Academic article descriptive statistics.

This study estimates the effect of data sharing on the citations of academic articles, using journal policies as a natural experiment. We begin by examining 17 high-impact journals that have adopted the requirement that data from published articles be publicly posted. We match these 17 journals to 13 journals without policy changes and find that empirical articles published just before their change in editorial policy have citation rates with no statistically significant difference from those published shortly after the shift. We then ask whether this null result stems from poor compliance with data sharing policies, and use the data sharing policy changes as instrumental variables to examine more closely two leading journals in economics and political science with relatively strong enforcement of new data policies. We find that articles that make their data available receive 97 additional citations (estimate standard error of 34). We conclude that: a) authors who share data may be rewarded eventually with additional scholarly citations, and b) data-posting policies alone do not increase the impact of articles published in a journal unless those policies are enforced.

This dataset groups all the tables supplementing the contents of the article "Data Journals: A Survey", which is going to be published by the Journal of the Association for Information Science and Technology (JASIST). Tables are published with no header. Any details can be found in the article.

Abstract Data occupy a key role in our information society. However, although the amount of published data continues to grow and terms like “data deluge” and “big data” today characterize numerous (research) initiatives, a lot of work is still needed in the direction of publishing data in order to make them effectively discoverable, available, and reusable by others. Several barriers hinder data publishing, from lack of attribution and rewards, vague citation practices, quality issues, to a rather general lack of data sharing culture. Lately, data journals came forward as a solution to overcome some of these barriers. In this study of more than 100 currently existing data journals, we describe the approaches they promote for description, availability, citation, quality and open access or datasets. We close by identifying ways to expand and strengthen the data journals approach as a means to actually promote datasets access and exploitation.

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.

Raw data supporting the Springer Nature Data Availability Statement (DAS) analysis in the State of Open Data 2024. SOOD_2024_special_analysis_DAS_SN.xlsx contains the DAS, DOI, publication date, DAS categories and related country by Insitution of any author.SOOD 2024_DAS_analysis_sharing.xlsx contains the summary data by country and data sharing type.Utilizing the Dimensions database, we identified articles containing key DAS identifiers such as “Data Availability Statement” or “Availability of Data and Materials” within their full text. Digital Object Identifiers (DOIs) of these articles were collected and matched against Springer Nature’s XML database to extract the DAS for each article. The extracted DAS were categorized into specific sharing types using text and data matching terms. For statements indicating that data are publicly available in a repository, we matched against a predefined list of repository identifiers, names, and URLs. The DAS were classified into the following categories:1. Data are available from the author on request. 2. Data are included in the manuscript or its supplementary material. 3. Some or all of the data are publicly available, for example in a repository.4. Figure source data are included with the manuscript. 5. Data availability is not applicable.6. Data are declared as not available by the author.7. Data available online but not in a repository.These categories are non-exclusive: more than one can apply to any one article. Publications outside the 2019–2023 range and non-article publication types (e.g., book chapters) that were initially included in the Dimensions search results were excluded from the final dataset. Articles were included in the final analysis after applying the exclusion criteria. Upon processing, it was found that only 370 results were returned for Botswana across the five-year period; due to this low number, Botswana was not included in the DAS focused country-level analysis. This analysis does not assess the accuracy of the DAS in the context of each individual article. There was no manual verification of the categories applied; as a result, terms used out of context could have led to misclassification. Approximately 5% of articles remained unclassified following text and data matching due to these limitations.

The final dataset and Supplementtary tables regarding to research entitled "Gross motor skills trajectory variation between WEIRD and LMIC countries: A Cross-cultural study" are available.

By downloading the data, you agree with the terms & conditions mentioned below:

Data Access: The data in the research collection may only be used for research purposes. Portions of the data are copyrighted and have commercial value as data, so you must be careful to use them only for research purposes.

Summaries, analyses and interpretations of the linguistic properties of the information may be derived and published, provided it is impossible to reconstruct the information from these summaries. You may not try identifying the individuals whose texts are included in this dataset. You may not try to identify the original entry on the fact-checking site. You are not permitted to publish any portion of the dataset besides summary statistics or share it with anyone else.

We grant you the right to access the collection's content as described in this agreement. You may not otherwise make unauthorised commercial use of, reproduce, prepare derivative works, distribute copies, perform, or publicly display the collection or parts of it. You are responsible for keeping and storing the data in a way that others cannot access. The data is provided free of charge.

