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  • We were already blown away by this success. But we knew our data pool still wasn’t showing the whole picture. What pockets of data were out there in other corners of the company?

    We talked to our support team, engineers, data scientists, analytics folks, social media people, and the email delivery team, all of whom have lots of valuable data. Soon large amounts of diverse data streamed in, and the ownership of the data pool shifted from the UX team to the entire company. The support team shared patterns they’d seen in emails and live chats. Engineers wrote scripts that grabbed aggregate data about popular pathways in the app and industry demographics of our users, and emailed them into the database weekly. Because Google Analytics lets you schedule emails with custom reports, we were able to stream in mobile device usage data and completion rates of important workflow funnels. Tweets, Facebook, and blog comments streamed in with even more customer feedback. Survey data, email delivery stats, industry research, and notes from each of our app releases—we added everything we could to the data pool to gain an even broader perspective.

    As we’ve opened our data up between teams, interesting things have happened. People who wouldn’t normally have occasion for conversation are meeting regularly to compare notes and share what they’ve learned. Our “data nerds” now get together for lunch, and share stories of the projects they’re working on. Data sharing is leading to new collaborations we never would have imagined.

    Design researchers collaborated with the DesignLab to turn a months-long user study into a series of beautiful posters illustrating MailChimp’s customer archetypes . After a recent major redesign of the app, support and design research teams collected feedback from customers, printed it out, and tacked the notes underneath each persona poster—helping us triage issues from different customers and devise solutions quickly.

    Each persona has a name and a detailed series of traits. When we do customer interviews, we store the transcripts and notes in our big database, and tag each with the name of a persona so we can see patterns in user types. What does “Andre” have to say about coding HTML email templates? How is “Ada” reacting to the redesign? Now we can answer questions like this by searching with both tags and keywords.

    Though we’re not a company that struggles with political drama, close collaboration between teams certainly helps bring our people together and builds respect for the work we’re all doing.

    We’ve found that when people are given the opportunity and the platform to share their data or do something new with existing data, they feel pride knowing their work is valuable to others. It feels good to see different areas of the company benefiting from the work you’re doing. Everyone wants their work to be valued and appreciated.

    The overwhelming majority of SCOR offerings are sold directly by the company. These offerings are frequently called "self-underwritten" offerings or "direct public offerings" (DPOs). Commissioned selling agents or finders may also be used. Mass solicitation may be used, including public meetings, advertisements, and the internet. Any type of investor may purchase any amount in the offering.

    Past regulatory problems by potential selling agents in the offering, the selling agents' management, or 10% or greater owners may result in the disqualification of the selling agents. Selling agents must sell only on behalf of the company and not on their own behalf. Accordingly, firmly underwritten offerings are prohibited.

    A selling agent or finder engaged in the business of selling securities must be registered as a Broker-Dealer with the Securities Division. Individuals receiving commissions or other compensation for selling securities in the offering must be registered as securities salespersons and have passed appropriate examinations. If the corporation is selling the securities directly without paying commissions, officers and directors of the company may become registered to sell the offering without taking any examinations. In that instance, registration is accomplished by filing a completed Nodality1 dress Green Talbot Runhof v2BcDWE
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    There are several requirements an offering must meet in order to qualify for SCOR registration. The general requirements for conducting a SCOR offering are detailed below.

    All American and Canadian corporations and LLCs may use Form SCOR, with certain exceptions. Specifically, the form may not be used:

    Form SCOR may be used to register common or preferred stock (including convertible preferred) and options, warrants, or rights, and membership interests in an LLC. If the company can show it will be able to meet debt service based on current earnings, Form SCOR may be used to register debt securities, including convertible debt. Common stock with lesser voting rights than other common shares may not be registered using Form SCOR.

    Up to $1 million may be raised each 12 month period using SCOR. In calculating this limit, sales in all jurisdictions must be included together with any other securities sold under SEC Rule 504 or under Section 3(b) of the Securities Act of 1933, or in violation of the registration provisions of federal securities laws. The offering price must be at least $1 per share (for LLCs, $1.00 per unit of interest), and the company may not split its stock or declare stock dividends for 2 years following effectiveness of the registration, except with the permission of the Securities Administrator or in connection with a subsequent registered public offering.

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    By far, the most common question asked of two sets of genomic features is whether or not any of the features in the two sets “overlap” with one another. This is known as feature intersection. bedtools intersect allows one to screen for overlaps between two sets of genomic features. Moreover, it allows one to have fine control as to how the intersections are reported. bedtools intersect works with both BED/GFF/VCF and BAM files as input.

    Note

    If you are trying to intersect very large files and are having trouble with excessive memory usage, please presort your data by chromosome and then by start position (e.g., for BED files) and then use the option. This invokes a memory-efficient algorithm designed for large files. This algorithm has been improved in recent (>=2.18.0) releases.

    Important

    As of version 2.21.0, the tool can accept multiple files for the option. This allows one to identify overlaps between a single query () file and multiple database files () at once!

    See also

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    Usage :

    By default, if an overlap is found, bedtools intersect reports the shared interval between the two overlapping features.

    As of version 2.21.0, the intersect tool can detect overlaps between a single -a file and multiple -b files (instead of just one previously). One simply provides multiple -b files on the command line.

    For example, consider the following query ( -a ) file and three distinct ( -b ) files:

    We can now compare query.bed to all three database files at once.:

    Clearly this is not completely informative because we cannot tell from which file each intersection came. However, if we use -wa and -wb , this becomes abundantly clear. When these options are used, the first column after the complete -a record lists the file number from which the overlap came. The number corresponds to the order in which the files were given on the command line.

    In many cases, it may be more useful to report an informative “label” for each file instead of a file number. One can do this with the -names option.

    Or perhaps it may be more useful to report the file name. One can do this with the -filenames option.

    Other options to intersect can be used as well. For example, let’s use -v to report those intervals in query.bed that do not overlap any of the intervals in the three database files:

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