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Throughout Sound of My Voice, in select theaters today, the big mystery is whether this beautiful, delicate cult leader is from the future, or just a big fake. Her followers are willing to do any absurd thing she demands of them, in exchange for a few morsels of future wisdom. » 4/27/12 9:00am 4/27/12 9:00am
You may have seen female cult leader Brit Marling luring her special flock of followers into her creepy basement in indie darling Sound of My Voice. But this two-time scifi screenwriter (Sound of My Voice and Another Earth) is more than just an actor and the face of indie SF. » 4/26/12 5:05pm 4/26/12 5:05pm
This week sees the big-screen debut of Sound of My Voice, the indie sensation centered around a mysterious cult leader (Brit Marling) who claims to be from the future. » 4/23/12 1:31pm 4/23/12 1:31pm
What if you were introduced to a spiritual guru who claimed to have traveled back in time from the year 2054? What would it take to make you believe her? Those are some of the questions raised by The Sound of My Voice, the Sundance sensation that's coming to select theaters on April 27. » 4/19/12 6:20am 4/19/12 6:20am
We've been excited about Sound of My Voice ever since it drew rave reactions at Sundance. Now you can check out the first trailer, watch the movie's first 12 minutes, and get our spoiler-y report from the movie's Wonder-Con panel. » 3/19/12 3:55pm 3/19/12 3:55pm
In the new movie Another Earth, a duplicate of our own planet appears from the far side of the sun, and starts moving towards "our" Earth. This could be the set-up for a disaster movie, or a story of war between Earths. » 7/22/11 9:00am 7/22/11 9:00am
Chances are, you've been hearing about Another Earth for months. It's the science fiction film with the tiny budget that wowed Sundance and won the Sloane Award. It's the introspective movie with a huge concept. It's a triumph of storytelling over razzle-dazzle. » 7/18/11 12:47pm 7/18/11 12:47pm


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US 7962487 B2 Abstract Techniques described herein allow for suggesting creation of tools for improving search engine performance. Specifically, these tools focus on producing more relevant search engine results via a URL-based query clustering method. These tools first extract tokens from Uniform Resource Locators associated to search queries. With these tokens, these tools form query clusters of common tokens. The resulting clusters can be used to help understand the similarities in user search queries via URL-based cluster queries to produce more relevant search results.
Claims(20) 1. A method of associating a search query to a cluster comprising:
receiving a Uniform Resource Locator (URL) returned in response to a search based on a search query;
extracting a first set of tokens from the URL, wherein the extracting comprises:
i. splitting the URL by predefined separators;
ii. removing URL stop-words; and
iii. merging identical tokens;
determining a similarity between the first set of tokens and each of multiple set of tokens extracted from multiple other URLs returned in response to other search queries; and
associating the search query with a cluster, wherein the associating is based at least in part on the similarities between the first set of tokens and each of the multiple set of tokens extracted from the other URLs.
2. The method of
3. The method of
4. The method of the
5. The method of
6. A method of associating a search query to a cluster comprising:
receiving a Uniform Resource Locator (URL) returned in response to a search based on a search query;
extracting a first set of tokens from the URL;
determining a similarity between the first set of tokens and each of a multiple set of tokens extracted from multiple other URLs returned in response to other search queries; and
associating the search query with a cluster, wherein the associating is based at least in part on the similarities between the first set of tokens and each of the multiple set of tokens extracted from the other URLs.
7. The method of the
splitting the URL by predefined separators; and
removing numerical characters.
8. The method of the
9. The method of the
10. The method of
11. The method of the
12. The method of
13. The method of the
14. One or more computer-readable media storing computer-executable instructions that, when executed on one or more processors, perform acts comprising:
extracting a first set of tokens from a Uniform Resource Locator (URL), wherein the extracting comprises:
i. splitting the URL by predefined separators; and
ii. removing numerical characters;
determining a similarity between the first set of tokens and each of a multiple set of tokens extracted from multiple other URLs; and
associating the search query with a cluster, wherein the associating is based at least in part on the similarities between the first set of tokens and each of the multiple set of tokens extracted from the other URLs.
