---
OA_place: publisher
OA_type: gold
_id: '19010'
abstract:
- lang: eng
text: Causal representation learning aims at recovering latent causal variables
from high-dimensional observations to solve causal downstream tasks, such as predicting
the effect of new interventions or more robust classification. A plethora of methods
have been developed, each tackling carefully crafted problem settings that lead
to different types of identifiability. The folklore is that these different settings
are important, as they are often linked to different rungs of Pearl's causal hierarchy,
although not all neatly fit. Our main contribution is to show that many existing
causal representation learning approaches methodologically align the representation
to known data symmetries. Identification of the variables is guided by equivalence
classes across different "data pockets" that are not necessarily causal. This
result suggests important implications, allowing us to unify many existing approaches
in a single method that can mix and match different assumptions, including non-causal
ones, based on the invariances relevant to our application. It also significantly
benefits applicability, which we demonstrate by improving treatment effect estimation
on real-world high-dimensional ecological data. Overall, this paper clarifies
the role of causality assumptions in the discovery of causal variables and shifts
the focus to preserving data symmetries.
acknowledgement: "We thank Jiaqi Zhang, Francesco Montagna, David Lopez-Paz, Kartik
Ahuja, Thomas Kipf, Sara\r\nMagliacane, Julius von Kügelgen, Kun Zhang, and Bernhard
Schölkopf for extremely helpful discussion. Riccardo Cadei was supported by a Google
Research Scholar Award to Francesco Locatello. We acknowledge the Third Bellairs
Workshop on Causal Representation Learning held at the Bellairs Research Institute,
February 9/16, 2024, and a debate on the difference between interventions and counterfactuals
in disentanglement and CRL that took place during Dhanya Sridhar’s lecture, which
motivated us to significantly broaden the scope of the paper. We thank Dhanya and
all participants of the workshop."
article_processing_charge: No
arxiv: 1
author:
- first_name: Dingling
full_name: Yao, Dingling
id: d3e02e50-48a8-11ee-8f62-c108061797fa
last_name: Yao
- first_name: Dario
full_name: Rancati, Dario
id: feb58f2e-72ef-11ef-b75a-8f0894539cd0
last_name: Rancati
- first_name: Riccardo
full_name: Cadei, Riccardo
id: 0fa8b76f-72f0-11ef-b75a-a5da96e5ad6b
last_name: Cadei
- first_name: Marco
full_name: Fumero, Marco
id: 1c1593eb-393f-11ef-bb8e-ab4f1e979650
last_name: Fumero
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Yao D, Rancati D, Cadei R, Fumero M, Locatello F. Unifying causal representation
learning with the invariance principle. In: 13th International Conference on
Learning Representations. ICLR; 2025.'
apa: 'Yao, D., Rancati, D., Cadei, R., Fumero, M., & Locatello, F. (2025). Unifying
causal representation learning with the invariance principle. In 13th International
Conference on Learning Representations. Singapore: ICLR.'
chicago: Yao, Dingling, Dario Rancati, Riccardo Cadei, Marco Fumero, and Francesco
Locatello. “Unifying Causal Representation Learning with the Invariance Principle.”
In 13th International Conference on Learning Representations. ICLR, 2025.
ieee: D. Yao, D. Rancati, R. Cadei, M. Fumero, and F. Locatello, “Unifying causal
representation learning with the invariance principle,” in 13th International
Conference on Learning Representations, Singapore, 2025.
ista: 'Yao D, Rancati D, Cadei R, Fumero M, Locatello F. 2025. Unifying causal representation
learning with the invariance principle. 13th International Conference on Learning
Representations. ICLR: International Conference on Learning Representations.'
mla: Yao, Dingling, et al. “Unifying Causal Representation Learning with the Invariance
Principle.” 13th International Conference on Learning Representations,
ICLR, 2025.
short: D. Yao, D. Rancati, R. Cadei, M. Fumero, F. Locatello, in:, 13th International
Conference on Learning Representations, ICLR, 2025.
