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THE SQL Server Blog Spot on the Web
I spent three days in Seattle at Microsoft Data Insight Summit, delivering a preconference day about data modeling for business users, and two workshops about DAX. All of these were sold-out, so even if you attended the conference, you might be interested in content that cover the same topics. And if you were in one of my sessions, you probably would like to get material to study. For this reason, I think it is useful to provide a complete recap here:
You do not have any excuse now: you can get some lecture and then make practice!
In the next three weeks I will deliver many speeches and training in United States. Here is the list:
The Microsoft Data Insight Summit is completely sold-out, so if you didn't register more than one month ago, you will not be able to attend. Luckily, several sessions will be broadcasted live, and others will be recorded (slides and audio, no video) and made available a few weeks after the conference.
There are still seats available for the workshops in Minneapolis and Chicago. If you want to attend to the user group session on June 19 organized by the Microsoft BI User Group Minnesota, take a look at the entire program for the afternoon (there is another presentation delivered by Dan English before mine). The community event is free but you have to register in advance (seats are limited there, too!).
Is there a secret recipe to create a beautiful dashboard? Why do we need to use "beautiful"? Would "useful" be a better adjective?
If you think for a moment, you know that we are attracted by the "beautiful". It is important for a first impression, but after that, you want to discover more, and at that point other factors are considered. I usually work in many of these "other factors". The data model, the accuracy of the numbers, the performances that could affect usability. You need all of this, and as "technical" people (data analysts and BI developers are both classified as "technicians"), too often we simply underestimate the importance and the efforts required to work on the "last mile" of a BI system.
I always tried to understand the "secret recipe" of a good dashboard. I'm not talking about a Power BI dashboard now, I'm considering the broader definition of a dashboard that you might implement in a single page of a Power BI report. Is it the graphic? The colors? The numbers? The position? The alignment?
I've never been a good dashboard designer, but over time I understood how to recognize dashboards that were not working well. However, fixing them is not easy. When I started working with Daniele Perilli, I've seen that "beautiful" was not enough. A dashboard is a communication tool, and there is a huge bibliography of what you should do and what you should not. Personally, I think that Stephen Few created the best reference for this industry, and if you spend 40 hours a week designing charts, reports, and dashboards, you should stop reading this post now, and spend your time reading his books.
However, I don't spend all the time designing dashboards, but that "last mile" is what communicate to end users the result of a large effort made by DBAs, developers, and data analysts. It is also the way to show the result of a one-man-show model you worked on for hours, days, or weeks. Where to start? How to apply rules that are easy to learn and to implement, in a limited amount of time?
I struggled with these questions for years. Working with Daniele, we started to think about a learning path that would be accessible to anyone, providing the basic principles that you need to apply in a good dashboard in a very direct and practical way. This initial idea started to grow, and it was one of the reasons we invested in the custom visuals that you can find on okviz website. Sometimes we didn't have the right tool to implement a few principles required for a good dashboard, so we (well, Daniele) created it.
Nevertheless, custom visuals are not the solution. They could be a tool, a useful and necessary tool, but you can always obtain a good dashboard with a very limited set of features (just think about Power BI Desktop in October 2015, if you tried it at that time). How is it possible? The reason is very simple. You can have a very powerful tool, but just because you have a feature, it doesn't mean you need it or you have to use it. You have to make choices, and you need a plan.
Thus, Daniele created the master plan of the 15 rules that all of us can apply to any dashboard to obtain a beautiful and useful result. When I say "all of us", I include myself. I don't have 1% of Daniele's design skills, but I can apply his 15 rules to my reports, and after "the cure", my reports are always much better. Not as beautiful as those designed by Daniele, but "good enough" to be shown to a larger audience without hearing people laughing or being distracted by useless details. They see the numbers, the trends, and get the message from the dashboard. This is the goal, after all.
Once Daniele completed this huge amount of work, we faced the issue of delivering such a content. After considering many other options, our decision has been to learn the content from Daniele, make practice, and record a video training using slides and demos prepared by him. This helped us (me and Alberto Ferrari) using a language that is probably less "technical" than what we typically use in our DAX classes, and this was another goal of our choice.
The third of the 15 rules is "keep it simple". Here is how a dashboard looks like before and after implementing this rule (please note there are other 12 rules to apply after that to complete "the cure").
Applying the rules could be harder than you think. You can watch the videos related to the "keep it simple" rule in the free preview of the Power BI Dashboard Design Video Course.
The course also includes a reference that will help you choosing the right chart, and you can find a complete description of the course content here.
It has been a very long journey, but I am really proud of this work. Now, I really look forward to read feedbacks and reviews!
In the last few years, I and Alberto Ferrari assisted many users of Power Pivot and Power BI who wanted to create their reports using these tools and were struggling with getting the desired numbers from their data. The first approach of an Excel user is to look for a function, or a more complex DAX expression, that can implement the calculation required. In a similar way, this is also the approach of Power BI users that don't have a database design background.
Several times, before looking for a correct DAX expression, the problem is defining the correct data model, putting data in the right tables, and creating the proper relationships. The tools to create queries in M (Power Query in Excel, now called Get Data, and the Query Editor in Power BI) are wonderful to help the users in doing the proper massage to the data. However, these tools cannot help users that don't know how to properly define the right data model. For this reason, when we thought to a new version of the book about Power Pivot (we previously wrote one for Excel 2013/2016 and one for Excel 2010) we considered that the DAX chapters were no longer necessary (because we have The Definitive Guide to DAX for that), so we decided to dedicate an entire book to the data modeling, targeted to business users that usually do not have such a skill, or that learned that by trial and errors and without a more structured approach.
Because the concepts are identical for Power Pivot and Power BI, we wrote a single book that target both products: Analyzing Data with Power BI and Power Pivot for Excel. Several examples are created in Power BI Desktop (because it is free and available to anyone), but certain reports are created using pivot tables in Excel. The goal is to teach the concepts, rather than providing formulas to copy and paste. In fact, the reader should try to apply the same ideas to its own data, recognizing the data modeling patterns described in the chapters of the book.
We tried to minimize the use of theoretical terms, trying to introduce with very practical examples and design patterns the terminology that is commonly used in data modeling (such as fact, dimensions, normalization, denormalization, star schema, snowflake schema, and so on). Thus, is this book for you?
At this point, you might wonder why we did not include the words "data modeling" in the book title. The simple answer could be "marketing", but the real answer is more complex. A business user hardly recognizes the need of data modeling skills. He/she just want to obtain a result, analyzing its own data. He/she might think that data modeling is something for DBAs, and is not related to reporting. However, when you create a report in Power Pivot and Power BI, usually you are creating a data model (unless you create a live connection to an existing Analysis Services database). For this reason, the title should help these users to find this book and to take look at its description. Yes, it is a book for data modeling targeted to readers that do not know that they need such a skill. Thus, the reason for this title is "communication": We hope that the book description is clear enough to avoid any misunderstanding!
This is the table of contents of the book:
Remember, when the data model is correct, the DAX code you need is simpler, shorter, and more efficient. The first rule is always "keep it simple", and a good data model is always the first step in the right direction!
If you run SSAS Tabular or Power BI, please help me in gathering some performance data for a study I'm doing about Formula Engine performance following these steps:
1) Copy the PowerShell code below in a script Benchmark.ps1, modifying serverName, ssasInstanceName and databaseName so that they point to an existing database of an instance of Analysis Services Tabular. If you use a default instance, assign an empty string to ssasInstanceName. Any database that exists is valid. If you want to connect to Power BI, retrieve the address using DAX Studio and write the complete address in the serverName variable (e.g. "localhost:1234")
2) Run the PowerShell code redirecting the output to a Benchmark.json file (eg. Test.ps1 > Benchmark.json)
3) Send me the JSON file in a mail (even copy/paste the content is ok) to marco (at) sqlbi [dot] com
Thanks in advance for your help!
## Specify the name of the server where SSAS runs$serverName = "."
## Specify the instance name of SSAS Tabular (use empty string to use the default instance)$ssasInstance = "TAB16"
#Specify any database available on SSAS Tabular (the database must exists, no data is read from there)$databaseName = "Contoso"
## End of parameters
if ($ssasInstance.Length -ne 0) { $ssasInstance = "\" + $ssasInstance}
$null = [System.Reflection.Assembly]::LoadWithPartialName("Microsoft.AnalysisServices")
$server = New-Object Microsoft.AnalysisServices.Server$server.Connect("$serverName$ssasInstance")
$null = [System.Reflection.Assembly]::LoadWithPartialName("Microsoft.AnalysisServices.AdomdClient")
$connStr = "data source=$serverName$ssasInstance;Initial catalog = $databaseName"[Microsoft.AnalysisServices.adomdclient.adomdconnection]$cnn = new-object Microsoft.AnalysisServices.adomdclient.adomdconnection($connStr)$cmd = new-Object Microsoft.AnalysisServices.AdomdClient.AdomdCommand$cmd.Connection = $cnn$cmd.CommandText = @"EVALUATEROW ( "x", COUNTROWS ( CROSSJOIN ( SELECTCOLUMNS ( CALENDAR ( 1, 10000 ), "Num1", INT([Date]) ), SELECTCOLUMNS ( CALENDAR ( 1, 10000 ), "Num2", INT([Date]) ) ) ))"@
$cnn.Open()$sw1 = New-Object Diagnostics.Stopwatch$sw2 = New-Object Diagnostics.Stopwatch$sw3 = New-Object Diagnostics.Stopwatch$sw1.Start()$dr = $cmd.ExecuteReader()$sw1.Stop()
$sw2.Start()$dr = $cmd.ExecuteReader()$sw2.Stop()$dr.Close()
$sw3.Start()$dr = $cmd.ExecuteReader()$sw3.Stop()$dr.Close()
$colItems = Get-WmiObject -class "Win32_Processor" -namespace "root/CIMV2" -computername $serverName
$s_version = $server.Version$run1 = $sw1.Elapsed$run2 = $sw2.Elapsed$run3 = $sw3.Elapsed
foreach ($objItem in $colItems) { $cpuId = $objItem.DeviceID $cpuModel = $objItem.Name $cpuCores = $objItem.NumberOfCores $cpuMaxSpeed = $objItem.MaxClockSpeed $cpuCurrentSpeed = $objItem.CurrentClockSpeed $cpuStatus = $objItem.Status
## We only consider the first CPU break}Write-output "{"Write-output " ""Benchmark"": ""FE CROSSJOIN 10k*10k"","Write-output " ""SSAS_Version"": ""$s_version"","
Write-output " ""Run_1"": ""$run1"","Write-output " ""Run_2"": ""$run2"","Write-output " ""Run_3"": ""$run3""," Write-output " ""CPU_ID"": ""$cpuId"","Write-output " ""CPU_Model"": ""$cpuModel"","Write-output " ""CPU_Cores"": ""$cpuCores"","Write-output " ""CPU_MaxSpeed"": ""$cpuMaxSpeed"","Write-output " ""CPU_CurrentSpeed"": ""$cpuCurrentSpeed"","Write-output " ""CPU_Status"": ""$cpuStatus"""Write-output "}"
I recently wrote an article about duplicated names in DAX to highlight a possible case where you can have a measure with the same name of a column. This is not a good idea, mainly because the syntax to reference a measure and a column is identical, and this similarity is the root of a number of issues. It would be much better if the measure reference had a syntax different from the column reference.
The syntax table[identifier] could identify either a column or a measure, and there is no secure way to understand that by just reading the code (unless you look at the color highlighting). I thought to a list of techniques to disambiguate that syntax, and the only available one is to modify the measure reference (not the column reference) in one of these ways:
If it was possible to refactor the DAX syntax, I would use a different syntax for a measure reference:
It's probably too late for a similar discussion, but you never know.
Feedbacks are welcome.
Last year I and Alberto Ferrari wrote a new edition of the Analysis Services Tabular book, which official title is Tabular Modeling in SQL Server Analysis Services (2nd Edition). Some delay in editing and production delayed the printing, but the book is finally available, so I can present it with a dedicated blog post, explaining its goals and contents, so you can evaluate whether it could be interesting for you.
First of all, this is the second edition of the book we wrote with Chris Webb a few years ago for Analysis Services 2012. Several chapters did not require particular changes, and even if we updated many parts of these chapters to the new version, a great thank you goes to Chris, who allowed us to base our work on his content, too. For an unfortunate series of events and crossing edits, we didn't include Chris in the acknowledgments of the book, so this blog post is just a first attempt to publicly address that – forgive me, Chris!
Compared with the previous edition, we removed all the chapters about DAX, keeping only a small introduction of this language, because we have an entire book dedicated to it! This choice provided us additional time to cover other topics in more detail. We covered only the compatibility level 1200 in this new book, so if you have to develop a model using the model 1100 or 1103, you probably will find a better reference in the previous edition.
This is the list of the chapters of this book, followed by a small comment of the main changes made in this edition.
CHAPTER 1 Introducing the tabular model CHAPTER 2 Getting started with the tabular model CHAPTER 3 Loading data inside Tabular CHAPTER 4 Introducing calculations in DAX CHAPTER 5 Building hierarchies CHAPTER 6 Data modeling in Tabular CHAPTER 7 Tabular Model Scripting Language (TMSL) CHAPTER 8 The tabular presentation layer CHAPTER 9 Using DirectQuery CHAPTER 10 Security CHAPTER 11 Processing and partitioning tabular models CHAPTER 12 Inside VertiPaq CHAPTER 13 Interfacing with Tabular CHAPTER 14 Monitoring and tuning a Tabular service CHAPTER 15 Optimizing tabular models CHAPTER 16 Choosing hardware and virtualization
The introduction has the same structure of the first chapter in the previous edition, and it covers the architecture of Analysis Services Tabular and its role in the Microsoft BI platform. Power BI was not even in the plans when we wrote the first edition, so you can imagine this was not just a simple search and replace of version numbers, many things changed since 2012. The chapter 4 is similar to the DAX Basics of the previous edition, but we updated a few details about the syntax, providing the foundation to write very simple calculations. This is the only chapter dedicated to DAX in this new book.
The chapter 5 about hierarchies and the chapter 6 about data modeling were present also in the previous edition. The chapter 7 is brand new chapter about the Tabular Scripting Model Language. Beside the technical information, I think that this chapter is useful to clarify the role of TMSL in the new compatibility level (1200). The chapter 8 has been updated considering Power BI as the main client tool for Tabular, instead of Power View (which was the reference in 2012).
The chapter 9 about DirectQuery has been completely rewritten, and we also wrote a separate whitepaper about DirectQuery that complements the content of this chapter in the book.
