Biomedical and Health Sciences

Human somatic label-free bright-field cell images


This cell images dataset is collected using an ultrafast imaging system known as asymmetric-detection time-stretch optical microscopy (ATOM)  for training and evaluation. This novel imaging approach can achieve label-free and high-contrast flow imaging with good cellular resolution images at a very high speed. Each acquired image belongs to one of the four classes: THP1, MCF7, MB231 and PBMC.

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Dataset Details

Citation Author(s):
Submitted by:
Nan Meng
Last updated:
Wed, 03/28/2018 - 10:48
DOI:
10.21227/H2QW97
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[1] , "Human somatic label-free bright-field cell images", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2QW97. Accessed: Apr. 23, 2018.
@data{h2qw97-18,
doi = {10.21227/H2QW97},
url = {http://dx.doi.org/10.21227/H2QW97},
author = { },
publisher = {IEEE Dataport},
title = {Human somatic label-free bright-field cell images},
year = {2018} }
TY - DATA
T1 - Human somatic label-free bright-field cell images
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2QW97
ER -
. (2018). Human somatic label-free bright-field cell images. IEEE Dataport. http://dx.doi.org/10.21227/H2QW97
, 2018. Human somatic label-free bright-field cell images. Available at: http://dx.doi.org/10.21227/H2QW97.
. (2018). "Human somatic label-free bright-field cell images." Web.
1. . Human somatic label-free bright-field cell images [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2QW97
. "Human somatic label-free bright-field cell images." doi: 10.21227/H2QW97

Mobile EEG Recordings in an Art Museum Setting


Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies.  Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.

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Citation Author(s):
Submitted by:
Justin Brantley
Last updated:
Wed, 03/28/2018 - 10:50
DOI:
10.21227/H2TM00
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[1] , "Mobile EEG Recordings in an Art Museum Setting", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2TM00. Accessed: Apr. 23, 2018.
@data{h2tm00-17,
doi = {10.21227/H2TM00},
url = {http://dx.doi.org/10.21227/H2TM00},
author = { },
publisher = {IEEE Dataport},
title = {Mobile EEG Recordings in an Art Museum Setting},
year = {2017} }
TY - DATA
T1 - Mobile EEG Recordings in an Art Museum Setting
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2TM00
ER -
. (2017). Mobile EEG Recordings in an Art Museum Setting. IEEE Dataport. http://dx.doi.org/10.21227/H2TM00
, 2017. Mobile EEG Recordings in an Art Museum Setting. Available at: http://dx.doi.org/10.21227/H2TM00.
. (2017). "Mobile EEG Recordings in an Art Museum Setting." Web.
1. . Mobile EEG Recordings in an Art Museum Setting [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2TM00
. "Mobile EEG Recordings in an Art Museum Setting." doi: 10.21227/H2TM00

Decoding local field potentials for neural interfaces


This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here: http://ieeexplore.ieee.org/document/7742994/

The DataPort Repository contains the data used primarily for generating Figure 1.

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Citation Author(s):
Submitted by:
Thomas Hall
Last updated:
Wed, 03/28/2018 - 10:53
DOI:
10.21227/H2VW5Z
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[1] , "Decoding local field potentials for neural interfaces", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2VW5Z. Accessed: Apr. 23, 2018.
@data{h2vw5z-17,
doi = {10.21227/H2VW5Z},
url = {http://dx.doi.org/10.21227/H2VW5Z},
author = { },
publisher = {IEEE Dataport},
title = {Decoding local field potentials for neural interfaces},
year = {2017} }
TY - DATA
T1 - Decoding local field potentials for neural interfaces
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2VW5Z
ER -
. (2017). Decoding local field potentials for neural interfaces. IEEE Dataport. http://dx.doi.org/10.21227/H2VW5Z
, 2017. Decoding local field potentials for neural interfaces. Available at: http://dx.doi.org/10.21227/H2VW5Z.
. (2017). "Decoding local field potentials for neural interfaces." Web.
1. . Decoding local field potentials for neural interfaces [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2VW5Z
. "Decoding local field potentials for neural interfaces." doi: 10.21227/H2VW5Z

ECG signals (744 fragments)


For research purposes, the ECG signals were obtained from the PhysioNet service (http://www.physionet.org) from the MIT-BIH Arrhythmia database. The created database with ECG signals is described below. 1) The ECG signals were from 29 patients: 15 female (age: 23-89) and 14 male (age: 32-89). 2) The ECG signals contained 17 classes: normal sinus rhythm, pacemaker rhythm, and 15 types of cardiac dysfunctions (for each of which at least 10 signal fragments were collected).

