cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14783323
title: Assessing Software Evolution with the Stickiness Score: Evaluating Code Persistence Across Files, Folders, and Developers- Replication Package

description: This package contains data and source code for the Devotion backend, frontend, and hypothesis testing. It is organized into two main folders:



Devotion: Contains the source code for both the frontend and backend of Devotion.

Hypothesis_Testing: Includes the preprocess-input folder, where Devotion’s output files are processed for hypothesis testing. The tokens folder requires a Git token. Other subfolders contain data, source code for hypothesis testing, test results, and plots corresponding to research questions.


Below are the instructions for running the Python scripts.



Instructions




Prerequisites


Before running the backend, install these dependencies using the following command:



pip install -r requirements.txt

 




Running the Backend


To run the backend of Devotion, navigate to the Devotion folder and use the following command:



python ./Devotion/runner.py <GitHub_Repo_URL> <Log_File_Name>

 




Running the Web Application


Before starting the web application, ensure all dependencies are installed by running:



npm install

 


To start the web application of Devotion, navigate to the client folder and run:



cd ./Devotion/frontend/client
npm run start

 




Running the Hypothesis Testing Code






Write your GitHub token:



Save your GitHub token in the Hypothesis_Testing/tokens/github-token.txt file to allow the scripts to access the GitHub API.





Place the required files:



Copy authors.json and folder_structure.json from the Devotion/frontend/client/src folder into the Hypothesis_Testing/preprocess-input folder, inside a new folder named after your project.





Execute the hypothesis testing for each research question:



First, run the Python script located in the preprocess folder.

Then, run the Python script in the hypothesis folder.





Output:



Results, including data points, plots, and the outcomes of normality and correlation tests, will be saved in the hypothesis-output folder.
DOI: 10.5281/zenodo.14783324
title: Assessing Software Evolution with the Stickiness Score: Evaluating Code Persistence Across Files, Folders, and Developers- Replication Package

description: This package contains data and source code for the Devotion backend, frontend, and hypothesis testing. It is organized into two main folders:



Devotion: Contains the source code for both the frontend and backend of Devotion.

Hypothesis_Testing: Includes the preprocess-input folder, where Devotion’s output files are processed for hypothesis testing. The tokens folder requires a Git token. Other subfolders contain data, source code for hypothesis testing, test results, and plots corresponding to research questions.


Below are the instructions for running the Python scripts.



Instructions




Prerequisites


Before running the backend, install these dependencies using the following command:



pip install -r requirements.txt

 




Running the Backend


To run the backend of Devotion, navigate to the Devotion folder and use the following command:



python ./Devotion/runner.py <GitHub_Repo_URL> <Log_File_Name>

 




Running the Web Application


Before starting the web application, ensure all dependencies are installed by running:



npm install

 


To start the web application of Devotion, navigate to the client folder and run:



cd ./Devotion/frontend/client
npm run start

 




Running the Hypothesis Testing Code






Write your GitHub token:



Save your GitHub token in the Hypothesis_Testing/tokens/github-token.txt file to allow the scripts to access the GitHub API.





Place the required files:



Copy authors.json and folder_structure.json from the Devotion/frontend/client/src folder into the Hypothesis_Testing/preprocess-input folder, inside a new folder named after your project.





Execute the hypothesis testing for each research question:



First, run the Python script located in the preprocess folder.

Then, run the Python script in the hypothesis folder.





Output:



Results, including data points, plots, and the outcomes of normality and correlation tests, will be saved in the hypothesis-output folder.
DOI: 10.5281/zenodo.14783148
title: InSAR-derived datasets of ground surface motion for Lisbon (2015-2020), East Madeira (2016-2021) and Belfast (2015-2021)

description: This archive contains InSAR datasets computed as part of the Platform for Atlantic Geohazard Risk Management (AGEO) project, funded by the Interreg Atlantic Area Programme through the European Regional Development Fund (https://ageoatlantic.eu/, Grant No. EAPA_884/2018). The following datasets are available:  



Vertical and horizontal (East-West) components of InSAR-based displacements for the Lisbon area (Portugal). The displacements were generated by using the Persistent Scatterer InSAR approach with Sentinel-1 Ascending (relative orbit: 45) and Sentinel-1 Descending (relative orbit: 125) images between 2014-10-08 and 2021-01-10; 

Line-of Sight (LOS) InSAR-based displacements for the Caniçal and Machico areas of Madeira Island, (Portugal). The displacements were generated by using the Distributed Scatterer InSAR approach (multi-reference) with 162 Sentinel-1 Ascending (relative orbit: 162) and Sentinel-1 Descending (relative orbit: 169) images between 2016-04-20 and 2021-03-19; 

Vertical and horizontal (East-West) InSAR-based displacements for the Belfast area (Nortern Ireland, United Kingdom). The displacements were carried out using the Distributed Scatterer InSAR approach (multi-reference) with Sentinel-1 Ascending (relative orbit: 103) and Sentinel-1 Descending (relative orbit: 125) images between 2015-04-22 and 2021-08-24.


