Personal SEO Is Essential For Your Career Success.

"Personal SEO" is the foundation technique for creating and managing personal online visibility, a requirement in today's search- and technology-obsessed world. Google alone processed no fewer than 2 trillion searches in 2016.

• SEO (search engine optimization) is a term used by web professionals to describe the art/science of making a web page appear in a search engine's results in a search for an appropriate and relevant term. For example, if the website is about sailboats, it should appear in the results when someone searches for "sailboats," hopefully near the top of page one.

• Personal SEO is the art/science of having a person's name appear in search engine results for a relevant search, like a name, job title or professional skill, again, hopefully on the top of the first page. Personal SEO also applies to searches done on sites like LinkedIn, Facebook and other relevant/searchable sites.

When someone does a search on our names, we must have positive content about us visible in search engines, as well as LinkedIn and other social networks and professional sites. Done well, personal SEO makes a person highly visible in search results on appropriate searches in appropriate websites.

Personal SEO has an impact in three main ways:

1. People search to expand their networks, find new potential business partners, customers and/or employers and also reconnect with people from their past.

2.Recruiters search to find candidates who are qualified for their job openings.

3. Recruiters also search to verify the qualifications of applicants before issuing interview invitations and job offers.

To consider anyone for a job, a business deal, or inclusion in a professional network, that person must be “findable.” Good personal SEO is how they are found.

Your Most Important Keywords: Your Name

Keywords are a significant component of SEO. For personal SEO, our name is our most important keyword phrase. According to a 2010 study funded by Microsoft, 79% of employers searched Google for the names of job applicants and candidates before seriously considering someone for their job opportunity. In all my discussions with recruiters since 2010, they consistently confirmed that they Googled job applicants, so my expectation is that this figure has greatly increased in 2017.

This relentless searching is the reason personal SEO is very important today. But it must be done correctly, with the right foundation. Today, that foundation is the version of a person's name used for their online professional visibility. The key is consistently using, and protecting, that name. Too many people use different versions of their names in different venues. They are "William A. Jones" on their business cards, "William Jones" on their LinkedIn Profile, "Bill Jones" on their resumes, and BillyJ-MBA on Facebook, Twitter and email. Anyone trying to connect those dots for Bill Jones will have difficulty.

Not being able to make that connection can be a serious problem if a professional network member, like a recruiter or potential client, is trying to find the right LinkedIn profile to go with the business card they received at a networking event. The names must match for quick confirmation and credibility.

If a recruiter has a name from an application or resume submittal, can they easily find the correct LinkedIn profile for that person? Recruiters are typically measured on how quickly they can fill a job (or beat other headhunters to fill the job). So, they are always in a hurry! If a recruiter must do extra research to confirm the identity of the candidate, they may move on to the next candidate, and, consequently, many opportunities may be lost.

Being invisible is not good either. If nothing (or nothing positive) is found related to the name being searched, negative assumptions are usually made that the person is out-of-date, hiding something, or using a fake name.

The Best Response: Defensive Googling

To monitor our online reputations, particularly what is associated with our names, I recommend what I call "defensive Googling." Defensive Googling is periodically (weekly or monthly) searching through Google and Bing on our names. If you don't know what someone will find associated with your name, you may have a serious problem – that you could address – but be totally unaware of it.

Defensive Googling will help uncover occurrences of:

• Mistaken online identity. People with the same name who have done something that would cause a recruiter or network connection to lose interest.

• Self-inflicted wounds. Public places where people have embarrassed themselves with unprofessional photos, language, ideas or suggestions, and publicly available information, like arrests for drunk driving.

• Famous/infamous doppelgänger who pushes someone so far down in search results that the person is never found, making that person look like a joker or invisible.

For example, I know a job seeker who has the same name as a deceased porn star, which complicated his job search because, when he used that version of his name, people didn't take him seriously after they did their usual search. They ignored him because they thought he was making a poor joke. Consequently, networking and job hunting were very tough.

The solution: he changed the version of his name used for his job search (email address, business cards, LinkedIn Profile, resume, public Facebook account, etc.). When he added his middle name to his professional identity, he separated himself from the issue because, fortunately, the star did not share (or make public) that same middle name.

The Best Strategy

We all need to build a strong foundation for our personal SEO and online reputation management, unavoidable aspects of 21st-century life. The keystone of that foundation is the name we use professionally. We must consistently use that name for all professional activities, and avoid doing anything unprofessional while using it. Finally, we need to consistently practice defensive Googling to monitor our professional name so we can learn about and manage potential threats. [source site]

Digital Science Adds Extra Dimensions to Scholarly Research Data by Terry Ballard 
Posted On January 23, 2018.

