Datasets:

InstaDeepAI
/

InstaNovo

---
license: other
license_name: embl-ebi-terms-of-use
license_link: https://www.ebi.ac.uk/about/terms-of-use/
task_categories:
  - feature-extraction
tags:
  - proteomics
  - mass-spectrometry
  - peptide-sequencing
pretty_name: InstaNovo-FM training corpus
configs:
  # Fully explicit names: no bare tier name exists, so a config can never be
  # ambiguous about which representation it refers to.
  #
  # splits/ uses data_dir because the filenames carry train/validation/test and
  # datasets detects them. by_project/ must NOT: with data_dir, split detection
  # runs inside the directory, finds no split keyword in the run names, and
  # collapses everything into a split called "train" -- advertising a training
  # split that does not exist. data_files with `split: full` names it honestly.
  - config_name: hcfm_splits
    default: true
    data_dir: splits/hcfm
  - config_name: mcfm_splits
    data_dir: splits/mcfm
  - config_name: lcfm_splits
    data_dir: splits/lcfm
  - config_name: hcfm_by_project
    data_files:
      - split: full
        path: by_project/hcfm/**/*.parquet
  - config_name: mcfm_by_project
    data_files:
      - split: full
        path: by_project/mcfm/**/*.parquet
  - config_name: lcfm_by_project
    data_files:
      - split: full
        path: by_project/lcfm/**/*.parquet
  - config_name: peptide_registry
    data_files:
      - split: full
        path: peptide_registry.parquet
---
# InstaNovo-FM training corpus

Tandem mass spectra with peptide-spectrum-match labels, uniformly reprocessed from
public PRIDE submissions, used to pretrain and evaluate InstaNovo-FM.

## Layout

Spectra are grouped into three **confidence tiers**. Each is a Foundational Model
dataset named for how stringently its peptide-spectrum matches (PSMs) were filtered:
**LCFM** (Low), **MCFM** (Medium) and **HCFM** (High). The names are relative, not
absolute — every labelled tier consists of high-confidence PSMs, and "low confidence"
marks LCFM as the broadest, least stringently filtered one, from which the stricter
subsets are derived. They are nested: HCFM ⊂ MCFM ⊂ LCFM. A fourth tier, **ACFM** (All
Confidence), is the unlabelled superset and is not published here.

Each tier is published twice, once under `splits/` and once under `by_project/`.
Which you want depends on whether you are consuming the corpus or rebuilding it:

