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new shortened readme, since doc is on different site
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vsbuffalo committed Nov 16, 2023
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Expand Up @@ -31,297 +31,11 @@ simple, minimal, human and machine readable specification. But you don't need
to know the specifics — the simple `sdf` command line tool handles it all for
you.

## A Simple Workflow Example
## Documentation

If you'd like to follow the example along, first [install
SciDataFlow](#installing-scidataflow).

The user interacts with the Data Manifest through the fast and concurrent
command line tool `sdf` written in the inimitable [Rust
language](https://www.rust-lang.org). The `sdf` tool has a Git-like interface.
If you know Git, using it will be easy, e.g. to initialize SciDataFlow for a
project you'd use:

```console
$ sdf init
```

Registering a file in the manifest:

```console
$ sdf add data/population_sizes.tsv
Added 1 file.
```

Checking to see if a file has changed, we'd use `sdf status`:

```console
$ sdf status
Project data status:
0 files on local and remotes (1 file only local, 0 files only remote), 1 file total.

[data]
population_sizes.tsv current 3fba1fc3 2023-09-01 10:38AM (53 seconds ago)
```

Now, let's imagine a pipeline runs and changes this file:

```console
$ bash tools/computational_pipeline.sh # changes data
$ sdf status
Project data status:
0 files on local and remotes (1 file only local, 0 files only remote), 1 file total.

[data]
population_sizes.tsv changed 3fba1fc3 → 8cb9d10b 2023-09-01 10:48AM (1 second ago)

```

If these changes are good, we can tell the Data Manifest it should update its
record of this version:

```console
$ sdf update data/population_sizes.tsv
$ sdf status
Project data status:
0 files on local and remotes (1 file only local, 0 files only remote), 1 file total.

[data]
population_sizes.tsv current 8cb9d10b 2023-09-01 10:48AM (6 minutes ago)

```

**⚠️Warning**: SciDataFlow does not do data *versioning*. Unlike Git, it does
not keep an entire history of data at each commit. Thus, **data backup must be
managed by separate software**. SciDataFlow is still in alpha phase, so it is
especially important you backup your data *before* using SciDataFlow. A tiny,
kind reminder: you as a researcher should be doing routine backups *already* —
losing data due to either a computational mishap or hardware failure is always
possible.

## Pushing Data to Remote Repositories

SciDataFlow also saves researchers' time when submitting supplementary data to
services like Zenodo or FigShare. Simply link the remote service (you'll need
to first get an API access token from their website):

```console
$ sdf link data/ zenodo <TOKEN> --name popsize_study
```

You only need to link a remote once. SciDataFlow will look for a project on the
remote with this name first (see `sdf link --help` for more options).
SciDataFlow stores the authentication keys for all remotes in
`~/.scidataflow_authkeys.yml` so you don't have to remember them.

SciDataFlow knows you probably don't want to upload *every* file that you're
keeping track of locally. Sometimes you just want to use SciDataFlow to track
local changes. So, in addition to files being registered in the Data Manifest,
you can also tell them you'd like to *track* them:

```console
$ sdf track data/population_sizes.tsv
```

Now, you can check the status on remotes too with:

```console
$ sdf status --remotes
Project data status:
1 file local and tracked by a remote (0 files only local, 0 files only remote), 1 file total.

[data > Zenodo]
population_sizes.tsv current, tracked 8cb9d10b 2023-09-01 10:48AM (14 minutes ago) not on remote
```

Then, to upload these files to Zenodo, all we'd do is:

```console
$ ../target/debug/sdf push
Info: uploading file "data/population_sizes.tsv" to Zenodo
Uploaded 1 file.
Skipped 0 files.
```

## Retrieving Data from Remotes

A key feature of SciDataFlow is that it can quickly reunite a project's *code*
repository with its *data*. Imagine a colleague had a small repository
containing the code lift a recombination map over to a new reference genome,
and you'd like to use her methods. However, you also want to check that you can
reproduce her pipeline on your system, which first involves re-downloading all
the input data (in this case, the original recombination map and liftover
files).

