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

Shape Index This data is part of the Global Ensemble Digital Terrain Model (GEDTM30) dataset. Check the related identifiers section below to access other parts of the dataset. Disclaimer This is the first release of the Multiscale Land Surface Parameters (LSPs) of Global Ensemble Digital Terrain Model (GEDTM30). Use for testing purposes only. This work was funded by the European Union. However, the views and opinions expressed are solely those of the author(s) and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is." The Open-Earth-Monitor project consortium, along with its suppliers and licensors, hereby disclaims all warranties of any kind, express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and non-infringement. Neither the Open-Earth-Monitor project consortium nor its suppliers and licensors make any warranty that the website will be error-free or that access to it will be continuous or uninterrupted. You understand that you download or otherwise obtain content or services from the website at your own discretion and risk. Description LSPs are derivative products of the GEDTM30 that represent measures of local topographic position, curvature, hydrology, light, and shadow. A pyramid representation is implemented to generate multiscale resolutions of 30m, 60m, 120m, 240m, 480m, and 960m for each LSP. The parametrization is powered by Whitebox Workflows in Python. To see the documentation, please visit our GEDTM30 GitHub (https://github.com/openlandmap/GEDTM30). Dataset Contents This dataset includes: Global Shape Index 120m Global Shape Index 240m Global Shape Index 480m Global Shape Index 960m Due to Zenodo's storage limitations, the high resolution LSP data are provided via external links: Global Shape Index 30m Global Shape Index 60m Related Identifiers Digital Terrain Model: GEDTM30 Landform: Slope in Degree, Geomorphons Light and Shadow: Positive Openness, Negative Openness, Hillshade Curvature: Minimal Curvature, Maximal Curvature, Profile Curvature, Tangential Curvature, Ring Curvature, Shape Index Local Topographic Position: Difference from Mean Elevation, Spherical Standard Deviation of the Normals Hydrology: Specific Catchment Area, LS Factor, Topographic Wetness Index Data Details Time period: static. Type of data: properties derived from Digital Terrain Model How the data was collected or derived: The data was derived using Whitbox Workflows. Methods used: LSP algorithms. Limitations or exclusions in the data: The dataset does not include data Antarctica. Coordinate reference system: EPSG:4326 Bounding box (Xmin, Ymin, Xmax, Ymax): (-180, -65, 180, 85) Spatial resolution: 120m, 240m, 480m, 960m Image size: 360,000P x 178,219L; 180,000P x 89,110L; 45,000L x 22,282L File format: Cloud Optimized Geotiff (COG) format. Additional information: Layer Scale Data Type No Data Difference from Mean Elevation 100 Int16 32,767 Geomorphons 1 Byte 255 Hillshade 1 UInt16 65,535 LS Factor 1,000 UInt16 65,535 Maximal Curvature 1,000 Int16 32,767 Minimal Curvature 1,000 Int16 32,767 Negative Openness 100 UInt16 65,535 Positive Openness 100 UInt16 65,535 Profile Curvature 1,000 Int16 32,767 Ring Curvature 10,000 Int16 32,767 Shape Index 1,000 Int16 32,767 Slope in Degree 100 UInt16 65,535 Specific Catchment Area 1,000 UInt16 65,535 Spherical Standard Deviation of the Normals 100 Int16 32,767 Tangential Curvature 1,000 Int16 32,767 Topographic Wetness Index 100 Int16 32,767 Support If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue here Naming convention To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, for twi_edtm_m_120m_s_20000101_20221231_go_epsg.4326_v20241230.tif, the fields are: generic variable name: twi = topographic wetness index variable procedure combination: edtm = derivative direct from global ensemble digital terrain model Position in the probability distribution/variable type: m = measurement Spatial support: 120m Depth reference: s = surface Time reference begin time: 20000101 = 2000-01-01 Time reference end time: 20211231 = 2021-12-31 Bounding box: go = global EPSG code: EPSG:4326 Version code: v20241230 = version from 2024-12-30

