The American Psychiatric Association has approved its new manual of diagnoses — Diagnostic and Statistical Manual of Mental Disorders — after five years of controversy [nytimes].

For example, it has removed Aspberger’s as a diagnosis, lumping it in with autism, but it has split out hoarding from the more general category of obsessive-compulsive disorder. Lumping and splitting are the two most basic activities of cataloguers and indexers. There are theoretical and practical reasons for sometimes lumping things together and sometimes splitting them, but they also characterize personalities. Some of us are lumpers, and some of us are splitters. And all of us are a bit of each at various times.

The DSM runs into the problems faced by all attempts to classify a field. Attempts to come up with a single classification for a complex domain try to impose an impossible order:

First, there is rarely (ever?) universal agreement about how to divvy up a domain. There are genuine disagreements about which principles of organization ought to be used, and how they apply. Then there are the Lumper vs. the Splitter personalities.

Second, there are political and economic motivations for dividing up the world in particular ways.

Third, taxonomies are tools. There is no one right way to divide up the world, just as there is no one way to cut a piece of plywood and no one right thing to say about the world. It depends what you’re trying to do. DSM has conflicting purposes. For one thing, it affects treatment. For example, the NY Times article notes that the change in the classification of bipolar disease “could ‘medicalize’ frequent temper tantrums,” and during the many years in which the DSM classified homosexuality as a syndrome, therapists were encouraged to treat it as a disease. But that’s not all the DSM is for. It also guides insurance payments, and it affects research.

Given this, do we need the DSM? Maybe for insurance purposes. But not as a statement of where nature’s joints are. In fact, it’s not clear to me that we even need it as a single source to define terms for common reference. After all, biologists don’t agree about how to classify species, but that science seems to be doing just fine. The Encyclopedia of Life takes a really useful approach: each species gets a page, but the site provides multiple taxonomies so that biologists don’t have to agree on how to lump and split all the forms of life on the planet.

If we do need a single diagnostic taxonomy, DSM is making progress in its methodology. It has more publicly entered the fray of argument, it has tried to respond to current thinking, and it is now going to be updated continuously, rather than every 5 years. All to the good.

But the rest of its problems are intrinsic to its very existence. We may need it for some purposes, but it is never going to be fully right…because tools are useful, not true.

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David Wood of 3RoundStones.com is talking about Callimachus, an open source project that is also available through his company. [NOTE: Liveblogging. All bets on accuracy are off.]

We’re moving from PCs to mobile, he says. This is rapidly changing the Internet. 51% of Internet traffic is non-human, he says (as of Feb 2012). 35hrs of video are uploaded to YouTube every minute. Traditionally we dealt with this type of demand via data warehousing: put it all in one place for easy access. But that’s not true: we never really got it all in one place accessible through one interface. Jeffrey Pollock says we should be talking not about data integration but interoperability because the latter implies a looser coupling.

He gives some use cases:

• BBC wanted to have a Web presence for all of its 1500 broadcasts per day. They couldn’t do it manually. So, they decided to grab data from the linked open data data cloud and assemble the pages automatically. They hired fulltime editors to curate Wikipedia. RDF enabled them to assuemble the pages.

• O’Reilly Media switched to RDF reluctantly but for purely pragmatic reasons.

• BestBuy, too. They used RDFa to embed metadata into their pages to improve their SEO.

• Elsevier uses Linked Data to manage their assets, from acquisition to delivery.

This is not science fiction, he says. It’s happening now.

Then two negative examples:

• David says that Microsoft adopted RDF in the late 90s. But Netscape came out a portal tech based on RDF that scared Microsoft out of the standards effort. But they needed the tech, so they’ve reinvented it three times in proprietary ways.

• Borders was too late in changing its tech.

Then he does a product pitch for Callimachus Enterperise: a content management system for enterprises.

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I’m at the Semantic Technology & Business conference in NYC. Matthew Degel, Senior Vice President and Chief Architect at Viacom Media Networks is talking about “Modeling Media and the Content Supply Chain Using Semantic Technologies.” [NOTE: Liveblogging. Getting things wrong. Mangling words. Missing points. Over- and under-emphasizing the wrong things. Not running a spellpchecker. You are warned!]

Matthew says that the problem is that we’re “drowning in data but starved for information” Tere is a “thirst for asset-centric views.” And of course, Viacom needs to “more deeply integrate how property rights attach to assets.” And everything has to be natively local, all around the world.

