In 2021, the share of internet users looking for and applying for jobs online in Turkey increased by 1.1 percentage points since 2019. With 8.97 percent, the share of internet users looking for jobs online thereby reached its highest value in the observed period.The EU survey on the use of Information and Communication Technologies (ICT) in households and by individuals is an annual survey conducted since 2002 aiming at collecting and disseminating harmonised and comparable information on the use of ICT in households and by individuals. Data presented in this domain are collected on a yearly basis by the National Statistical Institutes and are based on Eurostat's annual model questionnaire. This questionnaire is updated each year to reflect the evolving situation of information and communication technologiesFind more statistics on other topics about Turkey with key insights such as share of daily internet users, share of internet users seeking health information online, share of internet users informing themselves about goods and services online, share of internet users reading news online, share of internet users engaging in online learning activities, and share of people that upload self-created content.

As of March 2023, 17 percent of adults in Luxembourg said that the right to control one's own data online was very well applied in their country, while 53 percent said it was fairly applied. In contrast, only six percent of respondents in Greece said that the digital right to control personal data was very well applied.

As part of the “Biostatistics and Evidence Appraisal for Radiation Oncologists”, an online seminar series is sponsored by the University of Cincinnati Department of Radiation Oncology and ROECSG (Radiation Oncology Education Collaborative Study Group https://voices.uchicago.edu/roecsg/), Dr. Clifton fuller presented exemplar data from the following publications of prospective trial performed under the auspices of the University of Texas MD Anderson Cancer Center (Trial No, 88-001):-Peters LJ, Goepfert H, Ang KK, Byers RM, Maor MH, Guillamondegui O, Morrison WH, Weber RS, Garden AS, Frankenthaler RA, et al. Evaluation of the dose for postoperative radiation therapy of head and neck cancer: first report of a prospective randomized trial. Int J Radiat Oncol Biol Phys. 1993 Apr 30;26(1):3-11. doi: 10.1016/0360-3016(93)90167-t. PMID: 8482629.-Rosenthal DI, Mohamed ASR, Garden AS, Morrison WH, El-Naggar AK, Kamal M, Weber RS, Fuller CD, Peters LJ. Final Report of a Prospective Randomized Trial to Evaluate the Dose-Response Relationship for Postoperative Radiation Therapy and Pathologic Risk Groups in Patients With Head and Neck Cancer. Int J Radiat Oncol Biol Phys. 2017 Aug 1;98(5):1002-1011. doi: 10.1016/j.ijrobp.2017.02.218. Epub 2017 Jul 10. PMID: 28721881; PMCID: PMC5518636.Data from these publications was anonymized (I.e. stripped of 45 CFR § 164.514- defined PHI identifiers); age values were “scrambled” in random order, such that they are not associated directly with the index patient case-data. The resultant dataset is presented as a .csv file for use for training and statistical instruction purposes.

Financial overview and grant giving statistics of Applied Scholastics Online Academy

ABSTRACT In this paper we present the development of a modulated web based statistical system, hereafter MWStat, which shifts the statistical paradigm of analyzing data into a real time structure. The MWStat system is useful for both online storage data and questionnaires analysis, as well as to provide real time disposal of results from analysis related to several statistical methodologies in a customizable fashion. Overall, it can be seem as a useful technical solution that can be applied to a large range of statistical applications, which needs of a scheme of devolution of real time results, accessible to anyone with internet access. We display here the step-by-step instructions for implementing the system. The structure is accessible, built with an easily interpretable language and it can be strategically applied to online statistical applications. We rely on the relationship of several free languages, namely, PHP, R, MySQL database and an Apache HTTP server, and on the use of software tools such as phpMyAdmin. We expose three didactical examples of the MWStat system on institutional evaluation, statistical quality control and multivariate analysis. The methodology is also illustrated in a real example on institutional evaluation. A MWStat module was specifically built for providing a real time poll for teacher evaluation at the Federal University of São Carlos (Brazil).

