Hand Scraped Flooring: Points to Keep in Mind

The demand for hand-scraped flooring is growing. Yet, this type of flooring, in terms of appearance, isn't like any other. If you are one of the many considering it for your home, what points do you need to keep in mind as you look for the right type of hand-scraped hardwood?

First, nearly all species – domestic and exotic – are available as this distressed variety. Species from white oak to Brazilian cherry are all available with this distressed and rustic look. And, any floor of a building can have hand-scraped flooring, as both solid and engineered types are distressed. As you look at different types of hand-scraped flooring, think about where you will be installing it into your home, and plan accordingly with the right type of solid or engineered hardwood.

What's most notable about hand-scraped hardwood is its creation. All planks are distressed by hand, and as a result, no two appear similar. Multiple methods are used for distressing hardwood, including the following techniques for aging, scraping, or finishing.

Aged hardwood goes by one of two names: Time Worn Aged or Antique. Both are similar, but a lower grade is used for Antique flooring. In addition to being aged, the hardwood's distressed appearance is accented further through darker staining, highlighting the grain, or contouring.

Scraping techniques alter the texture of the hardwood, making an otherwise smooth surface rough. Wire Brushed is a term used to indicate hand-scraped flooring with removed sapwood and accented grain. Hand-sculpted, on the other hand, still has texture but is smoother than other varieties. Hardwood that is Hand Hewn and Rough Sawn has the roughest texture for hand-scraped flooring, with even saw marks visible.

Flooring that uses finish to give hardwood an aged texture is usually sold as French Bleed. Such hand-scraped flooring has deeper beveled edges, and the joints of the floor are highlighted with darker stain. Also a somewhat superficial type of hand-scraped flooring is pegged. Considered to be decorative only, pegged flooring must not be fastened directly onto a subfloor.

If you want an even less uniform appearance for your floor, consider having it custom distressed. In this case, after the unfinished hardwood is installed, a professional comes in to alter it through beating with chains, pickeling, fastening with antique nails, or bleaching. After, a finish is applied.

Also as you look at hand-scraped hardwood, think about your flooring long term. Will you want a distressed appearance a decade or more down the line? If not, plan ahead by going with flooring that can be sanded down: solid hardwood or an engineered variety with a thicker wear layer.

If, on the other hand, you plan to keep the hand-scraped flooring, think about how you will refinish it years down the line. Ideally, to keep up the distressed look without diminishing it through sanding, you will need a floor abrader to remove only the finish, or be prepared to have a professional refinish your floors.

Source:http://www.articlesbase.com/home-improvement-articles/hand-scraped-flooring-points-to-keep-in-mind-5435851.html

Damaged Or Affected Information Providers By Web Scraping Service

Data Scraping Services and computer hardware to grow. How is this possible? It's really simple. Computer systems installed and set in metal boxes and cabinets are a combination of electronic circuit cards. Conductive metal of choice because steel is very strong and affordable. Steel is often plated to prevent oxidation and corrosion.

Galvanizing material of choice because it is still relatively cheap, conductive, and provides a well finished appearance. Many computer enclosures are galvanized rack shelf supports, rails and other structural elements. Data Scraping Services are everywhere, they are not visible? Remember that Data Scraping Services thinner than a human hair and about You are looking for them to find them. Look for them to grow together.

Data Scraping Services exposed bridges and shorts of the circuit is still the potential to wreak havoc on a system. Remain important clues about what happens when the memory bus clock cycles during the installation of the latch is shorted? Maybe the data is corrupted. Perhaps the corruption will be detected and corrected by the error correction algorithms. Affect the data processor is actually an instruction

He logged on to various system disorders - are not logged in or track. If a reset clears the event, problem quickly annoying, but not - as significant is rejected. Often this is not the floor fixed management visibility. If the device must be set and they'll say: "Ask an IT manager ... No, why questions" Ask the operator to reset the equipment needs to be done and they will respond "... Of course, all the time why ask "

So if the Data Scraping Services are everywhere and are instruments to influence how it is not common knowledge? Most users of personal experience or get their information from reliable sources. If personal experience is unforgettable, it's human nature to discount and discard. If a jammed machine reset by filling a cup of coffee is memorable, it is not missed. Popping a diet is unusual and unforgettable. Clicking on the button is not. Data Scraping Services affected or influenced almost all providers.