Viacom has to model the content supply chain in a holistic way. So, how to structure the data? To answer, they need to know what the questions are. Data always has some structure. The question is how volatile those structures are. [I missed about 5 mins m-- had to duck out.]

He shows an asset tree, “relating things that are different yet the same,” with SpongeBob as his example: TV series, characters, the talent, the movie, consumer products, etc. Stations are not allowed to air a commercial with the voice actor behind Spoongey, Tom Kenney, during the showing of the SpongeBob show, so they need to intersect those datasets. Likewise, the video clip you see on your setup box’s guide is separate from, but related to, the original. For doing all this, Viacom is relying on inferences: A prime time version of a Jersey Shore episode, which has had the bad language censored out of it, is a version of the full episode, which is part of the series which has licensing contracts within various geographies, etc. From this Viacom can infer that the censored episode is shown in some geography under some licensing agreements, etc.

“We’ve tried to take a realistic approach to this.” As excited as they are about the promise, “we haven’t dived in with a huge amount of resources.” They’re solving immediate problems. They began by making diagrams of all of the apps and technologies. It was a mess. So, they extracted and encoded into a triplestore all the info in the diagram. Then they overlaid the DR data. [I don't know what DR stands for. I'm guessing the D stands for Digital, and the R might be Resource]] Further mapping showed that some apps that they weren’t paying much attention to were actually critical to multiple systems. They did an ontology graph as a London Underground map. [By the way, Gombrich has a wonderful history and appreciation of those maps in Art and Representation, I believe.]

What’s worked? They’re focusing on where they’re going, not where they’ve been. This has let them “jettison a lot of intellectual baggage” so that they can model business processes “in a much cleaner and effective way.” Also, OWL has provided a rich modeling language for expressing their Enterprise Information Model.

What hasn’t worked?

• “The toolsets really aren’t quite there yet.” He says that based on the conversations he’s had to today, he doesn’t think anyone disagrees with him.

• Also, the modeling tools presume you already know the technology and the approach. Also, the query tools presume you have a user at a keyboard rather than as a backend of a Web service capable of handling sufficient volume. For example, he’d like “Crystal Reports for SPARQL,” as an example of a usable tool.

• Visualization tools are focused on interactive use. You pick a class and see the relationships, etc. But if you want to see a traditional ERD diagram, you can’t.

• Also, the modeling tools present a “forward-bias.” E.g., there are tools for turning schemas into ontologies, but not for turning ontologies into a reference model for schema.

Matthew makes some predictions:

• They will develop into robust tools

• Semantic tech will enable queries such as “Show me all Madonna interviews where she sings, where the footage has not been previously shown, and where we have the license to distribute it on the Web in Australia in Dec.”

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I’m at the “Symposium on Digital Curation in the Era of Big Data” held by the Board on Research Data and Information of the National Research Council. These liveblog notes cover (in some sense — I missed some folks, and have done my usual spotty job on the rest) the morning session. (I’m keynoting in the middle of it.)

Alan Blatecky [pdf] from the National Science Foundation says science is being transformed by Big Data. [I can't see his slides from the panel at front.] He points to the increase in the volume of data, but we haven’t paid enough attention to the longevity of the data. And, he says, some data is centralized (LHC) and some is distributed (genomics). And, our networks are unable to transport large amounts of data [see my post], making where the data is located quite significant. NSF is looking at creating data infrastructures. “Not one big cloud in the sky,” he says. Access, storage, services — how do we make that happen and keep it leading edge? We also need a “suite of policies” suitable for this new environment.

He closes by talking about the Data Web Forum, a new initiative to look at a “top-down governance approach.” He points positively to the IETF’s “rough consensus and running code.” “How do we start doing that in the data world?” How do we get a balanced representation of the community? This is not a regulatory group; everything will be open source, and progress will be through rough consensus. They’ve got some funding from gov’t groups around the world. (Check CNI.org for more info.)

