OSCR

Everyday Activity Science and Engineering Table Setting Dataset.

Code ↔ Paper

9 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 9 matches
  1. [1] § Technical Validation ↔ 06_Model/baseline/windowed/overview.py, lines 147–172 · score 0.84 · motion la, motion ra, action ra, action la, action body, accuracy
  2. [2] § Technical Validation ↔ 06_Model/baseline/windowed/overview.py, lines 99–120 · score 0.79 · motion la, motion ra, action ra, action la, action body, baseline
  3. [3] § Technical Validation ↔ 03_Process/baseline/tsd/util.py, lines 3–81 · score 0.74 · tiers phase, motion ra, action la, action body, EMG, baseline
  4. [4] § Technical Validation ↔ 06_Model/baseline/windowed/overview.py, lines 99–120 · score 0.68 · motion ra, action la, action body, baseline, windows, phase
  5. [5] § Data Record › File Structure › Transcripts ↔ 03_Process/tsd1_pipeline2024/whisper_annotation.py, lines 20–45 · score 0.67 · begin_ts, end_ts, tier_name, whisper, transcripts
  6. [6] § Data Record › File Structure › Transcripts ↔ 03_Process/tsd1_pipeline2024/annotations.py, lines 14–85 · score 0.65 · begin_ts, end_ts, tier_name, transcripts
  7. [7] § Methods › Annotation ↔ examples/tsd/elan_cv.py, lines 218–290 · score 0.58 · controlled vocabulary, OWL, RDF, ontology, ELAN, schema
  8. [8] § Methods › Post-processing ↔ 03_Process/tsd1_pipeline2024/head_cams.py, lines 43–106 · score 0.53 · frame timestamps, consecutive, webcam, trimming, duplicating, computers
  9. [9] § Data Record › File Structure ↔ examples/tsd/create_eafs.py, lines 39–146 · score 0.51 · audio speech, trial id, wav, meta, timestamps

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Python · 176 lines · 6.6 KB · no license · 3 matches

