Browsing by Autor "Anamaria Mojica-Hanke"

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Dataset - What are the Machine Learning best practices reported by practitioners on Stack Exchange?
(European Organization for Nuclear Research, 2023) Anamaria Mojica-Hanke; Andrea Bayona; Mario Linares‐Vásquez; Steffen Herbold; Fabio A. González
The data correspond to the posts (questions and answers) retrieved by querying for posts related to the tag 'machine learning' and the phrase 'best practice(s).' The data were used as the basis for a study currently under review on discussing machine learning best practices as discussed by practitioners in question-and-answer communities such as Stack Exchange. The information from each type of post (i.e., questions and answers) is presented in multiple formats (i.e., .txt, .csv, and .xlsx). Answers - Variables AID: Unique identification of the answer in the Q&A website. ParentId: Unique identification of the question associated with the answer in the Q&A website AcceptedAnswerId : In the case in which an answer is the most voted question associated with the ParentId, and it is different from the accepted answer, a different identifier from the AID is available. In the case in which the accepted question had a score lower than 1, a -1 is assigned. ABody: HTML text of the answer. Score: Upvotes - downvotes of the answer. url_Answer: URL of the answer. The question URL can be from different websites. type: best or accepted. Accepted in the case that the information belongs to the accepted answer of the ParentId question and best in the case in which it is the most voted question of the ParentId question. Date: Creation date of the answer. Questions - Variables QID: Unique identification of the question in the Q&A website. AcceptedAnswerId: Unique identification of the accepted answer for a specific question in the Q&A website. In the case in which a question had a most-voted answer different from the accepted one, and the accepted one had a negative score, a -1 was assigned to the AcceptedAnswerId. BestAnswerId: Unique identification of the most voted answer for a specific question in the Q&A website. In the case in which the most voted and accepted questions were the same, then a -1 was assigned to the BestAnswerId. Qtitle: Title of the question. QBody: HTML text of the question. Score: Upvotes - downvotes of the questions. QTags: Tags that are associated with each question. url_question: URL of the question. The question URL can be from different websites. Date: Creation date of the question This dataset is a subset of the Stack Exchange dump of 03.2021 (https://archive.org/details/stackexchange_20210301) in which a series of filters were applied to obtain the data used in the study.
Dataset - What are the Machine Learning best practices reported by practitioners on Stack Exchange?
(European Organization for Nuclear Research, 2023) Anamaria Mojica-Hanke; Andrea Bayona; Mario Linares‐Vásquez; Steffen Herbold; Fabio A. González
The data correspond to the posts (questions and answers) retrieved by querying for posts related to the tag 'machine learning' and the phrase 'best practice(s).' The data were used as the basis for a study currently under review on discussing machine learning best practices as discussed by practitioners in question-and-answer communities such as Stack Exchange. The information from each type of post (i.e., questions and answers) is presented in multiple formats (i.e., .txt, .csv, and .xlsx). Answers - Variables AID: Unique identification of the answer in the Q&A website. ParentId: Unique identification of the question associated with the answer in the Q&A website AcceptedAnswerId : In the case in which an answer is the most voted question associated with the ParentId, and it is different from the accepted answer, a different identifier from the AID is available. In the case in which the accepted question had a score lower than 1, a -1 is assigned. ABody: HTML text of the answer. Score: Upvotes - downvotes of the answer. url_Answer: URL of the answer. The question URL can be from different websites. type: best or accepted. Accepted in the case that the information belongs to the accepted answer of the ParentId question and best in the case in which it is the most voted question of the ParentId question. Date: Creation date of the answer. Questions - Variables QID: Unique identification of the question in the Q&A website. AcceptedAnswerId: Unique identification of the accepted answer for a specific question in the Q&A website. In the case in which a question had a most-voted answer different from the accepted one, and the accepted one had a negative score, a -1 was assigned to the AcceptedAnswerId. BestAnswerId: Unique identification of the most voted answer for a specific question in the Q&A website. In the case in which the most voted and accepted questions were the same, then a -1 was assigned to the BestAnswerId. Qtitle: Title of the question. QBody: HTML text of the question. Score: Upvotes - downvotes of the questions. QTags: Tags that are associated with each question. url_question: URL of the question. The question URL can be from different websites. Date: Creation date of the question This dataset is a subset of the Stack Exchange dump of 03.2021 (https://archive.org/details/stackexchange_20210301) in which a series of filters were applied to obtain the data used in the study.
Dataset - What are the Machine Learning best practices reported by practitioners on Stack Exchange?
(European Organization for Nuclear Research, 2023) Anamaria Mojica-Hanke; Andrea Bayona; Mario Linares‐Vásquez; Steffen Herbold; Fabio A. González
The data correspond to the posts (questions and answers) retrieved by querying for posts related to the tag 'machine learning' and the phrase 'best practice(s).' The data were used as the basis for a study currently under review on discussing machine learning best practices as discussed by practitioners in question-and-answer communities such as Stack Exchange. The information from each type of post (i.e., questions and answers) is presented in multiple formats (i.e., .txt, .csv, and .xlsx).
