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Data Analytics Market Size 2025-2029

The data analytics market size is forecast to increase by USD 288.7 billion, at a CAGR of 14.7% between 2024 and 2029.

The market is driven by the extensive use of modern technology in company operations, enabling businesses to extract valuable insights from their data. The prevalence of the Internet and the increased use of linked and integrated technologies have facilitated the collection and analysis of vast amounts of data from various sources. This trend is expected to continue as companies seek to gain a competitive edge by making data-driven decisions. However, the integration of data from different sources poses significant challenges. Ensuring data accuracy, consistency, and security is crucial as companies deal with large volumes of data from various internal and external sources. Additionally, the complexity of data analytics tools and the need for specialized skills can hinder adoption, particularly for smaller organizations with limited resources. Companies must address these challenges by investing in robust data management systems, implementing rigorous data validation processes, and providing training and development opportunities for their employees. By doing so, they can effectively harness the power of data analytics to drive growth and improve operational efficiency.

What will be the Size of the Data Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleIn the dynamic and ever-evolving the market, entities such as explainable AI, time series analysis, data integration, data lakes, algorithm selection, feature engineering, marketing analytics, computer vision, data visualization, financial modeling, real-time analytics, data mining tools, and KPI dashboards continue to unfold and intertwine, shaping the industry's landscape. The application of these technologies spans various sectors, from risk management and fraud detection to conversion rate optimization and social media analytics. ETL processes, data warehousing, statistical software, data wrangling, and data storytelling are integral components of the data analytics ecosystem, enabling organizations to extract insights from their data. Cloud computing, deep learning, and data visualization tools further enhance the capabilities of data analytics platforms, allowing for advanced data-driven decision making and real-time analysis. Marketing analytics, clustering algorithms, and customer segmentation are essential for businesses seeking to optimize their marketing strategies and gain a competitive edge. Regression analysis, data visualization tools, and machine learning algorithms are instrumental in uncovering hidden patterns and trends, while predictive modeling and causal inference help organizations anticipate future outcomes and make informed decisions. Data governance, data quality, and bias detection are crucial aspects of the data analytics process, ensuring the accuracy, security, and ethical use of data. Supply chain analytics, healthcare analytics, and financial modeling are just a few examples of the diverse applications of data analytics, demonstrating the industry's far-reaching impact. Data pipelines, data mining, and model monitoring are essential for maintaining the continuous flow of data and ensuring the accuracy and reliability of analytics models. The integration of various data analytics tools and techniques continues to evolve, as the industry adapts to the ever-changing needs of businesses and consumers alike.

How is this Data Analytics Industry segmented?

The data analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ComponentServicesSoftwareHardwareDeploymentCloudOn-premisesTypePrescriptive AnalyticsPredictive AnalyticsCustomer AnalyticsDescriptive AnalyticsOthersApplicationSupply Chain ManagementEnterprise Resource PlanningDatabase ManagementHuman Resource ManagementOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Component Insights

The services segment is estimated to witness significant growth during the forecast period.The market is experiencing significant growth as businesses increasingly rely on advanced technologies to gain insights from their data. Natural language processing is a key component of this trend, enabling more sophisticated analysis of unstructured data. Fraud detection and data security solutions are also in high demand, as companies seek to protect against threats and maintain customer trust. Data analytics platforms, including cloud-based offerings, are driving innovatio

This dataset consists of three data folders including all related documents of the online survey conducted within the NESP 3.2.3 project (Tropical Water Quality Hub) and a survey format document representing how the survey was designed. Apart from participants’ demographic information, the survey consists of three sections: conjoint analysis, picture rating and open question. Correspondent outcome of these three sections are downloaded from Qualtrics website and used for three different data analysis processes.

Related data to the first section “conjoint analysis” is saved in the Conjoint analysis folder which contains two sub-folders. The first one includes a plan file of SAV. Format representing the design suggestion by SPSS orthogonal analysis for testing beauty factors and 9 photoshoped pictures used in the survey. The second (i.e. Final results) contains 1 SAV. file named “data1” which is the imported results of conjoint analysis section in SPSS, 1 SPS. file named “Syntax1” representing the code used to run conjoint analysis, 2 SAV. files as the output of conjoint analysis by SPSS, and 1 SPV file named “Final output” showing results of further data analysis by SPSS on the basis of utility and importance data.

Related data to the second section “Picture rating” is saved into Picture rating folder including two subfolders. One subfolder contains 2500 pictures of Great Barrier Reef used in the rating survey section. These pictures are organised by named and stored in two folders named as “Survey Part 1” and “Survey Part 2” which are correspondent with two parts of the rating survey sections. The other subfolder “Rating results” consist of one XLSX. file representing survey results downloaded from Qualtric website.

Finally, related data to the open question is saved in “Open question” folder. It contains one csv. file and one PDF. file recording participants’ answers to the open question as well as one PNG. file representing a screenshot of Leximancer analysis outcome.

Methods: This dataset resulted from the input and output of an online survey regarding how people assess the beauty of Great Barrier Reef. This survey was designed for multiple purposes including three main sections: (1) conjoint analysis (ranking 9 photoshopped pictures to determine the relative importance weights of beauty attributes), (2) picture rating (2500 pictures to be rated) and (3) open question on the factors that makes a picture of the Great Barrier Reef beautiful in participants’ opinion (determining beauty factors from tourist perspective). Pictures used in this survey were downloaded from public sources such as websites of the Tourism and Events Queensland and Tropical Tourism North Queensland as well as tourist sharing sources (i.e. Flickr). Flickr pictures were downloaded using the key words “Great Barrier Reef”. About 10,000 pictures were downloaded in August and September 2017. 2,500 pictures were then selected based on several research criteria: (1) underwater pictures of GBR, (2) without humans, (3) viewed from 1-2 metres from objects and (4) of high resolution.