- **`splits/`** — the train/validation/test partitions the model actually consumed:
  quality-filtered, shuffled, and peptide-disjoint. Take this to reproduce or extend
  the published results.
- **`by_project/`** — the same tier *before* filtering and splitting, one directory per
  PRIDE accession. Take this to apply your own quality criteria or derive your own
  partitions, which `splits/` cannot support because the filtering is lossy.
The tier sits *inside* the folder rather than above it — `splits/lcfm/`, not
`lcfm/splits/`. That nesting is deliberate: it means a download pattern like `--include
"by_project/*"` cannot stray outside the folder you named. Nested the other way,
`lcfm/*` would have matched both folders at once and quietly handed you ~900 GB
containing two overlapping copies of the same spectra, one filtered and one not.
```
InstaDeepAI/InstaNovo
│
├── splits/                                                   FILTERED · SHUFFLED · PEPTIDE-DISJOINT
│   ├── hcfm/                                                 11 files ·  11.4 GB ·   3,670,113 rows
│   │   ├── hcfm-train-00000-of-00007.parquet … 00006-of-00007
│   │   ├── hcfm-validation-00000-of-00001.parquet
│   │   └── hcfm-test-00000-of-00003.parquet … 00002-of-00003
│   ├── mcfm/                                                 44 files ·  52.5 GB ·  18,255,265 rows
│   │   ├── mcfm-train-00000-of-00029.parquet … 00028-of-00029
│   │   ├── mcfm-validation-00000-of-00001.parquet
│   │   └── mcfm-test-00000-of-00014.parquet … 00013-of-00014
│   └── lcfm/                                                 454 files · 467.1 GB · 181,777,591 rows
│       ├── lcfm-train-00000-of-00293.parquet … 00292-of-00293
│       ├── lcfm-validation-00000-of-00019.parquet … 00018-of-00019
│       └── lcfm-test-00000-of-00142.parquet … 00141-of-00142
│
├── by_project/                                               COMPLETE TIER · NOT FILTERED · NOT SPLIT
│   ├── hcfm/                                                 15,166 files ·  11.4 GB ·   3,684,448 rows
│   │   ├── PXD000561/                                        82 accessions in every tier
│   │   │   ├── Adult_Adrenalgland_Gel_Elite_49_f01.parquet
│   │   │   └── …                                             one file per instrument run
│   │   └── PXD000865/ …
│   ├── mcfm/                                                 15,244 files ·  51.7 GB ·  18,422,236 rows
│   │   └── PXD000561/ …                                      same runs, fewer rows each
│   └── lcfm/                                                 15,286 files · ~450 GB · 184,607,213 rows
│       └── PXD000561/ …
│
├── peptide_registry.parquet                                  5,613,657 peptides and their split
│
└── manifests/
    └── empty_runs.csv                                        162 runs with no PSMs at their threshold
```
**Pick one tier, from one folder.** Because the tiers are nested, a second tier
re-downloads the same spectra at a stricter threshold — and `splits/*` or
`by_project/*` fetches all three, roughly 531 GB and 510 GB.

### `splits/` — start here

`train`, `validation` and `test` parquet, exactly as the model consumed them. The
partitions are **peptide-disjoint** 80/10/10: a peptide sequence appears in only one
of the three, so evaluation does not reward memorisation. Rows are shuffled and have
passed the quality filters below.

Rows per split, measured:

| | train | validation | test |
|---|---:|---:|---:|
| `hcfm_splits` | 2,459,391 (67.0%) | 178,997 (4.9%) | 1,031,725 (28.1%) |
| `mcfm_splits` | 11,703,040 (64.1%) | 790,417 (4.3%) | 5,761,808 (31.6%) |
| `lcfm_splits` | 117,230,014 (64.5%) | 7,716,481 (4.3%) | 56,831,096 (31.3%) |

The 80/10/10 ratio is over **peptides**, not spectra: the registry assigns each peptide
to one split and every spectrum of that peptide follows it. Peptides differ in how many
spectra they have, and test peptides carry disproportionately many — 11.5% of peptides
but ~31% of rows — so the test partition is about 2.7x its nominal share and validation
about half of its own.

Use this to reproduce or extend the published results.

### `by_project/` — the input the splits came from
Each tier before filtering and splitting. Published because the filtering is lossy:
rows dropped by the quality gates are not recoverable from `splits/`.
Use this to apply different quality criteria, or to re-derive the partitions with
`peptide_registry.parquet` (see below).

### Quality filters applied to `splits/` but not to `by_project/`
| field | kept |
|---|---|
| retention time | <= 10800 s |
| lower isolation offset | <= 300 Da |
| precursor charge | 0-7 **inclusive** (0 is kept) |
| precursor *m/z* | <= 2000 |
| modification annotation | only if resolvable to a UNIMOD identifier |
A null in any numeric field **passes** its condition, so a reimplementation that
discards nulls instead would produce a different dataset. These tiers contain no nulls
in any filtered field, so the two behave identically here — but the distinction matters
if you apply the same criteria to new data.
Measured effect on LCFM (184,607,213 -> 181,777,591 rows, 98.5% kept): retention time
removes ~2.54 M, the unresolved-modification condition ~385 k, lower offset 8,147, and
the charge and *m/z* bounds remove **nothing** — no row in the corpus exceeds either.
The unresolved annotations are modifications the pipeline could not resolve to a UNIMOD identifier (`[IN:<digits>]`; 175 tokens over ids 3000–3174, predominantly N-glycans on asparagine but **not** exclusively — `K[IN:3174]` is on lysine). Glycopeptides whose glycan *does* have a
UNIMOD identifier are retained: 100 distinct UNIMOD modifications occur across 30.7% of
the corpus.
### `peptide_registry.parquet`

