To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

• About Dataset Safa S. Abdul-Jabbar, Alaa k. Farhan

• Context This is the first Dataset for various ordinary patients in Iraq. The Dataset provides the patients’ Cell Blood Count test information that can be used to create a Hematology diagnosis/prediction system. Also, this Data was collected in 2022 from Al-Zahraa Al-Ahly Hospital. These data can be cleaned & analyzed using any programming language because it is provided in an excel file that can be accessed and manipulated easily. The user just needs to understand how rows and columns are arranged because the data was collected as images(CBC images) from the laboratories and then stored the extracted data in an excel file.  Content This Dataset contains 500 rows. For each row (patient information), there are 21 columns containing CBC test features that can be described as follows:

• ID: Patients Identifier

• WBC: White Blood Cell, Normal Ranges: 4.0 to 10.0, Unit: 10^9/L.

• LYMp: Lymphocytes percentage, which is a type of white blood cell, Normal Ranges: 20.0 to 40.0, Unit: %

• MIDp: Indicates the percentage combined value of the other types of white blood cells not classified as lymphocytes or granulocytes, Normal Ranges: 1.0 to 15.0, Unit: %

• NEUTp: Neutrophils are a type of white blood cell (leukocytes); neutrophils percentage, Normal Ranges: 50.0 to 70.0, Unit: %

• LYMn: Lymphocytes number are a type of white blood cell, Normal Ranges: 0.6 to 4.1, Unit: 10^9/L.

• MIDn: Indicates the combined number of other white blood cells not classified as lymphocytes or granulocytes, Normal Ranges: 0.1 to 1.8, Unit: 10^9/L.

• NEUTn: Neutrophils Number, Normal Ranges: 2.0 to 7.8, Unit: 10^9/L.

• RBC: Red Blood Cell, Normal Ranges: 3.50 to 5.50, Unit: 10^12/L

• HGB: Hemoglobin, Normal Ranges: 11.0 to 16.0, Unit: g/dL

• HCT: Hematocrit is the proportion, by volume, of the Blood that consists of red blood cells, Normal Ranges: 36.0 to 48.0, Unit: %

• MCV: Mean Corpuscular Volume, Normal Ranges: 80.0 to 99.0, Unit: fL

• MCH: Mean Corpuscular Hemoglobin is the average amount of haemoglobin in the average red cell, Normal Ranges: 26.0 to 32.0, Unit: pg

• MCHC: Mean Corpuscular Hemoglobin Concentration, Normal Ranges: 32.0 to 36.0, Unit: g/dL

• RDWSD: Red Blood Cell Distribution Width, Normal Ranges: 37.0 to 54.0, Unit: fL

• RDWCV: Red blood cell distribution width, Normal Ranges: 11.5 to 14.5, Unit: %

• PLT: Platelet Count, Normal Ranges: 100 to 400, Unit: 10^9/L

• MPV: Mean Platelet Volume, Normal Ranges: 7.4 to 10.4, Unit: fL

• PDW: Red Cell Distribution Width, Normal Ranges: 10.0 to 17.0, Unit: %

• PCT: The level of Procalcitonin in the Blood, Normal Ranges: 0.10 to 0.28, Unit: %

• PLCR: Platelet Large Cell Ratio, Normal Ranges: 13.0 to 43.0, Unit: %

• Acknowledgements We thank the entire Al-Zahraa Al-Ahly Hospital Hospital team, especially the hospital manager, for cooperating with us in collecting this data while maintaining patients' confidentiality.

