SWISH: Scientific Workflow and Integration Software for Health
Keith, Dear
Bennett, Charmian
Hanigan, Ivan
Szarka, Ian
Dr Charmian Bennett and Dr Keith Dear from the Australian National University's Research School of Population Health, talk about SWISH - open source software that enables easier access and merging of large datasets on population and health.
 I'm Dr Shamian Bennett. I'm an epidemiologist at the Australian National University and I'm part of a research team that's looking at the impacts of climate change on human health. The Australian National Data Service Ground has allowed us to develop a scientific workflow system to access and merge large data sets on our environment, population and health. And we've built our workflows using Keplo. We've called our workflow system SWISH, which stands for Scientific Workflow and Integration Software for Health. And it's freely available to other researchers from our website. To assess the impacts of climate change on health, you need to gather a large amount of data, including weather, definitions of extreme weather events, population data, mortality data, including the cause of death, and all within a consistent spatial framework. And all of these steps have to happen before you actually get to the analysis of the health impacts linked to climate change. You can download complete workflows from our website that will set up your connection to the extreme weather events database. In this example, the actor Get Weather SLA connects to the database to locate daily weather data in the statistical local area of your choice. Double click on the string constant box and enter the name of the SLA that you want. In this example, we're looking at K-Lean. Then click commit. The second string constant shows you where the resulting file will be saved, in this case on the C drive in the temp folder. When you're done, click the run button in the toolbar and you will see the word executing appear in the bottom left corner. When the results window appears, scroll down and check that a CSV file with the suburb name has been created. This workflow takes the data to be obtained in the previous example and calculates how many days had an average temperature of above 35 degrees. You can use the search function to locate specific actors. To complete this workflow, it needs one more input using the string constant actor. You then drag and drop the actor into your workflow space and double click to edit the parameters. In this case, our value is 35 degrees. Then connect this box to the greater than actor using the connector arrows. To explore the many swish actors, go to the components window and open the folder called my workflows. Click the small plus sign to find the green actor icon. Then drag and drop into your workflow. Here we are adding an actor to the workflow to display our result at the end. When all of the actors are connected, click on the run button in the toolbar. The results are produced in the CSV file, which opens here in a text box, but can easily be opened in other statistical programs like Stata and R. Accessing data often requires high level programming expertise. Using swish can help reduce those barriers to data access by providing a user friendly drag and drop interface. Swish workflows are a single file, so it's very easy to share with other researchers. This can help us with collaborative research and improve the reproducibility of results. In addition, swish also packages multiple steps using Stata and R into a single executable flowchart. The documents you're working as you go. I'm Keith Thier. I'm a biostatistician and epidemiologist at the Australian National University. In 2007-2008, the Gano Climate Change Review Team asked a group of us here at NSF, the National Centre for Epidemiology and Population Health, to look at how health impacts of climate change might pan out in Australia out to 2100. To do this, I used several kinds of data from different sources. The demographic breakdown of the Australian population by age, sex and location around the country, daily records of mortality which are collected within each state, and the daily weather records collected by the Bureau of Meteorology. Merging these various sources of data into a final dataset that I could use for a statistical analysis was complicated. I did this initially back in 2007 using scripts in the statistical package Stata which I wrote specifically for the purpose. The final script in Stata that did this operation was quite long, several hundred lines of Stata code, and it took me a lot of time to debug it, make sure that I was reasonably confident it was correct. And even when I was confident myself, it was difficult to communicate it to other potential users of my methods. They would essentially have to start again from scratch. Five years on, we're now in the process of updating and revising this analysis, done back in 2007, 2008, and this time I have available to me the SWISH package of data management tools. I can see at least three advantages of using this package over the way I did it back then. First of all, it's just easier to write the code. That means it's easier for me to debug. I can spend less time and effort making sure that it's correct and I can have more confidence that it's correct. Secondly, it means that I can modify the analysis. I can try out different ways of doing the analyses, doing the data preparation and the analyses, and this means that we can be more sure that we're using optimal efficient methods for the work. And finally, and I think most importantly, it means that the methods can be communicated more easily to others. It's intrinsically better documented, and it means that we can communicate the methods we used so that others can use them and perhaps improve upon them. There are five main benefits to using the SWISH system to access and merge data sets. First of all, it improves data access by providing a drag and drop interface rather than relying on complex programming. You can create executable workflows that integrate both documentation and execution. Third, workflows are easy to share for collaborative research and teaching and training. The workflows are also easy to modify and update with new data or to change the spatial scale. Lastly, SWISH workflows allow you to incorporate existing procedures and analyses and use your preferred statistical packages. The SWISH system is free, open source software. You can find and download the SWISH tools and tutorials from our GitHub and product site. And there's some more information about our project at our blog.
