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Machine Learning Made Easy
Machine learning is ubiquitous. From medical diagnosis, speech, and handwriting recognition to automated trading and movie recommendations, machine learning techniques are being used to make critical business and life decisions every moment of the day. Each machine learning problem is unique, so it can be challenging to manage raw data, identify key features that impact your model, train multiple models, and perform model assessments.
In this session we explore the fundamentals of machine learning using MATLAB. Through several examples we review typical workflows for both supervised learning (classification) and unsupervised learning (clustering).
Highlights include
- Accessing, exploring, analyzing, and visualizing data in MATLAB
- Using the Classification Learner app and functions in the Statistics and Machine Learning Toolbox to perform common machine learning tasks such as:
- Feature selection and feature transformation
- Specifying cross-validation schemes
- Training a range of classification models, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, and discriminant analysis
- Performing model assessment and model comparisons using confusion matrices and ROC curves to help choose the best model for your data
- Integrating trained models into applications such as computer vision, signal processing, and data analytics.
About the Presenter: Shashank Prasanna is a product marketing manager at MathWorks, where he focuses on MATLAB and add-on products for statistics, machine learning, and data analytics. Prior to joining MathWorks, Shashank worked on software design and development at Oracle. Shashank holds an M.S. in electrical engineering from Arizona State University.
Recorded: 19 Mar 2015
Hello, and welcome to Machine Learning Made Easy. My name is Shashank Prasanna. I'm the product manager for statistics and machine learning products at MathWorks.
Here's the agenda for the rest of today's presentation. I'll start off with a high-level overview of what machine learning is and why you might want to consider it. We'll then see some key challenges in machine learning.
Next, I'll take a deeper dive into machine learning by solving an interesting problem that involves real-world data. To tackle this problem, I'll introduce a typical workflow that often gets used in solving machine learning problems. We'll then go through another interesting example that involves image data. I'll show you a live demonstration that uses video from a webcam to identify objects. We'll wrap the session up with a summary and some guidance on when to consider machine learning.
Since this is a Made Easy topic, there are no prerequisites. Basic familiarity with MATLAB is helpful, but not required. Machine learning is ubiquitous. These techniques are increasingly being used in today's world to make critical business and life decisions. Today, with machine learning, we are able to solve problems in automotive, finance, computer vision, and several other fields that were previously considered impossible.
So what is machine learning? Let's take a minute to quickly go over the high-level concepts of machine learning. Even if you're familiar with this topic, this should serve as a quick reminder of when it is actually useful to pursue machine learning.
We can define machine learning as a technique that uses data and produces a program to perform a task. Let me explain that in the context of an example. Consider a task that involves using sensor data from a mobile phone to detect a person's activity; for example, if the person is walking, sitting, standing, and so on. The standard approach to solving such a task is to either analyze the signal and write down a program with a set of well-designed rules, or you may know a priori a set of equations or a formula that uses the inputs and predicts the output.
The machine learning approach is to learn such a program directly from data. In this case, we provide the algorithm with input and output data and have it learn the program to solve this task. This step is often referred to as training. The output is a model that can now be used to detect activity from new sensor data.
Notice I don't explicitly mention a specific machine learning algorithm because there are plenty of algorithms to choose from, and each have their own strengths and weaknesses. We'll see that when we go through this particular example in detail. If there are two things I'd like for you to take away from this slide, that would be, one, you need data. If you don't have data, you can't do machine learning. This is a strong requirement.
Two, consider machine learning only when the task is complex, and there is no magic equation or formula to solve it. If you have a formula, you can go ahead and implement it. Machine learning will work, but it's not recommended approach in such a situation.
So let's go to MATLAB and take a look at what the machine learning approach looks like. The data we are working with consists of six inputs, three from the accelerometer and three from gyroscope of a mobile phone. The response, or the output, are the activities performed. These include walking, standing, running, climbing stairs, and laying.
This is Classification Learner. It's an interactive tool that lets you perform common machine learning tasks, such as interactively exploring your data, selecting features, specifying validation schemes, training models, and assessing results. On my left here, I have several different machine learning models that have already been trained on sensor data. Right next to each model is a percentage number that indicates the accuracy of the selected classifier on a separate validation set. I can use the visualization on the right here to explore my data to search for patterns and trends.
Training a new model is easy. I simply navigate to the Classifier gallery, choose a classifier of interest, and then hit Train. Once training is complete, you can see the new model in the History List along with the model's performance accuracy. The higher this number, the better the model may perform on new data. I will choose my best-performing model and click Export to start using my model in MATLAB, and I can see that the exported model is right here in the workspace.
I'd now like to use this model, along with some test data, to visualize the model's prediction results. This is a plot that shows two and a half seconds of streaming accelerometer and gyroscope data. The green bar here on the top is showing the actual activity the person was performing corresponding to the sensor signal right here. The bottom bar is green if the model is able to successfully predict the actual activity of the person, and it's red if it is—if it fails to predict the actual activity. You see that the model often gets confused between walking and climbing stairs.
Let's quickly recap what we just achieved. We use data to fit several different models using Classification Learner. We then chose the one that showed good results, and we tested it to make sure it was doing what it was supposed to. That seemed quite simple, so why does machine learning have a reputation for being so hard?
To answer that question, I'd like to use a quote from a famous statistician, who once wrote in a textbook, "All models are wrong, but some are useful." Why? Because models are approximations. Not just machine learning models—all models are approximations based on several assumptions, but that doesn't make them any less useful. Machine learning models can explain complex patterns in data, but to apply machine learning successfully, you need to find useful models, and that can be a challenging task.
In reality, there's not one, but several challenges in every step of the machine learning workflow. Data comes in all shapes and sizes. It could be simple numeric data, such as from financial data feeds or sensor signals, or could be streaming images from a camera or text data. Real-world datasets are usually messy, and not always tabular.
Pre-processing data requires domain-specific algorithms and tools. For example, signal or image processing algorithms are required to extract useful features from signal and image data. Statistics algorithms are required for feature selection and feature transformation. We often require tools from multiple domains, and when working with multiple machine learning algorithms, searching for the best model can be a daunting and time-consuming task.
At the heart of it, choosing the best machine learning model is a balancing act. Highly flexible models may be accurate but may also overfit your data and perform poorly on new data. At the other extreme, simple models may assume too much about the data. There's always a tradeoff between speed, accuracy, and complexity of the model.
Finally, machine learning workflows are never a convenient linear workflow. We always constantly have to go back and forth, iterate, and try different ideas before we converge onto a solution. My goal today: introduce you to a common set of tools and strategies to address these challenges.
Here's a simple two-step workflow we like to follow in solving a machine learning task. The first step is to train your model. We start by bringing in a data, which would come from a variety of different sources, such as databases, streaming devices, and so on. Next, we pre-process the data using feature extraction or other statistical tools. This step is critical to transform your data into a format that machine learning algorithm can work with.
After that, we are ready to learn from data. If the task is to predict labels or categories, we choose classification methods. If the task is to predict continuous values, then we choose regression methods, and then we go ahead and build a model. This, of course, is an iterative process that requires going back and forth to the pre-processing step, trying different machine learning algorithms, tweaking different parameters, and so on.
The second step of this workflow is to actually use the model, so on the left, I have new data, and on the right, I need to make predictions. So what do we need to go from data to predictions? First, we need to use all the pre-processing steps for the new data, so there's no additional work involved here. We just reuse all the hard work we spent during the training phase.
Next, we use a model from the training phase and make predictions. For several engineering problems, the second step is often deployed or integrated into production environments, like for example, onto a server executing automated traits using machine learning. Let's now use this workflow to go through an example in MATLAB.
The object of this example is to train a classifier to automatically identify human activities based on sensor measurements. The data consists of six inputs, three from accelerometer and three from gyroscope. The response of the output are the activities performed: walking, standing, running, climbing stairs, and laying.
The approach we'll take is as follows. First, we'll extract basic features from sensors signals. We'll then use Classification Learner to train and compare several different Classifiers and, finally, test the results on unseen sensor data.
So this is MATLAB. We'll start with the current folder window here. Since the first step of the workflow is to bring in data, I'm going to load some raw sensor data I have here in a MAT file. To import this data, all I have to do is pretty much drag and drop it into the workspace window. The workspace window helps you keep track of all the variables in MATLAB, whether they're existing variables or new variables that we create as we move along.
My data consists of seven variables. The first six are sensory input variables, three for gyroscope and three for accelerometer x, y, and z. The last variable, TrainActivity, contains the activity labels for each observations of the sensor measurements. Notice all the variables have around 7,000 observations. Let's take a look at what an individual sensory input looks like.
If I plot a single row of the x-axis gyroscope data, we see that it has around 128 points. Now, this is what the data represents. Sensor data are often collected and fixed with frames of windows. Each row, with 128 readings, corresponds to two and a half seconds of sensor data.
Now, this data is not in a form that can be used for machine learning. I must first process all these inputs to extract features for each of these 128 points or two-and-a-half-second windows, and I have to do this for all six sensor inputs. But before we start with the feature extraction, let's take a look at what the raw sensor data looks like.
