Dataset Description: Lab Data Extracted from Marham.pk This dataset contains comprehensive details about various medical tests and laboratory services extracted from Marham.pk, a popular healthcare platform in Pakistan. The dataset provides structured information on different tests, their types, pricing, locations, and other relevant attributes.

Key Features of the Dataset The dataset includes the following important attributes:

Test Name – The name of the medical test (e.g., CBC, Lipid Profile, Blood Sugar Test). Test Type – The category or type of test (e.g., Blood Test, Radiology, Urine Test). Lab Name – The name of the laboratory offering the test. Lab Location – The city or specific area where the lab is located. Test Price – The cost of the test in Pakistani Rupees (PKR). Test Description – A brief overview of the purpose and details of the test. Availability – Indicates whether the test is available in a specific lab. Sample Requirements – Specifies whether fasting, urine, or blood samples are needed. Processing Time – The estimated time required to complete and deliver the test results. Discounts & Offers – Any special discounts provided by labs. Potential Use Cases This dataset can be utilized for various applications, including:

✅ Healthcare Analysis – Understanding pricing trends and availability of medical tests across different regions. ✅ Price Comparison – Comparing the cost of tests across multiple labs. ✅ Medical App Development – Integrating with health applications for users to find and book tests online. ✅ Data Visualization – Creating dashboards to display test availability and pricing insights. ✅ Predictive Analytics – Analyzing trends to predict future demands for medical tests.

This dataset contains the default data provided with the Nutrient Explorer Downloadable application (SI: https://cfpub.epa.gov/si/si_public_record_Report.cfm?Lab=CPHEA&dirEntryId=358039), which is used for testing out the features and capabilities of the app. The dataset is based off of LAGOS-NE, lake total nitrogen and total phosphorus concentration data from lakes in the Northeast United States and is combined with a number of explanatory variables such as geology, land use, climate, nutrient inputs, and lake characteristics. Portions of this dataset are inaccessible because: The *.RData format is not allowed to be uploaded on ScienceHub (see above response). They can be accessed through the following means: The *.RData files can be accessed when the user downloaded the NutrientExplorer application zip files on the ScienceInventory link: https://cfpub.epa.gov/si/si_public_record_Report.cfm?Lab=CPHEA&dirEntryId=358039. The user need to have RStudio installed to open the application and be able to use the RData files. Format: Some of the data files associated with the Nutrient Explorer application are in the *.RData format, which can not be uploaded to ScienceHub. These files include hydrologic unit (HU8) watershed shapefiles and some datasets similar to the *.csv files uploaded here containing variables used for testing out the application's features. This dataset is associated with the following publication: Pennino, M., M. Fry, R. Sabo, and J. Carleton. Nutrient Explorer: An analytical framework to visualize and investigate drivers of surface water quality. ENVIRONMENTAL MODELLING & SOFTWARE. Elsevier Science, New York, NY, 170: 105853, (2023).

The data was created to share the lab results collected through the LakeWatchers Program with the public.This dataset can be used in conjunction with LakeWatchers Monitoring Sites which identifies the location of the lab results.Metadata

The UN Biodiversity Lab (UNBL) 2.0 was launched today at Day 1 of the Nature for Life Hub. The UNBL 2.0 is a free, open-source platform that enables governments and others to access state-of-the-art maps and data on nature, climate change, and human development in new ways to generate insight fo...

Dataset Description: The AI-Enhanced English and Chinese Language Learning Dataset is a comprehensive collection of data aimed at advancing language education through the use of artificial intelligence. This dataset includes detailed records from various language learning platforms, combining both traditional classroom activities and AI-driven learning applications. The dataset is suitable for exploring different AI techniques to improve English and Chinese language acquisition, focusing on adaptive learning, feedback analysis, and language practice. Data spans from February 2019 to August 2024, covering diverse language learning scenarios across multiple institutions, including digital language labs, mobile apps, and AI-powered tutoring systems.

