Skip to main content

Share your research data

Mendeley Data is a free and secure cloud-based communal repository where you can store your data, ensuring it is easy to share, access and cite, wherever you are.

Create a Dataset

Find out more about our institutional offering, Digital Commons Data

Search the repository

Recently published

92101 results
  • Unprocessed SARS-CoV-2 spike Nucleotide Sequences
    1. Sequence GenBank IDs of all 615,374 nucleotide spike sequences isolated from samples collected between December 2019 and July 2021. 2. Nucleotide alignment of the 16,808 unique spike sequences derived from the above. 3. Baseline Sequence IDs collected up to July 2021 4. B.1.1.7 Sequences IDs collected up to March 2022 5. P.1 Sequences IDs collected up to February 2022 6. AY.4 Sequences IDs collected up to February 2022 7. AY.4.2 Sequences IDs collected up to February 2022 8. BA.1 Sequences IDs collected up to February 2022 9. BA.1.1 Sequences IDs collected up to March 2022 10. BA.2 Sequences IDs collected up to March 2022 11. Biosample accession of deep sequenced patient samples 12. Newick tree for figure 1B - S3 Data 13. Newick tree for figure 2A -S4 Data 14. BA.4 Sequences IDs collected up to April 2023 15. BA.5 Sequences IDs collected up to April 2023
    • Dataset
  • Multifunctional material enables ultrafast and large-scale enrichment of glycopeptides and reveals distinct spatial patterns in oxidative stress
    Our study offers a convenient and robust site-specific chemproteomics tool for glycoproteome profiling, enabling simultaneous identification of O-GlcNAc sites and N-glycosites by a two-step enzymatic release strategy, and the application of this material revealed distinct spatial O-GlcNAcylation patterns upon oxidative stress.
    • Dataset
  • Lack of Period1 accelerates colorectal tumorigeneses in APCmin/+ mice
    Clock genes drive the circadian rhythm in each cell, and Period1 (Per1) is one of the core genes in mammals. When the clock genes lose their functions due to deficiency, various behavioral and physiological functions are altered. Although many pathological studies have been conducted on clock genes and cancers, the results could be more consistent. In the present study, we aimed to clarify how the lack of Per1 affects the development and progression of colorectal cancer. We recorded survival days and calculated survival rates, measured the number of polyps, performed histological evaluation, and measured β-catenin expression using ApcMin/+Per1-/- mice. The results showed that loss of Per1 caused variation in the survival rate of mice, increased the number of polyps, and increased β-catenin expression. These results suggest that Per1 plays a role in suppressing the development and progression of colorectal cancer.
    • Dataset
  • LDHU3_10.1360
    Archaic translocase of outer membrane 12 kDa subunit | ATOM12; Leishmania donovani (HU3 strain)
    • Dataset
  • Java Application to Design, Develop and Execute Preventative Tests Based on Synthetic App Monitoring Outcome and GenAI
    Software development is characterized by quick innovations, intricate designs, and changing customer requirements. Preventative testing finds and fixes flaws early in the development process, acting as a proactive approach to guarantee that software products fulfill quality requirements. Development teams may improve the user experience by drastically lowering the probability of faults making it into production by putting in place a strong preventive testing architecture. Preventative testing's capacity to find security holes and vulnerabilities in software programmes is one of its main advantages. Integrating security-focused preventive testing contributes to the overall integrity of digital ecosystems by protecting sensitive data and ensuring that software systems are resilient to possible breaches in the face of growing cyber threats. Cost-effectiveness is another crucial facet of preventative testing. Early detection and resolution of defects during the development phase lead to substantial cost savings compared to addressing issues post-deployment. By minimizing the need for emergency fixes and updates, preventative testing supports efficient resource allocation and reduces the economic burden associated with software maintenance. Preventative testing is also essential to keeping project deadlines and delivery dates on track. Early problem-solving and identification throughout the development life cycle helps teams better stick to project schedules, which guarantees on-time product delivery and satisfied customers. Preventative testing strategies, which incorporate automated testing, continuous integration, and other cutting-edge techniques, grow along with the complexity of software systems. Development teams may produce high-quality software more quickly, detect any bottlenecks, and optimize their testing procedures by using these breakthroughs. The application jar included with this