First, you'd clone the repository:

```console
$ git clone [email protected]:mclintock/maize_liftover
$ cd maize_liftover/
```

Then, as long as a `data_manifest.yml` exists in the root project directory
(`maize_liftover/` in this example), SciDataFlow is initialized. You can verify
this by using:

```console
$ sdf status --remotes
Project data status:
1 file local and tracked by a remote (0 files only local, 0 files only remote), 1 file total.

[data > Zenodo]
recmap_genome_v1.tsv deleted, tracked 7ef1d10a exists on remote
recmap_genome_v2.tsv deleted, tracked e894e742 exists on remote
```

Now, to retrieve these files, all you'd need to do is:

```console
$ sdf pull
Downloaded 1 file.
- population_sizes.tsv
Skipped 0 files. Reasons:
```

Note that if you run `sdf pull` again, it will not redownload the file (this is
to over overwriting the local version, should it have been changed):

```console
$ sdf pull
No files downloaded.
Skipped 1 files. Reasons:
Remote file is indentical to local file: 1 file
- population_sizes.tsv
```

If the file has changed, you can pull in the remote's version with `sdf pull
--overwrite`. However, `sdf pull` is also lazy; it will not download the file
if the MD5s haven't changed between the remote and local versions.

Downloads with SciDataFlow are fast and concurrent thanks to the [Tokio Rust
Asynchronous Universal download MAnager](https://github.com/rgreinho/trauma)
crate. If your project has a lot of data across multiple remotes, SciDataFlow
will pull all data in as quickly as possible.

## Retrieving Data from Static URLs

Often we also want to retrieve data from URLs. For example, many genomic
resources are available for download from the [UCSC](http://genome.ucsc.edu) or
[Ensembl](http://ensembl.org) websites as static URLs. We want a record of
where these files come from in the Data Manifest, so we want to combine a
download with a `sdf add`. The command `sdf get` does this all for you — let's
imagine you want to get all human coding sequences. You could do this with:

```console
$ sdf get https://ftp.ensembl.org/pub/release-110/fasta/homo_sapiens/cds/Homo_sapiens.GRCh38.cds.all.fa.gz
⠄ [================> ] 9639693/22716351 (42%) eta 00:00:08
```

Now, it would show up in the Data Manifest:

```console
$ sdf status --remotes
Project data status:
0 files local and tracked by a remote (0 files only local, 0 files only remote), 1 files total.

[data > Zenodo]
Homo_sapiens.GRCh38.cds.all.fa.gz current, untracked fb59b3ad 2023-09-01 3:13PM (43 seconds ago) not on remote
```

Note that files downloaded from URLs are not automatically track with remotes.
You can do this with `sdf track <FILENAME>` if you want. Then, you can use `sdf
push` to upload this same file to Zenodo or FigShare.

Since modern computational projects may require downloading potentially
*hundreds* or even *thousands* of annotation files, the `sdf` tool has a simple
way to do this: tab-delimited or comma-separated value files (e.g. those with
suffices `.tsv` and `.csv`, respectively). The big picture idea of SciDataFlow
is that it should take mere seconds to pull in all data needed for a large
genomics project (or astronomy, or ecology, whatever). Here's an example TSV
file full of links:

```console
$ cat human_annotation.tsv
type url
cdna https://ftp.ensembl.org/pub/release-110/fasta/homo_sapiens/cdna/Homo_sapiens.GRCh38.cdna.all.fa.gz
fasta https://ftp.ensembl.org/pub/release-110/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.alt.fa.gz
cds https://ftp.ensembl.org/pub/release-110/fasta/homo_sapiens/cds/Homo_sapiens.GRCh38.cds.all.fa.gz
```