The survey was created on Qualtrics website and launched on 4th October 2017 using Qualtrics survey service. Each participant rated 50 pictures randomly selected from the pool of 2500 survey pictures. 772 survey completions were recorded and 705 questionnaires were eligible for data analysis after filtering unqualified questionnaires. Conjoint analysis data was imported to IBM SPSS using SAV. format and the output was saved using SPV. format. Automatic aesthetic rating of 2500 Great Barrier Reef pictures –all these pictures are rated (1 – 10 scale) by at least 10 participants and this dataset was saved in a XLSX. file which is used to train and test an Artificial Intelligence (AI)-based system recognising and assessing the beauty of natural scenes. Answers of the open-question were saved in a XLSX. file and a PDF. file to be employed for theme analysis by Leximancer software.

Further information can be found in the following publication: Becken, S., Connolly R., Stantic B., Scott N., Mandal R., Le D., (2018), Monitoring aesthetic value of the Great Barrier Reef by using innovative technologies and artificial intelligence, Griffith Institute for Tourism Research Report No 15.

Format: The Online survey dataset includes one PDF file representing the survey format with all sections and questions. It also contains three subfolders, each has multiple files. The subfolder of Conjoint analysis contains an image of the 9 JPG. Pictures, 1 SAV. format file for the Orthoplan subroutine outcome and 5 outcome documents (i.e. 3 SAV. files, 1 SPS. file, 1 SPV. file). The subfolder of Picture rating contains a capture of the 2500 pictures used in the survey, 1 excel file for rating results. The subfolder of Open question includes 1 CSV. file, 1 PDF. file representing participants’ answers and one PNG. file for the analysis outcome.

Data Dictionary:

Card 1: Picture design option number 1 suggested by SPSS orthogonal analysis. Importance value: The relative importance weight of each beauty attribute calculated by SPSS conjoint analysis. Utility: Score reflecting influential valence and degree of each beauty attribute on beauty score. Syntax: Code used to run conjoint analysis by SPSS Leximancer: Specialised software for qualitative data analysis. Concept map: A map showing the relationship between concepts identified Q1_1: Beauty score of the picture Q1_1 by the correspondent participant (i.e. survey part 1) Q2.1_1: Beauty score of the picture Q2.1_1 by the correspondent participant (i.e. survey part 2) Conjoint _1: Ranking of the picture 1 designed for conjoint analysis by the correspondent participant

References: Becken, S., Connolly R., Stantic B., Scott N., Mandal R., Le D., (2018), Monitoring aesthetic value of the Great Barrier Reef by using innovative technologies and artificial intelligence, Griffith Institute for Tourism Research Report No 15.

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data esp3\3.2.3_Aesthetic-value-GBR

This repository contains the dataset for the study of computational reproducibility of Jupyter notebooks from biomedical publications. Our focus lies in evaluating the extent of reproducibility of Jupyter notebooks derived from GitHub repositories linked to publications present in the biomedical literature repository, PubMed Central. We analyzed the reproducibility of Jupyter notebooks from GitHub repositories associated with publications indexed in the biomedical literature repository PubMed Central. The dataset includes the metadata information of the journals, publications, the Github repositories mentioned in the publications and the notebooks present in the Github repositories.

Data Collection and Analysis

We use the code for reproducibility of Jupyter notebooks from the study done by Pimentel et al., 2019 and adapted the code from ReproduceMeGit. We provide code for collecting the publication metadata from PubMed Central using NCBI Entrez utilities via Biopython.

Our approach involves searching PMC using the esearch function for Jupyter notebooks using the query: ``(ipynb OR jupyter OR ipython) AND github''. We meticulously retrieve data in XML format, capturing essential details about journals and articles. By systematically scanning the entire article, encompassing the abstract, body, data availability statement, and supplementary materials, we extract GitHub links. Additionally, we mine repositories for key information such as dependency declarations found in files like requirements.txt, setup.py, and pipfile. Leveraging the GitHub API, we enrich our data by incorporating repository creation dates, update histories, pushes, and programming languages.

All the extracted information is stored in a SQLite database. After collecting and creating the database tables, we ran a pipeline to collect the Jupyter notebooks contained in the GitHub repositories based on the code from Pimentel et al., 2019.

Our reproducibility pipeline was started on 27 March 2023.

Repository Structure

Our repository is organized into two main folders:

• archaeology: This directory hosts scripts designed to download, parse, and extract metadata from PubMed Central publications and associated repositories. There are 24 database tables created which store the information on articles, journals, authors, repositories, notebooks, cells, modules, executions, etc. in the db.sqlite database file.
• analyses: Here, you will find notebooks instrumental in the in-depth analysis of data related to our study. The db.sqlite file generated by running the archaelogy folder is stored in the analyses folder for further analysis. The path can however be configured in the config.py file. There are two sets of notebooks: one set (naming pattern N[0-9]*.ipynb) is focused on examining data pertaining to repositories and notebooks, while the other set (PMC[0-9]*.ipynb) is for analyzing data associated with publications in PubMed Central, i.e.\ for plots involving data about articles, journals, publication dates or research fields. The resultant figures from the these notebooks are stored in the 'outputs' folder.
• MethodsWorkflow: The MethodsWorkflow file provides a conceptual overview of the workflow used in this study.

Accessing Data and Resources:

• All the data generated during the initial study can be accessed at https://doi.org/10.5281/zenodo.6802158
• For the latest results and re-run data, refer to this link.
• The comprehensive SQLite database that encapsulates all the study's extracted data is stored in the db.sqlite file.
• The metadata in xml format extracted from PubMed Central which contains the information about the articles and journal can be accessed in pmc.xml file.