conference:
end_date: 2025-04-28
location: Singapore
name: 'ICLR: International Conference on Learning Representations'
start_date: 2025-04-24
corr_author: '1'
date_created: 2025-02-05T09:23:25Z
date_published: 2025-01-22T00:00:00Z
date_updated: 2026-02-09T05:52:14Z
day: '22'
ddc:
- '000'
department:
- _id: FrLo
external_id:
arxiv:
- '2409.02772'
file:
- access_level: open_access
checksum: c4b5a4a644228c6d1b0283e1368bce9e
content_type: application/pdf
creator: flocatel
date_created: 2026-01-27T12:43:25Z
date_updated: 2026-01-27T12:43:25Z
file_id: '21048'
file_name: 4356_Unifying_Causal_Represent (1).pdf
file_size: 877014
relation: main_file
success: 1
file_date_updated: 2026-01-27T12:43:25Z
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '01'
oa: 1
oa_version: Published Version
publication: 13th International Conference on Learning Representations
publication_status: published
publisher: ICLR
quality_controlled: '1'
scopus_import: '1'
status: public
title: Unifying causal representation learning with the invariance principle
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2025'
...
---
OA_place: repository
OA_type: green
_id: '21068'
abstract:
- lang: eng
text: "Causal reasoning and discovery, two fundamental tasks of causal analysis,\r\noften
face challenges in applications due to the complexity, noisiness, and highdimensionality
of real-world data. Despite recent progress in identifying latent\r\ncausal structures
using causal representation learning (CRL), what makes learned\r\nrepresentations
useful for causal downstream tasks and how to evaluate them are\r\nstill not well
understood. In this paper, we reinterpret CRL using a measurement\r\nmodel framework,
where the learned representations are viewed as proxy measurements of the latent
causal variables. Our approach clarifies the conditions under\r\nwhich learned
representations support downstream causal reasoning and provides\r\na principled
basis for quantitatively assessing the quality of representations using\r\na new
Test-based Measurement EXclusivity (T-MEX) score. We validate T-MEX\r\nacross
diverse causal inference scenarios, including numerical simulations and\r\nreal-world
ecological video analysis, demonstrating that the proposed framework\r\nand corresponding
score effectively assess the identification of learned representations and their
usefulness for causal downstream tasks. Reproducible code can\r\nbe found at https://github.com/shimenghuang/a-measurement-perspective-of-crl."
acknowledgement: "This research was funded in whole or in part by the Austrian Science
Fund (FWF) 10.55776/COE12. For open access purposes, the author has applied a CC
BY public copyright license to any accepted manuscript version arising from this
submission.\r\n"
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Dingling
full_name: Yao, Dingling
id: d3e02e50-48a8-11ee-8f62-c108061797fa
last_name: Yao
- first_name: Shimeng
full_name: Huang, Shimeng
id: 989c2a06-fb4e-11ef-a992-ab766442255b
last_name: Huang
orcid: 0000-0001-6919-821X
- first_name: Riccardo
full_name: Cadei, Riccardo
id: 0fa8b76f-72f0-11ef-b75a-a5da96e5ad6b
last_name: Cadei
- first_name: Kun
full_name: Zhang, Kun
last_name: Zhang
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Yao D, Huang S, Cadei R, Zhang K, Locatello F. The third pillar of causal
analysis? A measurement perspective on causal representations. In: 39th Annual
Conference on Neural Information Processing Systems. Vol 38. Neural Information
Processing Systems Foundation; 2025.'
apa: 'Yao, D., Huang, S., Cadei, R., Zhang, K., & Locatello, F. (2025). The
third pillar of causal analysis? A measurement perspective on causal representations.
In 39th Annual Conference on Neural Information Processing Systems (Vol.