Note that this has a header, and the URLs are in the second column. To get this data, we'd use:

```console
$ sdf bulk human_annotation.tsv --column 2 --header
⠁ [ ] 0/2 (0%) eta 00:00:00
⠉ [====> ] 9071693/78889691 (11%) eta 00:01:22
⠐ [=========> ] 13503693/54514783 (25%) eta 00:00:35
```

**Columns indices are one-indexed** and `sdf bulk` assumes no headers by
default. Note that in this example, only two files are downloading — this is
because `sdf` detected the CDS file already existed. SciDataFlow tells you this
with a little message at the end:

```console
$ sdf bulk human_annotation.tsv --column 1 --header
3 URLs found in 'human_annotation.tsv.'
2 files were downloaded, 2 added to manifest (0 were already registered).
1 files were skipped because they existed (and --overwrite was no specified).
```

Note that one can also download files from URLs that are in the Data Manifest.
Suppose that you clone a repository that has no remotes, but each file entry
has a URL set. Those can be retrieved with:

```console
$ sdf pull --urls # if you want to overwrite any local files, use --ovewrite
```

These may or may not be `tracked`; tracking only indicates whether to *also*
manage them with a remote like Zenodo or FigShare. In cases where the data file
can be reliable retrieved from a steady source (e.g. a website like the UCSC
Genome Browser or Ensembl) you may not want to duplicate it by also tracking
it. If you want to pull in *everything*, use:

```console
$ sdf pull --all
```

## Adding Metadata

Some data repository services like Zenodo allow data depositions to be
associated with a creator's metadata (e.g. full name, email, affiliation).
SciDataFlow automatically propagates this from a file in
`~/.scidataflow_config`. You can set your user metadata (which should be done
early on, sort of like with Git) with:

```console
$ sdf config --name "Joan B. Scientist" --email "[email protected]" --affiliation "UC Berkeley"
```

Projects can also have store metadata, such as a title and description. This is
kept in the Data Manifest. You can set this manually with:

```console
$ sdf metadata --title "genomics_analysis" --description "A re-analysis of Joan's data."
```

## SciDataFlow Assets

Good scientific workflows should create shareable **Scientific Assets** that
are *trivial* to download and build upon in your own scientific work.
SciDataFlow makes this possible, since in essence each `data_manifest.yml` file
is like a minimal recipe specification for also how to *retrieve* data. The
`sdf asset` command simply downloads a `data_manifest.yml` from
SciDataFlow-Assets, another GitHub repository, or URL. After this is
downloaded, all files can be retrieved in one line:

$ sdf asset nygc_gatk_1000G_highcov
$ sdf pull --all

The idea of SciDataFlow-Assets is to have a open, user-curated collection of
these recipes at https://github.com/scidataflow-assets. Please contribute
an Asset when you release new data with a paper!
SciDataFlow has [extensive
documentation](https://vsbuffalo.github.io/scidataflow-doc/) full of
examples of how to use the various subcommands.

## SciDataFlow's Vision

Expand Down Expand Up @@ -390,21 +104,6 @@ asset; all it takes is a mere `sdf pull --overwrite`.

## Installing SciDataFlow

The easiest way to install SciDataFlow is to use the easy install script, which
detects if you have Rust on your system, and if not installs it. Then it will
install SciDataFlow via Rust's incredible `cargo` system. To run the easy
install script:

$ https://raw.githubusercontent.com/vsbuffalo/scidataflow/main/easy_install.sh | bash

If you are security-conscious, you can check the MD5 of SHA1 digests as below:

$ curl https://raw.githubusercontent.com/vsbuffalo/scidataflow/main/easy_install.sh | md5
75d205a92b63f30047c88ff7e3de1a9f

$ curl https://raw.githubusercontent.com/vsbuffalo/scidataflow/main/easy_install.sh | sha256sum
0a654048b932a237cb93a9359900919188312867c3b7aeea23843272bc616a71 -

If you'd like to the Rust Programming Language manually, [see this
page](https://www.rust-lang.org/tools/install), which instructs you to run:

Expand Down

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