Now Josh Greenberg from the Sloan Foundation. He points to the opportunities presented by aggregated Big Data: the effects on social science, on libraries, etc. But the tools aren’t keeping up with the computational power, so researchers are spending too much time mastering tools, plus it can make reproducibility and provenance trails difficult. Sloan is funding some technical approaches to increasing the trustworthiness of data, including in publishing. But Sloan knows that this is not purely a technical problem. Everyone is talking about data science. Data scientist defined: Someone who knows more about stats than most computer scientists, and can write better code than typical statisticians :) But data science needs to better understand stewardship and curation. What should the workforce look like so that the data-based research holds up over time? The same concerns apply to business decisions based on data analytics. The norms that have served librarians and archivists of physical collections now apply to the world of data. We should be looking at these issues across the boundaries of academics, science, and business. E.g., economics works now rests on data from Web businesses, US Census, etc.

[I couldn't liveblog the next two — Michael and Myron — because I had to leave my computer on the podium. The following are poor summaries.]

Michael Stebbins, Assistant Director for Biotechnology in the Office of Science and Technology Policy in the White House, talked about the Administration’s enthusiasm for Big Data and open access. It’s great to see this degree of enthusiasm coming directly from the White House, especially since Michael is a scientist and has worked for mainstream science publishers.

Myron Gutmann, Ass’t Dir of of the National Science Foundation likewise expressed commitment to open access, and said that there would be an announcement in Spring 2013 that in some ways will respond to the recent UK and EC policies requiring the open publishing of publicly funded research.

After the break, there’s a panel.

Anne Kenney, Dir. of Cornell U. Library, talks about the new emphasis on digital curation and preservation. She traces this back at Cornell to 2006 when an E-Science task force was established. She thinks we now need to focus on e-research, not just e-science. She points to Walters and Skinners “New Roles for New Times: Digital Curation for Preservation.” When it comes to e-research, Anne points to the need for metadata stabilization, harmonizing applications, and collaboration in virtual communities. Within the humanities, she sees more focus on curation, the effect of the teaching environment, and more of a focus on scholarly products (as opposed to the focus on scholarly process, as in the scientific environment).

She points to Youngseek Kim et al. “Education for eScience Professionals“: digital curators need not just subject domain expertise but also project management and data expertise. [There's lots of info on her slides, which I cannot begin to capture.] The report suggests an increasing focus on people-focused skills: project management, bringing communities together.

She very briefly talks about Mary Auckland’s “Re-Skilling for Research” and Williford and Henry, “One Culture: Computationally Intensive Research in the Humanities and Sciences.”

So, what are research libraries doing with this information? The Association of Research Libraries has a jobs announcements database. And Tito Sierra did a study last year analyzing 2011 job postings. He looked at 444 jobs descriptions. 7.4% of the jobs were “newly created or new to the organization.” New mgt level positions were significantly higher, while subject specialist jobs were under-represented.

Anne went through Tito’s data and found 13.5% have “digital” in the title. There were more digital humanities positions than e-science. She posts a lists of the new titles jobs are being given, and they’re digilicious. 55% of those positions call for a library science degree.

Anne concludes: It’s a growth area, with responsibilities more clearly defined in the sciences. There’s growing interest in serving the digital humanists. “Digital curation” is not common in the qualifications nomenclature. MLS or MLIS is not the only path. There’s a lot of interest in post-doctoral positions.

Margarita Gregg of the National Oceanic and Atmospheric Administration, begins by talking about challenges in the era of Big Data. They produce about 15 petabytes of data per year. It’s not just about Big Data, though. They are very concerned with data quality. They can’t preserve all versions of their datasets, and it’s important to keep track of the provenance of that data.

Margarita directs one of NOAA’s data centers that acquires, preserves, assembles, and provides access to marine data. They cannot preserve everything. They need multi-disciplinary people, and they need to figure out how to translate this data into products that people need. In terms of personnel, they need: Data miners, system architects, developers who can translate proprietary formats into open standards, and IP and Digital Rights Management experts so that credit can be given to the people generating the data. Over the next ten years, she sees computer science and information technology becoming the foundations of curation. There is no currently defined job called “digital curator” and that needs to be addressed.

Vicki Ferrini at the Lamont -Doherty Earth Observatory at Columbia University works on data management, metadata, discovery tools, educational materials, best practice guidelines for optimizing acquisition, and more. She points to the increased communication between data consumers and producers.

As data producers, the goal is scientific discovery: data acquisition, reduction, assembly, visualization, integration, and interpretation. And then you have to document the data (= metadata).

Data consumers: They want data discoverability and access. Inceasingly they are concerned with the metadata.

