The District of Columbia shares story maps that combine impacting narratives and multimedia with data and analytics. These examples support agency programs and help educate how DC is using its data.

This dataset contains examples of dashboards, data stories, data apps and other open data pages which we have developed for our Council clients in Australia.Each page or dashboard includes a screenshot, a short description and a hyperlink to the live page when the page is publicly accessible. The purpose of this dataset is to help Local Councils innovate with their data and see what their peers have done with their data.You can also browse the content of this dataset on the Get Inspired page of our website.

This dataset is about books. It has 5 rows and is filtered where the book subjects is Fathers and daughters-Anecdotes. It features 9 columns including author, publication date, language, and book publisher.

A collection of 22 data set of 50+ requirements each, expressed as user stories.

The dataset has been created by gathering data from web sources and we are not aware of license agreements or intellectual property rights on the requirements / user stories. The curator took utmost diligence in minimizing the risks of copyright infringement by using non-recent data that is less likely to be critical, by sampling a subset of the original requirements collection, and by qualitatively analyzing the requirements. In case of copyright infringement, please contact the dataset curator (Fabiano Dalpiaz, f.dalpiaz@uu.nl) to discuss the possibility of removal of that dataset [see Zenodo's policies]

The data sets have been originally used to conduct experiments about ambiguity detection with the REVV-Light tool: https://github.com/RELabUU/revv-light

This collection has been originally published in Mendeley data: https://data.mendeley.com/datasets/7zbk8zsd8y/1

Overview of the datasets [data and links added in December 2024]

The following text provides a description of the datasets, including links to the systems and websites, when available. The datasets are organized by macro-category and then by identifier.

Public administration and transparency

g02-federalspending.txt (2018) originates from early data in the Federal Spending Transparency project, which pertain to the website that is used to share publicly the spending data for the U.S. government. The website was created because of the Digital Accountability and Transparency Act of 2014 (DATA Act). The specific dataset pertains a system called DAIMS or Data Broker, which stands for DATA Act Information Model Schema. The sample that was gathered refers to a sub-project related to allowing the government to act as a data broker, thereby providing data to third parties. The data for the Data Broker project is currently not available online, although the backend seems to be hosted in GitHub under a CC0 1.0 Universal license. Current and recent snapshots of federal spending related websites, including many more projects than the one described in the shared collection, can be found here.

g03-loudoun.txt (2018) is a set of extracted requirements from a document, by the Loudoun County Virginia, that describes the to-be user stories and use cases about a system for land management readiness assessment called Loudoun County LandMARC. The source document can be found here and it is part of the Electronic Land Management System and EPlan Review Project - RFP RFQ issued in March 2018. More information about the overall LandMARC system and services can be found here.

g04-recycling.txt(2017) concerns a web application where recycling and waste disposal facilities can be searched and located. The application operates through the visualization of a map that the user can interact with. The dataset has obtained from a GitHub website and it is at the basis of a students' project on web site design; the code is available (no license).

g05-openspending.txt (2018) is about the OpenSpending project (www), a project of the Open Knowledge foundation which aims at transparency about how local governments spend money. At the time of the collection, the data was retrieved from a Trello board that is currently unavailable. The sample focuses on publishing, importing and editing datasets, and how the data should be presented. Currently, OpenSpending is managed via a GitHub repository which contains multiple sub-projects with unknown license.

g11-nsf.txt (2018) refers to a collection of user stories referring to the NSF Site Redesign & Content Discovery project, which originates from a publicly accessible GitHub repository (GPL 2.0 license). In particular, the user stories refer to an early version of the NSF's website. The user stories can be found as closed Issues.

