International interdisciplinary research journal Impact Factor 2024-2025 - ResearchHelpDesk - Aayushi International Interdisciplinary Research Journal (AIIRJ) publish articles, research papers etc. in every sphere of education for instance: Teaching-Learning process, Technologies in education and research etc. It publishes articles, research papers etc. in peer reviewed and refereed journal with excellent Impact Factor. The journal emphasis on originality, accuracy and relevance of the work. It ensures a strict vigil on plagiarism. Obviously the access of journal is online which is easy and free for all. It is purely intended for researchers in all discipline. The editorial and regional board members are committed to enhance the scope of interdisciplinary research. Their attempt is to explore innovative researchin education and to offer new direction and wings to the research beneficiaries. THE FEATURES OF AIIRJ World-wide access. Peer review process. Well-known and Experienced Editorial Board Members. Fast publication process. Special Issue for conference, workshops, Symposium, seminars etc. Reasonable and affordable publication charges Facility of On-line Submission. Journal Finder Option allows the researchers to find research paper either by author’s name or title of research article

This archive contains the files submitted to the 4th International Workshop on Data: Acquisition To Analysis (DATA) at SenSys. Files provided in this package are associated with the paper titled "Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices"

With the rapid development and usage of Internet-of-Things (IoT) and smart-home devices, researchers continue efforts to improve the ''smartness'' of those devices to address daily needs in people's lives. Such efforts usually begin with understanding evolving user behaviors on how humans utilize the devices and what they expect in terms of their behavior. However, while research efforts abound, there is a very limited number of datasets that researchers can use to both understand how people use IoT devices and to evaluate algorithms or systems for smart spaces. In this paper, we collect and characterize more than 50,000 recipes from the online If-This-Then-That (IFTTT) service to understand a seemingly straightforward but complicated question: ''What kinds of behaviors do humans expect from their IoT devices?'' The dataset we collected contains the basic information of the IFTTT rules, trigger and action event, and how many people are using each rule.

For more detail about this dataset, please refer to the paper listed above.

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to

establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data

Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered.
Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review:

Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection.
Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation.

See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

Given the increase in the use and profile of preprint servers – and alternative publishing hybrid platforms such as F1000 Research – in the life sciences, it is increasingly important to identify how many such servers and hybrids exist, to describe their scope in terms of the scientific disciplines they cover, and to compare and contrast their characteristics and policies.

We surveyed forty-four (44) platforms that host preprints relevant to life and biomedical sciences and that were active online and accepting submissions on 25 June 2019. Information on preprint platform policies, features and practices was collected through online research by the authors and by surveying preprint platform representatives directly.

Full data sheets include an additional 5 platforms hosted on OSF Preprints (rows 49-53) to fulfil the wider scope for the ASAPbio project, not in disciplinary scope (biology and medical sciences) for the manuscript with Jamie Kirkham.

Tables 1-5: Data (44 platforms, manuscript) are separated into five main tables of information and a list of preprint platform websites for reference.

Table 1: Scope and ownership of each server Table 2: Content-specific characteristics and information relating to submission, journal transfer options, and external discoverability Table 3: Screening, moderation, and permanence of content Table 4: Usage metrics and other features Table 5: Metadata Preprint platform websites

Data for each platform are listed as ‘Verified’ in the tables if these tables (V1.0 or V2.0) were seen and approved by a platform representative between January 13 and January 27, 2020.

Original online survey: a blank copy of the original survey form used by online researchers (the authors) and supplied pre-filled (or empty, in some cases) to preprint platform representatives for verification (or completion, in some cases).

Final data: survey data is presented in .txt and .xlsx, as follows:

Row 1: Heading (where field is included in manuscript tables, the heading presented here replaces any heading used in original survey. All columns are presented in the order the information was requested on the original survey form, with some supplementary columns added and columns removed (detailed below).

