From a1edb73ea2c7e63425901ced005ead768a274fd5 Mon Sep 17 00:00:00 2001 From: Bryan Newbold Date: Fri, 10 Aug 2018 19:01:42 -0700 Subject: update rfc copies --- python/fatcat/templates/about.html | 217 +++++++++++++++++++++---------------- 1 file changed, 122 insertions(+), 95 deletions(-) (limited to 'python') diff --git a/python/fatcat/templates/about.html b/python/fatcat/templates/about.html index 3ff67c19..85f100b7 100644 --- a/python/fatcat/templates/about.html +++ b/python/fatcat/templates/about.html @@ -1,86 +1,114 @@ {% extends "base.html" %} {% block body %} -

fatcat Design Document (RFC)

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Version: April 2018 - -

fatcat is a half-baked idea to build an open, independent, collaboratively editable bibliographic database of most written works, with a focus on published research outputs like journal articles, pre-prints, and conference proceedings.

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fatcat Design Document (RFC)

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Contact: Bryan Newbold bnewbold@archive.org. Last updated 2018-08-10

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fatcat is a proposed open bibliographic catalog of written works. The scope of works is somewhat flexible, with a focus on published research outputs like journal articles, pre-prints, and conference proceedings. Records are collaboratively editable, versioned, available in bulk form, and include URL-agnostic file-level metadata.

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fatcat is currently used internally at the Internet Archive, but interested folks are welcome to contribute to design and development.

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Goals and Ecosystem Niche

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For the Internet Archive use case, fatcat has two primary use cases:

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In the larger ecosystem, fatcat could also provide:

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Technical Architecture

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The canonical backend datastore would be a very large transactional SQL server. A relatively simple and stable back-end daemon would expose an API (could be REST, GraphQL, gRPC, etc). As little "application logic" as possible would be embedded in this back-end; as much as possible would be pushed to bots which could be authored and operated by anybody. A separate web interface project would talk to the API backend and could be developed more rapidly.

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A cronjob would make periodic database dumps, both in "full" form (all tables and all edit history, removing only authentication credentials) and "flat" form (with only the most recent version of each entity, using only persistent IDs between entities).

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A goal is to be linked-data/RDF/JSON-LD/semantic-web "compatible", but not necessarily "first". It should be possible to export the database in a relatively clean RDF form, and to fetch data in a variety of formats, but internally fatcat would not be backed by a triple-store, and would not be bound to a specific third party ontology or schema.

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Microservice daemons should be able to proxy between the primary API and standard protocols like ResourceSync and OAI-PMH, and bots could consume external databases in those formats.

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The canonical backend datastore exposes a microservice-like HTTP API, which could be extended with gRPC or GraphQL interfaces. The initial datastore is a transactional SQL database, but this implementation detail is abstracted by the API.

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As little "application logic" as possible should be embedded in this back-end; as much as possible would be pushed to bots which could be authored and operated by anybody. A separate web interface project talks to the API backend and can be developed more rapidly with less concern about data loss or corruption.

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A cronjob will creae periodic database dumps, both in "full" form (all tables and all edit history, removing only authentication credentials) and "flattened" form (with only the most recent version of each entity).

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A goal is to be linked-data/RDF/JSON-LD/semantic-web "compatible", but not necessarily "first". It should be possible to export the database in a relatively clean RDF form, and to fetch data in a variety of formats, but internally fatcat will not be backed by a triple-store, and will not be bound to a rigid third-party ontology or schema.

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Microservice daemons should be able to proxy between the primary API and standard protocols like ResourceSync and OAI-PMH, and third party bots could ingest or synchronize the databse in those formats.

Licensing

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The core fatcat database should only contain verifyable factual statements (which isn't to say that all statements are "true"), not creative or derived content.