The chapters 10-11-12 have been updated (considering also relationships with bidirectional filters) describing security, processing, partitioning, and VertiPaq internals. In particular, the VertiPaq chapter has many more details that will help you also in troubleshooting and optimization.
The chapter 13 is another chapter that has been written from scratch, because the previous APIs are no longer available, and a new (better but different) one is now the reference library for PowerShell and C# scripts.
The chapter 14 is an update of the chapter present also in the previous edition, whereas chapters 15 and 16 provide more space to topics (optimization of large structure and hardware/virtualization selections) that were cited in the previous edition very shortly, without the insights that you will find in this edition
I hope you will enjoy this book!
More than one year ago, I and Alberto Ferrari started to work on DirectQuery, exploring the new implementation appeared in Analysis Services 2016 for the Tabular models, which uses the same engine of Power BI. The previous version of DirectQuery appeared in SSAS Tabular 2012 was very slow and suffered of performance issues also using small models. We have never seen it adopted in a production environment, it was very hard to optimize, and there were too many restrictions to the data model (for example, no MDX queries = no PivotTable in Excel as a client, and no time intelligence = complex DAX code to write even for trivial calculations).
For these reasons, when we worked on Analysis Services 2016 and Power BI, we wanted to push the new version of DirectQuery, which solved many of the limitations of the first implementation, discovering its limits and where it could be used in a real production environment. The results of our efforts is now available in a white paper published by Microsoft: DirectQuery in Analysis Services 2016. The document contains a detailed step-by-step guide to implement DirectQuery in your model, and then investigate on how DirectQuery transforms a DAX or MDX query into one or more query in SQL. If you want a quick answer to the question "should we use DirectQuery?", then the answer is…. it depends!
You have to set your expectations to a correct level. You can use DirectQuery successfully in certain conditions (database size, frequency of refresh, capacity and performance of your RDBMS server). You certainly have to test the particular workload you want to apply to DirectQuery. The best suggestion is: try and measure it. The whitepaper will provide you many hints about what you should try and what you should expect. Specific measures we made could be different today, because there are often new release of this technology, and we can expect more improvements going forward. But you can certainly start to spend time testing it, and if you understand how it work, you can figure out what are the scenarios where you might want to adopt it.
The white paper can be used also for Power BI: the engine is the same, we will probably see many improvements very soon, and maybe that the version of the engine you are using is already much better than the ones we used writing the whitepaper. However, the basic concepts are the same, and you can see and measure the improvements by repeating the same tests we described in the document.
Out of the records, and in a very informal way, I would like to highlight that you should try DirectQuery with a correct attitude. Don't expect the magic box, and don't assume it will be bad, it could surprise you in both cases! Most important, think about why you might want to use this technology.
The right reasons for using DirectQuery are:
There are also two very wrong reasons for choosing DirectQuery:
I don't want to scare you. I just want to set the right level of expectations. With that in mind, you can implement successful projects using DirectQuery today.
One of the side effects that you might not expect from a table filter in a CALCULATE statement is the removal of an existing filter. In reality, this is a condition that happens when you write an explicit CALCULATE in a row context, because the table filter overrides the context transition in case you filter the same table that generated the context transition.
In practice, the following query return the same value for every color, ignoring the context transition for Product[Color]:
ADDCOLUMNS ( VALUES ( Product[Color] ), "Sales", CALCULATE( [Average Sales Amount], FILTER ( Sales, Sales[Quantity] > 3 ) ) )
Sales Amount],
Sales, Sales[Quantity] > 3
If you know the context transition and you think this is strange, Alberto wrote a good article explaining this particular situation: read Context Transition and Expanded Tables on SQLBI web site!
I recently wrote an article to described in more details the syntax of the IN operator in DAX, which is available in Power BI and in the CTP of the next release of Analysis Services.
Jeffrey Wang wrote a blog post about the IN operator a few months ago, so I waited some time to include more details about how it internally works and how to use it when there are two or more columns involved.
If you just need the simpler syntax for a single column, well, now instead of an infinite list of OR condition, you can write:
table[column] IN { 1, 3, 5, 7, 11 }
However, if you want to understand the implications of the new syntax of the IN operator and of the related table and row constructors, then I suggest you to read this article carefully!
One year ago I wrote an article describing how the time intelligence DAX functions work in Power BI (I just updated the article including the example described below). In a recent event in Sydney I observed a strange (or at least unexpected) behavior of Power BI during a demo, and I think it is interesting to share a few considerations about it.
As you can read in the article linked above, I wrote that when you apply a filter to the date column of a table, and this column is used in a relationship, then the table is considered a Date table even if it is not marked as a Date table (you cannot do that in Power BI). During my demo, the Date table had two relationships: one with Sales using an integer column, and one with Purchases using a date column. My expectation was that when I was using a calculation involving only Date and Sales tables, a time intelligence filter would have required an ALL ( Date ) condition in the CALCULATE statement, but this was not the case. Let me show this with an example.
Consider this formula:
SalesYTD1 := CALCULATE ( SUM ( Sales[Amount] ), DATESYTD ( 'Date'[Date] ) )
If the relationship between Sales and Date is made using an integer column in Power BI, I would expect the formula to not provide any YTD calculation unless I rewrite it as:
SalesYTD2 := CALCULATE ( SUM ( Sales[Amount] ), DATESYTD ( 'Date'[Date] ), ALL ( 'Date' ) )
However, SalesYTD1 was working during my demo, despite I had a relationship based on a DateKey column that was an integer value (such as 20170222). After some investigation, I realized that the other relationship connecting Purchases and Date, even if not used in the two measures above, was enough to consider the Date column as a primary key of the Date table, and this produces an automatic addition of the ALL ( 'Date' ) function in the CALCULATE statement when a filter is applied to 'Date'[Date].
Thus, the lesson is that even if you cannot have Mark as Date table in Power BI, you can obtain the same result by creating an hidden empty table with a column Date in the data model, creating a 1-to-many relationship between Date[Date] and the table you created. For example, you can use MarkAsDateTable=FILTER(CALENDARAUTO(),FALSE) to obtain such a table and then create the relationship and hide the table.
You can download a working version of this example in the updated demo of the article Time Intelligence in Power BI Desktop
One of the latest addition to DAX has been the ability to apply an external filter context to SUMMARIZECOLUMNS, which makes this function a perfect replacement for SUMMARIZE in DAX. In The Definitive Guide to DAX I introduced such a function, but at the time of writing it was used only by Power BI and it wasn't possible to include it in a measure, because of the limitation that now has been removed. Using SUMMARIZECOLUMNS you can write:
SUMMARIZECOLUMNS ( 'Date'[Calendar Year], 'Product'[Color], "Sales Amount", SUMX ( Sales, Sales[Quantity] * Sales[Unit Price] ) )
"Sales Amount"
Sales, Sales[Quantity] * Sales[Unit Price]
instead of:
SUMMARIZE ( Sales, 'Date'[Calendar Year], 'Product'[Color], "Sales Amount", SUMX ( Sales, Sales[Quantity] * Sales[Unit Price] ) )
SUMMARIZE ( Sales,
A more complete description of this new function is available in the Introducing SUMMARIZECOLUMNS article I wrote on SQLBI.
When you use the time intelligence functions in DAX, it is relatively easy to filter the same dates selection in the previous year by using the SAMEPERIODLASTYEAR or DATEADD functions. However, if you follow the best practices, it is likely that you have a full date table for the current year, which includes many days in the future. If you are in the middle of March 2017, you have sales data until March 15, 2017, so you might want to compare such a month with the same number of days in 2016. And the same when you compare the Q1, or the entire year.
A common solution is to translate this request in a month-to-date (MTD) or quarter-to-date (QTD) comparison, but depending on how you implement this, you might not obtain a reliable result. For example, you might assume that the current date on your PC is the boundary of the dates you want to consider, but you probably have a few hours if not days of latency in the data in your database, so you should constantly fix the offset between the current day and the last day available in your data.
Thus, why not simply relying on the data you have to make an automatic decision? This is the purpose of the technique described in the article Compare equivalent periods in DAX that I wrote on SQLBI, where I show several approaches optimized for Power BI, Excel, and SSAS Tabular, which are different depending on the version you use.
Personally, the version I prefer is the one with the variables in DAX:
[PY Last Day Selection] := VAR LastDaySelection = LASTNONBLANK ( 'Date'[Date], [Sales Amount] ) VAR CurrentRange = DATESBETWEEN ( 'Date'[Date], MIN ( 'Date'[Date] ), LastDaySelection ) VAR PreviousRange = SAMEPERIODLASTYEAR ( CurrentRange ) RETURN IF ( LastDaySelection > MIN ( 'Date'[Date] ), CALCULATE ( [Sales Amount], PreviousRange ) ) It's longer, but much more readable. More details and examples you can download for Excel and Power BI are available in the article.
[PY Last
Selection] :=
LastDaySelection =
], [Sales Amount]
CurrentRange =
, LastDaySelection
PreviousRange =
LastDaySelection >
[Sales Amount], PreviousRange
If you work with Power Pivot, Power BI, or Analysis Services Tabular, you probably already know VertiPaq Analyzer. If you never used it, with the new release 1.7 you have another couple of very good reason to try it.
VertiPaq Analyzer is a Power Pivot workbook that extracts information from data management views (DMVs) of a Tabular model. It can be connected to a SSAS Tabular or a Power BI database. If you have a Power Pivot data model, just import it in Power BI and extracts the information from there. You will quickly see what are the tables and columns that are more expensive, and you will have all the details about cardinality of columns and tables at a glance. This is of paramount importance when you have to optimize a DAX expression. However, these are the features we already had in VertiPaq Analyzer. What's new in this release?
This tool is amazing to do remote troubleshooting, or even just to document what are the DAX formulas you have in a model at a given point in time. It saves me hours every week. I hope it will be useful to you, too!
If you think that the end of the year would have been a quiet time because the Power BI team will skip their monthly release (but are we sure?), you might be reassured (or disappointed depending on your perspective) looking at the news I have in this blog post.
SSAS Tabular vNext CTP 1.1 In 2017 Microsoft will release a new version of Analysis Services. We already have a preview (CTP 1.1) that has been released last week. Christian Wade wrote a nice blog post about what's new in this preview (and Christian Wade donated the BISM Normalizer to the community as an open source tool – yes, it's free now!). We have a new compatibility level (1400) and I will not repeat the list of new features. But there is M integrated in SSAS Tabular. And you have full control over drillthrough. I waited for this feature since 2005, for Multidimensional. Next year we'll have full drillthrough control in Tabular. This will make Multidimensional developers jealous, I know.
If you want to test these features, you don't have to install the entire SQL Server vNext: just create a virtual machine and download SQL Server Data Tools for CTP 1.1. You can test everything using the integrated workspace. (don't use this release on your workstation or to create "real projects", it's unsupported and might have issues).
DAX Studio 2.6 If you use DAX, you probably use DAX Studio. If you don't, you have to! Darren Gosbell just announced the 2.6 release. If you use the Server Timings pane or you analyze the DAX queries generated by Power BI, this version has a big improvement, because it manages multiple results (you can execute multiple EVALUATE statements in a single operation). In any case, you should install this version because of the many bug fixes and the improved stability. For example, if you modify the model in Power BI (or SSAS Tabular, or Power Pivot), the existing connection in DAX Studio will nicely manage the changes, showing you the updated metadata automatically. And without crashing (I know, there were some bug changing metadata before…).
Updated Custom Visuals for Power BI from OkViz A few months ago, Microsoft released a new API for custom visuals in Power BI. This API will solve many stability issues, creating a shield for custom visuals that will not be impacted by changes in the underlying Power BI platform (something that happened at least once a month this year). The new API was not complete at the beginning, but in the latest version (1.3) it reached a level that allows to release a new version of the OkViz components based on the new APIs without losing existing features.
In the meantime, we also added new features to the components and if you use one or more of these custom visuals, I strongly suggest you to consider upgrading them in your reports. The price to pay is that this time the update requires a manual activity. The automatic upgrade provided by the Power BI Gallery would have broke too many reports, because we modified (and improved) the behavior of certain properties, following the many suggestions and feedback received. Breaking compatibility is an exceptional event and we don't want this to happen in the future, but we considered that this time it was a necessary operation. This choice is also conservative, because all the existing reports will continue to work. But you should consider that sooner or later Microsoft will remove support for "legacy" custom visuals, based on the old APIs. Thus, don't be in a hurry trying to upgrade. Take your time, try the new features, but make a plan for an upgrade path of your reports.
Ok, here is the list. It's big. Every component has an article describing the new features. We will provide demo videos later, but we didn't want to wait more before releasing them!
DAX and SSAS Training worldwide As you know, I and Alberto Ferrari deliver training on DAX, Power BI, Power Pivot, and SSAS Tabular. We try to organize courses where there is a demand (we receive many suggestions for cities we should visit), and I'm happy to announce that in 2017 we will deliver several workshops in United States, and we already open registration for dates in Australia and Europe.
Workshops in Australia I will be in Sydney and Melbourne between February and March 2017, delivering the workshops SSAS Tabular (Feb 20-21), Mastering DAX (Feb 22-24 and Feb 27-Mar 1), and Optimizing DAX (Mar 2-3). If you want to see more details, visit our page for training in Australia. If you are interested in some of these dates, hurry up and get the early bird discount before it's too late!
Workshops in United States The first dates are Seattle, WA (March 27-31, 2017 - Mastering DAX + Optimizing DAX) and Houston, TX (April 3-7, 2017 – SSAS Tabular + Mastering DAX). Registrations will open in January, and in the next few weeks we will announce workshops in other cities. If you are interested, vote your favorite city helping us to prioritize them. If you want to receive promptly notification about future dates, register to our newsletter.
Workshops in Europe Finally, we will continue to deliver our workshops in Europe, too: London, Amsterdam, Vienna, and Copenhagen are the dates already published in the first semester (visit links for more details and for registrations). We might also add some other city in January (Paris, Helsinki, and another city are under evaluation).