Row 2: Schema or description of field

Row 3: Whether and where included in manuscript tables. For supporting information for table data (e.g. source information, URLs), the table location for supported data is indicated in brackets, e.g. (Table 2) and supporting information is not included in tables. Data included in manuscript tables is presented in its final form, which in some cases is simplified from the original survey data. This simplified version of the data was presented to platform representatives for additional verification (v1.0/v2.0 verification). Data not included in manuscript tables is presented here as verified by platform representatives and/or found online. Some columns from the original survey have been removed due to the information not being informative or useful: specifically, Print ISSN (not reported for any platform); End date (no platforms have an end date; although two platforms stopped accepting submissions after survey completed; Personal contact information for platform representative(s) has been removed).

Rows 4 onwards: data for each preprint platform (44 included in manuscript (rows 4-47), plus 5 additional OSF platforms (rows 48-52)

Columns 3-6 (D-G) report online research and verification information and Column 13 (M) reports an additional data field (number of articles) – these are supplementary to the original survey columns

Verification status: Released V1/V2 data applies to data included in manuscript tables (as indicated in row 3); Online survey data applies to data used for manuscript tables and also to original survey data included here but not included in manuscript tables (‘Not included’ in row 3)

Note that data fields are presented as individual columns in these sheets, while some entries in Tables 1-5 combine several data fields.

These data were collected in collaboration and as part of: i. An ASAPbio project, led by Dr Naomi Penfold, to develop an online directory of preprint platforms ii. A research project led by Prof Jamie Kirkham These data are supplementary outputs for both projects.

Data v1.0 were presented during the ASAPbio January 2020 workshop – see Penfold, Naomi C, & Polka, Jessica. (2020, January). ASAPbio Preprint Platform Directory: 2019 data (presentation) (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.3626770.

Version 3.0 updates (December 14, 2020): added new files with updated information about servers from the ASAPbio preprint directory (https://asapbio.org/preprint-servers), provided by Jessica Polka (now included as author).

Topic Modeling for Research Articles

Researchers have access to large online archives of scientific articles. As a consequence, finding relevant articles has become more difficult. Tagging or topic modelling provides a way to give token of identification to research articles which facilitates recommendation and search process.

Given the abstract and title for a set of research articles, predict the topics for each article included in the test set.

Note that a research article can possibly have more than 1 topic. The research article abstracts and titles are sourced from the following 6 topics:

1. Computer Science
2. Physics
3. Mathematics
4. Statistics
5. Quantitative Biology
6. Quantitative Finance

Data Dictionary

train.csv

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

test.csv

Evaluation Metric

Submissions are evaluated on micro F1 Score between the predicted and observed topics for each article in the test set

### Public and Private Split Test reviews are further divided into Public (40%) and Private (60%)

Your initial responses will be checked and scored on the Public data. The final rankings would be based on your private score which will be published once the competition is over.

Guidelines for Final Submission

1. Please ensure that your final submission includes the following:
2. Solution file containing the predicted 1/0 for each of the 6 topics for every research article in the test set
3. Code file for reproducing the submission, note that it is mandatory to submit your code for a valid final submission

