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opencc-by-4.0Sep 2024View details →

High-resolution analysis of power plant land requirements for GODEEEP

This dataset contains data associated with Mongird et al. (under review). Files include output from the following three analyses found in the paper: (1) Projected power plant siting intersections with US Disadvantaged Communities (DACs), important farmland, and natural areas; (2) onshore wind and solar photovoltaic capacity factor availability under 27 different siting restriction cases, and (3) output from an analysis that determines how many DACs are projected to see both fossil fuel generation retirement and new renewable power plant development. Each of the files associated with these components are described below.  For more detailed information please refer to Mongird et al. (under review), "High-resolution analysis of power plant land requirements for the evolving Western United States power grid indicates coordinated land use policies will be essential" Outputs included in this dataset are associated with two different scenarios. Summaries of each of the two scenarios included are provided below. For additional information, see <a href="https://doi.org/10.1016/j.egycc.2023.100117">Ou et al. 2023.</a> <h2>Scenario Descriptions</h2> <ul> <li>business-as-usual: <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does include the US Inflation Reduction Act (IRA) incentives.</li> <li>It assumes that CCS technologies are available.</li> </ul> </li> <li>high renewables: <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does include US IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> </ul> </li> </ul> <h2>Data Descriptions</h2> <h3>1. Projected power plant siting intersections</h3> Description These files identify the intersection of projected power plant locations with three types of land: federall identified disadvantaged communities (DACs), important farmland, and land in close proximity to natural areas. Scenario Files: <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>bau_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with federally identified US DACs by technology type and Western US state</td> </tr> <tr> <td>bau_env_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state</td> </tr> <tr> <td>bau_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with important farmland by technology type and Western US state</td> </tr> <tr> <td>hr_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with federally identified US DACs by technology type and Western US state</td> </tr> <tr> <td>hr_env_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state</td> </tr> <tr> <td>hr_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with important farmland by technology type and Western US state</td> </tr> </tbody> </table>   Data Dictionary: <table> <tbody> <tr> <td>Column</td> <td>Description</td> <td>Units</td> </tr> <tr> <td>state</td> <td>Name of US state</td> <td>N/A</td> </tr> <tr> <td>technology</td> <td>Power plant technology type inclusive of turbine type, presence of CCS, and cooling type (as applicable)</td> <td>N/A</td> </tr> <tr> <td>technology_simple</td> <td>Power plant technology type excluding turbine type, presence of CCS, and cooling type (as applicable)</td> <td>N/A</td> </tr> <tr> <td>layer_name</td> <td>Descriptive name of geospatial raster layer used for intersection analysis</td> <td>N/A</td> </tr> <tr> <td>layer</td> <td>Name of geospatial raster layer used for intersection analysis</td> <td>N/A</td> </tr> <tr> <td>total_plants</td> <td>Number of projected power plants of specified technology in specified state under given scenario</td> <td>#</td> </tr> <tr> <td>intersection</td> <td>Number of projected power plant intersections of specified technology in specified state with given layer under given scenario </td> <td>#</td> </tr> <tr> <td>fraction</td> <td>ratio of intersection and total_plants</td> <td>fraction</td> </tr> </tbody> </table>   Scenario Difference Analysis Files: <table> <tbody> <tr> <td>difference_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with federally identified US DACs by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> <tr> <td>difference_env_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> <tr> <td>difference_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with important farmland by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> </tbody> </table>   