The split assignment of every peptide in the corpus. It is what makes the
peptide-disjoint partitions reproducible, and it lets new data be partitioned
consistently with these splits rather than at random — apply it to `by_project/`,
or to your own spectra, to keep a held-out set genuinely held out.

### Joining `by_project/` to the registry
Join on **`registry_key`** — not on `sequence`, and not on `unmodified_peptide`:

```python
from datasets import load_dataset
# hcfm is the smallest tier (11.4 GB); swap in mcfm or lcfm when you need more.
runs = load_dataset("InstaDeepAI/InstaNovo", "hcfm_by_project", split="full")
registry = load_dataset("InstaDeepAI/InstaNovo", "peptide_registry", split="full")
labelled = runs.to_polars().join(
    registry.to_polars(), left_on="registry_key", right_on="peptide", how="left"
)
```

Both calls download and cache from the Hub, so nothing needs fetching by hand.
`to_polars()` materialises the tier in memory, and `mz_array` and `intensity_array`
dominate that, so call `runs.select_columns(["registry_key", "experiment_name",
"scan"])` first if you only need the split assignment. To work through one accession at
a time rather than a whole tier, pass `streaming=True` and filter on `experiment_name`,
or fetch single files directly:

```python
from huggingface_hub import hf_hub_download
path = hf_hub_download(
    "InstaDeepAI/InstaNovo",
    "by_project/hcfm/PXD000561/Adult_Adrenalgland_Gel_Elite_49_f01.parquet",
    repo_type="dataset",
)
```

`registry_key` is `unmodified_peptide` with every `I` rewritten to `L`, which is the
convention the registry itself uses: its `peptide` column contains no `I` at all.
Isoleucine and leucine are isobaric, so a peptide pair differing only by I/L is
indistinguishable to MS/MS, and collapsing them into one key is what stops one variant
training while the other tests.

Mind the direction. Collapsing `L` to `I` instead yields keys that match nothing, and a
failed join returns *no rows* rather than visibly wrong ones — which reads naturally as
"these peptides are new".

The collapse applies to the key only. `sequence` and `unmodified_peptide` keep their
original I and L residues, and the model was trained on those unnormalised sequences.

There is no `normalised_peptide` column. Earlier internal copies carried one that was
never populated; it is removed here so nothing invites a join that would silently match
nothing.

## Confidence tiers

The tiers are nested subsets at increasing PSM-confidence thresholds: LCFM is the
full labelled corpus, MCFM and HCFM are progressively stricter. MCFM and HCFM inherit
LCFM's split assignments, so a peptide has the same split in every tier.

The unlabelled **ACFM** tier is not published here. It is approximately 5.7 TB and
comprises every MS/MS scan from the same raw files, so it is reconstructible from the
PRIDE accessions with the conversion pipeline deposited at Figshare, and is otherwise
available from the authors on request.

## Loading a specific tier

The configs above name every tier and flavour, so nothing is inferred from the
directory layout:

```python
from datasets import load_dataset
load_dataset("InstaDeepAI/InstaNovo", "hcfm_splits")         # train/validation/test
load_dataset("InstaDeepAI/InstaNovo", "lcfm_splits", split="test")
load_dataset("InstaDeepAI/InstaNovo", "hcfm_by_project")     # one "full" split
```

To fetch files without loading them, select by path — tier and flavour are
directory prefixes:

```bash
hf download InstaDeepAI/InstaNovo --repo-type dataset --include "hcfm/*"
```

## File naming

Split files follow the Hub's sharding convention,
`{split}-{index:05d}-of-{total:05d}.parquet`:

```
splits/hcfm/hcfm-train-00000-of-00007.parquet
splits/hcfm/hcfm-validation-00000-of-00001.parquet
```

The `-of-{total}` suffix makes an incomplete download self-evident, and the
zero-padding sorts correctly in a plain lexicographic listing.