System Requirements:

• Centos 7 (Documentation: https://www.centos.org/)
• Conda 4.9.4 (Installation Guide: https://docs.anaconda.com/anaconda/install/linux/)
• Python 3.7.6 (Download Link: https://www.python.org/downloads/)
• GitHub account (Get Started: https://github.com/, Requires GitHub Username and Token)
• gcc 7.3.0 (Installation Guide: https://gcc.gnu.org/install/)
• lbzip2 (Command: `conda install -c conda-forge lbzip2')

Running the pipeline:

• Clone the computational-reproducibility-pmc repository using Git:
  git clone https://github.com/fusion-jena/computational-reproducibility-pmc.git
  
• Navigate to the computational-reproducibility-pmc directory:
  cd computational-reproducibility-pmc/computational-reproducibility-pmc
• Configure environment variables in the config.py file:
  GITHUB_USERNAME = os.environ.get("JUP_GITHUB_USERNAME", "add your github username here")
  GITHUB_TOKEN = os.environ.get("JUP_GITHUB_PASSWORD", "add your github token here")
• Other environment variables can also be set in the config.py file.
  BASE_DIR = Path(os.environ.get("JUP_BASE_DIR", "./")).expanduser() # Add the path of directory where the GitHub repositories will be saved
  DB_CONNECTION = os.environ.get("JUP_DB_CONNECTION", "sqlite:///db.sqlite") # Add the path where the database is stored.
• To set up conda environments for each python versions, upgrade pip, install pipenv, and install the archaeology package in each environment, execute:
  source conda-setup.sh
• Change to the archaeology directory
  cd archaeology
• Activate conda environment. We used py36 to run the pipeline.
  conda activate py36
• Execute the main pipeline script (r0_main.py):
  python r0_main.py

Running the analysis:

• Navigate to the analysis directory.
  cd analyses
• Activate conda environment. We use raw38 for the analysis of the metadata collected in the study.
  conda activate raw38
• Install the required packages using the requirements.txt file.
  pip install -r requirements.txt
• Launch Jupyterlab
  jupyter lab
• Refer to the Index.ipynb notebook for the execution order and guidance.

References:

• Sheeba Samuel, Daniel Mietchen. (2024). Computational reproducibility of Jupyter notebooks from biomedical publications, https://doi.org/10.1093/gigascience/giad113, GigaScience
• Sheeba Samuel, Daniel Mietchen. (2022). Computational reproducibility of Jupyter notebooks from biomedical publications, https://arxiv.org/pdf/2209.04308.pdf, CoRR abs/2209.04308
• Sheeba Samuel, & Daniel Mietchen. (2022). Dataset of a Study of Computational reproducibility of Jupyter notebooks from biomedical publications [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6802158

Overview

The OpenResume dataset is designed for researchers and practitioners in career trajectory modeling and job-domain machine learning, as described in the IEEE BigData 2024 paper. It includes both anonymized realistic resumes and synthetically generated resumes, offering a comprehensive resource for developing and benchmarking predictive models across a variety of career-related tasks. By employing anonymization and differential privacy techniques, OpenResume ensures that research can be conducted while maintaining privacy. The dataset is available in this repository. Please see the paper for more details: 10.1109/BigData62323.2024.10825519

If you find this paper useful in your research or use this dataset in any publications, projects, tools, or other forms, please cite:

@inproceedings{yamashita2024openresume,

title={{OpenResume: Advancing Career Trajectory Modeling with Anonymized and Synthetic Resume Datasets}},

author={Yamashita, Michiharu and Tran, Thanh and Lee, Dongwon},

booktitle={2024 IEEE International Conference on Big Data (BigData)},

year={2024},

organization={IEEE}

}

@inproceedings{yamashita2023james,

title={{JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning}},

author={Yamashita, Michiharu and Shen, Jia Tracy and Tran, Thanh and Ekhtiari, Hamoon and Lee, Dongwon},

booktitle={2023 IEEE International Conference on Data Science and Advanced Analytics (DSAA)},

year={2023},

organization={IEEE}

}

This collection contains a snapshot of the learning resource metadata from ESIP's Data management Training Clearinghouse (DMTC) associated with the closeout (March 30, 2023) of the Institute of Museum and Library Services funded (Award Number: LG-70-18-0092-18) Development of an Enhanced and Expanded Data Management Training Clearinghouse project. The shared metadata are a snapshot associated with the final reporting date for the project, and the associated data report is also based upon the same data snapshot on the same date.

The materials included in the collection consist of the following:

• esip-dev-02.edacnm.org.json.zip - a zip archive containing the metadata for 587 published learning resources as of March 30, 2023. These metadata include all publicly available metadata elements for the published learning resources with the exception of the metadata elements containing individual email addresses (submitter and contact) to reduce the exposure of these data.
• statistics.pdf - an automatically generated report summarizing information about the collection of materials in the DMTC Clearinghouse, including both published and unpublished learning resources. This report includes the numbers of published and unpublished resources through time; the number of learning resources within subject categories and detailed subject categories, the dates items assigned to each category were first added to the Clearinghouse, and the most recent data that items were added to that category; the distribution of learning resources across target audiences; and the frequency of keywords within the learning resource collection. This report is based on the metadata for published resourced included in this collection, and preliminary metadata for unpublished learning resources that are not included in the shared dataset.

The metadata fields consist of the following:

