Digitize standard error from graph and extract data pdf
File Name: digitize standard error from graph and extract data .zip
- Data Extraction from Graphs Using Adobe Photoshop: Applications for Meta-Analyses
- Origin and OriginPro
- Subscribe to RSS
Despite recent open data initiatives in many countries, a significant percentage of the data provided is in non-machine-readable formats like image format rather than in a machine-readable electronic format, thereby restricting their usability. Various types of software for digitizing data chart images have been developed.
This is a generalization of a histogram function. A histogram divides the space into bins, and returns the count of the number of points in each bin. This function allows the computation of the sum, mean, median, or other statistic of the values or set of values within each bin. The data on which the statistic will be computed.
Data Extraction from Graphs Using Adobe Photoshop: Applications for Meta-Analyses
Often third party applications are used to do this e. It also provides users with options to conduct the necessary calculations on raw data immediately after extraction so that comparable summary statistics can be obtained quickly. Summaries will condense multiple figures into data frames or lists depending on the type of figure and these objects can easily be exported from R, or if using the raw data, analysed in any way the user desires. Conveniently, when needing to process many figures at different times metaDigitise will only import figures not already completed within a directory.
This makes it easy to add new figures at anytime. It has functions that allow users to redraw their digitisations on figures, correct anything and access the raw calibration data which is written automatically for each figure that is digitised into a special caldat folder within the directory. This makes sharing figure digitisation and reproducing the work of others simple and easy and allows meta-analysts to update existing studies more easily.
Installation will make the primary function for data extraction, metaDigitise , accessible to users along with its help file. The metaDigitise package is quite flexible. Users can extract single figures if this is all they have using the metaDigitise function with a path name to the directory with the file.
However, often many figures need extracting from a single paper or set of papers. This information is then all stored in a data frame or list at the end of the process, saving quite a bit of time. Users can stop mid-way through a folder by simply exiting after the last plot they have digitised. However, users can get creative in how they set up the directories of figures to facilitate extraction.
For example, one might have 3—4 figures from a single paper that need extracting and the user may want to focus on a single paper at a time while the information about a paper is on hand. This could be done by simply setting up a file structure as follows and then using metaDigitise with path names i. An alternative directory structure and probably the most flexible would be to simply have a set of different figures with an informative and relevant naming scheme to make it easy to identify the paper and figure the data come from.
This cuts out the need to change directories constantly. For example the directory structure could look like:. The above directory structure is probably the easiest in combination with a clear and unambiguous naming scheme for each figure.
Even if only figures from a single paper are digitised, one paper at a time, an overall figure directory will work perfectly because metaDigitise will only cycle through incomplete figures, so figures can be added at anytime. Nonetheless, how users set up their directory is really up to them.
However, it is important for users to think carefully about reproducibility at this stage. A versatile naming scheme that is consistent across papers and contains the relevant information the user desires will make digitising and sharing much easier if thought through carefully before starting a project.
Notice our naming of this file. Anderson in and the figure number. This makes it easy to keep track of the figures being digitised. Here is what this figure looks like:. Here, the output will be stored to the data object, which is great because we can access this after we have digitised. To start, the first thing a user must do is to tell metaDigitise what you would like to do:.
All the user needs to enter is the number related to the specific process they would like to execute. In this case, we have not digitised any data yet, and so, if we choose option 2 or 3 it will tell us that there are no digitised objects to work with. So, our only option is 1 , which will allow us to digitise the specific file within this directory. The next thing we are asked is whether we have different types of plot s in the folder.
This question is most relevant for a directory with lots of figures e. We are next asked whether we want to flip or rotate the figure image. This can be needed when box plots and mean and error plots are not orientated correctly. In some cases, older papers can give slightly off angled images which can be corrected by rotating. After we do this, metaDigistise will ask the user to specify the plot type.
Depending on the figure, the user can specify that it is a figure containing the mean and error m , a box plot b , a scatter plot s or a histogram h. If the user has specified d instead of s in response to the question about whether the plot types are the same or different, this question will pop up for each plot, but will only be asked once if plots are all the same.
After selecting the figure type a new set of prompts will come up that will ask the user first what the y and x-axis variables are. This is useful as you can keep track of the different variables across different figures and papers. Here, the user can just add this information in to the R console.
If we were working with a plot of mean and standard errors, the x-axis is rather useless in terms of calibration so metaDigitise just asks the user to calibrate the y-axis more on that soon.
Follow the instructions on screen step-by-step instructions above have been truncated by The user will then be asked to specify the x and y calibration points and whether or not the calibration has been set up correctly. If n is chosen because something needs to be fixed then the user can re-calibrate. The first few questions ask the user what the calibration points are. Often, plots might contain multiple groups that the meta-analyst wants to extract from.
After digitising the first group, and having exited i. They can add another group a , or simply continue c. The number of groups are not really limited and users can just keep adding in groups to accommodate the different numbers that may be presented across figures although it can get complicated with too many. It will also print the figure name, which is useful if the user needs to go back and find the paper to obtain information.
Printing this information on the figure is also useful so that input can be checked with actual values on the figure, and any mistakes can then be corrected if found. Prior to exiting the figure you will be prompted with this:. Choosing e allows the user to go back and edit a group already digitised, but also, d allows them to completely delete a group and re-digitise if necessary. In our case, all has gone well and we choose f to finish plotting.
This will exit metaDigitise since we only have a single figure and save the summary statistics to the data object that can be conveniently queried by printing the object:. Our summary output has all the relevant information about the means, standard deviations and standard errors if sample size is provided for each of the variables.