The dataset includes hourly data collected from language learners engaging in various activities such as grammar exercises, conversational practice, writing assessments, and interactive quizzes. The data is sourced from multiple regions, including English-speaking and Mandarin-speaking communities, making it ideal for comparative studies on AI-driven learning outcomes. The records encompass a variety of linguistic features and learning metrics, offering valuable insights into student engagement, progress, and performance across different learning contexts.

Features: Timestamp: Hourly timestamp indicating the time of each learning session. Learner ID: A unique identifier for each learner. Age: The age of the learner. Gender: Gender of the learner (Male, Female, Other). Native Language: The primary language spoken by the learner. Country of Residence: The country where the learner is based. Language Proficiency Level (Initial): The learner's initial language proficiency in English or Chinese (Beginner, Intermediate, Advanced). Type of Activity: Type of learning activity (Listening, Speaking, Reading, Writing). Lesson Content Type: The specific focus of the lesson (Grammar, Vocabulary, Pronunciation, etc.). Number of Lessons Completed: Cumulative count of lessons completed by the learner. Time Spent on Learning: Total time spent on language learning (in minutes). Learning Platform or Tool Used: Platform or tool used for learning (App, Website, Classroom Software). Homework Completion Rate: Percentage of homework assignments completed. Participation in Interactive Exercises: Frequency of participation in interactive exercises like quizzes and games. Frequency of Practice Sessions: Number of practice sessions per week. Test Scores: Scores from language proficiency tests, covering various areas such as grammar, listening, and vocabulary. Speaking Fluency Scores: Scores evaluating pronunciation accuracy and speech rate. Reading Comprehension Scores: Assessment scores for reading comprehension tasks. Writing Quality: Evaluation of writing quality based on grammatical accuracy and vocabulary use. Change in Proficiency Level: Measured change in language proficiency over time. Assignment Grades: Grades received on language assignments. Error Correction Rate: The rate at which learners correct their mistakes. Feedback from Instructors/Tutors: Qualitative feedback provided by instructors or AI tutors. Study Session Duration: Average duration of study sessions. Learning Consistency: Number of days per week studied. User Activity Type: Type of user activity (Active or Passive Participation). Engagement with Additional Learning Materials: Frequency of accessing extra learning resources (e.g., videos, articles). Peer Interaction Score: Score representing participation in study groups or discussion forums. Motivation Level: Self-reported level of motivation. Learning Environment: Type of learning environment (Home, School, Language Center). Learning Mode: Mode of learning (Self-Paced or Instructor-Led). Accessibility of Learning Resources: Availability of learning materials to the learner. Use of AI Tools: Whether AI tools like chatbots or speech recognition software were used. Language Learning Goals: Purpose of language learning (Academic, Professional, Personal). This dataset offers rich data for researchers and educators to analyze the impact of AI on language learning outcomes, make cross-linguistic comparisons, and develop personalized AI-driven language education models.

Overview: This is a lab-based dataset with videos recording volunteers (medical students) washing their hands as part of a hand-washing monitoring and feedback experiment. The dataset is collected in the Medical Education Technology Center (METC) of Riga Stradins University, Riga, Latvia. In total, 72 participants took part in the experiments, each washing their hands three times, in a randomized order, going through three different hand-washing feedback approaches (user interfaces of a mobile app). The data was annotated in real time by a human operator, in order to give the experiment participants real-time feedback on their performance. There are 212 hand washing episodes in total, each of which is annotated by a single person. The annotations classify the washing movements according to the World Health Organization's (WHO) guidelines by marking each frame in each video with a certain movement code.

This dataset is part on three dataset series all following the same format:

https://zenodo.org/record/4537209 - data collected in Pauls Stradins Clinical University Hospital

https://zenodo.org/record/5808764 - data collected in Jurmala Hospital

https://zenodo.org/record/5808789 - data collected in the Medical Education Technology Center (METC) of Riga Stradins University

Note #1: we recommend that when using this dataset for machine learning, allowances are made for the reaction speed of the human operator labeling the data. For example, the annotations can be expected to be incorrect a short while after the person in the video switches their washing movements.

Application: The intention of this dataset is to serve as a basis for training machine learning classifiers for automated hand washing movement recognition and quality control.