The six data sets were created for an undergraduate course at the Babes-Bolyai University, Faculty of Mathematics and Computer Science, held for second year students in the autumn semester. The course is taught both in Romanian and English with the same content and evaluation rules in both languages. The six data sets are the following: - FirstCaseStudy_RO_traditional_2019-2020.txt - contains data about the grades from the 2019-2020 academic year (when traditional face-to-face teaching method was used) for the Romanian language - FirstCaseStudy_RO_online_2020-2021.txt - contains data about the grades from the 2020-2021 academic year (when online teaching was used) for the Romanian language - SecondCaseStudy_EN_traditional_2019-2020.txt - contains data about the grades from the 2019-2020 academic year (when traditional face-to-face teaching method was used) for the English language - SecondCaseStudy_EN_online_2020-2021.txt - contains data about the grades from the 2020-2021 academic year (when online teaching was used) for the English language - ThirdCaseStudy_Both_traditional_2019-2020.txt - the concatenation of the two data sets for the 2019-2020 academic year (so all instances from FirstCaseStudy_RO_traditional_2019-2020 and SecondCaseStudy_EN_traditional_2019-2020 together) - ThirdCaseStudy_Both_online_2020-2021.txt - the concatenation of the two data sets for the 2020-2021 academic year (so all instances from FirstCaseStudy_RO_online_2020-2021 and SecondCaseStudy_EN_online_2020-2021 together)Instances from the data sets for the 2019-2020 academic year contain 12 attributes (in this order): - the grades received by the student for 7 laboratory assignments that were presented during the semester. For assignments that were not turned in a grade of 0 was given. Possible values are between 0 and 10 - the grades received by the student for 2 practical exams. If a student did not participate in a practical exam, de grade was 0. Possible values are between 0 and 10. - the number of seminar activities that the student had. Possible values are between 0 and 7. - the final grade the student received for the course. It is a value between 4 and 10. - the category of the final grade: - E for grades 10 or 9 - G for grades 8 or 7 - S for grades 6 or 5 - F for grade 4Instances from the data sets for the 2020-2021 academic year contain 10 attributes (in this order): - the grades received by the student for 7 laboratory assignments that were presented during the semester. For assignments that were not turned in a grade of 0 was given. Possible values are between 0 and 10 - a seminar bonus computed based on the number of seminar activities the student had during the semester, which was added to the final grade. Possible values are between 0 and 0.5. - the final grade the student received for the course. It is a value between 4 and 10. - the category of the final grade: - E for grades 10 or 9 - G for grades 8 or 7 - S for grades 6 or 5 - F for grade 4

This seminar is an applied study of deep learning methods for extracting information from geospatial data, such as aerial imagery, multispectral imagery, digital terrain data, and other digital cartographic representations. We first provide an introduction and conceptualization of artificial neural networks (ANNs). Next, we explore appropriate loss and assessment metrics for different use cases followed by the tensor data model, which is central to applying deep learning methods. Convolutional neural networks (CNNs) are then conceptualized with scene classification use cases. Lastly, we explore semantic segmentation, object detection, and instance segmentation. The primary focus of this course is semantic segmenation for pixel-level classification. The associated GitHub repo provides a series of applied examples. We hope to continue to add examples as methods and technologies further develop. These examples make use of a vareity of datasets (e.g., SAT-6, topoDL, Inria, LandCover.ai, vfillDL, and wvlcDL). Please see the repo for links to the data and associated papers. All examples have associated videos that walk through the process, which are also linked to the repo. A variety of deep learning architectures are explored including UNet, UNet++, DeepLabv3+, and Mask R-CNN. Currenlty, two examples use ArcGIS Pro and require no coding. The remaining five examples require coding and make use of PyTorch, Python, and R within the RStudio IDE. It is assumed that you have prior knowledge of coding in the Python and R enviroinments. If you do not have experience coding, please take a look at our Open-Source GIScience and Open-Source Spatial Analytics (R) courses, which explore coding in Python and R, respectively. After completing this seminar you will be able to: explain how ANNs work including weights, bias, activation, and optimization. describe and explain different loss and assessment metrics and determine appropriate use cases. use the tensor data model to represent data as input for deep learning. explain how CNNs work including convolutional operations/layers, kernel size, stride, padding, max pooling, activation, and batch normalization. use PyTorch, Python, and R to prepare data, produce and assess scene classification models, and infer to new data. explain common semantic segmentation architectures and how these methods allow for pixel-level classification and how they are different from traditional CNNs. use PyTorch, Python, and R (or ArcGIS Pro) to prepare data, produce and assess semantic segmentation models, and infer to new data.

The European questionnaire on Information and Communication Technologies Data reveals that there exists a disparity between the internet usage of people with a low, medium, and high formal education level. This disparity although present in most countries, differs widely in its severity.

In 2019, five percent of users with low formal education in the United Kingdom responded that they used the internet to search or apply for jobs. Among people with medium formal education the share is 19 percent higher, amounting to 24 percent. According to the survey 29 percent of users in the United Kingdom with a high degree of formal education searched and applied for jobs online.

The European questionnaire on Information and Communication Technologies Data reveals that there exists a disparity between the internet usage of people with a low, medium, and high formal education level. This disparity although present in most countries, differs widely in its severity.

In 2019, 12 percent of users with low formal education in France responded that they used the internet to search or apply for jobs. Among people with medium formal education the share is six percent higher, amounting to 18 percent. According to the survey 21 percent of users in France with a high degree of formal education searched and applied for jobs online.

The attached data with this journal article consists of an ESM zip containing three files. The file fedges.txt are the edges that define the network, the file tedges.txt are the edges between the different layers of the network, while data in the file twitter_foursquare_mapper.dat provides the basic info of each node of the network, as stated in the first row.

In this article, the link prediction problem in multiplex networks is studied. In the author's words: "As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%."