I have a custom plot here that shows three accelerometer raw sensor data, and they have different colors corresponding to the activity the person was performing. Plots in MATLAB are fairly interactive. I can zoom in to see if there are any visual patterns or if there are trends that can help us identify the activity of the person.
At a quick glance, we can see that the sensor values look different for each of these colors. For example, the orange values here, which correspond to climbing stairs, is quite different from the purple values, which correspond to standing. However, at the same time, it's hard to visually differentiate walking and climbing because both involve a lot of motion. Problems such as these are good candidates for machine learning, as it's not always obvious that we can define rules for each of these activities.
Before we start with the feature extraction step, I'm going to create a single table from these six sensor inputs. Tables are excellent tools for holding mixed-type data, which is common in machine learning. In this example, we have sensor measurements, which are numeric, and labels which are categorical values.
I now have a single table variable in my workspace that includes all the six individual sensor variables. Another advantage to using tables for machine learning is that I can apply a single feature extraction function to all the variables in this table with a single line of code. All I have to do is call VarFun, which stands for Variable Function, and pass in my feature extraction function. WMean here is a function that computes the average value of each row in the sensor variable. In this case, I am computing the average of two-and-a-half-second frame of sensor values.
WMean is a function in my current folder browser, and as you can see, it's a fairly simple function. But depending on your application and the features you want to extract, this could be as sophisticated as you please. In addition to the mean, I'd like to extract two other features, which is a standard deviation for each frame, and PCA, which stands for a principal component analysis, and I'd like to retain only the first principal component. Since tables can hold mixed-type data, I'm going to assign the label Activities as a new variable called Activity Within Table.
Let's run the section and take a look at what the human activity data table looks like. I have a total of 19 columns, and the first six correspond to the average value of the frame. The next six are the standard deviation of the frame, and the final six are the PCA outputs. My last column is the activity corresponding to each observation, which could be laying, sitting, climbing stairs, standing, or walking.
Now that we have a data ready, let's move the Classification Learner to train our models. Classification Learner is part of the Statistics and Machine Learning Toolbox, and you can launch it by typing Classification Learner on the MATLAB command line. Or you can find it under the AppStat under MAT Statistics and Optimization.
We first start by importing our data from the MATLAB workspace. Under step one of the import dialog box, we choose our dataset from the MATLAB workspace. Under step two, notice that the app automatically selects if a variable is a predictor or a response.
The app decides this based on the data type. However, you can also choose to change its role or completely remove the variable. For now, we leave this as it is.
Under step three, we can choose our validation method. Validation protects against problems like overfitting. Choose Cross Validation when you have a relatively smaller size dataset, as it makes efficient use of all the data. Choose Hold Out if you have sufficient data.
I'm going to choose this option for our problem since we have a lot of data points. Setting my Hold Out percentage to 20% instructs the app to use 80% of the data for training and 20% for validation of the model's performance. The last option is not usually recommended. Since all the data is used for training as well as testing, this leads to a biased estimate of the model's accuracy.
Classification Learner is a fairly interactive environment with a number of different windows and components, and I promise we'll go through every single one of them as and when they become relevant. The toolstrip on the top shows a left-to-right workflow all the way from importing your data to exporting your model. At the center here is pairwise scatter plot grouped by the response variable.
This plot can be useful for finding patterns. For example, this pair of predictors, we noticed that laying appears to be well separated from the rest of the activities. Sitting also appears to be well separated, but there is some overlap. The feature selection option on the toolstrip allows you to exclude predictors from the model. For this example, since we don't have too many predictors, we're going to leave this as it is.
When solving classification problems, there is no one size fits all. Different classifiers work best for different types of data and problems. Classification Learner lets you choose from decision trees, support vector machines, nearest neighbors, and ensemble classifiers, and for each classifier type, there are several presets that are excellent starting points for a range of classification problems. If you're not sure which to choose, a pop-up tool tip gives you a brief description of the classifier.
When working with MATLAB, help is always just a click away. For further assistance, simply click on the question mark on the top right, and this opens up the documentation for the app. You'll find all the information you need about the app right here. For now, let's go to the following section that has guidance on choosing a classifier.
Here's a nice table that gives you guidance on which classifier to choose, depending on the tradeoff you want to make. For example, decision trees are fast to fit, but have a medium predictive accuracy. On the other hand, nearest neighbors have high prediction accuracy for smaller problems, but also have high memory usage. Let's use this tip from the documentation and start with the decision tree first.
Training a model is easy. Simply choose a preset from the gallery and hit train, and this will produce a train model in the model history, along with its prediction accuracy. Let's also train a medium tree and a complex tree. The model that performs best on the validation set is always shown by a green box.
In addition to the prediction accuracy percentage, there are other diagnostic tools that are useful. Confusion matrix is a great tool that can tell you how a classifier's performing at a quick glance, so this is how you read a confusion matrix. Anything on the diagonal is correctly classified. Anything off diagonals have been misclassified. A perfect classifier would have 100% on the diagonal and 0% everywhere else.
Let's take a closer look at one activity: climbing stairs. The way to read this is 89.1% of the time, the model successfully predicted the activity. However, 10.9% of the time, the model thought that climbing stairs was walking, or in other words, the model misclassified climbing stairs as walking. We can always switch between different models in the modern history to compare confusion matrices.
Another diagnostic tool that's available in the app is the Auto C Curve. Auto C curves are used to describe the sensitivity of a binary classifier. The shape of the curve shows a tradeoff between sensitivity and specificity. As we move upward and to the right of the curve, we increase the chance of true positives, but also increase the chance of false positives.
In my opinion, one of the greatest advantages of using apps instead of writing code is the ability to train multiple models. In addition to decision trees, I'm now going to train nearest neighbor classifiers because I know they're fast to train and fast to predict. When training multiple models, you don't have to be confined to these presets.
If you are an advanced user, you can always bring up the advanced pop-up to tweak the classifier parameters. These changes are also shown in the model history for convenience. We now have about eight models or so in the model history, and I train all of them in a matter of seconds without writing a single line of MATLAB code.
There are two modes in which you can export your analysis. You can either export your model directly into MATLAB, or you can choose to generate MATLAB code that lets you automate all the steps we took in building this model without having to write any code. Let me choose this option to show you what the generated code looks like.
What I have here in MATLAB is a well-commented, completely autogenerated MATLAB code. We can also see the different steps in a workflow captured by the code. For example, extracting predictors and response, training a classifier using the KNN classifier, and setting a Hold Out validation and so on. You can always customize this generated code to integrate it into your applications.
Let's go back to Classification Learner and export a model instead, and we can see that the model is now available in the MATLAB workspace. We are now ready to test the model on new data. First, we load in some new sensor data from a MAT file. We then apply the same feature extraction steps from before. We then use the exported model to test results on this new sensor data.
As we saw before, the top green bar shows the actual activity the person was performing, and the bottom green bar, when the model successfully detects the activity. And it is red if it makes a mistake. If you want to try a new model, you can simply go back to Classification Learner and either export the model directly or generate MATLAB code and train new models to predict with new data.
Let's take a quick look at our workflow to summarize what we just did. Our training data was sensor signals obtained from mobile phone sensor. We applied basic pre-processing techniques to extract features like average standard deviation and PCA. We then used the Classification Learner app to arrive at our best model based on the holdout validation accuracy.
For prediction with new data, we applied the same pre-processing steps on new data, and then we used a trained model to make predictions and then visualize the results. Let's go through another example, where we'll train a machine learning model using image data. The objective of this example is to train a classifier to automatically detect cars from webcam video. The data consists of several images of four different toy cars. The response of the output is a label for each of these four cars.
The approach we'll take is as follows. First, we'll extract features from these images using a technique called bag-of-words. We then use these features to train and classify several different classifiers using Classification Learner. And finally—this is the interesting part—we'll see a live demonstration of the model we trained identifying cars in real time from a webcam feed in my office.
So let's switch to MATLAB. I'm going to clear out my workspace and start clean slate. So let's take a look at our data. So here are a bunch of images and folders, and each folder name is the label for the set of images in that folder. Let's take a look at what the images look like outside of MATLAB.
So here are a bunch of images for sand dune taken from several different angles and varying lighting conditions, and I have several such images for all the four cars. When working with images, loading all the images in a loop and keeping track of all the files and folders and the labels can be painful. The Computer Vision System Toolbox has convenient tools like Image Set that makes this task easy. I simply provide Image Set with a folder that has all my images and instructed to look at all the sub folders as well. Another important advantage of Image Set is that it doesn't load all the images to memory, so it's easy to work with when you have lots and lots of images.
To pre-process my data and to extract features, I'm going to use an approach called bag-of-features. This is a fairly sophisticated technique to do feature extraction from images and works particularly well. If you are interested in learning more about what the function does, the Computer Vision System Toolbox documentation has detailed explanation on what the algorithm does underneath the hood. For now, let's treat this function as a feature extraction tool that we'll use to extract features from images.
Like we did in the previous example, let's create a table and assign the labels to the table. Let's jump right into Classification Learner and start training our model. Let's take a quick look at our data. We have 200 new extracted features from the images, and the last variable is the response. I'll again choose holdout as my validation, and I'm now ready to start training models.