Raw data outputs 1-18 Raw data output 1. Differentially expressed genes in AML CSCs compared with GTCs as well as in TCGA AML cancer samples compared with normal ones. This data was generated based on the results of AML microarray and TCGA data analysis. Raw data output 2. Commonly and uniquely differentially expressed genes in AML CSC/GTC microarray and TCGA bulk RNA-seq datasets. This data was generated based on the results of AML microarray and TCGA data analysis. Raw data output 3. Common differentially expressed genes between training and test set samples the microarray dataset. This data was generated based on the results of AML microarray data analysis. Raw data output 4. Detailed information on the samples of the breast cancer microarray dataset (GSE52327) used in this study. Raw data output 5. Differentially expressed genes in breast CSCs compared with GTCs as well as in TCGA BRCA cancer samples compared with normal ones. Raw data output 6. Commonly and uniquely differentially expressed genes in breast cancer CSC/GTC microarray and TCGA BRCA bulk RNA-seq datasets. This data was generated based on the results of breast cancer microarray and TCGA BRCA data analysis. CSC, and GTC are abbreviations of cancer stem cell, and general tumor cell, respectively. Raw data output 7. Differential and common co-expression and protein-protein interaction of genes between CSC and GTC samples. This data was generated based on the results of AML microarray and STRING database-based protein-protein interaction data analysis. CSC, and GTC are abbreviations of cancer stem cell, and general tumor cell, respectively. Raw data output 8. Differentially expressed genes between AML dormant and active CSCs. This data was generated based on the results of AML scRNA-seq data analysis. Raw data output 9. Uniquely expressed genes in dormant or active AML CSCs. This data was generated based on the results of AML scRNA-seq data analysis. Raw data output 10. Intersections between the targeting transcription factors of AML key CSC genes and differentially expressed genes between AML CSCs vs GTCs and between dormant and active AML CSCs or the uniquely expressed genes in either class of CSCs. Raw data output 11. Targeting desirableness score of AML key CSC genes and their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 12. CSC-specific targeting desirableness score of AML key CSC genes and their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 13. The protein-protein interactions between AML key CSC genes with themselves and their targeting transcription factors. This data was generated based on the results of AML microarray and STRING database-based protein-protein interaction data analysis. Raw data output 14. The previously confirmed associations of genes having the highest targeting desirableness and CSC-specific targeting desirableness scores with AML or other cancers’ (stem) cells as well as hematopoietic stem cells. These data were generated based on a PubMed database-based literature mining. Raw data output 15. Drug score of available drugs and bioactive small molecules targeting AML key CSC genes and/or their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 16. CSC-specific drug score of available drugs and bioactive small molecules targeting AML key CSC genes and/or their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 17. Candidate drugs for experimental validation. These drugs were selected based on their respective (CSC-specific) drug scores. CSC is the abbreviation of cancer stem cell. Raw data output 18. Detailed information on the samples of the AML microarray dataset GSE30375 used in this study.

** RD DATASET ** RD dataset was created by the images from the melanoma community on the internet (https://reddit.com/r/melanoma). Consecutive images were included using a python library (https://github.com/aliparlakci/bulk-downloader-for-reddit) from Jan 25, 2020, to July 30, 2021. The ground truth was voted by four dermatologists and one plastic surgeon while referring to the chief complaint and brief history. A total of 1,282 images (1,201 cases) were finally included. Because of the deleted cases by users, the links of 860 cases are valid in July 2021.

1. RD_RAW.xlsx The download links and ground truth of the RD dataset are included in this excel file. In addition, the raw data of the AI (Model Dermatology Build2021 - https://modelderm.com) and 32 laypersons were included.

2. v1_public.zip "v1_public.zip" includes the 1,282 lesional images (full-size). The 24 images that were excluded from the study are also available.

3. v1_private.zip is not available here. Wide field images are not available here. If the archive is needed for research purpose, please email to Dr. Han Seung Seog (whria78@gmail.com) or Dr Cristian Navarrete-Dechent (ctnavarr@gmail.com).

References - The Degradation of Performance of a State-of-the-art Skin Image Classifier When Applied to Patient-driven Internet Search - Scientific Report (in-press)

** Background normal test with the ISIC images ** ISIC dataset (https://www.isic-archive.com; Gallery -> 2018 JID Editorial images; 99 images; ISIC_0024262 and ISIC_0024261 are identical images and ISIC_0024262 was skipped) was used for the background normal test. We defined 10% area rectangle crop to “specialist-size crop”, and 5% area rectangle crop to “layperson-size crop” a) S-crops.zip: specialist-size crops Format: CROPNO_AGE(0~99)_GENDER(1=male,0=female)[m]_FILENAME.png b) L-crops.zip: layperson-size crops Format: CROPNO_AGE(0~99)_GENDER(1=male,0=female)[m]_FILENAME.png c) result_S.zip: Background normal test result using the specialist-size crops d) result_L.zip; Background normal test result using the layperson-size crops