Fieldname	Description
abstract_data	A brief synopsis or abstract about the learning resource
abstract_format	Declaration for how the abstract description will be represented.
access_conditions	Conditions upon which the resource can be accessed beyond cost, e.g., login required.
access_cost	Yes or No choice stating whether othere is a fee for access to or use of the resource.
accessibililty_features_name	Content features of the resource, such as accessible media, alternatives and supported enhancements for accessibility.
accessibililty_summary	A human-readable summary of specific accessibility features or deficiencies.
author_names	List of authors for a resource derived from the given/first and family/last names of the personal author fields by the system
author_org - name - name_identifier - name_identifier_type	- Name of organization authoring the learning resource. - The unique identifier for the organization authoring the resource. - The identifier scheme associated with the unique identifier for the organization authoring the resource.
authors - givenName - familyName - name_identifier - name_identifier_type	- Given or first name of person(s) authoring the resource. - Last or family name of person(s) authoring the resource. - The unique identifier for the person(s) authoring the resource. - The identifier scheme associated with the unique identifier for the person(s) authoring the resource, e.g., ORCID.
citation	Preferred Form of Citation.
completion_time	Intended Time to Complete
contact - name - org - email	- Name of person(s) who has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user. - Name of organization that has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user. - (excluded) Contact email address.
contributor_orgs - name - name_identifier - name_identifier_type - type	- Name of organization that is a secondary contributor to the learningresource. A contributor can also be an individual person. - The unique identifier for the organization contributing to the resource. - The identifier scheme associated with the unique identifier for the organization contributing to the resource. - Type of contribution to the resource made by an organization.
contributors - familyName - givenName - name_identifier - name_identifier_type	- Last or family name of person(s) contributing to the resource. - Given or first name of person(s) contributing to the resource. - The unique identifier for the person(s) contributing to the resource. - The identifier scheme associated with the unique identifier for the person(s) contributing to the resource, e.g., ORCID.
contributors.type	Type of contribution to the resource made by a person.
created	The date on which the metadata record was first saved as part of the input workflow.
creator	The name of the person creating the MD record for a resource.
credential_status	Declaration of whether a credential is offered for comopletion of the resource.
ed_frameworks - name - description - nodes.name	- The name of the educational framework to which the resource is aligned, if any. An educational framework is a structured description of educational concepts such as a shared curriculum, syllabus or set of learning objectives, or a vocabulary for describing some other aspect of education such as educational levels or reading ability. - A description of one or more subcategories of an educational framework to which a resource is associated. - The name of a subcategory of an educational framework to which a resource is associated.
expertise_level	The skill level targeted for the topic being taught.
id	Unique identifier for the MD record generated by the system in UUID format.
keywords	Important phrases or words used to describe the resource.
language_primary	Original language in which the learning resource being described is published or made available.
languages_secondary	Additional languages in which the resource is tranlated or made available, if any.
license	A license for use of that applies to the resource, typically indicated by URL.
locator_data	The identifier for the learning resource used as part of a citation, if available.
locator_type	Designation of citation locatorr type, e.g., DOI, ARK, Handle.
lr_outcomes	Descriptions of what knowledge, skills or abilities students should learn from the resource.
lr_type	A characteristic that describes the predominant type or kind of learning resource.
media_type	Media type of resource.
modification_date	System generated date and time when MD record is modified.
notes	MD Record Input Notes
pub_status	Status of metadata record within the system, i.e., in-process, in-review, pre-pub-review, deprecate-request, deprecated or published.
published	Date of first broadcast / publication.
publisher	The organization credited with publishing or broadcasting the resource.
purpose	The purpose of the resource in the context of education; e.g., instruction, professional education, assessment.
rating	The aggregation of input from all user assessments evaluating users' reaction to the learning resource following Kirkpatrick's model of training evaluation.
ratings	Inputs from users assessing each user's reaction to the learning resource following Kirkpatrick's model of training evaluation.
resource_modification_date	Date in which the resource has last been modified from the original published or broadcast version.
status	System generated publication status of the resource w/in the registry as a yes for published or no for not published.
subject	Subject domain(s) toward which the resource is targeted. There may be more than one value for this field.
submitter_email	(excluded) Email address of

IntroductionImmune checkpoint inhibitors (ICIs) have significantly improved the therapeutic landscape of multiple malignancies. It becomes critical to understand the incidence, profile, and consequences of immune-related adverse events (irAEs) within real-world populations.AimWe aimed to assess the safety profile of ICIs in adult cancer patients at the National Center for Cancer Care and Research (NCCCR), Qatar, and explore the factors associated with irAEs, including the impact of irAEs on the survival outcomes.MethodsThis retrospective study included adult cancer patients who received at least one dose of an ICI between January 1, 2015, and January 1, 2020. Data was collected from electronic health records and institutional adverse drug reaction (ADR) reporting systems. irAEs were graded using Common terminology criteria of adverse events, version 5 (CTCAE v5). Logistic regression analysis was used to evaluate factors associated with irAEs. Kaplan–Meier and landmark analysis assessed associations between irAEs and progression-free survival (PFS) and overall survival (OS).Approvals were obtained from HMC IRB (MRC-01-20-251) and Qatar University IRB (073/2025-EM).ResultsA total of 236 patients (median age 57 years, 72% male) were included. Most patients had advanced solid tumors, with thoracic malignancies being the most common.Pembrolizumab was the predominant agent used. irAEs occurred in 55.9% of patients, with the most frequent side effects being endocrine (26.4%), dermatologic (13.5%), and hepatic (12.4%) toxicities. Sixteen patients (6.8%) experienced fatal irAEs, with pneumonitis being the most common cause of death.The median time to onset of irAEs was 55 days (IQR 16‐129.5 days). Most events occurred in the acute phase (21–180 days post-treatment). Resolution rates of irAEs varied, with gastrointestinal irAEs resolving in 92% of cases, compared to 40% for hematological events. Pulmonary irAEs were associated with the highest rate of treatment discontinuation.Factors associated with irAEs included a higher number of ICI treatment cycles (p=0.019), lower baseline and six-week platelet counts (p=0.015 and p=0.012, respectively), and elevated baseline TSH (p=0.048). In multivariable regression analysis, the only factor that remained statistically significant was the number of treatment cycles (p = 0.004).Dermatologic irAEs were significantly more common among patients aged ≥65 years (17.9% vs. 7.1%, p=0.018). Patients with poor performance status (PS ≥ 2) experienced a significantly higher rate of cardiac irAEs compared to those with good PS (10.9% vs. 1.7%, p=0.036).In the 30-day landmark analysis, patients who developed irAEs had significantly worse PFS (3.3 vs. 7.1 months, p=0.0085) and OS (4.37 vs. 9.0 months, p=0.0004) compared to those without irAEs. These finding were confirmed using adjusted landmark analysis where irAEs were associated with worse OS (HR 2.13, 95% CI 1.34–3.3, P = 0.001) and PFS (HR 1.88, 95% CI 1.22–2.87, P = 0.004). Additionally, time-dependent Cox regression also demonstrated worse OS (HR 1.86, 95% CI 1.23–2.79, P = 0.003) and PFS (HR 1.96, 95% CI 1.41–2.72, P = 0.001).ConclusionIn this real-world cohort, irAEs were frequent and clinically diverse. Using adjusted landmark analysis and time-dependent Cox regression, early-onset irAEs were associated with inferior survival in our cohort. Poor baseline PS was linked to an increased risk of cardiac irAEs. Older adults were at a higher risk of dermatological irAEs. Some factors such as higher number of ICI treatment cycles, thrombocytopenia and elevated TSH at baseline may aid in risk stratification. These findings reinforce the need for timely detection and multidisciplinary management of irAEs to optimize ICI safety and effectiveness.