I'm going to train few models quickly, starting with nearest neighbors and support vector machines. Now that I have a few more or less trained, let's do a quick visual diagnostics using the confusion matrix. The confusion matrix is mostly diagonal, so that's a good thing, and we'll quickly export one of these models and see how it performs on real streaming images.
Before I run this function, I'd like to show you the code and how easy it is to obtain streaming images and classify them in real time. The function takes in two inputs. First is the train model, and the second is a bag-of-features object, which I will use to extract features from new images.
Starting a webcam is easy. All I need to do is call the Webcam command. Inside a continuously running loop, first, I obtain a snapshot from the webcam and then convert it to grayscale, which means the model should be able to identify cars without the color information. The next step is to extract the features from this new image, and finally, use a predict function and the trained model to make predictions on the new image. Let's go back and run this function.
The top plot here shows images from my webcam, and the green bar here is the result of the model making predictions on this image. This is the classifier I'm using, and this bar chart shows how confident the classifier is that this car belongs to either Lightning, Mater, Nigel, or Sand Dune. So let's move the camera around to see how the model performs at different angles and orientation.
So the model mostly gets Lightning right, but as we move to Nigel, you'll see that the model is not completely confident which car it is. The probability plot on the bottom shows you how confident the model is in predicting which car this is. This is Sand Dune, and finally Mater.
Let's export another model from Classification Learner and see how two models perform side by side. So I'm going to use Linear SVM and choose Export with a default name. So I now have two classifiers, or two models in my workspace. One is KNN. The other is support vector machines.
Now let me run this piece of code which compares the performance of these two classifiers side by side. What we again see here is a video comparing the performance of two classifiers side by side. On the top left in red is a performance of our SVM classifier, and on the bottom right is the performance of KNN classifier. The important thing to note here is not only is it easy to compare and test models in Classification Learner, but it is just as easy to export these models to MATLAB and test them in real time.
That brings us to the end of this demonstration. Let's now switch back to our presentation. Let's again summarize what we just accomplished using our familiar workflow diagram.
Our inputs consisted of several labeled images on disk. We use the bag-of-words approach to generate new features. We then use the Classification Learner app to arrive at our best model. For prediction step, we obtain new data from a webcam, encoded the images and new features, and use a trained model to make real-time predictions.
This brings us to the summary and key takeaways. To summarize what we've seen so far, let me go back to the challenges I've set up at the beginning of this presentation. Hopefully, you've seen today how MATLAB is able to address these challenges.
For the first challenge of data diversity, we saw how MATLAB can work with different types of data. MATLAB can also access and download financial data feeds, work with text, geospatial data, and several other data formats. MATLAB also has libraries of vetted industry standard algorithms and functions. MATLAB also offers additional tools for specific engineering workflows in finance, signal processing, image processing, and several others.
We also saw how we could quickly build and prototype solutions, which interact to app-driven workflows that let you focus on machine learning, not programming and debugging. Best practices for machine learning, such as cross validation and model assessment tools, are integrated into apps and functions. As we saw, MATLAB also has rich documentation that has several guidelines that help you choose the right tools for the job. Finally, MATLAB is inherently a flexible modeling environment and a complete programming language with no restrictions to the customizations you can make to your analysis. This makes MATLAB an excellent platform for machine learning.
This brings us to the end of our presentation, and I'll try to keep this short and brief. I'd like to share with you some guidelines on when you should consider machine learning. Consider machine learning if you're working on a problem where handwritten rules and equations are too complex or impossible to formulate. Or when the rules of your task are constantly changing, and your program or model needs to constantly adapt since your task is a moving target. Or when the nature of your data changes, and the program needs to constantly adapt.
And hopefully, I've been able to convince you that MATLAB is a powerful platform for every step of the machine learning workflow. We're always interested in hearing from you, and you can find my contact information on the Statistics and Machine Learning Toolbox product page. If you're interested in learning more, please take a look at the product documentation. There are plenty of examples and concept pages that can not only help you get started, but also guide you as you master the tools.
To learn more about algorithms, application areas, examples, and webinars related to machine learning, feel free to visit the Machine Learning page. This brings us to the end of this session. Thank you all for listening.
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New technique to find copper deposits
A geologist at the University of Exeter has developed a new and relatively inexpensive way to establish whether certain types of magmatic rocks are more likely to contain valuable metal deposits.
In a study published today in Nature Geoscience, Dr Ben Williamson, of the University's Camborne School of Mines, together with Dr Richard Herrington from the Natural History Museum, London, have proposed a new method to explore for porphyry-type copper deposits. These deposits provide around 75 per cent of the world's copper and a significant amount of molybdenum and gold which makes them extremely important to the world economy. The deposits, which originally form at several kilometres depth below the Earth's surface, above large magma chambers, are relatively rare, particularly the largest deposits which are most economic to mine. In addition, most near-surface deposits have already been discovered. Any new method to locate deeper deposits is therefore of great interest to the mining industry.
The project, funded by Anglo American, a major global mining company, compared the chemical compositions of minerals from magmatic rocks that host porphyry deposits against those which are barren. A case study was then undertaken of a major new porphyry discovery in Chile, to test their theory. Minerals from magmatic rocks which host porphyry deposits have distinctive chemical characteristics which can be used as one of a suite of indicators to home-in on porphyry deposits. Unravelling the causes of the distinctive chemical signatures has also brought new insights into the formation of porphyry copper deposits, and more generally the generation of the magmatic rocks from which they form, which are an important component of the Earth's crust. The main finding in this regard is that the magma chamber below the porphyry undergoes discrete injections of water-rich melts or watery fluids which enhance the magma's ability to transfer copper and other metals upwards to form a porphyry copper deposit.
Dr Ben Williamson, of the University of Exeter's Camborne School of Mines, said: "This new method will add to the range of tools available to exploration companies to discover new porphyry copper deposits. Our findings also provide important insights into why some magmas are more likely to produce porphyry copper deposits than others, and add to our understanding of how their parent magmatic rocks evolve."
More information: "Porphyry copper enrichment linked to excess aluminium in plagioclase", B.J. Williamson, R.J. Herrington and A. Morris, Nature Geoscience, February 1. nature.com/articles/doi:10.1038/ngeo2651
Journal information: Nature Geoscience
Provided by University of Exeter

subset: commoncrawl


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Archival destruction by recycling. (Discontinued as of March 3, 2008.) The process of recycling Cartons that are stored with Iron Mountain upon Customer authorization. This includes retrieval, documentation and transfer to a 3rd party recycler. Iron Mountain's responsibility for the chain-of-custody of these records ends at its dock where the records are picked up by a 3rd party recycler.
As of March 3, 2008, Iron Mountain no longer supports Archival Destruction by Recycling due to the changes in regulatory environment and privacy laws. All archival destruction requests received after March 3, 2008 will be processed using the Archival Destruction by Secure Shredding method.
Assisted discovery. In the event that Customer requests Iron Mountain to retrieve data, or Iron Mountain discloses Customer Data under subpoena, Iron Mountain shall download Customer Data on to the same type of media provided by Customer or as otherwise reasonably requested. Customer shall pay for such media and shipping costs.
Autopay – The automatic payment of a customer's invoice from their credit card, debit card or bank account
Batch class. A batch class is a collection of rules and parameters, which define a document capture workflow
Billing cycle. The period of time between billing statements. Charges for storage are billed in advance, with billing in arrears for Cartons received during the month. Charges for service and transportation are billed in arrears.
Bulk 1 container / bulk 2 container. Generic term used for Collection Containers of a non-standard size.
Business days. Monday through Friday, excluding holidays.
Business hours. Begins 8:00 a.m. and ends at 5:00 p.m., local time, on a Business Day. Transportation services may be completed outside Business Hours.
Carton(s). A box or container used to hold records.
Collection container. Collection Containers are used for the temporary storage of awaiting secure destruction service. Collection Containers, designed to be lockable and secure, are available as 65 gallon bins, 40" security consoles, 27" mini consoles and other non-standard unit sizes designed to accommodate a range of environments. Collection containers are generally placed in common office areas within the Customer's facility (e.g. copy, mail rooms or other workplace locations) that are conducive to employee access and use.
Computer record change. An Iron Mountain service in which the customer contacts Iron Mountain and requests that we manually update the description of item(s) managed in our inventory system. Standard updates for Computer Record Changes are billed per item. In cases where the change relates to bulk moves or to edits for many items, you will be charged at an hourly rate for work performed. These fees will be identified on your invoice as a Computer Record Change. It is Iron Mountain's policy that record updates performed due to an Iron Mountain error will not be charged to the customer.
Container pickup/delivery. Container pickup/delivery includes the delivery and placement, relocation, or pickup of collection containers at the Customer Location.
Consolidation project. A process in which, upon request, Iron Mountain will adjust the allocated Linear Footage of shelf space dedicated to a customer for Open Shelf Storage. Open Shelf Storage charges are based upon the Linear Footage of shelf space allocated to a customer program or File series and not to the Linear Footage of actual Files in storage at a given time. Therefore, Files may be retrieved on a temporary basis, destroyed or withdrawn from storage on a permanent basis without impacting the allocated Linear Feet of shelf space. In cases where the customer's Open Shelf Storage needs exceed its allocated space, we automatically increase the storage allocation to accommodate the growth. When program needs change, customers may request a Consolidation Project to consolidate their Linear Feet of allocated shelf space and to reduce their storage charges. Charges for Consolidation Projects are quoted in advance of the project based upon the labor expected to be required for the project.