Reference - Automated Dermatological Diagnosis: Hype or Reality? - https://doi.org/10.1016/j.jid.2018.04.040 - Multiclass Artificial Intelligence in Dermatology: Progress but Still Room for Improvement - https://doi.org/10.1016/j.jid.2020.06.040

Abstract This dataset is a combined Excel spreadsheet dataset that integrates all available groundwater and surface water chemistry historical records. It includes field quality parameters, methane concentrations and major and minor ion concentrations. It is based on the following data sources: GA compiled hydrochemistry datasets: Surface water data from the Queensland water monitoring information portal (https://water-monitoring.information.qld.gov.au/), accessed and downloaded in January 2019; Data from EHS Support (2014) Water baseline assessment (ATP1087) prepared for Armour Energy. ## Attribution Geological and Bioregional Assessment Program ## History A hierarchical cluster analysis was conducted on groundwater and surface water datasets from the Isa GBA region. For this purpose, nine variables (Ca, Mg, Na, K, HCO3, Cl, SO4, electrical conductivity and pH) which were measured across most hydrochemical records were selected. Prior to the multivariate statistical analysis, all variables except for pH were log-transformed to ensure that each variable more closely follows a normal distribution. The multivariate statistical technique is described in more details by Raiber et al. (2012) and Raiber et al. (2016).

Presentation of normal and diabetic groups. (a) shows the glycemic values and the age in months for diabetic women (F+) and normal women (F-). (b) represents the legend of the colors for panel (a). (c) shows the average BMI for (F+) and (F-). (d) shows the average age for (F+) and (F-). (e) shows the average height and weight for (F+) and (F-). (f) shows the glycemic values and the age in months for diabetic men (M+) and normal men (M-). (g) represents the legend of the colors for panel (f). (h) shows the average BMI for (M+) and (M-). (i) shows the average age for (M+) and (M-). (j) shows the average height and weight for (M+) and (M-). (k) shows the average age of women (F) and men (M). (l) shows the average height and weight of women (F) and men (M). (m) shows the distribution of diabetic (F+) and normal (F-) women when glycemic values are ordered by t0 ... t4. (n) shows the distribution of diabetic (M+) and normal (M-) men when glycemic values are ordered by t0 ... t4. (o) shows the distribution of diabetics compared to non-diabetics when glycemic values are ordered by t0 ... t4. (p) shows the t4 distribution of diabetics compared to non-diabetics, and (q) the BMI values. (r) represents the legend of the colors for panel (o) and panel (p) (Constantin IONESCU-TIRGOVISTE, Paul A. GAGNIUC, Elvira GAGNIUC. The electrical activity map of the human skin indicates strong differences between normal and diabetic individuals: A gateway to onset prevention. Biosensors and Bioelectronics, 120 (2018) 188–194).

This data description explains the data of the 13 figures in the article, involving the overload of fuses and their fitting curves at 5 °, 7 °, 8 °, and 10 ° drop angles; The contribution rate of factors affecting the ignition mechanism of the fuse and the main effect diagram of each factor; The key influencing factors of whether the fuse firing mechanism fires under different variable ranges and their historical values in virtual shooting experiments; The velocity distribution diagram of the fuse needle piercing the detonator during virtual shooting experiment, variable optimization is carried out for fuses that fail to ignite normally, and the velocity distribution of the needle piercing the detonator after optimization, as well as the variable optimization process.The detailed description of the database is as follows.Figure 5 in the text shows the overload data of the fuse at 5 °, 7 °, 8 °, and 10 ° drop angles. The data is from an Excel table named "Different drop angle loads" in the database, and the fitting curves are different functions expressed as "Fitting formula" in a txt file.Figure 9 in the text shows the data of the firing needle velocity for the dynamic simulation of piercing detonators, which is stored in an Excel spreadsheet named "Dynamics simulation speed data" in the database.Figure 10 in the text shows the displacement data of the firing pin in the dynamic simulation of the detonator, which is stored in an Excel spreadsheet named "Dynamics simulation displacement data" in the database.Figure 15 in the text shows the velocity distribution of the firing pin piercing the detonator during the virtual shooting experiment, and the data is from an Excel table named "Speed distribution before optimization" in the database.Figure 16 in the text shows the main effect diagram of the influence of variable factors on the velocity of the firing pin piercing the detonator during virtual shooting experiments. The data is from an Excel table named "DOE Main Effect Diagram" in the database.Figure 17 in the text shows the contribution rate of variable factors to the velocity of the firing pin piercing the detonator during virtual shooting experiments. The data is from an Excel spreadsheet named "Effective on V" in the database.Figure 18 in the text is an iterative diagram of the optimization process for variable m2, and the data is from an Excel spreadsheet named 'History of simulation_m2' in the database.Figure 19 in the text is an iterative diagram of the optimization process for variable f, with data from an Excel spreadsheet named 'History of simulation_f' in the database.Figure 20 in the text is an iterative diagram of the optimization process for variable m1, and the data is from an Excel spreadsheet named 'History of simulation_m1' in the database.Figure 21 in the text shows the value curve of variable m2 before and after optimization, and the data is from an Excel spreadsheet named "Value of m2" in the database.Figure 22 in the text shows the value curve of variable f before and after optimization, and the data is from an Excel spreadsheet named "Value of f" in the database.Figure 23 in the text shows the value curves of variable m1 before and after optimization, with data from an Excel spreadsheet named "Value of m1" in the database.Figure 24 in the text shows the speed of the firing pin piercing the detonator in the optimized virtual shooting experiment, and the data is from an Excel table named "Speed distribution after optimization" in the database.