BackgroundAn understanding of the resources which engineering students use to write their academic papers provides information about student behaviour as well as the effectiveness of information literacy programs designed for engineering students. One of the most informative sources of information which can be used to determine the nature of the material that students use is the bibliography at the end of the students’ papers. While reference list analysis has been utilised in other disciplines, few studies have focussed on engineering students or used the results to improve the effectiveness of information literacy programs. Gadd, Baldwin and Norris (2010) found that civil engineering students undertaking a finalyear research project cited journal articles more than other types of material, followed by books and reports, with web sites ranked fourth. Several studies, however, have shown that in their first year at least, most students prefer to use Internet search engines (Ellis & Salisbury, 2004; Wilkes & Gurney, 2009).PURPOSEThe aim of this study was to find out exactly what resources undergraduate students studying civil engineering at La Trobe University were using, and in particular, the extent to which students were utilising the scholarly resources paid for by the library. A secondary purpose of the research was to ascertain whether information literacy sessions delivered to those students had any influence on the resources used, and to investigate ways in which the information literacy component of the unit can be improved to encourage students to make better use of the resources purchased by the Library to support their research.DESIGN/METHODThe study examined student bibliographies for three civil engineering group projects at the Bendigo Campus of La Trobe University over a two-year period, including two first-year units (CIV1EP – Engineering Practice) and one-second year unit (CIV2GR – Engineering Group Research). All units included a mandatory library session at the start of the project where student groups were required to meet with the relevant faculty librarian for guidance. In each case, the Faculty Librarian highlighted specific resources relevant to the topic, including books, e-books, video recordings, websites and internet documents. The students were also shown tips for searching the Library catalogue, Google Scholar, LibSearch (the LTU Library’s research and discovery tool) and ProQuest Central. Subject-specific databases for civil engineering and science were also referred to. After the final reports for each project had been submitted and assessed, the Faculty Librarian contacted the lecturer responsible for the unit, requesting copies of the student bibliographies for each group. References for each bibliography were then entered into EndNote. The Faculty Librarian grouped them according to various facets, including the name of the unit and the group within the unit; the material type of the item being referenced; and whether the item required a Library subscription to access it. A total of 58 references were collated for the 2010 CIV1EP unit; 237 references for the 2010 CIV2GR unit; and 225 references for the 2011 CIV1EP unit.INTERIM FINDINGSThe initial findings showed that student bibliographies for the three group projects were primarily made up of freely available internet resources which required no library subscription. For the 2010 CIV1EP unit, all 58 resources used were freely available on the Internet. For the 2011 CIV1EP unit, 28 of the 225 resources used (12.44%) required a Library subscription or purchase for access, while the second-year students (CIV2GR) used a greater variety of resources, with 71 of the 237 resources used (29.96%) requiring a Library subscription or purchase for access. The results suggest that the library sessions had little or no influence on the 2010 CIV1EP group, but the sessions may have assisted students in the 2011 CIV1EP and 2010 CIV2GR groups to find books, journal articles and conference papers, which were all represented in their bibliographiesFURTHER RESEARCHThe next step in the research is to investigate ways to increase the representation of scholarly references (found by resources other than Google) in student bibliographies. It is anticipated that such a change would lead to an overall improvement in the quality of the student papers. One way of achieving this would be to make it mandatory for students to include a specified number of journal articles, conference papers, or scholarly books in their bibliographies. It is also anticipated that embedding La Trobe University’s Inquiry/Research Quiz (IRQ) using a constructively aligned approach will further enhance the students’ research skills and increase their ability to find suitable scholarly material which relates to their topic. This has already been done successfully (Salisbury, Yager, & Kirkman, 2012)CONCLUSIONS & CHALLENGESThe study shows that most students rely heavily on the free Internet for information. Students don’t naturally use Library databases or scholarly resources such as Google Scholar to find information, without encouragement from their teachers, tutors and/or librarians. It is acknowledged that the use of scholarly resources doesn’t automatically lead to a high quality paper. Resources must be used appropriately and students also need to have the skills to identify and synthesise key findings in the existing literature and relate these to their own paper. Ideally, students should be able to see the benefit of using scholarly resources in their papers, and continue to seek these out even when it’s not a specific assessment requirement, though it can’t be assumed that this will be the outcome.REFERENCESEllis, J., & Salisbury, F. (2004). Information literacy milestones: building upon the prior knowledge of first-year students. Australian Library Journal, 53(4), 383-396.Gadd, E., Baldwin, A., & Norris, M. (2010). The citation behaviour of civil engineering students. Journal of Information Literacy, 4(2), 37-49.Salisbury, F., Yager, Z., & Kirkman, L. (2012). Embedding Inquiry/Research: Moving from a minimalist model to constructive alignment. Paper presented at the 15th International First Year in Higher Education Conference, Brisbane. Retrieved from http://www.fyhe.com.au/past_papers/papers12/Papers/11A.pdfWilkes, J., & Gurney, L. J. (2009). Perceptions and applications of information literacy by first year applied science students. Australian Academic & Research Libraries, 40(3), 159-171.