Conversion. Transformation of data from a physical format to an electronic file
Cubic foot/feet/footage (CF). The unit of measure for determining charges for storage and service of Cartons. Cubic Footage attributed to a specific Carton or type of Carton may vary from actual physical size.
Customer Id. A unique numeric, alphabetic or alphanumeric identifier, up to five digits in length, required to access your Iron Mountain Records Management or Shred account. If you maintain multiple Customer IDs with Iron Mountain, when requesting a file or carton, the Customer ID associated with that specific file or carton must be used to assure that it is located.
Customer location. For Records Management and Data Protection services, the transactional point where proof of service (delivery or pickup), as defined by the receipt of a Customer's signature on the completed work order, is required.
Delivery or pickup may be performed at a centralized delivery/pickup location within a loading dock or mail room or may be performed through multiple visits at several locations within the building. Each stop at which pickup or delivery service is completed and a Customer signature is obtained is considered a distinct Customer Location.
For Secure Shredding Services, the physical Customer site or facility where Iron Mountain collects materials that will be destroyed by the Secure Shredding process.
Custom reports. Any reporting requirements outside the core reporting offering requiring custom development.
Daily service. Recurring Secure Shredding Services performed each Business Day.
Decommissioning. The process of compiling, extracting, and returning a customer's assets, the off-boarding of their implementation and the deletion and destruction of a customer's data, information and environment from Iron Mountain's systems. Decommissioning may include, but not be limited to the following activities: Shutting down any VPNs, removing any FTP sites, return of assets, turning off any standard and customer reporting, closing support accounts, terminating Web access, asset destruction, database off-boarding, workflow clean up and contract, accounting and operations notifications.
Department Id. Information Management tool that enables the customer to breakdown the customer account into a logical series of business units under a Division ID. (e.g. Pediatrics, Cardiology, Obstetrics) Each Customer Account will have a default Master Department ID. A customer may choose to use only Departments to represent a business unit. The Departments would then be created under the default Master Division. * See Division ID.
Deposits. A Carton, File or other record that is serviced, transported, or stored in an Iron Mountain facility. A Deposit may be a Carton, individual File or other Item for storage. Also known as Item/Items.
Digital images scanned. Includes up to five- minutes of document preparation per file, quality control of scanned images, up to ten characters of manual indexing per image, up to 2 minutes of re-assembly per file, and delivery of image(s) to customer
Discount rate. The percentage reduction offered by Iron Mountain to the Customer to the List Price. The Discount Rate earned by the customer is determined by Iron Mountain and is calculated based on the total amount invoiced by Iron Mountain and paid by customer for records storage and services in the United States in the past 12 months. The List Price, adjusted for the Discount Rate applicable to the Customer, will be calculated to the third place to the right of the decimal point. In the event the List Price so adjusted has a number other than zero in the fourth place to the right of the decimal point, the effective price will be rounded up. For example, if the List Price reduced by the Customer's discount after applying the Discount Rate is $.3581, the Customer's effective price will be rounded up to $.359.
Division Id. Information Management tool that enables the customer to structure the customer account into a logical series of business units called Divisions. (e.g. Outpatient Care, Hospice Care, Ambulatory) Each Customer Account will have a default Master Division ID. * See Department ID.
Document imaging programming. Programming of batch classes which may include multiple outputs, PDF generation or other unique customer requirements requiring additional programming above and beyond the standard document imaging setup
Document imaging setup. Includes up to 5 hours of programming to create a batch class which indicates customer specific image settings: index fields, an output path for the image, and image formatting.
Document preparation. Document preparation includes the work associated with preparing a document for imaging. This includes document separation, removing all fasteners, adjusting bent corners, taping tears and other tasks necessary to cleanly convert a document to a digital image. The document preparation charge is determined for each document type and the same charge is applied for each page prepared.
Electronic payments – Any payment made by a customer to Iron Mountain via electronic transfer of funds (EFT) either through credit card or debit card, direct deposit payment, direct debit payment, wire transfer, digital wallet or ACH (automated clearing house).
File(s). A document or group of documents contained inside a folder. Files may be stored in Cartons on shelves or in Open Shelf Storage.
Floor walk service. A component of the materials collection process for Secure Shredding Service whereby personnel walk through a Customer's facility to access and service collection containers per the work order requirements.
Flat fee. Services billed at a fixed rate or price.
Fuel surcharge. An additional charge imposed on Transportation Visits to adjust for changes in fuel prices. For additional detail, click here.
Gigabyte. A unit of information or computer storage meaning exactly 1 billion bytes (10003 or 109) or approximately 1.07 billion bytes (10243 or 230). It is commonly abbreviated GB or Gbyte.
Governance, risk and compliance service fee: A monthly fee charged per account for governance, risk and compliance services and for maintenance and administrative services. Governance, risk and compliance services include Policy Center Essential, an information risk assessment tool, and information management courses on our Information and Economics Academy. Maintenance and administrative services include 24/7 call center availability, Iron Mountain Connect™, and Report Center standard reporting. These maintenance and administrative services were formerly billed separately as an Administrative Fee.
Handling charge. The charge for use of vehicle load capacity, the service of preparing Items for transportation, loading onto and off of Iron Mountain vehicles when making a pickup or delivery and, for Archival Destruction, transporting items to the Shredding Plant and staging for destruction. Handling Charges are based upon the Cubic Footage of Items transported. For transportation, Files are consolidated into Packages. Handling Charges for Packages are charged at one Cubic Foot per Package. All Cartons are subject to a minimum Handling Charge of one Cubic Foot. Find out more about special transportation requests and charges here.
Hold code. Record Management tool that enables the customer to manage/prevent the disposition of records for individual cartons, departments, record services or accounts.
* See Record Code and Retention Schedule.
Hour. Unit of billing for certain services defined as "per hour". Charges are calculated for the elapsed time required to perform the service, measured in fifteen minute increments.
Hourly document preparation. This includes extra actions performed during document preparation that were not anticipated in the pre-defined scope of work agreed with the customer. This may include photocopying or repairing damaged documents. Charges are calculated for the elapsed time required to perform the service and rounded up to the nearest 15 minute increment.
Image capture/scan. This is the process of taking physical paper documents and transferring them to digital images. Document preparation or document indexing may be included in this charge or may be priced separately.
Image delivery to ftp. Scanned images are delivered to a pre-defined, password-protected FTP (file transport protocol) site where they can be retrieved by authorized users.
Image indexing. Adding the customer defined indexes to the digitized images.
Image master to media. Images are written to a CD, DVD, or Film and delivered to the customer.
Image on demand monthly services. Under this pricing methodology, a customer pays a monthly fee for a set volume of image-on-demand orders. If the total number of orders placed by a customer in one month exceeds the purchased number of orders, the customer will incur an incremental charge calculated on a per-image basis.
Image on demand - overview. Individual files that are to be converted to digital format and delivered to the customer digitally can be requested through Iron Mountain's on-line customer portal, Iron Mountain Connect. Requests for files to be scanned under the IOD program must be requested by Authorized Users, whose credentials will be verified through Iron Mountain Connect.
Upon Iron Mountain's receipt of Customer's request, the file will be retrieved from storage, prepared for scanning, scanned and re-filed to the original storage location. When the digitized file is available to the Customer on Iron Mountain's electronic repository, the requestor will receive an email notification containing a hyperlink that can be followed to view the digitized file. In order to view and retrieve the digitized file from Iron Mountain's electronic repository, the requester's credentials must again be authenticated through Iron Mountain Connect. When authentication is complete, the user can download the digital file to his or her PC. All users must be authorized users of Iron Mountain Connect In order to request Image on Demand services and to view or retrieve digital files from the repository.
The IOD digitization process includes several steps: Document Preparation, Scanning, Quality Control, and Re-Assembly (scope of activities are defined below). Eight (8) minutes of labor are provided for each file requested for IOD digitization; this amount of labor time is sufficient to accommodate the majority of IOD file conversions. For files requiring more complex processes, additional labor costs may be applied and will be charged in 15-minute increments at the labor rates set forth in Customer's Agreement.
(a) Document preparation. Upon retrieval of a requested file, the requested pages of the file will be prepared for digitization. Typical preparation includes the insertion of a file header sheet, removal of fasteners and bindings, flattening bent corners, orienting the document properly, making minor repairs to the paper if required for scanning, and positioning smaller documents for auto feed scanning. Flatbed scanning may be required in some instances and will be charged per page (photocopy fee applies). A single file header sheet will be used for each file processed.
(b) Scanning and image processing. Physical source files will be converted to an electronic format at 300 DPI, in black and white and presented as a multi page PDF. The pages of the file will be scanned in duplex mode (front and back) with automatic blank page deletion at 5K or less per page; images over 5K will be delivered as an image in the digital file. Deskewing, auto orientation and edge cleanup will be an automated process with the output of these automated processes accepted as is.
(c) IOD quality control. Iron Mountain provides quality control by comparing certain physical pages of the file with the converted digital image.