The goal of data providers is to provide acccess, preservation and reuse. They care about data formats, metadata standards, interoperability, the diverse needs of users. [I've abbreviated all these lists because I can't type fast enough.].

At the intersection of these three domains is the data scientist. She refers to this as the “data stewardship continuum” since it spans all three. A data scientist needs to understand the entire life cycle, have domain experience, and have technical knowledge about data systems. “Metadata is key to all of this.” Skills: communication and organization, understanding the cultural aspects of the user communities, people and project management, and a balance between micro- and macro perspectives.

Challenges: Hard to find the right balance between technical skills and content knowledge. Also, data producers are slow to join the digital era. Also, it’s hard to keep up with the tech.

Andy Maltz, Dir. of Science and Technology Council of Academy of Motion Picture Arts and Sciences. AMPA is about arts and sciences, he says, not about The Business.

The Science and Technology Council was formed in 2005. They have lots of data they preserve. They’re trying to build the pipeline for next-generation movie technologists, but they’re falling behind, so they have an internship program and a curriculum initiative. He recommends we read their study The Digital Dilemma. It says that there’s no digital solution that meets film’s requirement to be archived for 100 years at a low cost. It costs $400/yr to archive a film master vs $11,000 to archive a digital master (as of 2006) because of labor costs. [Did I get that right?] He says collaboration is key.

In January they released The Digital Dilemma 2. It found that independent filmmakers, documentarians, and nonprofit audiovisual archives are loosely coupled, widely dispersed communities. This makes collaboration more difficult. The efforts are also poorly funded, and people often lack technical skills. The report recommends the next gen of digital archivists be digital natives. But the real issue is technology obsolescence. “Technology providers must take archival lifetimes into account.” Also system engineers should be taught to consider this.

He highly recommends the Library of Congress’ “The State of Recorded Sound Preservation in the United States,” which rings an alarm bell. He hopes there will be more doctoral work on these issues.

Among his controversial proposals: Require higher math scores for MLS/MLIS students since they tend to score lower than average on that. Also, he says that the new generation of content creators have no curatorial awareness. Executivies and managers need to know that this is a core business function.

Demand side data points: 400 movies/year at 2PB/movie. CNN has 1.5M archived assets, and generates 2,500 new archive objects/wk. YouTube: 72 hours of video uploaded every minute.

Takeways:

• Show business is a business.

• Need does not necessarily create demand.

• The nonprofit AV archive community is poorly organized.

• Next gen needs to be digital natvies with strong math and sci skills.

• The next gen of executive leaders needs to understand the importance of this.

• Digital curation and long-term archiving need a business case.

Q&A

Q: How about linking the monetary value of the metadata to the metadata? That would encourage the generation of metadata.

Q: Weinberger paints a picture of flexible world of flowing data, and now we’re back in the academic, scientific world where you want good data that lasts. I’m torn.

A: Margarita: We need to look how that data are being used. Maybe in some circumstances the quality of the data doesn’t matter. But there are other instances where you’re looking for the highest quality data.

A: [audience] In my industry, one person’s outtakes are another person’s director cuts.

A: Anne: In the library world, we say if a little metadata would be great, a lot of it would be great. We need to step away from trying to capture the most to capturing the most useful (since can’t capture the most). And how do you produce data in a way that’s opened up to future users, as well as being useful for its primary consumers? It’s a very interesting balance that needs to be played. Maybe short-term need is a higher thing and long-term is lower.

A: Vicki: The scientists I work with use discrete data sets, spreadsheets, etc. As we get along we’ll have new ways to check the quality of datasets so we can use the messy data as well.

Q: Citizen curation? E.g., a lot of antiques are curated by being put into people’s attics…Not sure what that might imply as model. Two parallel models?

A: Margarita: We’re going to need to engage anyone who’s interested. We need to incorporate citizen corporation.

Anne: That’s already underway where people have particular interests. E.g., Cornell’s Lab of Ornithology where birders contribute heavily.

Q: What one term will bring people info about this topic?

A: Vicki: There isn’t one term, which speaks to the linked data concept.

Q: How will you recruit people from all walks of life to have the skills you want?

A: Andy: We need to convince people way earlier in the educational process that STEM is cool.

A: Anne: We’ll have to rely to some degree on post-hire education.

Q: My shop produces and integrates lots of data. We need people with domain and computer science skills. They’re more likely to come out of the domains.