Children are increasingly exposed to ocular and environmental factors that could impact tear film function. The normal tear film function in children has not been systematically described as in adults. Reduced tear stability and tear secretion has been reported globally by studies in adult as the commonest factor in dry eye disease. The main focus of this data set is to systematically describe normal tear film function parameters in children. This data set shows the tear film stability and tear secretion using different methods from different studies which are chronologically presented on Excel 2016 worksheet. The study participants' population type, total sample size (as well as sample size by gender) and the age range (including mean and standard deviation) of the study participants were retrieved from individual articles. The study design, type of population and method of tear stability and tear secretion measurements were represented on the excel worksheet by numbers and the description of the numbers explained below the data set. The data was statistically analysed using the Stata/SE 14.2, StataCorp, 2015 and the results presented in forest plots and tables as reported in the manuscript.

This data provides the raw UMT data for a series of rubber slides over dry rough surfaces at two normal loads and velocities. The data shows that by changing the shape of the rubber tested here, a change in dynamic friction is observed.

The provided dataset is made up of an overview Excel file (CompleteData_RFES_Hale) and a file (RawData) which includes the individual raw data files which are mentioned in the overview file. These raw data files are the files directly provided from the UMT.

Static CoF is defined as the initial peak in CoF which occurs at which bulk sliding starts. Dynamic CoF is defined as the average of CoF readings as bulk sliding is maintained.

Please consult the corresponding research article "Rubber Friction and the Effect of Shape" for more information and detail relating to this dataset.

Materials and Methods The study was held in the Oral and Maxillofacial Surgery department and Kasturba Hospital, Manipal, from November 2019 to October 2021 after approval from the Institutional Ethics Committee (IEC: 924/2019). The study included patients between 18-70 years. Patients with associated diseases like cysts or tumors of the jaw bones, pregnant women, and those with underlying psychological issues were excluded from the study. The patients were assessed 8-12 weeks after surgical intervention. A data schedule was prepared to document age, sex, and fracture type. The study consisted of 182 subjects divided into two groups of 91 each (Group A: Mild to moderate facial injury and Group B: Severe facial injury) based on the severity of maxillofacial fractures and facial injury. Informed consent was obtained from each of the study participants. We followed Facial Injury Severity Scale (FISS) to determine the severity of facial fractures and injuries. The face is divided horizontally into the mandibular, mid-facial, and upper facial thirds. Fractures in these thirds are given points based on their type (Table 1). Injuries with a total score above 4.4 were considered severe facial injuries (Group A), and those with a total score below 4.4 were considered mild/ moderate facial injuries (Group B). The QOL was compared between the two groups. Meticulous management of hard and soft tissue injuries in our state-of-the-art tertiary care hospital was implemented. All elective cases were surgically treated at least 72 hours after the initial trauma. The facial fractures were adequately reduced and fixed with high–end Titanium miniplates and screws (AO Principles of Fracture Management). Soft tissue injuries were managed by wound debridement, removal of foreign bodies, and layered wound closure. Adequate pain-relieving medication was prescribed to the patients postoperatively for effective pain control. The QOL of the subjects was assessed using the 'Twenty-point Quality of life assessment in facial trauma patients in Indian population' assessment tool. This tool contains 20 questions and uses a five-point Likert response scale. The Twenty – point quality of life assessment tool included two zones: Zone 1 (Psychosocial impact) and Zone 2 (Functional and esthetic impact), with ten questions (domains) each (Table 2). The scores for each question ranged from 1- 5, the higher score denoting better Quality of life. Accordingly, the score in each zone for a patient ranged from 10 -50, and the total scores of both zones were recorded to determine the QOL. The sum of both zones determined the prognosis following surgery (Table 2). The data collected was entered into a Microsoft Excel spreadsheet and analyzed using IBM SPSS Statistics, Version 22(Armonk, NY: IBM Corp). Descriptive data were presented in the form of frequency and percentage for categorical variables and in the form of mean, median, standard deviation, and quartiles for continuous variables. Since the data were not following normal distribution, a non-parametric test was used. QOL scores were compared between the study groups using the Mann-Whitney U test. P value < 0.05 was considered statistically significant.