15. One or more computer-readable media as recited in
16. One or more computer-readable media as recited in
17. One or more computer-readable media as recited in
18. One or more computer-readable media as recited in
19. One or more computer-readable media as recited in
20. One or more computing devices comprising:
one or more processors; and
the one or more computer-readable media storing the computer-executable instructions as recited in
Description Search engines serve to locate information both on the World Wide Web and off. For example, search engines search for documents with keywords and return a list of the documents where the keyword was found. Search engines routinely index billions of documents in this manner, which gives users a way to find documents by entering a keyword search query. However, search engines do not always return the most relevant results in order. A user may enter a search query with a specific intent of the results she wants, but often times the desired documents are not highly ranked. This is typically because a search engine only reads meta-tags or only the first few hundred words of the document text which does not allow accurate result ranking. While existing search engine technology has proven very useful, there remains ample room for improvement of search engine technology. This document describes tools for improving search engine performance. Specifically, these tools focus on producing more relevant search engine results via a URL-based query clustering method. These tools first extract tokens from Uniform Resource Locators associated to search queries. With these tokens, these tools form query clusters of common tokens. The resulting clusters can be used to help understand the similarities in user search queries via URL-based cluster queries to produce more relevant search results. This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The detailed description is described with reference to accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different figures indicates similar or identical items. This document describes tools for associating two or more search queries whose search results produce similar Uniform Resource Locators (URLs). The association is produced via URL-based query clusters. These clusters are comprised of common tokens created from URL search results. The association is useful because URLs that share common tokens typically refer to topics that are relevant to each other. Associating the search queries can aid in revealing a user's actually intent behind a search query. Common tokens from URLs are useful in generating this association because they typically reflect a significant amount of information about the topic they refer to. Even though URLs are composed of a limited amount of text in comparison to, for example, actual text on a page that the URL refers to, URLs are typically very carefully designed by web masters to accurately represent the content of the topic. Thus tokens are rich in semantics hints. For example, URLs with common tokens typically pertain to relevant pages from the same domains or have common contents. In this manner, a first search query producing certain tokens is associated with a query cluster created from search queries with tokens that are similar to the tokens produced by the first search query. From the resulting query cluster information, data can be extracted to reveal a user's true intent in order to provide more accurate search results. In one embodiment, first common token clusters are created from a plurality of URLs produced from a plurality of search queries. A developer then assigns category names (such as "movie actors," "social network sites," etc.) to each cluster. These query cluster categories yield valuable information. Specifically, analysis of query cluster categories can for example, yield user popularity of specific clusters. With this information, search engine query output can then be re-ranked to produce more relevant search results. The claimed tools therefore provide for a method to provide more relevant search results based on query clusters created from common URL tokens from a plurality of search queries. The discussion begins with a section entitled "Illustrative Architecture", which describes one non-limiting environment that may implement the claimed tools. A second and final section, entitled "Illustrative Processes", describes processes for creating and employing query clusters illustrated in the architecture of This brief introduction, including section titles and corresponding summaries, is provided for the reader's convenience and is not intended to limit the scope of the claims, nor the proceeding sections. Illustrative Architecture Additionally, As illustrated, device 108 includes one or more processors 114, as well as memory 116, upon which a query clustering engine 118 may reside. Query clustering engine 118 serves to cluster queries via common URL tokens as illustrated in result 132. Query clustering engine 118 may include token creation engine 120, token similarity engine 122 and cluster creation engine 124. Computing device 108 meanwhile may include a cluster popularity engine 126, cluster evaluation engine 128, and a cluster based ranking engine 130. These engines serve to utilize the query clusters created by query clustering engine 118. In non-limiting architecture of Query clustering typically begins when the developer submits a query to search engine(s) 112. Search engine(s) 112 has previously indexed a plurality of files from content providers 110(1) and 110(N) and returns search results with corresponding URLs to query clustering engine 118 on device 108. Token creation engine 120 then extracts tokens from the URLs. In one embodiment, token creation engine breaks a URL into fragments that may be comprised of words, numbers etc that are used to form tokens. These tokens serve to carry useful information about the content of the location that the URL points to such as a domain name and/or specific topic. By being breaking the URL into fragments, analysis of the URL as well as comparisons to other URLs can be more easily done. Token similarity engine 122 then determines common token that are shared by URLs produced by other search queries. Cluster creation engine 124 analyzes the resulting common token search queries and then groups them with their corresponding search queries into query