A: Vicki: As long as you’re willing to take the step across the boundary, it doesn’t mater which side you start from.

Q: 7 yrs ago in library school, I was told that you need to learn a little programming so that you understand it. I didn’t feel like I had to add a whole other profession on to the one I was studying.

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Except Randall Munroe thinks going miscellaneous means giving up, rather than embracing the new organizational possibilities of blah blah blah.

(I am, of course, an awestruck fan of XKCD.)

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This is cross posted at the Harvard Digital Scholarship blog

Neil Jeffries, research and development manager at the Bodleian Libraries, has posted an excellent op-ed at Wikipedia Signpost about how to best represent scholarly knowledge in an imperfect world.

He sets out two basic assumptions: (1) Data has meaning only within context; (2) We are not going to agree on a single metadata standard. In fact, we could connect those two points: Contexts of meaning are so dependent on the discipline and the user's project and standpoint that it is unlikely that a single metadata standard could suffice. In any case, the proliferation of standards is simply a fact of life at this point.

Given those constraints, he asks, what's the best way to increase the interoperability of the knowledge and data that are accumulating on line at at pace that provokes extremes of anxiety and joy in equal measures? He sees a useful consensus emerging on three points: (a) There are some common and basic types of data across almost all aggregations. (b) There is increasing agreement that these data types have some simple, common properties that suffice to identify them and to give us humans an idea about whether we want to delve deeper. (c) Aggregations themselves are useful for organizing data, even when they are loose webs rather than tight hierarchies. 

Neil then proposes RDF and linked data as appropriate ways to capture the very important relationships among ideas, pointing to the Semantic MediaWiki as a model. But, he says, we need to capture additional metadata that qualifies the data, including who made the assertion, links to differences of scholarly opinion, omissions from the collection, and the quality of the evidence. "Rather than always aiming for objective statements of truth we need to realise that a large amount of knowledge is derived via inference from a limited and imperfect evidence base, especially in the humanities," he says. "Thus we should aim to accurately represent  the state of knowledge about a topic, including omissions, uncertainty and differences of opinion."

Neil's proposals have the strengths of acknowledging the imperfection of any attempt to represent knowledge, and of recognizing that the value of representing knowledge lies mainly in its getting linked it to its sources, its context, its controversies, and to other disciplines. It seems to me that such a system would not only have tremendous pragmatic advantages, for all its messiness and lack of coherence it is in fact a more accurate representation of knowledge than a system that is fully neatened up and nailed down. That is, messiness is not only the price we pay for scaling knowledge aggressively and collaboratively, it is a property of networked knowledge itself. 

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At the Future Forum conference in Dresden, I had the opportunity to hang out with Ranga Yogeshwar, a well-known television science journalist in Germany. We were deep into conversation at the speakers dinner when I mentioned that I work in a library, and he mentioned that his grandfather had been an earlly library scientist. It turns out that his grandfather was none other than S.R. Ranganathan, the father of library science. Among other things, Ranganathan invented the “Colon Classification System” (worst name ever) that uses facets to enable multiple simultaneous classifications, an idea that really needed computers to be fulfilled. Way ahead of his time.

So, the next day I took the opportunity to stick my phone in Ranga’s face and ask him some intrusive, personal questions about his grandfather:

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Rob Burnett, executive producer of Late Night with David Letterman is finishing up five hours of IAMA at Reddit, and 27 seconds ago posted a response to the question “Why is number 5 always the funniest out of the top 10?” What a dumb question! It’s always been obvious to me that #2 is the funniest.

And, well, I don’t mean to brag, but I’m right and gregorkafka (if that’s his real name) is wrong. Here’s Rob’s response to the question:

Don’t get me started. Every headwriter has their own approach to the Top 10. Here was mine:

10 Funny, but also straight forward. Reinforce the topic.

9 Medium strength. Start with two laughs. Get a tailwind.

8 Can be a little experimental. Maybe not everyone gets it, but ok.

7 Back on track. Something medium.

6 Crowd pleaser. One that will get applause. Will help bridge the first panel to the second.

5 Coming off #6, time to take a chance.

4 Starting to land the plane. Gotta be solid.

3 For me always the second funniest one you got.

2 Funniest one you have.

1 Funniest one that is short so the band doesn’t play over it.

I always tried to never give Dave two in a row that didn’t get a laugh. Of course you want all 10 to be killer, but you don’t always have that going in.

