This dataset provides structured academic information for a group of students across different programs and semesters. It includes personal identifiers (like Roll No. and Name), course codes, subject-wise theory/practical scores, and performance metrics.

The dataset can be used for:

Student performance prediction

Machine Learning classification/regression tasks

Educational data mining

Result analysis and visualization

Identifying academic trends & patterns

Comparative study of theory vs. practical performance

Dataset Columns Explained

This data provides results from the California Environmental Data Exchange Network (CEDEN) for field and lab chemistry analyses. The data set contains two provisionally assigned values (“DataQuality” and “DataQualityIndicator”) to help users interpret the data quality metadata provided with the associated result.

Due to file size limitations, the data has been split into individual resources by year. The entire dataset can also be downloaded in bulk using the zip files on this page (in csv format or parquet format), and developers can also use the API associated with each year's dataset to access the data.

Users who want to manually download more specific subsets of the data can also use the CEDEN Query Tool, which provides access to the same data presented here, but allows for interactive data filtering.

NOTE: Some of the field and lab chemistry data that has been submitted to CEDEN since 2020 has not been loaded into the CEDEN database. That data is not included in this data set (and is also not available via the CEDEN query tool described above), but is available as a supplemental data set available here: Surface Water - Chemistry Results - CEDEN Augmentation. For consistency, many of the conditions applied to the data in this dataset and in the CEDEN query tool are also applied to that supplemental dataset (e.g., no rejected data or replicates are included), but that supplemental data is provisional and may not reflect all of the QA/QC controls applied to the regular CEDEN data available here.

This data provides results from field analyses, from the California Environmental Data Exchange Network (CEDEN). The data set contains two provisionally assigned values (“DataQuality” and “DataQualityIndicator”) to help users interpret the data quality metadata provided with the associated result. Due to file size limitations, the data has been split into individual resources by year. The entire dataset can also be downloaded in bulk using the zip files on this page (in csv format or parquet format), and developers can also use the API associated with each year's dataset to access the data. Users who want to manually download more specific subsets of the data can also use the CEDEN Query Tool, which provides access to the same data presented here, but allows for interactive data filtering.

This dataset includes field and lab chemistry data that has been submitted to the California Environmental Data Exchange Network (CEDEN), but has not been loaded into the CEDEN database. It is a subset of the chemistry data that has been submitted to CEDEN since approximately December 2020, and supplements the data found in both the main Surface Water - Chemistry Results dataset and the CEDEN Query Tool (i.e., this augmentation data is not included in the data available from either of those sources). For consistency, many of the conditions applied to the other CEDEN data found on this portal and in the CEDEN query tool are also applied to this supplemental dataset (e.g., no rejected data or replicates are included). However, this supplemental data is provisional and may not reflect all of the QA/QC controls applied to the regular CEDEN data.

This dataset also contains two provisionally assigned values (“DataQuality” and “DataQualityIndicator”) to help users interpret the data quality metadata provided with the associated result (like the main Surface Water - Chemistry Results dataset referenced above).

The csv file contains aggregated data on the results of the experiment. The task is to analyze the results of the experiment and write your recommendations.

This dataset contains fecal indicator bacteria (FIB) monitoring results collected from surface waters across California. It includes results for Escherichia coli (E. coli), enterococci, fecal coliforms, and total coliforms - key indicators used to assess microbial water quality and potential health risks associated with recreational water use. The data has been cleaned and standardized to ensure consistency and usability. For each valid data point, the dataset also includes calculated 6-week and 30-day geometric means, which are commonly used to evaluate long-term water quality trends and compliance with bacteria water quality objectives. Some records are excluded from the final dataset during processing. These typically include duplicate or replicate results, records with missing or invalid result values, or any data that cannot be used to calculate a geometric mean. These records are saved to the "Excluded Records" data resource. This dataset serves as the primary data source for the Water Boards My Water Quality Portal Safe to Swim Map, which provides the public with timely information about potential health risks associated with water contact recreation at monitored sites. Data is sourced from the California Environmental Data Exchange Network (CEDEN) database, BeachWatch database, and the Central Valley Water Board Lower American River Recreational Water Quality Map. There is some overlap with the CEDEN Water Chemistry dataset. Both portal datasets draw from the same source, but the resources here are updated more frequently to support the Safe to Swim map.

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

Dataset is completed! Data was updated daily during the Olympic!
You can support the dataset via the upvote button!

The Paris 2024 Olympic Summer Games dataset provides comprehensive information about the Summer Olympics held in 2024. It covers various aspects of the event, including participating countries, athletes, sports disciplines, medal standings, and key event details. More about the Olympic Games on the official site Olympics Paris 2024 and Wiki.

Dataset Structure

Related Datasets

• Paralympic Summer Games Paris 2024 & Tokyo 2020
• Olympic Games, 1986-2022
• Horses - Olympic Equestrian
• Beijing 2022 Olympic Winter Games
• Tokyo 2020 Olympic Summer Games
• Olympic Games Hosts

ACKNOWLEDGMENT

I am very thankful to Luca Fontana, zenzombie and others for their efforts in helping me to make the dataset better. Luca Fontana did a manual check medalist.csv table and zenzombie cover dataset with tests.