38). San Diego, CA, United States: Neural Information Processing Systems Foundation.'
chicago: Yao, Dingling, Shimeng Huang, Riccardo Cadei, Kun Zhang, and Francesco
Locatello. “The Third Pillar of Causal Analysis? A Measurement Perspective on
Causal Representations.” In 39th Annual Conference on Neural Information Processing
Systems, Vol. 38. Neural Information Processing Systems Foundation, 2025.
ieee: D. Yao, S. Huang, R. Cadei, K. Zhang, and F. Locatello, “The third pillar
of causal analysis? A measurement perspective on causal representations,” in 39th
Annual Conference on Neural Information Processing Systems, San Diego, CA,
United States, 2025, vol. 38.
ista: 'Yao D, Huang S, Cadei R, Zhang K, Locatello F. 2025. The third pillar of
causal analysis? A measurement perspective on causal representations. 39th Annual
Conference on Neural Information Processing Systems. NeurIPS: Neural Information
Processing Systems, Advances in Neural Information Processing Systems, vol. 38.'
mla: Yao, Dingling, et al. “The Third Pillar of Causal Analysis? A Measurement Perspective
on Causal Representations.” 39th Annual Conference on Neural Information Processing
Systems, vol. 38, Neural Information Processing Systems Foundation, 2025.
short: D. Yao, S. Huang, R. Cadei, K. Zhang, F. Locatello, in:, 39th Annual Conference
on Neural Information Processing Systems, Neural Information Processing Systems
Foundation, 2025.
conference:
end_date: 2025-12-07
location: San Diego, CA, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2025-12-02
corr_author: '1'
date_created: 2026-01-29T14:24:56Z
date_published: 2025-12-15T00:00:00Z
date_updated: 2026-02-10T12:08:52Z
day: '15'
ddc:
- '000'
department:
- _id: FrLo
external_id:
arxiv:
- '2505.17708'
has_accepted_license: '1'
intvolume: ' 38'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2505.17708
month: '12'
oa: 1
oa_version: Preprint
publication: 39th Annual Conference on Neural Information Processing Systems
publication_identifier:
issn:
- 1049-5258
publication_status: epub_ahead
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/shimenghuang/a-measurement-perspective-of-crl
status: public
title: The third pillar of causal analysis? A measurement perspective on causal representations
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 38
year: '2025'
...
---
OA_place: publisher
OA_type: gold
_id: '21076'
abstract:
- lang: eng
text: In many scientific experiments, the data annotating cost constraints the pace
for testing novel hypotheses. Yet, modern machine learning pipelines offer a promising
solution—provided their predictions yield correct conclusions. We focus on Prediction-Powered
Causal Inferences (PPCI), i.e., estimating the treatment effect in an unlabeled
target experiment, relying on training data with the same outcome annotated but
potentially different treatment or effect modifiers. We first show that conditional
calibration guarantees valid PPCI at population level. Then, we introduce a sufficient
representation constraint transferring validity across experiments, which we propose
to enforce in practice in Deconfounded Empirical Risk Minimization, our new model-agnostic
training objective. We validate our method on synthetic and real-world scientific
data, solving impossible problem instances for Empirical Risk Minimization even
with standard invariance constraints. In particular, for the first time, we achieve
valid causal inference on a scientific experiment with complex recording and no
human annotations, fine-tuning a foundational model on our similar annotated experiment.
acknowledgement: We thank the Causal Learning and Artificial Intelligence group at
ISTA for the continuous feedback on the project and valuable discussions. We thank
the Social Immunity group at ISTA, particularly Jinook Oh, for the annotation program
and Michaela Hoenigsberger for supporting our ecological experiment. Riccardo Cadei
is supported by a Google Research Scholar Award and a Google Initiated Gift to Francesco
Locatello. This research was funded in part by the Austrian Science Fund (FWF) 10.55776/COE12).
It was further partially supported by the ISTA Interdisciplinary Project Committee
for the collaborative project “ALED” between Francesco Locatello and Sylvia Cremer.
