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Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.
Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either
with the same name or using synonyms or clearly linkable names;
Wrongly represented (WR) - A class in the domain expert model is
incorrectly represented in the student model, either (i) via an attribute,
method, or relationship rather than class, or
(ii) using a generic term (e.g., user'' instead of
urban
planner'');
System-oriented (SO) - A class in CM-Stud that denotes a technical
implementation aspect, e.g., access control. Classes that represent legacy
system or the system under design (portal, simulator) are legitimate;
Omitted (OM) - A class in CM-Expert that does not appear in any way in
CM-Stud;
Missing (MI) - A class in CM-Stud that does not appear in any way in
CM-Expert.
All the calculations and information provided in the following sheets
originate from that raw data.
Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,
including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.
Sheet 3 (Size-Ratio):
The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.
Sheet 4 (Overall):
Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.
For sheet 4 as well as for the following four sheets, diverging stacked bar
charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:
Sheet 5 (By-Notation):
Model correctness and model completeness is compared by notation - UC, US.
Sheet 6 (By-Case):
Model correctness and model completeness is compared by case - SIM, HOS, IFA.
Sheet 7 (By-Process):
Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.
Sheet 8 (By-Grade):
Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.
The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.
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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
Envestnet®| Yodlee®'s Consumer Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.
Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.
We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.
Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?
Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.
Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking
Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)
Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence
Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis
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This book is written for statisticians, data analysts, programmers, researchers, teachers, students, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using the R programming language. R is an open-source software and object-oriented programming language with a development environment (IDE) called RStudio for computing statistics and graphical displays through data manipulation, modelling, and calculation. R packages and supported libraries provides a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical softwares, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system.For all intents and purposes, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the R users or statisticians. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and non-parametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for reliability and validity of the available datasets. Different research experiments, case scenarios and examples are explained in this book. It is the first book to provide a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as Objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations. Thus, congruence of Statistics and Computer programming for Research.
The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.
The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.
Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.
Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences.
Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:
Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).
Consumer Graph Use Cases:
360-Degree Customer View:Get a comprehensive image of customers by the means of internal and external data aggregation.
Data Enrichment:Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment
Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity.
Advertising & Marketing:Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.
Using Factori Consumer Data graph you can solve use cases like:
Acquisition Marketing Expand your reach to new users and customers using lookalike modeling with your first party audiences to extend to other potential consumers with similar traits and attributes.
Lookalike Modeling
Build lookalike audience segments using your first party audiences as a seed to extend your reach for running marketing campaigns to acquire new users or customers
And also, CRM Data Enrichment, Consumer Data Enrichment B2B Data Enrichment B2C Data Enrichment Customer Acquisition Audience Segmentation 360-Degree Customer View Consumer Profiling Consumer Behaviour Data
Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.
The Portuguese footballer is the most-followed person on the photo sharing app platform with 628 million followers. Instagram's own account was ranked first with roughly 672 million followers.
How popular is Instagram?
Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.
Who uses Instagram?
Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.
Celebrity influencers on Instagram
Many celebrities and athletes are brand spokespeople and generate additional income with social media advertising and sponsored content. Unsurprisingly, Ronaldo ranked first again, as the average media value of one of his Instagram posts was 985,441 U.S. dollars.
In 2024, the number of data compromises in the United States stood at 3,158 cases. Meanwhile, over 1.35 billion individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. In 2024 the financial services, healthcare, and professional services were the three industry sectors that recorded most data breaches. Overall, the number of healthcare data breaches in some industry sectors in the United States has gradually increased within the past few years. However, some sectors saw decrease. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.
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This dataset includes bibliographic information for 501 papers that were published from 2010-April 2017 (time of search) and use online biodiversity databases for research purposes. Our overarching goal in this study is to determine how research uses of biodiversity data developed during a time of unprecedented growth of online data resources. We also determine uses with the highest number of citations, how online occurrence data are linked to other data types, and if/how data quality is addressed. Specifically, we address the following questions:
1.) What primary biodiversity databases have been cited in published research, and which
databases have been cited most often?
2.) Is the biodiversity research community citing databases appropriately, and are
the cited databases currently accessible online?
3.) What are the most common uses, general taxa addressed, and data linkages, and how
have they changed over time?
4.) What uses have the highest impact, as measured through the mean number of citations
per year?
5.) Are certain uses applied more often for plants/invertebrates/vertebrates?
6.) Are links to specific data types associated more often with particular uses?
7.) How often are major data quality issues addressed?
8.) What data quality issues tend to be addressed for the top uses?