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Dataset Details

Citation Author(s):
Submitted by:
Pawel Plawiak
Last updated:
Wed, 04/11/2018 - 19:59
DOI:
10.21227/H2GH2M
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[1] , "ECG signals (744 fragments)", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2GH2M. Accessed: Apr. 23, 2018.
@data{h2gh2m-18,
doi = {10.21227/H2GH2M},
url = {http://dx.doi.org/10.21227/H2GH2M},
author = { },
publisher = {IEEE Dataport},
title = {ECG signals (744 fragments)},
year = {2018} }
TY - DATA
T1 - ECG signals (744 fragments)
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2GH2M
ER -
. (2018). ECG signals (744 fragments). IEEE Dataport. http://dx.doi.org/10.21227/H2GH2M
, 2018. ECG signals (744 fragments). Available at: http://dx.doi.org/10.21227/H2GH2M.
. (2018). "ECG signals (744 fragments)." Web.
1. . ECG signals (744 fragments) [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2GH2M
. "ECG signals (744 fragments)." doi: 10.21227/H2GH2M

ISRMyo-I


The published sEMG database was captured by the Intelligent System and Biomedical Robotics Group at University of Portsmouth, leaded by Prof. Honghai Liu.

 

Six subjects were volunteered for data capturing, and the sEMG data were captured in ten separate days. We manually separated the whole database into two parts: training dataset (the first 7 days) and testing dataset(the last 3 days). For each subject, two folders exist, one for training and the other for test. 

 

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No Data files have been uploaded.

Dataset Details

Citation Author(s):
Submitted by:
Yinfeng Fang
Last updated:
Tue, 03/27/2018 - 19:47
DOI:
10.21227/H28364
 
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[1] , "ISRMyo-I", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H28364. Accessed: Apr. 23, 2018.
@data{h28364-18,
doi = {10.21227/H28364},
url = {http://dx.doi.org/10.21227/H28364},
author = { },
publisher = {IEEE Dataport},
title = {ISRMyo-I},
year = {2018} }
TY - DATA
T1 - ISRMyo-I
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H28364
ER -
. (2018). ISRMyo-I. IEEE Dataport. http://dx.doi.org/10.21227/H28364
, 2018. ISRMyo-I. Available at: http://dx.doi.org/10.21227/H28364.
. (2018). "ISRMyo-I." Web.
1. . ISRMyo-I [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H28364
. "ISRMyo-I." doi: 10.21227/H28364

Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection


TB (Tuberculosis) is a contagious disease which is caused by a bacterium named Mycobacterium Tuberculosis. Screening is done to confirm the presence of TB using different screening techniques available i.e. Chest X-ray, Microscopy, Gene Xpert and Culture etc. Medical image processing is a rapidly growing field of image processing that is used to automate different medical procedures. In this research we have designed two automated systems for the screening of TB patients. A sample of 50 images for microscopy slides and chest X-ray radiographs were taken.

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Dataset Details

Citation Author(s):
Submitted by:
SANA FATIMA
Last updated:
Mon, 02/26/2018 - 09:06
DOI:
10.21227/H2VD3C
Data Format:
 
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[1] , "Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2VD3C. Accessed: Apr. 23, 2018.
@data{h2vd3c-18,
doi = {10.21227/H2VD3C},
url = {http://dx.doi.org/10.21227/H2VD3C},
author = { },
publisher = {IEEE Dataport},
title = {Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection},
year = {2018} }
TY - DATA
T1 - Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2VD3C
ER -
. (2018). Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection. IEEE Dataport. http://dx.doi.org/10.21227/H2VD3C
, 2018. Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection. Available at: http://dx.doi.org/10.21227/H2VD3C.
. (2018). "Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection." Web.
1. . Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2VD3C
. "Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection." doi: 10.21227/H2VD3C

Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection


TB (Tuberculosis) is a contagious disease which is caused by a bacterium named Mycobacterium Tuberculosis. Screening is done to confirm the presence of TB using different screening techniques available i.e. Chest X-ray, Microscopy, Gene Xpert and Culture etc. Medical image processing is a rapidly growing field of image processing that is used to automate different medical procedures. In this research we have designed two automated systems for the screening of TB patients. A sample of 50 images for microscopy slides and chest X-ray radiographs were taken.

Dataset Files

No Data files have been uploaded.