Data Dictionary: <table style="width: 85.255198%; height: 152px;"> <tbody> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">Column</td> <td style="width: 82.845413%; height: 19px;">Description</td> <td style="width: 4.029241%; height: 19px;">Units</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">state</td> <td style="width: 82.845413%; height: 19px;">Name of US state</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">technology</td> <td style="width: 82.845413%; height: 19px;">Power plant technology type</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">layer</td> <td style="width: 82.845413%; height: 19px;">Name of geospatial raster layer used for intersection analysis</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">hr</td> <td style="width: 82.845413%; height: 19px;">Number of projected power plant intersections with given layer under the high renewables scenario</td> <td style="width: 4.029241%; height: 19px;">#</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">bau</td> <td style="width: 82.845413%; height: 19px;">Number of projected power plant intersections with given layer under the business-as-usual scenario</td> <td style="width: 4.029241%; height: 19px;">#</td> </tr> <tr style="height: 38px;"> <td style="width: 8.458634%; height: 38px;">intersection</td> <td style="width: 82.845413%; height: 38px;">Difference in projected power plant intersections between the high renewables scenario and the business-as-usual scenario</td> <td style="width: 4.029241%; height: 38px;">#</td> </tr> </tbody> </table> <h3> </h3> <h3>2. Projected onshore wind and solar photovoltaic capacity factor availability under 27 siting restriction cases</h3> Description: This file contains results from an analysis on the capability of reaching high renewables scenario solar and wind generation in 2050 under 27 different siting restriction cases. Relevant File: <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>capacity_factor_analysis_2050.csv</td> <td>Amount of solar PV or onshore wind generation projected to be available in a given state under a specified siting restriction case </td> </tr> </tbody> </table> Data Dictionary: <table> <tbody> <tr> <td>Column</td> <td>Description</td> <td>Units</td> </tr> <tr> <td>region_name</td> <td>Name of US state</td> <td>N/A</td> </tr> <tr> <td>technology</td> <td>Power plant technology type (either solar PV or Wind)</td> <td>N/A</td> </tr> <tr> <td>capacity_density_mw</td> <td>Assumed MW per square-km</td> <td>MW</td> </tr> <tr> <td>case</td> <td>Name of siting exclusion case</td> <td>N/A</td> </tr> <tr> <td>total_generation_mwh</td> <td>Projected total generation available given remaining available land after exclusions</td> <td>MWh</td> </tr> <tr> <td>target_generation_mwh</td> <td>Projected target annual generation in 2050 for technology type under high renewables scenario</td> <td>MWh</td> </tr> <tr> <td>gcam_trading_region</td> <td>Name of zonal representation of electricity trading regions as defined in the capacity expansion model</td> <td>N/A</td> </tr> </tbody> </table> <h3> </h3> <h3>3.  US DACs that see both fossil fuel generation retirement and new renewable power plant development by 2050</h3> Description: This data contains US census tract GEOIDs that see both new renewable sitings and the retirement of fossil generating resources. Relevant File: <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>dac_fossil_retire_analysis_2050.csv</td> <td>List of US census tracts that see both fossil fuel generation retirement and new renewable generation siting by 2050 </td> </tr> </tbody> </table> Data Dictionary: <table> <tbody> <tr> <td>Column</td> <td>Description</td> </tr> <tr> <td> census_tract</td> <td> US census tract GEOID</td> </tr> <tr> <td>state_name</td> <td>Name of US state</td> </tr> <tr> <td>county_name</td> <td>Name of US county</td> </tr> <tr> <td>scenario</td> <td>scenario name</td> </tr> </tbody> </table>   <h2>Funding statement</h2> This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.   <h2>Changelog</h2> v1.1  The following updates were made following manuscript revision: <ul> <li>"power_density_mw" variable name in `capacity_factor_analysis_2050.csv` file changed to "capacity_density_mw"</li> <li>More estimates are provided in `capacity_factor_analysis_2050.csv` reflecting additional capacity density and turbine hub height assumptions.</li> <li>Scenario naming adjusted to align with manuscript naming</li> </ul>

