Abstract

This synthetic dataset or phantom consists of 6 raw format databases, in the three–dimensional (3-D) domain, which are identified as follows:

DB1: Ground Truth

DB2: Poisson noise

DB3: Stair-step artifact

DB4: Streak artifact

DB5: Both artifacts

DB6: Hybrid.

 

 

 

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This dataset supports researchers in the validation process of solutions such as Intrusion Detection Systems (IDS) based on artificial intelligence and machine learning techniques for the detection and categorization of threats in Cyber Physical Systems (CPS). To that aim, data have been acquired from a water distribution hardware-in-the-loop testbed which emulates water passage between nine tanks via solenoid-valves, pumps, pressure and flow sensors. The testbed is composed by a real partition which is virtually connected to a simulated one.

Instructions: 

This dataset has related to the paper "A hardware-in-the-loop Water Distribution Testbed (WDT) dataset for cyber-physical security testing".
We provide four different acquisitions:
1) A normal acquisition without attacks ("normal.csv" for network traffic and "dataset_norm.csv" for physical measures)
2) Three acquisitions where different types of attacks and physical faults are reproduced ("attack_1.csv", "attack_2.csv" and "attack_3.csv" for network traffic and "dataset_att_1.csv", "dataset_att_2.csv" and "dataset_att_3.csv" for physical measures)
In addition to .csv files we provide four .pcap files ("attack_1.pcap", "attack_2.pcap", "attack_3.pcap" and "normal.pcap") which refer to network acquisitions for the four previous scenarios.
A README.xlsx file summarizes the key features of the entire dataset.

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1018 Views

BIMCV-COVID19- dataset is a large dataset with chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of no COVID-19 patients along with their radiographic findings, pathologies, polymerase chain reaction (PCR), immunoglobulin G (IgG) and immunoglobulin M (IgM) diagnostic antibody tests and radiographic reports from Medical Imaging Databank in Valencian Region Medical Image Bank (BIMCV).

Instructions: 

Once all the compressed files have been downloaded, use 00_extract_data.sh for their correct decompression. For more information, you could see the links on this page.

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439 Views

BIMCV-COVID19+ dataset is a large dataset with chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19 patients along with their radiographic findings, pathologies, polymerase chain reaction (PCR), immunoglobulin G (IgG) and immunoglobulin M (IgM) diagnostic antibody tests and radiographic reports from Medical Imaging Databank in Valencian Region Medical Image Bank (BIMCV).

Instructions: 

Once all the compressed files have been downloaded, use 00_extract_data.sh for their correct decompression. For more information, you could see the links on this page

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908 Views

This data set provides a list of the indexed journal by Scopus, Web of Science, and Directory of Open Access Journals (DOAJ) with the data in each row: Journal ID, Journal name, Publisher name, Publisher Address, Print-ISSN, E-ISSN, Scope, Coverage year, Status level (such as a Top-Level, etc), Cited Score, Languages, and many more.

Reference attached on the link.

 

Instructions: 

This excel (.xlxs) data has 9 sheets. this is the following information about each sheet:

1. Scopus

This sheet provides a list of journals indexed by Scopus. Last Updated: 10/2020
 

2. Scopus - More info Medline

This sheet provides information about Medical Literature Analysis and Retrieval System Online (MEDLINE) Journal 

Note: Scopus has a 100% overlap with Medline titles. The majority of those titles are also received via the publisher, however, approximately 20% of the Medline titles are fed directly from Medline into Scopus. As a result, these titles often have a delay in being loaded into Scopus, do not contain references and only the first author affiliation is available. 