  1. import marimo
  3. __generated_with = "0.7.20"
  4. app = marimo.App(width="medium")
  7. @app.cell
  8. def __():
  9. import marimo as mo
  10. return mo,
  13. @app.cell
  14. def __(mo):
  15. mo.md(r"""# Run Overview Windowed""")
  16. return
  19. @app.cell
  20. def __():
  21. import seaborn as sns
  22. import matplotlib.pyplot as plt
  24. # Set common parameters for seaborn plots
  25. custom_params = {"axes.spines.right": False, "axes.spines.top": False}
  26. sns.set_theme(rc=custom_params)
  27. sns.set_context("paper")
  28. return custom_params, plt, sns
  31. @app.cell
  32. def __():
  33. from glob import glob
  34. from tqdm import tqdm
  35. import numpy as np
  36. import pandas as pd
  37. from sklearn.metrics import accuracy_score
  39. _runs = list(glob("./06_Model/baseline/windowed/runs/run*sklearn*.npz"))
  40. _res = []
  41. predictions = []
  42. for _f in tqdm(_runs):
  43. # Main run
  44. _load = np.load(_f)
  45. _file_info = dict(zip(['Modality', 'Tier', 'Window Size', 'Model', 'Time'], _f.split('/')[-1].replace('.npz', '').split('_')[1:]))
  47. # Dummy run
  48. try:
  49. _load_dummy = np.load(_f.replace('sklearn', 'dummy'))
  50. _baseline = accuracy_score(_load_dummy['trg'], _load_dummy['prd'])
  52. # Assert target to be equal
  53. # assert all(_load['trg'] == _load_dummy['trg']), 'The target of all libs should be the same'
  54. except Exception as err:
  55. print(_file_info)
  56. print(err)
  57. _load_dummy = dict(trg=_load['trg'], prd=[])
  58. _baseline = None
  61. # append to results
  62. predictions.append({"Target": _load['trg'],
  63. 'Prediction': _load['prd'], 'Dummy Prediction': _load_dummy['prd']})
  64. _res.append({**_file_info,
  65. 'Accuracy': accuracy_score(_load['trg'], _load['prd']),
  66. 'Baseline': _baseline
  67. })
  69. df = pd.DataFrame(_res)
  70. df['Window Size'] = df['Window Size'].str[1:].astype(float)
  71. df['Time'] = df['Time'].str[1:].astype(float)
  72. df['Tier'] = df['Tier'].str[1:]
  73. df
  74. return accuracy_score, df, glob, np, pd, predictions, tqdm
  77. @app.cell
  78. def __(df):
  79. df[df['Time'] == 10800].pivot(index='Modality', columns='Tier', values='Accuracy')
  80. return
  83. @app.cell
  84. def __(df, plt, sns):
  85. _, (_ax1, _ax2) = plt.subplots(1, 2, figsize=(12, 4))
  86. sns.heatmap(df[df['Time'] == 10800].pivot(index='Modality', columns='Tier', values='Accuracy') * 100, square=True, cbar=True, vmin=0, vmax=100, annot=True, fmt='.3g', ax=_ax1)
  87. sns.heatmap(df[df['Time'] == 21600].pivot(index='Modality', columns='Tier', values='Accuracy') * 100, square=True, cbar=True, vmin=0, vmax=100, annot=True, fmt='.3g', ax=_ax2)
  88. plt.tight_layout()
  89. plt.gca()
  90. return
  93. @app.cell
  94. def __(df, np):
  95. df[np.logical_and(df['Time'] <= 21600, df['Modality'] == 'ACC')]
  96. return
  99. @app.cell
  100. def __(df, np, plt, sns):
  101. _plot_df = df.copy()
  102. _plot_df = _plot_df[_plot_df['Time'] <= 21600]
  103. _plot_df['Modality'] = _plot_df['Modality'].replace(dict(MoCapMatched='MoCap'))
  104. _plot_df = _plot_df.drop(_plot_df[np.logical_and(_plot_df['Time'] >= 21600, _plot_df['Modality'] == 'ACC')].index)
  105. _plot_df = _plot_df.pivot(index='Tier', columns='Modality', values='Accuracy')
  106. _plot_df = _plot_df.reindex(index = ['phase', 'action-body', 'action-ra', 'action-la', 'motion-ra', 'motion-la'])
  108. _, (_ax1, _ax2) = plt.subplots(1, 2, figsize=(5.5, 4))
  109. sns.heatmap(_plot_df[['ACC', 'EMG', 'EEG', 'MoCap']] * 100, square=True, cbar=False, vmin=0, vmax=100, annot=True, fmt='.3g', ax=_ax1)
  110. sns.heatmap(_plot_df[['PLUX', 'ALL']] * 100, square=True, cbar=True, vmin=0, vmax=100, annot=True, fmt='.3g', ax=_ax2)
  111. # _ax2.spines['left'].set_visible(False)
  112. # _ax2.set(xlabel=None, ylabel=None)
  113. _ax2.set_yticklabels([])
  114. _ax2.set(xlabel=None, ylabel=None)
  116. plt.tight_layout()
  117. # plt.suptitle('Auto-Sklearn')
  118. plt.savefig('06_Model/baseline/imgs/window_overview_accuracy.pdf')
  119. plt.gca(), _plot_df
  120. return
  123. @app.cell
  124. def __(df, np, plt, sns):
  125. _plot_df = df.copy()
  126. _plot_df = _plot_df[_plot_df['Time'] <= 21600]
  127. _plot_df['Modality'] = _plot_df['Modality'].replace(dict(MoCapMatched='MoCap'))
  128. _plot_df = _plot_df.drop(_plot_df[np.logical_and(_plot_df['Time'] >= 21600, _plot_df['Modality'] == 'ACC')].index)
  129. _plot_df = _plot_df.pivot(index='Tier', columns='Modality', values='Baseline')
  130. _plot_df = _plot_df.reindex(index = ['phase', 'action-body', 'action-ra', 'action-la', 'motion-ra', 'motion-la'])
  132. _, (_ax1, _ax2) = plt.subplots(1, 2, figsize=(5.5, 4))
  133. sns.heatmap(_plot_df[['ACC', 'EMG', 'EEG', 'MoCap']] * 100, square=True, cbar=False, vmin=0, vmax=100, annot=True, fmt='.3g', ax=_ax1)
  134. sns.heatmap(_plot_df[['PLUX', 'ALL']] * 100, square=True, cbar=True, vmin=0, vmax=100, annot=True, fmt='.3g', ax=_ax2)
  135. # _ax2.spines['left'].set_visible(False)
  136. # _ax2.set(xlabel=None, ylabel=None)
  137. _ax2.set_yticklabels([])
  138. _ax2.set(xlabel=None, ylabel=None)
  140. plt.tight_layout()
  141. # plt.suptitle('Auto-Sklearn')
  142. plt.savefig('06_Model/baseline/imgs/window_overview_baseline.pdf')
  143. plt.gca(), _plot_df
  144. return
  147. @app.cell
  148. def __(df, np, plt, sns):
  149. _plot_df = df.copy()
  150. _plot_df = _plot_df[_plot_df['Time'] <= 21600]
  151. _plot_df['Modality'] = _plot_df['Modality'].replace(dict(MoCapMatched='MoCap'))
  152. _plot_df = _plot_df.drop(_plot_df[np.logical_and(_plot_df['Time'] >= 21600, _plot_df['Modality'] == 'ACC')].index)
  153. _baseline = _plot_df.pivot(index='Tier', columns='Modality', values='Baseline')
  154. _acc = _plot_df.pivot(index='Tier', columns='Modality', values='Accuracy')
  155. _plot_df = _acc - _baseline
  156. _fmt_fn = lambda x: f'{x * 100:.3g}'
  157. _annot_df = (_acc.applymap(_fmt_fn) + '\n(' + _baseline.applymap(_fmt_fn) + ')').reindex(index = ['phase', 'action-body', 'action-ra', 'action-la', 'motion-ra', 'motion-la'])
  158. _plot_df = _plot_df.reindex(index = ['phase', 'action-body', 'action-ra', 'action-la', 'motion-ra', 'motion-la'])
  160. _, (_ax1, _ax2) = plt.subplots(1, 2, figsize=(5.5, 4))
  161. sns.heatmap(_plot_df[['ACC', 'EMG', 'EEG', 'MoCap']] * 100, fmt = '', annot=_annot_df[['ACC', 'EMG', 'EEG', 'MoCap']], square=True, cbar=False, vmin=-50, vmax=50, ax=_ax1, cmap='vlag_r')
  162. sns.heatmap(_plot_df[['PLUX', 'ALL']] * 100, fmt = '', annot=_annot_df[['PLUX', 'ALL']], square=True, cbar=True, vmin=-50, vmax=50, ax=_ax2, cmap='vlag_r')
  163. # _ax2.spines['left'].set_visible(False)
  164. # _ax2.set(xlabel=None, ylabel=None)
  165. _ax2.set_yticklabels([])
  166. _ax2.set(xlabel="Combination", ylabel=None)
  168. plt.tight_layout()
  169. # plt.suptitle('Auto-Sklearn')
  170. plt.savefig('06_Model/baseline/imgs/window_overview_diff.pdf')
  171. plt.gca(), _annot_df
  172. return
  175. if __name__ == "__main__":
  176. app.run()