opencc-by-4.0Mar 2018View details →

Microsat Data for 'Simulated Disperser Analysis: determining the number of loci required to genetically identify dispersers'

Microsattelite data from 94 samples (Stunus vulgaris) from 3 populations and including 29 loci. Used in the paper 'Simulated Disperser Analysis: determining the number of loci required to genetically identify dispersers'.

opencc-by-4.0Jun 2022View details →

Towards a Data-Driven Requirements Engineering Approach: Automatic Analysis of User Reviews

6000 French user reviews from three applications on Google Play (Garmin Connect, Huawei Health, Samsung Health) are labelled manually. We selected four labels: rating, bug report, feature request and user experience. <ul> <li>Ratings are simple text which express the overall evaluation to that app, including praise, criticism, or dissuasion.</li> <li>Bug reports show the problems that users have met while using the app, like loss of data, crash of app, connection error, etc.</li> <li>Feature requests reflect the demande of users on new function, new content, new interface, etc.</li> <li>In user experience, users describe their experience in relation to the functionality of the app, how does certain functions be helpful.</li> </ul> As we can observe from the following table, that shows examples of labelled user reviews, each review belongs to one or more categories. <table> <tbody> <tr> <th>App</th> <th>Total</th> <th>Rating</th> <th>Bug report</th> <th>Feature request</th> <th>User experience</th> </tr> </tbody> <tbody> <tr> <td>Garmin Connect</td> <td>2000</td> <td>1260</td> <td>757</td> <td>170</td> <td>493</td> </tr> <tr> <td>Huawei Health</td> <td>2000</td> <td>1068</td> <td>819</td> <td>384</td> <td>289</td> </tr> <tr> <td>Samsung Health</td> <td>2000</td> <td>1324</td> <td>491</td> <td>486</td> <td>349</td> </tr> </tbody> </table>   <h2>New Dataset</h2> Based on this dataset, we developed a labeled dataset containing 6,000 English and 6,000 French reviews for classification, as well as 1,200 bilingual reviews for clustering. The new dataset has been made publicly available on Zenodo at the following link: <a href="../records/11066414">https://zenodo.org/records/11066414</a>

opencc-by-4.0Nov 2022View details →

Annotation-based Modeling of Non-functional Requirements and Analysis Results in Domain-driven Design

This repo contains all supplementary data sets that we have created and used throughout this thesis. In particular, it contains - expert interview material (elicitation): consent form and question catalogue for requirements elicitation - expert interview material (evaluation): consent form and task description for expert evaluation - Diagrams related to our modeling concept and Dqualizer - Screenshots of the Domain Story Modeler with our Modeling Concept

opencc-by-4.0Dec 2023View details →

Photoactivation of the Orange Carotenoid Protein Requires Two Light-Driven Reactions Mediated by a Metastable Monomeric Intermediate – Absorption Spectra and Global Analysis Results, Molecular Dynamics Simulations

Time-resolved absorption and molecular dynamics trajectory datasets associated with: Rose, J. B.; Gascón, J. A.; Sutter, M.; Sheppard, D. I.; Kerfeld, C. A.; Beck, W. F. Photoactivation of the Orange Carotenoid Protein Requires Two Light-Driven Reactions Mediated by a Metastable Monomeric Intermediate. Phys. Chem. Chem. Phys. 2023, DOI: 10.1039/d3cp04484j.

opencc-by-4.0Apr 2024View details →

INTELLIMAN_WP2_Application Requirements and Integration_T2.4_Fresh food handling use case analysis, integration and validation_Apple 6D pose estimation dataset_v0

The dataset contains the data generated for the training of the 6D pose estimation neural network DOPE related to the publication: M. Costanzo, M. De Simone, S. Federico, C. Natale and S. Pirozzi, "Enhanced 6D Pose Estimation for Robotic Fruit Picking," 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT), Rome, Italy, 2023, pp. 901-906, doi: 10.1109/CoDIT58514.2023.10284072.

opencc-by-4.0May 2021View details →

Supplementary Material on "Early timing analysis based on scenario requirements and platform models"