Citation

Please cite our work as

@InProceedings{clef-checkthat:2022:task3, author = {K{"o}hler, Juliane and Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Wiegand, Michael and Siegel, Melanie and Mandl, Thomas}, title = "Overview of the {CLEF}-2022 {CheckThat}! Lab Task 3 on Fake News Detection", year = {2022}, booktitle = "Working Notes of CLEF 2022---Conference and Labs of the Evaluation Forum", series = {CLEF~'2022}, address = {Bologna, Italy},}

@article{shahi2021overview, title={Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection}, author={Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Mandl, Thomas}, journal={Working Notes of CLEF}, year={2021} }

Problem Definition: Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other (e.g., claims in dispute) and detect the topical domain of the article. This task will run in English and German.

Task 3: Multi-class fake news detection of news articles (English) Sub-task A would detect fake news designed as a four-class classification problem. Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other. The training data will be released in batches and roughly about 1264 articles with the respective label in English language. Our definitions for the categories are as follows:

False - The main claim made in an article is untrue.

Partially False - The main claim of an article is a mixture of true and false information. The article contains partially true and partially false information but cannot be considered 100% true. It includes all articles in categories like partially false, partially true, mostly true, miscaptioned, misleading etc., as defined by different fact-checking services.

True - This rating indicates that the primary elements of the main claim are demonstrably true.

Other- An article that cannot be categorised as true, false, or partially false due to a lack of evidence about its claims. This category includes articles in dispute and unproven articles.

Cross-Lingual Task (German)

Along with the multi-class task for the English language, we have introduced a task for low-resourced language. We will provide the data for the test in the German language. The idea of the task is to use the English data and the concept of transfer to build a classification model for the German language.

Input Data

The data will be provided in the format of Id, title, text, rating, the domain; the description of the columns is as follows:

ID- Unique identifier of the news article

Title- Title of the news article

text- Text mentioned inside the news article

our rating - class of the news article as false, partially false, true, other

Output data format

public_id- Unique identifier of the news article

predicted_rating- predicted class

Sample File

public_id, predicted_rating 1, false 2, true

IMPORTANT!

We have used the data from 2010 to 2022, and the content of fake news is mixed up with several topics like elections, COVID-19 etc.

Baseline: For this task, we have created a baseline system. The baseline system can be found at https://zenodo.org/record/6362498

Related Work

Shahi GK. AMUSED: An Annotation Framework of Multi-modal Social Media Data. arXiv preprint arXiv:2010.00502. 2020 Oct 1.https://arxiv.org/pdf/2010.00502.pdf

G. K. Shahi and D. Nandini, “FakeCovid – a multilingual cross-domain fact check news dataset for covid-19,” in workshop Proceedings of the 14th International AAAI Conference on Web and Social Media, 2020. http://workshop-proceedings.icwsm.org/abstract?id=2020_14

Shahi, G. K., Dirkson, A., & Majchrzak, T. A. (2021). An exploratory study of covid-19 misinformation on twitter. Online Social Networks and Media, 22, 100104. doi: 10.1016/j.osnem.2020.100104

Shahi, G. K., Struß, J. M., & Mandl, T. (2021). Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection. Working Notes of CLEF.

Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeno, A., Míguez, R., Shaar, S., ... & Mandl, T. (2021, March). The CLEF-2021 CheckThat! lab on detecting check-worthy claims, previously fact-checked claims, and fake news. In European Conference on Information Retrieval (pp. 639-649). Springer, Cham.

Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeño, A., Míguez, R., Shaar, S., ... & Kartal, Y. S. (2021, September). Overview of the CLEF–2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News. In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 264-291). Springer, Cham.

Scientific and technical journal articles in Lebanon was reported at 2091 in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Lebanon - Scientific and technical journal articles - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

This dataset provides the data underlying the scientific article "Researchers’ willingness and ability to openly share their research data: a survey of COVID-19 pandemic-related factors". The abstract of the article is as follows: While previous studies show that the drivers and inhibitors for openly sharing research data are diverse and complex, there is a lack of studies empirically examining the influence of the COVID-19 pandemic on researchers’ open data sharing behavior. Using a questionnaire (n=135), this study investigates the influence of COVID-19 pandemic-related factors on researchers’ willingness and ability to openly share their research data. Fifty-one respondents (37.8%) stated that factors related to the COVID-19 pandemic increased their willingness and ability to openly share their research data, while 80 (59.3%) reported that various pandemic-related factors did not influence their willingness and ability in this way. As one of the possible influencing factors, this study finds a significant association between the COVID-19-relatedness of researchers’ research discipline and whether or not the COVID-19 pandemic led to a change in their willingness and ability to share their research data openly: χ2 (1) = 5.77, p < .05. Social influences on open data sharing behavior, institutional support for open data sharing, and the fear of potential negative consequences of open data sharing were nearly similar for the respondents who were and were not involved in COVID-19-related research. This study contributes scientifically by going beyond conceptual studies as it provides empirically-based insights concerning the influence of COVID-19 pandemic-related factors on researchers’ willingness and ability to openly share their data. As a practical contribution, this study discusses recommendations that policymakers can use to sustainably support open research data sharing in post-COVID-19 times.