For open access purposes, the author has applied a CC BY public copyright license
to any author accepted manuscript version arising from this submission.
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
author:
- first_name: Riccardo
full_name: Cadei, Riccardo
id: 0fa8b76f-72f0-11ef-b75a-a5da96e5ad6b
last_name: Cadei
- first_name: Ilker
full_name: Demirel, Ilker
last_name: Demirel
- first_name: Piersilvio
full_name: De Bartolomeis, Piersilvio
last_name: De Bartolomeis
- first_name: Lukas
full_name: Lindorfer, Lukas
id: 85f0e6d3-06b3-11ec-8982-8c5049fa4455
last_name: Lindorfer
- first_name: Sylvia
full_name: Cremer, Sylvia
id: 2F64EC8C-F248-11E8-B48F-1D18A9856A87
last_name: Cremer
orcid: 0000-0002-2193-3868
- first_name: Cordelia
full_name: Schmid, Cordelia
last_name: Schmid
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Cadei R, Demirel I, De Bartolomeis P, et al. Prediction-powered causal inferences.
In: 39th Annual Conference on Neural Information Processing Systems. Vol
38. Neural Information Processing Systems Foundation; 2025.'
apa: 'Cadei, R., Demirel, I., De Bartolomeis, P., Lindorfer, L., Cremer, S., Schmid,
C., & Locatello, F. (2025). Prediction-powered causal inferences. In 39th
Annual Conference on Neural Information Processing Systems (Vol. 38). San
Diego, CA, United States: Neural Information Processing Systems Foundation.'
chicago: Cadei, Riccardo, Ilker Demirel, Piersilvio De Bartolomeis, Lukas Lindorfer,
Sylvia Cremer, Cordelia Schmid, and Francesco Locatello. “Prediction-Powered Causal
Inferences.” In 39th Annual Conference on Neural Information Processing Systems,
Vol. 38. Neural Information Processing Systems Foundation, 2025.
ieee: R. Cadei et al., “Prediction-powered causal inferences,” in 39th
Annual Conference on Neural Information Processing Systems, San Diego, CA,
United States, 2025, vol. 38.
ista: 'Cadei R, Demirel I, De Bartolomeis P, Lindorfer L, Cremer S, Schmid C, Locatello
F. 2025. Prediction-powered causal inferences. 39th Annual Conference on Neural
Information Processing Systems. NeurIPS: Neural Information Processing Systems,
Advances in Neural Information Processing Systems, vol. 38.'
mla: Cadei, Riccardo, et al. “Prediction-Powered Causal Inferences.” 39th Annual
Conference on Neural Information Processing Systems, vol. 38, Neural Information
Processing Systems Foundation, 2025.
short: R. Cadei, I. Demirel, P. De Bartolomeis, L. Lindorfer, S. Cremer, C. Schmid,
F. Locatello, in:, 39th Annual Conference on Neural Information Processing Systems,
Neural Information Processing Systems Foundation, 2025.
conference:
end_date: 2025-12-07
location: San Diego, CA, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2025-12-02
date_created: 2026-01-29T14:35:11Z
date_published: 2025-12-15T00:00:00Z
date_updated: 2026-02-16T11:39:33Z
day: '15'
ddc:
- '000'
department:
- _id: FrLo
- _id: SyCr
file:
- access_level: open_access
checksum: 92467fa566cd36671a6a3b9e71ae0f71
content_type: application/pdf
creator: flocatel
date_created: 2026-01-29T14:35:02Z
date_updated: 2026-01-29T14:35:02Z
file_id: '21077'
file_name: 17546_Prediction_Powered_Causa.pdf
file_size: 8489023
relation: main_file
success: 1
file_date_updated: 2026-01-29T14:35:02Z
has_accepted_license: '1'
intvolume: ' 38'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
publication: 39th Annual Conference on Neural Information Processing Systems
publication_identifier:
issn:
- 1049-5258
publication_status: epub_ahead
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
status: public
title: Prediction-powered causal inferences
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 38
year: '2025'
...