Statistics:

Frame rate: ~16 FPS (slightly variable, as the video are reconstructed from a sequence of jpg images taken with max framerate supported by the capturing devices).

Resolution: 640x480

Number of videos: 212

Number of annotation files: 212

Movement codes (in JSON files):

1: Hand washing movement — Palm to palm

2: Hand washing movement — Palm over dorsum, fingers interlaced

3: Hand washing movement — Palm to palm, fingers interlaced

4: Hand washing movement — Backs of fingers to opposing palm, fingers interlocked

5: Hand washing movement — Rotational rubbing of the thumb

6: Hand washing movement — Fingertips to palm

0: Other hand washing movement

Note #2: The original dataset of JPG images is available upon request. There are 13 annotation classes in the original dataset: for each of the six washing movements defined by the WHO, "correct" and "incorrect" execution is market with two different labels. In this published dataset, all incorrect executions are marked with code 0, as "other" washing movement.

Acknowledgments: The dataset collection was funded by the Latvian Council of Science project: "Automated hand washing quality control and quality evaluation system with real-time feedback", No: lzp - Nr. 2020/2-0309.

References: For more detailed information, see this article, describing a similar dataset collected in a different project:

M. Lulla, A. Rutkovskis, A. Slavinska, A. Vilde, A. Gromova, M. Ivanovs, A. Skadins, R. Kadikis, A. Elsts. Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines. Data. 2021; 6(4):38. https://doi.org/10.3390/data6040038

Contact information: atis.elsts@edi.lv

Context

The current research dataset has been developed within the context of a thesis of two students at the Department of Archival, Library and Information Studies of the University of West Attica. The dataset has been extracted from Google Play using a web scrapper namely the Data Miner. It contains information about more than 175 different Apps from the Cultural Heritage domain, including apps of museums, galleries, guides, cultural games, and so on.

GADM is a spatial database of the location of the world's administrative areas (or adminstrative boundaries) for use in GIS and similar software. Administrative areas in this database are countries and lower level subdivisions such as provinces, departments, bibhag, bundeslander, daerah istimewa, fivondronana, krong, landsvæðun, opština, sous-préfectures, counties, and thana. GADM describes where these administrative areas are (the "spatial features"), and for each area it provides some attributes, such as the name and variant names.

Scientific data provided by ENERGY-lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats. Dataset is available from the THREDDS Data Server to this url: https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html. Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores. ENERGY-lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.