The Journal Review Service market is anticipated to grow with a notable CAGR during the forecast period. This growth is attributed to the increasing demand for high-quality research publications, the growing number of research institutions, and the increasing adoption of online submission systems. The key drivers of the market include the growing emphasis on academic research, the increasing number of research publications, the growing adoption of online submission systems, and the increasing availability of peer review services. However, the high cost of journal review services and the lack of awareness about these services are some of the restraints that are expected to hinder the growth of the market.

This dataset is a collection of user reviews for various Google Apps available on the Play Store. It provides detailed insights into user feedback, ratings, and engagement with different applications. The dataset's primary purpose is to offer a rich resource for understanding user sentiment, identifying app performance issues, and tracking user satisfaction over time. It is a valuable asset for analytics and natural language processing tasks related to app reviews.

Columns

• reviewId: A unique identifier for each individual user review.
• userName: The name of the user who submitted the review.
• userImage: The URL pointing to the user's profile image.
• content: The textual review provided by the user about the app.
• score: The numerical rating given by the user for the app, typically on a scale of 1 to 5.
• thumbsUpCount: The total number of likes or "thumbs up" received by that specific review.
• reviewCreatedVersion: The version of the app that was being reviewed at the time the review was created.
• at: The date and time when the user's review was created.
• replyContent: The textual content of the reply provided by the app developer to the user's review. A significant portion of reviews do not have a developer reply.
• repliedAt: The date and time when the developer's reply was issued. Many entries in this column are null, indicating no developer response.

Distribution

The dataset contains over 90,000 app reviews. The score column shows a distribution across ratings, with substantial counts for scores like 1.00-1.20, 2.00-2.20, 3.00-3.20, 4.00-4.20, and 4.80-5.00. For thumbsUpCount, the majority of reviews have a relatively low number of likes (0-720), but there are instances with significantly higher counts, reaching up to over 14,000 likes. The reviewCreatedVersion column shows a variety of app versions, with some being more frequently reviewed than others. Review creation dates span a period from April 2014 to February 2021, with a notable increase in review volume towards the later years, particularly between May 2020 and February 2021.

Usage

This dataset is ideal for: * Sentiment analysis of app reviews. * Natural Language Processing (NLP) tasks, such as topic modelling, text classification, and entity recognition. * App performance monitoring and identifying user pain points. * Market research on user satisfaction and trends in app usage. * Developing AI and Machine Learning models for predicting app ratings or automatically classifying feedback.

Coverage

The dataset offers global coverage for app reviews. The time range for review creation spans from 10th April 2014 to 4th February 2021. While developer replies are included, the data on repliedAt primarily indicates a single latest date (4th February 2021) with the majority being null, suggesting that developer reply timestamps are not as broadly distributed across the dataset as review creation times.

License

CC0

Who Can Use It

• App Developers: To understand user feedback, identify bugs, and improve app features.
• Data Analysts: For trends analysis, user behaviour insights, and reporting.
• Researchers: In fields like computer science, internet studies, and data analytics for academic studies on online reviews.
• Machine Learning Engineers: To train models for sentiment analysis, user support automation, or content moderation.
• Product Managers: To gather insights for product iteration and strategic planning.

Dataset Name Suggestions

• Google Play Store App Reviews
• Play Store User Feedback
• Google Apps Ratings and Reviews
• Mobile App Review Data
• Playstore Review Analytics Data