---
OA_place: publisher
OA_type: gold
_id: '18847'
abstract:
- lang: eng
text: "Machine Learning and AI have the potential to transform data-driven\r\nscientific
discovery, enabling accurate predictions for several scientific\r\nphenomena.
As many scientific questions are inherently causal, this paper looks\r\nat the
causal inference task of treatment effect estimation, where the outcome\r\nof
interest is recorded in high-dimensional observations in a Randomized\r\nControlled
Trial (RCT). Despite being the simplest possible causal setting and\r\na perfect
fit for deep learning, we theoretically find that many common choices\r\nin the
literature may lead to biased estimates. To test the practical impact of\r\nthese
considerations, we recorded ISTAnt, the first real-world benchmark for\r\ncausal
inference downstream tasks on high-dimensional observations as an RCT\r\nstudying
how garden ants (Lasius neglectus) respond to microparticles applied\r\nonto their
colony members by hygienic grooming. Comparing 6 480 models\r\nfine-tuned from
state-of-the-art visual backbones, we find that the sampling\r\nand modeling choices
significantly affect the accuracy of the causal estimate,\r\nand that classification
accuracy is not a proxy thereof. We further validated\r\nthe analysis, repeating
it on a synthetically generated visual data set\r\ncontrolling the causal model.
Our results suggest that future benchmarks should\r\ncarefully consider real downstream
scientific questions, especially causal\r\nones. Further, we highlight guidelines
for representation learning methods to\r\nhelp answer causal questions in the
sciences."
acknowledgement: We thank Piersilvio De Bartolomeis, and the full Causal Learning
and Artificial Intelligence (CLAI) group at ISTA for the extremely helpful discussions.
Riccardo Cadei was supported by a Google Research Scholar Award and a Google Initiated
Gift to Francesco Locatello. We thank the Social Immunity team at ISTA particularly
Michaela Hönigsberger and Wilfrid Jean Louis, for supporting the ecological experiment
and Farnaz Beikzadeh Abbasi, Luisa Fiebig and Martin Estermann for annotating ant
behavior in ISTAnt.
article_processing_charge: No
arxiv: 1
author:
- first_name: Riccardo
full_name: Cadei, Riccardo
id: 0fa8b76f-72f0-11ef-b75a-a5da96e5ad6b
last_name: Cadei
- first_name: Lukas
full_name: Lindorfer, Lukas
id: 85f0e6d3-06b3-11ec-8982-8c5049fa4455
last_name: Lindorfer
- first_name: Sylvia
full_name: Cremer, Sylvia
id: 2F64EC8C-F248-11E8-B48F-1D18A9856A87
last_name: Cremer
orcid: 0000-0002-2193-3868
- first_name: Cordelia
full_name: Schmid, Cordelia
last_name: Schmid
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Cadei R, Lindorfer L, Cremer S, Schmid C, Locatello F. Smoke and mirrors in
causal downstream tasks. In: ICML 2024 Workshop AI4Science. Vol 38. Curran
Associates; 2024.'
apa: Cadei, R., Lindorfer, L., Cremer, S., Schmid, C., & Locatello, F. (2024).
Smoke and mirrors in causal downstream tasks. In ICML 2024 Workshop AI4Science
(Vol. 38). Curran Associates.
chicago: Cadei, Riccardo, Lukas Lindorfer, Sylvia Cremer, Cordelia Schmid, and Francesco
Locatello. “Smoke and Mirrors in Causal Downstream Tasks.” In ICML 2024 Workshop
AI4Science, Vol. 38. Curran Associates, 2024.
ieee: R. Cadei, L. Lindorfer, S. Cremer, C. Schmid, and F. Locatello, “Smoke and
mirrors in causal downstream tasks,” in ICML 2024 Workshop AI4Science,
2024, vol. 38.