`by_project/` deliberately does **not** use that convention. It has no train,
validation or test split, and any of those keywords in a filename would make the
Hub advertise a split that does not exist. Its files are named
`data-{index:05d}-of-{total:05d}.parquet` within each project accession directory,
so per-project selection stays possible:

```
by_project/lcfm/PXD012345/<instrument-run-name>.parquet
```

## Versioning

`v0.1` is the first release, and the one the paper's results were produced from. Pin it
rather than tracking `main`, so a later addition to the corpus cannot change what you
fetch:

```python
from datasets import load_dataset
ds = load_dataset("InstaDeepAI/InstaNovo", "hcfm_splits", revision="v0.1")
```

`main` moves as tiers are extended or corrected; a tag does not.

## Licence

The spectra derive from public submissions to
[PRIDE](https://www.ebi.ac.uk/pride/), so use of this dataset is governed by the
[EMBL-EBI terms of use](https://www.ebi.ac.uk/about/terms-of-use/).

The code that produced the corpus is available on
[Figshare](https://doi.org/10.6084/m9.figshare.33368752) under a CC BY 4.0 licence, and the
model checkpoints are CC BY-NC-SA 4.0.

## Citation

See the InstaNovo-FM manuscript. Code, including the pipeline that produced these
files, is at <https://github.com/instadeepai/InstaNovo-FM>.

	---
	license: other
	license_name: embl-ebi-terms-of-use
	license_link: https://www.ebi.ac.uk/about/terms-of-use/
	task_categories:
	- feature-extraction
	tags:
	- proteomics
	- mass-spectrometry
	- peptide-sequencing
	pretty_name: InstaNovo-FM training corpus
	configs:
	# Fully explicit names: no bare tier name exists, so a config can never be
	# ambiguous about which representation it refers to.
	#
	# splits/ uses data_dir because the filenames carry train/validation/test and
	# datasets detects them. by_project/ must NOT: with data_dir, split detection
	# runs inside the directory, finds no split keyword in the run names, and
	# collapses everything into a split called "train" -- advertising a training
	# split that does not exist. data_files with `split: full` names it honestly.
	- config_name: hcfm_splits
	default: true
	data_dir: splits/hcfm
	- config_name: mcfm_splits
	data_dir: splits/mcfm
	- config_name: lcfm_splits
	data_dir: splits/lcfm
	- config_name: hcfm_by_project
	data_files:
	- split: full
	path: by_project/hcfm/*/.parquet
	- config_name: mcfm_by_project
	data_files:
	- split: full
	path: by_project/mcfm/*/.parquet
	- config_name: lcfm_by_project
	data_files:
	- split: full
	path: by_project/lcfm/*/.parquet
	- config_name: peptide_registry
	data_files:
	- split: full
	path: peptide_registry.parquet
	---

	# InstaNovo-FM training corpus

	Tandem mass spectra with peptide-spectrum-match labels, uniformly reprocessed from
	public PRIDE submissions, used to pretrain and evaluate InstaNovo-FM.

	## Layout

	Spectra are grouped into three confidence tiers. Each is a Foundational Model
	dataset named for how stringently its peptide-spectrum matches (PSMs) were filtered:
	LCFM (Low), MCFM (Medium) and HCFM (High). The names are relative, not
	absolute — every labelled tier consists of high-confidence PSMs, and "low confidence"
	marks LCFM as the broadest, least stringently filtered one, from which the stricter
	subsets are derived. They are nested: HCFM ⊂ MCFM ⊂ LCFM. A fourth tier, ACFM (All
	Confidence), is the unlabelled superset and is not published here.