The description of current load monitoring practices may serve to highlight developmental needs for both the training ground, academia and related industries. While previous studies described these practices in elite men's football, no study has provided an overview of load monitoring practices in elite women's football. Given the clear organizational differences (i.e., professionalization and infrastructure) between men's and women's clubs, making inferences based on men's data is not appropriate. Therefore, this study aims to provide a first overview of the current load monitoring practices in elite women's football. Twenty-two elite European women's football clubs participated in a closed online survey (40% response rate). The survey consisted of 33 questions using multiple choice or Likert scales. The questions covered three topics; type of data collected and collection purpose, analysis methods, and staff member involvement. All 22 clubs collected data related to different load monitoring purposes, with 18 (82%), 21 (95%), and 22 (100%) clubs collecting external load, internal load, and training outcome data, respectively. Most respondents indicated that their club use training models and take into account multiple indicators to analyse and interpret the data. While sports-science staff members were most involved in the monitoring process, coaching, and sports-medicine staff members also contributed to the discussion of the data. Overall, the results of this study show that most elite women's clubs apply load monitoring practices extensively. Despite the organizational challenges compared to men's football, these observations indicate that women's clubs have a vested interest in load monitoring. We hope these findings encourage future developments within women's football.

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

In the rapidly moving proteomics field, a diverse patchwork of data analysis pipelines and algorithms for data normalization and differential expression analysis is used by the community. We generated a mass spectrometry downstream analysis pipeline (MS-DAP) that integrates both popular and recently developed algorithms for normalization and statistical analyses. Additional algorithms can be easily added in the future as plugins. MS-DAP is open-source and facilitates transparent and reproducible proteome science by generating extensive data visualizations and quality reporting, provided as standardized PDF reports. Second, we performed a systematic evaluation of methods for normalization and statistical analysis on a large variety of data sets, including additional data generated in this study, which revealed key differences. Commonly used approaches for differential testing based on moderated t-statistics were consistently outperformed by more recent statistical models, all integrated in MS-DAP. Third, we introduced a novel normalization algorithm that rescues deficiencies observed in commonly used normalization methods. Finally, we used the MS-DAP platform to reanalyze a recently published large-scale proteomics data set of CSF from AD patients. This revealed increased sensitivity, resulting in additional significant target proteins which improved overlap with results reported in related studies and includes a large set of new potential AD biomarkers in addition to previously reported.

BackgroundCurrently, evidence on the role of stressful life events in fatigue among the Chinese working adults is lacking. This study aimed at exploring the prospective associations between stressful life events and chronic fatigue among Chinese government employees.MethodsFrom January 2018 to December 2019, a total of 16206 government employees were included at baseline and they were followed-up until May 2021. A digital self-reported questionnaire platform was established to collect information on participants' health and covariates. Life events were assessed by the Life Events Scale (LES), fatigue was assessed by using a single item, measuring the frequency of its occurrence. Binary logistic regression analysis was used for the data analysis.ResultsOf the included 16206 Chinese government employees at baseline, 60.45% reported that they experienced negative stressful life events and 43.87% reported that they experienced positive stressful life events over the past year. Fatigue was reported by 7.74% of the sample at baseline and 8.19% at follow-up. Cumulative number of life events at baseline, and cumulative life events severity score at baseline were positively associated with self-reported fatigue at follow up, respectively. After adjusting sociodemographic factors, occupational factors and health behavior related factors, negative life events at baseline (OR: 2.06, 95% CI: 1.69–2.51) were significantly associated with self-reported fatigue at follow-up. Some specific life events including events related to work and events related to economic problems were significantly associated with self-reported fatigue. Specifically, work stress (OR = 1.76, 95%CI: 1.45–2.13), as well as not satisfied with the current job (OR = 1.95, 95%CI: 1.58–2.40), in debt (OR = 1.75, 95%CI: 1.40–2.17) were significantly associated with self-reported fatigue. The economic situation has improved significantly (OR = 0.62, 95%CI: 0.46–0.85) at baseline was significantly associated with lower incidence of self-reported fatigue.ConclusionNegative stressful life events were associated with fatigue among Chinese government employees. Effective interventions should be provided to employees who have experienced negative stressful life events.

Metadata ID: AAS_4046_Transects_2012-13 title: Windmill Islands vegetation transects, surveyed 2012/13 (10 years)

This record contains data associated with the Windmill Islands vegetation 10 year survey conducted in 2012/13, under AAS_4046. The transects were established in 2002/03, as described in metadata ID: ASAC_1313_Transects_2002-03, where details of experimental design and data collection are provided.