 

The acquisition mode of Sentinel-1 SAR data used to generate all datasets was Interferometric Wide Swath (IW). The displacement unit is mm. Conventional accuracy of measurement is: 5 mm for displacements, 1 mm/yr for displacement rates. 

 

Each dataset is formatted according to the European Ground Motion Service (EGMS) format. They are in .csv (text file) and can be read using any GIS software. The files are compatible with the guidelines provided by the EGMS and the reading function of the EGMStoolkit (https://github.com/alexisInSAR/EGMStoolkit). 

 

The files for the Lisbon dataset are as follows:



AGEO_DataSet_InSAR_1_Lisbon_EW.csv (horizontal); 

AGEO_DataSet_InSAR_1_Lisbon_UD.csv (vertical).


 

The files for the Madeira dataset are as follows:



AGEO_DataSet_InSAR_2_Madeira_LOS_162_Asc.csv (ascending);

AGEO_DataSet_InSAR_2_Madeira_LOS_169_Desc.csv  (descending).


 

The files for the Belfast dataset are as follows: 



AGEO_DataSet_InSAR_3_Belfast_EW.csv (horizontal)

AGEO_DataSet_InSAR_3_Belfast_UD.csv (vertical).


 

The coordinate system is ETRS89-extended / LAEA Europe (code EPSG:3035). Latitude and longitude coordinates are also given with the LOS displacement datasets. 0 and NaN values are used as no-data value.
DOI: 10.5281/zenodo.14783149
title: InSAR-derived datasets of ground surface motion for Lisbon (2015-2020), East Madeira (2016-2021) and Belfast (2015-2021)

description: This archive contains InSAR datasets computed as part of the Platform for Atlantic Geohazard Risk Management (AGEO) project, funded by the Interreg Atlantic Area Programme through the European Regional Development Fund (https://ageoatlantic.eu/, Grant No. EAPA_884/2018). The following datasets are available:  



Vertical and horizontal (East-West) components of InSAR-based displacements for the Lisbon area (Portugal). The displacements were generated by using the Persistent Scatterer InSAR approach with Sentinel-1 Ascending (relative orbit: 45) and Sentinel-1 Descending (relative orbit: 125) images between 2014-10-08 and 2021-01-10; 

Line-of Sight (LOS) InSAR-based displacements for the Caniçal and Machico areas of Madeira Island, (Portugal). The displacements were generated by using the Distributed Scatterer InSAR approach (multi-reference) with 162 Sentinel-1 Ascending (relative orbit: 162) and Sentinel-1 Descending (relative orbit: 169) images between 2016-04-20 and 2021-03-19; 

Vertical and horizontal (East-West) InSAR-based displacements for the Belfast area (Nortern Ireland, United Kingdom). The displacements were carried out using the Distributed Scatterer InSAR approach (multi-reference) with Sentinel-1 Ascending (relative orbit: 103) and Sentinel-1 Descending (relative orbit: 125) images between 2015-04-22 and 2021-08-24.


 

The acquisition mode of Sentinel-1 SAR data used to generate all datasets was Interferometric Wide Swath (IW). The displacement unit is mm. Conventional accuracy of measurement is: 5 mm for displacements, 1 mm/yr for displacement rates. 

 

Each dataset is formatted according to the European Ground Motion Service (EGMS) format. They are in .csv (text file) and can be read using any GIS software. The files are compatible with the guidelines provided by the EGMS and the reading function of the EGMStoolkit (https://github.com/alexisInSAR/EGMStoolkit). 

 

The files for the Lisbon dataset are as follows:



AGEO_DataSet_InSAR_1_Lisbon_EW.csv (horizontal); 

AGEO_DataSet_InSAR_1_Lisbon_UD.csv (vertical).


 

The files for the Madeira dataset are as follows:



AGEO_DataSet_InSAR_2_Madeira_LOS_162_Asc.csv (ascending);

AGEO_DataSet_InSAR_2_Madeira_LOS_169_Desc.csv  (descending).