This dataset is supplemental to the article "BBGD: an online database for blueberry genomic data," (2007); it is titled "list of genes printed on microarray slides."

The article, "BBGD: an online database for blueberry genomic data," (2007) involving blueberry cold hardiness experiments has a list of all the genes that were printed on microarray slides. This dataset, supplemental to the article, is called: "list of genes printed on microarray slides." 1471-2229-7-5-s1.xls 663k.
By using the BBGD database, researchers developed EST-based markers for mapping, and have identified a number of "candidate" cold tolerance genes that are highly expressed in blueberry flower buds after exposure to low temperatures.

BBGD (http://bioinformatics.towson.edu/BBGD/) is a public online database, and was developed for blueberry genomics. BBGD is both a sequence and gene expression database: it stores both EST and microarray data, and allows scientists to correlate expression profiles with gene function. Presently, the main focus of the database is the identification of genes in blueberry that are significantly induced or suppressed after low temperature exposure.

[NOTE: PLEXdb is no longer available online. Oct 2019.] PLEXdb (Plant Expression Database) is a unified gene expression resource for plants and plant pathogens. PLEXdb is a genotype to phenotype, hypothesis building information warehouse, leveraging highly parallel expression data with seamless portals to related genetic, physical, and pathway data. PLEXdb (http://www.plexdb.org), in partnership with community databases, supports comparisons of gene expression across multiple plant and pathogen species, promoting individuals and/or consortia to upload genome-scale data sets to contrast them to previously archived data. These analyses facilitate the interpretation of structure, function and regulation of genes in economically important plants. A list of Gene Atlas experiments highlights data sets that give responses across different developmental stages, conditions and tissues. Tools at PLEXdb allow users to perform complex analyses quickly and easily. The Model Genome Interrogator (MGI) tool supports mapping gene lists onto corresponding genes from model plant organisms, including rice and Arabidopsis. MGI predicts homologies, displays gene structures and supporting information for annotated genes and full-length cDNAs. The gene list-processing wizard guides users through PLEXdb functions for creating, analyzing, annotating and managing gene lists. Users can upload their own lists or create them from the output of PLEXdb tools, and then apply diverse higher level analyses, such as ANOVA and clustering. PLEXdb also provides methods for users to track how gene expression changes across many different experiments using the Gene OscilloScope. This tool can identify interesting expression patterns, such as up-regulation under diverse conditions or checking any gene’s suitability as a steady-state control. Resources in this dataset:Resource Title: Website Pointer for Plant Expression Database, Iowa State University. File Name: Web Page, url: https://www.bcb.iastate.edu/plant-expression-database [NOTE: PLEXdb is no longer available online. Oct 2019.] Project description for the Plant Expression Database (PLEXdb) and integrated tools.

The Conservation Practice Effectiveness Database compiles information on the effectiveness of a suite of conservation practices. This database presents a compilation of data on the effectiveness of innovative practices developed to treat contaminants in surface runoff and tile drainage water from agricultural landscapes. Traditional conservation practices such as no-tillage and conservation crop rotation are included in the database, as well as novel practices such as drainage water management, blind inlets, and denitrification bioreactors. This will be particularly useful to conservation planners seeking new approaches to water quality problems associated with dissolved constituents, such as nitrate or soluble reactive phosphorus (SRP), and for researchers seeking to understand the circumstances in which such practices are most effective. Another novel feature of the database is the presentation of information on how individual conservation practices impact multiple water quality concerns. This information will be critical to enabling conservationists and policy makers to avoid (or at least be aware of) undesirable tradeoffs, whereby great efforts are made to improve water quality related to one resource concern (e.g., sediment) but exacerbate problems related to other concerns (e.g., nitrate or SRP). Finally, we note that the Conservation Practice Effectiveness Database can serve as a source of the soft data needed to calibrate simulation models assessing the potential water quality tradeoffs of conservation practices, including those that are still being developed. This database is updated and refined annually. Resources in this dataset:Resource Title: 2019 Conservation Practice Effectiveness (CoPE) Database. File Name: Conservation_Practice_Effectiveness_2019.xlsxResource Description: This version of the database was published in 2019.