clusters. A graphical illustration is represented in results 132. Results 132 illustrate multiple search queries clustered together with other search queries that share common tokens. (See Once result 132 is attained, clusters are named and applications such as cluster popularity engine 126, cluster evaluation engine 128 and cluster based ranking engine 130 can then be applied to utilize results 132. Cluster popularity engine 126 serves to analyze clusters to reflect user intent via the query clusters. Cluster evaluation engine serves to aid in evaluating cluster information. For instance, specific clusters may contain cluster specific issues such as particularity relevant sites. Cluster ranking engine aids in re-ranking search results via cluster specific information. These engines can ultimately use the query clustering to provider the users operating computing devices 102 with more relevant search results. Illustrative Processes Referring to Tokens serve to carry useful information about the content of the page the URL points to such as a domain name and/or specific topics. In the illustrated example, token creation engine 120 extracts tokens from the URLs once the URLs are received. The URLs are broken into tokens because URLs are rich in meaningful semantic hints pertaining to the content of the location they refer to. For example, "Movie" is a common token among sites that deal with movie stars. "imdb"® (Internet Movie DataBase) can further distinguish queries on movies as this site hosts professional databases regarding movies. In other embodiments, instead of using only URLs, sources snippets, titles and clicked URLs with search URLs are also used to create tokens. In one embodiment, the URL is first separated into parts by token creation engine 120 by using predefined separators such as slashes, dots, dashes and hyphens. Second, numbers (other than domain names) and URL stop-words such as "com", "net", "index" and "php" are removed. Third, identical tokens in both lower case and uppercase are merged. For example, the URL: movies.nytimes.com/person/93341/Tom-Hanks is first separated into: movies, nytimes, com, person, 93341, Tom and Hanks. The number "93341" and the URL stop-word "com" are removed to get the tokens: movies, nytimes, person, Tom and Hanks. For instance, a desired number of selected URLs are chosen as ui1, ui2, . . . uik to represent a query as follows:
The URLs are then converted into tokens by the token creation engine 116 and yield:
Token similarity engine 122 is then used to find similarities between the resulting tokens belonging to the different URLs. In non-limiting examples, either a vector space model or a set model can be used to find similarities between URLs belonging to two different search queries as well as URLs belonging to the same search query. In the vector space model embodiment, the tokens are merged as if in a "bag" of tokens in a similar manner to using a vector space model in information retrieval. Then cosine similarity is used to measure the similarity between the tokens belonging to two search queries. Here, the query is represented by a certain number (k) of URLs. In this manner, a query becomes similar to a document in information retrieval. The query qi is represented by:
Here, wij is the weight of token tj in qi. The focus of Term Frequency-Inverse Document Frequency (TF-IDF) is to calculate the weights as follows:
TFij is a term frequency (such as how many times the token tj occurs in the query document qi. The document frequency is then counted upon all query documents and then the IDFj is calculated as follows:
Here, the denominator stands for the number of query document that contain token tj, and |Q| is the number of all query documents. Cosine similarity is then applied to measure query similarity via:
In contrast, in the vector space model embodiment, the set model keeps local information by treating each URL as a point in vector space. Thus a query is denoted by a set of points. Then a function is used to calculate the similarity between two sets or queries. Here, the focus is on the most similar parts of the returned URLs. Thus, we represent a query as a set of URLs. Each URL is denoted as a vector of tokens with TF-IDF weights as a query vector in Vector Space Model via:
Then, we calculate the similarity between a pair of URL vectors via:
Then, the similarity between queries as the sum of the m largest similarities of URL pairs is found via:
In one example, for the search queries 202(q 1) and 204 (q 2), if k=3 and m=2, the similarities of the 3*3 URL pairs between q1 and q2, are calculated. Then the resulting similarities are sorted and the largest two are selected. Finally, the sum of these two similarities becomes the similarity of the two queries. In this hierarchical clustering, the similarity between two clusters is defined as the largest similarity between their members. Cluster creation engine 124 then analyzes the resulting common tokens. During this analysis, each query is assigned to a distinct cluster based on the similarity of its tokens. Thus, unsupervised query clustering of method 200 is used to group search queries. In other embodiments the clustering can be supervised in which a developer would play an active role in making clustering decisions. In these embodiments, queries are not necessarily classified into a fixed taxonomy. Unsupervised clustering offers the flexibility to be able to detect new clusters that users are becoming interested in. In other words, common tokens are found from URLs, the URLs are clustered according to those common tokens and then a developer examines and then categorizes the new cluster. In one experiment, it was observed that URL-based clustering tends to produce two kinds of clusters. One kind of cluster is site related clusters. These clusters exhibit the queries that are related to a popular site. In one non-limiting example, the website Yahoo® is related to both "Yahoo® mail" and "Yahoo® search". Thus, these tokens would be clustered together, because they have the same domain name. A second kind of cluster that URL-based clustering creates is topical clusters. Topical clusters contain search queries with the same topic such as "movie stars." For example, "Mel Gibson" and "Tom Hanks" are both movie stars. At 304, the top URLs are determined for each search query. The top URLs can be determined in a multitude of