(Research) data and meta-data management

g08-frictionless.txt (2016) regards the Frictionless Data project, which offers an open source dataset for building data infrastructures, to be used by researchers, data scientists, and data engineers. Links to the many projects within the Frictionless Data project are on GitHub (with a mix of Unlicense and MIT license) and web. The specific set of user stories has been collected in 2016 by GitHub user @danfowler and are stored in a Trello board.

g14-datahub.txt (2013) concerns the open source project DataHub, which is currently developed via a GitHub repository (the code has Apache License 2.0). DataHub is a data discovery platform which has been developed over multiple years. The specific data set is an initial set of user stories, which we can date back to 2013 thanks to a comment therein.

g16-mis.txt (2015) is a collection of user stories that pertains a repository for researchers and archivists. The source of the dataset is a public Trello repository. Although the user stories do not have explicit links to projects, it can be inferred that the stories originate from some project related to the library of Duke University.

g17-cask.txt (2016) refers to the Cask Data Application Platform (CDAP). CDAP is an open source application platform (GitHub, under Apache License 2.0) that can be used to develop applications within the Apache Hadoop ecosystem, an open-source framework which can be used for distributed processing of large datasets. The user stories are extracted from a document that includes requirements regarding dataset management for Cask 4.0, which includes the scenarios, user stories and a design for the implementation of these user stories. The raw data is available in the following environment.

g18-neurohub.txt (2012) is concerned with the NeuroHub platform, a neuroscience data management, analysis and collaboration platform for researchers in neuroscience to collect, store, and share data with colleagues or with the research community. The user stories were collected at a time NeuroHub was still a research project sponsored by the UK Joint Information Systems Committee (JISC). For information about the research project from which the requirements were collected, see the following record.

g22-rdadmp.txt (2018) is a collection of user stories from the Research Data Alliance's working group on DMP Common Standards. Their GitHub repository contains a collection of user stories that were created by asking the community to suggest functionality that should part of a website that manages data management plans. Each user story is stored as an issue on the GitHub's page.

g23-archivesspace.txt (2012-2013) refers to ArchivesSpace: an open source, web application for managing archives information. The application is designed to support core functions in archives administration such as accessioning; description and arrangement of processed materials including analog, hybrid, and
born digital content; management of authorities and rights; and reference service. The application supports collection management through collection management records, tracking of events, and a growing number of administrative reports. ArchivesSpace is open source and its

By pairing water quality and meteorological data with visible impacts, reserves can illustrate storm impacts and connect local communities to science. The project This project developed through conversations among the southeast and Caribbean region National Estuarine Research Reserves while discussing the need to respond to regional hurricanes including Dorian, Michael, Florence, Maria, Irma, and Matthew. Storm events damage not only the built infrastructure of local communities, but also the natural areas within and surrounding the reserves. The reserves wanted tools to help communicate about storm impacts using monitoring data and information collected through the System-wide Monitoring Program (SWMP), including salinity, dissolved oxygen, wind speed and direction, rainfall, and water depth. By pairing water quality and meteorological data with visible impacts, reserves can illustrate storm impacts and connect local communities to science. The final communications products include pictures, hurricane path maps, SWMP data analyses and visualizations, and text to help connect the quantitative storm story to the visual impacts observed in reserve local communities. Tools that enable communication about storms with local communities allow reserve educators and local teachers to discuss storm event impacts with their students. They also enable the Coastal Training Program to communicate with natural resource managers and local decision makers about observed negative environmental changes such as fish kills, increases in invasive vegetation, and native vegetation die-off.

After catastrophes, international donors offering assistance must decide whether to channel resources via the local government or non-governmental organizations (NGOs). We examine how these channels differ in targeting aid by combining survey data on aid received by Nicaraguan households before and after Hurricane Mitch. In the short term, NGOs provided aid according to hurricane severity, while government aid allocations were not significantly higher in the hardest hit areas. However, government-provided aid matched that of NGOs several years later. Despite the lag in government aid, we do not find evidence of political manipulation of relief aid in either the short or long-term.

(:unav)...........................................

This statistic shows the share of adults reading novels or short stories in the United States from 2002 to 2017. The data shows that 41.8 percent of surveyed U.S. adults were reading novels or short stories in 2017, down from 45.2 percent five years previously.