 I'm Dr Shamian Bennett. I'm an epidemiologist at the Australian National University and  I'm part of a research team that's looking at the impacts of climate change on human  health.  The Australian National Data Service Ground has allowed us to develop a scientific workflow  system to access and merge large data sets on our environment, population and health.  And we've built our workflows using Keplo. We've called our workflow system SWISH, which  stands for Scientific Workflow and Integration Software for Health. And it's freely available  to other researchers from our website.  To assess the impacts of climate change on health, you need to gather a large amount  of data, including weather, definitions of extreme weather events, population data,  mortality data, including the cause of death, and all within a consistent spatial framework.  And all of these steps have to happen before you actually get to the analysis of the health  impacts linked to climate change.  You can download complete workflows from our website that will set up your connection to  the extreme weather events database. In this example, the actor Get Weather SLA connects  to the database to locate daily weather data in the statistical local area of your choice.  Double click on the string constant box and enter the name of the SLA that you want. In  this example, we're looking at K-Lean. Then click commit. The second string constant shows  you where the resulting file will be saved, in this case on the C drive in the temp folder.  When you're done, click the run button in the toolbar and you will see the word executing  appear in the bottom left corner. When the results window appears, scroll down and check  that a CSV file with the suburb name has been created. This workflow takes the data to be  obtained in the previous example and calculates how many days had an average temperature of  above 35 degrees. You can use the search function to locate specific actors. To complete this  workflow, it needs one more input using the string constant actor. You then drag and drop  the actor into your workflow space and double click to edit the parameters. In this case,  our value is 35 degrees. Then connect this box to the greater than actor using the connector  arrows. To explore the many swish actors, go to the components window and open the folder  called my workflows. Click the small plus sign to find the green actor icon. Then drag  and drop into your workflow. Here we are adding an actor to the workflow to display our result  at the end. When all of the actors are connected, click on the run button in the toolbar. The  results are produced in the CSV file, which opens here in a text box, but can easily be  opened in other statistical programs like Stata and R. Accessing data often requires  high level programming expertise. Using swish can help reduce those barriers to data access  by providing a user friendly drag and drop interface. Swish workflows are a single file,  so it's very easy to share with other researchers. This can help us with collaborative research  and improve the reproducibility of results. In addition, swish also packages multiple  steps using Stata and R into a single executable flowchart. The documents you're working as  you go. I'm Keith Thier. I'm a biostatistician and epidemiologist at the Australian National  University. In 2007-2008, the Gano Climate Change Review Team asked a group of us here  at NSF, the National Centre for Epidemiology and Population Health, to look at how health  impacts of climate change might pan out in Australia out to 2100. To do this, I used  several kinds of data from different sources. The demographic breakdown of the Australian  population by age, sex and location around the country, daily records of mortality which  are collected within each state, and the daily weather records collected by the Bureau of  Meteorology. Merging these various sources of data into a final dataset that I could  use for a statistical analysis was complicated. I did this initially back in 2007 using scripts  in the statistical package Stata which I wrote specifically for the purpose. The final script  in Stata that did this operation was quite long, several hundred lines of Stata code,  and it took me a lot of time to debug it, make sure that I was reasonably confident  it was correct. And even when I was confident myself, it was difficult to communicate it  to other potential users of my methods. They would essentially have to start again from  scratch. Five years on, we're now in the process of updating and revising this analysis, done  back in 2007, 2008, and this time I have available to me the SWISH package of data management  tools. I can see at least three advantages of using this package over the way I did  it back then. First of all, it's just easier to write the code. That means it's easier  for me to debug. I can spend less time and effort making sure that it's correct and I  can have more confidence that it's correct. Secondly, it means that I can modify the  analysis. I can try out different ways of doing the analyses, doing the data preparation  and the analyses, and this means that we can be more sure that we're using optimal efficient  methods for the work. And finally, and I think most importantly, it means that the methods  can be communicated more easily to others. It's intrinsically better documented, and  it means that we can communicate the methods we used so that others can use them and perhaps  improve upon them. There are five main benefits to using the SWISH system to access and merge  data sets. First of all, it improves data access by providing a drag and drop interface  rather than relying on complex programming. You can create executable workflows that integrate  both documentation and execution. Third, workflows are easy to share for collaborative research  and teaching and training. The workflows are also easy to modify and update with new data  or to change the spatial scale. Lastly, SWISH workflows allow you to incorporate existing  procedures and analyses and use your preferred statistical packages. The SWISH system is free,  open source software. You can find and download the SWISH tools and tutorials from our GitHub  and product site. And there's some more information about our project at our blog.