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The goal is to have a very permissively licensed database: CC-0 (no rights reserved) if possible. Under US law, it should be possible to scrape and pull in factual data from other corpuses without adopting their licenses. The goal here isn't to avoid all attibution (progeny information will be included, and a large sources and acknowledgements statement should be maintained), but trying to manage the intersection of all upstream source licenses seems untenable, and creates burdens for downstream users.

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Special care will need to be taken around copyright and original works. I would propose either not accepting abstracts at all, or including them in a partitioned database to prevent copyright contamination. Likewise, even simple user-created content like lists, reviews, ratings, comments, discussion, documentation, etc should go in separate services.

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The core fatcat database should only contain verifiable factual statements (which isn't to say that all statements are "true"), not creative or derived content.

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The goal is to have a very permissively licensed database: CC-0 (no rights reserved) if possible. Under US law, it should be possible to scrape and pull in factual data from other corpuses without adopting their licenses. The goal here isn't to avoid attribution (progeny information will be included, and a large sources and acknowledgments statement should be maintained and shipped with bulk exports), but trying to manage the intersection of all upstream source licenses seems untenable, and creates burdens for downstream users and developers.

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Special care will need to be taken around copyright, "original work" by editors, and contributions that raise privacy concerns. If abstracts are stored at all, they should be in a partitioned database table to prevent copyright contamination. Likewise, even simple user-created content like lists, reviews, ratings, comments, discussion, documentation, etc., should live in separate services.

Basic Editing Workflow and Bots

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Both human editors and bots would have edits go through the same API, with humans using either the default web interface or arbitrary integrations or client software.

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The usual workflow would be to create edits (or creations, merges, deletions) to individual entities one at a time, all under a single "edit group" of related edits (eg, correcting authorship info for multiple works related to a single author). When ready, the editor would "submit" the edit group for review. During the review period, humans could vote (or veto/approve if they have higher permissions), and bots can perform automated checks. During this period the editor can make tweaks if necessary. After some fixed time period (72 hours?) with no changes and no blocking issues, the edit group would be auto-accepted, if no auto-resolvable merge-conflicts have arisen. This process balances editing labor (reviews are easy, but optional) against quality (cool-down period makes it easier to detect and prevent spam or out-of-control bots). Advanced permissions could allow some trusted human and bot editors to push through edits more rapidly.

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Bots would need to be tuned to have appropriate edit group sizes (eg, daily batches, instead of millions of works in a single edit) to make human QA and reverts possible.

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Data progeny and citation would be left to the edit history. In the case of importing external databases, the expectation would be that special-purpose bot accounts would be used. Human editors would leave edit messages to clarify their sources.

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A style guide (wiki), chat room, and discussion forum would be hosted as separate stand-alone services for editors to propose projects and debate process or scope changes. It would be best if these could use federated account authorization (oauth?) to have consistent account IDs across mediums.

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Edit Log

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As part of the process of "accepting" an edit group, a row would be written to an immutable, append-only log table (which internally could be a SQL table) documenting each identifier change. This log establishes a monotonically increasing version number for the entire corpus, and should make interaction with other systems easier (eg, search engines, replicated databases, alternative storage backends, notification frameworks, etc).

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Itentifiers

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A fixed number of first class "entities" would be definied, with common behavior and schema layouts. These would all be semantic entities like "work", "release", "container", and "person".

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fatcat identifiers would be semanticly meaningless fixed length random numbers, usually represented in case-insensitive base32 format. Each entity type would have it's own identifier namespace. Eg, 96 bit identifiers would have 20 characters and look like:

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fcwork_rzga5b9cd7efgh04iljk
-https://fatcat.org/work/rzga5b9cd7efgh04iljk
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128-bit (UUID size) would have 26 characters:

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fcwork_rzga5b9cd7efgh04iljk8f3jvz
-https://fatcat.org/work/rzga5b9cd7efgh04iljk8f3jvz
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A 64 bit namespace is probably plenty though, and would work with most databse Integer columns:

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fcwork_rzga5b9cd7efg
-https://fatcat.org/work/rzga5b9cd7efg
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The idea would be to only have fatcat identifiers be used to interlink between databases, not to supplant DOIs, ISBNs, handle, ARKs, and other "registered" persistant identifiers.