If the  Services are plentiful, there are no problems?

Research has shown that Data Scraping Services to be reasonably attached to the host surface. Until a certain length, Data Scraping Services rub and rub until they are released by mechanical means such as related. After reaching a certain length, not only freedom from direct mechanical means is possible, but also as a more passive mode of vibration or air flow. Once expelled, Data Scraping Services are free to migrate within the environment.

Data Scraping Services need not be catastrophic failures. Bit errors, soft faults and other defects can be attributed to Data Scraping Services.

What is the treatment for Data Scraping Services?

In general, the accepted treatment to remove Data Scraping Services and is a pure version of the original source material. This tool is not suitable for every bad piece of the place, either a logistical or financial perspective. Does not mean that the problem should be ignored. . Will continue to grow Data Scraping Services. As they are today, they are potentially harmful.

Data Scraping Services through management training, all employees and visitors to the zinc whisker behavior are needed to sign the pledge. The promise Data Scraping Services staff and visitors are forced to treat seriously and will take no action that would aggravate the problem take. Their actions will reflect the best interests of users and reliable computing.

Conclusion

Data Scraping Services are more common than previously believed and accepted. At the same time we can keep up with Data Scraping Services can enjoy fairly reliable operation. But it is important to recognize and manage the situation - not ignore. Living with a chronic infectious disease is a useful model for operations.

Once a surface is the source of zinc whisker, it will always be a source of zinc whisker. Left alone, reliable operation can continue. When the need to interact with the surface, the material does not reveal the need for zinc whisker position.

Source:http://www.articlesbase.com/outsourcing-articles/damaged-or-affected-information-providers-by-web-scraping-service-5549982.html

Scraping table from any web page with R or CloudStat

Scraping table from any web page with R or CloudStat:

You need to use the data from internet, but don’t type, you can just extract or scrape them if you know the web URL.

Thanks to XML package from R. It provides amazing readHTMLtable() function.

For a study case,

I want to scrape data:

    US Airline Customer Score.
    World Top Chess Players (Men).

A. Scraping US Airline Customer Score table from

http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Airlines

Code:

airline = ‘http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Airlines’

airline.table = readHTMLTable(airline, header=T, which=1,stringsAsFactors=F)

Result:

> library(XML)

Warning message:

package "XML" was built under R version 2.14.1

> airline = "http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Airlines"
> airline.table = readHTMLTable(airline, header=T, which=1,stringsAsFactors=F)
> airline.table

                     Base-line 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10
1          Southwest        78 76 76 76 74 72 70 70 74 75 73 74 74 76 79 81 79
2         All Others        NM 70 74 70 62 67 63 64 72 74 73 74 74 75 75 77 75
3           Airlines        72 69 69 67 65 63 63 61 66 67 66 66 65 63 62 64 66
4        Continental        67 64 66 64 66 64 62 67 68 68 67 70 67 69 62 68 71
5           American        70 71 71 62 67 64 63 62 63 67 66 64 62 60 62 60 63
6             United        71 67 70 68 65 62 62 59 64 63 64 61 63 56 56 56 60
7         US Airways        72 67 66 68 65 61 62 60 63 64 62 57 62 61 54 59 62
8              Delta        77 72 67 69 65 68 66 61 66 67 67 65 64 59 60 64 62
9 Northwest Airlines        69 71 67 64 63 53 62 56 65 64 64 64 61 61 57 57 61

  11 PreviousYear%Change FirstYear%Change

1 81                 2.5              3.8
3 65                -1.5             -9.7
4 64                -9.9             -4.5
5 63                 0.0            -10.0
7 61                -1.6            -15.3
8 56                -9.7            -27.3
9  #                 N/A              N/A

>

B. Scraping World Top Chess players (Men) table from http://ratings.fide.com/top.phtml?list=men

Code:

chess = ‘http://ratings.fide.com/top.phtml?list=men’
chess.table = readHTMLTable(chess, header=T, which=5,stringsAsFactors=F)

Result:

> chess = "http://ratings.fide.com/top.phtml?list=men"
> chess.table = readHTMLTable(chess, header=T, which=5,stringsAsFactors=F)
> chess.table

