Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset consists of 2000 conversations from 5 programming related Q&A channels, hosted on Slack, and accompanies the paper "Automatically Identifying Archival-worthy, Software-related Slack Conversations". In addition to the text of the conversations, each conversation has been annotated as either archival worthy or not. Our definition of archival-worthiness is:
"If a conversation contains information that could be useful to other users, whether in the Slack channel or elsewhere, then it should be archived. These conversations have no determinate length and no need for objectivity. A conversation should be archived based on the availability and ease of identifying information that could help a person to gain useful software-related knowledge."
Data Origin: Numerous public Slack chat channels (https://slack.com/) have recently become available that are focused on specific software engineering-related discussion topics, e.g., Python Development (https://pyslackers.com/web/slack). The data reflects a portion of the conversations on public channels related to Python, Clojure, Elm and Racket programming.
Data Pre-Processing: To protect privacy, we replace usernames with fake names, and replace absolute times with relative times (in seconds). The conversations are disentangled from the overall chat stream with each unique thread in the dataset specifying a conversation in the channel. Archival-worthy conversations are marked with 1, while non-archival-worthy with 0.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This table contains Slack messaging data from 41 IBL researchers (rows) to 13 different IBL Working Groups (columns), which is analyzed in the Google Colab Python notebook to generate a network graph (also part of this repository).Column labels can be found in the Python notebook. Row labels are not used; researcher identity is anonymized by normalizing each row as a proportion of each user's total message count.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
GovHack 2017 Web service pilot - StreetMap A South Australian Street Basemap Service provides an underlying map with authoritative street locations and names. You can overlay other data on the street map in applications or analytics. The service provides the requested images and not the underlying data. A user can also use the basemap to coordinate a point location which can then be used to retrieve information via a location intersection service. Please read the user documentation published with this dataset record. A Location SA Data mentor will monitor the GovHack slack channel on the weekend for troubleshooting. This services will be free and active until 30 September 2017 (or the GovHack international Red Carpet Awards if after this date) to support the pilot and showcase GovHack concepts created using Location SA data services.
Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
License information was derived automatically
GovHack 2017 Web service pilot - StreetMap A South Australian Street Basemap Service provides an underlying map with authoritative street locations and names. You can overlay other data on the street map in applications or analytics. The service provides the requested images and not the underlying data. A user can also use the basemap to coordinate a point location which can then be used to retrieve information via a location intersection service. Please read the user documentation published with this dataset record. A Location SA Data mentor will monitor the GovHack slack channel on the weekend for troubleshooting. This services will be free and active until 30 September 2017 (or the GovHack international Red Carpet Awards if after this date) to support the pilot and showcase GovHack concepts created using Location SA data services.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This service identifies 2016 ABS census (ASGS) boundaries at a location coordinated point. Australian Statistical Geography Standard (ASGS) service is a point in a polygon. The boundary areas returned can be used as an additional layer over a basemap and can be used to perform analytics when mashed up with other statistical or spatial data. Please read the User documentation published with this record. A Location SA Data mentor will monitor the GovHack slack channel on the weekend for troubleshooting. This services will be free and active until 30 September 2017 (or the GovHack international Red Carpet Awards if after this date) to support the pilot and showcase GovHack concepts created using Location SA data services.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset consists of 2000 conversations from 5 programming related Q&A channels, hosted on Slack, and accompanies the paper "Automatically Identifying Archival-worthy, Software-related Slack Conversations". In addition to the text of the conversations, each conversation has been annotated as either archival worthy or not. Our definition of archival-worthiness is:
"If a conversation contains information that could be useful to other users, whether in the Slack channel or elsewhere, then it should be archived. These conversations have no determinate length and no need for objectivity. A conversation should be archived based on the availability and ease of identifying information that could help a person to gain useful software-related knowledge."
Data Origin: Numerous public Slack chat channels (https://slack.com/) have recently become available that are focused on specific software engineering-related discussion topics, e.g., Python Development (https://pyslackers.com/web/slack). The data reflects a portion of the conversations on public channels related to Python, Clojure, Elm and Racket programming.
Data Pre-Processing: To protect privacy, we replace usernames with fake names, and replace absolute times with relative times (in seconds). The conversations are disentangled from the overall chat stream with each unique thread in the dataset specifying a conversation in the channel. Archival-worthy conversations are marked with 1, while non-archival-worthy with 0.