Negative Openness This data is part of the Global Ensemble Digital Terrain Model (GEDTM30) dataset. Check the related identifiers section below to access other parts of the dataset. Disclaimer This is the first release of the Multiscale Land Surface Parameters (LSPs) of Global Ensemble Digital Terrain Model (GEDTM30). Use for testing purposes only. This work was funded by the European Union. However, the views and opinions expressed are solely those of the author(s) and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is." The Open-Earth-Monitor project consortium, along with its suppliers and licensors, hereby disclaims all warranties of any kind, express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and non-infringement. Neither the Open-Earth-Monitor project consortium nor its suppliers and licensors make any warranty that the website will be error-free or that access to it will be continuous or uninterrupted. You understand that you download or otherwise obtain content or services from the website at your own discretion and risk. Description LSPs are derivative products of the GEDTM30 that represent measures of local topographic position, curvature, hydrology, light, and shadow. A pyramid representation is implemented to generate multiscale resolutions of 30m, 60m, 120m, 240m, 480m, and 960m for each LSP. The parametrization is powered by Whitebox Workflows in Python. To see the documentation, please visit our GEDTM30 GitHub (https://github.com/openlandmap/GEDTM30). Dataset Contents This dataset includes: Global Negative Openness 120m Global Negative Openness 240m Global Negative Openness 480m Global Negative Openness 960m Due to Zenodo's storage limitations, the high resolution LSP data are provided via external links: Global Negative Openness 30m Global Negative Openness 60m Related Identifiers Digital Terrain Model: GEDTM30 Landform: Slope in Degree, Geomorphons Light and Shadow: Positive Openness, Negative Openness, Hillshade Curvature: Minimal Curvature, Maximal Curvature, Profile Curvature, Tangential Curvature, Ring Curvature, Shape Index Local Topographic Position: Difference from Mean Elevation, Spherical Standard Deviation of the Normals Hydrology: Specific Catchment Area, LS Factor, Topographic Wetness Index Data Details Time period: static. Type of data: properties derived from Digital Terrain Model How the data was collected or derived: The data was derived using Whitbox Workflows. Methods used: LSP algorithms. Limitations or exclusions in the data: The dataset does not include data Antarctica. Coordinate reference system: EPSG:4326 Bounding box (Xmin, Ymin, Xmax, Ymax): (-180, -65, 180, 85) Spatial resolution: 120m, 240m, 480m, 960m Image size: 360,000P x 178,219L; 180,000P x 89,110L; 45,000L x 22,282L File format: Cloud Optimized Geotiff (COG) format. Additional information: Layer Scale Data Type No Data Difference from Mean Elevation 100 Int16 32,767 Geomorphons 1 Byte 255 Hillshade 1 UInt16 65,535 LS Factor 1,000 UInt16 65,535 Maximal Curvature 1,000 Int16 32,767 Minimal Curvature 1,000 Int16 32,767 Negative Openness 100 UInt16 65,535 Positive Openness 100 UInt16 65,535 Profile Curvature 1,000 Int16 32,767 Ring Curvature 10,000 Int16 32,767 Shape Index 1,000 Int16 32,767 Slope in Degree 100 UInt16 65,535 Specific Catchment Area 1,000 UInt16 65,535 Spherical Standard Deviation of the Normals 100 Int16 32,767 Tangential Curvature 1,000 Int16 32,767 Topographic Wetness Index 100 Int16 32,767 Support If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue here Naming convention To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, for twi_edtm_m_120m_s_20000101_20221231_go_epsg.4326_v20241230.tif, the fields are: generic variable name: twi = topographic wetness index variable procedure combination: edtm = derivative direct from global ensemble digital terrain model Position in the probability distribution/variable type: m = measurement Spatial support: 120m Depth reference: s = surface Time reference begin time: 20000101 = 2000-01-01 Time reference end time: 20211231 = 2021-12-31 Bounding box: go = global EPSG code: EPSG:4326 Version code: v20241230 = version from 2024-12-30

Ring Curvature This data is part of the Global Ensemble Digital Terrain Model (GEDTM30) dataset. Check the related identifiers section below to access other parts of the dataset. Disclaimer This is the first release of the Multiscale Land Surface Parameters (LSPs) of Global Ensemble Digital Terrain Model (GEDTM30). Use for testing purposes only. This work was funded by the European Union. However, the views and opinions expressed are solely those of the author(s) and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is." The Open-Earth-Monitor project consortium, along with its suppliers and licensors, hereby disclaims all warranties of any kind, express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and non-infringement. Neither the Open-Earth-Monitor project consortium nor its suppliers and licensors make any warranty that the website will be error-free or that access to it will be continuous or uninterrupted. You