     Rank                       Name Title Country Rating Games B-Year

1      1           Carlsen, Magnus    g    NOR  2835   17  1990
2      2            Aronian, Levon    g    ARM  2805   25  1982
3      3         Kramnik, Vladimir    g    RUS  2801   17  1975
4      4        Anand, Viswanathan    g    IND  2799   17  1969
5      5         Radjabov, Teimour    g    AZE  2773    9  1987
6      6          Topalov, Veselin    g    BUL  2770    9  1975
7      7          Karjakin, Sergey    g    RUS  2769   16  1990
8      8         Ivanchuk, Vassily    g    UKR  2766   16  1969
9      9     Morozevich, Alexander    g    RUS  2763    6  1977
10    10           Gashimov, Vugar    g    AZE  2761    9  1986
11    11       Grischuk, Alexander    g    RUS  2761    8  1983
12    12          Nakamura, Hikaru    g    USA  2759   17  1987
13    13            Svidler, Peter    g    RUS  2749   17  1976
14    14    Mamedyarov, Shakhriyar    g    AZE  2747    9  1985
15    15       Tomashevsky, Evgeny    g    RUS  2740    0  1987
16    16            Gelfand, Boris    g    ISR  2739    9  1968
17    17          Caruana, Fabiano    g    ITA  2736   19  1992
18    18       Nepomniachtchi, Ian    g    RUS  2735   16  1990
19    19                 Wang, Hao    g    CHN  2733    6  1989
20    20              Kamsky, Gata    g    USA  2732    0  1974
21    21  Dominguez Perez, Leinier    g    CUB  2730    6  1983
22    22         Jakovenko, Dmitry    g    RUS  2729    0  1983
23    23        Ponomariov, Ruslan    g    UKR  2727   13  1983
24    24          Vitiugov, Nikita    g    RUS  2726    1  1987
25    25            Adams, Michael    g    ENG  2724   17  1971
26    26               Leko, Peter    g    HUN  2720    9  1979
27    27            Almasi, Zoltan    g    HUN  2717    8  1976
28    28               Giri, Anish    g    NED  2714   15  1994
29    29            Le, Quang Liem    g    VIE  2714    0  1991
30    30             Navara, David    g    CZE  2712    8  1985
31    31            Shirov, Alexei    g    LAT  2710   13  1972
32    32             Polgar, Judit    g    HUN  2710    0  1976
33    33     Riazantsev, Alexander    g    RUS  2710    0  1985
34    34       Wojtaszek, Radoslaw    g    POL  2706    8  1987
35    35      Moiseenko, Alexander    g    UKR  2706    7  1980
36    36   Vallejo Pons, Francisco    g    ESP  2705   15  1982
37    37        Malakhov, Vladimir    g    RUS  2705    0  1980
38    38            Jobava, Baadur    g    GEO  2704   23  1983
39    39           Bacrot, Etienne    g    FRA  2704   14  1983
40    40          Laznicka, Viktor    g    CZE  2704    8  1988
41    41            Sutovsky, Emil    g    ISR  2703    8  1977
42    42        Naiditsch, Arkadij    g    GER  2702   14  1985
43    43         Movsesian, Sergei    g    ARM  2700    9  1978
44    44       Sasikiran, Krishnan    g    IND  2700    9  1981
45    45   Vachier-Lagrave, Maxime    g    FRA  2699   13  1990
46    46            Dreev, Aleksey    g    RUS  2698    6  1969
47    47           Efimenko, Zahar    g    UKR  2695    8  1985
48    48         Volokitin, Andrei    g    UKR  2695    0  1986
49    49                 Wang, Yue    g    CHN  2694    6  1987
50    50        Fressinet, Laurent    g    FRA  2693   17  1981
51    51                Li, Chao b    g    CHN  2693    6  1989