data collection offers the preventative test solutions based on synthetic app monitoring outcome, code coverage information, performing browser synthetic app monitoring and provide valuable recommendation based on gen AI. Providing a platform that will function as an engine and offer preventative tests to identify errors early in the software development life cycle is the main problem statement driving this approach. This is done with the understanding that no further time will be spent on the procedure because the primary reason for not doing the preventive test is a time crisis. In addition to saving your time, parallel testing ensures that you receive the highest amount of test coverage. Preventative testing based on synthetic app monitoring gives you the ability to answer to support tickets with the greatest amount of ground testing before issues are reported. Please do visit our github repo for more updated code and artifacts on https://github.com/sohambpatel/PreventativeTests
    • Dataset
  • ML-GPS: machine learning-assisted genetic priority score
    Identifying genetic drivers of chronic diseases is crucial for drug discovery. We developed a Machine Learning-assisted Genetic Priority Score (ML-GPS) that incorporates genetic associations with predicted disease phenotypes to enhance target discovery.
    • Dataset
  • Role of natural isotopic fractionation in isotope geo- and cosmo-chronology: A theoretical investigation
    Data in Table 2 and Figures of the paper.
    • Dataset
  • Datacortech data and code
    Datacortech Data and Code
    • Dataset
  • AI Dataset
    Dataset for "A Study on Ethical Implications of Artificial Intelligence Adoption in Business: Challenges and Best Practices"
    • Dataset
  • User Preference distribution
    The data visualized in the plot represents a three-dimensional distribution of user preferences based on age and geographic region. X-axis (Age): The horizontal axis represents age, ranging from 20 to 70 years. Age values are evenly distributed within this range. Y-axis (Geographic Region): The vertical axis represents geographic regions, with values ranging from 1 to 10. Geographic regions are evenly distributed within this range. Z-axis (User Satisfaction): The depth axis represents user satisfaction, indicating the level of satisfaction associated with specific combinations of age and geographic region. User satisfaction values are calculated based on a user satisfaction function, which incorporates both age and geographic region as parameters. Plot: The plot depicts a surface that represents the distribution of user satisfaction across different age groups and geographic regions. The surface is generated using the plot_surface function from the mpl_toolkits.mplot3d module. Color Map: The color of the surface varies to indicate the magnitude of user satisfaction. A color map (cmap) called 'viridis' is applied to the surface, where different colors correspond to different levels of user satisfaction. Labels: The plot includes labels for each axis: 'Age' for the x-axis, 'Geographic Region' for the y-axis, and 'User Satisfaction' for the z-axis. Title: The title of the plot is "User Preference Distribution", providing an overview of what the plot represents. Overall, this visualization allows for the exploration of how user satisfaction varies across different age groups and geographic regions, providing insights into potential patterns or trends in user preferences.
    • Dataset
1
View more
GREI

The Generalist Repository Ecosystem Initiative

Elsevier's Mendeley Data repository is a participating member of the National Institutes of Health (NIH) Office of Data Science Strategy (ODSS) GREI project. The GREI includes seven established generalist repositories funded by the NIH to work together to establish consistent metadata, develop use cases for data sharing, train and educate researchers on FAIR data and the importance of data sharing, and more.

Find out more
GREI Collaborative Webinar Series on Data Sharing in Generalist Repositories

Why use Mendeley Data?

Make your research data citable
Unique DOIs and easy-to-use citation tools make it easy to refer to your research data.
Share data privately or publicly
Securely share your data with colleagues and co-authors before publication.
Ensure long-term data storage
Your data is archived for as long as you need it by Data Archiving & Networked Services.
Keep access to all versions
Mendeley Data supports versioning, making longitudinal studies easier.

The Mendeley Data communal data repository is powered by Digital Commons Data.

Digital Commons Data provides everything that your institution will need to launch and maintain a successful Research Data Management program at scale.

Find out more

Data Monitor provides visibility on an institution's entire research data output by harvesting research data from 2000+ generalist and domain-specific repositories, including everything in Mendeley Data.

Find out more