Biomedical and Health Sciences

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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: Jun. 22, 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:
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: Jun. 22, 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:
Sat, 06/16/2018 - 23:05
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: Jun. 22, 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

Design, Fabrication, and Testing of an Internet Connected Intravenous Drip Monitoring Platform


In the era of increasing connectivity, everything including upcoming medical devices is being connected to the internet. These medical devices are monitoring units which measure application specific parameters, process them, perform data encoding and send them to a cloud server. Intravenous (IV) therapy is a common medical procedure which requires a continuous monitoring of the setup to avoid complications which can be achieved by running the system for hours. Traditionally, doctors and nurses use their experience to estimate the time required by an IV to run.

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No Data files have been uploaded.

Dataset Details

Citation Author(s):
Submitted by:
Yara Baslan
Last updated:
Wed, 05/30/2018 - 15:39
DOI:
10.21227/H2DW9F
Data Format:
 
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[1] , "Design, Fabrication, and Testing of an Internet Connected Intravenous Drip Monitoring Platform", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2DW9F. Accessed: Jun. 22, 2018.
@data{h2dw9f-18,
doi = {10.21227/H2DW9F},
url = {http://dx.doi.org/10.21227/H2DW9F},
author = { },
publisher = {IEEE Dataport},
title = {Design, Fabrication, and Testing of an Internet Connected Intravenous Drip Monitoring Platform},
year = {2018} }
TY - DATA
T1 - Design, Fabrication, and Testing of an Internet Connected Intravenous Drip Monitoring Platform
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2DW9F
ER -
. (2018). Design, Fabrication, and Testing of an Internet Connected Intravenous Drip Monitoring Platform. IEEE Dataport. http://dx.doi.org/10.21227/H2DW9F
, 2018. Design, Fabrication, and Testing of an Internet Connected Intravenous Drip Monitoring Platform. Available at: http://dx.doi.org/10.21227/H2DW9F.
. (2018). "Design, Fabrication, and Testing of an Internet Connected Intravenous Drip Monitoring Platform." Web.
1. . Design, Fabrication, and Testing of an Internet Connected Intravenous Drip Monitoring Platform [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2DW9F
. "Design, Fabrication, and Testing of an Internet Connected Intravenous Drip Monitoring Platform." doi: 10.21227/H2DW9F

Segmented EEG Data from 12 Subjects during Left and Right Bicep Flexion


Electroencephalography (EEG) signal data was collected from twelve healthy subjects with no known musculoskeletal or neurological deficits (mean age 25.5 ± 3.7, 11 male, 1 female, 1 left handed, 11 right handed) using an EGI Geodesics© Hydrocel EEG 64-Channel spongeless sensor net. All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the University of Wisconsin-Milwaukee (17.352).

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Dataset Details

Citation Author(s):
Submitted by:
Joshua Myszewski
Last updated:
Sat, 05/19/2018 - 12:01
DOI:
10.21227/H2F66Q
Data Format:
 
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[1] , "Segmented EEG Data from 12 Subjects during Left and Right Bicep Flexion", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2F66Q. Accessed: Jun. 22, 2018.
@data{h2f66q-18,
doi = {10.21227/H2F66Q},
url = {http://dx.doi.org/10.21227/H2F66Q},
author = { },
publisher = {IEEE Dataport},
title = {Segmented EEG Data from 12 Subjects during Left and Right Bicep Flexion},
year = {2018} }
TY - DATA
T1 - Segmented EEG Data from 12 Subjects during Left and Right Bicep Flexion
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2F66Q
ER -
. (2018). Segmented EEG Data from 12 Subjects during Left and Right Bicep Flexion. IEEE Dataport. http://dx.doi.org/10.21227/H2F66Q
, 2018. Segmented EEG Data from 12 Subjects during Left and Right Bicep Flexion. Available at: http://dx.doi.org/10.21227/H2F66Q.
. (2018). "Segmented EEG Data from 12 Subjects during Left and Right Bicep Flexion." Web.
1. . Segmented EEG Data from 12 Subjects during Left and Right Bicep Flexion [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2F66Q
. "Segmented EEG Data from 12 Subjects during Left and Right Bicep Flexion." doi: 10.21227/H2F66Q

ISRMyo-I: A database for sEMG-based hand gesture recognition


 

This repository aims to publish a sEMG database for hand gesture recongnition, which is suitable for intra-session, inter-session, inter-day and inter-subject tests. Six subjects were involved in data collection on ten days, and two sessions a day with the interval of half an hour. In each session, one trial (10 secondes) for each geature was conducted. The electrode sleeve did not reweared between two sessions in a day. The utilised sEMG device was customised by the Intelligent System and Biomedical Robotics Group, which was discussed in [1]. 