52    52            Grachev, Boris    g    RUS  2693    0  1986
53    53      Nielsen, Peter Heine    g    DEN  2693    0  1973
54    54            Van Wely, Loek    g    NED  2692   13  1972
55    55    Bruzon Batista, Lazaro    g    CUB  2691   19  1982
56    56           McShane, Luke J    g    ENG  2691    8  1984
57    57            Eljanov, Pavel    g    UKR  2690   10  1983
58    58      Kasimdzhanov, Rustam    g    UZB  2689   14  1979
59    59         Inarkiev, Ernesto    g    RUS  2689    6  1985
60    60         Zvjaginsev, Vadim    g    RUS  2688    8  1976
61    61         Andreikin, Dmitry    g    RUS  2688    0  1990
62    62    Areshchenko, Alexander    g    UKR  2688    0  1986
63    63         Rublevsky, Sergei    g    RUS  2686    0  1974
64    64         Akopian, Vladimir    g    ARM  2685    8  1971
65    65          Potkin, Vladimir    g    RUS  2684    0  1982
66    66       Sargissian, Gabriel    g    ARM  2683   15  1983
67    67            Berkes, Ferenc    g    HUN  2682   16  1985
68    68           Bologan, Viktor    g    MDA  2680   15  1971
69    69          Bauer, Christian    g    FRA  2679   24  1977
70    70          Tiviakov, Sergei    g    NED  2677   22  1973
71    71            Short, Nigel D    g    ENG  2677   15  1965
72    72        Motylev, Alexander    g    RUS  2677    6  1979
73    73         Gharamian, Tigran    g    FRA  2676    0  1984
74    74          Kobalia, Mikhail    g    RUS  2673    0  1978
75    75              Meier, Georg    g    GER  2671    9  1987
76    76       Onischuk, Alexander    g    USA  2670   13  1975
77    77              Bu, Xiangzhi    g    CHN  2670    6  1985
78    78          Alekseev, Evgeny    g    RUS  2670    0  1985
79    79            Azarov, Sergei    g    BLR  2667    0  1983
80    80        Kryvoruchko, Yuriy    g    UKR  2666    0  1986
81    81             Balogh, Csaba    g    HUN  2665    8  1987
82    82           Harikrishna, P.    g    IND  2665    6  1986
83    83       Khismatullin, Denis    g    RUS  2664    8  1984
84    84   Nguyen, Ngoc Truong Son    g    VIE  2662    6  1990
85    85           Fridman, Daniel    g    GER  2660   11  1976
86    86              Smirin, Ilia    g    ISR  2660    7  1968
87    87               Ding, Liren    g    CHN  2660    6  1992
88    88         Sadler, Matthew D    g    ENG  2660    3  1974
89    89            Korobov, Anton    g    UKR  2660    0  1985
90    90          Cheparinov, Ivan    g    BUL  2659   18  1986
91    91          Timofeev, Artyom    g    RUS  2659    0  1985
92    92           Georgiev, Kiril    g    BUL  2658   17  1965
93    93           Bartel, Mateusz    g    POL  2658    9  1985
94    94          Zhigalko, Sergei    g    BLR  2658    8  1989
95    95         Feller, Sebastien    g    FRA  2658    0  1991
96    96            Ragger, Markus    g    AUT  2655   17  1988
97    97         Jones, Gawain C B    g    ENG  2653   27  1987
98    98                So, Wesley    g    PHI  2653    5  1993
99    99              Milov, Vadim    g    SUI  2653    0  1972
100  100           Gupta, Abhijeet    g    IND  2652    9  1989
101  101            Postny, Evgeny    g    ISR  2652    8  1981
102  102             Roiz, Michael    g    ISR  2652    6  1983
103  103           Gyimesi, Zoltan    g    HUN  2652    4  1977
104  104          Nikolic, Predrag    g    BIH  2652    2  1960