This application compares changes between aggregated 2011 National Land Cover Database land cover categories with similarly aggregated land cover categories from The Clark Labs 2050 Conterminous US Land Cover Prediction. It also provides a few summary statistics about possible changes in developed, forest and agricultural land cover. Look for the soon to be released Clark Labs American Land Change Explorer application, which provides exhaustive analysis and summaries of potential transitions from each of the NLCD categories to each of the projected 2050 categories.The Clark Labs 2050 Conterminous US Land Cover Prediction© 2016 Clark LabsIntroductionThe Clark Labs’ conterminous US land cover prediction for 2050 was produced as part of the development of the Land Change Explorer – a web application to illustrate the potential of predictive land change modeling and to introduce potential users to the Land Change Modeler – a cloud-based software service for land change modeling to be offered in the ArcGIS Marketplace.ProcedureThe prediction is based on an empirical modeling of the relationship between land cover change from 2001 to 2011 and a series of explanatory variables. The land cover data were at a 30 meter resolution from the National Land Cover Database (NLCD). The explanatory variables(1) were:ElevationSlopeProximity to primary roadsProximity to secondary roadsProximity to local roadsProximity to high intensity developmentProximity to open waterProximity to cropland (used only for transitions to cropland)Protected areasCounty subdivisions or counties/incorporated places (depending on the state)(2)The modeling procedure used is a newly developed algorithm suitable for distributed computing in a cloud computing environment(3). Briefly, the procedure is based of kernel density estimation of the normalized likelihood of change associated with varying levels of each independent variable. These estimates are then aggregated by means of a locally-weighted average where the weights are based on the degree of conviction each variable has about the outcome at that specific pixel. Testing has shown it to be comparable in skill to a multi-layer perceptron neural network with the added advantages of rapid calculation and capability of being distributed across multiple computer nodes.Because the drivers of change can vary over space, modeling was done separately for each state. All transitions that met or exceed 2 km2 in area (at the state level) were modeled independently. Within a single state, as many as 128 individual transitions might occur. In total, over the 48 conterminous states, 3330 transitions were modeled. The modeling process initially establishes the potential to transition. This potential is expressed as a continuous value from 0 to 1 at each pixel for each transition. The procedure then uses the Markovian assumption that the rate of transition experienced in the historical period (2001-2011 in this case) will continue into the future. A competitive greedy selection process then allocates the projected change(4).ValidationIn the training process for each transition, 50% of historical instances of change and 50% of an equal-sized sample of pixels eligible to change, but which did not (e.g., persistence), were reserved for model validation. The median accuracy over all 3330 transitions was 80% with 79% of change validation pixels being correctly predicted and 83% of persistence pixels being correctly predicted. Thus the models, on average, are quite balanced in their ability to predict change and persistence.The accuracy associated with more specific transitions varied. A key objective was to be able to monitor and project anthropogenic changes and thus the explanatory variables chosen were focused on such drivers. Consequently, the median accuracy of natural to developed transitions (such as deciduous forest to low intensity development) was 92%. Again, accuracy was evenly balanced (93% for change / 91% for persistence).Accuracy for transitions between developed categories was lower at 77% (80% change and 75% persistence). A large part of this is because of the inconsistent manner in which roads are classified in the NLCD system. Roads are classified as one of the developed categories (high, medium, low and open development) based on the amount of impervious surface detected within pixels. Alignment of image pixels can cause this to vary resulting in roads that frequently switch classes between the years mapped.Natural transitions, such as forest to shrub, had the lowest overall accuracy at 74%. This was expected because many drivers cannot be predicted with the variables used. An example would be forest fires caused by lightning. This is also reflected in the fact that accuracy for predicting change was only 71% while that for predicting persistence was 78%.Finally, in states with significant cropland development, natural to cropland transitions were modeled with a 79% overall median accuracy. Accuracies for change and persistence were 78% and 81% respectively.DisclaimerNote that there are many highly plausible future outcomes and the specific scenario presented is only one of these (albeit judged to be the most plausible). Also note that each state is modeled separately (on the assumption that drivers of change many differ between states). As a consequence, there may be some mismatches at the boundaries between states. Generally, these are only evident for states that have large quantities of natural to natural transitions (e.g., with forest plantation crop cycles or frequent fire) where the accuracy is lower. Also note that the protected areas layer does not include all protected areas. Some local conservation land may be missing. Finally, note that the modeling is based on the assumption that rate of change experienced within the historical period (2001-2011) will persist into the future.1 Elevation data were from the National Elevation Database while slope was derived from those data. All roads data were acquired from the Census Bureau TIGER line files for 2014. Earlier road data would have been preferred, but errors in earlier TIGER line files were deemed to be unacceptable. Country subdivisions, counties and incorporated places were also acquired from the Census Bureau. Protected areas came from the Protected Areas Database of the USGS National Gap Analysis Program. All proximity layers were derived by Clark Labs.2 In some states, planning jurisdiction is controlled by county subdivisions (such as in New England), while in others, planning is governed by a combination of counties and incorporated places (such as in many of the western states).3 Eastman, J.R., Crema, S.C., and Rush, H.R., (forthcoming) A Weighted Normalized Likelihood Procedure for Empirical Land Change Modeling.4 Greedy selection assumes that the specific pixels that will change are those that are ranked the highest. Conflicts are resolved by assigning them to the transition with the highest marginal transition potential.

Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats. Dataset is available from the THREDDS Data Server to this url: https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html. Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores. ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.

Scientific data provided by ENERGY-lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats. Dataset is available from the THREDDS Data Server to this url: https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html. Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores. ENERGY-lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.

Scientific data provided by ENERGY-lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats. Dataset is available from the THREDDS Data Server to this url: https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html. Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores. ENERGY-lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.