Q&A

If you have any questions or suggestions please start a discussion.

This deposit contains code and data for "Stability of Experimental Results: Forecasts and Evidence"Abstract: "How robust are experimental results to changes in design? And can researchers anticipate which changes matter most? We consider a real-effort task with multiple behavioral treatments, and examine the stability along six dimensions: (i) pure replication; (ii) demographics; (iii) geography and culture; (iv) the task; (v) the output measure; (vi) the presence of a consent form. We find near perfect replication of the experimental results, and full stability of the results across demographics, significantly higher than a group of experts expected. The results differ instead across task and output change, mostly because the task change adds noise to the findings."

Data extracts are raw data from company filings. Information Releases are annual reports from MassDEP.

BigBox API provides reliable, real-time Home Depot product, category, reviews, and offers data. All data includes comprehensive coverage of each of the search results in a cleanly structured output.

You can originate your request from any zip code (US) to see results as they would appear to customers in the specified location i.e. shipping info. BigBox APIs high-capacity, global infrastructure assures you the highest level of performance and reliability. For easy integration with your Home Depot data apps and services, data is delivered in JSON or CSV format.

Data is retrieved by search term, search results page URL, or for single products, by the Home Depot item ID or by global identifiers such as GTIN, ISBN, UPC and EAN. GTIN-based requests work by looking up the GTIN/ISBN/UPC on Home Depot first, then retrieving the product details for the first matching item ID.

So what's in the data from BigBox API?

Product: - Item & parent ID - UPC - Store SKU - In-store bay &/or aisle - Product specifications - Description - Imagery - Product videos - Buy Box winner: price and fulfillment info - Rating & reviews count - Descriptive attributes

Search results: - Product details per search result: - Position - Related queries - Pagination - Facets

How can BigBox API be used? - Product listing management - Price monitoring - Category & product trends monitoring - Market research & competitor intelligence - Location-specific shipping data - Rank tracking on Home Depot

...and more, depending on your request parameters or the search result.

Who uses BigBox API? This data is leveraged by software developers, marketers & business owners, sales & business development teams, researchers, and data analysts & engineers, in ecommerce, other retail business, agencies and SaaS platforms.

Anyone in your organization who works with your digital presence can develop business intelligence and strategy using this advanced product data.

This dataset provides results data from the Fare Compliance Survey Results reports from November 2012 to the latest report (the surveys were not conducted in 2020 due to Covid). The fare compliance survey is conducted twice yearly in May and November, and is designed to measure the incidence of non-compliance and associated revenue loss across the public transport network.

A report from each survey is also published on: https://www.transport.nsw.gov.au/news-and-events/reports-and-publication....

The LUCAS Topoil Survey-methodology, data and results dataset contains soil spectral reflectance data.

DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 test results by date of specimen collection, including total, positive, negative, and indeterminate for molecular and antigen tests. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests. Test results may be reported several days after the result. Data are incomplete for the most recent days. Data from previous dates are routinely updated. Records with a null date field summarize tests reported that were missing the date of collection. Starting in July 2020, this dataset will be updated every weekday.

The Pacific Community Results Report highlights the results achieved by SPC with our 26 Member countries and territories, and development partners. This dataset provides the data used in the Results Report provided in Excel and CSV formats.

This data has been visualised in the Results Explorer Dashboard: https://pacificdata.org/results-explorer

This dataset provides detailed laboratory test results, including test types, quantitative and qualitative outcomes, reference ranges, ordering physician information, specimen details, and timestamps. It enables clinical analysis, patient monitoring, and quality assurance in healthcare settings, supporting interoperability and regulatory compliance.

Access to affordable and reliable scientific instrumentation remains a significant barrier to the democratization of healthcare and scientific research. In the field of biotechnology, in particular, the complexity, cost, and infrastructure requirements of many instruments continue to limit their accessibility, especially in resource-limited environments. Despite the recent increase in the development of open-source tools, driven by advances in digital fabrication and electronic prototyping, few of these projects have reached large-scale implementation or validation in real-world settings. Here, we present qByte, an open-source, 8-tube isothermal fluorimeter designed to overcome these barriers by offering a cost-effective ($60) yet production-ready solution. qByte leverages standard digital manufacturing and Printed Circuit Board (PCB) assembly techniques and is designed to be portable, making it ideal for both laboratory and field use. The device has been benchmarked against commercial real-time thermocyclers and spectrophotometers, showing comparable results across four key applications: nucleic acid amplification and detection, including the on-site diagnosis of human parasites in Ghana, analysis of protein activity and stability, genetic construct characterization, and bacterial viability tests. Taken together, our results proved qByte as flexible and reliable equipment for a variety of biological tests and applications, while its affordability and open-source design simplify further development and allow adaptation to the needs of future users.

The following dataset provides the results of the TdF social and economic impact survey, conducted during the Tour de France 2014. Please note We are aware of data quality errors in this dataset. This is currently under review. Any personal data collected has been removed and postcode restricted to district area. This is a one off publication and will only be updated to improve the quality of data.

Compendium of all the Data & Results per each of the in-vitro workbench (1 and 2) and patient cohort (Bicuspid Aortic Valve) used.

simulation results raw data-MGGSA