>

Done. You had successfully scraping data from any web page with R or CloudStat.

Then, you can analyze as usual! Great! No more retype the data. Enjoy!

Source: http://www.r-bloggers.com/scraping-table-from-any-web-page-with-r-or-cloudstat/

Web Data Extraction Services and Data Collection Form Website Pages

For any business market research and surveys plays crucial role in strategic decision making. Web scrapping and data extraction techniques help you find relevant information and data for your business or personal use. Most of the time professionals manually copy-paste data from web pages or download a whole website resulting in waste of time and efforts.

Instead, consider using web scraping techniques that crawls through thousands of website pages to extract specific information and simultaneously save this information into a database, CSV file, XML file or any other custom format for future reference.

Examples of web data extraction process include:

• Spider a government portal, extracting names of citizens for a survey
• Crawl competitor websites for product pricing and feature data
• Use web scraping to download images from a stock photography site for website design

Automated Data Collection

Web scraping also allows you to monitor website data changes over stipulated period and collect these data on a scheduled basis automatically. Automated data collection helps you discover market trends, determine user behavior and predict how data will change in near future.

Examples of automated data collection include:

• Monitor price information for select stocks on hourly basis
• Collect mortgage rates from various financial firms on daily basis
• Check whether reports on constant basis as and when required

Using web data extraction services you can mine any data related to your business objective, download them into a spreadsheet so that they can be analyzed and compared with ease.

In this way you get accurate and quicker results saving hundreds of man-hours and money!

With web data extraction services you can easily fetch product pricing information, sales leads, mailing database, competitors data, profile data and many more on a consistent basis.

Should you have any queries regarding Web Data extraction services, please feel free to contact us. We would strive to answer each of your queries in detail.

Source:http://ezinearticles.com/?Web-Data-Extraction-Services-and-Data-Collection-Form-Website-Pages&id=4860417

ScraperWiki: A story about two boys, web scraping and a worm

“It’s like a buddy movie.” she said.
Not quite the kind of story lead I’m used to. But what do you expect if you employ journalists in a tech startup?
“Tell them about that computer game of his that you bought with your pocket money.”
She means the one with the risqué name.
I think I’d rather tell you about screen scraping, and why it is fundamental to the nature of data.

About how Julian spent almost a decade scraping himself to death until deciding to step back out and build a tool to make it easier.

I’ll give one example.
Two boys
In 2003, Julian wanted to know how his MP had voted on the Iraq war.
The lists of votes were there, on the www.parliament.uk website. But buried behind dozens of mouse clicks.
Julian and I wrote some software to read the pages for us, and created what eventually became TheyWorkForYou.

We could slice and dice the votes, mix them with some knowledge from political anaroks, and create simple sentences. Mini computer generated stories.

“Louise Ellman voted very strongly for the Iraq war.”
You can see it, and other stories, there now. Try the postcode of the ScraperWiki office, L3 5RF.

I remember the first lobbiest I showed it to. She couldn’t believe it. Decades of work done in an instant by a computer. An encyclopedia of data there in a moment.

Web Scraping

It might seem like a trick at first, as if it was special to Parliament. But actually, everyone does this kind of thing.

Google search is just a giant screen scraper, with one secret sauce algorithm guessing its ranking data.
Facebook uses scraping as a core part of its viral growth to let users easily import their email address book.

There’s lots of messy data in the world. Talk to a geek or a tech company, and you’ll find a screen scraper somewhere.

Why is this?
It’s Tautology

On the surface, screen scrapers look just like devices to work round incomplete IT systems.

Parliament used to publish quite rough HTML, and certainly had no database of MP voting records. So yes, scrapers are partly a clever trick to get round that.

But even if Parliament had published it in a structured format, their publishing would never have been quite right for what we wanted to do.

We still would have had to write a data loader (search for ‘ETL’ to see what a big industry that is). We still would have had to refine the data, linking to other datasets we used about MPs. We still would have had to validate it, like when we found the dead MP who voted.

It would have needed quite a bit of programming, that would have looked very much like a screen scraper.

And then, of course, we still would have had to build the application, connecting the data to the code that delivered the tool that millions of wonks and citizens use every year.

Core to it all is this: When you’re reusing data for a new purpose, a purpose the original creator didn’t intend, you have to work at it.

Put like that, it’s a tautology.
A journalist doesn’t just want to know what the person who created the data wanted them to know.
Scrape Through
So when Julian asked me to be CEO of ScraperWiki, that’s what went through my head.
Secrets buried everywhere.

The same kind of benefits we found for politics in TheyWorkForYou, but scattered across a hundred countries of public data, buried in a thousand corporate intranets.

If only there was a tool for that.
A Worm
And what about my pocket money?
Nicola was talking about Fat Worm Blows a Sparky.
Julian’s boss’s wife gave it its risqué name while blowing bubbles in the bath. It was 1986. Computers were new. He was 17.

Fat Worm cost me £9.95. I was 12.
[Loading screen]
I was on at most £1 a week, so that was ten weeks of savings.
Luckily, the 3D graphics were incomprehensibly good for the mid 1980s. Wonder who the genius programmer is.
I hadn’t met him yet, but it was the start of this story.