text:
Vegetable Yearbook, USDA/ERSEntry ID: USDA_89011
Abstract: To provide statistics on the U.S. vegetable industry.
"Vegetable Yearbook" includes data on production, acreage, value,
prices, imports, exports, per capita use, and beginning stocks for
major fresh market and processed vegetables; also includes data for
potatoes, sweet potatoes, dry beans and peas, and fresh and processed
Related URL
Geographic Coverage
Spatial coordinates
Data Set Citation
Dataset Title: Vegetable Yearbook
Data Presentation Form: onlineOnline Resource: http://usda.mannlib.cornell.edu/data-sets/specialty/89011/
Temporal Coverage
Start Date: 1971-07-01
Location Keywords
Science Keywords
Access Constraints The data are public domain and access is free and open to all.
Ancillary Keywords
Data Set Progress
Originating Center
Data Center
Distribution Media: online
Distribution Format: text, spreadsheets
Fees: None
Role: INVESTIGATOR
Phone: 202-219-0117
Fax: 202-501-6782
Email: glucier at econ.ag.gov
Contact Address:
USDA Economic Research Service Commercial Agriculture Division (CAD) 1301 New York Avenue, NW, Room 837B
City: Washington
Province or State: DC
Postal Code: 20005-4788
Country: USA
Extended Metadata Properties
(Click to view more)
Creation and Review Dates
DIF Creation Date: 1997-06-27
Last DIF Revision Date: 2017-08-24