overview.py at commit 3a0c0d1, no license · at the source

Overview

Authors: Moritz Meier1, Yale Hartmann1, Yasmina El Ouahabi1, Lars Bredereke1, Felix Putze1, Tanja Schultz1
ORCID iDs: Yale Hartmann, Felix Putze
  1. University Bremen, Cognitive Systems Lab (CSL), Bremen, 28359 Germany
Institutions: University of Bremen (Germany)
Journal: Scientific data, volume 13, issue 1, article 721
Dates: received 29 January 2025; accepted 12 March 2026; published online 12 May 2026
Type: Data paper · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41597-026-07077-7 · PMID 42120431 · PMCID PMC13168227 · OpenAlex W7160897596
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), other (modality), human (organism), methods / tools (subfield)
Methods: Preprocessing, Evoked potentials, Smoothing, state filtering, decompositions, Physiology & signal measures
Keywords: Scientific data, Computational science
MeSH: Human Activities*, Robotics*, Cognition, Electroencephalography, Humans, Motion Capture (* major topic)
Topic: Gaze Tracking and Assistive Technology (Human-Computer Interaction, Computer Science), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (German Research Foundation) (329551904)
Citations: not cited yet (Europe PMC); 68 references in the paper

Abstract

Understanding human everyday activity planning and execution is crucial to inform cognition-enabled robots and systems. In this paper, we describe the design, collection, validation, and dissemination of the Everyday Activity Science and Engineering Table Setting Dataset (EASE-TSD). EASE-TSD is a dataset of multimodal high-dimensional biosignals synchronously recorded from human subjects who are setting a table in a controlled laboratory setup. Data from 78 sessions are available, each recorded during six table-setting trials in which we capture the planning and execution of human behavior using eight synchronized biosignal streams: marker-based motion capturing, environmental and first-person video cameras, eye-tracking, electromyography, electrodermal activity, acceleration, microphones, and electroencephalography. Participants were instructed to think aloud concurrently and retrospectively to explain and comment on their table-setting actions and the corresponding cognitive processes. EASE-TSD is annotated with a 3-level annotation schema containing phases, activities, motions, and interacted objects. Additionally, the think-aloud (TA) protocols are annotated using TA codes. After recording, the EASE-TSD data undergo semi-automatic labeling, post-processing, and analysis procedures, leveraging latest biosignal processing and machine learning methods.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