Source:https://blog.scraperwiki.com/2011/05/scraperwiki-a-story-about-two-boys-web-scraping-and-a-worm/

The Hubcast #4: A Guide to Boston, Scraping Local Leads, & Designers.Hubspot.com

The Hubcast Podcast Episode 004

Welcome back to The Hubcast folks! As mentioned last week, this will be a weekly podcast all about HubSpot news, tips, and tricks. Please also note the extensive show notes below including some new HubSpot video tutorials created by George Thomas.

Show Notes:

Inbound 2014

THE INSIDER’S GUIDE TO BOSTON

Boston Guide
On September 15-18, the Boston Convention & Exhibition Center will be filled with sales and marketing professionals for INBOUND 2014. Whether this will be your first time visiting Boston, you’ve visited Boston in the past, or you’ve lived in the city for years, The Insider’s Guide to Boston is your go-to guide for enjoying everything the city has to offer. Click on a persona below to get started.

Are you the The Brewmaster – The Workaholic – The Chillaxer?

Check out the guide here

HubSpot Tips & Tricks
Prospects Tool – Scrape Local Leads
Prospects Tool

This weeks tip / trick is how to silence some of the noise in your prospect tool. Sometimes you might have need to just look at local leads for calls or drop offs. We show you how to do that and much more with the HubSpot Prospects Tool.

Watch the tutorial here

HubSpot Strategy
Crack down on your sites copy.

We talk about how your home page and about pages are talking to your potential customers in all the wrong ways. Are you the me, me, me person at the digital party? Or are you letting people know how their problems can be solved by your products or services.

HubSpot Updates
(Each week on the Hubcast, George and Marcus will be looking at HubSpot’s newest updates to their software. And in this particular episode, we’ll be discussing 2 of their newest updates)
Default Contact Properties

You can now choose a default option on contact properties that sets a default value for that property that can be applied across your entire contacts database. When creating or editing a new contact property in Contacts Settings, you’ll see a new default option next to the labels on properties with field types “Dropdown,” “Radio Select” and “Single On/Off Checkbox”.

Default Contact Properties

When you set a contact property as “default”, all contacts who don’t have any value set for this property will adopt the default value you’ve selected. In the example above, we’re creating a property to track whether your contact uses a new feature. Initially, all of them would be “No,” and that’s the default property that will be applied database-wide. As a result, this’ll get stamped on each contact record the value wasn’t present on.

Now, when you want to apply a contact property across multiple contacts, you don’t have to create a list of those contacts and then create a workflow that stamps that contact property across those contacts. This new feature allows you to bypass those steps by using the “default” option on new contact properties you create.

Watch the tutorial here
RSS Module with Images

Now available is a new option within modules in the template builder that will allow you to easily add a featured image to an RSS module. This module will show a blog post’s featured image next to the feed of recent blog content. If you are a marketer, all you need to do is simply check the “Featured Image” box off in the RSS Listing module to display a list of recent COS blog posts with images on any page. No developers or code necessary to do this!

If you are a designer and want to add additional styling to an RSS module with images, you can do so using HubL tokens.

Here is documentation on how to get started.

Default Contact Properties
Watch the tutorial here

HubSpot Wishlist

 The HubSpot Keywords Tool

Why oh why!!!! Hubspot why can we only have 1,000 keywords in our keywords tool? We talk about how for many companies a 1,000 keywords dont just cut it. For example Yale applaince can easily blow through those keywords.

Source: http://www.thesaleslion.com/hubcast-podcast-004/

Scraping Online Communities for your Outreach Campaigns

Online communities offer a wealth of intelligence for blog owners and business owners alike.

Exploring the data within popular communities will help you to understand who the major influencers are, what content is popular and who are the key content aggregators within your niche.

This is all fair and well to talk about, but is it feasible to be manually sorting through online communities to find this information out? Probably not.

This is where data scraping comes in.
What is Scraping and What Can it do?

I’m not going to go into great detail on what data scraping actually means, but to simplify this, here’s a definition from the Wikipedia page:

    “Data scraping is a technique in which a computer program extracts data from human-readable output coming from another program.”

Let me explain this with a little example…

Imagine a huge community full of individuals within your industry. Each person within the community has a personal profile page that contains information about their interests, contact details, social profiles, etc.