Attributes

Original Data Source: Google Apps Playstore Reviews

International Journal of Engineering and Advanced Technology FAQ - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. Recent Trends in Microelectronics and VLSI Design Process & Device Technologies Low-power design Nanometer-scale integrated circuits Application specific ICs (ASICs) FPGAs Nanotechnology Nano electronics and Quantum Computing 3. Challenges of Industry and their Solutions, Communications Advanced Manufacturing Technologies Artificial Intelligence Autonomous Robots Augmented Reality Big Data Analytics and Business Intelligence Cyber Physical Systems (CPS) Digital Clone or Simulation Industrial Internet of Things (IIoT) Manufacturing IOT Plant Cyber security Smart Solutions – Wearable Sensors and Smart Glasses System Integration Small Batch Manufacturing Visual Analytics Virtual Reality 3D Printing 4. Internet of Things (IoT) Internet of Things (IoT) & IoE & Edge Computing Distributed Mobile Applications Utilizing IoT Security, Privacy and Trust in IoT & IoE Standards for IoT Applications Ubiquitous Computing Block Chain-enabled IoT Device and Data Security and Privacy Application of WSN in IoT Cloud Resources Utilization in IoT Wireless Access Technologies for IoT Mobile Applications and Services for IoT Machine/ Deep Learning with IoT & IoE Smart Sensors and Internet of Things for Smart City Logic, Functional programming and Microcontrollers for IoT Sensor Networks, Actuators for Internet of Things Data Visualization using IoT IoT Application and Communication Protocol Big Data Analytics for Social Networking using IoT IoT Applications for Smart Cities Emulation and Simulation Methodologies for IoT IoT Applied for Digital Contents 5. Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. Advance Concept of Networking and Database Computer Network Mobile Adhoc Network Image Security Application Artificial Intelligence and machine learning in the Field of Network and Database Data Analytic High performance computing Pattern Recognition 9. Machine Learning (ML) and Knowledge Mining (KM) Regression and prediction Problem solving and planning Clustering Classification Neural information processing Vision and speech perception Heterogeneous and streaming data Natural language processing Probabilistic Models and Methods Reasoning and inference Marketing and social sciences Data mining Knowledge Discovery Web mining Information retrieval Design and diagnosis Game playing Streaming data Music Modelling and Analysis Robotics and control Multi-agent systems Bioinformatics Social sciences Industrial, financial and scientific applications of all kind 10. Advanced Computer networking Computational Intelligence Data Management, Exploration, and Mining Robotics Artificial Intelligence and Machine Learning Computer Architecture and VLSI Computer Graphics, Simulation, and Modelling Digital System and Logic Design Natural Language Processing and Machine Translation Parallel and Distributed Algorithms Pattern Recognition and Analysis Systems and Software Engineering Nature Inspired Computing Signal and Image Processing Reconfigurable Computing Cloud, Cluster, Grid and P2P Computing Biomedical Computing Advanced Bioinformatics Green Computing Mobile Computing Nano Ubiquitous Computing Context Awareness and Personalization, Autonomic and Trusted Computing Cryptography and Applied Mathematics Security, Trust and Privacy Digital Rights Management Networked-Driven Multicourse Chips Internet Computing Agricultural Informatics and Communication Community Information Systems Computational Economics, Digital Photogrammetric Remote Sensing, GIS and GPS Disaster Management e-governance, e-Commerce, e-business, e-Learning Forest Genomics and Informatics Healthcare Informatics Information Ecology and Knowledge Management Irrigation Informatics Neuro-Informatics Open Source: Challenges and opportunities Web-Based Learning: Innovation and Challenges Soft computing Signal and Speech Processing Natural Language Processing 11. Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level agreements (drafting,

Think of your current publishing practices in comparison with practices before the pandemic and tell us to what extent you agree or disagree with each statement (%).