ways. For instances, the top URLs may be determined based on an order of the URLs returned by the search engine. Additionally or alternatively, the top URLs may be determined based on the developer 106's own judgment regarding relevancy, popularity or similar factors. Other methods would be known to those skilled in the art. In addition, the exact number of URLs to be used can vary according to the desired complexity of the search. At 306, the common tokens are determined for each URL (see Moving onto 308 and 310, token similarities between queries are compared and each query is assigned to a cluster category based on those similarities (see Process 300 continues onto Cluster popularity serves to reflect user intent via the query clusters. This application seeks to understand "what users search for." User intent can be tracked in spatial and temporal dimensions. In one embodiment, this is done before selecting clusters for evaluation and ranking. In one embodiment, at least one dataset is submitted to a search engine. The URL based query clustering method is applied to the results to yield query clusters. The query clusters are then given category names. Then the sum of query frequency is then calculated to represent the hotness of query clusters. The popularity or hotness (Hi) of each cluster is then calculated below:
Here, the frequency fq of each query q from the cluster Cl is summed together. In this embodiment, the summed frequency is then normalized by the total frequency of queries in the query set Qm. In this non-limiting example, the top 10 clusters are picked based upon their hotness. One of the ways user intent or cluster popularity can be tracked is by examining the spatial difference of user intent. For instance, users' search behavior can vary by specific country. Knowing country-specific user intent will aid in determining user intent in specific markets. A benefit of URL clustering is that URLs are uniform regardless of what language the content that the URL refers to is written in. In one embodiment, the differences of user intents in China versus the United States was conducted. From this method, it was ascertained that Chinese users were more interested in query clusters pertaining to "beauty pictures" and "software." As opposed to U.S. users who were not nearly as interested in these clusters. Another method user intent can be tracked is by examining temporal changes of user intent. Specifically, it is useful to know what users are interested in at certain times as user intent may be time specific. Thus, URL query clustering can reveal user intent over time. For instance, user intent often changes with real-life events. For example, in China, users constantly seek search results in "beauty picture" clusters, however, user interest in, the movie "Lust, Caution" occurred only in the top ten clusters in November and December—which happens to be just after the movie was released. At step 314, the query clusters are evaluated as illustrated according to different search engines that produced the URLs via cluster evaluation engine 128. Additionally, it is useful to focus on evaluating information within a specific query cluster. For instance, specific clusters may contain cluster specific issues such as particularity relevant sites. In one embodiment, evaluating search engine query results via query clusters begins with selecting queries along with their query frequency. Query clusters are produced as above. The clusters are given category names and the desired clusters are selected for evaluation. Two commercial search engines are used to show query clustering's evaluation ability. The ranking performance is evaluated by Normalized Discounted Cumulative Gain (NDCG). This method yields information on the ranking of highly relevant results. Given a query, the search results retuned by the search engine are annotated with five tiers from "perfect match" to "bad match." The NDCG at K is computed via:
Where Mq is a normalization constant calculated so that a perfect ordering receives an NDCG of 1 and each r(i) is an integer relevant label ("perfect match"=5 to "bad match"=1) of a result at position i. By examining the NDCG scores from different clusters from the two search engines—search engine performance can be evaluated. Finally, at step 316, the search queries from a search engine are re-ranked as shown in 330, using query clusters via cluster based ranking engine 130. Query cluster re-ranking seeks to improve ranking in combination with a general model to yield more relevant search results. This can be accomplished in an off-line method. First, in the off-line method, useful cluster ranking features are added for specific clusters. In one embodiment, when a user submits a search query it is determined whether it is associated to a specific cluster. If it is, then the specific model can be used to assist in ranking returned search results. These features are may be determined in steps 320 (determining the popularity of clusters) and/or 324 (evaluating clusters). These features permit, training cluster-specific model to aid in ranking. In addition, features can be extracted from sites such as site importance for a specific cluster to further train the specific model for the cluster. In one embodiment, search results are returned in response to a search query via a search engine. Then the category the query belongs to is determined. This determination can be done via a sorted cluster label that is mined off-line in a quick-search table. Another method is to use a query classifier. A specific query cluster model is associated to that search query. The search results are then re-ranked by the specific model. This supplementation of generic search results with cluster specific information significantly improves the relevancy of the returned search results. Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. Patent Citations
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Hair and makeup lovers adore beautiful images, and it's no surprise that Pinterest is filled with beauty inspirations, tips, and tutorials. BellaSugar's Pinterest is filled with hair and makeup ideas from the site as well as stellar images from the online beauty community. This week, our most repinned posts included tutorials from the red carpet and DIY makeup solutions. We love to hear from you, so remember to follow us on Pinterest to see more.