subset: commoncrawl


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Ticket Giveaway: Akron/Family at the Echo
Filed under: Giveaways | Posted by: jody
We are giving away three pairs of tickets to see When You Awake Presents Akron/Family live on Wednesday, April 6th at the Echo with Delicate Steve supporting. To win tix to the all ages show, e-mail whenyouawakela @ gmail.com (subject: Akron/Family) and tell us what your ultimate dream song for Akron/Family to cover would be. Include your first and last name in the email. Three winners will be chosen and contacted.


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Today's Devotional
A Prayer for Powerless Moments - Your Daily Prayer - February 19, 2018
Top Music Videos
Christian Playlists
A collection of Christian music for different moments in life to encourage and grow your faith
- MercyMe - I Can Only Imagine - MercyMe Music
- Chris Tomlin - Good Good Father (Lyrics) - Chris Tomlin
- Hillsong United Performs 'Oceans' On The Sea Of Galilee - Hillsong United
- Mandisa - Overcomer (Official Music Video) - Mandisa .
- 'Holy Spirit' – Emotional Francesca Battistelli Will MOVE You - Francesca Battistelli
Lying Evangelist Exposes Himself!
Hugh Marjoe Ross Gortner, generally known as Marjoe Gortner (born January 14, 1944 in Long Beach, California), is a former evangelical minister who first gained a certain fame in the late 1940s and early to mid 1950s when he became the youngest ordained preacher at the age of four, and then outright notoriety in the 1970s when he starred in an Oscar-winning, behind-the-scenes documentary about the lucrative business of Pentecostal preaching. The name "Marjoe" is a combination of the names "Mary" and "Joseph".rnrnWhen Marjoe was three, his father, a second generation evangelical minister, noticed his son's talent for mimicry and overall fearlessness of strangers and public settings. His parents claimed Marjoe had received a vision from God during a bath and began training him to deliver sermons, complete with dramatic gestures and emphatic lunges. By the time Marjoe was four, his parents arranged for him to perform a marriage ceremony for a film crew from Paramount studios, referring to him as "the youngest ordained minister in history." Like much in Marjoe's early life it is hard to say for sure who exactly ordained him, if his father ordained him, or if he was even ordained at all.rnrnUntil the time he was a teenager, Marjoe and his parents traveled the rural United States, holding revival meetings. As well as teaching him scriptural passages, Marjoe's parents also taught him several money-making tactics, involving the sale of supposedly "holy" articles at revivals which promised to heal the sick and dying. By the time Marjoe was sixteen, he later estimated, his family had amassed maybe three million dollars; shortly after his sixteenth birthday, Marjoe's father absconded with the money, and a disillusioned Marjoe left his mother for San Francisco, where he was taken in by and became the lover of an older woman. Marjoe spent the remainder of his teenage years as an itinerant hippie until his early twenties, when, ha


