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These files are shared under a Creative Commons Attribution 4.0 International License.

MLA14 was the hashtag which corresponded to the 2014 Modern Language Association Annual Convention. The Convention was held in Chicago from Monday 9 to Sunday 12 January 2014.

The dataset includes tweets posted during the actual convention: the set starts with a tweet from Thursday 9 January 6:04:45 AM and ends with a tweet from Sunday 12 January 2014 23:32:46 Central Time. The total number of tweets in the dataset sums 21,915 tweets. The deposited .zip file contains 1 README.txt file and 5 CSV files including data from tweets harvested by Ernesto Priego (City University London) and Chris Zarate (MLA) using Martin Hawksey's TAGS 5.1. The data was deduplicated using OpenRefine. There is 1 CSV file per convention day and 1 CSV file with the combined tweets. An initial analysis of the data was posted as a series of blog posts by Ernesto Priego published between 16 January and 22 January 2014 at MLA Commons(http://remoteparticipation.commons.mla.org/2014/01/16/mla14-a-first-look/) (accessed 4 February 2014). To cite: Priego, Ernesto; Zarate, Chris (2014): #MLA14 Twitter Archive, 9-12 January 2014. figshare.http://dx.doi.org/10.6084/m9.figshare.924801

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.

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.

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This data release provides all data and code used in the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025)" to model stream temperature, evaluate, and assess results. The associated manuscript explores the effect of different ensemble construction techniques across different common machine learning (ML) architectures for predictions in unmonitored basins. Modeling was done using long short-term memory (LSTM), gated recurrent unit (GRU), temporal convolution network (TCN), and extreme gradient boosting (XGBoost) models, and stream site coverage spans 1362 locations across the conterminous United States. The ensemble construction techniques investigated include ensemble by random weight initialization, differing hyperparameters, different random subsets of training data, different subselections of input features, different architectures, and Monte Carlo Dropout. The data is organized into these items items:Code repository and data for the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025).Code: stream_temp_ml_regionalization.zip contains the code repositoryData to run the code:- data_dir.zip -- contains all files that should be moved to the "DATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- metadata_dir.zip -- contains all files that should be moved to the "METADATA_DIR" variable defined in the "set_env_vars.sh" scriptmore » in the code repositoryData produced by the code and used in the paper:- outputs_dir.zip - contains model output and results (outputs_dir/results), model weights (outputs_dir/models), and all other outputs used for the paper including feature importances.To cite this code, please use the following BibTeX or MLA entries:bibtex:@misc{willard2025streamensembles,author = {Jared Willard and Charuleka Varadharajan},title = {Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification"},year = {2024},doi = {10.15485/2527393},publisher = {ESS-DIVE Repository},url = {https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2527393}}MLA: Willard, Jared, et al. Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification". 2025. ESS-DIVE Repository, doi:10.15485/2448016.« less

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