International Journal of Engineering and Advanced Technology Publication fee - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. Recent Trends in Microelectronics and VLSI Design Process & Device Technologies Low-power design Nanometer-scale integrated circuits Application specific ICs (ASICs) FPGAs Nanotechnology Nano electronics and Quantum Computing 3. Challenges of Industry and their Solutions, Communications Advanced Manufacturing Technologies Artificial Intelligence Autonomous Robots Augmented Reality Big Data Analytics and Business Intelligence Cyber Physical Systems (CPS) Digital Clone or Simulation Industrial Internet of Things (IIoT) Manufacturing IOT Plant Cyber security Smart Solutions – Wearable Sensors and Smart Glasses System Integration Small Batch Manufacturing Visual Analytics Virtual Reality 3D Printing 4. Internet of Things (IoT) Internet of Things (IoT) & IoE & Edge Computing Distributed Mobile Applications Utilizing IoT Security, Privacy and Trust in IoT & IoE Standards for IoT Applications Ubiquitous Computing Block Chain-enabled IoT Device and Data Security and Privacy Application of WSN in IoT Cloud Resources Utilization in IoT Wireless Access Technologies for IoT Mobile Applications and Services for IoT Machine/ Deep Learning with IoT & IoE Smart Sensors and Internet of Things for Smart City Logic, Functional programming and Microcontrollers for IoT Sensor Networks, Actuators for Internet of Things Data Visualization using IoT IoT Application and Communication Protocol Big Data Analytics for Social Networking using IoT IoT Applications for Smart Cities Emulation and Simulation Methodologies for IoT IoT Applied for Digital Contents 5. Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. 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Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level

These data are being released as a preliminary version to facilitate early access to the study for research purposes. This collection has not been fully processed by ICPSR at this time, and data are released in the format provided by the principal investigators. As the study is processed and given enhanced features by ICPSR in the future, users will be able to download the updated versions of the study. Please report any data errors or problems to user support, and we will work with you to resolve any data-related issues. The American National Election Study (ANES): 2016 Pilot Study sought to test new instrumentation under consideration for potential inclusion in the ANES 2016 Time Series Study, as well as future ANES studies. Much of the content is based on proposals from the ANES user community submitted through the Online Commons page, found on the ANES home page. The survey included questions about preferences in the presidential primary, stereotyping, the economy, discrimination, race and racial consciousness, police use of force, and numerous policy issues, such as immigration law, health insurance, and federal spending. It was conducted on the Internet using the YouGov panel, an international market research firm that administers polls that collect information about politics, public affairs, products, brands, as well as other topics of general interest.

I. Executive summary

The Austrian Science Fund (FWF), which is Austria's main funding organisation for basic research, encourages and helps all project leaders and project staff members to make their peer-reviewed research results freely available on the Internet. Any exceptions must be clearly indicated and justified. For projects that started after 1 January 2015, open access has been compulsory for all peer-reviewed publications. All principal investigators in FWF-funded projects are obliged to submit a final report within four months of completing their projects.

In 2023, a total of 560 final reports were submitted to the FWF, 36 of which could not report any publications so far. The publications and other data mentioned in those reports are archived and analysed by the FWF. This report examines the state of compliance with open access requirements for peer-reviewed publications on the basis of final project reports submitted in the year 2023.

Main findings:

-A total of 6.389 publications were listed in the final project reports submitted in 2023.

-Of those publications, 5.296 were conclusively identified as peer-reviewed.

-Regarding compliance with the FWF's Open Access Policy, the analysis shows that 84% of the peer-reviewed publications listed in the final project reports submitted in 2023 were openly accessible (2017: 90%, 2018: 92%, 2019: 89%, 2020: 84%, 2021: 82%, 2022: 85%).

-The most frequently chosen option was hybrid open access (37%). The share of gold open access was 31%, the share of green open access 17%, and no open access 16%.

-The majority of peer-reviewed publications submitted were journal articles (85%), 91% of which were openly accessible.

-The lowest rate of compliance with the FWF's Open Access Policy could be found in editions, contributions to edited volumes, and monographs (34%).

-The absolute number of publications listed in final reports in 2023 included 265 publications that were mentioned several times in different projects; however, those repetitions did not affect the relative share of open access publications (84%).

II. Bias

Most of the publications were submitted to the FWF via a research documentation system (provided by ©Researchfish), in which FWF-funded researchers are able to enter and update their research data on an ongoing basis. The last check of the open access status was performed in March 2024. In some cases, the embargo period may have already expired, and publications labelled ‘closed’ in the FWF’s database may now be accessible through green open access.