gitlab.csl.uni-bremen.de/ease-public

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the text, “Methods”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: gitlab.csl.uni-bremen.de/ease-public/

cognitive-systems-lab/easelan

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 9131d4da1043b0832bbe63f65c656ca9883b55c0, 27 February 2026
Languages: Python (22), Shell (1), Jupyter (1)
Size: 63 files, 24 scripts
Software Heritage: not archived
Found in: the text, “Annotation”
Holds: README, license file, environment (pyproject.toml), continuous integration, 1 notebook
Not found: CITATION.cff, tests, documentation
Tools: pandas (5 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
26 files
At the source: github.com/cognitive-systems-lab/easelan

gitlab.csl.uni-bremen.de/ease-public/tsd-one

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 3a0c0d1cd3e9056ac33c4a6f89f2e6fe3552623f, 6 September 2024
Languages: Python (55), JavaScript (9), Shell (8), Jupyter (1)
Size: 604 files, 73 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (01_Plan/tsd1_data_stack/docker-compose.yml, 01_Plan/tsd1_data_stack/Dockerfile.spark_cron, 01_Plan/tsd1_data_stack/requirements.txt, 03_Process/baseline/setup.py, 03_Process/tsd1_pipeline2024/requirements.txt, 05_Analyze/baseline/requirements.txt, 05_Analyze/ln_vis/requirements.txt, 05_Analyze/ln_vis/ln_local_nodes/pyproject.toml, 05_Analyze/ln_vis/ln_local_nodes/setup.py), 1 notebook
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (34 files), pandas (26 files), Matplotlib (12 files), seaborn (11 files), PyTorch (5 files), scikit-learn (4 files), OpenCV (2 files), PyTorch Lightning (1 file), SciPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
74 files
At the source: gitlab.csl.uni-bremen.de/ease-public/tsd-one

gitlab.csl.uni-bremen.de/ease-public/g.nautilus-driver

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 116b0fb48b5b5aaa1b5733cd21e9fbb1ee599967, 24 March 2023
Languages: C/C++ (17), C++ (7)
Size: 58 files, 24 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
25 files
At the source: gitlab.csl.uni-bremen.de/ease-public/g.nautilus-driver

gitlab.csl.uni-bremen.de/ease-public/lsl-tools

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: e9089b6117910a11ac215ab80db7add9c67fb1be, 14 August 2024
Languages: Python (55), C/C++ (6), C++ (3)
Size: 95 files, 64 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (setup.py), tests
Not found: license file, CITATION.cff, continuous integration, documentation
Tools: NumPy (23 files), OpenCV (6 files), pandas (6 files), Matplotlib (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
65 files
At the source: gitlab.csl.uni-bremen.de/ease-public/lsl-tools

gitlab.csl.uni-bremen.de/ease-public/labc

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: e8ccc4aed0eae8e2bfa91fad304ed923950b1f5c, 12 September 2024
Languages: Python (26)
Size: 57 files, 26 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (pyproject.toml), tests, continuous integration, documentation
Not found: license file, CITATION.cff
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
27 files
At the source: gitlab.csl.uni-bremen.de/ease-public/labc

Code availability

The study and data analysis makes extensive use of many free libraries, such as Numpy52, Pandas53, Scikit-Learn54, Scipy55, Matplotlib56, Seaborn57 and AutoSklearn51,58, as well as a substantial amount of custom code. Below is a list of tools created, their purpose, and where they can be accessed.

• Nautilus Driver: The Nautilus Driver is a custom driver for the Nautilus EEG Cap. It is used to stream the received EEG signal via LSL. The driver is written in C++ and is available on GitLab (https://gitlab.csl.uni-bremen.de/ease-public/g.nautilus-driver).

• LSL Tools: The LSL Tools are a set of tools to send and receive data using the Lab Streaming Layer (LSL) protocol and include simple plotting functions. The tools are written in Python and are available on GitLab (https://gitlab.csl.uni-bremen.de/ease-public/lsl-tools).

• Experiment Control: The Experiment Control is a custom tool to control the experiment flow. It starts and stops each trial, randomly selects the trial condition, controls lighting, retrospection videos, and more. The tool is currently under further development and is not yet available publicly.

• Lab-Commander: Lab-Commander (labc) is a platform-independent pure-Python zero-conf user-friendly rootless network middle-ware that originated during the setup of EASE-TSD recording architecture. It is targeted at a fast setup of short-lived distributed applications within a computer network without any prior knowledge of computer networking required (https://gitlab.csl.uni-bremen.de/ease-public/labc).