(d) Document reassembly. Upon completion of the digitization process, pages are returned to the original folder, in original page order, without bindings (if bindings were removed for conversion).
Image transmission after hours. Service requested outside of normal business hours - Monday through Friday 8:00 AM to 5:00 PM. Requests for service on weekends and communicated holidays are also considered After Hours services
Implementation fee for image archiving. Implementation includes
- Kick-off meeting and definition of Customer requirements
- Creation of the database schema
- Application groups: up to three (3)
- Defined by the indexes associated with the document type(s) and by which groups of users are allowed access
- Applications: up to three (3)
- Defined by electronic format type (for example: PDF or TIFF)
- Document Types: up to three (3)
- Index fields per document: up to ten (10)
- Characters per index field: up to thirty-two (32)
- Implementation of user accounts and creation of up to two (2) security schemas (Customer is responsible for administering user accounts subsequent to initial implementation).
- Creation of related batch classes
- Testing of the data processes associated with Customer's business requirements
Individual listing. The process of data entry of standard File descriptions into the SafeKeeperPLUS® system to create Listed Files or the process of file level validation for files requested that have not been previously data entered.
Individual listing patient files. The process of capturing one or more unique patient file identifiers in the SafekeeperPlus® system, so that patient files can be easily accessed from open shelf or carton storage. Patient files include Medical Records, Radiology films, Cine films, Mammograms and Fetal Monitor Strips.
Initial move. For a new customer, the initial transfer of Items to Iron Mountain for storage, including Regular Pickup, transportation, Receiving and Entry and placement into storage. The period of time considered the Initial Move is set forth in the Pricing Schedule but normally does not exceed forty-five (45) days. The maximum number of Items included in an Initial Move may also be set forth in the Pricing Schedule. Charges in the Pricing Schedule assume that customer's cartons are available for pickup on a dock (palletized, wrapped, and inventoried) before the arrival of an Iron Mountain vehicle.
Initial move cartons. The Cartons that are included in the Initial Move from the customer and/or customer's vendor.
Initial service frequency. Anticipated service frequency that a Customer requests when initially engaging Iron Mountain to deliver and/or implement Secure Shredding services. Service schedule requirements and constraints will be determined by mutual agreement between the Customer and Iron Mountain with follow-on adjustments as necessary.
Interfile. A new File or document that is sent to storage and inserted into an existing Carton or File.
Item/items. A Carton, File, or other record that is serviced, transported or stored in an Iron Mountain facility. An Item may be a Carton, individual File or other record for storage. Also known as a Deposit.
Labor. The time required to perform any service requested by a Customer that is not specifically described and provided for in your Pricing Schedule or in the Additional Services section of this website (billed in fifteen minutes increments).
Linear foot/feet/footage (LF). The unit of measure for determining charges for storage and service in Open Shelf format. Linear Foot measurement includes shelving space allocated to a customer based on initial records moved in, plus growth in File volume. Linear Footage attributed to a customer does not vary because of temporary Retrievals or Permanent Withdrawals unless the customer undertakes a File Consolidation Project to reduce shelving allocated to the program.
List price. The price for commonly provided Storage and Service items as published by Iron Mountain from time to time.
Metro zone service: An additional charge assessed on Transportation Visits at Customer Locations located in certain metropolitan areas. You can find the affected metropolitan areas, the additional charge applicable to each, and other information about special transportation requests and charges here.
Metro-zone transportation charges: Transportation Visits may include applicable charges based on distance from an Iron Mountain facility or your presence in certain metropolitan areas.
Minimum dollar commitment. The minimum fee Customer agrees to pay each calendar month and is the total of the price per gigabyte and the per user fee. Each calendar month, the total monthly service cost is calculated and compared to the MMDC. If the total monthly service cost is less than the MMDC, the MMDC will be the fee Customer is charged for the calendar month.
Minimum service order charge. A minimum order charge applied to a customer's account when the service order, excluding transportation-related charges, does not meet the customer's contractual service order minimum, as set forth in the Pricing Schedule.
Minimum shredding service charge. All Secure Shredding Work Orders are subject to a minimum Service Charge. The charge will apply when the total fees associated with performing Secure Shredding services per the Customer Work Order are less than the stated minimum required service charge set forth in the Customer's Pricing Schedule.
Minimum storage charge. A minimum monthly storage charge that is applied to a customer's account when the storage charges do not meet the customer's contractual minimum storage requirement. Any cartons that are inbounded after the Month-End Cut-off date will not be included in the minimum storage calculation until the subsequent month.
Miscellaneous services. Charges for services requested by a customer that are not specifically described and provided for in the customer's Pricing Schedule or in the Additional Services section of this website. Miscellaneous Services are billed at the customer's contracted Labor rate. See Labor.
Mobile shredding unit (MSU). A vehicle outfitted with onboard commercial grade shredding equipment approved by Iron Mountain for the destruction of Paper-based Materials.
Month-end cut-off. The specific date at the end of a month that triggers the conclusion of a given Billing Cycle. For accounting purposes, Iron Mountain measures services as of a specific date, fixed annually in advance and a few days prior to the end of each month, in order to process invoices promptly. Services performed or Cartons added after the Month-end Cut-off date will be billed in the subsequent month. Upon request, Iron Mountain will advise the customer of future Month-end Cut-off dates.
Monthly storage. Monthly storage fee paid by customers of the Digital Record Center™ for Images based on total number of gigabytes stored.
Non paper materials. Any material that is not paper-based such as metal; plastic, bio-hazard, E-waste or wet material of any kind. Iron Mountain deems such materials unacceptable for destruction by standard, commercial shredding equipment.
Non-standard collection container. Any container that falls outside the parameters of Iron Mountain's three (3) Standard Collection Container options: 65 gallon bin; 40" security console and 27" mini security console.
Offsite shredding. Offsite Shredding refers to materials destruction performed at a secure facility. Includes collection and transport of the Customer's Paper-based Materials for destruction processing at a secure facility. Materials are subsequently destroyed to a non-recoverable form using proprietary procedures and commercial grade equipment. Following destruction, shredded materials are recycled.
Onsite shredding. Onsite Shredding refers to materials destruction performed at or near a Customer Location using a Mobile Shredding Unit. Includes collection, destruction and transfer of Customer's shredded material for subsequent processing. Following destruction, shredded materials are recycled.
On-call service fee: An additional fee charged to the work order for unscheduled service.
On-site training. Administrative training is delivered on site at the customer's location; it is a one time training overview of the security model set up for that customer. Customers will learn how to add a user, change a password, and add a user to an existing User Group. There is a two-day minimum associated with this training.
Open shelf storage. Storage of Items in File folders (and not within Cartons) generally used for records that require frequent access, such as medical records. See linear foot/feet/footage (LF).
Open shelf retrieval. The act of physically locating and pulling a File from an open shelf storage location in an Iron Mountain facility based on criteria provided by the Customer in the Order.
Order. A customer request for service related to its records. Multiple requests for service received at different times may result in multiple Orders, even if services are provided to the same Customer Location if the Iron Mountain dispatcher is unable to combine the Orders due to time or type of service.
Outside courier / customer representative handling. The service provided by Iron Mountain to prepare Items for shipment, or receive Items delivered, by a third party carrier, or to prepare or receive Items picked up or dropped off by the customer or the customer's representative. This service is billed per Item, except for customer pick-ups and third party carrier shipments of Files, in which case, where feasible, Iron Mountain will consolidate multiple Files into Packages and bill this service per Package.
Package. A bag, envelope, or container used to securely transport individual Files. In cases where customers send Files to Iron Mountain that are not contained within a Carton, or in cases where Iron Mountain sends a File back to the customer via a File Retrieval, Iron Mountain will insert the File into a Package. This process is designed to ensure the integrity of File records during transport.
Paper-based materials. Office paper, light cardboard and similar material such as stationery, envelopes, file folders etc. Excludes Non-Paper Materials.
Paper recycling surcharge. An additional charge imposed on Shred services to adjust for changes in the price of recycled paper. For additional detail, click here.
Permanent withdrawal. he act of processing a Permanent Withdrawal order to prepare and confirm Items retrieved at Iron Mountain's dock for transportation and to update the status of the Item in the inventory system. Permanent Withdrawal applies to Items that will not be returned to Iron Mountain for storage, but are not to be destroyed by Iron Mountain. Permanent Withdrawal charges will apply when Items are removed from Iron Mountain and will not be returned, regardless of whether the removal occurs during the contract term, at the end of the contact term, upon expiration of the contract, or as a result of a customer terminating the contract, by non-renewing or otherwise. Deposits processed for Permanent Withdrawal are deducted from the storage billing at the end of the month this process occurs. The Permanent Withdrawal process involves the Permanent Withdrawal charge, a Retrieval charge for each item to be withdrawn, and a Handling Charge. Permanent Withdrawal processing involves scanning to confirm each Deposit is being changed to permanently withdrawn status, a Handling Charge and closing the order within the system (up to the standard destruction volume of 1,200 cubic feet per Iron Mountain Market per month). In the context of a customer ending its relationship with Iron Mountain and closing its account, (whether by way of contract expiration, contract non-renewal, contract termination or otherwise), the maximum cubic feet of cartons that Iron Mountain will transfer back to the customer (or the customer's new vendor) is 1,200 cubic feet per month, per Iron Mountain market. Items that are not picked up by a customer within 30 days may be subject to additional charges, including but not limited to charges for refiling and retrieval.