ista: 'Cadei R, Lindorfer L, Cremer S, Schmid C, Locatello F. 2024. Smoke and mirrors
in causal downstream tasks. ICML 2024 Workshop AI4Science. ICML: International
Conference on Machine Learning vol. 38.'
mla: Cadei, Riccardo, et al. “Smoke and Mirrors in Causal Downstream Tasks.” ICML
2024 Workshop AI4Science, vol. 38, Curran Associates, 2024.
short: R. Cadei, L. Lindorfer, S. Cremer, C. Schmid, F. Locatello, in:, ICML 2024
Workshop AI4Science, Curran Associates, 2024.
conference:
end_date: 2024-07-26
name: 'ICML: International Conference on Machine Learning'
start_date: 2024-07-26
corr_author: '1'
date_created: 2025-01-14T07:27:26Z
date_published: 2024-09-25T00:00:00Z
date_updated: 2025-07-10T11:51:50Z
day: '25'
ddc:
- '000'
- '570'
department:
- _id: SyCr
- _id: FrLo
- _id: GradSch
external_id:
arxiv:
- '2405.17151'
file:
- access_level: open_access
checksum: beedf05388bbdb7ddda81ec3d5ec7026
content_type: application/pdf
creator: dernst
date_created: 2025-01-27T11:42:24Z
date_updated: 2025-01-27T11:42:24Z
file_id: '18896'
file_name: 2024_ICML_Cadei.pdf
file_size: 4453014
relation: main_file
success: 1
file_date_updated: 2025-01-27T11:42:24Z
has_accepted_license: '1'
intvolume: ' 38'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
publication: ICML 2024 Workshop AI4Science
publication_status: published
publisher: Curran Associates
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/CausalLearningAI/ISTAnt
record:
- id: '18895'
relation: research_data
status: public
- id: '19509'
relation: is_continued_by
status: for_moderation
scopus_import: '1'
status: public
title: Smoke and mirrors in causal downstream tasks
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 38
year: '2024'
...
---
OA_place: repository
OA_type: gold
_id: '18895'
abstract:
- lang: eng
text: 'ISTAnt is a new ecological dataset for social immunity and represents the
first real-world benchmark for causal inference downstream tasks on high-dimensional
observations. It analyzes grooming behavior in the ant Lasius neglectus in groups
of three worker ants. The workers for the experiment were obtained from their
laboratory stock colony, which had been collected from the field in 2022 in the
Botanical Garden Jena, Germany. Ant collection and all experimental work were
performed in compliance with international, national and institutional regulations
and ethical guidelines. For the experiment, the body surface of one of the three
ants was treated with a suspension of either of two microparticle types (diameter
~5 µm) to induce grooming by the two nestmates, which were individually color-coded
by application of a dot of blue or orange paint, respectively. The three ants
were housed in small plastic containers (diameter 28mm, height 30mm) with moistened,
plastered ground and the interior walls covered with PTFE (polytetrafluoroethane)
to hamper climbing by the ants. Filming occurred in a temperature- and humidity-controlled
room at 23°C within a custom-made filming box with controlled lighting and ventilation
conditions. We set up nine ant groups at a time (always containing both treatments)
and placed them randomly on positions 1-9 marked on the floor in a 3x3 grid, about
3mm from each other. The experiment was performed on two consecutive days. Videos
were acquired using a USB camera (FLIR blackfly S BFS-U3-120S4C, Teledyne FLIR)
with a high-performance lens (HP Series 25mm Focal Length, Edmund optics 86-572)
in OBS studio 29.0.0 \citep{bailey2017obs} at a framerate of 30 FPS and a resolution
of 2500x2500 pixels. From each original video (105x105 mm), we generated nine
individual videos .mkv (each ~32x32 mm, 770x770 pixels) by determining exact coordinates