If you were tasked with gathering all of this data on all of the individuals then you might start to hyperventilating at the thought of all the copy and pasting you’d need to do.

Well, an alternative is to scrape all of this content so that you can automate all of this process and easily export all of this information into a manageable, more consumable format in a matter of seconds. It’d be pretty awesome, right?
Luckily for you, I’m going to show you how to do just that!
The Example of Inbound.org

Recently, I wanted to gather a list of digital marketers that were fairly active on social media and shared a lot of content online within communities. These people were going to be some of my core targets to get content from the blog in front of.

To do this, I first found some active communities online where these types of individuals hang out. Being a digital marketer myself, this process was fairly easy and I chose Inbound.org as my starting place.

Scoping out Data Requirements
Each community is different and you’ll be able to gather varying information within each.

The place to look for this information is within the individual user profile pages. This is usually where the contact information or links to social media accounts are likely to be displayed.

For this particular exercise, I wanted to gather the following information:

    Full name
    Job title
    Company name and URL
    Location
    Personal website URL
    Twitter URL, handle and follower/following stats
    Google+ URL, follower count and list of contributor URLs
    Profile image URL
    Facebook URL
    LinkedIn URL

With all of this information I’ll be able to get a huge amount of intelligence about the community members. I’ll also have a list of social media accounts to add and engage with.
On top of this, with all the information on their websites and sites that they write for, I’ll have a wealth of potential link building prospects to work on.

Inbound.org Profiles

You’ll see in the above screenshot that a few of the pieces of data are available to see on the Inbound.org user profiles. We’ll need to get the other bits of information from the likes of Twitter and Google+, but this will all stem from the scraping of Inbound.org.

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Scraping the Data

The idea behind this is that we can set up a template based on one of the user profiles and then automate the data gathering across the rest of the profiles on the site.

This is where you’ll need to install the SEO Tools plugin for Excel (it’s free). If you’ve not used this plugin before, don’t worry – I’ve put together a full tutorial here.

Once you’ve installed the plugin, you’re good to go on the actual scraping side of things…
Quick Note: Don’t worry if you don’t have a good knowledge of coding – you don’t need it. All you’ll need is a very basic understanding of reading some code and some basic Excel skills.

To begin with, you’ll need to do a little Excel admin. Simply add in some column titles based around the data that you’re gathering. For example, with my example of Inbound.org, I had, ‘Name’, ‘Position’, ‘Company’, ‘Company URL’, etc. which you can see in the screenshot below. You’ll also want to add in a sample profile URL to work on building the template around.

spreadsheet admin
Now it’s time to start getting hands on with XPath.
How to Use XPathOnURL()

This handy little formula is made possible within Excel by the SEO Tools plugin. Now, I’m going to keep this very basic because there are loads of XPath tutorials available online that can go into the very advanced queries that are possible to use.

For this, I’m simply going to show you how to get the data we want and you can have a play around yourself afterwards (you can download the full template at the end of this post).

Here’s an example of an XPath query that gathers the name of the person within the profile that we’re scraping:

=XPathOnUrl(A2, "//*[@id='user-profile']/h2")

A2 is simply referencing the cell that contains the URL that we’re scraping. You’ll see in the screenshot above that this is Jason Acidre’s profile page.

The next part of the formula is the XPath.

What this essentially says is to scrape through the HTML to find a tag that has ‘user-profile’ id attached to it. This could be a div, span, a or whatever.

Once it’s found this tag, it then needs to look at the first h2 tag within this area and grab the text within it. This is Jason’s name, which you’ll see in the screenshot below of the code:

website code

Don’t be put off at this stage because you don’t need to go manually trawling through code to build these queries, there’s a much simpler way.

The easiest way to do this is by right-clicking on the element you want to scrape on the webpage (within Chrome); for example, on Inbound.org, this would be the profile name. Now click ‘Inspect element’.

inspect element

The developer tools window should now appear at the bottom of your browser (or in a separate window). Within that, you should see the element that you’ve drilled down on.

All you need to do now is right-click on it and press ‘Copy XPath’.
copy XPath

This will now copy the XPath code for your Excel formula to the clipboard. You’ll just need to add in the first part of the query, i.e. =XPathOnUrl(A2,

You can then paste in the copied XPath after this and add a closing bracket.

