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Nov 08, 2023

Circumvent the limitations of second-hand data portals: Orange with Dask enables you to process big data sets while ensuring the source's authenticity and enabling custom workflows.

Oct 28, 2023

An experimental version of Orange supports much larger data sets. It never loads the whole data set into the working memory; data is only processed in small chunks. We show a case study on a 13 GB spectroscopy data set.

Oct 24, 2023

Preparing large HDF5 datasets that load into Orange as on-disk data.

Oct 20, 2023

Highlighting our papers from the International Conference on Discovery Science

Visual Programming

Interactive data exploration for rapid qualitative analysis with clean visualizations. Graphic user interface allows you to focus on exploratory data analysis instead of coding, while clever defaults make fast prototyping of a data analysis workflow extremely easy. Place widgets on the canvas, connect them, load your datasets and harvest the insight!
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Interactive Data Visualization

Perform simple data analysis with clever data visualization. Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, MDS and linear projections. Even your multidimensional data can become sensible in 2D, especially with clever attribute ranking and selections.
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Add-ons Extend Functionality

Use various add-ons available within Orange to mine data from external data sources, perform natural language processing and text mining, conduct network analysis, infer frequent itemset and do association rules mining. Additionally, bioinformaticians and molecular biologists can use Orange to rank genes by their differential expression and perform enrichment analysis. Check out also Orange cousins Single Cell and Quasar.
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Orange users

When teaching data mining, we like to illustrate rather than only explain. And Orange is great at that. Used at schools, universities and in professional training courses across the world, Orange supports hands-on training and visual illustrations of concepts from data science. There are even widgets that were especially designed for teaching.
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My laboratory produces large amounts of data from RNA-seq, ChIP-seq and genome resequencing experiments. Orange allows me to analyze my data even though I don't know how to program. It also allows me to communicate with my collaborators, who are experts in data mining, and with my colleagues and trainees.

Gad Shaulsky, Ph.D.
Molecular biologist and Director of Graduate Studies (Baylor College of Medicine, Houston, USA)

The scientific community is in need of tools that allow easy construction of workflows and visualizations and are capable of analyzing large amounts of data. Orange is a powerful platform to perform data analysis and visualization, see data flow and become more productive. It provides a clean, open source platform and the possibility to add further functionality for all fields of science.

Ferenc Borondics, Ph.D.
Principal beamline scientist at SMIS (SOLEIL synchrotron, France)

I teach Orange workshops monthly to a diverse audience, from undergrad students to expert researchers. Orange is very intuitive, and, by the end of the workshop, the participants are able to perform complex data visualization and basic machine learning analyses. Most of our attendees have been able to incorporate this tool in their research practice.

Francesca Vitali, Ph.D.
Research Assistant Professor (Center for Biomedical Informatics & Biostatistics, The University of Arizona)

Contribute to Orange

If you love using Orange and want to support us, a donation would be very much appreciated. The funds help us fix bugs, implement new features, provide free educational content, and maintain computational infrastructure.

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Time series summary (csse_covid_19_time_series)

This folder contains daily time series summary tables, including confirmed, deaths and recovered. All data is read in from the daily case report. The time series tables are subject to be updated if inaccuracies are identified in our historical data. The daily reports will not be adjusted in these instances to maintain a record of raw data.

Two time series tables are for the US confirmed cases and deaths, reported at the county level. They are named time_series_covid19_confirmed_US.csv, time_series_covid19_deaths_US.csv, respectively.

Three time series tables are for the global confirmed cases, recovered cases and deaths. Australia, Canada and China are reported at the province/state level. Dependencies of the Netherlands, the UK, France and Denmark are listed under the province/state level. The US and other countries are at the country level. The tables are renamed time_series_covid19_confirmed_global.csv and time_series_covid19_deaths_global.csv, and time_series_covid19_recovered_global.csv, respectively.

Update frequency

Once a day around 23:59 (UTC).

Deprecated warning

The files below were archived here, and will no longer be updated. With the release of the new data structure, we are updating our time series tables to reflect these changes. Please reference time_series_covid19_confirmed_global.csv and time_series_covid19_deaths_global.csv for the latest time series data.

time_series_19-covid-Confirmed.csv

time_series_19-covid-Deaths.csv

time_series_19-covid-Recovered.csv

# data[].attributes.attachment_link.description
This folder contains daily time series summary tables, including confirmed, deaths and recovered. All data is read in from the daily case report. The time series tables are subject to be updated if inaccuracies are identified in our historical data. The daily reports will not be adjusted in these instances to maintain a record of raw data.

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기상청, 기상자료개방포털

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2. 오렌지3를 이용한 데이터분석 및 머신러닝