Relevant papers for this analysis include those that use online and openly accessible primary occurrence records, or those that add data to an online database. Google Scholar (GS) provides full-text indexing, which was important to identify data sources that often appear buried in the methods section of a paper. Our search was therefore restricted to GS. All authors discussed and agreed upon representative search terms, which were relatively broad to capture a variety of databases hosting primary occurrence records. The terms included: “species occurrence” database (8,800 results), “natural history collection” database (634 results), herbarium database (16,500 results), “biodiversity database” (3,350 results), “primary biodiversity data” database (483 results), “museum collection” database (4,480 results), “digital accessible information” database (10 results), and “digital accessible knowledge” database (52 results)--note that quotations are used as part of the search terms where specific phrases are needed in whole. We downloaded all records returned by each search (or the first 500 if there were more) into a Zotero reference management database. About one third of the 2500 papers in the final dataset were relevant. Three of the authors with specialized knowledge of the field characterized relevant papers using a standardized tagging protocol based on a series of key topics of interest. We developed a list of potential tags and descriptions for each topic, including: database(s) used, database accessibility, scale of study, region of study, taxa addressed, research use of data, other data types linked to species occurrence data, data quality issues addressed, authors, institutions, and funding sources. Each tagged paper was thoroughly checked by a second tagger.
The final dataset of tagged papers allow us to quantify general areas of research made possible by the expansion of online species occurrence databases, and trends over time. Analyses of this data will be published in a separate quantitative review.
The Exhibit of Datasets was an experimental project with the aim of providing concise introductions to research datasets in the humanities and social sciences deposited in a trusted repository and thus made accessible for the long term. The Exhibit consists of so-called 'showcases', short webpages summarizing and supplementing the corresponding data papers, published in the Research Data Journal for the Humanities and Social Sciences. The showcase is a quick introduction to such a dataset, a bit longer than an abstract, with illustrations, interactive graphs and other multimedia (if available). As a rule it also offers the option to get acquainted with the data itself, through an interactive online spreadsheet, a data sample or link to the online database of a research project. Usually, access to these datasets requires several time consuming actions, such as downloading data, installing the appropriate software and correctly uploading the data into these programs. This makes it difficult for interested parties to quickly assess the possibilities for reuse in other projects.
The Exhibit aimed to help visitors of the website to get the right information at a glance by: - Attracting attention to (recently) acquired deposits: showing why data are interesting. - Providing a concise overview of the dataset's scope and research background; more details are to be found, for example, in the associated data paper in the Research Data Journal (RDJ). - Bringing together references to the location of the dataset and to more detailed information elsewhere, such as the project website of the data producers. - Allowing visitors to explore (a sample of) the data without downloading and installing associated software at first (see below). - Publishing related multimedia content, such as videos, animated maps, slideshows etc., which are currently difficult to include in online journals as RDJ. - Making it easier to review the dataset. The Exhibit would also have been the right place to publish these reviews in the same way as a webshop publishes consumer reviews of a product, but this could not yet be achieved within the limited duration of the project.
Note (1) The text of the showcase is a summary of the corresponding data paper in RDJ, and as such a compilation made by the Exhibit editor. In some cases a section 'Quick start in Reusing Data' is added, whose text is written entirely by the editor. (2) Various hyperlinks such as those to pages within the Exhibit website will no longer work. The interactive Zoho spreadsheets are also no longer available because this facility has been discontinued.
The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Australia & Oceania and Asia.
The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.
Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).
The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.
The survey is focused on three core areas of research:
Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.
If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".
Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.
Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.
The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."
The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:
The survey data will be provided under embargo in both comma-delimited and statistical formats.
Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)
Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.
Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.
Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.
Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.
To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.
On 1 April 2025 responsibility for fire and rescue transferred from the Home Office to the Ministry of Housing, Communities and Local Government.
This information covers fires, false alarms and other incidents attended by fire crews, and the statistics include the numbers of incidents, fires, fatalities and casualties as well as information on response times to fires. The Ministry of Housing, Communities and Local Government (MHCLG) also collect information on the workforce, fire prevention work, health and safety and firefighter pensions. All data tables on fire statistics are below.
MHCLG has responsibility for fire services in England. The vast majority of data tables produced by the Ministry of Housing, Communities and Local Government are for England but some (0101, 0103, 0201, 0501, 1401) tables are for Great Britain split by nation. In the past the Department for Communities and Local Government (who previously had responsibility for fire services in England) produced data tables for Great Britain and at times the UK. Similar information for devolved administrations are available at https://www.firescotland.gov.uk/about/statistics/" class="govuk-link">Scotland: Fire and Rescue Statistics, https://statswales.gov.wales/Catalogue/Community-Safety-and-Social-Inclusion/Community-Safety" class="govuk-link">Wales: Community safety and https://www.nifrs.org/home/about-us/publications/" class="govuk-link">Northern Ireland: Fire and Rescue Statistics.