Note: When you use ‘Copy XPath’ it will wrap some parts of the code in double apostrophes (“) which you’ll need to change to single apostrophes. You’ll also need to wrap the copied XPath in double apostrophes.

Your finished code will look like this:
=XPathOnUrl(A2, "//*[@id='user-profile']/h2")

You can then apply this formula against any Inbound.org profile and it will automatically grab the user’s full name. Pretty good, right?

Check out the full video tutorial below that I’ve put together that talks you through this whole process:

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XPath Examples for Grabbing Other Data

As you’re probably starting to see, this technique could be scaled across any website online. This makes big data much more attainable and gives you the kind of results that an expensive paid tool would offer without any of the cost – bonus!

Here’s a few more examples of XPath that you can use in conjunction with the SEO Tools plugin within Excel to get some handy information.

Twitter Follower Count

If you want to grab the number of followers for a Twitter user then you can use the following formula. Simply replace A2 with the Twitter profile URL of the user you want data on. Just a quick word of warning with this one; it looks like it’s really long and complicated, but really I’ve just used another Excel formula to snip of the text ‘followers’ from the end.

=RIGHT(XPathOnUrl(D57,"//li[@class='ProfileNav-item ProfileNav-item--followers']"),LEN(XPathOnUrl(D57,"//li[@class='ProfileNav-item ProfileNav-item--followers']"))-10)

Google+ Follower Count

Like with the Twitter follower formula, you’ll need to replace A2 with the full Google+ profile URL of the user you want this data for.

=XPathOnUrl(H67,"//span[@class='BOfSxb']")

List of ‘Contributor to’ URLs

I don’t think I need to tell you the value of pulling in a list of websites that someone contributes content to. If you do want to know then check out this post that I wrote.

This formula is a little more complex than the rest. This is because I’m pulling in a list of URLs as opposed to just one entity. This requires me to use the StringJoin function to separate all of the outputs with a comma (or whatever character you’d like).

Also, you may notice that there is an additional section to the XPath query, “href”. This pulls in the link within the specific code block instead of the text.

As you’ll see in the full Inbound.org scraper template that I’ve made, this is how I pull in the Twitter, Google+, Facebook and LinkedIn profile links.

You’ll want to replace A2 with the Google+ profile URL of the person you wish to gather data on.

=StringJoin(", ",XPathOnUrl(A2,"//a[@rel='contributor-to nofollow']","href"))

Twitter Profile Image URL
If you want to get a large version of someone’s Twitter profile image then I’ve got just the thing for you.
Again, you’ll just need to substitute A2 with their Twitter profile URL.
=XPathOnUrl(A2,"//*[@class='profile-picture media-thumbnail js-tooltip']","data-resolved-url-large")


Some Findings from the Data I’ve Gathered

With all big data sets will come some interesting findings. Here’s a few little things that I’ve found from the top 100 influential users on Inbound.org.

average followers chart

The chart above maps out the average number of followers that the top 100 users have on both Twitter (12,959) and Google+ (9,601). As well as this, it shows the average number of users that they follow on Twitter (1,363).

The next thing that I’ve looked at is the job titles of the top 100 users. You can see the most common occurrences of terms within the tag cloud below:

Job titlesFinally, I had a look through all of the domains listed within each of the top 100 Inbound.org users’ Google+ ‘contributor to’ sections and mapped out the most frequently mentioned sites.

Here’s the spread of domains that were the most popular to be contributed to:

domain frequency
It Doesn’t Stop There

As you’ve probably gathered, this can be scaled out across pretty much any community/forum/directory/website online.

With this kind of intelligence in your armoury, you’ll be able to gather more intelligence on your targets and increase the effectiveness of your outreach campaigns dramatically.

Also, as promised, you can download my full Inbound.org scraper template below:

[sdfile url=”http://www.matthewbarby.com/goodies/MatthewBarby-Inbound-Scraper.xlsx” redirect=”http://www.matthewbarby.com/thanks-downloading-inbound-scraper/”]

TL;DR

    Online communities hold valuable data on your target audiences – use it!
    Scale out your intelligence gathering by brushing up on your XPath.
    Download my Inbound.org scraper template and let it work its magic.

Source: http://www.matthewbarby.com/scraping-communities-with-xpath/