Web Visualization of Massive Neuroscience Datasets using the Open Connectome Project & NeuroData

The share of internet users looking for and applying for jobs online in Sweden decreased by 2.8 percentage points in 2021 in comparison to the previous year. In total, the share of internet users looking for jobs online declined to 27.69 percent in 2021. This decrease was preceded by an increase in share of internet users looking for jobs online.The EU survey on the use of Information and Communication Technologies (ICT) in households and by individuals is an annual survey conducted since 2002 aiming at collecting and disseminating harmonised and comparable information on the use of ICT in households and by individuals. Data presented in this domain are collected on a yearly basis by the National Statistical Institutes and are based on Eurostat's annual model questionnaire. This questionnaire is updated each year to reflect the evolving situation of information and communication technologiesFind more statistics on other topics about Sweden with key insights such as share of daily internet users, share of internet users seeking health information online, share of internet users reading news online, share of internet users engaging in online learning activities, and share of people that upload self-created content.

Map shows the percentage change in number of occupied and unoccupied private dwellings between the 2018 and 2023 Censuses.Download lookup file from Stats NZ ArcGIS Online or Stats NZ geographic data service.FootnotesGeographical boundariesStatistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018. Caution using time series Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data). About the 2023 Census dataset For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings. Data quality The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.Quality rating of a variable The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable. Dwelling occupancy status quality rating Dwelling occupancy status is rated as high quality. Dwelling occupancy status – 2023 Census: Information by concept has more information, for example, definitions and data quality.Dwelling type quality rating Dwelling type is rated as moderate quality. Dwelling type – 2023 Census: Information by concept has more information, for example, definitions and data quality.Using data for good Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.Confidentiality The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.Symbol-998 Not applicable-999 Confidential

This web appendix formed part of the original submission and has been peer reviewed. The journal post it as supplied by the authors.Supplement to: Chao, F., Gerland, P., Cook, A.R. and Alkema, L., 2019. Systematic assessment of the sex ratio at birth for all countries and estimation of national imbalances and regional reference levels. Proceedings of the National Academy of Sciences, 116(19), pp.9303-9311.

This appendix formed part of the original submission and has been peer reviewed. The journal post it as supplied by the authors.Supplement to: Alkema L, Chao F, You D, Pedersen J, Sawyer CC. National, regional, and global sex ratios of infant, child, and under-5 mortality and identification of countries with outlying ratios: a systematic assessment. Lancet Glob Health 2014; 2: e521–30.

The share of internet users looking for and applying for jobs online in Finland increased by 2.1 percentage points in 2021 in comparison to the previous year. With 34.18 percent, the share of internet users looking for jobs online thereby reached its highest value in the observed period.The EU survey on the use of Information and Communication Technologies (ICT) in households and by individuals is an annual survey conducted since 2002 aiming at collecting and disseminating harmonised and comparable information on the use of ICT in households and by individuals. Data presented in this domain are collected on a yearly basis by the National Statistical Institutes and are based on Eurostat's annual model questionnaire. This questionnaire is updated each year to reflect the evolving situation of information and communication technologiesFind more statistics on other topics about Finland with key insights such as share of daily internet users, share of internet users seeking health information online, share of internet users informing themselves about goods and services online, share of internet users reading news online, share of internet users engaging in online learning activities, and share of people that upload self-created content.

This web appendix is for:

        Levels and trends in sex ratio at birth in

provinces of Pakistan from 1980 to 2020 with scenario-based missing female birth projections to 2050: a Bayesian modeling approach

The share of internet users looking for and applying for jobs online in Austria increased by 0.9 percentage points in 2021 in comparison to the previous year. While the share of internet users looking for jobs online increased significantly in the first phase of the observed period, the increase slowed down in the last years.The EU survey on the use of Information and Communication Technologies (ICT) in households and by individuals is an annual survey conducted since 2002 aiming at collecting and disseminating harmonised and comparable information on the use of ICT in households and by individuals. Data presented in this domain are collected on a yearly basis by the National Statistical Institutes and are based on Eurostat's annual model questionnaire. This questionnaire is updated each year to reflect the evolving situation of information and communication technologiesFind more statistics on other topics about Austria with key insights such as share of daily internet users, share of internet users seeking health information online, share of internet users informing themselves about goods and services online, share of internet users reading news online, share of internet users engaging in online learning activities, and share of people that upload self-created content.

Estimated probabilities of applying the statistics methods on sample data.

The share of internet users looking for and applying for jobs online in the Netherlands declined to 22.25 percent in 2021. This means a decline of 2.9 percentage points in comparison to the previous year.The EU survey on the use of Information and Communication Technologies (ICT) in households and by individuals is an annual survey conducted since 2002 aiming at collecting and disseminating harmonised and comparable information on the use of ICT in households and by individuals. Data presented in this domain are collected on a yearly basis by the National Statistical Institutes and are based on Eurostat's annual model questionnaire. This questionnaire is updated each year to reflect the evolving situation of information and communication technologiesFind more statistics on other topics about the Netherlands with key insights such as share of daily internet users, share of internet users seeking health information online, share of internet users informing themselves about goods and services online, share of internet users reading news online, share of internet users engaging in online learning activities, and share of people that upload self-created content.