 

The files for the Belfast dataset are as follows: 



AGEO_DataSet_InSAR_3_Belfast_EW.csv (horizontal)

AGEO_DataSet_InSAR_3_Belfast_UD.csv (vertical).


 

The coordinate system is ETRS89-extended / LAEA Europe (code EPSG:3035). Latitude and longitude coordinates are also given with the LOS displacement datasets. 0 and NaN values are used as no-data value.
DOI: 10.15468/dl.ne9yxg
title: Occurrence Download

description: A dataset containing 3662 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Arthrostemma ciliatum Pav. ex D.Don"
 ]
}

 The dataset includes 3662 records from 105 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007081-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k5f63e
title: Occurrence Download

description: A dataset containing 803469 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "OccurrenceStatus is Present",
 "PublishingOrg is 28eb1a3f-1c15-4a95-931a-4af90ecb574d",
 "TaxonKey is Laridae"
 ]
}

 The dataset includes 803469 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007077-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5rttsn
title: Occurrence Download

description: A dataset containing 261513 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (United States of America, Canada, Mexico)",
 "TaxonKey is Danaus plexippus (Linnaeus, 1758)",
 "Year 2004-2025"
 ]
}

 The dataset includes 261513 records from 69 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007078-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34943/2a1d8d84-beb2-4f41-8a60-f0042851a69d
title: CCGS John P. Tully Navigation System Deployed 2024-08-12

description: The C.C.G.S. John P. Tully Navigation 01 was deployed on 2024-08-12 on CCGS John P. Tully. The Canadian Coast Guard Ship (CCGS) John P. Tully is owned and operated by the Canadian Coast Guard. This device is a Navigation System. Navigational instruments are used to measure and record positional information of ships, buoys, and autonomous underwater vehicles (AUVs). Typical sensors include latitude, longitude, speed, heading, pitch, roll, and depth. It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.25345/c5q81547d
title: MassIVE MSV000097018 - Sacubitril treated mouse heart proteomics
title: Mouse heart samples processed by S-trap. S: sacubitril (n=6), V: vehicle (n=5)
DOI: 10.5064/f6fhtb9e/blyrx4
title: Sodre_Interview_with_Patricia_Rubim.pdf

description: Release form signed on 7 January 2024
DOI: 10.5064/f6fhtb9e/ghgams
title: Sodre_DataNarrative.pdf
DOI: 10.5064/f6fhtb9e/jgpoik
title: README_Sodre.txt
DOI: 10.5064/f6fhtb9e/6haubg
title: Sodre_Interview_with_Wilson_Belem.pdf

description: Release form signed on 31 January 2024
DOI: 10.5064/f6fhtb9e/abusfw
title: Sodre_Interview_with_Paulo_Andre.pdf

description: Release form signed on 4 December 2023
DOI: 10.5064/f6fhtb9e/amszvt
title: Sodre_DataNarrative.pdf
DOI: 10.5064/f6fhtb9e/aorirh
title: Sodre_Sample_Release_Form.pdf
DOI: 10.5064/f6fhtb9e/b91s7l
title: Sodre_Interview_with_Joice_Niskier.pdf

description: Release form signed on 23 December 2023
DOI: 10.5064/f6fhtb9e/cainxl
title: Sodre_Interview_with_Priscila_Castro.pdf

description: Release form signed on 22 March 2024
DOI: 10.5064/f6fhtb9e/egkxax
title: Sodre_Interview_with_Karla_Poppe.pdf

description: Release form signed on 26 November 2023
DOI: 10.5064/f6fhtb9e/gm7xe6
title: Sodre_Interview_with_Chico_Alencar.pdf

description: Release form signed on 15 April 2024
DOI: 10.5064/f6fhtb9e/hopzqi
title: Sodre_Interview_with_Adair_Rocha.pdf

description: Release form signed on 31 July 2024
DOI: 10.5064/f6fhtb9e/iutepe
title: Sodre_Interview_with_Ana_Luiza_Rabello.pdf

description: Release form signed on 12 December 2023
DOI: 10.5064/f6fhtb9e/mjbh9p
title: Sodre_Interview_with_Mauro_Vianna.pdf

description: Release form signed on 27 January 2024
DOI: 10.5064/f6fhtb9e/n4lsnk
title: Sodre_Interview_with_Isabel_Ortigao.pdf

description: Release form signed on 22 January 2022
DOI: 10.5064/f6fhtb9e/nkicpy
title: Sodre_Interview_with_Andrea_Ventura.pdf