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Serotonin Can Help You Cope
Twenty thousand years ago, there was an evolutionary advantage in running like hell if a saber-toothed tiger was eyeballing you for his evening snack. Our ancestors who hesitated or stayed to see if the beast really meant to eat them … well, they were not our ancestors, come to think of it, because they were summarily removed from the gene pool. Their friends who acted more quickly and wisely had a better chance to live and procreate, leading ultimately to us.
Fight-or-Flight—A Legacy from Our Ancestors
Our ancestors' gut reaction to mortal peril is now known as the fight-or-flight response—and it's still in our genes, even though the saber-toothed tiger is long gone. It entails a sudden cascade of physiological changes, taking anywhere from a few milliseconds to a few seconds, that prepare us to either stand and fight, or flee. This phenomenon, originally described by the great American physiologist Walter Cannon, is caused by the release of powerful hormones such as adrenaline, noradrenaline, and cortisol into the bloodstream, causing a variety of striking bodily changes, such as:
The Downside of the Fight-or-Flight Response
Most people would agree that an automatic, gut response to highly stressful life-and-death situations—such as being in the path of a speeding car or trapped in a dark alley by a mugger—is of great advantage. Yet the fact is that most stresses we face today are not of the life-and-death kind—they're more likely to be a financial or marital crisis, a rotten boss, or the daily traffic gridlock. They may not hurt us in any obvious way, but they sure do unleash the stress hormones on us, and they can still trigger the fight-or-flight response, to some degree.
Unlike our ancestors, though, we're trapped by our circumstances, unable to either fight or flee—we must "control ourselves," while frustration, fear, and anger seethe within us. When this happens repeatedly over months and years, it can greatly impair our health. In fact, many scientists now believe that chronic stress and the repeated activation of our fight-or-flight response can lead to a variety of illnesses, including depression, cancer, and heart disease.
That's bad enough, but in some people, the fight-or-flight response can strike out of the blue, with no consciously perceived stress, let alone threat. Psychiatrists refer to this condition as panic disorder.
Panic Disorder—A Modern Malady
Panic disorder affects at least 3 million American adults—more than the number of people who are afflicted annually by stroke, epilepsy, or AIDS.1 It is a chronic, relapsing condition that can have devastating effects on one's job, personal relationships, and family life. The hallmark symptom of panic disorder is panic attacks—acute surges of fear or almost paralyzing terror that occur spontaneously, often with no apparent trigger.
Along with the emotional distress, panic attacks have physical symptoms that resemble the fight-or-flight response, including racing or pounding heartbeat, chest pains, difficulty in breathing, trembling, sweating, and chills or hot flashes. On top of all that, patients who present with panic attacks almost always state that they feel like they're losing their mind.
What Causes Panic Attacks?
Even though all humans are hard-wired for the fight-or-flight response, most do not suffer from panic attacks. We don't know why some people do, but there are several theories. One is that they're hypersensitive to small changes in their bodies' biochemical milieu.* Psychiatric researchers can investigate this theory by inducing panic attacks experimentally, using small quantities of certain substances that are known to be anxiolytic (anxiety-producing) agents.
*For an example of this phenomenon, involving sensitivity to carbon dioxide levels in the air we breathe, see
"5-HTP Inhibits Panic Attacks" in the March 2004 issue.
One such agent is cholecystokinin-tetrapeptide (CCK-4), the smallest member of a family of peptide hormones that are found in the gastrointestinal tract and the central nervous system of most mammals, including humans. (See the sidebar.) People with panic disorder are unusually sensitive to CCK-4, but healthy people are susceptible when it's administered intravenously (it can't be taken orally, because our stomach acid would destroy it). Naturally, researchers want to find ways to reduce the susceptibility to panic attacks, and they've found that one way is with the amino acid 5-hydroxytryptophan (5-HTP), because of its connection with serotonin.
Cholecystokinin (CCK) is a family of peptide hormones (consisting of chains of amino acids) that play important roles in the gastrointestinal tract and the central nervous system. In the GI tract (where there are more than two dozen other hormones at work), CCK is secreted by the duodenum in response to partially digested food (chyme) entering from the stomach. It triggers the release of enzymes from the pancreas and bile from the gallbladder to finish the job of digestion (the name cholecystokinin means "to move the gallbladder"). When the food is gone, CCK secretion ceases.
CCK is sometimes called the satiety hormone, because it's believed to play a role in controlling food intake by signaling the brain (via the bloodstream) when the stomach has had enough; the brain then tells its owner to stop eating.* (It should be obvious, if you look around, that this system is highly fallible!)
*See the sidebar
"Phenylalanine for Weight Loss" in the article "Catecholamines Kick Out the Demons of Depression" in the September 2003 issue.
CCK is also widely distributed throughout the central nervous system (including the senile plaques in the brains of Alzheimer's victims), where it appears to be the most abundant neuropeptide. Our understanding of the roles of CCK in the brain, however, is rudimentary. It appears to modulate the activity of other hormones and of neurotransmitters such as dopamine and serotonin, and it may be involved in various mental disturbances, such as anxiety, depression, and schizophrenia.
This picture of CCK is complicated by the fact that there are numerous members of this family of peptides, and although they have certain features in common, they also differ in their sites and modes of action. The most active form in the central nervous system is CCK-8 (with 8 amino acids in the chain), but most are considerably larger. There are smaller ones too, though, and the smallest is CCK-4. Although it lacks the hormonal activity of its larger cousins, it does affect the brain as an anxiolytic, or anxiety-inducing agent, as discussed in the accompanying article.
Reduced Serotonin Activity May Be to Blame
Serotonin is a vital neurotransmitter that's involved in a wide variety of behaviors, including aggression, sleep, depression, anxiety, and panic.2 Its immediate precursor in the brain is 5-HTP, which is produced from its precursor, the amino acid tryptophan. Thus our serotonin levels depend on our dietary intake of tryptophan and 5-HTP. Although tryptophan is found in many food proteins, for a while it was unavailable as a nutritional supplement, owing to a misguided FDA ruling. 5-HTP, on the other hand, is not found in foods but is also available as a safe and effective natural supplement.
In people with panic disorder, it's thought that there is reduced serotonin activity in the brain's neural synapses (where most serotonin is found).* In fact, one way in which holistically oriented physicians treat panic attacks is with 5-HTP. It is well absorbed orally, with 70% entering the bloodstream, and it easily crosses the blood-brain barrier. Thus it can effectively increase the amount of serotonin produced in the brain.3
*Not all of the brain's neurons use serotonin as the neurotransmitter—the figure is 0.2%. Since the brain has about 100 billion neurons, that's about 200 million.
Researchers Use 5-HTP to Counter Panic Attacks
A bit of new light has recently been shed on the prevention of panic attacks.4 In a double blind, placebo-controlled study, researchers in Estonia examined the effects of oral 5-HTP in 32 healthy volunteers (14 men and 18 women, aged 18–32) in whom panic attacks were induced with CCK-4. The degree of panic (anxiety, mostly) was evaluated using the Panic Symptom Scale (PSS), a questionnaire regarding the intensity of 18 panic symptoms, of which 13 are somatic (physical) and 5 are cognitive (mental). The symptoms are rated on a scale from 0 (not present) to 4 (extremely severe). The Visual Analogue Scale (VAS) was used to measure the volunteers' subjective anxiety at frequent intervals throughout the experiment, and heart rate and blood pressure were also measured frequently.
The volunteers were randomized in such a way that two equally composed groups (seven men and nine women in each group) received a pretreatment of either two 100-mg capsules of 5-HTP or placebo. Ninety minutes later, the panic (anxiety) attack was induced with an IV infusion of CCK-4 (50 mcg added to a saline drip). And 5 minutes after that, the volunteers rated their symptoms on the PSS and VAS.
Attacks that could technically be classified as panic occurred in three members of the 5-HTP group and in seven of the controls, but this difference was not statistically significant, owing to the small number of volunteers in the study. One of the 18 individual panic symptoms—choking—showed a significant decrease with 5-HTP compared with placebo, and there was a downtrend in one other symptom (abdominal distress). On the VAS, there were significant increases in anxiety ratings in both groups after the CCK-4 infusion, but no difference between the two groups.
Women Appear to Benefit More than Men
One interesting result did emerge: an apparent gender difference. When the data were analyzed by gender, it turned out that the panic rate was significantly lower in the 5-HTP-treated women than in the female controls; this was not true of the men, however, in whom the panic rate was equally low in both groups. Furthermore, the cognitive symptoms were significantly lower in the 5-HTP-treated women compared with their controls, but the somatic symptoms were not. In the men, it was the other way around: there was a trend (though not statistically significant) toward lower somatic symptoms, but not cognitive symptoms, in the 5-HTP-treated men compared with their controls.
Thus, the principal finding regarding 5-HTP in this study was a significant reduction in the rate of panic attacks and in the intensity of cognitive symptoms in women versus men; in men the effect was limited to a nonsignificant reduction in the intensity of somatic symptoms. The authors speculated that these apparent gender differences could be due to a greater bioavailability of serotonin (following 5-HTP treatment) in women than in men, owing to the women's lower rate of serotonin metabolism, which has been observed in other studies. It's also possible that the two sexes differ in their physiological response to CCK-4.
More Research Needed
The cardiovascular data were puzzling. After the CCK-4 infusion, heart rate increased significantly in both groups—somewhat more so in the 5-HTP group than in the controls. Blood pressure also increased in both groups—significantly more so in the 5-HTP group than in the controls. The authors could not explain this (but the small size of the study may be a clue).
All in all, the results were intriguing but not definitive, and the apparent gender differences may be illusory, because, as the authors admit, the study was too small to allow such differences to be observed with statistical confidence. Larger studies are needed to resolve these questions—but not to validate the well-established efficacy of 5-HTP as a calming agent in general.
5-HTP May Calm Your Anxiety
For most people, the rush of a fight-or-flight response can be an exciting diversion from the dull aspects of everyday life. It can even be fun to have this evolutionarily beneficial response kick in when we're watching scary movies in the safety and comfort of our home or a movie theater. For people with panic disorder, however, this physiological vestige of our perilous past can be debilitating. For these people, 5-HTP may be just the supplement they need to relieve their anxiety and allow them to join the rest of us in enjoying the fruits of modern life.


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25 . 03 . 2011
Shiny Plastic Fox
My blogger loverat SHINY PLASTIC HAG has been kind enough to style me up a personalised OracleFox look and I'm dying over the selection! If you haven't seen some of the other goodies on SPH you can check it out here..
top : RACHEL COMEY


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Do I Still Sound like a Saint Lucian?
***Unedited Blog Entry***
Ever since leaving Saint Lucia in 2003, I've came across many ethnic groups from all around the world which have encourage me to be heard with my Saint Lucian accent by anyone who can hear me speak. This was my partial way of displaying pride and standing out among the rest. Although I've tried my best to maintain my native accent in Western Canada (or in any foreign country) but, recently a close friend made me realize that I was no longer sounding like a Saint Lucian. I thought to myself, Really? That day I almost had a heart attack. Gason, dat is pure papicho. How is me uh? (Saint Lucian Slang).
If I no longer sound like a Saint Lucian, then what or who do I sound like? Do I sound like an American? A place where I spent almost 6 years of my life. Or, do I sound like a Canadian where I am currently living for over 2 years? Maybe, I sound like an AmeriLucian or a CanaLucian. Again, How is me uh?
According to Google, an accent is a distinctive mode of pronunciation of a language, especially one associated with a particular nation, locality or social class.
I asked myself, a distinctive mode of pronunciation of a language? Hmm, so in that case, I've probably lost my Saint Lucian accent anytime in the many years I've been away from my birthplace. I cannot recall the first day it left me because today, I still hear myself sounding like a Saint Lucian. But hearing myself speak doesn't necessarily defines how someone else hears me out. The question is: when was I no longer sounding Saint Lucian? Perhaps, it was the first time an American told me that I need to learn how to speak English. Oh yes, that is a possibility.
Poor English or heavy accent? In my early years of migrating to a new country, my accent was a barrier to effective communication. And it was not due to the way I spoke my primary language (English) but it was the way I pronounced certain words. Still, that shouldn't have given these harsh citizens any rights to display their ignorance to foreigners like myself. They too, have accents. Having an accent never means that you cannot speak English. Furthermore, to prevent communication conflicts, I had to assimilate out of my comfort zone, into a universal dialect where my words can be understood by all nations.
But still I ask myself, Do I Still Sound like a Saint Lucian? Leave your comment below.


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Pilot Place Society's Lombard Residence is located at 70 Lombard Street in downtown Toronto and consists of the entire 1st & 2nd floors within a 10-storey apartment building. The residence includes 20 single units, each with a 4-piece washroom, plus common areas, such as living room, dining room, patio, and balcony – all fully furnished.
The Lombard Residence began operations in 1994, after a decade of hard-work, dedication, and financial investment by family members of adults with serious mental illness, who formed Pilot Place Society in response to a severe lack of supportive housing programs for mental health.
The Lombard program was launched with support from the Ministry of Health, the Ministry of Housing, the Labour Council, and Muriel Collins Housing Co-operative – which serves as the Lombard Residence landlord, receiving a Rent-Geared-to-Income (RGI) subsidy. Lombard operates with funding from the Toronto Central LHIN (Local Health Integration Network), as well as generous contributions from private donors.
DonatePilot Place Society's high-support & long-term residences are made possible through the generous support of private donors. Please click here for donation information.
Charitable Registration Number: 119093391RR0001
Contact70 Lombard Street
Toronto, Ontario
M5C 2W2, Canada
Administration: 416-368-5832
Fax: 416-368-4551
Email: click here for email form