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Well..... maybe with a whimper for a lot of us. It's been an interesting year. Slow business. Decisions to make some changes in our lives. Most of us tightened our belts to the last notch and things are still stretched. But.... 2012 looms just over the horizon and the lights are beaming brightly over there. So with that said!! I'm gonna go play hard in the new 2012 sandbox!! HAPPY NEW YEAR TO ALL MY ACTIVERAIN FRIENDS!! Let's all make 2012 the best year ever and ONE TO REMEMBER!!
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Berkshire Hathaway Home Services Florida Network Realty - Jacksonville Beach, FL
Global Realtor in Jax Beach, FL - CRS, CIPS, SRES
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Shaman - tips and tricks? (self.ForgeWar)
submitted 2 years ago by dr1s
I picked up the game yesterday and an after a few round playing a pyro I settled with the shaman class. I love the heal system, it's so much more fun than every other MMO/PVP I played.
Anyone has some tips or tricks playing with a Shaman? I feel like Heal isn't that effective and just a waste of mana compared to Blessed Aura.
[–]The_American_Tragedy 1 point2 points3 points 2 years ago (1 child)
There's a pretty neat post on the forums about Shamans if you want to take a look at it.
[–]dr1s[S] 0 points1 point2 points 2 years ago (0 children)
Thanks! I was browsing the forums at work today, but somehow did't see that.
[–]DeadlyBlind 1 point2 points3 points 2 years ago (3 children)
As shaman I start off by giving as many stacks of fervor as possible since there won't be any confrontation for a while. My usual rotation is to put (don't know all the names) the HoT and give a few quick heals then try to get full mana and use the AoE heal. If we're under attack by a lot of ranged or objective ill put up the bubble. Then I use spiritual burden to slow down people attacking me ( jump away and block), people attacking a low target or for a retreat. One tip I wish more shamans did is use fervor when your at full mana and everyone is at full health or there is downtime. Spamming the HoT when you are full mana and their full health doesn't benefit anyone.
[–]Calumfornia 0 points1 point2 points 2 years ago (0 children)
Does it say somewhere how many stacks you have?
[–]dr1s[S] 0 points1 point2 points 2 years ago (1 child)
The AoE heal is awesome! So thanks for the tip with Fervor. I didn't know it's that important.
[–]froibo 0 points1 point2 points 2 years ago (0 children)
It improves your direct heal and makes disables less effect on your teammate.
[–]cheldog 1 point2 points3 points 2 years ago (1 child)
I don't know ability names, but I believe T is Heal, correct? Anyway, it can be an awesome boost to keep someone alive, especially if you've got a full 5 stacks of Fervor (V) on them. With the stacks it heals for about 25% of a lifebar, which has saved my teammates' asses plenty of times. That said, it's fairly situational and really requires those 5 stacks of Fervor to be amazing.
You're correct. I didn't use Spiritual Fervor so far, thanks for the tip!
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Art Sale
SPECIAL SALE Cat in the Hat Thing 1 and Thing 2 $50.00 |
Items for Sale $10.50 |
Original Black Chinned Hummingbird Bird Painting Garden Flowers Art SALE 10 x10 $125.00 |
Art Sale
How much should I sell my art work for?
I have done a few paintings that I am going to try to sell at an arts and crafts sale at work. I have never sold anything before and I'm not sure how to price art. It seems like every time I go to an art show people are selling their stuff for a ton of money. I am not looking to be an artist, but I also don't want to get laughed at. My paintings are 8.5 x 11 on watercolor paper. I used water color and acrylics. They took about an hour each. I thought about selling them for $10/each.