Plastic media. Items such as Microfilm / Microfiche, Magnetic Media, Compact, Floppy and or Digital Video Disks, Magnetic Cartridges, VHS & Cassette Tapes and X-Rays. Iron Mountain accepts such media if collected separately for secure destruction by third party processing as a special work order. Iron Mountain will also invoice customers for non-paper destruction when non-paper assets are found in cartons marked for secure destruction. The rate for such destructions will equal the third party destruction rate minus any charges already incurred by the customer for standard paper shredding of the same carton.
Pound. A non-standard pricing measurement used for pricing certain secure shredding programs. Iron Mountain estimates weight based upon approximate paper volume collected from an individual container and the average weight of paper at full volume for that container type. For cartons, the weight is the Iron Mountain assigned weight of paper for that Carton type.
Professional services Fee. Fee for professional services engagements.
Program setup. The initial delivery and placement of Collection Containers at the Customer's site.
Re-boxing charge. Iron Mountain may replace Cartons when the condition of the Carton will not support other Cartons in a standard storage configuration, or when the Carton is not safe for transport or handling. Re-boxing charge includes both the price of the replacement Carton and Labor, at a rate per Carton, to carry out the re-boxing service. Re-box charge will show on your invoice as a "Repack Charge" plus the charge for a new carton.
Receiving and entry - cartons. The act of processing new Deposits when they are first received at an Iron Mountain facility, resulting in an increased storage volume. Includes the physical receipt, processing, assignment to a storage location and placement in the storage location. Items are scheduled to be completed into Iron Mountain's database not later than three Business Days after pickup. This does not include Individual Listing of Files - if applicable.
Receiving and Entry - open shelf files. The act of processing new Files when they are first received at an Iron Mountain facility. The service charge for Receiving and Entry is based upon the allocated Linear Feet of Files processed (shown on your invoice as Receiving & Entry). This does not include Individual Listing of Files, if applicable.
Record code. Record Management tool that enables the customer to group like records together and create a structure that defines record type, descriptions, and retention lengths. *See Hold Code and Retention Schedule.
Refile. A Carton, File or Item previously retrieved by a customer that is returned to storage at an Iron Mountain facility.
Regular pickup. A service where Items are picked up by Iron Mountain at a customer's location for transport to an Iron Mountain facility. Transportation from your site to Iron Mountain is normally performed using the Regular Pickup service, which typically occurs on a fixed weekly schedule. You can request a Rush Pickup - Business Day service if you have a need for faster pickup.
Regular retrieval charge. The charge associated with the Retrieval of Items in a regular service timeframe.
Regular retrieval - file from carton. The processing of a Retrieval Order for a File from a Carton in a regular service timeframe. Price based on Retrieval of Files from Carton that are individually listed into the Iron Mountain database. Files requested for Retrieval where the files are not individually listed will result in a Individual Listing Fee and possibly a Labor charge for Search Time.
Regular retrieval - file from open shelf. The processing of a Retrieval order for a File from an Open Shelf in a regular service timeframe. Price based on Retrieval of Files from Open Shelf that are individually listed into the Iron Mountain database. Files requested for Retrieval where the files are not listed may result in a Labor charge for Search Time.
Regularly scheduled service. Shredding service that occurs according to a pre-determined schedule.
Relid. Iron Mountain may replace the lid (top) of a Carton when the condition of the lid will not support other Cartons in a standard storage configuration, when the security of the information inside the Carton is no longer intact due to the damage to or construction of the lid, or when the Carton is not safe for transport or handling. Re-lidding charge includes both the price of the replacement lid and Labor, at a rate per carton, to carry out the re-lidding service. Re-lid charge will show on your invoice as a "Relid" plus the charge for a new lid.
Removable media encryption. All customer information released to removable media devices, created within the Iron Mountain's DCS (Document Conversion Services) Imaging Centers, is encrypted.
The encryption solution encrypts the media device using 256-bit Advanced Encryption Standard (AES). The encryption key is emailed to the customer. At the time of use, the customer will be prompted to enter the encryption key to decrypt the media device. This decrypt method does not require the customer to download a decryption application. Removable media consists of CD, DVD, external hard drive or flash drives.
Removal of docs or batches from storage. This is a Professional Services engagement where the customer has provided Iron Mountain with a listing all the meta data associated with the documents they would like deleted and those documents are deleted from the repository.
Retention schedule. Information Management Tool that enables the customer to set a foundation for company-wide compliant record management policies. * View best practice guidelines on corporate records retention policies posted on the Iron Mountain Connect system. * See Hold Code and Record Code.
Retrieval. The processing of a retrieval order and preparing Items for delivery. Includes locating and pulling a Carton or File from storage at an Iron Mountain facility. A Retrieval is considered a temporary removal of an Item from storage. Storage charges continue to be assessed for retrieved Items until they are either destroyed or permanently withdrawn. Any service performed in addition to a Retrieval will be billed as a separate line item on the customer's invoice (e.g., Permanent Withdrawal, Archival Destruction, Secure Shredding, imaging, faxing).
RFID – radio frequency identification – Wireless non-contact use of radio-frequency electromagnetic fields to transfer data. RFID tags come with a chip that has a unique RFID transponder number that represents your customer SKP box number printed on the tag. When conducting an audit, the RFID ready picks up the box number automatically.
Rush delivery - after hours. A service where Iron Mountain delivers Deposits at a customer's facility on an accelerated basis when placement of order is received after 3:00 p.m. or before 8:00 a.m. local time on a Business Day. Delivery service is scheduled for delivery not later than four (4) hours after placement of the Order. This delivery schedule is conditioned on the order's not exceeding forty (40) Items and the delivery destination not exceeding thirty (30) road miles from the Iron Mountain facility. Requests outside of this condition may be considered a Special Transportation Request.
Rush delivery - business day. A service where Iron Mountain delivers Deposits to a customer's facility on an accelerated basis. Items that are ordered not later than 3:00 p.m. on a Business Day are scheduled for delivery not later than three (3) hours of the placement of the Order. If an Order is placed after 3:00 p.m. local time on a Business Day, the Order will be delivered as a "Rush Delivery - After Hours". This delivery schedule is based on the order not exceeding forty (40) Items and the delivery destination not being more than thirty (30) miles from the Iron Mountain facility where the Items are stored.
Rush pickup - business day. A service where Iron Mountain picks up Items at a customer's facility on an accelerated basis, scheduled to be the next Business Day when the Order is placed by 4:00 p.m. local time on a Business Day. This delivery schedule is premised on the Order not exceeding one hundred (100) cubic feet per day per physical customer facility, and the delivery destination not exceeding fifty (50) road miles from the Iron Mountain facility.
Rush retrieval. The act of processing a Retrieval order and preparing Items for a rush level transportation service.
Rush retrieval - file from carton. The processing of a Rush Retrieval order for a File from a Carton in a rush service level timeframe. Price based on Retrieval of Files from Carton that are individually listed into the Iron Mountain database. Files requested for Retrieval where the files are not listed may result in a Labor charge for Search Time.
Rush retrieval - file from open shelf. The processing of a Rush Retrieval order for a File from an Open Shelf in a rush service level timeframe. Price based on Retrieval of Files from Open Shelf that are individually listed into the Iron Mountain database. Files requested for Retrieval where the files are not listed may result in a Labor charge for Search Time.
Rush service. A Service level assigned to a specific order when delivery is required at a faster time than under Regular Service. Specific delivery schedule for Rush Service is set forth in the Pricing Schedule and individual service descriptions in this Glossary.
Search time. The time required to locate a File that is not listed within Iron Mountain's database or when the customer does not know specifically which carton or file they need and they request Iron Mountain employees to search through various deposits. Charges for Search Time are based on Labor time.
Secure media destruction. A secure method for destroying Plastic Media materials to a non recoverable form via high heat processing.
Secure shredding. The service of shredding Paper-based Materials to a destruction standard that renders the materials practicably unreadable or non-reconstructable. Includes collection, transport and processing of Customer materials specified for destruction per the Customer Work Order. Secure Shredding services are performed via Offsite or Onsite destruction method, dependent upon Customer need or preference. Both Offsite and Onsite shredding are performed exclusively for Paper-based Materials.
Security schema. a security schema is defined as a set of rules which allow customers access to the Digital Record Center™ for Images. Permissions resident in a customer's unique security schema may include login in ID and password, which applications they have access to and which permissions they have within an application
Service frequency. The agreed upon number of shredding service events that are scheduled for a Customer Location.
Shipping / outside courier. The service provided when Iron Mountain utilizes a third party shipping carrier to deliver Items on an Order either at the Customer's request or outside the applicable service area. Common carriers utilized by Iron Mountain are UPS, DHL, Federal Express.
Shred contamination fee: The shred contamination fee will be charged only when shred transportation teams identify contamination upon "tipping" customers' shred collection container.
Shredding plant. A secure destruction facility where Paper-based Materials are destroyed using proprietary procedures and commercial grade shredding equipment.