	Each tier is published twice, once under `splits/` and once under `by_project/`.
	Which you want depends on whether you are consuming the corpus or rebuilding it:

	- `splits/` — the train/validation/test partitions the model actually consumed:
	quality-filtered, shuffled, and peptide-disjoint. Take this to reproduce or extend
	the published results.
	- `by_project/` — the same tier before filtering and splitting, one directory per
	PRIDE accession. Take this to apply your own quality criteria or derive your own
	partitions, which `splits/` cannot support because the filtering is lossy.

	The tier sits inside the folder rather than above it — `splits/lcfm/`, not
	`lcfm/splits/`. That nesting is deliberate: it means a download pattern like `--include
	"by_project/*"` cannot stray outside the folder you named. Nested the other way,
	`lcfm/*` would have matched both folders at once and quietly handed you ~900 GB
	containing two overlapping copies of the same spectra, one filtered and one not.

	```
	InstaDeepAI/InstaNovo
	│
	├── splits/ FILTERED · SHUFFLED · PEPTIDE-DISJOINT
	│ ├── hcfm/ 11 files · 11.4 GB · 3,670,113 rows
	│ │ ├── hcfm-train-00000-of-00007.parquet … 00006-of-00007
	│ │ ├── hcfm-validation-00000-of-00001.parquet
	│ │ └── hcfm-test-00000-of-00003.parquet … 00002-of-00003
	│ ├── mcfm/ 44 files · 52.5 GB · 18,255,265 rows
	│ │ ├── mcfm-train-00000-of-00029.parquet … 00028-of-00029
	│ │ ├── mcfm-validation-00000-of-00001.parquet
	│ │ └── mcfm-test-00000-of-00014.parquet … 00013-of-00014
	│ └── lcfm/ 454 files · 467.1 GB · 181,777,591 rows
	│ ├── lcfm-train-00000-of-00293.parquet … 00292-of-00293
	│ ├── lcfm-validation-00000-of-00019.parquet … 00018-of-00019
	│ └── lcfm-test-00000-of-00142.parquet … 00141-of-00142
	│
	├── by_project/ COMPLETE TIER · NOT FILTERED · NOT SPLIT
	│ ├── hcfm/ 15,166 files · 11.4 GB · 3,684,448 rows
	│ │ ├── PXD000561/ 82 accessions in every tier
	│ │ │ ├── Adult_Adrenalgland_Gel_Elite_49_f01.parquet
	│ │ │ └── … one file per instrument run
	│ │ └── PXD000865/ …
	│ ├── mcfm/ 15,244 files · 51.7 GB · 18,422,236 rows
	│ │ └── PXD000561/ … same runs, fewer rows each
	│ └── lcfm/ 15,286 files · ~450 GB · 184,607,213 rows
	│ └── PXD000561/ …
	│
	├── peptide_registry.parquet 5,613,657 peptides and their split
	│
	└── manifests/
	└── empty_runs.csv 162 runs with no PSMs at their threshold
	```

	Pick one tier, from one folder. Because the tiers are nested, a second tier
	re-downloads the same spectra at a stricter threshold — and `splits/*` or
	`by_project/*` fetches all three, roughly 531 GB and 510 GB.

	### `splits/` — start here

	`train`, `validation` and `test` parquet, exactly as the model consumed them. The
	partitions are peptide-disjoint 80/10/10: a peptide sequence appears in only one
	of the three, so evaluation does not reward memorisation. Rows are shuffled and have
	passed the quality filters below.