This dataset provides supplementary material on the submission “Early timing analysis based on scenario requirements and platform models” to the SoSyM theme issue on Model-Driven Requirements Engineering. It provides software and models for illustrating the paper's example application results as well as more detailed evaluation data. MSD-CCSL-TimingAnalysis.zip contains our approach and encompasses the following artifacts (Java 8 and not later required; if needed modify the GemocStudio.ini and point the vm to a corresponding Java version via "-vm <PathToJava8>\jre\bin"): <ul> <li>Development workspace: <ul> <li>ECL specification under /de.fraunhofer.iem.swt.msd.tam.dse/ecl/MSDLanguage.ecl</li> <li>MoCCML constraints under /de.fraunhofer.iem.swt.msd.tam.mocc/mocc/MSDLanguageComplete.moccml</li> <li>TAM profile under /de.fraunhofer.iem.swt.msd.tam.tamProfile/model/tam.profile.uml</li> </ul> </li> <li>Runtime workspace: <ul> <li>Models under "01_ExampleModels"</li> <li>Exemplary traces under "02_ExampleTraces"</li> <li>QVT-O Transformations (e.g., Preprocessing) needed when modifying the models</li> </ul> </li> </ul> Papyrus-CCSLEditor-Measurement.zip contains the plugins and artifacts that we used for measuring the particular modeling operations for the evaluation of the hypothesis H2 (see further documents below). It requires Java 11; if needed modify the eclipse.ini and point the vm to a corresponding Java version via "-vm <PathToJava11>\jre\bin". Contained plugins and artifacts: <ul> <li>Development workspace: <ul> <li>is.ru.cs.PapyrusActivityLogger: Our adapted version of ModRec, particularly extended by an Xtext document listener</li> <li>org.eclipse.gemoc.moccml.*: MoCCML editor prerequisites for the CCSL runtime model</li> <li>org.scenariotools.msd.profile and de.fraunhofer.iem.swt.msd.tam.tamProfile: Profiles that we partially use in the Papyrus runtime model</li> </ul> </li> <li>Runtime workspace: <ul> <li>CCSL Measuring Project: Measuring project for CCSL models</li> <li>Papyrus Measuring Project: Measuring project for Papyrus models</li> </ul> </li> </ul> Further documents: <ul> <li>MSD-CCSL-TimingAnalysisTutorial.pdf: Tutorial on starting the simulative timing analysis</li> <li>EvaluationData_H1_TimingEffectTestResults: Test results for the particular timing effects based on several models for hypothesis H1</li> <li>Files for hypothesis H2: <ul> <li>EvaluationData_H2.xlsx: Spreadsheet containing the particular model element amounts of MSD-spec-1--4 and CCSL-model-1--4, the measurements for the categorized atomic model operation kinds, the multiplication scheme for predicting the raw overall effort, and the measured transformation execution times</li> <li>EvaluationData_H2_MSD-specification-effort.pdf: PDF extract of the spreadsheet contents for the MSD specification effort and computation</li> <li>EvaluationData_H2_CCSL-model-effort.pdf: PDF extract of the spreadsheet contents for the CCSL model effort and computation</li> <li>EvaluationData_H2_transformationExecTimes.pdf: PDF extract of the spreadsheet contents for the transformation execution times</li> <li>EvaluationData_H2_MSD-specification_measurement-timestamps.txt: Raw timestamp logs for the conducted measurements for model operations on MSD specifications</li> <li>EvaluationData_H2_CCSL-model_measurement-timestamps.txt: Raw timestamp logs for the conducted measurements for model operations on CCSL models</li> </ul> </li> </ul>

opencc-by-4.0Jul 2013View details →

SatTerm experience: vocabulary control and facet analysis help improve the software requirement solicitation process

One of the most difficult steps in the software development process is moving from requirements written in natural, uncontrolled language, to the formalisms required by the design modelling languages. To solve this issue, practitioners should pay attention to the possibility of applying vocabulary control and knowledge representation techniques to produce better specifications. The use of controlled vocabularies and the modelling of the conceptual relationships between concepts in a specific domain are expected to improve the quality of the specifications. Vocabulary control and semantic modelling are promising tools to avoid the most frequent problems in the requirements specification process: lack of consistency and ambiguity. This paper provides a detailed description of the development process of an ontology used for requirements modelling in the area of satellite control systems. The process applied is based on well-established practices and guidelines applied for the construction of controlled vocabularies and faceted classifications schemas. Engineers can use the ontology when writing system specifications using predefined templates. The use of this ontology ensures the consistency of the specifications written by different engineers improves the communication with other parties involved in the system construction activities and sets the foundations for a semi-automated generation of models for subsequent design activities.

