Antibody Class(es) Predictor for Epitopes (AbCPE) User Experience Survey
Hello, we are Ph.D. students at the Bioinformatics Center, Savitribai Phule Pune University (SPPU). As a part of our Ph.D. work in the area of data curation, integration and extraction, we have developed various resources and customised data extraction pipelines. We have also been involved in co-creation of additional resources for which we have designed front-ends.
We have designed survey forms (questionnaires) to investigate user experience for the following resources:

1. AllerBase: https://forms.gle/RFKioUCtPASEew2LA
2. BioDB Extractor (BDE): https://forms.gle/ghVB6ojVr2H1Kaj77
3. Integrated Customized Extraction of Enzyme Data (iCEED):
       https://forms.gle/A7tsCVMkHB1AdZJS6
4. Antibody Class(es) Predictor for Epitopes (AbCPE):
       https://forms.gle/tkMm4qfokLGx1mQp9

The links to individual resources are included in the survey forms.
We request you to kindly take time and complete these short surveys, which are expected to take about 30 minutes. We intend to analyse the survey outcome to improve the user experience of these resources. Please be informed that your identity will not be revealed during the processing, analysis and documentation of the survey outcome.
Thank you in advance for your participation.

-Rajiv Karbhal & Kiran Kadam
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Antibody Class(es) Predictor for Epitopes (AbCPE)
AbCPE (http://bioinfo.unipune.ac.in/AbCPE/Home.html) web server implements a multi-label classification algorithm that predicts antibody class(es) for epitopes. This data driven machine learning based algorithm offers prediction of epitopes potentially recognised by an individual or multiple classes of antibodies viz. IgG, IgE, IgA and IgM (excluding IgD).
Publication: Kadam K, Peerzada N, Karbhal R, Sawant S, Valadi J and Kulkarni-Kale U. (2021) Antibody Class(es) Predictor for Epitopes (AbCPE): A Multi-Label Classification Algorithm. Front. Bioinform. 1:709951. doi: 10.3389/fbinf.2021.709951
(https://www.frontiersin.org/articles/10.3389/fbinf.2021.709951/full)
Do you have formal training in Bioinformatics, pertaining to databases and data analysis tools?
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How many years of experience do you have in using Bioinformatics resources?
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Which of the following best describes your profile?
What is the average internet bandwidth available to you?
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Do the AbCPE pages load in a reasonable time? (Caveat: The loading time often depends on the internet connectivity bandwidth. Please reply based on a sample of multiple attempts)
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Which browser did you use?
Was the interface and layout of AbCPE compatible with your browser?
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Is the design of the AbCPE homepage intuitive and user-friendly? If not, please provide suggestions for improvement in the space provided below for "other".
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Are the fonts readable?
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Is font size appropriate?
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Does the content on the Algorithm page help you to understand the AbCPE algorithm? If you have any suggestions, please mention them in  the space provided below for "other".
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Did you find the diagrammatic illustration of AbCPE algorithm on the Algorithm page useful?
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Are the links to download the datasets on the Datasets page working properly?
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Is the design of the input form on the home page user-friendly? If not, please provide suggestions in the space provided below for "other".
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How long did it take to load the prediction results after providing input? (Caveat: The loading time often depends on the internet connectivity bandwidth as well as complexity of the query and processing. Please reply based on a sample of multiple queries)
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Is the layout of prediction result/s on the result page easy to view and understand? If not, please give your suggestions in the space provided below for "other".
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Are the links to download result/s working properly?
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Did you find the help documentation adequate? Do you have any suggestions to improve the Help pages?  Please mention it in the space provided below for "other".
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Would you like to recommend AbCPE to your researcher colleagues/students to whom it may be useful?
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