Descriptions of data associated with this record are provided below under the following headings: 1. LOCATION (GPS) DATA (and MAPS) 2. QUADRAT PHOTOS 3. NOTEBOOK SCANS 4. MICROSCOPY SCORE SHEETS 5. FINESCALE SPECIES ABUNDANCE (MICROSCOPY) 6. BROADSCALE PERCENT COVER (IMAGE ANALYSIS) 7. ENVIRONMENTAL VARIABLES (e.g. MOISTURE, TEMPERATURE) 8. PROCESSED/COMPILED/WORKED

Descriptions of data provided: 1. LOCATION (GPS) DATA (and MAPS) Quadrat location data are provided in metadata ID: AAS_4046_quadrat_locations (http://data.aad.gov.au/aadc/metadata/metadata.cfm?entry_id=AAS_4046_quadrat_locations). And shown in two maps which are available via the AADC map catalogue: http://data.aad.gov.au/aadc/mapcat/display_map.cfm?map_id=14450 http://data.aad.gov.au/aadc/mapcat/display_map.cfm?map_id=14451

1. QUADRAT PHOTOS TO BE PROVIDED - all quadrat (and transect/site) photos collected 2013.

2. NOTEBOOK SCANS TO BE PROVIDED -

3. MICROSCOPY SCORE SHEETS TO BE PROVIDED - for samples collected 2013.

4. FINESCALE SPECIES ABUNDANCE (MICROSCOPY) TO BE PROVIDED - raw data from microscopy scoring for samples collected 2013.

5. BROADSCALE PERCENT COVER (IMAGE ANALYSIS) TO BE PROVIDED - has this data been generated? May be part of Diana King PhD thesis, which is due to be submitted 2016.

6. ENVIRONMENTAL VARIABLES (e.g. MOISTURE, TEMPERATURE) TO BE PROVIDED - raw stable isotope data collected 2013. Raw moisture content (CWC) data collected 2013.

7. PROCESSED/COMPILED/WORKED OPTIONAL to provide if relevant

8. MULTI-YEAR COMPILATIONS AND COMPARISONS

FILE: Transects Data Summary_2000-2013.xlsx This excel file provides a summary of transect data collected to 2013. This file was originally prepared by Taylor Benny (2013) and has been updated by Jane Wasley (2015).

Four worksheets: 1. Worksheet: "Vocabulary"- provides a detailed description of methods, terms and abbreviations. 2. Worksheet: "DataCollection" provides a summary of project personnel (including field collections, laboratory analyses and data analysis) for all survey years from 1999. 3. Worksheet: "Quadrat" provides a schematic of the quadrats used in this study, providing details of the size used for photos (25 x 25 cm), sample collection (20 x 20 cm) and grid interval details. 4. Worksheet "Data" includes the following data types: GPS locations of quadrats, species composition of vegetation quadrats (referred to as: fine scale vegetation analysis), moss moisture contents (referred to as: community water content; CWC) and vegetation temperature. The species composition data presented in this file are the overall relative abundance scores for each species/taxa for each quadrat. These data are based on presence/absence scores for nine samples collected per quadrat (raw individual sample data not provided here). The score range for each taxa for each quadrat is 0-9, where nine indicates taxa present in all nine samples in a given quadrat.

FILE: Taylor Benny 2013_Thesis.pdf PDF file of Honours thesis for Taylor Benny (2013).

FILE NAME: ASAC_4046-Transects 2013-SOE summary.pdf Public summary of results, describing the state and trends of continental Antarctic vegetation communities. Presentation format based on template from Australia: State of the Environment 2011 (Hatton et al. 2011). Trends presented based on results of transects surveyed 203 to 2013. The PDF file is an extract from Benny 2013 thesis (P80).

FILE NAME: AAS_4046-Transects-change maps-2013.pdf Maps produced by Taylor Benny (2013), showing schematic summary of biological change observed between survey periods (2002/03 vs 2007/08 and 2007/08 vs 2012/13) for Windmill Islands vegetation transects at Robinson Ridge and ASPA 135 sites.

The PDF file contains six pages, each page shows a map for each of the two study sites: ASPA 135 and Robinson Ridge (2 maps per page). Data collection as described in metadata ID: Windmill Islands Vegetation Transects. Unless otherwise provide in Benny 2013, details of the origin of the map imagery and quadrat position data are not known (data likely collected via octocopter instruments deployed by Arko Lucier, or his team). The six pages are an extract from Benny 2013, and are labelled with page numbers as indicated in brackets ( ) below, they present data for: 1. Ceratodon purpureus (P74) 2. Schistidium antarctici (P75) 3. Bryum pseudotriquetrum (P76) 4. crustose lichens (P77) 5. Community Water Content (P78) 6. % live moss (P79)

Data were collected from ASPA 135 and Robinson Ridge, as shown in maps 14450 and 14451 in the SCAR Map Catalogue.

Context

This dataset contains Kaggle ML & DS Survey data for 2022-2018. Cleaned and improved dataset.

In the original data (2018, 2019, 2020, 2021, 2022) answers to the questions were contained in different columns, the questions and answer options could differ. Single and multi-column columns had the same header type: Q1, Q2 ...

Improvements

In this dataset, questions are grouped into SA / GA categories - single answers and group answers. Also cleared columns from spaces and different answer options.

Rare categories/answers are grouped by value or categorized as "Other". Filling the category only if there is an empty value, not by simple summation, but by replacement.

Content

This dataset contains the following: - kaggle_survey_2018-2022_header.csv: the tabular dataset containing the header data - kaggle_survey_2018-2022_data.csv: the tabular dataset containing the aggregated data from 2018 to 2021 - code_samples.pdf: pdf file containing code examples

Source

Link : https://www.kaggle.com/c/kaggle-survey-2022 Link : https://www.kaggle.com/c/kaggle-survey-2021 Link : https://www.kaggle.com/c/kaggle-survey-2020 Link : https://www.kaggle.com/c/kaggle-survey-2019 Link : https://www.kaggle.com/kaggle/kaggle-survey-2018

Description: This dataset contains 5 sample PDF Electronic Health Records (EHRs), generated as part of a synthetic healthcare data project. The purpose of this dataset is to assist with sales distribution, offering potential users and stakeholders a glimpse of how synthetic EHRs can look and function. These records have been crafted to mimic realistic admission data while ensuring privacy and compliance with all data protection regulations.