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Both human editors and bots should have edits go through the same API, with humans using either the default web interface, integrations, or client software.

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The normal workflow is to create edits (or updates, merges, deletions) on individual entities. Individual changes are bundled into an "edit group" of related edits (eg, correcting authorship info for multiple works related to a single author). When ready, the editor would "submit" the edit group for review. During the review period, human editors vote and bots can perform automated checks. During this period the editor can make tweaks if necessary. After some fixed time period (72 hours?) with no changes and no blocking issues, the edit group would be auto-accepted if no merge conflicts have be created by other edits to the same entities. This process balances editing labor (reviews are easy, but optional) against quality (cool-down period makes it easier to detect and prevent spam or out-of-control bots). More sophisticated roles and permissions could allow some certain humans and bots to push through edits more rapidly (eg, importing new works from a publisher API).

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Bots need to be tuned to have appropriate edit group sizes (eg, daily batches, instead of millions of works in a single edit) to make human QA review and reverts managable.

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Data progeny and source references are captured in the edit metadata, instead of being encoded in the entity data model itself. In the case of importing external databases, the expectation is that special-purpose bot accounts are be used, and tag timestamps and external identifiers in the edit metadata. Human editors would leave edit messages to clarify their sources.

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A style guide (wiki) and discussion forum would be hosted as separate stand-alone services for editors to propose projects and debate process or scope changes. These services should have unified accounts and logins (oauth?) to have consistent account IDs across all mediums.

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Global Edit Changelog

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As part of the process of "accepting" an edit group, a row would be written to an immutable, append-only log table (which internally could be a SQL table) documenting each identifier change. This changelog establishes a monotonically increasing version number for the entire corpus, and should make interaction with other systems easier (eg, search engines, replicated databases, alternative storage backends, notification frameworks, etc.).

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Identifiers

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A fixed number of first-class "entities" are defined, with common behavior and schema layouts. These are all be semantic entities like "work", "release", "container", and "creator".

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fatcat identifiers are semantically meaningless fixed-length random numbers, usually represented in case-insensitive base32 format. Each entity type has its own identifier namespace.

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128-bit (UUID size) identifiers encode as 26 characters (but note that not all such strings decode to valid UUIDs), and in the backend can be serialized in UUID columns:

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work_rzga5b9cd7efgh04iljk8f3jvz
+https://fatcat.wiki/work/rzga5b9cd7efgh04iljk8f3jvz
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In comparison, 96-bit identifiers would have 20 characters and look like:

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work_rzga5b9cd7efgh04iljk
+https://fatcat.wiki/work/rzga5b9cd7efgh04iljk
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A 64-bit namespace would probably be large enought, and would work with database Integer columns:

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work_rzga5b9cd7efg
+https://fatcat.wiki/work/rzga5b9cd7efg
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The idea would be to only have fatcat identifiers be used to interlink between databases, not to supplant DOIs, ISBNs, handle, ARKs, and other "registered" persistent identifiers.

Entities and Internal Schema

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Internally, identifiers would be lightweight pointers to actual metadata objects, which can be thought of as "versions". The metadata objects themselves would be immutable once commited; the edit process is one of creating new objects and, if the edit is approved, pointing the identifier to the new version. Entities would reference between themselves by identifier.

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Internally, identifiers would be lightweight pointers to "revisions" of an entity. Revisions are stored in their complete form, not as a patch or difference; if comparing to distributed version control systems, this is the git model, not the mercurial model.

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The entity revisions are immutable once accepted; the editting process involves the creation of new entity revisions and, if the edit is approved, pointing the identifier to the new revision. Entities cross-reference between themselves by identifier not revision number. Identifier pointers also support (versioned) deletion and redirects (for merging entities).

