Problem 1: Have you ever wanted to reuse and build upon a research project's output or supplementary data, but can't find it?

SciDataFlow solves this issue by making it easy to unite a research project's data with its code. Often, code for open computational projects is managed with Git and stored on a site like GitHub. However, a lot of scientific data is too large to be stored on these sites, and instead is hosted by sites like Zenodo or FigShare.

Problem 2: Does your computational project have dozens or even hundreds of intermediate data files you'd like to keep track of? Do you want to see if these files are changed by updates to computational pipelines.

SciDataFlow also solves this issue by keeping a record of the necessary information to track when data is changed. This is stored alongside the information needed to retrieve data from and push data to remote data repositories. All of this is kept in a simple YAML "Data Manifest" (data_manifest.yml) file that SciDataFlow manages. This file is stored in the main project directory and meant to be checked into Git, so that Git commit history can be used to see changes to data. The Data Manifest is a 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.

If you'd like to follow the example along, first install SciDataFlow.

The user interacts with the Data Manifest through the fast and concurrent command line tool sdf written in the inimitable Rust language. 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:

$ sdf init

Registering a file in the manifest:

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

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

$ 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:

$ 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:

$ 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.

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):

$ 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:

$ sdf track data/population_sizes.tsv

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

$ 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:

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

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:

$ 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:

$ 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:

$ 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):

$ 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 crate. If your project has a lot of data across multiple remotes, SciDataFlow will pull all data in as quickly as possible.

Often we also want to retrieve data from URLs. For example, many genomic resources are available for download from the UCSC or Ensembl 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:

$ 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:

$ 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:

$ 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:

$ 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:

$ 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:

$ 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:

$ sdf pull --all

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:

$ 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:

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

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!

The larger vision of SciDataFlow is to change how data flows through scientific projects. The way scientific data is currently shared is fundamentally broken, which prevents the reuse of data that is the output of some smaller step in the scientific process. We call these scientific assets.

Scientific Assets are the output of some computational pipeline or analysis which has the following important characteristic: Scientific Assets should be reusable by everyone, and be reused by everyone. Being reusable means all other researchers should be able to quickly reuse a scientific asset (without having to spend hours trying to find and download data). Being reused by everyone means that using a scientific asset should be the best way to do something.

For example, if I lift over a recombination map to a new reference genome, that pipeline and output data should be a scientific asset. It should be reusable to everyone — we should not each be rewriting the same bioinformatics pipelines for routine tasks. There are three problems with this: (1) each reimplementation has an independent chance of errors, (2) it's a waste of time, (3) there is no cumulative improvement of the output data. It's not an asset; the result of each implementation is a liability!

Lowering the barrier to reusing computational steps is one of SciDataFlow's main motivations. Each scientific asset should have a record of what computational steps produced output data, and with one command (sdf pull) it should be possible to retrieve all data outputs from that repository. If the user only wants to reuse the data, they can stop there — they have the data locally and can proceed with their research. If the user wants to investigate how the input data was generated, the code is right there too. If they want to try rerunning the computational steps that produced that analysis, they can do that too. Note that SciDataFlow is agnostic to this — by design, it does not tackle the hard problem of managing software versions, computational environments, etc. It can work alongside software (e.g. Docker or Singularity) that tries to solve that problem.

By lowering the barrier to sharing and retrieving scientific data, SciDataFlow hopes to improve the reuse of data.

In the long run, the SciDataFlow YAML specification would allow for recipe-like reuse of data. I would like to see, for example, a set of human genomics scientific assets on GitHub that are continuously updated and reused. Then, rather than a researcher beginning a project by navigating many websites for human genome annotation or data, they might do something like:

$ mkdir -p new_adna_analysis/data/annotation
$ cd new_adna_analysis/data/annotation
$ git clone [email protected]:human_genome_assets/decode_recmap_hg38
$ (cd decode_recmap/ && sdf pull)
$ git clone [email protected]:human_genome_assets/annotation_hg38
$ (cd annotation_hg38 && sdf pull)

and so forth. Then, they may look at the annotation_hg38/ asset, find a problem, fix it, and issue a GitHub pull request. If the change is fixed, the maintainer would then just do sdf push --overwrite to push the data file to the data repository. Then, the Scientific Asset is then updated for everyone to use an benefit from. All other researchers can then instantly use the updated asset; all it takes is a mere sdf pull --overwrite.

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, which instructs you to run:

$ curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

Then, to install SciDataFlow, just run:

$ cargo install scidataflow

To test, just try running sdf --help.

If you are a user of SciDataFlow and encounter an issue, please submit an issue to https://github.com/vsbuffalo/scidataflow/issues!

If you are a Rust developer, please contribute! Here are some great ways to get started (also check the TODO list below, or for TODOs in code!):

• Write some API tests. See some of the tests in src/lib/api/zenodo.api as an example.

• Write some integration tests. See tests/test_project.rs for examples.

• A cleaner error framework. Currently SciDataflow uses anyhow, which works well, but it would be nice to have more specific error enums.

• Improve the documentation!

• [] sdf mv tests, within different directories.