Discussion of Norbert Wiener's text and vision of Cybernetics (1948) versus the conceptual knowledge of 'artificial intelligence.' The document was originally written in MLA format, and cited works are academic sources from online publication(s). The document may be used as a form of reference or analysis.

The datacitation extension for CKAN aims to facilitate proper data citation practices within the CKAN data catalog ecosystem. By providing tools and features to create and manage citations for datasets, the extension promotes discoverability and acknowledgment of data sources, enhancing the reproducibility and transparency of research and analysis based on these datasets. The available information is limited, but based on the name, the extension likely focuses on generating, displaying, and potentially exporting citation information. Key Features (Assumed based on Extension Name): * Dataset Citation Generation: Likely provides functionality to automatically generate citation strings for datasets based on metadata fields, adhering to common citation formats (e.g., APA, MLA, Chicago). * Citation Metadata Management: Potentially offers tools to manage citation-related metadata within datasets, such as author names, publication dates, and version numbers, which are essential elements for creating accurate citations. * Citation Display on Dataset Pages: It's reasonable to expect that the extension displays the generated citation information prominently on the dataset's display page, facilitating easy access for users. * Citation Export Options: May provide options to export citations in various formats (e.g., BibTeX, RIS) to integrate with reference management software popular among researchers. * Citation Style Customization: Possibly provides configuration options to customize the citation style used for generation, accommodating different disciplinary requirements. Use Cases (Inferred): 1. Research Data Repositories: Data repositories can utilize datacitation to ensure that researchers cite datasets correctly, which is crucial for tracking the impact of data and recognizing the contributions of data creators. 2. Government Data Portals: Government agencies can implement the extension to promote the proper use and attribution of open government datasets, fostering transparency and accountability. Technical Integration: Due to limited information, the integration details are speculative. However, it can be assumed that the datacitation extension likely integrates with CKAN by: * Adding a new plugin or module to CKAN that handles citation generation and display. * Extending the CKAN dataset schema to include citation-related metadata fields. * Potentially providing API endpoints for programmatic access to citation information. Benefits & Impact: The anticipated benefits of the datacitation extension include: * Improved data discoverability and reusability through proper citation practices. * Enhanced research reproducibility and transparency by ensuring that data sources are properly acknowledged. * Increased recognition of data creators and contributors. * Simplified citation management for users of CKAN-based data catalogs. Disclaimer: The above information is largely based on assumptions derived from the extension's name and common data citation practices. The actual features and capabilities of the datacitation extension may vary due to the unavailability of a README file.

MLA15 is the hashtag which corresponded to the 2015 Modern Language Association Annual Convention. The Convention was held in Vancouver from Thursday 8 to Sunday 11 January 2015.

This dataset is a .xlsx file including data from Tweets publicly published with #mla15 as harvested by Ernesto Priego (City University London) and Chris Zarate (MLA). This dataset includes Tweets posted during the actual convention with #mla15: the set starts with a Tweet from Thursday 08/01/2015 00:02:53 Pacific Time and ends with a Tweet from Sunday 11/01/2015 23:59:58 Pacific Time. The total number of Tweets in this dataset sums 23,609 Tweets. Only Tweets from users with at least two followers were collected. A combination of Twitter Archiving Google Spreadsheets (Martin Hawksey's TAGS 6.0; available at https://tags.hawksey.info/ ) was used to harvest this collection. OpenRefine (http://openrefine.org/) was used for deduplicating the data. Please note that both research and experience show that the Twitter search API isn't 100% reliable. Large tweet volumes affect the search collection process. The API might "over-represent the more central users", not offering "an accurate picture of peripheral activity" (González-Bailón, Sandra, et al. 2012). It is therefore not guaranteed this file contains each and every Tweet tagged with the archived hashtag during the indicated period, and is shared for comparative and indicative educational and research purposes only. Please note the data in this file is likely to require further refining and even deduplication. The data is shared as is. This dataset is shared to encourage open research into scholarly activity on Twitter. If you use or refer to this data in any way please cite and link back using the citation information above.

For the #MLA14 datasets, please go toPriego, Ernesto; Zarate, Chris (2014): #MLA14 Twitter Archive, 9-12 January 2014. figshare.http://dx.doi.org/10.6084/m9.figshare.924801

NB. The previous version of this dataset accidentally contained a typo in the title that has been corrected. Please use the most recent version.

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