Number 2! We’re Number 2!

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MetaFilter popped up a three-year-old post from Derek Sivers about how streeet addresses work in Japan. The system does a background-foreground duck-rabbit Gestalt flip on Western addressing schemes. I’d already heard about it — book-larnin’ because I’ve never been to Japan — but the post got me thinking about how things scale up.

What we would identify by street address, the Japanese identify by house number within a block name. Within a block, the addresses are non-sequential, reflecting instead the order of construction.

I can’t remember where I first read about this (I’m pretty sure I wrote about it in Everything Is Miscellaneous), but it pointed out some of the assumptions and advantages of this systems: it assumes local knowledge, confuses invaders, etc. But my reaction then was the same as when I read Derek’s post this morning: Yeah, but it doesn’t scale. Confusing invaders is a positive outcome of a failure to scale, but getting tourists lost is not. The math just doesn’t work: 4 streets intersected by 4 avenues creates 9 blocks, but add just 2 more streets and 2 more avenues and you’ve enclosed another 16 blocks. So, to navigate a large western city you have to know many many fewer streets and avenues than the number of existing blocks.

But of course I’m wrong. Tokyo hasn’t fallen apart because there are too many blocks to memorize. Clearly the Japanese system does scale.

In part that’s because according to the Wikipedia article on it, blocks are themselves located within a nested set of named regions. So you can pop up the geographic hierarchy to a level where there are fewer entities in order to get a more general location, just as we do with towns, counties, states, countries, solar system, galaxy, the universe.

But even without that, the Japanese system scales in ways that peculiarly mirror how the Net scales. Computers have scaled information in the Western city way: bits are tucked into chunks of memory that have sequential addresses. (At least they did the last time I looked in 1987.) But the Internet moves packets to their destinations much the way a Japanese city’s inhabitants might move inquiring visitors along: You ask someone (who we will call Ms. Router) how to get to a particular place, and Ms. Router sends you in a general direction. After a while you ask another person. Bit by bit you get closer, without anyone having a map of the whole.

At the other end of the stack of abstraction, computers have access to such absurdly large amounts of information either locally or in the cloud — and here namespaces are helpful — that storing the block names and house numbers for all of Tokyo isn’t such a big deal. Point your mobile phone to Google Maps’ Tokyo map if you need proof. With enough memory,we do not need to scale physical addresses by using schemes that reduce it to streeets and avenues. We can keep the arrangement random and just look stuff up. In the same way, we can stock our warehouses in a seemingly random order and rely on our computers to tell us where each item is; this has the advantage of letting us put the most requested items up front, or on the shelves that require humans to do the least bending or stretching.

So, I’m obviously wrong. The Japanese system does scale. It just doesn’t scale in the ways we used when memory spaces were relatively small.

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(Here’s a version of the text of a submission I just made to BoingBong through their “Submitterator”)

Harvard University has today put into the public domain (CC0) full bibliographic information about virtually all the 12M works in its 73 libraries. This is (I believe) the largest and most comprehensive such contribution. The metadata, in the standard MARC21 format, is available for bulk download from Harvard. The University also provided the data to the Digital Public Library of America’s prototype platform for programmatic access via an API. The aim is to make rich data about this cultural heritage openly available to the Web ecosystem so that developers can innovate, and so that other sites can draw upon it.

This is part of Harvard’s new Open Metadata policy which is VERY COOL.

Speaking for myself (see disclosure), I think this is a big deal. Library metadata has been jammed up by licenses and fear. Not only does this make accessible a very high percentage of the most consulted library items, I hope it will help break the floodgates.

(Disclosures: 1. I work in the Harvard Library and have been a very minor player in this process. The credit goes to the Harvard Library’s leaders and the Office of Scholarly Communication, who made this happen. Also: Robin Wendler. (next day:) Also, John Palfrey who initiated this entire thing. 2. I am the interim head of the DPLA prototype platform development team. So, yeah, I’m conflicted out the wazoo on this. But my wazoo and all the rest of me is very very happy today.)

Finally, note that Harvard asks that you respect community norms, including attributing the source of the metadata as appropriate. This holds as well for the data that comes from the OCLC, which is a valuable part of this collection.

• Press release

• Harvard’s Open Metadata policy

• NY Times coverage

• API info

• OCLC’s blog post – Thank you, OCLC

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