 

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Dataset Details

Citation Author(s):
Submitted by:
Yinfeng Fang
Last updated:
Fri, 05/04/2018 - 11:30
DOI:
10.21227/H26Q26
Data Format:
 
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[1] , "ISRMyo-I: A database for sEMG-based hand gesture recognition", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H26Q26. Accessed: Jun. 22, 2018.
@data{h26q26-18,
doi = {10.21227/H26Q26},
url = {http://dx.doi.org/10.21227/H26Q26},
author = { },
publisher = {IEEE Dataport},
title = {ISRMyo-I: A database for sEMG-based hand gesture recognition},
year = {2018} }
TY - DATA
T1 - ISRMyo-I: A database for sEMG-based hand gesture recognition
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H26Q26
ER -
. (2018). ISRMyo-I: A database for sEMG-based hand gesture recognition. IEEE Dataport. http://dx.doi.org/10.21227/H26Q26
, 2018. ISRMyo-I: A database for sEMG-based hand gesture recognition. Available at: http://dx.doi.org/10.21227/H26Q26.
. (2018). "ISRMyo-I: A database for sEMG-based hand gesture recognition." Web.
1. . ISRMyo-I: A database for sEMG-based hand gesture recognition [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H26Q26
. "ISRMyo-I: A database for sEMG-based hand gesture recognition." doi: 10.21227/H26Q26

ISRMyo-I: A database for sEMG-based hand gesture recognition


# ISRMyo-I: A Database for sEMG-based Hand Gesture Recognition

 

## Introduction

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No Data files have been uploaded.

Dataset Details

Citation Author(s):
Submitted by:
Yinfeng Fang
Last updated:
Fri, 05/04/2018 - 10:59
DOI:
10.21227/H2BH3R
Data Format:
 
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[1] , "ISRMyo-I: A database for sEMG-based hand gesture recognition", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2BH3R. Accessed: Jun. 22, 2018.
@data{h2bh3r-18,
doi = {10.21227/H2BH3R},
url = {http://dx.doi.org/10.21227/H2BH3R},
author = { },
publisher = {IEEE Dataport},
title = {ISRMyo-I: A database for sEMG-based hand gesture recognition},
year = {2018} }
TY - DATA
T1 - ISRMyo-I: A database for sEMG-based hand gesture recognition
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2BH3R
ER -
. (2018). ISRMyo-I: A database for sEMG-based hand gesture recognition. IEEE Dataport. http://dx.doi.org/10.21227/H2BH3R
, 2018. ISRMyo-I: A database for sEMG-based hand gesture recognition. Available at: http://dx.doi.org/10.21227/H2BH3R.
. (2018). "ISRMyo-I: A database for sEMG-based hand gesture recognition." Web.
1. . ISRMyo-I: A database for sEMG-based hand gesture recognition [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2BH3R
. "ISRMyo-I: A database for sEMG-based hand gesture recognition." doi: 10.21227/H2BH3R

Indian Diabetic Retinopathy Image Dataset (IDRiD)


Diabetic Retinopathy is the most prevalent cause of avoidable vision impairment, mainly affecting working age population in the world. Recent research has given a better understanding of requirement in clinical eye care practice to identify better and cheaper ways of identification, management, diagnosis and treatment of retinal disease. The importance of diabetic retinopathy screening programs and difficulty in achieving reliable early diagnosis of diabetic retinopathy at a reasonable cost needs attention to develop computer-aided diagnosis tool.

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Citation Author(s):
Submitted by:
Prasanna Porwal
Last updated:
Sat, 06/16/2018 - 23:06
DOI:
10.21227/H25W98
Data Format:
Links:
 
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[1] , "Indian Diabetic Retinopathy Image Dataset (IDRiD)", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H25W98. Accessed: Jun. 22, 2018.
@data{h25w98-18,
doi = {10.21227/H25W98},
url = {http://dx.doi.org/10.21227/H25W98},
author = { },
publisher = {IEEE Dataport},
title = {Indian Diabetic Retinopathy Image Dataset (IDRiD)},
year = {2018} }
TY - DATA
T1 - Indian Diabetic Retinopathy Image Dataset (IDRiD)
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H25W98
ER -
. (2018). Indian Diabetic Retinopathy Image Dataset (IDRiD). IEEE Dataport. http://dx.doi.org/10.21227/H25W98
, 2018. Indian Diabetic Retinopathy Image Dataset (IDRiD). Available at: http://dx.doi.org/10.21227/H25W98.
. (2018). "Indian Diabetic Retinopathy Image Dataset (IDRiD)." Web.
1. . Indian Diabetic Retinopathy Image Dataset (IDRiD) [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H25W98
. "Indian Diabetic Retinopathy Image Dataset (IDRiD)." doi: 10.21227/H25W98

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:
Mon, 04/30/2018 - 15:22
DOI:
10.21227/H2GH2M
Data Format:
Links:
 
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[1] , "ECG signals (744 fragments)", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2GH2M. Accessed: Jun. 22, 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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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: Jun. 22, 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

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