opencc-by-4.0May 2020View details →

Cloud-native Deploy-ability: An Analysis of Required Features of Deployment Technologies to Deploy Arbitrary Cloud-native Applications

Deployment modeling example used in the paper.

opencc-by-4.0Nov 2023View details →

D6.1 Review, analysis and crosswalk of the data requirements of key policies

Open the record for dataset details and reuse information.

opencc-by-4.0Feb 2022View details →

Data Set of Thesis on Architectural Data Flow Analysis for Detecting Violations of Confidentiality Requirements

The data set contains the results of the validation, the docker image for conducting the validation and the source code of all developed projects to conduct the validation.

opencc-by-4.0Apr 2022View details →

Replication Package - How Do Requirements Evolve During Elicitation? An Empirical Study Combining Interviews and App Store Analysis

This is the replication package for the paper titled "How Do Requirements Evolve During Elicitation? An Empirical Study Combining Interviews and App Store Analysis", by Alessio Ferrari, Paola Spoletini and Sourav Debnath.   The package contains the following folders and files.    **/Experiment Material** This folder contains the material used for the experiment, and provided to the participants. In particular, it includes the following files:   - Happy CampingTM_briefdescription.pdf/docx: brief description of the product for which requirements need to be elicited - Hw_description.pdf/docx: desciption of the tasks to be performed by the participants - Modeling_Intro_Slides.pdf: introductory slides to modelling for requirements engineering - Self-assessment Questionnaire.pdf: first questionnaire to self-assess the mistakes, from the SaPeer method (https://doi.org/10.1007/s00766-020-00334-0)  - Self-assessment Questionnaire (Second Interview).pdf: second questionnare to self-assess the mistakes, from the Sapeer method   **/R-analysis**   This is a folder containing all the R implementations of the the statistical tests included in the paper, together with the source .csv file used to produce the results. Each R file has the same title as the associated .csv file. The titles of the files reflect the RQs as they appear in the paper. The association between R files and Tables in the paper is as follows:   - RQ1-1-analyse-story-rates.R: Tabe 1, user story rates  - RQ1-1-analyse-role-rates.R: Table 1, role rates - RQ1-2-analyse-story-category-phase-1.R: Table 3, user story category rates in phase 1 compared to original rates - RQ1-2-analyse-role-category-phase-1.R: Table 5, role category rates in phase 1 compared to original rates - RQ2.1-analysis-app-store-rates-phase-2.R: Table 8, user story and role rates in phase 2 - RQ2.2-analysis-percent-three-CAT-groups-ph1-ph2.R: Table 9, comparison of the categories of user stories in phase 1 and 2 - RQ2.2-analysis-percent-two-CAT-roles-ph1-ph2.R: Table 10, comparison of the categories of roles in phase 1 and 2.     The .csv files used for statistical tests are also used to produce boxplots. The association betwee boxplot figures and files is as follows.    - RQ1-1-story-rates.csv: Figure 4  - RQ1-1-role-rates.csv: Figure 5 - RQ1-2-categories-phase-1.csv: Figure 8 - RQ1-2-role-category-phase-1.csv: Figure 9 - RQ2-1-user-story-and-roles-phase-2.csv: Figure 13 - RQ2.2-percent-three-CAT-groups-ph1-ph2.csv: Figure 14 - RQ2.2-percent-two-CAT-roles-ph1-ph2.csv: Figure 17 - IMG-only-RQ2.2-us-category-comparison-ph1-ph2.csv: Figure 15 - IMG-only-RQ2.2-frequent-roles.csv: Figure 18   NOTE: The last two .csv files do not have an associated statistical tests, but are used solely to produce boxplots.   **/Data-Analysis**   This folder contains all the data used to answer the research questions.    **RQ1.xlsx**: includes all the data associated to RQ1 subquestions, two tabs for each subquestion (one for user stories and one for roles). The names of the tabs are self-explanatory of their content.   **RQ2.1.xlsx**: includes all the data for the RQ1.1 subquestion. Specifically, it includes the following tabs:   * Data Source-US-category: for each category of user story, and for each analyst, there are two lines.  