A couple of examples of nifty tools that people have already shared:

• Facette, the faceted Delicious browser from MIT. (From Noah)
• Library of Congress on Flickr (From Andy, Becky, and Laura)
• ClerkDogs (From Nate)
• Cloudalicious (From James)

If it makes you think 202, we want to know about it.

In case your delicious bookmarks are still not as annotated/organized/categorized as much as you would like them to be, the fine folks over at MIT’s CSAIL have released a tool to help with that, dubbed Facette. Actaully seems to be an interesting project, tackling familiar issues and questions.

And if you find yourself casting about for concept map/mind map software, the folks at Mindomo have both a web based and an AIR app which can be installed locally for those times when you just need to diagram it out.

Maybe this will be useful for the Fall…

Gristmill points out that former USDA Economic Research Service researcher, Michael Roberts, argues that there may not actually be more small farms, there may simply be a difference in what “counts” as a small farm.

The important revelation here is that the USDA uses statistical weighting to arrive at the numbers for these micro-farms since many of these people don’t even self-identify as farmers — and so their precision is entirely a question of their methodology, i.e. how they decide to model the presence/frequency of these small operations. Census weighting is, of course, both controversial and necessary. Counting everything by hand can have a larger margin for error than rigorous statistical modeling. Indeed, this “controversy” is right now at the heart of a monumental battle between Democrats and Republicans over the U.S. Census (just ask Sen. Judd Gregg).

That said, there is nothing inherently wrong with the practice. However, even if your overall approach is solid, if you then change your weighting techniques from year to year, comparing annual changes is all but impossible. And that appears to be exactly what the USDA is doing.

Needless to say, this is a pretty big deal.  Are the number of small farms actually growing?  Or is the current political climate in this realm simply pushing the USDA to fudge their methods a little, causing a shift in their categorization schemes?

“The days of just reshelving a book are over,” said Ms. Rosalia, who came to P.S. 225 nearly six years ago after graduating at the top of her class at the Queens College Graduate School of Library and Information Studies. “Now it is the information age, and that technology has brought out a whole new generation of practices.”

The following statistics attest to the popularity and impact of the pilot. As of October 23, 2008,
there have been:
• 10.4 million views of the photos on Flickr.
• 79% of the 4,615 photos have been made a “favorite” (i.e., are incorporated into personal
Flickr collections).
• More than 15,000 Flickr members have chosen to make the Library of Congress a
“contact,” creating a photostream of Library images on their own accounts.
• 7,166 comments were left on 2,873 photos by 2,562 unique Flickr accounts.
• 67,176 tags were added by 2,518 unique Flickr accounts.
• 4,548 of the 4,615 photos have at least one community-provided tag.
• Less than 25 instances of user-generated content were removed as inappropriate.
• More than 500 Prints and Photographs Online Catalog (PPOC) records have been
enhanced with new information provided by the Flickr Community.

Kinda cool, no?

In the reading “Context Data in Geo-Referenced Digital Photo Collections”, a single GPS point is used as metadata for an image. In fact, GPS device could capture more than just a point; it could capture the whole track of a user’s journey, and using the GPS tracks as metadata may be more interesting than using point alone. The image below shows a trip around Tian’anmen in Beijing, with images connected together by a GPS track.

There are at least two benefits about using a GPS track, instead of a point:

1.Better visualization. Users could “replay” the whole journey on a map together with images in animation (which I implemented myself with Javascript and Live Map API)

2. GPS tracks reveal more about users’ behavioral patterns, and these patterns can be used to improve mobile/location search. On an individual level, car drivers care less about distance than pedestrians, and traffic mode can be inferred from GPS tracks using supervised learning [1]; on a social level, locations frequently visited could be regarded as more “popular” and thus be given higher ranking in local search. Rich geographic information in GPS tracks is invaluable to mobile and location-based IR.

[1] “Learning Transportation Mode from Raw GPS Data for Geographic Applications on the Web”, WWW2008

“In psychiatry no one knows the causes of anything, so classification can be driven by all sorts of factors” — political, social and financial.”

“What you have in the end,” Mr. Shorter said, “is this process of sorting the deck of symptoms into syndromes, and the outcome all depends on how the cards fall.”

http://www.nytimes.com/2008/12/18/health/18psych.html?hp

Google Advanced Search

Not all elements of advanced search are represented in the search box itself, so the product isn’t making itself dispensable, but the interface does make it much easier to learn some of Google’s tricks. I learned that you can search for a range of numbers with “\d..\d”. The first result for ‘cars $500..$1000′ find $690.

:: GraphML (Standard XML format for describing graph data structure) ::

<graphml xmlns=”http://graphml.graphdrawing.org/xmlns” xmlns:xsi=”http://www.w3.org/2001/XMLSchema-instance” xsi:schemaLocation=”http://graphml.graphdrawing.org/xmlns http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd” >
<graph edgedefault=”undirected” parse.nodes=”10000″ parse.edges=”20000″>
<node id=”0″ />
<node id=”1″ />
<node id=”2″ />
…..
<node id=”9997″ />
<node id=”9998″ />
<node id=”9999″ />
<edge source=”2″ target=”1″ />
<edge source=”2″ target=”0″ />
<edge source=”3″ target=”1″ />
…..
<edge source=”0″ target=”8068″ />
<edge source=”1″ target=”9731″ />
<edge source=”1″ target=”5549″ />
</graph>
</graphml>

:: Normal text format ::

Topology: ( 10000 Nodes, 20000 Edges )
Model (1 – RTWaxman)

Nodes: ( 10000 )
0
1
2
…..
9997
9998
9999

Edges: ( 20000 )
0    2    1
1    2    0
2    3    1
…..
19997    0    8068
19998    1    9731
19999    1    5549

The second format was much better both in terms of file size and parsing speed. The XML format spent too much on putting structured metadata on the data. Once the data will be used in a limited domain, costs for structuring and standardizing data could overwhelm the benefit of doing so.

This case reminded me the warning of Svenonius, which was “putting infinite number of metadata to data is economically impossible,” although my case did not involve “infinite” numbers of metadata. Anyway, I experienced the tradeoff of IO and IR again.