The user will notice an r column indicating the correlation coefficient between sepal width and length for each species provided because this is a scatterplot. These match reasonably well with the actual means of Sepal length and width for each of the species in the iris dataset:. This is an example of some of the challenges when extracting data from scatter plots, often data points will overlap with each other making it impossible without having the real data to know whether this is a problem.
However, a meta-analyst will probably realise that the sample sizes here conflict with what is reported in the paper. Nonetheless, it is important to recognise the impact that overlapping points can have particularly its effects on SD and SE. While this is a problem for any program digitising from figures, it is probably the best that can be done. Often a paper, and especially a single meta-analytic project, contains many figures needing extracting and having to open and re-open new files, save data, analyse or summarize data, make conversions etc.
Lets assume now that, after digitising our scatter plot, we have added two new figures from a different study done by a research group conducting experiments on the same species. Both figures contain data on sepal length and width for the same species but on a sample taken from different populations. This example will nicely demonstrate how users can easily pick up from where they left off and how all previous data gets re-integrated. It will also demonstrate how different plot types are handled.
All we have to do to begin, is again, provide the directory where all the figures are located:. All the prompts after this selection are essentially the same, but we now specify that we have different [i.
Notice that metaDigitise did not bring back up the scatter plot that we had already digitised. Here, we specify the new plot type as m for mean error because we have a plot of the mean and error of sepal length for each of the three species. The prompts, again, tell the user to calibrate the y-axis and enter these calibration values. After this we now have some new prompts, which tells metaDigitise whether we have sample sizes for all the groups in the plot.
If y we can enter the group name and its sample size straight after. This is important for back calculating standard errors, for example, in this plot. The user can then digitise each of the groups, being prompted after each group whether to add, delete for finish digitising the group. The user can continue adding groups to the plot until they are all completely digitised see figure below , at which point the user is asked to specify the type of error:.
When we are done the current plot, because there is another figure left to digitise, we get a message indicating how many figures are left and whether we want to continue. This allows the user to stop or automatically bring up the next figure for processing:. After selecting y the second plot pops up with all the same prompts.
Digitising information from histograms is a little bit more involved, however, than other plots because we need to characterize the entire distribution directly. The difference with histograms is that the user needs to click both the left and right corners of each bar, and continue adding until all bars of the histogram have digitised lines above them.
The user can just continue clicking each of the bars and metaDigitise will colour code the bars after each bar has been digitised to make it clear how each line corresponds to each bar on the histogram. A number is printed above the bar which is useful for editing as users can just type the bar number they want to change when editing.
Here, the output has all the relevant summary statistics we digitsied for each figure and specifies the plot type. The caldat folder also now contains files for the newly digitised figures see below. We can continue adding and digitising as new figures come up and it will automatically integrate these new statistics into the dataset, which can then be exported using write. One trick to digitising all kinds of figures all at once is to include the figure legends in the image, allowing you to quickly get information that is relevant should you need it as the figures come up.
The fact that metaDigitise only processes and digitises new figures from an image folder means there are two additional benefits afforded to meta-analysts. Second, if there are collaborators on the project, if the project folder and images are shared, then co-authors can pick up from where another colleague left off.
Now that all the relevant figures from papers included in the meta-analysis are digitised we can easily re-import these data if at any point in the future there is a need to view them again. But also, in case we need to get the raw data and process this in a unique way — this may be necessary from scatter plots. Again, this is seamless and easy with metaDigitise :. However, instead of selecting 1 we can just select 2 and import existing files:. Importantly, this will import the same summary statistics that we seen above, but what if we wanted the raw data because we wanted to access the data for the scatter plot.
We would like to thank our two reviewers for their helpful feedback. While they both 'approved' the paper, they provided some useful thoughts for clarifications throughout, and we have amended the paper accordingly. We have also included Supplementary Files 6 and 7 with the bibliographic details of the studies we used to identify the range of graphs included in the evaluation. Background: The extraction of data from the reports of primary studies, on which the results of systematic reviews depend, needs to be carried out accurately. To aid reliability, it is recommended that two researchers carry out data extraction independently.
Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up. Anybody have any experience with software preferably free, preferably open source that will take an image of data plotted on cartesian coordinates a standard, everyday plot and extract the coordinates of the points plotted on the graph? Essentially, this is a data-mining problem and a reverse data-visualization problem. Check out the digitize package for R.
Installation will make the primary function for data extraction, .png,.jpg,.tiff,.pdf images can be used) from many different papers and that are of different types. Throughout the process of digitising metaDigitise() walks the user If we were working with a plot of mean and standard errors, the x-axis is.
Origin and OriginPro
Origin is the data analysis and graphing software of choice for over half a million scientists and engineers in commercial industries, academia, and government laboratories worldwide. Origin offers an easy-to-use interface for beginners, combined with the ability to perform advanced customization as you become more familiar with the application. Origin graphs and analysis results can automatically update on data or parameter change, allowing you to create templates for repetitive tasks or to perform batch operations from the user interface, without the need for programming.
Subscribe to RSS
PDF is here to stay. But what are the options if you want to extract data from PDF documents? Manually rekeying PDF data is often the first reflex but fails most of the time for a variety of reasons. PDF files are the go-to solution for exchanging business data, internally as well as with trading partners.
I could write the author and wait several days for them to dig up the plot file and send me the digitized version, but I want to compare now! You can organize your digitized data into multiple datasets, which you can save as text files. Plus you can save the whole project, should you need to come back later and alter a fit. I used the curve-finding algorithm to follow one of the curves; the digitized points are shown by little red dots.
Join Stack Overflow to learn, share knowledge, and build your career. Connect and share knowledge within a single location that is structured and easy to search. I have thousands of pdf file that I need to extract data from. This is an example pdf. I want to extract this information from the example pdf. I am open to nodejs, python or any other effective method.