update paper and faq

4 files changed, 13 insertions, 13 deletions

diff --git a/docs/faq.md b/docs/faq.md
index 643f6bb..88803c9 100644
--- a/docs/faq.md
+++ b/docs/faq.md
@@ -8,6 +8,14 @@ Dat uses hyperdrive and a variety of other modules. Hyperdrive and Dat are compa
 
 ## How is Dat different than IPFS?
 
+IPFS and Dat share a number of underlying similarities. Both use Merkle DAGs to represent and deduplicate content-addressed pieces of data, have a mechanism for searching for peers who have a specific piece of data. Both have implementations which work in modern Web browsers, as well as command line tools.
+
+The two systems also have a number of differences. In order for IPFS to provide guarantees about interoperability, IPFS applications must use only the IPFS network stack. In contrast, Dat is only an application protocol and is agnostic to which network protocols (transports and naming systems) are used. As a result, Dat cannot make the same types of interoperability guarantees as IPFS.
+
+Dat keeps a secure version log of changes to a dataset over time which allows Dat to act as a version control tool. The type of Merkle tree used by Dat lets peers compare which pieces of a specific version of a dataset they each have and efficiently exchange the deltas to complete a full sync. It is not possible to synchronize or version a dataset in this way in IPFS without implementing such functionality yourself, as IPFS provides a CDN and/or filesystem interface but not a synchronization mechanism. In short, IPFS provides distribution of objects, Dat provides sychronization of datasets.
+
+Dat links are private and unadvertised unless they are explicitly shared publicly by the user, meaning that Dat supports private, end-to-end encrypted data exchange by default. In IPFS nodes must be encrypted to prevent them from being traversable (unlisted), but are still advertised.
+
 ## Is there JavaScript implementation?
 
 Yes, find it on GitHub: [dat-js](http://github.com/joehand/dat-js).
diff --git a/package.json b/package.json
index 2d8b6e8..16f54c7 100644
--- a/package.json
+++ b/package.json
@@ -13,7 +13,7 @@
     "deploy": "npm run build:deploy && echo docs.datproject.org > dist/CNAME && gh-pages -d dist",
     "watch:css": "nodemon -e scss -x \"npm run build:local\"",
     "start": "budo --dir dist --pushstate",
-    "paper": "pandoc --variable author=\"Maxwell Ogden, Karissa McKelvey, Mathias Buus\" --variable title=\"Dat - Distributed Dataset Synchronization And Versioning\" --variable date=\"Version 1.0.0, May 2016\" --variable classoption=twocolumn --variable papersize=a4paper -s dat-paper.md -o dat-paper.pdf"
+    "paper": "cd papers && pandoc --variable author=\"Maxwell Ogden, Karissa McKelvey, Mathias Buus\" --variable title=\"Dat - Distributed Dataset Synchronization And Versioning\" --variable date=\"UNFINISHED, October 2016\" --variable classoption=twocolumn --variable papersize=a4paper -s dat-paper.md -o dat-paper.pdf"
   },
   "repository": {
     "type": "git",
diff --git a/papers/dat-paper.md b/papers/dat-paper.md
index 9003214..3a4f750 100644
--- a/papers/dat-paper.md
+++ b/papers/dat-paper.md
@@ -1,6 +1,6 @@
 # Abstract
 
-Dat is a protocol designed for sharing datasets over networks such that their contents can be accessed randomly, be updated incrementally, and have the integrity of their contents be trusted. Dat clients can simultaneously be uploading and/or downloading, exchanging pieces of data with other clients in a swarm on demand. Datasets can be multi-homed such that if the original source goes offline clients can choose to automatically discover additional sources. As data is added to a Dat repository, updated files are split into pieces using Rabin fingerprinting and deduplicated against known pieces to avoid retransmission of data. Data are automatically verified using secure hashes meaning data is protected against tampering or corruption. Dat guarantees privacy if the Dat Link is kept secret, but does not provide authentication of sources, only authentication of data.
+Dat is a protocol designed for sharing datasets over networks such that their contents can be accessed randomly or fully replicated, be updated incrementally and streamed, and have the integrity of their contents be trusted. Dat clients can simultaneously be uploading and/or downloading, exchanging pieces of data with other clients in a swarm on demand. Datasets can be multi-homed such that if the original source goes offline clients can choose to automatically discover additional sources. As data is added to a Dat repository, updated files are split into pieces using Rabin fingerprinting and deduplicated against known pieces to avoid retransmission of data. Data are automatically verified using secure hashes meaning data is protected against tampering or corruption. Dat guarantees privacy if the Dat Link is kept secret, but does not provide authentication of sources, only authentication of data.
 
 # 1. Introduction
 
@@ -8,9 +8,7 @@ There are countless ways to share datasets over the Internet today. The simplest
 
 Cloud storage services like S3 ensure availability of data, but they have a centralized hub-and-spoke networking model and tend to be limited by their bandwidth, meaning popular files can be come very expensive to share. Services like Dropbox and Google Drive provide version control and synchronization on top of cloud storage services which fixes many issues with broken links but rely on proprietary code and services requiring users to store their data on cloud infrastructure which has implications on cost, transfer speeds, and user privacy.
 