	Rows per split, measured:

	\| \| train \| validation \| test \|
	\|---\|---:\|---:\|---:\|
	\| `hcfm_splits` \| 2,459,391 (67.0%) \| 178,997 (4.9%) \| 1,031,725 (28.1%) \|
	\| `mcfm_splits` \| 11,703,040 (64.1%) \| 790,417 (4.3%) \| 5,761,808 (31.6%) \|
	\| `lcfm_splits` \| 117,230,014 (64.5%) \| 7,716,481 (4.3%) \| 56,831,096 (31.3%) \|

	The 80/10/10 ratio is over peptides, not spectra: the registry assigns each peptide
	to one split and every spectrum of that peptide follows it. Peptides differ in how many
	spectra they have, and test peptides carry disproportionately many — 11.5% of peptides
	but ~31% of rows — so the test partition is about 2.7x its nominal share and validation
	about half of its own.

	Use this to reproduce or extend the published results.

	### `by_project/` — the input the splits came from

	Each tier before filtering and splitting. Published because the filtering is lossy:
	rows dropped by the quality gates are not recoverable from `splits/`.

	Use this to apply different quality criteria, or to re-derive the partitions with
	`peptide_registry.parquet` (see below).

	### Quality filters applied to `splits/` but not to `by_project/`

	\| field \| kept \|
	\|---\|---\|
	\| retention time \| <= 10800 s \|
	\| lower isolation offset \| <= 300 Da \|
	\| precursor charge \| 0-7 inclusive (0 is kept) \|
	\| precursor m/z \| <= 2000 \|
	\| modification annotation \| only if resolvable to a UNIMOD identifier \|

	A null in any numeric field passes its condition, so a reimplementation that
	discards nulls instead would produce a different dataset. These tiers contain no nulls
	in any filtered field, so the two behave identically here — but the distinction matters
	if you apply the same criteria to new data.

	Measured effect on LCFM (184,607,213 -> 181,777,591 rows, 98.5% kept): retention time
	removes ~2.54 M, the unresolved-modification condition ~385 k, lower offset 8,147, and
	the charge and m/z bounds remove nothing — no row in the corpus exceeds either.

	The unresolved annotations are modifications the pipeline could not resolve to a UNIMOD identifier (`[IN:<digits>]`; 175 tokens over ids 3000–3174, predominantly N-glycans on asparagine but not exclusively — `K[IN:3174]` is on lysine). Glycopeptides whose glycan does have a
	UNIMOD identifier are retained: 100 distinct UNIMOD modifications occur across 30.7% of
	the corpus.

	### `peptide_registry.parquet`

	The split assignment of every peptide in the corpus. It is what makes the
	peptide-disjoint partitions reproducible, and it lets new data be partitioned
	consistently with these splits rather than at random — apply it to `by_project/`,
	or to your own spectra, to keep a held-out set genuinely held out.

	### Joining `by_project/` to the registry

	Join on `registry_key` — not on `sequence`, and not on `unmodified_peptide`:

	```python
	from datasets import load_dataset

	# hcfm is the smallest tier (11.4 GB); swap in mcfm or lcfm when you need more.
	runs = load_dataset("InstaDeepAI/InstaNovo", "hcfm_by_project", split="full")
	registry = load_dataset("InstaDeepAI/InstaNovo", "peptide_registry", split="full")

	labelled = runs.to_polars().join(
	registry.to_polars(), left_on="registry_key", right_on="peptide", how="left"
	)
	```

	Both calls download and cache from the Hub, so nothing needs fetching by hand.
	`to_polars()` materialises the tier in memory, and `mz_array` and `intensity_array`
	dominate that, so call `runs.select_columns(["registry_key", "experiment_name",
	"scan"])` first if you only need the split assignment. To work through one accession at
	a time rather than a whole tier, pass `streaming=True` and filter on `experiment_name`,
	or fetch single files directly:

	```python
	from huggingface_hub import hf_hub_download

	path = hf_hub_download(
	"InstaDeepAI/InstaNovo",
	"by_project/hcfm/PXD000561/Adult_Adrenalgland_Gel_Elite_49_f01.parquet",
	repo_type="dataset",
	)
	```

	`registry_key` is `unmodified_peptide` with every `I` rewritten to `L`, which is the
	convention the registry itself uses: its `peptide` column contains no `I` at all.
	Isoleucine and leucine are isobaric, so a peptide pair differing only by I/L is
	indistinguishable to MS/MS, and collapsing them into one key is what stops one variant
	training while the other tests.