Edit objects represent a change to a single entity; edits get batched together into edit groups (like "commits" and "pull requests" in git parlance).

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SQL tables would probably look something like the following, though be specific to each entity type (eg, there would be an actual work_revision table, but not an actual entity_revision table):

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entity_id
-    uuid
-    current_revision
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SQL tables would probably look something like the (but specific to each entity type, with tables like work_revision not entity_revision):

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entity_ident
+    id (uuid)
+    current_revision (entity_revision foreign key)
+    redirect_id (optional; points to another entity_ident)
 
 entity_revision
-    entity_id (bi-directional?)
-    previous: entity_revision or none
-    state: normal, redirect, deletion
-    redirect_entity_id: optional
-    extra: json blob
-    edit_id
+    revision_id
+    <entity-specific fields>
+    extra: json blob for schema evolution
 
-edit
-    mutable: boolean
-    edit_group
-    editor
+entity_edit
+    timestamp
+    editgroup_id
+    ident (entity_ident foreign key)
+    new_revision (entity_revision foreign key)
+    previous_revision (optional; points to entity_revision)
+    extra: json blob for progeny metadata
 
-edit_group

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Additional type-specific columns would hold actual metadata. Additional tables (which would reference both entity_revision and entity_id foreign keys as appropriate) would represent things like external identifiers, ordered author/work relationships, citations between works, etc. Every revision of an entity would require duplicating all of these associated rows, which could end up being a large source of inefficiency, but is necessary to represent the full history of an object.

+editgroup
+    editor_id
+    description
+    extra: json blob for progeny metadata
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Additional entity-specific columns would hold actual metadata. Additional tables (which would reference both entity_revision and entity_id foreign keys as appropriate) would represent things like authorship relationships (creator/release), citations between works, etc. Every revision of an entity would require duplicating all of these associated rows, which could end up being a large source of inefficiency, but is necessary to represent the full history of an object.

Scope

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Want the "scholarly web": the graph of works that cite other works. Certainly every work that is cited more than once and every work that both cites and is cited; "leaf nodes" and small islands might not be in scope.

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Focusing on written works, with some exceptions. Expect core media (for which we would pursue "completeness") to be:

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The goal is to capture the "scholarly web": the graph of written works that cite other works. Any work that is both cited more than once and cites more than one other work in the catalog is very likely to be in scope. "Leaf nodes" and small islands of intra-cited works may or may not be in scope.

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Overall focus is on written works, with some exceptions. The expected core focus (for which we would pursue "completeness") is:

journal articles
-books
+academic books
 conference proceedings
 technical memos
-dissertations
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Probably in scope:

+dissertations +monographs +well-researched blog posts +web pages (that have citations) +"white papers" +

Possibly in scope:

reports
 magazine articles
-published poetry
 essays
+notable mailing list postings
 government documents
-conference
 presentations (slides, video)
-datasets
+datasets +well-researched wiki pages +patents

Probably not:

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patents
-court cases and legal documents
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court cases and legal documents
+newspaper articles
+social media
 manuals
 datasheets
-courses

+courses
+published poetry

Definitely not:

audio recordings
 tv show episodes
@@ -88,57 +116,49 @@ musical scores
 advertisements

Author, citation, and work disambiguation would be core tasks. Linking pre-prints to final publication is in scope.

I'm much less interested in altmetrics, funding, and grant relationships than most existing databases in this space.

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fatcat would not include any fulltext content itself, even for cleanly licensed (open access) works, but would have "strong" (verified) links to fulltext content, and would include file-level metadata (like hashes and fingerprints) to help discovery and identify content from any source. Typed file-level links should make fatcat more useful for both humans and machines to quickly access fulltext content of a given mimetype than existing redirect or landing page systems.