Quantifying lithospheric strength is essential to better understand seismicity in continental regions. In the manuscript “Using Syntectonic Calcite Veins to Reconstruct the Strength Evolution of an Active Low‐Angle Normal Fault, Woodlark Rift, SE Papua New Guinea”, we estimate differential stresses and principal stress orientations that drive rapid slip on the active Mai’iu fault (dipping ~16-24° at the Earth’s surface) in Papua New Guinea. We compile stress-depth snapshots by taking advantage of space-for-time relationships provided by progressive slip localization within the cooling and exhuming footwall of the Mai’iu fault. Estimated differential stresses are based on the mechanical twinning and/or recrystallized grain-size of deformed calcite veins that cross-cut the sequentially formed fault rock units (mylonites, foliated cataclasites, ultracataclasites and gouges). The orientation of principal stresses acting on the fault zone are estimated using stress-inversion techniques on crystallographic data for calcite-twins collected by electron backscatter diffraction (EBSD), and on fault-slip data of late outcrop-scale brittle faults cross-cutting the footwall and hangingwall of the Mai’iu fault. The data repository provides the raw dataset used for the paleostress analyses in this study from which we derive the fault’s peak strength (140–185 MPa) and the integrated strength of the extending brittle crust. The raw dataset includes:

(1) Calcite E-Twin Analysis: 12 subfolders with EBSD data on the analyzed calcite veins (.cpr, .crc), overview maps (.jpeg) of all analyzed calcite grains, excel-sheets with orientation and twin morphology data on the analyzed calcite grains, and EBSD Euler conversion output files (_out.xlsx; see below); (2) Calcite Grain-Size Piezometer: 8 subfolders with EBSD data on the analyzed calcite veins (.cpr, .crc), multiple overview maps (.png) of the analyzed calcite veins, and grain-size histograms of the relict and recrystallized grains; (3) Calcite Paleostress Analysis: twinning data that was made analogous to fault-slip data (.fdt) and best-fit stress orientations as calculated by the multiple inverse method (.mi4); (4) EBSD Euler Conversion: MATLAB code to calculate slip plane (e-plane) and glide direction from calcite host-twin pairs and to transform EBSD acquired orientation data from a sample reference frame into a geographic system; (5) Mai'iu Fault Structural Data: an excel-sheet with all structural data collected in the Suckling-Dayman Metamorphic Core Complex during the field campaigns in 2014, 2015 and 2016 (includes sample locations, fault-slip data, bedding data of the Gwoira Conglomerates, etc…; version 7, date: 26.09.2016).

All geothermometric data and explanations on how to reproduce the estimated paleostresses (using the provided raw dataset) can be found in the main manuscript. This unique dataset provides insights into the strength and stress evolution of the Woodlark Rift, Papua New Guinea.

1)This dataset contains information in excel format which comprises around 7489 data from social media, Facebook comments, etc. 2)All the people selected for data annotation are very well in English Language and are students , age range - 15 to 17 3)There are 5 columns in this dataset. Text , labels, Age , Age Category and Gender . 4)Normal and anxiety/depression text is present in the text columns, and the label column indicates whether the corresponding text denotes anxiety or depression.

