Fecal microscopic data set is a set of fecal microscopic images, which is used in object detection task. The datasets are collected from the Sixth People’s Hospital of Chengdu (Sichuan Province, China). The samples were went flow diluted, stirred and placed, and imaged with a microscopic imaging system. The clearest 5 images were collected for each view of each sample with Tenengrad definition algorithm. The dataset we collected includes 10670 groups of views with 53350 jpg images. The Resolution of images are 1200×1600. There are 4 categories, RBCs, WBCs, Molds, and Pyocytes.

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Recently, surface electromyogram (EMG) has been proposed as a novel biometric trait for addressing some key limitations of current biometrics, such as spoofing and liveness. The EMG signals possess a unique characteristic: they are inherently different for individuals (biometrics), and they can be customized to realize multi-length codes or passwords (for example, by performing different gestures).

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The ability to estimate the probability of a drug to receive approval in clinical trials provides natural advantages to optimizing pharmaceutical research workflows. Success rates of a clinical trials have deep implications to costs, duration of development, and under pressure due to stringent regulatory approval processes. We propose a machine learning approach that can predict the outcome of trial with reliable accuracies, using biological activities, physico-chemical properties of the compounds, target related features and NLP-based compound representation.

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The Open Big Healthy Brains (OpenBHB) dataset is a large (N>5000) multi-site 3D brain MRI dataset gathering 10 public datasets (IXI, ABIDE 1, ABIDE 2, CoRR, GSP, Localizer, MPI-Leipzig, NAR, NPC, RBP) of T1 images acquired across 93 different centers, spread worldwide (North America, Europe and China). Only healthy controls have been included in OpenBHB with age ranging from 6 to 88 years old, balanced between males and females.

Instructions: 

Please read carrefuly the following sections.

Dataset organization

This dataset comprises 3985 images for training and 666 images for test (kept hidden for the challenge), both dedicated to the OpenBHB challenge. Additionally, 628 images are available with missing label information (age, sex, or scanner details) and they are excluded for the current challenge. The exact content of this dataset is described in our paper.

The dataset is organized as follows:

  • All meta-data information (age, sex, site, acquisition setting, magnetic field strengh, etc.) can be found in participants.tsv.
  • Corresponding T1 images pre-processed with CAT12 (VBM), FSL (SBM) and Quasi-Raw can be found in training_data.
  • The pairs (site, acquisition setting) discretized used for the OpenBHB Challenge are in official_site_class_labels.tsv.
  • Additional T1 images with missing label information are in missing_label_data.
  • The metrics used for Quality Check (e.g Euler number for FreeSurfer) can be found in qc.tsv.

Resource:

  • the templates used during the VBM analysis can be found in cat12vbm_space-MNI152_desc-gm_TPM.nii.gz.
  • the templates used during the Quasi-Raw analysis can be found in quasiraw_space-MNI152_desc-brain_T1w.nii.gz.
  • the Region-Of-Interest (ROI) names corresponding to the default CAT12 atlas (Neuromorphometrics) and FSL Desikan and Destrieux atlases can be found in cat12vbm_labels.txt, freesurfer_atlas-desikan_labels.txt and freesurfer_atlas-destrieux_labels.txt respectively.
  • the surface-based feature names derived by FreeSurfer on both Desikan and Destrieux atlases are available in freesurfer_channels.txt.

Acknowledgements

If you use this dataset for your work, please use the following citation:

@article{dufumier2021openbhb,

      title={{OpenBHB: a Large-Scale Multi-Site Brain MRI Data-set for Age Prediction and Debiasing}},

      author={Dufumier, Benoit and Grigis, Antoine and Victor, Julie and Ambroise, Corentin and Frouin, Vincent and Duchesnay, Edouard},

      journal={Under review.},

      year={2021}

}

Licence and Data Usage Agreement

This dataset is under Licence CC BY-NC-SA 3.0. By downloading this dataset, you also agree to the most restrictive Data Usage Agreement (DUA) of all cohorts (see the Data Usage Agreement terms included in this dataset):

  • ABIDE 1 [1]. Licence term CC BY-NC-SA 3.0 (ShareAlike), DUA
  • ABIDE 2 [2]. Licence term CC BY-NC-SA 3.0, DUA
  • IXI [3]. Licence term CC0, DUA
  • CoRR [4] Licence term CC0, DUA
  • GSP [5]  Licence term  DUA
  • NAR [6] Licence term CC0
  • MPI-Leipzig [7] Licence term CC0
  • NPC [8] Licence term CC0
  • RBP [9,10] Licence term CC0
  • Localizer [11] Licence term CC BY 3.0