If you use assistive technology (for example, a screen reader) and need a version of any of these documents in a more accessible format, please email alternativeformats@communities.gov.uk. Please tell us what format you need. It will help us if you say what assistive technology you use.
Fire statistics guidance
Fire statistics incident level datasets
https://assets.publishing.service.gov.uk/media/686d2aa22557debd867cbe14/FIRE0101.xlsx">FIRE0101: Incidents attended by fire and rescue services by nation and population (MS Excel Spreadsheet, 153 KB) Previous FIRE0101 tables
https://assets.publishing.service.gov.uk/media/686d2ab52557debd867cbe15/FIRE0102.xlsx">FIRE0102: Incidents attended by fire and rescue services in England, by incident type and fire and rescue authority (MS Excel Spreadsheet, 2.19 MB) Previous FIRE0102 tables
https://assets.publishing.service.gov.uk/media/686d2aca10d550c668de3c69/FIRE0103.xlsx">FIRE0103: Fires attended by fire and rescue services by nation and population (MS Excel Spreadsheet, 201 KB) Previous FIRE0103 tables
https://assets.publishing.service.gov.uk/media/686d2ad92557debd867cbe16/FIRE0104.xlsx">FIRE0104: Fire false alarms by reason for false alarm, England (MS Excel Spreadsheet, 492 KB) Previous FIRE0104 tables
https://assets.publishing.service.gov.uk/media/686d2af42cfe301b5fb6789f/FIRE0201.xlsx">FIRE0201: Dwelling fires attended by fire and rescue services by motive, population and nation (MS Excel Spreadsheet, <span class="gem-c-attac
https://datafinder.stats.govt.nz/license/attribution-4-0-international/https://datafinder.stats.govt.nz/license/attribution-4-0-international/
Dataset contains counts and measures for households from the 2013, 2018, and 2023 Censuses. Data is available by statistical area 1.
The variables included in this dataset are for households in occupied private dwellings (unless otherwise stated). All data is for level 1 of the classification (unless otherwise stated):
Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.
Footnotes
Geographical boundaries
Statistical 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.
Concept descriptions and quality ratings
Data quality ratings for 2023 Census variables has additional details about variables found within totals by topic, for example, definitions and data quality.
Household crowding
Household crowding is based on the Canadian National Occupancy Standard (CNOS). It calculates the number of bedrooms needed based on the demographic composition of the household. The household crowding index methodology for 2023 Census has been updated to use gender instead of sex. Household crowding should be used with caution for small geographical areas due to high volatility between census years as a result of population change and urban development. There may be additional volatility in areas affected by the cyclone, particularly in Gisborne and Hawke's Bay. Household crowding index – 2023 Census has details on how the methodology has changed, differences from 2018 Census, and more.
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.
Measures
Measures like averages, medians, and other quantiles are calculated from unrounded counts, with input noise added to or subtracted from each contributing value during measures calculations. Averages and medians based on less than six units (e.g. individuals, dwellings, households, families, or extended families) are suppressed. This suppression threshold changes for other quantiles. Where the cells have been suppressed, a placeholder value has been used.
Percentages
To calculate percentages, divide the figure for the category of interest by the figure for 'Total stated' where this applies.
Symbol
-997 Not available
-999 Confidential
Inconsistencies in definitions
Please note that there may be differences in definitions between census classifications and those used for other data collections.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The aim of this survey was to collect feedback about existing training programmes in statistical analysis for postgraduate researchers at the University of Edinburgh, as well as respondents' preferred methods for training, and their requirements for new courses. The survey was circulated via e-mail to research staff and postgraduate researchers across three colleges of the University of Edinburgh: the College of Arts, Humanities and Social Sciences; the College of Science and Engineering; and the College of Medicine and Veterinary Medicine. The survey was conducted on-line using the Bristol Online Survey tool, March through July 2017. 90 responses were received. The Scoping Statistical Analysis Support project, funded by Information Services Innovation Fund, aims to increase visibility and raise the profile of the Research Data Service by: understanding how statistical analysis support is conducted across University of Edinburgh Schools; scoping existing support mechanisms and models for students, researchers and teachers; identifying services and support that would satisfy existing or future demand.
https://datafinder.stats.govt.nz/license/attribution-4-0-international/https://datafinder.stats.govt.nz/license/attribution-4-0-international/
Dataset contains counts and measures for households from the 2013, 2018, and 2023 Censuses. Data is available by statistical area 2.