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Mimi wo sumaseba1995
Based on a manga by Aoi Hiiragi, the gentle coming-of-age story Whisper of the Heart (1995) was scripted and storyboarded by Hayao Miyazaki, and directed by the late Yoshifumi Kondo. Shizuku Tsukishima is an absent-minded, insecure junior high school student who reads book after book of fairy tales, helps her friends, and does most of her chores, but remains dissatisfied. When she encounters a wonderfully supercilious cat on the train, she follows him and meets Seiji Amasawa, a student in her class who hopes to become a violin maker. Seiji's commitment to his dream helps Shizuku find her voice, literally and figuratively: she realizes she wants to become a writer. A statue of a cat in the antique store of Seiji's kindly grandfather inspires her first story, "Whisper of the Heart." Shizuku and Seiji exhibit believable strengths and flaws: their warm humanity contrasts markedly with the shabby superficiality of the characters in many American animated films. The lively performances of Brittany Snow and David Gallagher transcend the geographic limits of the story to make Shizuku and Seiji the kids the viewer wishes lived next door. Miyazaki's script suggests that a sense of magic can exist, even in everyday Tokyo. Whisper of the Heart was the only film Yoshifumi Kondo directed before his untimely death at age 47, but its understated charm stands as a monument to his talent. (Rated G, suitable for ages 10 and older: tobacco use) --Charles Solomon
Read the entire Mimi wo sumaseba movie script | Buy this movie on Amazon
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|Oracle FAQ
|Your Portal to the Oracle Knowledge Grid
Home -> Community -> Usenet -> c.d.o.server -> Re: When to use packages?
NetComrade wrote:
> Our application(s) are very dynamic in terms of being 'always in
> development'.
No excuse. Clean up your development environment.
Packages sometimes become invalid, b/c they depend on
> objects that might've changed (e.g. column added, view rebuilt).
Then develop processes and methologies.
> Aside from criticism for modifying production database
Why do you think your team deserves anything but heaps and piles of derisive criticism for perfecting bad practices?
But to answer your queestion ... when to use packages? ... Always.
Daniel Morgan Received on Thu Jul 22 2004 - 00:49:59 CDT
Original text of this message


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Universal Studios Home Entertainment and Netflix, Inc. announced agreements covering the distribution of Universal new release DVD and Blu-ray titles at Netflix while providing an expanded selection of the studio's movies that can be streamed from Netflix to TVs and computers.
According to the statement, new release titles on DVD and Blu-ray will be made available to Netflix members after a 28-day window, giving Universal a dedicated time period for sales of its physical and digital offerings. Netflix receives the benefits of reduced product costs, significantly more units and better in-stock levels four weeks after street date. At the same time, a license for Universal streaming content allows Netflix to provide its more than 12 million members more movies they can watch instantly.
"As the home entertainment market continues to evolve, we're exploring new and creative approaches to distribution with our key studio partners," said Ted Sarandos, chief content officer for Netflix. "Our intent is to forge agreements that make sense for the companies involved and that, on the whole, improve the consumer experience and the movie ecosystem. We believe the Universal deals accomplish that."
"We're extremely pleased to have reached this mutually beneficial arrangement that will allow Netflix subscribers to continue enjoying Universal's film content on DVD and Blu-ray," said Craig Kornblau, president of Universal Studios Home Entertainment.
The first release covered under the new agreement is the comedy "It's Complicated," which will be available to Netflix subscribers 28 days after its April 27 street date. New releases will continue to represent an important component of the Netflix service, and deals such as these with Universal will allow Netflix to be better in stock on new releases after a brief window while continuing to enhance the scope and attractiveness of the streaming content Netflix members receive.
The film streaming agreement makes premium domestic films like "Gosford Park" and "Billy Elliott" and library films including "The Pianist," "Being John Malkovich" and "Do the Right Thing" available to watch instantly.
For more information visit: www.netflix.com ; www.universalstudios.com


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Two skeletons and other artifacts dating to the Thirty Years War were found in Stralsund, Germany. Muskets engraved with the owners initials have helped identify the bodies as Hapsburg soldiers.
Swords, pikes, and morning stars were also found in the same trench. The skeletons were still wearing leather shoes and scraps of clothing.
The Thirty Years War took place bwteen 1618-1648 between Protestants and Catholics.


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If you were told "you're s£xy when you're angry"..how would you respond would it cool or infuriate you for the worse.?
I would break out in laughter
I would tell them to prepare for the most intense ****** of their life because you just pissed me off big time!
I'm never angry, but always sexy! lol :-)
it'd soften me up and make me try harder to reach a compromise
so he likes a women angry, give him his sexy angry women hehhehehe

get out the whip and show him sexy

my guess is he knows it shuts you down from ex<x>pression your issue

take the laugh and reaproach subject in different mannor
the second one I guess
It would make me laugh fer sure. It would probably make me forgt what i was mad about in the firßt place.
haha ya they say my eyes turn black
Depends who told that and who are around at that time.
It depends on the situation. I don't like when people deflect but if I see that it is an attempt to dial back a discussion, I'll take it for what it is.
id just ignore the compliment
I'd get annoyed and tell them to shut up and then probably just leave the room.


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Recent Members
Military Alumni
Honoring Our Heroes
This area is dedicated to our alumni that have served or are serving in our armed forces!
Lost Class Rings
Have you lost your Cumberland High School class ring? Have you found someone's class ring? Visit our Redskins lost class ring page to search for your class ring or post information about a found ring.
Honored Military Alumni
Anthony W. Day Class of 1989
Army, 19 Years Armored division currently serving in Iraq. We can not wait until he is home safe.
Brian Moyers Class of 1988
Air Force, 20+ Years Active duty air force. Served in the gulf...
Brian Moyers Class of 1988
Air Force, 20+ Years Served in the Air Force for 20 years and deployed to the middle east of several occasions.
CLARENCE TENTMAN Class of 1985
Navy, 5 Years U.S.S DEYO DD-989 U.S.S PETERSON DD-969
Edwin Hensley Class of 1979
Navy, 20+ Years Worked on various Naval Weapons Systems. Was stationed at Orlando Fla., Charleston SC., Holy Lock Scotland, San Diego Ca. and Seatle Wa.
Harvey Claude Brown Class of 1956
Air Force, 20+ Years After High School, graduation enlisted in USAF. Served 26+ years. Retired as CMSgt.
Randy Taylor Class of 1982
Marine Corps, 20+ Years Served in the Marine Corps for 24 years. Retired from the Corps in 2007. God bless all my old friends from the class of 82.
Classmates Spotlight
Roberta Roark
Alumni Stories
Read and submit stories about our classmates from Cumberland High School, post achievements and news about our alumni, and post photos of our fellow Redskins.
School News
Do you have any news to share? Keep our classmates informed on news and current events! Please post all reunion information in the reunion area.


