publication.

Number of observations for the methods used.

Parts of the publication’s text about the policy recommendations elaborated in the publication.

If the publication mentions a pathway.

Year of publication.

Author/s surname and name initial.

Author/s full surnames and names.

Keywords chosen by the author/s.

Abstract of the publication.

Name of the source or journal.

Type of publication.

Category/ies identified by Web of Science.

Publication’s language.

Keywords identified by Web of Science.

Number of references cited by the publication.

Number of times the publication has been cited in Web of Science Core Database.

Number of times the publication has been cited in Web of Science All Databases.

Name of the publisher.

Digital Object Identifier.

Digital Object Identifier link.

Publication’s number of pages.

Web of Science citation index.

Research area or areas of the publication.

Web of Science Unique Identifier.

Background This bibliometric analysis examines the top 50 most-cited articles on COVID-19 complications, offering insights into the multifaceted impact of the virus. Since its emergence in Wuhan in December 2019, COVID-19 has evolved into a global health crisis, with over 770 million confirmed cases and 6.9 million deaths as of September 2023. Initially recognized as a respiratory illness causing pneumonia and ARDS, its diverse complications extend to cardiovascular, gastrointestinal, renal, hematological, neurological, endocrinological, ophthalmological, hepatobiliary, and dermatological systems. Methods Identifying the top 50 articles from a pool of 5940 in Scopus, the analysis spans November 2019 to July 2021, employing terms related to COVID-19 and complications. Rigorous review criteria excluded non-relevant studies, basic science research, and animal models. The authors independently reviewed articles, considering factors like title, citations, publication year, journal, impact factor, authors, study details, and patient demographics. Results The focus is primarily on 2020 publications (96%), with all articles being open-access. Leading journals include The Lancet, NEJM, and JAMA, with prominent contributions from Internal Medicine (46.9%) and Pulmonary Medicine (14.5%). China played a major role (34.9%), followed by France and Belgium. Clinical features were the primary study topic (68%), often utilizing retrospective designs (24%). Among 22,477 patients analyzed, 54.8% were male, with the most common age group being 26–65 years (63.2%). Complications affected 13.9% of patients, with a recovery rate of 57.8%. Conclusion Analyzing these top-cited articles offers clinicians and researchers a comprehensive, timely understanding of influential COVID-19 literature. This approach uncovers attributes contributing to high citations and provides authors with valuable insights for crafting impactful research. As a strategic tool, this analysis facilitates staying updated and making meaningful contributions to the dynamic field of COVID-19 research. Methods A bibliometric analysis of the most cited articles about COVID-19 complications was conducted in July 2021 using all journals indexed in Elsevier’s Scopus and Thomas Reuter’s Web of Science from November 1, 2019 to July 1, 2021. All journals were selected for inclusion regardless of country of origin, language, medical speciality, or electronic availability of articles or abstracts. The terms were combined as follows: (“COVID-19” OR “COVID19” OR “SARS-COV-2” OR “SARSCOV2” OR “SARS 2” OR “Novel coronavirus” OR “2019-nCov” OR “Coronavirus”) AND (“Complication” OR “Long Term Complication” OR “Post-Intensive Care Syndrome” OR “Venous Thromboembolism” OR “Acute Kidney Injury” OR “Acute Liver Injury” OR “Post COVID-19 Syndrome” OR “Acute Cardiac Injury” OR “Cardiac Arrest” OR “Stroke” OR “Embolism” OR “Septic Shock” OR “Disseminated Intravascular Coagulation” OR “Secondary Infection” OR “Blood Clots” OR “Cytokine Release Syndrome” OR “Paediatric Inflammatory Multisystem Syndrome” OR “Vaccine Induced Thrombosis with Thrombocytopenia Syndrome” OR “Aspergillosis” OR “Mucormycosis” OR “Autoimmune Thrombocytopenia Anaemia” OR “Immune Thrombocytopenia” OR “Subacute Thyroiditis” OR “Acute Respiratory Failure” OR “Acute Respiratory Distress Syndrome” OR “Pneumonia” OR “Subcutaneous Emphysema” OR “Pneumothorax” OR “Pneumomediastinum” OR “Encephalopathy” OR “Pancreatitis” OR “Chronic Fatigue” OR “Rhabdomyolysis” OR “Neurologic Complication” OR “Cardiovascular Complications” OR “Psychiatric Complication” OR “Respiratory Complication” OR “Cardiac Complication” OR “Vascular Complication” OR “Renal Complication” OR “Gastrointestinal Complication” OR “Haematological Complication” OR “Hepatobiliary Complication” OR “Musculoskeletal Complication” OR “Genitourinary Complication” OR “Otorhinolaryngology Complication” OR “Dermatological Complication” OR “Paediatric Complication” OR “Geriatric Complication” OR “Pregnancy Complication”) in the Title, Abstract or Keyword. A total of 5940 articles were accessed, of which the top 50 most cited articles about COVID-19 and Complications of COVID-19 were selected through Scopus. Each article was reviewed for its appropriateness for inclusion. The articles were independently reviewed by three researchers (JRP, MAM and TS) (Table 1). Differences in opinion with regard to article inclusion were resolved by consensus. The inclusion criteria specified articles that were focused on COVID-19 and Complications of COVID-19. Articles were excluded if they did not relate to COVID-19 and or complications of COVID-19, Basic Science Research and studies using animal models or phantoms. Review articles, Viewpoints, Guidelines, Perspectives and Meta-analysis were also excluded from the top 50 most-cited articles (Table 1). The top 50 most-cited articles were compiled in a single database and the relevant data was extracted. The database included: Article Title, Scopus Citations, Year of Publication, Journal, Journal Impact Factor, Authors, Number of Authors, Department Affiliation, Number of Institutions, Country of Origin, Study Topic, Study Design, Sample Size, Open Access, Non-Original Articles, Patient/Participants Age, Gender, Symptoms, Signs, Co-morbidities, Complications, Imaging Modalities Used and outcome.

THIS DATA ASSET NO LONGER ACTIVE: This is metadata documentation for the National Priorities List (NPL) Publication Assistance Databsae (PAD), a Lotus Notes application that holds Region 7's universe of NPL site information such as site description, threats and contaminants, cleanup approach, environmental process, community involvement, site repository, and regional contacts. This database used to be updated annually, at different times for different NPLs, but it is currently no longer being used. This work fell under objectives for EPA's 2003-2008 Strategic Plan (Goal 3) for Land Preservation & Restoration, which are to clean up and reuse contaminated land.