The variables included in this dataset are for households in occupied private dwellings (unless otherwise stated). All data is for level 1 of the classification (unless otherwise stated):
Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.
Footnotes
Geographical boundaries
Statistical 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.
Concept descriptions and quality ratings
Data quality ratings for 2023 Census variables has additional details about variables found within totals by topic, for example, definitions and data quality.
Household crowding
Household crowding is based on the Canadian National Occupancy Standard (CNOS). It calculates the number of bedrooms needed based on the demographic composition of the household. The household crowding index methodology for 2023 Census has been updated to use gender instead of sex. Household crowding should be used with caution for small geographical areas due to high volatility between census years as a result of population change and urban development. There may be additional volatility in areas affected by the cyclone, particularly in Gisborne and Hawke's Bay. Household crowding index – 2023 Census has details on how the methodology has changed, differences from 2018 Census, and more.
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.
Measures
Measures like averages, medians, and other quantiles are calculated from unrounded counts, with input noise added to or subtracted from each contributing value during measures calculations. Averages and medians based on less than six units (e.g. individuals, dwellings, households, families, or extended families) are suppressed. This suppression threshold changes for other quantiles. Where the cells have been suppressed, a placeholder value has been used.
Percentages
To calculate percentages, divide the figure for the category of interest by the figure for 'Total stated' where this applies.
Symbol
-997 Not available
-999 Confidential
Inconsistencies in definitions
Please note that there may be differences in definitions between census classifications and those used for other data collections.
https://github.com/nytimes/covid-19-data/blob/master/LICENSEhttps://github.com/nytimes/covid-19-data/blob/master/LICENSE
The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.
Since the first reported coronavirus case in Washington State on Jan. 21, 2020, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.
We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.
The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.
https://datafinder.stats.govt.nz/license/attribution-4-0-international/https://datafinder.stats.govt.nz/license/attribution-4-0-international/
Dataset contains counts and measures for individuals from the 2013, 2018, and 2023 Censuses. Data is available by statistical area 1.
The variables included in this dataset are for the census usually resident population count (unless otherwise stated). All data is for level 1 of the classification.
The variables for part 2 of the dataset are:
Download lookup file for part 2 from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.
Footnotes
Te Whata
Under the Mana Ōrite Relationship Agreement, Te Kāhui Raraunga (TKR) will be publishing Māori descent and iwi affiliation data from the 2023 Census in partnership with Stats NZ. This will be available on Te Whata, a TKR platform.
Geographical boundaries
Statistical 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.
Subnational census usually resident population
The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city.
Population counts
Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts.
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).
Study participation time series
In the 2013 Census study participation was only collected for the census usually resident population count aged 15 years and over.
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.
Concept descriptions and quality ratings
Data quality ratings for 2023 Census variables has additional details about variables found within totals by topic, for example, definitions and data quality.
Disability indicator
This data should not be used as an official measure of disability prevalence. Disability prevalence estimates are only available from the 2023 Household Disability Survey. Household Disability Survey 2023: Final content has more information about the survey.
Activity limitations are measured using the Washington Group Short Set (WGSS). The WGSS asks about six basic activities that a person might have difficulty with: seeing, hearing, walking or climbing stairs, remembering or concentrating, washing all over or dressing, and communicating. A person was classified as disabled in the 2023 Census if there was at least one of these activities that they had a lot of difficulty with or could not do at all.
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.
Measures
Measures like averages, medians, and other quantiles are calculated from unrounded counts, with input noise added to or subtracted from each contributing value
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.
Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either
with the same name or using synonyms or clearly linkable names;
Wrongly represented (WR) - A class in the domain expert model is
incorrectly represented in the student model, either (i) via an attribute,
method, or relationship rather than class, or
(ii) using a generic term (e.g., user'' instead of
urban
planner'');
System-oriented (SO) - A class in CM-Stud that denotes a technical
implementation aspect, e.g., access control. Classes that represent legacy
system or the system under design (portal, simulator) are legitimate;
Omitted (OM) - A class in CM-Expert that does not appear in any way in
CM-Stud;
Missing (MI) - A class in CM-Stud that does not appear in any way in
CM-Expert.
All the calculations and information provided in the following sheets
originate from that raw data.
Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,
including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.
Sheet 3 (Size-Ratio):
The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.
Sheet 4 (Overall):
Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.
For sheet 4 as well as for the following four sheets, diverging stacked bar
charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:
Sheet 5 (By-Notation):
Model correctness and model completeness is compared by notation - UC, US.
Sheet 6 (By-Case):
Model correctness and model completeness is compared by case - SIM, HOS, IFA.
Sheet 7 (By-Process):
Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.
Sheet 8 (By-Grade):
Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.