	Mind the direction. Collapsing `L` to `I` instead yields keys that match nothing, and a
	failed join returns no rows rather than visibly wrong ones — which reads naturally as
	"these peptides are new".

	The collapse applies to the key only. `sequence` and `unmodified_peptide` keep their
	original I and L residues, and the model was trained on those unnormalised sequences.

	There is no `normalised_peptide` column. Earlier internal copies carried one that was
	never populated; it is removed here so nothing invites a join that would silently match
	nothing.

	## Confidence tiers

	The tiers are nested subsets at increasing PSM-confidence thresholds: LCFM is the
	full labelled corpus, MCFM and HCFM are progressively stricter. MCFM and HCFM inherit
	LCFM's split assignments, so a peptide has the same split in every tier.

	The unlabelled ACFM tier is not published here. It is approximately 5.7 TB and
	comprises every MS/MS scan from the same raw files, so it is reconstructible from the
	PRIDE accessions with the conversion pipeline deposited at Figshare, and is otherwise
	available from the authors on request.

	## Loading a specific tier

	The configs above name every tier and flavour, so nothing is inferred from the
	directory layout:

	```python
	from datasets import load_dataset

	load_dataset("InstaDeepAI/InstaNovo", "hcfm_splits") # train/validation/test
	load_dataset("InstaDeepAI/InstaNovo", "lcfm_splits", split="test")
	load_dataset("InstaDeepAI/InstaNovo", "hcfm_by_project") # one "full" split
	```

	To fetch files without loading them, select by path — tier and flavour are
	directory prefixes:

	```bash
	hf download InstaDeepAI/InstaNovo --repo-type dataset --include "hcfm/*"
	```

	## File naming

	Split files follow the Hub's sharding convention,
	`{split}-{index:05d}-of-{total:05d}.parquet`:

	```
	splits/hcfm/hcfm-train-00000-of-00007.parquet
	splits/hcfm/hcfm-validation-00000-of-00001.parquet
	```

	The `-of-{total}` suffix makes an incomplete download self-evident, and the
	zero-padding sorts correctly in a plain lexicographic listing.

	`by_project/` deliberately does not use that convention. It has no train,
	validation or test split, and any of those keywords in a filename would make the
	Hub advertise a split that does not exist. Its files are named
	`data-{index:05d}-of-{total:05d}.parquet` within each project accession directory,
	so per-project selection stays possible:

	```
	by_project/lcfm/PXD012345/<instrument-run-name>.parquet
	```

	## Versioning

	`v0.1` is the first release, and the one the paper's results were produced from. Pin it
	rather than tracking `main`, so a later addition to the corpus cannot change what you
	fetch:

	```python
	from datasets import load_dataset
	ds = load_dataset("InstaDeepAI/InstaNovo", "hcfm_splits", revision="v0.1")
	```

	`main` moves as tiers are extended or corrected; a tag does not.

	## Licence

	The spectra derive from public submissions to
	[PRIDE](https://www.ebi.ac.uk/pride/), so use of this dataset is governed by the
	[EMBL-EBI terms of use](https://www.ebi.ac.uk/about/terms-of-use/).

	The code that produced the corpus is available on
	[Figshare](https://doi.org/10.6084/m9.figshare.33368752) under a CC BY 4.0 licence, and the
	model checkpoints are CC BY-NC-SA 4.0.

	## Citation

	See the InstaNovo-FM manuscript. Code, including the pipeline that produced these
	files, is at <https://github.com/instadeepai/InstaNovo-FM>.