Whenever the status ‘peer-reviewed’ could not be clearly identified according to the FWF’s guidelines, the publications were classified as non-peer reviewed.

Openly available non-peer reviewed publications were entered as 'openly available’.

The dataset used in the research 'The distribution of online attention in publications in the field of Information Science & Library Science: an applied study in the Web of Science database (2012-2021)' submitted to Revista Española de Documentacion Científica (REDC). The name 'Autores' is used to ensure the blind review process and will be changed upon manuscript acceptance.

The data involves anonymous intake assessment and therapy progress/outcome information of 203 adult clients seeking treatment in a public outpatient unit specialized in problematic internet use in Greece (Psychiatric Hospital of Attica: 18-ano). Data was collected between 2011 and 2015. Informed consent was provided by the participants for the use of the data by researchers other than the data collection team for future research purposes. Data contains socio-demographic and internet abuse information, alongside measures of psychopathology (Symptoms Check List-90 Revised; Derogatis & Unger, 2010), personality traits (Five Factor Questionnaire; Asendorpf, 2002) and addictive internet use (Internet Addiction Test; Young 2009). Approval for the public deposit of the data has been provided by the current director of the unit S. Christidi (MD). PIU Treatment Data Date Submitted: 2020-04-03

This study aimed at analyzing the bibliometric characteristics and content of medical articles from North Korea in PubMed and characterizing the patterns of international cooperation of medical researchers in North Korea. We hypothesized that the number of publications from North Korea in PubMed has increased recently as a result of active cooperation with foreign researchers. PubMed was searched on July 19, 2017 using the search tem ((North Korea [Affiliation]) OR Democratic People's Republic of Korea [Affiliation]) OR DPRK [Affiliation]. The content of medical articles was analyzed and cooperative work with foreign researchers was noted. The number of medical articles in PubMed through July 2017 was 16, of which 2 were by North Korean authors only. From the content of these articles, it was found that researchers in top-notch institutions, including Kim Il Sung University, can access the internet, and that a dental caries prevention program supported by Finland has been in place for more than 10 years. The number of publications from North Korea in PubMed has increased recently, although the amount is still very small. Providing internet access to North Korean researchers will accelerate their submissions to international journals.

This dataset supports a research project in the field of digital medicine, which aims to quantify the impact of disseminating scientific information on social media—as a form of "meta-intervention"—on public adherence to Non-Pharmaceutical Interventions (NPIs) during health crises such as the COVID-19 pandemic. The research encompasses multiple sub-studies and pilot experiments, drawing data from various global and China-specific social media platforms.The data included in this submission has been collected from several sources:From Sina Weibo and Tencent WeChat, 189 online poll datasets were collected, involving a total of 1,391,706 participants. These participants are users of Sina Weibo or Tencent WeChat.From Twitter, 187 tweets published by scientists (verified with a blue checkmark) related to COVID-19 were collected.From Xiaohongshu and Bilibili, textual content from 143 user posts/videos concerning COVID-19, along with associated user comments and specific user responses to a question, were gathered.It is important to note that while the broader research project also utilized a 3TB Reddit corpus hosted on Academic Torrents (academictorrents.com), this specific Reddit dataset is publicly available directly from Academic Torrents and is not included in this particular DataHub submission. The submitted dataset comprises publicly available data, formatted as Excel files (.xlsx), and includes the following:Filename: scientists' discourse (source from screenshot of tweets)Description: This file contains screenshots of tweets published by scientists on Twitter concerning COVID-19 research, its current status, and related topics. It also includes a coded analysis of the textual content from these tweets. Specific details regarding the coding scheme can be found in the readme.txt file.Filename: The links of online polls (Weibo & WeChat)Description: This data file includes information from online polls conducted on Weibo and WeChat after December 7, 2022. These polls, often initiated by verified users (who may or may not be science popularizers), aimed to track the self-reported proportion of participants testing positive for COVID-19 (via PCR or rapid antigen test) or remaining negative, particularly during periods of rapid Omicron infection spread. The file contains links to the original polls, links to the social media accounts that published these polls, and relevant metadata about both the poll-creating accounts and the online polls themselves.Filename: Online posts & comments (From Xiaohongshu & Bilibili)Description: This file contains textual content from COVID-19 related posts and videos published by users on the Xiaohongshu and Bilibili platforms. It also includes user-generated comments reacting to these posts/videos, as well as user responses to a specific question posed within the context of the original content.Key Features of this Dataset:Data Type: Mixed, including textual data, screenshots of social media posts, web links to original sources, and coded metadata.Source Platforms: Twitter (global), Weibo/WeChat (primarily China), Xiaohongshu (China), and Bilibili (video-sharing platform, primarily China).Use Case: This dataset is intended for the analysis of public discourse, the dissemination of scientific information, and user engagement patterns across different cultural contexts and social media platforms, particularly in relation to public health information.