Dataset Details

Citation Author(s):
Submitted by:
SANA FATIMA
Last updated:
Mon, 02/26/2018 - 08:13
DOI:
10.21227/H2036N
Data Format:
 
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[1] , "Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2036N. Accessed: Apr. 23, 2018.
@data{h2036n-18,
doi = {10.21227/H2036N},
url = {http://dx.doi.org/10.21227/H2036N},
author = { },
publisher = {IEEE Dataport},
title = {Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection},
year = {2018} }
TY - DATA
T1 - Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2036N
ER -
. (2018). Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection. IEEE Dataport. http://dx.doi.org/10.21227/H2036N
, 2018. Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection. Available at: http://dx.doi.org/10.21227/H2036N.
. (2018). "Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection." Web.
1. . Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2036N
. "Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection." doi: 10.21227/H2036N

Investigating Velopharyngeal Closure Force with Linear Regression


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Dataset Details

Citation Author(s):
Submitted by:
Anish Sana
Last updated:
Tue, 02/20/2018 - 11:37
DOI:
10.21227/H2F95C
 
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[1] , "Investigating Velopharyngeal Closure Force with Linear Regression", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2F95C. Accessed: Apr. 23, 2018.
@data{h2f95c-18,
doi = {10.21227/H2F95C},
url = {http://dx.doi.org/10.21227/H2F95C},
author = { },
publisher = {IEEE Dataport},
title = {Investigating Velopharyngeal Closure Force with Linear Regression},
year = {2018} }
TY - DATA
T1 - Investigating Velopharyngeal Closure Force with Linear Regression
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2F95C
ER -
. (2018). Investigating Velopharyngeal Closure Force with Linear Regression. IEEE Dataport. http://dx.doi.org/10.21227/H2F95C
, 2018. Investigating Velopharyngeal Closure Force with Linear Regression. Available at: http://dx.doi.org/10.21227/H2F95C.
. (2018). "Investigating Velopharyngeal Closure Force with Linear Regression." Web.
1. . Investigating Velopharyngeal Closure Force with Linear Regression [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2F95C
. "Investigating Velopharyngeal Closure Force with Linear Regression." doi: 10.21227/H2F95C

Activities of Daily Living


Recognition of human activities is one of the most promising research areas in artificial intelligence. This has come along with the technological advancement in sensing technologies as well as the high demand for applications that are mobile, context-aware, and real-time. We have used a smart watch (Apple iWatch) to collect sensory data for 14 ADL activities (Activities of Daily Living). 

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Dataset Details

Citation Author(s):
Submitted by:
Walid Gomaa
Last updated:
Wed, 11/22/2017 - 09:06
DOI:
10.21227/H2PS74
Data Format:
 
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[1] , "Activities of Daily Living", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2PS74. Accessed: Apr. 23, 2018.
@data{h2ps74-17,
doi = {10.21227/H2PS74},
url = {http://dx.doi.org/10.21227/H2PS74},
author = { },
publisher = {IEEE Dataport},
title = {Activities of Daily Living},
year = {2017} }
TY - DATA
T1 - Activities of Daily Living
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2PS74
ER -
. (2017). Activities of Daily Living. IEEE Dataport. http://dx.doi.org/10.21227/H2PS74
, 2017. Activities of Daily Living. Available at: http://dx.doi.org/10.21227/H2PS74.
. (2017). "Activities of Daily Living." Web.
1. . Activities of Daily Living [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2PS74
. "Activities of Daily Living." doi: 10.21227/H2PS74

SUT-Lips-DB - A database of lips traces


A database of lips traces
Cheiloscopy is a forensic investigation technique that deals with identification of humans based on lips traces. Lip prints are unique and permanent for each individual, and next to the fingerprinting, dental identification, and DNA analysis can be one of the basis for criminal/forensics analysis.

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OPEN ACCESS Dataset Details

Citation Author(s):
Submitted by:
Dariusz Mrozek
Last updated:
Fri, 11/03/2017 - 09:56
DOI:
10.21227/H2R04P
Data Format:
Links:
 
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[1] , "SUT-Lips-DB - A database of lips traces", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2R04P. Accessed: Apr. 23, 2018.
@data{h2r04p-17,
doi = {10.21227/H2R04P},
url = {http://dx.doi.org/10.21227/H2R04P},
author = { },
publisher = {IEEE Dataport},
title = {SUT-Lips-DB - A database of lips traces},
year = {2017} }
TY - DATA
T1 - SUT-Lips-DB - A database of lips traces
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2R04P
ER -
. (2017). SUT-Lips-DB - A database of lips traces. IEEE Dataport. http://dx.doi.org/10.21227/H2R04P
, 2017. SUT-Lips-DB - A database of lips traces. Available at: http://dx.doi.org/10.21227/H2R04P.
. (2017). "SUT-Lips-DB - A database of lips traces." Web.
1. . SUT-Lips-DB - A database of lips traces [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2R04P
. "SUT-Lips-DB - A database of lips traces." doi: 10.21227/H2R04P

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