Special projects. A requested Labor service provided to a customer outside the scope of routine services. Some examples include: imaging, file packing and purging, Re-boxing, re-labeling, file room management, drop filing/interfiling, and data correction in the database, etc. Special Projects are quoted by project. The pricing for transactional services listed on the Pricing Schedule may differ when they are part of a special project.
Special transportation request. A delivery, pickup, or other service request that is outside normal service parameters because of time or distance requirements. Iron Mountain will consider Special Transportation Requests where feasible, and will quote prices associated to any such service.
Staged service. Shredding Service collection method where the Customer retrieves Collection Containers from its facility and pre-positions them for shredding service access. All Staging locations must be approved by Iron Mountain in advance of the Customer's first service visit.
Standard archive carton. The preferred and encouraged Carton type for storage with Iron Mountain. The Standard Archive Carton has an intended use to store a half-drawer of letter or legal records, has an external dimension of approximately 16.25" x 12.75" x 10.25". Iron Mountain encourages use of the Standard Archive Carton due to its ability to retain structural integrity during handling, transportation, and long term storage.
Standard delivery. A service where Iron Mountain delivers Items to a Customer Location. Delivery services for deliveries of two boxes and/or ten files or less will be delivered via third party carrier within two Business Days. Deliveries of greater than three boxes and/or eleven files will be delivered on a fixed weekly schedule with the day of week dependent on the postal code assignment of the Customer Location.
Storage. Storage of Customer Deposits at an Iron Mountain facility utilizing Cartons provided by the Customers or in Open Shelf Configuration, based upon Customer program needs.
Storage, LF. Shown on an invoice for the Linear Footage charges for Storage of Files in an open shelf location at an Iron Mountain facility.
Storage, new. Shown on an invoice for the charge for storage of new Deposits at an Iron Mountain facility for the month in which they are received, through the next regular billing cycle.
Storage, regular. Shown on an invoice for the recurring charge for storage of Deposits at an Iron Mountain facility.
Telecommunications fee. This is the fee for a telecommunications line running directly from an Iron Mountain facility to the customer site. Iron Mountain holds the contract for the line and passes the charges through to the customer.
Temporary retrieval. Removal of Items from storage on a temporary basis; i.e., with the expectation that the Items will be returned to storage. Items that are "out" on temporary retrieval continue to be billed for storage, as the storage location is retained for return of the Item.
Transportation service zones. Zones based on the customer's distance from an Iron Mountain facility or the customer's presence in certain metropolitan areas.
Transportation visit. The act of servicing a delivery or pickup at a Customer Location or the act of providing secure shredding services.
Trip charge. The charge that will appear on a Customer invoice to represent service performed for a Transportation Visit. Find out more about special transportation requests and charges here.
Unused container. A container at a customer site that has not been serviced for 90 days.
Unused container charge. A monthly charge imposed on an Unused Container.
User. A named user of the Digital Record Center™ for Images.
Vaccination surcharge: An additional charge imposed on a Transportation Visit when a customer requests service from a vaccinated-only (COVID) driver.
Wait time. A charge assessed in the event that the customer causes delay of the Iron Mountain courier in performing his or her tasks. Wait Time is charged based upon the Labor rates in the customer's Pricing Schedule. Delays can occur due to the shipment not being properly prepared or inability to access elevators or building docks at the customer's site. Billable Wait Time commences 15 minutes after the driver has arrived at the customer site and ceases when the driver can begin performing the service designated for the Transportation Visit.
Web-based raining. Remote training conducted via web-based, distance-learning.
Web Services integration. Iron Mountain offers a package that will enable integration with the Digital Record Center™ for Images. This package includes:
- Digital Record Center™ for Images Web Services API User Documentation
- A Kick-off meeting facilitated by Professional Services where design consultation is provided for the project
- Up to 20 hours of support within an 8 week timeframe
- Configuration of and assistance with a test environment for validating the implementation
- Testing should be completed within the 8 week timeframe
- Support for the test system is available 9:00am to 5:00pm EST
Weekly service. Secure Shredding services performed on a weekly basis at a Customer location
Work order. A written description of services prepared by Iron Mountain in response to Customer's request(s) Work Orders are the source documents for service, billing and invoicing purposes.


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Free Shipping
Free standard shipping.
Estimated to be delivered with in 5-7 days.
Free Shipping
Free Returns
Learn More.
Free Returns
Returns Policy
We make every attempt in Rug Gallery to give you 100% satisfaction with your purchase and want you in the future to come back with us. We maintain a complex refund policy for you if you're not completely satisfied with your order.
Our platform only offers a guideline for the processing times, the average time of available goods (ETA) and delivery times. Rug gallery does not promise time limits for shipping. Any special or consequential charges incurred by the customer due to delay in item delivery is the sole liability of the customer.
Product Description
Attribute | Value |
Product Name: | ADEN AD3370 Rug |
Collection Name: | ADEN |
Style | Contemporary |
Color: | Grey |
Construction Detail | 100% Olefin |
Key Features: | Naturally stain resistant and resists fading & easy to clean |
Group Size | Actual Size |
5x8 | 5.3' x 7.6' |
8x11 | 7.6' x 10.6' |
Customer Reviews


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Sku: MW33057
Availability: Ships in 1 business day.
Cardinal Garden Flag from Breeze Art by Studio M. (Stand not included - see Related Products below)
Breeze Art Premium Decorative Garden Flags are soft and silky with vivid colors and have superior fade and mildew resistance. All Breeze Art Garden Flags are printed and packed in the USA.


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NC_010723:801215 Clostridium botulinum E3 str. Alaska E43, complete genome
Host Lineage: Clostridium botulinum; Clostridium; Clostridiaceae; Clostridiales; Firmicutes; Bacteria
General Information: This strain was probably isolated from salmon eggs associated with a foodborne case of botulism in Alaska, however the exact details are not available. This genus comprises about 150 metabolically diverse species of anaerobes that are ubiquitous in virtually all anoxic habitats where organic compounds are present, including soils, aquatic sediments and the intestinal tracts of animals and humans. This shape is attributed to the presence of endospores that develop under conditions unfavorable for vegetative growth and distend single cells terminally or sub-terminally. Spores germinate under conditions favorable for vegetative growth, such as anaerobiosis and presence of organic substrates. It is believed that present day Mollicutes (Eubacteria) have evolved regressively (i.e., by genome reduction) from gram-positive clostridia-like ancestors with a low GC content in DNA. This organism produces one of the most potent and deadly neurotoxins known, a botulinum toxin that prevents the release of acetylcholine at the neuromuscular junction, thereby inhibiting muscle contraction and causing paralysis. In most cases the diseased person dies of asphyxiation as a result of paralysis of chest muscles involved in breathing. The spores are heat-resistant and can survive in inadequately heated, prepared, or processed foods. Spores germinate under favorable conditions (anaerobiosis and substrate-rich environment) and bacteria start propagating very rapidly, producing the toxin.Botulinum toxin, and C. botulinum cells, has been found in a wide variety of foods, including canned ones. Almost any food that has a high pH (above 4.6) can support growth of the bacterium. Honey is the most common vehicle for infection in infants. Food poisoning through C. botulinum is the most frequent type of infection caused by this bacterium. The wound botulism that occurs when C. botulinum infects an individual via an open wound is much rarer and is very similar to tetanus disease. There are several types of botulinum toxin known (type A through type F), all of them being neurotoxic polypeptides. The most common and widely distributed are strains and serovars of C. botulinum that produce type A toxin.


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De-icing for private jets
Just like a car in winter, a private jet left outside will be exposed to the weather and will become covered in ice and snow.
Unlike a car, it is a legal requirement to remove all ice and snow from a private jet charter aircraft before it takes off.
For more information on de-icing or winter flights call us on 020 7100 6960 (24 hours).
How to de-ice : De-icing costs : Avoiding de-icing costs : FAQs : De-icing Waiver
PrivateFly De-icing Waiver
For your safety, aircraft de-icing is often required during icy and snowy conditions. This is a legal requirement prior to takeoff. In severe winter weather, multiple de-icings may be required.
As it cannot be predicted if and how much de-icing fluid is required prior to a flight, for an on-demand private jet charter the client is liable after the flight for the cost of any ground de-icing required. This is industry standard practice and set out in PrivateFly's Standard Charter Terms. De-icing charges are usually invoiced at cost price after the flight is completed.
For our PrivateFly Jet Card clients, de-icing costs are included in the fixed hourly rates, so there are no additional fees to pay if and when de-icing is required. Find out more about the PrivateFly Jet Card.
For on-demand charter clients, during winter operations, PrivateFly offers a De-Icing Waiver as protection against these potential charges. This is similar to a collision damage waiver for hire cars.
De-Icing Summary
- For on-demand flights, the only extra cost not included in your PrivateFly contract is ground de-icing charges (invoiced to you at cost).
- Actual de-icing costs vary depending on the amount of fluid required. The average de-icing cost for a small jet is 1,000 EUR per application. De-icing larger aircraft can cost over 10,000 EUR.
- The PrivateFly De-Icing Waiver is calculated according to the risk of de-icing and is based on a percentage of the predicted cost. For example, on a small jet the waiver is often just 150 EUR per flight.
- PrivateFly Jet Card clients do not pay additional de-icing costs.