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text: Development of a Team Performance Scale to Assess Undergraduate Health ProfessionalsSigalet, Elaine PhD; Donnon, Tyrone PhD; Cheng, Adam MD; Cooke, Suzette MD, MSc; Robinson, Traci; Bissett, Wendy; Grant, Vincent MDAcademic Medicine: July 2013 - Volume 88 - Issue 7 - p 989–996 doi: 10.1097/ACM.0b013e318294fd45 Research Reports Abstract Author Information Article Outline Purpose Interprofessional simulation-based team training is strongly endorsed as a potential solution for improving teamwork in health care delivery. Unfortunately, there are few teamwork evaluation instruments. The present study developed and tested the psychometric characteristics of the newly developed KidSIM Team Performance Scale checklist. Method A quasi-experimental research design engaging a convenience sample of 196 undergraduate medical, nursing, and respiratory therapy students was completed in the 2010–2011 academic year. Multidisciplinary student teams participated in a simulation-based curriculum that included the completion of two acute illness management scenarios, resulting in 282 independent reviews by evaluators from medicine, nursing, and respiratory therapy. The authors investigated the underlying factors of the performance checklist and examined the performance scores of an experimental and a control team-training-curriculum group. Results Participation in the supplemental team training curriculum was related to higher team performance scores (P < .001). All teams at Time 2 achieved higher scores than at Time 1 (P < .05). The reliability coefficient for the total performance scale was α = 0.90. Factor analysis supported a three-factor solution (accounting for 67.9% of the variance) with an emphasis on roles and responsibilities (five items) and communication (six items) subscale factors. Conclusions When simulation is used in acute illness management training, the KidSIM Team Performance Scale provides reliable, valid score interpretation of undergraduates' team process based on communication effectiveness and identification of roles and responsibilities. Implementation of a supplementary team training curriculum significantly enhances students' performance in multidisciplinary simulation-based scenarios at the undergraduate level. Dr. Sigalet is KidSIM research and education specialist, Department of Pediatrics, Faculty Medicine, University of Calgary, Calgary, Alberta, Canada. Dr. Donnon is associate professor, Medical Education and Research Unit, Department of Community Health Sciences, Faculty of Medicine, University of Calgary, Calgary, Alberta, Canada. Dr. Cheng is associate professor and director of research and development, KidSIM-Aspire Simulation Research Program, Department of Pediatrics, Faculty of Medicine, University of Calgary, Calgary, Alberta, Canada. Dr. Cooke is hospital pediatrician and section director for human patient simulation, Department of Pediatrics, Faculty of Medicine, University of Calgary, Calgary, Alberta, Canada. Ms. Robinson is simulation educator/operations coordinator, KidSIM-Aspire Simulation Research Program, Department of Pediatrics, Alberta Health Services, Calgary, Alberta, Canada. Ms. Bissett is clinical nurse educator, Pediatric Intensive Care Unit, Department of Pediatrics, Alberta Health Services, Calgary, Alberta, Canada. Dr. Grant is associate professor and medical director, KidSIM-Aspire Pediatric Patient Simulation Program, Departments of Emergency Medicine and Pediatrics, University of Calgary, Calgary, Alberta, Canada. Correspondence should be addressed to Dr. Donnon, Medical Education and Research Unit, G13 Health Medical Research Building, Faculty of Medicine, University of Calgary, 3330 Hospital Dr., NW, Calgary, AB Canada, T2N 4N1; telephone: (403) 210-9682; fax: (403) 210-7507; e-mail: firstname.lastname@example.com. Abstract Background Method Research design Instrument development Curriculum Data collection Statistical analysis Results Discussion and Conclusions References The evidence supporting a positive relationship between effective team performance and patient outcomes1–5 has been the impetus behind the endorsement by the World Health Organization,6 and other national health organizations and medical licensing bodies,7–12 for strongly encouraging academic institutions to integrate team training curricula at all levels of education. Despite this, educators remain challenged by the scarcity of research in undergraduate health professions education to guide the design, delivery, and assessment of such interprofessional curricula.13 Of additional concern is the lack of measurement instruments to evaluate team-based performance during simulated resuscitation scenarios. Currently, the construct validity of existing instruments is limited, and they have been tested only within a postgraduate context.14–17 Back to Top | Article Outline Background The first step in developing any evaluation instrument is to understand the theoretical framework that supports the constructs to be measured.18 The concepts of team performance are drawn from the research in the assessment of persons from both aviation19 and military contexts.20,21 In aviation, the introduction of Crew Resource Management19—specifically, the concepts of leadership, roles and responsibilities, communication, situation awareness, and resource utilization—was used as a framework for the development of flight personnel training curriculum to improve the effectiveness of teamwork and, ultimately, passenger safety. Medicine adopted these same concepts and renamed it Crisis Resource Management (CRM)22 to align with health care professionals' team roles and processes. Today, CRM is the most frequently used framework for designing interprofessional education (IPE) curricula and evaluating team training in medical education.23 To date, only a few instruments exist for assessing interprofessional team training initiatives. The generalizability of all these instruments is limited to a postgraduate context. The first instrument developed, the Anesthesia Nontechnical Instrument,14 was designed specifically for evaluating the performance of health care professionals in the operating room. This instrument reports a moderate to strong reliability (α = 0.76–0.86) with evidence to support satisfactory content validity, based on fair to moderate interrater agreement. The Mayo High Performance Teamwork Scale (MHPTS), tested with 19 residents and 88 nurses, was designed for team members to evaluate their own team as they managed an emergency scenario.15 The 16-item scale is scored 0 (never or rarely), 1 (inconsistently), and 2 (consistently). Findings from the testing, however, exposed some scoring issues. Lack of an event in the scenario led evaluators to assign some zeros, which researchers felt negatively biased the analysis of the teams' performance. Consequently, these scores were changed to 1 (inconsistent) to reduce the scoring bias that resulted when the event did not occur during the scenario. This methodology is questionable and limits the trustworthiness of concepts evaluated. Furthermore, despite evidence of satisfactory internal consistency (α = 0.85), the use of Rasch analysis presumes a unidimensional construct but should be supported by a factor analysis to reinforce underlying factors examined. The Clinical Teamwork Scale (CTS) was developed as an instrument for evaluating team effectiveness of providers of obstetric care.16 Researchers focused on general behavioral descriptors to promote generalizability beyond the obstetric posteducation context. Videos portraying three different levels of team performance were shown to evaluators to provide evidence to support validity of score interpretations. Evaluators, trained in CRM, were blinded to the performance level of each video recording. First, Pearson correlations between evaluators varied between 0.94 and 0.96. The intraclass correlation (ICC) was 0.98 (CI 0.97–0.99). The scenario demonstrating average performance reported the most variance (0.905) compared with high performance (0.214) and low performance (0.729). In addition, the largest source of variance was attributable to the item-times-rater interaction.16 These results suggest that we are making progress with evaluation of teamwork in education. Second, the results reinforce the fact that experts know what good teamwork looks like, but challenge them to create unambiguous indicators to facilitate objective differentiation between poor and average performance. The most recently developed teamwork performance measure, the Team Emergency Assessment Measure (TEAM),17 published since the initiation of the research reported here, was created specifically to measure an emergency medical team's response to resuscitation events. Although supported by face, content, and a degree of construct validity, the results from factor analysis suggest that this 12-item scale measures only one construct, teamwork, explaining a majority of the variance (80.27%). Cohen kappa measuring agreement between two raters on six resuscitation video recordings was 0.55, interpretable as only fair. The ICC 0.60 was only fair. Cronbach alpha was 0.89, reflecting high internal consistency between items, which one would expect, because the scale measured only one construct. In short, although the introduction of IPE appears straightforward, there is an absence of psychometrically sound measures or instruments to assess the level of team performance with clear behavioral indicators, which demonstrates the challenges inherent in developing such an instrument. The purpose of this present study was to examine the psychometric characteristics of a newly developed assessment instrument, the KidSIM Team Performance Scale, used to evaluate team performance of undergraduate health professionals who had experienced a simulation-based interprofessional curriculum. The 12-item team-based performance scale was adapted from both the MHPTS15 and the CTS16 and includes a 5-point scale (with 5 representing optimal performance) anchored by behavioral descriptors to differentiate levels of achievement for each item. Back to Top | Article Outline Method Research design We used a two-group, quasi-experimental research design to analyze the psychometric properties of the newly developed KidSIM Team Performance Scale.* The course and clerkship directors from three disciplines at three institutions were involved in the coordination and recruitment for this interprofessional team training initiative. All of the students were actively engaged in their pediatric or emergency clinical rotations; however, participation was consensual and voluntary. The study participants were undergraduate medical students (n = 35; 17.9% of the total number of student participants) from the University of Calgary Faculty of Medicine in their pediatric emergency clinical rotation (the third year of their three-year program), nursing students (n = 127; 64.8% of the total number of student participants) from the University of Calgary School of Nursing in their pediatric rotation (third year of a four-year program), and respiratory therapy students (n = 34; 17.3% of the total number of student participants) from the Southern Alberta Institute of Technology in their final clinical practicum (third year of program). In all cases, each team consisted of a medical student, three to four nursing students, and, in all but three teams, a respiratory therapy student. To accommodate the medical students in their emergency rotations, 25% of the teams had two medical students. After receiving ethical approval from the conjoint health regional ethics board (CHREB) at the University of Calgary, the study started in September 2010 and was completed in April 2011. Throughout the 2010–2011 academic year, teams of five to six students from the participating disciplines were allocated to either a comparison group (total n = 95) or an intervention group (total n = 101) once a week, which occurred most Wednesday mornings. Back to Top | Article Outline Instrument development The KidSIM Team Performance Scale is a compilation or modification of existing items of performance indicators drawn from preexisting research and scales used to evaluate participants' team-based behaviors.15,16 Although the items developed for the scale were identified from the MHTPS15 and CTS,16 three of us (E.S., T.D., V.G.) developed all the behavioral indicators to adhere to the team performance learning objectives, and also developed behavioral indicators to reduce subjectivity from evaluator observations during the process of scoring team performance (see Appendix 1).18 Items 1, 3, 5, and 12 were taken verbatim from the MHPTS.15 Item 9 was taken verbatim from the CTS,16 and items 2, 4, 6, 7, 8, and 10 were modifications of items drawn from the MHPTS. We developed item 11 to examine situational awareness. The 12 items on the KidSIM Team Performance Scale were designed to assess team performance with respect to leadership, roles and responsibilities, communication, situational awareness, resource utilization, and patient-centered care. A five-point behavioral indicator scale was used to differentiate level of achievement with respect to each item. Appendix 1 Back to Top | Article Outline Curriculum Students in both the comparison and intervention groups participated in the simulation-based team training curriculum on acute illness management, which consisted of two scripted 20-minute scenarios each followed by 40 minutes of facilitated debriefing. Students from at least two of the three participating professional groups were assigned to teams of five to six members (e.g., one medical student, one to two respiratory therapy students, and to four nursing students) and were asked to manage an infant scenario (sepsis or seizure) as a team in the intensive care simulation setting and an older child scenario (asthma or anaphylaxis) in the emergency department simulation setting, both located at the Alberta Children's Hospital. Curriculum objectives focused on