per container from one frame in GIMP 2.10.36 and cropping of the videos with FFmpeg
6.1.1. Annotation was performed over two consecutive days by three observers who
had not been involved in the experimental setup or recording and were unaware
of the treatment assignments to ensure bias-free behavioral annotation. They annotated
the behavior of the ants during video observations, using custom-made software
that saves the start and end frames of behaviors marked in a .csv file (see ''annotations''
folder). In one of the videos, one of the nestmates'' legs got inadvertently stuck
to its body surface during the color-coding, interfering with its behavior, so
the video was discarded. This left 44 videos from 5 independent setups (n=24 of
treatment 1 and n=20 of treatment 2) of 10 minutes each for a total of 792 000
annotated frames (see ''video'' folder). For each video, we provide the following
information: the number of the set to which it belongs (1-5); the number of the
position within the set reflecting the position of the ant group under the camera
(1-9), for which we also provide ‘coordinates’ in the 3x3 grid (taking values
-1/0/1 for both X and Y axis); treatment (1 or 2); the hour of the day when the
recording was started (in 24h CEST); experimental day (A or B); the top left coordinate
of the cropping square from the original video (CropX/CropY); the person annotating
the video (given as A, B, C); the date of annotation (1: first day, 2: second
day) and in which order the videos were annotated by each person, both reflecting
a possible training effect of the person (see ''experiments_settings.csv'' file).'
article_processing_charge: No
author:
- first_name: Riccardo
full_name: Cadei, Riccardo
id: 0fa8b76f-72f0-11ef-b75a-a5da96e5ad6b
last_name: Cadei
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Sylvia M
full_name: Cremer, Sylvia M
id: 2F64EC8C-F248-11E8-B48F-1D18A9856A87
last_name: Cremer
orcid: 0000-0002-2193-3868
- first_name: Lukas
full_name: Lindorfer, Lukas
id: 85f0e6d3-06b3-11ec-8982-8c5049fa4455
last_name: Lindorfer
- first_name: Cordelia
full_name: Schmid, Cordelia
last_name: Schmid
citation:
ama: Cadei R, Locatello F, Cremer S, Lindorfer L, Schmid C. ISTAnt. 2024. doi:10.6084/M9.FIGSHARE.26484934.V2
apa: Cadei, R., Locatello, F., Cremer, S., Lindorfer, L., & Schmid, C. (2024).
ISTAnt. Institute of Science and Technology Austria. https://doi.org/10.6084/M9.FIGSHARE.26484934.V2
chicago: Cadei, Riccardo, Francesco Locatello, Sylvia Cremer, Lukas Lindorfer, and
Cordelia Schmid. “ISTAnt.” Institute of Science and Technology Austria, 2024.
https://doi.org/10.6084/M9.FIGSHARE.26484934.V2.
ieee: R. Cadei, F. Locatello, S. Cremer, L. Lindorfer, and C. Schmid, “ISTAnt.”
Institute of Science and Technology Austria, 2024.
ista: Cadei R, Locatello F, Cremer S, Lindorfer L, Schmid C. 2024. ISTAnt, Institute
of Science and Technology Austria, 10.6084/M9.FIGSHARE.26484934.V2.
mla: Cadei, Riccardo, et al. ISTAnt. Institute of Science and Technology
Austria, 2024, doi:10.6084/M9.FIGSHARE.26484934.V2.
short: R. Cadei, F. Locatello, S. Cremer, L. Lindorfer, C. Schmid, (2024).
corr_author: '1'
date_created: 2025-01-27T11:45:43Z
date_published: 2024-10-23T00:00:00Z
date_updated: 2025-01-27T11:58:38Z
day: '23'
ddc:
- '570'
department:
- _id: SyCr
- _id: FrLo
- _id: GradSch
doi: 10.6084/M9.FIGSHARE.26484934.V2
main_file_link:
- open_access: '1'
url: https://10.6084/M9.FIGSHARE.26484934.V2
month: '10'
oa: 1
oa_version: Published Version
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '18847'
relation: used_in_publication
status: public
status: public
title: ISTAnt
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...