If no De-Icing Waiver is purchased, we require a deferred credit card charge of a minimum 1500 EUR, prior to the flight. If de-icing is then required, we will contact you to arrange payment of the cost price (which may differ from the deferred payment). The deferred payment will be cancelled if no de-icing is required.
To book or for further information on a De-icing Waiver for private flights call 020 7100 6960 (24 hours).
How is a private jet de-iced?
Just like de-icing a car window screen, de-icing fluid is sprayed onto a private jet. This fluid has either (or both) de-icing or anti-icing properties.
At an airport, de-icing fluid is sprayed onto the private jet by a specially designed vehicle called a de-icing rig. Often a first time private jet customer can be alarmed at the amount of fluid sprayed onto the airframe, it is similar to a fireman's hose!
The de-icing fluid is often applied at a high temperature and is normally orange in colour. This helps to indicate where the fluid has been applied.
Private jet customers are often asked to be on-board during the de-icing process. This allows a quick take off after de-icing.
How much does it cost to de-ice a private jet
De-icing fluid is expensive. Each airport will have a cost per litre for de-icing fluid and often there is only one supplier a private jet can use. The average amount of de-icing fluid and cost for a private jet are:
Type | Fluid Quantity | Price per Litre | Total Cost |
Small Propeller | 50 | €5 | €250 |
Small Jet | 200 | €5 | €1000 |
Medium Jet | 300 | €5 | €1500 |
Long Range Jet | 500 | €5 | €2500 |
Regional Airliner | 700 | €5 | €3500 |
Airliner | 2000 | €5 | €10000 |
If you buy the PrivateFly De-icing Waiver, then you don't have to worry about the cost of de-icing the private jet. For full details contact us or call us on 020 7100 6960.
Who pays when a private jet is de-iced?
For an on-demand private jet charter, the charterer of the private jet (the client) is liable for the cost of de-icing a private jet. The cost of de-icing is not included in the price to hire a private jet. If de-icing is required, this will be invoiced at cost price after the flight. The cost of de-icing a private jet can be reduced by buying a De-icing Waiver.
How to avoid paying de-icing costs
Private jet pilots can avoid de-icing by arranging for their aircraft to be kept in a hangar. However at some airports the cost of overnight hangarage can be more expensive than de-icing. Many private jet airports do not have hangar space available and so the aircraft will be exposed to the weather prior to your private jet flight.
A private jet can also be de-iced by using a heated hangar for a shorter period of time before departure. This less expensive option can reduce the cost of de-icing required or prevent it being required. Often placing an aircraft that requires de-icing in a heated hangar for 1 hour, will melt the ice on the airframe completely.
At some private jet airports, passengers can board their private jet inside the hangar before it is towed outside. In winter periods this is a favourite customer service tip used by PrivateFly for our members.
The only way to guarantee you can avoid de-icing costs is to take out a De-icing Waiver.
Top 10 Tips for de-icing a private jet
Ask your private jet supplier the night before your flight if de-icing is expected to be required. This will encourage them to avoid de-icing if possible. See how to avoid paying de-icing costs.
Use a hangar to keep your private jet protected from the weather. A heated hangar can also be used to melt any ice that has formed.
Encourage your crew to remove layers of snow with a brush from the wings before the aircraft is de-iced. This will reduce the amount of de-icing fluid required. This manual procedure is only possible on small aircraft. On large private jets it is not safe to remove snow from the wings.
Keep your private jet crew informed of when you will be arriving at the FBO (private jet terminal) This will allow the de-icing to be timed just before your arrival and prevent it being required twice
Ask to be on-board your private jet when it is de-iced. This will allow your aircraft to depart immediately after the anti-icing fluid is applied. A further de-ice may be required if your departure is delayed.
Never argue against a de-ice. Your private jet Captain is making a safety decision and should not be influenced by financial or time considerations.
Ask your crew how much de-icing fluid is expected to be used before the de-ice starts. This will encourage the crew to remove excess snow from the airframe with a brush to reduce the amount of fluid required. In larger aircraft this will not be possible.
If you have a choice of airports, ask for the price of de-icing fluid at each FBO (Private Jet Terminal). Depart from the private jet airport that offers the lowest price of de-icing fluid. Often the de-ice is more expensive than the landing and handling fees.
If possible avoid flying very early in the morning. Often the sun will melt any thin layers of ice on a private aircraft.
Buy a De-icing Waiver from PrivateFly before your private jet flight call us on 020 7100 6960 or contact us.
Private Jet de-icing - FAQs
Why must a private jet be de-iced?
It is a legal requirement for all ice and snow to be removed from an aircraft before it departs from an airport.
- Snow and ice on a wing reduce its lift performance. Even the thinnest layer of frost affects how an aircraft flies.
- Snow and ice are heavy. The extra weight of snow and ice on a private jet will reduce its flight performance.
- The controls of a private jet (ailerons, elevators and rudders) can be blocked by ice and full movement may not be possible
What is the fluid used to de-ice a private jet?
A private jet is sprayed with a heated fluid that has two purposes:
- To de-ice, melt and remove existing snow and ice from the airframe.
- To form a protective layer that stops new ice forming, for a period known as the 'hold over time'.
Do all airports have de-icing fluid available?
In particularly cold winters, de-icing fluid can be limited at some airports. Some smaller airfields may not have de-icing fluids. At larger airports, and especially at peak airline departure times, there may be a long delay before your aircraft is de-iced.
Which countries are excellent at de-icing private jets?
German and Scandinavian airports are experts in de-icing procedures for private jets.
What does de-icing fluid smell like?
A private jet passenger should never come into contact with de-icing fluid. It is a dangerous chemical that can cause skin irritations and other medical problems. It does however smell of almonds!
For more information on de-icing or winter flights call us on 020 7100 6960 (24 hours).


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Sunday Scroungers
August 25 @ 9:30 am - 12:30 pm
An event every week that begins at 9:30 am on Sunday, repeating indefinitely
Dress: Mufti
(Everyone welcome, no experience necessary)
9.45am Corner to Corner.
Please be at the club no later than 9:30am to register.
$10 per person includes 14 ends, corner to corner, tee to tee.


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Discreet Bail Bonds in California
The last thing you want is to make a big deal out of the fact you've been arrested. Ideally, you'd like to get through the entire process without anyone, including your family, friends, employers, and co-workers learning anything about it.
While we can't promise that no one will notice your name if they scan the list of recent local court news, we can assure you, that when you turn to Bulldog Bail Bonds in Fresno, they won't learn anything from us. We take a great deal of pride in our outstanding reputation for being California's most discrete bail bonds services.
When you contact us, we won't contact everyone you know, asking for references and co-signers. The only people we'll discuss the case with are you and anyone you have assigned to stand in as your co-signer should you need one.
How We Handle Bail Bonds
The first step in the California bail bonds process is you reaching out to us. We offer two different ways you can contact us. You can give us a call or you can chat online. Either way, you'll immediately reach one of our highly experienced bail bonds experts who will provide you with a totally free consultation. We're open 24/7 so free to get in touch with us at your convenience.
If you decide to use our services, we'll have you sign a contract. Unless we require a co-signer, the only people who will know the terms of the contract are yourself and us.
Once the contract is signed and you've paid the 10% fee, we'll spring into action and post your bail. The process usually goes pretty quickly, though there are sometimes hold-ups with the paperwork when we're at the jail trying to post your bail. We move quickly, but we can't always persuade the officer handling matters to do the same, especially if they're extremely busy.
Benefits of Bulldog Bail Bonds in Fresno
Our reputation for discretion is just one of the reasons you should consider using Bulldog Bail Bonds in Fresno for your California bail needs. Other reasons include:
✅ 0% Interest payment plans
✅ 20% Discount
✅ Phone approvals
✅ No hidden fees
✅ No collateral for working signers
✅ Outstanding customer service


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Melissa and Doug Truck Crayon Set
Children can develop their creative abilities at "top speed" with this truck-shaped travel case full of colorful cargo! The triangular, non-roll design makes the 12 durable crayons easy to organize and a pleasure to use. The flip-top case makes this crayon set ideal for imaginations on the go. Part of Melissa & Doug's Art Essentials for kids--a thoughtful line of art supplies designed by parents and beloved by creative kids.
Durable non-roll crayons
12 vibrant colors
Triangular shape promotes writing-readiness skills
Handy flip-top case with handle
Perfect for art on the go!
Age Groups:
- Recommended for ages 3+


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- Information
- Resources
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Product information
Almond Opal Rods 6mm 3 pk - System 96
Pack of 3 System 96 fusing glass compatible rods for use in Glass fusing work, warm glass, casting and bead making.
Uroboros' System 96 Rods are machine pulled from System 96 tested compatible glass formulations. As a result they are straighter and more consistently round than hand pulled rods. Beadmakers who use System 96 rods love the low shock factor of these rods, which results in a safer torchworking experience.
- Pack of 3
- Each rod is 6mm diameter
- Approx. 50cm long
- COE 96 compatible with all System 96 fusing glass
The left bead in the image shows this colour rod wrapped with clear, and then added as dots to the outside of the clear. The right bead in the same image shows this colour rod spiraled onto white opal.
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