(:unav)...........................................

Data story "Korean War Veterans." Data sources: VA Veteran Population Projection Model 2018, Census 2000 Brief: Veterans, and the 2010 American Community Survey 1-Year Estimates.

Use this interactive map to explore data from the Marine Invader Monitoring and Information Collaborative (MIMIC), find descriptions of monitored species, and learn more about the program.

This story map covers many of the landmarks and attractions that can be found in Downtown and Central Baton Rouge. This part of the city holds the state Capitol and many other important legislative buildings. It also contains many important historical buildings, museums, and landmarks from Baton Rouge's early years as a settlement. There are also many modern amenities, the product of an extensive re-vitalization campaign over the last few decades.

Price quote data (for locally collected data only) and consumption segment indices that underpin consumer price inflation statistics, giving users access to the detailed data that are used in the construction of the UK’s inflation figures. The data are being made available for research purposes only and are not an accredited official statistic. From October 2024, private school fees and part-time education classes have been included in the consumption segment indices file. For more information on the introduction of consumption segments, please see the Consumer Prices Indices Technical Manual, 2019. Note that this dataset was previously called the consumer price inflation item indices and price quotes dataset.

This online appendix provides the materials of an experiment regarding the performance of individual subjects (24 students) in the extraction of conceptual models from a specification expressed:

[IV1] as user stories or use cases

[IV2] for one of three types of fictitious systems: hospital management system, urban traffic simulator, and international football association portal

We measure the performance in terms of validity (DV1) and completeness (DV2) against models that were created by domain experts (the three authors of the paper and of this online appendix) from each of the specifications.

The materials include

The description of the three systems (folder System Descriptions)

An Excel spreadsheet that includes raw data, charts, and statistical results

The guidelines that the authors used in assessing the quality of the subjects' models against the expert models

For each student who participated in the experiment and gave consent,

The specification, either via user stories or use cases, created by the student

The student model created by the student

The expert model created by one of the paper authors Note that these files are highlighted to denote how we applied the tagging guidelines

Edgar Allan Poe's Literary Works

Edgar Allan Poe's Complete Works

By Throwback Thursday [source]

About this dataset

The Week 24 - The Works of Edgar Allan Poe dataset is a comprehensive collection of the literary works of renowned author Edgar Allan Poe. This dataset provides detailed information about each work, including their titles, types or categories, years of writing, specific dates of creation (not available in this description), first publications, genres or styles, and additional notes.

The dataset contains multiple columns to facilitate easy access and analysis. The Title column specifies the titles of each literary work by Edgar Allan Poe. The Type column categorizes the works into different types such as poems, short stories, or essays. The Year column indicates the year in which each literary work was written.

Unfortunately,the specific dates are not available in this description so we do not know when exactly each piece was composed.The dataset also includes a separate column called Date, which would provide more precise information regarding the specific date on which each literary work was created.

The first publication details for each work can be found under the First published in column. This provides valuable insight into where and when these pieces were originally printed and made available to readers.

Furthermore,the datasets classifies each piece by genre using the Genre column.This allows researchers and enthusiasts to easily identify whether a particular work falls under genres like horror,mystery or gothic.

Lastly,it's worth mentioning that there is an extra field named Notes that provides useful additional information about certain aspects related to some literary works by Edgar Allan Poe.These notes could contain details about historical context,reception,personal anecdotes or any other relevant data that might enhance our understanding and appreciation for his writings.

Overall,this extensively detailed dataset serves as an invaluable resource for studying Edgar Allan Poe's literary contributions.Its inclusion of title,type/year/date/first published in/genre/notes offers a thorough understanding and accessibilityto his diverse body of work.This collection will appeal to scholars, literary enthusiasts, and anyone interested in exploring the works of this iconic writer

How to use the dataset

Dataset Overview

Understanding the Columns

To effectively use this dataset, it is important to understand the meaning of each column:

• Title: The title of Edgar Allan Poe's literary work.
• Type: The category or type of the literary work (poem, short story, essay).
• Year: The year in which the literary work was written.
• First published in: The publication where the literary work was first published.
• Genre: The genre or style of the literary work (horror, mystery, gothic).
• Notes: Additional notes or information about each individual piece.