The dataset was made in an attempt to investigate the state of information policy - the sum of principles guiding decisions about information - in archaeology and related areas. The aim of the study was to shed light on how information policy directs practice in archaeology, and to show that analysis of such policies is therefore vital.

Information policy in legislation and guidelines in Swedish archaeology serves as a case study, and examples from development-led archaeology and the museum sector illustrate how information policies have varied roles across different heritage sectors. There are historical and local trajectories in the policy documents specific to Sweden, but the discussion shows that the emergence of Swedish policies have many parallels with processes in other countries. The article provides recommendations for information policy development for archaeology and related areas.

See article "Information Policy for (Digital) Information in Archaeology: current state and suggestions for development" by Börjesson et al (2015) for further information.

The dataset was originally published in DiVA and moved to SND in 2024.

New Caledonia Imports of other articles of wood from Ecuador was US$46 during 2013, according to the United Nations COMTRADE database on international trade. New Caledonia Imports of other articles of wood from Ecuador - data, historical chart and statistics - was last updated on May of 2025.

This dataset supports the following publication: "Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States" (DOI:10.1016/j.rse.2020.112013). The data release allows users to replicate, test, or further explore results. The dataset consists of 4 separate items based on the analysis approach used in the original publication 1) the 'Phenocam' dataset uses images from a phenocam in a pinyon juniper ecosystem in Grand Canyon National Park to determine phenological patterns of multiple plant species. The 'Phenocam' dataset consists of scripts and tabular data developed while performing analyses and includes the final NDVI values for all areas of interest (AOIs) described in the associated publication. 2) the 'SolarSensorAnalysis' dataset uses downloaded tabular MODIS data to explore relationships between NDVI and multiple solar and sensor angles. The 'SolarSensorAnalysis' dataset consists of download and analysis scripts in Google Earth Engine and R. The source MODIS data used in the analysis are too large to include but are provided through MODIS providers and can be accessed through Google Earth Engine using the included script. A csv file includes solar and sensor angle information for the MODIS pixel closest to the phenocam as well as for a sample of 100 randomly selected MODIS pixels within the GRCA-PJ ecosystem. 3) the 'WinterPeakExtent' dataset includes final geotiffs showing the temporal frequency extent and associated vegetation physiognomic types experiencing winter NDVI peaks in the western US. 4) the "SensorComparison" dataset contains the NDVI time series at the phenocam location from 4 other satellites as well as the code used to download these data.

Comprehensive dataset of 2 Hygiene articles wholesalers in Jeollanam-do, South Korea as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

KAV 6727 First signed 11/30/1998 Last signed 11/30/1998 Entry into force (supplemented by last signed) 11/30/1998 stamped 04-389 C06541525 cover memo

China Other Daily Sundry Article: YoY: Product Inventory data was reported at 14.061 % in Oct 2015. This records an increase from the previous number of 11.419 % for Sep 2015. China Other Daily Sundry Article: YoY: Product Inventory data is updated monthly, averaging 10.963 % from Jan 2006 (Median) to Oct 2015, with 89 observations. The data reached an all-time high of 43.130 % in Feb 2008 and a record low of -3.172 % in May 2013. China Other Daily Sundry Article: YoY: Product Inventory data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIM: Daily Sundry Article: Other Daily Sundry Article.

China Jewelry & Related Article: Total Asset data was reported at 185.456 RMB bn in Oct 2015. This records a decrease from the previous number of 185.629 RMB bn for Sep 2015. China Jewelry & Related Article: Total Asset data is updated monthly, averaging 93.290 RMB bn from Dec 2003 (Median) to Oct 2015, with 97 observations. The data reached an all-time high of 200.020 RMB bn in Mar 2015 and a record low of 11.402 RMB bn in Dec 2003. China Jewelry & Related Article: Total Asset data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIH: Art and Craft: Jewelry and Related Article.

China Other Daily Sundry Article: Account Receivable data was reported at 9.647 RMB bn in Oct 2015. This records an increase from the previous number of 9.364 RMB bn for Sep 2015. China Other Daily Sundry Article: Account Receivable data is updated monthly, averaging 7.258 RMB bn from Dec 2003 (Median) to Oct 2015, with 97 observations. The data reached an all-time high of 9.781 RMB bn in Jul 2015 and a record low of 2.043 RMB bn in Dec 2003. China Other Daily Sundry Article: Account Receivable data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIM: Daily Sundry Article: Other Daily Sundry Article.