description: Release form signed on 15 December 2023
DOI: 10.5064/f6fhtb9e/nxlmm5
title: Sodre_Interview_with_Thais_Siervi.pdf

description: Release form signed on 7 February 2024
DOI: 10.5064/f6fhtb9e/oa8igp
title: Sodre_Interview_with_Adriana_Moretta.pdf

description: Release form signed on 22 November 2023
DOI: 10.5064/f6fhtb9e/oakcod
title: Sodre_Interview_with_Patricia_Rubim.pdf

description: Release form signed on 7 January 2024
DOI: 10.5064/f6fhtb9e/omvr2v
title: Sodre_Interview_with_Babeth_Bettencourt.pdf

description: Release form signed on 28 November 2023
DOI: 10.5064/f6fhtb9e/pqpqcv
title: Sodre_Interview_with_Rogerio_Souza.pdf

description: Release form signed on 2 July 2024
DOI: 10.5064/f6fhtb9e/rj6dxq
title: Sodre_Interview_with_Ronald_Valle.pdf

description: Release form signed on 12 June 2024
DOI: 10.5064/f6fhtb9e/smlwfk
title: Sodre_Interview_with_Maria_C._Centola.pdf

description: Release form signed on 15 December 2023
DOI: 10.5064/f6fhtb9e/txifgt
title: Sodre_Interview_with_Roberto_Caldas.pdf

description: Release form signed on 18 January 2024
DOI: 10.5064/f6fhtb9e/vltw2n
title: Sodre_Interview_with_Sandra_Oliveira.pdf

description: Release form signed on 26 February 2024
DOI: 10.5064/f6fhtb9e/wqpfyb
title: README_Sodre.txt
DOI: 10.5064/f6fhtb9e/wrvvty
title: Sodre_Interview_with_Ana_Carter.pdf

description: Release form signed on 30 December 2023
DOI: 10.5064/f6fhtb9e/wusiee
title: Sodre_Interview_with_Ligia_Aquino.pdf

description: Release form signed on 9 January 2023
DOI: 10.5064/f6fhtb9e/xjaj7c
title: Sodre_Interview_with_Lucia_Clontz.pdf

description: Release form signed on 20 November 2023
DOI: 10.5064/f6fhtb9e/ygyp4b
title: Sodre_Interview_with_Anna_Muggiati.pdf

description: Release form signed on 10 January 2024
DOI: 10.5064/f6fhtb9e/zgdqyi
title: Sodre_Interview_with_Renata_Muniz.pdf

description: Release form signed on 11 December 2023
DOI: 10.15468/dl.6zncb4
title: Occurrence Download

description: A dataset containing 41572 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-121.78909 32.31134,-115.33002 32.31134,-115.33002 35.79383,-121.78909 35.79383,-121.78909 32.31134))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sceloporus occidentalis Baird & Girard, 1852",
 "Year 2020-2025"
 ]
}

 The dataset includes 41572 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007082-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hwf6aw
title: Occurrence Download

description: A dataset containing 240 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Smilisca dentata (Smith, 1957)"
 ]
}

 The dataset includes 240 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007083-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14783445
title: Análisis Basado en Teoría Fundamentada Jacarandas (2025-01-31

description: Archivo exportado y guardado en formato “atlproj9” de Atlas Ti. versión 9.1.3.0. Contiene los 97 documentos. Esta todo el proceso de análisis realizados en el análisis de más de 3000 comentarios. Es su versión final, donde se puede detallar como las citas textuales se convierten en Red de Redes, que en terminos de la investigación se denominó perspectiva. Siendo 4 en total.
DOI: 10.5281/zenodo.14783446
title: Análisis Basado en Teoría Fundamentada Jacarandas (2025-01-31

description: Archivo exportado y guardado en formato “atlproj9” de Atlas Ti. versión 9.1.3.0. Contiene los 97 documentos. Esta todo el proceso de análisis realizados en el análisis de más de 3000 comentarios. Es su versión final, donde se puede detallar como las citas textuales se convierten en Red de Redes, que en terminos de la investigación se denominó perspectiva. Siendo 4 en total.
DOI: 10.5281/zenodo.14783461
title: Bibliografía en Zotero de artículos en ingles sobre el aborto

description: Archivo exportado y guardado en formato “BibTeX”. Puede ser importado tanto en Mendeley como en Zotero. Incluye un total de 61 artículos y libros en ingles (también traducidos al español). Contiene las anotaciones y resaltados.
DOI: 10.5281/zenodo.14783462
title: Bibliografía en Zotero de artículos en ingles sobre el aborto