Publication Support Services Market Outlook

The global publication support services market size was valued at approximately USD 2.5 billion in 2023 and is projected to reach USD 5.3 billion by 2032, registering a compound annual growth rate (CAGR) of 8.5% during the forecast period. This impressive growth is driven by increasing demand for quality content and the rising necessity for academic and corporate publications to maintain high standards of accuracy and presentation.

One of the primary growth factors in this market is the escalating volume of research output worldwide. With academic institutions, research organizations, and corporations producing a significant amount of research, there is a growing need for professional support to ensure that publications are clear, accurate, and meet the required standards. Furthermore, the globalization of research and the need for multi-lingual publications have amplified the demand for translation and peer review services, contributing to the marketÂ’s expansion.

Technological advancements have also played a crucial role in propelling the publication support services market. The advent of AI-powered editing tools, advanced formatting software, and digital illustration techniques have enabled service providers to deliver high-quality support efficiently and effectively. These technologies not only enhance the accuracy and quality of publications but also reduce the turnaround time, making it easier for researchers and authors to meet tight deadlines. Additionally, the widespread adoption of online platforms for manuscript submission and peer review processes supports market growth.

The growing emphasis on academic integrity and ethical publishing practices is another significant growth driver. With increased awareness of plagiarism and the importance of original research, there is a greater demand for meticulous editing and peer review services. Institutions and organizations are investing heavily in these services to ensure that their publications uphold high ethical standards, thereby enhancing their reputation and credibility in the academic and corporate world.

The rise of Electronic Publishing has significantly influenced the publication support services market, offering new avenues for content dissemination and accessibility. With the transition from traditional print to digital formats, electronic publishing has enabled faster and more efficient distribution of scholarly works. This shift has not only expanded the reach of academic and corporate publications but also necessitated the adaptation of support services to cater to digital platforms. As a result, service providers are increasingly focusing on enhancing digital formatting, ensuring compatibility with various e-readers and online platforms, and optimizing content for search engines. The evolution of electronic publishing continues to drive innovation in the industry, providing opportunities for service providers to offer specialized solutions tailored to the digital landscape.

Regionally, North America dominates the publication support services market, accounting for the largest share due to the presence of leading academic institutions, research organizations, and a high volume of scholarly publications. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth is fueled by increasing research activities, government initiatives to promote scientific research, and the rising number of academic institutions in countries such as China and India.

Service Type Analysis

The publication support services market is segmented by service type into editing, formatting, translation, peer review, illustration, and others. The editing segment holds a significant market share as it is essential for ensuring clarity, coherence, and compliance with publication standards. Professional editing services help refine the language, correct grammatical errors, and enhance the overall readability of manuscripts, making them suitable for publication in top-tier journals. The demand for specialized editing services, including technical and scientific editing, is particularly high in the academic and research sectors.

Formatting services also constitute a crucial segment of the publication support services market. Proper formatting ensures that manuscripts adhere to the specific guidelines of journals and publishers, which can be a complex and time-co

These are the appendices for the paper "Trust in Internet Voting: Preliminary Results of a QMethodology Experiment in Estonia", submitted at AMCIS Conference 2025.

Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.