Hall-of-Apps: The Top Android Apps Metadata Archive
(European Organization for Nuclear Research, 2020) Laura Bello-Jiménez; Camilo Escobar‐Velásquez; Anamaria Mojica-Hanke; Santiago Cortés-Fernández; Mario Linares‐Vásquez
The amount of Android apps available for download is constantly increasing, exerting a continuous pressure on developers to publish outstanding apps. Google Play (GP) is the default distribution channel for Android apps, which provides mobile app users with metrics to identify and report apps quality such as rating, amount of downloads, previous users comments, etc. In addition to those metrics, GP presents a set of top charts that highlight the outstanding apps in different categories. Both metrics and top app charts help developers to identify whether their development decisions are well valued by the community. Therefore, app presence in these top charts is a valuable information when understanding the features of top-apps. In this paper we present Hall-of-Apps, a dataset containing top charts' apps metadata extracted (weekly) from GP, for 4 different countries, during 30 weeks. The data is presented as (i) raw HTML files, (ii) a MongoDB database with all the information contained in app's HTML files (e.g., app description, category, general rating, etc.), and (iii) data visualizations built with the D3.js framework. A first characterization of the data along with the urls to retrieve it can be found in our online appendix: https://thesoftwaredesignlab.github.io/hall-of-apps-tools/
Hall-of-Apps: The Top Android Apps Metadata Archive
(2020) Laura Bello-Jiménez; Camilo Escobar‐Velásquez; Anamaria Mojica-Hanke; Santiago Cortés-Fernández; Mario Linares‐Vásquez
The amount of Android apps available for download is constantly increasing, exerting a continuous pressure on developers to publish outstanding apps. Google Play (GP) is the default distribution channel for Android apps, which provides mobile app users with metrics to identify and report apps quality such as rating, amount of downloads, previous users comments, etc. In addition to those metrics, GP presents a set of top charts that highlight the outstanding apps in different categories. Both metrics and top app charts help developers to identify whether their development decisions are well valued by the community. Therefore, app presence in these top charts is a valuable information when understanding the features of top-apps. In this paper we present Hall-of-Apps, a dataset containing top charts' apps metadata extracted (weekly) from GP, for 4 different countries, during 30 weeks. The data is presented as (i) raw HTML files, (ii) a MongoDB database with all the information contained in app's HTML files (e.g., app description, category, general rating, etc.), and (iii) data visualizations built with the D3.js framework. A first characterization of the data along with the urls to retrieve it can be found in our online appendix: https://thesoftwaredesignlab.github.io/hall-of-apps-tools/
Hall-of-Apps: The Top Android Apps Metadata Archive
(European Organization for Nuclear Research, 2020) Laura Bello-Jiménez; Camilo Escobar‐Velásquez; Anamaria Mojica-Hanke; Santiago Cortés-Fernández; Mario Linares‐Vásquez
The amount of Android apps available for download is constantly increasing, exerting a continuous pressure on developers to publish outstanding apps. Google Play (GP) is the default distribution channel for Android apps, which provides mobile app users with metrics to identify and report apps quality such as rating, amount of downloads, previous users comments, etc. In addition to those metrics, GP presents a set of top charts that highlight the outstanding apps in different categories. Both metrics and top app charts help developers to identify whether their development decisions are well valued by the community. Therefore, app presence in these top charts is a valuable information when understanding the features of top-apps. In this paper we present Hall-of-Apps, a dataset containing top charts' apps metadata extracted (weekly) from GP, for 4 different countries, during 30 weeks. The data is presented as (i) raw HTML files, (ii) a MongoDB database with all the information contained in app's HTML files (e.g., app description, category, general rating, etc.), and (iii) data visualizations built with the D3.js framework. A first characterization of the data along with the urls to retrieve it can be found in our online appendix: https://thesoftwaredesignlab.github.io/hall-of-apps-tools/
Hall-of-Apps: The Top Android Apps Metadata Archive
(European Organization for Nuclear Research, 2020) Laura Bello-Jiménez; Camilo Escobar‐Velásquez; Anamaria Mojica-Hanke; Santiago Cortés-Fernández; Mario Linares‐Vásquez
The amount of Android apps available for download is constantly increasing, exerting a continuous pressure on developers to publish outstanding apps. Google Play (GP) is the default distribution channel for Android apps, which provides mobile app users with metrics to identify and report apps quality such as rating, amount of downloads, previous users comments, etc. In addition to those metrics, GP presents a set of top charts that highlight the outstanding apps in different categories. Both metrics and top app charts help developers to identify whether their development decisions are well valued by the community. Therefore, app presence in these top charts is a valuable information when understanding the features of top-apps. In this paper we present Hall-of-Apps, a dataset containing top charts' apps metadata extracted (weekly) from GP, for 4 different countries, during 30 weeks. The data is presented as (i) raw HTML files, (ii) a MongoDB database with all the information contained in app's HTML files (e.g., app description, category, general rating, etc.), and (iii) data visualizations built with the D3.js framework. A first characterization of the data along with the urls to retrieve it can be found in our online appendix: https://thesoftwaredesignlab.github.io/hall-of-apps-tools/