Datasets

torchcell provides built datasets the way torchvision.datasets provides image datasets: a loader class per source publication, a versioned archive of the built records ({doc}../guide/downloads``), and a supported query that returns the records from the served knowledge graph. The pages under each organism take one group of datasets and show what the experiments measured, what one stored record looks like, how the values are distributed, the query that returns them, and how to download the built stores. Every table, figure, record dump and query result on these pages is written by a script under experiments/034-showcase-datasets/scripts/, and query results come from a slurm job against the served release named on the page. Caveats that change how a value should be read are stated on the page that shows the value.

Size of the collection

Release 2026.10.06-4b293d34 (knowledge-graph version 3.0, built 2026-10-06 from torchcell 1.6.2) serves 51 datasets and 52,668,931 experiments over 100,127,515 graph nodes, by organism: scerevisiae 51 datasets, 52,668,931 experiments. The release predates the organism switch and records no organism set; each dataset’s organism below is the one its loader class declares. One experiment is one genotype, environment and phenotype record with its reference; the guide’s supported-datasets table gives each dataset’s genotype count, environments, phenotype shape and graph role.

scerevisiae

dataset	experiments served	page
DmfCostanzo2016Dataset	20,705,612
DmiCostanzo2016Dataset	20,705,612
EnvChemgenHoepfner2014Dataset	3,083,827
HetHillenmeyer2008Dataset	2,712,677
HomHillenmeyer2008Dataset	1,063,034
DmfKuzmin2020Dataset	632,998
DmiKuzmin2020Dataset	632,797
Bloom2019Dataset	530,100
EnvChemgenWildenhain2015Dataset	430,820
DmfKuzmin2018Dataset	410,571
DmiKuzmin2018Dataset	410,399
TmfKuzmin2020Dataset	301,798
TmiKuzmin2020Dataset	301,798
CrisprMagicLian2019Dataset	266,304
EnvChemgenVanacloig2022Dataset	118,662
TmfKuzmin2018Dataset	91,111
TmiKuzmin2018Dataset	91,111
EnvChemgenCostanzo2021Dataset	61,430
SmfCostanzo2016Dataset	20,484	Gene essentiality and single-mutant fitness
SynthLethalityYeastSynthLethDbDataset	13,996
FattyAcidSmith2006Dataset	12,747
CrispriChemgenSmith2016Dataset	7,053
SynthRescueYeastSynthLethDbDataset	6,942
NadalRibellesPerturbSeq2025Dataset	6,188
SmfBaryshnikova2010Dataset	5,993
BetaxanthinCachera2023Dataset	4,719	Amino acids and betaxanthin
ScmdOhya2005Dataset	4,718
ProteomeMessner2023Dataset	4,699
AminoAcidMulleder2016Dataset	4,678	Amino acids and betaxanthin
IsobutanolScreenLopez2024Dataset	4,554
CarotenoidOzaydin2013Dataset	4,474
AminoAcidCooper2010Dataset	4,313	Amino acids and betaxanthin
ScmdOhnuki2022Dataset	1,979
SmfKuzmin2018Dataset	1,539
MicroarrayKemmeren2014Dataset	1,484
SmfODuibhir2014Dataset	1,312
EnvChemgenMota2024Dataset	1,270
GeneEssentialitySgdDataset	1,140	Gene essentiality and single-mutant fitness
ScmdOhnuki2018Dataset	1,112
CaudalPanTranscriptome2024Dataset	943
EnvChemgenAuesukaree2009Dataset	525
SmfKuzmin2020Dataset	472
IsobutanolValidatedLopez2024Dataset	224
FattyAcidXue2025Dataset	176
MetaboliteZelezniak2018Dataset	129
MetaboliteDaSilveira2014Dataset	127
ProteomeZelezniak2018Dataset	97
SmMicroarraySameith2015Dataset	82
DmMicroarraySameith2015Dataset	72
OrganicAcidYoshida2012Dataset	17
CrispriMormino2022Dataset	12
scerevisiae total	52,668,931

The full list of loaders, with the phenotype class, the genotype count and the record count of each, is on the guide’s Datasets page; the served counts above come from the committed release snapshot, so the two agree only for datasets admitted at that release.

What every dataset page provides

A dataset page is a fixed set of sections, in this order, so two pages can be read the same way. essentiality-smf and amino-acid-betaxanthin are the reference pages; a new page copies their structure.

1. Introduction and terms. One paragraph naming each loader class, what it records and from which paper, and the supported query that joins them. A term list, alphabetical, defines every abbreviation and every torchcell term (entry, processed record, reference, source key) before the text uses it.

2. What the experiments measured. One draw.io diagram (notes/assets/drawio/<page>.drawio, exported headless to SVG) with three panels: a the sources and what each screen measured, in which strain, medium and temperature; b the stored record (experiment, reference, publication) and the phenotype class; c the query, the conversion or aggregation, and what one processed record holds. The diagram is composed from the loaders and the query, never redrawn from a published figure, and follows the repository figure standard (Nature print size, the palette, the font ladder).

3. The records. One record per dataset as its loader stored it, re-validated through the pydantic class and printed from model_dump(), cut to the named fields with every cut marked by the number of fields it hides. The records show the same gene where the datasets share one.

4. The data. Counts per dataset (records, genes, measurement type, medium, temperature, parent strain, replicate counts, uncertainty stored), the overlap of genes between the datasets, the distribution of every stored value as a table and at least one figure, and one exploration that a reader of the group would ask for (agreement between two datasets on the same gene, a histogram by strain type, a per-key summary). Figures follow the repository plotting standard (torchcell.utils.apply_paper_style: Arial 6 pt, the palette, boxed axes, a standard panel width, true-size SVG). A figure with more than one panel carries bold lowercase panel letters drawn by torchcell.utils.panel_label, and its caption names the panels in the paper’s form: **a**, ...; **b**, .... Every figure has a :name:, Sphinx numbers it (Fig. 1), and the text refers to it with {numref}, never “the figure above”.

5. Querying the graph. The supported query as a literalinclude of its .cql file, the Neo4jCellDataset construction with the converter, deduplicator and aggregator the group needs, the source of any converter as a literalinclude, and what the query returned from a named served release: the slurm job id, entries per dataset at each pipeline stage, processed records by dataset membership, and one processed record per membership pattern.

6. Getting the data. The tc-data slug of each dataset, the DatasetClient and curl forms, what the loader does with and without TC_DATA_URL, and the state of the public endpoint.

7. Caveats. Every statement that changes how a value should be read, each with its source: the mirror OCR file, line and sha256, the verification report and claim id, the loader line, or the issue number. A caveat with no issue names that fact.

8. Provenance. A table per dataset: loader, dev store, raw input with sha256, source of record, paper mirror with sha256, DOI and PubMed id. Then the generating scripts.

Two rules hold for every number on a page. Every table, figure, record dump and query count is written by a committed script under experiments/034-showcase-datasets/scripts/ into the page’s _generated/ directory, and the page only includes those fragments; nothing is typed by hand. Every query against the served graph runs as a slurm job whose id the fragment records, and the page names the release the job read. The page path is registered as docs_page on its supported query in torchcell/knowledge_graphs/supported_queries/registry.json, and the Sphinx build must add no warnings.

• S. cerevisiae
  • Gene essentiality and single-mutant fitness
  • Amino acids and betaxanthin