understand that you download or otherwise obtain content or services from the website at your own discretion and risk. Description LSPs are derivative products of the GEDTM30 that represent measures of local topographic position, curvature, hydrology, light, and shadow. A pyramid representation is implemented to generate multiscale resolutions of 30m, 60m, 120m, 240m, 480m, and 960m for each LSP. The parametrization is powered by Whitebox Workflows in Python. To see the documentation, please visit our GEDTM30 GitHub (https://github.com/openlandmap/GEDTM30). Dataset Contents This dataset includes: Global Ring Curvature 120m Global Ring Curvature 240m Global Ring Curvature 480m Global Ring Curvature 960m Due to Zenodo's storage limitations, the high resolution LSP data are provided via external links: Global Ring Curvature 30m Global Ring Curvature 60m Related Identifiers Digital Terrain Model: GEDTM30 Landform: Slope in Degree, Geomorphons Light and Shadow: Positive Openness, Negative Openness, Hillshade Curvature: Minimal Curvature, Maximal Curvature, Profile Curvature, Tangential Curvature, Ring Curvature, Shape Index Local Topographic Position: Difference from Mean Elevation, Spherical Standard Deviation of the Normals Hydrology: Specific Catchment Area, LS Factor, Topographic Wetness Index Data Details Time period: static. Type of data: properties derived from Digital Terrain Model How the data was collected or derived: The data was derived using Whitbox Workflows. Methods used: LSP algorithms. Limitations or exclusions in the data: The dataset does not include data Antarctica. Coordinate reference system: EPSG:4326 Bounding box (Xmin, Ymin, Xmax, Ymax): (-180, -65, 180, 85) Spatial resolution: 120m, 240m, 480m, 960m Image size: 360,000P x 178,219L; 180,000P x 89,110L; 45,000L x 22,282L File format: Cloud Optimized Geotiff (COG) format. Additional information: Layer Scale Data Type No Data Difference from Mean Elevation 100 Int16 32,767 Geomorphons 1 Byte 255 Hillshade 1 UInt16 65,535 LS Factor 1,000 UInt16 65,535 Maximal Curvature 1,000 Int16 32,767 Minimal Curvature 1,000 Int16 32,767 Negative Openness 100 UInt16 65,535 Positive Openness 100 UInt16 65,535 Profile Curvature 1,000 Int16 32,767 Ring Curvature 10,000 Int16 32,767 Shape Index 1,000 Int16 32,767 Slope in Degree 100 UInt16 65,535 Specific Catchment Area 1,000 UInt16 65,535 Spherical Standard Deviation of the Normals 100 Int16 32,767 Tangential Curvature 1,000 Int16 32,767 Topographic Wetness Index 100 Int16 32,767 Support If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue here Naming convention To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, for twi_edtm_m_120m_s_20000101_20221231_go_epsg.4326_v20241230.tif, the fields are: generic variable name: twi = topographic wetness index variable procedure combination: edtm = derivative direct from global ensemble digital terrain model Position in the probability distribution/variable type: m = measurement Spatial support: 120m Depth reference: s = surface Time reference begin time: 20000101 = 2000-01-01 Time reference end time: 20211231 = 2021-12-31 Bounding box: go = global EPSG code: EPSG:4326 Version code: v20241230 = version from 2024-12-30

The Land use of Australia 2010-11 is the latest in a series of digital national land use maps at national scale. Agricultural land uses and their spatial distributions are based on the Australian Bureau of Statistics' 2010-11 agricultural census data. The spatial distribution of the agricultural land uses is modelled and was determined using Advanced Very High Resolution Radiometer (AVHRR) satellite imagery with training data to make agricultural land use allocations. The non-agricultural land uses are drawn from existing digital maps covering seven themes: topographic features, catchment scale land use, protected areas, World Heritage Areas, tenure, forest type and vegetation condition. \r \r The Land use of Australia 2010-11 is a product of the Australian Collaborative Land Use and Management Program (ACLUMP). ACLUMP, coordinated by ABARES, is a collaborative cross-government approach producing land use mapping products for Australia underpinned by common technical standards. The Department of Agriculture and Water Resources is acknowledged for its financial support of the Land use of Australia 2010-11.\r \r The Land Use of Australia datasets are recognised as Foundation Spatial Data by the Australia New Zealand Land Information Council and as an Essential Statistical Asset for Australia by the Australian Bureau of Statistics. Common applications of the datasets are in strategic planning and continental modelling.\r