London-based technology company Digital Science announced the launch of Dimensions, a new platform that aims to transform scholarly search, making previously hidden information available to researchers. The service went live on Jan. 15 with an offering of more than 9 million OA articles, 124 million formerly siloed documents (i.e., those hidden from view in universities and other research organizations), 86 million articles and books, and 34 million patents. All of this is linked through 3.7 billion connections. Driven by requests and feedback from the company’s development partner community, the publication and citation data is freely available to individual researchers.

Digital Science was created in 2010 as a digital spinoff of Macmillan and Nature and today is separately owned and managed. It began investing in, acquiring, and creating a number of related companies in the IT space. Launched in 2015, the Global Research Identifier Database (GRID) identifies research institutions around the world. The Digital Science Consultancy comprises a team working on data cleaning, linking, and text mining for scholarly projects. figshare is a data management tool to store, preserve, and share scholarly research information. Altmetric provides comprehensive data on the attention that a researcher’s work is having in the field at large. ReadCube is a bibliographic citation management tool that allows users to work with every type of device to search, store, and manage information in the chosen field of research, as well as offer updates based on known interests. Symplectic is a research information management tool. ÜberResearch is a software company that provides solutions to nonprofits and organizations seeking funding. Digital Science has also collected more than 100 research funders and universities in its stable of research partners. (The company once offered another product named Dimensions, which was centered on funding.)

The Components of Dimensions

Dimensions has taken components of Digital Science’s diverse products and melded them into one platform that provides a universe of synthesized and accessible data to the serious researcher. One search leads the user to the citations and abstracts on any topic; the clinical trials, grants, and patents associated with it; the citations that any publication has gathered; and even data on which projects are generating tweets, blog articles, Facebook posts, and newspaper stories.

Furthermore, it reports data about the people who are generating this attention. These reports include geographic breakdowns as well as information about what type of researcher is involved. Digital Science management maintains that most such products on the market are one-dimensional, as they focus on just one aspect of research, namely publications. Dimensions will allow subscribers to trace a concept from grant awards through clinical trials, publications, and entry into the market. The company also points out that much ongoing research is siloed. This has the effect of producing redundant research when institutions are working on the same problem, unaware of the duplication of effort.

What Makes Dimensions Different

Dimensions eliminates barriers to discovery by making more than 860 million academic citations freely available and delivering one-click access to a massive and unique set of data. Built using real-world use cases, it has advanced semantic tools developed over 7 years by experts in the Digital Science companies.

The most basic form of Dimensions will be available for free to any interested researcher. A more functional version will be available to institutions using a pricing model designed to reduce the strain on budgets, allowing them to provide their community with a more comprehensive view of the research landscape. This will give institutional libraries the capability of integrating full-text access to existing subscriptions within the institution. “We believe that access to scholarly data should be available at a fair price,” says Christian Herzog, CEO of ÜberResearch, who is leading the Dimensions efforts within Digital Science. Management team members told me that the institutional price is generally more than 50% less than that of comparable products, which largely provide only publication and citation access.

According to Stephen Leicht, COO of Digital Science Discovery & Analytics Group and co-founder of ÜberResearch, in his work with Digital Science’s many partners, there was nearly unanimous interest in somebody creating a comprehensive database like this. The partners include major players in government, the private sector, and higher education. The management team members finally took it upon themselves to be the pioneers. Leicht says that after 23 years in the IT field, he sees this as a chance to help create a product that is world-changing. He believes that even in its infancy, Dimensions is worthy of being considered in the company of Google Scholar and Scopus.

Daniel Hook, CEO at Digital Science, says, “The Dimensions project is a response to an urgent need for a more modern and inclusive research information platform, one which truly services the needs of both researchers and research institutions. Digital Science has always placed a focus on close collaboration with the scholarly community to develop and deliver solutions that will directly benefit the future of research; in creating Dimensions, we are empowering researchers, institutions, government, funders and publishers to redefine the ways in which scholarly work is discovered and evaluated.”

Getting a First Look

Digital Science gave NewsBreaks an advance look at the subscription version of Dimensions. (I was told that the free version allows users to search only for publications, whereas the subscription version also has searches for grants, clinical trials, and patents. This extra material does show up to enhance the data in the publication searches.) With a search bar at the top and facets on the left, it was intuitive and easy to perform searches. In addition, to date, there are 13 facets for limiters such as location, funder, and field of research. There is also a chance to refine searches to OA publications only.

I noted that on a machine that was loaded with the Kopernio and Unpaywall applications in Chrome, numerous full-text articles were made available in Dimensions, whereas otherwise, only the citation and abstract would be provided. Additionally, Dimensions is making access to full texts available directly from the application without a browser extension required, and it is working actively with the Unpaywall team.