Exploring and Analyzing Data

Once you have familiarized yourself with these columns' meanings and relevant context information for your analysis purpose:

• Explore Titles: You can browse through different titles to gain an understanding of what kind of literature Edgar Allan Poe produced.
• Filter by Type: If you are specifically interested in reading his poems only or looking for short stories within certain years mentioned in his works.
• Analyze Genre Distribution: Observe how many works fall into specific genres like horror or mystery.
• Examine First Publications: Dive into exploring where these works were initially published—revealing fascinating insights into their reach during that time period.

Remember that analyzing this data independently requires critical thinking and interpretation to uncover interesting patterns, insights, or correlations within Poe's literary works.

Leveraging Additional Notes

One useful aspect of this dataset is the column for additional notes. These notes might provide insights into themes, recurring motifs, or historical contexts related to specific works. Make sure to explore these notes while examining the data to enhance your understanding and interpretations.

Data Cleaning Notes

While going through this dataset, it is essential to consider potential data cleaning requirements. Ensure that you account for any missing values, inconsistencies in formatting (typos), or potential conflicts between columns. Cleaned and standardized data will contribute to better analysis outcomes

Research Ideas

• Analyzing the evolution of Edgar Allan Poe's writing style and themes...

Graph and download economic data for Civilian Labor Force in Story County, IA (IASTOR7LFN) from Jan 1990 to Aug 2025 about Story County, IA; Ames; IA; civilian; labor force; labor; and USA.

Global interactive map containing, among others, data from Armenian architectural websites as well as individuals.

About this dataset

Context

The dataset tabulates the Story City population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Story City across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Story City was 3,388, a 0.53% increase year-by-year from 2022. Previously, in 2022, Story City population was 3,370, a decline of 0.21% compared to a population of 3,377 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Story City increased by 143. In this period, the peak population was 3,451 in the year 2014. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Story City is shown in this column.
• Year on Year Change: This column displays the change in Story City population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Story City Population by Year. You can refer the same here

Seven data sharing stories, in seven minutes or less. The video recordings, presentation slides and live scribe illustrations are included.Featuring:The citation advantage of linking publications to research data - Barbara McGillivray, The Alan Turing Institute and University of CambridgeBibliographic Data Science: Open Ecosystems for Scalable Collaboration - Leo Lahti, University of Turku Reproducible Research - Why and How? -Yasemin Turkyilmaz-van der Velden, TU DelftResearch Data Management Using Open Source Off The Shelf Tools -Graham Addis, Nuffield Department of Experimental MedicineX chromosome genetic data in a Spanish children cohort, dataset description and analysis pipeline - Augusto Anguita-Ruiz, University of GranadaParticipatory Science to Empower: Building a Citizen Science Platform - Georgia Aitkenhead, The Alan Turing Institute‘Open Science’ opens doors: How #Scidata18 helped me unlock career opportunities - Connie Clare, University of Nottingham

This is the VetPop2023 Data Story. The Veteran Population Projection Model (VetPop) provides the official Veteran population projections from the Department of Veterans Affairs (VA). VetPop is a deterministic projection model developed by the VA's National Center for Veterans Analysis and Statistics to estimate and project the Veteran Population by key demographic characteristics such as age, sex, period of service, and race/ethnicity at various geographic levels for the next 30 years. VetPop is produced every 2 or 3 years and the latest model, VetPop2023, uses the best available Veteran data at the end of Fiscal Year (FY) 2023 as the base population and projects the Veteran population from FY2024 to FY2053. See VetPop2023: A Brief Description for information on the methodology and data sources used in VetPop2023.