References

  1. [1] http://fcon_1000.projects.nitrc.org/indi/abide/abide_I.html
  2. [2] http://fcon_1000.projects.nitrc.org/indi/abide/abide_II.html
  3. [3] https://brain-development.org/ixi-dataset
  4. [4] Zuo, X.N., et al., An Open Science Resource for Establishing Reliability and Reproducibility in Functional Connectomics, (In Press)
  5. [5] Buckner, Randy L.; Roffman, Joshua L.; Smoller, Jordan W., 2014, "Brain Genomics Superstruct Project (GSP)", https://doi.org/10.7910/DVN/25833, Harvard Dataverse, V10
  6. [6] Nastase, S. A., et al., Narratives: fMRI data for evaluating models of naturalistic language comprehension. https://doi.org/10.18112/openneuro.ds002345.v1.0.1
  7. [7] Babayan, A., Erbey, M., Kumral, D. et al. A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. Sci Data 6, 180308 (2019). https://doi.org/10.1038/sdata.2018.308
  8. [8] Sunavsky, A. and Poppenk, J. (2020). Neuroimaging predictors of creativity in healthy adults. OpenNeuro. doi: 10.18112/openneuro.ds002330.v1.1.0
  9. [9] Li, P., & Clariana, R. (2019) Reading comprehension in L1 and L2: An integrative appraoch. Journal of Neurolinguistics, 50, 94-105.(https://doi.org/10.1016/j.jneuroling.2018.03.005)
  10. [10] Follmer, J., Fang, S., Clariana, R., Meyer, B., & Li, P (2018). What predicts adult readers' understanding of STEM texts? Reading and Writing, 31, 185-214.(https://doi.org/10.1007/s11145-017-9781-x)
  11. [11] Orfanos, D. P., Michel, V., Schwartz, Y., Pinel, P., Moreno, A., Le Bihan, D., & Frouin, V. (2017). The brainomics/localizer database. NeuroImage, 144, 309-314.
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Walking disorders are common in post-stroke. Body weight support (BWS) systems have been proposed and proven to enhance gait training systems for recovering in individuals with hemiplegia. However, the fixed weight support and walking speed increase the risk of falling and decrease the active participation of the subjects. This paper proposes a strategy to enhance the efficiency of BWS treadmill training.

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This dataset includes 70 sets of 3D confocal high-resolution images. All images were imaged using an LSM800 Zeiss microscope with a Plan-apochromat 1.40-NA, 63× objective, and Zeiss ZEN Blue 2.6 software was used to acquire the images. Three channels were used to acquire transmitted light (TL), SYBR GoldTM- (Thermo Fisher Scientific, Inc.) labeled (nuclear and mitochondrial DNA), and TMRM-labeled (mitochondria) images. Each confocal image consists of 32 slices with an interval of 0.15 µm and a YX resolution of 917 × 917 pixels.

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93 Views

This is a comprehensive dataset of human arm motion during Activities of Daily Living (ADL). The Cartesian locations of the head, torso, and arm segments were recorded using a motion capture system (Vicon) from 12 participants (ages 18-72, 6 male, 6 female) performing 24 unique tasks. These include both standing and sitting tasks, as well as repetitions, selected based on what would be most useful for prosthesis users, resulting in 72 recorded trials per subject.

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This is the protein PDB dataset for the article "Novel Algorithm for Improved Protein Classification Using Graph Similarity".  This dataset consists of 9 classes of proteins.

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<p>All life on Earth is related, so that some molecular interactions are common across almost all living cells, with the number of common interactions increasing as we look at more closely related species. In particular, we expect the {\it protein-protein interaction} (PPI) networks of closely-related species to share high levels of similarity. This similarity may facilitate the transfer of functional knowledge between model species and human. {\it Multiple Network Alignment} is the process of uncovering the connection similarity between 3 or more networks simultaneously.

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All life on Earth is related, so that some molecular interactions are common across almost all living cells, with the number of common interactions increasing as we look at more closely related species. In particular, we expect the {\it protein-protein interaction} (PPI) networks of closely-related species to share high levels of similarity. This similarity may facilitate the transfer of functional knowledge between model species and human. {\it Multiple Network Alignment} is the process of uncovering the connection similarity between 3 or more networks simultaneously.

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38 Views

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