Key Features: 1. Synthetic Data: Entirely artificial data created for testing and demonstration purposes. 1. PDF Format: Records are presented in PDF format, commonly used in healthcare systems. 1. Diverse Use Cases: Useful for evaluating tools related to data parsing, machine learning in healthcare, or EHR management systems. 1. Rich Admission Details: Includes admission-related data that highlights the capabilities of synthetic EHR generation.

Potential Use Cases:

• Demonstrating EHR-related tools or services.
• Benchmarking data parsing models for PDF health records.
• Showcasing synthetic healthcare data in sales or marketing efforts.

Feel free to use this dataset for non-commercial testing and demonstration purposes. Feedback and suggestions for improvements are always welcome!

BackgroundAtrial fibrillation (AF) is a common persistent arrhythmia characterized by rapid and chaotic atrial electrical activity, potentially leading to severe complications such as thromboembolism, heart failure, and stroke, significantly affecting patient quality of life and safety. As the global population ages, the prevalence of AF is on the rise, placing considerable strains on individuals and healthcare systems. This study utilizes bioinformatics and Mendelian Randomization (MR) to analyze transcriptome data and genome-wide association study (GWAS) summary statistics, aiming to identify biomarkers causally associated with AF and explore their potential pathogenic pathways.MethodsWe obtained AF microarray datasets GSE41177 and GSE79768 from the Gene Expression Omnibus (GEO) database, merged them, and corrected for batch effects to pinpoint differentially expressed genes (DEGs). We gathered exposure data from expression quantitative trait loci (eQTL) and outcome data from AF GWAS through the IEU Open GWAS database. We employed inverse variance weighting (IVW), MR-Egger, weighted median, and weighted model approaches for MR analysis to assess exposure-outcome causality. IVW was the primary method, supplemented by other techniques. The robustness of our results was evaluated using Cochran's Q test, MR-Egger intercept, MR-PRESSO, and leave-one-out sensitivity analysis. A “Veen” diagram visualized the overlap of DEGs with significant eQTL genes from MR analysis, referred to as common genes (CGs). Additional analyses, including Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and immune cell infiltration studies, were conducted on these intersecting genes to reveal their roles in AF pathogenesis.ResultsThe combined dataset revealed 355 differentially expressed genes (DEGs), with 228 showing significant upregulation and 127 downregulated. Mendelian randomization (MR) analysis identified that the autocrine motility factor receptor (AMFR) [IVW: OR = 0.977; 95% CI, 0.956–0.998; P = 0.030], leucine aminopeptidase 3 (LAP3) [IVW: OR = 0.967; 95% CI, 0.934–0.997; P = 0.048], Rab acceptor 1 (RABAC1) [IVW: OR = 0.928; 95% CI, 0.875–0.985; P = 0.015], and tryptase beta 2 (TPSB2) [IVW: OR = 0.971; 95% CI, 0.943–0.999; P = 0.049] are associated with a reduced risk of atrial fibrillation (AF). Conversely, GTPase-activating SH3 domain-binding protein 2 (G3BP2) [IVW: OR = 1.030; 95% CI, 1.004–1.056; P = 0.024], integrin subunit beta 2 (ITGB2) [IVW: OR = 1.050; 95% CI, 1.017–1.084; P = 0.003], glutaminyl-peptide cyclotransferase (QPCT) [IVW: OR = 1.080; 95% CI, 1.010–0.997; P = 1.154], and tripartite motif containing 22 (TRIM22) [IVW: OR = 1.048; 95% CI, 1.003–1.095; P = 0.035] are positively associated with AF risk. Sensitivity analyses indicated a lack of heterogeneity or horizontal pleiotropy (P > 0.05), and leave-one-out analysis did not reveal any single nucleotide polymorphisms (SNPs) impacting the MR results significantly. GO and KEGG analyses showed that CG is involved in processes such as protein polyubiquitination, neutrophil degranulation, specific and tertiary granule formation, protein-macromolecule adaptor activity, molecular adaptor activity, and the SREBP signaling pathway, all significantly enriched. The analysis of immune cell infiltration demonstrated associations of CG with various immune cells, including plasma cells, CD8T cells, resting memory CD4T cells, regulatory T cells (Tregs), gamma delta T cells, activated NK cells, activated mast cells, and neutrophils.ConclusionBy integrating bioinformatics and MR approaches, genes such as AMFR, G3BP2, ITGB2, LAP3, QPCT, RABAC1, TPSB2, and TRIM22 are identified as causally linked to AF, enhancing our understanding of its molecular foundations. This strategy may facilitate the development of more precise biomarkers and therapeutic targets for AF diagnosis and treatment.

Protein interactions enable much more complex behavior than the sum of the individual protein parts would suggest and represents a level of biological complexity requiring full understanding when unravelling cellular processes. Cross-linking mass spectrometry has emerged as an attractive approach to study these interactions, and recent advances in mass spectrometry and data analysis software have enabled the identification of thousands of cross-links from a single experiment. The resulting data complexity is, however, difficult to understand and requires interactive software tools. Even though solutions are available, these represent an agglomerate of possibilities, and each features its own input format, often forcing manual conversion. Here we present Cross-ID, a visualization platform that links directly into the output of XlinkX for Proteome Discoverer but also plays well with other platforms by supporting a user-controllable text-file importer. The platform includes features like grouping, spectral viewer, gene ontology (GO) enrichment, post-translational modification (PTM) visualization, domains and secondary structure mapping, data set comparison, previsualization overlap check, and more. Validation of detected cross-links is available for proteins and complexes with known structure or for protein complexes through the DisVis online platform (http://milou.science.uu.nl/cgi/services/DISVIS/disvis/). Graphs are exportable in PDF format, and data sets can be exported in tab-separated text files for evaluation through other software.