-Distributed file sharing tools can become faster as files become more popular, removing the bandwidth bottleneck and making file distribution effectively free. They also implement discovery systems which prevents broken links meaning if the original source goes offline other backup sources can be automatically discovered. However these file sharing tools today are not supported by Web browsers and/or do not provide a mechanism for updating files without redistributing a new dataset which could mean entire redownloading data you already have.
-
-Decentralized version control tools for source code like Git provide a protocol for efficiently downloading changes to a set of files, but are optimized for text files and have issues with large files. Solutions like Git-LFS solve this by using HTTP to download large files, rather than the Git protocol. GitHub offers Git-LFS hosting but charges repository owners for bandwidth on popular files. Building a distributed distribution layer for files in a Git repository is difficult due to design of Git Packfiles which are delta compressed repository states that do not easily support random access to byte ranges in previous file versions.
+Distributed file sharing tools can become faster as files become more popular, removing the bandwidth bottleneck and making file distribution cheaper. They also implement discovery systems which prevents broken links meaning if the original source goes offline other backup sources can be automatically discovered. However these file sharing tools today are not supported by Web browsers and/or do not provide a mechanism for updating files without redistributing a new dataset which could mean entire redownloading data you already have.
 
 Scientists are an example of a group that would benefit from better solutions to these problems. Increasingly scientific datasets are being provided online using one of the above approaches and cited in published literature. Broken links and systems that do not provide version checking or content addressability of data directly limit the reproducibility of scientific analyses based on shared datasets. Services that charge a premium for bandwidth cause monetary and data transfer strain on the users sharing the data, who are often on fast public university networks with effectively unlimited bandwidth that go unused. Version control tools designed for text files do not keep up with the demands of data analysis in science today.
 
@@ -22,6 +20,8 @@ Dat is inspired by a number of features from existing systems.
 
 Git popularized the idea of a directed acyclic graph (DAG) combined with a Merkle tree, a way to represent changes to data where each change is addressed by the secure hash of the change plus all ancestor hashes in a graph. This provides a way to trust data integrity, as the only way a specific hash could be derived by another peer is if they have the same data and change history required to reproduce that hash. This is important for reproducibility as it lets you trust that a specific git commit hash refers to a specific source code state.
 
+Decentralized version control tools for source code like Git provide a protocol for efficiently downloading changes to a set of files, but are optimized for text files and have issues with large files. Solutions like Git-LFS solve this by using HTTP to download large files, rather than the Git protocol. GitHub offers Git-LFS hosting but charges repository owners for bandwidth on popular files. Building a distributed distribution layer for files in a Git repository is difficult due to design of Git Packfiles which are delta compressed repository states that do not easily support random access to byte ranges in previous file versions.
+
 ## 2.2 LBFS
 
 LBFS is a networked file system that avoids transferring redundant data by deduplicating common regions of files and only transferring unique regions once. The deduplication algorithm they use is called Rabin fingerprinting and works by hashing the contents of the file using a sliding window and looking for content defined chunk boundaries that probabilistically appear at the desired byte offsets (e.g. every 1kb).
@@ -64,14 +64,6 @@ WebTorrent implements the BitTorrent protocol in JavaScript using WebRTC as the
 
 IPFS is a family of application and network protocols that have peer to peer file sharing and data permanence baked in. IPFS abstracts network protocols and naming systems to provide an alternative application delivery platform to todays Web. For example, instead of using HTTP and DNS directly, in IPFS you would use LibP2P streams and IPNS in order to gain access to the features of the IPFS platform.
 
-IPFS and Dat share a number of underlying similarities. Both use Merkle DAGs to represent and deduplicate content-addressed pieces of data, have a mechanism for searching for peers who have a specific piece of data. Both have implementations which work in modern Web browsers, as well as command line tools.
-
-The two systems also have a number of differences. In order for IPFS to provide guarantees about interoperability, IPFS applications must use only the IPFS network stack. In contrast, Dat is only an application protocol and is agnostic to which network protocols (transports and naming systems) are used. As a result, Dat cannot make the same types of interoperability guarantees as IPFS.
-
-Dat keeps a secure version log of changes to a dataset over time which allows Dat to act as a version control tool. The type of Merkle tree used by Dat lets peers compare which pieces of a specific version of a dataset they each have and efficiently exchange the deltas to complete a full sync. It is not possible to synchronize or version a dataset in this way in IPFS without implementing such functionality yourself, as IPFS provides a CDN and/or filesystem interface but not a synchronization mechanism. In short, IPFS provides distribution of objects, Dat provides sychronization of datasets.
-
-Dat links are private and unadvertised unless they are explicitly shared publicly by the user, meaning that Dat supports private, end-to-end encrypted data exchange by default. In IPFS nodes must be encrypted to prevent them from being traversable (unlisted), but are still advertised.
-
 ## 2.8 Certificate Transparency/Secure Registers
 
 The UK Government Digital Service have developed the concept of a register which they define as a digital public ledger you can trust. In the UK government registers are beginning to be piloted as a way to expose essential open data sets in a way where consumers can verify the data has not been tampered with, and allows the data publishers to update their data sets over time.
diff --git a/papers/dat-paper.pdf b/papers/dat-paper.pdf
index aa92b1c..1595327 100644
--- a/papers/dat-paper.pdf
+++ b/papers/dat-paper.pdf