 

3. Scopus - ASJC class codes

This sheet provides information about the classification code of scope/sub-scope on the journal. this data has information related to Sheet 1: Scopus

 

4. WoS - SSCI 

This sheet provides a list of journals indexed by Web of Science (WoS) in the index category: Social Sciences Citation Index (SSCI). Last Updated: 02/2021

 

5. WoS - SSCIE

This sheet provides a list of journals indexed by Web of Science (WoS) in the index category: Science Citation Index Expanded (SSCIE). Last Updated: 02/2021

 

6. WoS - ESCI

This sheet provides a list of journals indexed by Web of Science (WoS) in the index category: Emerging Sources Citation Index (ESCI). Last Updated: 02/2021

 

7. WoS - AHCI  

This sheet provides a list of journals indexed by Web of Science (WoS) in the index category: Arts & Humanities Citation Index (AHCI). Last Updated: 02/2021

 

8. DOAJ - Added List

This sheet provides a list of journals indexed by Directory of Open Access Journals (DOAJ). Last Updated: 02/2021

Note: The DOAJ Seal

The DOAJ Seal is awarded to journals that demonstrate best practices in open access publishing. Around 10% of journals indexed in DOAJ have been awarded the Seal. Journals do not need to meet the Seal criteria to be accepted into DOAJ.

 

9. DOAJ - Removed List

This sheet provides a list of journals NOT LONGER indexed by Directory of Open Access Journals (DOAJ). Last Updated: 02/2021

 

More information about this dataset can reach at muhammadsabirinhadis@ieee.org

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295670 Views

Diabetic Retinopathy is the second largest cause of blindness in diabetic patients. Early diagnosis or screening can prevent the visual loss. Nowadays , several computer aided algorithms have been developed to detect the early signs of Diabetic Retinopathy ie., Microaneurysms. The AGAR300 dataset presented here facilitate the researchers for benchmarking MA detection algorithms using digital fundus images. Currently, we have released the first set of database which consists of 28 color fundus images, shows the signs of Microaneurysm.

Instructions: 

The files corresponding to the work reported in paper titled " A novel automated system of discriminating Microaneurysms in fundus images”. The images  are taken from Fundus photography machine with the resolution of 2448x3264. This dataset contains Diabetic Retinopathy images and users of this dataset should cite the following article.

 

D. Jeba Derwin, S. Tamil Selvi, O. Jeba Singh, B. Priestly Shan,”A novel automated system of discriminating Microaneurysms in fundus images”, Biomedical Signal Processing and Control,Vol.58, 2020, pages: 101839,ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2019.101839.

(http://www.sciencedirect.com/science/article/pii/S1746809419304203)

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1797 Views

A collection of about 30K images that represents figures and tables from each track of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST).

Instructions: 

These files are in PNG format. Due to upload size limit, these files are divided into five zip files organized by year. 

The full collection in one-file is about 21.2G and can also be found online at http://www.cse.osu.edu/~chen.8028/VIS30K/VIS30K.tar.gz. 

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875 Views

Improving performance and safety conditions on industrial sites remains a key element of the company's strategy. The major challenges require, the ability to dynamically locate people and goods on the site. Security and regulation of access to areas with different characteristics (types of tasks, level of risk or confidentiality...) are often ensured by doors or badge barriers. These means have several weaknesses when faced with inappropriate movements of people, but also an inappropriate use of objects or tools.

Instructions: 

Dataset of each position of person.

We provide two modalities :

-A motion capture system called Mocap with an millimetric accuracy -An Ultra Wide Band system (The MDEK1001 from Decawave) with a centimeter accuracy.

The dataset is composed of two '.zip' :

  1. Raw_datas_UWB_Mocap.zip : Raw datas of both UWB and Mocap in the same frame of reference. It contains each person (Rig1 to Rig6).

  2. Filtered_datas_UWB.zip : UWB datas filtered.

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145 Views

The dataset consists of 60285 character image files which has been randomly divided into 54239 (90%) images as training set 6046 (10%) images as test set. The collection of data samples was carried out in two phases. The first phase consists of distributing a tabular form and asking people to write the characters five times each. Filled-in forms were collected from around 200 different individuals in the age group 12-23 years. The second phase was the collection of handwritten sheets such as answer sheets and classroom notes from students in the same age group.

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490 Views