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fatcat would not include any fulltext content itself, even for cleanly licensed (open access) works, but would have "strong" (verified) links to fulltext content, and would include file-level metadata (like hashes and fingerprints) to help discovery and identify content from any source. File-level URLs with context ("repository", "author-homepage", "web-archive") should make fatcat more useful for both humans and machines to quickly access fulltext content of a given mimetype than existing redirect or landing page systems. So another factor in deciding scope is whether a work has "digital fixity" and can be contained in a single immutable file.

Ontology

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Loosely following FRBR, but removing the "manifestation" abstraction, and favoring files (digital artifacts) over physical items, the primary entities are:

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Loosely following FRBR (Functional Requirements for Bibliographic Records), but removing the "manifestation" abstraction, and favoring files (digital artifacts) over physical items, the primary entities are:

work
-    type
-    <has> contributors
-    <about> subject/category
-    <has-primary> release
+    <a stub, for grouping releases>
 
 release (aka "edition", "variant")
     title
     volume/pages/issue/chapter
-    open-access status
+    media/formfactor
+    publication/peer-review status
+    language
     <published> date
-    <of a> work
-    <published-by> publisher
-    <published in> container
-    <has> contributors
-    <citation> citetext <to> release
+    <variant-of> work
+    <published-in> container
+    <has-contributors> creator
+    <citation-to> release
     <has> identifier
 
 file (aka "digital artifact")
-    <of a> release
-    <has> hashes
-    <found at> URLs
-    <held-at> institution <with> accession
+    <instantiates> release
+    hashes/checksums
+    mimetype
+    <found-at> URLs
 
-contributor
+creator (aka "author")
     name
-    <has> aliases
-    <has> affiliation <for> date span
-    <has> identifier
+    identifiers
+    aliases
 
-container
+container (aka "venue", "serial", "title")
     name
     open-access policy
     peer-review policy
     <has> aliases, acronyms
     <about> subject/category
     <has> identifier
-    <published in> container
-    <published-by> publisher
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-publisher
-    name
-    <has> aliases, acronyms
-    <has> identifier
+ <published-in> container + <published-by> publisher

Controlled Vocabularies

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Some special namespace tables and enums would probably be helpful; these should live in the database (not requiring a database migration to update), but should have more controlled editing workflow... perhaps versioned in the codebase:

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Some special namespace tables and enums would probably be helpful; these could live in the database (not requiring a database migration to update), but should have more controlled editing workflow... perhaps versioned in the codebase:

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These could also be enforced by QA bots that review all editgroups.

Unresolved Questions

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How to handle translations of, eg, titles and author names? To be clear, not translations of works (which are just separate releases).

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Are bi-directional links a schema anti-pattern? Eg, should "work" point to a primary "release" (which itself points back to the work), or should "release" have a "is-primary" flag?

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Should identifier and citation be their own entities, referencing other entities by UUID instead of by revision? This could save a ton of database space and chunder.

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Should contributor/author contact information be retained? It could be very useful for disambiguation, but we don't want to build a huge database for spammers or "innovative" start-up marketing.

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Would general purpose SQL databases like Postgres or MySQL scale well enough told hold several tables with billions of entries? Right from the start there are hundreds of millions of works and releases, many of which having dozens of citations, many authors, and many identifiers, and then we'll have potentially dozens of edits for each of these, which multiply out to 1e8 * 2e1 * 2e1 = 4e10, or 40 billion rows in the citation table. If each row was 32 bytes on average (uncompressed, not including index size), that would be 1.3 TByte on it's own, larger than common SSD disk. I think a transactional SQL datastore is the right answer. In my experience locking and index rebuild times are usually the biggest scaling challenges; the largely-immutable architecture here should mitigate locking. Hopefully few indexes would be needed in the primary database, as user interfaces could rely on secondary read-only search engines for more complex queries and views.

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How to handle translations of, eg, titles and author names? To be clear, not translations of works (which are just separate releases), these are more like aliases or "originally known as".