The first one reports the number of user stories in that category for phase 1, and the second one reports the number of user stories in that category for phase 2, considering the specific analyst.    * Data Source-role: for each category of role, and for each analyst, there are two lines.  The first one reports the number of user stories in that role for phase 1, and the second one reports the number of user stories in that role for phase 2, considering the specific analyst.    * RQ2.1 rates: reports the final rates for RQ2.1.  NOTE: The other tabs are used to support the computation of the final rates.   **RQ2.2.xlsx**: includes all the data for the RQ2.2 subquestion. Specifically, it includes the following tabs:   * Data Source-US-category: same as RQ2.1.xlsx   * Data Source-role: same as RQ2.1.xlsx   * RQ2.2-category-group: comparison between groups of categories in the different phases, used to produce Figure 14   * RQ2.2-role-group: comparison between role groups in the different phases, used to produce Figure 17   * RQ2.2-specific-roles-diff: difference between specific roles, used to produce Figure 18   **NOTE:** the other tabs are used to support the computation of the values reported in the tabs above.    **RQ2.2-single-US-category.xlsx**: includes the data for the RQ2.2 subquestion associated to single categories of user stories. A separate tab is used given the complexity of the computations.    * Data Source-US-category: same as RQ2.1.xlsx   * Totals: total number of user stories for each analyst in phase 1 and phase 2   * Results-Rate-Comparison: difference between rates of user stories in phase 1 and phase 2, used to produce the file "img/IMG-only-RQ2.2-us-category-comparison-ph1-ph2.csv", which is in turn used to produce Figure 15   * Results-Analysts: number of analysts using each novel category produced in phase 2, used to produce Figure 16. NOTE: the other tabs are used to support the computation of the values reported in the tabs above.    **RQ2.3.xlsx**: includes the data for the RQ2.3 subquestion. Specifically, it includes the following tabs:   * Data Source-US-category: same as RQ2.1.xlsx   * Data Source-role: same as RQ2.1.xlsx   * RQ2.3-categories: novel categories produced in phase 2, used to produce Figure 19   * RQ2-3-most-frequent-categories: most frequent novel categories   **/Raw-Data-Phase-I** The folder contains one Excel file for each analyst, s1.xlsx...s30.xlsx, plus the file of the original user stories with annotations (original-us.xlsx). Each file contains two tabs:   - Evaluation: includes the annotation of the user stories as existing user story in the original categories (annotated with "E"), novel user story in a certain category (refinement, annotated with "N"), and novel user story in novel category (Name of the category in column "New Feature"). **NOTE 1:** It should be noticed that in the paper the case "refinement" is said to be annotated with "R" (instead of "N", as in the files) to make the paper clearer and easy to read.    - Roles: roles used in the user stories, and count of the user stories belonging to a certain role.   **/Raw-Data-Phaes-II** The folder contains one Excel file for each analyst, s1.xlsx...s30.xlsx. Each file contains two tabs:   - Analysis: includes the annotation of the user stories as belonging to existing original  category (X), or to categories introduced after interviews, or to categories introduced  after app store inspired elicitation (name of category in "Cat. Created in PH1"), or to  entirely novel categories (name of category in "New Category").   - Roles: roles used in the user stories, and count of the user stories belonging to a certain role.   **/Figures**   This folder includes the figures reported in the paper. The boxplots are generated from the  data using the tool http://shiny.chemgrid.org/boxplotr/. The histograms and other plots are  produced with Excel, and are also reported in the excel files listed above.