$10 each sounds about right. You want to price your work for the amount of time it took to make them. Other facters include any awards they may have recieved and the subject and quality.
Does Art Sell On The Internet?
If you are interested at all in art and photography, you will no doubt have noticed the huge number of impressive looking online galleries all over the internet. Judging by the numbers of signed up artists and photographers they seem extremely popular. But do these online galleries actually sell the work they display?
Well yes, quite often. It is a fact that many artists do very well selling art and photography on the internet. However, as with any offline gallery, showing work simply provides the opportunity to be seen by a larger audience thereby increasing chances of making sales. That said, the internet is a gateway to a worldwide audience and by joining an online gallery an artist has the potential of tapping into it. A word of caution though; some galleries are far easier to find than others. This is very important because no matter how attractive the layout of an online gallery appears, if customers can't find it then the artwork is not going to be seen.
So where are the best places to sell art online? Well, there are now many options open to both professional and aspiring artists and photographers. It all depends on individual objectives. For high visibility, joining one of the larger online galleries who get many thousands of visitors daily might be a wise consideration. A visitor count is usually provided somewhere on the homepage of these sites. There are some excellent ones to choose from but each is slightly different in terms of selection and joining procedures, how they assist in promoting work and how work is displayed on the website. Also, attention should be paid to areas such as who takes payment for work sold and how money is received after a sale. Commission taken from a sale is another important factor and can vary from 0% to 40+%.
Can one really make any money from selling art online then? The short answer to that is 'probably'. A good indication is whether the art sells in the 'real' world or not. If it does, then it is likely to be also successful online. If an artist hasn't yet ventured into selling, then there is nothing to lose by dipping a toe in the water by joining an online gallery. One place to test the waters is eBay but another word of caution here. If work is already selling at top end prices, it is very unlikely that these can be commanded on eBay. It is very important to avoid dropping prices for the purposes of selling on eBay too, or anywhere else online for that matter. Current customers won't like it. It devalues their purchases and also makes them feel they have been ripped off by paying higher prices. That said, some artists are making a good living on eBay with its phenomenal number, and still growing, of daily visitors.
What about fine art and photography prints, do they sell well online? Well, yes actually! Very well. In fact it would be fair to say that although the art print market is very competitive online, with huge websites offering everything from impressionist style canvases to Disney posters, there is still room for original art and fine art photography prints. The fact is that whilst people may be hesitant to spend a huge amount of money on an original artwork without seeing it 'in the flesh', prints are a safer gamble as they are much more affordable. As more and more people become comfortable with shopping online for a variety of goods, this can only help online art sales too.
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Part 1.Early Miocene of Uganda, including the first full account and discussion of the recently discovered oldest known hominoid, Morotopithecus bishopi.
Part 2. Middle Miocene to Pleistocene of the Tugen hills, Kenya, a comprehensive account of the hominids and their environmental context from members of the Baringo Basin Project.
Part 3. Quaternary Environments, with particular emphases on the English Midlands, western Scotland and southern Africa.
Every paper presents new, unpublished research e.g. the first full account of the oldest known hominoid (Uganda), Morotopithecus bishopi; the first integrated account of the Baringo Basin (Kenya) hominids and their environmental contexts; the climatic implications of the ages and directions of the dune winds of southern Africa; new ideas on physical and biological aspects of the British Quaternary.
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Development of a European resource on the origins of pathogens of aquaculture: The Europa Project
Publication: Research › Article in proceedings – Annual report year: 2004
This workshop described the EUROPA project, an EU-funded program aimed at creating a web-based database of molecular sequence data-sets related to significant pathogens of aquaculture. The project aims to focus the efforts of fish health researchers into generating large, evolving and readily available data-sets suited to the purpose of molecular epidemiology. This workshop reviewed the progress of this project, demonstrated the scope of the database developed to date, and sought input regarding the future development of this resource.
Original language | English |
Title of host publication | European Association of Fish Pathologists. Bulletin |
Number of pages | 84 |
Volume | 24/1 |
Place of publication | Scotland |
Publisher | European Association of Fish Pathologists |
Publication date | 2004 |
Edition | 1 |
Pages | 54-57 |
State | Published |
Conference | European Association of Fish Pathologists |
City | Copenhagen |
Period | 01/01/05 → … |
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