description: Archivo exportado y guardado en formato “BibTeX”. Puede ser importado tanto en Mendeley como en Zotero. Incluye un total de 61 artículos y libros en ingles (también traducidos al español). Contiene las anotaciones y resaltados.
DOI: 10.15468/dl.ugx994
title: Occurrence Download

description: A dataset containing 219742 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Belgium",
 "TaxonKey is one of (Aponogeton distachyos L.f., Arthurdendyus triangulatus (Dendy, 1895), Cabomba caroliniana A.Gray, Callosciurus finlaysonii (Horsfield, 1823), Cherax destructor Clark, 1936, Corvus splendens Vieillot, 1817, Crassula helmsii (Kirk) Cockayne, Elodea densa (Planch.) Casp., Eriocheir sinensis H.Milne Edwards, 1853, Erythranthe guttata (DC.) G.L.Nesom, Faxonius immunis (Hagen, 1870), Faxonius juvenilis (Hagen, 1870), Faxonius rusticus (Girard, 1852), Faxonius virilis (Hagen, 1870), Gambusia affinis (Baird & Girard, 1853), Heracleum mantegazzianum Sommier & Levier, Heracleum persicum Desf. ex Fisch., C.A.Mey. & Avé-Lall., Heracleum pubescens (Hoffm.) M.Bieb., Houttuynia cordata Thunb., Hydrocotyle ranunculoides L.f., Impatiens glandulifera Royle, Koenigia polystachya (Wall. ex Meisn.) T.M.Schust. & Reveal, Lagarosiphon major (Ridl.) Moss, Lepomis gibbosus (Linnaeus, 1758), Limnoperna fortunei (Dunker, 1857), Ludwigia grandiflora (Michx.) Greuter & Burdet, Ludwigia peploides (Kunth) P.H.Raven, Lysichiton americanus Hultén & H.St.John, Morone americana (Gmelin, 1789), Muntiacus reevesi (Ogilby, 1839), Myriophyllum aquaticum (Vell.) Verdc., Myriophyllum heterophyllum Michx., Pacifastacus leniusculus (Dana, 1852), Petasites japonicus (Siebold & Zucc.) Maxim., Pontederia cordata L., Procambarus acutus (Girard, 1852), Procambarus clarkii (Girard, 1852), Procambarus virginalis Lyko, 2017, Pseudorasbora parva (Temminck & Schlegel, 1846), Pycnonotus cafer (Linnaeus, 1766), Saururus cernuus L., Sciurus carolinensis Gmelin, 1788, Sciurus niger Linnaeus, 1758, Solenopsis invicta Buren, 1972, Solenopsis richteri Forel, 1909, Threskiornis aethiopicus (Latham, 1790), Vespa velutina Lepeletier, 1836, Zizania latifolia (Griseb.) Hance ex F.Muell.)",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 219742 records from 82 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007080-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x29rj7
title: Occurrence Download

description: A dataset containing 1095315 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Belgium, Germany, Netherlands)",
 "TaxonKey is one of (Ondatra zibethicus (Linnaeus, 1766), Myocastor coypus (Molina, 1782))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1095315 records from 77 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007079-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/ds/3962521
title: MASC dataset
DOI: 10.5281/zenodo.14783065
title: MASC Dataset: A Novel Resource for Classifying Mobile Application Screens using Machine Learning

description: 🚀 MASC Dataset: Mobile Application Screen Classification 

📌 OverviewMASC (Mobile Application Screen Classification) is a manually curated dataset containing 7,065 mobile UI screens classified into 10 distinct categories. Designed for UI/UX research and ML applications, it enables:- 📱 Accurate screen type classification - 🤖 Automated UI testing - 🎨 Design pattern analysis
DOI: 10.5281/zenodo.14783064
title: MASC Dataset: A Novel Resource for Classifying Mobile Application Screens using Machine Learning

description: 🚀 MASC Dataset: Mobile Application Screen Classification 

📌 OverviewMASC (Mobile Application Screen Classification) is a manually curated dataset containing 7,065 mobile UI screens classified into 10 distinct categories. Designed for UI/UX research and ML applications, it enables:- 📱 Accurate screen type classification - 🤖 Automated UI testing - 🎨 Design pattern analysis
DOI: 10.15468/dl.r23b9f
title: Occurrence Download

description: A dataset containing 250 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (United States of America, Canada, Mexico, Argentina, Cuba, Peru)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Danaus plexippus (Linnaeus, 1758)",
 "Year 2025-2025"
 ]
}

 The dataset includes 250 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007084-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.