🧪🌈 Laboratory Test Results – Anonymized Dataset

💎 Where Precision Medicine Meets the Vibrance of Data Science Unlock insights, drive innovations, and explore healthcare analytics with a colorful, interactive, and thematically bold dataset.

🌟 Overview

This dataset delivers fully anonymized laboratory test results with a visually rich and research-ready design. Each element—from clear unit descriptions to color-coded status flags—is crafted for maximum clarity and engagement.

💡 Ideal For:

• 📊 Data Analysis – Spot trends, detect anomalies.
• 🤖 Machine Learning – Build predictive healthcare models.
• 🎓 Education – Train students in medical data interpretation.
• 🖥 Dashboards – Create vibrant, widget-based visual analytics.

📂 Dataset Structure

Format: CSV – ready to use with Python, R, Excel, Tableau, Power BI, or any BI/ML platform.

🧾 Common Test Units & Meanings

Here’s what some of the common units mean in a medical context:

• ug/L (Microgram per Liter) – Common for Ferritin; measures very small concentrations in blood.
• % (Percentage) – Used for HbA1c to express average blood sugar over time.
• KU/L (Kilo Unit per Liter) – For Total IgE; measures antibodies in blood.
• mU/L (Milli Unit per Liter) – For Insulin or TSH; measures hormone activity.
• ng/dL (Nanogram per Deciliter) – For Free T4; measures tiny amounts of thyroid hormone.
• g/dL (Gram per Deciliter) – Common for Hemoglobin; measures hemoglobin concentration in blood.
• 10^3/uL (Thousand per Microliter) – Used for White Blood Cell or Platelet count.
• fL (Femtoliter) – For MCV, RDW; measures cell size.
• mg/dL (Milligram per Deciliter) – Used for glucose, bilirubin; measures substance concentration in blood/urine.

These units help clinicians determine how much of a substance is present and compare it with healthy reference ranges.

🎯 Why It Stands Out

• 🌈 Color-coded Status Flags – Instantly spot outliers.
• 📌 Detailed Annotations – Context for every measurement.
• 📊 Widget & Dashboard Ready – Perfect for embedding in BI tools.
• 🔒 Privacy Assured – 100% anonymized.
• 📚 Educational Value – Includes unit definitions and usage.

⚠️ Disclaimer

This dataset is for educational and research purposes only. It is not intended for actual medical diagnosis or treatment.

📜 License

CC0 1.0 Public Domain Dedication – Free to use, share, remix, and adapt.

💡 Inspiration

Crafted to inspire data-driven healthcare solutions, this dataset empowers researchers, educators, and developers to transform raw lab results into vivid, interactive, and actionable insights.

BackgroundIntraocular pressure is the only modifiable risk factor for the development and progression of glaucoma. Raised intraocular pressure could cause progressive visual field loss and blindness if left uncontrolled. Adherence to ocular hypotensive medications is vital to prevent optic nerve damage and its consequences. This study was conducted to systematically summarize the magnitude of glaucoma medication adherence and factors influencing adherence to glaucoma medications among adult glaucoma patients in Ethiopia.MethodsDatabase searches to identify research articles were conducted on PubMed, EMBASE, Cochrane, AJOL, SCOPUS, and Google Scholar without restriction on the date of publication. Data extraction was done using a data extraction Excel sheet. Analysis was performed using STATA version 16. Heterogeneity was assessed using I2 statistics. Pooled prevalence and pooled odds ratio with a 95% confidence interval using a random effect model were computed.ResultWe included six studies with a total of 2101 participants for meta-analysis. The magnitude of adherence to glaucoma medication was found to be 49.46% (95% CI [41.27–57.66]). Urban residents (OR = 1.89, 95% CI; 1.29–2.49) and those with normal visual acuity (OR = 2.82, 95% CI; 0.85–4.80) had higher odds of adherence to glaucoma medications. Patients who pay for the medications themselves (OR = 0.22, 95% CI; 0.09–0.34) were found to have 78% lower odds of adherence than their counterparts.ConclusionThe magnitude of glaucoma medication adherence is lower than expected. Place of residence, visual acuity, and payment means had statistically significant associations with glaucoma medication adherence. Tailored health education on medication adherence and subsidization of glaucoma medication is recommended.