• Processing Scripts: The Processing Scripts are a set of Python scripts to cut all sensor data into a shared length, ensuring data presence where possible. The scripts are available on GitLab (https://gitlab.csl.uni-bremen.de/ease-public/tsd-one).

• Machine Learning/Analysis Scripts: The Machine Learning (ML) Scripts Analysis are a set of Marimo (http://marimo.io/) notebooks and python scripts to segment the data, train, and evaluate the models. The scripts are available on GitLab (https://gitlab.csl.uni-bremen.de/ease-public/tsd-one).

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 6 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 211 scripts, each with its path and the digest of its content;
  • 9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

The data of 78 participants, who consented to data publication, is freely available at https://osf.io/rbyfk, accompanied by comprehensive documentation and annotation. The data is structured by session and contains seperate files for each modality and annotation type, split by trials within the session. A detailed breakdown is given in the Data Record section.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 2 keywords, 6 MeSH terms, 1 funder, 47 references.

Cite

This paper

Meier, M., Hartmann, Y., El Ouahabi, Y., Bredereke, L., Putze, F., & Schultz, T. (2026). Everyday Activity Science and Engineering Table Setting Dataset. Scientific data, 13(1), 721. https://doi.org/10.1038/s41597-026-07077-7

BibTeX

@article{meier2026everyday,
author = {Meier, Moritz and Hartmann, Yale and El Ouahabi, Yasmina and Bredereke, Lars and Putze, Felix and Schultz, Tanja},
title = {{Everyday Activity Science and Engineering Table Setting Dataset}},
journal = {Scientific data},
year = {2026},
month = may,
volume = {13},
number = {1},
pages = {721},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-07077-7},
url = {https://doi.org/10.1038/s41597-026-07077-7},
pmid = {42120431},
pmcid = {PMC13168227}
}

RIS

TY - JOUR
AU - Meier, Moritz
AU - Hartmann, Yale
AU - El Ouahabi, Yasmina
AU - Bredereke, Lars
AU - Putze, Felix
AU - Schultz, Tanja
TI - Everyday Activity Science and Engineering Table Setting Dataset
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/05/12
VL - 13
IS - 1
SP - 721
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-07077-7
UR - https://doi.org/10.1038/s41597-026-07077-7
LA - en
ER -

CSL-JSON

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"title": "Everyday Activity Science and Engineering Table Setting Dataset",
"container-title": "Scientific data",
"author": [
{
"family": "Meier",
"given": "Moritz"
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{
"family": "Hartmann",
"given": "Yale"
},
{
"family": "El Ouahabi",
"given": "Yasmina"
},
{
"family": "Bredereke",
"given": "Lars"
},
{
"family": "Putze",
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"container-title-short": "Sci Data",
"volume": "13",
"issue": "1",
"page": "721",
"DOI": "10.1038/s41597-026-07077-7",
"PMID": "42120431",
"PMCID": "PMC13168227",
"ISSN": "2052-4463",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41597-026-07077-7",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
12
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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Deep Learning-Based Temporal Gait Analysis Using a Smartphone IMU in Older Adults with and Without Non-Specific Low Back Pain.
Journal: Bioengineering (Basel, Switzerland)
In common: PyTorch Lightning, PyTorch, seaborn, 5 other tools, other, methods / tools, 1 reference
[5] doi:10.1162/imag.a.1207 [code]
Investigating the temporal dynamics and modeling of mid-level feature representations in humans.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: PyTorch Lightning, OpenCV, PyTorch, 6 other tools, EEG
[6] doi:10.1371/journal.pcbi.1014571 [code]
SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification.
Journal: PLoS computational biology
In common: OpenCV, PyTorch, seaborn, 5 other tools, 2 references
[7] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: PyTorch Lightning, OpenCV, PyTorch, 6 other tools
[8] doi:10.1038/s41598-026-61605-4 [code]
Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations.
Journal: Scientific reports
In common: PyTorch Lightning, OpenCV, PyTorch, 4 other tools, methods / tools, 1 reference
[9] doi:10.1038/s42003-026-10259-z [code]
Spatial transcriptomic profiling of developing mouse hearts reveals a spatially patterned signaling environment.
Journal: Communications biology
In common: PyTorch Lightning, OpenCV, PyTorch, 6 other tools
[10] doi:10.1038/s41598-026-68186-2 [code]
NeuroStream: spectral-spatio-temporal deep learning for visual stimulus classification from EEG.
Journal: Scientific reports
In common: PyTorch, seaborn, scikit-learn, 4 other tools, methods / tools, EEG, 2 references

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