BackgroundRheumatoid arthritis (RA) is a chronic autoimmune disease in which autophagy is pivotal in its pathogenesis. This study aims to identify autophagy-related genes associated with RA and investigate their functional roles.MethodsWe performed mRNA sequencing to identify differentially expressed genes (DEGs) between RA and osteoarthritis (OA) and intersected these with autophagy-related genes to obtain autophagy-related DEGs (ARDEGs) in RA. Bioinformatics and machine learning approaches were used to identify key biomarkers. Functional experiments, including real-time cellular analysis (RTCA), scratch healing, and flow cytometry, were conducted to examine the effects of gene silencing on the proliferation and migration of MH7A cells.ResultsA total of 37 ARDEGs were identified in RA. Through bioinformatics analysis, interferon regulatory factor 4 (IRF4) emerged as a key hub gene, with its high expression confirmed in RA synovial tissues and RA FLS cells. IRF4 knockdown inhibited the proliferation and migration and promoted the death of MH7A cells.ConclusionIRF4 is an autophagy-related diagnostic biomarker for RA. Targeting IRF4 could serve as a potential diagnostic and therapeutic strategy for RA, although further clinical studies are required to validate its effectiveness.

BackgroundSepsis is one of the leading causes of preventable deaths in hospitals. This study presents the evaluation of a quality collaborative, which aimed to decrease sepsis-related hospital mortality.MethodsThe German Quality Network Sepsis (GQNS) offers quality reporting based on claims data, peer reviews, and support for establishing continuous quality management and staff education. This study evaluates the effects of participating in the GQNS during the intervention period (April 2016–June 2018) in comparison to a retrospective baseline (January 2014–March 2016). The primary outcome was all-cause risk-adjusted hospital mortality among cases with sepsis. Sepsis was identified by International Classification of Diseases (ICD) codes in claims data. A controlled time series analysis was conducted to analyze changes from the baseline to the intervention period comparing GQNS hospitals with the population of all German hospitals assessed via the national diagnosis-related groups (DRGs)-statistics. Tests were conducted using piecewise hierarchical models. Implementation processes and barriers were assessed by surveys of local leaders of quality improvement teams.ResultsSeventy-four hospitals participated, of which 17 were university hospitals and 18 were tertiary care facilities. Observed mortality was 43.5% during baseline period and 42.7% during intervention period. Interrupted time-series analyses did not show effects on course or level of risk-adjusted mortality of cases with sepsis compared to the national DRG-statistics after the beginning of the intervention period (p = 0.632 and p = 0.512, respectively). There was no significant mortality decrease in the subgroups of patients with septic shock or ventilation >24 h or predefined subgroups of hospitals. A standardized survey among 49 local quality improvement leaders in autumn of 2018 revealed that most hospitals did not succeed in implementing a continuous quality management program or relevant measures to improve early recognition and treatment of sepsis. Barriers perceived most commonly were lack of time (77.6%), staff shortage (59.2%), and lack of participation of relevant departments (38.8%).ConclusionAs long as hospital-wide sepsis quality improvement efforts will not become a high priority for the hospital leadership by assuring adequate resources and involvement of all pertinent stakeholders, voluntary initiatives to improve the quality of sepsis care will remain prone to failure.

To elucidate the role of transcriptional regulators (TRs) associated with invasiveness in multiple myeloma (MM), we conducted a systematic network analysis to identify key master regulators (MRs) that govern MM invasiveness. We employed a consensus clustering method based on a 24-gene signature to classify MM patients into high invasiveness (INV-H) and low invasiveness (INV-L) groups. Subsequently, we identified TRs specific to the INV-H and INV-L phenotypes as MRs using a network-based approach, and we validated the MR activities that correlated with the INV-H phenotype across multiple independent datasets. We evaluated the effect of MRs on patient outcomes in relation to the prognosis of MM. By utilizing siRNA to disrupt ERG expression in U266 and RPMI8226 cell lines, we evaluated the effects of the master regulator ERG on the proliferation, apoptosis, invasion, and migration of myeloma cell lines, and we confirmed the expression of ERG in patients with extramedullary MM. We assessed invasiveness using a 24-gene signature, categorizing patients into INV-H and INV-L groups. Our network identified MRs linked to MM invasiveness and revealed enriched signaling pathways. High ERG expression correlated with poor prognosis. ERG silencing reduced cell invasiveness, migration, and apoptosis, while promoting proliferation. Elevated ERG was found in extramedullary MM, and potential drug candidates, including Idarubicin, were identified for treatment. This study provides a comprehensive analysis of master regulators in EMM, contributing to targeted therapeutic strategies. We identified ERG as a marker for extramedullary invasion in MM, suggesting it as a potential therapeutic target for future interventions.

IntroductionChronic obstructive pulmonary disease (COPD) is projected to become the third leading cause of death worldwide. Despite extensive research over the past few decades, effective treatments remain elusive, making disease prevention and control a global challenge.MethodsThis study aimed to identify diagnostic key genes for COPD. We utilized the Gene Expression Omnibus database to obtain gene expression data specific to COPD. Differentially expressed genes (DEGs) were identified and analyzed through Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis. Integrated weighted gene co-expression network analysis was employed to examine related gene modules. To pinpoint key genes, we used SVM-RFE, RF, and LASSO.ResultsA total of 1782 DEGs were discovered, many of which were enriched in various biological pathways and activities. Four key genes—MRC1, BCL2A1, GYPC, and SLC2A3—were identified. We observed a significant difference in immune infiltration between COPD and normal groups, indicating potential interactions between immune cells and these genes. The identified key genes were further validated using external datasets.DiscussionOur findings suggest that MRC1, BCL2A1, GYPC, and SLC2A3 are potential biomarkers for COPD. Targeting these diagnostic genes with specific drugs may potentially offer new avenues for COPD management; however, this hypothesis remains preliminary and requires further investigation, as the study does not directly assess therapeutic interventions.