The Altmetric data in the enhanced Dimensions is particularly impressive. In the article “Signatures of Positive Selection and Local Adaptation to Urbanization in White-Footed Mice (Peromyscus leucopus),” we learn that there is an Altmetric score of 56, meaning that the article, published only in October 2017, has generated considerable attention from the press, on Twitter, and from Mendeley users. The entry shows that the article is in the top 5% in terms of attention. Further, we learn that the most likely readers are other scientists. Other articles show readership, including graduate students, faculty members, and medical practitioners. Each such entry includes a map of the world indicating which countries have noticed the research.

One important feature of each initial results screen is seen on the right-hand side of the display—a line graph showing activity on each topic within the last 10 years. Prospective grant applicants would obviously be more interested in a topic that is generating rising interest than a subject that peaked 8 years ago.

The default in each search set is to display the hits by date, with the most recent showing at the top. There is also a drop-down menu for other options. When there is a large set of hits (usually the case), I found it more useful to sort by relevance.

Sara Rouhi, director of engagement and advocacy for Dimensions, says, “In a nutshell, Dimensions makes exponentially more data and metrics—better linked and curated than anything currently available—freely available to researchers and sustainably priced to institutions.”

Dimensions is available now at dimensions.ai. For more information on licensing options, please get in touch with the Dimensions team at info@dimensions.ai.

[Source Site]

Quintessentially search, no matter what the terminology

Oct 30, 2017

Judith Lamont, Ph.D.

This article appears in the issue November/December 2017, [Volume 26, Issue 9]

Insight engines, cognitive search and knowledge discovery are among the terms that evolved as new descriptors because the label of “search engine” was no longer adequate to describe what those solutions can now do. The techniques achieving fruition after years of development include the ability to fully integrate with other enterprise applications, explore and proactively present relevant information, allow use of natural language, use cognitive search and machine learning to continuously improve and personalize results, and retrieve and analyze in real time information from repositories throughout the enterprise.

Search engages on many levels

Coveo’s intelligent search software has been built out as a platform over the past year, so rather than serving only as a standalone search tool, it can also be embedded much more easily in such environments as intranets, community sites and contact centers. “The product is fundamentally the same,” says Mark Floisand, CMO of Coveo, “but now it is designed to be accessed from within other applications.”

In the broader context of information access, search engines have developed features that take into account much more information about users. “Coveo uses machine learning to learn from what has been proven successful for previous users, which allows it to continuously improve results for those who are exhibiting similar behavior,” Floisand says.

Coveo indexes disparate information from multiple data sources, securely ranks them by relevance to a search topic and is able to automatically compare and contrast documents in the repository. “The functionality is less dependent now on taxonomies and maintaining a thesaurus,” Floisand explains. “Coveo’s software uses proprietary techniques to explore the content for the most relevant set of responses based on user inquiries.” Machine learning allows Coveo to identify patterns as they change in real time. “Instead of worrying about specific queries, we think about the model to refine it and improve relevance,” Floisand says.

As with so many other enterprise software products, development is being driven by user expectations in the consumer market. “People have a much lower tolerance now for user experiences that are not personalized,” Floisand says. Personalization is generally something that must be developed over time, both because the applications need time to learn and the users need time to mature in their understanding and acceptance of the technology.

“Search needs to be delivered in three progressive stages,” Floisand advises. “First, if the user wants something specific, the information needs to be delivered efficiently—we call this being responsive. Then the system can begin to suggest other relevant information proactively that relates to the topic at hand. Finally, it can begin to deliver predictively, using machine learning to detect the user’s true intent and anticipate what they need next, having observed the behavior of others before them.”

Analyze this

Another example of the increased sophistication of search software products is that some now include advanced analytics. Sinequa was developed from the beginning with content analytics already integrated with the search function. As a cognitive search solution, Sinequa uses natural language processing (NLP) to interact with users and machine learning to continuously improve results. It provides out-of-box capability to extract elements from unstructured text, configurable to incorporate terms and phrases to specific businesses or domains.

Those text-mining agents (TMAs) are integrated into Sinequa’s indexing engine and allow detection of either standard expressions or complex “shapes” that represent the likely meaning of terms and phrases. “Once defined,” says Scott Parker, senior product marketing manager of Sinequa, “they can be normalized and used throughout the enterprise to extract relationships and concepts.” Natural language processing and machine learning capabilities are other elements that support its advanced search.

TMAs can sift through large volumes of text and data to identify authors and concepts even if the queries and documents do not include the exact terms. “They can be used to verify whether an author has been consulted on certain topics via email,” Parker points out, “and determine the volume of publications and correspondence.” In this way, the TMAs can map implicit networks and create links between them.

[Read more]