the CRM concepts of leadership, roles and responsibilities, communication, situation awareness, and resource utilization. In addition, the medical learning objectives focused on basic medical management. Led by experienced facilitators from nursing, medicine, and respiratory therapy, debriefing focused on student and facilitator observations of task work and teamwork which led to effective and ineffective team performance affecting the medical management of scenarios. Students were instructed to perform all diagnostic or therapeutic actions in real time and to verbalize diagnosis and treatment during scenarios to facilitate scoring accuracy. We used the Laerdal infant mannequin (Simbaby–Laerdal, Toronto, Ontario, Canada) and the METI child mannequin (Pediasim HPS, Sarasota, Florida) in respective scenarios to create a realistic depiction of CRM issues. Students in the intervention group received an additional 30-minute formalized team training module prior to participation in the simulation-based team training curriculum. This module consisted of a short PowerPoint presentation to introduce teamwork concepts, a discussion, and a short video that role-modeled effective teamwork before the students began engaging in the simulation-based curriculum. Back to Top | Article Outline Data collection Two wide-angle cameras were used to digitally record team performances. This provided evaluators with four views: three views of the team caring for the patient (to secure views of task work by all team members) and one view of up-to-date vital signs of heart rate, oxygen saturation, respiratory rate, and blood pressure. Digital recordings were uploaded to a central server for retrospective scoring by six blinded evaluators from medicine, nursing, and respiratory therapy backgrounds, who all participated in a two-hour training session to familiarize themselves with the measurement instrument. On completion of the study, all digital recordings were erased as dictated by the requirements of the CHREB. Back to Top | Article Outline Statistical analysis Descriptive statistics were performed using SPSS Version 19.0 (SPSS Inc., Chicago, Illinois) to derive the means and standard deviations for each questionnaire item, overall total scale scores, and subscale scores for both the comparison and intervention groups. Paired-sample t tests were used to examine the team performance scores between Time 1 and Time 2. Independent-sample t tests were used to examine team performance scores between the intervention (team training module) and comparison group. An exploratory factor analysis (principal components analysis) with varimax rotation was completed to assess the construct validity of the 12-item performance scale and the variance attributed to each of the underlying factors. The resulting eigenvalue-greater-than-one test was used to identify the number of resulting factors. Items loading over 0.32 (i.e., accounting for greater than 10% of the variance) were retained and grouped according to where they loaded the highest on an identified factor. An analysis of the internal reliability (Cronbach alpha = α) was completed for each of the resulting subscale factors and the total performance scale. Cohen d was used to examine mean effect size differences, where a "small" effect size is from 0.20 to 0.49, a "medium" effect size is from 0.50 to 0.79, and a "large" effect size is equal or greater to 0.80. The intraclass correlation (ICC) was also analyzed for Time 1 and Time 2 to examine rater agreement between scenarios. Back to Top | Article Outline Results Of the 196 participants in this study, 161 (82%) were female. A total of 182 students (93%) had reported previous team-based learning, with most identifying either a course-related (61; 33.5%) or work-related experience (75; 41.2%). A total of 131 students (73%) had no critical care experience; 19 students (9.7%) reported less than one month, and 37 students (18.9%) reported greater than one month. Only 3 students (1.6%) reported completion of an advanced life support course. Only 23 participants (11.7%) reported previous IPE. The internal consistency of the KidSIM Team Performance Scale was α = 0.90. A total of 57 simulation-based team performances were evaluated by 1 of 6 examiners with an average of 4.9 examiners per scenario. The examiners were faculty and staff members from medicine (2 emergency medicine physicians), nursing (3 registered nurses), and 1 respiratory therapist. The interclass correlation for the 6 examiners was found to be ICC = 0.90 at Time 1 and 0.87 at Time 2. As shown in Table 1, mean (SD) scores for each of the 12 items at Time 1 were as low as 2.35 (SD = 1.31) for item 11, "The team leader provides summaries of patient status and needed interventions to the team," and as high as M = 3.74 (SD = 1.10) for item 1, "A leader is clearly recognized by all team members." Mean (SD) scores for scenario two ranged from a low of 2.89 (SD = 1.21) for item 11, to a high of M = 4.02 (SD = 0.95) for item 1 and M = 4.02 (SD = 0.92) for item 3. In a separate analysis of variance, it was found that regardless of the team composition there was no significant difference in performance on the KidSIM Team Performance Scale. Table 1 There was a significant relationship between teams receiving the supplemental curriculum and performance scores (Table 2). Intervention teams achieved overall higher scores at both Time 1 and Time 2; t = 4.85 (220), P < .001. Comparison teams, however, also experienced a significant improvement in score by Time 2 (P < .001). The intervention group did not experience a significant gain in score by Time 2. The effect size calculations show that the intervention group (i.e., those that received the formalized team training module) achieved higher mean scores at Time 1 (large effect size, d = 0.92) and 2 (medium, d = 0.61) in relation to the comparison group scores. During the second scenario (Time 2), team performance scores were shown to improve for both the intervention and comparison groups. Table 2 As shown in Table 3, an exploratory factor analysis of the KidSIM Team Performance Scale resulted in a three-factor solution accounting for 65.5% of the variance. These results indicate that we assessed subscales related to communication (6 items: 27.6% variance, α = 0.84), roles and responsibilities (5 items: 27.4% variance, α = 0.86), and patient-centered care (1 item, 10.5% variance). Further analysis of the factors identified exposed the relationship between time slot and quantity of curriculum, with the intervention group outperforming the comparison group with all three factors at Time 1 (P < .01), but only the factor of communication (P < .05) at Time 2. Table 3 Back to Top | Article Outline Discussion and Conclusions Educators today are challenged by the lack of standardized assessment measures with evidence to support reliable and valid score interpretation to evaluate both the learners and the curriculum. Furthermore, there is a scarcity of research in this area, especially in undergraduate medical education. Our findings contribute to this important body of knowledge by supporting the validity and reliability of team performance scores obtained using the KidSIM Team Performance Scale to assess teams of undergraduate health professionals. The scale was shown to evaluate two key dimensions of team performance: (1) roles and responsibilities, and (2) communication. Unfortunately, a third dimension (patient-centered care) that was identified only had a single item and thus needs to be either deleted or supported further with more similar items and then retested. The dimension roles and responsibilities reflected team organization, and the dimension communication reflected the active interdependence between team members trying to complete tasks. The two factors (i.e., dimensions) underlying team performance in our results are congruent with literature from team performance experts like Salas and colleagues,24 who describe team performance as differentiating between the active team process and each team member's role. Urban and colleagues25 identify team structure and communication as the two concepts underscoring team performance. This information is important because in previously tested scales, team performance has been regarded as a unidimensional construct. Our evaluation instrument is the first scale to identify team performance as a two-dimensional construct consistent with previous interpretations by team performance experts. Furthermore, the reliability coefficient for the KidSIM Team Performance Scale had higher internal consistency than did the other team-based measures identified. Additionally, the reliability for the communication and roles and responsibilities factors, or subscales, were much higher than reported in other instruments (α = 0.85 and 0.87, respectively). Together the two factors account for just over 50% of the variance, which was equally distributed between each factor. Only the TEAM performance instrument17 accounted for more variance (80.3%), but again failed to measure more than one dimension of team performance. Furthermore, our results provide evidence to support a degree of construct validity for the scores obtained using the KidSIM Team Performance Scale instrument. The relationships between the performance scores and time slot, and between performance scores and presence of a team training curriculum, were significant despite having evaluators blinded to both conditions. This suggests that evaluators from medicine, nursing, and respiratory therapy were able to use this instrument to discern between teams of learners who received a supplemental curriculum and teams who had more experience. Mean scores for each item on the instrument ranged from a low of 2.35 (SD = 1.31) to a high of 4.02 (SD = 0.92), suggesting the adequacy of a one-to-five scale to evaluate team performance for this level of learner. Additionally, evaluators assigned lower scores for teamwork tasks that continue to challenge even postgraduates like the provision of situation summaries (item 11; see Table 1).26,27 Again this is suggestive of the instrument's ability to discriminate between team process concepts that may reflect an advanced expectation for this level of learner. Nevertheless, those teams that received a formalized team training module were found to perform higher at Time 1 and 2, suggesting that there may be a benefit to using a multiblended approach when delivering team training initiatives. This also provides an indication of the construct validity of the KidSIM Team Performance Scale, as it is anticipated that those teams that were provided with the formalized team training module would have performed better on the scale. There are several limitations to our study. Our results would have been strengthened with a randomized controlled research design; however, this was impossible because of the logistics of scheduling participants in three health professions. In particular, those teams that were allocated to the comparison group completed their clinical rotations before the end of the study and consequently did not receive the formalized team training module. The largest limitation resides in determining appropriate anchors to differentiate levels of performance using a five-point scale for each of the 12 items. In part, a pilot of the instrument might have assisted in the testing of the items and the appropriateness of the behavioral anchors. Many of the behavioral indicators refer to how well (on a five-point scale) the team members performed on any one of the specific behaviors. We based these numbers on performance on a 20-minute scenario. Unfortunately, there are no standards for setting such criteria, and as such the scale may need to be modified to accommodate an experienced interprofessional team and a longer scenario. The validity of using the instrument in different contexts requires further research to evaluate the construct validity of items on the instrument. Furthermore, generalizability of the findings is limited to an undergraduate context. Nevertheless, the team performance scale has the potential to be used in similar interprofessional settings where simulation is used to depict and deliver training in acute illness management. In conclusion, we propose the use of the KidSIM Team Performance Scale as an assessment measure appropriate for providing reliable and valid information about interprofessional undergraduate teams' ability to communicate and engage in appropriate roles and task work. We recognize the need for further research with more rigorous testing of scale items in different contexts to improve the trustworthiness of our findings and the generalizability of the scale. In addition, to enhance our understanding of the validity of the scale, research assessing multiple approaches to the delivery of team-based curricula is required. As educators, we need instruments such as this to further our knowledge of how best to assess team training curricula. Funding/Support: None. Other disclosures: None. Ethical approval: This study was reviewed and received ethics approval from the conjoint health research ethics board at the University of Calgary. Previous presentations: A poster summarizing these data was presented at the International Meeting for Simulation Healthcare, January 30, 2012, San Diego, California.* KidSIM is the name of the pediatric patient simulation program that has been run at the Alberta Children's Hospital since October 2005, and is simply the combination of the word "Kid" and the abbreviation "SIM," for simulation. Cited Here... Back to Top | Article Outline References1. Capella J, Smith S, Philp A, et al. Teamwork training improves the clinical care of trauma patients. J Surg Educ. 2010;67:439–443 Cited Here... 2. Baker GR, Norton PG, Flintoft V, et al. The Canadian Adverse Events Study: The incidence of adverse events among hospital patients in Canada. CMAJ. 2004;170:1678–1686 Cited Here... 3. Baker DP, Day R, Salas E. 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