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I'm Dr Shamian Bennett. I'm an epidemiologist at the Australian National University and I'm part of a research team that's looking at the impacts of climate change on human health. The Australian National Data Service Ground has allowed us to develop a scientific workflow system to access and merge large data sets on our environment, population and health. And we've built our workflows using Keplo. We've called our workflow system SWISH, which stands for Scientific Workflow and Integration Software for Health. And it's freely available to other researchers from our website. To assess the impacts of climate change on health, you need to gather a large amount of data, including weather, definitions of extreme weather events, population data, mortality data, including the cause of death, and all within a consistent spatial framework. And all of these steps have to happen before you actually get to the analysis of the health impacts linked to climate change. You can download complete workflows from our website that will set up your connection to the extreme weather events database. In this example, the actor Get Weather SLA connects to the database to locate daily weather data in the statistical local area of your choice. Double click on the string constant box and enter the name of the SLA that you want. In this example, we're looking at K-Lean. Then click commit. The second string constant shows you where the resulting file will be saved, in this case on the C drive in the temp folder. When you're done, click the run button in the toolbar and you will see the word executing appear in the bottom left corner. When the results window appears, scroll down and check that a CSV file with the suburb name has been created. This workflow takes the data to be obtained in the previous example and calculates how many days had an average temperature of above 35 degrees. You can use the search function to locate specific actors. To complete this workflow, it needs one more input using the string constant actor. You then drag and drop the actor into your workflow space and double click to edit the parameters. In this case, our value is 35 degrees. Then connect this box to the greater than actor using the connector arrows. To explore the many swish actors, go to the components window and open the folder called my workflows. Click the small plus sign to find the green actor icon. Then drag and drop into your workflow. Here we are adding an actor to the workflow to display our result at the end. When all of the actors are connected, click on the run button in the toolbar. The results are produced in the CSV file, which opens here in a text box, but can easily be opened in other statistical programs like Stata and R. Accessing data often requires high level programming expertise. Using swish can help reduce those barriers to data access by providing a user friendly drag and drop interface. Swish workflows are a single file, so it's very easy to share with other researchers. This can help us with collaborative research and improve the reproducibility of results. In addition, swish also packages multiple steps using Stata and R into a single executable flowchart. The documents you're working as you go. I'm Keith Thier. I'm a biostatistician and epidemiologist at the Australian National University. In 2007-2008, the Gano Climate Change Review Team asked a group of us here at NSF, the National Centre for Epidemiology and Population Health, to look at how health impacts of climate change might pan out in Australia out to 2100. To do this, I used several kinds of data from different sources. The demographic breakdown of the Australian population by age, sex and location around the country, daily records of mortality which are collected within each state, and the daily weather records collected by the Bureau of Meteorology. Merging these various sources of data into a final dataset that I could use for a statistical analysis was complicated. I did this initially back in 2007 using scripts in the statistical package Stata which I wrote specifically for the purpose. The final script in Stata that did this operation was quite long, several hundred lines of Stata code, and it took me a lot of time to debug it, make sure that I was reasonably confident it was correct. And even when I was confident myself, it was difficult to communicate it to other potential users of my methods. They would essentially have to start again from scratch. Five years on, we're now in the process of updating and revising this analysis, done back in 2007, 2008, and this time I have available to me the SWISH package of data management tools. I can see at least three advantages of using this package over the way I did it back then. First of all, it's just easier to write the code. That means it's easier for me to debug. I can spend less time and effort making sure that it's correct and I can have more confidence that it's correct. Secondly, it means that I can modify the analysis. I can try out different ways of doing the analyses, doing the data preparation and the analyses, and this means that we can be more sure that we're using optimal efficient methods for the work. And finally, and I think most importantly, it means that the methods can be communicated more easily to others. It's intrinsically better documented, and it means that we can communicate the methods we used so that others can use them and perhaps improve upon them. There are five main benefits to using the SWISH system to access and merge data sets. First of all, it improves data access by providing a drag and drop interface rather than relying on complex programming. You can create executable workflows that integrate both documentation and execution. Third, workflows are easy to share for collaborative research and teaching and training. The workflows are also easy to modify and update with new data or to change the spatial scale. Lastly, SWISH workflows allow you to incorporate existing procedures and analyses and use your preferred statistical packages. The SWISH system is free, open source software. You can find and download the SWISH tools and tutorials from our GitHub and product site. And there's some more information about our project at our blog.
I'm Dr Shamian Bennett. I'm an epidemiologist at and merge large data sets on our environment, population and health. And we've built our epidemiologist at the Australian National to using the SWISH system to access and merge packages. The SWISH system is free, open source