Biomedical and Health Sciences

Investigating Velopharyngeal Closure Force with Linear Regression


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Citation Author(s):
Submitted by:
Anish Sana
Last updated:
Sun, 02/18/2018 - 16:19
DOI:
 
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[1] , "Investigating Velopharyngeal Closure Force with Linear Regression", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/. Accessed: Feb. 19, 2018.
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url = {http://dx.doi.org/},
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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 -
ER -
. (2018). Investigating Velopharyngeal Closure Force with Linear Regression. IEEE Dataport. http://dx.doi.org/
, 2018. Investigating Velopharyngeal Closure Force with Linear Regression. Available at: http://dx.doi.org/.
. (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/
. "Investigating Velopharyngeal Closure Force with Linear Regression." doi:

Investigating Velopharyngeal Closure Force with Linear Regression


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Dataset Files

No Data files have been uploaded.

Dataset Details

Citation Author(s):
Submitted by:
Anish Sana
Last updated:
Sun, 02/18/2018 - 16:03
DOI:
 
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[1] , "Investigating Velopharyngeal Closure Force with Linear Regression", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/. Accessed: Feb. 19, 2018.
@data{-18,
doi = {},
url = {http://dx.doi.org/},
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 -
ER -
. (2018). Investigating Velopharyngeal Closure Force with Linear Regression. IEEE Dataport. http://dx.doi.org/
, 2018. Investigating Velopharyngeal Closure Force with Linear Regression. Available at: http://dx.doi.org/.
. (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/
. "Investigating Velopharyngeal Closure Force with Linear Regression." doi:

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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No Data files have been uploaded.

Dataset Details

Citation Author(s):
Submitted by:
Nan Meng
Last updated:
Thu, 02/08/2018 - 00:52
DOI:
10.21227/H2QW97
Data Format:
Links:
 
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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: Feb. 19, 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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Dataset Details

Citation Author(s):
Submitted by:
Justin Brantley
Last updated:
Fri, 12/22/2017 - 11:40
DOI:
10.21227/H2TM00
Data Format:
Links:
 
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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: Feb. 19, 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

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: Feb. 19, 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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No Data files have been uploaded.

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: Feb. 19, 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

Health Timeline Insight-based Dataset


Electronic Health Records and clinical longitudinal data have been visualized in a wide range of applications to assist the understanding of the status and evolution of patients. Few studies have objectively assessed these applications. We utilized the insights-based method to objectively assess the effectiveness of an application that visualizes longitudinal data from the Australian national electronic health record. Five professional psychiatrists took part in the assessment study.

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Citation Author(s):
Submitted by:
Andres Ledesma
Last updated:
Mon, 10/16/2017 - 07:30
DOI:
10.21227/H2KD14
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[1] , "Health Timeline Insight-based Dataset", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2KD14. Accessed: Feb. 19, 2018.
@data{h2kd14-17,
doi = {10.21227/H2KD14},
url = {http://dx.doi.org/10.21227/H2KD14},
author = { },
publisher = {IEEE Dataport},
title = {Health Timeline Insight-based Dataset},
year = {2017} }
TY - DATA
T1 - Health Timeline Insight-based Dataset
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2KD14
ER -
. (2017). Health Timeline Insight-based Dataset. IEEE Dataport. http://dx.doi.org/10.21227/H2KD14
, 2017. Health Timeline Insight-based Dataset. Available at: http://dx.doi.org/10.21227/H2KD14.
. (2017). "Health Timeline Insight-based Dataset." Web.
1. . Health Timeline Insight-based Dataset [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2KD14
. "Health Timeline Insight-based Dataset." doi: 10.21227/H2KD14

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:
Thu, 10/12/2017 - 13:48
DOI:
10.21227/H2VW5Z
Data Format:
Links:
 
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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: Feb. 19, 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

BHI 2017: Big Data Analytics Competition


Data Science is all about the processes and methods to access and analyze data to gain insights for informed decision making. To promote the awareness and analytic technology of Big Data, IEEE EMBS and the IEEE Big Data Initiative are organizing a Data Analytics Competition. The competition will be held during the International Conference on Biomedical and Health Informatics (IEEE BHI2017), 16-19 February 2017 in Orlando, Florida, and is open to all participants of the conference.


The submission period for this data competition has ended.

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Dataset Details

Citation Author(s):
Submitted by:
Chris Franzino
Last updated:
Tue, 08/08/2017 - 10:52
DOI:
10.21227/H2W886
Data Format:
Links:
 
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[1] , "BHI 2017: Big Data Analytics Competition", IEEE Dataport, 2016. [Online]. Available: http://dx.doi.org/10.21227/H2W886. Accessed: Feb. 19, 2018.
@data{h2w886-16,
doi = {10.21227/H2W886},
url = {http://dx.doi.org/10.21227/H2W886},
author = { },
publisher = {IEEE Dataport},
title = {BHI 2017: Big Data Analytics Competition},
year = {2016} }
TY - DATA
T1 - BHI 2017: Big Data Analytics Competition
AU -
PY - 2016
PB - IEEE Dataport
UR - 10.21227/H2W886
ER -
. (2016). BHI 2017: Big Data Analytics Competition. IEEE Dataport. http://dx.doi.org/10.21227/H2W886
, 2016. BHI 2017: Big Data Analytics Competition. Available at: http://dx.doi.org/10.21227/H2W886.
. (2016). "BHI 2017: Big Data Analytics Competition." Web.
1. . BHI 2017: Big Data Analytics Competition [Internet]. IEEE Dataport; 2016. Available from : http://dx.doi.org/10.21227/H2W886
. "BHI 2017: Big Data Analytics Competition." doi: 10.21227/H2W886

TST Intake Monitoring dataset v2


The dataset contains depth frames collected using Microsoft Kinect v1 during the execution of food and drink intake movements.

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Dataset Details

Citation Author(s):
Submitted by:
Susanna Spinsante
Last updated:
Fri, 01/06/2017 - 16:44
DOI:
10.21227/H2BC7W
Data Format:
Links:
 
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[1] , "TST Intake Monitoring dataset v2 ", IEEE Dataport, 2016. [Online]. Available: http://dx.doi.org/10.21227/H2BC7W. Accessed: Feb. 19, 2018.
@data{h2bc7w-16,
doi = {10.21227/H2BC7W},
url = {http://dx.doi.org/10.21227/H2BC7W},
author = { },
publisher = {IEEE Dataport},
title = {TST Intake Monitoring dataset v2 },
year = {2016} }
TY - DATA
T1 - TST Intake Monitoring dataset v2
AU -
PY - 2016
PB - IEEE Dataport
UR - 10.21227/H2BC7W
ER -
. (2016). TST Intake Monitoring dataset v2 . IEEE Dataport. http://dx.doi.org/10.21227/H2BC7W
, 2016. TST Intake Monitoring dataset v2 . Available at: http://dx.doi.org/10.21227/H2BC7W.
. (2016). "TST Intake Monitoring dataset v2 ." Web.
1. . TST Intake Monitoring dataset v2 [Internet]. IEEE Dataport; 2016. Available from : http://dx.doi.org/10.21227/H2BC7W
. "TST Intake Monitoring dataset v2 ." doi: 10.21227/H2BC7W

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