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Are bi-directional links a schema anti-pattern? Eg, should "work" point to a "primary release" (which itself points back to the work)?

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Should identifier and citation be their own entities, referencing other entities by UUID instead of by revision? Not sure if this would increase or decrease database resource utilization.

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Should contributor/author affiliation and contact information be retained? It could be very useful for disambiguation, but we don't want to build a huge database for spammers or "innovative" start-up marketing.

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Can general-purpose SQL databases like Postgres or MySQL scale well enough to hold several tables with billions of entity revisions? Right from the start there are hundreds of millions of works and releases, many of which having dozens of citations, many authors, and many identifiers, and then we'll have potentially dozens of edits for each of these, which multiply out to 1e8 * 2e1 * 2e1 = 4e10, or 40 billion rows in the citation table. If each row was 32 bytes on average (uncompressed, not including index size), that would be 1.3 TByte on its own, larger than common SSD disks. I do think a transactional SQL datastore is the right answer. In my experience locking and index rebuild times are usually the biggest scaling challenges; the largely-immutable architecture here should mitigate locking. Hopefully few indexes would be needed in the primary database, as user interfaces could rely on secondary read-only search engines for more complex queries and views.

I see a tension between focus and scope creep. If a central database like fatcat doesn't support enough fields and metadata, then it will not be possible to completely import other corpuses, and this becomes "yet another" partial bibliographic database. On the other hand, accepting arbitrary data leads to other problems: sparseness increases (we have more "partial" data), potential for redundancy is high, humans will start editing content that might be bulk-replaced, etc.

There might be a need to support "stub" references between entities. Eg, when adding citations from PDF extraction, the cited works are likely to be ambiguous. Could create "stub" works to be merged/resolved later, or could leave the citation hanging. Same with authors, containers (journals), etc.

References and Previous Work

The closest overall analog of fatcat is MusicBrainz, a collaboratively edited music database. Open Library is a very similar existing service, which exclusively contains book metadata.

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Wikidata seems to be the most successful and actively edited/developed open bibliographic database at this time (early 2018), including the wikicite conference and related Wikimedia/Wikipedia projects. Wikidata is a general purpose semantic database of entities, facts, and relationships; bibliographic metadata has become a large fraction of all content in recent years. The focus there seems to be linking knowledge (statements) to specific sources unambigiously. Potential advantages fatcat would have would be a focus on a specific scope (not a general purpose database of entities) and a goal of completeness (capturing as many works and relationships as rapidly as possible). However, it might be better to just pitch in to the wikidata efforts.

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Wikidata seems to be the most successful and actively edited/developed open bibliographic database at this time (early 2018), including the wikicite conference and related Wikimedia/Wikipedia projects. Wikidata is a general purpose semantic database of entities, facts, and relationships; bibliographic metadata has become a large fraction of all content in recent years. The focus there seems to be linking knowledge (statements) to specific sources unambiguously. Potential advantages fatcat would have would be a focus on a specific scope (not a general-purpose database of entities) and a goal of completeness (capturing as many works and relationships as rapidly as possible). However, it might be better to just pitch in to the wikidata efforts.

The technical design of fatcat is loosely inspired by the git branch/tag/commit/tree architecture, and specifically inspired by Oliver Charles' "New Edit System" blog posts from 2012.

There are a whole bunch of proprietary, for-profit bibliographic databases, including Web of Science, Google Scholar, Microsoft Academic Graph, aminer, Scopus, and Dimensions. There are excellent field-limited databases like dblp, MEDLINE, and Semantic Scholar. There are some large general-purpose databases that are not directly user-editable, including the OpenCitation corpus, CORE, BASE, and CrossRef. I don't know of any large (more than 60 million works), open (bulk-downloadable with permissive or no license), field agnostic, user-editable corpus of scholarly publication bibliographic metadata.

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RFC Changelog

+ {% endblock %} -- cgit v1.2.3